XA0102854* NAHRES-63
Vienna, 2001
CO-ORDINATED RESEARCH PROJECT
ON
VALIDATION AND APPLICATION OF PLANTS AS BIOMONITORS OF TRACE ELEMENT ATMOSPHERIC POLLUTION, ANALYZED BY NUCLEAR AND RELATED TECHNIQUES
Report on the Second Research Co-ordination Meeting
Vienna, Austria, 20 to 24 March 2000
INTERNATIONAL ATOMIC ENERGY AGENCY EDITORIAL NOTE
This report is not a formal publication of the International Atomic Energy Agency (IAEA). Although all rights are reserved by the IAEA the report may nevertheless be freely reviewed, abstracted, reproduced and translated, in part or in whole with appropriate citation- but not for sale nor for use in conjunction with commercial purposes.
The views expressed in this report do not necessarily reflect those of the IAEA or of governments of the Member States or organizations under whose auspices the work described herein was carried out. The use in this report of particular designations of countries or territories doesnot imply any judgment by the IAEA as to the legal status of such countries or territories, of their authorities and institutions or of the delimitation of their boundaries. The mention of specific companies or of their products or brand names does not imply any endorsement or recommendation on the part of the IAEA.
Some of the reports in the NAHRES series are recorded in the IAEA's International Nuclear Information System (INIS). PLEASE BE AWARE THAT ALL OF THE MISSING PAGES IN THIS DOCUMENT WERE ORIGINALLY BLANK VALIDATION AND APPLICATION OF PLANTS AS BIOMONITORS OF TRACE ELEMENT ATMOSPHERIC POLLUTION, ANALYZED BY NUCLEAR AND RELATED TECHNIQUES
Report on the Second Research Co-ordination Meeting
Vienna, Austria, 20 to 24 March 2000
NAHRES-63, IAEA, Vienna (2001)
A report prepared by the
Section of Nutritional and Health-Related Environmental Studies Division of Human Health Department of Nuclear Sciences and Applications International Atomic Energy Agency P.O. Box 100, A-1400 Vienna, Austria
Single copies of this report are available cost-free on request from the above address CONTENTS
PART I: SUMMARY REPORT
PART n: HIGHLIGHTS AND ACHIEVEMENTS 13
PART HI: COUNTRY REPORTS
AIR POLLUTION BIOMONITORING IN ARGENTINA, APPLICATION OF NEUTRON ACTIVATION ANALYSIS TO THE STUDY OF BIOMONITORS MARIA LUISAPlGNATA AND RlTAR.PLA 37
DETERMINATION OF TRACE ELEMENTS IN LICHEN SAMPLES BY INSTRUMENTAL NEUTRON ACTIVATION ANALYSIS MITIKO SADU, LIDIAK. HORIMOTO, MARINAB.A. VASCONCELLOS, MARCELOP. MARCELLI, NAIROM. SUMLTA, PAULO H. N. SALDIVA 65
STUDY OF AIR POLLUTION IN CHILE USING BIOMONITORS EDUARDO CORTES, NURI GRAS, IRIS PEREIR, OSCAR ANDONI, SUSANA SEPULVED 15
A GLANCE OF AIR POLLUTION OF BEIJING CITY BY USING POPLAR LEAVES AND NAA TECHNIQUES NIBANGFA, TIAN WELZHL. WANGPINGSHENG, ZHANGYANGMEI, CHAOLEL,HEGAOKUI 91
CORRELATION OF ATMOSPHERIC DEPOSITION AND DISEASES IN THE EUROREGION NEISSE O. WAPPELHORST, I. KUHN, J. OEHLMANN, S. KORHAMMER, B. MARKERT 97
BIOMONITORING OF AIR POLLUTION THROUGH TRACE ELEMENT ANALYSIS SAMUEL AKOTO BAMFORD, E. K. OSAE, Y. SERFOR-ARMAH, B. NYARKO, F.OFOSU, I. J. ABOH, G. T. ODAMTTEN 107
BIO-MONITORING STUDIES USING NUCLEAR AND RELATED TECHNIQUES FOR THE STUDY OF AIR POLLUTION IN AND AROUND THE CITY OF HYDERABAD, INDIA J. ARUNACHALAM, M.V. BALARAMAKRISHNA, CHRISTOPHER DAVID, D. KARUNASAGAR AND SANJIV KUMAR 115 AIR-BIOMONITORING BY TRANSPLANTED LICHENS IN THE NEGEV DESERT, ISRAEL JACOB GARTY 125
BIOMONITORING OF AIR POLLUTION IN JAMAICA THROUGH TRACE-ELEMENT ANALYSIS OF EPIPHYTIC PLANTS USING NUCLEAR AND RELATED ANALYTICAL TECHNIQUES MiTKO VUTCHKOV 141
LOCAL VARIANCES IN BIOMONITORING H.TH. WOLTERBEEK, T.G. VERBURG 157
FURTHER PROMOTION OF THE USE OF MOSSES AND LICHENS FOR STUDIES OF ATMOSPHERIC DEPOSITION OF TRACE ELEMENTS EILIV STEMNES 169
STUDY OF ATMOSPHERIC DISPERSION OF POLLUTANTS IN THE INDUSTRIAL REGION OF THE SADO ESTUARY USING BIOMONITORS M.C. FREITAS, M.A. REIS, A.P. MARQUES, C. COSTA, H.TH. WOLTERBEEK 173
ATMOSPHERIC DEPOSITION OF HEAVY METALS IN RURAL AND URBAN AREAS OF ROMANIA STUDIED BY THE MOSS BIOMONITORING TECHNIQUE EMPLOYING NUCLEAR AND RELATED ANALYTICAL TECHNIQUES AND GIS TECHNOLOGY ADRIANALUCACIU, MARINA FRONTASYEVA, OTILIASTAN, E. STEINNES, N. SASARAN,KATALINACZIPLE 189
BIOMONITORING AIR POLLUTION IN CHELYABINSK REGION (URAL MOUNTAINS, RUSSIA) THROUGH TRACE-ELEMENTS AND RADIONUCLIDES: TEMPORAL AND SPATIAL TRENDS V.D. CHERCHINTSEV, M.V. FRONTASYEVA, S.M. LYAPUNOV, L.I. SMIRNOV 197
PART IV: APPENDICES
Appendix 1: Agenda of the Meeting 209 Appendix 2: List of Discussion Topics 213 Appendix 3: List of Participants 215 PART I: SUMMARY REPORT SUMMARY REPORT
1. INTRODUCTION
Environmental pollution is a cause of ever increasing concern in the world. The UN Conference on Environment and Development (Rio, Brazil, 1992) reaffirmed the importance of protecting the environment within the context of sustainable development. Arising out of this conference, the Rio Agenda 21 declaration called for a number of nationally determined action programmes, with international assistance and co-ordination under "Capacity 21", concerning environmental monitoring and assessment, including the use of biological markers.
For an objective assessment of the impact which anthropogenic activities may have on the environment there is a need for reliable information on the concentrations of elements and chemical compounds of environmental relevance that might serve as reference levels in these studies. At present, in developing countries, information on levels of various pollutants is; very scarce. It is well known that direct measurements of airborne pollutants require enormous efforts as to investments in infrastructure and manpower. Application of direct measurements in air (e.g. airborne particulate matter) on a large scale is extremely costly and manpower intensive, therefore impractical and almost impossible. Airborne pollutants can be transported over large distances, raising a high public interest also in remote areas.
Biomonitoring is an appropriate tool for assessing the levels of air pollution. In several developed countries biomonitoring is used on a regular basis for such surveys. Application of biomonitors has several advantages compared to the use of direct measurements of contaminants, related primarily to the permanent and common occurrence in the field, the ease of sampling and trace element accumulation. Furthermore, biomonitors provide a measure of integrated exposure over an extended period of time, are present in remote areas and no expensive technical equipment is involved in collecting them. Suitably chosen biomonitors accumulate contaminants over certain periods of time, concentrate them, thus allowing more reliable analytical measurements. Simple and cheap sampling procedures (in contrast to direct measurements) allow a very large number of sites to be included in the same survey, permitting detailed geographical patterns to be drawn. In combination with the specimen banking (long-term storage) of selected samples, biomonitoring can be an effective tool for pollutant mapping and trend monitoring by real time and retrospective analysis. By application of appropriate statistical tools, information can also be obtained on the type and location of pollution sources as well as on the short, medium and long range trans-boundary transport of environmental pollutants.
In Europe, nuclear and related analytical techniques have been shown to be particularly appropriate for the analysis of air pollution biomonitors, such as moss and lichen, being multi element, reliable, extremely sensitive for many toxic elements, matrix independent and suitable for all concentration ranges (i.e. from nanogram to percent levels). However, it is not yet known whether the proposed biomonitors are equally applicable in all areas of the world. Environmental protection and control is a matter of high priority in all developing countries' governmental policies in view of its implications for the welfare of the present and future populations. Therefore it is expected that regional atnd national organisations responsible for legislation and environmental policy, municipal organisations, which could use the data collected for establishing emission levels, organisations responsible for pollutant emission control and public health-related institutions will benefit from the proposed CRP. The CRP is expected to exploit possibilities of developing and validating tools for using appropriate biomonitors to map the distribution of air pollution over wide areas in developing countries. If successful, this would be a powerful way for developing countries to monitor air pollution.
2. UPDATING SPECIFIC RESEARCH OBJECTIVES
The specific objectives remains the same as defined during the first RCM.
To identify suitable bioindicators of atmospheric pollution for local and/or regional application (e.g. moss and/or lichen). Whenever possible these bioindicators should be validated for general environmental monitoring.
Approaches to achieve this objectives:
• Development of sampling design and Guidelines for sample collection, sample preparation and analysis, including quality control; • Sample collection, preparation and analysis in accordance with the Guidelines; • Statistical analysis and creation of graphical plots showing the geographical distributions of the elements, the levels of selected environmental pollutants, identification of pollution sources and the data on time trends; • Calibration of elemental content in biomonitoring species against absolute data for bulk deposition or air concentrations, whichever approach, or against another biomonitoring species already validated.
3. ASSESSING THE OUTPUTS
• Established procedures for conducting environmental monitoring campaigns pertained to air pollution using biomonitors, thereby providing national governments with some of the capabilities that they need in order to implement national and regional programmes of sustainable development. • Improved ability to conduct measurements of environmental samples in conformity with modern quality management concepts. • Reliable information on potential pollutants and their sources. • Appropriate presentation of the data on trace elements of environmental and health concern such as As, Cd, Cr, Cu, Hg, Ni, Pb, Sb, Se, and V: geographical distributions of the elemental contents and time trends. • Publication of final reports in the form of IAEA TECDOC and in national and international scientific j ournals. • Establishment of a Web site for the CRP. • Closer collaboration among the CRP participants.
The present status of the expected outputs is summarised in Table 1. Table 1. PRESENT STATUS OF THE EXPECTED OUTPUTS
Tasks Expected output Present status Sampling Sample collection procedures Written protocols including QC Sample preparation Sample preparation procedures Written protocols including QC • Written protocols including QC Elemental analysis Validated analytical method • Successful participation in NAT-5 intercomparison of lichen samples Physiology Additional knowledge about the role of the • Ongoing for some of the participants physiological process in biomonitoring • Planned for majorities. Interpretation Reliable information on pollutants and potential • Proved by majority of the participants; sources • Additional sampling including quantification is planned. Data presentation Appropriate presentation of the data on trace • Some participant already developed maps elements; geographical distribution of the • Planned for others elemental contents and time trends. Note: It was proposed IAEA to provide a simple mapping software. Publications Publication of reports and papers in • Final Report in the IAEA TECDOC international journals • Journal Papers (Listed in Attachment) Dissemination Establishment of a Web site for the CRP • Web address established • Web page to be designed Collaboration Closer collaboration among the CRP • Biogroup listserver created participants • Norway/Russia/Romania collaboration • Collaboration of the ARCAL participants • Tillandsia workgroup. 4. ASSESSING THE CRP ACTIVITIES
The main activities considered are: quantification, time resolution, geographical resolution, survey, mapping and physiology. Table 2 lists the present status for each participant.
Quantification: Assessment of a quantitative relationship between the elemental content of the biomonitor species and the (wet or bulk) deposition or the atmospheric concentrations.
Time resolution: Assessment of the element accumulation rate in the biomonitor to permit estimates of the time needed for the monitor to reflect new elemental atmospheric/deposition conditions.
Geographical resolution: of the resolution-strength of the biomonitor in a spatial sense. This means that local variabilities in biomonitor responses, survey design (grid density) and spatial variabilities in deposition and atmospheric concentrations should be considered simultanuously.
Survey: Assessment of geographical differences and/or time trends in deposition and/or atmospheric concentrations by the determination of the elemental content of the sampled biomonitor species.
Mapping: Graphical representation of the biomonitor-results, mostly referred to in cases where local and/or regional surveys are carried out.
Impact: Assessment of the changes in biomonitor parameters as a result of occurring ambient and/or internal conditions. This means that selected physiological/biochemical parameters are quantified in relationship with varying extent of deposition and/or atmospheric concentrations. Furthermore, changes in selected parameter values are studied in order to get insight in the consequences: changed values may lead to changed relationships between monitor and deposition and/or atmospheric concentrations. Table 2. PRESENT STATUS OF THE ACTIVITIES
Time Geographical Survey Mapping Physiology Country Quantification resolution resolution Argentina Lichen (Ramalina) and 3, 6, 9 month Regional Status: ongoing Status: Status: ongoing Tillandsia against air ongoing parti culates Status: planned Status: ongoing Status: planned Brazil Lichens and plants For plants Local Lichens No calibration against air Status: planned Status: planned Status: planned Status: filters Plants planned Status: planned Chile Inter-calibration of 3, 8 months Local Status: ongoing Status: Under lichens with PMIO data Status: ongoing. planned consideration Status: planned Status: Regional Status: planned. China Poplar leaves calibrated 3, 6 months Regional Status: Status: ongoing Status: Under against PMIO Status: ongoing ongoing planned consideration Status: planned Germany Quantification using 1 month for 20 x 20km Done for the Arclnfo Link with the mosses sampling regional trace metals in health data 1 year for the different niants Status: Status: completed indicator Status: Status: ongoing Status: ongoing Status: completed completed completed Ghana Lichens 6 months Local, specific Under Status: planned (Dry/Wet pollution source Status: planned Status: consideration ferns leaves season), Status: planned planned Status: planned Status: planned India Calibration of mosses 4 months Local Status: Planned; against air filters period. Status: ongoing Status: ongoing planned Speciation - Status: planned Status: ongoing Regional, Status: ongoing Status: planned Israel No N/A, Regional, No Status: Status: ongoing New case study transplanted planned using lichens Jamaica Calibration of Dry and Wet Local Status: planned; Tillnadsia against season Status: ongoing Status: ongoing Status: Particle size PMIO and deposition, Status: Regional ongoing speciation, Status: planned Status: planned Status: ongoing
Netherlands N/A Quality of local Quality of local No, Provide advise to other observations, observations, Status: planned Status: Metal and participants Status: ongoing ongoing ongoing particle size speciation, Status: planed Norway Status: ongoing Status: ongoing Status: ongoing Status: ongoing Status: No ongoing Portugal Calibration of lichens 3,6,9 months. Regional and National Metal speciation against aerosols and transplanted transplanted Status: meteorological data, Status: ongoing Status: ongoing Status: ongoing ongoing Status: planned Status: ongoing Mosses national Status: planned
Romania Mosses against 12 moths Regional Status: Status: Under deposition Status: ongoing Status: ongoing Ongoing ongoing consideration Status: planned Russia No No Regional Status: Status: No Status: ongoing Ongoing ongoing 5. OVERALL ASSESSMENT OF PROGRESS TOWARDS ACHIEVING THE OBJECTIVES
1. All participants have shown satisfactory qualifications as far as chemical analyses are concerned, as shown by interlaboratory comparison organized by the IAEA. 2. All participants have tested at least one biomonitor species with respect to sample collection, sample preparation, and analysis. 3. From the experience achieved so far in the CRP it is obvious that conditions in the various participating countries are so different that the possibilities of harmonizing operating procedures are much more limited than originally assumed. 4. The status with respect to actual field studies differs greatly between the participating countries. Some groups had extensive experience from organizing biomonitoring programmes already before the start of the present CRP, whereas others are still at early stage. 5. There has been some collaboration and interaction between different participants Progress might have been faster on some points if consultations between participants had been more frequent. 6. Validation of the selected biomonitors against bulk deposition or air concentration of the elements studied still remains to be done in most countries. It is not clear at this point to what extent such validation is feasible for all experimental situations encountered in the programme.
6. TECHNICAL ASPECTS
The discussion of the technical aspects from the first RCM remain the same. The following new aspects were considered:
6.1. Quantification
The participant from India expressed opinion that quantification using un-washed and washed samples may be necessary to calibrate against total deposition..
6.2. Time resolution
Time resolution with respect to exposure and pollution was discussed. Study of the time resolution will be mostly done by transplants.
6.3. Geographical resolution
Geographical resolution studies should be done simultaneously considering the local variability in biomonitor responses, survey design (grid density) and spatial variability in deposition and atmospheric concentrations.
6.4. Survey
No general rules on the design of the survey could be recomended. 6.5. Mapping
If possible, the 25, 50, 75 and 95 percentiles maps should be used to facilitate the comparison of results. Outliers should remain visible on the maps.
6.6. Impact
A simple test of membrane and chlorophyll integrity was recommended. Membrane integrity can be tested by conductivity measurements complemented by potassium determination in the leachates. Further or more advanced measurement of impact physiology are considered by individual participants.
Action: The participant of Israel will prepare a written protocol.
7. MAIN SCIENTIFIC AND TECHNICAL CONCLUSIONS
1. The group agrees upon the protocols and work plans for carrying out national projects. The group gets more basic, explicit, and practical experience in discussions on all issues covered. There is considerable improvement in approaches by participants not familiar with biomonitoring at the start of the CRP. 2. More investigations on quantitative aspects of biomonitoring are needed in dry areas with high concentrations of airborne particles. The diversity of environmental conditions worldwide needs specific biomonitoring programs for various sites of investigation. This means that the term "harmonization" should be interpreted in terms of approaches and lines of thinking rather than in terms of strict rigid protocols. 3. The CRP is already giving valuable scientific outcomes as evidenced by the published scientific papers 4. Physiological, biochemical and genetic information is important for the interpretation of biomonitor data. Measurements can be performed on various physiological levels; the problem is how to use the data in the interpretation of biomonitoring response. Indices of pollution should be developed, possibly in terms of heavy metals and sulfur occurrence, together with the development of indices for selected biomonitor physiological parameters. 5. There is no clear approach in designing surveys. 6. There is no clear approach of the presentation of data through maps. 7. Tillandsia may prove to be a good biomonitoring organism in the Latin-American area. It's response needs to be investigated in a quantification study. 8. Procedures aimed at reducing the influence of soil particles are essential. Further work on the effects of washing, and on the need for washing in dry dusty areas is necessary. Procedures need testing for all practical situations 9. Quantification should be performed predominantly by using transplants (lichens), plant segments (mosses), or plant mass formed during the exposure period. Relationships may be established for wet or bulk deposition or for atmospheric concentrations. Validation can be perfomed by the performance of a quantification study or by calibration against a species for which a quantification result exists. 10. Results from the chemical speciation studies are not necessarily related to the physiology. 8. ORGANIZATIONAL ASPECTS
8.1. Funding
A number of participants have finances (partially) covered in the context of government- funding of their regular national biomonitoring programmes (Norway, Germany), or are in the process of raising funds for regional/national surveys, thereby using the IAEA-CRP reference (Argentina, Russia, Romania).
Others raised funding for specific source/location-dedicated surveys (Norway: local point sources, surveys comprising determination of stable Pb isotopes; Portugal: assessment of effects from waste incinerator plants, Jamaica: Pb pollution biomonitoring), or benefited from the IAEA-CRP reference in national scientific positioning of the monitoring programmes (Germany).
Argentina and Chile arrived at bilateral institute's agreements to join forces in both the programmes. In Europe, a moss survey proposal (EPI-DEP project) with 18 countries participating has been submitted to the EU FP5, aimed at the assessiment of deposition by biomonitoring and linking these data to epidemiological information.
8.2. Co-operation
A biogroup listserver was created by the participant of Portugal to facilitate the collaboration among the participants. Action requested: Update the participants e-mail addresses and encourage the participamts to use the listserver for closer co-operation.
An Web address for the CRP was created using the ICENS server in Jamaica Action requested: The participant of Jamaica will design the first draft of the CRP Web page and distribute through IAEA to participants for discussion.
8.3. Next research co-ordination meeting
Argentina, Russia, Portugal and Ghana proposed to host the next RCM. PART II: HIGHLIGHTS AND ACHIEVEMENTS TITLE: AIR POLLUTION BIOMONITORING IN ARGENTINA, APPLICATION OF NEUTRON ACTIVATION ANALYSIS TO THE STUDY OF BIOMONITORS
CSI: MARIA LUISA PIGNATA1 AND RITA R. PLÁ2
INSTITUTE: Cátedra de Química General. Facultad de Ciencias Exactas, Físicas y Naturales. Universidad Nacional de Córdoba. Avda. Vélez Sarsfield 299, 5000 Córdoba, Argentina 2Comisión Nacional de Energía Atómica (CNEA), Unidad Radioquímica, Sector Técnicas Analíticas Nucleares. Avda. del Libertador 8250, 1429, Buenos Aires ARGENTINA
SCIENTIFIC BACKGROUND
Due to low population density, total air pollutant emission in Argentina are still low if compared with highly industrialized countries. Although a significant deterioration of air quality has been observed for a long tine, air monitoring did not begin until the 90's and only in a few cities. The use of air pollution biomonitors represents an important contribution to Argentina, when measurements of air pollutants in large areas would require especial technical equipment, not easily available and operated.
SCIENTIFIC SCOPE
The general objective of this project is to study the use of biomonitors, especially lichens, for evaluating pollutant levels over a wide geographic area of Argentina and for establishing baseline values and assessing time trends.
Two lichen species (Ramalina ecklonii (Spreng.) Mey. & Flot. And Usnea amblyoclada (Mull. Arg.) Zahlbr.) and a Bromeliaceae (tillandsia capillaris f. incana (Mez.)) are used as air pollution biomonitors at a 50,000 km2 area in Cordoba province (central Argentina). Elemental characterization is done using NAA and AAS and some physiological parameters are determined.
OUTPUTS SINCE THE LAST RCM
• Samples were collected at 49 points, gathering the three biomonitors species when possible • Five-replicate samples were collected to study variability of sources • Chemical characterization (30 elements) of samples using NAA and AAS (five-replicates only by AAS) • Participation in NAT-5 (only NAA) • Chemical physiological parameters determination • Data evaluation: descriptive statistics, Spearman correlation, factor analysis • Sources of variability analysis and signal-to-noise ratio • Drawing of preliminary maps
13 PROGRAMME OF WORK FOR 2000
• To complete sampling • To analyze samples by NAA and AAS (already taken ones and new ones) • To take part in NAT-6 (only NAA) • To perform statistical analysis of the data • To draw distribution maps • To enlarge studies of variability of sources • To prepare material for transplants for Ramalina and Tillandsia
EXPECTED OUTPUT FOR 2000
• Complete collection over the sampling grid • Chemical analysis and physiological determinations for all the samples • Evaluation of the whole data set • Mapping • Set of transplants for the selected biomonitors
APPROACH FOR 2001
To perform transplant studies at certain selected sites, to complete comparison of selected biomonitors and to complete data evaluation
PROGRAMME OF WORK FOR 2001
• To complete transplant setting • To analyze transplant samples by NAA and AAS and to perform the physiological determinations • To work on the comparison of the selected biomonitors • To try validation of the selected biomonitors
14 TITLE: DETERMINATION OF TRACE ELEMENTS IN LICHEN SAMPLES BY INSTRUMENTAL NEUTRON ACTIVATION ANALYSIS
CSI: MITIKO SAIKI
INSTITUTE: Institute de Pesquisas Energeticas e Nucleares Caixa Postal 11049 CEP 05422-970, Sao Paulo SP, BRAZIL
SCIENTIFIC BACKGROUND
Studies on environmental pollution by trace elements have gained increasing concern and lichen have been analysed to assess the baseline levels of pollutants and to identify contamination sources. Besides the occurrence of about 2,800 lichen species in the Brazilian territory has been published, however, data concerning their use in monitoring studies and their elemental composition are very scarce. Consequently studies concerning use of biomonitors are of interest due to the serious problems of pollution encountered mainly in big cities like Sao Paulo and due to great extension of the country. There is also interest to obtain reliable information on the elemental concentrations that might serve as reference levels in the studies of the impact which the industrial, mining or agricultural activities may be causing on the environment.
SCIENTIFIC OBJECTIVE
During the last decades, lichen, moss and plant analyses have played an important role in studies on environmental pollution monitoring. The accumulation of various air pollutants, including heavy metals by lichens is well documented and they are considered as useful monitors for air quality. The advantages of using biomonitors, instead of direct measurement of pollutants in several materials of the environment such as water and air, are their easy sampling and their wide geographical distribution. This allows comparison of metal concentrations from several regions as well as to draw more reliable pollution maps of a large area. The main objective of the project is to establish baseline data of pollutants by using Cano parmelia texana lichen as biomonitor and the analytical method of instrumental neutron activation analysis. In order to validate the chosen lichen species, analyses are carried out in samples collected in different levels of pollution. Tradescantia pallida plant is being sludied to be used to monitor regions where lichens are not present.
OUTPUTS SINCE THE LAST RCM
• Analyses of lichens and their respective bark tree substrates. • Definition of the procedure for collection and preparation of the samples. • Collection and analyses of lichens collected in different sites to obtain preliminary information on the quality of air in these regions. • Cultivation, collection and analyses of T. pallida plant to be used as biomonitor of regions where lichens are not present.
15 PROGRAM OF WORK FOR 2000
Our laboratory will concentrate in the following activities concerning biomonitoring of trace elements:
• Conclusion of the analyses of lichens collected in control region • Collection and analysis of lichen samples collected mainly near the industries (car battery industry and mercury recycling plants). This work will be carried out in collaboration with Instituto de Botanica and with Companhia de Tecnologia de Saneamento Ambiental (CETESB), a governmental institution responsible for air quality control • Analyses of T. pallida plant cultivated in vases and kept in three regions of different levels of pollution • Analyses of the soil utilised in the T. pallida cultivation.
EXPECTED OUTPUT/RESULTS FOR 2000
• Data of lichens analyses from control region (Base line) • Results for lichen samples collected in polluted region • Analytical results for T. pallida plant • Treatment of data and interpretation
APPROACH FOR 2001
• The work started in 2000 will be continued
16 TITLE: STUDY OF AIR POLLUTION IN CHILE USING BIOMONITORS
CSI: EDUARDO CORTES
INSTITUTE: Dept. of Nuclear Applications, La Reina Nuclear Centre, Chilean Nuclear Energy Commission, Santiago CHILE
SCIENTIFIC BACKGROUND
Chile in general and Santiago, its capital city, in particular have serious air pollution problems [1,2]. During winter time the air pollution in Santiago reaches dramatic levels producing dangerous health problems in children and adults. The problem has been studied by the local authorities from several sides, however, only recently it has been possible to identify the main sources and origins of the main pollutants. The use of biomonitors will greatly reduce the cost of equipment and manpower. Santiago, in particular, has implemented a decontamination plan of the atmosphere and it is necessary to follow closely the results of the recently enforced measurements. In addition, background information from known clean areas but which are far from the laboratories are needed for comparison purposes.
SCIENTIFIC SCOPE The present project aims at the monitoring of air pollution using appropriate bioindicators to (I) study the applicability of biomonitors to monitor the pollution levels of the atmosphere, (ii) determine the concentration levels of toxic elements in the atmosphere of cities and rural areas using PM-10 samplers, (iii) to determine the concentration levels of those toxic elements on the membrane filters and in the selected biomonitors using NAA, complemented by AAS, (iv) to establish correlations, if any, between the concentration levels of toxic trace elements in airborne particulate matter and those in the biomonitors, (v) determine the sources of pollutants and (vi) to determine the applicability of biomonitors to study air pollution in large areas, using indicators either naturally grown or artificially introduced to the region under examination.
OUTPUT SINCE LAST RCM
• Selection of local and regional areas where the study will be carried out • Collection of several species of lichens for intercalibration purposes and determination of more suitable ones for large scale biomonitoring • Establishment of procedures for sample collection, preparation for analysis and analysis using INAA • Study of the homogeneity of the samples prepared for analysis • Participation in an intercomparison run on lichen analysis organised by the IAEA • Determination of relationships, as regards elemental composition, between different lichen species • Installation of transplants of lichens in the city of Santiago. The lichen were collected in the south of the country (clean native forest) and tranplanted to Santiago in different neibourghoods
17 PROGRAMME OF WORK FOR 2000
• Collection, sample preparation and analysis of the transplants from Santiago • Survey (selection of region and collection of lichens) of trace elements using lichens on a regional base • Collect PM-10 airborne particulate matter at some places where the transplants are, to attempt a calibration between lichens contents and deposition
EXPECTED OUTPUTS FOR 2000
• Analytical results of the transplants from Santiago • Mapping of the levels of different elements in the transplant according to the place • Collected lichens from target region
APPROACH FOR 2001
Extend the study to other cities and region of the country depending on resources and availability of biomonitors. Attempt to identify other plants, different from lichens, to be used as biomonitors on a more extended and regional areas
PROGRAMME FOR 2001
• Analysis of lichens collected from the target region • Collection of lichens from other cities and regions in Chile with relevant air pollution problemss • Mapping and interpretation of the data
18 TITLE: A GLANCE OF AIR POLLUTION OF BEIJING CITY BY USING POPLAR LEAVES AND NAA TECHNIQUES
CSI: NlBANGFA
INSTITUTE: China Institute of Atomic Energy P.O. Box 275-50 Beijing 102413 CHINA
SCIENTIFIC BACKGROUND
Air pollution was increased along with the developing of economy during last two decades in China. The main means of air pollution monitoring are TSP and size-fractionated air particulate matter. Biomonitor as indicator of air pollution have a long history. But most work have been done by Europe countries. In order to monitor air pollution seasonally/yearly in the nation wide, it is very necessary to study the biomonitor as a pollution indicator.
SCIENTIFIC OBJECTIVE
Using poplar leaves as an indicator to monitor the air pollution in case that moss or lichen samples are difficult to be collected.
OUTPUT SINCE LAST MEETING
Forty six Chinese poplar leave samples were collected from different districts of Beijing city proper and outskirts. 32 trace elements have been determined by using NAA technique. The results indicated that high concentrations of Br was appeared in heave traffic area; Sb and Ni in 4 sites of south of Beijing shown a strong correlation; REE at high technical special zone shown more than 4 times of average; Fe and Br were extremely high at the site close to the Iron and Steel Factory. Mapping all the high concentration for each sampling site. It indicates that southwest of Beijing is relative dirty or polluted. The reason may be explained as the Iron and Steel Factory and a Oil refinery Complex are located in this area; Northeast of Beijing is a clean area or less polluted. A paper has been published.
PROGRAM OF WORK FOR 2000
1) study on the relationship of pollution indicators from poplar leaves, airborne particles, and soils. Collecting airborne particles once a week by using Gent sampler. Collecting poplar leaves surrounding the airborne particles sampling sitess. Collecting soils from 50cm depth of ground surface under the poplar tree. Samplers analyzing by NAA Data interpretation.
2) sampling in existing sites will continue, to study the elemental concentration variation with time and the change of surrounding situations.
19 EXPECTED OUTPUT/RESULTS OF 2000 Finding the relationship of the indicator among poplar leaves, airborne particles and soils. Comparing the results of indicator between 1999 and 2000 Paper publishing.
APPROACH FOR 2001
Mapping the pollutants in Beijing city
PROGRAM OF WORK FOR 2001
According to the results of 2000, making a leaves sampling sites in Beijing city, and sampling, analyzing and mapping.
20 TITLE: CORRELATION OF ATMOSPHERIC DEPOSITION AND DISEASES IN THE EUROREGION NEISSE
CSI: BEKND ALBERT MARKERT
INSTITUTE: International Graduate School Zittau Markt 23 02763 Zittau GERMANY
SCIENTIFIC BACKGROUND
A biomonitoring system using the mosses Pleurozium schreberi and Polytrichum formosum as biomonitors has been used to determine the degree of pollution in the Euroregion Neisse (ERN). This region, located in Central Europe where the borders Germany, Poland and Czech Republic meet (see Figure 1), was one of the most highly polluted areas in Europe until the early 1990s. For clarity and ease of access the results have been presented visually using a Geographical Information System (GIS). The deposition of 37 elements in the Euroregion as found in the moss study is compared with the incidence of various diseases, using data from regional hospitals. Connections between diseases of the respiratory tract and Ce, Fe, Ga and Ge deposition as well as between cardiovascular diseases and Tl were determined. The results will be validated by further studies with an even greater amount of data.
APPROACH FOR 2001
There are different methods to estimate and predict effects of chemical elements and corresponding speciation forms in biochemistry and toxicology, including statements on essentiality and antagonisms. Two approaches will be followed for 2001:
- the preparation of so called "ecotoxicological idendity cards" describing biologically fundamental aspects of element chemistry and
- quantitative discussions which assume the existence of (indirect ways into) chemical autocatalysis for maintaining reproduction
21 TITLE: BIOMONITORING OF AIR POLLUTION THROUGH TRACE ELEMENT ANALYSIS
CSI: SAMUEL AKOTO BAMFORD
INSTITUTE: National Nuclear Research Institute P.O. Box LG 80 Legon, Accra GHANA
SCIENTIFIC BACKGROUND AND SCOPE
Some of the gold mining processing methods being currently applied in Ghana involves the high temperature roasting of the gold ore. This leads to the release of heavy metals into the surrounding atmosphere. Taking also into consideration the fact that most of the gold mines are located in the forest zones of the country, make biomonitoring a viable option in atmospheric pollution studies. The main project objective is therefore to develop and validate an indirect, simpler, and less expensive method for studying heavy metal pollutants in ambient air of gold mining industries using biomonitors (lichens). Work done so far includes the validation of the neutron activation analysis quantitative method for the analysis of lichens. This was achieved through the analysis of an NBS orchard leaves samples, BCR-CRM No. 279, and the participation in an intercomparison run of two unknown lichen samples. Also on-going are the analysis of moss samples in another intercomparison exercise, and a field survey to identify local lichen species. The programme of work for the year 2000 is as summarized below:
1. Field trip to Prestea Gold Mines March • Survey of lichen species • Sampling and storage of lichens D Air sampling for PMio and PM2.5 particulates 2. Identification of lichen species April 3. Sample Preparation April • Separation and cleaning of lichen species • Grinding (lichen powder) and pelletization • Wet digestion of lichen samples with HNO3 and H2O2 4. Laboratory Analysis April/May/June • Analysis of lichen samples by INAA and EDXRF • Analysis of digested lichen samples by TXRF • Analysis of reference materials D Gravimetric analysis and elemental analysis of Airborne particulates 5. Field trip to Prestea Gold Mines July/August • Repeat sampling of lichens and aerosols 6. Laboratory Analysis August D Repeat analysis of lichens and aerosols 7. Data Reduction and Evaluation September • Lichen mapping (abundance and concentration distribution)
22 D Establishing empirical relationship (aerosol - lichen concentration linkages) 8. Development of a protocol for biomonitoring air October pollution. 9. Project reports November
THE EXPECTED PROJECT OUTPUTS FOR THE YEAR 2000 ARE AS INDICATED BELOW
1. The quantitative methods of the applied nuclear-related analytical techniques, of instrumental neutron activation analysis (INAA) and x-ray fluorescence spectrometry (XRF), validated for the analysis of lichen samples. 2. Map of the abundance and distribution of identified local lichen species in the project areas. 3. Report on heavy metal concentration levels in selected lichen species, (baseline and prevailing levels). 4. Report on concentrations of atmospheric trace elements through analysis of loaded air filters. 5. Empirical relationship developed linking elemental concentration in lichen samples to that in ambient air. 6. Protocol and procedures in the use of local lichens for biomonitoring air pollution produced.
For the year 2001 attention shall be focused on the selection of the best candidate lichen species to be recommended from among the other species identified. Attempts shall also be made to study the heavy metal uptake using artificially grown lichen samples, and also carry out a factor analysis of both the elemental concentrations in the loaded filters and in the lichen samples. The Ecological Laboratory of the University of Ghana will also be interested in studying the nitrogen fixation characteristics of lichens and their possible linkage with elemental uptake.
23 TITLE: BIO-MONITORING STUDIES USING NUCLEAR AND RELATED TECHNIQUES FOR THE STUDY OF AIR POLLUTION IN AND AROUND THE CITY OF HYDERABAD, INDIA
CSI: JAYARAMAN ARUNACHALAM
INSTITUTE: National Centre for Compositional Characterisation of Materials (CCCM) Board of Radiation and Isotope Technology Department of Atomic Energy Hyderabad, 500 062 INDIA
SCIENTIFIC BACKGROUND
Plants (moss. Lichen and some higher plants) have been shown to be useful for the recognition and estimation of anthropogenically induced air pollution of toxic trace elements.
SCIENTIFIC OBJECTIVES
1. To study some of the plant species around Hyderabad, India (Funaria (a moss) and Lanatana (a shrub) and evaluate their suitability as bio-monitors. 2. To expand this activity to other parts of the country through our own studies and by participation of other institutes.
OUTPUT SINCE LAST RCM
1. Sampling of Funaria and Lantana were carried out. Lantana samples were collected from about 35 sampling sites covering industrial, commercial, residential and areas of heavy traffic. 2. As per agreed protocol, these samples were analysed for several elements using ICP-MS and PIXE (mainly for F). 3. The elemental data from these locations were studied using PCA to study inter element associations; a "soil component" is identified. 4. Electron Microscopic images of the Funaria (washed and unwashed samples) were obtained which revealed the trapping of fine particulate matter in the plant. 5. The local variability of the concentrations of different elements in the lantana was studied and found to be smaller; the concentrations around some hot spots were much higher. 6. Participated in the inter-comparison analysis on the lichen samples.
PROGRAMME OF WORK FOR 2000
1. To carry out sampling from the southern parts of the city (the northern part was covered so far) 2. To collect additional samples from the previously investigated locations to look for consistency of the observations. 3. To collect air particulate matter in some locations and sample the Lantana plants also around the same locations. 4. To collect and analyse fine particulate matter trapped in Funaria.
24 EXPECTED OUTPUT FOR 2000
1. Elemental concentrations in Lantana from about 30 newer locations from the City 2. Detailed elemental distribution patterns of the trace elements 3. Correlation of data on air filter with those of the bio-monitor from certain locations. 4. HPLC/ICP-MS analysis of the funaria moss (already collected) to identify trace elements bound to some organic fractions.
APPROACH FOR 2001
1. To expand the approach to other cities and regions in and around Hyderabad. (Dept. of Experimental Botany, University of Delhi have expressed their interest to carry out similar studies in Delhi); to look for mosses and lichen in the western ghats in collaboration with University of Kerala. 2. To involve the local pollution control board through the dissemination of the information gathered so far. 3. Seek funds from some Scientific Departments to carry out national level surveys.
PROGRAMME OF WORK FOR 2001
Carry out analysis of samples from other Institutes through ICP-MS To obtain info on the availability of moss/lichens from other parts of India.
25 TITLE: AIR-BIOMONITORING BY TRANSPLANTED LICHENS IN THE NEGEV DESERT, ISRAEL
CSI: JACOB GARTY
INSTITUTE: Department of Plant Sciences and Institute for Nature Conservation Research Tel Aviv University Tel Aviv 69978 ISRAEL
SCIENTIFIC BACKGROUND AND SCOPE
1. There are no regular measurements of air quality in the Negev Desert, therefore we try to evaluate the air quality especially around an industrial area south of Been Sheva town.
2. The objective is to study the elemental content and the alteration of physiological parameters in a transplanted lichen.
3. In the last year we obtained all the data about the elemental content of the lichen transplanted in the period August 1997 - April 1998. We obtained also the results of the physiological measurements of the lichens transplanted in the period May-November 1999.
4. In the year 2000 we will measure the elemental content of the lichens exposed to field conditions in the May-November 1999 period.
5. We expect to be able to evaluate the air quality status in the study area according to the elemental content results.
6. In the year 2001 we will use another lichen to evaluate the air quality around a coal- fired power station in central Israel.
7. We will transplant Ramalina lacera around the Oroth-Rabin coal-fired power plant in the year 2001, in order to evaluate the air quality in central Israel.
26 TITLE: BIOMONITORING OF AIR POLLUTION IN JAMAICA THROUGH TRACE ELEMENT ANALYSIS OF EPIPHYTIC PLANTS USING NUCLEAR AND RELATED ANALYTICAL TECHNIQUES
CSI: MlTKO VUTCHKOV
INSTITUTE: International Centre for Environmental and Nuclear Sciences University of the West Indies Kingston 7 JAMAICA
SCIENTIFIC BACKGROUND AND SCOPE
The results of the first phase of the project indicated that the most abundant epiphytic plants in Jamaica are of the genus Tillandsia. The main objective during the second stage of the project is to validate the epiphytic Tillandsia recuvrata for biomonitoring of the atmospheric pollution in Jamaica using the nuclear analytical faculty at the International Centre for Environmental and Nuclear Sciences.
As a first step, a harmonized operating procedure for sampling of epiphytic plants of the genus Tillandsia was developed and a filed card form was produced. The sampling media considered in the filed card are the Tillandsia and optionally lichen specks, if available, or tree barks with associated soil samples. The field information recorded includes the sample location, climatic conditions, description of the ecosystem and sample collection details.
The study area of the biomonitoring programme spreads over the entire country with emphasis on some anomalous areas with elevated levels of heavy metals. To cover the study area, 20x20km grid-sampling strategy was used which produced a total of 33 sampling sites. The electron microscopy study of the samples collected from different heights shows that the size fraction of the particles trapped by Tillandsia is very fine: and can provide valuable information for health risk assessment if collected above 2 meters height.
The laboratory preparation of the Tillandsia recuvrata samples included the separation of the plant tissue and the dust particulate matter captured on the Tillandsia "ball" during the growing period. The plant tissue was pre-concentrated by dry-ashing to enhance the sensitivity for some elements of specific interest. Plant, ash and dust samples were analysed using Neutron Activation Analysis (NAA) and Energy-Dispersive X-Ray Fluorescence (EDXRF) spectrometry.
The comparison of the results obtained for 24 Tillandsia - dust pair samples indicates that both the plant tissues and associated particulate matter can provide information on the elemental status of the atmospheric particulate deposition without necessity of electronic sample collecting devices.
27 TITLE: LOCAL VARIANCES IN BIOMONITORING
CSI: H. TH. WOLTERBEEK
INSTITUTE: University of Technology Interfaculty Reactor Institute Department of Radiochemistry Nuclear Environmental Studies Mekelweg 15 2629 JB Delft THE NETHERLANDS
SCIENTIFIC BACKGROUND
Long experience in biomonitoring, data processing and multi-elemental analysis
SCIENTIFIC OBJECTIVES
Judgement of survey quality, in terms of measurable levels of time and spatial resolution
OUTPUT SINCE LAST RCM
• data on signal-to-noise ratios in limited numbers of observations • judgement of present day possibilities to assess survey quality
EXPECTED OUTPUT 2000 scientific publication on 1999 work • further work on quality assessment by studying local vs survey variances • indication of the possibility to use results on nearby sites to judge site cluster quality
APPROACH 2000 data processing on various survey outcomes, both in moss, bark and soil data
PROGRAMME 2001 further assessment of possibilities to judge survey quality, reporting from the Moss-2001 survey in the Netherlands
28 TITLE. FURTHER PROMOTION OF THE USE OF MOSSES AND LICHENS FOR
STUDIES OF ATMOSPHERIC DEPOSITION OF TRACE ELEMENTS
CSL EE.IV STEINNES
INSTITUTE: Department of Chemistry Norwegian University of Science and Technology N-7491 Trondheim NORWAY
SCIENTIFIC BACKGROUND
Long experience in biomonitoring
SCIENTIFIC OBJECTIVE
Further development of the biomonitoring work Transfer of knowledge to less experienced participants
OUTPUT SINCE THE LAST RCM
Progress in • Analytical developments (ICP-MS, NAA) • Calibration of moss data • Retrospective studies • Collaboration with eastern European countries
PROGRAMME FOR 2000
Carry out the 5th nationwide survey of atmospheric heavy metal deposition in Norway using the moss technique.
PROGRAMME FOR 2001
Report from the Moss-2000 survey Further collaboration with Russia and Romania
29 TITLE: STUDY OF ATMOSPHERIC DISPERSION OF POLLUTANTS IN THE INDUSTRIAL REGION OF THE SADO ESTUARY USING BIOMONITORS
CSI: MARIA DO CARMO MOREIRA FREITAS
INSTITUTE: ITN - Institute Tecnologico e Nuclear Departamento de Quimica Estrada Nacional 10 2686-953 Sacavem PORTUGAL
OUTPUT
Lichen transplants, collected in March, June and September 1998, were analyzed by INAA. Lichen transplants have been exposed facing and opposing the wind, in a whole there were 98 transplants facing the wind and 104 transplants opposing the wind. Previously to analysis they were cleaned, freeze dried, ground and pelletized. Some results were published in the Journal of Radioanalytical Nuclear Chemistry, following a presentation at MTAA -/o Conference '*', and in Biological Trace Element Research .
Aerosol samples, collected from October 1997 to October 1998, in three places at Setubal region, were analyzed by INAA. In total 324 aerosol filters were analyzed.
Statistical procedures were applied to results using STATISTICA program and mapping was performed using SURFER.
PLAN FOR 2000
For 2000, PIXE analysis on these samples (lichen transplants and aerosol filters) will be performed.
The effect of the sample particle grain size on the relation between outcomes of INAA and PIXE will be investigated, based on an iterative procedure consisting of two steps: grinding to smaller size followed by PIXE analysis.
Correlations between data on lichen transplants, aerosol filters and meteorology will be started.
APPROACH FOR 2001
Monte Carlo Aided Target Transform Analysis will be applied to the facing and opposing lichen transplants data sets obtained by both PIXE and INAA. The same procedure will be applied to the aerosol data.
The final goal of the project - elemental dispersion in the Setubal area - will be accomplished by conjugating all the data sets originated in the lichen transplants, aerosols and meteorology.
30 TITLE: ATMOSPHERIC DEPOSITION OF HEAVY METALS IN RURAL AND URBAN AREAS OF ROMANIA STUDIED BY THE MOSS BIOMONITORING TECHNIQUE EMPLOYING NUCLEAR AND RELATED ANALYTICAL TECHNIQUES AND GIS TECHNOLOGY
CSI: ADRIANA LUCACIU
INSTITUTE: Institute of Physics and Nuclear Engineering P.O. Box MG-6 R-76 900 Bucharest ROMANIA
SCIENTIFIC BACKGROUND
Romania is one of the European countries not yet examined for heavy metal atmospheric deposition. This study was undertaken to cover one more "white spot" on the map of Europe in accordance with the requirements of the European guidelines of the Programme: Atmospheric Heavy Metal Deposition in Europe - Estimation based on moss analysis.
The most important results expected are:
• identification of contaminated areas of serious environmental concern • creation of a data base for continued studies at regular intervals
Altogether, the results will form a basis for local authorities to implement the necessary measures to reduce the emissions up to acceptable levels.
SCIENTIFIC SCOPE
To get reliable information on MAP in Romania the following has been done:
• the sampling in Transilvania Plateau including 63 of Hypnum cupressiforme moss samples, i.e. a network of 63 squous each having 20 x 20 km2 (25.200 km2) • sample preparation for short- and long-term irradiation and atomic absorption spectroscopy • multielemental analysis of moss samples by NAA in Dubna (Russia) and by AAS in Baia More (Romania) • data processing of the result • statistical analysis of the results • preparing an annual report
31 PROGRAMME FOR THE YEAR 2000
Atmospheric deposition of heavy metals in rural and urban areas of Romania (Danube course and Delta, Black-Sea coast line) by the moss.
• Analysis of samples collected 2000, by NAA Dubna, and AAS in Baia More • Continuation and completion of NAA and AAS of mosses • Data processing • Sampling in Danube course and Delta, Black Sea coast line • Writing reports and papers based on previously done work
PROGRAMME FOR THE YEAR 2001
• Analysis of samples collected 2000 by NAA in Dubna and AAS in Baia Mare • Continuation and completion of NAA and AAS of mosses • Data processing • Composition with data obtained for the Eastern Carpathians • Sampling in the Southern of Romania for the moss-survey 2001 • Writing reports and papers based on done work on the Eastern, Southern and Western Carpathians and Transilvania Plateau. Analysis of samples collected in summer 2000 (NAA in Dubna and AAS in Baia More).
32 TITLE: BIOMONITORTNG AIR POLLUTION IN CHELYABINSK REGION (URAL MOUNTAINS, RUSSIA) THROUGH TRACE-ELEMENTS AND RADIONUCLIDES: TEMPORAL AND SPATIAL TRENDS
CSI: V. D. CHERCHINTSEV, M.V. FRONTASYEVA
INSTITUTE: Dept. of Activation Analysis & Radiation Research Frank Laboratory of Neutron Physics Joint Institute for Nuclear Research 141980 Dubna, Moscow Region RUSSIAN FEDERATION
SCIENTIFIC BACKGROUND AND SCOPE OF THE PROJECT
The South Ural Mountains are among of the most polluted areas in the world where human impact on the environment is practically irreversible. The impact of these emissions; on a regional scale is not known, as well as their influence on the natural environment and their possible impact on human health. Monitoring efforts of the present study will help to elucidate these problems.
This report contains the results on the analysis of the moss species; Hylocomium splendens and Pleurozium schreberi which were used to study heavy metal atmospheric deposition, as well as other toxic elements, in the Chelyabinsk Region (the South Ural Mountains) characterized by intense anthropogenic impact from various industries incuding plutonium production - the source of radionuclides of great potential hazard. A two years summer field work followed by the applying two most appropriate analytical techniques to the analyses of the collected moss - NAA and AAS - allowed us to determine the atmospheric deposition of about 38 elements over the examined areas. Applying a graphical technique to the results obtained allowed to distinguish between contribution from air pollution and a crystal component from windblown soil particles through enrichment factor EF = (x/sc) moss/ (x/sc) crust. Maps of heavy metal deposition in two examined areas on the basis of the developed EIS are prepared.
PLANS FOR FUTURE WORK
The detailed working plan is as follows:
Months 1-3
• Sample preparation for short- and long-term irradiation at IBR-2 reactor, Dubna • Multi-elemental analysis of moss and soil samples collected during summer, 1999 by epithermal NAA in Dubna and by AAS in Moscow.
Months 4-6
• Data processing of the results obtained for moss and soil samples by epithermal NAA in Dubna and AAS in Moscow. • Developments of GIS technology for the examined territory of the Chelyabinsk Region at the University of Dubna.
33 Months 7-8
• Field work: sampling of mosses and soils in the Central part of the Chelyabinsk Region (to the South of enterprise "Mayak"), according to a detailed plan approved by the expert consultant of the Project, Prof. E. Steinnes (Norway). A sampling grid of 30 x 30 km will be applied, giving a total of about 50 sampling sites.
Months 9-12
• Statistical analysis of the results obtained for the samples collected during summer, 1997- 1999, including principal component analysis. • Preparation of maps of heavy metal distribution in Chelyabinsk Region on the basis of the developed GIS.
• Preparation of interim (third year) report.
The most important results expected after completion of the project are as follows:
• establishment of a regional sampling network for future monitoring programmes; • identification of areas with high contamination levels not previously considered to be of environmental risk; • creation of a database for continued studies at regular intervals; • comparison of environmental contamination levels in the Ural region with other strongly polluted areas in Europe, such as the "Black Triangle", Copper Basin in Poland and the Romanian Carpathians.
Altogether, the results from the project will form a basis for local authorities to implement the necessary measures to reduce emissions to environmentally acceptable levels. Moreover, the responsible public health authorities will have a significantly improved basis for assessing possible risk to the population from previous and current emissions.
34 PART III: COUNTRY REPORTS XAO102855 AIR POLLUTION BIOMONITORING IN ARGENTINA, APPLICATION OF NEUTRON ACTIVATION ANALYSIS TO THE STUDY OF BIOMONITORS
^ARÍA LUISA PIGNATA AND 2RUA R. PLÁ
'Cátedra de Química General, Facultad de Ciencias Exactas, Físicas y Naturales Universidad Nacional de Córdoba. Avda. Vélez Sársñeld 299, 5000 Córdoba, Argentina. E-mail: [email protected] - E-mail2: [email protected] 2Comisión Nacional de Energía Atómica (CNEA), Unidad Radioquímica, Sector Técnicas Analíticas Nucleares. Avda. Libertador 8250, 1429 Buenos Aires, Argentina. E-mail: [email protected]
Abstract:
Due to low population density, total air pollutant emissions in Argentina are still low if compared with highly industrialised countries. Although a significant deterioration of air quality has been obsewed for a long time, air monitoring did not begin until the 90's and only in a few cities. The use of air pollution biomonitors represents an important contribution to Argentina, as measurements of air pollutants in large areas would require especial technical equipment not easily available and operated. In this project, two lichen species (Ramalina ecklonii (Spreng) Mey & Flot and Usnea amblyoclada (Müll. Rg.) Zahlbr.) and a Bromeliaceae (Tillandsia capillaris) are used as biomonitors of air pollution at a 50,000 km2 area in Córdoba province (central Argentina). AAS andlNAA have been applied for the analysis of samples, determining As, Ba, Br, Ce, Co, Cr, Cs, Cu, Eu, Fe, Hf, Gd, K, La, Lu, Mn, Na, Ni, Pb, Rb, Sb, Se, Sm, Ta, Tb, Th, U, Yb and Zn. The following physiological parameters were also determined: chlorophyll a, chloropyll b, phaeophytin a, phaeophytin b, hydroperoxy conjugated dienes, malonaldehide and sulphur. Some of these parameters were used for calculating a pollution index. These determinations were carried out on pools collected at the sampling sites. AAS and physiological parameters were also applied to the analysis of five-replicate samples in order to study variability sources. For data evaluation, different statistical and other evaluating tools were used: descriptive statistics and Spearman's correlation analysis were used on data from the three biomonitor species while factor analysis and mapping, only for R. ecklonii results.
1. INTRODUCTION
Due to the low population density, the total air pollutant emissions of Argentina are probably still low in comparison with highly industrialised countries. However, and like in some other South American countries, they are rapidly increasing and, even though the detailed emission inventories of urban and industrial centres are being compiled by state environmental agencies, they are scarce and inefficient [1].
Although a significant deterioration of ambient air quality, caused by the locally and regionally high air pollutant emissions from different kinds of sources, has been observed for a long time, the establishment of environmental agencies and the installation of air-monitoring networks did not take place until the 90's and only in a few cities. Major air pollution problems are occurring at urban and industrial centres, increasing pollution levels, however, they can also be observed at remote sites as a consequence of agricultural practices and mineral mining and processing [2].
Thus, the use of air pollution biomonitors represents an important contribution to Argentina where measurements of paniculate matter or other types of pollutants in big areas would require expensive technical equipment, not available in the country at this moment.
37 In this project, we set to assess the behaviour of two lichen species {Ramalina ecklonii (Spreng.) Mey. & Flot. and Usnea amblyoclada (Mull. Arg.) Zahlbr.) and of one Bromeliaceae {Tillandsia capillaris f. incana (Mez.)) as biomonitors of air quality, considering both their accumulative properties and their physiological response.
Although several studies have shown that trace element concentrations in biomonitors show average concentration of particulate matter in air and both wet or dry depositions of pollutants over a certain time [3-4], this kind of research has not been carried out so far on a large scale in Argentina.
The possibility to systematically assess air quality from the lichens' physiological response was described about a decade ago [5] and has been studied for some species in Argentina [6]. Due to the specific response to different pollutants, some species transplanted to urban and industrial areas are excellent markers of different emission sources and good for biomonitoring atmospheric quality [7-8-9].
Besides lichens, other epiphytic plants have been used as biomonitors for metal and trace elements depositions [10-11]. Tillandsia genus, which is highly spread in South America, shows a large number of species, characterised because of their high tolerance to hydric stress. However, there are few works reporting on their physiological response to pollutants, and the species growing in Argentina have not been studied considering their biomonitoring ability.
2. METHODS
2.1. Study Area
The study area covering 50,000 km2 in central Argentina, is a quadrilateral area whose four corners are at the following co-ordinates: to the West, 31° 25' 21" S, 65° 24' W; to the East, 31° 41' 15" S, 62° 38' 34" W; to the North, 30° 36' S, 64° 15' W; to the South, 32° 48' S, 64° 10' 12" W. Land morphology is highly variable, ranging from a mean altitude of about 250 meters in the south-east to more than 2,500 m to the mid-west. There are cities (high and medium sized) and many small villages in the area; industrial plants, mainly metallurgical, petrochemical, chemical, food, vegetable oil and cement, are mostly located in the centre and south where the highest population density is recorded.
For sampling purposes, the chosen area was divided according to a square pattern, each square of 25 by 25 km (80 sampling points in the area) collecting samples of selected species at each intersection point. At them, the collection sites were located at least 500 m from major routes and highly populated areas, and at least 300 m from streets with lower traffic density.
2.2. Sampling collection
Three bioindicators were selected, two lichen species: Ramalina ecklonii (Spreng.) Mey. & Flot and Usnea amblyoclada (Mull. Arg.) Zahlbr. and a Bromeliaceae, Tillandsia capillaris f. incana (Mez.). They are present in the study area but they are not evenly distributed as they represent different environments or phytogeographic provinces. R. ecklonii and T. capillaris are found in tree trunk substrates, whereas U. amblyoclada is found in rocks. The two lichens are fruticose species.
38 2.3. Experimental Design
Pools of the three different species were collected (when possible) at the sampling sites. Each pool consisted of 40 - 50 individuals, randomly taken along the four cardinal directions within an area of 100 x 100 m. Extraneous material was removed from each sample and they were put in paper bags. The collection was done using plastic gloves to avoid any risk of sample contamination [12].
In order to analyse intra-site variability, five-replicate samples were collected at 20 % of the sites [13]. At these sites, five pools were collected, from the same substrate if possible.
Sampling was conducted only if a five-days-without-rain condition was fulfilled. Once at the laboratory, the samples were let to dry on filter paper, in a clean enclosure at room temperature, for 24-48 hours.
2.4. Sample Pools
Each species pool was prepared mixing several individuals collected along the four cardinal directions, at each sampling site, no more than 100 m from the geographically referenced point. When possible, the samples were collected from the same kind of substrate (forophyte species). For T. Capillaris, similar diameter individuals were collected. Chemical determinations were done in triplicate, from independent sub-samples in each sample corresponding to each pool.
2.5. Replicates
The points for collecting the five-replicates were set using a random numbers table. Each replicate was formed by a pool of individuals collected from the same substrate, at the four cardinal directions and no more than 100 m from the point. The samples were put in paper bags and labelled. Chemical determinations were done in triplicate, from three independent sub-samples in each sample corresponding to each replicate
2.6. Analytical methods
Multielemental analysis was carried out using Instrumental Neutron Activation Analysis (NAA) and Atomic Absorption Spectrometry (AAS) was used for some metal determination. For chemical-physiological parameter quantification, different methodologies were used [13].
2.7. Sample treatment previous to AAS
The samples (kept at room temperature) were washed with cold (4 °C) bi-distilled Avater in a relation 1:50 W/V. For washing purposes, each sample was put into a white nylon mesh bag (previously washed with bi-distilled water) and submerged in a glass of bi-distilled water for about 5 seconds while rotating it 180°. This procedure was repeated three times without changing the water.
Once washed, the samples were put in a Petri capsule and dried in oven at 50 ± 2 °C for 72 hours. Afterwards, R. ecklonii and T. capillaris were ground in a porcelain mortar, while U. amblyoclada was separated into small segments using Teflon covered tweezers. After
39 being ground and homogenised, the samples were dried in oven till constant weight. From this material, 0.5 g (dry weight) was taken for metal quantification by AAS.
2.8. Sample preparation for NAA
The samples were washed as described in point 2.7 and let dry at room temperature in a clean area. Plant material was ground in an agate mortar with the aid of liquid nitrogen addition and then freeze-dried for 24 hours.
2.9. AAS determinations
Masses of about 0.5 g of dry material were ground and reduced to ashes at 650°C for 4 hours. The ashes were digested with HC1 (18%): HNO3 (3:1) at mild heat and the solid residue was separated by centrifugation. Finally, the volume was adjusted to 50 ml with Milli Q water and analysed by AAS using a Buck Spectrophotometer Model 210-VGP in order to determine the concentration of Co, Cu, Fe, Mn, Ni, Pb and Zn. Likewise, blanks of the digest were prepared and analysed [14].
2.10. NAA determinations
Masses of about 200 mg of freeze-dried material were sealed in high purity quartz ampoules for their irradiation, together with two certified reference materials, NIST 1633b Coal Fly Ash and IAEA V-10 Hay Powder. The irradiations were done at the RA-3 reactor of the Ezeiza Atomic Centre (Argentine Atomic Energy Commission), of thermal flux 3.1013 cm" 2.s"1 and 4,5 Mw, during 8 hours. Instrumental Neutron Activation Analysis was performed, measuring twice after 6 and 30 day decay, for medium and long-lived nuclides.
The measurements were carried out using GeHP detectors (30 % efficiency, 1,8 keV resolution for the 1332,5 keV 6OC0 peak) coupled to both a Canberra Series 85 multichannel analyser and to an Ortec 919 buffer multichannel module plus Gamma Vision software for data acquisition. Concentrations for the determined elements (As, Ba, Br, Ca, Ce, Co, Cr, Cs, Eu, Fe, Gd, Hf, K, La, Lu, Na, Rb, Sb, Sc, Sm, Ta, Tb, Th, U, Yb and Zn) were calculated using softwares developed at the NAA laboratory. The precision on 12 sets of three replicates was estimated. All the determined elements had a precision better than 12%, except for Ba (14%), Gd (17%) and U (14%). Control charts (z-values) [15] for inspection of the normalized concentrations of all elements in one control sample, for a series of measurements, were used. None of the values was outside the I z I > 3 range. As control samples, NIST SRM 1547 Peach Leaves, 679 Brick Clay and 2709 San Joaquin Soil were used.
2.11. Chemical-physiological parameter quantification
Lichen samples were dried and shredded to achieve homogeneity and then, freeze-dried. T. capillaris was kept and analysed without previous homogenisation of the initial material due to its high water content. Three sub-samples were run for all the determinations.
2.11.1. Dry weight /fresh weight ratio
The dry weight / fresh weight (DW/FW) ratio of the samples was determined by drying 1 g of fresh material at 60 ± 2 °C until constant weight. The results were expressed in g DW. g"
40 2.11.2. Chlorophylls
A portion of 100 mg of plant material was homogenised in 10 ml EtOH 96 % v/v with an Ultra Turrax homogeniser and the supernatant separated. Afterwards, HC1 0.06 M was added to clear chlorophyll extract (1 ml HC1 and 5 ml chlorophyll extract) in order to produce phaeophytin formation. Absorption of chlorophylls and phaeophytins, and phaeophytins alone (after addition of HC1) was measured with a spectrophotometer Beckman DU 7000. Concentrations of chlorophylls and phaeophytins were calculated on a dry weight basis [16]. The ratios chlorophyll b/ chlorophyll a (Chi b /Chi a) and phaeophytin a /chlorophyll a (Phaeoph a / Chi a ) were also calculated.
2.11.3. Sulphur content
Five millilitres of Mg (NC^ saturated solution were added to 0.5 g of plant and dried in an electric heater. Subsequently, the sample was heated in an oven for 30 min at 500 °C. Ashes were then suspended in HC1 6 M, filtered and the resulting solution, boiled for 3 minutes. The solution was brought to 50 ml with distilled water.
The amount of SO42" in the solution was determined by the acidic suspension method with BaCl2 [17] which subsequently allowed for the calculation of sulphur contents in each sample. The concentration was expressed in mg of total sulphur g"1 DW.
2.11.4. Per oxidation product estimation
Malondialdehyde (MDA) was measured by a colorimetric method. The amount of MDA present was calculated from the extinction coefficient of 155 mM"1 cm"1 [18]. Results were expressed in |xmol g"1 DW.
Hydroperoxy conjugated dienes (HPCD) were extracted by homogenisation of the plant material in 96 % v/v EtOH at a ratio of 1:50 FW/V. The absorption was measured in the supernatant at 234 nm and its concentration was calculated by means of s = 2.65 X 104 M"1 cm"1 [8]. Results were expressed as mmol g"1 DW.
2.12. Statistical Analysis and other data evaluation tools
The following statistical analysis and other data evaluation tools were applied:
1. Descriptive statistics, factor analysis and distribution maps for chemical elements and physiological parameters, using SPSS 8.0 and Surfer softwares.
2. Spearman's correlation coefficient for studying the relationships among chemical elements and physiological variables.
3. Hierarchical analysis of variance to evaluate the different sources of variability in the study. The GLM procedure of SAS was used to estimate the components of variance from the expected mean squares for Type III Square Sums.
41 3. RESULTS AND DISCUSSION
3.1. Ramalina ecklonii
For this species the determinations included: i) Pools from 37 sites and five-replicates from five sites were analysed by AAS ii) NAA was used for the analysis of pools from 26 sampling sites. iii) Physiological parameters (chlorophyll a, chlorophyll b, phaeophytin a, phaeophytin b, HPCD, MDA and S were determined on pools from 33 sites and five-replicates from five sites . A pollution index (PI) was calculated based on some of the above mentioned parameters [19]:
PI = ((Phaeoph a/Chl a)+ (S/ S mean)) (MDA/ MDA mean) a) Descriptive statistics Results are in Table I. The highest concentrations corresponded to Ca, K, Fe, Na, Mn, Zn, Ba, Cu, Rb, Ce, Cr, Ni, Nd and La. Physiological parameters and S values were found within the same range as reported for this species in previous works. b) Spearman's correlation analysis b.l) Chemical element correlation analysis
High positive correlation coefficients were found among Na, K, Sc, Cr, Fe, Co, Se, Cs, Ba, La, Ce, Nd, Sm, Eu, Gd, Tb, Yb, Lu, Hf, Ta, Th and U. Zn showed positive correlation with Sc, Se, Ba, Ce, Sm, Eu, Gd, Tb, Yb, Lu and negative with Sb while Sb showed positive correlation with K, Co, As, La y U. Ni correlated with Na, K, Co, Ba, La, Nd and Eu and Cu with Na, Cr, As, La, Ta, and Pb, S showed positive correlation with Ni and Se, and a negative one with Pb, and Pb correlated positively with Cu, As and negatively with S, Zn and Gd.
b.2) Correlation analysis among physiological parameters and among physiological parameters and chemical elements
Significant positive correlations were observed for S with HPCD and for DW/FW with Sc, Cr, Fe, Co, Cs, La, Sm, Yb, Hf, although for this species, DW/FW has showed to be a poor physiological damage indicator caused by pollutants. In general, pigments didn't show correlation with chemical elements with the exception of Chi a which showed positive correlation with Mn, Co and Ba, Chi b/Chl a with Cs and Br and Phaeoph b, and HPDC which showed negative correlation with Pb. Pollution index PI presented significant positive correlations with Mn, Fe, Se and Nd and negative with Pb and Zn.
c) Factor analysis d) Chemical results from 25 sampling sites were analysed by factor analysis using SPSS software. By keeping those factors with eigenvalues higher than one, six factors were chosen (Table II). The first factor could be assigned to natural soil source and also probably, to fossil fuel combustion. Factor 2 was probably due to agricultural activities and refuse incineration
42 and factors 3, 4 and 5, to anthropogenic activities. Factor 7, although could be connected to a natural source, will require further investigation. A new analysis was tried including physiological results and 10 factors were obtained. Those factors with an important contribution of chemical elements were similar to the ones obtained previously. Factor 1, connected with soil and possibly fossil fuel combustion, was similar to factor 1 obtained using only chemical data analysis. Factors 3 and 6 reflected anthropogenic origin and factor 4 could be connected to an agricultural source. Factors 2, 5 and 9 included some physiological parameters. Factors 7 and 8 and also factor 10 (similar to factor 6 from the analysis using only chemical results) will require further investigation. d) Distribution maps
Preliminary distribution maps for certain selected elements and physiological parameters were constructed based on correlation results and analysis of variability sources. Only some of these maps are presented here (see Figure 1, 2, 3 and 4). e) Sources of Variability Analysis
Data from five sample points with five replicates were used to estimate components of variances of the physiological parameters, and data from eight sample points, for the analysis of variances of metals determined by AAS. Table in (a) and IV (a) show the estimates of variance components obtained from a hierarchical analysis of variance, in which three random effects or sources of variability were considered: • The variability among sample points (only sample points with five replicates), which will be called survey variance. • The variability among localizations within a sample point (five replicates in each sample point), which is called "local variance". • The variability among different sub-samples and chemical determinations, which will be named "experimental variance".
It must be pointed out that these estimates of variances are "components" of the total variance of each observation, and therefore, none of them includes any component of a lower level of variability.
If experimental variance were negligible compared to local variance, the signal-to-noise ratio proposed by Wolterbeek et al. [20], could be obtained as the ratio between the survey and local variance in Tables III (a) and VI (a). This was not the case as can be seen in Table III (a). Some variables were found in which experimental variance is at least as important as local variance, and others in which experimental variance is more important than local variance.
Tables III (b) and IV (b) show the estimates of variance with the following two sources of variability: The variability among sample points (survey variance) « The variability among localizations within a sample point (local variance*) that in these cases includes a contribution due to the experimental variability.
Local variance* = Local variance + experimental variance / n
With n = number of sub-samples.
43 Survey variancel = Survey variance for sample points with five replicates, using the mean value of the sub-samples determinations.
Survey variance2 = Survey variance for sample points with five replicates + Local variance / k + experimental variance / (k * n)
Survey variance3 = Survey variance of all the sample points + Local variance / k + experimental variance / (k * n)
With k = number of local replicates and n = number of sub-samples.
From Tables HI (b) and IV (b) it can be seen that there are substantial changes in considering this new proposal for estimating the variance associated to "noise" in connection with the estimation obtained in Tables HI (a) and IV (a) for local variances. As it has been proposed by Wolterbeek et al. [20], the denominator of the signal-to-noise ratio should contain all sources of variability others than the variability among sample points that are far enough to represent different environmental conditions.
Based on these estimates of survey and local variances, a signal-to-noise ratio was constructed and labelled 1. For those variables in which the experimental variance was negligible, the signal-to-noise ratio 1 was almost the same as the ratio obtained in the first table (see for example, Mn and Zn in Table IV). But in those variables with important experimental variances, the ratio could diminish noticeably (see for example, Chi b and Phaeoph b in Table HI or Ni and Pb in Table IV).
Signal-to-noise ratios were calculated from these estimates of survey variances, using the local variance* estimate obtained from the analysis of variance of the data from those points with five replicates.
As can be observed in Table IE (b) and IV (b), survey variance 2 was a little higher than the survey variance 1, as expected from the above expression. There were no important differences in calculating the signal-to-noise ratio using the estimate 1 or 2 of the survey variance. The only difference was that the survey variance 2, was not exactly a variance component but it contained a contribution of the lower components of variance, which would be less important with the increase in the number of replicates or sub-samples.
However, the estimates of survey variance based on all the data of the survey (survey variance 3) could be noticeably different from those based on sample points with five replicates (see for example Chi b, Phaeoph b or Chi b/Chl a ratio in Table III (b)). These results could be explained in connection with the fact that in using all the points a wider range of environmental conditions are included, giving more chances for extreme situations to appear. For metals (Table IV (b)) there were situations in which survey variance 3 (or signal- to-noise3) was lower than survey variance2 (or signal-to-noise2).
3.2. Tillandsia capillaris
For this species the determinations included:
44 i) Pools from 34 sites and five-replicates from five sites were analysed by AAS ii) Physiological parameters (chlorophyll a, chlorophyll b, phaeophytin a, phaeophytin b, HPCD, MDA and S were determined on pools from 33 sites and five-replicates from eight sites A pollution index (PI) was calculated based on some of the above mentioned parameters (unpublished data)
PI - [(Chi b/Chl a)+ (S/S mean)] [(MDA/MDA mean) + (HPCD/HPCD mean)] (DW/FW) a) Descriptive Statistics
Results of mean, standard deviation, median and range are in Table V. The highest concentrations corresponded to Zn, Cu and Ni. Although Cu, Zn, Co and Ni mean values were similar to thosie found in R. ecklonii, the highest values were significantly lower. For Pb, values were one magnitude level lower than those for R. ecklonii. b) Spearman's correlation analysis
Significant positive correlation coefficients were found between Ni and Co and between them and Pb. Cu showed positive correlation with Zn. S showed negative correlation with DW/FW and positive with Chi a, Phaeoph a, and Phaeoph b. HPCD correlated positively with Cu, and MDA negatively with the DW/FW ratio. c) Distribution Maps
Preliminary distribution maps for metals and physiological parameters were drawn presenting here only those that according to the statistical analysis were considered as more representative (see Figures 5, 6, 7, 8). d) Sources of Variability Analysis
Results for T. capillaris are shown in Tables VI and VII. Data from eight sample points with five replicates were used to estimate components of variances of the physiological parameters and data from five sample points with five replicates were used to estimate components of variance of metals determined by AAS. As for R. ecklonii, Tables VI (a) and VII (a) show the estimates of variance components obtained from a hierarchical analysis of variance in which three random effects or source of variability were considered. Tables VI (b) and VII (b) show the estimates of variance obtained from this analysis under the names: Survey variance 1, Survey variance 2, Survey variance 3 and Local variance*.
It can be seen from Tables VI (b) and VII (b), that there were substantial changes in considering this new proposal for estimating the variance associated to "noise" compared to the estimation obtained in Tables VI (a) and VII (a) for local variances. As can be observed in Tables VI (b) and VII (b), survey variance 1, was slightly higher than survey variance 2. Signal-to-noise ratio using the estimate survey variance 1 was higher than 2 for some parameters (Chi a, Chi b, Phaeoph a, Phaeoph b and sulphur), whereas for others it was lower (Chi b/Chl a ratio, Phaeoph a/Chl a ratio, MDA, and heavy metals).
45 For Tillandsia, the estimates of survey variance based on all the survey data (survey variance 3) and signal-to-noise 3 were not noticeably different from those based on sample points with five replicates.
3.3. Usnea amblyoclada
Chemical determinations in this species included: i) Pools from 10 sites and five-replicates from three sites were analysed by AAS ii) Physiological parameters (chlorophyll a, chlorophyll b, phaeophytin a, phaeophytin b, HPCD, MDA and S were determined on pools from 10 sites and five-replicates from two sites A pollution index (PI) was calculated based on some of the above mentioned
parameters [21].
PI = [(Chi b/Chl a)+ (Sulphur/Mean of Sulphur)] (MD A/Mean of MDA) a) Descriptive Statistics Results are in Table Vffl. Highest concentration values corresponded to Zn, Cu y Ni. Mean values for Cu as well as for Zn, Co and Ni were similar to those found in R. ecklonii while the highest values were significantly lower. Pb values were in the same order that those obtained for T. capillaris, whereas the highest values were one order lower than those of T. capillaris and two orders than the ones for R. ecklonii. b) Correlation Analysis
Due to the fact that only a few point were available for this species, the results for the correlation analysis should be confirmed in future studies using more data. Pb correlated positively with Co and PI, and negatively with Phaeoph a and Phaeoph b. Zn correlated positively with sulphur and Phaeoph a/Chl a ratio and negatively with Chi a and Chi b. Cu showed a significant positive correlation with Co, Chi b/Chl a ratio and HPCD. Ni correlated positively with Phaeoph a, Phaeoph b and negatively with MDA and PI. Co correlated positively with Pb, Cu, PI and negatively with Paeoph a and Phaeoph b. Sulphur correlated positively with Zn. PI showed a positive correlation with Co, Pb and sulphur and a negative one with DW/FW ratio, Phaeoph a, Phaeoph b and Ni. MDA correlated positively with Phaeoph a/Chl a ratio and negatively with Chi b, Chi a and Ni. HPCD correlated positively with Chi b/Chl a ratio and Cu. c) Distribution maps Due to the scarce number of points obtained for this species, no distribution maps are presented. d) Sources of Variability Analysis
Results for U. amblyoclada are presented in Tables IX and X. Data from three sample points with five replicates were used to estimate components of variances of the physiological parameters and metals determined by AAS.
In the same way as for the other two species, Tables IX (a) and X (a) show the estimates of variance components obtained from a hierarchical analysis of variance in which three
46 random effects or sources of variability were considered. It must be pointed out that these estimates of variance are components of the total variance of each, observation, and therefore, none of them includes any component of a lower level of variability.
Tables IX (b) and X (b) show the estimates of variance obtained from this analysis under the names: Survey variance 1, Survey variance 2, Survey variance 3 and Local variance* obtained in the same way as explained for R ecklonii and T. capillaris.
From Tables IX (b) and X (b) it can be seen that there were substantial changes in considering this new proposal for estimating the variance associated to "noise" if compared with the estimation obtained in Tables IX (a) andX (a) for local variances.
As can be observed in Tables IX (b) and X (b), survey variance 2, was slightly higher than survey variance 1, as expected. For this species, except for HPCD, signal-to-noise ratio using the estimate survey variance 1 was lower than 2.
However, as for R. ecklonii, in this species the estimates of survey variance based on all the data of the survey, can be noticeably different from those based on sample points with five replicates (see for example Chi a, Chi b, b, Phaeoph a, Phaeoph b, Chi b/Cl a ratio, MDA and sulphur in Table IX (b) and Zn in Table X (b)). Signal-to-noise ratio 3 also was noticeably higher.
3.4. Interspecies comparisons
Spearman's correlation analysis was performed among all parameters taking pairs of species and considering data from the points where the two species were present. As for U. amblyoclada the number of common points with the other two species was low, only the results for the intercomparison between R ecklonii and T. capillaris are reported. Chi b/Chl a, MDA and Cu showed high positive correlation coefficients between two species. However, due to the fact that data for trace elements were not available, these preliminary results should be considered as preliminary ones.
4. PLANS FOR FUTURE WORK
Chemical analysis of trace elements by NAA as well as the analysis and interpreting of results will be completed and new samples will be taken considering these results, in order to complete the study.
At those sites where high elemental concentrations were found for certain elements of anthropogenic origin, transplants will be tried. R ecklonii and T. capillaris will be used in order to determine if the observed high-level results correspond to present high-value emissions or to accumulation over an extended period.
The results of this Project will be transferred to the Environmental Agency of the Government of the Cordoba province. This agency will set air quality standards and maximum emission levels for industry in the province of Cordoba. The data produced within this study are the first trace-element baseline level results for the area that will be used by organisms responsible for environmental legislation and control.
47 REFERENCES
[I] WORLD RESEARCH INSTITUTE. International Institute for Environment and Development, Internationaler Umveltatlas: Jahrbuch der Welt-Ressourcen, Vol. 1, Ecomed, Landsberg, Germany (1988). [2] MORETTON, J., GUASCHINO, H., AMICONE, C, BELETZKY, V., SANCHEZ, M., SANTORO, V., NOTO, B., "Contamination del Aire en Argentina: aspectos generales, legislation y situation en Capital Federal y provincia de Buenos Aires", Ediciones Universo, Buenos Aires (1996) 1-127. [3] STEINNES, E., Biomonitors of air pollution by heavy metals. In "Control and fate of Atmospheric Trace Metals", (PACYNA, J.M, OTTAR, B. Eds.), Kluwer Academic Publishers, Dordrecht (1989) 321-338. [4] MARKERT, B., Instrumental Analysis of Plants. In "Plants as Biomonitors - Indicators for Heavy Metals in the Terrestrial Environment", (MARKERT, B., Ed.), VCH- Publisher, Weinheim, New York (1993) 65-103. [5] GARTY, J., KARDISH, N., HAGEMEYER, J., RONEN, R., Correlations between the concentration of adenosine tri phosphate, chlorophyll degradation and the amounts of airborne heavy metals and sulphur in a transplanted lichen. Arch. Env.Contam. Toxicol., 17(1988)601-611. [6] PIGNATA, M.L., Studies About Lichens and the Atmospheric Pollution in Argentina, In "Lichenology in Latin America: history, current knowledge and applications", (MARCELLI, M.P., SEAWARD, M.R.D. Eds.), CETESB, Sao Paulo (1998) 155-164. [7] GARTY, J., KARARY, Y.,HAREL, J., The impact of air pollution on the integrity of cell membranes and chlorophyll in the lichen Ramalina duriaei (De Not.) Bagl. transplanted to industrial sites in Israel. Arch. Env.Contam. Toxicol., 24 (1993) 455- 460. [8] LEVIN, A.G., PIGNATA, ML., Ramalina ecklonii (Spreng.) Mey. and Flot. as bioindicator of atmospheric pollution in Argentina, Can. J. Bot, 73 (1995) 1196-1202. [9] GONZALEZ, CM, CASANOVAS, S.S, PIGNATA, Ml, Biomonitoring of air polution in Cordoba, Argentina employing Ramalina ecklonii (Spreng.) Mey. and Flot, Env. Pollut, 91(1996) 269-277. [10] SHACKLETTE, H.T, CONNOR, J.J, Airborne Chemical elements in Spanish Moss. Statistical studies in field geochemistry. Geological Survey Professional Paper 574-E. United States. Government Printing Office, Washington, (1973). II1] PYATT, F.B, GRATTAN, J.P, LACY, D, PYATT, A.J, SEAWARD, M.R.D, Comparative effectiveness of Tillandsia usneoides L. and Parmotrema praesorediosum (Nyl.) hale as bio-indicators of atmospheric pollution in Louisiana (S.S.A.). Water, Air, and Soil Pollution 111 (1999) 317-326. [12] SLOOF, J.E., Environmental Lichenology: Biomonitoring trace-element air pollution, Thesis, Interfacultair Reactor Instituut, Technische Unversiteit Delft (1993). [13] PIGNATA, M.L, PLA, R.R., Air pollution biomonitoring in Argentina, application of neutron activation analysis. First Research Co-ordination Meeting of the Co-ordinated Research Project on Validation and application of plants as biomonitors of trace- element atmospheric pollution, analyzed by nuclear and related techniques. IAEA, Vienna, Austria, 28 Sept.-l Oct., 1998. NAHRES-45, IAEA, Vienna, (1999) [14] PFEIFFER, H.N, BARCLAY-ESTRUP, P, The use of a single lichen species, Hypogimnia physodes, as an indicator of air quality in Northwestern Ontario. Bryologist, 95 (1992) 3-41. [15] BODE, P, VAN DDK, C.P, Operational management of results in INAA utilizing a versatile system of control charts. J. Radioanal. Nucl. Chem, 215 (1) (1997) 87-94.
48 [16] WINTERMANS, J.F.G.M., DE MOTS, A., Spectrophotometric characteristics of chlorophylls a and b and their pheophytins in ethanol. Biochim. Biophys. Acta 169 (1965)448-453. [17] GONZALEZ, CM., PIGNATA, M.L., The influence of air pollution on soluble proteins, chlorophyll degradation, MDA, sulphur and heavy metals in a transplanted lichen, Chem. andEcol., 9(1994)105-113. [18] KOSUGI, H., JOJEMA, T., KDCUGAWA, K., Thiobarbituric acid-reactive substances from peroxidized lipids. Lipids 24 (1989) 873 -881. [19] GONZALEZ, CM., PIGNATA, M.L., Effect of pollutants emitted by different urban- industrial sources on the chemical response of the transplanted lichen Ramalina ecklonii (Spreng)Mey. andFlot. Toxicol. Env. Chem., 69 (1999) 61-73. [20] WOLTERBEEK, H.TH., BODE, P., VERBURG, T.G., Assessing the quality of biomonitoring via signal-to-noise ratio analysis, The Science of the Total Environment, 180 (1996) 107-116. [21] CARRERAS, H.A., GUDINO, G.L., PIGNATA, MX., Comparative biomonitoring of atmospheric quality in five zones of Cordoba city (Argentina) employing the transplanted lichen Usnea sp. Env. Pollut, 103 (1998) 317-325.
49 66 -30.50^
4.40
-31.00
12 61
13 62 •- 2.80 -31.50- • 72 63 « * 73 -2.00 ..64
65, -32.00 16 1.20 .8 27
0.40
-65.00 -64.50 -64.00 -63.50 -63.00
Figure 1. Distribution map of As (NAA) concentration inR. ecklonii (ug/g DW).
66 -30.50-T
58 * 2600.00
-31.00- 40 50 41 • 2200.00 12 32 61 23 • 13 / 62 1800.00 -31.50- .- »~. 43 72 • • * V + 63 - « 73 1 • 1400.00
-32.00 16 1000.00 27 * 47 --- ' • 1 ••id' —^600.00 -- •• ,-- -65.00 -64.50 -64.00 -63.50 -63.00
Figure 2. Distribution map of Fe (NAA) concentration in R ecklonii (ug/g DW).
50 -30.50
Ill 3.10
J2.50
2.20
1.90
-32.50 1.60
-65.00 -64T.5O " -64.00 -63.50 -63.00
Figure 3. Distribution map of Ni (AAS) concentration in R. ecklonii (ug/g DW).
58 — •— 6(3 -30.50 '57 * \
-31.00
01 80 1.05 -31.50 72
0.95 64 7<« 6S -32.00 0.85
-32.50-^-'- 0.75 20 11
-65.00 -64.50 -64.00 -63.50 -63.00
Figure 4. Distribution map of Pb (AAS) concentration in R. ecklonii (ug/g DW).
51 -30.50-i
-31.00- 17.15
6.90
-31.50- 6.65
6.40
-32.00- 6.15
J5.90
-32.50
-65.00 -64.50 -64.00 -63.50 -63.00
Figure 5. Distribution map of Cu (AAS) concentration in T. capillaris (ug/g DW).
66 -30.50
49 -31 .OO- I 0.12 4*. ™ *- '
•JO »0.10
-31.50-, ,'43-
0.09
-32.00-;- ? t H0.08 i 7 Vi * -I 0.06
-32.50 5
?0 -65.00 -64.50 -64.00 -63.50 -63.00
Figure 6. Distribution map of Pb (AAS) concentration in T. capillaris (ug/g DW).
52 66 -30.5q 5P 57 48
49
-31.0C 30 40 26 50 21 12 61 80 22 32 22 \52 1 3 62 -31.50 # / 33 34\ \'i 18
14 - 26 ^ 32.0C 16 V *Tv 8 \ * 10 55-
18 32.5G 20 -65.00 ^64.50 -6^.00 ^63'.'50 -63.00"
Figure 7. Distribution map of Zn (AAS) concentration in T. capillaris (ug/g DW).
66 -30.50
-31.00- 40 50 3.25 sti
3.00
-31.50 r Z75
Z50 S -32.00 Z25 f \ • ZOO -32.50 - i- ?. -•'- t J
-65.00 -64.50 -64.00 -63.50 -63.00
Figure 8. Distribution map of Ni (AAS) concentration in T. capillaris (ng/g DW).
53 TABLE I. SIMPLE STATISTICS FOR THE RESULTS OF RAMALINA ECKLONII (SPRENG.) MEY. & FLOT
Variable N Mean Std Dev Median Minimum Maximum Cu 37 6.76782 4.09012 5.14393 2.49931 16.89425 Zn(AAS) 37 20.60368 17.75915 12. 92046 2.42585 75.40479 Pb 37 0.95756 0.13384 0.92762 0.77375 1.24090 Co(AAS) 37 0.06620 0.00997 0.06723 0.03815 0.08346 Ni 37 2.21406 0.37340 2. 11883 1.63300 3.20637 Mn 37 53.99938 13.33885 52.41594 26.33140 80.75305 Fe (AAS) 37 171.50776 87.57934 145. 09551 61.98080 513.14609 MDA 33 0.12946 0.03004 0.12754 0.06742 0.20363 HPCD 33 1.12489 0.56637 1.01963 0.43395 3.66428 Chi a 33 1.40365 0.40450 1.36106 0.59363 2.09438 Chi b 33 0.54812 0.20475 0.50857 0.28348 1.25758 Phaeoph a 33 1.68747 0.45312 1.68505 0.86275 2.59750 Phaeoph b 32 0.66151 0.34219 0.56930 0.32001 1.97857 Sulphur 33 1.92900 1.66718 1.79961 0.07186 6.30557 Chi b/Chl a 33 0.62131 1.08232 0.36305 0.22813 5.81522 Phaeoph a/Chl a 33 1.33828 0.73236 1.14878 0.96114 5.22704 PI 33 2.28517 1.00647 2. 07966 1.04352 4.87969 As 26 2.33212 0.99164 2. 30500 0.30000 4.95000 Ba 25 21.60840 10. 04755 20. 98500 9.62000 44.19000 Br 26 2.24904 1.38911 1.75750 0.68000 6.12000 Ca 23 3321 2160 2853 307.47000 8532 Ce 26 3.29135 1.31922 3.46500 1.33000 6.82000 Co(NAA) 26 0.64596 0.29683 0.64000 0.24000 1.38000 Cr 26 2.41981 2.20208 1.87000 0.80500 11.94000 Cs 26 0.35385 0. 12521 0. 35000 0.21000 0.68000 Eu 26 0.05982 0.02570 0.05880 0.02010 0.12000 Fe(NAA) 26 1555 667. 92302 1424 695.87500 2959 Gd 26 0.25788 0.10598 0.23500 0.09500 0.48000 Hf 26 0.23250 0.10759 0.20000 0.10000 0.48500 K 23 2875 1570 2998 399.91000 6410 La 26 1.59606 0.84687 1.52750 0.01260 3.28000 Lu 26 0.02210 0.00990 0.02035 0.00900 0.04670 Na 26 466.53461 278. 87335 401. 11250 73.50000 1302 Nd 25 1.67680 0.85509 1.53000 0.58000 4.39000 Rb 26 5.93173 2.10817 5.76500 2.37500 12.51000 Sb 26 0.11465 0.07125 0.09585 0.04740 0.38500 Sc 26 0.54865 0.23874 0.54500 0.25000 1.13500 Se 24 0.26375 0.10743 0.23500 0.16000 0.53000 Sm 26 0.32596 0.14925 0.32500 0.12000 0.66500 Ta 26 0.04315 0.01519 0.03885 0.01850 0.08030 Tb 24 0.04026 0.01667 0.03750 0.01200 0.07590 Th 26 0.53692 0.20419 0.54500 0.21000 1.02500 U 26 0.12112 0.06771 0.09970 0.03470 0.34000 Yb 26 0.12940 0.06028 0.11740 0.04450 0.28000 Zn(NAA) 26 43.83769 40.48961 26. 57500 5.07000 154.06000
54 TABLE II. RAMALINA ECKLONII (SPRENG.) MEY. & FLOT, FACTOR ANALYSIS
Componen t 1 2 3 4 5 6 Tb .931 -.126 .120 Ce .927 .175 .152 Co .925 .281 .136 Eu .925 .292 .142 Lu .905 .369 .117 Yb .890 .354 .145 Th .880 .286 .231 .150 Sc .868 .354 .217 .184 Hf .836 .368 .223 .226 .131 Fe .835 .375 .153 .201 .210 Se .802 .120 .144 .150 -.109 .178 Cs .783 .308 .339 -.107 .247 Sm .748 .473 .226 .157 .298 -.115 Ta .687 .324 .174 .429 .307 Ba .683 .504 .306 .239 -.157 La .674 .383 .325 .219 .358 -.158 U .660 .308 .527 .106 Mn .637 .205 -.527 Gd .582 .425 .178 .113 -.435 .151 Cr .335 .821 K .154 .755 .422 .278 Br .430 .718 -.108 .359 Na .406 .707 .240 .442 .107 Zn .367 .598 -.389 -.239 -.301 Ca .335 .493 .301 -.154 .102 .409 Sb .154 .890 As .102 .207 .601 .154 .589 -.153 Ni .151 .803 .174 -.177 Nd .483 .142 .620 -.354 .120 Cu .226 .132 .387 .596 .380 .140 Pb -.172 .196 .171 .817 .210 Rb .350 .126 .764
55 TABLE m. PHYSIOLOGICAL PARAMETERS MEASURED IN RAMALINA ECKLONII (SPRENG.) MEY. & FLOT. SURVEY VARIANCE, LOCAL VARIANCE, EXPERIMENTAL VARIANCE AND SIGNAL-TO-NOISE RATIOS.
(a)
Chlb/ Pha/ DW Chi a Chlb Phaeo a Phaeo b Chi a Chi a MDA HPCD /FW Sulphur Survey variance 0.123 0.0088 0.165 0.015 0.0018 0.013 0.00046 0.038 0.00013 2.19 Local variance 0.022 0.00067 0.025 0.0002 0.00187 0.0017 0.00062 0.069 0.00014 3.84 Experimental variance 0.053 0.013 0.064 0.031 0.0076 0.033 0.0016 1.43 0.0016 2.14 Signal to Noise ratio 5.44 13.16 6.49 75.85 0.94 7.52 0.77 0.55 0.93 0.57
(b)
Chi a Chlb Phaeo a Phaeo b Chlb/ Pha/ MDA HPCD DW/ Sulphur Chi a Chi a FW Survey variance 1 0.13 0.010 0.188 0.018 0.0015 0.0119 0.0004 0.00 0.0001 1.074 Survey variance 2 0.14 0.012 0.199 0.021 0.0025 0.0139 0.0007 0.00 0.0003 2.69 Survey variance 3 0.16 0.042 0.205 0.117 1.171 0.536 0.0009 0.321 0.0008 2.78 Local variance* 0.053 0.005 0.057 0.011 0.0047 0.010 0.0013 0.565 0.0007 8.08 Signal to Noise ratio 1 2.525 1.962 3.27 1.692 0.326 1.173 0.295 0.00 0.2024 0.133 Signal to Noise ratio 2 2.72 2.17 3.47 1.88 0.526 1.37 0.508 0.00 0.3937 0.333 Signal to Noise ratio 3 3.08 7.91 3.58 10.64 249.23 52.58 0.69 0.57 1.1581 0.34
56 TABLE IV. HEAVY METALS MEASURED IN RAMALINA ECKLONII (SPRENG.) MEY. & FLOT. SURVEY VARIANCE, LOCAL VARIANCE, EXPERIMENTAL VARIANCE AND SIGNAL-TO-NOISE RATIOS
(a)
Co Cu Fe Mn Ni Pb Zn Survey variance 0.00018 25.58 4228.49 288.39 0.079 0.0435 314.901 Local variance 0.00008 3.005 3173.30 41.60 0.0044 0.0031 259.523 Experiment al variance 0.00034 7.872 1448.28 12.14 0.283 0.073 35.810 Signal to Noise ratio 2.25 8.51 1.33 6.93 17.93 14.50 1.21
(b)
Co Cu Fe Mn Ni Pb Zn Survey variance 1 0.00020 29.0033 4326.9911 277.0919 0.0552 0.0382 279.7265 Survey variance 2 0.00025 27.6403 5322.4320 278.2558 0.1279 0.0422 318.1942 Survey variance 3 0.00010 16.7291 7670.1410 177.9250 0.1394 0.0179 315.3874 Local variance* 0.00010 10.9315 3398.7271 44.4513 0.3392 0.0350 252.5633 Signal to Noise 1.9557 2.6532 1.2731 6.2336 0.1628 1.0923 1.1076 ratio 1 Signal to Noise 2.4649 2.5285 1.5660 6.2598 0.3772 1.2066 1.2599 ratio 2 Signal to Noise 0.9932 1.5304 2.2568 4.0027 0.4110 0.5118 1.2487 ratio 3
57 TABLE V. SIMPLE STATISTICS FOR THE RESULTS OF TILLANDSIA CAPILLARIS F. INC AN A (MEZ.).
Variable N Mean Std Dev Median Minimum Maximum
Cu 31 6.35193 0.30838 6.33163 5.85035 7.28475
Pb 31 0.09621 0.01139 0.09383 0.06723 0.12020
Zn 31 15.86639 4.47128 14.86651 6.72220 30.60101
Co 31 0.07551 0.01406 0.07456 0.04563 0.10419
Ni 31 2.77213 0.27997 2.79166 2.00959 3.45611
DW/FW 26 0.38099 0.04468 0.37106 0.31096 0.46771
MDA 23 0.10394 0.01692 0.10196 0.06875 0.13616
HPCD 22 3.01767 1.24467 3.17057 1.23506 5.67427
Chi a 22 0.65691 0.26624 0.61919 0.28952 1.23786
Chi b 22 0.36592 0.12203 0.36018 0.20854 0.62182
Phaeoph a 22 0.87544 0.31743 0.83189 0.39748 1.55916
Phaeoph b 22 0.45049 0.22993 0.38832 0.21658 1.32828
Sulphur 26 0.05162 0.02198 0.05021 0.01319 0.10043
Chi b/Chl a 22 0.57930 0.07987 0.55167 0.48490 0.73866
Ph a/Chl a 22 1.36343 0.12440 1.33717 1.13333 1.67002
PI 19 1.23801 0.44509 0.99108 0.64973 2.22431
58 TABLE VI. PHYSIOLOGICAL PARAMETERS MEASURED IN TILLANDSIA CAPILLAMS F. INC AN A (MEZ.). SURVEY VARIANCE, LOCAL VARIANCE, EXPERIMENTAL VARIANCE AND SIGNAL-TO-NOISE RATIOS
(a)
Chlb/ Pha/ Chi a Chlb Phaeo a Phaeo b Chi a Chi a MDA HPCD DW/FW Sulphur Survey variance 0.0027 0.00131 0.0033 0.0169 0.0017 0.0021 0.00005 0.656 0.00080 0.00008 Local variance 0.0222 0.00341 0.0421 0.0085 0.0025 0.0084 0.00023 0.502 0.00041 0.00017 Experimental variance 0.0107 0.0018 0.0081 0.0026 0.0043 0.0183 0.00059 0.218 0.00047 0.00013 Signal to Noise ratio 0.12 0.38 0.079 1.98 0.68 0.23 0.22 1.31 2.0 0.47
(b)
Chi a Chlb Phaeo a Phaeo b Chlb/ Pha/ MDA HPCD DW/FW Sulphur Chi a Chi a Survey variance 1 0.0448 0.0129 0.0676 0.0190 0.0021 0.0003 0.0001 0.5031 0.0008 0.0004 Survey variance 2 0.0097 0.0030 0.0201 0.0139 0.0029 0.0008 0.0001 6.1E-11 0.0009 0.0001 Survey variance 3 0.0709 0.0149 0.1008 0.0529 0.0064 0.0155 0.0003 1.5492 0.0020 0.0005 Local variance* 0.0162 0.0029 0.0257 0.0059 0.0036 0.0088 0.0002 0.5991 0.0004 0.0009 Signal! to Noise ratio 1 2.7618 4.5051 2.6278 3.1901 0.5847 0.0293 0.2850 0.8397 2.1437 0.3923 Signal to Noise ratio 2 0.5965 1.0318 0.7835 2.3640 0.7921 0.0853 0.7321 0.0000 2.2801 0.1419 Signal to Noise ratio 3 4.3755 5.1347 3.9206 8.9605 1.7719 1.7585 1.4312 2.5859 4.9908 0.5368
59 TABLE VH. HEAVY METALS MEASURED IN TILLANDSIA CAPILLARIS F. INCANA (MEZ.). SURVEY VARIANCE, LOCAL VARIANCE, EXPERIMENTAL VARIANCE AND SIGNAL TO NOISE RATIOS
(a)
Co Cu Ni Pb Zn Survey variance 0.00007 0.0239 0.0582 0.00001 3.576 Local vaiance 0.00006 0.0208 0.0149 0.00002 7.718 Experiment al variance 0.00060 0.213 0.0871 0.00042 5.727 Signal to Noise ratio 1.17 1.15 3.90 0.5 0.46
(b)
Co Cu Ni Pb Zn Survey variance 1 0.0001 0.0205 0.0556 0.00001 3.4497 Survey variance 2 0.0001 0.0403 0.0726 0.0001 5.5084 Survey variance 3 0.0002 0.0951 0.0784 0.0001 19.9923 Local variance* 0.0004 0.1012 0.0648 0.0002 11.1978 Signal to Noise 0.1536 0.2021 0.8584 0.0082 0.3081 ratio 1 Signal to Noise 0.3192 0.3980 1.1200 0.2249 0.4919 ratio 2 Signal to Noise 0.4941 0.9397 1.2096 0.5522 1.7854 ratio 3
60 TABLE VIII. SIMPLE STATISTICS FOR THE RESULTS OF USNEA AMBLYOCLADA (MULL. ARG.) ZAHLBR.
Variable N Mean Std Dev Median Minimum Maximum Cu 9 5.84172 0.33953 6.00299 5.21211 6.25839 Pb 9 0.01064 0.00116 0.01066 0.00922 0.01245 Zn 9 24.50533 17.74014 15.42933 9.39576 54.98085 Co 9 0.04827 0.00720 0.04726 0.03713 0.06172 Ni 9 2.48339 0.18477 2.52414 2.14964 2.74553 DW/FW 10 0.92474 0.03021 0.92912 0.87205 0.96739 Sulphur 10 0.08235 0.02058 0.07423 0.06404 0.12802 Chi a 4 0.32734 0.29779 0.23523 0.08169 0.75719 Chi b 4 0.30016 0.21364 0.25808 0.10347 0.58099 Phaeoph a 4 0.41000 0.41617 0.21626 0.17399 1.03349 Phaeoph b 4 0.40182 0.19034 0.47303 0.12049 0.54073 HPCD 4 0.000696 0.000504 0.00C691 0.000153 0.00125 MDA 9 0.16192 0.03502 0.17669 0.10614 0.20491 Chi b/Chl a 4 1.21971 0.69037 1.14986 0.45835 2.12077 Phaeoph a/Chl a 4 1.34814 0.58106 1.25791 0.74649 2.13027 PI 3 2.05128 0.53105 1.97317 1.56361 2.61707
61 TABLE IX. PHYSIOLOGICAL PARAMETERS MEASURED IN USNEA AMBLYOCLADA (MULL. ARG.) ZAHLBR. SURVEY VARIANCE, LOCAL VARIANCE, EXPERIMENTAL VARIANCE AND SIGNAL TO NOISE RATIOS
(a)
Chlb/ Pha/ Chi a Chlb Phaeo a Phaeo b Chi a Chi a MDA HPCD DW/FW Sulphur Survey variance 0.00038 0.00127 0.00169 0.00171 0.0527 0.176 0.000702 2.328 0.00020 0.000033 Local variance 0.0194 0.00559 0.00193 0.00661 0.00826 0.177 0.000127 0.347 0.000002 0.000076 Experimental variance 0.00083 0.0027 0.00211 0.00682 0.039 0.0842 0.000719 0.3 0.00167 0.000045 Signal to Noise ratio 0.19 0.23 0.88 0.26 6.37 0.99 5.50 6.71 7.47 0.44
(b)
Chi a Chlb Phaeo a Phaeo b Chlb/ Pha a/ MDA HPCD DW/ Sulphur Chi a Chi a FW Survey variance 1 0.0000 0.0009 0.0001 0.0001 0.0528 0.1770 0.0007 2.3528 0.0002 0.00003 Survey variance 2 0.0002 0.0018 0.0006 0.0013 0.0577 0.2221 0.0008 0.0000 0.0003 0.00005 Survey variance 3 0.0887 0.0456 0.1732 0.0362 0.4766 0.3376 0.0012 0.0000 0.0009 0.0004 Local variance* 0.0011 0.0037 0.0020 0.0052 0.0216 0.2007 0.0004 1.2546 0.0007 0.0001 Signal to Noise ratio 1 0.0000 0.2524 0.0653 0.0245 2.4496 0.8819 1.9110 1.8754 0.2445 0.3135 Signal to Noise ratio 2 0.1731 0.4745 0.2977 0.2492 2.6711 1.1067 1.9460 0.0000 0.4425 0.4803 Signal to Noise ratio 3 80.6167 12.3360 86.5994 6.9675 22.0650 1.6823 3.0665 0.0000 1.3041 4.2356
62 TABLE X. HEAVY METALS MEASURED IN USNEA AMBLYOCLADA (MULL. ARC) ZAHLBR. SURVEY VARIANCE, LOCAL VARIANCE, EXPERIMENTAL VARIANCE AND SIGNAL TO NOISE RATIOS
(a)
Co Cu Ni Pb Zn Survey variance 0.000155 0.0574 0.0112 0.00026 9.832 Local variance 0.000114 0.0151 0.0394 0.00031 57.892 Experiment al variance 0.000070 0.194 0.0691 0.00056 20.278 Signal to Noise ratio 1.36 3.83 0.28 0.83 0.17
(b)
Co Cu Ni Pb Zn Survey variance 1 0.0002 0.0572 0.0143 0.0002 0.0000 Survey variance 2 0.0002 0.0748 0.0281 0.0003 3.1350 Survey variance 3 0.0001 0.1153 0.0341 0.0000 314.7127 Local variance* 0.0001 0.0879 0.0686 0.0006 54.5363 Signal to Noise 1.2789 0.6511 0.2090 0.3741 0.0000 ratio 1 Signal to Noise 1.8769 0.8510 0.4090 0.5400 0.0575 ratio 2 Signal to Noise 0.5190 1.3115 0.4977 0.0023 5.7707 ratio 3
63 DETERMINATION OF TRACE ELEMENTS IN LICHEN SAMPLES BY INSTRUMENTAL NEUTRON ACTIVATION ANALYSIS
^ITIKO SAIKI, !LIDIAK. HORMOTO, 'MARINA B.A. VASCONCELLOS, 2MARCELOP. MARCELLI, 3NAIROM. SUMTTA, 3PAULOH. N. SALDIVA
instituto de Pesquisas Energéticas e Nucleares, Caixa Postal 11049, CEP 05422-970, Sao Paulo, SP, Brazil. instituto de Botánica, Seçao de Micologia e Liquenologia, Caixa Postal 4005, CEP 01061-970, Sao Paulo, SP, Brazil 3Laboratório de Poluiçâo Atmosférica, Faculdade de Medicina da USP, Av. Dr Arnaldo 455, CEP 01246-903, Sao Paulo, SP, Brazil
Abstract: XA0102856
Samples of Canoparmelia texana lichen collected in different sites of Säo Paulo and Paraná States, Brazil, were analysed by neutron activation analysis in order to obtain preliminary information on the air quality in these regions and also to select a region of interest for biomonitoring studies. Also Tadescantia pallida plant has been analysed in order to study the viability of using this specimen in environmental pollution monitoring. Lichens samples were collected from tree barks which were also collected to investigate the contribution of substrate derived elements to elements present in lichens. Young and old leaves of T. pallida were collected separately in order to study the leaf age effects on their elemental levels. The samples were cleaned, washed with distilled water, dried and ground for the analyses. Samples and standards were irradiated at the IEA-Rlm nuclear reactor for short and long periods and concentrations of the elements Al, As, Ca, Cd, Cl, Co, Cr, Cs, Fe, HfMg, Mi, Rb, Sb, Se, Ti, Th, U, V, In and lanthanides were determined. Preliminary results obtained for T. texana lichen indicated that three sites (Ibiúna, Botanical Garden and Parque de Vila Velha) present low concentrations of the most elements analysed. Therefore lichens from these regions could be analysed to establish baseline levels of elements for monitoring purposes. Samples collected in open areas presented high concentrations of some elements probably due to the accumulation of elements originating from soil and from heavy vehicular traffic. Elemental concentrations obtained in outer barks were similar or smaller than those results obtained for lichens. Results obtained for T. pallida indicated that concentrations of elements in old leaves of this plant are of the same magnitude or slightly higher than those presented in young ones.
1. SCIENTIFIC BACKGROUND AND SCOPE OF THE PROJECT
During the last decades, lichen analyses have played an important role in studies on environmental pollution monitoring. The accumulation of various air pollutants, including heavy metals by lichens is well documented and they are considered as useful monitors for air quality [1-3],
The advantages of using lichens as biomonitors, instead of direct measurement of pollutants in several materials of the environment such as water and air, are their easy sampling and their wide geographical distribution allowing comparison of metal concentrations from several regions and to draw more reliable pollution maps of a large area.
The occurrence of about 2,800 lichen species in the Brazilian territory has been published [4], however works with strictly ecological studies on communities of lichens are rare. This way, data on trace elements in lichens collected in Brazil as biomonitor of atmospheric paniculate matter and deposition are also practically non-existing.
65 Consequently the development of this project is being of great interest due to the serious problems of pollution encountered here specially in big cities like Sao Paulo or in industrialised areas. Also this project is providing an opportunity to improve our knowledge concerning the validation of the use of plants as biomonitors of air pollution. In this second year of this project, samples of the lichen Canoparmelia texana collected in different sites of States of Sao Paulo and Parana, Brazil, were analysed by neutron activation analysis in order to obtain preliminary information on the air quality in these regions and also to select a region of interest for biomonitoring studies.
Also a plant named Tradescantia pallida is being analysed in this project in order to evaluate the viability of using this plant in the regions where lichens are not present. T. pallida was chosen for biomonitoring purposes due to its wide distribution in Sao Paulo city and its easy propagation, even in regions with high level of pollution, such as central area of Sao Paulo city. These conditions allow us to use T. pallida specimens specially cultivated for monitoring purposes or those spontaneously appearing in the interest areas.
The harmful effects of environmental pollution caused to T. pallida have been evidenced by micronucleus assay by several researchers [5-7]. The nuclei of plant DNA molecules which were submitted to high pollution levels are split in micronuclei so that the T. pallida might used as an indicator of environmental pollution. The number of microhuclei raises with the air pollution increase, that is, the more DNA molecules are split, the more polluted is the air.
Also T. pallida is considered an ideal plant for air monitoring and testing and this species has been used to test for mutagenicity of radioisotpes - contaminated air after nuclear accident and monitoring around power plants [8]. The analytical methodology used in the analyses of C. texana lichen and T. pallida plant was the instrumental neutron activation analyses (INAA).
2. MATERIALS AND METHOD
2.1. Collection and preparation of the samples
2.1.1. Collection of lichen samples and sampling points
C. texana (Tuck) Elbe & Hale is an epiphytic lichen species of the family Parmeliaceae. This foliose lichen with large thallus (5 to 20 cm in diameter) and radial growth was collected from the tree barks. The samples were carefully removed using a titanium knife and placed in a paper bags. Plastic bags were not adequate for storing lichen samples because of the their humidity and the mould formation. They were collected at the following sampling sites:
• Site Nr. 1 - Ibiuna city, SP, that is a part of the green ring (horticultural and touristical region) located in the country about 100 km from Sao Paulo city, 850 meters above sea level, and it is a region considered not polluted, originally covered by a Mesophyllous Forest however not far from Serra do Mar. • Site Nr.2 - Alto da Serra, Paranapiacaba, Sao Bernardo do Campo city, SP is a region supposed to be clean, located in a high place near the top of the Serra do Mar, about 720 meters above sea level, rounded by Tropical Rain Forest.
66 • Site Nr.3 - Jardim Casqueiro, Pra9a Independencia, Cubatao, SP. Cubatao is a city situated in a coastal region, in a place originally covered by mangroves, about two meters above sea level. Cubatao is well known as one of the most polluted cities of the world however, the lichen samples were collected in a clean place located before the south winds cross the industrial part of the city. • Site Nr.4 - Campo Limpo Paulista town, SP, situated about 100 Km from Sao Paulo city, 750 meters above sea level. The lichen sample was collected near a not paved road and in a place particularly submitted to heavy dust from the soil. This region was originally covered by Cerrado vegetation. • Site Nr.5 - Botanical Garden, SP, that is situated 15 km far from downtown of Sao Paulo city, inside the urban zone, 680 meters above sea level. • Site Nr.6 - Institute de Pesquisas Energeticas e Nucleares (IPEN), SP, located at the Campus of the Sao Paulo University, in the urban zone of Sao Paulo city, with vehicular traffic. • Site Nr.7 - Campus of Sao Paulo University, SP, situated about 650 meters above sea level and with heavy vehicular traffic and building contructions. • Site Nr.8 - Parque Vila Velha situated in Ponta Grossa city, Parana State, is an open field with great rock formations and it is a not polluted area in the countryside, 950 m above sea level.
2.1.2. Treatment of the lichen samples
In the laboratory the thalli were examined under an Olympus stereoscopic microscope model SZ4045 and they were cleaned in order to remove substrates or other adhered material. Then the samples were washed in distilled water where the samples remained immersed for about 5 minutes to remove dust and sand. Next the samples wen; freeze-dried for 16 hours under a pressure of about 4.10"2 mbar. A fine powder of the lichen sample was obtained by manual grinding in an agate mortar.
2.2. Samples of Tradescantia pallida plant
Tadescantia pallida (Rose) Hunt. Cv purpurea is an indigenous popular ornam ental plant, which is widely grown in the garden, roadside and streets of Sao Paulo city. This plant was cultivated in vases by researchers of Medicine School of Sao Paulo University to investigate the suitability of Tradescantia micronucleus bioassay to detect the toxicity of environmental pollutants and also to use this plant as biomonitor of toxic elements. The same lot of soil sample "was used in all vases and they were kept in three sites of different levels of air pollution: • Bandeirantes Avenue - is one of most polluted avenues from Silo Paulo city, SP. • Medicine School of Sao Paulo University, one polluted area of Sao Paulo city, and • Caucaia, SP is a clean region of the countryside situated about 50 km from Sao Paulo central area. Caucaia was considered as a control region and the vases of these plants were kept in a green house.
2.2.1. Treatment of T. pallida samples for analysis
Young and old leaves of T. pallida were collected separately in plastic bags. In the laboratory they were previously washed using distilled water, dried in an oven at 37°C during 48 hours and then they were ground in an agate mortar to obtain the samples in a powder form. A loss weight percentage of about 93% was obtained in this drying process.
67 2.3. Preparation of Standards
Multielement standards were used for the INAA determinations. Stock solutions of elements were provided from Spex Chemical or they were prepared by dissolving high purity metals, oxides or salts in high purity reagents or distilled water. Single or multielement solutions were prepared by using appropriate amounts of these stock solutions and they were then pipetted onto a sheets of Whatman 42 filter paper. After drying these sheets in a desicator, they were placed in polyethylene bags that were heat sealed for irradiation together with the samples.
2.4. Instrumental neutron activation analysis
The samples, ranging in mass from 100-180 mg, were weighed in polyethylene envelopes. Short irradiations of 5 minutes were carried out using a pneumatic transfer system of the IEA-Rlm nuclear reactor and under a thermal neutron flux of 4 1011 n cm"2 s"1 for the determinations of Al, Cl, Mg, Mn, Na, Ti and V. Longer irradiations of 16 h under thermal neutron flux of about 1012 n cm"2 s"1 were carried out for As, Br, Ca, Cd, Co, Cr, Cs, Fe, Hf, K, lanthanides, Rb, Sb, Sc, Th, U and Zn determinations.
After adequate decay times, gamma ray measurements were performed using a Canberra GX2020 hyperpure Ge detector which was coupled to Model 1510 Integrated Signal Processor and System lOOMCA Card, both from Canberra. The detector used had a resolution (FWHM) of 0.9 keV for 122 keV gamma rays of 57Co and 1.78 keVfor 1332 kev gamma rays of 60Co. Samples and standards were measured at least twice and the sample-to-detector distances of 3.0 and 0.5 cm were used for first and second measurements, respectively. The gamma-ray spectra were processed using VISPECT software[9] that evaluates peak areas (counting rates) and gamma ray energies. The standard comparative method was used for calculating the elemental concentrations.
Certified reference materials IAEA 336 Lichen and NIST 1570 Peach Leaves were irradiated with the samples and analysed to control the quality of the results. The accuracy and the precision for most of elements were, generally, found to be within 11%.
3. RESULTS
Table I presents results obtained in the samples of C. texana collected in seven sites of Sao Paulo State and one site of Parana State. In all these samples, Ca was found in higher concentrations at levels of percentages. The elements Al, Br, Cl, Cr, Fe, K, Mg, Mn, La, Ce, Nd, Na, Rb, Ti, V and Zn are present at the levels of ug g"1 and the elements As, Cd, Co, Cs, Hf, Sm, Eu, Tb, Lu, Sb, Sc, Se, Th and U at the levels of |ag kg"1.
The number of analytical results presented here is not sufficient to have a final conclusion however some observations could drawn. A preliminary comparison between the results obtained for samples collected in different sites indicates that lichens from Ibiuna, Botanical Garden and Parque de Vila Velha present low concentrations for most elements analysed. These results indicate that samples from these regions should be analysed to establish baseline levels of elements in C. texana for monitoring purposes.
68 As expected, low concentrations of elements were obtained in samples collected in regions considered clean. The concentrations of several elements in the lichens collected in the open places like Campus of Sao Paulo University were higher than those found in lichens from clean region of Parque Vila Velha and Ibiuna, presumably due to accumulation of elements originating from heavy vehicular traffic and from soil. Also sample from Campo Limpo presented high levels of Hf, Th, Sc and lanthanide elements probably due to this air contamination by elements present in the soil and sample from this site was collected along a not paved road.
The lichen sample from Cubatao presented a relatively high level of Mn and that from Paranapiacaba presented slightly high levels of Fe, Se and lanthanides. This high level of elements collected in these samples may be explained due to the air contamination by the smoke from the industries and refineries of the highly polluted areas of Cubatao city. Regarding to lanthanide elements, their biological effects of long exposure even at low concentrations are unknown and should be matter of concern. The; highest concentrations of Na and Cl were expected for lichen collected in Cubatao since this city is situated at coastal region, however the their concentrations were not so high.
Elemental concentrations obtained in lichen and their respective substrates are presented in Table II. These results indicate that elemental concentrations of outer barks depend on the tree substrate but the outer bark is not significant source of metals for T. texana. As it can be seen in Table n, concentrations of elements obtained for outer barks were similar or smaller than those results found for lichens.
Table in shows the mean values obtained in the analyses of ten samples of young and old leaves of T. pallida plant cultivated in vases and kept in the same place. These results exhibited inter-vase variability as well as the concentrations of Ce, Co, Cr, Fe, La, Sb, Sc and Zn in old leaves were slightly higher than those presented in the young ones and for the elements Ca, Cl, K, Mn, Rb and Sr there was no difference related to the leaf age. 4. FUTURE PLANS
During the next few years our laboratory will concentrate In the following activities concerning biomonitoring of trace elements:
• Collection and analysis of lichen samples collected mainly near the industries (car battery industry and mercury recycling plants). This work will carried out in collaboration with Companhia de Tecnologia de Saneamento Ambiental (CETESB), a governmental institution responsible for quality control of Sao Paulo State • Analyses of T. pallida plant cultivated in vases and kept in tree regions of different levels of pollution • Analyses of the soil utilised in the T. pallida cultivation. • Treatment of data and interpretation
69 REFERENCES
[1] SLOF, J.E., Environmental lichenology : biomonitoring trace element air pollution. PhD Thesis Technische Universiteit, Delft, (1993). [2] GARTY J., Lichens as biomonitor for heavy metal pollution. In Market B. ed. Plants as biomonitors, VCHPublisher, Weinhein (1993)193-263. [3] RICHARDSON D. H. S., Pollution monitoring with lichens. Richarmond Publishing Co. Ltd., Slough, England, (1991) p. 25. [4] MARCELLI, M. P., History and current knowledge of Brazilian lichenology. In M. P. MARCELLI & M.R.D. SEAWARD, Lichenology in Latin America: history, current knowledge and applications: 25-45. CETESB, Sao Paulo, 1998, 179 p. [5] STEINKELLNER, H. et al. Genotoxic effects of heavy metals: Comparative investigation with plant bioassays. Environ. Mol. Mutagen. 31, 2(1998)183-191. [6] KNASMULLER, S. et al. Detection of genotoxic effects of heavy metal contaminated soils with plant bioassays. Mutat. Res. Genetic Toxicol. Environ. Mutag. 420, 1- 3(1998)37-48. [7] BATALHA, J. R. F. et al. Exploring the clastogenic effects of air pollutants in Sao Paulo (Brazil) using Tradescantia micronuclei assay. Mutat. Res. Fund. Mol. Mutag., 426 (1999) 239-232. [8] GRANT, W. F. Higher plant assay for the detection of genotoxicity in air polluted environment. Ecosyst. Heath 4 4(1998)210-229. [9] PICCOT, D., Laboratoire Pierre Sue, CEA-CNRS, Centre d'Etudes de Saclay, F-91191, Gif-sur Yvette, CEDEX, France, personal communication, 1991
70 TABLE I. ANALYSES OF LICHEN SAMPLES COLLECTED IN SEVEN DIFFERENT SITES FROM SAO PAULO STATE AND ONE SITE OF PARANA STATE.
Samples ftom the sites Element Nr.l Nr.2 Nr.3 Nr.4 Nr.5 Nr. 6 Nr. 7 Nr.8 Ibiuna Paranapiacaba Cubatao Campo Limpo Bot. Garden IPEN Campus USP Parque V. Velha AI, jig g'1 2747 ± 44 1164 + 33 930 ± 20 306 ±9 426 ±17 789 ± 24 7129 ±137 733+22 As, jig kg"1 411 ±14 708 + 9 450 ±6 786 ±9 274 ±7 469 ±11 1057 ±14 343 ±6 Br, ng g"1 39.40 + 0.07 H.00 + 0.05 6.59 ±0.01 5.70 ±0.03 1.3 ±0.4 23.0 ±3.8 24.80 ± 0.05 3.30 + 0.01 Ca, % 4.67 + 0.08 4.79 + 0.08 1.90 ±0.02 1.68 ±0.03 1.96 ±0.06 2.67 ±0.02 4.13 ±0.07 5.74 + 0.03 Cd, ng kg1 459 ±59 139±5 266 ±36 600 ±15 799 ± 43 640 ±105 3917 ±209 665 + 122 Cl, Hg g1 639 ±14 308+17 946 ±43 601 ±24 449 ±11 529 ±21 284 ±39 665 + 15 Co, tig kg'1 219 + 4 358±6 353 ±5 584 + 8 - (*) 295 ±4 1063 ±14 110 + 2 Cs, (ig kg'1 117±4 356 + 7 185 ±12 276 ±5 213 ±1 155+4 1016+9 95 ±3 Fe, ng g"1 1033 ±6 4276 + 19 1351 ±12 1637 ±7 366 ±3 540 ±3 4135 ±21 540 ±3 Hf^gkg1 378 ±3 182±4 103 ±7 639 ±4 - 120 ±2 1464 ± 5 100 ±2
1 O/"\<^ 1 1 1 y^ K, j-ig g- 1OVZ I11O 1957 + 468 1491 ±72 2516 ±87 3849 ±233 96+42 La, |ag g"1 1.454 ±0.006 8.31+0.03 2.096 ± 0.004 38.7 + 0.1 0.936 ±0.004 1.238 ±0.005 7.05 ±0.05 0.77 + 0.04 Ce, ng g"1 3.30 + 0.02 15.8 ±0.04 3.07 ±0.01 11.82 ±0.03 1.70 ±0.01 2.89 + 0.01 16.58 ±0.04 1.85 + 0.01 Nd, ng g"1 1.62 ±1.09 6.87 ± 0.07 1.47 ±0.03 4.05 ±0.05 0.65 ±0.05 1.53 ±0.15 6.52 ±0.21 0.66 ±0.02 Sm, ng kg" 180.7 + 0.4 842.8 + 1.2 203.0 + 0.3 523.2 ±0.7 113.1 ±1.4 117.3 ±0.3 1055 ±1 102.6 ±0.3
11O £. J_ O C in o _L n T i r» -i i r% j>.-r _!. ^.T £, 1 O.VI _i_ ^..^ J7.i _L U. / 108.2 ±1.4 15.8 ±0.4 AO.y x v.y 101 X Z 21.9 + 0.6 |
71 Tb, ng kg"1 20.7 ±1.9 73.5 ±3.5 14.811.2 60.712.5 11.2 + 0.7 11.1 + 1.6 203.613.2 14.611.4 Yb, ng kg1 53.1 ±4.1 120.017.6 36.813.3 134.216.2 28.0 + 3.0 44.7+ 1.6 346.6 + 6.7 53.3 11.0 Lu, p,g kg"1 10.8 + 0.4 17.510.4 10.510.7 23.3 10.3 3.410.2 9.9 + 0.3 60.110.5 10.710.3 Mg, |ig g"1 251+13 865 196 8551151 122 + 18 570 1 54 781 176 35401437 699 1 77 Mn, jj,g g'1 37.8 ±0.9 73.211.3 36619 11512 11.4 + 0.2 13812 16411 62.710.6 Na, |ag g"1 77.2 + 0.1 132.81 11.2 81.810.1 96.017.1 33.9 + 0.8 53.010.5 422.910.5 20.610.3 Rb, lag g'1 6.010.1 9.110.2 7.710.3 13.810.2 9.110.1 12.910.2 20.210.2 1.110.1 Sb, |ag kg1 280 + 6 19212 25016 17712 19111 20012 2000 110 57.710.8 Sc, ng kg"1 315 + 1 30611 16111 56812 56.910.2 125.710.7 119013 163.110.7 Se, |ag kg'1 201118 873 1 29 283 111 278 122 10419 141 117 665 124 98115 Th.ngkg1 327 + 2 41214 19811 80214 83 11 278 12 1933 15 133 12 TUgg"1 195 139 64 + 11 3717 171+25 4717 342 1 77 510189 114132 U, |ag kg1 55 ±6 128 14 45 15 15214 - 6412 19019 2713 v^gg"1 1.510.3 3.910.2 3.510.2 0.40 1 0.03 1.2610.08 2.710.2 14.010.9 1.910.1 Zn, ng g"1 137.010.5 72.810.4 58.010.2 73.010.3 66.1+0.2 97.810.4 145.7 + 0.5 31.910.2 The uncertainties of the results were calculated using statistical counting errors of the samples and standards.
72 TABLE II. ELEMENTAL CONCENTRATIONS IN LICHENS SAMPLES AND THEIR RESPECTIVE SUBSTRATES
Sampie 1 Sam )le 2 Element Trunk 1 Lichen 1 Trunk 2 Lichen 2 Al, (xg g"1 728 ±9 789 ± 24 846 ±18 733 ± 22 Br, \xg g"1 1.90 ±0.03 23.0 + 3.8 5.6 ±0.2 3.30 ±0.01 Ca, % 0.246 ±0.004 2.67 ± 0.02 5.37 ±0.07 5.74 ± 0.03 Cd^gkg"1 681 ±31 640 ±105 158 ±3 665 +122 cingg1 146 ±9 529 ±21 105 ±2 665 ±15 Co, \xg kg'1 277 ±4 295 ±4 158 ±3 110 + 2 Cs, ng kg"1 33 ±2 155 ±4 77 ±3 95 ±3 Fe, \xg g"1 285 ±2 540 ±3 191 ±1 540 ±3 Hf^gkg"1 326 ±3 120 ±2 96 ±2 100 + 2 La, \ig g"1 1.19±0.01 1.238 ±0.005 0.498 ±0.005 0.77 ± 0.04 Ce, ng g"1 2.45 ±0.01 2.89 ±0.01 0.99 ±0.01 1.85 ±0.01 Nd^gg1 1.49 ±0.04 1.53 ±0.15 0.61 ±0.03 0.66 ± 0.02 Sm, |ng kg' 111.9±0.9 117.3 ±0.3 78.7 ±0.4 102.6 + 0.3 Eii, \xg kg"1 19.5 + 0.6 26.9 ±0.9 16.9 + 0.6 21.9 ±0.6 Tb^gkg"1 7.4 ±1.3 11.1 ± 1.6 14.5 ±1.9 14.6 ±1.4 Yb^gkg"1 19.7 ±2.1 44.7 ±1.6 30.5 ±4.5 53.3 ±1.0 Lu, |ig kg"1 5.2 ±0.4 9.9 ±0.3 9.2 ±0.3 10.7 ±0.3 Mn, \ig g"1 122 ±1 138 ±2 73.0 ±0.4 62.7 ±0.6 Na, \xg g"1 67 + 4 53.0 + 0.5 12.6 + 0.4 20.6 + 0.3 Rb^gg1 2.0 ±0.1 12.9 ±0.2 1.00 ±0.08 1.1 ±0.1 Sb^gkg"1 219±5 200 ±2 80.5 ±6.8 57.7 ±0.8 Se, \xg kg"1 143 ±21 141 + 17 98 ±15 98 ±15 Th, \xg kg"1 87 ±1 278 ±2 125 ±2 133 ±2 U^gkg"1 55 ±6 64 ±2 34±4 27 ±3 V^gg'1 2.6 ±0.1 2.7 ±0.2 1.2 ±0.1 1.9±0.1 Zn, (igg"1 105.3 ±0.3 97.8 ±0.4 51.6±0 2 31.9 + 0.2
73 TABLE III ELEMENTAL CONCENTRATIONS IN YOUNG AND OLD LEAVES OF TRADESCANTIA PALLIDA
Young leaves Old leaves ELEMENTS Mean + s* Range Mean + s Range
Br^gg1 42 ±14 77-21 26.4 ±6.8 36.5-14.8
Ca, % 2.5 ± 0.4 3.05-1.91 2.3 ±0.3 2.7-1.9
Ce, |ag kg'1 471 ±281 1174-281 974 ± 323 1396-251
Cl, % 0.88 ±0.17 1.15-0.69 0.74 ± 0.23 1.12-0.41
Co.ngkg1 180±91 376 - 73 245 ± 97 445 - 130
C^ugkg1 194 ±125 502 - 94 254 ±112 353 - 127
Fe, |ag g'1 84.5 ±30.4 172 -70 124±31 167-64
K,% 4.36 ± 2.43 10.9-2.2 3.12 ±1.04 5.27-1.84
La^gkg1 286 ± 205 806-136 568 ±232 927 - 114
Mn, ug g"1 140 ±57 245 - 89 133 ±70 248-51
Na, |ag g"1 50.7 ±15.7 75.2-25.4 85.9 ±42.1 163 -35
R^ugg1 37.8 ±14.9 66.0-13.5 23.0 ±10.4 38.4-10.9
Sb, |ag kg1 55 ±45 173-17 136 ±62 282 - 29
Sc^gkg1 8.2 ±5.4 23.0-4.3 15.3 ±5.1 25.8-6.6
Sr, ugg1 286 ± 98 519-166 274 ± 65 392-211
Zn, |ag g1 109 ±61 278 - 63 149 ±35 192 - 77 (*) - Arithmetic mean and standard deviation of results obtained in 10 samples cultivated in 10 different vases
74 STUDY OF AIR POLLUTION IN CHILE USING BIOMONITORS xA0102857
^DUARDO CORTES, !NURI GRAS, 2IRIS PEREIRA, * OSCAR ANDONIE, ^USANA SEPULVEDA
'Dept. of Nuclear Applications, La Reina Nuclear Centre, Chilean Nuclear Energy Commission, Santiago, Chile 2Inst. For Vegetal Biology and Biotechnology, University of Talca, Talca, Chile
Abstract:
A project has been undertaken within the framework of a Co-ordinated Research Programme (CRP) supported by the International Atomic Energy Agency (IAEA) to carry out a long term study on atmospheric air pollution in Chile using biomonitors. The present paper describes the activities undertaken within the framework of this project. Sampling of different lichens species has been performed in clean areas (native forest), preparation of such samples has been done under controlled, cryogenic conditions and analysed by neutron activation analysis. Participation in an intercomparison run organized by the IAEA for the determination of trace and minor elements in two lichens samples, has also been carried out. Transplant of lichens collected in clean areas has been done in Santiago.
1. INTRODUCTION
Chile in general and Santiago, its capital city, in particular have serious air pollution problems [1,2]. During winter time the air pollution in Santiago increases to levels which might be detrimental to elder and children. A number of studies; have identified the main sources of this pollution. It has been determined that the main problem is the airborne particulate matter coming from fixed and mobile sources, in particular buses and cars. In 1992 the catalytic converter has been requested to all new cars but this has also contribute to an increase in the ozone levels in the city, not only in winter but also, and more remarkably in summer [3,4]. Since 1995, the Chilean Nuclear Energy Commission (CCHEN) has worked closely with The Metropolitan Commission for the Environment (COREMA) and international research institutions (i.e., The University of Sao Paulo, Brazil, USP) to determine the main sources of the contamination. The role of CCHEN has been the sampling of airborne paniculate matter using Gent type PM-10 samplers provided by the IAEA, and the analyses of samples by NAA and ion chromatography. The data evaluation and interpretation were jointly carried out by staff of COREMA, CCHEN and USP [5-13]. Recently, the so- called "Decontamination Plan for the Santiago Metropolitan Area" has been extensively review and modified accordingly to make it more effective. The authorities have realized that the air pollution problem is being extended to areas outside the city limits and would extend their monitoring network to zones outside the Metropolitan area.
Some of the new target areas do not have an adequate infrastructure for operation of traditional air samplers (i.e., electricity) and other ways of monitoring the levels of pollutants are becoming relevant. Among these alternatives are the use of passive tubes for the collection and identification of gases and biomonitors for minor and trace elements. The present project will help in the identification of the more suitable biomonitor for such purpose and the possibility of doing transplants to areas where there suitable biomonitors do not exist. The possibility of "planting" appropriate monitors in suspected areas or near sources of pollutants is quite interesting and can help to detect the origin of contaminants for control and surveillance purposes.
75 The experience on biomonitoring at CCHEN started in 1996 when a project on the determination of reference levels of elements of environmental importance in the sea, using different molluscs and sediments [14-16]. This project, carried out jointly with the National Commission for the Environment (CONAMA), established the "natural or background" concentration levels of some elements in given matrices for regulatory and control purposes. A sample preparation laboratory has been implemented for handling the samples in a contamination free environment. Available is a class 100 clean room, several laminar flow fume hoods, cryogenic mills, a large capacity freeze-drier and homogenizers. This laboratory prepares all samples for analyses by the different analytical techniques at CCHEN and also plays an important role in the preparation of reference materials.
The present project, as originally planned, aims at (I) the study of the applicability of biomonitors to monitor elemental air pollution, (ii) to determine the concentration levels of elements in the atmosphere of cities and rural areas using PM-10 samplers, (iii) to determine the concentration levels of those toxic elements on the membrane filters and in the selected biomonitors using NAA, complemented by AAS, (iv) to establish correlations, if any, between the concentration levels of trace elements in airborne particulate matter and those in the biomonitors, (v) to determine the sources of pollutants and (vi) to determine the applicability of biomonitors to study air pollution in large areas, using indicators either naturally grown or transplanted to the region under examination.
2. BIOMONITORING AIR POLLUTION
Various monitor materials have been used in trace-element air monitoring programs, including lichens, mosses, ferns, grass, tree bark, tree rings, tree leaves and pine needles [19- 21]. Evaluation of the criteria mentioned above for the various biomonitor materials, leads to lichens and mosses as the best suitable monitors. For all biomonitors used, the mechanisms of trace element uptake and retention are still not sufficiently known. For monitors other than lichens and mosses, the contribution from sources other than atmospheric, such as soil have to be taken into account. Where comparisons have been made, lichens and mosses show consistently higher metal levels than higher plants which simplifies the analytical process for the determination of trace-element. According to some authors, differences between element concentrations in bark on different trees are often significant to the trace elements levels in lichens. Some lichen species exist in large geographical areas, occurring more abundant in rural zones than in urban and industrial areas. The morphology of lichens and mosses does not vary with seasons, thus accumulation can occur throughout the year. Lichens and mosses usually have considerable longevity, which led to their use as long-term integrators of atmospheric deposition.
3. SELECTION OF STUDY SITES
Sampling sites and areas of study remains the same as originally planned taking into account the special characteristics of the country (Fig. 1) and the real possibilities for field work: (i) Santiago city, the capital, (ii) Valparaiso, the main port of Chile and (iii) Talca, a city at about 300 km south of Santiago (Fig. 2). The reasons for the selection of these places are explained in the Report on the First Research Co-ordination Meeting (RCM, 28 September-1 October 1998, Vienna, Austria) for this CRP.
76 Lm-1-
,-uc > Jiel'ci Lloiuuritt>r- - iun h-.^—"..••.* •:JIB*. Sat I'dtila
Santiago,.." sPcirto Alftgre
Fig 1. Location of Chile in South America
©Mendoza ©PfoCutdo g Puente Alto
> llV.;'-
Temuen&
Fig. 2. Location of the areas of interest for air pollution studies
77 4. SAMPLING AND SAMPLE PREPARATION
Sampling has taken place in the mountain area near Talca, in a native forest at about 1100 m above sea level. This first sampling campaign had two purposes: (i) to establish the working group for this project, integrating botanists and chemists to exchange experience and information about lichens species, procedures for collecting samples (without contamination) and analytical methodologies, and (ii) to collect samples of lichens to select the appropriate one for monitoring purposes. Collection has been done following procedures described in the literature and adapted to our possibilities and limitations [18]. A list of the sample collected, as well as their substrate is shown below in Table I.
TABLE I. SPECIES OF LICHENS AND THEIR SUBSTRATE COLLECTED IN THE AREA OF TALCA
Specie Date Substrate Location Hypogymmia 26 Mar 1999 Arbol Donbeyi Altos de Vilches. Plalismatia glauca 26 Mar 1999 Arbol Dombeyi Altos de Vilches Usnea 26 Mar 1999 Arbol Dombeyi Altos de Vilches Plalismatia glauca 26 Mar 1999 Arbol Donbeyi Altos de Vilches Plalismatia glauca 26 Mar 1999 Arbol Donbeyi Altos de Vilches Parmelia caperata 26 Mar 1999 Obligua Var Macrocarpa Altos de Vilches Parmelia perlata 26 Mar 1999 Obligua Var Macrocarpa Altos de Vilches Usnea flenda 26 Mar 1999 Obligua Var Macrocarpa Altos de Vilches Hypogymmia 26 Mar 1999 Obligua Var Macrocarpa Altos de Vilches
A second sampling campaign was carried out in a valley in the Andean Mountains, known as Laguna del Maule. There is little vegetation and lichens are not abundant, however, it was possible to collect some samples which are indicated in Table II.
78 TABLE II. SPECIES OF LICHENS AND THEIR SUBSTRATE COLLECTED IN THE AREA OF LAGUNA DEL MAULE
Specie Date Substrate Location Rhizoplaca melanophthalma 27 May Laguna del 3ra. Parada 1999Maule Umbilicania 27 May Laguna del 3ra. Parada 1999Maule Rhizoplaca melanophthalma (de) Leuck 27 May Laguna del 4ra. Parada 1999 Maule Usnea acmomelana stirt 27 May Laguna del 5ra. Parada 1999 Maule Rhizoplaca melanophthalma 27 May Lagima del 5ra. Parada 1999 Maule Umbilicania 27 May Laguna del 5ra. Parada 1999 Maule
One of the most important aspects of this study is the use of biomonitors for air pollution studies in large cities. Therefore, a third sampling campaign was performed in Santiago to identified species of potential use as biomonitors. However, no lichens were found in the city despite an studied carried out in 1988 were a number of species were identified in several zones of the city. Only few species, in very bad conditions were found in a park in a rather remote area. This means that the environmental conditions of the cities has damaged al lichens living there 12 years ago. A list of the samples collected in this area is in Table ffl.
TABLE III. SPECIES OF LICHENS AND THEIR SUBSTRATE COLLECTED IN THE METROPOLITAN AREA OF SANTIAGO
Specie Date Substrate Location Mosses 27 Jun Saxicola P. Muni. De La Rein a 1999 Ramalina 27 Jun Litraca caustica P. Muni. De La Reina 1999 Teloschistes 27 Jun Acacia caven espino P. Muni. De La Reina chrysophthalmus 1999Maulino Ramalina striatula ecklonii 27 Jun Acacia caven espino P. Muni. De La Reina 1999Maulino Parmelia 27 Jun Saxicola P. Muni. De La Reina 1999
At the botanical laboratory at the University of Talca the species of lichens where identified and then transported to the CCHEN laboratories. There, the samples were cleaned, using only clean plastic materials, milled at liquid nitrogen temperature and freeze dried. The solid material was then re-homogenized and stored at low temperature.
79 5. ANALYSIS AND QA/QC
The samples were analysed by INAA and a few elements were determined using radiochemical NAA. Some of the samples were also analysed by solid atomic absorption spectrometry, with direct introduction of the solid sample in the graphite furnace. This technique has recently been implemented in the laboratory and is planned to be mainly used for homogeneity studies in sample preparation procedures as well as in the preparation of reference materials. This technique require minimum sample mass of around 1-2 mg or less with the advantage that it does not require sample treatment before the instrumental measurement. This technique has already been used to study the homogeneity of some test materials. One example of the determination of the homogeneity, homogeneity factor and minimum mass for analysis, is shown in Table IV and Figures 3 and 4.
FIG. 3. Cadmium distribution in a te 51 m a te rla I
1 f) 0 9. 0.8 - 0 7 0.6 - V* • • ^ ^ ^ -a 0.5 - o 6 0.4 - c o 0.3 - O y = 0.0041X + 0.5 325 0 2 - 0 1 - 0.0 - 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 a .80
Sam p)e m ass [m g]
Fig. 4. Dispersion in each bottle
1 n 0 .9 - "3 n 8 3 0 ./ . i 1 MI 0 .6 . 1 - o m • m m --• • m u b - r I F1 ' F9 I F1 '14 f ™6
snc . F2 7F8 F1 2 f 1 3 '15 n 4 V 7 6 0 .3 . F1 n 2 . 0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 n° of measurement
80 TABLE IV. DESCRIPTIVE STATISTICS OF THE HOMOGENEITY OF TEST MATERIAL
Descriptive Statistics Average 0,536 Typical error 0,005 Median 0,530 Mode 0,5695 Standard Deviation 0,047 Variance of the sample 0,0022522 Kurtosis -0,097530 Coefficient of asymmetry 0,48131 Range 0,2079 Minimum 0,4444 Maximum 0,6523 Addition 54,6334 Counts 102 Confidence level (95,0%) 0,00932
Results S% 8,86 He 7,697 M[mg] 11,82
Emphasis have been placed in quality control and quality assurance of the analyses. This is routinely done using appropriate reference material. The NAA laboratory also took part in the Intercomparison Run for the Determination of Minor and Trace Elements in Two Lichen Samples organised by the IAEA. The laboratory performed well and most of the results are within the confidence intervals of the averaged calculated for all participating laboratories. Table V shows the results of the laboratory as well as the overall average of all participating laboratories.
81 TABLE V. RESULTS OF AN INTERCOMPARISON RUN FOR THE DETERMINATION OF TRACE AND MINOR ELEMENTS IN TWO LICHENS SAMPLES
Iiitercomparison sample L-l Intercomparison sample L-2 Analyte Our data Unc. Overall mean Std. Dev. Our data Unc. Overall mean Std. Dev. of lab. aver. of lab aver. Al 1100 1065 252 700 14 634 134 As 0.85 0.028 0.97 0.1 0.65 0.025 0.68 0.07 Ba 28 1.3 26.1 1.81 7.4 0.68 7.1 0 Br 17 0.58 18.9 1.51 12.2 0.24 11.8 1.15 Ca 3560 54 3927 316 2400 180 2489 194 Cd Ce 1.5 0.38 1.76 0.26 0.98 0.057 1.23 0.2 Cl 2540 26 2438 206 2160 59 1964 181 Co 0.4 0.037 0.36 0.05 0.34 0.016 0.29 0.04 Cr 6.4 0.59 6.04 1.71 1.13 0.096 1.05 0.26 Cs 0.35 0.025 0.39 0.04 0.113 0.0022 0.122 0.02 Cu Eu 0.03 0.0036 0.03 0 0.024 0.0013 0.025 0 Fe 800 67 902 92 415 28 444 42 Hg 0.27 0.026 0.31 0.03 0.15 0.019 0.167 0.01 K 3100 170 3138 266 1900 120 1814 197 La 0.87 0.027 0.9 0.13 0.68 0.024 0.63 0.05 Mg Mn 54.4 0.12 52.6 1.6 69 3 64 7 Na 109 3.9 124 9.4 325 9.5 303 24 Ni Pb Rb 20 1.2 20.9 1.42 1.8 0.18 1.73 0.07 Sb 0.41 0.03 0.46 0.06 0.071 0.0044 0.085 0.01 Sc 0.22 0.019 0.25 0.02 0.161 0.0085 0.169 0.02 Ti V 3.54 0.085 3.58 0.34 1.4 0.1 1.45 0.2 Zn 109 8.6 112 6.4 33 1.9 31.9 1.94
6. RESULTS OF THE ANALYSIS
Table VI present the results of the analyses of the samples of lichens collected during the first three sampling campaigns. These results have been obtained using NAA.
To help in the selection of the more appropriate monitors, an attempt to find similar behaviour between the similar and different species was made. Figure 5 shows a clear relation, as regards the content of trace elements for the same specie. Figure 6 presents a graphical relationship between the same species, collected in different zones from the same substrate.
82 TABLE VI. PARTIAL RESULTS OF THE ANALYSES OF LiCHENS SAMPLES COLLECTED DURING THE FIRST, SECOND AND THIRD SAMPLING CAMPAIGN (1) Specie Substrate Location Al Al-abs As As-abs Br Br-abs Ca
Hypogymmia Arbol Donbeyi Altos de Vilches 2da. 2600 390 1.9 0.1 13.3 1.1 93000 Par. Plalismatia Arbol Dombeyi Altos de Vilches, 2do. 3200 522 1.6 0.1 3800 glauca Puente Usnea Arbol Dombeyi Altos de Vilches 1000 150 2.6 0.2 3.9 0.3 3400 Plalismatia Arbol Donbeyi Altos de Vilches 2da. 2800 199 2.1 0.2 3500 glauca Par. Plalismatia Arbol Donbeyi en cabezas 2da. Par. 3300 495 2.6 0.2 7.1 0.6 3000 glauca Parmelia Obligua Var Altos de Vilches pie. 5200 780 3.4 0.3 5.3 0.4 27000 caperata Macrocarpa Tac. Parmelia perlata Obligua Var Altos de Vilches 1900 285 3.1 0.2 7.0 0.6 7700 Macrocarpa Usnea flenda Obligua Var Altos de Vilches 630 95 1.3 0.1 1.6 0.1 4700 Macrocarpa Hypogymmia Obligua Var Altos de Vilches 3900 585 2.3 0.2 13.8 1.1 22900 Macrocarpa Rhizoplaca Laguna del Maule 3ra. Parada 9800 470 2.2 0.2 28000 melanophthalma Umbilicania Laguna del Mauie 3ra. Parada 3470 42 1.8 0.1 1900 Rhizoplaca Laguna del Maule 4ra. Parada 10300 577 3.4 0.3 7000 melanophthalma (de) Leuck Usnea Laguna del Maule 5ra. Parada 1700 119 2.3 0.2 840 acmomelana stirs Rhizoplaca Laguna del Maule 5ra. Parada 8400 336 2.9 0.2 3100 melanophthalma Umbilicania Laguna del Maule 5ra. Parada 4800 341 2.5 0.2 970 Musgo Saxicola P. Muni. La Reina 860m 34500 1208 6.3 0.5 17400 S.M.M. Ramalina Litraca caustica P. Muni. La Reina 6410 64 4.7 0.4 4810 Teloschistes Acacia caven espino P.Muni. De la Reina 18300 2745 6.8 0.5 6.3 0.5 6600 chrysophthalmus Maulino Ramalina Acacia caven espino P.Muni. De la Reina 3700 555 4.7 0.3 3.3 0.3 2500 ouiciiuia cuniui in IVIOUIII iU Parmelia Saxicola P.Muni. De la Reina 13800 2070 5.5 0.4 8.2 0.7 19300
(1 ) "abst' means absolute 2s standard deviation 83 TABLE VI. PARTIAL RESULTS OF THE ANALYSES OF LICHENS SAMPLES COLLECTED DURING THE FIRST, SECOND AND THIRD SAMPLING CAMPAIGN (1)
Specie Ca-abs Ce Ce-abs Cl Cl-abs> Co Co-abs Cr Cr-abs Cs Cs-abs Eu Eu-abs Fe Hypogymmia 2790 2.10 0.16 770 123 0.68 0.07 1.69 0.33 0.23 0.02 <0,01 1700 Plalismatia 232 1430 45 0.39 0.03 1.26 0.14 0.034 0.005 1360 glauca Usnea 214 0.68 0.07 170 29 0.31 0.03 1.20 0.22 0.10 0.01 0.020 0.004 550 Plalismatia 490 1280 47 0.52 0.03 1.97 0.21 0.038 0.005 1460 qlauca Plalismatia 210 1.92 0.14 1200 180 0.70 0.07 2.24 0.39 0.33 0.02 0.051 0.009 1900 glauca Parmelia 972 3.13 0.23 110 24 1.01 0.10 3.15 0.53 0.47 0.03 0.073 0.013 3000 caperata Parmelia perlata 1001 1.15 0.17 1030 185 0.44 0.05 <0,8 0.20 0.03 0.050 0.012 890 Usnea flenda 517 0.38 0.03 <60 0.13 0.01 0.39 0.08 <0,02 <0,001 190 Hypogymmia 802 2.65 0.19 980 147 0.78 0.07 2.35 0.39 0.23 0.02 0.072 0.013 2300 Rhizoplaca 1624 113 9 1.35 0.06 1.88 0.23 0.169 0.018 4720 melanophthalma Umbilicania 380 78 15 0.72 0.04 0.93 0.15 0.074 0.009 1730 Rhizoplaca 161 130 26 1.49 0.07 1.70 0.21 0.168 0.018 4440 melanophthalma (de) Leuck Usnea 151 170 11 0.24 0.02 0.90 0.12 0.060 0.007 780 acmomelana stirs Rhizoplaca 341 129 5 0.93 0.05 3.25 0.30 0.113 0.012 3140 melanophthalma Umbilicania 12 134 8 0.49 0.03 2.59 0.25 0.089 0.010 1530 Musgo 278 460 12 5.73 0.26 21.46 1.68 0.396 0.042 18300 Ramalina 96 170 4 1.35 0.06 7.65 0.62 0.095 0.011 4010 Teloschistes 436 9.34 0.66 250 45 3.96 0.37 18.93 3.04 1.10 0.08 0.257 0.044 12600 chrysophthalmus Ramalina 173 2.25 0.17 120 22 3.66 0.34 4.93 0.81 0.30 0.02 0.072 0.013 2900 striatula ecklonii Parmelia 869 8.53 0.60 210 40 2.92 0.27 12.93 2.09 0.73 0.05 0.229 0.039 9100
(1) "absf means absolute 2s standard deviatio
84 TABLE VI. PARTIAL RESULTS OF THE ANALYSES OF LICHENS SAMPLES COLLECTED DURING THE FIRST, SECOND AND THIRD SAMPLING CAMPAIGN (1)
Specie Fe-abs Hg Hg-abs K K-abs La La-abs Lu Lu-abs Mg Mg-abs Mn Mn-abs Na
Hypogymmia 112 <0,22 2600 297 1.14 0.09 0.023 0.004 860 129 97 6 820 Plalismatia 51 1800 432 840 54 155 5 470 glauca Usnea 38 0.39 0.06 3000 277 0.40 0.03 0.026 0.006 690 104 94 6 144 Plalismatia 55 2200 264 950 44 126 3 530 glauca Plalismatia 122 <0,15 3100 301 0.96 0.08 0.020 0.004 1100 121 114 7 670 glauca Parmelia 192 <0,18 4000 382 1.58 0.13 0.029 0.006 1700 156 270 17 860 caperata Parmelia perlata 68 <0,35 5300 584 0.81 0.07 <0,01 <1200 360 24 270 Usnea flenda 14 <0,08 2900 259 0.20 0.02 <0,002 <490 95 7 71 Hypogymmia 146 <0,13 3300 304 1.31 0.10 0.024 0.005 1400 129 170 11 750 Rhizoplaca 160 2800 672 2610 31 125 6 2090 melanophthalma Umbilicania 63 2100 153 1100 150 49 2 790 Rhizoplaca 151 3200 256 2380 83 125 4 2020 melanophthaima (de) Leuck Usnea 31 1600 209 500 42 26 2 330 acmomelana stirs Rhizoplaca 108 3400 300 2200 107 88 3 2320 melanophthalma Umbilicania 56 3000 459 1200 24 53 2 1520 A f\<\ C\C\ "7O/"\ Musgo cnn IU IW I OU 04UU 656 6 6600 Ramalina 136 3400 340 1900 186 100 1 1380 Teloschistes 796 <0,29 7100 682 4.33 0.34 0.081 0.016 4800 346 200 13 4400 chrysophthalmus Ramalina 185 <0,17 4500 414 1.09 0.09 0.020 0.004 1500 113 57 4 890 striatula ecklonli Parmelia 576 <0,29 7400 681 4.09 0.32 0.080 0.016 3500 294 160 10 3400
(1) stabs" means absolute 2s standard deviatic
85 TABLE VI. PARTIAL RESULTS OF THE ANALYSES OF LICHENS SAMPLES COLLECTED DURING THE FIRST, SECOND AND THIRD SAMPLING CAMPAIGN (1)
Specie Na-abs Sb Sb-abs Sc Sc-abs Se Sm Sm-abs Th Th-abs V V-abs Zn Zn-abs
Hypogymmia 53 0.13 0.01 0.65 0.10 <0,33 0.20 0.01 4.8 0.9 47 3 Plalismatia 17 0.55 0.01 0.20 0.02 4.1 1.1 20 1 glauca Usnea 9 0.10 0.01 0.27 0.04 <0,19 0.07 0.01 1.8 33 2 Plalismatia 20 0.58 0.01 0.22 0.02 4.5 0.3 20 1 glauca Plalismatiaglauca 44 0.13 0.01 0.78 0.12 (1) "abs" means absolute 2s standard deviatio 86 7. TRANSPLANTS OF LICHENS Since one of the main objectives of the project is the use of appropriate lichens in rather highly polluted cities an areas in the country, lichens collected in clean areas where transplanted to Santiago, one of the target cities. Samples of Parmelia perlata and of Usnea where collected and carefully placed into "envelopes" of nylon net. Two of such transplants where placed under a cover to protect the lichen from rain and distributed among colleagues of the Nuclear Centre living in the Metropolitan area and distributed throughout the city. The transplants were carried out in January 2000 and one "envelope" will be collected at the end of April 2000 (beginning of Fall) and the second at the end of September, when the winter is over. Figure 7 shows where the transplants were installed. 8. FUTURE ACTIVITIES During the next period of this CRP, the collection and analyses of the transplanted lichens will take place. Also, a systematic collection of lichens will start in the more relevant cities and zones of the country where air pollution may be an important factor for the health of the persons. The determination of some elements which is not possible to be done using NAA will be carried out by solid atomic absorption spectrometry. In addition, the collection of airborne particulate matter using Gent type PM-10 samplers at the same location where the biomonitor(s) will be sampled (if electricity and other services are available) will continue as well as the preparation of analytical quality control materials to assure reliable and useful data. 9. ACKNOWLEDGMENT The authors are grateful to the International Atomic Energy Agency (IAEA) and the Chilean Nuclear Energy Commission for their financial and technical support to this project. 87 REFERENCES [I] UNIVERSE)AD DE CHILE, Study on the physical and chemical characterization of airborne paniculate matter in the Metropolitan Area, Report, University of Chile, Santiago, Chile (1985) [2] ANDONIE, O., ORTIZ, J., Analysis of atmospheric aerosols of Santiago using neutron activation analysis, Thesis, University of Chile, Santiago, Chile (1977) [3] TORO, P., CORTES, E., Air pollution in Santiago (Chile) as studied by nuclear and other techniques, Report on the First Research Coordination Meeting for the Coordinated Research Programme on Applied Research on Air Pollution Using Nuclear-Related Analytical Techniques, IAEA, Vienna, Austria (1993) [4] TORO, P., CORTES, E., Caracterizacion del material atmosferico en suspension en Santiago de Chile: Parte I. Diseno, muestreo y analisis para una campana experimental, Proceedings III Simposio Centroamericano y del Caribe en Quimica Analitica Ambiental y Sanitaria, San Jose, Costa Rica (1995) and in Nucleotecnica 15 (1995)21-47 [5] HOPKE, P.K., Receptor modeling in environmental chemistry, John Wiley, New York (1985) [6] HARMAN, H.H., Modern factor analysis, Third Ed. Revised, University of Chicago Press, Chicago (1976) [7] TORO, P., CORTES, E., MORALES, R, DINATOR, M.I, Material particulado en la atmosfera de Santiago: contribution del carbon negro y balance de masa elemental, Actas XXII Congreso Latinoamericano de Quimica, Conception, Chile, Enero 1996 [8] DINATOR, M.I, MORALES, R, ROMO, C, TORO, P, CORTES, E, KLOCKOW, D, A multiparametric study of suspended particulate matter in the atmosphere of Santiago, Chile, Procedings of 10th World Clean Air Congress, Helsinki, Finland, 1995 [9] TORO, P, CORTES, E, ARTAXO, P, Caracterizacion quimica elemental del material particulado en la atmosfera de Santiago y su interpretation estadistica, Actas II Seminario International en Contamination Atmosferica, Santiago, Chile, Noviembre 1995 [10] TORO, P, CORTES, E, ARTAXO, P, Size distribution of atmospheric aerosol in Santiago, Chile, Proceedings of the International Colloquium on Process Related Analytical Chemistry in Environmental Investigations, Gramado, Brazil, May 1996 [II] TORO, P, CORTES, E, Air pollution in Santiago (Chile) as studied by nuclear and other techniques, in Report on the Second Research Coordination Meeting for the International Atomic Energy Agency Coordinated Research programme on Applied Research on Air Pollution using Nuclear-Related Analytical Techniques, University of West Indies, Kingston, Jamaica, 21-25 October 1996 [12] CORTES, E, TORO, P, ARTAXO, P, Chemical characterization and source identification of airborne particulate matter in Santiago, Chile. Proceedings of the International Symposium on Harmonization of Health Related Environmental Measurements Using Nuclear and Isotopic Techniques, Hyderabad, India, 4-7 November 1996 [13] TORO, P, CORTES, E, Assessment of the chemical characteristics and sources of airborne particulate matter in Santiago, Chile, J. Radioanal. Nucl. Chem. 221(1-2) (1997) 127-136 [14] CORTES, E, GRAS, N, Biomonitors for the determination of baseline concentrations of environmentally important elements and their use as reference materials, IV Simposio Centroamericano y del Caribe de Quimica Analitica Ambiental y Sanitaria, Ciudad de Panama, Panama, 22-26 de septiembre de 1997 88 [15] CORTÉS, E., GRAS, N., Utilización de biomonitores para la determinación de una línea base de concentraciones de elementos de importancia para el medio ambiente y su uso como material de referencia, Primer Congreso Iberoamericano de Química Ambiental y Primeras Jornadas Chilenas de Física y Química Ambiental, Jahuel, Chile, 19-22 de octubre 1997 [16] GRAS, N., CORTÉS, E., Determinación de línea base de concentración de elementos de importancia ambiental usando biomonitores, XXII Jornadas Chilenas de Química, Osorno, Chile, 12-15 de noviembre de 1997 [17] SLOOF, J.E., de BRUIN, M., WOLTERBEEK, H.TH., Critical evaluation of some commonly used biological monitors for heavy metal air pollution, Proc. Of the Int. Conf. On Environmental Contamination, Venice, Spetember 1988, 296-298 [18] SLOOF, J.E., Environmental lichenology: Biomonitorin Trace Element Air Pollution, Thesis, Interfacultair Reactor Instituut, Technische Universiteit Delft, September 1993 [19] LOPPI, S., NELLI, L., ANCORA, S., BARGAGLI, R, Passive monitoring of trace elements by means of tree leaves, epiphytic lichens and bark substrate, Environ. Monit. Assess., 45(1) (1997) 81-88 [20] FREITAS, MC, REIS, M.A., AL VES, L.C., WOLTERBEEK, HT., VERBUG, T., GOUVEIA, M.A., Biomonitoring of trace elements air pollution in Portugal. Qualitative survey, J. Radioanal. Nucí. Chem., 217(1) (1997) 21-30 [21] KOVACS, M., PENKSZA, K., TURCSANYI, G., SILLER, I., KASZAB; L., Multielement analysis of a montane beech forest in Hungary, Verh. Ges. OekoL, 25 (1996) 147-152 [22] MAENHAUT, W., FRANCOIS, F., CAFMEYER, J., The Gent Stacked Filter Unit (SFU) sampler for the collection of atmospheric aerosols in two size fractions: description and instructions for installation and use, Report on the First Research Coordination Meeting for the Coordinated Research Programme on Applied Research on Air Pollution Using Nuclear-Related Analytical Techniques, IAEA, Vienna, Austria (1993) 89 A GLANCE OF AIR POLLUTION OF BEIJING CITY BY USING POPLAR LEAVES AND NAA TECHNIQUES NIBANGFA, TIAN WEEK, WANGPINGSHENG, ZHANG YANGMEI, CHAO LEI, HE GAOKUI China Institute of Atomic Energy, P.O. Box 275-50, Beijing, 102413, China XAO102858 Abstract: Forty six Chinese White Poplar (CWP) leave samples were taken from different districts of Beijing proper and outskirt. 32 trace elements, As, Au, Ba, Br, Ca, Cd, Ce, Co, Cr, Cs, Eu, Fe, Iff Hg, K, La, Lu, Mo, Na, Rb, Sb, Sc, Se, Sm, Sr, Ta, Tb, Th, U, W, Yb and Zn have been determined by using relative and KQ method of instrumental neutron activation analysis (NAA) techniques. Each element was normalized by the average. Values of two times of average are considered as notable or suspected pollution. Three times of average are considered as very notable or polluted. In this case, the results indicated: 1) Clues of prominent suspected element pollution: Br is prominent in the area of heavy traffic, sites 4, 13, 27 and 38; Four sites (6, 7, 8, 9) at south of downtown are indicated the very notable level for Ni and Sb; Ca is relatively higher in the constructing area, sites 27 and 29; Sites 34 to 36 and sites 43 to 45 indicate the high concentration ofRb, both group sites are located at west of Beijing; At northwest, sites 25 to 28 show the notable level of Co. 2) Very notable pollution sites: In site 6, close to a railway station, concentration of 12 elements are more than two times higher than the average, especially Hf, Ni and Sb; In site 26, a high technical special zone, the concentrations of rare earth elements (REE) are more than 4 times of the average, Hfand Ta are extraordinary high. It might be some source from high technical zone; In site 38, the place close to the Iron and Steel Factory, has a extremely high concentration ofFe and Br, 8 and 5 times higher than the average, respectively. Co, Cr, Hf, K, Na, Ni, Rb and Se are also at notable level; In sampling site 42, located between downtown and outskirt, REE and As, Ba,Cr, Fe, Hf, Hg, Sc, Se, Ta, Th and Zn are shown in the notable level. 1. INTRODUCTION Chinese economy developed rapidly in the past 20 years. At the beginning, environmental pollution was as long with the increase of economy, especial the air pollution. It once affected the human's health. In recent years, government has paid more attention and allocated a huge amount financial support to improve the environmental pollution problem. In Beijing, the capital of China, the government has took a series of measures to decrease the air pollution, such as replacing some coal by natural gas in industry and household; Leaded gasoline has been banned for 2 years; The air quality is reported to public every day, which include the contents of SO2, CO, NO2, O3 and density of inhalable air particles (toxic trace elements and organic phases are taking account). This is part of a program so-called "blue sky project". At the same time, another project so-called "green land program" is also bringing into effect. More and more places were covered by grasses, flowers and trees nowadays. At present, the main means of air pollution monitoring is total suspended paniculate in China. Trace elements from size-fractionated air particulate matter (PM-10 and PM-2.5 sampler) are also used for air pollution studies. Biomonitors as indicator of air pollution have a long history. Most work have been done by Europe countries, such as The Netherlands! 1], Norway[2-3], Portugal[4-5] and Russian[6]. And lichen, moss and trees are usually used as biomonitors. In China, the application of biomonitor for air pollution studies started recently. Air pollution in Chengde city has recently been studied using barks from different trees as 91 biomonitors[7-8]. Three kind of plant leaves, Chinese white poplar, arborvitae and pine trees as biomonitor for air pollution monitoring in Beijing area were studied[9] in last year. The results indicated poplar leaves is relatively better as a biomonitor, in case lichen and moss are difficult to collect. In present work, 46 Chinese White Poplar leave samples were taken from different districts of Beijing downtown and suburb area to monitor the air pollution of Beijing during the season of spring to autumn. The results indicated that Br is prominent in the areas of heavy traffic; A high technical special zone shows the high concentration of REE and Cr, Hf, Sb, Sc, Ta, Th; Fe and Br are extraordinary high in the area around of the Iron and Steel Factory; Ca is relatively higher in the constructing area; Sb and Ni show strong correlation at south sampling sites. 2. SAMPLING Forty six Chinese White Poplar leave samples were taken from different districts of Beijing proper and outskirt. It covers 70 km diameters of ellipse from west to east of Beijing city. The sampling sites are listed in Table 1 and marked in fig. 1, map of Beijing city proper (lined square attached at the bottom left comer is part of Beijing city map, shaded area is proper). Leaves were taken from the height of 2 to 3 meters above ground. Samples were collected during first week of August 1999, representing the integrated airborne paniculate matter status during spring and summer of 1999. 3. SAMPLE TREATMENT Samples were washed by using a kind of soft detergent and distilled water. The washed samples were dried in an oven at a constant temperature of 50 °C for 48 hours, and then 80 °C for 24 hours. About 25 grams of the dried samples were crushed by using an agate roller. And grinded materials with the size of less than 40 mashes were used as the analytical samples. 4. SAMPLES AND STANDARDS PREPARATION Around 300 mg of crushed sample was packed into a pure aluminum foil bag. 150 mg of NBS-1632a (Coal) was prepared in the same procedure for analytical quality controls. Pure metal wires or chemical compounds were dissolved and mixed into a reasonable multi- elements solution, and then dropped on the ashless filet paper with the size of O7X2 mm pad. Around 1 mg each of pure zirconium foil and iron wire were prepared as the neutron flux monitor and comparator for Ko method, respectively. All the samples, controls, monitor and comparator were packed together and put into an irradiation can. 5. IRRADIATION AND COUNTING Samples was put into a vertical channel of the 15 MW heave water research reactor, China Institute of Atomic Energy, and irradiated for 48 hours at position with the neutron flux of 3xlO13 n cm"2 S"1. Irradiated samples with the Al bag removed were counted in 3000 seconds by using a HPGe, PCA-II multichannel board and computerized gamma spectrometer system (with resolution of 2.1keV and relative efficiency of 30% for 1332.5 keV and peak/Computton ratio of 50:1) after 5 days decay for the determination of As, Au, Br, Ca, Cd, K, La, Lu, Mo, Na, Sb, Sm, U, W and Yb. And another 5000 seconds was counted after 12 days decay for the determination of Ba, Ce, Co, Cr, Cs, Eu, Fe, Hf, Hg, Rb, Sc, Se, Sr, Ta, Tb, 92 industry factory, such as the Capital Iron and Steel Factory and Oil Refinery Complex. Both of them are located at southwest area of Beijing. 4) Quality Control: Two procedures were used for analytical quality control. First procedure is relative chemical standard and Ko method, each analytical indicator has two independent results: One is from relative and the other from ko method. They are listed in two columns at same time. If the difference is greater than 5% of outside uncertainties, the reason must be found. Another procedure is to use certified reference materials, such as NBS-1632a. In present work, the values between analytical and certified have a good agreement for 1632a, listed in Table 2. Table 2, Analytical Results and literature Values of NBS-1632a, Quality Control Element Anl. Error Literature Element Anl. Error Literature Value Value Value Value As 9.2 0.7 9.3 La 14.5 0.4 16 Ba 127 11 130 Na 846 18 840 Br 41 2 43 Rb 29 2 31 Ce 28.5 1.9 30 Sb 0.63 0.06 0.6 Co 6.6 0.2 6.8 Sc 6.22 0.32 6.3 Cr 35.4 1.6 34.4 Sm 2.5 0.3 2.3 Eu 0.51 0.03 0.54 Ta 0.36 0.04 0.4 Fe 11100 400 11100 Th 4.7 0.3 4.5 Hf 1.3 0.1 1.6 Yb 1.07 0.07 1.1 K 4400 200 4200 Zn 25.5 2.5 28 7. WORK IN THE FUTURE 1) Environmental and meteorological conditions for suspected sites (Very notable and notable sites) will be further investigated to reach more reasonable explanations of possible pollution sources. 2) Sampling in exiting sites (especially, the suspected sites) will continue, to study the elemental concentration variations with time and the change of surrounding situations. Sampling and NAA of CWP leaves will also be carried out in 10 to 15 new sites, to fill-up the relatively less sampled north areas. 3) Sampling sites will also be arranged around the Oil Refinery Complex in order to match the project contents. 4) Considerations have to be taken on the contributions of elements in CWP leaves from media other than airborne paniculate matter, e.g. soil and ground/rain water. Some lichen samples (although rarely found in Beijing) will be taken and analyzed to try to clarify the problem. Hopefully, that may bring some information on pollution from soil and water, as a by-product. 5) Sampling sites will be arranged close to the airborne particulate sampling site, where airborne particles is sampled once a week, in order to compare the pollution results from both biomonitor and air particle monitor. 93 REFERENCES [1] J.E. Sloof, Ph. D. Thesis, Tu Delft, The Netherlands, Sep. 1993. [2] E. Steinnes, et al., J. Radioanal. Nucl. Chem., 114(1987)69. [3] E. Steinnes, et al., J. Radioanal. Nucl. Chem., 192(1995)205. [4] M.C. Freitas, et al., J. Radioanal. Nucl. Chem., 217(1997)17, [5] M.C. Freitas, et al., J. Radioanal. Nucl. Chem., 217(1997)22, [6] M.V. Frontasyeva, et al, J. Radioanal. Nucl. Chem., 181(1994)363, [7] Jiang Gaoming, Chinese J. of Applied Ecology, 7(1995)310. [8] Shang He, et al., J. shenyang Agr. Univ., 25(1994)98. [9] Ni Bangfa, at all, Biological Trace Elements Research, 1999(71 -72)267-272 94 Table 1, Beijing Poplar Sampling Sites and suspected pollutants Sampling Site and Name High Concentration Sub-High Cone. Element elements in sample 1. Tuoli Rb, Sb Cs 2. Liangxiang Ba, Se ,Hg 3. Daxing-Liangxiang Ba, Sr 4. Daxing Br,Ni Lu, Na, Rb, Sb 5. Daxing(2) As, Sb 6. Baiguanglu Hf, Ni, Sb, Ta Ce, Cr, Na, Sc, Sm, Th 7. Xuanwumen Ni K, Rb, Sb 8. Hufangqiao Ni, Sb Zn 9. Tiantan Ni, Sb As, Zn 10. Muxidi 11. Fangzhuang Lu 12. Langjiayuan Sb, 13. Shuangqiao Br,Ni Co,Rb 14. Tongxian Lu 15. Tongxian(2) 16. Guanzhuang Lu 17. Balizhuang Ni 18. Hongmiao Sb 19. Changhongqiao Lu, Ni, Sr, Zn 20. Siyuanqiao Hg,Se,Br 21. Xiaoying Sr 22. Yayuncun Ni, 23. Datun Lu, Zn 24. Jianxiangqiao Sb, Sr 25. Zhongguancun Sr, Lu Co, Na, Rb, Zn 26. Sitongqiao Ce,La,Lu,Eu,SmTa, Cr, Sb, Sc, Th 27. Weigongcun Br Hf 28. Baishiqiao Sb Co, Lu 29. Tianwenguan Rb,Ni 30. Chegongzhuang 31. Fuwaidajie 32. Ganjiakou As, Cr, Hf, Lu, Th 33. Fuchenglu 34. Gongzhufen Rb,Ni 35. Wanshoulukou Rb Zn 36. Yuquanlu Rb Sr, Lu, Zn 37. Shijingshan 38. Shougangqiangwai Fe,Br Co, K, Rb, Se 39. Xinqiaodajie Ba 40. Mentougou Ba 41. Liuliqu Ni 42. Wulidian Ta REE,As,Ba,H£Hg,Sc,Se,Th,Zn Fe,Tb 43. Lugouqiao Na, Ni, Rb Lu, Ta, K 44. Changxindian Hf, Ni, Rb, Ta 45. Zhaoxindian Rb 46. Changyang Co Hg,Ni,Zn 95 Fl . ESl , E=1 , u 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |20 J15 10 i 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 16 4 \2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Fig. 2 Map of Br, Co, Ni, Sb and Rb for 46 sampling sites Si t e- fi •I AsBaBrOaCeCoCrQsEuFeHfi Hg K—La-4 j Ma N—Fb Sb Sc Se Sm Sr Ta Th Zn- ^PopSite-26 JSL ra , EM . ra 10 laustourtsbui-eTtt rcarc rososcsesmsrl a in zn Ba Br Ca p /ftsBaBrCaCeOoacsEuFeHfHgK La Lu Na M Ri Sb Sc Se Sm Sr Ta Th Zn Fig. 3 Elements Distribution of sampling sites 6, 26, 38 and 42 96 CORRELATION OF ATMOSPHERIC DEPOSITION AND DISEASES IN THE EUROREGION NEISSE O. WAPPELHORST, I. KtJHN, J. OEHLMANN, S. KORHAMMER, B. MARKERT |||||||||||||||||||||||||||||||||||||| International Graduate School Zittau Markt 23 02763 Zittau Germany XA0102859 Tel. ++49 3583 7715 0, Fax ++49 3583 7715 34, E-Mail: [email protected] Abstract: A biomonitoring system using the mosses Pleurozium schreberi and Polytrichum formosum as biomonitors has been used to determine the degree of pollution in the Euroregion Neisse (ERNj. This region, located in Central Europe where the borders Germany, Poland and Czech Republic meet (see Figure 1), was one of the most highly polluted areas in Europe until the early 1990s. For clarity and ease of access the results have been presented visually using a Geographical Information System (GIS) (Markert et al, 1999; Wappelhorst, 1999; Wappelhorst et al, 1999). The deposition of 37 elements in the Euroregion as found in the moss study is compared with the incidence of various diseases, using data from regional hospitals. Connections between diseases of the respiratory tract and Ce, Fe, Ga and Ge deposition as well as between cardiovascular diseases and Tl were determined. The results will be validated by further studies with an even greater amount of data. 1. INTRODUCTION The use of epiphytic plants as passive biomonitors is an established method of determining atmospheric deposition. In Scandinavia, mosses have been used as biomonitors for determining pollution with heavy metals since the late 1960s (Riihling & Tyler, 1968). Numerous projects have since been carried out with mosses; the method has been developed systematically (Ellison et al., 1976; Maschke, 1981; Engelke, 1984; Steinnes, 1984; Ross, 1990) and also used in large-scale European studies (Ruhling, 1994; Siewers & Herpin, 1998; Markert et al., 1999). Mainly the endohydric mosses Pleurozium schreberi and Hylocomium splendens have been used in these studies. Due to the fact that mosses have no cuticle layer, heavy metals and other elements are taken up through the surface of the mosses. This makes it safe to assume that the element concentrations correlate closely with the actual deposition of thes e elements, providing that disruptive factors such as soil particles or leaching processes can be excluded. Atmospheric pollution causes different diseases in humans, although definite proof as to which substance triggers or causes a disease is very difficult. 2. METHODS An investigation into the effects of atmospheric pollution on individuals over large areas involves the collection of data throughout the region. Biomonitoring is an excellent means of doing this. Such an approach was used by Cislaghi & Nimis (1997) in a study in which they compared the mortality rate from various pulmonary diseases with a biodiversity index for lichens. High correlation coefficients were found between the index and the number of deaths from lung cancer. However, no direct conclusions were drawn in respect to levels of individual elements. 2.1. Biomonitoring In the years 1995 and 1996 the atmospheric deposition of elements in the Euroregion Neisse (ERN), located in Central Europe at the border of Poland, the Czech Republic and 97 Germany, was determined in a biomonitoring project using mosses. The mosses Pleurozium schreberi and Polytrichum formosum were chosen as biomonitors because of their wide distribution in the area studied and because their ability to take up metals from the atmosphere makes them optimal biomonitors. The present study was the first attempt to compare pollution with numerous elements - detected by using mosses as biomonitors - with the incidence of disease (Wappelhorst, 1999; Wappelhorst et al., 1999). _ESchwarze State border Borders of ERN ower station -**., r Figure 1: The Euroregion Neisse The sampling sites were located at least 300 m from main roads or populated areas and at least 100 m from any road or single house. The moss samples were dried at 45°C without previous washing. A microwave-assisted digestion in closed PTFE vessels was done with 300 mg samples, 4 ml nitric acid and 2 ml hydrogen peroxide. The samples were analyzed by ICP-MS and ICP-OES for their concentrations of 37 chemical elements (Ag, Al, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, La, Mg, Mn, Mo, Na, Nd, Nd, Ni, Pb, Pr, Rb, Sn, Sr, Ti, Tl, Th, U, V, Y, Zn and Zr). Using the Geographical Information System ARC/INFO the element contents were then interpolated (inverse distance method) and presented in map form. Standard reference material was used to check the quality of the analytical results (Markert, 1996). In evaluating the general contamination, all elements analysed must be considered simultaneously. As the number of elements in question increases, the difficulty of this task increases as well, especially considering that the distribution curves can differ greatly from element to element. For this reason we calculated a standardised contamination level which took all the element contents which had been determined into consideration. To accomplish this all element contents in the mosses were first standardised. Then the average standardised element content was calculated for each sampling point and interpolated exactly as for the individual element contents. Thus in areas where the standardised contamination level is higher than 0.3, the level of pollution, taking the average of all measured elements, lies more 98 than 30% over the ERN average. 2.2. Health data For every region investigated the average element content was calculated and then correlated with the health data. The numbers of patients discharged from hospitals, including deaths, in the years 1993 to 1997 were taken as the basic data for the frequency with which a disease occurs. In Germany this data, which includes the diagnosis and the patient's sex, age and place of residence, is provided annually by hospitals to the Statistical Offices of the individual states. The diseases are classified according to ICD 9 (International Statistical Classification of Diseases and Related Health Problems, ninth revision, WHO) (Table 1). Table 1: Classification of the diseases studied according to ICD 9. ICD 9 Description of the D isease 140 - 208 Neoplasms 162 Malignant neoplasms of the trachea, bronchus and lung 172, 173 Malignant melanomas of the skin and other malignant neoplasms of the skin 204 - 208 Leukaemia 390 - 459 Diseases of the circulatory system 393-398,410-429 Heart diseases 410 Acute myocardial infarction 411 - 414 Other forms of ischaemic heart disease 401 - 405 Essential and secondary hypertension 430 - 438 Diseases of the cerebrovascular system 460 - 519 Diseases of the respiratory system 480 - 486 Pneumonia 466, 490, 491 Bronchitis 490 - 496 Chronic obstructive lung disease 493 Asthma 680 - 709 Diseases of the skin and subcutaneous tissue 710 - 739 Diseases of the musculoskeletal system and connective tissue: 99 Table 2: The European Standard Population according to Waterhouse (1976) Age group Number of people 0 1600 1 - <5 6400 5-<10 7000 10 -<15 7000 15-<20 7000 20 - <25 7000 25 - <30 7000 30 - <35 7000 35 - <40 7000 40 - <45 7000 45 - <50 7000 50 - <55 7000 55 - <60 6000 60 - <65 5000 65 - <70 4000 70-<75 3000 75 - <80 2000 80-<85 1000 85+ 1000 Total 100 000 We were able to evaluate the data from the districts of Bautzen, Kamenz, Lobau-Zittau, the Upper Lusatia district of Lower Silesia (NOL) and the county boroughs of Gorlitz and Hoyerswerda. The data are classified according to sex and age (0 - <5, 5 - <10, ..., 80 - <85, 85 and over). The age structure, which has a bearing on the incidence of disease, differs from one area to another. In order to compare the disease figures for the individual areas it was nevertheless necessary to standardize them. Two possible methods of standardization were available. In the first method the data were converted in accordance with the European Standard Population (see Table 2) (Waterhouse, 1976). The age groups are taken into account in the standardized overall incidence of a disease with a weighting that corresponds to their share of the standard population. In the second method the numbers of cases of the disease are converted to a figure per 100,000 inhabitants divided according to sex and age; the overall incidence of the disease per 100,000 inhabitants is then calculated. This method has the advantage that the data can be evaluated separately according to age and sex. But such a breakdown can lead to very small case numbers per group and thus cause major errors in the results. This does not happen with the first method. A further advantage of the first method is a great reduction of the individual data to be processed. Both methods were carried out. To determine possible connections between deposition and disease, the incidence of the diseases was correlated with the element concentrations in the mosses. The concentrations of a number of elements in the mosses Pleurozium schreberi and, to a lesser extent, in Polytrichum formosum differ only slightly from one sampling site to another in the ERN. A priori, high correlation coefficients would result between these elements and all diseases that also show little geographic difference in incidence. But such correlations yield extremely little information; for this reason, only elements with a mean relative deviation of at least 35 % from the median were considered. In the ERN, differences of this magnitude were found 100 for Ag, Al, Be, Bi, Ce, Cr, Cs, Fe, Ga, Ge, La, Li, Mn, Mo, Na, Nb, Nd, Pb, Pr, Rb, Sn, Th, Ti, Tl, U, V, Y and Zr in Polytrichum formosum and for Be, Bi, Cs, Mn, Na and Tl in Pleurozium schreberi. The incidences of disease converted in accordance with the standard population (Method 1) and the numbers of cases broken down according to age and sex (Method 2) were correlated with the element concentrations in the moss samples from the various districts. The results are similar; for this reason, the results of Method 2 are only given in part here. 3. RESULTS The element levels for Ce, Cr, Fe, Ge, La, Li, Nb, Nd, Ni, Pr, Th, Ti, U, V, Y and Zr in Polytrichum formosum around the Turow power plant and from the city of Liberec to the southern part of the ERN along the Neisse river valley are higher than the background values. These elements have highly significant correlation coefficients among themselves. A good example of this is the element distribution of Zr in Polytrichum formosum as well as the standardized contamination for Polytrichum formosum, shown in Figures 2 and 3, respectively. Dry deposition coming from sources south of the ERN are brought by atmospheric currents along the Neisse river valley. To the east the Iser Mountains and to the west the Zittau Mountains and the Lusatia Hills form natural barriers to a broader deposition pattern. Because of the topographical conditions in this part of the ERN, winds from the: south and southwest are common, whereas in general westerly winds predominate. „„ \ Polytnchum formosum 1996 Zr 10 20 30 km Figure 2: Zirkonium concentration in jjg/g dry matter in Polytrichum formosum 101 I, • i_i _•.'., n't Polynchum standardized contamination ' G <-0.3 @ -0.3--0.1 • 0.1-0.3 • >0.3 0 10 20 30 km Figure 3: Standardized element concentration in pg/g dry matter in Polytrichum formosum Significant correlations (p < 0.1) between the element concentrations in the mosses and the incidence of the diseases covered by the survey are shown in Table 3. For the sake of simplicity the remaining correlation coefficients are not included. The fact that a disease is caused or promoted by increased rates of deposition of an element would be reflected in a positive correlation; positive correlation coefficients are therefore printed in bold type. Negative correlations may be interpreted as a protective effect" of an element against the disease concerned. The concentrations of the elements Ce, Fe, Ga and Ge in Polytrichum formosum correlate significantly with the incidence of malignant neoplasms of the trachea, bronchus and lung (ICD 162) and the incidence of diseases of the skin and subcutaneous tissue (ICD 680- 709). There is a significant positive correlation between the thallium (Tl) concentrations detected in the moss Polytrichum formosum and the incidence of diseases of the circulatory system in general (ICD 390-459); in particular there is a correlation with essential and secondary hypertension (ICD 401 - 405), acute myocardial infarction (ICD 410), other forms of ischaemic heart disease (ICD 411-414) and chronic obstructive lung disease (ICD 490- 496). The Tl concentration in Pleurozium schreberi also correlates significantly with the incidence of essential and secondary hypertension (Table 3). The evaluation according to age groups (Method 2) gives a very clear indication of the connection between Tl concentrations and the occurrence of acute myocardial infarction and hypertension in the age groups over 40 (Pleurozium schreberi, Table 4) and over 25 years of age (Polytrichum formosum, Table 5). In the older age groups there are highly significant correlations for both men and women. Since Tl and K have a similar ionic radius (150 pm and 151 pm respectively), Tl has an effect on the conduction system of the heart and the cardiac muscle (Marquart & Schafer, 1997). 102 d 0> y -< < c H r! o CD (0 W -0.7 4 0.8 0 o Malignant o o O 6 Malignant S| CO s| o neoplasms OS 00 neoplasms o ca o 0.7 3 o ... of trachea, p O o o ... of trachea, « 3 2 !s| 96' 0 ooeff i CB Bronchitis Bronchitis p CD 3 ient j co m Chronic obstructive O Chronic obstructive dis e o lung disease 00 lung disease sas e Asthma Asthma I 6 o e o CO Skin disease Skin disease bo si eo co SI Ol d D. of the musculo- D. of the musculo- skeletal system & CO skeletal system & to 3" connective tissue to o connective tissue CD Table 4: Coefficients of correlation between element concentrations in Pleurozium schreberi and the incidence of essential and secondary hypertension (ICD 401-405) in the years 1993-1997, broken down according to age groups. Significant correlations are printed in bold type. >8 5 80-8 5 50-5 5 55-6 0 65-7 0 70-7 5 75-8 0 25-3 0 30-3 5 40-4 5 60-6 5 20-2 5 35-4 0 45-5 0 Be -0.10 -0.22 -0.02 -0.18 -0.14 -0.08 -0.01 -0.18 -0.12 -0.03 -0.12 -0.05 -0.10 -0.05 Be Bi 0.33 0.38 -0.13 -0.08 0.44 0.45 0.01 0.20 0.59 0.76 0.69 0.81 0.82 0.86 Bi Cs 0.46 0.54 -0.10 0.13 0.61 0.60 0.11 0.42 0.76 0.85 0.84 0.90 0.94 0.92 Cs Mn 0.14 -0.23 0.63 0.44 -0.07 -0.33 0.29 -0.09 -0.36 -0.57 -0.48 -0.60 -0.63 -0.71 Mn Na 0.46 0.07 0.75 0.78 0.31 0.10 0.66 0.36 0.05 -0.25 -0.12 -0.31 -0.36 -0.49 Na Tl 0.49 0.39 -0.08 0.20 0.63 0.71 0.19 0.44 0.81 0.96 0.92 0.98 0.99 0.99 Tl Significant correlations were found between concentrations of the elements Nd, Sn and Th in Polytrichum formosum and the incidence of leukaemia (ICD 204 - 208). In contrast to this, the correlation coefficient for Tl is significantly negative for leukemia. Practically nothing is known about the toxicity of Nd. The toxicity of inorganic tin (Sn) compounds is generally considered to be low, but tin organyls are suspected of having a carcinogenic effect (Oehlmann & Markert, 1997). Thorium (Th) may have a carcinogenic effect because of its radioactivity. Chromates are carcinogenic; they mainly cause tumours of the nose and lungs (Marquart & Schafer, 1997; Oehlmann & Markert, 1997). The Cr concentrations detected in Polytrichum formosum show a positive but not significant correlation (r = 0.63) with the malignant neoplasms of the trachea, bronchus and lung (ICD 162). Significant correlations were found between the concentrations of such elements as Ce, Fe, Ga and Ge in the biomonitors and diseases of the respiratory tract. Ce may be regarded as non-toxic, and Fe is essential to all organisms. Ga is slightly toxic and has a stimulant effect like that of Ce. Ge is also thought to be non-toxic, but some Ge compounds are poisonous. These elements are found in dust deposits. Their sources are the burning of fossil fuels and the drifting of dust on the ground. High Fe concentrations in the mosses indicate a generally high level of pollution with dust, which may result in respiratory tract disease. 104 Table 5: Coefficients of correlation between thallium concentrations in Polytrichum formosum and the incidence of diseases in the ERN. Significant coefficients are printed in bold type. For a listing of the diseases indicated by the classification according to ICD 9 see Table 1. U) e v> •o o to o> o to o o o •0 8 CM «o eo ^ *t to 4. SUMMARY This is the first study comparing the level of pollution with numerous elements, determined by moss monitoring, with the incidence of various types of disease. For most of the elements, the region studied was found to have a level of pollution similar to that of many other European regions and can thus be regarded as a model case. Since humans take these elements in chiefly by inhalation, a connection between pollution and diseases of the respiratory tract was to be expected. A connection was indeed found between such diseases and levels of the elements Ce, Fe, Ga and Ge in the mosses. Unexpectedly, however, a correlation was also proved to exist between thallium concentrations and heart disease. In the case of some other elements, such as Cr, there seems to be a connection with certain diseases but no significant correlation was observed. The reason may be that the element concentrations in the deposits are only one of many factors involved in pollution. Other factors such as indoor air contaminants or personal habits may overlay the effects of atmospheric deposition. The significant correlations found between the element concentrations in the mosses Pleurozium schreberi and Polytrichum formosum and the incidence of a disease can only provide indications as to the possible causes of the disease. Causality is not taken into account when the correlation coefficients are calculated. This means that correlations can never prove that a connection exists. To do so will be the task of further studies, for which these results may offer initial hints. 105 REFERENCES [I] Cislaghi, C, Nimis, P.L., 1997: Lichens, air pollution and lung cancer. Nature 387, pp. 463-464. [2] Ellison, G., Newham, J., Pinchin, M.J., Thompson, I., 1976: Heavy metal content of moss in the region of Consett (North-East England). Environ. Poll. 11, pp. 167-174. [3] Engelke, R., 1984: Schwermetallgehalte in Laubmoosen des Hamburger Stadtgebietes und Untersuchungen zur Sensibilitat bei experimenteller Belastung, Dissertation, Universitat Hamburg. [4] Markert, B., Wappelhorst, O., 1999: Biomonitoring of heavy metals and trace elements in the Euroregion Neisse. Ed.: International Atomic Energy Agency, Co-ordinated research project on validation and application of plants as biomonitors of trace element atmospheric pollution, analysed by nuclear and related techniques, Report on the First Research Co-ordinated Meeting (RCM), Vienna, Austria, Sept. 28th - Oct. lSt, 1998, pp. 66-73. [5] Markert, B., 1996: Instrumental element and multi-element analysis of plant samples - methods and applications. J. Wiley & Sons, Chincester, 296p. [6] Maschke, J., 1981: Moose als Bioindikatoren von Schwermetallimmissionen. In: Cramer, J. (Hrsg.): Bryophytum Bibliotheka 22, Vaduz. [7] Marquart, H., Schafer, S.G. (Hrsg.), 1997: Lehrbuch der Toxikologie, Spektrum Akademischer Verlag. Heidelberg. [8] Oehlmann, J., Markert, B., 1997: Humantoxikologie. Eine Einfiihrung fur Apotheker, Arzte, Natur- und Ingenieurswissenschaftler, Wissenschaftliche Verlagsbuchhandlung mbH, Stuttgart, 26lp. [9] Ross H.B., 1990: On the use of mosses (Hylocomium splendens and Pleurozium schreberi) for estimating atmospheric trace metal deposition. Water, Air Soil Poll. 50, pp. 63-76. [10] Riihling, A., Tyler, G., 1968: An ecological approach to the lead problem. Bot. Notisier 121, pp. 321-342. [II] Riihling, A., 1994: Atmospheric heavy metal deposition in Europe - estimations based on moss analysis, Nord 1994, 9, 53p. [12] Siewers, U., Herpin, U., 1998: Schwermetalleintrage in Deutschland - Moos- Monitoring 1995/96. Hrsg.: Bundesanstalt fur Geowissenschaften und Rohstoffe. E. Schweizerbart'sche Verlagsbuchhandlung, Stuttgart. 199p. [13] Steinnes, E., 1984: Monitoring of trace element distribution by means of mosses. Fresenius J. Anal. Chem. 317, pp. 87-97. [14] Waterhouse, J. (Hrsg.), 1976: Cancer incidence in five continents. Vol. 3. IARC, Lyon. [15] Wappelhorst, O., Kuhn, I, Oehlmann, J., Markert, B., 2000, Deposition and Disease - A moss monitoring project as an approach to ascertaining potential connections. Science of the Total Environment, In prep. [16] Wappelhorst, O., 1999: Charakterisierung atmospharischer Depositionen in der Euroregion NeiBe durch ein terrestrisches Biomonitoring. Dissertation, Internationales Hochschulinstitut Zittau, 189p. [17] Wappelhorst, O., Winter, S., Heim, M., Korhammer, S., Markert, B., 1999: Terrestrisches Biomonitoring in der Euroregion NeiBe - Grenzen und Moglichkeiten. In: Oehlmann, J., Markert, B.: Okosystemare Ansatze und Methoden. Ecomed-Verlag. Mtinchen. pp. 208-224. 106 XAC) 102860 BIOMONITORING OF AIR POLLUTION THROUGH TRACE ELEMENT ANALYSIS 1 SAMUEL AKOTO BAMFORD, E. K. OSAE, Y. SERFOR-ARMAH, B. NYARKO, F.OFOSLJ, I. J. ABOH, 2G. T. ODAMTTEN 'National Nuclear Research Institute, P. O. Box LG 80, Kwsibenya, Ghana, Legon, Accra, Ghana, Tel/Fax : 233-21-401272, E-mail: [email protected], [email protected] 2Botany Department, University of Ghana, Univ. Post Office, Legon, Accra, Ghana Abstract: Research work is currently going on to determine the suitability in the use of local lichen species for biomonitoring air pollution in Ghana. The study area being investigated are the gold-mining areas situated in the Moist Evergreen and Semi-Deciduous forests in Ghana. The nuclear analytical techniques being used in this work are instrumental neutron activation analysis and tube-excited x-ray fluorescence spectrometry. The present report covers results of quality control exercise carried out to validate the quantitative methods being used. This includes our participation in an intercomparison exercise carried out among participants of the IAEA coordinated research project. The samples analyzed were two lichen samples from two completely different areas using neutron activation analysis. Only short- and medium- lived irradiations were carried out. Satisfactory results were obtained for most of the elements identified and quantified. 1. INTRODUCTION The gold-mining processing methods of the type of gold ore predominant in the country, requires high temperature roasting of the ore. This roasting therefore leads to the release of oxides and sulfides of heavy metal pollutants into the environment [1, 2]. Consequently, for those mines situated in the forest zones, biomonitoring of air pollution offers a convenient and cheaper method if properly developed and tested. The main project objective is therefore to develop and validate an indirect, simpler, and less expensive method for studying heavy metal pollutants in ambient air of gold mining industries using biomonitors (lichens). The expected project outputs are: 1. The quantitative methods of the applied nuclear-related analytical techniques, of Instrumental neutron activation analysis (INAA) and x-ray fluorescence spectrometry (XRF), validated for the analysis of lichen samples. 2. Map of the abundance and distribution of identified local lichen species in the project areas. 3. Report on heavy metal concentration levels in selected lichen species, (baseline and prevailing levels). 4. Report on concentrations of atmospheric trace elements through analysis of loaded air filters. 5. Empirical relationship developed linking elemental concentration in lichen samples to that in ambient air. 6. Protocol and procedures in the use of local lichens for biomonitoring air pollution produced. 107 Figure 1. shows at a glance the project Objective and outputs. Objective To develop and validate a biomonitoring method for atmospheric monitoring in the gold-mining areas v v Output 1 Output 3 Output 4 Output 6 Quantitative methods of Results on atmospheric Map of abundance Protocol and procedures the analytical techniques trace elements through and distribution of for biomonitoring air (NAA/XRF) validated analysis of loaded filters local lichens pollution produced Output 5 V Output 2 Heavy metal concentrations In selected lichen species 108 The above project is being done in collaboration with the Botany Department of the University of Ghana, who are providing the personnel for the species identification. It has also been possible to get the Ecological Laboratory of the University of Ghana on board in an active participation and support for the project. The present studies is continuing on the basis of the last renewal of the contract No. 9934/RO for the period 1998/12/01 to 1999/11/30, and this report covers essentially our participation in the quality control exercise being carried out through the intercomparison analysis of two unknown lichen samples with different levels of elemental concentrations. 2. METHODS 2.1. Sample Preparation The lichen samples were prepared by weighing 100 - 200 mg from the homogeneously ground sample. They were then wrapped in thin polythene foils, port into polythene capsules and sealed with a hot soldering iron. Replicate analyte samples were prepared, two each for short irradiation, medium irradiation It was not possible to carry out long irradiation and further replicate analysis since the MNSR reactor developed some temporal electronic problem. This problem has, however, been solved. 2.2. Sample Irradiation Irradiation of the samples was done using the GHARR-1 reactor operating between 10 - 15 kW and at neutron flux in the range of 1 - 5x10* ins'lcnT^. Each of the capsules was sent into the reactor for irradiation by means of a pneumatic transfer system operating at a pressure of 25 psi. The irradiation was also categorized according to the half-lives of the elements of interest. At the end of the irradiation, the capsules were returned from the reactor, allowed to cool down until the activities have reached the acceptable level for handling. Each of the samples was then placed on the detector for counting. The irradiation schemes and corresponding ( n,y) product radionuclides was presented in the first report at the first RCM 2.3. Qualitative and Quantitative Analysis The PC-based gamma - ray spectroscopy system consists of an N-type High Purity Germanium (HPGe) detector model GR2518, an HV Power Supply Model 3103, a Spectroscopy Amplifier Model 2020, all manufactured by Canberra Industries Inc., a Silena EMCAPLUS Multi-Channel Analyzer (MCA) Emulation Software Card and a 486 micro- computer for data evaluation and analysis. The detector operates on a bias voltage of 3000(- ve)V, and has resolutions of 0.85 keV and 1.8 keV for Co-60 gamma-ray energies of 122 keV and 1332 keV respectively. The relative efficiency of the detector is 25%. By means of the MCA, the spectra intensities for the samples were accumulated for some pre-set times. The qualitative analysis which involves the determination of the various elements in the samples and the quantitative determination of their concentrations were achieved using the Gamma Spectrum Analysis Software SPAN 5.0 109 3. RESULTS AND DISCUSSIONS Two lichen materials labeled L-l and L-2 were analyzed using instrumental neutron activation analysis. The following elements were identified and quantified in the lichen sample L-l: Al, As, Ca, CI, Co, Cr, Fe, K, La, Mn, Na, Sb, Sc, V, and Zn. For the lichen sample L-2, for the few irradiations carried out , these were the elements identified and quantified: Co, Fe, K, La, Na, Sc, and Zn. Evaluation of the results of participating countries by the IAEA has been published [4], with graphical presentation of the results for the samples L-l and L-2 . In the L-l sample the concentrations of the following elements fell within the 95% confidence interval (CI) of the laboratory mean: CI, Co, K, Mn, Sc, and V. Those that fell within three times the 95% confidence interval are: Al, Cr, Na, Sb, and Sc. The elements whose determined values fell outside the three times 95% confidence interval (3x CI) include: As, Ca, Co, Fe, La, and Zn. In the case of As it has been discovered that the error was due to transposition of results from print out into the table of results. Hence for the L-l lichen analyzed 40% of the samples had elemental concentrations within the 95% CI, 73% within 3x CI, and 33% outside the 3X CI range. In the L-2 sample the elements falling within the confidence interval are: K, La, and Sc. Those elements lying outside the range of confidence interval include: Co, Fe, and Zn. These results were obtained from a maximum of two replicate analysis. Increasing the number of replicate samples would have improved further the quality of the results obtained. In both the L-l and L-2 lichen samples, the concentration of the following elements fell outside the 3x CI range : Co, Fe, and Zn. The irradiation and counting scheme for these elements need be investigated further through the analysis of some carefully selected biological plant reference materials to help improve the accuracy of the determination of Co, Fe, and Zn 4. PLANS FOR THE FUTURE ACTIVITY PERIOD 1. Field trip to Prestea Gold Mines March • Survey of lichen species • Sampling and storage of lichens • Air sampling for PMio and PM2.5 particulates 2. Identification of lichen species April 3. Sample Preparation April • Separation and cleaning of lichen species • Grinding (lichen powder) and pelletization • Wet digestion of lichen samples with HNO3 and H2O2 4. Laboratory Analysis April/May/June • Analysis of lichen samples by INAA and EDXRF • Analysis of digested lichen samples by TXRF • Analysis of reference materials • Gravimetric analysis and elemental analysis of Airborne particulates 110 5. Field trip to Prestea Gold Mines July/August • Repeat sampling of lichens and aerosols 6. Laboratory Analysis August • Repeat analysis of lichens and aerosols 7. Data Reduction and Evaluation September • Lichen mapping (abundance and concentration distribution) • Establishing empirical relationship (aerosol - lichen concentration linkages) 8. Development of a protocol for biomonitoring air October pollution. 9. Project reports November 111 PROJECT OUTPUTS, ACTIVITIES, AND STATUS (BAMFORD, GHANA) SPECIFIC OUTPUTS ACTIVITIES STATUS 1. Quantitative methods of • Analysis of CRMs an SRMs • Validation carried out analytical techniques (NAA/XRF) • Participation in on NAA using NBS validated intercomparison runs Orchard Leaves, and BCR-CRMNo. 279 • Interccomparison exercise carried out with 2 lichen samples • Intercomparison exercise with moss samples is on-going 2. Heavy metal concentrations in • Field survey of local lichens • On-going selected lichens • Sampling and sample preparation of lichens • NAA and XRF analysis • Data evaluation 3. Atmospheric elemental • Sampling atmospheric • Yet to be done concentrations from analysis of particulates for PMio and 2.5 loaded filters • Gravimetric analysis • Trace element analysis • Data evaluation • On-going 4. Map of abundance and • Field survey distribution of local lichens • Sample collection • Species identification • Data evaluation • Generation of a lichen map 5. Linkage between elemental • Statistical evaluation of • Vf*t to hp Hnnp concentrations in lichens and the results from outputs 2 & 3 atmosphere established • Development of an empirical relationship 6. Protocol and procedures for • Protocol for sampling and A \< 0+ +rt r\^ ziAMp biomonitoring of air pollution sample preparation • I CL LU UC UUIIC • Protocol for analysis 112 REFERENCES [1] BAMFORD, S. A., OSAE, E., ABOH, I., BINEY, C, ANTWI, L., Environmental impact of the gold-mining industry in Ghana, Biological Trace Element Research, 26/27,(1990)279-285. [2] ABOH, I, BAMFORD, S. A., TETTEH, G. K., model for the multi-element analysis of Prestea gold ore with energy dispersive x-ray fluorescence analysis, X-ray Spectrometry, 21 (1992) 119-125 [3] BAMFORD, S. A., et al., Biomonitoring of air pollution through trace element analysis, IAEA Coordinated research project on validation and application of plants as biomonitors of trace element atmospheric pollution, analyzed by nuclear and related techniques, NAHRES-43 Rep., Vienna, Austria (1998). [4] INTERNATIONAL ATOMIC ENERGY AGENCY, Intercomparison run NAT-5 for the determination of trace and minor elements in two lichen samples, Vienna, (1999) 113 BIO-MONITORING STUDIES USING NUCLEAR AND RELATED TECHNIQUES FOR THE STUDY OF AIR POLLUTION IN AND AROUND THE CITY OF HYDERABAD, INDIA !J. ARUNACHALAM, 'M.V. BALARAMAKRISHNA, 2CHRISTOPHER DAVED, !D. KARUNASAGAR AND 2SANJW KUMAR Trace Analysis Laboratory, 2Surface and Profile Measurement Laboratory National Centre for Compositional Characterisation of Materials (CCCM), Board Of Radiation and Isotope Technology, Department of Atomic Energy, ECIL Post, Hyderabad, INDIA 500 062 Abstract: XA0102861 Passive bio-monitoring using different plant species affords a cost effective approach to studies on environmental trace element pollution. Lower plants like mosses and lichens have already been demonstrated to be effective bio monitors. As part of our participation in the IAEA's Co-Ordinated Research Program on "Validation and application of plants as bio-monitors of trace element atmospheric pollution analyzed by nuclear and related techniques", we have carried out studies on the use of a moss (Funaria hygrometrica) and a shrub ( Lanatana Camera). About 35 sampling locations covering industrial zones, locations with heavy traffic, commercial and residential areas were identified. The samples have been analyzed using 1CP-MS and PIGE to provide elemental concentrations on a number of elements. Electron microscopic images of the moss show physical trapping of fine particulate matter. The data are examined with a view to assess the use of these plants as bio-monitors of toxic trace elements. The moss, available only during the monsoon, shows, on a dry weight basis, much higher levels of concentrations for many elements, than the shrub (leaves). The concentration profiles in relation to the sampling locations suggest that metallic pollution can be easily discerned. The elemental data are examined using principle component analysis. While the qualitative identification of the metallic pollutants is easier with reference to the sampling locations, it would also require complimentary information using other techniques to provide a quantitative estimate of total environmental trace element burden. 1. INTRODUCTION As part of out participation in this CRP, we had earlier analysed a set of lower plants: mosses (Funaria sp. and Cyathodium sp.) and a set of higher plants( a weed: Parthenium hysterophorus L. and a common bushy shrub Lantana Camera L.). Samples from different locations were collected and analyzed using PIGE and ICP-OES These studies had shown that vehicular pollution is easy to discern. The weed Parthenium though available in plenty during the monsoon seasons and is near perennial, is identified to be a health hazard to both humans and animals and hence we discontinued the collection and study of this plant for the bio-monitoring purposes. The collections and study of Funaria and Lanatana were continued and the results obtained subsequent to the first RCM are presented. 115 2. METHODS 2.1. Sampling Moss samples: The moss Funaria is available during monsoon periods only and grows on old brick walls (without cement mortar cover). It grows as broad patches, usually on the earlier dried patches. The Funaria was sampled during the last monsoon period (July-August 1999) from different locations in the city where the growth was found to be abundant to enable the collection of 0.2-0.5 kg of sample in each location.. The plant is hand picked from about a height of 2-3 metres from the ground level. The soil bound to the basal parts (root like fibres called rhizoids) of the plant are removed to the extent possible and stored in perforated polythene bags in a fridge. Lantana Samples: Lantana Camera is widely distributed and is a near perennial shrub. The leaves of this plant were sampled from nearly 35 sampling locations, covering industrial estates, commercial zones with of heavy traffic, housing areas and isolated places with very little human activity. The leaves were sampled from healthy plants from a height of about 1-2 m above the ground levels. In each location, leaf samples from 4 to 5 individual plants were sampled and mixed to form a common sample. Leaves with infection or perforations were discarded. They were stored in perforated polythene bags and stored in a fridge until analysis. 2.2. Sample Preparation Samples of the moss, cleaned from much of the soil fraction from the basal parts, were taken (100-200g portions) in a beaker and washed with DI water with agitation in an ultrasonic bath for 1 minute. Where the soil particles were still seen to be released, one more agitation cycle was repeated. The water was drained by keeping the washed mass on a plastic sieve for some time, taken in a wide mouth beaker and oven dried for 24 hours at 45-50°C. The dried samples were ground in an agate grinding assembly and stored in polythene bottles. The composite Lantana (leaf) samples (100-200g aliquots) were also washed once with DI water for about 30 sees., drained of water by keeping on a plastic sieve and dried under conditions similar to those of the moss samples. The powdered samples were stored in polythene bottles until analysis. 2.3. Analysis of samples The powdered sampled were digested using microwave digestion with conc.nitric acid. Multi-element analysis of these digests samples were carried out using ICP-MS (VG- PlasmaQuad in isntrument, VG Elemental, U.K), located in the Ultratrace Analysis laboratory. Proton Induced Gamma ray Emission(PIGE) analyses were carried out using the 3MV Tandetron Accelerator( High Voltage Engineering Europa, Holland) facility at the Surface and Profile Measurement lab. of our Centre. 116 2.3.1. Sample Digestion 200-300 mg aliquots of the powdered sample were taken in Parr PTFE digestion vessels(45 ml volume). 3ml of concentrated nitric acid was added. The digestion was carried out for 3 minutes at high power ( 650 watts). Upon cooling, the digest was later taken in standard flasks, diluted to 25 ml with DI water. Appropriate blanks were also prepared in the same way. The digests of the Lantana samples were always clear but digests of Fumaraia showed some sediment on standing indicating some unattacked soil fractions clinging to samples despite the wash cycle. Only clear supernatant portions of the Funaria digests were analyzed. 2.3.2. Analysis using ICP-MS The Lantana and the funaria digests were suitably diluted with high purity water obtained using a Milli-Q water system and analyzed using external calibration procedure. NIST Multi-element standards 3171A (mix Al) and 3172A(mix Bl) were used. NIST SRM Oyster 1566a tissue was used for validating the analytical procedure. Rh and Re were used as internal standards for the analysis. 2.3.3. Analysis using PIGE The analysis of some the Funaria and Lantana samples were carried out using PIGE. The powdered sample (ca. 300 mg) was mixed with high purity graphite powder and pelletized. The pellets were irradiated using a proton beam and the ensuing gamma spectra were recorded, using a HPGe detector. 3. RESULTS AND DISCUSSION The elemental data obtained on the Lantana and Funaria samples are given in Tables I and Table II respectively. As is seen the concentrations in Funaria are much higher than the Lanatana indicating the ability of the moss samples to accumulate the trace elements. 3.1. Lantana samples 3. J.I. Local Variations and exposure The variations within a sampling locality were studied and are given for two locations (JDM2,JDM 4 and Pattancheru 2 and 3) in Table III. As is seen, variations of concentrations on many elements seem to be very small within a locality. Also given in Table IE, an; the results on the Lanatana samples collected near a foundry (JDM3), which show considerably enhanced levels of concentrations for all the elements. JDM 2 and JDM4 are about 500m away from this location. This would essentially suggest that the exposure to and accumulation of trace elements in Lantana is within a restricted zone. Thus establishing the local variations within a plant species is important prerequisite to identify 'local hot spots' and establish the source and the area likely to be affected by the source. 117 3.1.2. Principal Component Analysis (PCA) of the elemental concentrations PCA is a powerful multivariate statistical approach for the study of associations among the variables; in addition in can also help infer 'independent causal factors' which can be used to account for the dispersion in the data set. The multi-elemental data obtained on the Lanatana samples from different locations were examined using PCA, which suggested 4 'factors' to account for the variability; however the first two components account for nearly 70% of the total variance. The plot of the eigen vector coefficients of the first two components is given in Fig.l. 0.6- •Mn •Ti 0.4- 0.2- e o 0.0- S o 0.2- 0.4- 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 Component 1 The Communalities, i.e., the fraction of variance accounted for by each component for the given set of variables (elements) is given in Table IV. The first component alone accounts for nearly 80-95% of the total variance of Al, Cr, Co, Ba and La; it also accounts for 61% ,67% and 74% of the variances of V, Ni and Zn respectively. This information, in association with the plot given in Fig would suggest that the 'soil fraction' could be the dominant factor. Only a fraction of Pb and Cd are associated with this component. The coefficients of the first and second components suggest an association of Ti,Mn and Pb with the second component accounting for 39%, 52% and 32% of the total variance of these elements. The first and third component suggest an association of V, Pb and Cd with the third component accounting for 20%,36% and 56% of the variances associated with these elements. The physical factors that would account for the suggested association of Ti,Mn and Pb; and V,Pb and Cd are not apparent, in this juncture. Further, copper is not accounted for in any of first three factors and a fourth component accounts for nearly 87% of the variance in copper. The plot of the 'component scores' on the first two components of the Lanatana samples from different locations are given in Fig. showing areas of high metallic pollution. The cluster of other sample points can be used to establish the base values of the concentrations of the elements (in Lantana) and locations where significant deviations occur can be probed for possible sources of pollutants. 118 3.2. Funaria Samples 3.2.1. Physical Trapping of paniculate matter in Funaria: As mentioned earlier, a lot of soil fraction is adhered to the Funaria and it not easily removed. The scanning electron microscopic image given in Fig.2 shows the trapping of such particulate matter in Funaria. Fig.2. SEM image of soil particles trapped in a Funaria moss sample. As is seen these particles are of about 10 microns or lower in size. The X-ray spectra obtained on these particles partciles show that they are essentially composed of Si, Al and oxygen indicating that they are mainly soil particles. A typical x-ray spectrum obained on one such particles is given in Fig.3. 119 Energy Dispersive X-ray Spectrum of atypical trapped particle ( in Funaria) OK 0.70 1.10 1.50 1.90 2.30 2.70 3.10 3.50 3.90 4.30 Fig. 3 :EDXRF spectrum of a typical soil particle trapped in Funaria The images of the washed samples of Funaria show a typically smaller density if trapped particles and also of smaller sizes, indicating that the washing cycle is able to remove particles of larger size. The effect of washing on the elemental concentrations on the Lantana samples was studied. Leaves of lantana collected from a single plant, near a foundry in an industrial area, visibly covered by dust, were analyzed with and without washing them. The washing resulted in a reduction of nearly 20% in the concentrations of Al, Ti, V, Cr, Sr, Ba and Pb but showed almost no change in rest of the elements, suggesting either 'in situ' biological levels these elements or substantial fine soil fraction still adhering to the leaf surface. Analysis using multivariate statistical approaches like PCA can help elucidate such information. Future work: The second phase of our work under the CRP has suggested that local variations in the elemental concentrations in the plants are smaller and samples collected from locations with identifiable pollution sources have significantly higher concentrations. But the dispersal of these metallic pollutants seem to be within a 'restricted zone' around the source. It is planned to establish this 'zone of dispersal' around the some of the locations already studied. Atmospheric dust forms the predominant form of air pollution in Hyderabad and the particles trapped in Funaria show a size distribution of 10 microns or less. Thus another activity that is planned is to collect these fine particulate matter so as to examine feasibility of using these plants to study the elemental concentrations in these fine fractions. 120 4. ACKNOWLEDGEMENTS The authors wish to express their deep sense of appreciation to Dr.S.Gangadharan, Chief Executive, BRIT/CCCM for his guidance and advice. We are grateful to Dr.V.S.Raju and Dr.S.C.Chaurasia, Officers-In-Charge of Surface and Profile: Measurement Laboratory and Bulk Analysis Laboratory, respectively, for extending the analytical support for the programme. We thank Ms.Ritu Singh who carried out the microwave digestions; Mr. K.Chandrasekaran for measurements using ICP-MS and Mr.Ranjan Mishra and Mr.S.M.Dhavile, for providing additional analytical data through AAS measurements. 121 Table II. Elemental Concentration in Funaria from some locations. Eleme K-Nag CPalli M-Ali Lguda Nachr Secbd Seri CCC Pchr nt M Al 506 335 388 878 1112 1080 810 1600 1000 Ti 76 91 124 141 56 88 45 159 113 V 7.1 11.6 4.9 5.9 5.9 5.5 5.4 1.3 10 Cr 5.6 3.6 3.8 4.9 8.1 7.1 6.2 7.4 8.7 Mn 43 27 41 34 70 57 50.6 82 51 Co 1.1 0.33 0.93 1.0 1.7 2 1.4 3.6 2.4 Ni 3.4 2.2 3.4 4.1 10.7 5.7 6.2 5.2 5.3 Cu 5.0 4.0 5.2 7.3 7.6 10.6 4.5 6.5 9.9 Zn 46 29 49 86 104 165 102 44 230 As 3 8.6 9.1 8.5 2.2 2.6 2.2 2.2 2.8 Cd 0.25 0.14 0.25 0.35 0.35 0.4 0.16 0.07 0.11 Sb 0.03 0.03 0.26 0.12 0.02 0.08 0.04 0.01 0.16 Ba 43 34 40 74 77 116 88 41 65.3 La 8.0 26 14.4 8.1 10.3 13.7 11.3 34.8 5.2 Pb 14.4 10 8.6 420 27 39 19 10 13 Description: Knag, Cpalli, M-Ali: Residential Areas Lguda : Residential colony, near Railway workshop Nachr: : Industrial Estate, ferrous&non-ferrous industry Secbd : Area in the middle of city, heavy traffic location CCCM : Behind our laboratory Seri : near an Aluminum works factory Pchr : Industrial area, mainly organic/pharmaceutical industry 122 Table III: Variation in elemental concentrations in two locations ( Lantana) Element Location: Industrial Estate Industrial Estate 2 1 mg/kg JDM2 JDM4 JDM3 PCH2 PCH3 Al 49.2 35.5 154 39 35 Ti 14.9 16.7 20.2 16 17 V 0.34 0.7 6.8 0.2 0.2 Cr LO.74 0.95 3.6 1.3 0.8 Mn 8.2 13.5 18.1 9.9 10.9 Co 0.09 0.09 0.25 0.14 0.12 Ni L!I4 1.5 2.4 1.7 2.6 Cu 4.0 3.1 4.4 4.2 4.5 Zn 13.4 15.2 25.6 15.2 14.2 Ba 13.3 8.4 16.7 21.5 19.1 Pb 1.8 2.7 30.1 1.5 1.2 JDM3 Collected near a foundry. JDM2 and 4 are about 500 m away from this location. PCH3 and PCH2 are Lantana samples collected in another industrial estate. (all are composite samples) Table IV. Communalities of the elemental variables in the PCA of the Lantana data Element Component 1 Component 2 Component 3 Al 0.942 0.003 0.03 Ti 0.109 0.394 0.011 V 0.614 0.136 0.199 Cr 0.901 0.065 0.005 Mn 0.208 0.518 0.046 Co 0.932 0.004 0.046 Ni 0.674 0.126 0.076 Cu 0.008 0.004 0.026 Zn 0.737 0.126 0.008 Ba 0.817 0.025 0.125 La 0.926 0.001 0.026 Pb 0.08 0.318 0.356 Cd 0.154 0.216 0.557 123 AIR-BIOMONITORING BY TRANSPLANTED LICHENS IN THE NEGEV DESERT, ISRAEL JACOB GARTY XAO'102862 Department of Plant Sciences and Institute for Nature Conservation Research, Tel Aviv University, Tel Aviv 69978, Israel Abstract: The present report summarizes two time periods of study: 1) August 1997 - April 1998 2) May 1999 - November 1999 1) In August 1997 thalli of the lichen Ramalina maciformis (Del.) Bory were collected in the Negev Desert and transplanted with their substrate, flintstones, to 24 biomonitoring sites in the Negev Desert. In April 1998 the lichens were retrieved and their elemental content was determined by ICP-AES. In addition, we examined physiological parameters as presented in report #1. 2) In May 1999 thalli of the lichen were collected in the control site and transferred together with the substrate to 10 biomonitoring sites in the Negev. These thalli were retrieved in November 1999. We examined: a) the electric conductivity, indicative of cell membrane integrity; b) the production ofethylene indicative of stress; c) the chlorophyll a fluorescence as a means to monitor aspects of photosystem II (PSII) activities in the lichen. Final results of the first period experiment show that lichens exposed to air contaminants at a site of toxic waste deposition, accumulated large amounts of Al, Ba, Ca, Cu, Fe, Mg, Na, Pb, S, Sr and Zn. Preliminary results of the second period of exposure show that physiological parameters, indicative of lichen-viability, detected stress in thalli retrieved from sites in and around the Ramat Hovav Industrial Area in the Negev. 1. INTRODUCTION The main objective of the project is to establish a network of biomonitoring sites in and around the Ramat Hovav Industrial Area in the Negev Desert. In the absence of instrumental monitoring, we believe that the capability of Ramalina maciformis to monitor air-pollution should be utilized in full. The use of transplants of an epilithic fruticose desert lichen to monitor air pollutants in the desert is innovative and leads to the development of a biotechnological procedure to be used for environmental quality research projects all over the world. 2. METHODS In August 1997 we collected flintstones carrying the fruticose lichen Ramalina maciformis (Del.) Bory on a hill near Tellalim in the Negev Desert, and transferred them to 24 biomonitoring sites in the region (Fig. 1). In April 1998 the stones carrying the lichens were retrieved and transferred to the laboratory at Tel Aviv University. The lichens were detached from the substrate and rinsed immediately for 10-20 s with double-distilled water. Then the thalli were air-dried and divided into samples. 2.1. Determination of the elemental content of the lichen (first period of the project) For the measurement of the Al, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Pb, S, Sr, V and Zn content of the lichen, subsamples of 1-2 g of rinsed and air-dried thalli were ground with 125 liquid nitrogen to make a powder. This powder was dried for 24 h at 105°C. Subsamples of 250 mg were digested in 10 ml of concentrated analytical HNO3 in test tubes of 50 ml in a heating block for a duration of 8 h at a temperature of 120°C. The elemental content was determined by inductively coupled plasma - atomic emission spectrometry (ICP-AES) by the use of a Spectroflame ICP (Spectro, Kleve, Germany). 2.2. Transplantation and examination of physiological parameters (second period of the project) In May 1999 lichens were collected in the Tellalim site and transferred to ten biomonitoring sites in and around the Ramat Hovav Industrial Area. Lichen-carrying stones were also relocated in the control site at Tellalim (Fig. 2). All the lichens were retrieved in November 1999. Sections 2.2.1.-2.2.3. describe the examination of the physiological parameters. 2.2.1. Measurement of chlorophyll fluorescence For the measurement of chlorophyll fluorescence, the thalli collected in November 1999 were immersed in distilled water for five min to revive full photosynthetic activity. Subsequently, the thalli were dark-adapted for 10 min by means of 'dark leaf clips. Finally, the shutter of one clip at a time was opened and a 0.7 sec long, saturating flash of white light 2 1 (12,000 umol photons m" s" , found to be saturating for Fm) was administered by a pulse- modulated fiuorometer (Diving PAM, Walz, Effeltrich, Germnay) through an optical fiber. The potential quantum yield of PSII was calculated as Fv/Fm , as Fv is variable fluorescence defined as maximal fluorescence (Fm) during the saturating flash minus minimal fluorescence 2 1 (Fo), as sampled in virtual darkness (0.1 umol photons m" s" from the modulated red measuring light) just before the saturating flash. 2.2.2. Assessment of membrane integrity The integrity of cell membranes was assessed for batches of thalli collected in November 1999, divided into subsamples of one g, kept in moist chambers overnight and immersed in 100 ml of double distilled water for 60 min. The electric conductivity of the water indicating the membrane integrity, was measured by an electric-conductivity meter (TH-2400, El Hamma Instruments, Mevo Hamma, Israel). 2.2.3. Measurement of ethylene production For this analysis we used samples of 1 g of thalli collected in November 1999. Each sample consisted of a few thalli with only one damaged surface each and not of thalli fragments, to avoid and/or minimize the production of stress-ethylene as a result of wounding. The samples were soaked in 20 ml of either double distilled water, pH 5.6 or 5 mM FeCk, pH 3.5 for 30 min. The submersion of the second group of samples in a solution of iron salt at a low pH was performed to create unfavorable conditions and obtain a noticeable production of ethylene, to indicate stress brought about by the absorption and accumulation of air pollutants. After the soaking procedure, the samples were wiped gently with filter paper to remove excess moisture and then placed in sealed 50 ml Erlenmeyer flasks. After 3 h, 4 ml of the gas in each flask was withdrawn with a syringe and 1 ml was injected into a gas chromatograph Varian 3350 equipped with an activated alumina column and a flame ionization detector. The 126 and its loss through corroded cell membranes. The significant low K content of thalli exposed in the vicinity of the evaporation ponds (site 12) in the Ramat Hovav Industrial Area, may be indicative of the presence of organic volatiles characterized by strong odors. If this is the case, the leakage of K should be linked with the degradation of cell membranes in the presence of the organic evaporating solvents, in addition to the mineral elements deposited with high amounts of local dust. 3.2. Physiological parameters in the lichen in the second period of the project Table II shows that the potential quantum yield of PSII expressed as the fluorescence ratio Fv/Fm was high in the control site at Tellalim and in most of the other sites. However, low Fv/Fm ratios were obtained for four sites, all located in the Ramat Hovav Industrial Area. Table II also shows that high values of electric conductivity were obtained for four sites located in the Ramat Hovav Industrial Area, indicating injury of cell membranes. Most of the biomonitoring sites, including the Tellalim control site, exhibited low electric conductivity values indicating non-corroded cell membranes. Table III shows that lichens retrieved from most of the biomonitoring sites and soaked in water, produced very low amounts of ethylene. Relatively high ethylene concentrations were produced by thalli retrieved from six sites located in the Ramat Hovav Industrial Area. Lichens retrieved from most of these sites and soaked in FeCl:2, produced high ethylene concentrations indicating stress. 4. PLANS FOR FUTURE WORK In the period January - October 2000 we plan to analyse the elemental content of the lichens retrieved in November 1999 and to test the statistical relations between the investigated physiological parameters and the elemental content of the lichen. 127 REFERENCES [1] GOUGH, L.P., ERDMAN, J.A., Influence of a coal-fired power plant on the element content ofParmelia chlorochroa, Bryologist 80 (1977) 492-501. [2] GARTY, J., GALUN, M., KESSEL, M., Localization of heavy metals and other elements accumulated in the lichen thallus, New Phytol. 82 (1979) 159-168. [3] YAALON, D.H., GINZBOURG, D., Sedimentary characteristics and climatic analysis of easterly dust storms intheNegev (Israel), Sedimentology 6 (1966) 315-332. [4] YAALON, D.H., GANOR, E., "East Mediterranean trajectories of dust carrying storms from the Sahara and Sinai", Saharan Dust (1979) (MORALES, C, Ed.), Wiley and Sons, London, pp. 187-193. 128 TABLE I-a. THE Al AND Ba CONTENT OF THALLI OF RAMALINA MAC1F0M41S COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Al Ba Site a Meanb (SD) na Mean6 (SD) 1 RamatBeka n10 738 ( 93) A 10 10.6 (1.2) ABCD 2 Red Sculpture, Ramat Hovav 10 601 (63) BCDE 10 9.5 (1.0) BCD 3 Ramat Hovav (gravel area) 10 111 (99) A 10 11.4 (1.6) ABC 4 Yona Efrat site 10 590 ( 67) BCDE 10 9.1 (0.7) CD 5 Power Station 10 507 (51) DE 10 8.8 (1.0) CD 6 Local council, Ramat Hovav 10 563 (114) BCDE 10 8.1 (1.9) D 7 Toxic waste site, Ramat Hovav 10 690 (85) AB 10 11.1 (1.2) ABCD 8 Old Turkish railway 10 517 (107) CDE 10 10.2 (2.8) ABCD 9 Telecommunication antenna 10 546 (102) BCDE 10 10.0 (1.5) ABCD 10 IDF antenna 10 686 (111) AB 10 10.8 (2.1) ABCD 11 Pumping site, Ramat Hovav 10 529 (43) CDE 10 8.9 (3.0) CD 12 Evaporation ponds, Ramat Hovav 10 671 (111) ABC 10 10.3 (1.0) ABCD 13 Tel Sheva, near Beer Sheva 10 573 (138) BCDE 10 9.5 (3.0) BCD 14 Kibbutz Revivim 10 541 (100) BCDE 10 9.3 (1.6) BCD 15 Mitzpe Revivim, east 9 546 (73) BCDE 10 9.6 (1.9) BCD 16 Mitzpe Revivim, west 10 519 ( 69) CDE 10 8.9 (2.2) CD 17 Kibbutz Mashabbe Sade 10 481 ( 98) E 10 12.1 (1.4) AB 18 Meitar (Efrati's house) 10 571 (107) BCDE 10 8.1 (1.2) D 19 Eshel HaNassi School 10 587 (78) BCDE 10 9.5 (1.3) BCD 20 Kibbutz Sede Boker (cemetery) 10 542 (78) BCDE 10 10.4 (1.7) ABCD 21 Pistachio orchard, Sede Boker 10 645 (182) ABCD 10 11.6 (2.9) ABC 22 Motorola antenna 10 671 (177) ABC 10 11.7 (2.4) ABC 23 Mishol Girit, Beer Sheva 10 519 ( 85) CDE 10 10.4 (2.3) ABCD 24 Shekhunat Vered, Beer Sheva 10 749 (129) A 10 12.6 (3.7) A 25 Control site 20 560 ( 68) BCDE 20 8.7 (1.4) CD ANOVAF ratio 6.73 4.07 ANO VA F probability 0.00 0.00 n = number of replicates. b Elemental content is given in micrograms per gram on dry-weight basis. Values in eacli vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 129 TABLE I-b. THE Ca AND Cr CONTENT OF THALLI OFRAMALINA MACIFORMS COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Ca Cr Site na Mean6 (SD) na Meanb (SD) 1 RamatBeka 10 82,585 (10,835) B 10 2.7 (0.6) BCDE 2 Red Sculpture, Ramat Hovav 10 74,350 ( 6,541) CDE 10 2.2 (0.5) BCDE 3 Ramat Hovav (gravel area) 10 76,110 ( 5,514) BCD 10 3.0 (1.2) ABC 4 Yona Efrat site 10 67,030 ( 4,697) DEFG 10 1.9 (0.5) DE 5 Power Station 10 75,603 ( 4,053) BCD 10 1.7 (0.7) E 6 Local council, Ramat Hovav 10 75,420 ( 6,016) BCD 10 2.5 (0.7) BCDE 7 Toxic waste site, Ramat Hovav 10 95,070 ( 7,592) A 10 3.6 (0.5) A 8 Old Turkish railway 10 70,858 ( 2,946) CDEF 10 2.6 (0.6) BCDE 9 Telecommunication antenna 10 69,559 ( 5,982) CDEFG 10 2.0 (0.5) CDE 10 DDF antenna 10 74,170 ( 6,084) CDE 10 2.2 (0.7) BCDE 11 Pumping site, Ramat Hovav 10 77,583 ( 5,875) BC 10 2.0 (0.6) CDE 12 Evaporation ponds, Ramat Hovav 10 92,022 ( 4,978) A 10 2.6 (0.6) BCDE 13 Tel Sheva, near Beer Sheva 10 60,978 ( 7,837) G 10 2.6 (0.5) BCDE 14 Kibbutz Revivim 10 64,357 ( 7,494) FG 10 2.8 (0.8) BCD 15 Mitzpe Revivim, east 10 68,588 ( 7,981) CDEFG 10 2.4 (0.7) BCDE 16 Mitzpe Revivim, west 10 64,248 ( 5,002) FG 10 2.4 (0.6) BCDE 17 Kibbutz Mashabbe Sade 10 71,086 (7,799) CDEF 10 2.1 (1.0) CDE 18 Meitar (Efrati's house) 10 67,574 (4,397) DEFG 10 1.9 (0.6) DE 19 EshelHaNassi School 10 60,490 (4,104) G 10 2.3 (0.4) BCDE 20 Kibbutz Sede Boker (cemetery) 10 63,861 (6,961) FG 10 2.1 (0.4) CDE 21 Pistachio orchard, Sede Boker 10 64,739 (6,807) FG 10 2.5 (0.5) BCDE 22 Motorola antenna 10 64,263 (6,056) FG 10 3.2 (0.1) AB 23 Mishol Girit, Beer Sheva 10 61,860 (5,152) FG 10 2.5 (0.6) BCDE 24 Shekhunat Vered, Beer Sheva 10 65,562 (8,010) EFG 10 2.7 (0.6) BCDE 25 Control site 20 60,529 (6,753) G 20 1.7 (0.6) DE ANOVAF ratio 20.91 5.02 ANOVAF probability 0.00 0.00 n = number of replicates. b Elemental content is given in micrograms per gram on dry-weight basis. Values in each vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 130 TABLE I-c. THE Cu AND Fe CONTENT OF THALLI OFRAMALINA MACIFORMIS COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Cu Fe Site na Mean6 (SD) na Mean0 (SD) 1 RamatBeka 10 5.2 (0.3) EF 10 813 ( 95) AB 2 Red Sculpture, Ramat Hovav 10 4.9 (0.4) EFG 10 640 ( 65) CDEF 3 Ramat Hovav (gravel area) 10 5.6 (1.3) DE 10 862 ( 97) A 4 Yona Efrat site 10 3.9 (0.3) FG 10 642 (71) CDEF 5 Power Station 10 5.2 (0.7) EF 10 541 ( 64) F 6 Local council, Ramat Hovav 10 7.4 (0.9) C 10 600 (125) DEF 7 Toxic waste site, Ramat Hovav 10 20.4 (3.5) A 10 793 ( 97) ABC 8 Old Turkish railway 10 4.8 (0.6) EFG 10 575 (121) EF 9 Telecommunication antenna 10 4.6 (0.5) EFG 10 592 (104) DEF 10 DDF antenna 10 4.2 (0.3) FG 10 758 (130) ABCD 11 Pumping site, Ramat Hovav 10 5.6 (1.0) DE 10 557 ( 50) F 12 Evaporation ponds, Ramat Hovav 10 6.4 (0.6) D 10 742 (110) ABCDE 13 Tel Sheva, near Beer Sheva 10 4.0 (0.4) FG 10 628 (152) DEF 14 Kibbutz Revivim 10 3.7 (0.4) G 10 583 ( 99) EF 15 Mitzpe Revivim, east 10 4.1 (0.5) FG 10 710 (224) ABCDEF 16 Mitzpe Revivim, west 10 4.3 (0.5) FG 10 587 (77) EF 17 Kibbutz Mashabbe Sade 10 8.8 (1.2) B 10 539 (106) F 18 Meitar (Efrati's house) 10 4.0 (0.3) FG 10 607 (117) D]iF 19 EshelHaNassi School 10 3.9 (0.3) FG 10 658 ( 97) BCDEF 20 Kibbutz Sede Boker (cemetery) 10 3.9 (0.2) FG 10 557 ( 86) F 21 Pistachio orchard, Sede Boker 10 3.9 (0.4) FG 10 691 (176) BCDEF 22 Motorola antenna 10 3.8 (0.4) FG 10 714 (178) ABCDEF 23 Mishol Girit, Beer Sheva 10 4.3 (0.2) FG 10 561 (101) F 24 Shekhunat Vered, Beer Sheva 10 5.0 (0.6) EFG 10 801 (140) ABC 25 Control site 20 3.9 (0.3) FG 20 578 (75) EF ANOVAF ratio 142.43 7.05 ANOVAF probability 0.00 0.00 n = number of replicates. b Elemental content is given in micrograms per gram on dry-weight, basis. Values in each vertical column followed by the same capital letter do not differ significantly at/><0.05 using one-way ANOVA and SNK test. 131 TABLE I-d. TEE K AND Mg CONTENT OF THALLI OYRAMAUNA MACIFORMS COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. K Mg Site na Meanb (SD) na Meanb (SD) 1 RamatBeka 10 1,577 ( 80) ABC 10 537 (50) BCD 2 Red Sculpture, RamatHovav 10 1,596 (159) AB 10 543 (59) BC 3 Ramat Hovav (gravel area) 10 1,691 (132) A 10 683 (54) A 4 Yona Efrat site 10 1,482 (71) BCDE 10 484 (36) CDEFGH 5 Power Station 10 1,400 ( 84) DEF 10 421 (26) GHIJ 6 Local council, Ramat Hovav 10 1,428 ( 82) CDEF 10 387 (50) J 7 Toxic waste site, RamatHovav 10 1,047 ( 67) G 10 694 (58) A 8 Old Turkish railway 10 1,345 ( 83) DEF 10 390 (54) J 9 Telecommunication antenna 10 1,377 (153) DEF 10 425 (53) GHIJ 10 IDF antenna 10 1,638 (164) A 10 579 (95) B 11 Pumping site, RamatHovav 10 1,286 ( 85) F 10 405 (38) HIJ 12 Evaporation ponds, RamatHovav 10 846 ( 51) H 10 473 (57) CDEFGH 13 Tel Sheva, near Beer Sheva 10 1,376 (128) DEF 10 475 (59) CDEFGH 14 Kibbutz Revivim 10 1,443 (141) BCDEF 10 453 (44) EFGHU 15 Mitzpe Revivim, east 10 1,465 ( 98) BCDE 10 493 (85) CDEFG 16 Mitzpe Revivim, west 10 1,499 (73) BCD 10 451 (35) EFGHU 17 Kibbutz Mashabbe Sade 10 1,340 (128) DEF 10 517 (44) BCDE 18 Meitar (Efrati's house) 10 1,496 ( 91) BCD 10 459 (43) DEFGHLF 19 EshelHaNassi School 10 1,470 ( 77) BCDE 10 458 (46) DEFGHU 20 Kibbutz Sede Boker (cemetery) 10 1,370 ( 87) DEF 10 394 (33) IJ 21 Pistachio orchard, Sede Boker 10 1,672 (124) A 10 512 (64) BCDEF 22 Motorola antenna 10 1,328 (37) EF 10 477 (77) CDEFGHJ 23 Mishol Girit, Beer Sheva 10 1,359 (100) DEF 10 435 (36) FGHU 24 Shekhunat Vered, Beer Sheva 10 1,576 (188) ABC 10 524 (58) BCDE 25 Control site 20 1,488 (116) BCDE 20 408 (70) HIJ ANOVA.F ratio 27.68 21.18 ANOVA.F probability 0.00 0.00 a n = number of replicates. b Elemental content is given in micrograms per gram on dry-weight basis. Values in each vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 132 TABLE I-e. THE Mn AND Na CONTENT OF THALLI OYRAMAUNA MAC1FORMS COLLECTED IN SITE 2.5 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Mn Na Site na Meanb (SD) na Mean" (SD) 1 RamatBeka 10 28.5 (3.3) E 10 335 ( 24) CD 2 Red Sculpture, Ramat Hovav 10 21.1 (1.8) JK 10 325 (26) CD 3 Ramat Hovav (gravel area) 10 27.3 (2.0) EFG 10 390 ( 59) BC 4 Yona Efrat site 10 24.1 (3.5) GHIJ 10 266 (33) DEF 5 Power Station 10 23.4 (1.2) HIJ 10 271 ( 15) DEF 6 Local council, Ramat Hovav 10 39.3 (2.9) D 10 327 ( 54) CD 7 Toxic waste site, Ramat Hovav 10 45.9 (3.0) C 10 569 ( 80) A 8 Old Turkish railway 10 26.3 (1.6) EFGH 10 244 (37) DEI7 9 Telecommunication antenna 10 24.4 (1.8) GHIJ 10 189 ( 15) F 10 DDF antenna 10 24.1 (2.0) GHIJ 10 215 ( 21) EF 11 Pumping site, Ramat Hovav 10 54.6 (4.7) A 10 552 (196) A 12 Evaporation ponds, Ramat Hovav 10 50.9 (4.4) B 9 522 (136) A 13 Tel Sheva, near Beer Sheva 10 22.8 (2.7) HIJ 10 400 (41) BC 14 Kibbutz Revivim 10 23.7 (2.6) GHIJ 10 259 (26) DEF 15 Mitzpe Revivim, east 10 24.9 (3.3) FGHIJ 10 253 (33) DEI' 16 Mitzpe Revivim, west 10 24.0 (1.0) GHIJ 10 293 (31) DEF 17 Kibbutz Mashabbe Sade 10 28.0 (1.8) EF 10 233 ( 18) DEF 18 Meitar (Efrati's house) 10 22.9 (1.7) HIJ 10 293 (27) DEF 19 Eshel HaNassi School 10 21.6 (1.7) IJK 10 197 (20) EF 20 Kibbutz Sede Boker (cemetery) 10 19.0 (1.9) K 10 192 ( 17) F 21 Pistachio orchard, Sede Boker 10 28.1 (4.3) EF 10 207 (21) EF 22 Motorola antenna 10 21.8 (3.0) IJK 10 203 ( 19) EF 23 Mishol Girit, Beer Sheva 10 21.3 (1.6) IJK 10 444 (29) B 24 Shekhunat Vered, Beer Sheva 10 25.1 (2.8) FGHI 10 426 (35) B 25 Control site 20 21.2 (1.9) IJK 20 299 (154) DE ANOVAF ratio 129.19 25.14 ANOVAF probability 0.00 0.00 n = number of replicates. b Elemental content is given in micrograms per gram on dry-weight basis. Values in each vertical column followed by the same capital letter do not differ significantly atp<0.05 using one-way ANOVA and SNK test. 133 TABLE I-f. THE Pb AND S CONTENT OF THALLI OFRAMAUNA MACIFORMS COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Pb s Site na Meanb (SD) na Meanb (SD) 1 RamatBeka 6 10.5 (3.5) B 10 2,664 (220) A 2 Red Sculpture, Ramat Hovav - n.d. - 10 2,531 (205) AB 3 Ramat Hovav (gravel area) 8 9.2 ( 1.8) B 10 2,561 (208) AB 4 Yona Efrat site - n.d. - 10 2,356 (130) BCD 5 Power Station 3 13.1 ( 0.7) B 10 2,157 (145) DEF 6 Local council, Ramat Hovav 6 11.7 (2.3) B 10 2,468 (151) ABC 7 Toxic waste site, Ramat Hovav 10 99.8 (21.3) A 10 2,624 (142) A 8 Old Turkish railway 7 16.1 (2.7) B 10 2,264 (122) CDE 9 Telecommunication antenna 5 11.1 ( 3.2) B 10 1,954 (173) FG 10 IDF antenna 6 10.0 ( 1.8) B 10 2,270 ( 85) CDE 11 Pumping site, Ramat Hovav - n.d. - 10 2,245 (104) DE 12 Evaporation ponds, Ramat Hovav 8 9.9 (2.5) B 10 2,252 (135) DE 13 Tel Sheva, near Beer Sheva - n.d. - 10 2,142 (184) DEF 14 Kibbutz Revivim - n.d. - 10 2,045 (196) EFG 15 Mitzpe Revivim, east 4 8.7 ( 1-9) B 10 2,249 (133) DE 16 Mitzpe Revivim, west - n.d. - 10 2,360 (122) BCD 17 Kibbutz Mashabbe Sade 3 n.d. (1.3) B 10 2,040 (160) EFG 18 Meitar (Efrati's house) 5 9.9 (2.4) B 10 2,120 (123) EF 19 Eshel HaNassi School - n.d. - 10 2,368 ( 70) BCD 20 Kibbutz Sede Boker (cemetery) - n.d. - 10 1,990 (159) FG 21 Pistachio orchard, Sede Boker 5 10.5 (2.9) B 10 2,055 (100) EFG 22 Motorola antenna - n.d. - 10 1,939 (140) FG 23 Mishol Girit, Beer Sheva - n.d. - 10 2,211 (110) DE 24 Shekhunat Vered, Beer Sheva 8 12.3 (4.1) B 10 2,555 (306) AB 25 Control site - n.d. - 20 1,867 (193) G ANOVAF ratio 82.45 21.70 ANOVAF probability 0.00 0.00 n = number of replicates. b Elemental content is given in micrograms per gram on dry-weight basis. Values in each vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 134 TABLE I-g. THE Sr, V AND Zn CONTENT OF THALLI OFRAMAUNA MACIFORMS COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Sr V Zn Site na Mea (SD) na Mea (SD) na Mean0 (SD)" 1 RamatBeka 10 87 ( 8) DEF 10 5.2 (1.5) C 10 16.9 ( 3.9) CDEFG 2 Red Sculpture, RamatHovav 10 105 (13) B 10 7.0 (1.3) A 9 22.9 ( 9.3) BCDE 3 Ramat Hovav (gravel area) 10 96 (10) C 10 6.0 (0.6) B 10 22.8 ( 7.9) BCDE 4 YonaEfratsite 10 94 ( 4) CD 10 5.1 (0.6) C 10 23.7 ( 3.2) BCDE 5 Power Station 10 86 ( 6) DEFG 10 4.5 (0.8) CDE 10 25.8 ( 6.8) BCD 6 Local council, Ramat Hovav 10 89 ( 6) CDEF 10 6.8 (0.8) A 10 11.3 ( 5.3) G 7 Toxic waste site, RamatHovav 10 118 ( 4) A 10 3.6 (0.7) DEFGH 10 35.0 ( 5.8) A 8 Old Turkish railway 10 92 ( 7) CD 10 4.8 (0.9) CD 10 21.0 ( 9.0) BCDE 9 Telecommunication antenna 10 80 ( 5) FGH 10 3.5 (0.9) EFGH 10 33.8 ( 8.1) A 10 IDF antenna 10 90 ( 4) CDE 10 4.4 (0.7) CDEF 10 21.3 ( 7.6) BCDE 11 Pumping site, Ramat Hovav 10 108 ( 7) B 10 3.3 (0.9) FGH 10 26.2 (13.7) BC 12 Evaporation ponds, Ramat Hovav 10 104 ( 6) B 10 4.7 (0.7) CDE 10 19.2 ( 2.0) BCDEF 13 Tel Sheva, near Beer Sheva 10 69 ( 8) IJK 10 3.3 (0.8) FGH 10 19.1 ( 2.8) BCDEF 14 Kibbutz Revivim 10 77 ( 7) GHI 10 3.5 (0.6) EFGH 10 18.6 ( 5.2) BCDEF 15 Mitzpe Revivim, east 9 81 ( 7) EFGH 10 3.5 (0.9) EFGH 10 17.6 ( 1.8) BCDEF 16 Mitzpe Revivira, west 10 73 ( 4) HIJ 10 2.9 (0.4) H 10 19.6 ( 2.2) BCDEF 17 Kibbutz Mashabbe Sade 10 86 ( 6) DEFG 10 3.8 (0.6) DEFGH 10 23.3 (3.1) BCDE 18 Meitar (Efrati's house) 10 73 ( 3) HIJ 10 2.9 (0.9) GH 10 12.9 (1.9) FG 19 Eshel HaNassi School 10 65 ( 4) JK 10 4.1 (0.5) CDEFGH 8 26.9 (8.2) B 20 Kibbutz Sede Boker (cemetery) 10 82 ( 7) EFGH 10 3.1 (1.0) GH 10 19.7 (6.0) BCDEF 21 Pistachio orchard, SedeBoker 10 86 (10) DEFG 10 3.8 (1.3) DEFGH 10 11.6 (2.3) G 22 Motorola antenna 10 75 ( 6) HI 10 3.6 (0.5) DEFGH 10 15.9 (6.5) EFG 23 Mishol Girit, Beer Sheva 10 74 ( 6) HI 10 4.2 (0.7) CDEFG 10 36.5 (7.4) A 24 ShekhunatVered, Beer Sheva 10 64 ( 6) K 10 5.0 (0.9) C 10 16.6 (4.7) DEFG 25 Control site 20 70 ( 7) IJK 20 4.2 (0.8) CDEFG 20 15.6 (2.9) EFG ANOVAF ratio 43.32 17.38 11.71 ANOVA.F probability 0.00 0.00 0.00 a n = number of replicates. b Elemental content is given in micrograms per gram on dry-weight basis. Values in each vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 135 TABLE I-h. THE Ni AND P CONTENT OF THALLI OFRAMAUNA MACIFORMS COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APRIL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Ni P Site na Meanb (SD) na Mean6 (SD) 1 RamatBeka 10 3.8 (1.0) BCD 10 687 (66) A 2 Red Sculpture, Ramat Hovav 10 1.3 (0.5) F 10 569 (64) BCD 3 Ramat Hovav (gravel area) 10 1.5 (1.0) F 10 561 (46) BCDE 4 Yona Efrat site 8 1.6 (0.3) F 10 565 (41) BCD 5 Power Station 8 3.4 (0.6) CD 10 486 (26) EF 6 Local council, Ramat Hovav 10 4.7 (0.6) AB 10 544 (51) CDEF 7 Toxic waste site, Ramat Hovav 9 1.7 (1.0) F 10 516 (37) DEF 8 Old Turkish railway 10 3.1 (0.7) CD 10 535 (73) DEF 9 Telecommunication antenna 10 3.5 (0.4) CD 10 524 (57) DEF 10 IDF antenna 10 1.2 (0.5) F 10 530 (49) DEF 11 Pumping site, Ramat Hovav 10 4.1 (1.1) BC 10 505 (24) DEF 12 Evaporation ponds, Ramat Hovav 10 5.1 (1.1) A 10 623 (88) B 13 Tel Sheva, near Beer Sheva 8 3.3 (0.6) CD 10 469 (28) F 14 Kibbutz Revivim 5 3.1 (0.3) CD 10 503 (59) DEF 15 Mitzpe Revivim, east 9 2.1 (1.0) EF 10 543 (49) CDEF 16 Mitzpe Revivim, west 10 2.0 (0.7) EF 10 551 (38) CDE 17 Kibbutz Mashabbe Sade 10 2.9 (0.5) DE 10 543 (57) CDEF 18 Meitar (Efrati's house) 10 3.8 (1.0) BCD 10 513 (30) DEF 19 Eshel HaNassi School 8 1.7 (0.7) F 10 570 (45) BCD 20 Kibbutz Sede Boker (cemetery) 10 1.1 (0.6) F 10 556 (58) BCDE 21 Pistachio orchard, Sede Boker 10 3.7 (0.5) BCD 10 612 (33) BC 22 Motorola antenna 7 3.4 (0.9) CD 10 529 (23) DEF 23 Mishol Girit, Beer Sheva 10 1.3 (0.5) F 10 568 (38) BCD 24 Shekhunat Vered, Beer Sheva 10 3.8 (1.3) BCD 10 611 (71) BC 25 Control site 20 1.2 (0.7) F 20 505 (50) DEF ANOVAF ratio 24.58 9.10 ANOVAF probability 0.00 0.00 n = number of replicates. Elemental content is given in micrograms per gram on dry-weight basis. Values in each vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 136 TABLE I-i. THE Si AND Ti CONTENT OF THALLI OFRAMALINA M4CIF0RMIS COLLECTED IN SITE 25 (CONTROL), AUGUST 1997, TRANSFERRED TO SITES 1-24, RETRIEVED APEJL 1998, AND OF IN SITU THALLI COLLECTED IN SITE 25, APRIL 1998. Si Ti Site na Mean6 (SD) na Mean" (SD) 1 RamatBeka 10 98 (39) ABC 10 18.5 (2.8) BCD 2 Red Sculpture, RamatHovav 10 82 (10) ABCDE 10 15.8 (2.1) CD 3 Ramat Hovav (gravel area) 10 109 (16) A 10 22.6 (3.2) ABC 4 Yona Efrat site 10 77 (36) ABCDEF 10 19.5 (2.3) BCD 5 Power Station 10 14 01) I 10 16.3 (2.5) CD 6 Local council, Ramat Hovav 10 73 (19) BCDEFG 10 15.7 (4.5) CD 7 Toxic waste site, Ramat Hovav 10 97 (15) ABC 10 21.2 (4.4) ABC 8 Old Turkish railway 10 38 (10) Gffl 10 17.1 (4.3) BCD 9 Telecommunication antenna 9 71 (25) BCDEFG 10 16.1 (4.2) CD 10 IDF antenna 10 92 (29) ABCD 10 21.5 (4.2) ABC 11 Pumping site, Ramat Hovav 10 48 (31) EFGffl 10 19.1 (6.1) BCD 12 Evaporation ponds, RamatHovav 9 105 (34) AB 10 18.6 (3.8) BCD 13 Tel Sheva, near Beer Sheva 10 47 (16) EFGFfl 10 18.5 (5.2) BCD 14 Kibbutz Revivim 10 18 (9) I 10 15.6 (3.9) CD 15 Mitzpe Revivim, east 10 50 (15) EFGFfl 10 16.4 (5.7) CD 16 Mitzpe Revivim, west 10 42 (13) FGFfl 10 13.0 (2.2) D 17 Kibbutz Mashabbe Sade 10 16 ( 8) I 10 26.2 (6.1) A 18 Meitar (Efrati's house) 10 68 (29) CDEFGH 10 16.9 (4.3) CD 19 Eshel HaNassi School 10 58 (24) DEFGH 10 17.1 (3.5) BCD 20 Kibbutz Sede Boker (cemetery) 10 48 (15) EFGffl 10 15.6 (3.0) CD 21 Pistachio orchard, Sede Boker 10 109 (70) A 10 17.7 (7.5) BCD 22 Motorola antenna 7 34 (13) HI 10 19.8 (6.2) ABCD 23 Mishol Girit, Beer Sheva 10 64 (16) CDEFGH 10 17.8 (3.2) BCD 24 Shekhunat Vered, Beer Sheva 10 83 (24) ABCDE 10 23.2 (7.1) ABC 25 Control site 14 14 (6) I 10 24.5 (8.5) AB ANOVA.F ratio 14.96 4.57 ANOVAF probability 0.00 0.00 a n = number of replicates. Elemental content is given in micrograms per gram on dry-weight basis. Values in each vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 137 TABLE H. POTENTIAL QUANTUM YIELD OF PHOTOSYNTHESIS EXPRESSED AS FLUORB SCENCE 1 RATIO Fv/Fm AND VALUES OF ELECTRIC CONDUCTIVITY (mSm ) IN THALLI OF RAMALINA MACIFORMS COLLECTED IN CONTROL SITE, MAY 1999, TRANSPLANTED TOSITES 1-12, RETRIEVED, NOVEMBER 1999. F, Electric conductivity Site na Meanb '(SD) na Meanb (SD) 1 Ramat Beka 30 0.63 (0.07) a 15 3.13 (0.62) e 2 Red Sculpture, Ramat Hovav 31 0.58 (0.12) ab 10 2.80 (0.46) e 3 Ramat Hovav (gravel area) 30 0.62 (0.11) a 10 4.89 (0.61) d 4 Yona Efrat site 30 0.61 (0.07) a 15 3.60 (0.84) e 6 Local council, Ramat Hovav 32 0.47 (0.22) c 15 7.75 (3.73) b 7 Toxic waste site, Ramat Hovav 30 0.32 (0.20) d 10 11.29 (2.22) a 8 Old Turkish railway, Ramat Hovav 30 0.53 (0.10) be 10 6.39 (0.93) c 9 Telecommunication antenna 30 0.63 (0.07) a 15 2.24 (0.34) e 10 IDF antenna 30 0.65 (0.05) a 15 2.25 (0.47) e 12 Evaporation ponds, Ramat Hovav 30 0.50 (0.11) c 10 6.72 (1.31) be 25 Control site, Tellalim, 60 0.64 (0.10) a 25 2.73 (0.59) e in situ stones 26 Control site, Tellalim, 30 0.62 (0.09) a 15 3.07 (0.64) e relocated stones ANOVA F ratio 20.65 47.63 ANOVA F probability 0.00 0.00 a n = number of replicates. b Values in each vertical column followed by the same capital letter do not differ significantly at/?<0.05 using one-way ANOVA and SNK test. 138 TABLE HI. ETHYLENE PRODUCTION (nl g1 h"1) OF THALLI OF RAMALINA MACIFORMS COLLECTED IN CONTROL SITE, MAY 1999, TRANSPLANTED TO SITES 1-12, RETRIEVED, NOVEMBER 1999, SOAKED IN H2O (pH 5.6) OR IN 5 mM FeCl2 (pH 3.5)FOR30MIN. Soaked in H2O Soaked in FeCl2 Site na Meanb (SD) n3 Meanb (SD) 1 RamatBeka 15 0.11 (0.11) d 15 4.78 (2.21) a 2 Red Sculpture, Ramat Hovav 13 0.54 (0.28) be .15 4.27 (1.35) a 3 Ramat Hovav (gravel area) 13 1.07 (0.50) a 15 2.76 (0.36) b 4 Yona Efrat site 13 0.32 (0.10) cd 15 0.99 (0.48) d 6 Local council, Ramat Hovav 15 0.80 (0.35) b 13 5.35 (1.19) a 7 Toxic waste site, Ramat Hovav 13 0.65 (0.42) b 14 4.66 (1.44) a 8 Old Turkish railway, Ramat Hovav 14 1.27 (0.54) a 14 2.52 (0.64) be 9 Telecommunication antenna 8 0.31 (0.11) cd 9 1.03 (0.47) d 10 IDF antenna 9 0.08 (0.13) d 10 2.62 (1.14) be 12 Evaporation ponds, Ramat Hovav 14 0.71 (0.37) b 15 4.47 (1.48) a 25 Control site, Tellalim, 28 0.20 (0.15) d 28 1.67 (0.58) cd in situ stones 26 Control site, Tellalim, 15 0.24 (0.19) cd 14 1.02 (0.32) d relocated stones ANOVA F ratio 22.17 31.14 ANOVA F probability 0.00 0.00 a n = number of replicates. b Values in each vertical column followed by the same capital letter do not differ significantly at/K0.05 using one-way ANOVA and SNK test. 139 BIOMONITORING OF AIR POLLUTION IN JAMAICA THROUGH TRACE- ELEMENT ANALYSIS OF EPIPHYTIC PLANTS USING NUCLEAR AND RELATED ANALYTICAL TECHNIQUES MITKO VUTCHKOV XA0102863 International Centre for Environmental and Nuclear Sciences, University of the West Indies, Kingston 7, Jamaica, Tel: (876) 927-1777, Fax: (876) 977-0768 1. SCIENTIFIC BACKGROUND OF THE PROJECT The main goal of the Coordinated Research Project (No:9937/R0), entitled "Biomonitoring of Air Pollution in Jamaica Through Trace-Element Analysis of Epiphytic Plants Using Nuclear And Related Analytical Techniques", is to identify and validate site specific epiphytic plants for biomonitoring the atmospheric pollution in Jamaica using nuclear analytical techniques at the International Centre for Environmental and Nuclear Sciences (ICENS). The specific objectives for the second year of the project were: i Development of HOP for sampling epiphytic plants in Jamaica. ii Sampling design and sample collection iii Sample preparation and analysis iv Development of an in-house SRM and participation in the NAT-5 inter-laboratory study v. Data analysis and interpretation of the results, vi. Development of a work plan of the third year of the project. 2. MATERIALS AND METHODS 2.1. Development of HOP for sampling of epiphytic plants in Jamaica The results of the first year of the project indicated that the most abundant epiphytic plants in Jamaica are the genus Tillandsia and some lichens. Therefore, an integrated harmonized operating procedure (HOP) sampling of epiphytic plants was developed considering the specificity of the higher (Tillandsia) and lower (lichens) plants. The Filed Card form used for collection of the epiphytic plants is shown in Appendix I. The sampling media considered in the Filed card are the Tillandsia and/or lichens, and optionally tree barks and soil/dust samples. The following field information was recoded: (a). Sample location The sample location information includes the place name, topographic sheet No, easting, northings and altitude. (b). Climatic conditions The climatic conditions recorded during the sample collection included weather information such as sunny/cloudy, rain/fog, wind, temperature and cither factors. 141 (c). Description of the ecosystem The ecosystem description included: a) Sampling area: urban (commercial, residential, parking or other) and rural/industrial (forest, pasture, cultivation, wasteland, marsh, dwelling, highway or industry). Note: The approximate distance of the sampling site from the potential pollutants was also recorded. b) Terrain and soil information: degree of erosion, inclination, soil type, etc. c) Vegetation information: abundance, predominant vegetation, presence of other epiphytic plants, presence of deteriorated, discoloured or underdeveloped epiphytic plants. (d). Collection of the samples The following information was recorded: sample substrate, type/name (tree, shrub, rock or soil), number and height of the sampling points. 2.2. Sampling design and sample collection The study area of the biomonitoring programme, shown in Figure 1, primarily included some priority area at risk with a view of further extension at islandwide seal »/"# Figure 1. Study area considered for biomonitoring of air pollution. The guiding principle in designing the sampling scheme was to develop a comprehensive database, which is: (a) compatible with the existing geochemical databases at the Centre on soils, sediments and water, and (b) suitable for geographically referenced mapping of the atmospheric status in Jamaica. Four recognized sampling approaches were considered in development of the biomonitoring sampling scheme, namely: systematic, random stratified or biased grid sampling. The systematic sampling is often recommended over random sampling for detecting long-term trends such as climatic or atmospheric studies. This method has the smallest bias but requires a larger number of samples than does a biased sampling scheme. Considering all above, a systematic grid sampling was adopted to guarantee complete coverage of the island and facilitate the map development using Geographical Information 142 System (GIS). Figure 2 shows the study area overlapped with 20km x 20km sampling grid, yielding a total of 33 sampling points. 3 /« i 12 ] 16 17 21 \ 25 ---"" *. 7 \ 26-i» ,-„-.. Y 10 ! 14 \ 18 (2 Figure 2. Sampling design of the study area (grid size = 20km x 20km). Within each grid, a biased or stratified sampling approach was used to account for non- homogeneity of the sampling population. Biased (judgmental) sampling was used in areas with known sources of contamination, while a stratified sampling was applied in areas where the sampling population is split into non-overlapping strata that individually are more homogeneous than the population as a whole. At least, one composite sample was collected at each grid following the sampling protocol in Appendix I. In the case on known or expected non-homogeneity of the sampled population, two or three additional samples were taken by biased or stratified sampling approach. Sampling sites typically contained 1-5 medium sized trees in open areas, which were used for composite plant sample. The sampling sites were visually located on the topographic map and the exact coordinates were determined using a Magellan ProMARK-X Global Positioning System (GPS). Each site location was also marked on the topographic map quadrangle. A photograph of the site and the sample collected was taken using a digital camera. Most of the sampled epiphytic plants were Tillansia recuvrata species. Site information was recorded on the associated Filed Cards. The field data as well as the pictures were entered in the Database system of the Centre. The information which was recorded included the date, topographic map quadrangle, parish, location, brief habitat description, dominant tree species, the types of epiphytic plants present, overall environmental conditions. The environmental conditions of a given site were evaluated on the base of the size, overall appearance and abundance of Tillansia species. The large sized dense balls with blue to violet flowers are most probably from an area with blight light and higher humidity, whereas smaller size plants with thinner leaves and non-developed flowers are most likely from an area of lower light and humidity. Figures 3 and 4 illustrate well and poor developed sampling sites. 143 (a) (b) Figure 3. Tillandsia sampling sites: (a) well developed and (b) less abundant. The sampling was done manually using protective gloves, if the species were accessible or a specialised telescopic sampler designed to access species at higher locations. The specimens were stored in paper bags and transported to the lab for analysis. The current status of the biomonitoring programme is shown in Figure 4. A total of 10 sites (No.3 to 13) were sampled during the second year of the project. I 24 | 16 2&^ \25 17 21 28/: 10 1 (22 26 7 -^30 32^ ^1 1 \ * ^8 29 V /'31 \19 33 i '• Figure 4. Current status of the biomonitoring programme. 2.3. Sample preparation and analysis 2.3.1. Preparation of plant samples The laboratory preparation of the Tillandsia recuvrata samples included cleaning and separation of the individual species from the composite "ball" using Teflon tweezers. The separated individual plants were rinsed with bi-distilled water to wash the adhered surface dust similar to the rain, followed by oven drying at 45°C. Dried samples were ground manually using liquid nitrogen and transferred to a Fritsch mortar-grinder for further grinding and homogenisation. The dried analytical samples were divided in two subsamples, one for direct analysis and the second for pre-concentration by dry-ashing. Samples for direct analysis were stored in sealed plastic bags at low temperature to reduce the rate of microbial decay. For dry-ashing, a subsample of about lOg was weighed 144 with a high precision balance. The Pyrex glass beaker containing the weighed material was covered and put in a furnace whose temperature was slowly increased to 450°C over 8 hours. After 24 hours, the beakers were removed from the furnace and the ashed material was weighed. The weight of the ash samples ranged typically from 0.3 to 1.8 grams. The ashed samples were stored in sealed plastic bags for analytical sample preparation. 2.3.2. Dust paniculate matter The atmospheric dust, deposited between individual plants, was released during the cleaning and plant separation process. The plant/dust subsample left-out after separation of the individual Tillandsia species was sieved through 150 macron mesh and the -150micron dust fraction was collected for elemental analysis. The particle size: distribution was assessed by Scanning Electron Microscopy (SEM). 2.3.3. Multielement analysis Plant, ash and dust samples were analysed by using Neutron Activation Analysis (NAA) and Energy-Dispersive X-Ray Fluorescence (EDXRF) spectrometry. EDXRF was mainly applied for plant materials, which were prepared as 25mm pressed pellets using ().8g materials. Dust samples, before submitting to NAA, were analysed by EDXRF by thin-film method for Pb, Cu and other metals. NAA was used for analysis of plant, ash and dust samples. Plant and ash samples were prepared in hermetically sealed 25x25 mm double plastic bags, while the dust material was encapsulated in 1.5ml heat sealed polyethylene vials. The in-house standard reference material TILJAM, developed during the first year of the project, was used as a matrix reference material for NAA and included within the routine sample batches. The TILJAM normalised concentration ratios of the analysed samples were used to categorize the analysed plant, ash or dust materials as "biological" or "geological" in view of their quantification. 3. RESULTS AND DISCUSSION 3.1. Multielement analyses ICENS participated in NAT-5 inter-laboratory study for the determination of trace and minor elements in two lichen samples, L-l and L-2. The samples were analysed by NAA for short- and long-lived elements. Table 1 compares the ICENS results with the NAT-5 accepted values. 145 Table 1. Comparison of ICENS data on L-l and L-2 with the NAT-5 accepted values. Elements L- 1 L-2 NAT-5 ICENS NAT-5 iCENS Al 1064.84 1383 634.142 802.96 As 0.966 0.95 0.682 0.68 Ca 3926.634 4123.63 2489.291 2491.34 Cl 2437.991 2406.33 1964.02 1875.14 Cr 6.041 7.29 1.051 1.35 Fe 902.296 920.6 443.597 433.24 K 3138.261 3378.39 1813.917 1902.64 La 0.895 0.85 0.625 0.61 Mn 52.626 52.99 64.288 58.11 Na 124.455 125.8 302.869 295.78 Sb 0.457 0.5 0.085 0.11 Sc 0.249 0.22 0.169 0.14 V 3.582 3.62 1.452 1.5 Zn 111.73 104.83 31.928 29.58 The results obtained on analysis of 24 Tillandsia-dust samples were submitted to statistical analysis and the medians of dust/tillandsia concentration ratios for some selected elements are plotted in Figures 5 and 6. Figure 5 is a pie chart, showing the balance of the dust and plants elemental ratios. nDy QEU HV EjMn gCa Figure 5. Percent contribution of elements in dust/Tillandsia concentration ratios. 146 100.00 Figure 6. Dust/Tillandsia concentration ratios of elements, sorted in decreasing order. The graph in Figure 6 suggests three groups of elements: > Elements (Ti, Al, Dy, Eu and V) with high dust/plant ratios (>10), i.e. typical soil elements, non-essential for plants, > Elements (Mn, Ca) with moderated dust/plant ratios (about 1 -3), > Elements (K, Cl, Na) with low dust/plant ratios (<1), essential for plants. The above results make obvious that Tilandsia species can provide information on the elemental status of the atmospheric deposition compatible with these by direct measurement of the air particulates. 3.2. Tillansia biology and air participate trapping The detailed knowledge of Tillandsia biology is important in the interpretation of biomonitoring data. Tillandsia recuvrata, used in our study initially occur as small, grey-green tufts that develop within a relatively short time into a dense "ball" composed of numerous individual plants. A single strand of Tillandsia, laid on a flat surface, will reveal its scoipoid dichotomous growth pattern. This pattern is the result of alternate branching of the plant at each growth point or node. Alternating branches elongate, resulting in a zigzagging pattern. The distance between nodes appears to be an indicator of the plant's response to its habitat and as it can be used to compare growth rates of plants. The leaves of all Tillandsias have scales, known as trichomes, which serve two major functions vital to the plant's survival: act as pumps that facilitate the capture and conservation of water, minerals and nutrients from the surrounding environment and also help reflect the intense sunlight from their leaf surface. These trichomes are what give many of the air plants their characteristic grey colour. Their density can also indicate the kind of physical environment the plant has grown in, as the denser the trichome covering, the hotter and dryer 147 the growing environment. They are also suspected of playing a vital role in particulate trapping in tillandsia, as was further investigated in this study. To examine the role of trichomes in air particulate trapping, two individual T. recuvrata species (young and aged) separated from the same "ball" were investigated by SEM. Figure 7 illustrates the Tillandsia specie under investigation. Figure 7. EM image of Tillandsia recuvrata. The electron microscopy images obtained at various sections in Figure 7 are shown in Figures 8 through 12. (a) (b) 148 1*1 (c) (d) Figure 8. Section of leaf base at "C" showing the trapped particles. Scale bar: (a) lmm, (b)= lOOmkm, (c) = lOmkm, d=10mkm. ~ • • * — (a) (b) Figure 9. Section of the youngest leaf at "Y"; scale bar = 1 mm (a) and lOOmkm (b). (a) (b) Figure 10. Section of the oldest leaf at "OL"; scale bar = lOOmkm (a) and lOmkm (b). 149 The above SEM images of Tillandsia, clearly demonstrate that: (a) the trichomes do serve a vital role in air particulate trapping, (b) the embedded plant particulates levels are age dependent, (c) Tillandsia is tolerant to high particulate pollution, which does not have an apparent physiological effect on the plants. The electron microscopy images on Figures 11 and 12 illustrate the particles trapped by the Tillandsia flowers. 'fi Figure 11. Tillandsia flower at "F" (scale bar =lmm) Figure 12. Particle trapped in the marked area of flower in Fig. 11 (scale bar =100 mkm). 150 4. CONCLUSION AND RECOMMENDATIONS In conclusion, the results of the second year of the CRP project demonstrated the potential of Tillandsia recuvrata in biomonitoring of the atmospheric pollution in Jamaica. The outdoor dust recovered from Tilllandsia species can be analysed and used for direct assessment of the atmospheric pollution without using electronic equipment. The mane advantages of Tillandsia recuvrata in biomonitoring of air pollution are: > Due to its faster grow rate compared with lichens, Tillandsi as are well-suited for biomonitoring of the short- to medium-term pollution. > The analysis of leaves of different ages of Tillandsia could be used for retrospective biomonitoring. > The physiology and size of Tillandsia trichomes offer favourable microtopography for trapping of the fine dust fraction, which is important in health related studies. 151 REFERENCES [1] Shimizu, Hideo and Takizawa, Hiroyuki New Tillandsia Handbook, Fukushima, Japan Cactus Planning Co. Press, 1998.. [2] Benzing, David H. The Biology of the Bromeliads, Eureka CA, Mad River Press, 1980. [3] Johnson, A., G.C. Lalor, H. Robotham, and M. Voutchkov. J. Radioanal. Nucl. Chem, 209,No.l, 101-111, 1996. [4] M. Davis, C. Grant, G. Ho-Yorck-Krui, A. Johnson, G. C. Lalor, H. Robotham and M. Vutchkov. Env.Geochem.Health, 19,23-28, 1997. [5] Grant, C, Lalor, G.C., Vutchkov, J. Radioanal. Nucl. Chem., 237, No. 1-2,109-112, 1998. [6] Grant, G. C. Lalor, J. Preston, R. Rattray, M. Vutchkov. Jam. J. Sc. Tech., 9, 63, 1999. [7] Lalor, G. C, Vutchkov, M., Grant, C, Preston, J., Figueiredo, M. G., Favaro, D.I.T., J. Radioanal. Nucl. Chem., 244, 263,2000 . 152 Appendix: FIELD CARD FOR EPIPHYTIC PLANTS INTERNATIONAL CENTRE FOR ENVIRONMENTAL AND NUCLEAR SCIENCES Lichen/Tillandsia Number of samples Substrate (To fill in laboratory) Soil Field Team (Initials) Date dd-mm-yy Time hh-mm Project name: Topographic sheet No. Place name: Site Easting Northing Altitude [2, CLIMATIC CONDITIONS Sunny • Cloudy • Rain • Fog • Dry D Wind • D Dusty • Temperature <10°C a 10-20 °c D >20°C • Others llfililllllll 3.1. Description of the area URBAN AREA Industrial LJ Commercial • Residential D Parking D Others Distance of the sampling site from the main road m 153 Rural/ Industrial area Forest • Pasture U Cultivation • Wasteland Marsh U • Dwelling D Distance from the sampling site m Highway • Distance from the sampling site m Industrial D Distance from the sampling site m Others 3.2. Terrain conditions Soil type (To fill in the laboratory) Degree of erosion Inclination High D High • Medium LJ Medium LJ Low • Low LJ Negligible U Negligible LJ 3.3 Vegetation Abundance of vegetation High • Medium • Low D Scanty LH Predominant Vegetation Trees U Shrubs • Grass D Presence of other epiphytic plants (lichens, mosses, others ) Yes • No • Presence of Usneaa rubicunda Yes No u 154 Presence of Tillansia Yes • No u Presence of lichens deteriorated, discolored or underdeveloped Yes D No • 4. COLLECTION OF THE SAMPLE Sample Substrate (To be filled in the laboratory) Tree [] Type/Name Shrub [] Type/Name Rock [] Type/Name Soil [] Type/Name Others Height of the sampling point < 1 m • 1-l.Sm D 1.5-2m • Other |j 5. REMARKS 155 LOCAL VARIANCES IN BIOMONITORING XA0102864 H.TH. WOLTERBEEK, T.G. VERBURG University of Technology, Interfaculty Reactor Institute, Department of Radiochemistry, Nuclear Environmental Studies, Mekelweg 15 2629 JB Delft, The Netherlands. Tel: +31-15-2787053, Fax: +31-15-2783906, E-mail: [email protected]. Abstract: The present study was undertaken to explore possibilities to judge survey quality on basis of a limited and restricted number of a-priori observations. Here, quality is defined as the ratio between survey and local variance (signal-to-noise ratio). The results indicate that the presented surveys do not permit such judgement; the discussion also suggests that the 5-fold local sampling strategies do not merit any sound judgement. As it stands, uncertainties in local determinations may largely obscure possibilities to judge survey quality. The results further imply that surveys will benefit from procedures, controls and approaches in sampling and sample handling, to assess both average, variance and the nature of the distribution of elemental concentrations in local sites. This reasoning is compatible with the idea of the site as a basic homogeneous survey unit, which is implicitly and conceptually underlying any survey performed. 1. INTRODUCTION The present paper deals with the (larger-scaled) biomonitoring survey and specifically focuses on the survey quality. In earlier work (Wolterbeek et al. 1996), the survey quality was proposed as measurable by analysis of the signal-to-noise ratio, which, in turn, can be determined by the ratio between survey and local variance. The inherent drawback of this analysis is of course that quality can only be judged afterwards: the assessment asks for the survey variance. The present study is concerned with possibilities to estimate the survey's quality before the start-up of the full survey. This implies an important prepositioning of thinking: the a-priori set-up of the survey should contain measurable information about it's quality. As a first collection of thoughts, both survey and local variances were studied in more detail. Here, the aims of the study make that attention should not only be focused on analytical (instrumental) variances (Wolterbeek et al. 1996, Wolterbeek and Bode 1995)., but that especially the total local and survey variances (including biological and all further unspecified variances) should be investigated (Wolterbeek et al., 1996; Wolterbeek and Bode, 1995; Shantangeeva, 1994 1995; Wyttenbach et al., 1994). In most surveys, the sampling site is simply selected or defined as a spot of (geographical) dimensions which is small relative to the dimensions of the total survey area. Implicitly it is assumed that the sampling site is essentially homogeneous relative to the variations in survey parameters. As such, the sampling site is mostly regarded as "the basic unit" of the survey. As a logical consequence, the local (sampling site) variance should also be seen as a basic and important characteristic of the survey. In the present study, work was carried out to gain more knowledge of the local variance. Multiple sampling was carried out at a specific site, multi-elemental analyses were carried out by NAA, and local variances were investigated by conventional statistics and bootstrapping (Hall, 1986; Efron and Tibshirani, 1986; Diciccio and Romano, 1988). The outcomes were set in the context of the total survey. 157 For the survey, the approach was essentially similar: outcomes from a limited number of sites, processed by bootstrapping, were used to estimate the true survey variance. The present paper addresses outcomes, differences between estimated and true variances and discusses possibilities for "a-priori" judgement of survey quality. 2. MATERIALS AND METHODS 2.1. Sampling, sample handling and elemental analysis Multiple samples were taken from tree bark (local site at Delft, The Netherlands), and from mosses and soil (local site at Hattem, The Netherlands), all in the context of national biomonitoring surveys on trace element air pollution (Kuik and Wolterbeek, 1994 1995; Riihling and Steinnes, 1995). Tree bark and moss samples were handled as in (Kuik and Wolterbeek, 1994 1995), immersed in liquid N2, and milled in a FRITSCH (pulverisette 114) Rotor Speed Mill. Soil samples were dried at 40 oC after presieving through a 5 mm sieve (Linker Industrie-Technik); after drying sieving was repeated through a 2.8 mm sieve. Elemental analyses were carried out by INAA, following instrumental methods as essentially described by Blaauw (1993). Quality control was performed by the regular analysis of standard reference materials (NBS-1572 "Citrus leaves", NIST SRM 2711 "Montana soil"). For the Hattem tree bark, separate from INAA on the initially taken samples, the rest of the sample masses were mixed thoroughly (a total of about 1000 g mass), after which 32 sub-samples were taken and also processed by INAA. The rest of the mass was analyzed as a bulk sample by the IRI BISNIS large volume facility (Bode and Overwater, 1993; Overwater et al, 1993). 2.2. Data processing General and initial processing of data was by straightforward statistics. Bootstrap procedures (Hall, 1986; Efron and Tibshirani, 1986; Diciccio and Romano, 1988) were used to construct confidence regions for local and survey concentrations. In general terms, and wihout going into detailed mathematics, the bootstrap method is a computer-based method, used to assess the accuracy of an estimation of an unknown (statistical) parameter, thereby substituting considerable amounts of computation in place of theoretical analysis (Efron and Tibshirani, 1986). Thus, in the present study, the initial data were considered as estimates of the true populations of elemental concentrations: bootstrapping can be regarded as the use of simulation to approximate the true statistical distributions (Diciccio and Romano, 1988). 3. RESULTS AND DISCUSSION Calculations were carried out on data obtained in moss and soil surveys, and in tree bark. Initial approaches were aimed at getting more insight into local data: the question asked here was whether the general survey set-up (5-fold local sampling, mixing before analysis) permits an adequate expression of local circumstances. Similar questions were addressed on survey level, since surveys are based on a series of local concentrations. The main underlying question was whether survey quality could be reasonably assessed by a restricted number of a-priori observations, that is, by a very limited number of sites, selected throughout the survey area of interest In the following paragraphs, the local problems are discussed, after which their consequences are indicated on survey level. 158 3.1. Local data The determination of the local variance implies that all aspects of the survey are taken into account: biomonitor selection, definition of the sampling site, sampling, sample handling, elemental analysis etc. The local variance is suggested as not related to concentration levels (Wolterbeek et al., 1996; Sloof, 1993); furthermore, analytical uncertainties hardly contribute to local uncertainties (Wolterbeek et al., 1996). This means that in larger-scaled surveys any effort to improve analytical precision may be regarded as meaningless. Considering the Delft 32-fold tree bark sampling, mixing of all initial samples, milling, and the elemental analysis of the then-taken 32 sub-samples did not invariably show increased homogeneity." success varied between elements (Table 1). This means that average local levels and uncertainties may only be obtained by analysis of a multitude of initially taken local samples. The IRI large volume facility (analysis of 1000 g mass) was used to avoid homogeneity problems. The results (Table 1) indicate differences in outcomes for a number of elements; furthermore, the single analysis approach makes that any information on distribution characteristics and local variance is lost. For Ba, bootstrapping resulted in strongly reduced variances, probably indicating differences from normal distributions. Considering the sampling sites at Delft and Hattem for tree bark, moss and soil, repeated randomized n=5 trials were taken out of the total number of local samples (n=32, 25 and 25 for tree bark, moss and soil respectively). Table 2 gives results obtained after 500 trials, and presents the mean and maximal increment factors by which the trial local variance should be increased to ensure full compatibility with the actual local elemental concentration population (T-test threshold approach). This testing implies a very strict verification of the local trial outcomes: although survey quality is using local variance only, the test compares both means and variance of the trial with the concentration population characteristics. Table 2 also presents the number of cases in which the factor value 1 1.0. The data indicate that statistically speaking n=5 trials give reasonable results (agreement with local populations for all selected elements in > 90 % of the trails). However, the remaining up to 10 % of all selected cases, and both mean and maximal values also suggest effects from outlyers and/or from skewness and/or kurtosis of the local distributions. Here should also be noted that in the 25-32 fold local sampling, the repeated calculations on randomized n=5 trials undoubtly suffered from overlaps: the F and E values may strongly underestimate reality. 3.2. Survey data The trial approach (Table 2 for local sites) was also followed with survey data: 500 trials, each comprising 5 randomized samples, and each T-tested to compare with survey characteristics, yielded results as given in Table 3. The data indicate that agreement between trial and survey was highly variable, probably due to skewed distributions for several elements. It should be noted here that the survey data in Table 3 inevitably comprise all local problems discussed sofar (Table 2). It may also be clear that the suivey quality Q, defined as the ratio between survey and local variance, will suffer from both local and survey problems. This is illustrated in Table 4, where Q is expressed both on the direct survey data (Ql) and on the bootstrapped data (Q2), and is also calculated in repeated trials (N=500) of randomly selected 20 % and 40 % of the available local and survey data (Q3). The differences between Ql and Q2 (suggesting skewed distributions) and the relatively large variances in sub-set Q3's all indicate the difficulties in reproducing survey behaviour in restricted randomized 159 sub-sets. After devision of the moss survey into two sub-sets of 25 locations each, or into three sub-sets of 18 locations each, in all approaches ensuring comparable aera coverage, Q data were obtained as given in Table 5. Here, the calculations were simulating parallel surveys, with comparable area coverage although using different locations: the outcomes are comparable to those of Table 4, and underline the element-specific variability in Q values. In a series of calculations, the Hattem local site (n=25) was taken for both moss and soil. Out of the total number of observations repeated (N=18) randomized n=5 trials were processed as if they represented a survey: this was performed three times, and the outcomes were compared to the actually performed moss and soil surveys, each in turn devided into three area-ensured sub-surveys. The results are presented in Table 6, for a number of selected elements. The data indicate that 5-fold local sampling could not be used to express the local population's elemental average and variance. The variability of the outcomes was expressed as a Qloc, which was about 0.3-0.4 in all cases. This means that the local Hattem outcomes, build up from 5-fold sampling, may be seen as a low-quality survey, of which the variance is due to the fact that 5-fold sampling is inadequate in fully expressing the local circumstances. Here, as with the results presented in Table 2, should be noted that the real variance may be underestimated due to the fact that in initial 25 local samples the repeated (three times N=18) calculations on randomized n=5 trials may have led to overlaps. 4. CONCLUSIONS The study was undertaken to gain more knowledge on local and survey variances, all ment to explore possibilities to judge survey quality on basis of a limited and restricted number of observations. The results indicate that the present survey data cannot be used to assess "a-priori judgement" possibilities. The present surveys comprise 5-fold local sampling, which, on basis of the data on Hattem and Delft multi-fold local sites, is not always adequate in expressing local averages and concentrations. This means that, strictly speaking, the present surveys do not permit sound judgement, it goes wrong in about 10 % of all cases. The discussion underlines the basic importance of the local sampling site in the total survey. In future set-ups, local sites and sampling strategies should be examined, aimed at the assessment of local (normal) distributions, and expressed in number of samples to be taken, sample handling and elemental analysis. Furthermore, in surveys, strict procedures and control should be agreed upon with respect to sample handling, in all cases where multiple samples are to be processed into single analysis procedures for the assessment of elemental concentrations. 5. FUTURE WORK Future work will be focused on strategies in selecting local sites, and on local sampling; furthermore the use of clusters of nearby-sites and the effects and control of- and by (selected techniques in) interpolation will be studied. 160 REFERENCES [I] Blaauw, M. : 1993, 'The holistic analysis of gamma-ray spectra in instrumental neutron activation analysis', Thesis, Delft University of Technology, Delft, The Netherlands, ISBN 90-73861-16-0. [2] Bode, P., and Overwater, R.M.W. : 1993, 'Trace-element determinations in very large samples: a new challenge for neutron activation analysis', J. Radioanal. Nucl. Chem. 167, 169-176. [3] Diciccio, T.J. , and Romano, J.P.: 1988, 'A review of bootstrap confidence intervals', J. Royal Statistical Soc. B50(3), 338-354. [4] Efron, B., and Tibshirani, R.: 1986, 'Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy', Statistical Sci. 1, 54-77. [5] Hall, P.: 1986, 'On the bootstrap and confidence intervals', Annals of Statistics 14, 1431-1452. [6] Kuik, P., and Wolterbeek, H.Th.: 1994, 'Factor analysis of trace element data from tree- bark samples in The Netherlands', Environm. Monit. Assessm. 32, 207-226. [7] Kuik, P., and Wolterbeek, H.Th.: 1995, 'Factor analysis of atmospheric trace-element data in THe Netherlands obtained by moss monitoring', Water, Air Soil Pollut. 83, 323- 346. [8] Overwater, R.M.W., Bode, P. , and De Goeij, J.J.M.: 1993, 'Gamma-ray spectroscopy of voluminous sources. Corrections for source geometry and self-attenuation', Nucl. Instrum. Meth. A324, 209-218. [9] Riihling, A., and Steinnes, E. (eds): 1995, 'Atmospheric Heavy Metal Deposition in Europe 1995-1996', NORD 1998:15, Nordic Council of Ministers, Copenhagen 1998, 67 pp., ISBN 92-893-0254-2. [10] Shtangeeva, I.V. : 1994, 'Variation of the chemical composition of plants annd soils', J. Radioanal. Nucl. Chem. 177, 381-391. [II] Shtangeeva, I.V. : 1995, 'Behaviour of chemical elements in plants and soils', Chem. Ecol. 11,85-95. [12] Sloof, J.E.: 1993, 'Environmental Lichenology: biomom'toring trace-element air pollution', Thesis, Delft University of Technology, Delft, The Netherlands, ISBN 90- 73861-12-8. [13] Wolterbeek, H.Th., Bode, P., and Verburg, T.G.: 1996, Assessing the quality of biomonitoring via signal-to-noise ratio analysis', Sci. Tot. Environm. 180, 107-116. [14] Wolterbeek, H.Th., and Bode, P.: 1995, 'Strategies in sampling and sample handling in the context of large-scale biomonitoring surveys of trace element air pollution', Sci. Tot. Environm. 176, 33-43. [15] Wyttenbach, A., Schleppi, B., Bucher, J., Furrer, V. ,and Tobler, L.: 1994, 'The accumulation of the rare earth elements and of Scandium in successive needle age classes of Norway Spruce', Biol. Trace Elem. Res. 41, 13-29. 161 TABLE 1. Element concentrations (mg/kg+SD) in tree bark (Delft, The Netherlands, n~32), in initial 32 samples, in the bootstrap-derived population, in 32 sub-samples after mixing, and in the 1 kg total mass (IRI BISNIS facility, single analysis, no SD, * = different from average after mixing) Bark, Bark, Bark, Bark, Element initial bootstrap mixed BISNIS As 0.29 ±0.06 0.29+0.05 0.30+0.02 0.21* Ba 527+1028 414+619 433+156 440 Br 13.3+2.6 13.2+2.6 12.6+0.3 12.8 Cr 13.1+7.3 13.2+6.5 13.9+0.8 14.5 Cs 0.06+0.02 0.06+0.02 0.06+0.01 - Cu 32+5 32+5 31+5 - Fe 850+220 850+210 860+23 808* K 1240+244 1230+277 1260+40 1300 Mg 666+91 665+93 675+88 - Zn 60+11 60+10 61+1 84* 162 TABLE 2. Repeated (N=500) randomized sub-sampling (n—5) in local populations, and T- tests on trial with population. Outcomes are expressed in increment factors F (means and maxima) in the local variance necessary to maintain full compatibility between trial outcome and population. E= number of trials with F' &1.0. Initial local sampling was 32 for bark, and 25 both for moss and soil. El. F (Bark) F(Moss) F(Soil) mean max E mean max E mean max E As 1.34 32 33 1.20 11 42 1.13 9 25 Br 1.04 6 11 1.32 16 46 1.07 7 14 Cr 1.12 13 30 - - - 1.18 9 43 Cs 1.05 7 12 1.08 6 24 1.16 13 33 Cu 1.16 30 24 ------Fe 1.15 13 21 1.07 10 10 1,04 5 17 K 1.06 13 8 1.14 8 33 LI 2 9 22 Mg 1.11 12 12 1.17 8 38 - - - Zn 1.36 26 41 1.17 29 18 1.03 7 6 163 TABLE 3. Repeated (N=500) randomized sub-sampling (n=5 sites) in survey populations, and T-testson trial with population. Outcomes are expressed in increment factors F (means and maxima) in the survey variance necessary to maintain full compatibility between trial outcome and population. E= number of trials with F ^ 1.0. Initial number of sampling sites was 54 both for moss and soil. F(Moss) F(Soil) El mean max E mean max E As 1.00 3 1 1.46 22 33 Br 1.00 1 0 1.82 16 130 Cr 1.34 7 88 1.00 1 0 Cs 1.00 1 0 1.77 56 77 Fe 1.38 10 101 1.00 1 0 K 1.56 17 70 1.34 20 50 Mg 1.00 1 0 - - - Mn 1.44 9 135 1.02 5 4 Zn 1.01 3 3 1.20 6 66 164 TABLE 4. Signal-to-noise ratio's (Q)for moss and soil surveys (54 sites). Qj ~Qon actual survey. Q2 = Qon bootstrapped survey. Qs = Q based on a fraction of the survey's sampling sites (A= 20 % survey and 5-fold local; B = 40 % survey and 10-fold local). Qs determined in 500 trials, av = average, N = number of cases (out of 500 trials) that Qs is outside range ofQj+50%. Moss survey Soil survey El. Qs Qs a/ Qi Q2 N Qi Q2 N a/b Av+SD b Av+SD a 1.6+1.2 136 a 1.9+1.4 111 As 1.4 2.8 1.5 1.6 b 1.5+0.5 78 b 1.6+0.4 34 a 2.2+1.3 165 a 1.6+1.0 128 Br 1.9 4.9 1.3 1.2 b 2.0+0.7 76 b 1.4+0.4 56 a a 5.2+5.3 266 Cr - 2.9 6.3 b b 3.9+2.6 190 a 2.3 ±1.7 175 a 1.9+1.1 155 Cs 1.8 4.7 1.5 1.3 b 2.1+0.9 117 b 1.6+0.6 57 a 6.4+6.8 233 a 3.8+2.9 152 Fe 4.7 3.3 2.8 4.4 b 5.2+3.2 179 b 3.2+1.1 63 a 2.0+1.6 227 a 2.1 ±1.4 163 K 1.3 1.2 1.6 1.4 b 1.5+0.7 127 b 1.8+0.8 102 a 1.4+1.2 118 a Mg 1.1 2.9 - b 1.2+0.3 36 b a 6.2+6.4 250 a 5.5+5.2 217 Mn 4.9 3.1 4.2 5.9 b 5.3+2.8 147 b 4.8+2.2 136 a 4.9+3.4 105 ., 11. a 8.4+13.8 247 Zn 4.2 8.3 4.6 b 4.4+1.3 35 6 b 5.9+3.4 150 165 TABLE 5. Repeatibility calculations for moss and soil surveys. Quality Q determined for the total surveys (Qi), for 50 % of the survey data, ensuring area coverage (Q2, n=2), and for 30 % of the survey data, ensuring area coverage (Q3, n—3). For Q2, individual outcomes are presented. For Q3, both the individual outcomes and the mean (Av) and variances (SD) are presented. Moss survey Soil survey Hit. Qi Q2 Q3 Q3Av±SD Qi Q2 Qi Q3Av±SD 1.8 1.7 As 1.4 1.7 1.1 1.4±0.4 1.5 1.5 1.7 1.5±0.4 1.1 1.5 1.3 1.1 1.0 1.4 2.2 1.5 Br 1.9 1.6 1.7 1.8±0.8 1.3 1.0 1.2 1.3 ±0.1 2.5 1.4 3.6 Cr - - - - 2.9 3.4 2.4 2.9±0.7 2 4 2.5 2.3 1.6 Cs 1.8 2.0 1.4 1.8 ±0.5 1.5 1.5 1.2 1.5±0.2 1.6 1.5 1.6 1.7 7.3 3.3 Fe 4.7 5.9 2.8 4.3 ±2.7 2.8 3.2 2.9 2.8+0.5 3.3 2.5 2.6 2.3 1.3 1.9 K 1.3 1.4 1.1 1.2 ±0.1 1.6 1.7 1.1 1.6±0.5 1.1 1.5 1.3 1.8 3.8 4.6 Mn 4.9 5.0 7.0 4.5±2.2 4.2 5.0 5.3 3.8±2.0 4.9 3.1 2.8 1.5 5.0 3.8 4.3 4.0 Zn 4.2 4.1 4.1 4.2±0.8 4.6 5.1 1.1 2.1 ±1.5 3.5 1.4 166 TABLE 6. Qsuf-values for moss and soil surveys in 30 % coverages (18 locations), expressed asAv.±SD (n=3), Q\oc-values, Av±SD (moss and soil) for the Hattem location (n=25 total), for which a "survey" (n=18) was three times simulated by randomly taking n=5 sub-samples, and the residual survey Q (Qres, AvdtSD), after correcting Qsurfor contributions from Qioc. P- values denote outcomes of a Kolmogorov-Smirnov test on the local distributions (P < 0.05: the location does not have a normal distribution of elemental concentrations). Moss survey Soil survey Hit. Qsur Qloc \lres P \Jsur Qloc Qres P Al 3.65 ±2.33 0.3 7±0.0 3 3.29 ±2.34 - 0.16±0.05 0.41 ±0.04 -0.25±0.06 .00 As 1.38 ±0.39 0.49 ±0.10 0.89±0.41 .86 1.49 ±0.35 0.48 ±0.09 1.01 ±0.37 .51 Au - - - 0.71 ±0.45 0.42±0.01 0.28 ±0.45 .01 Cr - - - 2.86±0.66 0.41±0.05 2.45 ±0.66 .14 Cs 1.77±0.51 0.41 ±0.07 1.37±0.51 .61 1.51 ±0.25 0.47±0.13 1.04 ±0.28 .99 Fe 4.25 ±2.68 0.38±0.10 3.87±2.68 .29 2.82 ±0.52 0.41 ±0.12 2.41 ±0.53 .40 K 1.22±0.10 0.47±0.06 0.75 ±0.12 .44 1.61±0.44 0.3 7±0.0 5 1.24 ±0.44 .75 Mg 1.10±0.09 0.47±0.07 0.63±0.11 .99 - - - - Mn 4.53±2.24 0.44±0.0 3 4.09±2.23 .45 3.79 ±2.04 0.47±0.04 3.3 2 ±2,04 .52 Tl 4.44±3.65 0.44±0.08 4.00 ±3.65 .03 - - - - V 2.47±0.53 0.40±0.08 2.07±0.53 .84 0.24±0.04 0.41 ±0.16 -0.16±0.17 .00 Zn 4.19 ±0.75 0.45±0.09 3.75 ±0.7 6 .88 4.45 ±1.21 0.54±0.07 3.91±1.21 .18 167 FURTHER PROMOTION OF THE USE OF MOSSES AND LICHENS FOR STUDIES OF ATMOSPHERIC DEPOSITION OF TRACE ELEMENTS EELJV STEINNES Department of Chemistry, Norwegian University of Science: and Technology, N-7491 Trondheim, Norway Abstract: XA0102865 Some recent and ongoing studies related to the use of mosses as biomonitors of atmospheric metal deposition are briefly reviewed. Issues discussed in particular are the conversion of concentration in moss to absolute deposition values, introduction of a second-generation ICP-A4S instrument for moss analysis, determination of stable lead isotope ratios in mosses for source apportionment, and temporal trends of lead and cadmium deposition in Norway. A novel nuclear technique for the determination of fluorine in mosses surrounding an aluminium smelter is presented. 1. INTRODUCTION Biomonitoring of air pollutants using mosses has been employed in Norway for the: last 25 years employing nuclear as well as non-nuclear methods of analysis. Nationwide metal deposition surveys based on the moss technique were carried out in 1977, 1985, 1990, and 1995, each time including about 500 sites. These studies form an integral part of the national monitoring system for long range transported pollutants. In addition considerable research has been carried out in order to critically assess the moss technique. A summary of this extensive work was presented in a previous report (1). In the following text current work in the author's laboratory will be briefly described and some plans for the future will be discussed. During the last decade collaboration has been started with scientists in a number of countries, particularly in eastern Europe, in order to implement the experience from the Norwegian studies in areas where such studies were not done before. Some of these studies will be described by other participants at this meeting (2,3), while others will be mentioned in the following. 2. METHODS In the routine monitoring in Norway using the moss technique ICP-MS has been the preferred analytical technique since 1990 since it is able to provide reasonably good data for most elements of primary interest in this work. Among the ten elements in focus eight (V, Cr, Fe, Ni, Cu, Zn, Cd, Pb) normally do not represent any problem. Considering the two remaining elements the determination of As is sometimes difficult because of interference from 40Ar35Cl at mass 75. This problem is now about to be solved due to the introduction of a new field sector ICP-MS instrument with a mass resolution far superior to that of the previously used quadrupole istrument. In the case of Hg erratic results were obtained by ICP- MS, and atomic fluorescence has therfore been routinely used for this element. Except for collaboration projects where JINR, Duibna has been involved, neutron activation analysis has not normally been used in our recent moss projects. An interesting exception is a study of fluorine pollution around an aluminium smelter in western Norway where a method employing epithermal irradiation and cyclic activation was shown to be 169 suitable for the determination of F in mosses and surface soils. The method is based on measurement of 26.9 sec 19O produced by (n,p) reaction in 19F. 3. RECENT INVESTIGATIONS 3.1. Conversion of concentrations in moss to absolute deposition values Since 1990 parallel studies of metal deposition have been performed in Norway using respectively the moss technique and conventional bulk precipitation sampling. In 1990 deposition values for some metals from precipitation collected at 6 stations were compared with concentrations of the same elements in moss (Hylocomium splendens) near the same stations, and calibration curves were drawn (5). A very high correlation was observed for lead, whereas for other metals the relations were not so clear. In 1995 the experiment was repeated, this time with 13 precipitation stations (6), and with sampling of both Hylocomium splendens and Pleurozium schreberi, the two mosses employed in the joint European moss surveys. Significant positive correlations were found for V, Fe, Co, As, Y, Mo, Cd, Sb, Tl, and Pb. Concentrations of most of the 48 elements studied were quite similar in the two moss species, but the data may indicate that particles of geogenic origin were more effectively trapped in Hylocomium splendens. No variations were observed in the concentrations of the studied elements over the sampling season. 3.2. Temporal trends in atmospheric deposition of Pb and Cd in Norway Over the years the analytical methods used in the nationwide moss surveys in Norway have varied to some extent, and it was suspected that this might be a source of uncertainty when using these data to study temporal trends in atmospheric deposition in different regions. This suspicion was particularly strong for Pb and Cd, which were determined by respectively flame AAS, graphite furnace AAS, and ICP-MS in different surveys. Moreover the lack of suitable reference materials in the early surveys made it difficult to assess the accuracy of these data. Therefore samples from 1977, 1985, 1990, and 1995 collected at the same site at 70 different geographical locations in Norway were re-analysed for Pb and Cd by graphite furnace AAS. The samples were analysed in random order, and the accuracy of the analyses checked against certified reference materials. From the results it is obvious that data from some of the previous surveys are either systematically high or low. In one case the Pb level was as much as 25% low (7). 3.3. Source apportionment of lead in mosses by stable isotope ratios. Moss samples from 7 different sites taken at different times were analysed for the stable lead isotope composition using thermal ionization mass spectrometry (8). It was apparent from the data that the lead deposited in different parts of Norway was at least partly derived from a different mixture of sources. Whereas the 206Pb/207Pb ratio varied considerably over time in the south-western part of the country, it was distinctly different and very constant during the same period in the far north. The project is now extended in time and space, and the new data so far appear to support the conclusions drawn from the more limited material. 170 4. FUTURE WORK 4.1. Year 2000 moss survey in Norway The 2000 moss survey in Norway will be run in a similar way as previous nationwide surveys, and the data will also be submitted to the joint European survey as before. A part of the material will be analysed by both ICP-MS instrument types mentioned above, and the feasibility of the new sector field instrument for this kind of survey will be tested. In addition to the nationwide study the Norwegian Pollution Control Authority has asked for more detailed monitoring around some local pollution sources. 4.2. Atmospheric deposition in the catchment of Prut, a border river between tlhree countries Within the framework of the "NATO Science for Peace" program a study of heavy metals in the river Prut will be performed. The river Prut catchment is shared between three countries: Romania, Ukraina, and the Republic of Moldova, and this prosject is a co-operative effort between our laboratory and colleagues in Iasi (Romania) and Chisinau (Moldova). As a part of this project an attempt will be made to determine the atmospheric supply of the metals in question to the catchment by moss analysis. Since it is not yet clear that the naturally growing mosses in the area are suitable for this purpose, it may be necessary to use transplanted mosses. 171 REFERENCES [1] STEINNES, E., Further promotion of the use of mosses and lichens for studies of atmospheric deposition of trace elements. Report NAHRES-43, IAEA, Vienna (1999) 122-126 [2] FRONTASYEVA, M.V, these Proceedings. [3] LUCACIU, A., these Proceedings. [4] PARRY, S.J., BENZING, R., BOLSTAD, K.L., STEINNES, E., Epithermal/fast neutron cyclic activation analysis for the determination of fluorine in environmental and industrial materials. J. Radioanal. Nucl. Chem. 244 (2000) 67-72. [5] BERG, T., R0YSET, O., STEINNES, E., Moss {Hylocomium splendens) used as biomonitor of atmospheric trace element deposition: Estimation of uptake efficiencies. Atmos. Environ. 29 (1995) 353-360. [6] BERG, T., STEINNES, E., Use of mosses (Hylocomium splendens and Pleurozium schreberi) as biomonitors of heavy metal deposition: From relative to absolute values. Environ. Pollut. 98 (1997) 61-71. [7] FILLAN, T.J., thesis, Norwegian University of Science and Technology, in prep. 172 STUDY OF ATMOSPHERIC DISPERSION OF POLLUTANTS IN THE INDUSTRIAL REGION OF THE SADO ESTUARY USING BIOMONITORS 'M.C. FREITAS, JM.A. REIS, JA.P. MARQUES, lC. COSTA, 2H.TH. WOLTERBEEK - Instituto Tecnologico e Nuclear, 2686-953 Sacavem, Portugal 2IRI - TUDelft, Mekelweg 15, 2629 JB Delft, The Netherlands XAO102866 Abstract: The region of Lisbon and south of Lisbon (Sado estuary) is densely industrialised ', and therefore air pollution should be studied in a more detailed scale there. Also the topography of the Sado estuary region and the predominant wind direction from the north-west contribute to the influence of the industries located in the north onto this region. The region selected in this work includes a oil-fired power station. Transplants of the lichen Parmelia sulcata were suspended in nylon bags in a region within a rectangle of 15 km wide and 25 km long on a grid 2.5 km x 2.5 km, centred in a oil powered station. In each of the 47 places two sets of four transplants each were hanged. Care was taken i) in covering the sets with a polyethylene roof to prevent leaching of elements in the lichen, ii) in building a hanging system which could rotate according to the wind direction, Hi) in orienting one set towards the wind and the other set against the wind. For a 9 month period and every three months, one transplant of each set was collected. We have no knowledge of any other study on differentiation elemental uptake of Parmelia sulcata. where the component wind direction is taken into account. Some information upon local and distant sources is expected to be accessible. The transplants were analysed by INAA. Contents on Cl, Na, Ca, V and Zn are mapped and discussed. 1. INTRODUCTION The sensitivity of lichens to atmospheric pollution was demonstrated in the last two decades in various international and national investigations. Recent studies showed that the survey of a large territory with lichens makes identification of atmospheric pollution sources possible1"7. The use of lichens is the only plausible possibilrty to perform a large scale monitoring, for which otherwise an enormous amount of air samplers and samples would be needed. Modelling of lichens response to the environmental pollution has recently been studied in our Institute7'8. This is performed with lichen transplants whose response to airborne particulate matter, and total deposition is followed experimentally. 2. METHODS 2.1. Lichen transplants The trace element monitoring was carried out with epiphytic lichen transplants of Parmelia sulcata Taylor. The procedure has been described in detail4, showing the lichen transplants support used (47 hanging systems were installed). The transplants were suspended in December 1997 according the grid on Fig.l. Thirty-nine transplants oriented towards the wind (designated as F-transplants) and 39 transplants opposing the wind (designated as T- transplants) were collected in March 1998, 34 F-transplants and 34 T-transplants were collected in June 1998, and 25 F-transplants and 31 T-transplants were collected in September 1998. The lichen transplants were analysed by INAA, following the procedure described previously1"5. In short, the lichen transplants were washed for 30 sec. in de-ionised water, freeze-dried, and ground in a Teflon mill. Pellets of 500 mg were irradiated together with 0.1% Au-Al wires as comparators. Short (18 sec.) and long irradiation (5 hours) were performed at the Portuguese Nuclear Reactor. Samples were measured with a high-purity germanium detector (FWHM=1.75 keV at 1.33 MeV of 60Co) for 5 min. (short irradiation) 173 after 3 min. waiting time, and 2-4 hours (long irradiation) after 4 and 30 days of waiting time. Quality control was pursued by analysing the IAEA-336 lichen9 and CTA-OTL-1 tobacco leaves10 reference materials. We also participated in the NAT - 5 intercomparison. PIXE analysis were done for the 1st and the 2nd campaigns. 2.2. Air particulate matter Gent PM10 aerosol were working in Palmela, Faralhao and Troia for one year period. The filters (fine and coarse) were picked in a week basis. In the laboratory the filters were cut. One half for INAA analysis and one quarter for PIXE analysis. INAA analysis were performed, PIXE results are not available yet. Due to the unavailability of suitable reference materials quality assurance of filters is being made by comparison of INAA and PIXE results for common elements. We have also participated in the NAT - 3 intercomparison. 3. RESULTS AND DISCUSSION Due to the limited number of pages, only the elements determined by INAA in the three campaigns of lichen transplants will be presented. Some elements are discussed. Tables I and II show the INAA values for the 0 - month exposure of lichen transplants. Statistical data for the 3, 6 and 9 month exposed transplants are presented too. Figs. 2-6 show the variation of the elemental contents after 3, 6 and 9 months exposure in units of the standard variation of the reference values (contents in transplants before exposure). Cl and Na (Figs. 2 and 3) in both F and T transplants collected in March 98 (3 month exposure) are very similar. Therefore the source should be the same - sea spray - which is spreading through the south, crossing the Troia peninsula and the Sado estuary. The effect is more intense in F transplants than in T transplants. During spring (June 98 sampling) there is leaching of the two elements in a few sites although the patterns keep similar which means they were leached together. Therefore during spring there is no evidence of sea spray affecting this area. During summer (September 98 sampling) the area was again affected by the sea spray, and the similarity of F and T for both elements still remains. In short, F transplants are more extensively influenced by sea spray than the T transplants; the Cl and Na contents in the transplants has a seasonal variation. For Ca (Fig. 4), originated in this area mainly by cement processing (site 31 in Fig. 1), it is evident that the F transplants are more influenced by the emitting source than the T transplants. The variation along the 9 months does not seem seasonally influenced, rather there is a progressive extension of Ca to the whole area with the increasing time of exposure. This extension is noticed in both transplant types but differently. The main vanadium (Fig. 5) source in the area is the oil power station (local 33 in Fig. 1). F and T transplants do not show relevant differentiation between them; then we conclude that the emitting source is so intense that influences the surrounding area, not depending of the exposure direction to the wind. The vanadium emission is gradually spreading over the whole exposure area. Zinc (Fig. 6) is being spread so fast in F and T transplants that we do not believe it is emitted by one single source. There is evidence of a local source on the right margin of Sado estuary (probably the oil power station) but we think that the area is also mfluenced by remote 174 sources, namely the Siderurgia Nacional for iron processing. For the two first campaigns it seemed that F transplants could give information about local sources and T transplants about distant ones. After 9 months exposure that isn't so evident. For some elements (Na, Al, Cl, Ca, Co, Zn, As, Br, Sb and Ba) F transplants showed higher concentrations and for the remained elements T transplants equal F transplants. Results on the quality control are presented on the report on the intercomparison run NAT - 5 for the determination of trace and minor elements in two lichen samples. 4. CONCLUSIONS In this work some local sources were found: . cement processing emitting Ca . oil power station emitting V and Zn . sea spray emitting Cl and Na Also remote sources were identified because of the large extension of transplants affected with Zn, the F as well as the T transplants. 5. PLANS FOR FUTURE WORK One of the main points of attention will be the effect of the sample particle grain size on the relationships between outcomes of INAA and PIXE. A detailed investigation will be carried out for clarification, mainly based in an iterative procedure consisting of two steps: grinding to smaller size followed by PIXE analysis. The procedure will come to an end as soon as results of PIXE are similar to INAA ones. Monte Carlo Aided Target Transformation Analysis (MCATTFA), a method developed at Interfaculty Reactor Institute (IRI, Delft), will also be applied to the F and T data sets. In air pollution studies TTFA has been used to identify the composition of atmospheric aerosols or element concentrations (obtained from lichen data sets) and to transform them into factors. These factors may represent emission sources. 175 REFERENCES [1] FREITAS, M.C., REIS, M.A. ALVES, L.C. WOLTERBEEK, H.TH., VERBURG T., GOUVEIA, M.A., Bio-monitoring of trace-element air pollution in Portugal: Qualitative survey, J. Radioanal. Nucl. Chem., Articles 217(1) (1997) 21-30. [2] FREITAS, M.C. and NOBRE, A.S., Bioaccumulation of heavy metals using Parmelia sulcata and Parmelia caperata for air pollution studies, J. Radioanal. Nucl. Chem., Articles 217(1) (1997) 17-20. [3] FREITAS, M.C., REIS, M.A., ALVES, L.C., WOLTERBEEK, H.TH., Distribution in Portugal of some pollutants in the lichen Parmelia sulcata, Environmental Pollution 106(1999)229-235. [4] FREITAS, M.C., REIS, M.A., ALVES L.C., MARQUES, A.P., COSTA, C, Environmental assessment in an industrial area of Portugal, Biological Trace Element Research, 71/72 (1999) 471-479 [5] M.C. Freitas, M.A. Reis, A.P. Marques, H.T. Wolterbeek, Dispersion of chemical elements in an industrial environment studied by bimonitoring using Parmelia sulcata, Journal of Radioanalytical Nuclear Chemistry, 244 1 (2000) 109 - 113. [6] SLOOF, J.E., Environmental Lichenology Biomonitoring Trace Element Air Pollution, Ph.D. Thesis, Delft University of Technology, The Netherlands, (1993). [7] REIS, M.A., ALVES, L.C., FREITAS, M.C., VAN OS, B., WOLTERBEEK, H.TH., Lichens {Parmelia sulcata) time response model to environmental availability, The Sci. Total Env. 232 (1999) 105-115. [8] Reis, M.A., Quantitative biomonitoring of atmospheric trace elements in Portugal; methods, response modelling and nuclear analytical techniques, Ph.D. Thesis, Delft University of Technology, The Netherlands, (2000). [9] STONE, S.F., FREITAS, M.C., PARR, R.M., ZEISLER, R., Elemental characterisation of a candidate lichen research material-IAEA 336, Fresenius J Anal Chem. 352 (1995) 227-231. [10] DYBCZYNSKI, R., POLKOWSKA-MOTRENCO, H., SAMCZYNSKI, Z., SZOPA, Z., Preparation and Certification of the Polish Reference Material "Oriental Tobacco Leaves" for Inorganic Analysis, Raporty IchTJ. SERIA A nr 1/96, Inst. Nucl. Chem. And Techn., Warsaw(1996). 176 Caption of Figures and Tables Fig. 1 Grid selected in this study. Site 1: 1st of the grid; site 6: last of the 1st row; site 31: cement processing; site 33: oil powered station; site 42: largest contents in Troia peninsula. Fig. 2. Variation of Cl contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. Fig. 3. Variation of Na contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. Fig. 4. Variation of Ca contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. Fig. 5. Variation of V contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. Fig. 6. Variation of Zn contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. Table I Statistical data for the elemental contents determined by ESfAA in P. sulcata transplants facing the wind after 3, 6, and 9 months exposure. The values for the 0 - month exposure are also indicated. Table II Statistical data for the elemental contents determined by INAA in P. sulcata transplants opposing the wind after 3, 6, and 9 months exposure. The values for the 0 - month exposure are also indicated. 177 Power Station Figure 1. Grid selected in this study. Site 1: 1st of the grid; site 6: last of the 1st row; site 31: cement processing; site 33: oil powered station; site 42: largest contents in Troia peninsula. 178 Lichen transplants content variation after 3 month exposure Lichen transplants content variation after 3 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for Cl Results for Cl WMFaS 40 Oppositfimg INAAdata INAAdm S2/T*v- Lichen transplants content variation after 6 month exposure Lichen transplants content variation after 6 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results art? in unto of reference standard deviation. Results for Results for Cl INAAiata I L^~»L~^- ^l}'->2!"> s content variation after 9 month exposure XicAen transplants content variation after 9 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviatimt. Results for Cl Results for Cl WtndFacbig Opposufacbig INAA data INAA data 20 it 6 2 •2 -i Fig. 2. Variation of Cl contents in lichen transplants exposed: 3, 6, and 9 months, facing and opposing the wind. 179 Lichen transplants content variation after 3 month exposure Lichen transplants content variation after 3 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for Na Results for Na HmdFaang itfamg IN.U data MM data Lichen transplants content variation after 6 month exposure Lichen transplants content variation after 6 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for Na Results for Na HMFaJng ^^ Oppositfacing JNAAiatu m Lichen transplants content variation after 9 month exposure Lichen transplants content variation after 9 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for Na Results for Na WmdFaang 140 OpposUfacmg mAAiata INMdrtu Y r iv2 Fig. 3. Variation of Na contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. 180 Lichen transplants content variation after 3 month exposure Lichen transplants content variation after 3 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for Ca Results for Ca Hind Facing 40 Opposil facing \40 INAA iota I20 ho fc Lichen transplants content variation after 6 month exposure Lichen transplants content variation after 6 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results art in units of reference standard deviation. Results for Ca Results for Ca Wind Facing itfadng INAAJma WAAim Lichen transplants content variation after 9 month exposure Lichen transplants content variation after 9 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results art! in units of reference standard deviation. Results for Ca Results for Ca Wind Fating Oppositfadng &***,^ M40 IN AA iota INAAiatu 9 ihk^^^t/ " 10 v-c£ /428O t •2 -t Fig. 4. Variation of Ca contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. 181 Lichen transplants content variation after 3 month exposure Lichen transplants content variation after 3 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for V Results for V Wind Facing Opposilfacing .<£=>*=•?>: INAA data IN A A data Lichen transplants content variation after 6 month exposure Lichen transplants content variation after 6 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for V Results for V Opposit facing HmdFaang 40 INA4 data lNAAdata 20 It %£&'*& 6 %S?y427O 2 f* km IVorte 426O •2 -6 510 k-m Esle Lichen transplants content variation after 9 month exposure Lichen transplants content variation after 9 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard deviation. Results for V Results for V mttdFadng Opposit facing lNAAdata lNAAdata S&Szr*- /42BO Fig. 5. Variation of V contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. 182 Lichen transplants content variation after 3 month exposure Lichen transplants content variation after 3 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results are in units of reference standard demtion. Results for Zn Results for Zn WmdFaSng Oppositfacing INAAdm INAA data Lichen transplants content variation after 6 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results art in units of reference standard deviatfon. Results for Zn Results for Zn ntndFachtg Opposilfadng INAAdata Lichen transplants content variation after 9 month exposure Lichen transplants content variation after 9 month exposure in Sado Estuary. Results are in units of reference standard deviation. in Sado Estuary. Results art! in units of reference standard deviatun. Results for Zn Results for Zn mndfafa Opposufadng MM data IN AA data 120 W -t2SO I; -2 '•6 sio Fig. 6. Variation of Zn contents in lichen transplants exposed 3, 6, and 9 months, facing and opposing the wind. 183 F Na Al Cl K Ca Sc V Cr Mn Fe Co Zn As Se Br o - month 1448.26 3649.80 0.67 9.44 4.78 36.68 1894.40 0.77 *9.49 0.79 0.3JS 1$,99 Std 4.66 36.63 128.94 618.50 148.72 0.16 0.74 1.16 7.46 64.96 0.17 9.30 0.16 0.38 2.19 3 - months Mean 17SM4 1109.26 3035,42 2456.92 7431 £4 0.47 %M 5.32 40.05 1604.44 0.73 90.72 1.35 a.33 28.47 Var 700358.26 45692.41 1706179.42 691499.72 9302486.07 0.01 2.07 6.13 66.64 157176.79 0.04 10060.41 3.72 0.00 74.41 Std 836.87 213.76 1306.21 831.56 3050.00 0.11 1.44 2.48 8.16 396.46 0.19 100.30 1.93 0.07 8.63 Count 39.00 39.00 39.00 38.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 Median 1655.91 1067.05 3038.99 2469.00 6831.50 0.47 3.04 4.63 38.44 1604.70 0.75 60.36 0.96 0.32 27.94 Mean/REF 7.86 0.77 23.80 0.67 9.38 0.83 1.04 1.11 1.09 0.85 0.95 1.63 1.71 1.02 1.42 Max/REF 16.48 1.10 45.60 1.13 25.57 1.42 2.80 2.47 1.58 1.43 1.82 13.53 16.28 1.49 2.50 Min/REF 1.40 0.51 7.17 0.27 4,82 0.40 0.62 0.54 0.73 0.44 0.47 0.76 0.52 0.70 0.62 6 - months Mean - $830,12 1615.36 wwm 1944.86 , vrwM 0.57 6,17 5.93 29.90 1983.15 18$ '" 1.66 ' HM 52.02 Var 1267223.72 90031.20 1622744.61 309902.68 8849588.63 0.02 7.12 12.52 230.09 229182.49 1.72 3189.00 1.10 0.01 137.87 Std 1125.71 300.05 1273.87 556.69 2974.83 0.12 2.67 3.54 15.17 478.73 1.31 56.47 1.05 0.08 11.74 Count 34.00 34.00 33.00 33.00 32.00 33.00 34.00 33.00 33.00 33.00 33.00 33.00 34.00 33.00 34.00 Median 1631.85 1539.74 1404.00 1922.10 8575.25 0.55 5.52 5.31 24.84 1920.01 1.05 98.16 1.32 0.40 31.03 Mean/REF 8.21 1.12 13.49 0.53 11.05 1.00 1.79 1.24 0.82 1.05 2.01 2.23 2.02 1.22 1.64 Max/REF 22.71 1.62 40.37 0.87 20.28 1.53 3.96 5.05 1.87 1.62 7.80 6.68 5.49 1.96 3.65 Min/REF 2.10 0.70 1.82 0.19 1.75 0.55 0.93 0.59 0.26 0.53 0.81 0,78 0.11 0.81 0.96 9 - months Mean 3034.97 2479.66 3029,94 2357.10 tl»»J64 0.66 6.03 36.72 2346.89 1.2$ 177,8$ 1-S$ 0.47 $$.94 Var 2738371.16 250932.30 2139423.12 900179.00 11373684 44 0.03 30 49 3.93 77.94 448940.98 0.25 16956.64 1.51 0.02 193.93 Std 1654.80 500.93 1462.68 948.78 3372.49 0.17 5.52 1.98 8.83 670.03 0.50 130.22 1.23 0.14 13.93 Count 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00 24.00 25.00 25.00 25.00 22.00 25.00 25.00 Median 3181.40 2322.81 2696.76 2621.10 11286.00 0.65 7.87 6.00 35.18 2293.90 1.17 120.87 1.27 0.41 35.25 Mean/REF 13.62 1.71 23.75 0.65 14.14 1.15 2.81 1.26 1.00 1.24 1.66 3.59 2.01 1.47 1.95 Max/REF 34.66 2.54 61.26 1.04 21.32 1.73 9.04 1.86 1.66 2.20 3.78 10.76 7.62 2.84 4.01 Min/REF 1.70 1.24 4.53 0.14 5.05 0.61 1.48 0.31 0.67 0.66 0.92 1.01 0.31 1.02 0.81 Table I - Statistical data for elemental contents determined by INAA in P. sulcata transplants facing the wind after 3, 6, and 9 months exposure. The values for 0 - month exposure are also indicated. 184 F Rb Sb Ba La Ce Nd Sm Eu Tb Lu Hf Ta Hg Th u o - month 1.15 0 30 16.67 2.43 6.24 2.67 0.47 0.86 0.06 0.02 0.32 0.06 &.1$ Std 2.46 0.46 3.87 0.87 1.12 0.61 0.16 0.28 0.13 0.76 0.82 0.17 0.64 0.14 0.49 3 - months Mean ?m pM 2.06 4.26 2.01 0.36 0.07 0.05 0.02 0.28 0.05 'QM ' 0,24 ' ' Var 4.04 0.01 10.78 0.27 0.86 0.26 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.11 Std 2.01 0.12 3.28 0.52 0.93 0.51 0.10 0.02 0.01 0.01 0.06 0.01 0.03 0.11 0.33 Count 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 Median 7.82 0.26 15.63 2.08 4.38 1.90 0.36 0.07 0.05 0.02 0.28 0.05 0.14 0.51 0.15 Mean/REF 6.88 0.95 0.98 0.85 0.81 0.78 0.76 0.08 0.08 0.08 0.86 0.09 0.91 0.93 1.52 Max/REF 11.86 1.89 1.45 1.33 1.29 1.41 1.21 0.13 0.12 0.14 1.35 0.15 1.57 1.39 12.84 Min/REF 4.26 0.47 0.66 0.35 0.42 0.29 0.39 0.04 0.04 0.02 0.44 0.04 0.53 0.49 0.46 6 - months Mean &S3 0,42 2W8 2.46 5.15 2.26 0.44 0.10 0.06 0.03 0.33 0.07 • " 0,33 0,8f'' 0,34 Var 1.89 0.05 24.35 0.38 1.29 0.43 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.42 Std 1.38 0.23 4.93 0.62 1.13 0.65 0.12 0.07 0.05 0.03 0.07 0.06 0.07 0.13 0.65 Count 33.00 33.00 33.00 34.00 33.00 30.00 30.00 33.00 33.00 33.00 33.00 33.00 33.00 33.00 29.00 Median 8.73 0.38 20.56 2.37 4.80 2.17 0.40 0.09 0.05 0.02 0.33 0.06 0.33 0.60 0.22 Mean/REF 7.47 1.42 1.33 1.01 0.98 0.88 0.92 0.12 0.11 0.12 1.03 0.12 2.09 1.10 2.14 Max/REF 10.28 3.94 1.96 1.59 1.46 1.40 1.46 0.57 0.58 0.73 1.45 0.67 2.96 1.53 23.55 Min/REF 5.29 0.50 0.79 0.54 0.53 0.40 0.50 0.06 0.02 0.06 0.53 0.07 1.08 0.59 0.76 9 - months Mean 1&.34 0,63 3.01 6.24 2.61 0.46 0.10 0.06 0.03 0.41 0.07 0,22" 0,74 o.a$ Var 5.31 0.30 B0.23 0 °9 1 OO n to u.uu 0.00 0.00 0.01 0.00 0.02 0.04 0.02 Std 2.30 0.54 8.96 0.94 1.68 0.75 0.15 0.04 0.02 0.01 0.11 0.02 0.13 0.20 0.14 Count 25.00 25.00 25.00 25.00 25.00 24.00 25.00 24.00 25.00 25.00 25.00 25.00 25.00 24.00 22.00 Median 10.21 0.46 25.04 2.83 5.90 2.45 0.45 0.09 0.06 0.03 0.40 0.07 0.18 0.75 0.26 Mean/REF 8.95 2.11 1.74 1.24 1.19 1.02 0.97 0.12 0.12 0.13 1.28 0.13 1.39 1.35 1.75 Max/REF 13.98 9.42 3.26 1.93 1.96 1.74 1.52 0.25 0.16 0.24 1.96 0.20 4.33 2.24 3.75 Min/REF 5.17 0.68 0.87 0.57 0.63 0.61 0.39 0.02 0.07 0.06 0.68 0.07 0.45 0.77 0.65 Table I (cont.) 185 T Na Al Cl K Ca Sc V Cr Mn Fe Co Zn As Se Br o - month m»o 1448.26 3649.80 •mm 0.67 '£44 4.78 36.68 1894.40 " ' 4S.49 "• QM QM'" ' 'WM Std 4.66 36.63 128.94 618.60 148.72 0.16 0.74 1.16 7.46 64.96 0.17 9.30 0.16 0.38 2.19 3 - months Mean 1873.76 1343.66 2438.04, 2778.08 €962.69 0.54 3.84 5.18 43.18 1783.31 0J7 76.77 1.24 0.39 26.36 Var 995907.29 99684.80 1401947.44 783373.10 2795570.38 0.03 1.32 2.36 158.14 260066.64 0.04 6366.68 0.21 0.01 63.56 Std 997.95 315.73 1184.04 885.08 1672.00 0.16 1.15 1.54 12.58 509.97 0.19 79.79 0.46 0.07 7.97 Count 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 Median 1447.74 1346.21 2387.34 2732.00 6606.00 0.48 3.63 4.66 42.74 1640.80 0.72 55.68 1.16 0.38 26.50 Mean/REF 7.06 0.93 19.11 0.76 8.79 0.94 1.11 1.08 1.18 0.94 1.00 1.53 1.56 1.21 1.32 Max/REF 20.28 1.66 46.72 1.21 12.89 1.59 2.09 1.78 1.98 1.53 1.60 10.30 3.35 1.90 2.41 Min/REF 1.20 0.66 2.46 0.29 5.17 0.60 0.54 0.66 0.65 0.59 0.61 0.79 0.65 0.84 0.59 6 - months Mean t«34>27 1200.76 2417.16 • mnm ' 0.52 &7? 4.94 27.67 1771.23 11&7S ; 147 wm 27,37 ! Var 1628694.41 210837.46 796719.17 309437.42 2983345.55 0.03 4.51 4.52 51.88 380633.90 0.06 34917.40 0.68 0.01 127.02 Std 1276.20 459.17 892.59 556.27 1727.24 0.18 2.12 2.13 7.20 616.96 0.24 186.86 0.83 0.10 11.27 Count 34.00 22.00 34.00 34.00 34.00 34.00 34.00 34.00 34.00 34.00 34.00 34.00 34,00 34.00 34.00 Median 1474.00 1118.50 1664.01 2480.70 8122.45 0.49 5.28 4.21 26.78 1609.20 0.80 76.37 1.17 0.32 26.09 Mean/REF 8.23 0.83 13.25 0.66 10.27 0.92 1.68 1.03 0.75 0.93 1.11 2.28 1.73 1.08 1.37 Max/REF 26.83 1.60 30.01 0.99 16.71 1.90 4.15 2.53 1.14 1.88 1.87 23.38 6.12 1.94 3.84 Min/REF 2.14 0.10 2.82 0.29 6.66 0.49 0.76 0.52 0.42 0.47 0.57 0.81 0.61 0.57 0.65 9 - months Mean • m4$r 2508.20 31fl$,06 2560.38 tO147,$9 0.56 $J? 5.71 33.99 1986.95 %m 150,07 148 Mi 3iei Var 2619570.91 240218.93 4798229.64 539126.02 12170417.15 0.02 44.04 8.74 59.82 400099.94 0.12 37573.90 1.27 0.01 101.80 Std 1618.51 490.12 2190.49 734.25 3488.61 0.15 6.64 2.96 7.73 632.53 0.35 193.84 1.13 0.10 10.09 Count 30.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 30.00 31.00 31.00 31.00 31.00 31.00 31.00 Median 2363.00 2518.56 2484.00 2649.00 9598.80 0.58 7.67 5.14 33.43 1939.50 0.99 107.60 1.34 0.38 30.59 Mean/REF 11.91 1.73 24.37 0.70 12.80 0.99 2.84 1.19 0.93 1.05 1.31 3.05 2.13 1.26 1.58 Max/REF 36.38 2.65 98.29 1.05 28.51 1.59 11.19 3.94 1.42 2.05 2.74 23.15 6.58 2.33 3.01 Min/REF 2.92 1.08 10.17 0.22 6.49 0.59 1.50 0.66 0.65 0.64 0.62 0.81 0.85 0.72 0.78 Table II - Statistical data for elemental contents determined by INAA in P. sulcata transplants opposing the wind after 3, 6, and 9 months exposure. The values for 0 - month exposure are also indicated. 186 T Rb Sb Ba La Ce Nd Sm Eu Tb Lu Hf Ta Hg Th u o - month . WO 2.43 5.24 2.57 0.47 0.86 0.06 0.02 0.32 0.06 0.16 0.56 0.16 Std 2.4S 0.46 3.87 0.87 1.12 0.61 0.16 0.28 0.13 0.76 0.82 0.17 0.64 0.14 0.49 3 - months Mean §.32 6.33 16.88 2.29 5.04 2.22 0.43 0.09 0.05 0.02 0.31 0.06 0.16 0.61 0.19 Var 5.71 0.16 17.09 0.41 2.04 0.41 0.02 0.00 0.00 0.00 0.01 0.00 0.00 0.03 0.00 Std 2.39 0.39 4.13 0.64 1.43 0.64 0.13 0.03 0.01 0.01 0.10 0.02 0.04 0.18 0.05 Count 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 39.00 Median 8.85 0.24 15.88 2.13 4.77 2.07 0.41 0.08 0.05 0.02 0.28 0.05 0.16 0.57 0.19 Mean/REF 8.07 1.11 1.08 0.94 0.96 0.86 0.92 0.10 0.10 0.10 0.98 0.10 1.04 1.11 1.24 Max/REF 13.01 8.76 1.86 1.61 1.70 1.58 1.83 0.17 0.17 0.20 1.91 0.17 1.80 2.22 2.29 Min/REF 4.74 0.50 0.69 0.58 0.62 0.48 0.58 0.06 0.06 0.07 0.50 0.06 0.54 0.71 0.79 6 - months Mean • &,44 &ae wm 2.24 4.80 2.24 0.40 0.08 0.05 0.02 0.30 0.05 0.16 0.57 0.18 Var 3.46 0.03 29.52 0.73 1.53 0.51 0.02 0.00 0.00 0.00 0.01 0.00 0.00 0.04 0.00 Std 1.86 0.18 5.43 0.85 1.24 0.72 0.13 0.03 0.02 0.01 0.11 0.02 0.05 0.19 0.06 Count 34.00 34.00 34.00 34.00 34.00 34.00 34.00 33.00 34.00 34.00 34.00 34.00 34.00 34.00 32.00 Median 8.04 0.26 17.08 2.06 4.56 2.08 0.39 0.08 0.05 0.02 0.27 0.05 0.15 0.54 0.17 Mean/REF 7.30 1.08 1.16 0.92 0.92 0.87 0.83 0.10 0.09 0.09 0.94 0.09 1.04 1.04 1.14 Max/REF 12.29 3.32 2.14 2.13 1.77 1.83 1.74 0.25 0.16 0.20 2.34 0.19 2.09 2.35 2.38 Min/REF 3.88 0.53 0.59 0.51 0.58 0.55 0.18 0.06 0.04 0.05 0.60 0.06 0.50 0.59 0.53 9 - months Mean $.45 0.4? 22.0$ 2.52 5.14 2.63 0.46 0.09 0.06 0.03 0.35 0.06 0.20 0.65 0.22 Var 5.83 0.14 U.D5 z.uu 0.61 0.02 0.00 0.00 0.00 0.01 0.00 0.01 0.03 0.01 Std 2.41 0.37 7.38 0.81 1.41 0.78 0.14 0.02 0.02 0.01 0.11 0.02 0.07 0.17 0.10 Count 31.00 31.00 31.00 31.00 31.00 30.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 29.00 Median 9.15 0.34 20.65 2.41 5.00 2.48 0.45 0.08 0.06 0.02 0.34 0.06 0.18 0.63 0.20 Mean/REF 8.19 1.58 1.42 1.03 0.98 1.02 0.97 0.10 0.11 0.10 1.10 0.11 1.25 1.17 1.42 Max/REF 13.27 6.40 2.43 1.87 1.65 1.80 1.55 0.17 0.20 0.19 2.34 0.18 3.06 2.00 4.24 Min/REF 3.95 0.53 0.86 0.63 0.56 0.52 0.53 0.06 0.06 0.06 0.66 0.06 0.57 0.67 0.72 Table II (cont.) 187 XAO102867 ATMOSPHERIC DEPOSITION OF HEAVY METALS IN RURAL AND URBAN AREAS OF ROMANIA STUDIED BY THE MOSS BIOMONITORING TECHNIQUE EMPLOYING NUCLEAR AND RELATED ANALYTICAL TECHNIQUES AND GIS TECHNOLOGY 1 ADRIANA LUCACIU, 2MARINAFRONTASYEVA, 2OTILIA STAN, 3E. STEINNES, 4N. SASARAN , 4KATALINA CZIPLE 'institute of Physics and Nuclear Engineering, Po Box MG-6, R-76900, Bucharest, Romania, Fax: (40) 131 222 45, e-mail: [email protected] 2Frank Laboratory of Nuclear Physics, Joint Institute for Nuclear research, Dubna 141980, Moscow Region, Russia, Fax: 7 (09621) 65085; e-mail: [email protected], [email protected] 3Department of Chemistry, Norway University of Science and Technology, Trondheim 7034, Norway, Fax: 47 (73) 59 69 40; e-mail: [email protected], 4Mining Research and Design Institute, Baia Mare 4800, Romania, Fax: (40) 62. 423 643 This paper presents data for 39 elements in moss samples collected in the Transilvanian Plateau of Romania. The analyses were carried out by ENAA with the exception of Cu and Pb which were determined by AAS. Extremely high values are observed for elements such as Cu, Zn, As, Cd, and Sb in parts of this territory affected by local metal industries. The levels are among the highest observed in the world, and could be partly responsible for the unfortunate health situation in some of these areas. 1. INTRODUCTION In most European countries, the increased efforts to establish heavy metal monitoring have led to a number of environmental programmes at the national and international levels. The moss technique, introduced in the Scandinavian countries about 25 years ago, has shown to be the most suitable for studying the deposition of the heavy metals. It has found numerous applications and is now being widely used for large scale deposition studies. Matters of environmental protection are also being considered most attentively in Romania, especially with respect to the intense local pollution problems resulting from intensive industrial and agricultural activities. In Romania, however, the available resources for the next 10-20 years necessary for the improvement of the environmental conditions are very limited, whereas the cost of attaining certain targets connected with the environment is very high. For the first time, the moss technique was applied in Romania in 1995 to a systematic study of air pollution with heavy metals and other trace elements in several industrialized and urban areas of the Eastern Romanian Carpathians. This was done to cover one more "white spot" on the heavy metal atmospheric deposition map of Europe The study has continued later in other regions of Southern and Western Carpathians and most recently in 1999 in the Transilvanian Plateau. 189 The most important results to be expected by this study are as follows: • identification of areas with high contamination levels to be considered for the evaluation of environmental risk; • creation of database for continued studies at regular intervals; • establishment of a regional sampling network for future monitoring programs; • comparison of the environmental contamination levels of Romania regions with other strongly polluted areas in Europe, such as the "Black Triangle", the Copper Basin in Poland, the Ural region, etc. Romania, known for its rich mineral resources, is a highly industrialized country where a great number of metal processing plants as well as coal-fired power plants are operating. The most important metals are iron, chromium, nickel, aluminum, gold, silver, copper and zinc; other important elements are arsenic, mercury, vanadium and rare earth elements. A high concentration of industrial activity is clustered within a limited geographical region in Transilvania. As a result, the environment in the area has reached a state of deep ecological stress. For example, non-ferrous metal processing plants pollute the surroundings of Copsa Mica, Zlatna and Baia Mare with heavy metals such as lead, tin, copper and cadmium, the maximum values of the concentrations exceeding by far the permitted norms [1]. 2. METHODS 2.1. Sampling Samples of the moss Hypnum cupressiforme were collected during the summer of 1999 according to guidelines described in detail elsewhere [2—4]. The sampling sites were located at least 300 m from main roads and populated areas and at least 100 m from smaller roads or single houses. From each sampling site, 5 to 10 subsamples were taken within a 50 x 50 m area and mixed in the field. The samples were collected with plastic gloves and stored in clean plastic bags. Unwashed green parts of moss plants, cleaned and dried at 40 °C, were taken for analysis. No further homogenization of the samples was performed [5]. 2.2. Analysis Moss samples of about 0.3 g were packed in aluminum cups for long-term irradiation and samples of about 0.3 g were heat-sealed in polyethylene foil bags for short-term irradiation. Elements yielding long-lived isotopes were determined using Cd-screened channel 1 (Chi) (epithermal neutron activation analysis, ENAA) at the IBR-2 reactor in Dubna, Russia. Samples were irradiated for 5 days, re-packed, and then measured twice after 4-5 and 20 days of decay, respectively. Measurement time varied from 1 to 5 hours. To determine the short-lived isotopes of Na, Mg, Al, Cl, K, Ca, Mn, I, and Br (80Br), channel 2 (Ch2) was used (conventional NAA). Samples were irradiated for 5 min and measured twice after 3-5 min of decay for 5-8 and 20 min, respectively. 190 Table 1. Characteristics of the irradiation channels Neutron flux density / lO1^ n cm"z s" Irradiation site Thermal Resonance Fast T(°C) Chi Cd-screened 0.23±0.03 1.4±0.16 70 Ch2 0.54±0.06 0.12±0.014 0.64±0.04 60 Data processing and element concentration determination was performed on the basis of certified reference materials and flux comparators, using software developed in FLNP JINR [6]. For long-term irradiation in Chi, single comparators of Au (lug) and Zr (10 (ig) were used. For short-term irradiation in Ch2 a comparator of Au (10 |ig) was employed. Concentrations of elements yielding long-lived isotopes were also determined using certified reference materials: SDM sediment (International Atomic Energy Agency, Vienna), Montana Soil (NIST) and moss DK-1, prepared for calibration of laboratories participating in the corresponding 1990 Nordic survey [7]. Interference from the 56Fe(n,p)56Mn and 54Fe(n,a)51Cr reactions was estimated at less than 0.1% for the given concentrations of Fe. The high density of fast neutrons in the irradiation channels used provided favorable conditions for the determination of Ni by the 58Ni(n,p)58Co reaction. However, problems with interfering nuclear reactions are evident in a number of instances, as shown in Table 2. The elements Cu and Pb were determined in Mining Research and Design Institute, Baia Mare, by atomic absorption spectrometry (AAS) after nitric acid decomposition. Table 2. Interference by fast neutron reactions* Intended reaction Interfering reaction Level of interference/ng ZiNa(n,y)24Na ^Mg(n,p)^Na 3xlOJ 27Al(n,a)24Na 1.5 xlO6 26Mg(n,y)Mg27 27Al(n,p)27Mg 9xlO8 27Al(nj)28Al 28Si(n,p)28Al 3xlO7 31P(n,a)28Al 9x 106 41 42 42 42 K(n,Y) K Ca(n,P) K f 45Sc(n,a)42K 1.5 x 106 51V(n,y)52V 52Cr(n,p)52V lxlO4 50 5I 54 51 5 Cr(n,Y) Cr Fe(n,a) Cr 4xlO 55Mn(n,Y)56Mn 56Fe(n,p)56Mn 7xlO4 58Fe(n,a)59Fe 59Co(n,p)59Fe 2.3 x 10 8 63Cu(n,Y)64Cu 64Zn(n,p)64Cu 5xlO6 * Cr and Fe were determined in Ch 1, all other elements mentioned here in Ch2 (see Table 1). ** As compared with 1 gram of the interfering element. * Cross-section not available. 191 3. RESULTS AND DISCUSSIONS The obtained results for Transilvanian Plateau are shown in Table 3, in comparison with other relevant areas from Russia (Ural), Poland (Copper Basin) and Norway (Mo, local iron smelter). Polymetalic mining industries show pollution of a vast territory with Fe, Pb, Cr, etc. Non-ferrous metal industries in Copsa Mica, Zlatna and Baia Mare are responsible for pollution with elements such as Pb and Cu. The iron and steel factories of Hunedoara and Calan show emissions of iron and non-ferrous metals. From Table 3 the following observations can be made regarding the concentrations of metals such as As, Cr, Cu, Fe, Mg, Mn, Ni, Pb and Zn: • in Transilvanian Plateau the concentrations of these elements exceed the values from Russia, Poland and Norway; very serious is the fact that As, Cu, Pb and Zn show concentrations about ten times greater in Romania; • the concentrations of Mg, Cr and Mn in Romania, Poland and Norway are comparable and lower than in the South Ural Mountains (Russia); • the Ni concentration is lower in Romania that in the other areas The average life expectancy in Romania is one of the lowest in Europe: 66.56 years for men and 73.17 years for women. The highest mortality figures are evident in the districts of Arad, Giurgiu, Teleorman with 31-33 % above the national average value, and Bihor, Mehedinti, Salaj, with some 20% above the national average. Most of the registered deaths in Romania are due to the circulatory system diseases (60-65 %). The serious air pollution situation is likely to be one of the factors responsible for these unfortunate health conditions. In the period after 1989, and especially during the last three years extensive measures have been taken in order to improve the environmental conditions in Romania. 4. PLANS FOR FUTURE WORK The detailed working plan for 2000 is as follows: Month 1-3 • Analysis of samples collected in summer 1999 by NAA and AAS Month 4-5 • Continuation and completion of NAA and AAS analyses; • Data Processing; • Comparison with previous data obtained for the Eastern Carpathians Month 6-7 • Sampling in the Eastern Carphatians for the European Moss-survey 2000 Month 8-9 • Writing of papers based on previous work on the Eastern, Southern and Western 192 Carpathians and Transilvanian Plateau; • Application of SEM XRF (Scanning Electron Microscopy and X-Ray Fluorescence) in Dubna to mosses taken from the most polluted areas under examination in order to obtain pictures of aerosol particles captured by the moss Month 10-11 • Analysis of samples collected in the summer of 2000 Month 12 • Preparation of GIS maps Working plan for 2001: Continuation of the program in areas of Romania not studied so far (Danube course and Delta, Black-Sea coast line) Month 1-3 • Analysis of samples collected in 2000 Month 4-5 • Continuation and completion of NAA and AAS of mosses Month 6-7 • Sampling in Danube course and Delta, and along the Black Sea coast line; Month 8-9 • Writing reports and papers based on previous work; • Further examination of mosses by SEM XRF in Dubna; Month 10-11 • Analysis of samples collected in the summer of 2001; Month 12 • Preparation of GIS maps. 5. ACKNOWLEDGEMENT The authors are grateful to the International Atomic Energy Agency (IAEA) for theiir financial support to this project 193 REFERENCES [1] Romania Environment Protection Strategy, Bucharest, 1996. [2] RUHLING A., RASMUSSEN L., PILEGAARD K., MAKINEN A., STEINNES E., Survey of atmospheric heavy metal deposition in the Nordic countries in 1985- monitored by moss analysis. NORD 1987-21, 44p. [3] BERG O., R0YSET O., STEINNES E., Moss used as a biomonitor of atmospheric trace element deposition. Estimation of uptake efficiences. Atmos. Environ. 29 (1995) 325-360. [4] MARKERT B. Quality Assurance in Environmental Monitoring Sampling and Sample Pretreatment. VCH-Publisher, Weinheim, New York, 1995, 215-254. [5] STEINNES E., HANSSEN J.E., RAMBAEK J.P., VOGT N.B. Atmospheric deposition of trace elements in Norway. Temporal and spatial trends studied by moss analysis. Water, Air, SoilPollut. 74 (1994), 121-140. [6] OSTROVNAYA T.M., NEFEDYEVA, L.S., NAZAROV, V.M., BORZAKOV,S.B., SRELKOVA, L.P. "Software for INAA on the Basis of Relative and Absolute Methods Using Nuclear Data Base". Activation Analysis in Environment Protection, D-14-93 -325, Dubna, 1993, p. 319-326. [7] FRONTASYEVA, M.V., NAZAROV, V.M., and STEINNES, E., Moss as Monitor of Heavy Metal Deposition: Comparison of Different Multi-Element Analytical Techniques, 181 (1994)363-371. 194 PUBLICATIONS ROMANIA -ADRIANA LUC ACIU 1 Multielemental content study of moss as biomonitor of environmental pollution by thermal neutron activation analysis. Adriana Lucaciu, Stefania Spiridon, L. Staicu, L.Timofite Rom. Joura. Phys., Vol. 42, Nos 5-6, P. 423- 427, Bucharest, 1997. 2. Atmospheric Deposition of Heavy Metals in Romania Studied by the Moss Biomonitoring Technique Employing Nuclear and Related Analytical Techniques and GIS Technology A.Lucaciu,M.V.Frontasyeva,E.Steinnes,Ye.N.Cheremisina, C.Oprea,T.B.Progulova,S.Spiridon,L.Staicu Second Prize, JINR,Frank Laboratory of Neutron Physics, Universersity Training Center, 1998. 3. Determination of Atmospheric Trace Elements As, Br, Hg and V Deposited on Moss Samples By Neutron Activation. A. Lucaciu, S. Spiridon, S. Staicu TFD - 17, Turkish Physical SocietySociet , 17th Physics Conference, 27- 31 Oct. 1998, Alanya- Turkey. 4. Atmospheric Deposition of Heavy Metals in Romania Studied by the Moss Biomonitoring Technique Employing Nuclear and Related Analytical Techniques and GIS Technology A.Lucaciu, M.V.Frontasyeva, E.Steinnes, Ye.N.Cheremisina, C.Oprea, T.B.Progulova, S.Spiridon, L.Staicu Journal of Radioanalytical and Nuclear Chemistry,vol.240,No.2(1999), 457-458 5. The use of moss as biomonitors regarding the study of atmospherical deposition in Romania A.Lucaciu,S.Spiridon, L.Staicu, L. Craciun The Tenth International Conference on Modern Trends in Activation Analysis (MTAA-10), 19-23,April, 1999,Gaithersburg, USA. 195 TABLE 3. Element concentration {ppm) in moss from Transilvania Plateau (Romania) and in some other relevant areas used for comparison Norway [3] Transil. Plateau Element Ural (Russia) Copper Basin (Poland) Tula (Russia) [14] Mo (Norway) [12] background (Romania) level Mean Range Mean Range Mean Range Mean Range Mean Range Mean Na 941 192-2210 394 174-1051 152 74-302 403 147-882 294 93-635 200 Mg 2420 654-5470 5003 1353-15400 1694 800-6480 2878 1160-4982 1861 556-4230 1200 A! 5410 828-14600 2819 810-7000 815 237-2590 2486 402-6015 1244 243-3100 350 Cl 500 186-1310 314 44-1114 226 113-537 859 232-2521 294 50-1110 200 K 9220 4470-20000 6842 2642-13260 5005 515-8708 19628 8910-42230 3845 1930-7160 3000 Ca 13000 1450-19000 5093 2030-13800 2229 1190-12800 7260 3290-12380 2871 1450-6740 1500 Sc 1.29 21.5-6.13 0.60 0.10- 1.45 0.15 0.03-0.63 0.39 0.09-1.19 0.41 0.06-1.41 0.06 V 9.69 1.95-25.2 8.50 2.0-22.4 2.60 1.14-8.13 11.2 1.38-62 5.72 1.05-31.0 2 Cr 13.6 2.72-51.9 18.6 2.2-194 1.43 0.80-3.16 2.95 0.88-9.6 11.7 0.5-50 1.5 Mn 283 27.1-818 344 88 - 1402 287 65-847 391 100-817 384 89-1460 200 Fe 4600 815-21300 1888 335-7438 520 219-1405 3030 471-19670 12280 700-72100 400 Co 1.57 0.31-7.05 0.64 0.14-1.95 0.32 0.11-1.96 0.13 0.05-0.29 0.61 0.06-2.2 0.3 Ni 1.06 0.14-3.91 8.4 0.96-94 2.49 0.21-38 0.53 0.21-1.21 1.69 <0.5-6.96 1.6 Cu* 1310 11- 19100 34 5-200 73 3.11-2040 Zn 498 39.2-2950 72 14.8-304 41 21-83 69 27.15-105 99 31-397 36 As 15.1 0.594- 119 2.17 0.63 - 9.7 0.73 0.12-6.04 0.51 0.11-1.47 0.62 0.06-2.20 0.3 Se 1.01 0.0754- 5.01 0.34 0.02- 1.1 0.32 0.10-0.77 0.13 0.05-0.20 0.47 0.21-1.17 0.25 Br 7.19 2.03- 15.3 6.20 1.52-25 1.38 0.89-2.85 4.05 1.44-12.7 6.94 3.6-12.2 5 Rb 21.5 5.76- 135 10.3 2.8-39 21 1.95-45.51 19 6.2-32 17.2 6.7-46.2 10 Sr 61.8 1.84-289 18 1.96-65 . 12.4 0.69-339 Mo 2.76 0.286- 14.6 0.29 0.041 -0.71 0.29 0.05-2.42 Ag 0.837 0.0326- 4.54 0.124 0.011-0.47 0.12 0.02-1.74 0.06 0.02-0.15 0.059 <0.03-0.16 0.04 Cd* - - 0.63 0.16-2.86 0.30 0.03-1.07 Sb 8.93 0.160-50.9 2.63 0.08 - 29 0.26 0.12-0.79 0.13 0.05-0.7 0.25 <0.05-0.76 0.09 I 2.12 0.795-5.55 1.35 0.51-3.41 1.14 0.35-2.68 1.58 0.51-4.3 2.26 <1.0-4.3 2 Cs 0.602 0.122-3.40 0.22 0.04-0.61 0.43 0.08-1.29 0.20 0.06-0.48 0.37 <0.05-1.03 0.18 Ba 116 21.6-658 44 6.3-125 13.6 5.47-79 65 10-145 33.1 12.0-83.0 24 La 1.67 0.362- 5.06 2.43 0.47- 13 0.52 0.14-1.61 2.40 0.42-6.75 0.69 <0.10-2.87 0.3 Ce 4.44 0.930- 18.4 3.24 0.53- 11.7 1.27 0.24-3.74 3.45 0.64-10.9 - Sm 0.253 0.00773- 1.51 0.29 0.07-1.05 0.13 0.06-0.63 0.40 0.08-1.05 0.33 0.05-1.34 0.06 Tb 0.0793 0.00556- 0.375 0.035 0.004-0.17 0.01 0.003-0.09 0.04 0.008-0.126 0.019 <0.005-0.067 0.015 Yb 0.275 0.0355- 1.48 0.107 0.005-0.55 0.04 0.01-0.18 0.13 0.028-0.38 0.069 <0.010-0.230 0.03 Hf 0.807 0.122-4.66 0.276 0.023-1.78 0.13 0.01-0.58 0.45 0.08-1.51 0.179 <0.04-0.71 0.05 Ta 0.0873 0.0134-0.394 0.045 0.004-0.48 0.02 0.004-0.13 0.04 0.01-0.13 0.043 <0.003-0.180 0.005 W 0.464 0.115- 1.27 0.34 0.06- 1.27 0.17 0.02-0.62 0.14 0.05-0.40 1.71 <0.6-6.4 - Au 0.451 0.00331-0.135 0.011 0.002 - 0.086 0.005 0.0004-0.02 0.02 0.005-0.067 0.0002 <0.0001-0.010 - Pb* 32 40- 17000 8.18 2.30-24 32 5.63-411 3 Tit 1.15 0.219-4.70 0.36 0.054-1.72 0.13 0.05-0.45 0.47 0.095-1.46 0.267 0.04-1.10 0.08 U 0.257 0.0419-0.737 0.15 0.057 - 0.73 0.10 0.02-0.99 0.18 0.052-0.59 0.143 <0.03-0.51 0.05 *- results obtained by AAS 196 BIOMONITORING AIR POLLUTION IN CHELYABINSK REGION (URAL MOUNTAINS, RUSSIA) THROUGH TRACE-ELEMENTS AND RADIONUCLIDES: TEMPORAL AND SPATIAL TRENDS V.D. CHERCHINTSEV, M.V. FRONTASYEVA*, S.M. LYAPUNOV*, L.I. SMIRNOV* Magnitogorsk State Academy of Mining and Metallurgy, Lenin Prospect, 38, 455000 Magnitogorsk, Russia, *Joint Institute for Nuclear Research, 141980 Dubna, Moscow Region, Russia, ** Geological Institute of RAS, Pyizhevskij per., 7, 109017 Moscow, Russia Abstract: XA0102868 This report contains the results on the analysis of the moss species Hylocomium splendens and Pleurozium schreberi -which were used to study heavy metal atmospheric deposition, as -well as other toxic elements, in the Chelyabinsk Region (the South Ural Mountains) characterized by intense anthropogenic impact from various industries including plutonium production - the source of radionuclides of great potential hazard. A two years summer field work followed by the applying two most appropriate analytical techniques to the analyses of the collected moss•— NAA and AAS— allowed us to determine the atmospheric deposition of about 40 elements over the examined areas. One on them is considered to be the most polluted place in the world, the copper mining and reprocessing centre of the Russian Federation in Karabash, and the other adjacent area is of no less ecological stress, the area to the north of the Mayak complex for plutonium production, next to the city of Ozersk. The element concentrations in moss samples from the Urals are compared with those available for the Copper Basin in Poland, Tula Region (Russia), Germany and Norway, obtained by the same moss biomonitoring technique. Information on radionuclides in soils collected during the same fieldwork in the northern part of the Chelyabinsk region in July, 1998 is given. Plans for moss and soil -survey-2000 are reported. 1. SCIENTIFIC BACKGROUND AND SCOPE OF THE PROJECT The South Ural Mountains are among of the most polluted areas in the world where human impact in the environment is practically irreversible. These areas include several industrial cities such as Karabash, Chelyabinsk, Ozersk and many others, where the clustering of heavy industry has produced extremely high levels of air and water pollution. In addition, substantial emissions of radioactive substances have occurred in the Ural region as the result of full-scale activities of the radiochemical "Mayak" Production Association (PA), from liquid radioactive waste disposal into the river Techa and lake Karachay, the Kyshtym accident in 1957, and the Karachay wind dispersion in 1967. The latter is reviewed in a report "Behind the Nuclear Curtain. Radioactive Waste Management in this Former Soviet Union" of Battelle Memorial Institute, USA, 1997 [1]. Thus two the most important groups of pollutants emitted into the atmosphere in this region are heavy metals and long-lived radionuclides. Studies of atmospheric emissions of heavy metals from metal mining and processing and other industries have so far been limited to pollution within and in the near vicinity of the factories. The impact of these emissions on a regional scale is not known, as well as their influence on the natural environment and their possible impact on human health. Monitoring efforts of the present study will help to elucidate these problems. 197 This project originated at a NATO Advanced Research Workshop "Air Pollution in the Ural Mountains" (May 25-29, 1997, Magnitogorsk, Russia) organized by Harvard University (USA) and Magnitogorsk Academy of Mining and Metallurgy. It was hosted by the Chief Scientific Investigator, Prof. V.D. Cherchintsev, a Co-Chairman of the ARW. The present CRP's expert, Prof. E. Steinnes (Norway), who participated in that Workshop, suggested a Prospectus "Monitoring of Heavy Metals and Radionuclides in the Ural Region: Temporal and Spatial Trends". It was emphasized, discussed and adopted at the Round Table by a distinguished group of the international experts from 16 countries. During that Workshop Dr. M.V. Frontasyeva initiated fieldwork that has been done on a small geographical scale, in the vicinity of Lake Bannoe, situated 30 km from the Joint-Stock Company «Magnitogorsk Iron and Steel Works», South Ural Mountains. The results of this pilot study has been published as a reprint of JINR, Dubna [2] (in English) and in the collection of papers "Ecology of Industrial Regions at the Boarder of the XXI Century", Ural, 1999 [3] (in Russian). It was also accepted by the Journal of Radioanalytical and Nuclear Chemistry. 2. METHODS A combination of epithermal neutron activation analysis (ENAA) and atomic absorption spectrometry (AAS) provides data for concentration of about 45 chemical elements (Al, As, Ba, Br, Ca, Cd, Ce, Cl, Co, Cr, Cs, Cu, Dy, Eu, Fe, Hf, I, In, La, Lu, Mg, Mn, Na, Nd, Ni, Pb, Rb, Sb, Sc, Se, Sm, Ta, Th, V, W, Yb, Zn). Not all of the above trace elements are strictly relevant as air pollutants, but they come additionally from the multi-element analyses with insignificant extra cost, and most of them can be used as air mass tracers. GREENVEIW with raster and vector graphics will be used to generate colored raster- based pollution contour maps of the chemical elements and radionuclides of interest for the entire studied area. The GIS integrated system analysis of different level information will be provided with the interfaces for all international standard GIS systems: ARC-Info, MAP-Info, GIS-INTEGRO, etc. 2.1. Sampling Sampling sites are shown in maps in Annex 1. Sampling strategy and procedure followed those adopted by the Atmospheric Heavy Metal Deposition in Europe Project [4]. All sampling sites are placed at least 300 m away from main roads, villages and at least 100 m away from smaller roads and houses. The sampling was carried out according to the standard procedure described in detail in [5]. For each sampling site around 10 subsamples were taken within 50x50 sq. m area and combined to one collective sample. The unwashed samples were air-dried at 30 °C and extraneous plant material was removed. The three fully developed segments of each Hylocomium plant or green part of Pleurosium were taken for analysis. No further homogenization of samples was performed. Disposable polyethylene gloves were used during all handling of samples. 2.2. Analysis Moss samples of about 0.3 g were heat-sealed in polyethylene foil bags for short-term irradiation and packed in aluminum cups for long-term irradiation. Neutron flux density characteristics and the temperature in the channels equipped with a pneumatic system are given in Table I. 198 TABLE I. CHARACTERISTICS OF THE IRRADIATION CHANNELS [6] 12 u Temp Oth 10" 3>epi 10 Ofast 10 Chi Cdcoat 0.023 3.31 4.32 0.88 70 Ch2 1.23 2.96 4.10 0.92 60 Long-lived isotopes of Sc, Cr, Fe, Co, Ni, Zn, As, Se, Br, Rb, Ag, Sb, Cs, Ba, La, Ce, Sm, Tb, Yb, Hf, Ta, W, Au, Th and U, were determined using channel 1 (Chi). Samples were irradiated for 4 d, repacked and then measured twice after 4-5 d of decay, Respectively. Measuring time varied from 1 to 5 h. To determine the short-lived isotopes Na, Mg, Al, Cl, V, Mn, Cu, Al, In, and I, channel 2 (Ch2) was used. Samples were irradiated for 5 min and measured twice after 3-5 min and 20 min of decay for 5-8 and 20 min, respectively. To determine Cu, K, Na the same samples were irradiated for an additional 30 min, and after 12- 15 h of decay measured for 30 min. Gamma spectra were measured using Ge(Li) detectors with a resolution of 2.5 keV for the 60Co 1332.5 keV line, with an efficiency of about 6% relative to a 3D3" Nal detector for the same line. Radionuclides and y-energies used are given in Table II. Table II. Radionuclides and energies of y-lines used for calculations Element Radionuclide Ey measured Element Radionuclide Ey measured used (keV) used (keV) Na ^Na 2753.6 Rb 0DRb 1876.6 Mg z'Mg 1014.4 Ag liumAg 657.7 Al Z8A1 1778.9 In llomIn 1097.1 Cl 38C1 2166.8 Sb lz"Sb 1691.0 K «K 1524.7 I 442.7 Ca "Ca 3084.4 Cs 13Ts 795.8 Sc 40Sc 889.2 Ba 1J1Ba 496.8 V 1434.1 La lwLa 1596.5 J1~v 1HO Cr Cr 310.1 Ce Ce 145.4 Mn JOMn 1810.7 Sm JMSm 103.2 Fe 3yFe 1291.6 Tb iOTb 879.4 Co ouCo 1332.4 Yb 1DyYb 198.0 Ni "8Co 810.8 Hf i01Hf 482.0 Cu °°Cu 1039.2 Ta lszTa 1221.4 Zn raZn 1116.0 W JO'W 685.7 As /oAs 559.1 Au 'y6Au 411.8 Se "Se 264.7 Th '"Pa 312.0 Br 8ZBr 776.5 U "jyNp 228.2 Data processing and element concentration determination was performed using software developed in FLNP JINR [7]. For long-term irradiation in Chi single comparators of Au (1 Hg) and Zr (10 ug) were used. For short-term irradiation in Ch2 a comparator of Au (10 ug) was used. Concentrations of elements yielding above mentioned isotopes were determined using certified reference materials: SDM (International Atomic Energy Agency, Vienna) and Nordic moss DK-1 [8]). Lead, cadmium and copper were determined by flame AAS at the Geological Institute of RAS, Moscow. 199 3. RESULTS The results obtained for the moss samples collected along Lake Bannoe are presented in Table in. A total of 38 elements were determined, including most of the heavy metals. The data for the Ural moss samples were compared with those obtained by one of the authors of the present project, Dr. M.V. Frontasyeva, for Copper Basin (Poland) [9], Tula Region (Central Russia) [10], around iron smelter in northern Norway [11], as well as for Germany [12] and typical background concentrations of the elements observed in Hylocomium splendens in areas little affected by air pollution from previous studies in Norway (background levels in moss) [13,14]. It appeared that the concentration of Sb has the highest ever published for mosses level in atmospheric deposition: 2,63 ppm, showing maximum value of 29 (!) ppm (near "town of steel" Magnitogarsk). High values for antimony clearly indicate strong pollution with this element in the Chelyabinsk Region. In this connection it may be noted that in a study of trace elements in human cancer mammae carried out in Magnitogorsk [15], the reported tissue concentrations of Sb were about 5 times higher than the normal level. Patients suffering from cancer had significantly higher level of antimony than healthy persons. Our data for copper allowed us to examine the gradient of its concentration from the pollution source. It is clearly seen from Fig.l that pollution pattern is quite local, and covers area of 40 km in radius (if to consider background value to be equal to 10 ppm, as recommended in [12]). 0 20 40 60 80 100 120 km Fig.l. Karabash, Chelyabinsk Region, South Ural Mountains In order to better distinguish between contribution from air pollution and a crustal component from windblown soil particles enrichment factors (EF = (X/Sc)moSs/(X/Sc)crUst) were calculated and plotted in Fig. 2. Typical crustal components, such as Al, REE, Th, etc. show EF values near unity, whereas values appreciably above that level indicate that the element in question is either enriched in the moss by active biological processes (K, Ca) or stems from atmospheric deposition. 200 1000 o # An # CO 100 r HI i- .... ID 1 J_ 0) $^ o 10 % 1 CO i>- : ^fa:::: -JJ-- i—< • • "IT (F u_ - —SE: it1 : m 3SEEB -1— ' =«:"" Hna In 0.1 II i i . ——L-.. 0.0001 0.001 0.01 0.1 10 100 1000 10000 (X/Sc)crust Fig. 2. Enrichment factors for elements It may be noted that V and Fe, in spite of there high concentrations in the moss, are enriched only a factor 2-3 over the expected crustal contribution, whereas other heavy metals such as Cr, Zn, As, Se, Ag, Cd, Sb, and Au enriched 10 times or more, clearly indicating that these elements represent a regional problem. High correlation coefficient for such elements as Cu and Ag, Fe and Cr reflect the type industry responsible for contamination of the environment in the areas where moss samples were collected (Fig.3). Examples of GIS maps are given in Figs.4 and 5. 7000 6000 5000 HI&MEM- •I O 4000 S ft LL 3000 2000 Fi:=0.78 1000 0 50 100 150 200 Cr/Sc 201 TABLE EL Element concentration (ppm) in moss from Ural and in some other relevant areas used for comparison Element Ural (Russia) Copper Basin Poland [9] Tula (Russia) [10] Mo (Norway) [11] Germany [12] Norway [13,14] background levels Mean Range Mean Range Mean Range Mean Range Mean Range Mean Na 394 174-1051 152 74-302 403 147-882 294 93-635 200 Mg 5003 1353- 15400 1694 800-6480 2878 1160-4982 1861 556-4230 1200 Al 2819 810-7000 815 237-2590 2486 402-6015 1244 243-3100 350 Cl 314 44-1114 226 113-537 859 232-2521 294 50-1110 200 K 6842 2642 - 13260 5005 515-8708 19628 8910-42230 3845 1930-7160 3000 Ca 5093 2030 - 13800 2229 1190-12800 7260 3290-12380 2871 1450-6740 1500 Sc 0.60 0.10-1.45 0.15 0.03-0.63 0.39 0.09-1.19 0.41 0.06-1.41 0.06 V 8.50 2.0-22.4 2.60 1.14-8.13 11.2 1.38-62 5.72 1.05-31.0 3.11 1.7-17.0 2 Cr 18.6 2.2- 194 1.43 0.80-3.16 2.95 0.88-9.6 11.7 0.5-50 2.11 0.97-7.98 1.5 Mn 344 88-1402 287 65-847 391 100-817 384 89-1460 200 Fe 1888 335 - 7438 520 219-1405 3030 471-19670 12280 700-72100 720 386-6830 400 Co 0.64 0.14-1.95 0.32 0.11-1.96 0.13 0.05-0.29 0.61 0.06-2.2 0.3 Ni 8.4 0.96-94 2.49 0.21-38 0.53 0.21-1.21 1.69 <0.5-6.96 2.61 1.03-6.29 1.6 Cu* 34 5-200 73 3.11-2040 Zn 72 14.8-304 41 21-83 69 27.15-105 99 31-397 55 33-463 36 As 2.17 0.63 - 9.7 0.73 0.12-6.04 0.51 0.11-1.47 0.62 0.06-2.20 0.39 0.3 Se 0.34 0.02-1.1 0.32 0.10-0.77 0.13 0.05-0.20 0.47 0.21-1.17 0.25 Br 6.20 1.52-25 1.38 0.89-2.85 4.05 1.44-12.7 6.94 3.6-12.2 5 Rb 10.3 2.8-39 21 1.95-45.51 19 6.2-32 17.2 6.7-46.2 10 Sr 18 1.96-65 12.4 0.69-339 Mo 0.29 0.041-0.71 0.29 0.05-2.42 Ag 0.124 0.011-0.47 0.12 0.02-1.74 0.06 0.02-0.15 0.059 <0.03-0.16 0.04 Cd* 0.63 0.16-2.86 0.30 0.03-1.07 SI. 2.63 0.08-29 0.26 0.12-0.79 0.13 0.05-0.7 0.25 <0.05-0.76 0.09 I 1.35 0.51-3.41 1.14 0.35-2.68 1.58 0.51-4.3 2.26 <1.0-4.3 2 Cs 0.22 0.04-0.61 0.43 0.08-1.29 0.20 0.06-0.48 0.37 <0.05-1.03 0.18 Ba 44 6.3- 125 13.6 5.47-79 65 10-145 33.1 12.0-83.0 24 La 2.43 0.47- 13 0.52 0.14-1.61 2.40 0.42-6,75 0.69 <0.10-2.87 0.3 Ce 3.24 0.53-11.7 1.27 0.24-3.74 3.45 0.64-10.9 - Sm 0.29 0.07-1.05 0.13 0.06-0.63 0.40 0.08-1.05 0.33 0.05-1.34 0.06 Tb 0.035 0.004-0.17 0.01 0.003-0.09 0.04 0.008-0.126 0.019 <0.005-0.067 0.015 Yb 0.107 0.005-0.55 0.04 0.01-0.18 0.13 0.028-0.38 0.069 <0.010-0.230 0.03 Hf 0.276 0.023- 1.78 0.13 0.01-0.58 0.45 0.08-1.51 0.179 <0.04-0.71 0.05 Ta 0.045 0.004 - 0.48 0.02 0.004-0.13 0.04 0.01-0.13 0.043 <0.003-0.180 0.005 W 0.34 0.06-1.27 0.17 0.02-0.62 0.14 0.05-0.40 1.71 <0.6-6.4 - Au 0.011 0.002 - 0.086 0.005 0.0004-0.02 0.02 0.005-0.067 0.0002 <0.0001-0.010 - Pb* 8.18 2.30-24 32 5.63-411 14.6 8-269 3 Th 0.36 0.054-1.72 0.13 0.05-0.45 0.47 0.095-1.46 0.267 0.04-1.10 0.08 V 0.15 0.057-0.73 0.10 0.02-0.99 0.18 0.052-0.59 0.143 <0.03-0.51 0.05 *- results obtained by AAS 202 u < 0.9 i [J 0.9-1.2 i 1.2 - 1.5 2 - 2.5 F] 2.5-3 izhno-Uralsk I > 10 dneu ra Isk < O.9 U 0.9 - 1.2 1.2 - 1.5 1 n i-5-; 2 -2.5 4 2.5 - 3 3-5 5-10 > 10 .as. FIG. 4. DEPOSITION PATTERNS OF U AND TH (Relative units, normalized to the Norwegian background) 203 r < 0.9 0.9- 1.2 1.2-1.5 • 1.5-2 2-2.5 n 2.5 - 3 3-5 5-10 iJuzhno-Uralsk =» 10 Jfcl -L :S redneura lsk < 0.9 iTERINB O.9 - 1.2 ralska 1.2-1.5 1.5-2 n2-5- 3-5 5-10 > 10 FIG. 5. DEPOSITION PATTERNS OF CS AND CU (Relative units, normalized to the Norwegian background) 204 4. PLANS FOR FUTURE WORK The detailed working plan is as follows: Months 1-3 • Sample preparation for short- and long-term irradiation at IBR-2 reactor, Dubna • Multi-elemental analysis of moss and soil samples collected during summer, 1999 by epithermal NAA in Dubna and by AAS in Moscow • Measurements of radionuclides in soil samples at the Institute of Biophysics («Mayak»), and at JINR, Dubna Months 4-6 • Data processing of the results obtained for moss and soil samples by epithermal NAA in Dubna and AAS in Moscow. • Developments of GIS technology for the examined territory of the Chelyabinsk Region at the University of «Dubna» Months 7-8 • Field work: sampling of mosses and soils in the Central part of the Chelyabinsk Region (to the South of enterprise "Mayak"), according to a detailed plan approved by the expert consultant of the Project, Prof. E. Steinnes (Norway). A sampling grid of 30 x 30 km will be applied, giving a total of about 50 sampling sites Months 9-12 • Statistical analysis of the results obtained for the samples collected during summer, 1997- 1999, including principal component analysis. • Preparation of maps of heavy metal distributions in Chelyabinsk Region on the basis of the developed GIS. • Preparation of interim (third year) report. Since the same moss species may not be found at all sites, extension of the sampling to include additional moss species or lichens as well as some species intercalibration work will be necessary. We expect that some peat cores will be analyzed during autumn 2000. To succeed with fieldwork in summer 2000, we requested UDS 8,000 from the IAEA having in mind difficult conditions of sampling in the mountains lacking transport roads, distant location of the areas of investigation from the central Russia where the project analytical studies are carried out. The most important results expected after completion of the project are as follows: • establishment of a regional sampling network for future monitoring programmes; • identification of areas with high contamination levels not previously considered to be of environmental risk; • creation of a database for continued studies at regular intervals; • comparison of environmental contamination levels in the Ural region with other strongly polluted areas in Europe, such as the "Black Triangle'7, Copper Basin in Poland and the Romanian Carpathians. Altogether, the results from the project will form an excellent basis for local authorities to implement the necessary measures to reduce emissions to environmentally acceptable levels. Moreover, the responsible public health authorities will have a significantly improved basis for assessing possible risk to the population from previous and current emissions. 205 REFERENCES [I] BRADLEY, Don J., Behind the Nuclear Curtain. Radioactive Waste Management in the Former Soviet Union. Edited by David R. Payson, Battelle Press, Columbus Richland, 1977, p. 371-450. [2] FRONTASYEVA, M.F., STEINNES, E., LYAPUNOV, S.V., CHERCHINTSEV, V.D., SMIRNOV. L.I., Biomonitoring of Heavy Metal Deposition in the South Ural Region: Some Preliminary Results obtained by Nuclear and Related TechniqueSyPreprint of JINR, El4-99-257, Dubna (in English); [3] FRONTASYEVA, M.F., STEINNES, E., LYAPUNOV, S.V., CHERCHINTSEV, V.D., SMIRNOV, L.I., Biomonitoring of Heavy Metal Deposition in the South Ural Region:"Ecology of Industrial Regions at the Boarder of the XXI Century", Ural, 1999, p. 7-13. (in Russian). [4] RUEHLING, A., Survey of Atmospheric Heavy Metal Deposition in Europe: Organization of the Project. In the Book of Abstracts Workshop Monitoring of Natural and Man-Made Radionuclides and Heavy Metal Waste in Environment. E-14-99-290, Dubna, 1999, p. 20-21. [5] RUEHLING, A. and TYLER, G., Sorption and Retention of Heavy Metals in the Woodland Moss Hylocomium splendens, Oikos. 21 (1970) 92-97. [6] PERESEDOV, V.F., ROGOV, A.D., Simulation and Analysis of Neutron Energy Spectra from Irradiation Channels of the Reactor IBR-2. JINR Rapid Communications. No. l[75]-96 (1996) 69-74 (in Russian). [7] OSTROVNAYA, T.M., NEFEDYEVA, L.S., NAZAROV, V.M., BORZAKOV, SB., STRELKOVA, L.P. "Software for INAA on the Basis of Relative and Absolute Methods Using Nuclear Data Base". Activation Analysis in Environment Protection, D-14-93-325, Dubna, 1993, p. 319-326. [8] FRONTASYEVA, M.V., NAZAROV, V.M., and STEINNES, E., Moss as Monitor of Heavy Metal Deposition: Comparison of Different Multi-Element Analytical Techniques, J.RadioanalNucl.Chem. 181 (1994) 363-371. [9] FRONTASYEVA M.V., GRODZINSKA K., STEINNES E.. Atmospheric Deposition of Heavy Metals in Two of The Most Polluted Areas in The World: The Copper Basin in Poland and the South Ural Mountains in Russia. In Proceedings, Int. Conf. Modern Trends in Activation Analysis, MTAA-10, 19-23 April, 1999, Bethesda, Maryland, USA, p. 117. [10] YERMAKOVA, Ye. V., FRONTASYEVA M.V., STEINNES E., Reliability of Mosses {Hylocomium Splendens, Pleurozium Schreberi and Calliergon Geganteum) as Biomonitors of Heavy Metal Atmospheric Deposition in Central Russia. IV Conference of Young Scientists and Specialists of JINR, (31 January - 4 February, 2000, Dubna, Russia) [II] FRONTASYEVA M.V., STEINNES E., Epithermal Neutron Activation Analysis of Mosses Used to Monitor Heavy Metal Deposition Around an Iron Smelter Complex, The Analyst, May 1995, Vol.120, p. 1437-1440. [12] RUEHLING, A., Atmospheric Heavy Metal Deposition in Europe - Estimation Based on Moss Analysis, Nordic Council of Ministers, Nord 1994, 9. [13] STEINNES, E., RAMBAEK, J.P., HASSEN, J.E., Large Scale Multi-Element Survey of Atmospheric Deposition Using Naturally Growing Moss as Biomonitor, Chemosphere, 25, 1992, p.735-752. [14] BERG, T, ROYSET, O., STEINNES, E.,. VADSET, M., Atmospheric Trace Element Deposition: Principal Component Analysis of ICP-MS Data from Moss Samples, Environ. Pollut.,%% 1995, p. 67-77. [15] KOSHKINA, V.S., KOTLYAR, N.N., A Cancer Mammae Under Technogenic Impact on Population of the Town of Magnitogorsk, Environment and Health, Magnitogorsk, 1998, p.33- 39. 206 PART IV: APPENDICES APPENDIX 1: AGENDA OF THE MEETING SECOND RESEARCH CO-ORDINATION MEETING (RCM) FOR THE CO-ORDINATED RESEARCH PROJECT (CRP) ON VALIDATION AND APPLICATION OF PLANTS AS BIOMONITORS OF TRACE ELEMENT ATMOSPHERIC POLLUTION, ANALYSED BY NUCLEAR AND RELATED TECHNIQUES MONDAY, 20 MARCH 2000 9:00- 9:10 Registration 9:10— 9:40 Opening of the meeting Representative of the Division of Human Health (NAHU) Representative of the Nutritional and Health-Related Environmental Studies Section (NAHRES) Election of the rapporteur Adoption of the agenda Administrative arrangements for the meeting Status report on the CRP 9:40 - 10:00 Coffee break 10:00 - 12:30 SESSION 1: PROJECT REPORTS Chair: M.C. Freitas Argentina (M L. Pignata): Air pollution biomonitoring in Argentina, application of neutron activation analysis to the study of biomonitors. Brazil (M Saikf): Determination of trace elements in lichen samples by instrumental neutron activation analysis. Chile (£. Cortes): Study of air pollution in Chile using biomonitors. 12:30 -14:00 Luncheon 14:00 - 15:40 SESSION 2: PROJECT REPORTS (continuation) Chair: J. Garty China (B. Ni): Study on air pollution around China's largest oil refinery complex using multielements in biomonitors through NAA. Germany (B. Markert): Biomonitoring of air pollution through trace element analysis 15:40-16:30 Coffee break 16:30 - 17:20 SESSION 3: PROJECT REPORTS (continuation) Chair: A. Lucaciu Ghana (S. Akoto Bamford): Biomonitoring of air pollution through trace element analysis. 17:30 RECEPTION 209 TUESDAY, 21. MARCH 2000 9.00 - 10:40 SESSION 4: PROJECT REPORTS (continuation) Chair: M. Saiki India (J. Arunachalam): Studies using nuclear and complementary non-nuclear analytical techniques for biomonitoring of air pollution. Israel (J. Garty): Air biomonitoring by transplanted lichens in the Negev desert, Israel. 10:40 -11:00 Coffee break 11:00-12:40 SESSION 5: PROJECT REPORTS (continuation) Chair: B. Ni Jamaica (M Voutchkov): Biomonitoring of air pollution in Jamaica through trace-element analysis of epiphytic bromeliads using nuclear and related analytical techniques. The Netherlands (H.Th. Wolterbeek): Study of local variance in biomonitoring. 12:40-13:40 Luncheon 13:40-16:10 SESSION 6: PROJECT REPORTS (continuation) Chair: J. Arunachalam Norway (E. Steinnes): Further promotion of the use of mosses and lichens for studies at atmospheric deposition of trace elements. Portugal (M.C. Freitas): Study of atmospheric dispersion of pollutants in the industrial region of the Sado estuary using biomonitors. Romania (A. Lucaciu): Atmospheric deposition of heavy metals in rural and urban areas of Romania studied by the moss biomonitoring technique employing nuclear and related analytical techniques and GIS technology. 16:10 - 16:40 Coffee break 16:40 - 17:30 SESSION 7: PROJECT REPORTS (continuation) Chair: S. Akoto Bamford Russian Federation (M Frontasyeva): Biomonitoring air pollution in Chelyabinsk region (Ural Mountains, Russia) through trace-elements; temporal and spatial trends. WEDNESDAY, 22 MARCH 2000 8:30- 12:30 SESSION 8: GENERAL DISCUSSION (see separate list of discussion topics): • Summaries (highlights) of individual projects • Updating specific research objective of CRP • Assessing the outputs Chair: ML. Pignata 12:30 -13:30 Luncheon 210 13:30-17:30 SESSION 9: GENERAL DISCUSSION (continuation) • Assessing activities • Overall assessment of progress towards achieving objective • Adjustment to proposed work plan until next RCM • Technical aspects Chair: E. Cortes THURSDAY, 23 MARCH 2000 8:30-12:30 SESSION 10: GENERAL DISCUSSION (continuation) • Technical aspects (continuation) • Technical aspects (continuation) Chair: B. Markert 12:30 - 13:30 Luncheon 13:30-17:30 SESSION 11: GENERAL DISCUSSION (continuation) • Main scientific/technical conclusions from the meeting • Organisational aspects • Formulation and drafting of conclusions and recommendations • Drafting of the Meeting Report Chair: H.Th. Wolterbeek FRIDAY, 24 MARCH 2000 9:00-12:30 SESSION 12: GENERAL DISCUSSION (continuation) • Drafting of the Meeting Report (continuation) • Drafting of the Meeting Report (continuation) Chair: E. Steinnes 12:30 - 13:30 Luncheon 13:30-15:30 SESSION 14: CONCLUDING SESSION • Discussion and approval of the Meeting Report Chair: M. Frontasyeva 15:30 Open end, personal discussions 211 APPENDIX 2: LIST OF DISCUSSION TOPICS FIRST RESEARCH CO-ORDINATION MEETING (RCM) FOR THE C0- ORDINA TED RESEARCH PROJECT (CRP) ON VALIDA TION AND APPLICATION OF PLANTS AS BIOMONITORS OF TRACE ELEMENT ATMOSPHERIC POLLUTION, ANALYSED BY NUCLEAR AND RELATED TECHNIQUES LIST OF DISCUSSION TOPICS STATUS OF THE CRP 1. Summaries (highlights) of individual projects 1.1. Scientific background 1.2. Scientific objective 1.3. Outputs since the last RCM 1.4. Programme of work for 2000 1.5. Expected output/results for 2000 1.6. Approach for 2001 1.7. Programme of work for 2001 2. Updating specific research objective of CRP 3. Assessing the outputs 3.1. Creation of a table with expected outputs and present status for each participant 3.2. Creation of a table with expected outputs and present status for the CRP 3.3. Creation of a list of scientific publications 4. Assessing the activities 4.1. Creation of a table with expected activities and present status for each participant 4.2. Creation of a table with expected activities and present status for the CRP 5. Overall assessment of progress towards achieving objective 6. Adjustment to proposed work plan until next RCM TECHNICAL ASPECTS 7.1. Selection of sampling sites and types of samples to be collected 7.2. Sampling techniques and equipment 7.3. Analysis 7.4. Data processing and interpretation 7.5. Quality assurance 8. Main scientific/technical conclusions from the Meeting 9. Formulation and drafting of conclusions and recommendations ORGANIZATIONAL ASPECTS Next RCM 213 APPENDIX 3: LIST OF PARTICIPANTS LIST OF PARTICIPANTS Dr. Maria Luisa Pignata Departamento de Química Facultad de Ciencias Exactas, Físicas y Naturales Universidad Nacional de Córdoba Avda. Vélez Sársfíeld 299, 5000 Córdoba ARGENTINA Tel: 0054-351-4332114 Fax: 0054-351-4332097 E-mail: [email protected] pignata@com .uncor. edu Dr. Mitiko Saiki Instituto de Pesquisas Energéticas e Nucleares Commisâo Nacional de Energía Nuclear Departamento de Applicaciones de Técnicas Nucleares Rua do Matao Travessa R400 Caixa Postal, 11049 CEP: 05442-970 Sao Paulo/SP BRAZIL Tel: 0055-11-816-9288 or 816-9180 Fax: 0055-11-8169188 E-mail: [email protected] Dr. Eduardo Cortés Toro Comisión Chilena de Energía Nuclear P.O.Box 118-D Santiago CHILE Tel: 0056-2-3646280 Fax: 0056-2-3646277 E-mail: [email protected] Dr. Ni Bangfa China Institute of Atomic Energy Department of Nuclear Physics P.O. Box 275 (50) Beijing 102413 CHINA Tel: 0086-10-69357308 Fax: 0086-10-69357008 E-mail: [email protected] 215 Dr. Bernd Albert Markert International Graduate School Zittau Dept. of High Environmental Technology Markt 23 02763 Zittau GERMANY Tel: 0049-358-377150 Fax: 0049-358-3771534 E-mail: [email protected] Dr. Akoto Bamford Ghana Atomic Energy Commission National Nuclear Research Institute Physics Department P.O. Box 80 Legon - Accra GHANA Tel: 0023-321-400303 Fax: 0023-321-400807 E-mail: [email protected] Dr. Jayaraman Arunachalam National Centre for Compositional Characterization of Materials Board of Radiation and Isotope Technology Department of Atomic Energy ECEL Post, Hyderabad 500062 INDIA Tel: 0091-407-125463 Fax: 0091-407-125463 or 0091225562161 E-mail: [email protected] Dr. Jacob Garty Tel Aviv University George S. Wise Faculty of Life Sciences Department of Plant Sciences 69978 Tel Aviv ISRAEL Tel: 00972-3-6409376,6409841 Fax: 00972-3-6409380 E-mail: [email protected] Dr. Mitko K. Voutchkov International Centre for Environmental and Nuclear Sciences University of the West Indies Mona Campus, Kingston 7, JAMAICA, W.I. Tel: 001-876-9271777 Fax: 001-876-9770768 E-mail: [email protected] 216 Dr. H.Th. Wolterbeek Interfaculty Reactor Institute Delft University of Technology Mekelweg 15 2629 JB Delft THE NETHERLANDS Tel: 0031-15-278-7053 or 6171 Fax: 0031-15-2783906 E-mail: [email protected] Dr. Eiliv Steinnes Norwegian University of Science and Technology Department of Chemistry N-7034 Trondheim NORWAY Tel: 0047-73596237 Fax: 0047-73550877 E-mail: [email protected] Dr. Maria do Carmo Moreira Freitas Institute Tecnologico e Nuclear Departamento de Quimica Estrada Nacional 10 2685 Sacavem PORTUGAL Tel: 00351-19550021 Fax: 00351-19550117 or 0035119941455 E-mail: [email protected] Dr. Adriana Lucaciu Institute of Physics and Nuclear Engineering "Horia Hulubei" Department 360 P.O. Box MG-6 Magurele, Bucharest ROMANIA Tel: 0040-1-7807040 int. 4614 or 4600 or 40017807040 Fax: 0040-1-7896295 E-mail: [email protected] Dr. Marina V. Frontasyeva Dept. of Activation Analysis & Radiation Research Frank Laboratory of Neutron Physics Joint Institute for Nuclear Research 141980 Dubna, Moscow Region RUSSIAN FEDERATION Tel: 007-(09621)-65609, 63653 or 63676 Fax: 007-(09621)-65085 E-mail: [email protected] 217 Observer: Dr. Rita R. Pla Comision Nacional de Energia Atomica Unidad Radioquimica y Quimica de las Radiaciones Av. Libertador 8250 1429 Buenos Aires Argentina Tel: 0054-1-147041201 or 00541143798572 Fax: 0054-1-3798554 E-mail: [email protected] Scientific Secretary: Dr. Borut Smodis Section of Nutritional and Health-Related Environmental Studies Division of Human Health IAEA P.O. Box 100 A-1400 Vienna AUSTRIA Tel: 0043-1-2600-21652 Fax: 0043-1-26007-21652 or 0043-1-26007 E-mail: [email protected] 218