GIS-based source identification and apportionment of diffuse water pollution: Perfluorinated compound pollution in the Tokyo Bay basin

Chemosphere 85 (2011) 1340–1346

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GIS-based source identification and apportionment of diffuse water pollution: Perfluorinated compound pollution in the Tokyo Bay basin Yasuyuki Zushi, Shigeki Masunaga ⇑ Graduate School of Environment and Information Sciences, Yokohama National University, Japan

a r t i c l e

i n f o

Article history: Received 4 April 2011 Received in revised form 22 July 2011 Accepted 25 July 2011 Available online 31 August 2011 Keywords: Perfluorinated compounds (PFCs) Geographical information system (GIS) Source identification Source apportionment Nonpoint source Tokyo Bay

a b s t r a c t To efficiently reduce perfluorinated compound (PFC) pollution, it is important to have an understanding of PFC sources and their contribution to the pollution. In this study, source identification of diffuse water pollution by PFCs was conducted using a GIS-based approach. Major components of the source identification were collection of the monitoring data and preparation of the corresponding geographic information that was extracted from a constructed GIS database. The spatially distributed pollution factors were then explored by multiple linear regression analysis, after which they were visually expressed using GIS. Among the 35 PFC homologues measured in a survey of the Tokyo Bay basin, 18 homologues were analyzed. Pollution by perfluorooctane sulfonate (PFOS) was explained well by the percentage of arterial traffic area in the basin, and the 84% variance of the measured PFOS concentration was explained by two geographic variables, arterial traffic area and population. Source apportionment between point and nonpoint sources was conducted based on the results of the analysis. The contribution of PFOS from nonpoint sources was comparable to that from point sources in several major rivers flowing into Tokyo Bay. Source identification and apportionment using the GIS-based approach was shown to be effective, especially for ubiquitous types of pollution, such as PFC pollution. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Perfluorinated compounds (PFCs) have been detected in various environmental matrices worldwide, even the Arctic (Shoeib et al., 2006), and they are known to be ubiquitous pollutants. Perfluorooctane sulfonate (PFOS) and its synthetic starting material, perfluorooctyl sulfonyl fluoride (PFOSF), have been added to the list of the Stockholm Convention on POPs (May 2009) (UNEP, 2009). Fluorotelomer alcohols (FTOHs) and fluorooctane sulfonamidoethanol (FOSE), which can be degraded to perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS), respectively, have spread worldwide via the atmosphere, and are considered to be major contributors to the ubiquitous pollution caused by PFCs (Shoeib et al., 2006; Stock et al., 2007). In addition, nonpoint sources of PFCs have contributed to water pollution, especially in urban regions (Kim and Kannan, 2007; Adams et al., 2008; Zushi et al., 2008; Murakami et al., 2009b), as a result of the use of distributed consumer products containing PFCs (USEPA, 2009b; Zushi and Masunaga, 2009b). The PFCs emitted from these sources can reach rivers via leaching or rain runoff (Murakami et al., 2009b; Zushi and Masunaga, 2009a), where they contribute to aquatic PFC pollution. ⇑ Corresponding author. Address: Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogaya, Yokohama 240-8501, Japan. Tel.: +81 45 339 4352; fax: +81 45 339 4373. E-mail address: [email protected] (S. Masunaga). 0045-6535/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.chemosphere.2011.07.052

Although the most important contributor to PFC pollution might be point sources (Mclachlan et al., 2007), several reports have suggested that nonpoint sources make a comparatively large contribution (Kim and Kannan, 2007; Zushi et al., 2008; Murakami et al., 2009b; Zushi and Masunaga, 2009b). In a survey of an urban river with no fluoropolymers or related product factories in its basin, approximately 60% of PFOS loading was from nonpoint sources, including loading from rain runoff (Zushi and Masunaga, 2009b). A clear declining trend in PFC pollution levels has not been observed, despite various attempts to decrease their levels, such as regulations on their production (e.g., in Tokyo Bay) (Zushi et al., 2010), the phase-out of PFOSF production by 3M Ltd. (3M, 2000), the PFOA stewardship program (USEPA, 2009a), and an EU directive (EU Directive, 2006). This may be due to the contribution of PFC loading from nonpoint sources. However, nonpoint sources of PFCs have not been well characterized, and their contributions to pollution have not been sufficiently evaluated, despite their potentially significant contribution. To enable an efficient reduction of PFC pollution, an understanding of PFC sources (both point and nonpoint) and their contribution to pollution is important. Geographic indices have been used to identify the relationship between chemical pollutants, such as pesticides (Chen et al., 2002) and heavy metals (Xiao and Ji, 2007), and their sources/fates. In addition, a correlation between population density and PFC

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concentration was found, and the population density was used as a simple indicator of distributed sources of PFCs (Murakami et al., 2008a; Pistocchi and Loos, 2009). However, more effective geographic indices have not been sufficiently searched or evaluated to identify and characterize nonpoint sources of PFC pollution. In our previous study, a geographic information system (GIS) based approach of source identification was applied to evaluate PFC nonpoint sources in a small river basin (Zushi and Masunaga, 2009b), and the results indicated that PFCs emanated from an area with a high proportion of commercial and/or transportation land use. The study showed that the GIS based approach was well suited for identification of nonpoint source. As mentioned above, (1) PFCs are spatially distributed, which is probably due in part to nonpoint sources and (2) PFC nonpoint sources are not well characterized. Therefore, evaluation of spatially distributed PFC pollution by spatial analysis and identification of its sources are important for managing PFC pollution. In this study, we applied a GIS-based approach to the source identification of spatially distributed sources of PFC, as well as for evaluation of their contribution to PFC pollution. The results showed that a GIS-based approach is effective for source identification and apportionment, especially for ubiquitous types of pollution, such as PFC pollution. 2. Methods 2.1. Framework of GIS-based source identification The framework of a GIS-based approach for the identification of spatially distributed sources is illustrated in Fig. 1. The monitoring data of the PFC concentration and the corresponding geographic information extracted from a constructed GIS database were prepared, and exploratory analysis of the pollution factors was conducted by multiple linear regression analysis. The pollution factors, which were spatially distributed and contributed to the PFC pollution, were obtained from the regression analysis. The spatial distribution of the pollution factors in the target area was calculated by regression analysis and was visually presented using GIS. 2.2. Study area and analytical sample collection The Tokyo Bay basin in Japan was selected for the identification of spatially distributed sources. A detailed description of the PFC pollution survey in the Tokyo Bay basin is available elsewhere (Zushi et al., 2011). The abbreviations and chemical structures of PFCs are shown in Table S-1. River water samples (n = 50) were collected from the downstream end of a river in each watershed whose samples are considered to represent the characteristics of the watershed, such as population density, and level of industrial

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development (Zushi and Masunaga, 2009b). The sampling stations and the watersheds they cover are shown in Fig. S-1. The PFC concentration data quantified in the survey were used for the spatial analysis. Samples were collected based on the following criteria to prevent bias influencing the source identification: (i) The watersheds were independent from each other. (ii) The water quality of the samples was not affected by tidal action. (iii) The samples were collected as close to the down-stream end of the river in each watershed as possible. (iv) The samples were collected when there was no rain on the day of and the day before sampling to prevent contamination by rainwater runoff. 2.3. Geographic indices A GIS database of the study area was constructed for source identification and apportionment (Table S-2). The geographic information for each watershed was extracted from the GIS database, and geographic indices were used as explanatory variables (i.e., possible pollution factors) for the analysis. Geographic indices that met the following criteria were used for the analysis: (i) The indices were interpretable as possible pollution factors for PFC pollution. (ii) Correlation between the indices was low (r < 0.7), and the indices were considered to be independent of each other. Eleven explanatory variables were selected as candidates for pollution factors for source identification. These 11 candidates were extracted from the GIS database as density in the watershed: percentages of the agricultural area, the area except forest and waste land, arterial traffic area, river and lake area, golf course, other land use area, sewage treatment area, sewage treatment plant catchment area, buffer area around the train station (50 m in radius), number of waste disposal sites, and population in each watershed. The variable ‘‘the area except forest and waste land’’ can be regarded as an indicator of combined characteristics of various artificial land use, such as ‘‘buildings’’, ‘‘arterial traffic areas’’, and ‘‘agricultural areas’’, and the defined indicator is counted as ‘‘featureless artificial areas’’. The variable ‘‘buffer area around the train station’’ (50 m in radius) can be regarded as an indicator of a densely urbanized area, especially for commercial and human activity. The variable ‘‘catchment area of sewage wastewater’’ represents the percentage of the water treatment area serviced by a specific sewage treatment plant (STP) in each watershed. This variable can be considered the impact of STP effluent. If a STP existed in the watershed, the percentage of its catchment area covered by the STP was calculated. As mentioned above, population density has a positive relationship with PFC pollution and is considered a good source indicator of diffuse pollution of PFC (Murakami et al., 2008a; Pistocchi and Loos, 2009). Despite the high correlation (r P 0.7) between population and other variables, such as sewage-treatment area, buffer area around train station, population density was included in the candidates for pollution factors for evaluation of the feasibility of the variables as indicators of distributed PFC sources. Variable selection was conducted using stepwise selection in the multiple linear regression analysis. 2.4. Procedure of GIS-based source identification of PFC

Fig. 1. Schematic illustration of the GIS-based source identification.

The procedure for the source identification and apportionment is shown in Fig. 2. The dataset of PFC concentrations from the

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results of the survey of the Tokyo Bay basin was prepared as objective variables in a multiple linear regression analysis. The dataset of geographic indices was prepared as explanatory variables in the multiple linear regression analysis. The dataset of PFC concentrations and geographic indices in their respective watersheds was appended using the watershed ID as an indicator, after which the data matrix for the multiple linear regression analysis was prepared. The multiple linear regression analysis was conducted to obtain the pollution factors for PFC pollution. Because the PFC concentrations in the environment are considered to have a log-normal distribution, they were converted to logarithmic values. Some PFCs (PFHpA and PFOA) were found not to have log-normal distributions (see Table S-3 and Fig. S-2); however, logarithmic conversions were conducted for all PFCs to enable their comparison with each other. Furthermore, the relationship between logtransformed PFC concentrations and geographic indices was loglinear in many cases in this study. Therefore, the log-transformed values of geographic indices were used. Finally, a multiple linear regression model with log-transformed values of objective and explanatory variables was conducted. A stepwise procedure (p-in and p-out: 0.05) was used for variable selection in the analysis. The obtained multiple linear regression model was then reverted to the antilogarithm of PFC concentration. The final regression equation for each PFC homologue is shown as:

the variance inflation factor (VIF) and the validity of the coefficient of the variables was checked. The value of the VIF was calculated based on the following equation:

VIF ¼

1 1  Rvar2

ð2Þ

ð1Þ

where Rvar2 equals the square of the correlation coefficient between the explanatory variables. A VIF value greater than 10 indicates multicollinearity. When the sample distribution is according to the normal distribution, the median +3r of the samples is approximately equal to 99.86%ile of the distribution. Thus, the 99.86%ile of prediction interval of sample, which was calculated from the distribution of predicted PFCconc values during analysis, was defined as the threshold value for identifying outliers among the monitored PFC concentrations. The criterion of the 99.86% is randomly exceeded with a probability of 7% for all samples (n = 50) and was rarely observed (exceeded with a probability of 0.14% for each sample). We assumed that outliers appeared as a result of loading from point sources such as PFC-using facilities, and this was confirmed by map inspection (see Table S-4 for detail). Those outliers caused by point sources were omitted from the multiple linear regression analysis, after which the samples were reanalyzed. The obtained model represents the pollution by spatially distributed sources (in other words, nonpoint sources). A map of pollution potential that exhibited the PFCconc in each watershed was drawn.

where fj is the score of the pollution factor j, and bj is an estimated parameter derived from the multiple linear regression analysis. In the case of j = 0, fj = e is accepted. PFCconc indicates the PFC concentration attributed to the pollution factors existing in each watershed. To avoid generation of multicollinearity, which is a statistical phenomenon in which two or more explanatory variables in a multiple regression model are highly correlated, the value of

2.4.1. Source apportionment between point and nonpoint source PFCconc was calculated to exhibit the spatial distribution of pollution potential. The map of the pollution potentials only represents the estimated PFC concentrations at the end of the individual watersheds from nonpoint sources, and the process of dilution or mixing of the PFC concentrations by the river flow from upstream watershed to downstream watershed is not considered.

PFC conc ¼

n Y

bj

fj

j¼0

Fig. 2. Procedure for source identification and apportionment of PFCs.

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Therefore, the map does not represent the virtual PFC concentrations by the nonpoint sources in the downstream of river, which reflect mixed contribution of upper stream watersheds. In the next step, we calculated the virtual PFC concentration in the river based on Eq. (1) while considering the river connection from upstream to downstream to account for dilution and mixing of PFCs. The processes of loss/generation of PFCs by physical and chemical effects such as degradation and sedimentation were not considered in the calculation. The calculated PFC concentration was assumed to represent only nonpoint sources. Conversely, the measured PFC concentrations in a river consist of contributions from both point and nonpoint sources. Therefore, the point source contribution can be estimated by subtracting the calculated PFC concentration (caused by the nonpoint sources) from the measured PFC concentration (caused by the point and nonpoint sources). The source apportionment between point and nonpoint sources in the rivers of the Tokyo Bay basin was conducted based on this calculation. 3. Results & discussion 3.1. The results of GIS-based source identification The pollution factors and their estimated parameters were obtained by multiple linear regression analysis. Among the 35 PFC homologues measured in a survey of the Tokyo Bay basin (Zushi et al., 2011), only 18 (PFHxA, PFHpA, PFOA, PFNA, PFDA, PFUnDA, PFOAisomer, PFNAisomer, PFDAisomer, PFUnDAisomer, PFPeS, PFHxS, PFOS, PFOSisomer1, PFOSisomer2 FOSA, NMeFOSAA, and NEtFOSAA; see full names in Table S-1) were analyzed. Other homologues were below the LOQ for more than 55% of their samples. The dissolved organic carbon (DOC), suspended solid (SS), and electrical conductivity (EC) were also analyzed for comparison to the results of the PFC homologues. The results of the PFOS analysis are shown in Fig. 3. Pollution by PFOS was explained well by the percentage of arterial traffic area in the basin area, and 84% of the variance of the measured PFOS concentration was explained by two geographic variables (arterial traffic area and population). Statistically high measurements of PFC concentration were omitted from the analysis, as they were considered to be a result of the high contribution of point sources. Three outliers were identified during the PFOS analysis. Those

samples were obtained in the down-stream stretches of rivers with basins that included the training field of a self-defence force, numerous facilities using electrical equipment, and an improper landfill site (see Table S-4 for details). Outlying data points identified during analysis were suggestive of the presence of point sources in a geographical location. The results obtained during analysis for PFOA, PFNA, PFOS, DOC, SS, and EC are shown in Table 1 (see Table S-5 for all measurements in this study). Among the 18 homologues analyzed, R2-ad values (adjusted for the degrees of freedom) for PFOAisomer, PFDAisomer, PFUnDAisomer, PFPeS, and NMeFOSAA were low (<0.5). PFPeS and NMeFOSAA were the lowest among the 18 homologues with respect to the number of >LOQ samples (52% and 48% of all samples respectively). These findings show that the variation in the measured concentrations of PFCs with the small number of >LOQ samples could not be explained well by the analysis. A rapid increase in DOC concentration in river water fed by rain runoff was observed (Zushi and Masunaga, 2009a), which indicated that a nonpoint source of DOC also exists. In addition, a rapid increase in SS owing to the washout of nonpoint sources during the rainy period was reported (Furumai et al., 2002). Therefore, DOC and SS can be considered indicators of nonpoint source type pollution. However, a high correlation between geographical indices and the concentration of DOC and SS was not observed in this study (Table 1). Moreover, no relationship between the concentration of SS and percentage of urbanized area was observed (Brett

Table 1 The results of the multiple linear regression model.*

PFOA

PFNA

b1

b2

b3

b0

R2ad

>LOQ (%)

Area except forest and waste landa 1.3b 0.91c

–

–

Constant

0.84

98

– –

– –

2.2b –

Area except forest and waste landa

Catchment area of sewage wastewatera 0.15b 0.22c

–

Constant

0.68

94

– –

4.4b –

Arterial traffic areaa 0.83b 0.51c

Populationa

–

Constant

0.84

94

0.42 0.46c

– –

1.6 –

Arterial traffic areaa 0.75b 0.3c

–

–

Constant

0.07

100

– –

– –

2.0b –

Area except forest and waste landa

Number of waste disposal sitesa 0.34b 0.44c

Agricultural areaa

Constant

0.39

98

0.24b 0.32c

1.2b –

–

–

Constant

0.22

92

– –

– –

8.0b –

1.4b 0.77c PFOS

DOC

SS

0.56b 0.55c EC

* a

Fig. 3. Concentration predicted by the regression model and the measured concentration of PFOS. R2-ad: R2 adjusted for the degrees of freedom.

b c

Buffer area of train station (50 m in radius)a 0.39b 0.48c

b

b

Variables are the percentage of the total basin area (or the number in unit area). Selected variable. Partial regression coefficient. Standardized partial regression coefficient.

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Fig. 4. Map of pollution potential for various PFCs. Map representing the spatial distribution of PFCconc formed by pollution factors. Dark color indicates the relatively high density of pollution factors for each PFC in the Tokyo Bay basin. The concentration range was 0.1–5.0 ng L1 for PFHxA, 0–3.3 ng L1 for PFHpA, 0.1–10.0 ng L1 for PFOA, 0.1– 110.0 ng L1 for PFNA, 0–2.4 ng L1 for PFDA, 0–4.4 ng L1 for PFUnDA, 0.0–18.0 for PFHxS, 0–60.0 for PFOS, 0–0.33 for PFOSisomer1, 0–13.2 ng L1 for PFOSisomer2, 0.01– 1.4 ng L1 for FOSA and 0–1.0 ng L1 for NEtFOSAA.

et al., 2005). Conversely, there was a relationship between both total phosphorus (TP) and soluble reactive phosphorus (SRP), which are contaminants from nonpoint sources, and the percentage of urbanized area in that study. Therefore, we should clarify the two types of nonpoint source pollutants; one is emitted continuously from households or other sources and has high soil penetrability because of its solubility in water and its continuous access to aqueous media, while the other is residual on land surfaces and occasionally washes out during surface runoff events. The source of the latter type could not be characterized by this GIS approach, because the data were collected under dry conditions. Soil penetrability of PFOS/FOSA (Murakami et al., 2008b) and groundwater pollution by PFCs due to their soil penetrability (Murakami et al.,

2009a) have been reported. In addition, PFCs have been used in household objects as mentioned in the introduction and continuously emitted from households. Therefore, PFCs are considered to be pollutants with continuous access to water media independent of weather conditions, and a nonpoint source of PFC was identified by this GIS approach. Conversely, the source of SS and DOC, which drained during runoff events, could not be identified. For successful analysis by GIS-based source identification, discriminating between the behavior characteristics of pollutants is important. The behavior of most branched isomers of perfluorocarboxylates (PFCAs) could not be explained well by the geographic indices (see Table S-5 for detailed results). These findings indicated that emanation from specific land surfaces did not contribute greatly

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to the pollution of branched isomers of PFCAs. The wide distribution of branched isomers of PFCAs transported through the atmosphere as branched isomers of volatile precursors (FTOHs) has been suggested (Zushi et al., 2011). Such unique dynamics and sources might cause difficulty in predicting the sources of branched PFCAs isomers by the GIS-based approach. Other detailed information regarding the behavior and source of branched isomers of PFCAs has been reported (Furdui et al., 2008; Zushi et al., 2010). Regression models with a high R2-ad were obtained for PFCAs, PFSAs, branched isomers of PFOS and precursors of PFOS. The percentage of arterial traffic area was selected for PFOS, its isomers, NEtFOSAA and FOSA (PFOS-related compounds), as the most dominant variable. Thus, traffic-related activities and construction likely contributed to the PFOS related pollution in the study area. In a previous study, higher concentrations of PFOS were detected from the fine fraction of road dust collected from heavily travelled areas when compared with residential areas, and emanation of PFOS from road surfaces was suggested (Murakami and Takada, 2008). However, the higher concentrations were not only observed for PFOS, but also for other PFCs, such as PFOA, PFNA, and PFDA in their study. Thus, these findings do not support our suggestion that arterial traffic areas should be a source of only PFOS and its related compounds. A large amount of PFOS containing fire fighting foams (148.8 ton of PFOS) has been stocked for possible fires in airports, industrial complexes, and parking lots in Japan (NITE, 2009). Therefore, continued emanation of PFOS from the stockpile in parking lots could explain why PFOS and its related compounds were identified as the most dominant pollution factor in arterial traffic areas. Further research to identify the source of PFOS and its related compounds based on the result of this analysis is necessary. The percentage of area except forest and waste land) was chosen as a variable for PFCAs. This variable can be regarded as an indicator of the uniform contribution of artificial land use to pollution. This variable was only selected for PFOA and PFHpA, which suggests that nonpoint sources of these homologues were more widely distributed than those of other homologues. In the case of PFNA, the percentage of the catchment area of sewage water for each treatment plant was also selected as an indicator to represent the impact of STP effluent. Thus, STP effluent was shown to contribute to PFNA pollution. These findings were consistent with the results of a survey of the PFC pollution in the Tokyo Bay basin, which showed a high level of PFCAs, especially PFNA, in STP effluents when compared with the levels found in river water (Zushi et al., 2011). Population density, which was used as a simple indicator of distributed sources of PFC in previous studies (Murakami et al., 2008a; Pistocchi and Loos, 2009), was not selected as a primary factor for most PFCs. Therefore, we suggest that exploring and identifying sources by the GIS-based approach is very important for the highly accurate prediction of PFC pollution by distributed sources. 3.2. Spatial distribution of pollution factors A map representing the spatial distribution of the PFCconc formed by pollution factors (i.e., pollution potential) from nonpoint sources that was generated from the regression model is shown in Fig. 4. The map for PFOS is also shown in Fig. S-3. The spatial trend of PFC pollution potential is obvious from this visual expression. The pollution potential of PFOS, branched isomers of PFOS, and precursors of PFOS were high in the center of Tokyo city. Conversely, the pollution potential of PFHpA and PFOA was more spatially uniform. The pollution potential of other PFCAs also tended to be spatially uniform when compared with PFOS and its related compounds. Fractionally high PFNA values were distinctively ob-

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Fig. 5. Source apportionment between point and nonpoint sources of PFOS in major rivers in the Tokyo Bay basin. The loadings from point sources were estimated by subtracting the calculated PFC concentration caused by the nonpoint sources from the measured PFC concentration. Error bars show the range of standard deviations derived from the prediction by the model. The deviation in measured value was not included because only one time measurement was conducted.

served in the map. The concentrations of PFNA varied much more discretely, with high concentrations occurring in watersheds that contained STPs. Thus, the high measured levels of PFNA are a result of the high contribution of PFNA from STP effluent. Overall, the distribution of the pollution potential of PFCAs was spatially uniform when compared with PFOS and its related compounds. As previously mentioned, an advantage of the GIS-based approach is the identification of spatially distributed pollution factors. Additionally, visual expression of the spatial distribution of pollution factors could be a major advantage in comparative identification of the sources of several pollutants. 3.3. Contribution of point and nonpoint sources to PFC pollution We conducted source apportionment between point and nonpoint sources using the results calculated from the regression model. The results for PFOS are shown in Fig. 5, and the results for other PFCs are shown in Fig. S-3. These six major rivers flowing into Tokyo Bay comprise the bulk of the water flow into the bay. The contribution of PFOS loadings from nonpoint sources was comparable to that from point sources in the basins of the Naka River, Edo River, and Tsurumi River. In the remaining three river basins (Tama, Sumida and Ara Rivers), PFOS loadings from point sources were 5 to 7 times higher than non point sources. The error bars for the point source loadings of PFOS shown in Fig. 5 do not include the deviations of the measured values since no repetitive measurements were recorded. Thus, a larger fluctuation in the results for PFOS loading from point sources would be expected. Moreover, when the input of PFOS by rain runoff (Zushi and Masunaga, 2009a) is taken into account, the contribution of nonpoint sources should be larger than the results shown in Fig. 5. Although calculation of the PFC input by rain runoff is difficult, it is an important issue for understanding the impact from nonpoint sources, and several assumptions were used for the estimation of PFC loading. The GIS-based approach allows calculation of the impact of nonpoint sources in the steady state. This case study shows that GIS-based source identification and apportionment is effective, especially for ubiquitous pollution such as nonpoint source pollution. Accordingly, it can be further used as an effective approach for the source identification and apportionment of spatially distributed water-soluble pollutants. Acknowledgments The data used in this study were obtained from a survey conducted in collaboration with the Center for Environmental Science

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in Saitama (Dr. Mamoru Motegi, Dr. Kiyoshi Nojiri, and Dr. Sigeo Hosono) and the Tokyo Metropolitan Institute of Public Health (Dr. Toshinari Suzuki, Ms. Yuki Kosugi, and Dr. Kumiko Yaguchi). We also thank laboratory members in Yokohama National University for their help in the sampling campaign. We thank Dr. Takehiko Hayashi and Dr. Kyoko Ono for their valuable advice regarding this paper. This study was supported by a JSPS Research Fellowships for Young Scientists Grant (ID: 213467) and in part by the River Fund in charge of the Foundation of River and Watershed Environment Management (FOREM), Japan. Appendix A. Supplementary material Supplementary data describing the sampling location, target PFCs and their acronyms, GIS database, and the results of regression analysis (Figs. S-1 to S-2 and Tables S-1 to S-3). Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.chemosphere.2011.07.052. References 3M, 2000. Phase-out Plan for POSF-based Products. US EPA Public Docket AR2260600. Adams, J., Houde, M., Muir, D., Speakman, T., Bossart, G., Fair, P., 2008. Land use and the spatial distribution of perfluoroalkyl compounds as measured in the plasma of bottlenose dolphins (Tursiops truncatus). Mar. Environ. Res. 66, 430–437. Brett, M.T., Arhonditsis, G.B., Mueller, S.E., Hartley, D.M., Frodge, J.D., Funke, D.E., 2005. Non-point-source impacts on stream nutrient concentrations along a forest to urban gradient. Environ. Manage. 35, 330–342. Chen, W., Hertl, P., Chen, S., Tierney, D., 2002. A pesticide surface water mobility index and its relationship with concentrations in agricultural drainage watersheds. Environ. Toxicol. Chem. 21, 298–308. EU Directive, 2006. Directive 2006/122/EC of the European Parliament and of the Council. . Furdui, V.I., Helm, P.A., Crozier, P.W., Lucaciu, C., Reiner, E.J., Marvin, C.H., Whittle, D.M., Mabury, S.A., Tomy, G.T., 2008. Temporal trends of perfluoroalkyl compounds with isomer analysis in lake trout from Lake Ontario (1979– 2004). Environ. Sci. Technol. 42, 4739–4744. Furumai, H., Balmer, H., Boller, M., 2002. Dynamic behavior of suspended pollutants and particle size distribution in highway runoff. Water Sci. Technol. 46, 413– 418. Kim, S.K., Kannan, K., 2007. Perfluorinated acids in air, rain, snow, surface runoff, and lakes: relative importance of pathways to contamination of urban lakes. Environ. Sci. Technol. 41, 8328–8334.

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