Environ. Sci. Techno/. 1995,29,2267-2272
Relationships between Chlorinated Hydrocarbons in Vegetation and Socioeconomic Indices on a Global Scale DAVIDE CALAMARI,* PAOLO TREMOLADA, AND VINCENZO NOTARIANNI Group of Ecotoxicology, Institute of Agricultural Entomology, University of Milan, Via Celoria 2, Milan 1-20133, Italy
Many advances have been made in the comprehension of the environmental distribution of persistent organic chemicals. Previous studies on chlorinated hydrocarbons in vegetation have shown that physicochemical properties in combination with environmental characteristics are the most important factors in determining the distribution pattern in remote areas, while present and past use seem to be the most important element near source areas. To better understand how a country’s technological level and socioeconomic conditions impact on the environment in terms of organic contamination, several published data on contamination in vegetation by HCHs, HCB, and DDTs along with a new set of results from different countries were considered in relation to socioeconomic indices (Gross National Product per capita and Human Development Index). HCB and secondly y H C H seem to be linked to the economic development, while among the DDTs only the DDTI DDE ratio shows a significant relationship with the considered indices. The proposed relationships between pollution and development highlight new elements that contribute to the understanding of the inputs and distribution of organic contaminants.
Introduction Investigations on global contamination by persistent organic chemicals have increased in the last few years, and advances have been made in understanding the rules and the pathways of transfer of these chemicals around the world. Among many articles that discuss the subject, reviews are provided by Kurtz (I),Ballschmiter (21, deVoogt and Jansson (3), and Wania and Mackay (4). Physicochemical properties of the substances and climatic and metereological features regulate the “global distillation” (3,the volatilization-deposition cycle (s),and the “cold trap effect” (7). However, Calamari et al. (8)in a work on global distribution of some organochlorinated compounds (HCB, a-HCH, y-HCH, DDE, and DDT) in * Fax: +39-2-26680320.
0013-936X/95/0929-2267$09.00/0
G 1995 American Chemical Society
vegetation, identifying some outliers, stated that while physicochemical properties and environmental characteristics play the most important role in the distribution patterns in remote areas, unpredictable and scattered distribution are found in proximity to source areas. In the attempt to solve this last problem, they resorted to the concept of the “fingerprint”, whereby relative differences in composition are used to identify pollutant mixtures for each geographical-economical homogeneous area (91,and they concluded in a following paper that the history of use and the socioeconomic conditions appear to be the most important factors determining the distribution pattern of these contaminants in areas of utilization (10). The present work is an attempt to more precisely identify economic descriptors and socioeconomic indices that can help in explaining the relevance of inputs on the detection and the distribution of these chemical substances in the environment.
Materials and Methods Chlorinated Hydrocarbons in Vegetation. Contamination data of chlorinated hydrocarbons (HCB, a-HCH, y-HCH, p,p’-DDE, and p,p’-DDT) in vegetation from different countries were considered. Some of the data come from papers already published by this same research group (8101, excluding those coming from high mountains, small islands, or towns in the elaboration of the results. Remote areas were not considered because, by definition, no anthropogenic activitiesare present; while the samples from towns were excluded because they represent only specific conditions. In order to enrich and complete the spectrum of countries of different economic levels, another set of analyses was performed with 43 new samples from eight countries. The new set of data allows us to have results from another developed non-European country (Japan)and to add a consistent number of countries at different economic levels in various continents. The samples were collected in Burkina-Faso,Jordan,India, Indonesia, Japan, Chile, Mexico, and Greece using pine needles or mango leaves. The number of samples in each area was variable, but in each country, vegetation samples were collected by transect sampling mode according to the cited papers. The sampling procedure as well as the analyticalmethod were as previously described (IO): vegetation samples (about 10 g of each) were collected from the ground at the end of their life cycle, wrapped in aluminium foil, kept cold (4 “C), and stored at -20 “C until analysis was performed. Samples were partially oven-dried, minced, and extracted by a Soxhlet apparatus using n-hexane. Purification was performed using Florisil columns and n-hexane as eluent. The cleaned samples were analyzed by a Perkin-Elmer 8500 gas chromatograph with a split-splitless injector and an electron capture detector (ECD). For more details in sample preparation, extraction and cleanup, analytical detection and recovery, and reproducibility check results, see the previously cited publications. Statistical Treatment of Data Correspondence Factor Analysis (CFA) was used for the analysis of the 43 new samples and for the analysis of the mean concentrations of the samples of each country after converting the mass
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units of the contaminants in the concentration values from nanograms to picomoles. The analysis was performed according to a statistical computer program STAT-ITCF Version 3.0 (1987), originally produced by ECOSOFT and translated and corrected by the “Institut Technique des CCrCales et des Fourrages”, Paris. Correlations between concentration data and socioeconomic indices were performed using the equation y = axb because variables were scaled on logarithmic axes. Economic and Social Indices. Two different indices were selected among those available for presenting the development level of a country: Gross National Product (GNP) per capita and Human Development Index (HDI). The World Bankuses the latest GNP per capita estimates in U.S. dollars to classify and broadly distinguish the different stages of economic development. Every economy is classified as low income, middle income (subdividedinto lower middle and upper middle), or high income. In this paper, the same criteria has been followed, and the data came from the World Development Report 1992 (11). The Human Development Report of United Nations Development Programme in 1990 introduced the Human Development Index (HDI),which combines indicators of national income, life expectancy, and educational attainment in order to give a composite measure of human progress and to address aid to people’s needs. HDI does not use nominal GNP but adjusts it to reflect real purchasing power and incorporates social indicators such as life expectancy, adult literacy, and mean years of schooling. How this index is calculated and HDI ranking for countries is referred to in the Human Development Report 1992 (12). These indices are commonly used by the United Nations system to evaluate the level of development of the country and the welfare of a population. They are taken as economic indicators of the richness of a nation. In developed countries, several aid agencies are using these indices as a method for ranking priorities among developing countries to receive economic aid. Other indices of socioeconomic interest, such as gross domestic product (GDP) (E),agricultural production as percentage of total GDP ( E )percentage , of distribution of GDP for industry ( I 11,fertilizer consumption for hectare of arable land and permanent crops (13) have been tested however, the results and the correlations are unchanged because these last indices are correlated with the GNP and HDI.
Results and Discussion The new set of 43 samples coming from Burkina-Faso, Jordan, India, Indonesia, Japan, Chile, Mexico, and Greece are presented in Figure 1 by means of Correspondence Factor Analysis (CFA) representation. This statistical technique allows us to find out relations among multiresidual contamination data by similarities or differences in their relative composition (14). It gives a statistical evaluation of a set of data without any transformation or elimination of outliers. CFA allows as many two-dimensional representations as combinations obtained by the axes. The axes are linear combinations of the original variables, and they define a multidimensional space in which samples and variables are placed. From an analysis of the axes, the percentage of the total variance carried by them is deduced, and those carrying the higher variance percentage have to be taken 2268
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FIGURE 1. Correspondence .-ctor analysis of the 43 new samples by five variables: a-HCH, HCB, y-HCH, p,p’-DDE, and p,p’-DDT. The graphical display is obtained by the projection of the points on the first and second axes. The variability percentage explained by each axis is reported in parentheses. Samples are identified by the code as follows: BU = Burkina-Faso; JO = Jordan; IN = India; ID = Indonesia; JA = Japan; CH = Chile; ME = Mexico; GR = Greece.
as more explicative for the relative differences in the composition of the samples (14). Figure 1 shows the most significant representation of the CFA of the 43 new samples. It is obtained by the use of the first and second axes, which explains 77.6 and 13.9% of the total variability, respectively. Samples are grouped by typical contamination patterns, and this is indicated by the relative distances among samples and variables. For example, India appears characterized by a high contamination of both a-HCH and DDT residues because its cloud lies in-between those compounds. Data from literature confirm these results: high levels of these chemicals are reported in the air around the Indian peninsula by Iwata and al. (19, and data of use (15, 16) indicate that high quantities of DDT and technical HCH (composed prevalently by a-HCH isomer) were utilized in India and that high quantities of technical HCH were utilized in Japan. Looking at Figure 1, as general feature, samples coming from the same country appear to be grouped together, at least for most of them. Burkina-Faso, Mexico, and Indonesia samples are tightly clustered together with DDT variables. Greece and Chile data lie in detached clouds and appear associated with HCB and y-HCH. Jordan and India are grouped even if less clearly separated in comparison to the previous countries. Japan samples on the contrary are more scattered. A second result is the association among the variables. They appear divided in three different groups: one composed by p,p’-DDT and its metabolite, the second composed with y-HCH and HCB, and the last one composed by a-HCH alone. The association of HCB and y-HCH can be explained considering that these two molecules are mostly used and
HCB
nglg d.w.
(71 0 %)
Mau Mex 100
300
1,000
3,000
10,WO
30,000
1W,WO
GNP percapita
FIGURE 3. HCB mean concentrations in vegetation in nanogram/ gram dry weight versus Gross National Product (GNP) per capita in U.S. dollars for the 23 countries. Different marks refer to the economic classification in four classes as in Table I (* = low income economies; = lower middle income economies; 0 = upper middle income economies; 0 = high income economies).
+
FIGURE 2. Correspondencefactor analysis of the mean concentrations in Vegetation in 23 countries by five variables: a-HCH, HCB, y-HCH, p,p’-DDE, and p,p’-DDT. The graphical display is obtained by the projection of the points on the first end second axes. The variability percentage explained by each axis is reported in parentheses. Samples are identified by the code as follows: Nep = Nepal; Si-le = Sierra Leone; Bu-Fa = Burkina-Faso; lndi = India; Ben = Benin; Ken = Kenya; Gha = Ghana; Gui = Guinea; lndo = Indonesia; Iv-Co = Ivory Coast; Gua = Guatemala; Jor = Jordan; Chi = Chile; Mau = Mauritius; Mex = Mexico; Van = Venezuela; Cze = Czechoslovakia; Gre = Greece; Ita = Italy; Neth = The Netherlands; Aus = Austria; Jap = Japan; Fin = Finland.
produced in technological areas. HCB is known to be inadvertently produced in large quantities as a byproduct and/or impurity in a number of chemical processes (17); pure y-HCH (lindane) is still registered for restricted applications in Canada and the United States and is the main HCH product in Europe (18). By the CFA representation of Figure 1, one can deduce that the contamination is relatively homogeneous for each country and that a mean level can be calculated for each region representing the typical contamination status of the area. Geometric mean was chosen to avoid the misleading weight of a few highly contaminated samples. The new set of chlorinated hydrocarbon residues in the vegetation has been considered together with several previous data analyzed by the same group in order to better understand the distribution patterns of antropized areas. Figure 2 shows the most significant graphical representation of the CFA of the mean concentrations in vegetation for all the countries for which data were available. This plot is obtained by the use of the first and second axes, which explain 71.8 and 21.4% of the total variability, respectively. Observing the positions of observations and variables in Figure 2, the following associations are deduced: African countries and most of the tropical regions are tightly bound with DDT compounds; European countries and Chile are grouped together with the presence of y-HCH and HCB molecules; and a third well-distinguished cloud, composed by a more heterogeneous group of countries, is associated with a-HCH compound. The association of European countries with the most technological compounds (HCB and y-HCH) and the one of African and tropical areas with DDT residues together with the conclusion of a previous paper of the contamination in Europe (10) seem to accredit the hypothesis of
possible relations between contamination levels and socioeconomic and technological conditions of a country. We are conscious that the distribution and the contamination levels are determined by and derived from the interaction of many factors that can act differently in time and for different areas. The fundamental role of the physicochemical properties has been extensively proved and described in literature. Wania and Mackay (4) describe the global distillation process of semivolatile organic compounds as a function to the properties of the molecule; environmental conditions such as cold temperature have been found determining the HCB global distribution referring to remote areas (8). Tanabe et al. (19) have reported that the atmospheric circulation can influence the distribution levels too. In the meantime, loads and the history of use, as expected, play a fundamental role especially dealing with man-impacted regions, such as European countries (10). In the present paper, we deliberately try to emphasize this last aspect for two main reasons: first because it has not been highlighted enough and secondly because most of the data available in vegetation deal with areas impacted by man activities. Because loads and the history of use are linked with socioeconomic conditions and the technological level of a country, a correlation has been tried between the data of chlorinated hydrocarbons in vegetation and socioeconomic indices. Mean chlorinated hydrocarbon concentrations in vegetation, GNP per capita in U.S. dollars, and HDI for all the 23 countries considered are listed in Table 1by increasing income. Figures 3 and 4 describe the relationships between HCB concentrations in vegetation, on a logarithmic scale, GNP, and HDI, respectively. Figures 5 and 6 report the correlations between ZHCH and the same indices, while Figures 7 and 8 show the relations between GNP and a-and y-HCH, respectively. Figure 9 shows the distribution of2DDT (p,p’DDE and p,p’-DDT) against GNP, and in Figure 10 the ratio DDT/DDE is plotted against GNP. As we are dealing with the contamination of wide and relatively ununiform areas, e.g., contamination of “India”, and economic indices are a combination of so many factors, the aim of a correlation is not to define a precise relationship VOL. 29, NO. 9, 1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1
Mean Chlorinated Hydrocarbon Concentrations in Vegetation, GNP per Capita in U.S. Dollars, and HDI for 23 Countries no. of sample
GNP
per capita
HDI
species
170 240 330 350 360 370 390 440 570
0.168 0.062 0.074 0.297 0.111 0.366 0.310 0.052 0.491
lichens-mosses mango leaves mango leaves mango leaves mango leaves mango leaves mango leaves mango leaves mango leaves
Ivory Coasta Guatemalaa JordanC ChileC Mauritiusb
750 900 1240 1940 2250
0.289 0.485 0.586 0.863 0.793
mango leaves mango-lichens pine needles pine needles mango leaves
MexicoC Venezuelaa Czechoslovakiad GreeceC
2490 2560 3140 5990
0.804 0.824 0.897 0.901
mango leaves lichens-mosses pine needles pine needles
16830 17320 19060 25430 26040
0.922 0.968 0.950 0.981 0.953
pine needles pine needles pine needles pine needles pine needles
nanogrem/gram dry weight a-HCH
HCB
y-HCH
22 0.11 0.40 13 0.60 0.82 0.30 0.50 0.04
0.1 0.02 0.12 0.08
