assessment of heavy metals contamination in soil: the
ASSESSMENT OF HEAVY METALS CONTAMINATION IN SOIL: THE IMPACT OF MSWI W.C. MA*, L.Y. TAI*, Z. WANG*, K.X. FU*, L. ZHONG*, Z. QIAO*, G.Y. CHEN* **, B.B.YAN*, Z.J.CHENG* *School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China ** School of Science, Tibet University, Lhasa 850000, Tibet, China Corresponding author: [email protected] (Guanyi Chen); [email protected] (Lei Zhong) SUMMARY: There are few studies considering the pollution level assessment and
source identify of soil heavy metals in the vicinity of municipal solid waste incineration (MSWI) in Tianjin, northeast of China. In this study, 9 elements in total 128 surface and sub-surface soil samples were collected and analyzed by FAAS and AFS. The single pollution index, integrated Nemerow pollution index and enrichment factor were used to quantify the pollution levels of soil metals. The principal component analysis (PCA) was applied to identify the potential sources. The results showed that the soils around the MSWI were lightly polluted by Cu, Pb, Zn, and Hg and heavily polluted by As, and Cd. The overall level of soil heavy metals contamination in study area is seriously high. PCA and correlation analysis indicated that the MSWI, natural source, industrial discharges and coal combustion were the four main potential sources of soil heavy metals. The MSWI had a significant influence on Pb, Cu, Zn and Cd. Keywords: Municipal Solid Waste Incineration (MSWI), Heavy Metals, Principle Component Analysis (PCA), Soil
1. INTRODUCTION Along with the rapid economic growth, the amount of municipal solid waste (MSW) increased from 164 million tons (2011) to 191 million tons (2015). Waste disposal became a crucial part of the social sustainable development (Li et al., 2015; Li, Wan, Ben, Fan, & Hu, 2017). Incineration is an extremely important waste disposal method in China and the capacity of 61.8 million tons in 2015 was more than double of that in 2011. Although incineration can significantly reduce the waste mass and volume, the potential toxic elements emitted from MSWIs cannot be ignored. Heavy metals present in MSW evaporate through the combustion process and mainly transferred to the fly ash and flue gas (Weibel, Eggenberger, Schlumberger, & Mader, 2017). Then heavy metals in the fly ash and flue gas could emit into the atmosphere from chimney or
Proceedings Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium/ 2 - 6 October 2017 S. Margherita di Pula, Cagliari, Italy / © 2017 by CISA Publisher, Italy
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
as fugitive emissions and eventually accumulated in soil by wet and dry deposition(Rimmer et al., 2006). Heavy metals in soils can destroy the ecological system and threaten human health (Tepanosyan, Maghakyan, Sahakyan, & Saghatelyan, 2017). Although many studies focused on the soil heavy metals contamination in the vicinity of MSWI, the effect of MSWI on the soil heavy metals concentrations remain debatable. In one study, the soil heavy metals concentrations were irrelevant to the distance apart the MSWI in Catalonia and did not show temporal variations (Rovira, Vilavert, Nadal, Schuhmacher, & Domingo, 2015). Similarly, no evidence for increased heavy metals pollution due to MSWI emissions can be found in a study in Newcastle(Rimmer et al., 2006). The soil Hg concentrations in the vicinity of MSWI in south China was not significantly different from control area (Deng et al., 2016). In contrast, the heavy metals concentrations were relatively high in the downwind zone indicating an effect by MSWI in Beijing (Han et al., 2016). Li et al found that the concentrations of Pb, Cr and Mn in soil decreased with the increasing distance from MSWI in Shenzhen, China(Li et al., 2017). Therefore, whether the MSWI had significant influence on the heavy metals contamination in surrounding soils needs further investigation. Tianjin is one of the most populous cities in China and the soil heavy metals concentrations background values of Cr, Cu, Ni, Zn and Hg in Tianjin are higher than the mean values in China(Wei, Chen, Wan, & Zheng, 1991). It was more likely to get soil heavy metals contamination. There was a MSWI located in the high population density area in Tianjin which was very near to a primary school and residential area. Therefore, it is urgent to investigate the influences of MSWI to the soil heavy metals contaminations. The present study investigated an MSWI in Tianjin, northeastern China. The goals of this study were (1) to quantify the pollution levels of soil heavy metals using the single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the enrichment factor (EF) ; (2) to identified the potential sources by principal component analysis (PCA); (3) to provide a reference for the environmental governance. 2. MATIERIALS AND METHODS 2.1 Study area The research MSWI is located in Tianjin (116°43′- 118°04′E and 38°34′- 40°15′N), Northeastern China (Fig. 1), which belongs to the warm temperate zone, with a continental monsoon climate. The prevalent wind is southwest and the average annual temperature and precipitation are 11.9℃ and 556 mm. Light expanded clay and silty clay are dominant soils. The MSWI began operating in 2005 with a capacity of 1200 tons per day. The flue gas treatment system in MSWI consisted of a semi-dry scrubber, an activated carbon adsorption unit, a bag filter and an 80-m-high stack.
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Fig.1. Locations of the soil sampling sites around the MSWI and wind rose map in Tianjin 2.2 Sample collection A total of 128 surface soil samples (0-20cm) and sub-surface soil samples (20-40cm) from 64 sampling sites within 3km around the MSWI were collected in December 2016. The study area was divided into four sectors (NW, NE, SW, SE), because of the dominant southwest wind direction. Each sector consisted of four distance bands (0-0.5km, 0.5-1km, 1-2km, 2-3km) and four sampling sites were distributed in each band (Fig. 1). The coordinates of sampling sites were recorded by GPS. Each sample (more than 1 kg dry weight) was composed of 3 subsamples obtained by stainless steel hand auger. The soil samples were sealed in PVC bags, then brought back to the lab. One fly ash sample from the MSWI was also collected in December 2016 to examine the relationship between heavy metals concentrations in soil and MSWI. 2.3 Sample treatment and analysis After air-dried, ground and sieved through a 0.15 mm mesh, the soil samples (approximately 0.5g) were digested with a 8 ml solution of 5:2:1 HNO3 :HF :H2O2 (v/v) by microwave digestion method. The concentrations of Cr, Pb, Cu, Ni, Zn, Cd and Fe were measured by a flame atomic absorption spectrophotometer (FAAS-Z-5000, HITACHI). Hg and As were determined by Atomic fluorescence spectrophotometer (AFS-8220, TITAN). For quality assurance (QA) and quality control (QC), national standard soil samples (GBW07428; Center for Certified Reference Materials, China), blank control and duplicate samples were employed. The recoveries rates were within the range of 90%-110% and the relative standard deviations (RSD) were less than 3%. 2.4 Evaluation of heavy metal contamination in soils To quantify the heavy metal contamination risk, the single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the enrichment factor (EF) were applied in this study. The single pollution index (PIi) is defined as: , where Ci is the detected concentration of heavy metal (i), and Si is the reference values. In this study the average background values of soils in Tianjin were applied as reference values. The PIi was classified as non-pollution (PIi ≤1),
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light pollution (1< PIi ≤2), moderate pollution (2< PIi ≤3), or heavy pollution (PIi >3). The integrated Nemerow pollution index (PIN) can be obtained using the equation: , where MaxPIi and AvePIi are the maximum and average value of the single pollution indexes. According to the PIN, the soil quality was classified as safety (PIN ≤ 0.7), warning (0.7 < PIN ≤ 1), light pollution (1 < PIN ≤ 2), moderate pollution (2 < PIN ≤ 3), and heavy pollution (PIN > 3)(Zhu, Guo, Xiao, Chen, & Yang, 2017). The enrichment factor (EF) is expressed as follows: , where Cref is the concentration of reference element for normalization(Chen et al., 2016). Fe was employed as reference element because of the high concentration in soil(Rodriguez-SeijoLuisa Andrade & Vega, 2017). The soils were classified as no enrichment (EF < 2), moderate enrichment (2 ≤ EF < 5), severe enrichment (5≤ EF < 20), very severe enrichment (20≤ EF < 40) and extremely severe enrichment (EF ≥ 40). 2.5 Multivariate statistical analysis The correlation coefficient could be used to measure the intensity of inter-relationship between the heavy metals. Heavy metals with high correlation coefficient are probably have common origin or similar pollution sources(Li et al., 2013). Principal component analysis (PCA) is a mathematical process using an orthogonal transformation to convert the possibly correlated variables into linearly uncorrelated variables. In the present study, correlation analysis and principal component analysis (PCA) were applied to identify the sources of heavy metal contamination found in soil using SPSS 21.0 software. 3. RESULTS AND DISCUSSION 3.1 General characteristic of heavy metal concentrations The mean concentrations of Cr, Pb, Cu, Ni, Zn, Cd, Hg, and As were 76.35, 26.74, 30.49, 31.82, 120.16, 0.38, 0.14, and 31.21 mg·kg-1, respectively. Besides Cr and Ni, the mean concentrations of rest of 6 heavy metals (Pb, Cu, Zn, Cd, Hg, and As) exceeded the background values for Tianjin (CNEMC, 1990). When compared with the limit value of Grade Ⅱ environmental quality standard for soil in China (GB15618-1995)(CEPA, 1995), approximately 3.1%, 4.7%, 3.1%, and 48.4% of soil samples were high of Zn, Cd, Hg, and As, respectively. Therefore, the soils of study area were polluted by heavy metals in various degrees. Zn, Cd, Hg, and As were in a relatively highly contaminated level. The concentrations of Zn, Cd, Hg, and As showed greater spatial heterogeneity than other heavy metals. In comparison, Ni was quite homogeneous in study area, with the coefficients of variation (CV) of 0.21, which is quite low indicating that the heavy metals were dominated by natural source. Conversely, heavy metals with high CV values were more likely to be affected by anthropogenic sources(Han, Du, Cao, & Posmentier, 2006; Zhu et al., 2017). According to that, Zn, Cd, Hg, and As tend to be affected by anthropogenic activities (Table 1), while Ni mainly originated from natural source. Comparison of the heavy metals concentrations in fly ash with the soils in the study area revealed that the concentrations of Pb, Cu, Zn, and Cd in fly ash were 15, 10, 33, 192 times higher than soils, while the concentrations of Cr, Ni, Hg, and As in fly
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ash were no more than 1.5 times higher than soil. The soils in study area are more likely to get significant input of Pb, Cu, Zn, and Cd from MSWI.
II)
Table 1 Summary statistics of heavy metals concentrations of surface soils around MSWI (mg·kg-1) Cr Pb Cu Ni Zn Cd Hg Arithmetic mean 76.35 26.74 30.49 31.82 120.16 0.38 0.14 Standard deviation 24.98 9.00 7.14 6.83 42.73 0.13 0.22 Coefficient of variance (CV) 0.33 0.34 0.23 0.21 0.36 0.35 1.52 Fly ash of MSWI 93.26 428.87 335.72 33.48 4140.44 73.56 0.32 Background of Tianjina 84.00 21.00 29.00 33.00 79.00 0.09 0.08 Chinese soil guidelines (Grade 200.00 300.00 200.00 50.00 250.00 0.60 0.50 b
As 31.21 18.57 0.60 44.33 9.60 30.00
a
CNEMC (China National Environmental Monitoring Center), The Backgrounds of Soil Environment in China, Environment Science Press of China, 1990. b Chinese Environmental Quality Standard for Soils. State Environmental Protection Administration of China, 1995. 3.2 Evaluation of the contamination levels The single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the enrichment factor (EF) were applied in this study. The mean values of PIi of each heavy metal increase in the order of Cr (0.91) < Ni (0.96) < Cu (1.1) < Pb (1.3) < Zn (1.5) < Hg (1.7) < As (3.3) < Cd (4.2). The study area performed non-pollution by Cr, and Ni, light pollution by Cu, Pb, Zn, and Hg, heavy pollution by As, and Cd. The proportion of PIi of soil samples for each metal is shown in Fig. 2. According to the integrated Nemerow pollution index (PIN), 7.81% of soil samples are lightly polluted, 23.44% are moderately polluted, 68.75% are heavily polluted. The mean value of PIN is 3.73, which indicated that the overall level of heavy metals contamination is seriously high. To estimate the degree of anthropogenic influence on heavy metals contaminations in soil and mask the interference of natural procedure, the enrichment factor (EF) of heavy metals in soils was calculated. The mean EF values decrease in the order of Hg (1.68) > As (1.20) > Cd (1.19) > Zn (1.08) > Cr (0.98) > Pb (0.89) > Cu (0.87) > Ni (0.81). The mean EF values of Hg, As, Cd, and Zn were greater than 1, implying anthropogenic influences. Approximately 20.3% EF values for Hg, 14.1% for Cd, and 12.5% for As exceed 2, indicating that the soils around MSWI were moderately polluted by Hg, Cd and As. It is noteworthy that the EF value of Cr (which is non-pollution identified by PIi) is higher than Pb and Cu (which is light pollution identified by PIi). Furthermore, approximately 6.3% EF values of Cr lay between 2-5, suggesting that the soils in the study area were lightly polluted by Cr. Therefore, the two pollution indices PIi and EF can complement each other in estimating soils heavy metals contamination level.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Proportion of different pollution levels (%)
100
80
60
40
20
0
Cr
Pb
Cu
Ni
heavy pollution light pollution
Zn
Cd
Hg
As
moderate pollution non-pollution
Fig. 2. Proportion of different pollution levels for heavy metals identified by PIs 3.3 Spatial distribution of heavy metals in surface soils Considering the differences of heavy metals concentrations from distances, only Ni, Cd, Hg, As (NE); Cr, Cu, Ni, Cd (NW), Cr (SE) and Cr, Pb, Cu, Ni (SW) showed significant differences in distances (p < 0.1) (Table S1). As shown in Table S2, most of these heavy metals have maximum concentrations in the distance of 500m to 1000m from their source point. This phenomenon accords with the dilution and dispersion regularities of point source pollution. It indicated that MSWI might have obvious influence on soil heavy metals contamination. For other heavy metals concentrations which did not show significant difference or did not decrease with the increasing distances, it might be due to the effect of MSWI which was masked by other contamination sources. Bretzel & Calderisi (2011) and Han et al. (2016) (Bretzel & Calderisi, 2011; Han et al., 2016)also found that the interferences of anthropogenic activity could mask the influence of MSWI. As for the directions, it was expected that heavy metals concentrations were highest in NE part and lowest in SW part, because of the dominant prevailing southwest wind direction (Han et al., 2016; Rimmer et al., 2006). However, there was no maximum concentration located in NE part or minimum concentrations located in SW part. Most heavy metals concentrations (Cr, Pb, Cu, Ni, and Hg) were highest in SW part, and the concentrations of Cr, Pb, Cu, Ni, and As were lowest in NW part (Table 2). It may be attributed to the different land use. There are many industrial factories located in SW part of MSWI, while the NW part mainly consists of residential area and schools. Industrial factories (automotive production, printing plant, electronic equipment production) could release more heavy metals pollutant to soil than residential area and schools. Furthermore, the frequency of prevailing wind direction was not extremely significantly higher than that of wind direction (Fig. 1). It may cause homogeneity in the effect of MSWI on the soils heavy metals concentrations in different directions rather than greater effect on downwind area of dominant wind direction. According to the distributions of heavy metals in different distances and directions, the MSWI has the potential effect on the contamination in soil, but partially masked by natural sources and
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other anthropogenic activities like industrial production, traffic emission, the utilization of pesticides and fertilizers and so on. Table 2 Average concentrations of heavy metals in soils at different directions (mg· kg–1) direction Cr Pb Cu Ni Zn Cd NE 67.30 26.63 32.04 33.89 121.06 0.32 NW 65.02 24.30 26.38 25.12 125.68 0.47 SE 86.00 27.92 31.23 34.06 113.26 0.34 SW 87.09 28.09 32.32 34.23 120.67 0.38
Hg 0.14 0.13 0.12 0.19
As 32.21 27.98 35.51 29.14
3.4 Correlation coefficient and principal component analysis The Pearson’s correlation coefficients for heavy metals in surface soils are presented in Table 3. Inter-element relationships could provide many information on the sources and pathways of heavy metals(Zhu et al., 2017). Generally, the high positive correlations among heavy metals indicate that they significantly share similar origins or geochemical characteristics, and the marker elements can make contribution to sources identification (Zhong et al., 2016). Cr, Pb, Cu, and Ni were positively correlated with each other at P < 0.01. Because the coefficient of variance (CV) values of Cr, Pb, Cu, and Ni are relatively low and Cr and Ni are in non-pollution level identified by PIi values, they may mainly originate from natural source. Zn is significantly positively correlated with Pb, Cu, and Cd. As the soils in study area were heavily polluted by Cd and lightly polluted by Zn, Pb, and Cu, these four heavy metals may have been influenced by the same anthropogenic activities. Hg and As were not correlated with other heavy metals, hence it is assumed that they may have exclusive sources. PCA was adopted to identify the sources of heavy metals in soils. The results of KMO (0.685) and Bartlett’s test (P < 0.001) indicated a good fit for PCA. The results of the rotated component matrix of heavy metals concentrations in soils are shown in Table 4. In this study, four principal components were extracted which explain 84.143% of the total variance. Component PC1 explains 29.179% of the total variance and loads heavily on Pb, Cu, Zn, and Cd. Component PC2, dominated by Cr, Pb, Cu, and Ni, accounts for 28.552% of the total variance. Component PC3 dominated by As and Component PC4 dominated by Hg account for 13.382% and 13.031% of the total variance, respectively. Compared with the background values of Tianjin soil, the concentrations of Ni, Cr, and Cu were lower or approximately equal to their corresponding values, indicating that they may originate from natural source, while the significantly higher concentrations of Pb, Zn, Cd, Hg, and As suggest anthropogenic sources. Meanwhile, the concentrations of Pb, Cu, Zn, and Cd in fly ash are much higher than soils and the spatial distribution of these metals in soil accords with the deposition regularities of flue gas from MSWI, so these metals may be significantly influenced by MSWI and Component PC1 was the MSWI. According to the previous reports, Ni and Cr were dominantly present at natural background concentrations, controlled by the weathering of the parent rock material and pedogenesis (Li et al., 2013; Ma, Chen, Li, Bi, & Huang, 2016; Zhao, Xu, Hou, Shangguan, & Li, 2014). Combined with the low PIi value of Ni and Cr, they are considered mainly came from natural source. Component PC2 was regarded as natural source. Pb and Cu had elevated loading in PC1 and moderate loading in PC2. This suggests that Pb and Cu might be joint controlled by MSWI and natural source. According to previous studies, high As concentration in soil might be associated with
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anthropogenic wastes and industrial activities, including sewage sludge and industrial discharges (Charlesworth, Everett, McCarthy, Ord Ez, & de Miguel, 2003; Zhao et al., 2014). Discharges from industrial factories in the study area, such as glass plants, perfume factories, pharmaceutical factories, and wood conversion factories, could contribute to As pollution. The Component PC3 was industrial discharges. Hg was uncorrelated with other metals, indicating that it might have different source from other heavy metals and different distribution mechanism. Hg in the surface soil can be released into the air and exchanged between the air and soil, causing it to be transported over long distances (Zhu et al., 2017). There was a coal-fired city central heating company located in the study area. The consumption of coal in heating company and the industrial factories was thought as the main source of Hg contamination. The Component PC4 was coal combustion. Table 3 Pearson’s correlations matrix for the heavy metals concentrations. Cr Pb Cu Ni Zn Cd Hg As Cr 0.005 0.000 0.000 0.806 0.150 0.725 0.398 Pb 0.350** 0.000 0.003 0.000 0.023 0.120 0.287 Cu 0.451** 0.631** 0.000 0.000 0.266 0.203 0.293 Ni 0.659** 0.370** 0.543** 0.822 0.001 0.980 0.782 Zn 0.031 0.543** 0.594** -0.029 0.000 0.310 0.198 Cd 0.182 0.284* 0.141 -0.400** 0.584** 0.098 0.049 Hg 0.045 0.196 0.161 0.003 0.129 0.209 0.712 As 0.107 0.135 0.133 -0.035 -0.163 -0.247* 0.047 The left lower part is correlation coefficient; the right upper part is significant level. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed). Table 4 Rotated component matrix for soil heavy metals (PCA loadings > 0.4 are shown in bold). PC1 PC2 PC3 PC4 Cr 0.08 0.832 -0.13 0.061 Pb 0.733 0.401 -0.031 0.133 Cu 0.701 0.571 0 0.052 Ni 0.046 0.925 0.042 -0.032 Zn 0.923 -0.08 -0.064 0.004 Cd 0.652 -0.489 -0.306 0.208 Hg 0.107 0.023 0.037 0.986 As -0.095 -0.067 0.976 0.042 Eigenvalue (>1) 2.334 2.284 1.071 1.042 % of variance 29.179 28.552 13.382 13.031 Cumulative % 29.179 57.731 71.112 84.143 Extraction method: principal component analysis.
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4 CONCLUSIONS This study investigated the soil heavy metals contamination in the vicinity of MSWI in order to quantify the pollution levels and identify their potential sources. The results showed that the soils around MSWI were lightly polluted by Cu, Pb, Zn, and Hg and heavily polluted by As, and Cd identified by the single pollution index (PIi). The overall level of soil heavy metals contamination in the study area is seriously high (PIN > 3). PCA and correlation analysis indicated that the MSWI, natural source, industrial discharges and coal combustion were the four main potential sources of soil heavy metals. The MSWI had a significant influence on Pb, Cu, Zn and Cd. More attention should be paid to the contamination of Pb, Cu, Zn and Cd in soil around the MSWI. ACKNOWLEDGEMENT The authors would like to thank the National Science and Technology Support Program of China (2014BAC02B03), and High Technology Support Project of Tianjin (16PTGCCX00170, 16YFXTSF00540), Tianjin Research Program of Application and Advanced Technology (15JCQNJC06600). REFERENCES Bretzel, F. C., & Calderisi, M. (2011). Contribution of a municipal solid waste incinerator to the trace metals in the surrounding soil. ENVIRONMENTAL MONITORING AND ASSESSMENT, 182(1-4), 523-533. CEPA. (1995). Environmental Quality Standard for Soils (GB15618-1995). In C. E. P. Administration (Ed.). Charlesworth, S., Everett, M., McCarthy, R., Ord Ez, A., & de Miguel, E. (2003). A comparative study of heavy metal concentration and distribution in deposited street dusts in a large and a small urban area: Birmingham and Coventry, West Midlands, UK. Environment International, 29(5), 563-573. Chen, H., Teng, Y., Lu, S., Wang, Y., Wu, J., & Wang, J. (2016). Source apportionment and health risk assessment of trace metals in surface soils of Beijing metropolitan, China. Chemosphere, 144, 1002-1011. CNEMC. (1990). The Background Values of Elements in Chinese Soils: Environmental Science Press of China. Deng, C., Xie, H., Ye, X., Zhang, H., Liu, M., & Tong, Y., et al. (2016). Mercury risk assessment combining internal and external exposure methods for a population living near a municipal solid waste incinerator. ENVIRONMENTAL POLLUTION, 219, 1060-1068. Han, Y. M., Du, P. X., Cao, J. J., & Posmentier, E. S. (2006). Multivariate analysis of heavy metal contamination in urban dusts of Xi'an, Central China. SCIENCE OF THE TOTAL ENVIRONMENT, 355(1-3), 176-186. Han, Y., Xie, H., Liu, W., Li, H., Wang, M., & Chen, X., et al. (2016). Assessment of pollution of potentially harmful elements in soils surrounding a municipal solid waste incinerator, China. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING, 10(76). Li, N., Kang, Y., Pan, W., Zeng, L., Zhang, Q., & Luo, J. (2015). Concentration and transportation of heavy metals in vegetables and risk assessment of human exposure to bioaccessible heavy metals in soil near a waste-incinerator site, South China. SCIENCE OF
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THE TOTAL ENVIRONMENT, 521, 144-151. Li, T., Wan, Y., Ben, Y., Fan, S., & Hu, J. (2017). Relative importance of different exposure routes of heavy metals for humans living near a municipal solid waste incinerator. Environ Pollut, 226, 385-393. Li, X., Liu, L., Wang, Y., Luo, G., Chen, X., & Yang, X., et al. (2013). Heavy metal contamination of urban soil in an old industrial city (Shenyang) in Northeast China. GEODERMA, 192, 50-58. Ma, Z., Chen, K., Li, Z., Bi, J., & Huang, L. (2016). Heavy metals in soils and road dusts in the mining areas of Western Suzhou, China: a preliminary identification of contaminated sites. JOURNAL OF SOILS AND SEDIMENTS, 16(1), 204-214. Rimmer, D. L., Vizard, C. G., Pless-Mulloli, T., Singleton, I., Air, V. S., & Keatinge, Z. (2006). Metal contamination of urban soils in the vicinity of a municipal waste incinerator: One source among many. SCIENCE OF THE TOTAL ENVIRONMENT, 356(1-3), 207-216. Rodriguez-Seijo, A., Luisa Andrade, M., & Vega, F. A. (2017). Origin and spatial distribution of metals in urban soils. JOURNAL OF SOILS AND SEDIMENTS, 17(5), 1514-1526. Rovira, J., Vilavert, L., Nadal, M., Schuhmacher, M., & Domingo, J. L. (2015). Temporal trends in the levels of metals, PCDD/Fs and PCBs in the vicinity of a municipal solid waste incinerator. Preliminary assessment of human health risks. WASTE MANAGEMENT, 43, 168-175. Tepanosyan, G., Maghakyan, N., Sahakyan, L., & Saghatelyan, A. (2017). Heavy metals pollution levels and children health risk assessment of Yerevan kindergartens soils. Ecotoxicol Environ Saf, 142, 257-265. Wei, F., Chen, J., Wan, Y., & Zheng, C. (1991). The study on the background values of soil environment in China. Environmental Science(04), 12-19. Weibel, G., Eggenberger, U., Schlumberger, S., & Mader, U. K. (2017). Chemical associations and mobilization of heavy metals in fly ash from municipal solid waste incineration. WASTE MANAGEMENT, 62, 147-159. Zhao, L., Xu, Y., Hou, H., Shangguan, Y., & Li, F. (2014). Source identification and health risk assessment of metals in urban soils around the Tanggu chemical industrial district, Tianjin, China. SCIENCE OF THE TOTAL ENVIRONMENT, 468, 654-662. Zhong, C., Yang, Z., Jiang, W., Hu, B., Hou, Q., & Yu, T., et al. (2016). Ecological geochemical assessment and source identification of trace elements in atmospheric deposition of an emerging industrial area: Beibu Gulf economic zone. SCIENCE OF THE TOTAL ENVIRONMENT, 573, 1519-1526. Zhu, G., Guo, Q., Xiao, H., Chen, T., & Yang, J. (2017). Multivariate statistical and lead isotopic analyses approach to identify heavy metal sources in topsoil from the industrial zone of Beijing Capital Iron and Steel Factory. Environ Sci Pollut Res Int.
source identify of soil heavy metals in the vicinity of municipal solid waste incineration (MSWI) in Tianjin, northeast of China. In this study, 9 elements in total 128 surface and sub-surface soil samples were collected and analyzed by FAAS and AFS. The single pollution index, integrated Nemerow pollution index and enrichment factor were used to quantify the pollution levels of soil metals. The principal component analysis (PCA) was applied to identify the potential sources. The results showed that the soils around the MSWI were lightly polluted by Cu, Pb, Zn, and Hg and heavily polluted by As, and Cd. The overall level of soil heavy metals contamination in study area is seriously high. PCA and correlation analysis indicated that the MSWI, natural source, industrial discharges and coal combustion were the four main potential sources of soil heavy metals. The MSWI had a significant influence on Pb, Cu, Zn and Cd. Keywords: Municipal Solid Waste Incineration (MSWI), Heavy Metals, Principle Component Analysis (PCA), Soil
1. INTRODUCTION Along with the rapid economic growth, the amount of municipal solid waste (MSW) increased from 164 million tons (2011) to 191 million tons (2015). Waste disposal became a crucial part of the social sustainable development (Li et al., 2015; Li, Wan, Ben, Fan, & Hu, 2017). Incineration is an extremely important waste disposal method in China and the capacity of 61.8 million tons in 2015 was more than double of that in 2011. Although incineration can significantly reduce the waste mass and volume, the potential toxic elements emitted from MSWIs cannot be ignored. Heavy metals present in MSW evaporate through the combustion process and mainly transferred to the fly ash and flue gas (Weibel, Eggenberger, Schlumberger, & Mader, 2017). Then heavy metals in the fly ash and flue gas could emit into the atmosphere from chimney or
Proceedings Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium/ 2 - 6 October 2017 S. Margherita di Pula, Cagliari, Italy / © 2017 by CISA Publisher, Italy
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
as fugitive emissions and eventually accumulated in soil by wet and dry deposition(Rimmer et al., 2006). Heavy metals in soils can destroy the ecological system and threaten human health (Tepanosyan, Maghakyan, Sahakyan, & Saghatelyan, 2017). Although many studies focused on the soil heavy metals contamination in the vicinity of MSWI, the effect of MSWI on the soil heavy metals concentrations remain debatable. In one study, the soil heavy metals concentrations were irrelevant to the distance apart the MSWI in Catalonia and did not show temporal variations (Rovira, Vilavert, Nadal, Schuhmacher, & Domingo, 2015). Similarly, no evidence for increased heavy metals pollution due to MSWI emissions can be found in a study in Newcastle(Rimmer et al., 2006). The soil Hg concentrations in the vicinity of MSWI in south China was not significantly different from control area (Deng et al., 2016). In contrast, the heavy metals concentrations were relatively high in the downwind zone indicating an effect by MSWI in Beijing (Han et al., 2016). Li et al found that the concentrations of Pb, Cr and Mn in soil decreased with the increasing distance from MSWI in Shenzhen, China(Li et al., 2017). Therefore, whether the MSWI had significant influence on the heavy metals contamination in surrounding soils needs further investigation. Tianjin is one of the most populous cities in China and the soil heavy metals concentrations background values of Cr, Cu, Ni, Zn and Hg in Tianjin are higher than the mean values in China(Wei, Chen, Wan, & Zheng, 1991). It was more likely to get soil heavy metals contamination. There was a MSWI located in the high population density area in Tianjin which was very near to a primary school and residential area. Therefore, it is urgent to investigate the influences of MSWI to the soil heavy metals contaminations. The present study investigated an MSWI in Tianjin, northeastern China. The goals of this study were (1) to quantify the pollution levels of soil heavy metals using the single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the enrichment factor (EF) ; (2) to identified the potential sources by principal component analysis (PCA); (3) to provide a reference for the environmental governance. 2. MATIERIALS AND METHODS 2.1 Study area The research MSWI is located in Tianjin (116°43′- 118°04′E and 38°34′- 40°15′N), Northeastern China (Fig. 1), which belongs to the warm temperate zone, with a continental monsoon climate. The prevalent wind is southwest and the average annual temperature and precipitation are 11.9℃ and 556 mm. Light expanded clay and silty clay are dominant soils. The MSWI began operating in 2005 with a capacity of 1200 tons per day. The flue gas treatment system in MSWI consisted of a semi-dry scrubber, an activated carbon adsorption unit, a bag filter and an 80-m-high stack.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Fig.1. Locations of the soil sampling sites around the MSWI and wind rose map in Tianjin 2.2 Sample collection A total of 128 surface soil samples (0-20cm) and sub-surface soil samples (20-40cm) from 64 sampling sites within 3km around the MSWI were collected in December 2016. The study area was divided into four sectors (NW, NE, SW, SE), because of the dominant southwest wind direction. Each sector consisted of four distance bands (0-0.5km, 0.5-1km, 1-2km, 2-3km) and four sampling sites were distributed in each band (Fig. 1). The coordinates of sampling sites were recorded by GPS. Each sample (more than 1 kg dry weight) was composed of 3 subsamples obtained by stainless steel hand auger. The soil samples were sealed in PVC bags, then brought back to the lab. One fly ash sample from the MSWI was also collected in December 2016 to examine the relationship between heavy metals concentrations in soil and MSWI. 2.3 Sample treatment and analysis After air-dried, ground and sieved through a 0.15 mm mesh, the soil samples (approximately 0.5g) were digested with a 8 ml solution of 5:2:1 HNO3 :HF :H2O2 (v/v) by microwave digestion method. The concentrations of Cr, Pb, Cu, Ni, Zn, Cd and Fe were measured by a flame atomic absorption spectrophotometer (FAAS-Z-5000, HITACHI). Hg and As were determined by Atomic fluorescence spectrophotometer (AFS-8220, TITAN). For quality assurance (QA) and quality control (QC), national standard soil samples (GBW07428; Center for Certified Reference Materials, China), blank control and duplicate samples were employed. The recoveries rates were within the range of 90%-110% and the relative standard deviations (RSD) were less than 3%. 2.4 Evaluation of heavy metal contamination in soils To quantify the heavy metal contamination risk, the single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the enrichment factor (EF) were applied in this study. The single pollution index (PIi) is defined as: , where Ci is the detected concentration of heavy metal (i), and Si is the reference values. In this study the average background values of soils in Tianjin were applied as reference values. The PIi was classified as non-pollution (PIi ≤1),
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
light pollution (1< PIi ≤2), moderate pollution (2< PIi ≤3), or heavy pollution (PIi >3). The integrated Nemerow pollution index (PIN) can be obtained using the equation: , where MaxPIi and AvePIi are the maximum and average value of the single pollution indexes. According to the PIN, the soil quality was classified as safety (PIN ≤ 0.7), warning (0.7 < PIN ≤ 1), light pollution (1 < PIN ≤ 2), moderate pollution (2 < PIN ≤ 3), and heavy pollution (PIN > 3)(Zhu, Guo, Xiao, Chen, & Yang, 2017). The enrichment factor (EF) is expressed as follows: , where Cref is the concentration of reference element for normalization(Chen et al., 2016). Fe was employed as reference element because of the high concentration in soil(Rodriguez-SeijoLuisa Andrade & Vega, 2017). The soils were classified as no enrichment (EF < 2), moderate enrichment (2 ≤ EF < 5), severe enrichment (5≤ EF < 20), very severe enrichment (20≤ EF < 40) and extremely severe enrichment (EF ≥ 40). 2.5 Multivariate statistical analysis The correlation coefficient could be used to measure the intensity of inter-relationship between the heavy metals. Heavy metals with high correlation coefficient are probably have common origin or similar pollution sources(Li et al., 2013). Principal component analysis (PCA) is a mathematical process using an orthogonal transformation to convert the possibly correlated variables into linearly uncorrelated variables. In the present study, correlation analysis and principal component analysis (PCA) were applied to identify the sources of heavy metal contamination found in soil using SPSS 21.0 software. 3. RESULTS AND DISCUSSION 3.1 General characteristic of heavy metal concentrations The mean concentrations of Cr, Pb, Cu, Ni, Zn, Cd, Hg, and As were 76.35, 26.74, 30.49, 31.82, 120.16, 0.38, 0.14, and 31.21 mg·kg-1, respectively. Besides Cr and Ni, the mean concentrations of rest of 6 heavy metals (Pb, Cu, Zn, Cd, Hg, and As) exceeded the background values for Tianjin (CNEMC, 1990). When compared with the limit value of Grade Ⅱ environmental quality standard for soil in China (GB15618-1995)(CEPA, 1995), approximately 3.1%, 4.7%, 3.1%, and 48.4% of soil samples were high of Zn, Cd, Hg, and As, respectively. Therefore, the soils of study area were polluted by heavy metals in various degrees. Zn, Cd, Hg, and As were in a relatively highly contaminated level. The concentrations of Zn, Cd, Hg, and As showed greater spatial heterogeneity than other heavy metals. In comparison, Ni was quite homogeneous in study area, with the coefficients of variation (CV) of 0.21, which is quite low indicating that the heavy metals were dominated by natural source. Conversely, heavy metals with high CV values were more likely to be affected by anthropogenic sources(Han, Du, Cao, & Posmentier, 2006; Zhu et al., 2017). According to that, Zn, Cd, Hg, and As tend to be affected by anthropogenic activities (Table 1), while Ni mainly originated from natural source. Comparison of the heavy metals concentrations in fly ash with the soils in the study area revealed that the concentrations of Pb, Cu, Zn, and Cd in fly ash were 15, 10, 33, 192 times higher than soils, while the concentrations of Cr, Ni, Hg, and As in fly
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
ash were no more than 1.5 times higher than soil. The soils in study area are more likely to get significant input of Pb, Cu, Zn, and Cd from MSWI.
II)
Table 1 Summary statistics of heavy metals concentrations of surface soils around MSWI (mg·kg-1) Cr Pb Cu Ni Zn Cd Hg Arithmetic mean 76.35 26.74 30.49 31.82 120.16 0.38 0.14 Standard deviation 24.98 9.00 7.14 6.83 42.73 0.13 0.22 Coefficient of variance (CV) 0.33 0.34 0.23 0.21 0.36 0.35 1.52 Fly ash of MSWI 93.26 428.87 335.72 33.48 4140.44 73.56 0.32 Background of Tianjina 84.00 21.00 29.00 33.00 79.00 0.09 0.08 Chinese soil guidelines (Grade 200.00 300.00 200.00 50.00 250.00 0.60 0.50 b
As 31.21 18.57 0.60 44.33 9.60 30.00
a
CNEMC (China National Environmental Monitoring Center), The Backgrounds of Soil Environment in China, Environment Science Press of China, 1990. b Chinese Environmental Quality Standard for Soils. State Environmental Protection Administration of China, 1995. 3.2 Evaluation of the contamination levels The single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the enrichment factor (EF) were applied in this study. The mean values of PIi of each heavy metal increase in the order of Cr (0.91) < Ni (0.96) < Cu (1.1) < Pb (1.3) < Zn (1.5) < Hg (1.7) < As (3.3) < Cd (4.2). The study area performed non-pollution by Cr, and Ni, light pollution by Cu, Pb, Zn, and Hg, heavy pollution by As, and Cd. The proportion of PIi of soil samples for each metal is shown in Fig. 2. According to the integrated Nemerow pollution index (PIN), 7.81% of soil samples are lightly polluted, 23.44% are moderately polluted, 68.75% are heavily polluted. The mean value of PIN is 3.73, which indicated that the overall level of heavy metals contamination is seriously high. To estimate the degree of anthropogenic influence on heavy metals contaminations in soil and mask the interference of natural procedure, the enrichment factor (EF) of heavy metals in soils was calculated. The mean EF values decrease in the order of Hg (1.68) > As (1.20) > Cd (1.19) > Zn (1.08) > Cr (0.98) > Pb (0.89) > Cu (0.87) > Ni (0.81). The mean EF values of Hg, As, Cd, and Zn were greater than 1, implying anthropogenic influences. Approximately 20.3% EF values for Hg, 14.1% for Cd, and 12.5% for As exceed 2, indicating that the soils around MSWI were moderately polluted by Hg, Cd and As. It is noteworthy that the EF value of Cr (which is non-pollution identified by PIi) is higher than Pb and Cu (which is light pollution identified by PIi). Furthermore, approximately 6.3% EF values of Cr lay between 2-5, suggesting that the soils in the study area were lightly polluted by Cr. Therefore, the two pollution indices PIi and EF can complement each other in estimating soils heavy metals contamination level.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Proportion of different pollution levels (%)
100
80
60
40
20
0
Cr
Pb
Cu
Ni
heavy pollution light pollution
Zn
Cd
Hg
As
moderate pollution non-pollution
Fig. 2. Proportion of different pollution levels for heavy metals identified by PIs 3.3 Spatial distribution of heavy metals in surface soils Considering the differences of heavy metals concentrations from distances, only Ni, Cd, Hg, As (NE); Cr, Cu, Ni, Cd (NW), Cr (SE) and Cr, Pb, Cu, Ni (SW) showed significant differences in distances (p < 0.1) (Table S1). As shown in Table S2, most of these heavy metals have maximum concentrations in the distance of 500m to 1000m from their source point. This phenomenon accords with the dilution and dispersion regularities of point source pollution. It indicated that MSWI might have obvious influence on soil heavy metals contamination. For other heavy metals concentrations which did not show significant difference or did not decrease with the increasing distances, it might be due to the effect of MSWI which was masked by other contamination sources. Bretzel & Calderisi (2011) and Han et al. (2016) (Bretzel & Calderisi, 2011; Han et al., 2016)also found that the interferences of anthropogenic activity could mask the influence of MSWI. As for the directions, it was expected that heavy metals concentrations were highest in NE part and lowest in SW part, because of the dominant prevailing southwest wind direction (Han et al., 2016; Rimmer et al., 2006). However, there was no maximum concentration located in NE part or minimum concentrations located in SW part. Most heavy metals concentrations (Cr, Pb, Cu, Ni, and Hg) were highest in SW part, and the concentrations of Cr, Pb, Cu, Ni, and As were lowest in NW part (Table 2). It may be attributed to the different land use. There are many industrial factories located in SW part of MSWI, while the NW part mainly consists of residential area and schools. Industrial factories (automotive production, printing plant, electronic equipment production) could release more heavy metals pollutant to soil than residential area and schools. Furthermore, the frequency of prevailing wind direction was not extremely significantly higher than that of wind direction (Fig. 1). It may cause homogeneity in the effect of MSWI on the soils heavy metals concentrations in different directions rather than greater effect on downwind area of dominant wind direction. According to the distributions of heavy metals in different distances and directions, the MSWI has the potential effect on the contamination in soil, but partially masked by natural sources and
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other anthropogenic activities like industrial production, traffic emission, the utilization of pesticides and fertilizers and so on. Table 2 Average concentrations of heavy metals in soils at different directions (mg· kg–1) direction Cr Pb Cu Ni Zn Cd NE 67.30 26.63 32.04 33.89 121.06 0.32 NW 65.02 24.30 26.38 25.12 125.68 0.47 SE 86.00 27.92 31.23 34.06 113.26 0.34 SW 87.09 28.09 32.32 34.23 120.67 0.38
Hg 0.14 0.13 0.12 0.19
As 32.21 27.98 35.51 29.14
3.4 Correlation coefficient and principal component analysis The Pearson’s correlation coefficients for heavy metals in surface soils are presented in Table 3. Inter-element relationships could provide many information on the sources and pathways of heavy metals(Zhu et al., 2017). Generally, the high positive correlations among heavy metals indicate that they significantly share similar origins or geochemical characteristics, and the marker elements can make contribution to sources identification (Zhong et al., 2016). Cr, Pb, Cu, and Ni were positively correlated with each other at P < 0.01. Because the coefficient of variance (CV) values of Cr, Pb, Cu, and Ni are relatively low and Cr and Ni are in non-pollution level identified by PIi values, they may mainly originate from natural source. Zn is significantly positively correlated with Pb, Cu, and Cd. As the soils in study area were heavily polluted by Cd and lightly polluted by Zn, Pb, and Cu, these four heavy metals may have been influenced by the same anthropogenic activities. Hg and As were not correlated with other heavy metals, hence it is assumed that they may have exclusive sources. PCA was adopted to identify the sources of heavy metals in soils. The results of KMO (0.685) and Bartlett’s test (P < 0.001) indicated a good fit for PCA. The results of the rotated component matrix of heavy metals concentrations in soils are shown in Table 4. In this study, four principal components were extracted which explain 84.143% of the total variance. Component PC1 explains 29.179% of the total variance and loads heavily on Pb, Cu, Zn, and Cd. Component PC2, dominated by Cr, Pb, Cu, and Ni, accounts for 28.552% of the total variance. Component PC3 dominated by As and Component PC4 dominated by Hg account for 13.382% and 13.031% of the total variance, respectively. Compared with the background values of Tianjin soil, the concentrations of Ni, Cr, and Cu were lower or approximately equal to their corresponding values, indicating that they may originate from natural source, while the significantly higher concentrations of Pb, Zn, Cd, Hg, and As suggest anthropogenic sources. Meanwhile, the concentrations of Pb, Cu, Zn, and Cd in fly ash are much higher than soils and the spatial distribution of these metals in soil accords with the deposition regularities of flue gas from MSWI, so these metals may be significantly influenced by MSWI and Component PC1 was the MSWI. According to the previous reports, Ni and Cr were dominantly present at natural background concentrations, controlled by the weathering of the parent rock material and pedogenesis (Li et al., 2013; Ma, Chen, Li, Bi, & Huang, 2016; Zhao, Xu, Hou, Shangguan, & Li, 2014). Combined with the low PIi value of Ni and Cr, they are considered mainly came from natural source. Component PC2 was regarded as natural source. Pb and Cu had elevated loading in PC1 and moderate loading in PC2. This suggests that Pb and Cu might be joint controlled by MSWI and natural source. According to previous studies, high As concentration in soil might be associated with
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
anthropogenic wastes and industrial activities, including sewage sludge and industrial discharges (Charlesworth, Everett, McCarthy, Ord Ez, & de Miguel, 2003; Zhao et al., 2014). Discharges from industrial factories in the study area, such as glass plants, perfume factories, pharmaceutical factories, and wood conversion factories, could contribute to As pollution. The Component PC3 was industrial discharges. Hg was uncorrelated with other metals, indicating that it might have different source from other heavy metals and different distribution mechanism. Hg in the surface soil can be released into the air and exchanged between the air and soil, causing it to be transported over long distances (Zhu et al., 2017). There was a coal-fired city central heating company located in the study area. The consumption of coal in heating company and the industrial factories was thought as the main source of Hg contamination. The Component PC4 was coal combustion. Table 3 Pearson’s correlations matrix for the heavy metals concentrations. Cr Pb Cu Ni Zn Cd Hg As Cr 0.005 0.000 0.000 0.806 0.150 0.725 0.398 Pb 0.350** 0.000 0.003 0.000 0.023 0.120 0.287 Cu 0.451** 0.631** 0.000 0.000 0.266 0.203 0.293 Ni 0.659** 0.370** 0.543** 0.822 0.001 0.980 0.782 Zn 0.031 0.543** 0.594** -0.029 0.000 0.310 0.198 Cd 0.182 0.284* 0.141 -0.400** 0.584** 0.098 0.049 Hg 0.045 0.196 0.161 0.003 0.129 0.209 0.712 As 0.107 0.135 0.133 -0.035 -0.163 -0.247* 0.047 The left lower part is correlation coefficient; the right upper part is significant level. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed). Table 4 Rotated component matrix for soil heavy metals (PCA loadings > 0.4 are shown in bold). PC1 PC2 PC3 PC4 Cr 0.08 0.832 -0.13 0.061 Pb 0.733 0.401 -0.031 0.133 Cu 0.701 0.571 0 0.052 Ni 0.046 0.925 0.042 -0.032 Zn 0.923 -0.08 -0.064 0.004 Cd 0.652 -0.489 -0.306 0.208 Hg 0.107 0.023 0.037 0.986 As -0.095 -0.067 0.976 0.042 Eigenvalue (>1) 2.334 2.284 1.071 1.042 % of variance 29.179 28.552 13.382 13.031 Cumulative % 29.179 57.731 71.112 84.143 Extraction method: principal component analysis.
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4 CONCLUSIONS This study investigated the soil heavy metals contamination in the vicinity of MSWI in order to quantify the pollution levels and identify their potential sources. The results showed that the soils around MSWI were lightly polluted by Cu, Pb, Zn, and Hg and heavily polluted by As, and Cd identified by the single pollution index (PIi). The overall level of soil heavy metals contamination in the study area is seriously high (PIN > 3). PCA and correlation analysis indicated that the MSWI, natural source, industrial discharges and coal combustion were the four main potential sources of soil heavy metals. The MSWI had a significant influence on Pb, Cu, Zn and Cd. More attention should be paid to the contamination of Pb, Cu, Zn and Cd in soil around the MSWI. ACKNOWLEDGEMENT The authors would like to thank the National Science and Technology Support Program of China (2014BAC02B03), and High Technology Support Project of Tianjin (16PTGCCX00170, 16YFXTSF00540), Tianjin Research Program of Application and Advanced Technology (15JCQNJC06600). REFERENCES Bretzel, F. C., & Calderisi, M. (2011). Contribution of a municipal solid waste incinerator to the trace metals in the surrounding soil. ENVIRONMENTAL MONITORING AND ASSESSMENT, 182(1-4), 523-533. CEPA. (1995). Environmental Quality Standard for Soils (GB15618-1995). In C. E. P. Administration (Ed.). Charlesworth, S., Everett, M., McCarthy, R., Ord Ez, A., & de Miguel, E. (2003). A comparative study of heavy metal concentration and distribution in deposited street dusts in a large and a small urban area: Birmingham and Coventry, West Midlands, UK. Environment International, 29(5), 563-573. Chen, H., Teng, Y., Lu, S., Wang, Y., Wu, J., & Wang, J. (2016). Source apportionment and health risk assessment of trace metals in surface soils of Beijing metropolitan, China. Chemosphere, 144, 1002-1011. CNEMC. (1990). The Background Values of Elements in Chinese Soils: Environmental Science Press of China. Deng, C., Xie, H., Ye, X., Zhang, H., Liu, M., & Tong, Y., et al. (2016). Mercury risk assessment combining internal and external exposure methods for a population living near a municipal solid waste incinerator. ENVIRONMENTAL POLLUTION, 219, 1060-1068. Han, Y. M., Du, P. X., Cao, J. J., & Posmentier, E. S. (2006). Multivariate analysis of heavy metal contamination in urban dusts of Xi'an, Central China. SCIENCE OF THE TOTAL ENVIRONMENT, 355(1-3), 176-186. Han, Y., Xie, H., Liu, W., Li, H., Wang, M., & Chen, X., et al. (2016). Assessment of pollution of potentially harmful elements in soils surrounding a municipal solid waste incinerator, China. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING, 10(76). Li, N., Kang, Y., Pan, W., Zeng, L., Zhang, Q., & Luo, J. (2015). Concentration and transportation of heavy metals in vegetables and risk assessment of human exposure to bioaccessible heavy metals in soil near a waste-incinerator site, South China. SCIENCE OF
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