University of Zimbabwe Faculty of Science
Atmospheric aerosol particles and transport: a climatological perspective for Zimbabwe
by Daniel NYANGANYURA
The thesis is submitted in partial fulfilment of the requirements for the Doctor of Philosophy Degree
2006
To my wife Maybe and to our two boys Pedro and Danilo who always give me the will to go on, and to my parents who although they lived in a different epoch, have contributed to what I became. Abstract Atmospheric aerosol particles were collected in the coarse (2-10 µm diameter) and fine (< 2 µm diameter) size fractions at Rukomechi research station, (16.1 °S, 29.4 °E, 500 m a.s.l.), northern Zimbabwe from September 1994 to January 2000. The collected samples were analysed for particulate mass (PM), black carbon and 47 elements at the Gent Institute for Nuclear Sciences. The main sources of coarse and fine aerosols were found to be crustal matter, sea salt and biomass burning for both wet and dry seasons. However, additional sources for copper and biogenic aerosols were detected during the wet and dry seasons, respectively, while anthropogenic influences were only found in the fine aerosols. The Absolute Principal Component Analysis (APCA) attributed 29% of the total wet season coarse particulate mass to the mixed biogenic/biomass burning component, while the major contribution (32%) was attributed to biomass burning during the dry season. The biomass burning component also provided the major contribution to the total fine particulate mass, accounting for 44% and 79% in the wet and dry seasons, respectively. The climatology of air mass transport to northern Zimbabwe was examined through an analysis of 5-day kinematic back trajectories arriving daily at Rukomechi at ~ 850 hPa for the period of 1994 to 1999. Classification of trajectories into different flow regimes was done using a non-hierarchical cluster algorithm. The dominant transport features include the eastern corridor, the south-east corridor, southern and north north-westerly flows, as well as regionally re-circulated air. The eastern corridor is comprised of fast and slow air masses that contribute 35% of the total number of trajectories. The air masses in these corridors bring late dry season to wet season air masses driven by an anticyclone that wraps around the subcontinent and stretches into the Mozambique Channel. The south-east corridor is also composed of fast and slow winds that contribute late wet season to dry season air masses and account for 44% of the total number of trajectories. These winds are associated with the Atlantic Ocean anticyclone and the Indian Ocean tropical depression. The fast dry season southern flow contributes 8% of the total number of trajectories and is driven by a continental anticyclone over South Africa coupled to an Atlantic Ocean anticyclone. The north north-westerly flow contributes least to the total number of trajectories (6%) and is associated with Inter- Tropical Convergence Zone while the regionally re-circulated air, as a result of differential heating at the surface, is the slowest. Of the seven air flows, only four were found carrying air significantly loaded with aerosols to northern Zimbabwe. These are namely: the fast easterly that carries aerosols mainly from biomass burning related sources; the slow south easterly that is associated with aerosols from various sources; the southerly containing mineral dust, sea salt and anthropogenic aerosols; and the regional flows that contain aerosols of anthropogenic origin. The potential sources of biomass burning aerosols were found to be due to fire activities from northern Zimbabwe and central Mozambique. The other pathways carry anthropogenic aerosols, especially copper (from areas around the Copper-belt regions), sulphur (from South African industrial areas), and lead (from the major Southern African road network).
i Acknowledgements I am grateful for the contributions of several institutions and individuals who made this PhD thesis possible. I am grateful to the University of Zimbabwe, Department of Physics, for giving me the opportunity to do my PhD study. I am gratefully indebted to the German Academic Exchange Service (DAAD) and the Max Planck Society (partially through the Max Planck International Research School of Atmospheric Chemistry and Physics) for the financial support provided during the course of this research and to the Belgian Federal Science Policy Office for the financial support for the aerosol chemical analysis. I regard myself very privileged to have the opportunity to carry out this research under the guidance of my supervisors Dr A. Makarau, Dr M. Mathuthu and Prof F. X. Meixner. I’m particularly indebted to Prof F. X. Meixner whose commitment to my work is unmatched, and I appreciate his guidance, wisdom, and attention to detail. Big thanks also go to the Max Planck Institute for Chemistry, Biogeochemistry department group members, my office colleague Dr Ivonne Trebs and particularly to Dr. Günter Helas, for their unwavering support and for providing me with an excellent research environment. I am also indebted to Prof Meinrat O. Andreae who supported my studies and also provided logistical support. My gratitude also goes to Prof. W. Maenhaut (Institute of Nuclear Sciences, University of Gent, Belgium) for his assistance with data analysis, his valuable advice and support. I am also grateful to Prof. H. Wernli (Institute of Atmospheric Sciences, University of Mainz, Germany) for his valuable help and advice, and the European Centre for Medium Range Weather Forecasting for providing data for plotting the surface pressure maps and Dr G. Kirkman whose assistance in developing the fire plot procedure was very valuable and Dr A Lupu for developing the potential source contribution function procedure that was used in this study. I also thank M.-T. Fernández-Jiménez, I. Rajta, S. Dubtsov, D.J.A. De Ridder and N. Raes from Ghent University for the chemical analyses of the samples. I am also grateful to P. Chimanga, M. Chiranda and J. Mlambo from the Tsetse Control Branch, Harare, Zimbabwe, for collecting the aerosol samples at the Rukomechi site. I also thank Dr. Steve Torr (Natural Resources Institute, Chatham/U.K.) for providing the 1992-1999 data set of the automated meteorological station at Rukomechi. Finally, I would like to express my heart-felt gratitude to my wife, Maybe and our three boys Pedro, Danilo and Daniel Junior (a family that made a difference) for tolerating years of my absence from the family.
ii
Table of contents
Abstract ...... i Acknowledgements...... ii List of tables ...... vi List of figures ...... vii
Chapter 1 Introduction...... 1 1.1 Background ...... 1 1.2 Motivation for the research...... 2 1.3 Objectives of the research ...... 3 1.4 Layout of the chapters...... 4
Chapter 2 Theory ...... 5 2.1 Aerosols...... 5 2.2 Sources of aerosols ...... 7 2.2.1 Mineral dust aerosols ...... 8 2.2.2 Biogenic aerosols ...... 8 2.2.3 Anthropogenic aerosols...... 9 2.2.4 Marine aerosols ...... 9 2.2.5 Biomass burning...... 10 2.3 Effects of aerosols...... 11 2.3.1 Direct and indirect effect on climate...... 11 2.3.2 Aerosol effects on human health and environment ...... 13 2.4 Aerosol sampling...... 13 2.5 Physical and elemental analysis of aerosols...... 16 2.5.1 Gravimetric technique...... 16 2.5.2 Light reflectance technique...... 16 2.5.3 Elemental analysis...... 17 2.5.3.1 Basis of the Proton Induced X-ray Emission (PIXE) analysis ...... 18 2.5.3.1.1 Detection limit ...... 22 2.5.3.1.2 The Advantages of Proton Induced X-ray Emission ...... 24 2.5.3.2 Instrumental Neutron Activation Analysis...... 25 2.5.3.2.1 The Neutron Activation Analysis Method ...... 25 2.5.3.2.2 Irradiation and counting procedures...... 27 2.5.3.2.3 Detection limit ...... 28 2.6 Statistical analysis ...... 28 2.6.1 Principal component analysis...... 29 2.6.2 Absolute principal component analysis ...... 33 2.7 Trajectory calculations and cluster analysis ...... 34 2.7.1 Trajectories...... 35 2.7.2 Trajectory calculation...... 35 2.7.3 Errors in trajectory calculation...... 37 2.7.4 Trajectory cluster analysis...... 39 2.7.5 The potential source contribution function technique...... 41
iii Chapter 3 Materials and methods ...... 44 3.1 Study area...... 44 3.2 Sampling equipment ...... 45 3.3 Determination of particulate mass and black carbon...... 47 3.4 Proton Induced X-ray emission analysis...... 48 3.5 Instrumental neutron activation analysis ...... 49 3.5.1 Irradiation and Counting for Short - Lived Isotopes ...... 50 3.5.2 Irradiations and Counting for Medium and Long - Lived Isotopes ...... 50 3.6 Principal component analysis and absolute principal component analysis...... 51 3.7 Trajectory calculation using HYSPLIT model ...... 53 3.8 Trajectory cluster analysis procedure...... 54 3.9 Speed along the median trajectory...... 55 3.10 Source regions of aerosols in the subcontinent...... 55 3.10.1 The fire plot technique ...... 55 3.10.2 The potential source contribution function technique...... 56
Chapter 4 Results and discussion ...... 58 4.1 Meteorological situation ...... 58 4.1.1 Wind variability...... 59 4.1.2 Relative humidity and rainfall...... 60 4.1.2.1 Variation in relative humidity...... 60 4.1.2.2 Rainfall around Rukomechi research station...... 64 4.1.3 The mixing layer over Rukomechi research station ...... 66 4.2 Elemental composition and sources of tropospheric aerosols from northern Zimbabwe: Results and discussion...... 74 4.2.1 Principal component analysis and source identification ...... 75 4.2.1.1 Coarse size fraction...... 75 4.2.1.2 Fine size fraction...... 79 4.2.1.3 Multiple sources for elements in the coarse and fine size fraction...... 82 4.2.2 Source apportionment ...... 83 4.2.3 Enrichment factors ...... 87 4.2.4 Descriptive Statistics...... 90 4.2.4.1 Means ...... 90 4.2.4.2 Medians and variability...... 92 4.2.4.3 Contribution of individual elements to the total particulate mass...... 95 4.2.5 Temporal variations...... 96 4.2.5.1 Seasonal variation and long terms trends ...... 97 4.2.5.2 Annual cycle of elemental concentrations...... 101 4.2.6 Aerosol data comparison...... 105 4.3 Trajectory climatology: Results and discussion...... 109 4.3.1 Optimization of number of clusters ...... 109 4.3.2 Results from cluster analysis...... 110 4.3.3 Spatial and dynamic patterns of the flow...... 116 4.3.4 Temporal distribution of trajectories ...... 121 4.3.5 Influence of the inter-tropical convergence zone ...... 124 4.3.6 Air mass flows and pressure systems over southern Africa...... 125 4.3.7 Comparison of regional air flow climatologies ...... 129
iv 4.4 Aerosol concentration over northern Zimbabwe: A climatological perspective...... 130 4.4.1 Combining elemental concentration data with meteorological information...... 131 4.4.2 Determination of source regions of aerosols measured at Rukomechi research station...... 134 4.4.2.1 Source regions of biomass burning related aerosols ...... 135 4.4.2.1.1 Source regions of biomass burning related aerosols resulting from fire plots and individual trajectories ...... 135 4.4.2.1.2 Source regions of biomass burning related aerosols resulting from the potential source contribution function method...... 147 4.4.2.1.3 Comparison of biomass burning regional sources obtained from the potential source contribution function and the fire plots...... 149 4.4.2.2 Regional source of anthropogenic aerosols...... 151 4.4.2.2.1 Source regions of copper containing aerosols...... 155 4.4.2.2.2 Source regions of fine S...... 160 4.4.2.2.3 Source region of fine Pb ...... 165
Chapter 5 Summary and conclusions...... 169 5.1 Summary of meteorological situation ...... 169 5.2 Summary of aerosol elemental composition and source types...... 170 5.3 Summary of trajectory climatology...... 172 5.4 Summary of climatological perspective of aerosol loadings over northern Zimbabwe ...... 174 5.5 Conclusions...... 177
References ...... 180
Appendix A Relative humidity correction procedure ...... 189 Appendix B Trajectory calculation...... 196 Appendix C Program to detect fires within the buffer zone...... 200 Appendix D Local meteorology around Rukomechi research station...... 203 D.1 Wind variability...... 203 D.1.1 Diurnal variations of the wind speed...... 203 D.1.2 Daytime wind direction vs. wind speed ...... 205 D.2 Solar radiation and temperature ...... 207
v List of tables
Table 4.1 The total annual rainfall and the number of rainy days for each of the seasons from weather stations around Rukomechi research station...... 66 Table 4.2 The wet and dry season, and overall Varimax rotated principal component analysis (PCA) matrices showing bivariate correlations between the elements and the components for the coarse aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000...... 76 Table 4.3 The overall, wet and dry season Varimax rotated principal component analysis (PCA) matrices showing bivariate correlations between the elements and the components for the fine aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000 ...... 80 Table 4.4 Dry season, wet season and overall mean elemental concentrations of particulate mass and selected elements together with their associated standard error in the mean...... 91 Table 4.5 Median concentrations of aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000 for the overall, dry and wet season (with range: maximum-minimum) for the coarse and fine size fractions...... 93 Table 4.6 Percentage contributions of individual elements to the total particulate mass for aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000 ...... 95 Table 4.7 The percentage contribution of each of the air corridors to the total flows to Rukomechi from January 1994 to January 2000...... 120 Table 4.8 The correlation between the monthly occurrences of flow from a certain cluster to the monthly median elemental concentrations ...... 132 Table A.1 The original P(0.983406) data points, regression data and maximum relative humidity values used in the correction of the upper limit of the relative humidity ...... 192 Table A.2 The original values of the relative humidity at P(0.0010275), regression data and observed minimum relative humidity values used for the correction of the lower limit of the recorded relative humidity data...... 193 Table D.1 Nine-year mean percentage contributions of each direction to the three wind speed bands ...... 206
vi List of figures
Figure 1.1 The location of Rukomechi research station in Mana Pools National Park (Zambezi valley) in relation to southern Africa...... 2 Figure 2.1 Physical processes leading to atmospheric particulate matter and the idealized scheme of the distribution of particle surface area of an atmospheric aerosol...... 6 Figure 2.2 Schematic diagram of Proton Induced X-ray Emissions Analysis...... 18 Figure 2.3 The Proton Induced X-ray Emission spectrum of an aerosol sample showing the characteristic X-ray lines and intensities from the trace elements in the aerosol particles superimposed on a the background radiation...... 19 Figure 2.4 Diagram illustrating the process of neutron capture by a target nucleus followed by the emission of γ-rays ...... 26 Figure 3.1 The Gent PM10 stacked filter unit sampler used to collect aerosol samples at Rukomechi research station (Maenhaut et al., 1994)...... 45 Figure 3.2 Schematic diagram showing the arrangement of the main components used for the Proton Induced X-ray Emission Analysis ...... 48 Figure 4.1 Flow direction of the daytime and night-time winds and the percentage contribution of each direction to the daytime and night-time wind flows ...... 59 Figure 4.2 Daily median relative humidity (corrected) at Rukomechi research station from 1992 to April 1999 ...... 61 Figure 4.3 Annual variations of the daily median and inter-quartile range of sensor corrected relative humidity...... 63 Figure 4.4 Variations of the monthly total rainfall for Zimbabwe meteorological service stations around Rukomechi research station...... 64 Figure 4.5 Vertical profiles of the potential temperature at different times of the day at Victoria Falls in September 1992 and at Rukomechi research station in August 1997 ...... 69 Figure 4.6 The vertical profile of the potential temperature and ozone mixing ratio measured from the descending aircraft to Rukomechi research station on 2 October 1992 at 1500 hrs local time ...... 73 Figure 4.7 Seasonal and overall source apportionment to total particulate mass of fine and coarse aerosols collected at Rukomechi research station from September 1994 to January 2000 ...... 85 Figure 4.8 Crustal enrichment factors calculated relative to the composition of average crustal rock of Mason and Moore (1982) with Al as reference element and for sea-water enrichment factors calculated relative to sea-water abundance of Riley and Chester (1977) with Na as the reference elements for coarse and fine PM and several elements for components obtained by PCA...... 88 Figure 4.9 The median monthly particulate mass and elemental concentrations for some of the elements associated with mineral dust and biomass burning aerosols collected at Rukomechi research station from September 1994 to January 2000 ...... 98
vii Figure 4.10 Monthly variations of elemental concentrations for aerosols from sea salt and anthropogenic sources ...... 100 Figure 4.11 The normalised (with respect to the maxima) monthly median elemental concentration roses showing the annual cycles of particulate mass, and indicator elements for mineral dust biomass burning, sea salt and anthropogenic sources...... 102 Figure 4.12 Comparison of median elemental concentrations measured at Rukomechi research station during the dry season and at Skukuza (South Africa) during SAFARI-92 for coarse and fine aerosols...... 107 Figure 4.13 Percentage change of the total root mean square deviation for incremental reduction (step change of one unit) of the potential cluster numbers ...... 110 Figure 4.14 Cluster membership plots of 5 day back trajectories arriving at Rukomechi station at 1180 m above ground level (~800 hPa) together with the 75th median and 25th percentile trajectories...... 111 Figure 4.15 Median trajectories for clusters A to G arriving at Rukomechi research station from 1994 to 1999 arriving at 1180 m above ground level...... 115 Figure 4.16 Median height of all trajectories in an individual Cluster (A to G) as a function of trajectory running time ...... 118 Figure 4.17 Percentage contribution of the seven identified trajectory clusters to the total air flow to Rukomechi from January 1994 to December 1999...... 119 Figure 4.18 Percentages of the monthly long-term occurrences of trajectories in each cluster for the air masses arriving at Rukomechi for the period January 1994 to December 1999 ...... 122 Figure 4.19 Total number of trajectories from the north-north-westerly and south- easterly flows to Rukomechi station for the months of January and February during the period 1995 to 1999...... 124 Figure 4.20 Composite surface pressure patterns that characterise the air mass flows to Rukomechi station for the period of January 1994 to December 1999...... 126 Figure 4.21 Southern African fire plots for August to November detected by the Second European Remote Sensing Satellite and the locations of fires picked up 50 km on either side of the trajectories when trajectory height above ground level was less than the model’s mixing layer in 1996...... 138 Figure 4.22 All the fires that occurred in September from 1996 to 1999 together with the corresponding fires detected by trajectories within the 50 km buffer zone ...... 140 Figure 4.23 The year-by-year fires within the 50 km buffer zone for August to November detected from 1996 to 1999...... 141 Figure 4.24 The location of the regional fires that are associated with concentration above the 75th percentile levels of fine BC at Rukomechi for the biomass burning seasons from 1996 to 1999 ...... 143 Figure 4.25 The variation of the number of fires that contribute to fine BC concentrations above the 75th percentile as a function of the radial distance from Rukomechi research station during the biomass burning season (August to November) from 1996 to 1999...... 144
viii Figure 4.26 The variations of the measured elemental concentrations (above the 90th percentile level) of biomass burning elements and the corresponding fires selected by trajectories ...... 146 Figure 4.27 Potential source contribution function spatial distribution of fine black carbon, with 75th percentile of the concentration as threshold for the biomass burning season (August to November) from 1996 to 1999 ...... 148 Figure 4.28 Comparison of biomass burning regional sources obtained from the potential source contribution function and the fire plots ...... 150 Figure 4.29 The percentages of cases when the trajectories carrying concentrations below the 25th percentile, within the inter-quartile range and above the 75th percentile for each of the elements for each of the air mass pathways...... 152 Figure 4.30 Potential source contribution function spatial distribution of coarse copper, with 75th percentile of the concentration distribution as threshold ...... 156 Figure 4.31 Ensemble of two categories of trajectories that arrive at Rukomechi research station while carrying high copper concentrations ...... 158 Figure 4.32 Trajectories associated with relatively low concentrations of copper after passing over the Copper Belt ...... 159 Figure 4.33 Potential source contribution function spatial distribution of fine S, with 75th percentile of the concentration distribution as threshold for the period from April to November (1995 to 1999)...... 161 Figure 4.34 Major transport routes of fine S pollutants to Rukomechi research station.. 164 Figure 4.35 Potential source contribution function showing the regional sources of fine Pb, with 75th percentile of the concentration distribution as threshold...... 166 Figure A.1 Trends of the annual maximum, mean and minimum observed relative humidity ...... 189 Figure A.2 The observed maximum relative humidity and some of the percentiles used to get the best regression of the upper limit of the recorded relative humidity data...... 191 Figure D.1 15-day moving average showing the variation of the daytime and night- time wind speed...... 204 Figure D.2 Variation of the daily solar radiation received at Rukomechi research station from 1995 to 1999 ...... 207 Figure D.3 Variation of daytime temperature at Rukomechi research station, 1992- 1999 ...... 209 Figure D.4 Normalised values (with respect to their maxima) of the daytime temperature, solar radiation and sensor corrected relative humidity for Rukomechi research station ...... 210
ix Chapter 1 Introduction
1.1 Background
The continuous and rapid changes in social, economic, and political environments in southern Africa have resulted in large-scale changes in land use and land cover
[Nyanganyura, 1999]. In the central regions comprising Zimbabwe, Mozambique, Zambia and Malawi, there has been a resurgence of intensive and extensive agricultural production, resulting increased land clearing and forest fires and use of wood as sources of energy emanating from increased electricity power shortages. There is also a growing need for energy to drive the growing mining, processing and metallurgical industries in the region. A combination of these factors contributes to high levels of regional aerosol and trace gas emissions [Held et al., 1996] which merit scientific attention.
This research is based on an aerosol data set collected at the Rukomechi research station
(16.1 °S, 29.4 °E, see Figure 1.1) from September 1994 to January 2000. The samples were collected by the Tsetse control branch and analysed for coarse and fine aerosols at the University of Ghent (Belgium) by the Analytical Chemistry group led by Prof Dr W.
Maenhaut [Fernandez-Jimenez, 1999]. The goal of this study was to make a comprehensive description of this data set to determine the aerosol composition, transport pathways, aerosol sources and to quantify source contributions with emphasis to seasonal variations. This analysis was done by the author at the Max Planck Institute for
Chemistry, Mainz, Germany.
Rukomechi research station is located in the semi-arid region of northern Zimbabwe that is characterised by low and erratic rainfall received between November and April
[Nyanganyura, 1999; Vincent and Thomas, 1960]. The site lies in an area reserved for the
Mana Pools National Park, away from the inhabited areas, both rural and urban, making it
1 suitable for studying the background chemical composition of aerosols and the influence of anthropogenic sources to remote regions. This location is also under the influence of various air masses throughout the year making it ideal for studying the influence of different synoptic weather systems on the aerosol loading over northern Zimbabwe.
5 5N 15.0-15.0 S
Equator0 DRC ZAMBIA 15.5 S 5-5 S TAN -15.5 1300 m
10-10 S ZAM 1100 m ANG MAL 16.0-16.0 S Rukomechi 900 m MOZ 15-15 S Rukomechi 700 m ZIM MAD 20-20 S NAM BOT 16.5-16.5 S 500 m 25-25 S SWA ZIMBABWE 300 m LES 30-30 S SA 17.0-17.0 S 28.528.5 E 29.0 29.0 E 29.5 E 30.0 30.0 E 35.5 30.5 E 15 E 35 E 35 E 1525 25 E 354545 45 EE
Figure 1.1 The location of Rukomechi research station in Mana Pools National Park (Zambezi valley), showing the following countries: Democratic Republic of Congo (DRC), Tanzania (TAN), Zambia (ZAM), Angola (ANG), Malawi (MAL), Mozambique (MOZ), Zimbabwe (ZIM), Namibia (NAM), Botswana (BOT), Madagascar (MAD), Swaziland (SWA), Lesotho (LES), South Africa (SA). Contour elevation is in meters above sea level. The purple line in the right figure indicates the national borders (right upper corner belongs to Mozambique)
1.2 Motivation for the research
Air pollution awareness has resulted in numerous studies of the chemical composition of ambient aerosols and determination of pollution sources. Already, for decades, the chemical composition and sources of atmospheric aerosols have been studied in different parts of the world. In southern Africa, however, extensive work on aerosol composition has only been done over South Africa and in Kenya and so gave very little or no attention to the region between 10 °S and 22 °S where dry season biomass burning is dominant. To
2 increase the knowledge of aerosol composition and transport, source types and source
contributions in this part of southern Africa, a long term monitoring program was
performed from September 1994 to January 2000 at Rukomechi research station, northern
Zimbabwe. The aerosol data was collect by the Tsetse Control Branch of Zimbabwe in
conjunction with the technical and financial support from the Gent Institute of Nuclear
Sciences, Belgium and the Max Planck Institute for Chemistry, Germany. The chemical
analysis was done at the Gent Institute of Nuclear Sciences. To the author’s knowledge,
the Rukomechi aerosol data set is the longest set of aerosol measurements that exists for
southern Africa. The main thrust of this study is to jointly apply different techniques
(which normally were used individually) to analyse this aerosol data and come up with a
comprehensive study of aerosols sources, contribution of the sources, aerosol transport
and identification of potential source regions.
1.3 Objectives of the research
The key concepts for the research are built on two fundaments; (a) atmospheric chemistry
(particulate mass, black carbon and elemental composition of atmospheric aerosol
particles), and (b) meteorology (air transport by kinematic trajectories). The study
addresses a six- year climatology of large-scale lower tropospheric air flow over northern
Zimbabwe and its relation to the long-range transport of airborne material, with the
following objectives in mind:
(i) to specify the types and characteristics of tropospheric aerosols over northern
Zimbabwe;
(ii) to identify basic seasonal patterns and long-term trends of aerosol concentrations;
(iii) to establish general aerosol source-receptor relationships;
(iv) to document the climatology of large-scale lower tropospheric air flow over northern
Zimbabwe and its relation to the long-range transport of airborne material
3 (v) to identify the influence of meteorological conditions on the aerosol loading over
northern Zimbabwe.
1.4 Layout of the chapters
This work is organised into five chapters. Chapter 1 gives an introduction to the research,
Chapter 2 contains a review of the general background information about atmospheric
aerosols, their sources and effects, suitable sampling techniques, general information
about the physical, chemical and statistical analysis procedures, as well as techniques to
determine and characterise long range transport of aerosols (trajectory calculation and
trajectory cluster analysis). Chapter 3 outlines the aerosol monitoring site and also
provides a description of the Gent PM10 stacked filter sampling unit that is used to collect
samples. The methods employed in the physical, chemical and statistical analysis of the
aerosol data are also discussed. The description of the operational trajectory model and
trajectory cluster algorithm used in this study is also presented in this chapter. Chapter 4
presents the results and discussion of the meteorology at and around the site, the aerosol
chemical composition and sources of aerosols, the large scale tropospheric air masses to
northern Zimbabwe as well as the influence of meteorological parameters on the
elemental concentration of aerosols over northern Zimbabwe. An overall summary and
conclusions of the study are presented in Chapter 5.
4 Chapter 2 Theory
2.1 Aerosols
Aerosols are small particles suspended in the air long enough to enable observation and measurement [Willeke and Baron, 1993]. While technically the term “aerosols” can refer to both particles and cloud droplets, it is common to use this term only when referring to liquid/solid particles which are equal to or smaller than 10 µm in diameter.
Aerosol particles are broadly classified according to their size. Figure 2.1 shows the three main categories (nucleation range (transient or Aitken nuclei), accumulation range and coarse particles) classified depending on the principal size fractions, sources and particle formation, and the removal mechanisms [Seinfeld and Pandis, 1998]. Aerosols in the nucleation range, also called “ultra fine particles” [Oberdorster et al., 1995], are particles whose diameters range from about 0.005 µm to 0.1 µm and are formed mainly from condensation and nucleation of gaseous atmospheric species. These particles are also emitted directly from combustion sources or are produced by condensation from cooled gases. Atmospheric residence times for the particles in the nucleation range are usually less than one hour because they rapidly coagulate to form larger particles or serve as nuclei for cloud and/or fog droplets. Coagulation is the main removal mechanism of these smaller aerosols. The nucleation range is observed only when emission sources are close to the measuring site or when new particles have been recently formed in the atmosphere.
Aerosols in the accumulation range have diameters between 0.1 µm and 2.5 µm and are a result of coagulation of particles from the nucleation range and also of the condensation of vapours onto existing particles. However, removal mechanisms of particles in the accumulation range are least efficient. These particles tend to stay in the atmosphere for a considerably longer time than the nucleation range and coarse aerosols. Main removal
5 mechanisms of accumulation range aerosols are precipitation scavenging (in-cloud and below cloud) processes. Coarse particles are composed of particles with radii greater than
2.5 µm. These particles result from mechanical processes (grinding activities) and are dominated by natural and anthropogenic dust particles. Gravitational settling is the main removal mechanism for coarse particles.
Figure 2.1 Physical processes leading to atmospheric particulate matter and the idealized scheme of the distribution of particle surface area of an atmospheric aerosol [Seinfeld and Pandis, 1998]. Principal size fractions, sources, and particle formation and removal mechanisms are indicated.
Particles with diameter less than 2.5 µm (i.e., in the nucleation and accumulation ranges) are referred to as “fine” while those whose diameter is greater than 2.5 µm are called
“coarse”. In most cases, the fine and coarse particles originate from different sources, are
6 transformed separately, and have significantly different effects on human health and climate. The time spent in the atmosphere by an aerosol particle is a complex function of its physical and chemical characteristics, the time and location of its release, and the meteorology and topography of the region [Wanta and Lowry, 1976]. The efficiency of these processes is strongly dependent upon particle size. The lifetime of sub-micron aerosol particles in the lower troposphere is estimated to be of the order of days to a week
[Seinfeld and Pandis, 1998]. Fine and coarse air-borne particles, and their effects on human health and climate are fundamental discussions by atmospheric and aerosol scientists.
2.2 Sources of aerosols
Atmospheric aerosols originate from natural sources (e. g. volcanic emissions, sea spray and plant particles) and anthropogenic sources (e. g. fossil fuel burning, mining activities) as well as gas-to-particle conversion processes. Aerosol emissions from sources like biomass burning and soil dust might be influenced by both natural and anthropogenic activities [Andreae, 1991; Kleinman, et al., 1980]. The aerosol sources (whether natural or anthropogenic), can furthermore be classified either as primary or secondary. Primary sources emit particles directly into the atmosphere and include volcanic emissions and sea spray, while secondary sources involve the formation of aerosols in the atmosphere by chemical transformation of gaseous emissions such as sulphur dioxide and nitrogen oxides. In general, particles in the coarse particles are mainly a result of primary sources, while most fine particle are formed as a result of a variety of chemical processes and the coagulation of smaller particles [Seinfeld and Pandis, 1998]. Generally, major sources of air-borne particles are mineral dust, biogenic, anthropogenic, and maritime and biomass burning.
7 2.2.1 Mineral dust aerosols
Mineral dust aerosols come from wind blown fugitive dust, volcanoes and remnants of meteorites. Wind-blown dust is also found to occur mostly in the deserts, dry lake beds and semi-arid desert fringes [Prospero, 1999]. Areas in drier regions where vegetation has been reduced or soil surfaces have been disturbed by human activities can also strongly enhance dust mobilisation. Soil-dust related aerosols are not always transported very far from where they are released. This is because they are emitted very close to the ground and are often trapped by surface features like shrubbery, vegetation, buildings, etc. The atmospheric lifetime of dust depends on its particle size; large particles are quickly removed from the atmosphere by gravitational settling, while sub-micron sized particles can have atmospheric lifetimes of a week. Aerosol particles which contain Al, Si, Ti and
Fe are identified as generic ‘mineral dust’ sources [Marcazzan et al., 2003].
2.2.2 Biogenic aerosols
Primary biogenic particles are airborne solid particles (dead or alive) that are derived from living organisms. These particles are often released from plants in the form of seeds, pollen, spores, leaf waxes and resins, ranging in size from 1 to 250 µm [Warneck, 1988].
Fungal spores and animal debris such as insect fragments are also found in ambient aerosol samples in this size range. Smaller bio-aerosol particles include viruses, individual bacteria, protozoa, and algae [Matthias-Maser and Jaenicke, 1994], partially decomposed plant matter from wind blown areas and carbon-rich soot from bush fires or biomass burning [Matthias-Maser, 1998]. Secondary biogenic aerosols are formed from oxidation of volatile gaseous species emitted from biological organisms. Phosphorus (P), calcium
(Ca) and strontium (Sr) are often used as indicators of biogenic aerosols [W. Maenhaut,
2003, personal communication].
8 2.2.3 Anthropogenic aerosols
The major human related direct sources of aerosols include unpaved roads, industrial, mining and processing activities. In populated areas, the main sources of fugitive dust are unpaved roads, agricultural tilling and construction sites. The incomplete combustion of gasoline and other petrochemical products produces additional soot aerosols. Coal combustion, cement manufacturing, mining, metallurgy, and waste incineration are among the industrial and technical activities that produce primary aerosol particles. Sulphur abundance is high in coal power plant emissions, organic lead compounds are used as additives in leaded gasoline [Marcazzan et al., 2003], and arsenic compounds are added to certain insecticides and herbicides used in forest management and agriculture.
Recent estimates for the current emission of anthropogenic aerosols range from about 100
Tg/yr [Andreae, 1995] to about 200 Tg/yr [Wolf and Hidy, 1997]. Growing industrialisation without stringent emission controls, especially in Asia, may lead to increases in this source to values above 300 Tg/yr by 2040 [Wolf and Hidy, 1997].
2.2.4 Marine aerosols
In some respect, the production of sea salt aerosols is like that of wind blown dust in that both are produced by wind agitation of the surface. Sea salt particles are generated by various physical processes, especially the sea spray [Fitzgerald, 1991] and the bursting of entrained ocean water bubbles caused by the agitation of the ocean surface during whitecap formation [Blanchard, 1983]. Primary coarse sea-salt aerosols are generated by the sea spray of waves at high wind speed while fine particles are formed from the thin film of water that breaks up as bubbles burst [Blanchard, 1982]. As maritime aerosol particles are typically enriched with sodium chloride, Na and Cl are used as indicators to identify sea salt related aerosols [Annegarn et al., 1983]. Sea salt particles cover a wide
9 size range (about 0.05 µm to 10 µm diameter), and have a correspondingly wide range of atmospheric lifetimes depending on their sizes. For the present-day climate, the total sea salt flux from ocean to atmosphere is estimated to be within the range of 1,000 to 3,000
Tg/yr [Erickson and Duce, 1988].
Due to their excellent solubility, sea salt particles are very efficient cloud condensation nuclei (CCN), and therefore characterization of their ambient concentrations is of major importance for aerosol indirect effects and cloud formation. For example, Feingold et al.,
[1999] showed that in concentrations of 1 particle per litre, giant salt particles are able to modify strato-cumulus drizzle production and cloud albedo significantly.
2.2.5 Biomass burning
Biomass burning is a significant global source of many atmospheric trace constituents and is a frequent and widespread phenomenon in the tropical regions [Crutzen and Andreae,
1990]. Emissions from biomass burning include a wide range of chemically active gaseous compounds such as nitric oxide, carbon monoxide and volatile organic compounds (VOC) which have impact on local, regional, and even global atmospheric chemistry and biogeochemical cycles [Meixner et al., 1997]. The formation of ozone and other photo-oxidants due to the turnover of VOCs from biomass burning may have a significant impact on the regional environment and on the health of the population living in the vicinity of biomass burning events as well as thousands of miles away [Jaffe et al.,
2004].
The composition of biomass burning aerosols is dominated by tar-like, condensed hydrocarbons and soot particles that are usually spherical in shape [Radke et al., 1991].
Although particles in biomass burning plumes from a number of different fires have three distinguishable size ranges, namely a nucleation range, an accumulation range, and a
10 coarse particles, approximately 70% of the particulate mass was found in particles less than 3.5 µm in aerodynamic diameter [Radke et al., 1991]. For woodstove emissions
[Susott et al., 1991], the composition of biomass burning emissions is strongly dependent on the stage of combustion (i.e., flaming, smouldering, or mixed), and the type of vegetation (e.g., forest, scrub).
Fine potassium (K) is a major trace element found in woodstove emissions [Watson and
Chow, 1994] while black carbon (BC) originates from burning of open biomass due to natural fires [Andreae, 1983]. Hence BC and K are used as indicators for biomass burning
[Andreae, 1983; Lewis et al., 1988; Maenhaut et al., 2002].
2.3 Effects of aerosols
Aerosols in the atmosphere have direct and indirect effects on the Earth's climate and can also adversely affect human health. In general, the influence of aerosols on climate can be divided into three categories: direct, indirect (cloud) and the indirect effects of aerosols on heterogeneous atmospheric chemical reactions.
2.3.1 Direct and indirect effect on climate
Aerosols directly affect the radiation budget by scattering and absorption of incoming shortwave (solar) radiation, thereby changing the planetary albedo. Absorption of solar and/or longwave (terrestrial) radiation enhances the atmospheric greenhouse effect and produces changes in the atmospheric temperature profile. Absorption of incoming solar radiation by elevated concentrations of aerosols such as black carbon and mineral dust reduce the solar radiation that reaches the Earth’s surface and warms the atmosphere at height. This, in turn, decreases the thermal lapse rate and thus may increase the static stability of the atmosphere resulting in suppression of convection, cloud formation that may lead to a loss of cloud cover, a decrease in cloud albedo, and a further warming of the 11 Earth system. ([Hansen et al., 1997; Lohmann and Feichter, 2001; Ramanathan et al.,
2001]. Suppression of precipitation shifts rainfall patterns, and increases cloud lifetime which changes global mean fractional cloudiness, albedo, and radiative forcing [Albrecht,
1989]. The thermal forcing may also act to evaporate cloud droplets in clouds that do form. The tendency to warm the Earth by “cloudburning” from aerosol absorption is called the "semi-direct effect" [Hansen et al., 1997].
Indirect forcing by aerosols is the overall process by which aerosols perturb the Earth- atmosphere radiation balance by changing the cloud albedo and cloud amount. The aerosols that act as cloud condensation nuclei (CCN) and/or ice nuclei modify the cloud microphysics, radiative properties of clouds and clouds lifetime. The aerosol indirect effect can be split into two components: (i) indirect effect in which an increase in aerosols leads to an increase in the cloud droplet concentration and a decrease in cloud droplet size, resulting in the increase in cloud albedo (also termed as “cloud albedo effect”) [Twomey,
1974]; and (ii) a reduction in cloud droplet size also tends to lower the precipitation efficiency, prolonging cloud lifetime (also termed as “cloud lifetime effect”) [Albrecht,
1989] and increase cloud thickness [Pincus and Baker, 1994].
Therefore, both direct and indirect effects tend to reduce the amount of solar radiation reaching to the Earth’s surface, while the semi-direct effect also increases solar heating in the atmospheric column. However, the magnitude of the semi-indirect effect is far much less than that of the direct effect [Ramanathan et al., 2001; Schwartz and Andreae, 1996].
It is important to note that aerosols do not always have negative climatic effects. The presence of aerosols with moderate concentrations that act as CCN promotes the formation of cloud droplets resulting in precipitation. Homogeneous nucleation of water droplets (no aerosols present) occurs only at very high levels of super-saturation, which are rarely observed in the Earth's atmosphere. Consequently, heterogeneous nucleation
12 (water drops nucleating on aerosols) is the main mechanism for the formation of clouds.
The nucleation and growth rates of cloud droplets depend on the physico-chemical properties of aerosols.
2.3.2 Aerosol effects on human health and environment
Aerosols also have adverse effects on human health and the environment in a variety of ways. Fine particles (less than 100 nm) may adversely affect human health, especially in regard to cardio-vascular illnesses [Dockery and Pope, 1994]. They also reduce visibility by their scattering and absorption of radiation [Husar et al., 2000]. On the environment, aerosols can affect photosynthesis and C-uptake rates of ecosystems subjected to large aerosol loadings by perturbing leaf temperatures and the amount of photosynthetic active radiation available to green plants [Bergin et al., 2001].
2.4 Aerosol sampling
Most of the knowledge concerning properties and behaviour of airborne particles has been gained through experimental means. The pace of development and use of new measurement methods and instruments in basic aerosol research and practical field work has improved over the last decades. Types of aerosol measurements can be classified by the quantities to be determined, which are: particulate mass, size, shape, concentration and chemical composition. One of the commonly measured aerosol quantities is particle mass concentration (the particle mass in a unit volume of air or gas). Most often, a measured value of particulate mass may refer to the total mass of particles, particle range size, element or chemical compound. To appropriately represent the irregular shape of most types of aerosols, an equivalent aerodynamic particle diameter (the diameter of a sphere of unit-density with the same gravitational settling velocity as the shaped particle under
13 consideration [Willeke and Baron, 1993]) is often used when dealing with the measurement of the particle size [Lehtimaki and Willeke, 1993].
Measurement of aerosol particles can be done by depositing the particles onto filters that typically consist of glass, cellulose, plastic fibers or porous membranes [Willeke and
Baron, 1993]. The loaded filters are sent to the laboratory for chemical analysis during which the different chemical constituents of aerosols on the filters are determined.
Aerosols can also be sampled directly by a real-time, dynamic instrument that allows in situ determination of particulate mass by beta-ray attenuation [Macias and Husar, 1976] or by an inertial microbalance [Patashnick and Rupprecht, 1991]. In situ monitoring has great potential to reduce particle volatilisation because environmental variables like temperature and relative humidity do not normally fluctuate over hourly sample durations.
Because of its flexibility, simplicity and low cost, filtration (filter collection) is the most widely used method for removing particles from an air flow for subsequent analysis.
Filtration is due to the simultaneous action of several collection mechanisms (inertial impaction, gravitational settling, centrifugal, and thermal precipitation) [Willeke and
Baron, 1993]. The inertial impaction technique is widely used, while settling chambers, which include centrifuges and gravitational settling devices, are less widely used. Thermal precipitators are seldomly used. The relative importance of each mechanism depends on the particle size, density, shape, electric charge, filtrate flow velocity and properties of the filter.
As air penetrates a filter, the trajectories of the particles deviate from the streamlines due to many mechanisms, resulting in particles colliding with the fiber surface and being deposited on them [Lee and Ramamurthi, 1993]. The overall efficiency E, of a filter composed of many fibers in a mat can be related to the single fiber efficiency, η, as follows [Willeke and Baron, 1993]:
14 − 4ηαL E = 1− exp ………………………….. 2.1 πd f (1− α)
where α = (1-porosity) is the packing density of the filter, L, is the filter thickness and df is the fiber diameter.
During collection, the filter retains all or a fraction of the particles suspended in the incident aerosol flow. As air passes through filter media, the filter structure causes a resistance that is a measure of air permeability or the pressure drop. Measurements of the pressure drop across filter media play an important role in estimating the filtration efficiency. A comparison of the calculated pressure drop based on an idealised flow model
[Lee and Lui, 1982] with actual measured pressure drop gives an indication of how uniformly the media structure elements (fibers or pores) are arranged. The pressure drop factor, β, is calculated by
∆P β = ex ……………………………………….. 2.2 ∆Pth
where ∆Pex and ∆Pth are experimental and model predicted pressure drops respectively.
The pressure drop factor is then applied to the theoretically calculated filtration efficiency values as
E ex = βE th ……………………………………….. 2.3
where Eex and Eth are filter efficiencies measured experimentally and predicted by the model, respectively [Lee and Liu, 1982].
The duration of filter exposure depends on whether the sampling site is located in highly polluted areas or is situated in relatively aerosol-free and remote areas [Maenhaut et al.,
1994]. In remote locations that are characterised by low atmospheric aerosols loadings,
15 sampling can go on for as long as 48 hours so that enough aerosol material can be collected on the filter, while in a polluted urban environment, sampling can be done over
12 hours or less [Maenhaut et al., 1994].
2.5 Physical and elemental analysis of aerosols
Loaded filters are taken to the laboratory for analysis of the physical properties and chemical content of the substrates. Several procedures are used depending on the type of filter and the information to be extracted.
2.5.1 Gravimetric technique
The gravimetric technique is the most common physical analysis tool used almost exclusively to obtain mass measurements of the filter in the laboratory environment. The technique determines the net particulate mass by weighing filters (in the laboratory) before and after sampling, using a balance under controlled temperature and relative humidity. Equilibration at low temperature (15 to 30 °C) and relative humidity (20% to
45%) effectively removes liquid water associated with the particle deposit. If the filters are exposed to ambient air for more than a day or two some of the particles may volatize
[Witz et al., 1990]. The main interference in gravimetric analysis of filters arises from non-gravimetric forces between the filter and the balance [Engelbrecht et al., 1980] as a result of electrostatic charge in the filters. This charge can be removed by exposing the filters to a low-level radioactive source prior to and during weighing.
2.5.2 Light reflectance technique
Black carbon (BC) (also known as elemental carbon or soot carbon) concentration is determined from the increase of light absorption [Andreae et al., 1998] and the specific attenuation cross section is used to convert the absorption measured by the instrument into
16 black carbon (BC) concentration. Black carbon is often estimated by measuring the transmission of He/Ne laser light (wavelength 0.633 µm) through the filters before and after exposure. The BC (in µg m-3) estimate is given by the following expression:
A I BC = 100 ln 0 …………………………… 2.4 εV I where A is the filter collection area (cm2), є is the mass absorption coefficient (cm2 g-1), V
3 is the total sampled volume (m ), I0 and I are respectively, the pre-exposure and the post- exposure transmitted laser intensities.
Generally for particles produced by combustion processes, the aerodynamic diameters are less than a micron and values of the mass absorption coefficient ranging from 5 to 10 cm2 g-1 are appropriate for black carbon estimates [Fuller et al., 1999]. The mass absorption coefficient is a function of the wavelength as well as the absorbing particle diameter and density [Horvath, 1993]. For absorbing particles with diameters much smaller than 0.633
µm, (the He/Ne laser wavelength), the mass absorption coefficient is a slowly varying function of particle diameter and can be considered as constant for particles of similar density. Calibration measurements by [Cohen et al., 2002] on smoke from acetylene flames and candle soot showed є = 7 cm2 g-1 to be a reasonable value for black carbon estimates of fine particles collected on filters where the particle diameters were less than
0.6 µm.
2.5.3 Elemental analysis
To quantify the elemental measurements of aerosol samples, modern analytical tools have been developed. These include a variety of techniques such as Proton Induced X-ray
Emission (PIXE), Instrumental Neutron Activation Analysis (INAA), Atomic Absorption
Spectrophotometry (AAS), Photon-Induced X-ray Fluorescence (XRF), Inductively
17 Coupled Plasma with Atomic Emission Spectroscopy (ICP/AES) and Scanning Electron
Microscopy with X-Ray Fluorescence (SEM/XRF). Of these, the widely used elemental analysis techniques are the PIXE and the INAA and are briefly discussed in this study.
2.5.3.1 Basis of the Proton Induced X-ray Emission (PIXE) analysis
PIXE is a powerful yet non-destructive elemental analysis technique now used routinely by aerosol scientists, geologists, archaeologists and other researchers to help answer questions of provenance of aerosols. The principle is based on the quantum theory which states that orbiting electrons of an atom must occupy discrete energy levels in order to be stable.
Bombardment of the atom with particles of sufficient energy (usually 2 to 5 MeV protons) produced by a particle accelerator will cause inner shell ionization of atoms in a specimen
(Figure 2.2(a)), resulting in an unstable electron configuration. The subsequent filling of the resulting inner shell vacancies by atomic transitions from outer shells leads to the emission of characteristic X-rays for each element (Figure 2.2(b)).
a) b) ejected inner shell electron characteristic x-rays
Incident proton the resulting vacancy is filled by outer shell electron
Figure 2.2 Schematic diagram of Proton Induced X-ray Emissions Analysis showing the incident proton as blue, the atomic nuclei green, while atomic electrons are red. a) An incident proton ejects an inner shell electron from a specimen atom, b) an outer shell electron fills the resulting inner shell vacancy and emits an x-ray.
18 The X-ray signal from the trace elements of interest may be partially or totally masked by unwanted background radiation arising from the host material. The background may be in the form of a continuum due to bremsstrahlung, or it may contain discrete peaks arising from interfering characteristic X-rays from the matrix material [Tapper et al., 1994].
Figure 2.3 shows a typical X-ray spectrum obtained with a Si(Li) detector when 2.6 MeV protons with a 10 nA beam current are incident on an aerosol sample.
S Cl 1000 K Si Ca Fe Al Continuous background spectrum 1000 Ti Characteristic X-ray peaks Fe Mn Zn Pb (Kβ) (Lα) Br 100 Zn Pb Cu (Lβ) (Kβ)
er channel
p Sr Pb 10 (Lγ)
Counts Counts
1
0 2 4 6 8 10 12 14 16 18 X-ray Energy (MeV)
Figure 2.3 The Proton Induced X-ray Emission spectrum of an aerosol sample showing the characteristic X-ray lines (red) and intensities from the trace elements in the aerosol particles superimposed on a the background radiation (green). Unless otherwise stated, the X-ray emission is composed of Kα lines.
The area of each peak is related directly to the concentration of the corresponding element in the specimen. Quantitative analysis of the PIXE spectrum presents difficulties when a sample contains many trace and minor component elements. Each of these elements may give rise to one or more X-ray peaks due to the K, L and M shell transitions, subdivided to give peaks corresponding to Kα, Kβ, Lα, Lβ, Lγ etc, some of which are capable of being
19 resolved, giving rise to a large number of both large and small peaks [Alfassi, 1994].
There is a high probability that a number of X-ray peaks will overlap and interfere with each other, complicating data reduction and interpretation.
X-rays from multiple elements are simultaneously detected using solid state energy dispersive detectors and the intensities are then converted to elemental concentrations with help of appropriate standardised curves [Cahill, 1980]. Several well documented spectral analysis software codes are available to perform data reduction of the acquired X-ray spectrum to unravel overlapping peaks, de-convolve spectra and calculate peak areas with high degrees of accuracy producing quantitative results [Maxwell et al., 1994; Nass et al.,
1978].
For any measurement of an absolute nature to be made by PIXE analysis, the cross-section
X X for X-ray production, σ Kp , has to be calculated. The X-ray production cross-section σ Kp for the X-ray characteristic line of element p for the K-shell is given by [Cohen et al.,
2002]:
X i σ Kp = σ K ωK k ……………………………….. 2.5
i where σ K is the ionization cross-section for the K-shell, ωK is the fluorescence yield (the ratio of emitted X-ray intensity due to transition to a particular shell to the number of primary vacancies created in that shell) and k is the relative X-ray transition probability
(the fraction of a specific X-ray line (p) emitted with respect to the total number of X-rays emitted from K-shell).
The quantification of the PIXE spectrum of an element is based on the direct relationship between the net area under the K or L shell X-ray curve of the element and the quantity of the element in the sample. Assuming that the specimen sample is uniform and the cross-
20 sectional area of the proton beam is smaller than the total surface area of the specimen, then this relation is given by [Fernandez-Jimenez, 1999]:
X N A σ pZ (E 0 )ε p NC Zρt Yp (Z) = ……………….. 2.6 A Zsinθ
where Yp(Z) in the number of counts for a characteristic X-ray line of element p with atomic number Z and mass number AZ, NA is Avogadro’s number, εp is the absolute detection efficiency for element p X-rays, N is the number of the incident protons, Cz is the concentration of the element with atomic number Z, ρ is the density of the sample, t is the thickness of the specimen, θ is the incident angle of the proton beam to the surface of
X the specimen and σ pZ ()E 0 is the X-ray production cross-section for element p and the beam of incident energy Eo [Johansson and Campbell, 1988]. The energy of the incident proton beam can be accurately determined using Rutherford Backscattering Spectrometry, assuming that there is elastic interaction between the nuclei of the specimen atoms and the incident protons. The energy of the beam can be determined by:
2 ()m + M E E0 = 2 , …….. 2.7 2 2 2 (mcosθ + M − m sin θ ) where m is the mass in the incident proton, M is the mass of the nuclei of the specimen, θ is the scattering angle and E is the scattered proton.
From the spectral analysis, the mass per unit area of an element is found using previously measured efficiency curves [Alfassi, 1994]. PIXE is an absolute analysis method in that the efficiency curve is fundamentally due to the physics of the atom, as reflected in the X- ray production cross-section curve for specific proton energy. Such curves are functions that mirror the changes in the electronic structure of the inner shells of atoms as the atomic number increases. The measured efficiency, kp(Z), in the calibration of a particular 21 laboratory system also includes the effects of solid angle subtended by the detector and the absorption of X-rays in reaching the detector. It is given by
X NAσpZ (E0 )εp k p (Z) = …………………….. 2.8 AZsinθ
The measured efficiency, referred to as the sensitivity curve, has units of counts microgram per centimetre squared per micro Coulomb of collected charge (counts µg cm-2
µC-1). To convert the output of the spectral fitting to a quantifiable value, µg cm-2, the number of integrated counts in a peak (equation 2.6) is divided by the sensitivity for that element (equation 2.8) and the amount of charge collected to acquire the spectrum.
The atmospheric aerosol concentrations (mass per unit volume) of sampled air are obtained by multiplying the quantifiable value of the measured efficiency (in mass/area) by the sample area and dividing by the volume of the sampled air Vsmp during collecting.
This volume is corrected to Environmental Protection Agency’s (EPA) standard temperature (25 °C) and standard pressure (1.013 x 105 Pa). The sampled air volume corrected to EPA reference conditions is given by [Nelson and Lapp, 1999]:
298 P Vsmp = Vs …………………….. 2.9 T 760
3 where Vs the volume of ambient air pulled through the sampler in m , T is the ambient temperature in K and P is the barometric pressure during measurement condition (in Pa).
2.5.3.1.1 Detection limit
The Detection Limit (DL) is the statistically calculated minimum concentration that can be measured with 99% confidence that the reported value is greater than zero [Nelson and
Lapp, 1999]. It is defined here as the mass per unit area of a filter that is required to
22 produce a peak spectral area that exceeds the background by a factor of three. DL values are calculated on the basis of 3 standard deviations of the background areas over the one full width at half maximum (FWHM) region centred about the element’s principal peak centroid. In counts, the detection limit, DL, is calculated as [Fernandez-Jimenez, 1999]
DL = 3 R B …………………….. 2.10
where RB is the background area over the one FWHM region. Assuming that the background originates from the element of the matrix of atomic mass AB and if one assigns σB as the production probability of continuous background radiation per unit X- ray energy, then the intensity of the background radiation can be expressed as
N tσ (E )n(FWHM)ε Nρ A B 0 p R B = …………………….. 2.11 ABsinθ where n(FWHM) indicates the spectral interval used in the summation of the background noise. Equating equations 2.6 and 2.10 taking into account 2.11, the detection limit for concentration Cz is given by
3A Z σB (E0 )n(FWHM)sinθ x L (Z) = X ………………….. 2.12 σpZ NBtA0ε B Nρ
Theoretical and experimental calculations using thin film references established that the detection limit for elements with atomic numbers 42 or less is determined using the K- shell spectra and for the rest, L-shell spectra lines are used [Puri et al., 1999]. To get the detection limits for aerosol concentrations, protons from an optimized PIXE equipment that produces a proton dose of 100 µC were used to bombard a filter sample of aerosols for 15 minutes [Nelson and Lapp, 1999].
23 2.5.3.1.2 The Advantages of Proton Induced X-ray Emission
PIXE analysis has several advantages that were discussed extensively by [Nelson and
Lapp, 1999]. Below is a brief summary of some of these advantages.
High sensitivity: Compared to electron based X-ray analytical techniques such as energy dispersive spectroscopy (EDS), PIXE offers better signal to noise ratios and consequently much higher trace element sensitivities.
Measurements at atmospheric pressure: A MeV proton beam can be brought out from the high vacuum environment of the accelerator into the ambient air of the laboratory.
This technology makes it possible to measure a valuable artefact or precious material at atmospheric pressure outside the confines of an evacuated chamber.
Multi-element capability: Major elemental analysis is performed for any element from sodium to uranium in a single spectrum. X-rays from elements below sodium cannot be seen because they are absorbed in either the detector window, the atmosphere between the sample and the detector, or through any filters used.
The proton beam is non-destructive: PIXE is a non-destructive analytical technique such that the filters can be used in other techniques for further analysis of elements and compounds that cannot be detected, using PIXE.
The use of standards: PIXE systems rely on standard efficiency curves to determine accurate specimen compositions.
The benefit of X-ray filters: At very high count rates, Si(Li) X-ray detectors behave in a less than ideal fashion. Dominant low energy x-rays may be filtered out so that the detector will only see contributions due to the higher energy trace or minor elements.
24 Beam current may now be increased with an overall effect of much greater trace element sensitivity while keeping the detector at a low count rate.
2.5.3.2 Instrumental Neutron Activation Analysis
Instrumental Neutron Activation Analysis (INAA) [Dams, 1970; Weaver, 1999; Zoller and Gordon, 1970] is a non-destructive, highly precise and accurate analytical technique capable of determining up to 48 elements in almost all types of sample matrices. The
INAA procedure involves irradiating the samples and appropriate standard reference materials with neutrons to produce unstable radioactive nuclides. Many of these radionuclides emit γ-rays with characteristic energies that can be measured utilizing high- resolution semiconductor detectors. The rate that the γ-rays are emitted from an element in the sample is directly proportional to its concentration. Detection limits are in the parts per million (ppm) to parts per billion (ppb) range depending on the element and sample matrix
[Dams, 1992].
The basic essentials required to carry out an analysis of samples by INAA are a source of neutrons, instrumentation suitable for detecting γ-rays, and a detailed knowledge of the reactions that occur when neutrons interact with target nuclei. Brief descriptions of the
INAA method, reactor neutron sources, and γ-ray detection are given in the following section.
2.5.3.2.1 The Neutron Activation Analysis Method
The sequence of events occurring during the most common type of nuclear reaction used for INAA, namely the neutron capture or (n,γ) reaction, is illustrated in Figure 2.4. When a neutron (from a reactor, an accelerator, or radio-isotopic neutron emitter) interacts with the target nucleus via a non-elastic collision, a compound nucleus forms in an excited state. The compound nucleus will almost instantaneously de-excite into a more stable
25 configuration through emission of one or more characteristic prompt γ-rays. In many cases, this new configuration yields a radioactive nucleus which also decays by emission of one or more characteristic delayed γ-rays. This happens at a much slower rate depending on the half-life of the radioactive isotope, which can range from fractions of a second to millions of years.
Figure 2.4 Diagram illustrating the process of neutron capture by a target nucleus followed by the emission of γ-rays [Alfassi, 1990].
With respect to the time of measurement, the INAA falls into two categories [Glasclock,
1998]: (i) prompt γ-ray neutron activation analysis (PGNAA), where measurements take place during irradiation, or (ii) delayed γ-ray neutron activation analysis (DGNAA), where the measurements follow radioactive decay. The latter technique (DGNAA) is commonly used, hence when one mentions INAA it is generally assumed that measurement of the delayed γ-rays is intended [Weaver, 1999]. As in the PIXE technique, the peaks in the γ- spectrum at certain energy determine the element while the peak area gives information about the amount of this element present in the sample.
INAA is based on the measurement of characteristic γ-rays from a radionuclide formed by a specific neutron reaction. The activity (Bq) from an expected reaction is given by
[Alfassi, 1998]:
26 m A = σΦ N (1− e −λti )e −λtd (1− e −λtc )θP ε , ………. 2.13 M A γ where σ is the activation cross-section (cm2), Φ is the activating flux (n cm-2 s-1), m is the amount of the element determined (g), M is the atomic weight of the element to be
-1 −λti determined ( g mol ), NA is the Avogadro constant, 1− e is the saturation factor (λ is
−λtd the decay constant of the radioactive product, ti is the duration of irradiation), e is the
−λtc decay factor (td is the duration of the decay), 1− e is the correction factor for nuclide decay during the counting time (tc is the duration of counting), θ is the relative natural isotopic abundance of the activated isotope, Pγ is the probability of emission of a γ-photon with energy E, and ε is the detector efficiency for the measured radiation energy.
The comparator method can be used to simplify the above expression. If equal weights of the sample and the standard (with known concentration of the element of interest) have the same irradiation, decay and counting times, then the above equation leads to [Weaver,
1999]:
A sam Csam = Cstd ………………….. 2.14 A std
where Csam and Cstd are respectively the unknown concentration of the element in the sample and the known concentration in the standard, Astd and Asam are the activities of the standard and the sample respectively.
2.5.3.2.2 Irradiation and counting procedures
The irradiation, decay and counting times may be varied depending on the neutron flux density, the mass of the sample and the efficiency of the detector. Two irradiations are
27 typically performed, one for determining short-lived radionuclides and the other for medium/long-lived radionuclides [Glasclock, 1998; Weaver, 1999]. To produce short- lived radionuclides, irradiation times varying from 5 seconds to 10 minutes are employed, depending on the sample type. A decay time of at least 5 minutes is needed for most samples to let the usually high 28Al activity decrease by two half-lives. For the analysis of the medium-lived radionuclides, the samples are counted for about 1 hr after an irradiation of 3 hours and a cooling for 3-5 days while long-lived radionuclides are counted for about
6 hrs after cooling for 8-14 days.
2.5.3.2.3 Detection limit
The minimum detectable concentration is a priori estimate of the detection level based on a specified probability of a false detection and non-detection [Weaver, 1999]. For a five- percent chance that a false conclusion will be reached regarding the presence or absence of the element of interest in the sample, Currie, [1968] defined the quantity called the lower limit of detection (LLD) via the following equation:
LLD = 2.71+ 4.65 B ………………….. 2.15 where B is the gross background counts.
2.6 Statistical analysis
The elemental concentration data obtained from the PIXE and/or INAA techniques can be statistically analysed to identify the different sources of aerosols. For source identification, the Principal Component Analysis (PCA) technique is often applied while the Absolute
Principal Component Analysis (APCA) is commonly used to quantify the contributions of each source to the total particulate mass.
28 2.6.1 Principal component analysis
PCA is a statistical technique that can be applied to a set of variables in order to reduce their dimensionality and is often used to replace a large data set of inter-correlated variables with a smaller number of independent variables [Henry and Hidy, 1979]. PCA is a well-established tool for analyzing structure in multivariate data sets. It starts with a large number of correlated variables (e.g., elemental concentrations) and seeks to identify a smaller number of independent components that can be used to explain the variance in the data [Henry and Hidy, 1979; Maenhaut and Cafmeyer, 1987].
The basic objectives of the PCA are (i) to determine how many components are needed to explain the set of variables, (ii) to find the extent to which each variable is associated with each set of common components, and then (iii) to provide interpretations to the components and determine the amount of each component possessed by each observation.
When PCA is used to study aerosol concentration, the elements will be the variables while the pollutant sources will represent the components. The initial step in PCA is to transform the elemental concentration data set into a dimensionless standardised dataset using the Z-score as follows
Cik − Ci Zik = ………………………….. 2.16 σi where i = 1,……,n, is the total number of elements in the analysis; k = 1,….,m is the total
th number of observed samples; Zik is the Z-score of the i element for sample k; Cik is the
th th concentration of the i element for sample k; C i is the mean concentration for the i element over all the observed samples, and σi is the standard deviation of the distribution of concentration for element i. The PCA assumes that the total concentration of each
29 element is made up of the sum of elemental contributions from each of the j source components. Hence, the ith normalised concentration,
p Zik = ∑ G ij H jk ………..………………….. 2.17 j=1 where j = 1,….,p, is the number of pollutant sources influencing the concentration data
th set; Hjk is the matrix of factor scores showing the relative impact of the j component’s value on sample k, Gij is the matrix factor loadings that represents the correlation of element i with component j. In matrix notation, equation 2.17 can be expressed as
[]Z ik = []G ij [H ]jk ………..………………….. 2.18 where G and H are the matrices of factor loadings and of factor scores respectively. The factor loadings are determined from an eigenvector decomposition of the matrix of pair- wise correlation coefficients for the n elements:
1 R = ZZ T ………..………………….. 2.19 n −1
T where Z is the transpose of [Z]ij.
If is an n x n matrix whose columns are the eigenvectors of R and λ a diagonal matrix of its eigenvalues, then the full factor loading matrix G is determined by
2 G = Λλ . ………..………………….. 2.20
is evaluated by diagonalising the correlation matrix by finding a matrix Q such that,
-1 [Q ]ixi []R ixi [Q]ixi = [Λ]ixi ……….………….. 2.21
H (the factor score matrix) is calculated by inverting Equation 2.18 yielding
30 = B Z [] []jxi [ ]ixk ………..………………….. 2.22
[]G jxi where [] = is the principal component scoring matrix and λj is the eigenvalue λ j associated with principal component, Pj.
The principal component scoring matrix is derived such that the first principal component
(P1) accounts for a maximal amount of total variance in the observed elements. The second component extracted (P2) will have two important characteristics; (i) it will account for a maximal amount of remaining variance in the original elements of the data set that was not accounted for by the P1, (ii) the coefficients of the P2 and P1 are not correlated. The remaining components that are extracted in the analysis display the same two characteristics: each component accounts for a maximal amount of variance in the observed elements that was not accounted for by the preceding components, and is uncorrelated with all of the preceding components. PCA proceeds in this fashion, with each new component accounting for progressively smaller and smaller amounts of variance (which is why only the first few components are usually retained and interpreted). When the PCA procedure is complete, the resulting components will display varying degrees of correlation with the observed elements, but are completely uncorrelated with one another.
In order to make the interpretation of the components that are considered relevant, the first selection step is generally followed by a rotation of the components that were retained.
Consequently, the eigenvectors obtained by decomposing [R] are sorted by descending order of the corresponding eigenvalues and low-ranking eigenvectors are discarded.
Two main types of rotation are used: (i) Varimax (orthogonal) rotation in which the new axes are orthogonal to each other and the components remain uncorrelated, (ii) oblique
31 rotation whose solution yields correlated components. However, oblique solutions are more complicated to interpret as compared to orthogonal solutions; hence the orthogonal rotation is most often used.
A varimax rotation developed by Henry Kaiser [Kaiser, 1960] redistributes the variance to give a more interpretable structure to the components. It is applied to maximize the number of values that are close to either zero or unity in the factor loading matrix
(equation 2.22) without changing either the total variance or the variance of the single element in the model. After rotation, elements coming from the same source can be found in the same component with a high weight. The result gives a new matrix, which shows a clustering of the elements on single components, thus enhancing the interpretability of the principal components. The researcher's job is to identify and give a name to each component by examining the clusters of elements.
In reality, the number of components extracted in PCA is equal to the number of observed elements being analyzed. However, only the first few components accounting for meaningful amounts of variance will be important enough to be retained for interpretation and used in subsequent analyses. The criterion that is mostly used in selecting the optimum number of components is the eigenvalue-one criterion.
The eigenvalue-one criterion or the Kaiser criterion [Kaiser, 1960] is one in which any component with an eigenvalue greater than 1 is retained. The rationale for this criterion is based on the fact that each observed element contributes one unit of variance to the total variance in the data set, hence, any component that displays an eigenvalue greater than 1 is accounting for a greater amount of variance than had been contributed by one element.
Such a component is therefore accounting for a meaningful amount of variance, and is worthy of being retained. On the other hand, a component with an eigenvalue less than 1 is accounting for less variance than had been contributed by one element. As the purpose
32 of PCA is to reduce a number of observed elements into a relatively smaller number of components; this cannot be effectively achieved if components that account for less variance than had been contributed by individual elements are to be retained.
Using indicator elements (see section 2.2) for different aerosol sources, each of the components identified by the PCA can be assigned to a specific aerosol source type.
Hence, the PCA is used as an explanatory tool to identify the major sources of air pollutant emissions while the Absolute Principal Component Analysis method is then used to quantify the contributions of all sources to each measured pollutant.
2.6.2 Absolute principal component analysis
Details of how the Absolute Principal Component Analysis (APCA) technique can be used to estimate source contributions to each pollutant were described by Thurston and
Spengler, [1985], while a typical application to aerosol problems is treated in the paper by
Maenhaut and Cafmeyer [1987]. A brief description is given below.
Because the factor scores obtained from PCA are normalized, with mean zero and standard deviation equal to unity, the true zero for each factor score is calculated by introducing an artificial sample with concentrations equal to zero (case 0) for all elements.
The artificial zero concentrations (from Equation 2.16) are calculated as
0 − Ci Zoi = ………..………………….. 2.23 σi
Factor scores of elements (including the case 0) are then calculated from PCA by analysis of normalised elemental concentrations. The absolute principal component scores (APCS) for each component are then estimated by subtracting the factor scores of the artificial sample from the factor scores of each of the true samples. Regressing the aerosol
33 particulate mass (fine and coarse) on these APCS gives estimates of the coefficients which convert the APCS into particle mass contributions from each source for each sample. For each source (component) identified by the fine and coarse particulate mass, the weighted regression of each element's concentration on the predicted mass contributions yields estimates of the content of that element in each source. Hence, the source contributions to the concentration of element i during observation k, Cik, can be calculated by using a multiple linear regression procedure according to the relationship:
n
Cik = ()b0 i + ∑ a j APCS jk ………..…………… 2.24 j=1
Where (b0)i is the constant term of multiple regression for pollutant i, aj is the coefficient of multiple regression representing the mean mass fraction of source j’s particles represented by element i, and APCSjk is the scaled value of the rotated factors for the considered sample that represents the mass concentration of source j for sample k. The product, aij*APCSjk, represents the contribution of source j to Ci in sample k. The mean of asi*APCSjk on all samples represents the average contribution of the sources.
2.7 Trajectory calculations and cluster analysis
While identification of aerosol source types and the quantification of source contributions can be achieved by the PCA and APCA respectively, one has to make use of the aerosol chemical information (from PCA and APCA) and the air mass flow information to locate the potential source regions of air pollutants. The large scale air flow information necessary for understanding the transport processes and pathways of pollutants to a certain region can be obtained from an ensemble of air mass trajectories.
34 2.7.1 Trajectories
In meteorological terms, a trajectory is the time integration of the change in position of an air parcel as it is transported by the wind [Doty and Perkey, 1993]. Air mass trajectories are typically calculated in a backward mode (path of air movement arriving at a receptor location) or forward mode (path of air movement leaving from a source location). The study of air mass trajectories can be used (i) to understand the three-dimensional transport of trace substances (pollutants and particles) in the atmosphere, (ii) to monitor the long- range transport pathways of atmospheric pollutants, and (iii) to locate source regions of both anthropogenic and natural aerosols. Backward trajectories have been used to explore predominant source regions of ozone, particulate matter and regional haze for various receptor locations and time periods in Asia [Poirot and Wishinski, 1986], to characterise atmospheric transport of air pollution over Africa [Piketh and Walton, 2004], to investigate the vertical and horizontal dominant circulation patterns over southern Africa
[Garstang et al., 1996], and to establish the flow patterns and transportation range in southern Africa [Piketh et al., 2002; Tyson and D'Abreton, 1998]. In central Africa, air mass trajectories were used to characterize the transportation of aerosols over equatorial eastern Africa by Gatebe et al., [2001] and to study the inter-regional transport of air masses in the southern hemisphere [Sturman et al., 1997].
2.7.2 Trajectory calculation
To determine individual trajectories, most methods use observed or model analyzed wind data to compute the horizontal advection components. Fluid motion and any constituent transported by fluid motion can be described from two frames of reference; the Eulerian and the Lagrangian [Seinfeld and Pandis, 1998]. In the Eulerian perspective, the flow and its scalar constituents are described with respect to fixed spatial positions,
35 X = (x, y, z) and with respect to time(t), and are written, like for example V (X ,t) and
C(X ,t). The Lagrangian perspective focuses on the individual air parcels as they move through space and time with the flow, and trace the history of individual fluid particles.
Unlike the Eulerian description, in which spatial position is a fixed reference, in the
Lagrangian perspective, the spatial position is another variable of the particle. The flow
variables are written with respect to time and a single, initial reference position, X 0 , the
particle position at t = 0, such that the variables recorded are X (X 0 ,t) and C(X 0 ,t)
[Stohl, 1998]. Although the Eulerian perspective is generally easier to represent, the physics and transport of flow are, however, more fundamentally related to the Lagrangian perspective.
In most cases, trajectory calculation is done applying the isentropic or the kinematic method to calculate the 3 D-components of the trajectory. In the isentropic approach, the trajectory is assumed to be moving along a sloping surface of constant potential temperature where the potential temperature of the air parcels remains conserved as they undergo an adiabatic and frictionless flow. The assumptions are made so that the vertical motion of the air parcels can be estimated. The methodology of the isentropic model is the
. same as that of the kinematic model with the exception that the vertical wind speed X z , is replaced by the potential temperature (Appendix B). The conservation of the potential temperature is used to compute the pressure (and hence height) of the air parcel [Merrill et
. . al., 1985]. The temperature and horizontal wind components (X x and X y ) are interpolated from the start point using the same interpolation procedure as in the kinematic model (see Appendix B).
The potential temperature, θ, is calculated from
36 R/c 1000 p θ = T , ………………………………………….2.25 P where T is the interpolated temperature from the model grid at the selected start pressure
P, R is the gas constant and cp the specific heat capacity of air at constant pressure. The air parcel is advected along the isentropic surface as in the kinematic model (see Appendix
. . B), interpolating the temperature, Xx and Xy wind components at the new position. The new pressure, P’, level along the isentropic plane is calculated by the expression
7.211T' P' = 0.286 , ………………………………………. 2.26 θ where T' is the interpolated temperature at the new position and θ is the potential temperature calculated from equation 2.25.
The kinematic trajectories make use of the derived vertical wind component assuming that the trajectory moves with the vertical wind fields generated by the meteorological model grid [Rolph et al., 2002]. This approach uses the wind velocity components to trace the three-dimensional components of the air parcels. The trajectory calculation procedure was discussed in details by Stohl, [1996; 1998] and the summary is given Appendix B.
2.7.3 Errors in trajectory calculation
Uncertainties in trajectory calculation may be caused by interpolation procedures, sub grid-scale processes, observation and modelling errors [Miller, 1981]. Stohl, [1998] and
Harris, [1992] gave a detailed discussion of trajectory uncertainties. Below is a brief discussion of errors in trajectory calculation.
Truncation errors. The so-called truncation errors result when equation B.4 is approximated by a finite-difference scheme that neglects the higher order terms of the
37 Taylor series. The truncation error is proportional to ∆t for the zero acceleration method
(equation B.6) and proportional to (∆t)2 for the constant acceleration method (equation
B.8) and for the variable acceleration method [Walmsley and Mailhot, 1983]. This type of error can be kept below any desired limit by using sufficiently small values of ∆t.
Interpolation errors. Wind data are available only at discrete locations in space and time, either as irregularly spaced observations or as the gridded output of meteorological models. Most trajectory models are based on gridded wind fields, but since radiosondes are the most important data for analyzing wind fields, errors arise when interpolating radiosonde winds. Hence, the wind speed must be estimated at the trajectory position by the trajectory model.
Errors resulting from certain assumptions regarding the vertical wind. Trajectory errors are also related to different assumptions regarding the vertical wind
. component, X z , . In contrast to the horizontal wind, there are no routine observations of
. . X z , . Fields of X z , are a sole product of meteorological models, and hence they are less accurate than the fields of the horizontal wind.
Wind field errors. In many cases, errors of the underlying wind fields are the largest single source of error for trajectory calculations. Wind field errors can be due to either observational errors, analysis errors or forecast errors, depending on the type of wind fields used. Trajectory errors caused by erroneous forecasts are relatively simple to evaluate by comparing forecast with analysis trajectories.
Starting position errors, amplification of errors, and ensemble methods. The starting positions of the trajectories are often not exactly known. For example, estimations of the effective source height of accidentally released material are usually very inaccurate.
Another uncertainty is due to the differences between the model topography and the real
38 topography, making the selection of a starting height difficult. Although the initial trajectory position error may be rather small, it can strongly amplify in divergent (forward trajectories) or convergent (back trajectories) flow.
2.7.4 Trajectory cluster analysis
An individual trajectory should not be viewed as an accurate representation of an air mass pathway; however, classifying a sufficiently large set of individual trajectories will generally give a good indicator of general large-scale airflow. Trajectory cluster analysis is an exploratory data analysis tool for solving classification problems [Stohl, 1996]. The objective of this analysis is to sort trajectories into groups, or clusters, so that the degree of association is strong between members of the same cluster and weak between members of different clusters [Harris and Kahl, 1990]. The method is used to categorize a large set of trajectories into groups of similar trajectories that represent different synoptic regimes.
In general, cluster analysis is a variety of multivariate statistical analysis techniques, which are used to explore the existing structure within data sets [Romesburg, 1984].
There are many different clustering algorithms with great variation in the computational requirement to the interpretation of the data. The clustering algorithm commonly used
(and also used in this study) is non-hierarchical, designed for large databases and has relatively small computational storage requirements [Dorling et al., 1992]. The general sequence of the procedure is as follows:
(i) From the large number of backward trajectories calculated by a trajectory model (e.g.
the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, (see
section 3.7), an optimum number of average trajectories (,x pk (),y pk which covers the
spread of the real trajectories used in the analysis, is generated. The calculated
39 quantities x pk and y pk are the average coordinates (longitude, latitude) of cluster p for
hourly time step k.
(ii) To minimize the differences among trajectories in a cluster and to maximize the
differences among clusters, each of the real trajectories is assigned to the average
trajectory which is closest in terms of the Euclidean distance (ED) between their
corresponding hourly coordinates. Computation of the distance between each
individual trajectory and the mean cluster trajectory is done as follows:
2 2 ED = ()xik − x pk + (yik − y pk ) ……………………… 2.37
th where xik and yik are respectively the longitudinal and latitudinal coordinates for the i
trajectory at time step k. The similarity among trajectories in each cluster is
maximised considering the full length of each trajectory.
(iii) Calculation of the cluster mean trajectories may result in the some real trajectories
being assigned to wrong clusters in terms of their distance from the cluster means.
Hence, each real trajectory is checked by re-computing the Euclidean distance and the
cluster means are recalculated again (as in stages (i) and (ii) after the check. Several
iterations over all trajectories may be necessary each time and the recalculation of the
cluster means at the end of each iteration made before all the real trajectories are
correctly assigned (defined by a number representing the Euclidean distance between
two successive trajectories in each cluster).
(iv) The Root Mean Square Deviation (RMSD) of each real trajectory from its cluster
average is calculated and the sum of the RMSD gives the total Root Mean Square
Deviation (tRMSD) which is the RMSD for the chosen number of clusters. The
40 change in the tRMSD as a function of cluster number is used to determine the
optimum number of clusters to be used in the analysis (see section 3.8).
The output of the air mass trajectory cluster analysis is useful for estimating pollutant source regions [Gomez and Martin, 1987], quantifying the relationship between atmospheric transport and the aerosol chemical composition [Moody and Galloway,
1988], and identifying similar meteorological scenarios for case studies [Moody and
Samson, 1989].
2.7.5 The potential source contribution function technique
If one combines aerosol chemical data and trajectory information, then potential source regions of air pollutants can be identified and defined more quantitatively. One of the methods used is the potential source contribution function (PSCF). PSCF is a statistical technique developed to systematically analyse aerosol concentration data [Ashbaugh et al., 1985] and its methodology concentrates on (a) ascertaining the probability that a polluted air mass originates from a given geographical location; (b) determining the most frequently used pathways that an air mass takes as it passes over pollution sources and moves towards the receptor site; and (c) finding the probability that an air mass originating from a given geographical location is polluted.
The potential source contribution function (PSCF) [Ashbaugh et al., 1985] is the probability that an air parcel with aerosol concentration greater than a specified threshold arrives at the receptor site after having passed through a certain grid cell of the spatial domain of interest. It is calculated as the ratio of the grid cell number of backward trajectory segment endpoints associated with concentrations above the threshold to the total number of trajectory segment endpoints for the specified cell.
41 Calculations are performed on a longitude-latitude grid which covers the spatial domain covered by (most of) the calculated trajectories. It is assumed that aerosols which are emitted within a certain grid cell are taken by the air parcel and transported to the receptor site without loss through diffusion, chemical transformation or atmospheric scavenging
[Cheng et al., 1993]. Let nij be the total number of trajectory segment endpoints falling in the grid cell (i,j) over the period of study, and mij the number of endpoints in grid cell (i,j) corresponding to trajectories associated with concentration values at the receptor site exceeding a specified threshold. Then the probability Pij (PSCF), that an air parcel passing over the cell (i,j) on its way to the receptor site arrives with concentration values above the threshold, is given by the ratio
mij Pij = ……………… 2.38 nij
High values in the spatial distribution of Pij will therefore correspond to geographical regions that are likely to produce high concentration values at the receptor site.
In order to exclude high PSCF values that might have arisen purely by chance, the PSCF values are usually tested against the null hypothesis that there is no association between concentrations and trajectories [Vasconcelos et al., 1996]. The statistical significance of the spatial distribution of the PSCF values is examined by making use of a nonparametric bootstrap method [Wehrens et al., 2000], which assumes that the concentration values are independent and identically distributed. In this procedure, B random sub-samples of size
N (equal to the length of the original concentration data), of concentration data set
* * * * * C = {c1 ,c2 ,c3 ,.....cN } is drawn with replacement from the original concentration data set
C = {c1 ,c2 ,c3 ,.....cN }. Calculations of the PSCF are then made for each bootstrapped
42 * sample k giving the corresponding PSCF spatial distributions Pk;ij , for each bootstrapped sample k (k = 1, . . ., B ).
* * Assuming that Pk;ij ,< ...... < PB;ij , are the ordered values of the PSCF spatial distribution
* (Pk;ij ), then if
* * Pk;ij ≥ P()()()B+1 1−α ;ij , ……………… 2.39 the null hypothesis is rejected at (1 − α)*100% confidence level where α is the chosen significance level. Only the PSCF values satisfying the above relation are retained for subsequent analysis.
The PSCF method has successfully been applied to locate regional sources and preferred transport pathways of atmospheric constituents identified by multivariate receptor models
[Lupu and Maenhaut, 2002; Poirot and Wishinski, 1986; Stohl and Krompkolb, 1994; Xie et al., 1999].
43 Chapter 3 Materials and methods
Chapter 3 covers the data, equipment and methodology used in the present research.
Starting with the location of Rukomechi, it discusses the aerosol sampling technique used and the necessary analysis techniques, which include the determination of particulate mass and black carbon, Proton Induced X-ray Emission analysis, instrumental neutron activation analysis, principal component analysis, trajectory calculation and cluster analysis procedure. The chapter finishes by indicating how the potential source regions of aerosols in the subcontinent were identified.
3.1 Study area
Sampling of aerosols was performed at Rukomechi research station (Figure 1.1 left), northern Zimbabwe (16.1 °S, 29.4 °E) from September 1994 to January 2000. The site is situated at an altitude of 500 m above sea level in the Zambezi valley that runs east-west along the Zimbabwean-Zambian border (Figure 1.1 right). The site is close to the southern escarpment (about 5 km away) but the valley stretches northwards towards the Zambian border for about 80 km. Its remoteness makes it suitable for studying aerosols originating from distance sources within the subcontinent.
The location is under the influence of various air masses throughout the year making it suitable for studying the influence of different synoptic weather systems on the aerosol loading over northern Zimbabwe. Occasionally, there are northern winds associated with the inter-hemispheric tropical convergence zone (ITCZ). The ITCZ is a global belt of low pressure produced by the convergence of air masses coming out from the high pressure belts to the north and south of the equator [Gedzelman, 1985] and is associated with rainfall over southern Africa [Makarau, 1995]. The ITCZ sometimes comes close to the station and/or passes across the station twice a year; that makes Rukomechi an interesting
44 site to study the influence of meteorological systems on aerosol concentrations over the
site.
3.2 Sampling equipment
Sampling was done from September 1994 to January 2000 by the Tsetse Control Branch
that manned the station. The filter method was used to collect the atmospheric aerosol
concentrations over Rukomechi and main components of the Gent PM10 stacked filter
unit (SFU) sampling system used for aerosol measurements at the site are shown in Figure
3.1.
To pump Wooden pole Wooden board White Teflon washer Nylon connector Threaded part Transparent tubing Brass connector Container lid Steel clamps Fine filter Plastic clamps Flat gasket Rain protection cover Plastic container with stacked filter cassette (SFU) inside Coarse filter Stacked filter cassette Flexible POLY-flow tubing 3 rods from pre- O-rings Brass connector impaction plate to polyethylene tube
Pre-impaction plate (greased)
Air flow in Pump Time switch
Hour meter
Figure 3.1 The Gent PM10 stacked filter unit sampler used to collect aerosol samples at Rukomechi research station [Maenhaut et al., 1994]
The pump is the source of air movement that enables particle-laden air to be aspirated into
the sampling system through the sample inlet and the volume of the air pumped through
the system is measured by the volume meter. The pre-impaction plate is used to keep
particles with diameter greater than 10 µm out of the system.
45 Aerosol-laden air (with particles less than 10 µm diameter) is drawn by the pump through the sampling probe into a filter holder containing the filter medium. Depending on the filter characteristics, flow velocity and properties (size, density, etc) of particles, aerosols are separated from the flow stream. As particles were to be separated in two discrete size ranges, a two-stacked filter configuration of decreasing pore size was employed such that air passes from the first stage to the second stage.
From the filter unit (two-stage cascaded filters), air is drawn through a rotameter (flow measuring device) and the valve regulates the air flow. As the flow rate depends on the resistance imposed by the filter and its collected aerosols, flow rates were checked periodically by local personnel of the Rukomechi research station during sampling and adjusted accordingly if necessary. Nevertheless, the pump unit employed flow control sensors built into a feedback loop to minimise such regular attention during sampling.
The Gent PM10 stacked filter unit (SFU) sampler collects bulk aerosol samples on 47 mm diameter Nuclepore filters of different pore size. An 8 µm pore size filter collects coarse particles (equivalent aerodynamic diameter 2.0 - 10 µm) while a 0.4 µm pore size filter collects fine particles (with equivalent aerodynamic diameter < 2.0 µm). The flow rate was typically 16 litres per minute. As the location is situated in a remote area, sampling was performed during two consecutive days in order to collect enough particulate mass on the filter and each aerosol sample was collected during the daytime only, i.e., from 06:00 to 18:00 local time.
Sampling was done during daytime only because the station was powered by a generator that would be switched off during night-time. Even if electrical power to run the system would have been available, it would have been inconvenient to sample during the night due to the strong temperature inversion that develops during the night (see section 4.1.3).
This inversion would inhibit vertical mixing, trap aerosols near the ground making night
46 time measurements more representative of local than regional emissions.
A total of 494 samples were collected, with each sample containing separate coarse and fine size fraction filters. The collected samples were stored at the site and then later air- freighted to the Institute of Nuclear Sciences (Gent University, Belgium) for physical and chemical analysis, which has been performed as part of the PhD project of M.-T.
Fernandez-Jimenez [Fernandez-Jimenez, 1999]). Physical and chemical analysis was done at the Gent Institute of Nuclear Science by Prof W Meanhaut’s group. The analytical methods applied and the specifications of the equipment used are discussed in the following sections.
3.3 Determination of particulate mass and black carbon
The particulate mass was determined by weighing the Nuclepore filters before and after sampling at a temperature of 20 °C and 50% relative humidity using an electronic microbalance with 1 µg sensitivity. Approximately 24 hours before gravimetry, the filters were placed in this controlled environment to equilibrate. Before actual weighing (pre- and final- samples), the samples were exposed to 210Po radioactive source in order to remove electrostatic charges on the filters that could cause errors in the measurement due to electrostatic forces. Aerosol particulate masses were finally determined as the difference between the pre-sample and final masses.
Black carbon concentrations were determined from the increase of light absorption (see section 2.5.2). It was estimated by measuring the transmission of He/Ne laser light
(wavelength 0.633 µm) through the filters before and after exposure. The specific attenuation cross section was used to convert the measured absorption into black carbon concentration in µg m-3 (Equation 2.4).
47 3.4 Proton Induced X-ray emission analysis
The Proton Induced X-rays Emission (PIXE) method was used to identify and quantify the different atomic species in the collected samples (see section 2.5.3.1). Figure 3.2 shows the experimental set up for PIXE employed.
Gamma ray Detector
Funny filter
Figure 3.2 Schematic diagram showing the arrangement of the main components used for the Proton Induced X-ray Emission Analysis [Govil, 2001]
PIXE analysis system used consists of an accelerator that produces a proton beam in the range of 2 to 5 MeV, a beam transport system and an analysis vacuum chamber (vacuum pressures of 0.133 Pa) that contains several targets (samples) and detectors. The collimators produce a focussed proton beam on the target. The resultant X-rays are detected by the energy-dispersive X-ray detection system (Lithium drifted Silicon (SiLi) detector) and the data is sent to the computer coupled with amplifiers and a multi-channel analyzer. The data output from the system is processed by computer to yield micrograms of trace element per square centimetre (µg cm-2) and subsequently µg m-3 of air (see section 2.5.3.1). The proton beam intensity required for quantitative analysis is determined by beam charge measurements at the Faraday cup.
48 The samples (targets) were bombarded in a vacuum irradiation chamber using a 2.4 MeV proton beam supplied by a compact isochronous cyclotron. The beam diameter at the target was 8 mm with the beam current of 150 nA and the preset charge (bombarding time) per target up to 60 µC. The X-rays emerging from the target were detected with the
Si(Li) detector and a composite “funny filter" [Maenhaut and Raemdonck, 1984] was placed in front it. A “funny filter” is a filter with a hole (one 10th of the detector active area) drilled at its centre. It is used to reduce low energy transmission (since X-ray spectrum is weighted towards low energy end) that would lead to high energy count rates in the detector, which may cause an overloading of electronics, worse resolution and peak shift.
The PIXE spectra were analysed using the AXIL-84 computer code [Maenhaut and
Vandenhaute, 1986], which performs data reduction of the acquired X-rays and evaluates the X-ray spectra to produce quantitative results. The computer code uses a library of measured X-ray intensities for each element, as determined from gravimetric thin film standards [Cohen et al., 2002], to simultaneously fit the entire spectrum. The background continuum is fitted by a ninth order constrained polynomial. Interferences are accounted for in the fitting process since the library contains all the lines of each element and their ratios to one another. From the spectral analysis, the mass per unit area of an element is found using the previously measured efficiency curves from which the elemental concentration is determined.
3.5 Instrumental neutron activation analysis
The basic instrumentation used to perform Instrumental Neutron Activation Analysis
(INAA) (see section 2.5.3.2) consists of a nuclear reactor for irradiating the samples, nuclear detectors for detecting the gamma emissions, and various types of multi-channel
49 analyzer systems that range from simple data acquisition systems to complex computerized data acquisition and processing systems.
The INAA counting facility is equipped with a High Purity Germanium (HPGe) detector, multi-channel analyzer, and Genie 2000 Gamma spectrum analysis system. The HPGe detector is a coaxial detector which can measure γ-rays with energies over the range from about 60 keV to 3.0 MeV. The multi-channel analyzer is a device whose primary function is to sort and store the signals coming from the nuclear detector due to gamma interaction with the detector.
3.5.1 Irradiation and Counting for Short - Lived Isotopes
The aerosol samples were irradiated individually for 20 s to 10 min in the pneumatic transfer system of the nuclear reactor at a neutron flux of 4.0 x 1013 cm-2 s-1. After a monitored decay, each sample vial is rinsed in a radioactive hood and samples placed in non-irradiated counting vials. After a decay time (determined by the results of preliminary sample analyses), each sample was counted for 30 to 300 s on designated HPGe detection systems. Data was analyzed and spectra are stored on disk with appropriate nuclear parameters included. The elements typically analyzed for short-lived isotopes are Al, V,
Ti, Mn, Mg, Cl, Cu, F, O, Na, K and I.
3.5.2 Irradiations and Counting for Medium and Long - Lived Isotopes
The aerosol samples were again exposed for 1 to 24 hr at a neutron flux of 4.0 x 1013 cm-2 s-1 in the rotating wet vertical exposure irradiation facility of the nuclear reactor. After approximately a 5-7 day radioactive decay (to allow interfering activities to decay away), the samples were counted for 400-1200 s on the gamma detection systems. Data were analyzed and spectra were again stored on disk with appropriate decay time, dead time and other nuclear data included. The elements typically analyzed by medium-lived
50 isotopes are Na, K, As, Sm, Cd, La, Mo, Br, Sb, U, Hg, Au, W and Pt. After a 14-21 day decay, a 1000-1800 s gamma count of each sample was made for long-lived isotopes. The elements typically analyzed by long-lived isotopes are Ce, Ca, Lu, Th, Cr, Eu, Yb, Nd, Zr,
Ag, Cs, Fe, Sc, Zn, Co, Sr, Rb, Ni and Ba. The elemental concentrations were calculated as discussed in section 2.5.3.2.1.
3.6 Principal component analysis and absolute principal component
analysis
For source identification, the principal component analysis (PCA) (see section 2.6.1) with
Varimax rotation (to enhance the interpretability of the principal components) was applied. A Statistical Package for Social Sciences (SPSS) [SPSS, 1999] analytical tool was used to calculate the principal components needed to identify the sources of aerosols and the absolute principal component analysis (APCA) was used to quantify the contributions of the sources to the total particulate mass. Before applying the PCA method to the chemical data (split into wet and dry season data), the measured concentrations which were below the detection limit were given as negative concentrations. PCA method cannot handle negative values or blank cells, so the measured concentrations below the detection limit were replaced by half of their absolute values to minimize their influence to the real concentrations.
For the 47 chemical elements (i.e., : Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe,
Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ag, Cd, In, Sn, Sb, I, Cs, Ba,
La, Ce, Sm, Eu, Lu, W, Au, Pb, and Th) that were determined by a combination of PIXE spectrometry and INAA, only a subset was used for further analysis. The basic criterion to retain an element for PCA was that its concentration had to be above the detection limit in over 70% of all the samples (Willy Maenhaut; personal communication). Hence, PCA involved 24 variables (PM, BC, Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Mn, Fe, Co, 51 Cu, Zn, Sr, I, La, Sm, and Th) for the coarse size fraction and 22 (PM, BC, Na, Al, S, Cl,
K, Ca, Sc, Ti, V, Mn, Fe, Co, Zn, As, Br, Sb, I, La, Sm, and Pb) for the fine size fraction that satisfy the above mentioned criterion.
To decide upon the number of components to retain for PCA, each of the components had to explain at least 1 unit of variance after the Varimax rotation; in addition, the communality (i.e., the variance explained by the retained components) was preferably greater than 0.70 for each of the elements. To categorize the principal component (PC) loadings of the elements on the components, the following criterion was used: (a) low loading when PC was less that 0.30; (b) moderate loading for PC loadings ranging from
0.30 to 0.60; (c) moderately high loading when the PC loading was greater than 0.60 but smaller than 0.80, and (d) high loading for PC loadings greater than 0.80.
To identify and give a name to each component, certain elements were used as indicators for specific aerosol sources. As discussed in section 2.2, the component that shows high
Principal Component loadings of Al, Si, Ti and Fe is identified as the generic ‘mineral dust’ source and that with high Principal Component loadings in Cl and Na is identified as the result of the sea salt sources. Black carbon and K were used as indicators of biomass burning, Pb and As were taken for indicators for anthropogenic sources while P and Sr were used to identify biogenic sources.
Absolute Principal Component Analysis (APCA) was used to quantify the contribution of each source to the concentration of each element in each sample for the overall, wet season and dry season data. Approaches to perform these conversions for the selected elements are given in section 2.5.2.
52 3.7 Trajectory calculation using HYSPLIT model
The HYSPLIT_4 (HYbrid Single-Particle Lagrangian Integrated Trajectory model 4)
[Draxler and Hess, 1997] was used to compute backward air mass trajectories employing the kinematic approach (see section 2.7.2) using the re-analyzed National Oceanic and
Atmospheric Administration (NOAA) wind dataset of resolution 2.5° x 2.5° (latitude, longitude) as input. The HYSPLIT model is a hybrid simulation of the transport and dispersion of atmospheric pollutants over mesoscale/regional distances by performing calculation procedures employing the Lagrangian (transport) and Eulerian (dispersion) approaches [Draxler and Hess, 1997; Draxler and Hess, 1998]. Trajectory calculation was done at the Max Planck Institute for Chemistry, Mainz, Germany.
The kinematic method was used in this study because the comparison of the kinematic and isentropic methods [D'Abreton, 1996; Fuelberg et al., 1996] showed that the kinematic approach was a preferable method in trajectory modelling for southern Africa [Garstang et al., 1996; Tyson and D'Abreton, 1998]. Furthermore, the isentropic trajectory modelling is best suited for cloud- and rain-free conditions and for areas with relatively short advection periods [Fuelberg et al., 1996], hence in this study where both the wet and dry season analysis was needed, the kinematic model would give more appropriate results.
To sample a variety of flow regimes, five-day back trajectories were calculated on a daily basis (start time: 1200 hrs local time (=GMT + 2 hour) from January 1994 to December
1999). Five-day back trajectories were selected because they went far enough back in distance to cover the major source regions of the sub-continent. The trajectories were calculated starting at a height of 1180 m above ground level (~ 800 hPa pressure level).
The starting height of 1180 m was chosen because it is above the escarpment (to minimize local valley channelling effects) but within the daytime mixing layer (section 4.1.4) and of the HYSPLIT that was found to about 1570 m on average. This height is also below the
53 region’s maximum mixing layer of approximately 4 km [Anderson et al., 1996; Tyson et al., 1996a].
The model computes backwards trajectories for air masses arriving at Rukomechi research station with position (latitude, longitude), height above ground level, relative humidity and height of the mixing layer (above ground level) given every hour. In total, 2172 trajectories were calculated and the trajectory information was stored in ASCII file format for cluster analysis (sections 2.7.4 and 3.8) and identification of potential source region of aerosols (sections 2.7.5 and 3.10.2).
3.8 Trajectory cluster analysis procedure
To analyze trajectory data from a large population of Rukomechi kinematic back trajectories, the cluster analysis technique (see section 2.7.4) was used in order to obtain a grouping of generalised air flow pathway. The large data set of trajectories has been reduced by using latitude and longitude information every 3 hours (instead of 1 h) to reduce computation time. As mentioned already in section 2.7.4, the cluster algorithm used is non-hierarchical, which minimizes the differences among air mass trajectories in a cluster and maximizes the differences between clusters by minimizing the Euclidean distances between the corresponding coordinates of the individual trajectories
(considering the full length of each 5-day backward trajectory).
To determine the optimum number of clusters to retain during the cluster analysis, the method of Dorling et al. [1992] was applied. Starting with a high cluster number, (n = 20), the total root mean square deviation (tRMSD) of the chosen number of potential clusters is calculated. Then the number of potential clusters is subsequently reduced stepwise (by one unit) and the corresponding tRMSDs are calculated again. A steep increase in the tRMSD is then observed when a significant number of different trajectories formerly
54 located in distinct clusters are put together into one cluster. Hence the optimum number of
clusters to be used for the dataset was taken to be that number before the steep increase in
the tRMSD (see section 4.3.1).
3.9 Speed along the median trajectory
The distance (in km) between two points on the earth’s surface (P1 = P1(x1,y1) and P2 =
P2(x2,y2)) can calculated by the following expression
c cos−1[]sin()y c sin(y c )+ cos(y c )cos(y c )cos(c (x − x )) X = 2 1 1 2 1 1 1 2 1 1 1 2 … 3.1 P1P2 c1
where x and y are respectively the longitude and latitude of point P (in degrees), c1 =
2π/360 and c2 = 111.12 km.
Bearing in mind that the distance between any two successive median trajectory points
(equation 3.1) was covered in 3 hours, the wind speed between the points can be
calculated from which the trajectory mean speed and/or mean wind speed of any section
of the trajectory can be determined. This procedure was used to determine the average
wind speed of the median trajectories associated with each of the final seven flow regimes
and of mean wind speed of the section of some median trajectories (see section 4.3.3).
3.10 Source regions of aerosols in the subcontinent
3.10.1 The fire plot technique
Biomass burning events may be observed in a number of ways from space and active fires
detected by satellite provide a good indication of the spatial and temporal patterns of
global fire incidence [Langaas, 1992]. Satellite sensors with high resolution and a limited
number of spectral channels such as the ATSR sensor can provide detailed visual
55 information on the distribution of fires and smoke. The fire data used in this study were obtained from the European Space Agency (ESA) World Fire Atlas
(http://dup.esrin.esa.int/ionia/wfa/index.asp). This data is retrieved from the Along Track
Scanning Radiometer (ATSR-2) carried onboard the Second European Remote Sensing
Satellite (ERS-2). The ATSR-2 has seven visible and infrared channels centred at 0.55µm,
0.67 µm, 0.87 µm, 1.6 µm, 3.7 µm, 11.0 µm and 12.0 µm [Mota et al., 2006]. Its spatial resolution is 1 km at nadir (satellite sub-point on the earth's surface that is centred directly below the satellite). Additional information about the ATSR-2 can be found at http://www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml.
The fire data is available from January 1995 but the fire analysis was restricted to the biomass burning season (July to November) from July 1995 to November 1999. An algorithm (Appendix C) was developed that combines the fire data along each of the trajectories calculated by the HYSPLIT model. In this algorithm, all fires found within a buffer zone of 50 km on either side of the trajectories were counted and the point information (date, trajectories running hour, position (longitude, latitude), mixing layer and height of trajectory) determined. The combination of the fires found within the buffer zone while the trajectories were within the mixing layer and the aerosol concentration data was used to locate the potential source regions of biomass burning related aerosols over northern Zimbabwe (see section 4.4.2.1.1).
3.10.2 The potential source contribution function technique
In the present study, the potential source contribution function technique (section 2.7.5) was applied to quantitatively identify and define the locations of the aerosol source regions in the subcontinent using the multi-species and multi-annual concentration time series of the Rukomechi research station aerosol data set, as well as the corresponding trajectory data set (see section 2.7.5). Here, only those trajectories were considered which
56 were within the mixing layer and centred around the period of high aerosol loadings for each of the indicator elements associated with biomass burning (BC) and anthropogenic
(Cu, S and Pb) sources. The threshold concentration (see section 2.7.5) was set at the 75- percentile level and the nonparametric bootstrap method [Lupu and Maenhaut, 2002] was used to estimate the statistical significance of the calculated PSCF values with the significance level, α, set at 0.95. Given that sampling was done over two consecutive days, only the corresponding daily trajectories were assigned to one concentration value.
The trajectories of the days during which no measurements were made are not included in the PSCF analysis.
57 Chapter 4 Results and discussion
The intricate consortium of aerosols measured at Rukomechi are presented and discussed in this chapter. This aims to present (a) an insight of the meteorology at and around the measuring site, (b) the aerosol source types and source apportionment; (c) the regional air mass flows and pathways to northern Zimbabwe, and (d) and the potential sources regions of biomass burning and anthropogenic related aerosols.
4.1 Meteorological situation
Description of the local meteorology at Rukomechi is based on the hourly meteorological data collected at the station from 1992 to 1999. At the site, the following meteorological parameters were monitored: wind speed and direction, relative humidity, air temperature and solar radiation, using a standard automatic meteorological station (DELTA-T, U.K.) which was provided by the Natural Resources Institute, Chatham/U.K. The time series from the hourly meteorological measurements are investigated from the point of view of annual, seasonal, monthly and diurnal variations. As rainfall data for Rukomechi were not available, the daily rainfall information (from 1991 to 1999) is derived from meteorological stations around the Rukomechi research station. The rainfall data were provided by the Department of Meteorological Services of Zimbabwe, Harare.
In this section, only the meteorological parameters that affect the aerosol concentrations measured at Rukomechi are discussed. However, the detailed discussion of other parameters that define the local meteorology at Rukomechi research station is presented in
Appendix D.
58 4.1.1 Wind variability
The analysis of near surface (approximately 1 m above ground level) winds shows that the wind regime at Rukomechi varies substantially in both wind speed (presented in Appendix
D) and direction between day and night (presented here). Wind at Rukomechi research station, tends to blow from distinct directions during day and night time (Figure 4.1a). The long term average of wind direction shows that the dominant wind flow direction shifts by an angle of about 90 degree, i.e., from ENE during the day to SSE or S during the night.
On average, close to 40% of the cases (Figure 4.1b), the day time winds blow from ENE and the flows are sharply defined between the NE and E directions.
(a) (b) N Day time 40 Night time 40 NNW NNE Day time 30 35 Night time NW NE Noon Time 20 30 WNW ENE 25 10 20 W 0 E 15
10
WSW ESE total the to % contribution 5
SW SE 0 E S N W SE NE SW NW ESE SSE ENE SSW NNE SSE SSW NNW WSW WNW S Wind direction
Figure 4.1 (a) Flow direction of the daytime and night-time winds and (b) the percentage contribution of each direction to the daytime and night-time wind flows
It is important to note that although the average day time winds direction is mainly ENE, the noon time wind flow is predominantly NNE direction (Figure 4.1b). This shift in the noon time surface winds flows could be due to the anabatic winds caused by the differential heating between the valley and the escarpment. The spread in the direction of night time wind is bimodal with a major component (24% of the time) from the south. The other night wind direction component shows that 13% of the time, the flow is coming from the east. This difference shows the influence of the valley channelling effect of the
59 (main) Zambezi valley during the day and the katabatic winds caused by the Rukomechi canyon in the southern escarpment. As the aerosol sampling was done only during daytime, the local effects due to the night time wind flows can be ruled out.
4.1.2 Relative humidity and rainfall
The relative humidity sensor of the Rukomechi automatic weather station that was first setup in 1992 operated for 9 years without interruption and without recalibration. As a result of unavoidable shift of the sensor, the display of saturation relative humidity (i.e.
100%) increased to values greater than 100% (reaching about 135% in 1999). Since the in-situ calibration could never be done (for logistical reasons), a suitable procedure to correct the sensor drift had to be found. The correction procedure was done for upper and lower range relative humidity values. The steps in the correction procedure are shown in
Appendix A and the corrected values of the relative humidity values were calculated using the expression (Equation A.9; Appendix A):
-3 4 rhcorr(t) = -49.51 + 1.48x10 t + (4.78 - 1.10x10 t)rhobs(t)
4.1.2.1 Variation in relative humidity
Equation A.9 (Appendix A) was applied to the hourly relative humidity data set measured from January 1992 to April 1999 and the synthetic daily median relative humidity values derived from the hourly corrected relative humidity data were used for further analysis.
The variation of the corrected sensor daily median relative humidity for Rukomechi is shown in Figure 4.2. The results show that the relative humidity conditions over
Rukomechi exhibit seasonal variability marked by low (20-30%) and high (90-100%) values. The change from high to low happens gradually starting in mid-March and continues until the end of October/beginning of November where it is lowest. Over the years of this study, it has been noted that this gradual decrease in daily median relative
60 humidity is occurring at the rate of 0.16% per day. This slow decrease in daily median
relative humidity (wet to dry) may be due to the maintenance of high relative humidity
values as a result of evapotranspiration. The transition from low to high daily median
relative humidity happens around mid November and is a sudden change. This abrupt
change in relative humidity marks the beginning of the rainy season, indicating that there
is a clear cut between the end of the dry season (low daily median relative humidity) and
the start of the wet season (high relative humidity).
110 rh = 0.0047*t + 52 DailyDaily relativemedian relative humidity humidity LinearLinear (Daily(Daily median relative relative humidity) humidity) 100
90
80
70
60
50
Daily relative humidity (%) 40
30
20 1-Jan-92 1-Jan-93 1-Jan-94 1-Jan-95 1-Jan-96 1-Jan-97 1-Jan-98 1-Jan-99 1-Sep-92 1-Sep-93 1-Sep-94 1-Sep-95 1-Sep-96 1-Sep-97 1-Sep-98 1-May-92 1-May-93 1-May-94 1-May-95 1-May-96 1-May-97 1-May-98
Figure 4.2 Daily median relative humidity (corrected) at Rukomechi research station from 1992 to April 1999 (where t is the time in days from 1 January 1992 to 30 April 1999)
The rate of change of the daily median relative humidity during the transition period from
low to high was found to be about 0.68% per day and it happens over a period of three
months. Assuming that the changes are a first order linear relation, then the decrease and
61 increase of daily median relative humidity (rh) can be summarised by the following equations:
rhwet-to-dry = -0.16t + 82 …………. 4.10a
rhdry-to-wet = 0.68t + 36 …………. 4.10b where t is the time in days from 1 January 1992 to 30 April 1999.
From Figure 4.2, the relative lengths of the rainy seasons for different years can be deduced. For that we have chosen to use, by comparing, the area between the 60% relative humidity line and the daily median relative humidity curve. For instance, the 1993-1994 rainy season was shorter while that of 1996-1997 season was relatively longer. The 1993-
94 and 1994-95 rainy seasons were punctuated by episodes of low daily median relative humidity, which reflects the mid-wet season dry periods that occurred during the particular seasons.
Throughout the period of study, the daily median relative humidity shows an increasing trend. This could be attributed to sensor drift which may not have been captured by the applied correction procedure (Appendix A). But as will be shown later with measured rainfall trends (in section 4.1.2.5), it is possible that this trend could be due to the changes in the actual relative humidity in the environment. If that is the case, then statistically, it can be stated with 95% confidence that the median relative humidity over Rukomechi has been increasing at rate of 1.7% per year from 1992 to 1999.
The annual cycle of the relative humidity was used to define the dry and wet seasons. The daily median relative humidity derived from sensor corrected hourly measurements at
Rukomechi from January 1992 to December 1999 was calculated for each day of the year.
The annual variation in the daily median relative humidity values and inter-quartile range
62 are shown in Figure 4.3. The dry season and wet season were defined according to relative humidity of 60%. The dry season is the period from mid April to mid November when the median relative humidity was less than or is around 60%. Mid November was considered as the end of the dry season as this is the time when a sudden increase in relative humidity has been noted. The wet season is defined as the period from mid
November to mid April when the median relative humidity is more than 60%. Mid April was taken to mark the end of the wet season because it is the first time the relative humidity falls below 60%.
100 Wet Dry 90 80 70 60 50
Relative humidity (%) (%) humidity Relative 40 30 1-Jul 1-Apr 1-Oct 1-Jan 1-Mar 1-Jun 1-Nov 1-Feb 1-Dec 1-Aug 1-Sep 1-May
Day of the year
Figure 4.3 Annual variations of the daily median (line) and inter-quartile range (grey shaded) of sensor corrected relative humidity.
These definitions for the dry and wet season were later used to split the aerosol data for statistical analysis and in the discussion of the seasonal variation of the elemental concentrations. The wet and dry season classification using Rukomechi relative humidity data is considered to be representative for the region around the site as confirmed (at least qualitatively) by the following analyses of the rainfall data of stations around Rukomechi.
63 4.1.2.2 Rainfall around Rukomechi research station
As the rainfall data at Rukomechi research station were not available, daily rainfall data from meteorological stations around Rukomechi were used. These stations are situated within a 200 km distance from Rukomechi. The variations in the total monthly rainfall from January 1991 to December 1999 for the surrounding meteorological stations are shown in Figure 4.4, while the values of the total annual rainfall and the number of rainy days for each of the stations are presented in Table 4.1.
400 500 Kanyemba Banket 450 Linear (Banket) Linear (Kanyemba) 350 Monthly total rainfall = 0.0069*t - 173 Monthly total rainfall= 0.36*t - 355 400 300 350
250 300
200 250
150 200 150 100 Monthly total rinfall (mm) Monhly total rainfall (mm) (mm) rainfall total Monhly 100 50 50
0 0 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Sep-91 Sep-92 Sep-93 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Sep-93 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 May-91 May-92 May-93 May-94 May-95 May-96 May-97 May-98 May-99 May-93 May-94 May-95 May-96 May-97 May-98 May-99
450 450 Chinhoyi Guruve 400 Linear (Chinhoyi) 400 Linear (Guruve) Monthly total rainfall = 0.17*t - 133 Monthly total rainfall = 0.36*t - 348 350 350
300 300
250 250
200 200
150 150 Montly rainfall (mm) rainfall Montly 100 Monthly total rainfall (mm) rainfall total Monthly 100 50 50 0 0 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Sep-92 Sep-93 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 May-92 May-93 May-94 May-95 May-96 May-97 May-98 May-99 Sep-91 Sep-92 Sep-93 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 May-91 May-92 May-93 May-94 May-95 May-96 May-97 May-98 May-99
400 Karoi 500 Mvurwi Linear (Karoi) 350 450 Linear (Mvurwi) Monthly total rainfall= 0.23*t - 207 Monthly total rainfall = 0.62*t - 636 400 300 350 250 300 200 250
150 200 150 100 Monthly total rinfall (mm) rinfall total Monthly Monthly total rinfall (mm) 100 50 50 0 0 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Jan-94 Jan-93 Jan-92 Jan-91 Sep-99 Sep-98 Sep-97 Sep-96 Sep-95 Sep-94 Sep-93 Sep-92 Sep-91 May-99 May-98 May-97 May-96 May-95 May-94 May-93 May-92 May-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Sep-92 Sep-93 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 May-92 May-93 May-94 May-95 May-96 May-97 May-98 May-99
Figure 4.4 Variations of the monthly total rainfall for Zimbabwe meteorological service stations around Rukomechi research station (also see Table 4.1)
64 One of the meteorological stations used is Kanyemba (15.63°S, 30.41°E, 329 masl), which is located about 100 km east of Rukomechi in the Zambezi valley and it experiences the same climatic conditions as both are found in the same natural region
[Vincent and Thomas, 1960]. The monthly total rainfall at Kanyemba (Figure 4.4a) closely mimics the variations in the daily median relative humidity data of Rukomechi research station (Figure 4.2) over the period of study. The low daily median relative humidity values that appeared during the 1993-94 and 1994-95 rain seasons are reflected in the rainfall data as mid wet season dry episodes in December 1993 and January 1995.
The 1996-97 rainy season was an abnormal season that registered total annual rainfall of
1169 mm with a total of 78 rainy days as compared to the mean total annual rainfall of
603 mm and mean annual total rainy days of 55.
For all the weather stations around Rukomechi, the general trend of the total monthly rainfall shows an increasing tendency like that of the relative humidity. This consistency between the observed rainfall and the corrected daily median relative humidity data can be used to infer that the increasing trend observed in the corrected daily median relative humidity data is an actual trend and not due to the un-calibrated sensor trend. This also serves as a good indication that the correction procedure derived is correct.
65
Banket Chinhoyi Guruve Kanyemba Karoi Mvurwi (30.2 °E, 17.3 °S) (30.1 °E, 17.2 °S) (30.4 °E, 16.4 °S) (30.4 °E, 15.6 °S) (29.5 °E, 16.5 °S) (30.6 °E, 17.0 °S) D (km) 155 142 114 120 43 160 Rainfall Rainy Rainfall Rainy Rainfall Rainy Rainfall Rainy Rainfall Rainy Rainfall Rainy Season (mm) days (mm) days (mm) days (mm) days (mm) days (mm) days 1991-92 535 64 404 41 492 69 1992-93 713 75 557 69 739 65 930 84 862 87 1993-94 638 72 511 71 697 55 515 41 396 43 728 72 1994-95 648 58 499 49 583 56 522 45 347 57 415 61 1995-96 964 99 826 82 786 74 571 63 799 102 769 82 1996-97 909 87 1010 101 1141 79 1169 78 1099 100 1340 92 1997-98 857 77 916 78 650 67 823 59 824 75 1099 83 1998-99 1068 93 1036 107 1197 75 832 70 1019 94 1461 94
Table 4.1 The total annual rainfall and the number of rainy days for each of the seasons from weather stations around Rukomechi research station. D is the distance (in km) of the station from Rukomechi.
The rainfall measured at meteorological stations around Rukomechi also shows two
distinct seasons (dry and wet) that are consistent with the daily median relative humidity
measured at Rukomechi. As in the relative humidity analysis, variation in the rainfall
received in the area around Rukomechi can influence aerosol loading. The seasonality was
also reflected in the atmospheric aerosol concentrations measured at the site (section
4.2.5).
4.1.3 The mixing layer over Rukomechi research station
Air pollutants are transported horizontally by advection and vertically, in the atmospheric
boundary layer (also mixing layer), mainly by turbulence. The turbulence occurring in the
mixing layer influences vertical concentration of the atmospheric constituents and the
meteorological conditions, especially convection processes and temperature inversions
[Beyrich et al., 1996]. Vertical exchange caused by turbulence dilutes the pollutant
throughout the mixing layer [Shaw et al., 2005] and this results (ideally) in a constant
vertical distribution of concentrations throughout the mixing layer [Husar et al., 1978].
66 A characterization of the mixing layer (in terms of vertical aerosol distribution) over
Rukomechi research station was not performed. As a substitute, results of some vertical soundings of potential temperature will be used, which were carried out on 21-22 August
1997 at Rukomechi research station using a tethered balloon system (Meixner F X personal communication). To demonstrate that these results are typical for the region, some vertical profiles of potential temperature (measured in a small aircraft) are added, which were obtained on 25 September 1992 over Victoria Falls (500 km west of
Rukomechi) during the SAFARI'92 experiment [Meixner et al., 1993].
Balloon and aircraft flights were performed at different times of the day to probe the vertical distribution of potential temperature up to a height of about 600 m above ground level (a.g.l.) at Rukomechi and 1700 m a.g.l. at Victoria Falls. The vertical profiles of potential temperature were found to be very similar in both cases. Figure 4.5 shows the diurnal variation in the vertical structure of the potential temperature measured at the two sites. It should to be noted that the use of potential temperature (instead of actual temperature) corrects for the adiabatic decrease of actual temperature with height (see equations 2.25, section 2.7.2). Therefore, a constant vertical distribution of potential temperature in the mixing layer is equivalent to complete mixing of the mixing layer.
Consequently, a positive slope of vertical potential temperature represents a stable stratification of a corresponding sub-layer (weak mixing), and negative slope for unstable stratification. Abrupt and/or marked changes of vertical temperature, particularly sudden increases, indicate the so-called (temperature) inversions, which limit the extent of sub- layers.
Much of the importance of inversions of temperature stems from the crucial role they play in controlling the vertical transport of aerosols and trace gases in the atmosphere. The inversions inhibit vertical exchange and strong inversions may decouple layers below and
67 above the inversion. This is generally the case for the sub-continental mixing layer of southern Africa, where a strong temperature inversion at the top of the mixing layer (about
3 km a.g.l.) decouples the (surface-adjacent) mixed layer from the free troposphere [Tyson et al., 1996b]. On a more local to regional scale, this is also the case for the so-called nocturnal boundary layer, a surface-adjacent sub-layer (of some tens to hundreds meters thickness), which develops after sunset due to strong infrared cooling of the surface, preferably under calm surface wind conditions [Stull, 1988].
68 (a) (b) 1700 600 1600
1500
1400 500
1300
1200
1100 400
1000
900
800 300
700
Height above ground level (m) 600 Height above ground level (m)
200 500
400
300 100 200
100
0 0 295 300 305 310 315 320 300 302 304 306 308 301 Potential temperature (K) Potential temperature (K)
25 September 1992 : 6.001800 amhrs 21 August 1997 : 9.002100 pm hrs 25 September 1992 : 7.001900 amhrs 22 August 1997 : 11.001100 hrsam 25 September 1992 : 8.002000 amhrs 22 August 1997 : 19:451945 hrspm 25 September 1992 : 9.002100 amhrs
25 September 1992 : 15003.00 phrsm 25 September 1992 : 16004.00 phrsm 25 September 1992 : 18006.00 phrsm
Figure 4.5 Vertical profiles of the potential temperature at different times of the day at (a) Victoria Falls in September 1992 and at (b) Rukomechi research station in August 1997
A distinct early morning evolution of the vertical potential temperature distribution at
Victoria Falls was observed which started at 0600 hrs (Figure 4.5a). At 0600 hrs, the
nocturnal boundary layer has a height of about 150 m, indicated by the first major change
69 of the slope of vertical potential temperature. Another layer, from 150 to 700 m, may have developed for orographical reasons (Meixner F X, personal communication). However, the increase of potential temperature within the first 150 m (approx. 10 K) indicates that the air in the nocturnal boundary layer was decoupled from the air above. As the ground heats up in the early morning, the increase of potential temperature was about 8 K at 0700 hrs and is further reduced to less than 1 K by 0800 hrs. At 0900 hrs, any nocturnal boundary layer(s) are completely eliminated and a constant potential temperature (~311
K) profile is an indication that the air is completely well mixed (at least to 1650 m a.g.l., the upper end of the profile). It is important to note that the temperature above 800 m a.g.l. has always remained constant over the period from 0600 hrs to 0900 hrs. The shift in the potential temperature from about 311 K in the morning to 314 K in the afternoon could be a result of regional surface heating and heat transport from the ground by turbulent mixing. At 1500 hrs and 1600 hrs, the ground is over heated showing in slightly higher potential temperature (~1 K) near the ground than throughout the mixing layer. At around
1800 hrs, indication of cooling of the ground starts to appear and the formation of the nocturnal boundary layer commences again.
Summarizing, the fate of the nocturnal boundary and mixing layers may be described as follows [Stull, 1988]: By sunset (approx. 1800 hrs, local time) the nocturnal boundary layer (NBL) will start to develop over the region as a consequence of widespread ground cooling which causes temperature inversion (at the top of the NBL). The NBL is considered to be a sub-layer forming the lowermost part of the formerly well mixed atmospheric boundary layer (the "mixing layer"). With proceeding time, the NBL will slowly grow in height and thereby will cut off the near surface air from the former mixed layer. The layer above the NBL (the "residual layer") will keep the characteristics of the mixed layer of the day before. However, the inversion at the top of the NBL will prevent exchange of air between the NBL and the residual layer. Any pollutants emitted at and/or
70 near the ground will be trapped in the NBL and enhance the pollutant concentration by accumulation. On the other hand, above the inversion, within the (still well mixed) residual layer, the pollutants that were left from previous day(s) tend to be "conserved" and can be transported during the night with the prevailing winds. In the morning as the sun starts to heat up the ground, convective thermals and turbulent eddies initiating from the surface, will (a) dissolve the NBL inversion by vertical mixing, and (b) force the growth of the (daytime) mixing layer. As the mixing layer grows, pollutants from the
(night time) residual layer will be mixed downward, while pollutants trapped in the former
NBL will be mixed upward. Around 0900 hrs to 1000 hrs a well mixed, uniform atmospheric boundary layer (the "mixing layer") will generally be established, showing vertically constant concentrations. For completeness, it should be mentioned, that (at least) during daytime there is also some exchange between the mixing layer and the free troposphere [Stull, 1988].
Results of the vertical potential temperature profile obtained from the Rukomechi balloon dataset are very similar to the Victoria Falls aircraft dataset. They also show a well developed nocturnal boundary on 21 August 1997 at 2100 hrs, with the temperature inversion higher (about 60 m) than that of 22 August at 1930 hrs (30 m). The shift in the potential temperature (~ 3.5 K) in the layers above the nocturnal boundary layer between
21 August 1997 (2100 hrs) and 22 August 1992 (1930 hrs) is a result of advection of
(surface generated) heat during this time period. The vertical potential temperature profile at 1100 hrs (22 August 1997) is constant with height. This demonstrates, as in the case of the Victoria Falls aircraft measurements, that the entire mixing layer is well mixed at least at this time of the day (most likely already around 0900 hrs). The intense mixing during the day is due to convective heating of the surface by the sun which produces buoyant thermals that establish the mixing layer even higher than indicated by the Victoria Falls aircraft and the Rukomechi balloon measurements. This layer can go up to 4 km
71 [Anderson et al., 1996; Tyson et al., 1996a]. From the vertically constant potential temperature throughout the mixed layer, the general conclusion of the well mixed state of the layer (including any scalar quantity), and in turn the uniform vertical distribution of a pollutant (i.e. constant concentration with height) is justified [Husar et al., 1978; Stull,
1988]. Hence, measurements taken at any point within the mixed layer (particularly at surface level) are representative of the entire mixed layer concentration of a pollutant.
A remarkable example of the similarity between vertical profiles of potential temperature and ozone mixing ratio in a well mixed (afternoon) atmospheric boundary layer is given in
Figure 4.6. During the SAFARI'92 experiment a surveillance flight to explore large scale horizontal distribution of ozone mixing ratio has been performed from Victoria Falls to
Rukomechi research station on 02 October, 1992. During the aircraft descent (1526 hrs to
1536 hrs, local time) to Rukomechi airstrip the potential temperature and ozone mixing ratios were measured [Meixner et al., 1993]. Vertical profiles of potential temperature as well as ozone mixing ratio are constant from ground to (at least) 700 m a.g.l., which is the average height of the southbound escarpment of the Zambezi valley near Rukomechi.
There are three major conclusions for the present study from the results shown above:
(1) night time sampling of aerosols, even when hypothetically possible, would have biased
the Rukomechi aerosol dataset substantially due to co-sampling aerosol matter which
would potentially be accumulated in the NBL from (local) pollutant sources,
(2) only the fully developed mixing layer during the day guarantees that aerosol
concentrations inferred from near-surface sampling can be considered to be
representative of the entire mixing layer where ambient constituents are transported
from distance sources to Rukomechi, therefore
72 (3) the trajectory starting height of 1680 m a.s.l (1180 m a.s.l., mainly selected to reduce
the channelling effect of the Zambezi valley (see sections 3.7 and 4.3), can be assumed
to be correct for starting the trajectory calculations of the daily transport routes of
aerosols from distant sources to Rukomechi research station.
Potential temperature
Ozone
Figure 4.6 The vertical profile of the potential temperature and ozone mixing ratio measured from the descending aircraft to Rukomechi research station on 2 October 1992 at 1500 hrs local time
73 4.2 Elemental composition and sources of tropospheric aerosols from
northern Zimbabwe: Results and discussion
The elemental composition, concentrations and source types of aerosol data from filter samples collected at Rukomechi research station from September 1994 to January 2000 were identified. Only part of the chemical data set (up to March 1998) obtained from this campaign has been presented previously [Fernández-Jiménez, 1999; Maenhaut et al.,
2000]. A preliminary identification of the main sources of both fine and coarse particles was made and the temporal variation of the concentrations of particulate mass, black carbon and aluminium was discussed pointing to strong seasonal variation of the aerosol concentration at the site [Fernandez-Jimenez, 1999]. Due to this significant seasonal variation of the elemental aerosol concentration, it was found worthwhile to analyze the present data set in terms of the dry (mid-April to mid-November) and wet (mid-November to mid-April) seasons (see section 4.1.2.1), and to compare the source types and their contribution to the total particulate mass (PM). In this study, the complete Rukomechi data set from September 1994 to January 2000 was re-analysed to provide a comprehensive description of the chemical composition, aerosol source types and to quantify the source contributions with more emphasis given to dry and wet season characterisation. Within each season, the long term means, medians and inter-quartile ranges of selected elemental concentrations are presented. The aerosol sources and their seasonal contributions are also discussed.
The coarse and fine size fraction filters of all samples were analysed (as already discussed in sections 3.3 to 3.5) for the particulate mass (PM), black carbon (BC), and 47 chemical elements at the University of Ghent (Belgium) by the Analytical Chemistry group led by
Prof Dr W. Maenhaut. The 47 chemical elements were determined by a combination of
Proton-Induced X-ray Emission (PIXE) spectrometry and instrumental neutron activation
74 analysis (INAA). Of the total particulate mass, only 37.2% of coarse fraction and 29.7% of the fine mode were chemically identified by PIXE and INAA. The rest of the PM may be attributed to organic, or other compounds e.g. ammonia, nitrates and sulphates, which could not be detected by the analytical techniques employed. The 47 elements identified were: Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As,
Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ag, Cd, In, Sn, Sb, I, Cs, Ba, La, Ce, Sm, Eu, Lu, W, Au,
Pb, and Th.
However, not all these elements were used for further analysis. The principal component analysis (PCA) involved 24 variables (i.e., PM, BC, Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc,
Ti, V, Mn, Fe, Co, Cu, Zn, Sr, I, La, Sm, and Th) for the coarse size fraction and 22 (i.e.,
PM, BC, Na, Al, S, Cl, K, Ca, Sc, Ti, V, Mn, Fe, Co, Zn, As, Br, Sb, I, La, Sm, and Pb) for the fine size fraction. The elements satisfy the basic criterion discussed before (see section 3.6) which states that for an element to be retained for further analysis; its concentration had to be above the detection limit in over 70% of all the samples.
4.2.1 Principal component analysis and source identification
The overall, wet and dry season Varimax rotated PCA (see section 3.6) matrices showing bivariate correlations between the elements and the principal components (aerosol source types) for the coarse and fine aerosols collected at Rukomechi are given in Tables 4.2 and
4.3, respectively.
4.2.1.1 Coarse size fraction
Wet season: Four different aerosol source types (Table 4.2) associated with wet-season coarse aerosols at Rukomechi were found to be: (a) mineral dust (MD), (b) sea salt (SS),
(c) a mixed source of biomass burning and biogenic (BB/BIO) components, and (d) a separate source type (CU-component) that contributes to high copper loading. All together
75 the four components explained 71% of the total variance of the coarse aerosol
concentrations during the wet seasons. The major source, MD, accounted for 29% of the
total variance and the least component, the CU-component, accounted for 8% of the total
variance.
Wet season coarse size fraction Dry season coarse size fraction Overall coarse size fraction Variable MD SS BB/BIO CU Com MD BB BIO SS Com MD BB BIO SS Com PM 0.63 0.53 0.27 0.08 0.76 0.43 0.69 0.44 0.07 0.86 0.51 0.62 0.45 0.14 0.87 BC 0.43 0.18 0.58 0.08 0.56 0.49 0.73 0.39 -0.06 0.93 0.53 0.69 0.42 0.00 0.93 Na 0.22 0.96 -0.05 -0.04 0.97 0.05 0.20 0.13 0.93 0.92 0.14 0.21 0.14 0.92 0.94 Mg 0.50 0.75 0.07 -0.05 0.81 0.73 0.36 0.37 0.30 0.89 0.73 0.37 0.37 0.32 0.91 Al 0.96 0.21 0.09 0.07 0.98 0.91 0.31 0.28 0.00 0.99 0.90 0.32 0.29 0.06 0.99 Si 0.96 0.18 0.16 0.05 0.98 0.87 0.35 0.34 0.01 0.99 0.86 0.35 0.35 0.06 0.99 P -0.07 -0.38 0.81 0.10 0.81 0.47 0.42 0.73 -0.05 0.93 0.45 0.39 0.72 -0.12 0.89 S 0.22 0.87 0.11 0.03 0.82 0.45 0.78 0.18 0.20 0.88 0.45 0.75 0.21 0.26 0.89 Cl 0.03 0.93 -0.13 -0.02 0.89 -0.06 -0.10 -0.07 0.94 0.91 -0.04 -0.05 -0.07 0.95 0.92 K 0.81 0.26 0.47 0.04 0.95 0.66 0.64 0.37 -0.01 0.98 0.68 0.61 0.39 0.03 0.98 Ca 0.81 0.29 0.39 -0.02 0.88 0.37 0.25 0.80 0.06 0.85 0.44 0.27 0.75 0.12 0.85 Sc 0.97 0.18 0.09 0.08 0.98 0.92 0.30 0.26 0.02 1.00 0.91 0.31 0.27 0.06 1.00 Ti 0.97 0.17 0.10 0.07 0.98 0.92 0.30 0.24 0.01 0.99 0.90 0.31 0.27 0.06 0.99 V 0.95 0.23 0.04 0.09 0.96 0.90 0.33 0.25 0.01 0.99 0.90 0.33 0.27 0.06 0.99 Mn 0.94 0.15 0.25 0.02 0.97 0.85 0.36 0.37 0.00 0.99 0.85 0.36 0.38 0.05 0.99 Fe 0.97 0.18 0.07 0.09 0.98 0.92 0.30 0.24 0.02 0.99 0.91 0.31 0.26 0.06 0.99 Co 0.92 0.20 0.00 0.20 0.93 0.91 0.30 0.27 0.00 0.99 0.91 0.30 0.28 0.05 0.99 Cu 0.18 -0.07 0.04 0.89 0.82 0.66 0.23 0.42 -0.12 0.69 0.62 0.15 0.43 -0.11 0.61 Zn 0.55 0.09 0.31 0.48 0.63 0.77 0.49 0.35 -0.01 0.95 0.77 0.46 0.37 0.02 0.95 Sr 0.67 0.35 0.41 -0.07 0.75 0.50 0.36 0.71 0.13 0.90 0.53 0.37 0.69 0.16 0.91 I 0.32 0.32 0.35 -0.29 0.41 0.29 0.90 0.20 0.01 0.94 0.32 0.89 0.22 0.03 0.94 La 0.94 0.24 0.16 0.05 0.96 0.91 0.29 0.27 0.01 0.99 0.90 0.31 0.29 0.06 0.99 Sm 0.96 0.15 0.16 0.05 0.97 0.91 0.30 0.26 0.01 0.99 0.90 0.31 0.28 0.05 0.99 Th 0.82 0.16 0.05 0.12 0.72 0.93 0.25 0.22 0.01 0.98 0.92 0.27 0.24 0.04 0.98 λ 15.1 2.9 1.4 1.1 18.0 2.1 1.6 0.98 18.5 2.0 1.3 0.96 %Var 29.5 22.8 10.1 8.13 36.2 25.2 11.3 6.7 35.9 26.3 10.3 6.8
Table 4.2 The wet and dry season, and overall Varimax rotated principal component analysis (PCA) matrices showing bivariate correlations between the elements and the components for the coarse aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000. MD is the mineral dust component; BB is the biomass burning component; BIO is the biogenic component; SS is the sea salt component; CU is the copper component, Com is the communality associated with each variable, λ is the eigenvalue of the principal component and %Var is the percentage of the total variance. The species which constitute a component are highlighted in bold.
The MD component is characterised by high loadings for Al, Si, Sc, Ti, V, Mn, Fe, Co,
La, Sm and Th while moderate loadings were found for Mg, K, Ca, Zn and Sr. The
76 dominance of the soil derived coarse aerosol particles can be ascribed to erosion from the degraded part of the Mana Pools National Park, where grass is partly dried out most of the year, and to dust from the increasing clearing of land for subsistence agriculture around the eastern peripherals of the National Park. The second component (SS) is characterized by Na, S and Cl, all having high principal component (PC) loadings. High PC loadings of
S in the wet season suggest that marine sources are an important source of natural coarse
S, a result that is of potential value in understanding the global cycling of S in the atmosphere.
The third component, (BB/BIO), responsible for moderately high loading for P and moderate loadings for BC, K, Ca, Sr and I, is not very influential in the wet season.
Primary biogenic aerosols in this component are thought to be produced by the disintegration of plant and animal material from the National Park. A fourth source (CU- component) showing a high loading for copper and moderate loading for Zn can be attributed to the mineral processing in the Zambian Copper-belt; Zambia’s industrial hub about 400 km north of Rukomechi. The transport of aerosols by the ITCZ related winds from Zambia to the site is predominantly during the wet season. (The air mass transport will be discussed in detail later in section 4.4).
It is interesting to note that the coarse aerosols associated with some elements originate from more than one source during the wet season. For example, while elements like Ca and Sr get admixed to soil generated elements with moderately high loadings, they also have moderate loadings attributed to the maritime sources and the BB/BIO component.
Mg exhibits SS origins where it has a moderately high loading but the soil-derived sources also have some input during the wet season. To a lesser extent, iodine (I) is also attributed to a maritime origin.
Dry season: PCA also extracted four components (MD, BB, BIO and SS) for the coarse
77 aerosol particles during the dry season (Table 4.2). Together these components accounted for 79% of the total variance. The first component (MD) has high loadings for typical crustal elements (Al, Si, Sc, Ti, V, Mn, Fe, Co, La, Sm and Th) and represents the crustal matter sources. Unlike during the wet season, biomass burning (BB) and biogenic (BIO) components were detected separately during the dry seasons. The presence of moderately high and high loadings for S, K and I suggests that the second component (BB) is associated with biomass burning emissions. Besides, this second component has moderately high loadings in BC; this allows clear identification of emissions from biomass burning. The third component (BIO) is highly loaded with P, Ca and Sr and was attributed to biogenic emissions. These bio-aerosols in the coarse mode could be a result of fungal spores, insect and plant debris [Warneck, 1988]. The fourth identified component (SS) is characterized by high loadings for Na and Cl and represents the contribution of the oceans.
Seasonal changes in the origins of some elements have been observed. Dry season PCA reveals that coarse size fraction copper related aerosols are attributed to MD with a moderate contribution coming from the BIO component while wet season analysis indicates that they originate from a purely copper component. Maritime sources are the major sources of coarse S during the wet season but in the dry season, the ocean influence is replaced by the BB component with additional moderate loading coming from soil related sources. Dimethysulfide (DMS) emissions from the oceans are thought to be the major source of wet season coarse S. The absence of oceanic influence on the dry season coarse sulphur may indicate chemical reactions between acidic sulphur compounds and dust particles [Sheridan, 1989]. K is one of the elements which portray multiple sources.
Dry season, coarse K is associated with the MD component while the wet season loading is split between MD, BB and BIO components.
78 The PC loadings and source identification for the overall coarse fraction data set closely mimic those for the dry season. This can be explained by the fact that the concentrations of the variables are highest in the dry season samples and, in addition, there are substantially more samples in the dry (312) than in the wet (183) data set. Although mineral dust is the dominant component for the overall coarse aerosols at Rukomechi, sea- salt, biogenic and biomass burning components were also clearly identified.
Other than biomass burning, which could also be due to human-caused fires and the copper related sources, there is however no evidence of other anthropogenic source contributions from elements like Pb and As [Marcazzan et al., 2003] to the coarse size fraction aerosols at Rukomechi, in contrast to the fine fraction aerosols discussed in the following section. The fact that BIO and BB coarse aerosols are found in one component during the wet season might be explained by the fact that the PCA could not resolve the two due to effect of the influence of the wet environment and the strength of emission of
BB aerosols in the coarse size fraction.
4.2.1.2 Fine size fraction
Wet season: The Varimax rotated PCA solution for the wet season fine aerosols yielded four significant components namely; MD, BB, non-ferrous smelters (NFS) and SS (Table
4.3). Together these components accounted for 77% of the variance in the data set.
The first component (MD) explained 32% of the total variance and exhibited high loadings for Al, Sc, Ti, Fe and Sm. This component is attributed to mineral dust sources.
The second component (BB) explained 25% of the variance and is attributed to biomass burning emissions as it has high loadings for BC, K, Br and I. As the forest and savannah fires are suppressed by the rains most of the time during this period, it is most likely that the aerosols related to the BB component are the result biomass burning activities during
79 early and/or late wet season, or might also be a result of long range transport of particles
from domestic cooking fires. The third component (SS), with high PC loading for fine Na
and moderately high loadings for Cl and Ca, represents maritime emissions from the
Indian Ocean (see section 4.3.2).
Wet season fine size fraction Dry season fine size fraction Overall fine size fraction Variable MD BB SS NFS Com MD BB NFS SS Com MD BB NFS SS Com PM 0.21 0.91 0.07 0.09 0.88 0.18 0.95 0.05 0.03 0.94 0.26 0.94 0.05 0.05 0.95 BC 0.34 0.90 -0.06 -0.03 0.92 0.17 0.96 -0.03 -0.01 0.96 0.26 0.94 -0.01 0.01 0.96 Na 0.08 0.32 0.91 0.10 0.94 -0.08 0.37 -0.07 0.82 0.82 0.04 0.43 0.01 0.81 0.84 Al 0.97 0.12 -0.01 0.06 0.96 0.97 0.13 0.11 -0.05 0.97 0.95 0.22 0.13 -0.02 0.97 S 0.20 0.59 0.28 0.33 0.57 0.07 0.47 0.56 -0.08 0.55 0.18 0.57 0.48 0.05 0.60 Cl 0.01 -0.12 0.78 -0.18 0.65 -0.05 -0.13 -0.11 0.86 0.77 0.01 -0.07 -0.09 0.89 0.81 K 0.23 0.93 0.00 0.00 0.92 0.18 0.94 0.03 0.00 0.92 0.27 0.93 0.03 0.02 0.93 Ca 0.44 0.49 0.67 0.09 0.90 0.69 0.46 0.14 0.30 0.80 0.68 0.52 0.14 0.32 0.86 Sc 0.95 0.20 -0.01 0.07 0.96 0.98 0.12 0.08 -0.05 0.97 0.96 0.21 0.11 -0.02 0.98 Ti 0.96 0.12 -0.02 0.02 0.94 0.98 0.08 0.05 -0.06 0.97 0.97 0.17 0.08 -0.03 0.98 V 0.76 0.27 0.34 0.07 0.76 0.90 0.20 0.21 -0.04 0.90 0.88 0.31 0.20 0.04 0.91 Mn 0.77 0.47 0.13 0.14 0.85 0.92 0.20 0.24 -0.07 0.95 0.90 0.31 0.23 0.00 0.95 Fe 0.94 0.19 0.04 0.05 0.93 0.97 0.10 0.11 -0.07 0.98 0.96 0.20 0.13 -0.02 0.98 Co 0.47 0.23 0.19 -0.10 0.32 0.83 0.01 0.11 -0.01 0.70 0.82 0.10 0.11 0.06 0.70 Zn 0.56 0.24 -0.14 0.42 0.56 0.22 0.89 0.22 -0.09 0.90 0.30 0.83 0.24 -0.05 0.83 As -0.06 0.03 0.12 0.78 0.62 0.13 0.02 0.85 -0.11 0.75 0.13 0.08 0.83 -0.03 0.71 Br 0.19 0.92 0.16 -0.02 0.91 0.09 0.95 -0.07 0.19 0.95 0.20 0.94 -0.04 0.17 0.96 Sb 0.02 -0.02 -0.07 0.44 0.20 0.17 -0.06 0.75 0.05 0.60 0.13 -0.04 0.69 -0.01 0.49 I 0.14 0.92 0.15 -0.07 0.90 0.11 0.92 -0.11 0.11 0.89 0.22 0.91 -0.06 0.14 0.91 La 0.63 0.17 0.43 0.23 0.68 0.94 0.18 0.06 0.03 0.91 0.91 0.25 0.10 0.08 0.91 Sm 0.82 0.17 0.11 0.17 0.74 0.96 0.16 0.06 -0.03 0.94 0.94 0.24 0.09 0.00 0.95 Pb 0.37 0.00 -0.02 0.82 0.82 0.18 -0.07 0.92 -0.11 0.90 0.22 0.04 0.90 -0.06 0.87 λ 10.9 3.2 2.4 1.9 10.3 4.8 2.7 1.4 11.9 3.7 2.4 1.4 %Var 32.2 24.8 11.0 8.9 39.5 27.5 12.4 7.3 39.1 28.7 11.2 7.4
Table 4.3 The overall, wet and dry season Varimax rotated principal component analysis (PCA) matrices showing bivariate correlations between the elements and the components for the fine aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000. MD is the mineral dust component; BB is the biomass burning component; NFS is the non-ferrous smelters component; BIO is the biogenic component; SS is the sea salt component; Com is the communality associated with each variable; λ is the eigenvalue of the principal component and %Var is the percentage of the total variance. The species which constitute a component are highlighted in bold
The elements As and Pb are associated with the fourth component (NFS) that is attributed
to anthropogenic emissions related to mining activities, especially the non-ferrous metal
80 processing operations. This component of anthropogenic pollutants may be attributed to different sources mixed with aerosol long-range transport. Fine Pb aerosols at Rukomechi may probably be associated with mining activities in central Zimbabwe and South Africa although part of it could originate from regional automobile sources. As and Sb may also originate from nonferrous metal mining, nonferrous metal primary and secondary smelting and refining [Crecelius et al., 1974] in the Zambian Copper-belt situated about 540 km to the north of the site. Tsetse control in the Zambezi Valley may be the additional source for
As since it is a component of insecticides used in forest management and agriculture.
Dry season: The PCA also extracted four components for the fine size fraction during the dry season that account for 87% of the total variance. Identical components (MD, BB,
NFS and SS) were retained as in those for the wet season (Table 4.3). The major difference between the seasons is in the composition of the elements that constitute each component.
For the wet season, elemental fine Ca is evenly associated with MD (0.44), BB (0.49) and
SS (0.67) components while the dry season Ca is mainly attributed to MD (0.69) with some moderate loadings attributed biomass burning activities (0.46). On the other hand, the sea-derived aerosols have very little influence (0.30) on to the dry season fine Ca aerosol concentration. Dry season fine S is mainly loaded on NFS (0.56) and lesser on
BB (0.47), while the wet season fine S is more associated with BB (0.59) than NFS (0.33).
The moderately high loadings of fine S may be an indication of the contribution of long- range transport processes from coal combustion sources in the region (see section
4.4.2.2.2). No biogenic component was identified in the fine fraction data sets for
Rukomechi. This may be at least in part due to the absence of biogenic indicator elements, such as P, which were below the detection limit in most of the fine fraction samples. As in
81 the coarse size fraction, the PC loadings and source identification for the overall fine fraction data are similar to those of the dry season.
The high loadings for the elements from the soil dust particles are an indication that the mineral-dust component was much better separated from the other sources in the coarse mode than in the fine size fraction. The fine size aerosols at Rukomechi do not show significant biogenic influences.
4.2.1.3 Multiple sources for elements in the coarse and fine size fraction
Although the sources of most aerosols remain the same, there are some types of aerosols whose sources shift between seasons and the contributions are from different sources for the coarse and fine mode. Up to this point, the associations of the variables with the various components were discussed separately for the coarse and fine size fractions. If the statistical loadings for the two size fractions within each season are compared, notable shifts among components are observed. This holds, in particular, for the elements K, Ca,
Zn, and S.
Fine K (Table 4.3) is highly loaded on BB (around 0.93), while coarse K (Table 4.2) is associated with MD as well as the BB and the BIO components. Similarly, fine (Table
4.3) Zn is mainly associated with BB, whereas coarse Zn (Table 4.2) is mainly associated with MD, BB and BIO. Coarse S is mainly associated with SS during the wet season and has a moderately high loading on BB and a moderate loading on MD during the dry season. In contrast, fine S is mostly associated with BB during the wet season and with the
NFS and BB components during the dry season. Since S and As are known to be emitted by coal combustion [Zhao and Sun, 1986], their dry season levels may in part also be due to coal combustion sources in the region (for example, the Hwange coal power station located about 440 km to the west, the Harare power station which is about 250 km to the
82 south, and the South African power stations situated about 1000 km to the south of the
Rukomechi site).
During the dry season, the SS factor is the major source for the S coarse size fraction
(Table 4.2) with a PC loading of 0.88. S in the coarse mode is derived from sulphate in sea water but can also arise from the sulphate produced during the oxidation of DMS
(CH3SCH3) [Hertel et al., 1994]. The presence of coarse S in the maritime aerosols during the wet season also reflects the occurrence of displacement reactions which enrich sea salt particles with sulphate [Song and Carmichael, 1999].
4.2.2 Source apportionment
The APCA receptor model (see section 2.6.2 and 3.6) was applied to the components extracted by the PCA in order to quantify the contribution of each source to the total PM at Rukomechi research station. Figure 4.7 shows APCA source apportionment for fine and coarse aerosols during the wet and dry seasons as well as the overall source apportionment.
The source contribution estimates show that in the wet season, major contributions to the total coarse PM (Figure 4.7a) come from the SS and the BB/BIO components. The SS component contributes 32% while BB/BIO accounts for 29% of the total coarse PM in the wet season. Only 16% of the total coarse PM is attributed to the MD component while the
CU-component is responsible for 8% of the apportioned coarse PM. The dry season coarse PM is dominated by BB (32%) related aerosols followed by the BIO and MD components which respectively account for 23% and 21% of the total coarse PM. The relatively low contribution of soil dust related aerosols during the dry season is, however, expected as the sampling site is located in the National Park where there is minimal disturbance to the soil surface in most areas. Due to the fact that coarse soil dust aerosols
83 are typically not transported over long distances [Seinfeld and Pandis, 1998], most of the coarse aerosols probably originate from the degraded areas of the Park. While the contribution of other sources to the total coarse PM increases during the dry season, the maritime contribution decreases by a factor of five from 32% in the wet season to 6% in the dry season. The CU-component is no longer evident in the dry season, which is apparently due to the fact that the copper related sources are masked by copper aerosols that come from other areas.
84 (a) Wet season coarse (d) Wet season fine Unexplained 19% Unexplained Biomass CU 28% burning Biomass 44% 4% burning/ Biogenic 29%
Smelters Sea salt 4% 32% Sea salt Mineral dust 7% Mineral dust 16% 17%
(b) Dry season coarse (e) Dry season fine Biogenic 30% Biomass Unexplained burning 12% Unexplained 79% 2%
Smelters 3% Sea salt 12% Sea salt 3%
Biomass Mineral dust 13% Mineral dust burning 16% 30%
(c) Overall coarse Biogenic (f) Overall fine 23% Unexplained Biomass 10% Unexplained burning 18% 63% Smelters 4%
Sea salt 5% Sea salt Biomass 6% burning 32% Mineral dust Mineral dust 18% 21%
Figure 4.7 Seasonal and overall source apportionment to total particulate mass of fine and coarse aerosols collected at Rukomechi research station from September 1994 to January 2000. (wet season: mid November to mid April; dry season: mid April to mid November)
In the fine size fraction, results show that biomass burning emissions are by far the most
abundant ambient atmospheric aerosol constituent over Rukomechi. APCA results
indicate that the BB component is the main source of fine particles at Rukomechi for both
85 the dry and wet seasons. Overall, 58% of the particulate mass is composed of BB related aerosols. The dominance is, however, more pronounced in the dry season than in the wet season. Of the total fine PM, 44% and 79% was attributed to biomass burning emissions for the wet and dry season respectively (Figures 4.7a and 4.7e). Since the forest and savannah fires [Swap et al., 2003] are suppressed by wet conditions during the rainy season and aerosol concentrations are reduced by in- and below cloud scavenging, contributions to the BB may be mainly from biomass burning activities that occur at the beginning and end of each wet seasons. Moreover, biofuels are extensively used for cooking and heating purposes during the wet season [Marufu et al., 1999; Marufu et al.,
1997] and regional transport of smoke particles from such fires to Rukomechi research station is likely.
While the fine aerosol mass apportioned to BB, MD and NFS components (Figure 4.7) have increased from the wet to the dry season, the contribution from oceanic emissions decreased from 7% in the wet season to 3% in the dry season. The increase of the SS contribution to total PM (Figure 4,7) at Rukomechi during the wet season may be due to the chemical and physical processes in the atmosphere. Considering that the primary production of sea-salt aerosols by the mechanical disruption of the ocean surface might be the same during the dry and wet seasons, the increase of the SS contributions during the wet season may be attributed to the secondary aerosols formed by gas-to-particle conversion processes such as binary homogeneous nucleation, heterogeneous nucleation and condensation [Twomey, 1977]. This increase in the maritime contributions during the wet season can be explained by the air flow patterns to the site which are dominated by air masses that come from the Indian Ocean during the wet season [Nyanganyura et al., 2005]
(see also section 4.3).
86 4.2.3 Enrichment factors
APCA yielded source profiles and contributions for the identified components. The source profiles of the mineral dust and sea-salt components were compared with the compositions of average crustal rock of Mason and Moore [1982] and of sea water of
Riley and Chester (1971). These comparisons were done in terms of crustal and sea-water enrichment factors (EFs), whereby Al was used as the crustal reference element and Na as sea-water reference element [Maenhaut et al., 1996].
An enrichment factor is given by the double ratio of the element of interest in the sample
(Xi) to a reference element in the sample (Ci) divided by the ratio of the same element found in a reference material (e.g. rock or sea salt). It is expressed as follows:
X i C i sample …………………………………. 4.12 EF = Xi C i reference
In general, most of the crustal and sea salt elements have EF values close to unity; higher
EF values of the elements indicate that they are not from the source in question.
Crustal and sea-water enrichment factors calculated for coarse and fine PM and several elements are presented in Figure 4.8. The crustal EFs for the PM and several elements in the coarse (Figure 4.8a) and fine (Figure 4.8b) MD components are consistent with the result presented in Table 4.2. The EFs for the typical crustal elements were all found between 0.4 and 3.0 in the various coarse and fine MD source profiles. The fact that the values of EF are close to 1 is an indication that the attribution to mineral dust of these components extracted by the PCA (section 4.2.1) is fully justified. Some elements like Mg and Cu, which have different sources in the wet season have relatively high EFs as compared to the values when they were attributed to mineral dust.
87 (a) 1000 Dry season Wet season Overall
100
10
1 Crustal enrichment factor for coarse aerosols coarse for factor enrichment Crustal
0.1 PM Na Mg Al Si P S Cl K Ca Sc Ti V Mn Fe Co Cu Zn I La Sm Th Element
(b) 10000 Dry season Wet season Overall
1000
100
10
1 Crustal enrichment factor for fine aerosols fine for factor enrichment Crustal
0.1 PM Na Al S Cl K Ca Ti V Mn Fe Co Zn As Br Sb I La Sm Pb Element
Figure 4.8 Crustal enrichment factors {(a) coarse & (b) fine} calculated relative to the composition of average crustal rock of Mason and Moore (1982) with Al as reference element and for sea-water enrichment factors {(c) coarse & (d) fine} calculated relative to sea-water abundance of Riley and Chester (1977) with Na as the reference elements for coarse and fine PM and several elements for components obtained by PCA.
88 (c) 100000000 Dry season 10000000 Wet season Overall
1000000
100000
10000
1000
100
10
1 Sea-water enrichment factor for coarse aerosols 0.1 PMNaMg Al Si P S Cl K Ca Sc Ti V MnFe Co Cu Zn I La SmTh Element
(d)
10000000 Dry season wet season 1000000 Overall fine
100000
10000
1000
100
10
1 Sea-water enrichment factor for fine aerosols fine for Sea-water enrichment factor 0.1 PM Na Al S Cl K Ca Ti V Mn Fe Co Zn As Br Sb I La Sm Pb Element
Figure 4.8 continued
For the coarse SS components, the sea-water EFs for Cl, Mg and wet season S (Figure
4.8c) were typically between 0.9 and 1.8 while the fine sea-water EFs ranged from 0.8 to
1.5 for Cl and Ca (Figure 4.8d). As with the crustal component, these EFs which are close to 1 are consistent with the attribution of different elements to the sea-salt source (Table
89 4.3). Coarse S that was attributed to the maritime component during the wet season has an
EF closer to unity as compared to dry and overall where it was attributed to biomass burning sources.
The results of the enrichment factors indicate the source components identified by the
PCA are justified, indicating that PCA is a useful explanatory tool for identifying the major sources of air pollutant emissions.
4.2.4 Descriptive Statistics
4.2.4.1 Means
The elemental concentrations and the associated standard error in the mean for particulate mass (PM) and various elements in the coarse and fine fractions are presented in Table
4.4. The results exhibit variations of the different individual elemental concentrations between dry and wet seasons as well as in the overall mean elemental concentrations.
Blank cells in Table 4.4 indicate that the concentration of the variable was not observed above the detection limit in over 70% of all the samples.
The elements that contribute most to the dry season fine aerosols at Rukomechi are BC, S,
K and Na. They have long term mean elemental concentrations of 2.6 ± 0.2 µg m-3, 680 ±
30 ng m-3, 600 ± 40 ng m-3 and 222 ± 9 ng m-3 respectively (Table 4.4). The same elements also have significantly higher concentrations during the dry season than the wet season. The mean wet season elemental concentrations for fine BC, S, Na and K are 0.42
± 0.04 µg m-3, 310 ± 20 ng m-3, 130 ± 9 ng m-3 and 70 ± 10 ng m-3 respectively. The fine mode in both seasons is dominated by BC (1.8 ± 0.09 µg m-3), S (540 ± 20 ng m-3), K
(410 ± 20 ng m-3), Na (188 ± 6 ng m-3) and Cl (55 ± 3 ng m-3). These elements originate either from biomass burning or the oceans.
90 Algebraic Means and standard error in the mean Variable Overall Coarse Dry coarse Wet coarse Overall fine Dry fine Wet fine PM 9.2 ± 0.3 11.8 ± 0.4 4.7 ± 0.2 11.2 ± 0.4 15.1 ± 0.7 4.9 ± 0.3 BC 0.31 ± 0.01 0.41 ± 0.02 0.122 ± 0.005 1.82 ± 0.09 2.6 ± 0.2 0.42 ± 0.04 Na 340 ± 10 400 ± 10 240 ± 10 188 ± 6 222 ± 9 130 ± 9 Mg 155 ± 5 195 ± 8 86 ± 3 Al 480 ± 30 680 ± 40 130 ± 10 46 ± 2 64 ± 4 16 ± 2 Si 910 ± 50 1290 ± 70 260 ± 20 P 27.1 ± 0.9 31 ± 1 20.0 ± 0.6 S 87 ± 4 107 ± 5 52 ± 2 540 ± 20 680 ± 30 310 ± 20 Cl 270 ± 10 280 ± 20 260 ± 20 55 ± 3 61 ± 5 45 ± 6 K 200 ± 10 280 ± 20 72 ± 4 410 ± 20 600 ± 40 70 ± 10 Ca 180 ± 10 250 ± 10 53 ± 3 22.0 ± 0.7 28 ± 1 11.2 ± 0.6 Sc 0.112 ± 0.006 0.159 ± 0.009 0.030 ± 0.003 0.0103 ± 0.0005 0.0141 ± 0.0009 0.0037 ± 0.0004 Ti 38 ± 2 54 ± 3 10 ± 1 3.7 ± 0.2 5.1 ± 0.4 1.2 ± 0.1 V 0.89 ± 0.05 1.25 ± 0.06 0.26 ± 0.03 0.155 ± 0.005 0.197 ± 0.008 0.082 ± 0.005 Mn 9.0 ± 0.5 12.7± 0.7 2.5 ± 0.2 1.06 ± 0.05 1.47 ± 0.08 0.36 ± 0.03 Fe 370 ± 20 520 ± 30 100 ± 10 34 ± 2 47 ± 3 12 ± 1 Co 0.21 ± 0.01 0.29 ± 0.01 0.072 ± 0.006 0.041 ± 0.001 0.047 ± 0.002 0.031 ± 0.001 Cu 0.58 ± 0.03 0.70 ± 0.04 0.36 ± 0.03 Zn 1.80 ± 0.08 2.4 ± 0.1 0.74 ± 0.04 2.4 ± 0.1 3.3 ± 0.2 0.8 ± 0.1 As 0.140 ± 0.008 0.16 ± 0.01 0.10 ± 0.01 Br 5.4 ± 0.2 7.4 ± 0.4 1.9 ± 0.1 Sr 1.77 ± 0.09 2.4 ± 0.1 0.59 ± 0.04 Sb 0.039 ± 0.004 0.043 ± 0.006 0.030 ± 0.006 I 0.32 ± 0.02 0.39 ± 0.03 0.178 ± 0.008 1.85 ± 0.08 2.6 ± 0.1 0.62 ± 0.07 La 0.39 ± 0.02 0.55 ± 0.03 0.11 ± 0.01 0.052 ± 0.002 0.065 ± 0.003 0.029 ± 0.002 Sm 0.062 ± 0.004 0.088 ± 0.005 0.016 ± 0.002 0.0075 ± 0.0003 0.0098 ± 0.0005 0.0037 ± 0.0002 Pb 0.91 ± 0.05 1.08 ± 0.08 0.62 ± 0.06 Th 0.121 ± 0.006 0.165 ± 0.009 0.045 ± 0.003
Table 4.4 Dry season, wet season and overall mean elemental concentrations of particulate mass and selected elements together with their associated standard error in the mean. PM and BC concentrations are measured in µg m-3 while other elemental concentration are measured in ng m-3
For the coarse aerosols, the dry season aerosol concentrations are dominated by BC, Si,
Al, Fe Na and Cl which have mean elemental concentrations that are over 200 ng m-3
(Table 4.4). Cl, Si, Na, Al and Fe have highest concentrations during the wet season. Their
mean elemental concentrations are respectively 260 ± 20 ng m-3, 260 ± 20 ng m-3, 240 ±
10 ng m-3, 130 ± 10 ng m-3 and 100 ± 10 ng m-3. Overall, the elemental concentrations of
most elements are higher in the coarse size fraction than in the fine fraction except for BC,
S, K, Zn and I, which are associated with biomass burning sources. This suggests the
influence of biomass burning activities that occur in the region during that period
[Andreae et al., 1998].
91 4.2.4.2 Medians and variability
Median concentrations of the coarse and fine aerosols showing the overall, dry and wet seasons (with range: maximum-minimum in brackets) are displayed in Table 4.5. With the exception of coarse Cl and fine As, the maximum values of the aerosol loading at
Rukomechi research station always occur during the dry period. High values of Cl during the wet season might be due to the transport of sea salt aerosols from the oceans and might also be due to the formation of secondary aerosols (see above). Higher levels of As may be caused by mining activities in the Zambian Copper-belt since the air masses (discussed later) originate mostly from the north during this period.
The dry season mean (median) elemental concentrations are always higher than the overall means (medians) and this, again points to the fact that aerosol loading around the research site is greater during the dry period than during the wet. This can also be seen as an indication that the wet weather conditions suppress the emissions and that scavenging of aerosols over Rukomechi by cloud and rain is effective.
Suppression of aerosol emission and scavenging (dry and wet) may have a greater effect on the fine size than the coarse size fraction. This is supported by the fact that the monthly median of coarse PM is reduced by half while fine PM is reduced three fold from the dry to wet season (Table 4.5). While maximum concentration levels for most elements occur during the dry season, maximum Na and Cl concentrations occur during the wet season for both the coarse and fine size factions.
92
Coarse size fraction Fine size fraction Overall Dry Wet Overall Dry Wet Variable Overall Dry Wet Overall Dry Wet Vari Vari Vari Vari Vari Vari PM 7.41 (56.6-0.06) 7.6 9.67 (56.6-0.06) 5.8 4.12 (11.3-0.11) 8.2 8.80 (59.5-0.03) 6.8 13.1 (59.5-1.43) 4.4 3.92 (23.5-0.03) 5.8 BC 0.21 (2.56-0.01) 11.9 0.32 (2.56-0.01) 8.1 0.11 (0.39-0.01) 6.3 0.91 (9.71-0.01) 10.6 2.14 (9.71-0.08) 4.5 0.22 (3.59-0.01) 16.3 Na 321 (1268-0.48) 3.9 358 (1268-15.5) 3.5 186 (1105-0.48) 5.9 176 (647-0.48) 3.7 204 (647-8.37) 3.1 109 (515-0.48) 4.7 Mg 129 (1381-22.3) 10.6 163 (1381-42.7) 8.2 75.6 (230-22.3) 6.5 Al 360 (6249-8.09) 17.3 538 (6249-19.2) 11.6 72.5 (626-8.09) 40.5 30.5 (403-0.24) 13.1 50.5 (403-2.50) 7.9 10.3 (210-0.24) 20.5 Si 699 (12120-9.4) 17.3 1039 (12120-31) 11.6 149 (1111-9.43) 31.3 P 21.7 (181-5.52) 8.1 24.4 (181-9.10) 7.1 17.4 (65.6-5.52) 4.1 S 72.3 (937-11.1) 12.8 86.8 (937-12.6) 10.6 51.9 (151-11.1) 2.7 452 (2332-21.2) 5.1 603 (2332-74.3) 3.7 216 (2065-21.2) 9.4 Cl 156 (1797-2.54) 11.5 166 (1645-3.06) 9.9 91.7 (1797-2.54) 19.6 29.2 (710-3.15) 24.2 40.0 (710-3.15) 17.7 14.0 (684-3.17) 48.4 K 138 (2350-17.5) 16.8 208 (2350-30.3) 11.1 59.7 (241-17.5) 15.4 195 (3036-2.77) 15.6 480 (3036-24.3) 6.3 28.3 (960-2.77) 33.3 Ca 119 (1553-4.82) 13.0 193 (1553-10.4) 14.1 36.5 (322-4.82) 15.9 19.0 (106.6-1.3) 5.5 25.8 (106-3.97) 4.0 9.73 (35.3-1.3) 5.4 Sc 0.08 (1.6-0.003) 20.0 0.12 (1.58-0.004) 12.8 0.02 (0.15-0.003) 7,4 0.01 (0.11-0.001) 10.9 0.01 (0.11-0.001) 9.6 0.002 (0.036-0.001) 21.8 Ti 28.2 (570-0.49) 20.2 43.1 (570-0.60) 13.2 5.74 (51.6-0.49) 38.8 2.38 (37.4-0.28) 15.6 3.82 (37.4-0.33) 9.7 0.47 (15.1-0.28) 41.3 V 0.68 (11.5-0.01) 16.9 1.01 (11.5-0.03) 11.4 0.16 (1.21-0.01) 37.6 0.13 (0.86-0.01) 6.5 0.17 (0.86-0.03) 4.8 0.07 (0.39-0.01) 8.1 Mn 6.73 (119-0.16) 17.7 10.1 (119.2-0.24) 11.8 1.40 (11.5-0.16) 36.9 0.79 (7.63-0.02) 9.6 1.18 (7.63-0.09) 6.4 0.22 (2.19-0.02) 19.4 Fe 266 (5305-6.41) 19.9 412 (5305-8.13) 12.9 56.3 (524-6.41) 43.3 23.2 (324-0.77) 13.9 36.5 (324-3.13) 8.8 7.25 (114-0.77) 24.2 Co 0.15 (2.62-0.01) 17.4 0.23 (2.62-0.02) 11.3 0.05 (0.28-0.01) 27.9 0.03 (0.19-0.01) 5.3 0.04 (0.19-0.01) 4.6 0.03 (0.09-0.01) 3.2 Cu 0.35 (5.10-0.11) 14.3 0.59 (5.10-0.14) 8.5 0.20 (4.33-0.11) 21.1 Zn 1.37 (11.1-0.13) 8.0 1.89 (11.1-0.19) 9.3 0.57 (2.59-0.13) 14.5 1.52 (12.9-0.11) 14.4 2.94 (12.9-0.14) 4.3 0.44 (4.71-0.11) 49.1 As 0.08 (1.77-0.00) 21.3 0.10 (1.36-0.00) 13.4 0.05 (1.77-0.02) 32.0 Br 3.51 (25.4-0.25) 7.2 6.36 (25.4-0.33) 3.9 1.45 (10.3-0.25) 6.9 Sr 1.19 (16.8-0.11) 14.1 1.87 (16.8-0.22) 8.9 0.31 (4.21-0.11) 15.1 Sb 0.02 (0.22-0.002) 10.9 0.03 (0.22-0.002) 51.4 0.02 (1.12-0.002) 59.5 I 0.22 (4.13-0.02) 18.4 0.30 (4.13-0.04) 13.8 0.15 (1.24-0.02) 8.1 1.24 (8.60-0.04) 6.9 2.14 (8.60-0.15) 4.0 0.34 (6.69-0.04) 19.4 La 0.29 (5.17-0.01) 17.7 0.44 (5.17-0.02) 11.8 0.07 (0.44-0.01) 35.8 0.04 (0.39-0.01) 9.3 0.05 (0.39-0.01) 7.1 0.02 (0.13-0.01) 7.7 Sm 0.05 (0.88-0.001) 19.0 0.07 (0.88-0.002) 12.8 0.01 (0.07-0.001) 46.5 0.01 (0.07-0.001) 11.7 0.01 (0.07-0.001) 8.5 0.003 (0.02-0.001) 6.3 Pb 0.60 (7.59-0.14) 17.2 0.85 (5.46-0.17) 12.1 0.26 (7.59-0.14) 28.5 Th 0.09 (1.66-0.02) 19.3 0.13 (1.66-0.02) 12.3 0.03 (0.17-0.02) 19.2
Table 4.5 Median concentrations of aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000 for the overall, dry and wet season (with range: maximum-minimum) for the coarse and fine size fractions. The concentrations are expressed in (a) µg m-3 for particulate mass (PM) and black carbon (BC), (b) ng m-3 for the rest of the elements. “Dry” is the period from mid April to mid November and “wet” is that from mid November to mid April, Vari is the variability. 93 As a measure of variability within the dataset of each element, the ratio of the range to the median (range/median) was used (Table 4.5). The 75th percentile variability values were used to mark the lower end of high variability. Overly, high variability for coarse size fraction (over 17.9) was observed for Sc, Ti, Fe, I, Sm and Th. The general picture for the dry season coarse aerosol variability is roughly the same as that of the overall coarse size fraction with Ca, Sc, Ti, Fe, I and Sm show relatively high variability (12.4). The wet season coarse aerosols exhibit strongest variability where the 75th variability is 36.4. The variability of elemental concentrations in the fine mode are similar to those of the coarse size fraction where highest values are found in wet season as compared to the overall and the dry season.
High variability observed during the wet season might be due to the fact that wet seasons are often punctuated with dry spells which may be associated with high ambient concentrations while wet episodes have relatively clean air due to wet scavenging. The variability can also be explained in terms of the origin of these aerosols, i.e., whether they originate from single or multiple sources.
For both the dry and wet seasons, it was observed that high variability is associated with a single source, especially those related to crustal origin, biomass burning and/or biogenic sources. Multiplicity of source emissions tends to dampen the variability in the concentration of a variable in that when one source is less active, then the other sources could still be emitting thereby keeping the aerosol loading relatively high, thus reducing the difference between the maximum and minimum concentration levels over northern
Zimbabwe
94 4.2.4.3 Contribution of individual elements to the total particulate mass
The contribution of identified sources was discussed in section 4.2.2. The total contribution of each of the individual elements to the overall particulate mass (PM) as well as for the wet and dry season are presented in Table 4.6.
Dry Wet Dry Wet Overall Overall Variable season season season season coarse fine coarse coarse fine fine BC 3.3 3.5 2.6 16.0 17.4 8.5 Na 3.7 3.4 5.2 1.7 1.5 2.6 Mg 1.7 1.7 1.9 Al 5.2 5.8 2.8 0.41 0.42 0.32 Si 9.9 11.0 5.5 P 0.30 0.27 0.43 S 0.94 0.91 1.1 4.8 4.5 6.2 Cl 3.0 2.4 5.5 0.49 0.40 0.91 K 2.2 2.3 1.6 3.6 4.0 1.5 Ca 2.0 2.15 1.14 0.19 0.19 0.23 Ti 0.41 0.46 0.22 0.03 0.03 0.02 V 0.01 0.01 0.01 0.0014 0.0013 0.002 Mn 0.10 0.11 0.05 0.01 0.01 0.01 Fe 4.0 4.4 2.15 0.30 0.31 0.23 Cu 0.01 0.01 0.01 Zn 0.02 0.02 0.02 0.02 0.02 0.02 As 0.001 0.001 0.002 Br 0.05 0.05 0.04 Sr 0.02 0.02 0.01 Sb 0.0003 0.0003 0.001 I 0.003 0.003 0.004 0.02 0.02 0.01 Sm 0.001 0.001 0.0003 0.0001 0.0001 0.0001 Pb 0.01 0.01 0.01 Th 0.001 0.001 0.001
Table 4.6 Percentage contributions of individual elements to the total particulate mass for aerosols collected at Rukomechi research station, Zimbabwe, from September 1994 to January 2000
Looking at individual elements, Si, Al, Fe, Na, Cl, K and Ca provide the largest average contribution to the overall coarse PM, i.e., 9.9%, 5.2%, 4.0%, 3.7%, 3.0%, 2.2% and
2.0%, respectively (Table 4.6). During the dry season, the contributions of the same elements to the coarse PM are 11.0%, 5.8%, 4.4%, 3.4%, 2.4%, 2.3% and 2.2%, respectively, and thus are larger than in the overall data set, except for Na and Cl. During
95 the wet season, the contributions of the typical crustal elements (Al, Si, Fe) to the coarse
PM is only half of that in the dry season, whereas, in contrast, the contribution to the coarse PM shows a significant increase from the dry to the wet season for the sea-salt elements Na and Cl. Cl and Na respectively contribute 5.5% and 5.2% to the total coarse
PM during the wet season, compared to 2.4% and 3.4% during the dry season. The results indicate that the relative contributions of sea salt and mineral dust to the coarse PM are quite different in the two seasons, and that the impact from sea salt is more substantial during the wet season.
The highest contribution to the total fine PM comes from BC, with 16%, on average, for the overall data set (Table 4.6). Elements that provide a significant contribution are S, K,
Na, and Cl, which respectively account for 4.8%, 3.6%, 1.7% and 0.5% of the overall fine
PM. The same variables dominate in both the dry and wet season. As in the coarse size fraction, the contribution to the total PM from Na and Cl increases by twofold during the wet season compared to the dry season (Table 4.6). Metals of anthropogenic origin (Cu,
Zn, As, Sb, and Pb) provide a minor contribution (less than 0.1% in total) to the coarse and fine PM at Rukomechi.
4.2.5 Temporal variations
To consider the inter-annual changes in aerosol loadings over Rukomechi, monthly median elemental concentrations were used. The median was used in this analysis because its magnitude is usually less affected by extreme values in the data set. The temporal variations of concentration levels for elements belonging to a certain source were found to be similar, hence only certain elements (considered to be representative for the sources type, described in section 4.2.1) were chosen to study the behaviour of aerosol loadings with time. The median monthly particulate mass and elemental concentrations for indicator elements associated with the different sources of aerosols collected at
96 Rukomechi research station from September 1994 to January 2000 are plotted in Figure
4.9 and 4.10 together with their linear long term trends. The long term trends were determined by adding a linear trend-line through the whole data-set of the corresponding elements.
4.2.5.1 Seasonal variation and long terms trends
Monthly median elemental concentrations of soil dust, biomass burning and related aerosols, and total particulate mass (Figure 4.9) show high pollutant concentration levels in the dry period and low in the wet season. The aerosol concentrations from mineral dust show remarkable seasonal features with a distinct annual cycle for both the coarse and fine aerosols. Their temporal concentration trends reveal a gradually decreasing trend over the 5.5 years as shown in the Si, Al and Fe (Figure 4.9a). For example, coarse Si was found to be decreasing at a rate of about 4 ng m-3 per month over the period of study, which at 95% confidence level, is statistically significant.
Biomass burning related aerosols (represented by fine BC (Figure 4.9b) and fine K
(Figure 4.9c)) also exhibit strong seasonal variability. However, the concentration of biomass burning aerosols over Rukomechi has steadily been increasing over the 5.5 years.
It can be stated with 95% confidence (using the Student T-test) that the amount of fine aerosols from biomass burning emissions have increased over this period. On average, the median concentration of black carbon increased at a rate of 7.26 ng m-3 per month and that of fine K increased at a lower rate of 1.31 ng m-3 per month. This increasing trend mirrors the decreasing trend of aerosols associated with soil dust sources. While the decrease of the aerosol loadings from mineral dust sources may have been due to the recovery of vegetation from the 1991-92 drought season, the resultant increase in biomass burning activities can be attributed to the recovery of the bio-fuels from the 1991-92 drought, especially due to improved rainfall regimes (see section 4.1.2).
97
(a) Mineral dust (b) Biomass burning 3500 7000 Coarse Si = -4.01*t + 900 Coarse Fe Fine BC = 7.26*t + 1469 Fine K Coarse Al ) ) 3000 6000 Fine BC -3 -3 Coarse Si Linear (Fine BC) Linear (Coarse Si) 2500 5000
2000 4000
1500 3000
1000 2000 Elemental concentration (ng m Elemental concentration (ng m 500 1000
0 0 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 May-95 May-96 May-97 May-98 May-99 May-95 May-96 May-97 May-98 May-99 Time (months) Time (months) (c) Mineral dust and biomass burning (d) Particulate mass 1600 35 K-coarse Coarse PM Coarse K = -0.51*t + 184 Coarse PM = -0.021*t + 8.87 1400 K-fine 30 Fine PM ) Fine K = 1.31*t + 320 Fine PM = 0.019*t + 10.16
-3 Linear (Fine PM)
Linear (K-coarse) ) -3 Linear (Coarse PM) 1200 Linear (K-fine) 25 g m 1000 µ 20 800 15 600 10 400 concentrationPM (
Elemental concentration m (ng 5 200
0 0 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 Jan-00 Jan-99 Jan-98 Jan-97 Jan-96 Jan-95 Sep-99 Sep-98 Sep-97 Sep-96 Sep-95 Sep-94 May-95 May-96 May-97 May-98 May-99 May-99 May-98 May-97 May-96 May-95 Time (months) Time (months)
Figure 4.9 The median monthly particulate mass and elemental concentrations for some of the elements associated with mineral dust and biomass burning aerosols collected at Rukomechi research station from September 1994 to January 2000 98
Abundant and widespread rainfall resulted in an increase of the vegetation biomass providing enough fuel for fires through the dry season and an inhibition of aerosol emissions from the soil. The increase in elemental concentration of biomass burning aerosols is consistent with other results from this region. Anyamba et al., [2003] detailed the inter-annual variability of vegetation conditions in southern Africa, contrasting the rainfall and Normalized Difference Vegetation Index (NDVI) measurements during contrasting years 1992 (a dry phase with extreme drought in southern Africa,) and 2000
(above normal rainfall year). A significant increase in the areas burned is reported during
2000 as compared to 1992.
In section 4.2.1.3, it has been found that coarse K is ascribed to mineral dust sources while fine K is associated with biomass burning emissions. Therefore the mirror image between the mineral dust (Figure 4.9a) and biomass burning (Figure 4.9b) related aerosols can be further illustrated by the trends of coarse and fine K (Figure 4.9c). Like elemental concentrations of indicator elements of mineral dust origin, coarse K was found to be decreasing with time (at rate of 0.51 ng m-3 per month) while fine K (like BC) showed an increasing trend (at about 1.3 ng m-3 per month).
The particulate mass data also revealed strong contrasts between the dry and wet season for both the fine and coarse mode fractions (Figure 4.9d). Fine PM increased with time while coarse PM decreased. The increasing trend of fine PM can be explained by the direct influence of the biomass burning sources on the fine PM while the decrease in coarse PM can be due to the influence of mineral dust sources. Statistically, it can be stated with 95% confidence that the fine PM loading in northern Zimbabwe steadily increased at a rate of 19 ng m-3 per month for the period and with 80% confidence that the coarse PM levels decrease monthly by 21 ng m-3.
99 Monthly variations of elemental concentrations for aerosols from sea salt and anthropogenic sources are presented in Figure 4.10. Unlike the mineral dust and biomass burning aerosols, the long term trend for the elemental concentration of aerosols that originate from sea salt and anthropogenic sources were not statistically significant, hence the absence of trend lines in Figure 4.10.
(a) Sea salt
800 Coarse Na
) 700 Fine Na -3 Coarse Cl 600 Fine Cl 500
400
300
200
Elemental concentration (ng m (ng concentration Elemental 100
0 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 May-95 May-96 May-97 May-98 May-99 Time (months)
(b) Anthropogenic
10.00 Fine Pb )
-3 Fine As
1.00
0.10 Elemental concentration (ng m
0.01 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98 Sep-99 May-95 May-96 May-97 May-98 May-99 Time (months)
Figure 4.10 Monthly variations of elemental concentrations for aerosols from sea salt and anthropogenic sources
100 The main indicator elements for sea salt aerosols (Cl and Na) do not reveal strong seasonal variations in their elemental concentrations as has been revealed by mineral dust and the biomass burning elements (Figure 4.9a and b). However, it can be noted that Cl and Na have low concentrations in the months of January and February (Figure 4.10a).
Measurements of elemental concentration of the sea salt indicators elements show that there is no significant change in the ambient levels of maritime aerosols in the long term.
As for the sea salt sources, elemental concentration of indicator elements for anthropogenic aerosols (Pb and As) do not exhibit strong seasonality. Although the long term trend of fine As seems to show a decreasing tendency, this could not be statistically substantiated.
4.2.5.2 Annual cycle of elemental concentrations
Besides the inter-annual changes in elemental concentrations discussed in the above section, there are also intra-annual variations over Rukomechi research station. To obtain the elemental concentration annual cycles, the long term monthly median elemental concentrations for PM and for indicator elements of different sources were calculated by considering aerosol data for each particular month.
There are large differences between the measured elemental concentration levels (Table
4.5) of the elements originating from the same source (see Tables 4.2 and 4.3). For example, the maximum monthly median concentration values for Al, Si and Fe (mineral dust) were found to be 720 mg m-3, 1527 mg m-3and 545 mg m-3 respectively. Hence, to study the similarities and differences between the annual variations of the concentrations for such elements, their monthly median concentration values were normalised with respect to the maximum. The resulting normalised concentration roses for the different aerosol sources types are shown in Figure 4.11.
101 (a) (b) Jan Jan Coarse PM 1.0 Coarse Al 1.0 Fine PM Dec Feb Dec Feb Coarse Si 0.8 0.8 Coarse Fe 0.6 0.6 Nov Mar Nov Mar 0.4 0.4 0.2 0.2 Oct 0.0 Apr Oct 0.0 Apr
Sep May Sep May
Aug Jun Aug Jun Jul Jul
(c) (d) Jan Fine BC Coarse Na Jan 1.0 1.0 Dec Feb Fine K Fine Na Dec Feb 0.8 Fine I Coarse Cl 0.8 0.6 0.6 Nov Mar Fine Br Fine Cl Nov Mar 0.4 0.4 0.2 0.2 Oct 0.0 Apr Oct 0.0 Apr
Sep May Sep May
Aug Jun Aug Jun Jul Jul (e) (f) Jan Jan Coarse Cu 1.0 Fine As 1.0 Dec Feb Fine Sb Dec Feb 0.8 Fine Pb 0.8 0.6 0.6 Nov Mar Fine S Nov Mar 0.4 0.4 0.2 0.2 Oct 0.0 Apr Oct 0.0 Apr
Sep May Sep May
Aug Jun Aug Jun Jul Jul
Figure 4.11 The normalised (with respect to the maxima) monthly median elemental concentration roses showing the annual cycles of particulate mass (a), and indicator elements for mineral dust (b), biomass burning (c), sea salt (d) and anthropogenic sources (e & f)
102 The annual cycle of PM levels (Figure 4.11a) closely follow that of mineral dust (for coarse PM) and biomass burning (for fine PM). This indicates the contribution of these sources to the total PM.
The annual cycle for elemental concentrations from mineral dust elements (Figure 4.11b) are lowest in January but gradually increase from January to peak in September. The steady increase between January and September closely mirrors the steady decrease in relative humidity (Figure D.4) though with a phase difference of 1 month between the highest mineral dust concentration levels (Figure 4.11b) and the trough of the relative humidity (Figure D.4). The phase difference might be due to early rainfall events, which often occur around Rukomechi in October (Figure 4.4) that would weaken the emissions from the soil.
High normalised concentrations for biomass burning related aerosols from July to
November (Figure 4.11c) might be a good indication of the biomass burning period.
Highest elemental concentrations of biomass burning aerosols occur in the month of
September which is also associated with low relative humidity (Figure D.4) and it seldom rains during this month (Figure 4.4). October, the hottest month, reveals relatively low concentrations of aerosols related to mineral dust and biomass burning due to occasional early season rainfall episodes (Figure 4.4). For example, Kanyemba meteorological station received a total rainfall of 72 mm in six days in October 1994. Such rainy episodes can easily dampen the emissions from the soil and suppress forest fires.
The increase of elemental concentrations of biomass burning related aerosols that starts in
May is punctuated by fairly constant aerosol loading in July and August (Figure 4.11c).
This could be due to the “Guti” weather phenomenon that may occur any time of the year but is more common during July and August. “Guti” is a type of weather system having a low overcast of stratiform cloud from which drizzle falls, sometimes continuously, but
103 more typically in bursts during which the cloud base lowers and visibility is considerably reduced [Torrance, 1981]. Equally important is the wind, a moderate fresh south-easterly wind, cool and rather gusty. In severe or moderate “Guti” conditions, low cloud that spreads into a blanket and covers south, central and east Zimbabwe. This weather system is normally associated with the southerly and/south-easterly flows. As the flow is southerly during the “Guti” weather, the moist conditions therefore suppress the mineral dust and biomass burning emissions along the path of the wind. This weather system is not revealed by the rainfall and relative humidity data from meteorological stations around
Rukomechi because it mainly affects the south-eastern half of Zimbabwe, but impacts biomass burning emissions from distant sources. The drizzles may also not be strong enough to be captured adequately by standard rain gauge systems but strong enough to inhibit biomass burning.
Sea salt elemental concentration levels show a bi-modal distribution with different maxima for Na and Cl. Their elemental concentrations behave in the same manner in the first half of the year from January to July but the variations in the aerosol loading become very different thereafter for both, the fine and coarse mode (Figure 4.11d). The changes in the concentration levels, however, follow a similar trend for fine and coarse fraction sizes for each element. The concentration levels of sea salt elements are lowest in January but increase steadily to reach the first maxima in April. This month is the first of two maxima for Na and Cl. The second maximum for Cl is experienced in July (August) for the coarse
(fine) modes. The concentration levels for Na drop in May but then continue to increase steadily to create a second and greater maximum in September. The annual cycle of Cl concentration variation differs from that of Na in that it shows a decreasing trend from
July to November whereas Na reveals a relatively high concentration. This decrease in Cl concentration levels while Na increases can be attributed to chloride depletion reactions that occur when sea salt aerosols are mixed with polluted air masses [Song and
104 Carmichael, 1999]. The depletion would be mainly due to the formation of nitrates during the reaction of sea salts with NOx compounds from biomass burning and fossil fuel burning activities [Weingartner et al., 1997]. As maritime air masses also pass over most polluted areas in the sub-region (see section 4.3.2), Cl depletion can also be due to the formation of sulphates from industrial activities.
Elemental concentrations of Cu are generally high during the dry season (Figure 4.11a) from May to November showing a maximum in September and two minima, one in
December and the other one in March. Between the two minima, there is a period of relatively high aerosol loadings in January and February. Sb concentration levels are highest in June with another smaller peak in January. It is important to note that Sb and
Cu are the only two source types that show some activity in January. This minor peak might be a result of pollutants from the Zambian Copper-belt region as most of the air masses originate from this area during this period (discussed later).
Anthropogenic related elemental concentrations also exhibit an annual cycle. As and Pb have respectively distinctive isolated maxima in February and June (Figure 4.11f).
However, a higher and broader elemental concentration band for Pb and As is found from
May to August but Pb concentrations reach the peak in May while the highest As concentration levels are found in June.
4.2.6 Aerosol data comparison
The purpose of this section is to discuss the results given above in the light of other
(previous) aerosol studies performed in (southern) Africa.
Size fractionised atmospheric aerosols were collected at Skukuza National Park, South
Africa [Maenhaut et al., 1996] during the field work for SAFARI-92 from 30 August to 12
October 1992. Comparison of results from Rukomechi research station with those from
105 Skukuza shows that the major sources of coarse and fine aerosols in southern African are mineral dust, biomass burning and sea salt. However, results from Rukomechi also show that aerosols in this region have a biogenic origin. The absence of biogenic sources at
Skukuza could be due to the effect of the depleted vegetation due to the 1991-92 drought year while the measurements at Rukomechi were done over a period of 5 years that saw the recovery of vegetation.
The median elemental concentrations measured at the two sites are presented in Figure
4.12. In general, the results show that elemental concentrations of mineral dust (e.g. Al, Si and Fe), sea salt (Na, Cl) and anthropogenic (e.g. Pb, As) related elements over Skukuza were higher than those at Rukomechi for both the coarse and fine size fractions. For the coarse size fraction, the ratios of the median elemental concentrations for mineral dust elements for both sites are close to 1 (ratio of Rukomechi median concentrations to
Skukuza ranges between 0.8 and 1.2), which shows that the results at the two sites are comparable. The same ratios for sea salt aerosols were 0.3 for Na and 0.1 for Cl. The greater concentration of sea salt aerosols over Skukuza could be due to the fact that this site is much closer to the Indian Ocean than Rukomechi. Fine mode anthropogenic aerosols were also found to have elevated concentrations over Skukuza compared to
Rukomechi; which might be attributed to the proximity of the site to the South African industrial area.
106 (a) 100000 Dry season Rukomechi Skukuza
) 10000 -3
1000
100
10
Coarse size fraction concentration (ng m (ng concentration size fraction Coarse 1
0.1 PM BC Na Mg Al Si P S Cl K Ca Ti V Mn Fe Co Zn Sr I La Element (b) 100000 )
-3 Dry season Rukomechi Skukuza 10000 ) -3
1000
100
10 Fine size fractionconcentration m (ng
1
Coarse size fraction concentration (ng m (ng concentration size fraction Coarse 0.1
0.01 PM BC Na Al S Cl K Ca Ti V Mn Fe Co Zn As Br Sb I La Pb Element
Figure 4.12 Comparison of median elemental concentrations measured at Rukomechi research station during the dry season and at Skukuza (South Africa) during SAFARI-92 for (a) coarse and (b) fine aerosols
On the other hand, the elemental concentrations of biomass burning related aerosols reveal that during the dry season, the concentrations over Rukomechi are greater than those measured at Skukuza. The ratios of the median elemental concentrations (Rukomechi to
107 Skukuza) of fine BC and K were found to be 2.0 and 2.2, respectively. This is not a surprise as Rukomechi is situated closer than Skukuza to the region in the sub-continent that experiences a high frequency of fires [Anyamba et al., 2003] (also see section
4.4.2.1.1). In summary, although the sources of atmospheric aerosols in southern Africa are the same, the aerosol concentration at a particular site strongly depends on the proximity of the sources.
Furthermore, the mean daily total particulate mass (coarse and fine) loadings at
Rukomechi research station (Table 4.4) are comparable to those obtained in South Africa between Johannesburg and Vereeniging over the period 1989-1993 [Rorich and Turner,
1994]. The mean daily concentration of total particulate mass for the two data sets was found to be the same (20 µm-3) while the peak values exceeded 120 µm-3 for the South
African data set and was found to be 116 µm-3 at Rukomechi (Table 4.5). In Kenya, the mean concentration values for black carbon in the air sampled around the equator in May and June 1999 are much lower (1 – 4 µg m-3) [Gatari and Boman, 2003] than those measured at Rukomechi which range from 0.01 µg m-3 to about 9.7 µg m-3 (Table 4.5).
These results show that the aerosol concentration values at Rukomechi research station are more similar to those in southern Africa than concentration values close to the equator.
108 4.3 Trajectory climatology: Results and discussion
As described in section 3.7, daily back trajectories calculated by the HYSPLIT model were apportioned (section 3.8) into clusters (groups) that are thought to represent the major air-flow transport regimes to northern Zimbabwe. The air-flow climatology for
Rukomechi (in terms of direction, speed and preferential flow height) is represented by the clusters of all the five-day back trajectories for the period January 1994 to December
1999.
4.3.1 Optimization of number of clusters
The total root mean square deviation (tRMSD) of the trajectories (calculated as in section
3.8) gradually increases as the number of potential clusters is decreased. A steep increase in the tRMSD is often used to determine the optimal number of clusters to be used in the analysis. However, it is often difficult to identify an appropriate steep increase that can be used for cluster number optimization. Hence, to get a better measure of the step changes in tRMSD, the percentage change in the tRMSD, (tRMSD(%)) from n clusters to (n-1) was determined. The percentage change is expressed as
tRMSD − tRSMD tRMSD(%) = n n−1 *100 ………… 4.13 tRMSDn
In Figure 4.13, the percentage change of the tRMSD is shown as a function of the step change of the potential cluster numbers. Reducing the number of potential clusters stepwise from n = 20 downwards, the first major steep increase is observed when reducing the cluster numbers from seven to six. According to Dorling et al [1992], seven was then chosen as the optimum number of clusters for best representing the different air flows to
Rukomechi research station during the period January 1994 to December 1999.
109 10
8
6
4 tRMSD (%)
2
0 2 to 1 2 to 2 3 to 3 4 to 4 5 to 5 6 to 6 7 to 7 8 to 8 9 to 10 to 9 10 to 11 to 10 11 to 11 12 to 12 13 to 13 14 to 14 15 to 15 16 to 16 17 to 17 18 to 18 19 to 19 20 to Step change in cluster number
Figure 4.13 Percentage change of the total root mean square deviation (tRMSD) for incremental reduction (step change of one unit) of the potential cluster numbers
4.3.2 Results from cluster analysis
Seven distinct clusters were identified for the period of study. Figure 4.14 illustrates how all the daily trajectories (left diagrams) from January 1994 to December 1999 were assigned to the seven clusters identified by the clustering algorithm. These plots together with the 25th percentiles, median and 75th percentile trajectories (right diagrams Figure
4.14) give a qualitative impression of the spread of the trajectories within each cluster.
Although considerable variability within each individual cluster is evident, there is evidence that the clustering procedure categorised the trajectories into seven distinct clusters.
110 (a) Slow easterly (Cluster A)
5 N5 Equator0
5 S-5
10 -10S 15 -15S 20 -20S Latitude (deg) Latitude (deg)
25 -25S
30 -30S
15 20 25 30 35 40 45 50 20 E 30 E 40 E 50 E Longitude (deg) Longitude (deg)
(b) Fast easterly (Cluster B)
5 N 5
Equator 0 5 S -5 10 S-10 15 S-15 20 S-20
Latitude (deg) -25
Latitude (deg) 25 S
30 S-30
15 20 25 30 35 40 45 50 15 E 25 E 35 E 45 E 55 E Longitude (deg) Longitude (deg)
Figure 4.14 Cluster membership plots (left hand side) of 5 day back trajectories arriving at Rukomechi station at 1180 m above ground level (~800 hPa) together with the 75th (blue), median (purple) and 25th (red) percentile trajectories (right hand side). Data basis are trajectories (starting at 1200 hr local time) for the period from January 1994 to December 1999. The seven clusters have been identified by trajectory cluster analysis (see 4.3.1).
111
(c) Fast south-easterly (Cluster C)
5 N 5
Equator 0
5 S -5
10 S-10
15 S-15
20 S-20
25 S-25
30 S-30
35 S-35 15 20 25 30 35 40 45 50 Longitude (deg) 15 E 25 E 35 E 45 E 55 E Longitude (deg)
(d) Slow south-easterly (Cluster D)
5 5 N Equator 0 5 S-5
10 S-10
15 S-15
20 S-20
25 S-25
30 S-30
15 20 25 30 35 40 45 50 20 E 30 E 40 E 50 E 20 E 30 E 40 E 50 E Longitude (deg) Longitude (deg)
Figure 4.14 (continued)
112
(e) Southern flow (Cluster E) 5 Equator 0 -5 10 S-10 -15 20 S-20 -25 30 S-30 Latitude (deg) Latitude (deg)
-35 -60 W -40 W -20 W 0 30 E 40 E 40 S-40 Longitude (deg) -45 5 101520253035404550 10 E 20 E 30 E 40 E 50 E Longitude (deg)
(f) North-north westerly flow (Cluster F) 5 N 5 Equator 0 NF 5 S-5
10 S-10 15 S-15 20 S-20
Latitude (deg) Latitude (deg) 25 S-25
30 S-30
15 20 25 30 35 40 45 50 20 E 30 E 40 E 50 E Longitude (deg) Longitude (deg)
Figure 4.14 (continued)
113
(g) Local flow (Cluster G)
5 N 5 Equator 0
5 S-5
10 S-10
15 S-15
20 S-20
25 S-25 Latitude (deg) Latitude (deg)
30 S-30
15 20 25 30 35 40 45 50 20 E 30 E 40 E 50 E Longitude (deg) Longitude (deg)
Figure 4.14 (continued)
To avoid the influence of extreme values in the trajectory endpoint values, the median was
used as a measure of the central tendency to represent general cluster flow direction. The
spread of trajectories within each cluster is represented by the inter-quartile range (right
diagrams Figure 4.14). The seven median trajectories shown in Figure 4.15 represent the
seven general air mass pathways to Rukomechi. The length of each median trajectory is
five days and the distance between two successive data points represents a three-hour
interval.
114 5 N5.0 SlowSlow Easterly easterly (Cluster A) Equator0.0 (Cluster A) Fast Easterly DRC -5.0 (ClusterFast B) easterly (Cluster B) 5 S TAN FastFast South-easterly south-easterly (Cluster C) -10.0 MAL 10 S ZAM (Cluster C) ANG SlowSlow South-easterly south-easterly (Cluster D) 15 -15.0S MOZ (Cluster D) ZIM MAD Southern (Cluster E) -20.0 Southern flow (Cluster E) 20 S NAM BOT
-25.0 North-North Westerly 25 S SWA North-north westerly (Cluster F)
Latitude (deg) Latitude (deg) (Cluster F) LES 30 -30.0S Local (Cluster G) SA Local flow (Cluster G)
35 -35.0S
40 -40.0S
1010.0 E 2030 20.0 E E 30 30.0 E 40 40.0 E 50 50.0 E Longitude (deg)
Figure 4.15 Median trajectories for clusters A to G arriving at Rukomechi research station from 1994 to 1999 arriving at 1180 m above ground level (~800 hPa)
The main transport routes to Rukomechi are (i) the eastern corridor with two easterly flows, one that originates from eastern Africa (Cluster A) and the other, which originates from northern Madagascar (Cluster B); (ii) the south-eastern corridor also with two flows, one originating from the Indian ocean off southern Madagascar (Cluster C) and the other from the Mozambique Channel (Cluster D); (iii) the southern corridor that carries air from the Atlantic Ocean round the tip of southern Africa (Cluster E); (iv) the north-north-west corridor (Cluster F) which originates from Zambia and Angola; and (v) the local recirculation air (Cluster G). The general flow pathways (clusters) can easily be distinguished in terms of flow direction, wind speed, preferential transport height, and the dominant months of transport which will be presented in the following sub-chapters 4.3.3 and 4.3.4.
115 4.3.3 Spatial and dynamic patterns of the flow
Eastern corridor. The slow easterly flow (Figures 4.14 and 4.15, Cluster A) contains all trajectories coming generally from easterly and north north-easterly directions with relatively low average wind speeds of about 2.9 m s-1. The fast easterly flow (Figures 4.14 and 4.15, Cluster B) brings in air from the northern part of Madagascar and northern
Mozambique with a mean wind speed of 5.4 m s-1. Bearing in mind that the time interval between two successive points represents a three hour interval, then it is interesting to note that the air masses in the fast easterly are faster over land (more spaced) than over the ocean (less spaced). The corresponding mean wind speeds over land and over sea along this trajectory are respectively 6.4 m s-1 and 4.7 m s-1. This apparently contradicts the usual sea/land contrast effect, where movement of air masses are slower over land (due to surface friction) and faster over the sea (southern corridor, Cluster E). This contrast can be explained in terms of the increased pressure gradient force that could arise due to the incursion of the Indian Ocean anti-cyclone into the Mozambique Channel (Figure 4.20b) that overrides the frictional forces.
South-eastern corridor. The airflow in this corridor also consists of a fast (6.1 m s-1,
Cluster C) and a relatively slow (4.1 m s-1, Cluster D) mean wind speed component. Air masses constituting the slow south-easterlies (Cluster D) originate mainly from the
Mozambique Channel and those in the fast south easterly corridor (Cluster C) originate from the Indian Ocean off the southern coast of Madagascar.
Southern corridor. Air flow to Rukomechi from the south Atlantic round the tip of southern Africa is shown by the median trajectory of Cluster E (Figure 4.15). Considering that the length of each trajectory is five days, the existence of (very) long trajectories in the southern corridor shows that the fastest flow of air masses to Rukomechi research station occurs along this pathway (average wind speed of 10.6 m s-1). Over the ocean, the
116 average speed is 12.1 m s-1 which is reduced to about 7.1 m s-1 as soon as air passes over the continent (enhanced surface friction) and is channelled through the Zambezi valley.
Air parcels in this flow will have turned through an angle of about 180° by the time they reach the receptor site.
Furthermore, median trajectories in the southern (and south-eastern) corridors all show anticyclonic curvature when arriving from east the at Rukomechi research station suggesting that the airflow associated with these pathways is part of the anticyclonic recirculation modes of air over southern Africa [Garstang et al., 1996] (see also section
4.3.6.
North-north-western corridor. Mostly continental air from Zambia and Angola reaches the receptor site in this corridor; the corresponding median trajectory (average speed of 4.1 m s-1) is shown in Figure 4.15 as Cluster F. Some of the individual trajectories in this corridor (Figure 4.14c) reach Rukomechi from the Atlantic Ocean and the general trajectories' curvature tends to be cyclonic in contrast to other trajectories mostly in the eastern, south-eastern, and south corridors, where anticyclonic curvature prevails (see section 4.3.6).
Local winds. Air flows which have been apportioned into Cluster G generally loop around
Rukomechi with the lowest mean wind speed of 1.8 m s-1. As for Cluster F, the transport regime of Cluster G mainly brings continental air to Rukomechi.
While Figure 4.15 shows the horizontal pathways of the clusters' median trajectories,
Figure 4.16 depicts the median height (in metres above ground level) of the median trajectories during the 5 days before their arrival at the receptor site.
117 3000
Cluster A Cluster E ) 2500 Cluster B Cluster F Cluster C Cluster G 2000 Cluster D
1500
1000
Height above ground level (m level ground above Height 500
0 0 1224364860728496108120 Time (h)
Figure 4.16 Median height of all trajectories in an individual Cluster (A to G) as a function of trajectory running time
The fast easterly (Cluster B), the fast (Cluster C) and slow (Cluster D) south-easterly, and the north-north westerly (Cluster F) flows show a tendency of persistent horizontal transport at an average height of about 850 m above ground level. As would have been expected, the fast southern winds (Cluster E) have subsided from higher altitudes; this is associated with the descent of air along isentropic surfaces sloping down towards the equator [Holton, 1993]. A similar behaviour, but less pronounced, is observed for the local trajectories associated with Cluster G. The slow easterly flow (Cluster A) is characterised by rising air masses.
The air-flow corridors’ contributions to the total air-flow to Rukomechi are shown in
Figure 4.17 and the year to year pathway contributions are presented in Table 4.7.
118 North-north Southern flow westerly (Cluster E) (Cluster F) Local flow 8% 6% (Cluster G) 7% Slow south easterly Slow easterly (Cluster D) (Cluster A) 28% 15%
Fast easterly Fast south (Cluster B) easterly 20% (Cluster C) 16%
Figure 4.17 Percentage contribution of the seven identified trajectory clusters (A to G, see text) to the total air flow to Rukomechi from January 1994 to December 1999
The eastern corridor (clusters A and B) contributes 35% while the south-eastern corridor
(Clusters C and D) accounted for 44% to the total flow to Rukomechi. The dominance of clusters C and D indicates the strong influence of the south-east trade winds that generally dominate the air flow between 30 °S and the equator [Ahrens, 2002]. The high speed southern flow (Cluster E) contributed only to 8% of the cases. The north-north western flow (Cluster F, associated with ITCZ) and local flow contributed 6% and 7%, respectively, to the total air-flow to Rukomechi over the six year study period.
Since the meteorological parameters show variations from year to year, similar variations were also observed in the percentage contribution to the total flow by each of the clusters.
Table 4.7 shows the annual percentage contributions of each air corridor from years 1994-
95 to 1998-99 of the meteorological calendar taken from July to June the following year.
The results reveal that the predominant air pathway is the slow south-easterly which had a relatively high contribution for all the years under study. Although the inputs of each of
119 these pathways of air masses remain fairly constant over the years, there is however a
marked difference in the flow contribution from the years that were associated with
extreme weather conditions like 1994-95 (drought year) and 1996-97 (abnormally wet
year).
Pathway 1994-95 1995-96 1996-97 1997-98 1998-99 Overall Corridor
Slow easterly (Cluster A) 16 14 15 15 10 15 35 Fast easterly (Cluster B) 22 22 6 16 18 20
Fast south-easterly (Cluster C) 17 12 18 10 19 16 44 Slow south-easterly (Cluster D) 28 23 35 30 30 28
Southern flow (Cluster E) 8 5 8 16 5 8 8
North-north westerly (Cluster F) 2 9 17 8 8 6 6
Local flow (Cluster G) 6 16 2 5 10 7 7
Table 4.7 The percentage contribution of each of the air corridors to the total flows to Rukomechi from January 1994 to January 2000
In a relatively dry year (1994-95), the percentage contribution from the north-north
westerly (Cluster F) is minimal (2%). The absence of these rain bearing winds associated
with the ITCZ (the Congo air) is reflected in the lack of the rains in January observed in
several meteorological stations in northern Zimbabwe around Rukomechi (Figure 4.5) and
some other parts of the country [Nyanganyura, 1999].
1996-97 was marked by the drop in the fast easterly contribution from about 20% to 6%
while there was an increased activity from about 6% to 17% in the north-north westerly’s
air mass pathway. To explain these abrupt changes in the flow regimes in these two
pathways, it has to be considered that these two flows are predominantly wet season flows
(see section 4.3.5) and that the 1996-97 rainy season was an abnormally high rainfall
season caused by an Indian Ocean tropical depression. The presence of a cyclone in the 120 Indian Ocean south of Madagascar and/or in the Mozambique Channel will induce an air movement that inhibits the easterly flows but enhances the air movement from the north- north westerly corridor. Hence, the decrease in the easterly flows and the increase in the north-north westerly flows can be attributed to the weather system leading to the development and presence of an Indian Ocean tropical depression.
The changes that affected the wet season flows (Clusters B and F) show how synoptic conditions can influence the contributions of air mass pathways to Rukomechi. The fact that the January 1997 cyclone was preceded by the low contributions from the fast easterly, then the decrease in the early wet season fast easterly flows might be used as an indicator for possible mid wet season cyclones.
4.3.4 Temporal distribution of trajectories
Temporal distributions of air-flows were investigated in terms of mean annual cycles with particular consideration of subtropical seasons. The six year trajectory climatology shows definite patterns of seasonal variations. The variation in the trajectory frequencies over the course of the year is evident from the results shown in Figure 4.18, which shows the monthly contribution of each flow to the total number of trajectories (total flow).
The slow south-easterly flow (Cluster D) alone contributes most (28% see Figure 4.17) to the total flow and is present all year round, indicating again the general influence of the trade winds. This flow is more evident from the onset of the dry season (April to
September) when its monthly contribution is always above 3%, while during the rainy season, particularly in January and February, the monthly contribution drops to 1.3% and
0.98%, respectively.
The fast easterly flow (Cluster B), the second largest individual contribution to the total flow (20%, see Figure 4.17), starts to appear significantly in August (mid dry season), it
121 reaches its maximum monthly contribution in November (4.7%), and undergoes rapid reduction to 2.2% in the early wet season (December). Although there is a sudden reduction of the fast easterlies in December, the flow to Rukomechi is still predominantly easterly particularly due to the contribution of slow easterly flow (Cluster A).
5.0 4.5 Cluster A Cluster E Cluster B Cluster F 4.0 Cluster C Cluster G 3.5 Cluster D 3.0 2.5 2.0 1.5 1.0 Contribution to the total flow (%) flow total the to Contribution 0.5 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month
Figure 4.18 Percentages of the monthly long-term occurrences of trajectories in each cluster for the air masses arriving at Rukomechi for the period January 1994 to December 1999
The fast south-easterly flow (Cluster C), third largest contributor to the total flow (16%, see Figure 4.17), becomes most active from mid-wet season onwards and contributes most to the air mass transport at the end of the rainy season (2.9% in March, see Figure 4.18).
Strengthening of this flow at the expense of the north-north-westerly flow (Cluster F) indicates that the fast south-easterlies play a major role in the final northwards movement of the ITCZ (see section 4.3.5). As the fast south-easterlies (Cluster C) weaken at the change between wet and dry seasons, the slow south-easterlies (Cluster D) become the major contributor to the total flow, and an annual cycle of south-eastern, eastern, and the
122 north north-western starts all over again. Moreover, it should be noted that for paired air corridors (eastern and south-eastern), the dominance of fast transports is always followed immediately by slow transports. This phenomenon may be due to the dynamics of synoptic weather systems that are responsible for the different flow patterns (see section
4.3.6).
The fastest air transport (Cluster E), originating from the south Atlantic (Figure 4.15), is limited mainly to the period from May to July. Its highest monthly contribution is 1.5%
(June), whereas its lowest contributions occur in the middle of the rainy season, particularly in December and January. Although trajectories of Cluster E contribute only
8% to the total flow, they may comprise a considerably influential pathway of air pollutant transport to Rukomechi because they frequently pass over the most industrialized regions of South Africa (see Figure 4.14), and they occur during the cold and relatively dry season when there is considerable coal combustion for heating in South Africa.
The north-north-westerly flow (Cluster F) that contributes 7% to the total flow (Figure
4.17) significantly appears only in the mid wet season, January and February, where its monthly percentage contributions are 2.8% and 2.3%, respectively (Figure 4.18). The role of the ITCZ in the coexistence of two opposite, south-easterly and north north-westerly flows is discussed in section 4.3.5.
Local winds (Cluster G), contributing 7% to the total flow (see Figure 4.17), originate mainly from the north north-west to south-south-west and the fetch of this flow describes a semi-circle of 600 km radius. Local winds significantly influence the flow during the beginning of the dry season where its monthly contribution is highest in May (1.5%).
123 4.3.5 Influence of the inter-tropical convergence zone
As shown in Figure 4.18, the monthly percentage contributions of the fast south-easterly flow (Cluster C) in January and February (1.3% and 2.0%, respectively) are roughly of the same order of magnitude as of those in the north north-westerly flow (Cluster F) (2.8% and 2.3%). This apparent co-existence of air mass flows (at 1180 m above ground level
(~800 hPa)) from opposite directions is indicative of the movement (positioning) of the
ITCZ. Figure 4.19 shows the number of days (trajectories) for occurrence of north north- westerly air flow (Cluster F - hatched bar) and south-easterly flow (Clusters C and D - grey bar) for the months of January and February, during the period from 1995 to 1999.
20
16
12
8
4
Total number of trajectories 0 Jan95 Jan96 Jan97 Jan98 Jan99 Feb95 Feb96 Feb97 Feb98 Feb99
Figure 4.19 Total number of trajectories from the north-north-westerly (hatched bar) and south-easterly (grey bar) flows to Rukomechi station for the months of January and February during the period 1995 to 1999
In 1995 and 1997, the influence of the north-north-westerly flow was weaker than the south-easterly, while in 1996 and 1999, the mid wet season flow was mainly north-north- westerly. This is an indication that the southwards migration of the ITCZ had not reached
124 Rukomechi for the first case and for the latter, the ITCZ was positioned south of the site for a couple of days.
Using the relative number of daily trajectories between the two flows for January and
February (Figure 4.19), some of the history of the ITCZ's position (in relation to
Rukomechi research station) can be deduced. In 1996, 1998 and 1999, the ITCZ had already passed over Rukomechi in January, whereas in January 1995 and 1997, the ITCZ was still north of Rukomechi. However, 1997 shows less influence of the north-north- westerly flow. This could be an indication that the southwards movement of the ITCZ might have been affected by the presence of a tropical cyclone in the Indian Ocean that produced a wetter than average 1996-97 rainy season in north-eastern Australia and southern Africa [Bell and Halpert, 1998]. On the other hand no explanation was found for
1995 which has a similar air-flow distribution between January and February. In 1998, the
ITCZ retreated much earlier than in any other year as indicated by the number of February south-easterly trajectories compared to the north-north westerly.
A closer inspection of the individual trajectories showed that the air flows to Rukomechi research station oscillate either from north-north west or from south-east with a period of
2 to 4 days. This alternating of north-north-westerly and south-easterly flows indicates that the southward and northward translational motion of the ITCZ is accompanied by oscillatory motion in the south-northern direction.
4.3.6 Air mass flows and pressure systems over southern Africa
To study the influence of the surface pressure systems on the individual flows, the synoptic pressure systems of trajectories (of each cluster) whose time-step coordinates that were found within 25% of the median cluster trajectory were used. Trajectories within the inter-quartile range were selected in order to reduce the ‘noise’ in the composite surface
125 pressure that could be introduced by synoptic systems associated with trajectories located
further away from the median trajectory. The European Centre for Medium-Range
Weather Forecast (ECMWF) surface pressure data was used to construct composite
surface pressure maps for the selected days. The resultant composite surface maps
together with median cluster trajectories are presented in Figure 4.20.
Equator Equator A 1024 hPa B 1028 hPa 10 S 10 S 1018 hPa 1022 hPa 1016 hPa 20 S 1012 hPa 20 S 1006 hPa 1010 hPa 30 S 1000 hPa 30 S 1004 hPa 994 hPa 998 hPa 40 S 40 S 988 hPa 992 hPa
50 S 50 S 0 10 E 20 E 30 E 40 E 50 E 60 E 0 10 E 20 E 30 E 40 E 50 E 60 E (a) Slow easterly (Cluster A) (b) Fast easterly (Cluster B)
Equator Equator C D 1026 hPa 1034 hPa 1022 hPa 10 S 10 S 1030 hPa 1018 hPa 1026 hPa 1014 hPa 1022 hPa 20 S 1010 hPa 20 S 1018 hPa 1006 hPa 1014 hPa 30 S 1002 hPa 30 S 998 hPa 1010 hPa 994 hPa 1006 hPa 40 S 40 S 990 hPa 1002 hPa
50 S 50 S 0 10 E 20 E 30 E 40 E 50 E 60 E 0 10 E 20 E 30 E 40 E 50 E 60 E (c) Fast south easterly (Cluster C) (d) Slow south easterly (Cluster D)
Equator Equator E 1028 hPa F 1022 hPa 10 S 1022 hPa 10 S 1016 hPa 1016 hPa 1010 hPa 20 S 1010 hPa 20 S 1004 hPa 1004 hPa 998 hPa 998 hPa 30 S 30 S 992 hPa 992 hPa 986 hPa 986 hPa 40 S 40 S 980 hPa 980 hPa
50 S 50 S 0 10 E 20 E 30 E 40 E 50 E 60 E 0 10 E 20 E 30 E 40 E 50 E 60 E (e) Southern flow (Cluster E) (f) North-north westerly (Cluster F)
Figure 4.20 Composite surface pressure patterns that characterise the air mass flows to Rukomechi station for the period of January 1994 to December 1999
126 The easterlies (Cluster A and B) are influenced by a ridge of high pressure wrapping around the subcontinent and the development of a continental cyclone between the borders of Angola and Zambia (Figures 4.20a and b). The incursion of the high pressure system into the Mozambique Channel pushes the flows to Rukomechi further north and finally easterly as they approach the receptor site. This incursion adds speed to the motion of the air giving it an unusual higher speed over land than over the sea (see Figure 4.15
Cluster C). The strength of the pressure cells differentiates between the easterly flows. For the fast easterly (Cluster B), the high pressure ridge reaches the central part of the
Mozambique Channel. This flow is also associated with two cyclones; a continental cyclone cell situated in the south-east of the Democratic Republic of Congo (DRC) and a maritime cyclone located in the Indian Ocean south of Madagascar (Figure 4.20b). In the case of the slow easterlies (Cluster A), the incursion of the high pressure system remains south of the Mozambique Channel (Figure 4.20a), and a continental cyclone is found around the Caprivi Strip, stretching along the subcontinent to the southern part of South
Africa (Figure 4.20a).
The fast south-easterly flow (Cluster C) is mainly influenced by the Atlantic Ocean
Anticyclone (AOA), which is centred off the southern coast of South Africa and exerts influence also in north-west South Africa and southern Zimbabwe (Figure 4.20c). The other dominant feature is the Indian Ocean tropical depression centred south of
Madagascar. Observing the pressure pattern's development over a number of consecutive days shows that the AOA strengthens as it moves eastwards and curves around South
Africa, while the low pressure centre moves north-eastwards and sits in the Indian Ocean.
The development and positioning of these cells stir fast south-east air towards Rukomechi.
The slow south-easterly flow (Cluster D) is characterised by generally high surface pressure levels over the region (minimum 1002 hPa) and two anticyclones, one far south-
127 west in the Atlantic Ocean and the other south east in the Indian Ocean (Figure 4.20d).
Both subtropical anticyclones are too far away to have direct influence on the flow to the receptor site. The flow is therefore thought to be influenced be the general circulation of the Hadley Cell.
The southern flow (Cluster E) is characterised by a continental anticyclone which is centred over South Africa/southern Zimbabwe, and connected to the AOA (at 5 °E) by a ridge of high pressure (Figure 4.20e). Time analysis of the sequence leading to this pressure pattern indicates that the continental anticyclone “bud off” [Hattle, 1972] from the AOA. The development and strengthening of an anticyclone over South Africa and a cyclone over central DRC induces a south-to-north pressure gradient between South
Africa/southern Zimbabwe and northern DRC/northern Zambia that stirs fast southerly air towards the site. The coupled system of high pressure cells is responsible for the dominating winds emanating from the South Atlantic Ocean. A trough of low pressure between the twin anticyclones and a weak Indian Ocean tropical depression also helps to stir the winds from the south Atlantic, but as air masses approach the coast, the only driving force will be the continental anticyclone. Apart from the effect of enhanced friction (over land), this might be one of the reasons why the wind speed of this flow decreases as it approaches the coast en route to Rukomechi.
The continental air-flow (Cluster F) is driven by a continental cyclone situated over western Zambia stirring clockwise winds round Rukomechi. Though it is a weak cyclone, its presence might also be responsible for the cyclonic circulation of air over the entire subcontinent. The subtropical high pressure belt (along 40 °S) shows marked cells of high pressure on either ocean, but too far away to influence the flow over Rukomechi. It is suggested that the north-north-west flow is mainly due to the low pressure system caused by the ITCZ.
128 There is no distinct surface pressure system associated with the flow patterns of the local winds (Cluster G), hence the exclusion from Figure 4.20. It is however suggested that air motions in this flow regime may be due to the differential heating at the surface.
4.3.7 Comparison of regional air flow climatologies
Anti-cyclonic recirculation is a persistent feature in the air flow over southern Africa and is governed by semi-permanent subtropical high pressure systems and by the transient ridging highs in the westerlies [Tyson et al., 1996b]. The analysis of the dominant synoptic-scale occurring over southern Africa during the period 1986 to 1992 revealed that anti-cyclonic conditions are common in June and July where the occurrence is around
70% to 80%. These results are consistent with the findings of this study (Figure 4.14) where most of the air masses flowing to northern Zimbabwe describe anti-cyclonic flows with the southerly (Cluster E) and local flows (Cluster G) tend to re-circulate air over the greater southern African region under anti-cyclonic conditions. Furthermore, the occurrence of the air masses in these clusters tends to be highest also during the months of
May, June and July (Figure 4.18), a period that coincides with the maximum occurrence of the re-circulation flows discussed by Tyson [1996a]. Tyson [1996b] also discussed in detail the four major air circulation types that occur over southern Africa.
The different flow regimes of air masses to northern Zimbabwe are comparable with the air flows to the Zambian Copper-belt region obtained by Meter [2000] who identified the easterly, the south-easterly and the north westerly flows. These flow directions coincide with the easterly, the south-easterly and the north westerly air corridors identified in this study. However, the easterlies and the south-easterlies are herein split into the fast and slow components. Furthermore, both studies indicate that the southward and northward translational motion of the ITCZ is accompanied by oscillatory motion in the south- northern direction. Seasonal transport climatology for Kenya [Gatebe et al., 1999] reveals
129 that predominant atmospheric pathways conveying air to Kenya are the north easterly flow, the south easterly flow, the westerly flow, the anti-cyclonic Saharan transport, circum Kenyan transport and the localised within Kenyan transport.
As discussed above, the results from other air flow studies in the region and those from this study indicate that the air movement in the subcontinent is composed of direct and re- circulation flows. Apart from the anti-cyclonic re-circulation flows that occur in the subcontinent, the results from this study also revealed that the north north-westerly flow to northern Zimbabwe is composed of air masses that tend to flow in cyclonic pathways
(Figure 4.14f). These rain bearing winds that affect northern Zimbabwe are associated with cyclonic flows due to the semi-permanent cyclone located over the Caprivi Strip
(Figure 4.20a).
4.4 Aerosol concentration over northern Zimbabwe: A climatological
perspective
Transport of aerosols and trace gases by large-scale circulation fields of the atmosphere has received increasing attention with the growing need to identify aerosol sources and sinks. Evaluating the atmospheric pathways of atmospheric pollutants to a particular geographical region requires a comprehensive understanding of the transport-climatology of the region, as discussed in the previous section 4.3. The meteorological information necessary for understanding the transport processes and pathways of pollutants are the prevailing large-scale synoptic features, which have also been discussed (section 4.1). A combination of chemical data set, air mass climatology and synoptic weather conditions is now used to yield a comprehensive insight into the influence of meteorological parameters on the aerosol concentration levels over northern Zimbabwe.
130 4.4.1 Combining elemental concentration data with meteorological information
The clusters presented in section 4.3 represent flow regimes ranging from most polluted to clean air flows. The classification of “polluted” and “clean” clusters depends on the direction of flow and the dominant flow season (wet or dry). As the median monthly elemental concentrations (Figure 4.11) of aerosols associated with different sources
(Tables 4.2 and 4.3) and the total monthly occurrences of trajectories in each cluster
(Figure 4.18) show distinct annual cycles, a correlation procedure was used to determine polluted and clean air-flow pathways. High correlations between the median monthly concentrations and the total monthly occurrences of trajectories in each cluster indicate that trajectories in a particular cluster will be associated with a high concentration of a certain element; hence these correlations were used to identify which elements are mostly transported along which pathway. The median monthly elemental concentrations for each element were correlated with the total monthly trajectory occurrences of each cluster and only correlation coefficients greater than 0.5 were considered to define polluted pathways.
In cases of weak correlation, the highest two or three elements showing highest correlations were included in the results for illustration. The resulting correlation coefficients of the trajectory occurrences in different clusters with individual elemental concentrations are presented in Table 4.8. The horizontal sort order of the table is in the order of decreasing correlation coefficients.
131
Sb_f Cluster A 0.08 I_f Na_f PM_f Br_f Ca P I BC_f Ca_f K_f Cluster B 0.70 0.67 0.63 0.63 0.62 0.60 0.54 0.52 0.52 0.49 Cl Cl_f Cluster C 0.40 0.18 Mn_f S_f Fe_f Ti_f Al_f Mg_f Na Fe Cl_f Al Ti Zn 0.80 0.80 0.79 0.76 0.76 0.73 0.72 0.72 0.72 0.71 0.71 0.70 Cluster D Si Cl As_f PM Mg Pb_f Zn_f Mn BC Ca_f K_fBC_f 0.68 0.66 0.66 0.63 0.63 0.62 0.61 0.60 0.59 0.59 0.55 0.51 Pb_f Mn_f S_f Al_f Fe_f Ti_f As_f Cl_f Al Mg_f Fe Ti 0.78 0.77 0.70 0.74 0.74 0.73 0.66 0.64 0.63 0.63 0.63 0.61 Cluster E Cl Sb_f Si Na Mn Zn Ca_f Zn_f Mg 0.61 0.60 0.58 0.56 0.55 0.54 0.54 0.54 0.51 Sb_f Cu Cluster F 0.18 0.35 As_f Sb_f Pb_f Cluster G 0.68 0.65 0.60
Table 4.8 The correlation between the monthly occurrences of flow from a certain cluster to the monthly median elemental concentrations. The fine size fractions of elements are represented by “_f” otherwise they are coarse size fraction.
The correlation between the monthly occurrences of trajectories from a certain cluster and the monthly median elemental concentrations makes the large-scale influence of synoptic factors evident. Results in Table 4.8 show that the fast easterly (Cluster B), the slow south-easterly (Cluster D), the southerly (Cluster E) and the local flows (Cluster G) carry air masses with enhanced aerosol concentrations while the slow easterly (Cluster A), the fast south-easterly (Cluster C) and the north-north-westerly (Cluster F) are relatively clean air masses. The difference between the clean and polluted air masses can be explained in terms of the time of the year (wet or dry season) when the air masses arrive at the site
(section 4.4.2.2). This can also be explained in terms of the relationship between the transport height of the air masses (trajectory height) and the mixing layer (section 4.4.2).
132 It is, therefore, important to note at this juncture that the climatic factors play an important role in the variations of these correlation values. Generally, clean flows (Clusters A, C and
F) are mainly wet season air masses while the polluted air masses (Cluster B, D, E and G) are mostly dry season flows (discussed in section 4.3.4). It can also be noted that the different air mass pathways (Table 4.8) with enhanced concentrations tend to transport pollutants originating from different aerosol source types (Tables 4.2 and 4.3).
The fast easterly flow (Cluster B) is highly correlated with elements that are associated with forest and savannah fires. The correlations with elemental concentrations of fine I,
Br, BC and K range from 0.49 to 0.7 make this cluster a biomass burning pathway. The air masses in this cluster commonly occur in September, October and November (see section
4.3.4 and Figure 4.18), a period that is marked by relatively high temperatures and low relative humidity (see section D.2 and also Figure D.4 in Appendix D). However, the peak values of the elemental concentrations of biomass related aerosols are found in September and October (Figure 4.14c) while the high frequencies of trajectories from the fast easterlies occur in November (Figure 4.18). As November marks the start of the wet season (Figure 4.3), the shift in the peak concentration levels and that of the frequency of trajectories signifies that wet weather conditions greatly influence the aerosol concentrations at Rukomechi research station by suppressing forest fires and scavenging of biomass burning aerosols en route to the station.
Cluster D (slow south-easterly) carries aerosols related to a variety of sources that include mineral dust, biomass burning, sea salt and of anthropogenic origin. The bulk of these aerosols are related to mineral dust with correlation above 0.7. The only anthropogenic related elements found in this flow are fine size fractions of S and Pb which have correlations of 0.8 and 0.62 respectively. All sea salt related aerosols, except for fine Na,
133 are correlated with these air masses with correlations above 0.66. Biomass burning aerosols have relatively low correlations (less than 0.61) with this cluster.
The fact that this pathway is associated with aerosols related to the different source types is an indication of the temporal and spatial spread of the trajectories constituting this cluster. The monthly frequency of the cluster membership shows that this corridor is made up of prevailing winds that dominate from April to September. The trajectories are spread over a wider area throughout the sub-region. A large variety of source types in the region are active at different locations (within the trajectory spread) during different times of the year, hence, the presence of aerosols from a variety of sources.
Cluster E (the southerly flow) tends to transport air from the subcontinent loaded with aerosols emitted from anthropogenic, mineral dust and sea salt sources. These air masses have high correlations with anthropogenic related elements, fine S (0.7), As (0.66) and Pb
(0.78) indicating that it is the pathway through which most of the human related aerosols from the sub-continent are transported to northern Zimbabwe. The human influence along this pathway is especially evident from Pb that is most likely coming from the combustion of leaded-fuels by vehicles in the region (see section 4.4.2.2.3).
Cluster G (local flow) is the only flow regime associated with anthropogenic related aerosols with correlations of 0.68, 0.65 and 0.60 for fine As, Sb and Pb respectively. The winds in this flow are responsible for transporting aerosols from the non-ferrous smelters around the sub-continent in an anti-cyclonic motion.
4.4.2 Determination of source regions of aerosols measured at Rukomechi research
station
As the previous analysis done to this point did not bring up a clear (quantitative) picture of the origin of the aerosols collected, the potential source contribution function (PSCF)
134 method (section 3.10.3) was used to define the source regions of the aerosols. The PSCF approach combines the information obtained from the aerosol chemical data (section 4.2) and the air flow climatology (section 4.3) to identify the potential source regions of aerosols measured at Rukomechi research station. Aerosol concentration information was attached to individual daily trajectories during the aerosol peak months and only those trajectories whose transport heights were below the mixing layer were considered. The comparisons of the results from the PSCF method with known regional sources of S and copper, and of BC with the satellite fire data are also discussed. The potential regional sources of biomass burning related aerosols were also estimated from the relationship between the trajectories and the fire spots observed by satellite.
4.4.2.1 Source regions of biomass burning related aerosols
4.4.2.1.1 Source regions of biomass burning related aerosols resulting from fire plots
and individual trajectories
Figure 4.21 (left side) shows all sub-continental fires detected by the Second European
Remote Sensing Satellite (ERS-2) (section 3.10.1) during the 1996 biomass burning season (August to November). The right side presents the corresponding location of fires for 1996 that were picked up by the trajectories within 50 km on either side of the trajectories while the trajectories were within the mixing layer. The regional fire density
(number of fires per pixel (1° x 1°)) for each month during the biomass burning season was found to be similar from 1996 to 1999. To illustrate the fire behaviour activities in the sub-region and to analyse their influence in northern Zimbabwe, 1996 was chosen because it presented the highest concentration levels of biomass burning related aerosols measured at Rukomechi from 1994 to 1999 (Figure 4.9b).
135 The temporal and spatial evolution of monthly fire counts (Figure 4.21 (left diagrams)) shows that the southern African fire activities are mainly during the months of August to
November, and drift south and eastwards during the same period. In August, the fires are concentrated mainly in central Angola, southern DRC and western Tanzania while
September fire activities are located in northern South Africa, central Botswana and northern Zambia. The central Botswana and northern Zambia fire zones are linked by a fire belt that runs along the Zambezi valley through Rukomechi research station. October fires are mainly located in central Zambia, northern Zimbabwe and central Mozambique.
In November, fire activities are mainly in southern Tanzania and northern Madagascar.
As the fire activities shift south-eastwards, the total number of fires detected by satellite decreases over the four month period. For 1996, 12939 fires were detected by satellite in
August, 10228 in September, 5353 in October and 5023 in November. The pattern of vegetation production and the vegetation moisture content determine the extent and distribution of the fires in the sub-continent. The decline in the fire counts and spatial distribution from August to November is mostly related to bio-fuel availability in the areas where biomass burning fires are concentrated and is also influenced by the positioning of the inter-tropical convergence zone (ITCZ). The bio-fuel diversity in the sub-continent ranges from dense equatorial forests and miombo woodlands in southern DRC to savannah grasslands and shrubs in central Botswana [Anyamba et al., 2003].
The seasonal movement of the ITCZ is an important determining factor in the south- eastwards drift of fire activities in southern Africa. The encroachment, from the north, of the ITCZ, which crosses the equator in August [Waliser and Gauntier, 1993] and the wet conditions associated with this system suppress the fires in the sub-continent giving rise to the trough in biomass burning activities in the DRC observed in August (Figure 4.21). As
136 the ITCZ shifts southwards from August to November, encroaching on the sub-region from the north-west direction, the fire activities also travel south-eastwards.
From the satellite data, the peak of the biomass burning season is in August, when the highest number of fires occurs in the region; however, aerosol concentration measurement at Rukomechi shows that the highest concentration levels of biomass burning related aerosols in northern Zimbabwe do not occur in August but in September. To explain this discrepancy between the source intensity (number of fires) and the chemical data, the number of fires seen by the trajectories on their way to Rukomechi has to be taken into consideration.
Diagrams on the right side of Figure 4.21 show only those fires that were detected within a 100 km buffer zone of the daily air mass trajectories en route to Rukomechi. These are the fires picked up in this zone when the trajectory height above ground level was less than the model’s mixing layer. The colour scheme was used to classify the number of the fires per pixel (1° x 1°) detected by the trajectories.
137 August 5 N 5
# ## # #
# # # Equator 0 # # # ## # # # # # # # # # # # ## # # # # # # # # # # ## # # # ## # # ## ## # # # # # ## # # # ## # # # # # # # ### # # # # ### ## # # # ### # # # # # # # # # # # ##### # # # ## # ### # # ## ## ## # # # # # # # ## # # ### ## # # ## # # # # # # ##### -5 # # # # ## # # 5 S # # # # # # # # ## # # # # # ## # # # # # ## # # # # # # ## # # # ### # ## ### # ## # # # # # # # ## ### ## # # # # # # # ## ## # ## ##### ## # # # ## # # # # # # ### ## # # ## # # # # # # # ## # # # # # # # # # # # # # # # # # # # ## # # ## # # ## ## # # # ## # ## # # # # # #### # ### # ## # ## # # # # # # ## # ## # # ## # # # # # ## ### ## # # # ## ### # # # ## # # ## # # # # # # # # # ### # # # # # # ## # # # # # # # # # # # ### # # ## # ## ## # # ### # ### # # # ### # # # ## # # # # # # # # # # #### # # # ## # ## # # ### # # # ## # # # ### # # # # # # # ## # # ### # ## # # ## # # # # # -10 # # # # # ## # # # # ### # # # # # # # ## ## # 10 S # # # # # ## # # # # # # # # # ## # # # ## # # # ## # # ## # ## # # # ## ### # ## # # # # ##### # # # # # # # # # # # ## ### # # # # # # # # ### # # # # # ## # ## ## # # # # # #### # # # ## # # # ### # # # # # # # # # # ## # ## # # # # # # # # # # # # ## # # # # # # # # # ## # ## # ## # # # # ### # # ### # ## # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # -15 # # ## # 15 S # # # ## # # # # # # ## # # # # ## # # # # # # ## # # # # # # ## # # ## ## # # ## # # ## # # ## # # # ## ### # # # # # # # #
# # ## # ## # # 20 S-20 #
# # # # # # # ## # # # ## # # # (deg) Latitude # ## # # # ## # # # # ## ## # ## # # # # # ### # # # 25 S-25 # ## # # # # # # # # # # # # #### # # # # # # # # # ## ## ## # # 30 S-30 ## ### # # ##
1515 E 20 25 25 E 30 35 35 E 40 45 45 E 50 Longitude (deg)
5 N 5 # # # # September # # # Equator 0
#
# # # # # # # # # # # # # # # 5 S # -5 # # # # ## # # # # # # ## # # # ## # # # # # # # # # # # # # ### # # ## # # # # # # # ## # # # # ## # # # # # # # # # # # # # # # # # # # # ## # # # # # # ## # # # # # # # # # # # ### # # # ## # # #### # # # # # # # # # # # # # # # ## # # # # # # # # ## # # # -10 ## # # # # # # # ## # # # # # 10 S # # # ## # # # # ### ## ## # ## # # ## ##### # ##### # # # # ## # # ##### ## # # # ## # ## ## # ## # ### # ####### # ### #### ## # # # ## ## # ## ## ## ## # # ## ## ## # ###### # ### ####### #### # ## # # # ### # # # # # # # ## ## # # # ##### # #### # ## # # # ## # # # # ## ### # # # # ##### # # # # # # # ## # # ## # ## # # # # # ## # # # # ## # # # # # # # # # # # # -15 # 15 S # # # # ## # # ## # # # ## # # ### # ## # # # # # ## ## # ## # # # # # # # # # # # # # ## # # # # # # # # # # # ### ### # # # # ## # ### # ## # ## # # # ### # # # # # # # # # ## # # # ## # ## # # # # # ## # ## # # # ## # ###### # #### # # # # # # # ## # # # # # # # # ### # ## # # # ## # # ### # ### # # # ## # ## ### ## # # # # # ## # # # ### # # ## # # # # # # # #### # #### ### ## # # # # # # # # ## ## ### # # # # # # # ## # # # ## # # # ### # # # # # # ## # # # -20 # # # ## 20 S ## #### #### # # # # ## # # # ## # # ## ####### # # # # # ## # (deg) Latitude ####### # ##### # ##### ## # ## ###### # # # ### ############# ## # # # # ## # ##### # # #### # ####### ## # # # ## # # ## #### # # ## #### ###### ###### ### # ## # ## # #### # ## ## ## ### # # ## # # # ## # ## # # # # # # # ### # # ## 25 S # # # # -25 # # ## # # # # # ### # # # # ### ## # # # # # # # ### ## ## # # # # # # # # # ## # # ## # # # ## # # # # # # # # # # # ## # # ### # ### ## # # # # # # # 30 S-30 # ### # # #
# #
# # # # Fires counts between 1 and 5 1515 E 20 25 25 E 30 35 E 40 45 E 50 Longitude (deg) Fires counts between 5 and 10 Fires counts above 10 October
### # # ## ## # # # # #### ### # ### # # # ### # ## # # ## # ### # ##### # # # # # 5 N 5 ## # 0 # Equator #
#
# # # # # # # ## # # # # ## -5 ## # # # # 5 S # # # # # # ### ##### # ## # # #### ## ## ## # # ## # # # ## ## # ## ### #### # ### ### ## # # ## # # # # # # # # ###### ## # # # # # # ## -10 # # # # # # ## # ## # # ## # # 10 S # # # # ## # #### # # # ##### # # # ## # # # # ### # # # ## # ##### # # # ### # # # # # # # #### # ## # # # ### # # # ### # # #### # # #### # # ### # ## # ## ## # ### # # # ### # # # # ### # # # ## ## # # # ##### # ### -15 # # # # ## # # # # ## ## # ## # # # # # ### # # # # 15 S # # # # # ## # # # # # ## # # # # ## # # # # ## ### # # # # # # # # ### ## # # # # ## ## # # ## # # # # ### # # # # # # # # # # # # # # # # # # # # # # -20 # ## # # ## ### ## 20 S # # # # # ## ## # # # # # # # # # # # # # (deg) Latitude # # -25 # # 25 S # # 30 S-30 1515 E 20 25 25 E 30 35 35 E 40 45 45 E 50 55 Longitude (deg)
November 5 N 5 # # # # # # # ## # # ##### # ### # # # Equator 0
#
# # # #
# # # # # -5 # # # # # 5 S # # #### # # # # # # # # # # # # # # # # # ## # # # ## # # # # # ## ## # # # # # # # # -10 # ## # # # # ## ## # # # # # 10 S # # # # ## # #### ## ## # ##### # # # # # # # # # # ## # # ## # # # # # # # # # # ## # # # ## ### # ## # ## # # # # # # ## # # # # ## # ### # # # # # # # # # # ## # # # ## ## # # # # # # ## # # # # ## ## # # ### # # # # # ## ## # # # # # # # # # # # # # ## # ## # ## # # # ## ### # # # # # # ## # # # ## # ## # ## # # # ## # ## # # ## # # ## # ## ## # -15 ## # # ## # ## # # # # # ## # # # # ## 15 S # # # # # # # # # # # # ## # # # ## ## # ### # # ## ### # # # ## # ### # # # # # ### # # # # # # ## ## # ## # # ## ### # # # # # # # ## # ## # # # # # # ## # # # # # # # # ## # # # # # # # # # # # # ## # # # # # # ### # # # # # # # # ## # # # # # # # # # ### # ## # # # # # # ####### # # ## ## # # # # ## # ## # # # # # # # # # # # ### # # # ## ### # # # # -20 # # # # # ## # # # 20 S ## ### ## ## ## # # # # # # # # # # # # ## # # # # ## # # # # # # # # # ## # # ## # ####### # ## #### # ### # ## # # # # # # ## # ## #### ## # ## # # # # # # ## (deg) Latitude -25 # 25 S # #
# 30 S-30
1515 E 20 25 25 E 30 35 35 E 40 45 45 E 50 Longitude (deg)
Figure 4.21 Southern African fire plots for August to November detected by the Second European Remote Sensing Satellite (left side) and the locations of fires picked up 50 km on either side of the trajectories when trajectory height above ground level was less than the model’s mixing layer in 1996 (right side).
138 Although August was the month with the highest number of fires detected by satellite, only 86 (about 0.7% of the total fires) were found within the corresponding trajectory buffer zones. The surface density of the detected fires at any given location was less than
5 fire counts per 12300 km2 (pixel size 1° x 1°). The spread of the fires detected in
August are found along the 15ºS latitude on either side of Rukomechi research station.
More fires within the buffer zone were detected in September (511) than in any other month and these constitute about 5% of the total fires detected by the satellite in
September 1996. A large portion of these fires are located in northern Zimbabwe and central Mozambique, though some fires were picked up in central Botswana and northern
Zambia. The fires with surface density more than 5 per location (per 12300 km2) are mainly located in northern Zimbabwe and central Mozambique. For October and
November 1996, significantly fewer buffer zone fires were registered, 369 and 292 respectively. The fires selected by the trajectories in these two months are mainly located in northern Zimbabwe, central Mozambique and northern Madagascar. However, these fires constitute about 7% and 6% of the total fires detected by the satellite in October and
November respectively. This narrows down to the fact that the most important factor responsible for high concentration levels of biomass burning related aerosols at
Rukomechi is not the total number of fires occurring in the sub-region, but where the fires occur in the region in relation to the air mass flow en route to Northern Zimbabwe.
To locate the potential source areas for biomass burning aerosols, fire data for the month corresponding to the highest concentration levels (September) were considered. All the fires that occurred in September from 1996 to 1999 together with the corresponding fires detected within the 100 km buffer zone are shown in Figure 4.22. The regional distribution of the fires detected by satellite is similar to the distribution for September
1996 (discussed above).
139 Fires counts between 1 and 5 Fires counts between 5 and 10 Fires counts above 10
#### # # ## # # ### # ### # # # # ## # ## # # # #### # ## # #### # # ## # # # ## # # ## # # # # ## # # # # # ## ## # 5 N 5 # # # ## ### # # # ## ### # # ## # ## # ## ## # # # ### #### # # # # # ## ## ## # # # Equator # # # # # # # ### # 0 # # # ## ## # # # # ## # # # ## # # # # # # # # # # # #### # # # # ## ### # # # # ###### ## # # # # # ## ## # # ## # # # # # # #### # # # # # ## ## # ## ## ## # # # ### # # # # ## # #### ### # ### # # ## ## ## ## #### ## # # # # ## ####### # ##### ### # ##### ## ## # # # # # # # # # # # ### # # # ## # ## # # # # ## # # # # # # # # # # # #### # # # # # ## ## # # # ### # ## # # # # # # # # # # # # # # # # # # # # # ### ### # # # ### # # ## # 5 S # # -5 # # # # # # # ### # ### # # # # # # # # ## ## ## # # ## # ## # # # # ## # # # # # # # ## ### # # # # ## ### # # # # # # # # # # ####### ## #### # ##### ## ## # # ## # ##### # #### #### ### # #### ### # # # ## # # ####### ## ## ## # ##### # # ## # #### # # # # # ### ###### #### ## # # # # # # # # # # # # ####### # # # ## ## ## ## # # # # ## #### # # # ### # # # # # # # ## # # # #### # # ##### # # # # # # ## ### # ## # ### ## ######## # # ## # # ## # # ## ## ### # ### ## ###### # # # # # # # # # ### #### # ## # ### ########### # ### ### # # ### ## # ## # # # ### ### ######### ###### ## # # ######### ## # # # # ## ################ ### ########## # # # # # ## # # ### ############## ######## # # ### # # # # # # # # ######### ################## # ### # # # # # # # # # # # ## ################################## ####### # ## 10 S ## ## #### ## # -10 # # ###### # ## # # # # # ###### ############### ### #### # # # ## # # # ############ ## ######### # # # # # # ############# ############# ## # # # ## # # # ######################## ### #### # # ## # #### # # # ######### # ###### ######## # # ### ##### # # #### # ## #### #### ### ############ ####### # # # # # # # ## # # # ### ## # ## ### #### # # ## # # # # # # ####### ## # ## # # # # ## ## # # ## ####### # ### # # # # ## ## # #### # #### # ## ## # # ## # # # # # ######## #### # # ### # ##### # ## # ########## # # # # # # # # ## # # # # # # # # # # ## # #### ## # # #### # ## #### ## ## ## # # ## # ##### # ## # # ##### # # # ## ## # # # # # ### #### # ######### ## # # ## ## ## # # # ### ###### # #### # # ### # # # ## ## ## ## # # ## ### ## ## # # # #### # ##### # # ## # ## # # # # # # # # # # # # ## # # ## ###### # #### # 15 S ## # # ## # # # # #### # # ## -15 ## # ## # # # ## # # # ##### # ### ## ## #### # # # # #### ######## ### # # ########## # ## ##### # # # # # # # # # # ## ## # ##### ## ## ################## # # # # ## # # # # ## # # # #### # # # #### ##### #### # # # ## # # ## # # ## ## ### ### ## ## ### ## ## #### # # # #### # # # # # # # ### ########## ####### ## ## # # ######## # ## # ### # # # # # ### ### ## ## ## # # #### ## # # ## ## ## ####### ## # # # # # ## # # # ## # ### # # # # # # # ### ## ### # # # # # # ## # # ## ## ### # ##### # ### ## # ## # # ### # # ## # # ### #### ## # # # # ### ### # # ## ### # # # # # # # # # ### # ########## ## # ## # # # # # ## # # ## ## ## # # # # ## # ### ########### ### #### # ## # # # # # # # # #### #### ##### ## # # # # # ### # # # ## ## ## # ### ####### ## # # # # ## ## # # # ### ### # # ## ### # # # # ## # # ### # # # # # # # ## ######## # # # # # # ###### #### ## # ## ## # 20 S ######### # # # # -20 # ## ######### ## # ## ### # # # ## ########## ### ## # ## # ## # # # ####### # # ### #### # # # # # # # #### ## ## ##### # # # ## ##### # # # # # # ##### ####### ## ## # # # # # ##### # ### # ### # # ### ############## ## # # # # ## ### ## ##### # ## ## # ##### ############# # ## # # # # ### #### # ## # ### # # #### ### ## # # ### ## ## ## ## # # ## # ## # ###### ###### #### ## # ## # # ### ## ## # ### # # #### # ### # # # # (deg) Latitude ### # ### # # # # # ## # ## # #### # ## ### ## #### # # 25 S # ### #### # ## -25 ## # # ## # # ## # # ### # ## # ## # # # # # ## ###### ## ## # # #### ## ### # # # ##### ##### # ## # ## # # ## ## ## ## ### ## ## #### ## # # # ######### ## # ### # ######## ## # ### # # ## ## # ## # ## # # # # ## ## ## ### ### ##### # # ######## #### # # # ### ##### # # ## # # ## ### ### #### # # ##### ### ### # ##### ### # ## # ## #### ## ## ### # # # # # ### 30 S # -30 ## # ## # # # # # ## ## # # ## ## # # # # # # #
# # # # 1515 E 20 25 25 E 30 35 E 40 45 45 E 50 55 55 E Longitude (deg)
Figure 4.22 All the fires that occurred in September from 1996 to 1999 together with the corresponding fires detected by trajectories within the 50 km buffer zone
Fire plots for September and the number of fires picked by trajectories show that the
region with fires within the buffer zone tends to be more in the eastern direction. A total
of 1785 fires were picked up by trajectories within the buffer zone and most of these fires
are located in northern Zimbabwe, central Mozambique and northern Madagascar, making
these potential source regions for biomass burning related aerosols measured at
Rukomechi. The results are qualitatively consistent with those discussed earlier, thus
demonstrating the importance of the temporal and spatial scales of fire activities in the
region.
For the overall picture, the fires seen by trajectories within the buffer zone from August to
November for the years 1996 to 1999 are shown in Figure 4.23. The total number of fires
detected in the buffer zone for 1996, 1997, 1998 and 1999 were 1714, 614, 1241 and 601
respectively. Most fires were detected in 1996 followed by 1998, which is consistent with
140 the measured time series of the monthly median elemental concentrations of biomass
burning related aerosols presented on Figure 4.9 that shows considerably higher levels of
BC, I and K in the sampled air during these years.
5 N 5 5 N 5 1997 Equator 0 1996 Equator 0
5 S-5 5 S -5
10 S-10 10 S-10
15 S-15 15 S-15
20 S-20 20 S-20 Latitude (deg) (deg) Latitude 25 S-25 25 S-25
30 S-30 30 S-30
15 20 25 30 35 40 45 50 55 15 20 25 30 35 40 45 50 15 E 25 E 35 E 45 E 55 E 15 E 25 E 35 E 45 E Longitude (deg) Longitude (deg) 8.00August to 9.00 9.00September to 10.00 10.00October to 11.00 11.00November to 11.01
5 N 5 5 N 5 Equator 1997 0 1998 Equator 0 5 S -5 5 S -5 10 S -10 10 S-10 15 S -15 15 S-15 20 S Latitude (deg) (deg) Latitude -20 20 S-20 25 S -25 25 S-25 30 S -30 30 S-30
15 20 25 30 35 40 45 50 55 15 E 25 E 35 E 45 E 55 E 1515 E20 2525 E30 35 E40 45 E 50 Longitude (deg) Longitude (deg)
Figure 4.23 The year-by-year fires within the 50 km buffer zone for August to November detected from 1996 to 1999
141 As in Figure 4.21 and Figure 4.22, the results from Figure 4.23 again indicate that northern Zimbabwe and central Mozambique are regions where biomass burning activities seen by trajectories en route to Rukomechi are mainly located. There are also some biomass burning activities that occur on Reunion Island (about 55 ºE, 21 ºS) that seem to have distant influence in aerosol loadings over northern Zimbabwe (Figure 4.23).
Up to now, only the occurrence of fires along the trajectories (±50 km) has been considered. However, not all the trajectories that select fires (shown in Figure 4.23) are associated with the high concentration levels of biomass burning related aerosols measured at Rukomechi. Due to dispersion of pollutants as they are transported by winds, the fire distance from the receptor site is a major factor in the measured concentration levels. Only those fire spots that were located within the buffer zone while the trajectories were within the mixing layer during the biomass burning season (August to November) from 1996 to 1999 and account for fine BC concentrations greater than the 75th percentile concentration level were considered. These fires are shown in Figure 4.24.
142
5 N 5
Equator 0
5 S-5
g) 10 -10S de (
15 -15S Latitude 20 -20S
25 -25S
30 -30S
1515 E 20 20 E 25 25 E 30 E 35 35 E 40 E 45 E 50 50 E Longitude (deg)
Figure 4.24 The location of the regional fires that are associated with concentration above the 75th percentile levels of fine BC at Rukomechi for the biomass burning seasons from 1996 to 1999
The 75th percentile level was used as the cut-off (lower limit) concentration for high concentrations. Most of the fires that contribute more than 75th percentile concentration of fine BC were found to be located east of the site within a radial distance of 200 km
(Figure 4.25) in the region around northern Zimbabwe and central Mozambique (Figure
4.24). This is the region with fires that are considered to be contributing most to the biomass burning aerosol loading at Rukomechi. Although some trajectories selected distant fires, the number of fires responsible for high BC concentration levels decreases considerably with distance from the receptor site. Figure 4.25 shows how the numbers of fires that contribute concentration levels of fine BC above the 75th percentile vary with the radial distance from the receptor site.
143 1600
7 .-2.2 Nfires = 4x10 X 1400 r −2.2 N = 1500 Power (Series1) fires 100 1200 R = 0.98 1000
800
600 Total number offires 400
200
0 <100 100-200 200-300 300-400 400-500 500-600 600-700 700-800 800-900 Radial distance from the Rukomechi (km)
Figure 4.25 The variation of the number of fires that contribute to fine BC concentrations above the 75th percentile as a function of the radial distance, r, from Rukomechi research station during the biomass burning season (August to November) from 1996 to 1999
Most of the fires are found closest to Rukomechi, within the first 100 km. The number of fires decreases drastically within the next 100 km band of radial distances. The variation in the calculated least squares trend line that fits through points in Figure 4.25 is given by the following expression:
r−2.2 Nfires = 1500 ……………………. 4.14 100
R = 0.98
where Nfires is the total number of fires within radial distance r (in km) starting at 100 km.
Equation 4.14 shows that within the radial distance of 900 km from Rukomechi, the
144 relationship between the number of fires that account for concentrations greater than the
75th percentile level and the distance from the receptor site obeys approximately an inverse square law. The fires within the first radial distance band (<100 km) account for
69% of the total number of fires while distant fires (in the 800 km to 900 km zone) account for an insignificant percentage (0.4%) of fires that influence high levels of fine
BC at the site.
This variation of the number of fires with distance signifies that the fires close to the receptor site are more responsible for high level concentration than the distant ones.
Assuming that the number of fires within a certain distance range is a measure of the upwind influence of fire to the ambient concentration of biomass burning related aerosols, then the influence of any fire from the receptor site is inversely proportional to the square of the distance. A plot of the drop of the influence according to the r-2 emphasizes the rapid loss of the influence of sources with respect to the inverse square law. The slightly larger power may point to additional loss processes.
Assuming that the measurements of biomass burning related aerosols at Rukomechi are largely influenced by the fires detected by trajectories within 200 km radial distance from the sources and that their concentration levels are negligibly affected by the dispersion processes, then a source-receptor relationship for biomass burning related aerosols can be established. The quantification of the source-receptor relationship for biomass burning aerosols was achieved by considering the measured elemental concentrations (above the
90th percentile concentration level) of biomass burning indicator elements (BC, K and I).
The 90th percentile level was chosen in this case because the quantitative expression
(equation 4.15) does not hold for concentration below this level. The samples associated with concentration levels above the 90th percentile show a general trend that is consistent
145 with the fire spots within the 100 km (±50 km) buffer zone, a trend that is not reflected by
concentrations below this level.
Figure 4.26 presents the elemental concentrations above the 90th percentile concentration
levels of BC, K and I, and the corresponding number of fires on the respective sampling
days found within 200 km from Rukomechi research station. Samples associated with
high concentrations are generally associated with trajectories that have picked up more
fires en route to the receptor site. There is some consistency of measured very high
concentration levels and number of fires seen by trajectories. Although the graphs look
identical, what is more striking from the results presented in Figure 4.26 is that the fire
count curve fits exactly in that of K except for the last few samples. The fine BC curve
also closely follows the fire count curve.
40 3000 ) Fine black carbon -3 35 Fine Iodine 2500 )
-3 Number of fires 30 Fine Potassium 2000 25
20 1500
15 ) and Iodine (ng m (ng Iodine ) and 1000 -3 10 500 5
0 0 (ng m Potassium fine of Concentrations Number of fires, concentrations of fine black black of fine of fires, concentrations Number (carbon m g 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Samples (consecutive numbers) with concentrations greater th than the 90 percentile concentration level
Figure 4.26 The variations of the measured elemental concentrations (above the 90th percentile level) of biomass burning elements and the corresponding fires selected by trajectories
146 The relationship between the number of fires selected by the trajectories, Nfires, to the measured elemental concentrations of BC, K and I was found to be summarised by the following expression:
c BC + c K + c I N = 1 2 3 …………………….. 4.15 fires 3
where c1 = 3.5, c2 = 0.012, and c3 = 4.8 are the number of fires per concentration measured in (µg m-3) for BC, (ng m-3) for K and (ng m-3) for I respectively. The constants c1, c2 and c3 are regression coefficients derived from the original data used to plot graphs in Figure 4.16. Equation 4.15 expresses the linear relation between the sources (fires) and the measured concentrations at the site and holds for high concentrations due to local sources. Therefore when dispersion effects can be neglected, a linear relationship exists between the elemental concentrations of biomass burning aerosols the number of fires selected by individual trajectories. Thus, the fires directly influence the concentration level at the site according to equation 4.15.
4.4.2.1.2 Source regions of biomass burning related aerosols resulting from the
potential source contribution function method
In order to better characterise the sources of fires impacting Rukomechi research station, the potential source contribution function (PSCF) method [Ashbaugh et al., 1985; Lupu and Maenhaut, 2002] was applied (see section 2.7.5). The results were presented in a spatial grid of 1º x 1º (latitude, longitude) on a probability value between 0 and 1. Values close to 1 mark the cell as a potential source area for aerosols containing the element under consideration. As the areas that have low probabilities (less than 0.4) are not likely to be potential sources, only cells with probability values greater than 0.4 were considered in this study.
147 For biomass burning, fine black carbon is used as an indicator and its probability function
values are plotted on the map shown in Figure 4.27. The plot points to the Zambezi valley
as a distinct area, whose bio-fuel combustion related aerosols obviously impact
Rukomechi. Interestingly, however, there are additional, partially isolated, areas
contributing as well. The Malawi area, the Gaza-Imbaubane area of Mozambique and
further away, the Highveld and approximately the Karroo of South Africa act as sources.
5 N5 0.8 to 1.0 Equator 0 0.80.6 toto 0.81.0
5 S-5 0.60.4 toto 0.60.8
0.4 to 0.6 10 -10S
15 -15S
20 -20S
25 -25S
30 -30S
35 -35S
40 -40S
1010 E 15 20 20 E 25 30 E 35 40 E 45 5050 E 55
Figure 4.27 Potential source contribution function spatial distribution of fine black carbon, with 75th percentile of the concentration as threshold for the biomass burning season (August to November) from 1996 to 1999
148 4.4.2.1.3 Comparison of biomass burning regional sources obtained from the
potential source contribution function and the fire plots
The PSCF is a statistical trajectory technique that is based on combining aerosol data (BC in this case) with calculated air parcel backward trajectories; while the fire plot technique is a Geographical Information System based procedure that combines satellite detected fire data (source of BC) with calculated air parcel backward trajectories. Comparing
Figure 4.24 and Figure 4.27, the regions depicted as potential source areas for biomass burning by the two techniques agree for the probability above 0.8.
Figure 4.28 shows that the region corresponding to the fires associated with more than
75th percentile concentrations at Rukomechi of the fire count procedure fits in the region of the PSCF spatial distribution (0.8 to 1.0) of fine black carbon. It should be borne in mind that the fire plots only correspond to those fires that were detected when the trajectories were within the mixing layer and that information about the strength of the fires is not available from the satellite data. Therefore, the absence of the detected fires in the regions that are depicted as potential source areas by the PSCF technique indicate the higher sensitivity of the latter technique.
Although the two methods are based on different computational techniques and basic assumptions, a visual inspection of Figure 4.28 shows that they both identify the same main potential source regions for black carbon. The PSCF is a well documented procedure and has been extensively used to identify source locations [Lupu and Maenhaut, 2002;
Polissar et al., 2001] and preferred transport pathways [Lin et al., 2001; Zhang et al.,
2002] of atmospheric trace elements and particulate species. The fact that it agrees well with our method shows that the fire plot method is an accurate method of locating potential source regions of biomass burning aerosols. Vice versa, this serves as evidence to show that the PSCF technique can be applied accurately to the southern African
149 situation to identify potential sources of atmospheric pollutants. Hence the PSCF
procedure has been used hereafter to locate potential source regions of human related
aerosols in the sub-continent.
5 N 5 0.8 to 1.0
Equator 0 0.80.6 toto 0.81.0
5 S 0.60.4 toto 0.60.8 -5 0.4 to 0.6 10 -10S Fire plots
15 -15S
20 -20S
Latitude (deg) 25 -25S
30 -30S
35 -35S
40 -40S
1010 E 1 5 20 E 2 5 3030 E 35 40 40 E 4 5 5050 E 55
Longitude (deg)
Figure 4.28 Comparison of biomass burning regional sources obtained from the potential source contribution function and the fire plots
150 4.4.2.2 Regional source of anthropogenic aerosols
The strengths of most anthropogenic sources tend to remain constant throughout the year as human activities, like mining and traffic, are not necessarily affected by the season. The variation of their ambient concentrations is then most likely due to changes in the meteorological parameters. To study the effects of meteorological parameters on the variation of anthropogenic aerosols at Rukomechi, the elemental concentrations of the coarse Cu, fine S, Pb, As and BC will be considered in the following sections.
To study the climatological perspective of aerosol loading, the distribution of elemental concentration levels (of coarse Cu, fine S, Pb, As and BC) below the 25th percentile were used to analyse low concentration level cases while those above the 75th percentile levels were used to study high concentration episodes. Elemental concentrations within the inter- quartile range represent moderate levels. The percentages of total samples whose concentrations (for each element) were found below the 25th percentile, within the inter- quartile range and above 75th percentile concentration levels were determined. The distribution characteristics for each element were analysed with respect to the established seven air pathways (section 4.3.3). The percentages of trajectories associated with concentrations above the 75th and below 25th percentiles were used to study the characteristics of contaminated and clean trajectories respectively within each pathway.
Figure 4.29 presents the percentage distributions of trajectories carrying concentrations above the below 25th percentile level, within the inter-quartile range and above the 75th percentile level for Cu, S, Pb, As and BC in each of the air mass pathways.
The concentration distribution for air masses that arrive at Rukomechi during the wet season (Clusters A, C and F) shows that a large number of these air masses carry relatively clean air and the distributions are heavily skewed towards low concentration.
During the wet season, the number of cases when the concentration levels are greater than
151 the 75th percentile concentration is considerably lower than when the concentrations are
below the 25th percentile level.
Coarse Cu Fine S 20 20
16 16
12 12
8 8
Percentage of the total 4 Percentage of the total 4
0 0 AB CD E F G AB CD E F G Cluster Cluster Fine Pb Fine As 24 20
20 16
16 12 12 8 8
Percentage of the total Percentage of the total 4 4
0 0 AB CD E F G AB C D E F G Cluster Cluster
Fine BC 16.016 Below 25th percentile
Inter-quartile range 12.012
Above 75th percentile 8.08
A: Slow easterly E: Southern flow 4.04 B: Fast easterly F: North-north westerly Percentagethe of total C: Fast south easterly G: Local flow 0 0.0 D: Slow south easterly ABCDEFG Cluster
Figure 4.29 The percentages of cases when the trajectories carrying concentrations below the 25th percentile, within the inter-quartile range and above the 75th percentile for each of the elements for each of the air mass pathways
152 The early wet season air masses (Cluster B) show that the concentration levels along this pathway exhibit a more or less normal distribution, except for the coarse Cu and fine BC, where the concentration distributions are heavily skewed towards high concentrations.
Both fine BC and coarse Cu concentrations describe a J-curve distribution with coarse Cu having no recordings of low concentration (below the 25th percentile level). The relatively high concentrations of Cu in this pathway might be due to mineral dust related copper aerosols which occur just before the rainy season; however, the sources associated with these high levels of Cu from this direction were not detected by the PSCF technique
(section 4.4.1.2.1). The positive skewness observed for fine S concentration distribution might be attributed to the influence of biomass burning activities.
On the other hand, dry season air masses (Clusters E and G) tend to carry relatively high concentration levels of human related aerosols. Figure 4.29 shows that the elemental concentration distributions along these air masses are general positively skewed, except for fine S and BC in Cluster G when it is slightly negatively skewed. The highest bias towards high concentration levels is shown by the As concentration distribution, which exhibits a J-curve distribution. Because the air pollutants are transported under dry conditions, aerosol removal by in cloud and below cloud scavenging is minimal during the dry, leading to relatively high concentration tendencies along these pathways. The only factor that would influence the concentration levels would be dispersion of pollutants during transport.
Cluster D (the slow south-easterly) is the air pathway that has the highest number of cases with high percentages of concentration found within each of the three concentration ranges. This is as would be expected because the slow south-easterly flow contributes most to the total air mass flows to Rukomechi (Figure 4.17) and the trajectories are well spread in space (Figure 4.14) and time (Figure 4.18). The elemental concentration
153 distributions along this pathway were found to be either negatively or positively skewed.
The air parcels tend to carry air masses that have low elemental concentrations (less than
25th percentile level) of coarse Cu and fine Pb while the elemental concentrations of fine
S, As and BC are found more in the high concentration band (above the 75th percentile level) than in the lower band (less than the 25th percentile). This could be due to the fact that air masses in this pathway tend to pass over areas (Figure 4.14d) that are most likely to be potential sources of fine S (section 4.4.2.2.2) and BC ((section 4.4.2.1) but not those for coarse Cu (section 4.4.2.2.1) and fine Pb (section 4.4.2.2.3).
The results from the inspection of these elemental concentration distributions showing the contaminated and clean air masses (Figure 4.29) compare very well with those from the correlations between the monthly median concentrations with the monthly occurrences of trajectories in each cluster (Table 4.8). As in the results presented in Table 4.8, the air masses associated with the wet season (Clusters A, C and F) have a larger portion of clean air (concentrations below 25th percentile level) when arriving at Rukomechi. On the other hand, air masses associated with the dry season tend to show enhanced elemental concentration distribution (above 75th percentile level). This shows that wash-out and rain- out of aerosols might be major factors that influence atmospheric concentration levels over northern Zimbabwe.
The concentration levels and distributions also depend on the direction from which the wind is blowing. Air masses from different directions pass over locations that contribute different aerosol species depending on the human activities in the region. Hence attaching chemical data to the different trajectories may help to locate the potential source areas of the anthropogenic aerosols and this was achieved by using the PSCF technique. To validate the PSCF procedure, the results were compared with known regional copper mines, coal power stations and the road network system for Cu, S and Pb respectively.
154 4.4.2.2.1 Source regions of copper containing aerosols
The spatial distribution of the potential source contribution function that represents the potential source regions of coarse Cu is shown in Figure 4.30. As expected, the potential source regions for fine copper are located mainly around the Zambian Copper-belt regions and Shaba mining region in the DRC. Some other potential source areas were found to be in northern Namibia, central Angola, central Zimbabwe, and northern and south-west
South Africa.
Except for the region in central Angola and in Madagascar, most of the hot spots for coarse Cu are known regions of copper mining and processing activities
(http://pubs.usgs.gov/of/2006/1135/2006-1135.pdf). The results reveal the possible transport of Cu aerosols resulting from the mining activities in the Copper-belt (Zambia and the DRC) and central Zimbabwe, but also of long-range transport from copper mining regions of Namibia and South Africa. Again, due to the pollutant dispersion, distant potential source regions are detected as minor (low probability regions). However, no database information could be found to explain the copper sources in central Angola and
Madagascar. It is postulated that the copper from Angola could be from undocumented mining activities (due to the civil war that was going on) while the sources from
Madagascar might be due to copper smelters for copper from other regions.
155 5 N 5 0.8 to 1.0
0.8 to 1.0 Equator 0 0.6 to 0.8
0.60.4 toto 0.60.8
5 S-5 0.4 to 0.6
Copper mine 10 -10S
15 -15S
Latitude (deg) 20 -20S
25 -25S
30 -30S
35 -35S 1515 E 20 25 25 E 30 35 35 E 40 45 45 E 50
Longitude (deg)
Figure 4.30 Potential source contribution function spatial distribution of coarse copper, with 75th percentile of the concentration distribution as threshold
Trajectories that are associated with high levels of coarse copper after passing the Copper-
belt region can be classified into two categories depending on the number of cases the
trajectory was found within the model’s mixing layer and the mean relative humidity
along the trajectory path. An ensemble of a selected number of trajectories associated with
high copper content measured at Rukomechi is considered to illustrate the importance of
each of these factors and is presented in Figure 4.31.
156 Trajectories belonging to Categories Cu1.1 (Figure 4.31a) represent relatively low air masses that pass directly over the Copper-belt and are transported below 1200 m above ground level in most cases. On average, the trajectory heights were found to be within the model’s mixing layer (94% of the cases) and that these trajectories have encountered low relative humidity (46% mean relative humidity) en route to Rukomechi. Trajectories in
Category Cu1.2 (Figure 4.32b) are characterised by air flow at high altitude and in most cases are descending air masses. Although the trajectory heights are always greater than
800 m above ground level, in most cases, the transportation height of air masses is within the mixing layer (about 86 % of the cases). As in Category Cu1.1, the air masses in
Category Cu1.2 trajectories have encountered less relative humidity conditions that have on average (45%). Regardless of the different transport heights of the two sets of trajectories, these air masses are found within the mixing layer in most cases and have encountered low relative humidity; a combination which might be used to explain the high copper concentrations.
157
(a) Category Cu1.1 (b) Category Cu1.2
5 N 5 5
Equator 0 0
5 S -5 -5
10 S-10 -10
15 S-15 -15
20 S-20 -20
Latitude (deg) Latitude 25 S-25 -25
30 S-30 -30
1010 E 15 20 20 E 25 30 30 E 35 40 40 E 45 50 50 E 5 10 101520253035404550 E 20 E 30 E 40 E 50 E Longitude (deg) Longitude (deg)
< 400 m 400 - 800 m 800 - 1200 m 1200 - 1600 m > 1600 m
Figure 4.31 Ensemble of two categories of trajectories that arrive at Rukomechi research station while carrying high copper concentrations. The height of the trajectories is measured in metres above ground level
However, not all the trajectories passing over the Zambian Copper-belt regions exhibit
high levels of Cu when reaching Rukomechi. As in the contaminated case, two categories
of air masses with relatively low concentration of coarse Cu were observed. Results
presented in Figure 4.32 show a subset of trajectories that were selected to represent the
two categories of air masses that have relatively low elemental concentration of copper.
The preferential transport height of these air masses in each of these categories is similar
to those observed for copper contaminated air masses but the main difference is found in
the percentage of cases the trajectories were within the mixing layer and the average
relative humidity along the trajectory paths.
158 (a) Category Cu2.1 (b) Category Cu2.2 5 N 5 5
Equator 0 0
5 S -5 -5
10 S-10 -10
15 S-15 -15
-20 -20 Latitude (deg) Latitude 20 S
25 S-25 -25
30 S-30 -30
1515 E 20 25 25 E 30 35 35 E 40 45 4 E5 50 10 15 15 E 20 25 25 E 30 35 E 40 45 E 50 Longitude (deg) Longitude (deg)
< 400 m 400 - 800 m 800 - 1200 m 1200 - 1600 m > 1600 m
Figure 4.32 Trajectories associated with relatively low concentrations of copper after passing over the Copper Belt
Although air masses in Category Cu2.1 (Figure 4.32a) follow relatively low trajectories
(below 1200 m above ground level), their preferential transport height is above the mixing
layer in most cases. On average, these trajectories were found to be above the mixing
layer (62% of the cases). At the same time, the air masses in Category Cu2.1 have passed
through locations that have registered higher relative humidity values (89% relative
humidity on average). The same can be said about the trajectories found in the second
group (Figure 4.32b) Category Cu2.2 that are mainly transported above 800 m altitude,
particularly in the height band between 1200 and 1600 m above ground level. These are
typically descending air masses associated with moderately high relative humidity (about
66% relative humidity) and are found above the mixing layer (in 94% of the cases).
Although the major parameters that influence high elemental concentration levels of Cu in
northern Zimbabwe could be the air masses passing over contaminated locations like the
Copper-belt region, it is also important that the air masses are found within the mixing
159 layer and have encountered low relative humidity en route to the sampling site. The air within the mixing layer is well mixed and the pollutants emitted by sources near or at the ground are taken to greater heights due to the vertical mixing by eddies. Shallow mixing layer traps pollutants emitted below and cannot mix with air above (discussed in section
4.1.4) and hence the air masses can pass over pollutant emitting regions yet not be contaminated.
4.4.2.2.2 Source regions of fine S
The values of the PSCF for fine S were obtained using the concentration and trajectory information for the period April to November (1995 to 1999) when the elemental concentrations of fine S are relatively high (Figure 4.11). The resultant probability spatial distribution that represents the possible source regions of fine S is shown in Figure 4.33.
The major potential source areas of fine S (represented by PSCF values greater than 0.6) are located in the region around central and northern South Africa, while some source regions were found to be in south eastern Zimbabwe, with a localised source region in eastern Africa. Relatively low values of the PSCF also show that minor potential sources are located central Zimbabwe, central Mozambique and in Madagascar.
As the major source area in South Africa coincides with the industrialised and power production regions, the fine S concentrations measured at Rukomechi can be assumed to be mainly influenced by the industrial activities within the sub-continent. The petroleum production activities around Johannesburg and Durban
(http://minerals.usgs.gov/minerals/pubs/country/maps/92359.gif) could also be potential sources of fine S. There are no known sources of S found around south-eastern
Zimbabwe, and no explanation could be found to augment these findings. The region with low probability values in central Zimbabwe could be traces of fine S emitted from coal
160 combustion from Bulawayo thermal power station and smelters from central Zimbabwe’s
Great Dyke mining region.
5 N 5 0.8 to 1.0
0.8 to 1.0 Equator 0 0.6 to 0.8
0.60.4 toto 0.60.8
-5 5 S 0.4 to 0.6
10 -10S
15 -15S
Latitude (deg) 20 -20S
25 -25S
30 -30S
35 -35S 1515 E 20 25 25 E 30 35 35 E 40 45 45 E 50
Longitude (deg)
Figure 4.33 Potential source contribution function spatial distribution of fine S, with 75th percentile of the concentration distribution as threshold, for the period from April to November (1995 to 1999)
There are no known sources of fine S in central Mozambique, but the mineral map
(http://minerals.usgs.gov/minerals/pubs/country/maps/92279.gif) shows that there are
mining activities in this region at and around Alto Ligonha. The mining activities and
probably smelters could be contributing to the fine S emissions in the depicted areas. As
161 in the area in central Mozambique, the potential source region in Madagascar has mining activities, in addition, petroleum refinery activities are found around Majunga and
Toamasina (http://minerals.usgs.gov/minerals/pubs/country/maps/92239.gif) that have the potential of emitting fine S detected in northern Zimbabwe. Further north, in east Africa, the localised source area around Dar es Salaam shows the influence of S emitted from the
Ubungo thermal power plant (in Dar es Salaam) that was found to be a potential source of mineral oil pollution [Mato, 2002] and also from petroleum production activities around
Dar es Salaam (http://minerals.usgs.gov/minerals/pubs/coun-try/maps/92389.gif).
It is important to note that in spite of its physical proximity to the receptor site, the
Copper-belt region was not detected as a potential source for fine S over northern
Zimbabwe although it would have been expected from the mining and processing activities [Meter, 2000; Meter et al., 1999]. S emissions from the Copper-belt region are not transported south-wards during the dry season and the air masses that carry high S concentration levels to Rukomechi come from the opposite direction. The only case when
Copper-belt S was supposed to be transported to the receptor site could be by the north- north westerly flows and as these flows occur mainly during the rainy season, the S containing pollutants may most likely be removed by scavenging from the air masses before they could reach the receptor site.
The air masses that carry S to Rukomechi are generally high trajectories associated with fast and descending air. There are three major trajectory types that contribute to high levels of fine S in northern Zimbabwe. The common feature of these air masses is that they occur during the dry season when the relative humidity is low (less than 50% of average). The major difference however lies in the pathways they follow en route to
Rukomechi. The three major transport route of fine S to northern Zimbabwe are shown in
Figure 4.34.
162 The circulating air masses (group S1) are relatively high but relatively slow trajectories that circulate air masses over the sub-region and are associated with highest fine S concentrations. This recirculation of air is a common feature in May and June; they are part of the local flows (Cluster G, Figure 4.14). Although they constitute a small percentage (7%) of the total flow, they are part of the anticyclonic circulation system
[Tyson et al., 1996a] that circulates air and pollutants over the sub-continent. As these air masses pass over some of the most polluted regions in southern Africa, the recirculating air picks up S in all these regions, hence the highest fine S concentration levels are associated with these air masses.
Group S2 is also composed of relatively high, fast and descending trajectories that carry air masses to the receptor site after passing over the industrial regions of South Africa and
Zimbabwe. These air masses are most common from May to July and are associated with the southerly flow (Cluster E, Figure 4.17) that account for about 8% of the total flow of air to Rukomechi. Although most of the air masses in this pathway pass over the Ocean, those that pass over South Africa are faster and more direct to Rukomechi than those from group S1. They are associated with high fine S concentrations which, however, are reduced in comparison to those found in group S1.
Group S3 is made up of trajectories transporting air masses at lower altitudes than those in groups S1 and S2. These air masses are part of the fast easterlies that account for 20% of the total flow to Rukomechi. This group transports air masses with fine S concentrations in the lower spectrum of those concentrations found above the 75th percentile levels.
These are air masses that are responsible for transporting fine S pollutants from the petroleum production activities in Madagascar and biomass burning regions in central
Mozambique and northern Zimbabwe.
163 5 N 5
Equator 0
5 S-5
10 -10S
15 S -15 S3
S1 Latitude (deg) Latitude (deg) 20 -20S
25 -25S
30 -30S
S2
1515 E 20 25 25 E 30 35 E 40 45 45 E 50
Longitude (deg)
Figure 4.34 Major transport routes of fine S pollutants to Rukomechi research station
Not all the trajectories passing over South Africa’s industrial areas register high S levels
in northern Zimbabwe. As the air masses in the southerly flow are descending (Figure
4.16), the S concentration levels also depend on the nature of the descending trajectories,
i.e., whether they get to the mixing layer before or after passing the polluted areas of
southern Africa.
The transport of fine S to northern Zimbabwe comprises of two components: the direct (S2
and S3 pathways), in which air masses are advected directly with little delay from the sub-
164 continent to the site, and the recirculation (S1), in which air masses are re-circulated back towards the point of origin. The recirculation exerts a controlling influence on the accumulation of aerosols within the greater southern African region.
4.4.2.2.3 Source region of fine Pb
The possible source regions of fine Pb as revealed by the distribution of the potential source contribution function are shown in Figure 4.35 (left diagram). This distribution was calculated using trajectory and concentration data for April to September when the normalised (with respect to the maximum) elemental concentrations of Pb were high
(greater than or equal to 0.5) (Figure 4.11). Although these PSCF values are not very high
(mostly below 0.8), the distribution of the potential sources of fine Pb in the sub-region obviously closely follows the major road network (Figure 4.35, right diagram).
The mosaic of the 0.4 to 0.6 PSCF distributions is mapped along the highways that lead from South Africa to Zambia through Harare and Bulawayo (Zimbabwe) and the Caprivi
Strip road running between Angola and Botswana. It is also interesting to note that in northern Zimbabwe, the source distributions with relatively high values (0.6 to 0.8) map the major exit roads: the northern road that links southern Africa and Central Africa through Chirundu border post and the other major road linking Southern Africa and eastern Africa through Nyamapanda border post.
165 Chirundu border post
Caprivi Strip 5 Nyamapanda border post
5 S 0
10 S-5
-10 15 S
-15
20 S 0.8 to 1.0 -20 0.80.6 toto 0.81.0 25 S
Latitude (deg) Latitude (deg) -25 0.60.4 toto 0.60.8 30 S National borders -30 0.4 to 0.6 Major roads
35 -35S 10 15 15E 20 25 E 25 30 35 E 35 45 40 E 45 55 50E 55
Longitude (deg)
Figure 4.35 Potential source contribution function showing the regional sources of fine Pb, with 75th percentile of the concentration distribution as threshold
166 The fact that the potential regional sources are along the major highways in the sub- continent should not come as a surprise because leaded-fuels are common in most southern African countries. Traffic is one of the biggest sources of air pollution in urban areas, however, the potential source areas of lead in southern Africa also suggests that the countryside through which major roads are passing are not an exception. Motor vehicles contribute significantly to emission inventories in the regions along the highways and the pollution severity is demonstrated here by the lead in gasoline. The fact that low values of the PSCF values were used to map the source regions does not necessarily mean that the sources are weak or that the concentrations of Pb along the sub-continent’s highways are low. On the contrary, owing to the fact that southern Africa is not highly urbanised and that emissions from traffic can be easily diluted by clean air masses in the countryside, there should be a cause of concern for the population living along the major roads in the sub-region.
In spite of great improvements in trying to reduce lead emissions by most developed countries through reduced use of leaded fuels, sub-Saharan countries (at the time of study) still use leaded fuels, hence highway emissions of lead remain a persistent air quality problem. As sub-Saharan African cities and link roads experience increased urbanization and motorization, they have seen a rapid increase in pollutants emitted by motorized vehicles. Air pollution, particularly from vehicles, most of which are still using leaded gasoline, is worsening. This pollution is becoming a major environmental and health concern in sub-Saharan Africa to populations living along the major roads. The use of leaded fuels needs to be reviewed by countries in the region so as to reduce the effects on human health [Lovei, 1998]. Leaded gasoline from vehicular emissions is the primary source of childhood lead poisoning. Young children are more susceptible than adults to lead poisoning, with elevated blood lead levels affecting brain development, reducing learning ability and IQ, and causing behavioural disorders. Many of those affected will be
167 poor children who live in homes along the highways that are susceptible to poisonous emissions from vehicle exhaust fumes. Eliminating lead as an additive is the key to rapidly eliminating childhood lead poisoning on a large scale, as has been done successfully in other developing countries like Thailand [World Bank, 1992] and
Indonesia [Heinze et al., 1998].
168 Chapter 5 Summary and conclusions
The major findings and conclusions of the research are presented in this section, starting with a summary of the meteorological situation at and around Rukomechi research station, followed by discussion of the elemental composition and sources of aerosols that affect northern Zimbabwe and the air transport fields together with the climatological perspective of aerosol loadings over northern Zimbabwe.
5.1 Summary of meteorological situation
The meteorology at Rukomechi is characterised by considering the wind speed and direction, relative humidity and rainfall. The preferential direction of wind flow during the day is from the ENE parallel to the axis of the Zambezi Valley. In more than 40% of the cases, daytime winds are blowing from the direction between the north-east and east
(Figure 4.1).
The relative humidity around Rukomechi was found to have a distinct annual cycle
(Figure 4.2) with high values from January to March and low values around October. The relative humidity values reveal two distinct seasons: dry season (mid April to mid
November) when the relative humidity is less that 60% and wet season (mid November to mid April) when the relative humidity is above 60%. There is an abrupt increase in relative humidity (0.68% per day) from dry to wet season and a gradual decrease (0.16% per day) from wet to dry. The sudden increase in the relative humidity (dry to wet season) is caused by a change in the meteorological system leading to the occurrence of rainfall while the slow decrease in relative humidity (wet to dry) is due to the maintenance of relative humidity values as a result of evapo-transpiration. Despite these inter-annual variations, the relative humidity around Rukomechi increased (at a rate of 1.7% per year)
169 from 1992 to 1999. The rainfall values around Rukomechi closely follow the variations in relative humidity with all the measured values of rainfall from stations around it showing an increasing trend from 1991-92 to 1998-99 rainy seasons (Figure 4.4). The increasing tendency in the rainfall regimes would be accompanied by an improvement in the vegetation cover in the region.
Around Rukomechi research station, the air is well mixed during the day to a height of more than 1700 m. The mixing layer is fully developed from about 0830 hrs to about 1800 hrs (Figure 4.5). During the night, a temperature inversion causes the formation of the nocturnal boundary layer that decouples near surface the air from the residual layer. The formation of the nocturnal boundary layer would trap ambient particles near the ground and inhibit mixing and transportation of pollutants. This would have affected night time measurements of atmospheric aerosols; hence, only daytime measurements were made when the air was well mixed.
5.2 Summary of aerosol elemental composition and source types
The 5.5-year atmospheric aerosol monitoring study at the Rukomechi research station that includes the determination of coarse and fine particulate mass and elemental composition of aerosols provides a general picture for ambient aerosols in a remote area of northern
Zimbabwe. For the aerosol samples collected in fine (<2 µm diameter) and coarse (2-10
µm diameter) fraction sizes from September 1994 to January 2000, the chemical data set was analyzed for particulate mass (PM), black carbon (BC) and 22 elements in the coarse size fraction and 20 elements in the fine size fraction. There is a strong seasonality in the atmospheric concentrations of the particulate mass and of most elements for both size fractions, with highest concentration levels occurring during the dry season.
170 Aerosol loadings at Rukomechi arise due to a variety of natural processes and anthropogenic activities (Table 4.2 and Table 4.3). Using the principal component analysis (PCA), several natural and anthropogenic aerosol sources were identified for dry and wet seasons. The main sources of fine and coarse aerosols for both the dry and wet seasons are mineral dust, biomass burning, and sea salt. Other sources are biogenic emissions, only associated with the coarse size fraction, and a copper source found only for the coarse size fraction during the wet season. The major anthropogenic sources are from regional non-ferrous smelters that only contribute to the fine size fraction.
Certain elements are found to originate from different sources during different seasons, while at the same time, coarse and fine aerosols of certain elements come from different sources. Over the entire period (September 1994 - January 2000), elemental concentrations measured at Rukomechi reveal that biomass burning related aerosols increased while soil dust aerosols decreased (Figure 4.9), and the aerosol loadings from sea salts remained relatively constant.
The contributions of the various sources to the total particulate mass are different for the fine and coarse size fractions, and exhibit a marked seasonality (Figure 4.9). Biomass burning emissions are dominant for both the coarse and fine size fractions during the dry season, which is consistent with the extensive forest and savannah fires that occurred in southern Africa during this period. Although the fine size fraction is dominated by biomass burning emissions in both seasons, their contribution is higher during the dry season than in the wet season (Figure 4.7). All sources, except sea salt, show a higher relative contribution to the particulate mass in the dry season as compared to the wet season. This is caused by (a) air masses moving predominantly via the Indian Ocean during the wet season and (b) the impact of the wet environment on the suppression of emissions during the wet season
171 As the Rukomechi site is situated within a National Park, mineral dust emissions, although a significant source, are not the major source of the particulate mass. Overall, source apportionment shows that biomass burning sources contribute most of total particulate mass (63%) for the fine size fraction followed by mineral dust (18%). Overall coarse biogenic aerosols have a relatively high contribution to total particulate mass
(30%), the same as biomass burning, while mineral dust aerosols account for 16%.
5.3 Summary of trajectory climatology
The analysis of five-day backward trajectories established a six-year (January 1994 to
December 1999) trajectory climatology for northern Zimbabwe. Classification of the trajectories by objective cluster analysis elucidated pathways and origin of air parcels, as well as the surface pressure systems responsible for each of the clustered air-flows (Figure
4.14 and Figure 4.15). The established trajectory climatology shows that air masses are characterised by fast and slow transport regimes, each with a specified direction, wind speed, period of predominant flow and preferential transport height (Figure 4.16). Four corridors (with six major pathways) and a local flow have been identified through which air masses reach Rukomechi:
(a) the south-eastern corridor that contributes most of the air flow to Rukomechi (44%)
carries air masses from the Indian Ocean south of Madagascar. It consists of a fast (6.1
m s-1) and a slower (4.1 m s-1) wind components, which contribute more effectively in
March (wet season) and April-September (dry season), respectively. The fast south
eastern flow is mainly driven by an Atlantic Ocean anticyclone and the Indian Ocean
tropical depression, while the slow component can mainly be attributed to general
global circulation processes.
172 (b) the eastern corridor brings in air from northern Madagascar and Mozambique. This
flow is driven by an anticyclone wrapping around the subcontinent that sometimes
stretches into the Mozambique Channel. The eastern corridor also consists of a fast
component (5.4 m s-1) that occurs during end of dry season to the beginning of wet
season and a slower flow component (2.9 m s-1) that brings in air during mid wet
season. Both flows of the eastern corridor account for 35% to the total flow to
Rukomechi.
(c) the southern pathway is most active during the southern-hemispheric winter (June to
August) and brings in air from the south Atlantic around the tip of South Africa. This
flow comprises the fastest air masses (10.6 m s-1) to Rukomechi and contributes 8% to
the total flow. A continental anticyclone over South Africa, coupled to an anticyclone
in the Atlantic Ocean, is the main feature stirring these fast winds.
(d) a north-north-westerly flow from Zambia and Angola brings in mostly continental air
during the mid-wet season (January/February) and accounts for 6% of the total flow to
Rukomechi. The air masses in this pathway are driven by a continental cyclone
situated over western Zambia. The Inter-tropical Convergence Zone is also another
important factor that influences the continental air masses. The north-north west flow
is the only pathway in which the winds to northern Zimbabwe are cyclonic while other
pathways are characterised by anti-cyclonic flows.
(e) lastly, the occurrence of regionally re-circulated air (7% of the total flow) is slightly
enhanced in June and July, but never dominates any monthly contribution. No
pronounced surface pressure system could be attributed to this flow regime which may
be caused by differential heating at the surface.
173 The vertical distribution of air mass trajectories reveals that most of the air parcels originate from the lower troposphere; however, the southerly flow and the recirculation air masses tend to be composed of descending air masses.
5.4 Summary of climatological perspective of aerosol loadings over
northern Zimbabwe
The combination of the air flow pattern to Rukomechi with atmospheric aerosol information at the site yielded improved knowledge of the causes of concentration variations, preferential transport pathways of pollutants to northern Zimbabwe and potential source areas (Table 4.7). Of the seven major pathways of air masses to
Rukomechi, only four were found to be major transport routes for aerosols to northern
Zimbabwe, namely; the fast easterly, the slow south-easterly, the southerly and the local flows. Each of these four pathways carry aerosols that contain different atmospheric constituents depending on the flow direction, the time of the year, and the relative position of the preferential transport height to the height of the mixing layer. The common feature between these four (contaminated) flows is that they are predominantly dry season air masses.
By correlating information of (a) classification of backward trajectories and (b) aerosol concentration and composition at the receptor site, the following results can be stated:
(i) The fast easterly air masses carry predominantly biomass burning aerosols that
originate mainly from savannah fires which occur from regions around northern
Zimbabwe, Mozambique and Madagascar.
(ii) The slow south-easterly flow carries aerosols which originate from a variety of sources
that include mineral dust, biomass burning, biogenic, sea salt and anthropogenic.
174 According to the spread of the trajectories in this flow, the sources of these aerosols
may be from a wider area within the sub-continent.
(iii) Aerosols in the southerly air masses that pass mostly over South Africa are a mixture
of mineral dust, sea salts and anthropogenic sources while the local winds are
associated with anthropogenic related aerosols.
Climatologically, high and low concentration levels of atmospheric pollutants in each of these pathways largely depend on the relative humidity conditions encountered by the air masses en route to northern Zimbabwe and how often the air masses were found within the mixing layer as they pass over known polluted regions. The highest concentrations
(greater than the 75th percentile concentration levels) were recorded for air masses that encountered low relative humidity (less that 50%) and have been transported within the mixing layer most of the time. Low concentration levels (less than the 25th percentile concentration levels) were associated with trajectories that often encounter high relative humidity environments (>60%) en route to northern Zimbabwe.
Combining (a) information on individual backward trajectories, (b) information on aerosol concentration and composition at the receptor site, and (c) independent knowledge of known sources, for example occurrence of fires, location of coal power stations, industrial areas and mining regions, it is found that the fire plot technique shows that most of the fires that influence high concentration levels (greater than 75th levels) of fine black carbon
(indicator for biomass burning aerosols) at Rukomechi research station are located in the regions around northern Zimbabwe and central Mozambique. Of these fires, greater influence on the biomass burning aerosol loadings is due to fires located closer to the receptor site. For fires within a 200 km radial distance from Rukomechi, there is a linear relationship between the fire occurrence and the elemental concentration of black carbon
(BC), potassium (K) and Iodine (I).
175 To complement the results of the fire plot technique, the potential source contribution function technique also detected the areas around northern Zimbabwe and central
Mozambique as potential source regions of fine black carbon. The results of the areas depicted as source regions for biomass burning aerosols by the fire plots (Geographical
Information System) approach agrees well with those depicted by the potential source contribution function (statistical) approach. The agreement of both approaches, which are based on different computational techniques and basic assumptions leads to the conclusion that biomass burning aerosols over Rukomechi come from fires that occur in northern
Zimbabwe and central Mozambique.
The PSCF method shows that potential source regions of anthropogenic related aerosols are mostly located around industrialised, mining and processing regions in the sub- continent. Potential sources of coarse copper are found around active copper mines, such as the Zambian Copper-belt, while potential source areas of fine sulphur occur mainly around the South African industrial area (the Mpumalanga escarpment region), where the coal power stations and industrial areas of South Africa are situated.
The potential source regions for Pb are found along the major roads in southern Africa, especially along the major exit roads from Zimbabwe; through Chirundu (to Zambia) and
Nyamapanda (to Mozambique) border posts. The coincidence of Pb source regions with the road network points to the health risk the rural population living along the highways is exposed to. Hence, countries in southern Africa should move towards eliminating the use of lead-fuels, which contribute to poisonous fumes that affect human health, especially of children.
176 5.5 Conclusions
The main conclusions of the present study are:
(a) The Principal Component Analysis (PCA) has proved to be a useful explanatory tool
for identifying the major source types of pollutant emissions; results of enrichment
factors indicate that the source type components identified by the PCA are justified.
Tropospheric aerosols over northern Zimbabwe are characterised by fine (diameter
less than 2 µm) and coarse (diameter between 2 µm and 10 µm) size fractions, and
originate from biomass burning, mineral dust, sea salt, biogenic and anthropogenic
sources. Most of the fine aerosols are related to biomass burning activities, while
coarse aerosols are mainly related to biomass burning and biogenic sources.
(b) The aerosol loadings from biomass burning have been increasing over the period from
September 1994 to January 2000, while the emissions from the soil have been
decreasing over the same period. By considering the behaviour of emissions from
these two sources, it can be concluded that the vegetation in the sub-continent (which
serves as a source of bio-fuels for forest fires and as a cover for emissions from the
ground) has been recovering from the 1991-1992 drought year. Aerosol loadings
related to the sea salt and anthropogenic sources remained fairly constant.
(c) The climatological conditions (wet and dry) of this part of Africa (between 10 ºS and
22 ºS) have been investigated (to the knowledge of the author) for the first time and
have been shown to affect the source strength of biomass burning and mineral dust
sources but not those of sea salt or anthropogenic origin. This conclusion is drawn
from the fact that aerosol loadings from biomass burning and mineral dust sources
strongly depend on the season, with the dry season exhibiting higher concentration
levels than the wet season. The concentrations of aerosols from sea salt and
177 anthropogenic sources are less affected by the seasonal variations of the
meteorological parameters as they run independent of season. Enhanced aerosol
loadings during the dry season are largely due to long range transport of aerosols from
the sub-continent.
(d) Kinematic back trajectory modelling is an important technique for characterising
regional and large-scale lower tropospheric air flow. It has been found − also for the
first time (to the knowledge of the author) − that there are seven distinct transport
pathways of air masses to northern Zimbabwe (the slow easterly, the fast easterly, the
slow south easterly, the fast south easterly, the southerly, the north north-westerly and
the local flows). These pathways are dominant during different periods of the year.
Their flow characteristics are affected by the development and positioning of semi-
permanent subtropical anticyclones in the sub-continent.
(e) The combination of trajectory information and aerosol concentration data is a useful
way of determining the pollutant sectors and pathways. It is one of the major findings
of this thesis, that of the seven identified pathways, only four (the fast easterly, the
slow south easterly, the southerly and the local pathways) are associated with
contaminated air masses. Most of the biomass burning aerosols are mainly transported
along the fast easterly pathway while the slow south-easterly air masses contain
aerosols from all the detected sources. The southerly pathway is associated with
aerosols that originate from mineral dust, sea salt and anthropogenic sources, while the
local flows only carry air masses that contain anthropogenic related aerosols. The
other three pathways (the slow easterly, the fast south-easterly and the north north-
westerly) carry relatively clean air masses as these are mostly wet season flows.
(f) The potential source contribution function (PSCF) technique is an excellent statistical
tool for tracing aerosols back in space and time to ascertain their origin and to locate
178 potential source areas using back air mass trajectory information and chemical aerosol data. This technique has been successfully applied for non-African regions of the world. But for the first time, it was shown in this thesis, that it also proved to be applicable to southern Africa. Furthermore, it has also been never shown before, that there are independent proofs for the successful application of the PSCF technique: our
PSCF results for southern African compare excellently with those of the fire plot technique, and with known source regions of anthropogenic related aerosols in the sub-continent. The results point to the area around northern Zimbabwe and central
Mozambique as potential source regions of biomass burning aerosols. The PSCF alone indicated that anthropogenic related aerosols (fine S and coarse Cu) are located around the industrial and mining regions. Potential source regions of fine Pb are found mainly along the major roads in the sub-continent due to the use of leaded fuels in most countries in the region. The latter finding is being considered to be the most exciting highlight of this PhD thesis.
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188 Appendix A Relative humidity correction procedure
A.1 Correction of the upper range of the relative humidity data
Figure A.1 shows the annual trends for the maximum, mean and minimum data, measured by at Rukomechi research station from 1992 to 1999 (Meixner, 2006, pers. com- munication).
140 minimum 130 maximum average 120 110 100 90 80 70 60 50 40 relative humidity (raw data) [%] data) humidity (raw relative 30 20 10 0 1992 1993 1994 1995 1996 1997 1998 1999
Figure A.1 Trends of the annual maximum, mean and minimum observed relative humidity
The quasi-linear increase, especially of the maximum from 1994 to 1999, suggests that the increasing readings of the relative humidity sensor can be considered to be linear. As the maximum relative humidity values may show a certain variability depending on individual and specific sensor conditions (± 2.5%), the 98 percentile P(0.99) values or greater were used for further analysis with the following correction hypothesis:
(i) there is only one natural calibration point, namely the air water vapour saturation (i.e.
100% relative humidity, for example during and/or after the rainfall, dense fog, etc).
189 (ii) the observed trend with maximum and P(0.99) data is used to establish a correction
procedure for the upper limit (which is physically constrained).
(iii) linear regression using maximum and/or P(0.99) data versus EXCEL-time will make
use of the information in this time series.
(iv) the question whether the maximum, or P(0.99), or (P0.98), etc values will be used was
attempted to be answered by the following procedure: Assuming that the sensor was
produced in 1991 in a well calibrated state (i.e. at water vapour saturation it has
displayed 100%), the percentile was varied to find one that estimated 100.00% relative
humidity results (based on the regression line) in 1991. Figure A.2 shows the observed
maximum relative humidity and some of the percentiles. P(0.983406) (0.9834th
percentile) fulfils the requirement with the following regression equation:
rh(t) = -361.526+ 0.0138rhP(0.98) …………. A.1 with R = 0.9759 . The time data are the mid-points of each year which were found by the
MEDIAN function.
190 150 P(0.99) regr.P(0.99) P(0.999) 140 regr.P(0.999) MAX regr.MAX P(0.983406) 130 regr.P(0.983406)
120 relative humidity (raw data) [%]relative humidity data) (raw 110
100 1991 1992 1993 1994 1995 1996 1997 1998 1999
Figure A.2 The observed maximum relative humidity and some of the percentiles used to get the best regression of the upper limit of the recorded relative humidity data.
For the trend correction, the P(0.983406) regression data was used rather than the
original P(0.983406) data points, to induce the consistent time series information
content into the correction procedure. Table A.1 shows the original P(0.983406) data
points (rhP(0.983406)), its regression data (regrP(0.983406)) and the maximum relative
humidity (rhMax). The difference between the P(0.983406) data and the maximum is
approximately 4% (with the exception of 1992 which is 8%). It is assumed that the
noise around the maximum display of the relative humidity sensor is a few percent;
another possible argument to choose the P(0.983406) percentile.
191 Year 1991 1992 1993 1994 1995 1996 1997 1998 rhP(0.983406) (%) - 103.04 112.93 115.33 118.66 125.31 131.71 134.27 regrP(0.983406) (%) 100.00 105.05 110.09 115.13 120.18 125.22 130.27 135.31 rhMax(%) - 111.74 118.14 119.17 124.03 128.90 133.50 139.39
Table A.1 The original P(0.983406) data points, regression data and maximum relative humidity values used in the correction of the upper limit of the relative humidity
A.2 Correction of the lower range data
Unlike the upper limit, there is no physical lower limit of the recorded relative humidity data. The temporal (potential increasing) trend of the relative humidity around the minimum annual relative humidity could be caused by (i) sensor trend, (ii) decreasing
(minimum) temperature and inter-annual constant specific humidity, (iii) constant
(minimum) temperatures and increasing specific humidity, or (iv) a combination of the three. However, the specific humidity and temperature effects on the observed trend in relative humidity were considered to be most unlikely and hence the sensor trend was only considered as responsible for the observed increase of relative humidity in the lower limit.
Since the total dryness (0%) is never observed, the following approach was chosen for lower limit relative humidity correction: The percentage level for calculation of the lowest percentiles of the annual relative humidity data for Rukomechi was varied as long as the correction coefficient of the relation P(xx) (where xx is the percentile) versus time was highest and the best correlation that best fit this condition was found to be that for
P(0.0010275), described by the following regression equation
rh(t) = -118.08 + 0.0039rhP(0.00102), …………. A.2 with R = 0.929.
192 Table A.2 showing the original values of the relative humidity at P(0.0010275), rhP(0.0010275), its regression data, regrP(0.0010275) and the observed minimum relative humidity values.
Year 1991 1992 1993 1994 1995 1996 1997 1998 rhP(0.0010275) (%) - 14.637 17.850 16.024 17.336 19.833 22.752 23.480 regrP(0.0010275) 13.108 14.543 15.978 17.411 18.843 20.278 21.713 23.146 (%) rhMin (%) - 11.984 13.860 14.512 12.456 17.240 21.712 22.264
Table A.2 The original values of the relative humidity at P(0.0010275), regression data and observed minimum relative humidity values used for the correction of the lower limit of the recorded relative humidity data.
A.3 Correction procedure
The procedure aims to develop correction relations, based on the observed temporal trends of P(0.983406) and P(0.0010275) data of the annual relative humidity values observed at
Rukomechi research station. Following Meixner (2006, pers. communication) we start by postulating a linear relationship
rhcorr = a + brhobs ……………….. A.3
where rhcorr is the corrected relative humidity, rhobs is the observed, a and b are time dependent variables.
For the solution of this equation, two different time references (t1 and t2) are needed.
Using the following notation:
rhcorr = f; and rhobs = g ……………….. A.4
then for t = t1, f1 = a1 + b1g and for t = t2, f2 = a2 + b2g …….. A.5
193 Hence, for given t = t1, the minimum extrapolated (f11) and the observed minimum (g11) as well as the maximum extrapolated (f12) and its corresponding observed maximum (g12); and for t = t2, the corresponding extrapolated (minimum, maximum) values (f21, f22) and the observed (minimum, maximum) values (g21, g22), the constants (a1, b1) and (a2, b2) can be determined.
Following Meixner (2006, pers. communication), we assume that the temporal change of time dependent variables a1 to a2 and of b1 to b2 is linear (first order approximation) with time, then the following relationship can be deduced:
a(t) = c1 + d1t and b(t) = c2 + d2t
Evaluating in equation A.3 the following linear relation is obtained
rhcorr = (c1 + d1t) + (c2 + d2t)rhobs ……………….. A.6
where c1, d1, c2 and d2 are constants.
To determine the time dependent variables a and b in equation A.3, the constants in equation A.6 have to be obtained. Consider t = t1 = 1991 (02-Jul-1991, 11:30 EXCEL time) when the extrapolated and the observed (minimum and maximum) are the same, i.e., f11 = g11 = 13.108% and f12 = g12 = 100.00%, and taking t = t2 = 1998 (02-Jul-1998, 11:30
EXCEL time), then f21 = 13.108% and f22 = 100.00%; g21 = 23.146% and g22 = 135.310%.
Rewriting A.3 for t1 and t2 considering notation in equation A.4, then
(i) for t = t1: f11 = a1 + b1g11 and f12 = a1 + b1g12, and solving for a1 and b1, the following is deduced
f11 g12 − f12 g11 f12 − f11 a1 = and b1 = . g12 − g11 g12 − g11
194 Since f11 = g11 and f12 = g12, then a1 = 0 and b1 = 1.
For t = t2, f21 = a2 + b2g21 and f22 = a2 + b2g22
Similarly, solving for a2 and b2
f12 g 22 − f 22 g 21 f 22 − f 21 a2 = and b2 = g 22 − g 21 g 22 − g 21
Evaluating for f21 = 13.108% ; f22 = 100%; g21 =23.146% and g22 = 135.310% then
a2 = 3.788 (%) and b2 = 0.711049.
For a(t) and b(t): a1 = c1 + d1t1 and a2 = c1 + d1t2 ……….. A.7
and b1 = c2 + d2t1 and b2 = c1 + d1t2 ….…….. A.8
and t = t1 and t = t2 (as above)
Solving for c1, c2, d1 and d2, therefore
a1t2 − a2t1 b1 f 2 − b2 f1 c1 = and c2 = t2 − t1 t2 − t1
a2 − a1 b2 − b1 d1 = and d1 = t2 − t1 t2 − t1
using the determined values of a1, a2, b1 and b2; and t1 (=33421.47917) and t2
(=35978.47917).
c1 = -49.50755102 (%) and c2 = 4.77676097 (%)
-3 4 d1 = 1.481309 x 10 and d2 = -1.1004 x 10
Therefore, substituting these values in equation A.6, the final expression derived for correcting the observed relative humidity at Rukomechi is given by the following equation:
195 -3 4 rhcorr(t) = -49.51 + 1.48x10 t + (4.78 - 1.10x10 t)rhobs(t) ……. A.9
Appendix B Trajectory calculation
The air parcel's transportation by the wind is computed from the average of the three-
. dimensional velocity vectors X [X(t)] at the particle's initial-position X (X 0 ,t). If it is assumed that there is a specific infinitesimally small air parcel, then its trajectory can be defined by the differential trajectory equation
dX . = X[X(t)] ………..………………….. B.1 dt
. where t is the time, X is the position vector and X is the wind velocity vector. Knowing the air parcel’s initial position X0 at time t0, then its path is determined through equation
(B.1). Hence the air parcel’s position at time t can be written as
X (t) = X(X0 , t) ………..………………….. B.2
The inverse transformation of (B.2) can be written as
X 0 (t = t0 ) = X 0 (X ,t) ….……………….. B.3 and this gives the initial coordinates of the air parcel at time t and position X. The air parcels thus may be followed either forward (forward trajectories) or backward (back trajectories) in time. The spatial coordinates X0 at time t0 provide a way of identifying each air parcel for all time. These initial coordinates are called Lagrangian coordinates
[Dutton, 1986].
196 An important feature of trajectories is that particles which are initially adjacent will remain neighbours for all time. A line of particles at time t0 remains an unbroken line at time t, no matter how it is distorted by the motion. This can be expressed by
lim X(X0 + ∆X 0 , t) − X(X0 , t) = 0 ………….. B.4 ∆X0 →0
The most important property of equation (B.4) is that particles that are inside a closed volume at time t0 are forever separated from those outside. The closed volume that moves with the flow is called a material surface [Stohl, 1998].
The idealized concept discussed above is not fully applicable in the real atmosphere. With the limited information available, it is not possible to pick an infinitesimal air parcel and follow its path with infinite accuracy. A real parcel of finite size may become distorted so strongly in a divergent flow that it is torn apart. Hence, a computed trajectory is representative of the path of an air parcel only for a limited period of time.
Equation (B.1) can be solved analytically only for simple flow fields where its finite- difference approximation is used for meteorological applications. Taylor series expansion of X(t) about t = t0 and evaluated at t1=t0+∆t results in the following expression:
2 dX 1 2 d X X (t1 ) = X (t0 ) + (∆t) + (∆t) 2 + ...... …….. B.5 dt t 2 dt 0 t0
The first approximation to equation (B.5) is
. X(t1 ) = X(t 0 ) + (∆t) X(t 0 ) ……..………………….. B.6 a “zero acceleration” solution of equation (B.1). This can be easily computed since it does not involve iteration. It is accurate to the first order, which means that differences between the real and the numerical solution occur from the omission of the second- and higher- 197 order terms. Sufficient accuracy of equation B.6 can be achieved if trajectories are calculated using very short integration time steps. However, more accurate approximations at acceptable computational costs exist. If X(t) is also expanded in a
Taylor series about t = t1 and evaluated a t = t0, then:
2 dX 1 2 d X X(t0 ) = X(t1) − (∆t) + (∆t) 2 − ...... ……….. B.7 dt t 2 dt 1 t1
Combining equations (B.5) and (B.7), we obtain
. . 1 . . 1 d X d X X(t ) = X(t ) + (∆t)[X(t ) + X(t )] + (∆t)2 − + ...... B.8 1 0 2 0 1 4 dt dt t 0 t 0
Retaining only the first two terms on the right-hand side of equation (B.8), the “constant acceleration” solution is achieved [Walmsley and Mailhot, 1983].
1 . . X(t ) ≈ X(t ) + (∆t) X(t ) + X(t ) …………….. B.9 1 0 2 0 1
This approximation is identical to the original graphical method used for constructing isobaric trajectories manually from weather charts [Petterssen, 1940]. Equation (B.9) is accurate to the second order. The air parcel's transportation by the wind is computed from
. the average of the three-dimensional velocity vectors X (t ) at the particle's initial-
positionX 0 (t ) . The velocity vectors are interpolated in both space and time by solving
i iteratively starting with equation (B.6). Iteratively, the guess positions X (t1) of the air parcel are given by:
198 . 1 X (t1 ) ≈ X(t 0 ) + (∆t) X(t 0 )
. . 2 1 1 X (t ) ≈ X(t ) + (∆t)X(t ) + X (t ) 1 0 2 0 1
. . i 1 i-1 X (t ) ≈ X(t ) + (∆t)X(t ) + X (t ) …………….. B.10 1 0 2 0 1
. i The superscripts, i, indicate the number of iteration, and the velocity X (t1 ) is taken at
i position X (t1). Equation B.10 gives the final position of the air parcel and ∆t is the time integration step.
If the third term on the right-hand side of equation (B.8) is also retained, then we have the
“variable acceleration” method. In principle, this solution gives higher accuracy at the cost of increased computing time, but it has the disadvantage that the accelerations at two times must be evaluated. This can be inaccurate because wind fields are often available only at large temporal intervals. Hence, the variable acceleration method may even be less accurate than the constant acceleration method. If linear interpolation of the wind speed is used, the third term on the right-hand side of equation (B.8) vanishes, and the “variable acceleration” method reduces to the “constant acceleration” method.
199 Appendix C Program to detect fires within the buffer zone
/* ------/*By G. Kirkiman & D Nyanganyura /* ------/* Program : HYFIRE.AML /* Purpose : Count the no. of fires intersected by a prescribed distance buffer and output these hits /* per day into a result file, which is used to add data to the AAT. /* ------/* Pre : must run hy2arc.aml first to generate the coverages /* Calls : init /* Called by: none /* ------/* Arguments: traj_cov - Trajectory coverage /* land mass_cov - Land mass coverage, to limit map extent /* buf_dist - buffer distance, 0.1 = 10 km, 0.01 = 1km /* lastfield - field after which the fire count data will be appended /* Globals : none /* ------/* Big do loop, input trajectory files &do traj_cov &list tr_apr23_800 tr_apr24_800 tr_apr25_800 tr_apr26_800 tr_apr27_800 tr_apr28_800 ~ tr_apr29_800 tr_apr30_800 tr_may01_800 tr_may02_800 tr_may03_800 tr_may04_800 ~ tr_may05_800 tr_may06_800 tr_may07_800 tr_may08_800 tr_may09_800 tr_may10_800 ~ tr_may11_800 tr_may12_800 tr_may13_800 tr_may14_800 tr_may15_800 tr_may16_800 ~ tr_may17_800 tr_may18_800 tr_may19_800 tr_may20_800 tr_may21_800 tr_may22_800 ~ tr_may23_800 tr_oct01_800 tr_oct02_800 tr_oct03_800 tr_oct04_800 tr_oct05_800 ~ tr_oct06_800 tr_oct07_800 tr_oct08_800 tr_oct09_800 tr_oct10_800 tr_oct11_800 ~
/* &args traj_cov
&severity &error &routine BAILOUT &severity &warning &ignore
&s landmass_cov = continent
&s buf_dist = 0.5 /* 50km buffer &s lastfield = FIRE0.5
/* Check for valid coverages etc..... &call init
/* Assemble the traj_file date as a string &s today = [locase[trim[substr %traj_cov% 1 9]]]
/* Define and open a result file, add a header; delete if existant &s result = result_%today%%buf_dist% &s fileunit1 = [open %result% open_status -w] &s header = rec_no,hits &s write_stat = [write %fileunit1% %header%]
&if %open_status% ne 0 &then &return Could not open file %result%....2
&if [exists %result% -file] &then &s delete_status [delete %result% -file]
/* Step through each arc in the landfall trajectory, query the no of hits with a buffer &describe %traj_cov% &s rec_no = 1 &do i = 1 &to %dsc$arcs%
200 &type .....processing arc/hour %i%......
/* Pay a visit to arcplot ap mape %landmass_cov%
/* Select each hour arc seperately resel %traj_cov% arcs %traj_cov%# = %i%
&s year = [show select %traj_cov% arcs 1 item year] &s month = [show select %traj_cov% arcs 1 item month] &s day = [show select %traj_cov% arcs 1 item day] &s hour = [show select %traj_cov% arcs 1 item hour] &s press = [show select %traj_cov% arcs 1 item pres]
&if %day% lt 10 &then &s day = 0%day%
&if %month% lt 10 &then &s month = 0%month%
resel fire_%year%%month% points datetext = %year%%month%%day%
resel fire_%year%%month% point OVERLAP %traj_cov% arcs %buf_dist% PASSTHRU /* Buffer
&s hits = [before [show select fire_%year%%month% point] ,]
&type .....%hits% hits during %day%/%month%/19%year%:%hour%.00 for %press%hPa......
&s data_hits = %rec_no%,%hits%
&s write_stat = [write %fileunit1% %data_hits%]
clearsel
quit &s rec_no = %rec_no% + 1 &end
/* Close all open files &s closestat = [close -all]
/* Define the path of the look-up table for INFO &s path = [path %result%]
/* Create INFO table that will store the attribute data &data arc info arc define HITS.CODE [translate %traj_cov%]-ID,4,5,B FIRE%buf_dist%,4,5,I
add from %path% q stop &end
/* Join the data from the "hits table" to the .AAT joinitem %traj_cov%.aat hits.code %traj_cov%.aat %traj_cov%-id %lastfield% linear
/* Delete the temp files &s delete_stat = [delete hits.code -info] &s delete_stat = [delete %result% -file]
&end &return Fire hit calculator completed successfully.....
201
/**************************************************************************** /* Subroutine INIT /**************************************************************************** /* /* Check for proper input and existence of input coverages, lines, and item /* &routine init
&if [exists HITS.CODE -info] &then &s delete_stat = [delete HITS.CODE -info]
&if [exists HITS.CODE -info] &then &s delete_stat = [delete result -file]
&if [show program] ne ARC &then &return Program stopped: This AML must be run from the Arc: prompt
&if [null %traj_cov%] &then &return Usage: args
&describe %traj_cov% &if %dsc$arcs% < 1 &then &return Program stopped: coverage [quote %traj_cov%] has no arcs
&describe %landmass_cov% &if %dsc$polygons% < 1 &then &return Program stopped: coverage [quote %landmass_cov%] has no polygons
&return /**************************************************************************** &routine BAILOUT &s .error$flag 1 &s ok [close -all] &return &inform Error condition in this AML code!
202 Appendix D Local meteorology at Rukomechi research station
The purpose of this section is to establish the diurnal, seasonal and inter-annual variations of local meteorological parameters at and around Rukomechi. Local meteorological parameters have only limited importance for the interpretation of the long-term aerosol data set (see section 4.1). However, the Automatic Weather Station at Rukomechi research station was never part of the network of meteorological stations of the Zimbabwean
Meteorological Service. Consequently, there exists no climatological analysis of this station, unlike other Zimbabwean stations [Torrance, 1981]. Therefore, the purpose of
Appendix D is to complete information related to the thesis and to document a short climatology of the Rukomechi research station (1992 − 1999) .
D.1 Wind variability
The analysis of near surface winds (approximately 1 m above ground level) shows that the wind regime at Rukomechi varies substantially in both wind speed and direction between day and night (wind direction is discussed in section 4.1.1).
D.1.1 Diurnal variations of wind speed
Near surface wind speed conditions (Figure D.1) show that while night wind speed variations mimic those of daytime for the greater part of the year, it is windier during the daytime than at night. The 15-day running mean of the daytime wind speed is lowest around February/March (1 m s-1) but gradually increases and peaks in October (2.5 m s-1)
(Figure D.1). There is a sharp drop in the daytime wind speed in the month of November, which is occasionally punctuated by a sharp increase and subsequent decrease in the month of December. When the peak value of the running mean is relatively high (greater than 2.5 m s-1), then there is drop in the daytime wind speed that occurs between the end
203 of October and the beginning of November (1992, 1994 and 1995). In all cases, there is a
sharp drop in the wind speed from late November to the first half of December and this is
more evident in the years 1993, 1997 and 2000. The observed drop in the daytime wind
speed is from about 1.8 m s-1 to about 0.8 m s-1 in a time period of less than 30 days.
3.0 15 per. Mov. Avg. (Nighttime wind speed) 15 per. Mov. Avg. (Daytime wind speed) 2.5
) 2.0 -1
1.5
Wind speed (m s 1.0
0.5
0.0 01-Jan-92 01-Jan-93 01-Jan-94 01-Jan-95 01-Jan-96 01-Jan-97 01-Jan-98 01-Jan-99 01-Jan-00 01-Sep-92 01-Sep-93 01-Sep-94 01-Sep-95 01-Sep-96 01-Sep-97 01-Sep-98 01-Sep-99 01-Sep-00 01-May-92 01-May-93 01-May-94 01-May-95 01-May-96 01-May-97 01-May-98 01-May-99 01-May-00
Figure D.1 15-day moving average showing the variation of the daytime and night- time wind speeds
It is important to note that in most cases, the rise in the wind speed during the day is
closely followed by the rise in the night-time wind speeds. However, the months of June
and July present a reverse scenario for the wind speed patterns over Rukomechi when the
decrease in daytime wind speed is accompanied by an increase in the night time wind
speed. Occasionally, during this period, individual daily data shows that night time wind
speed is greater than that of daytime. The length of the reverse varies from year to year
but it always occurs during the second half of June to the first half of July. This anomaly
can be explained by considering wind direction and forces driving the day and night
winds. During June and July, day time winds are mostly from the south or south-west,
204 which is almost perpendicular to the axis of the Zambezi valley. Bearing in mind that the slope wind system is up the southern escarpment of the valley during daytime, that is, from north to south (opposite to the general winds direction at this time of the year), then the result is the reduction in the general south/south-east to north daytime winds speed.
Thus the flow in wind speed is reduced by the forces driving the anabatic winds up the escarpment.
On the other hand, as the wind speed is preferential from the south down the slope, at night, the wind speed is enhanced by the katabatic winds due to the differential cooling between the valley and the upper part of the southern escarpment. As this period coincides with the southern hemispheric winter, the ground cools fast giving rise to strong pressure gradient forces responsible for the katabatic winds. The night time winds are strengthened because the two wind components are in the same direction while the daytime winds are weakened because the two wind components are in opposite directions.
D.1.2 Daytime wind direction vs. wind speed
Over the period of the nine years (1992-1999), it was observed that the near surface wind speed varies from near 0 m s-1 to a maximum of 2.8 m s-1. To categorise the wind speed, three groups were set up: (a) calm winds with v <= 1.0 m s-1; (b) moderate winds when
1.0 m s-1
205 (=< 1.0 m s-1) tend to come from a broader angle (N and ESE) with the central tendency around the ENE.
Over the years, the day time wind regime at Rukomechi is dominated by calm to medium winds that account for 95% of the cases that arrive at Rukomechi from the NE, ENE and
E directions. This shows that near surface winds are influenced by the valley channelling effect.
Wind speed (m s-1)/ v<= 1.0 1.0
Table D.1 Nine-year mean percentage contributions of each direction to the three wind speed bands
As the aerosol sampling was done only during daytime, the local effects due to the night time wind flows can be ruled out. The major flow directions (E and ENE) coincide with the main valley axis and the main large scale air mass flow to Rukomechi.
206 D.2 Solar radiation and temperature
Daily means of global radiation values were synthesised using hourly measurements
collected from 1995 to 1999 and the variation is represented in Figure D.2. As expected,
the variation of the mean daily solar radiation received at Rukomechi shows low values of
solar radiation (100-400 W m-2) during the southern hemispheric winter and high values
(650-800 W m-2) during summer. This is consistent with the astronomical factors that
affect the relative path length of solar radiation through the atmosphere, i.e. the irradiance
on the horizontal area and the optical path [Coulson, 1975].
0.90
0.80 )
-2 0.70
0.60
0.50
0.40
0.30
0.20 Daily solar radiation (kW m
0.10
0.00 1-Jul-95 1-Jul-96 1-Jul-97 1-Jul-98 1-Jul-99 1-Jan-95 1-Jan-96 1-Jan-97 1-Jan-98 1-Jan-99 1-Oct-95 1-Oct-96 1-Oct-97 1-Oct-98 1-Oct-99 1-Apr-95 1-Apr-96 1-Apr-97 1-Apr-98 1-Apr-99
Figure D.2 Variation of the daily solar radiation (daytime average) received at Rukomechi research station from 1995 to 1999
The local insolation forcing is influenced by the atmospheric circulation and annual
position of the sun. While these seasonal variations are mainly due to the effect of
astronomical factors, they are also moderated by the solar beam attenuation due to the
207 scattering and absorption by gases and aerosols in the atmosphere. Low values of solar radiation observed during the months of December, January and February are mainly due to cloudiness, which occurs during the rainy season. The effects of clouds are clearly observed in the troughs of the solar radiation curve for January and February 1997, which was an abnormally high rainfall season with the highest number of rainy days (ranging from 78 to 101) observed during the rainy season. The troughs in the solar radiation during the southern hemispheric winter may be due to the cloud cover caused by the
‘Guti’ weather system, which is common during that period, while small variations could be due to attenuation of solar radiation by aerosols.
The variation of daily mean day time air temperature (Figure D.3) closely mimics the variation in solar radiation in that the troughs and the peaks coincide. However, the changes from high to low temperatures (summer to winter) and that from low to high temperatures (winter to summer) closely mimic the variations of the daily relative humidity. As in the sensor corrected relative humidity data (Figure 4.2), there is a gradual decrease (at a rate of 0.026 °C per day) from summer to winter followed by a sharp increase (at a rate of about 0.11 °C per day) from winter to summer. These linear variations of daily mean temperatures between seasons can be described by the following two expressions:
Tempsummer-to-winter = -0.026t + 30 …………. D.1a (15 August < t < 15 November)
Tempwinter-to-summer = 0.11t + 24 …………. D.1b (1 December < t < 30 May)
208 40
35
30
25 Daytime temperature (°C) temperature Daytime 20
15 1-Jul-92 1-Jul-93 1-Jul-94 1-Jul-95 1-Jul-96 1-Jul-97 1-Jul-98 1-Jul-99 1-Jul-00 1-Jan-92 1-Jan-93 1-Jan-94 1-Jan-95 1-Jan-96 1-Jan-97 1-Jan-98 1-Jan-99 1-Jan-00
Figure D.3 Variation of daytime temperature at Rukomechi research station, 1992- 1999
The mean daily temperatures were observed to vary between about 15°C in July (winter) to about 39°C just before the rainy season (end October/beginning November). It is however important to note that the hottest period at Rukomechi is just prior to the rainy season from mid September to mid November when the mean daytime temperatures are above 30°C.
The observed daytime surface temperature change over the entire observation period can be reasonably well explained by the insolation variation associated with the variations in the Sun-earth distance, and elevation of the Sun. The Figure D.4 shows the normalised values (with respect to the maxima) of the monthly variations of the daytime temperature, solar radiation and relative humidity.
209 Jan
Day temperature Dec 0.95 Feb Solar radiation Relative humidity 0.85
Nov 0.75 Mar
0.65
0.55
Oct 0.45 Apr
Sep May
Aug Jun
Jul
Figure D.4 Normalised values (with respect to their maxima) of the daytime temperature, solar radiation and sensor corrected relative humidity for Rukomechi research station
The local insolation variation is in phase with the daytime temperature curve, which indicates that the daytime temperature could be driven by the incoming solar radiation.
The troughs in the annual cycle of these two parameters coincide with the southern hemispheric winter and both have maximum values in October when the sun is almost directly over southern Africa. Although the annual cycle of the daytime temperature closely mimics that of the relative humidity, the two are out of phase by about 90° (Figure
D.4). In the annual cycle, both the solar radiation and the daytime temperature lead the relative humidity by four months. Lowest solar radiation and daytime temperatures occur in July while lowest relative humidity values occur four months later in October. On the other hand, the highest values of solar radiation and daytime temperature are found in
October with the relative humidity maximum observed in January (four months later).
210 While the temperature variation is controlled by the incoming solar radiation received at the site, the relative humidity variation is a function of the availability of water evaporating from open water bodies and evapo-transpiration from plants, both of which are not readily available in October (dry season) and are abundant in January (mid wet season).
211