Environ. Sci. Technol. 2010, 44, 3162–3168
Application of Self-Organizing Maps for PCDD/F Pattern Recognition of Environmental and Biological Samples to Evaluate the Impact of a Hazardous Waste Incinerator M O N T S E M A R I , † , ‡ M A R T ´I N A D A L , † M A R T A S C H U H M A C H E R , †,‡ A N D ´ L . D O M I N G O * ,† JOSE Laboratory of Toxicology and Environmental Health, School of Medicine, IISPV, Universitat Rovira i Virgili, Sant Llorenc¸ 21, 43201 Reus, Catalonia, Spain, and Environmental Engineering Laboratory, ETSEQ, Universitat Rovira i Virgili, Av. Paı¨sos Catalans 26, 43007 Tarragona, Catalonia, Spain
Received January 4, 2010. Revised manuscript received March 7, 2010. Accepted March 9, 2010.
Kohonen’s self-organizing maps (SOM) is one of the most popular artificial neural network models. In this study, SOM were used to assess the potential relationships between polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/ Fs) congener profiles in environmental (soil, herbage, and ambient air) and biological (plasma, adipose tissue, and breast milk) samples, and the emissions of a hazardous waste incinerator (HWI) in Spain. The visual examination of PCDD/F congener profiles of most environmental and biological samples did not allow finding out any differences between monitors. However, the global SOM analysis of environmental and biological samples showed that the weight of the PCDD/F stack emissions of the HWI on the environmental burden and on the exposure of the individuals living in the surroundings was not significant in relation to the background levels. The results confirmed the small influence of the HWI emissions of PCDD/Fs on the environment and the population living in the neighborhood.
Introduction Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/ Fs) are environmental pollutants, which are unintentionally formed as byproduct in a wide range of activities that entail the combustion of organic matter in the presence of chlorine atoms. These include manufacture of other organic chemicals containing chlorine atoms, combustion processes such as waste incineration, as well as other industrial activities, encompassing power plants and sintering plants among others (1-3). PCDD/Fs are also released by very different sources such as traffic, home heating, crematories, or accidental fires (1). The varying number (up to eight) and the different position of the chlorine atoms in the benzene rings of the PCDD/F molecules lead to 210 congeners (75 PCDDs and 135 PCDFs). However, only the 17 2,3,7,8substituted congeners, as being the most toxic, are generally analyzed. Each PCDD/F source is characterized by its own * Corresponding author phone: +34 977 759 380; e-mail:
[email protected]. † Laboratory of Toxicology and Environmental Health. ‡ Environmental Engineering Laboratory. 3162
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congener profile, which is the proportion of each congener in the mixture, also known as PCDD/F fingerprint (4-6). However, the particular profile of certain sources may change depending on diverse operational parameters, such as the conditions of the combustion (7). In turn, the profiles in the different environmental matrices are influenced by the physicochemical properties that determine the behavior of the different congeners (Kow, half-lives, etc.) (8, 9). Moreover, the pharmacokinetics of the different congeners are other determinant factors for the PCDD/F profiles in biological tissues (10). In 1999, a new hazardous waste incinerator (HWI) initiated its regular operations in Constantı´ (Tarragona County, Catalonia, Spain). To date, this facility is still the only one in Spain, and it is settled in an industrial area whose main characteristics were previously described (11). The HWI periodically controls the PCDD/F stack emission concentrations in order to ensure that the cleaning systems operate properly. Moreover, to determine the potential environmental impact and the human health risks derived from emissions of the HWI, in 1996 a wide environmental and biological monitoring program (“background”) was started and continued since then. Measurements of PCDD/Fs (and heavy metals) were performed in environmental matrices collected in the vicinity of the facility. Soils and herbage samples have been used to assess environmental pollution, as well as direct exposure for the population living in the area under potential influence of the emissions of the HWI (11, 12). PCDD/F concentrations were also recently determined in ambient air samples in the area under study (13). On the other hand, to assess human health risks, samples of adipose tissue, plasma, and breast milk were collected in individuals living in the vicinity of the facility (14-23). All those monitoring data have been used to investigate the temporal trends of PCDD/F (and metal) concentrations. However, studies on the PCDD/F profiles have not been performed. Kohonen’s self-organizing maps (SOM) is one of the most popular neural network models. SOM is a multivariate technique which enables pattern recognition and classification without preliminary knowledge of the process. Other multivariate techniques have been largely used for the same purpose in environmental data analysis. Among them, principal component analysis (PCA) is one of the most accepted. However, SOM has some potential advantages over PCA such as its straightforward interpretation, clustering power and ability to deal with nonlinear problems (24). The SOM algorithm is based on unsupervised competitive learning. It means that the training is entirely data-driven, with the neurons of the map competing among them (25). One of the main strengths of this methodology is that it makes possible to transform highly dimensional data set into simple visual information (18, 24). SOM has been already applied in various environmental studies (26, 27). In the present study, we used Kohonen self-organizing maps to assess the potential relationships between PCDD/F profiles in environmental (soil, herbage and air) and biological (plasma, adipose tissue, and breast milk) samples and the emissions of the facility. This information should give an insight on the behavior of PCDD/Fs in the environment, as well as of the human exposure and pharmacokinetics. The main goal of this study was to use all this information to evaluate the real impact of the HWI on the population exposure to PCDD/Fs, and therefore, the associated health risks. 10.1021/es1000165
2010 American Chemical Society
Published on Web 03/18/2010
Materials and Methods Preprocessing and Data Sets. The original PCDD/F concentrations in the environmental matrices were preprocessed to give the same importance to the different monitors and variables. Since the main objective was to compare profiles, instead of absolute concentrations, the percentage of contribution of each congener to the sum of the 17 2,3,7,8substituted congeners in the corresponding sample was calculated. Afterward, the resulting matrix was scaled to unit variance to give an equal importance to each variable. To get different information, three SOMs were run for the 17 variables (the 2,3,7,8-substituted PCDD/F congeners). Hence, three different data sets were made up according to the type of monitors: (a) Environmental Samples. The environmental matrix was made up by using the PCDD/F congener profiles (percentages) of soil, herbage, and air samples together with the PCDD/F emissions from the HWI. The soil samples (160 samples) were taken during 1998, 2004, 2006, and 2008 (11, 12, 20, 28), while herbage samples (156 samples) were taken during 1998, 2005, 2007, and 2008 (20, 28, 29). In addition, the samples of ambient air (eight samples) were collected in the same area in 2007 (13). With regard to the stack emissions of the HWI, 12 samples were analyzed between 2005 and 2008. They were also included to evaluate the influence of the HWI on the surrounding environment. (b) Biological Samples. The biological matrix was made up by using the PCDD/F congener profiles of plasma, adipose tissue and breast milk samples of people who had been living during at least the last 10 years in the vicinity of the HWI. Analyses of these matrices were performed during 1998, 2002, and 2007. Forty-five samples of breast milk and adipose tissue, and 60 samples of plasma were collected (14-17, 19-23). (c) Total Samples. A global matrix was built using soils, herbage, ambient air and emission samples, together with plasma, adipose tissue, and breast milk samples. Relationships between emissions of the facility and the different monitors were investigated. Kohonen’s Self Organizing Maps. Kohonen’s selforganizing maps are a special type of neural networks, which provides projection of multidimensional data into one-, two-, or in special cases, into a three-dimensional space. The SOM algorithm is based on unsupervised competitive learning, which means that the training is entirely data-driven, competing among them the neurons of the map (25). SOM algorithm relies on two main aspects: the input data set, and the output data set or map. Input Data. Each data item is associated with an n-length vector of elements. These elements are commonly called features, attributes, or properties of the data. Output Map. The map is an array of nodes (also called neurons). This array is usually two-dimensional, although it could be of a higher order. It is often laid out in a rectangular or hexagonal lattice. Each node has an associated reference vector of the same size as each input feature vector. The input vectors are compared to these references. The number of map units determines the accuracy and generalization capability of SOM. A reasonable optimum solution of compromise among the quantization and topographic errors to determine the side lengths of the map is the heuristic formula,M ) 5N, where M is the number of map units, and N is the number of samples of the training data. To calculate the number of rows and columns, the ratio between them is the ratio between the two highest eigenvalues (n1)/(n2) ) (e1)/(e2), where n1 and n2 are the number of rows and columns, being e1 and e2 the highest and the second eigenvalues, respectively (24, 30). The results are visualized as component planes (c-planes), where correlation patterns among the variables can be observed. Component planes display the behavior of a given
input variable through the whole data set. These planes are built in color levels to show the value of a given input feature for each SOM unit in grid. Additionally, it is important to find out how the observations can be naturally grouped based on their similarity. For this purpose, it is useful to analyze the distances between neighboring neurons on the whole map. In the present study, for the different SOM runs the learning process was broken down with 10 000 steps for the organizing phase and 10 000 steps for the tuning phase.
Results and Discussion Apparently, PCDD/F profiles of most environmental and biological samples look very similar. As it can be noticed in Figure 1, the three environmental monitors (soil, vegetation, and ambient air) presented a common PCDD/F profile pattern, with OCDD being the most abundant, followed by OCDF, 1,2,3,4,6,7,8-HpCDD and 1,2,3,4,6,7,8-HpCDF. Because soils are a cumulative matrix, slightly higher percentages of OCDD and OCDF were found in this monitor, while PCDD/F congener profiles in vegetation and air were almost identical. Similar patterns have been found in previous studies around MSWIs in Catalonia (31, 32), as well as worldwide (33, 34). Notwithstanding, the PCDD/F congener profiles in environmental compartments were very different from that corresponding to PCDD/Fs emitted by the stack gas, where the contribution of the most chlorinated dioxins (OCDD and 1,2,3,4,6,7,8-HpCDD) was reduced. This would be a first indication that the influence of the HWI on the surrounding environment is low. These results would be in agreement with those obtained by authors such as Gao et al. (35), who reported that 1,2,3,4,6,7,8-HpCDF was the most abundant congener in emissions from hospital waste incinerators in China. Concerning biological monitors, all of them showed a very similar PCDD/F congener profile, with OCDD being again the predominant. These findings would be in accordance with those previously reported in the literature. Fromme et al. (36) also noted that OCDD was the dominant congener when analyzing PCDD/Fs in blood of the German population, while Lo´pez-Espinosa et al. (37) found the same profile in adipose tissue from women living in Southern Spain. Although the PCDD/F congener profiles could look like very similar in environmental and biological monitors, some differences were noted. Biological matrices were characterized by an increase of the contribution of 1,2,3,6,7,8-HxCDD, as well as a reduction of 1,2,3,4,6,7,8-HpCDF and OCDF to the total concentration of PCDD/Fs. These variations would be associated to the pharmacokinetics of these chemicals together with differences in intake/elimination of dioxinlike compounds according to the degree of chlorination. In relation to this, excretion rates of some persistent organic compounds, such as 1,2,3,6,7,8-HxCDD, have been found to be significantly greater for older subjects than for younger people (38). Moreover, Guruge et al. (39) identified this congener as one of those showing a greatest biomagnification factor in animals, which might explain its relative importance in biological tissues. In addition to this preliminary evaluation, a deeper analysis applying data mining techniques such as SOM, might help to state differential patterns among monitors. Environmental Data. The SOM algorithm was applied to the PCDD/F profiles of the 336 environmental samples composed of soils, herbage, and air, as well the stack air samples from the HWI. The number of neurons was 88 [11 × 8]. The component planes displaying the congener composition of each virtual unit map are depicted in Figure 2a. They allow the comparison between variables. In turn, Figure 2b shows the distribution of the samples through the grid. This distribution indicates a different behavior depending on the environmental matrix type. Additional explanation of SOM interpretation is given as Supporting Information VOL. 44, NO. 8, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Profiles of PCDD/F Congeners Corresponding to Environmental and Biological Samples. (SI). Soil samples were grouped at the lowest part of the map, irrespective of the year of collection. For these samples, elevated OCDD and OCDF ratios were observed. Herbage samples were found at the top half of the map, where the lighter congeners had a major importance. Some clusters were identified according to the year of collection of herbage. For instance, samples collected in 2008 showed elevated concentrations of 1,2,3,4,7,8- HxCDF, 1,2,3,6,7,8-HxCDF, and 2,3,4,6,7,8-HxCDF, whereas those collected in 2005 showed high levels of 1,2,3,7,8,9-HxCDF, 1,2,3,4,7,8-HxCDD, and 1,2,3,6,7,8-HxCDD. Most air samples were located at the very top of the map, showing a high percentage of 2,3,7,8-TCDD. The only exceptions were Air 2, 3, and 4, which appeared at the right-hand side. By contrast, all emission samples appeared clustered on the left-hand side of the map, showing increased concentrations of heptafurans. 3164
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Following atmospheric deposition, soils tend to accumulate ambient PCDD/Fs. Therefore, soils are natural sinks for PCDD/Fs, being a typical long-term accumulating matrix for PCDD/Fs (11). When adsorbed to the organic material of soils, PCDD/Fs remain quite immobile. The longer half-lives of OCDD and OCDF in soils with respect to the lighter congeners, determine the accumulation of the former, while the latter are more easily degraded. Consequently, soil profiles are characterized by higher concentrations of OCDD and OCDF. On the other hand, the load of those heaviest congeners in herbage samples was not as important as in soils. However, they were also the most abundant (Figure 1). The fact that herbage samples appeared clustered according to the year of collection confirms the use of herbage as a suitable indicator of atmospheric emissions during short periods of time. Based on their position in the map, air, and
FIGURE 2. SOM results for environmental samples. (a) Component planes, in percentage of each PCDD/F congener. (b) Distribution of samples.
FIGURE 3. SOM results for biological samples. (a) Component planes, in percentage of each PCDD/F congener. (b) Distribution of samples. emission samples resemble to herbage samples. However, they have different patterns from each other. It indicates that air samples in the area are influenced not only by the HWI, but also by other different sources. Biological Data. The SOM algorithm was applied to the PCDD/F profiles of the 150 biological samples. Plasma, breast milk, and adipose tissue were used to get information regarding the PCDD/F pattern in these biological monitors, and thus, some insights on the pharmacokinetics and behavior of PCDD/Fs in the human body. As a result of the learning process, a map with 63 virtual nodes [9 × 7] was obtained. The congener composition of each virtual map in the component planes of the SOM results is depicted in Figure 3a, whereas Figure 3b shows the distribution of the biological samples on the grid. Three main clusters can be observed (Figure 3b). Plasma samples appeared at the right top corner of the SOM grid, being characterized by high OCDD concentrations. Breast milk samples were located at the left part of the grid, showing high concentrations of 1,2,3,7,8PeCDD, 2,3,7,8-TCDD, 1,2,3,6,7,8-HxCDD, and 1,2,3,7,8,9HxCDD. Finally, adipose tissue samples were placed at the
bottom left side of the map. Although it was more complex to correlate adipose tissue with a specific congener, they were mainly differentiated by higher concentrations of 2,3, 7,8-TCDF, 1,2,3,6,7,8-HxCDF, and 1,2,3,7,8,9-HxCDF. PCDD/Fs are detected in blood or other lipid containing tissues such as adipose tissue or breast milk (10). Blood is one of the most widely biological monitors used to assess human exposure to toxics (40). Once the uptake of PCDD/Fs is produced, congeners are metabolized and those that are not metabolized are stored in the fat. In addition, during lactation PCDD/Fs, as other lipophilic compounds, are eliminated via milk. Body burdens of PCDD/Fs are generally result of environmental exposure to these chemicals, mainly of dietary intake (10, 18, 41), together with distribution, metabolism and excretion of those compounds. The current SOM results for human milk and plasma are in agreement with data from recent studies where blood and breast milk of the same mothers were collected (42). Higher concentrations of high chlorinated PCDD/Fs (especially OCDD) in blood were found in comparison to breast milk. Todaka et al. (43) reported that OCDD concentrations in blood of VOL. 44, NO. 8, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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primiparous and multiparous mothers were 9.0 and 9.9 times higher, respectively, than those found in breast milk, whereas Wittsiepe et al. (44) found OCDD concentrations 3.3-fold higher in blood than in milk. According to Wittsiepe et al. (44), the difference could be explained at least in part, by a difference in the lipophilicity of the congeners. However, the main reason would be the molecular weight of the most chlorinated congeners, which makes difficult the transfer through the membranes. With regard to adipose tissue, the results of the present investigation indicate that in addition to OCCD, other congeners such as 2,3,7,8-TCDF, 1,2,3,6,7,8HxCDF and 1,2,3,7,8,9-HxCDF might be also notably bioaccumulated in fatty tissue. However, it should be taken into account that some pharmacokinetic studies concluded that half-lives of PCDD/Fs are dependent on the total body fat content of the organism, together with other factors like dose or body burden (the lower body burden the higher halflives) (10, 45). Global Data. Finally, the SOM algorithm was applied to the PCDD/F profiles of the 486 environmental and biological samples (soil, herbage, air, emissions, plasma, breast milk, and adipose tissue) in order to get information about the relationship between emissions of the HWI and the environmental and biological patterns. As result of the learning process, a map with 112 virtual nodes [14 × 8] was obtained. Graphical results of the SOM application to the global data set are presented in the SI. The congener composition of each virtual map in the component planes of the SOM results is depicted in SI Figure 1S, whereas SI Figure 2S displays the distribution of the samples on the grid. Soil and biological samples appeared on the top half of the map showing high OCDD concentrations, whereas samples of herbage, air, and emissions of the HWI were located at the low part of the grid, showing elevated levels of furans. The first finding to be highlighted is that emission samples appeared at the opposite part of the SOM map with respect to the biological samples. The patterns of PCDD/Fs reflect their sources. It is recognized that the measured dioxin congener patterns in blood (or other tissues) can be informative of the exposure (10). According to this, our results would indicate a small influence of the HWI on the total PCDD/F exposure of the subjects living in the area under evaluation. In fact, previous studies in that area clearly showed that the diet was the main route of exposure for the local population, contributing to more than 99% of total exposure to PCDD/Fs (12, 41). Other recent studies also noted that the diet is the main route of human exposure to PCDD/ Fs, making up more than 95% of total daily intake (28, 46, 47). Therefore, for the general population, the major correlation should be noticed between biological patterns and those of dietary intake. However, when a considerable exposure to PCCD/Fs occurs due to accidents, or occupationally, different patterns have been observed in human tissues evidencing the characteristics of the agent that caused the massive exposure (10). The global PCDD/F pattern analysis of the environmental and biological samples showed that the weight of the PCDD/F stack emissions of the HWI on the environmental burden and on exposure of the individuals living in the surroundings is not significant in relation to the background levels. These results are in agreement with those of recent studies (48-52) where the repercussion of incinerators, especially in the EU, has been evaluated. These investigations clearly state that waste-to-energy plants using best available techniques (BAT) and best environmental practices (BEP) to meet the stringent emission standards, result in a high reduction in expected risks with respect to older plants with no specific PCDD/F control measures (48). Moreover, values of maximum individual excess risk are largely insignificant with respect to the regulatory reference level. Another key issue when 3166
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addressing PCDD/F pollution in areas under potential influence of incinerators is the presence of other PCDD/F emission sources, such as industrial facilities. It is evident that the concentration of pollutant sources results in an increase of PCDD/F levels in the environment and, therefore, in the body burden. However, the weight of waste incinerators as releasers of PCDD/Fs has decreased in recent years. According to the U.S. Environmental Protection Agency (53), in 1987 and 1995, municipal waste combustion was the leading source of dioxin emissions to the U.S. environment, but it dropped to the third ranked source in 2000. In an inventory of PCDD/Fs made in Tarragona Province, it was noted that the amount of these chemicals emitted by a MSWI and the HWI here evaluated, both in compliance with the legislation, was only 0.04% and 0.09%, respectively, of the total (54). Therefore, it is clear that, if waste incineration plants are equipped with BAT, the presence of these facilities must not result in additional human health risks of exposure to PCDD/Fs. However, all those studies agree that the public acceptance of these facilities is still very low and emphasize the importance of performing this type of studies not only to ensure that all the actions taken to protect the environment and human health are properly working, but also to spread the results to the global population. This is a fundamental issue to change the public perception in front of these facilities. The SOM algorithm is able of detecting differences in PCDD/F patterns of matrices that apparently are very similar. Consequently, complementary information can be extracted. In the current particular example, different profiles have been observed for soils and herbage. The results confirm these matrices as long- and short- term monitors, being air samples, as expected, quite similar to herbage. With respect to biological samples, the present results also showed different profiles according to the monitor, which indicated that the behavior of the congeners in the human body is different. In summary, the current global comparison of environmental and biological samples confirms the small influence of the HWI emissions of PCDD/Fs on the environment and the population.
Acknowledgments This study was financially supported by the Age`ncia Catalana de Residus, Generalitat de Catalunya, Barcelona, Catalonia, Spain.
Supporting Information Available The SOM Algorithm and two additional figures. This material is available free of charge via the Internet at http:// pubs.acs.org.
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