Correlation of Atmospheric Deposition and Diseases in the Euroregion Neisse
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CORRELATION OF ATMOSPHERIC DEPOSITION AND DISEASES IN THE EUROREGION NEISSE O. WAPPELHORST, I. KtJHN, J. OEHLMANN, S. KORHAMMER, B. MARKERT |||||||||||||||||||||||||||||||||||||| International Graduate School Zittau Markt 23 02763 Zittau Germany XA0102859 Tel. ++49 3583 7715 0, Fax ++49 3583 7715 34, E-Mail: [email protected] Abstract: A biomonitoring system using the mosses Pleurozium schreberi and Polytrichum formosum as biomonitors has been used to determine the degree of pollution in the Euroregion Neisse (ERNj. This region, located in Central Europe where the borders Germany, Poland and Czech Republic meet (see Figure 1), was one of the most highly polluted areas in Europe until the early 1990s. For clarity and ease of access the results have been presented visually using a Geographical Information System (GIS) (Markert et al, 1999; Wappelhorst, 1999; Wappelhorst et al, 1999). The deposition of 37 elements in the Euroregion as found in the moss study is compared with the incidence of various diseases, using data from regional hospitals. Connections between diseases of the respiratory tract and Ce, Fe, Ga and Ge deposition as well as between cardiovascular diseases and Tl were determined. The results will be validated by further studies with an even greater amount of data. 1. INTRODUCTION The use of epiphytic plants as passive biomonitors is an established method of determining atmospheric deposition. In Scandinavia, mosses have been used as biomonitors for determining pollution with heavy metals since the late 1960s (Riihling & Tyler, 1968). Numerous projects have since been carried out with mosses; the method has been developed systematically (Ellison et al., 1976; Maschke, 1981; Engelke, 1984; Steinnes, 1984; Ross, 1990) and also used in large-scale European studies (Ruhling, 1994; Siewers & Herpin, 1998; Markert et al., 1999). Mainly the endohydric mosses Pleurozium schreberi and Hylocomium splendens have been used in these studies. Due to the fact that mosses have no cuticle layer, heavy metals and other elements are taken up through the surface of the mosses. This makes it safe to assume that the element concentrations correlate closely with the actual deposition of thes e elements, providing that disruptive factors such as soil particles or leaching processes can be excluded. Atmospheric pollution causes different diseases in humans, although definite proof as to which substance triggers or causes a disease is very difficult. 2. METHODS An investigation into the effects of atmospheric pollution on individuals over large areas involves the collection of data throughout the region. Biomonitoring is an excellent means of doing this. Such an approach was used by Cislaghi & Nimis (1997) in a study in which they compared the mortality rate from various pulmonary diseases with a biodiversity index for lichens. High correlation coefficients were found between the index and the number of deaths from lung cancer. However, no direct conclusions were drawn in respect to levels of individual elements. 2.1. Biomonitoring In the years 1995 and 1996 the atmospheric deposition of elements in the Euroregion Neisse (ERN), located in Central Europe at the border of Poland, the Czech Republic and 97 Germany, was determined in a biomonitoring project using mosses. The mosses Pleurozium schreberi and Polytrichum formosum were chosen as biomonitors because of their wide distribution in the area studied and because their ability to take up metals from the atmosphere makes them optimal biomonitors. The present study was the first attempt to compare pollution with numerous elements - detected by using mosses as biomonitors - with the incidence of disease (Wappelhorst, 1999; Wappelhorst et al., 1999). _ESchwarze State border Borders of ERN ower station -**., r Figure 1: The Euroregion Neisse The sampling sites were located at least 300 m from main roads or populated areas and at least 100 m from any road or single house. The moss samples were dried at 45°C without previous washing. A microwave-assisted digestion in closed PTFE vessels was done with 300 mg samples, 4 ml nitric acid and 2 ml hydrogen peroxide. The samples were analyzed by ICP-MS and ICP-OES for their concentrations of 37 chemical elements (Ag, Al, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, La, Mg, Mn, Mo, Na, Nd, Nd, Ni, Pb, Pr, Rb, Sn, Sr, Ti, Tl, Th, U, V, Y, Zn and Zr). Using the Geographical Information System ARC/INFO the element contents were then interpolated (inverse distance method) and presented in map form. Standard reference material was used to check the quality of the analytical results (Markert, 1996). In evaluating the general contamination, all elements analysed must be considered simultaneously. As the number of elements in question increases, the difficulty of this task increases as well, especially considering that the distribution curves can differ greatly from element to element. For this reason we calculated a standardised contamination level which took all the element contents which had been determined into consideration. To accomplish this all element contents in the mosses were first standardised. Then the average standardised element content was calculated for each sampling point and interpolated exactly as for the individual element contents. Thus in areas where the standardised contamination level is higher than 0.3, the level of pollution, taking the average of all measured elements, lies more 98 than 30% over the ERN average. 2.2. Health data For every region investigated the average element content was calculated and then correlated with the health data. The numbers of patients discharged from hospitals, including deaths, in the years 1993 to 1997 were taken as the basic data for the frequency with which a disease occurs. In Germany this data, which includes the diagnosis and the patient's sex, age and place of residence, is provided annually by hospitals to the Statistical Offices of the individual states. The diseases are classified according to ICD 9 (International Statistical Classification of Diseases and Related Health Problems, ninth revision, WHO) (Table 1). Table 1: Classification of the diseases studied according to ICD 9. ICD 9 Description of the D isease 140 - 208 Neoplasms 162 Malignant neoplasms of the trachea, bronchus and lung 172, 173 Malignant melanomas of the skin and other malignant neoplasms of the skin 204 - 208 Leukaemia 390 - 459 Diseases of the circulatory system 393-398,410-429 Heart diseases 410 Acute myocardial infarction 411 - 414 Other forms of ischaemic heart disease 401 - 405 Essential and secondary hypertension 430 - 438 Diseases of the cerebrovascular system 460 - 519 Diseases of the respiratory system 480 - 486 Pneumonia 466, 490, 491 Bronchitis 490 - 496 Chronic obstructive lung disease 493 Asthma 680 - 709 Diseases of the skin and subcutaneous tissue 710 - 739 Diseases of the musculoskeletal system and connective tissue: 99 Table 2: The European Standard Population according to Waterhouse (1976) Age group Number of people 0 1600 1 - <5 6400 5-<10 7000 10 -<15 7000 15-<20 7000 20 - <25 7000 25 - <30 7000 30 - <35 7000 35 - <40 7000 40 - <45 7000 45 - <50 7000 50 - <55 7000 55 - <60 6000 60 - <65 5000 65 - <70 4000 70-<75 3000 75 - <80 2000 80-<85 1000 85+ 1000 Total 100 000 We were able to evaluate the data from the districts of Bautzen, Kamenz, Lobau-Zittau, the Upper Lusatia district of Lower Silesia (NOL) and the county boroughs of Gorlitz and Hoyerswerda. The data are classified according to sex and age (0 - <5, 5 - <10, ..., 80 - <85, 85 and over). The age structure, which has a bearing on the incidence of disease, differs from one area to another. In order to compare the disease figures for the individual areas it was nevertheless necessary to standardize them. Two possible methods of standardization were available. In the first method the data were converted in accordance with the European Standard Population (see Table 2) (Waterhouse, 1976). The age groups are taken into account in the standardized overall incidence of a disease with a weighting that corresponds to their share of the standard population. In the second method the numbers of cases of the disease are converted to a figure per 100,000 inhabitants divided according to sex and age; the overall incidence of the disease per 100,000 inhabitants is then calculated. This method has the advantage that the data can be evaluated separately according to age and sex. But such a breakdown can lead to very small case numbers per group and thus cause major errors in the results. This does not happen with the first method. A further advantage of the first method is a great reduction of the individual data to be processed. Both methods were carried out. To determine possible connections between deposition and disease, the incidence of the diseases was correlated with the element concentrations in the mosses. The concentrations of a number of elements in the mosses Pleurozium schreberi and, to a lesser extent, in Polytrichum formosum differ only slightly from one sampling site to another in the ERN. A priori, high correlation coefficients would result between these elements and all diseases that also show little geographic difference in incidence. But such correlations yield extremely little information; for this reason, only elements with a mean relative deviation of at least 35 % from the median were considered. In the ERN, differences of this magnitude were found 100 for Ag, Al, Be, Bi, Ce, Cr, Cs, Fe, Ga, Ge, La, Li, Mn, Mo, Na, Nb, Nd, Pb, Pr, Rb, Sn, Th, Ti, Tl, U, V, Y and Zr in Polytrichum formosum and for Be, Bi, Cs, Mn, Na and Tl in Pleurozium schreberi. The incidences of disease converted in accordance with the standard population (Method 1) and the numbers of cases broken down according to age and sex (Method 2) were correlated with the element concentrations in the moss samples from the various districts.