Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Sampling
2.2. Auxiliary Variables Data
2.3. Single Pollution Index (SPI)
2.4. Ordinary Kriging (OK)
2.5. Principle Component Analysis (PCA)
2.6. Finite Mixture Distribution Model (FMDM)
2.7. Data Analysis
3. Results and Discussion
3.1. Summary Statistical Analysis of Soil Trace Elements
3.2. Analysis of Auxiliary Variable Data
3.3. Assessment of Trace Element Pollution
3.4. Spatial Distribution of Soil Trace Elements
3.5. Source Identification Based on PCA
3.6. Source Identification Based on FMDM
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Arrouays, D.; Saby, N.P.A.; Thioulouse, J.; Jolivet, C.; Boulonne, L.; Ratié, C. Large trends in French topsoil characteristics are revealed by spatially constrained multivariate analysis. Geoderma 2011, 161, 107–114. [Google Scholar] [CrossRef]
- Lequy, E.; Saby, N.P.A.; Ilyin, I.; Bourin, A.; Sauvage, S.; Leblond, S. Spatial analysis of trace elements in a moss bio-monitoring data over France by accounting for source, protocol and environmental parameters. Sci. Total Environ. 2017, 590–591, 602–610. [Google Scholar] [CrossRef] [PubMed]
- Marchant, B.P.; Saby, N.P.A.; Arrouays, D. A survey of topsoil arsenic and mercury concentrations across France. Chemosphere 2017, 181, 635–644. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hu, B.F.; Zhao, R.Y.; Chen, S.C.; Zhou, Y.; Jin, B.; Li, Y.; Shi, Z. Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China. Int. J. Environ. Res. Public Health 2018, 15, 710. [Google Scholar] [CrossRef] [PubMed]
- Shi, T.Z.; Guo, L.; Chen, Y.Y.; Wang, W.X.; Shi, Z.; Li, Q.Q.; Wu, G.F. Proximal and remote sensing techniques for mapping of soil contamination with heavy metals. Appl. Spectrosc. Rev. 2018, 3. [Google Scholar] [CrossRef]
- Hu, B.F.; Wang, J.Y.; Jin, B.; Li, Y.; Shi, Z. Assessment of the potential health risks of heavy metals in soils in a coastal industrial region of the Yangtze River Delta. Environ. Sci. Pollut. Res. 2017, 24, 19816–19826. [Google Scholar] [CrossRef] [PubMed]
- Marchant, B.P.; Saby, N.P.A.; Lark, R.M.; Bellamy, P.H.; Jolivet, C.C.; Arrouays, D. Robust analysis of soil properties at the national scale: Cadmium content of French soils. Eur. J. Soil Sci. 2010, 61, 144–152. [Google Scholar] [CrossRef]
- Lacarce, E.; Saby, N.P.A.; Martin, M.P.; Marchant, B.P.; Boulonne, L.; Meersmans, J.; Jolivet, C.; Bispo, A.; Arrouays, D. Mapping soil Pb stocks and availability in mainland France combining regression trees with robust geostatistics. Geoderma 2012, 170, 359–368. [Google Scholar] [CrossRef]
- Schneider, A.R.; Morvan, X.; Saby, N.P.A.; Cancès, B.; Ponthieu, M.; Gommeaux, M.; Marin, B. Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter. Environ. Sci. Pollut. Res. 2016, 23, 15164–15174. [Google Scholar] [CrossRef] [PubMed]
- Rambeau, C.M.C.; Baize, D.; Saby, N.; Matera, V.; Adatte, T.; Foellmi, K.B. High Cadmium concentrations in Jurassic limestone as the cause for elevated cadmium levels in deriving soils: A case study in Lower Burgundy, France. Environ. Earth Sci. 2010, 61, 1573–1585. [Google Scholar] [CrossRef]
- Marchant, B.P.; Saby, N.P.A.; Jolivet, C.C.; Arrouays, D.; Lark, R.M. Spatial prediction of soil properties with copulas. Geoderma 2011, 162, 327–334. [Google Scholar] [CrossRef]
- Hu, B.F.; Wang, J.Y.; Fu, T.T.; Li, Y.; Shi, Z. Application of spatial analysis in soil heavy metal pollution. Chin. J. Soil Sci. 2017, 48, 11. (In Chinese) [Google Scholar]
- Saby, N.P.A.; Thioulouse, J.; Jolivet, C.C.; Ratie, C.; Boulonne, L.; Bispo, A.; Arrouays, D. Multivariate analysis of the spatial patterns of 8 trace elements using the French soil monitoring network data. Sci. Total Environ. 2009, 407, 5644–5652. [Google Scholar] [CrossRef] [PubMed]
- Cheng, H.F.; Hu, Y.N. Planning for sustainability in China’s urban development: Status and challenges for Dongtan eco-city project. J. Environ. Monit. 2010, 12, 119–126. [Google Scholar] [CrossRef] [PubMed]
- Zelenka, M.P.; Wilson, W.E.; Chow, J.C.; Lioy, P.J. A combined TTFA CMB receptor modeling approach and ITS application to air-pollution sources in China. Atmos. Environ. 1994, 28, 1425–1435. [Google Scholar] [CrossRef]
- Krumal, K.; Mikuska, P.; Vecera, Z. Application of organic markers in identification of sources of organic aerosols. Chem. Listy 2012, 106, 95–103. [Google Scholar]
- Li, P.Z.; Duan, X.L.; Cheng, H.G.; Lin, C.Y. Application of Lead stable isotopes to identification of environmental source. Environ. Sci. Technol. 2013, 36, 63–67. [Google Scholar]
- Vedantham, R.; Landis, M.S.; Olson, D.; Pancras, J.P. Source identification of PM2.5 in Steubenville, Ohio Using a hybrid method for highly time-resolved Data. Environ. Sci. Technol. 2014, 48, 1718–1726. [Google Scholar] [CrossRef] [PubMed]
- Shaw, P.M.; Johns, R.B. The identification of organic input sources of sediments from the Santa-Catalina basin using factor-analysis. Org. Geochem. 1986, 10, 951–958. [Google Scholar] [CrossRef]
- Kim, E.; Hopke, P.K. Source identifications of airborne fine particles using positive matrix factorization and US environmental protection agency positive matrix factorization. J. Air Waste Manag. Assoc. 2007, 57, 811–819. [Google Scholar] [CrossRef] [PubMed]
- Qu, M.K.; Wang, Y.; Huang, B.; Zhao, Y.C. Source apportionment of soil heavy metals using robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR) receptor model. Sci. Total Environ. 2018, 626, 203–210. [Google Scholar] [CrossRef] [PubMed]
- Zhong, B.Q.; Liang, T.; Wang, L.Q.; Li, K.X. Applications of stochastic models and geostatistical analyses to study sources and spatial patterns of soil heavy metals in a metalliferous industrial district of China. Sci. Total Environ. 2014, 490, 422–434. [Google Scholar] [CrossRef] [PubMed]
- Hu, B.F.; Chen, S.C.; Hu, J.; Xia, F.; Xu, J.; Li, Y.; Shi, Z. Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution. PLoS ONE 2017, 12, e0172438. [Google Scholar] [CrossRef] [PubMed]
- Critchley, F. Influence in Princepal Components-Analysis. Biometrika 1985, 72, 627–636. [Google Scholar] [CrossRef]
- Parra, S.; Bravo, M.A.; Quiroz, W.; Moreno, T.; Karanasiou, A.; Font, O.; Vidal, V.; Cereceda-Balic, F. Source apportionment for contaminated soils using multivariate statistical methods. Chemom. Intell. Lab. Syst. 2014, 138, 127–132. [Google Scholar] [CrossRef]
- Dong, R.Z.; Jia, Z.M.; Li, S.Y. Risk assessment and sources identification of soil heavy metals in a typical county of Chongqing municipality, Southwest China. Process Saf. Environ. Prot. 2018, 113, 275–281. [Google Scholar] [CrossRef]
- Hu, Y.N.; Cheng, H.F. Application of Stochastic Models in Identification and Apportionment of Heavy Metal Pollution Sources in the Surface Soils of a Large-Scale Region. Environ. Sci. Technol. 2013, 47, 3752–3760. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lin, Y.P.; Chang, C.R.; Chu, H.J.; Cheng, B.Y. Identifying the spatial mixture distribution of bird diversity across urban and suburban areas in the metropolis: A case study in Taipei Basin of Taiwan. Landsc. Urban Plan. 2011, 102, 156–163. [Google Scholar] [CrossRef]
- Chu, H.J.; Yu, H.L.; Kuo, Y.M. Identifying spatial mixture distributions of PM2.5 and PM10 in Taiwan during and after a dust storm. Atmos. Environ. 2012, 54, 728–737. [Google Scholar] [CrossRef]
- Zhou, Y.; Biswas, A.; Ma, Z.Q.; Lu, Y.L.; Chen, Q.X.; Shi, Z. Revealing the scale-specific controls of soil organic matter at large scale in Northeast and North China Plain. Geoderma 2016, 271, 71–79. [Google Scholar] [CrossRef]
- Hu, B.F.; Jia, X.L.; Hu, J.; Xu, D.Y.; Xia, F.; Li, Y. Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in. the Yangtze River Delta, China. Int. J. Environ. Res. Public Health. 2017, 14, 1042. [Google Scholar] [CrossRef] [PubMed]
- Wang, S.F.; Li, R.A.; Ye, Z.J. Zhejiang Soil, 2nd ed.; Yu, Z.Y., Yan, X.Z., Wei, X.F., Eds.; Zhejiang Science and Technology Press: Hangzhou, China, 1990; Volume 1, pp. 103–477. ISBN 7-5341-0599-4. (In Chinese) [Google Scholar]
- China National Environmental Protection Agency (CEPA). Environmental Quality Standard for Soils; Report No. GB15618–1995; China National Environmental Protection Agency: Beijing, China, 1995. (In Chinese)
- Cressie, N. Spatial prediction and ordinary kriging. Math. Geol. 1988, 20, 405–421. [Google Scholar] [CrossRef]
- Armstrong, M.; Boufassa, A. Comparing the robustness of ordinary kriging and lognormal kriging: Outlier Resistance. Math. Geol. 1988, 20, 447–457. [Google Scholar] [CrossRef]
- Wold, S.; Esbensen, K.; Geladi, P. Principal Component Analysis. Chemom. Intell. Lab. Syst. 1987, 2, 37–52. [Google Scholar] [CrossRef]
- Zhi, Y.Y.; Li, P.; Shi, J.C.; Zeng, L.Z.; Wu, L.S. Source identification and apportionment of soil cadmium in cropland of Eastern China: A combined approach of models and geographic information system. J. Soils Sediments 2015, 16, 467–475. [Google Scholar] [CrossRef]
- Lin, Y.P.; Zhao, Y.; Hu, G.R.; Su, G.M. Application of multivariate statistics in source analysis of heavy metal pollution in soil. Earth Environ. 2011, 39, 6. (In Chinese) [Google Scholar]
- Batzoglou, C. What is the expectation maximization algorithm? Comput. Biol. 2008, 26, 3. [Google Scholar]
- R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Australia, 2009; Available online: http://www.R-project.org (accessed on 10 February 2015).
- Fraley, C.; Raftery, A.E. Enhanced model-based clustering, density estimation, and discriminant analysis software: MCLUST. J. Classif. 2003, 20, 263–286. [Google Scholar] [CrossRef]
- Brock, J.E. Variation of coefficients of simultaneous linear equation. Q. Appl. Math. 1953, 11, 234–240. [Google Scholar] [CrossRef]
- Wang, Q.H.; Dong, Y.X.; Zhou, G.H.; Zheng, W. Soil Geochemistry Standard. Value and Environmental Background Value of Zhejiang Province. J. Ecol. Rural Environ. 2007, 23, 8. (In Chinese) [Google Scholar]
- Cheng, J.L.; Shi, Z.; Li, Y.; Yang, D.Z.; Li, H.Y.; Zhu, Y.W. Assessing environmental quality of agricultural soils using GIS and multivariate analysis in Zhejiang province, China. J. Environ. Sci.-China 2005, 19, 315–319. [Google Scholar]
- Wu, X.; Pan, G. Distribution of heavy metals in urban soils different in function zone. Acta Pedologica Sin. 2005, 42, 513–517. (In Chinese) [Google Scholar]
- De la Cueva, A.V.; Marchant, B.P.; Quintana, J.R.; de Santiago, A.; Lafuente, A.L.; Webster, R. Spatial variation of trace elements in the peri-urban soil of Madrid. J. Soils Sediments. 2014, 14, 78–88. [Google Scholar] [CrossRef]
- Yuan, Y.; Bian, J.; Liu, X. Biotreatment of the heavy metal pollution in the environment. Chin. J. Vet. Sci. 2009, 29, 1089–1092. [Google Scholar]
- Weissmannova, H.D.; Pavlovsky, J. Indices of soil contamination by heavy metals—Methodology of calculation for pollution assessment (minireview). Environ. Monit. Assess. 2017, 189, 616. [Google Scholar] [CrossRef] [PubMed]
Element | Cr | Cd | Hg | As | Cu | Zn | Ni |
---|---|---|---|---|---|---|---|
Mean | 67.72 | 0.197 | 0.288 | 6.58 | 34.77 | 110.67 | 29.22 |
Median | 69.7 | 0.18 | 0.19 | 6.26 | 33 | 106 | 29.8 |
SD | 29.4 | 0.099 | 0.301 | 2.65 | 16.69 | 36.52 | 16.64 |
CV | 43.41% | 50.68% | 104.55% | 40.33% | 47.99% | 33.00% | 56.96% |
Range | 6.04–326.0 | 0.03–1.84 | 0.02–2.26 | 0.87–19.2 | 4.28–315.0 | 34.30–714.0 | 2.89–293.0 |
Background Value | 68.7 | 0.157 | 0.11 | 6.23 | 29.2 | 89.9 | 26.3 |
Background Interval | 43.0–94.4 | 0.101–0.213 | 0.048–0.231 | 3.94–8.52 | 9.7–48.6 | 51.6–128.2 | 20.6–32.1 |
Category | Enterprise Quantity | Sample Quantity | Cr | Cd | Hg | As | Cu | Zn | Ni |
---|---|---|---|---|---|---|---|---|---|
Textile Industry | 0–4 | 1692 | 65.40 * | 0.197 | 0.256 | 6.58 | 33.80 | 108.30 | 28.59 * |
4–8 | 144 | 76.94 * | 0.203 | 0.407 | 6.57 | 39.18 | 124.22 | 31.25 * | |
8–20 | 161 | 78.03 * | 0.193 | 0.385 | 6.39 | 37.97 | 116.75 | 32.21 * | |
>20 | 54 | 85.01 * | 0.200 | 0.695 | 7.46 | 43.92 | 130.57 | 34.52 * | |
Chemical materials | 0–4 | 1241 | 63.10 | 0.194 | 0.213 * | 6.42 | 32.04 * | 105.64 * | 27.87 * |
4–8 | 298 | 70.35 | 0.206 | 0.336 * | 6.79 | 36.94 * | 115.59 * | 30.16 * | |
8–20 | 397 | 76.33 | 0.195 | 0.416 * | 6.78 | 38.61 * | 116.72 * | 31.48 * | |
>20 | 115 | 81.00 | 0.219 | 0.536 * | 7.12 | 45.39 * | 131.29 * | 33.48 * | |
Metal products industry | 0–4 | 817 | 60.21 * | 0.188 | 0.174 * | 6.50 | 31.14 * | 104.03 * | 27.02 * |
4–8 | 286 | 67.84 * | 0.204 | 0.275 * | 6.49 | 32.73 * | 107.25 * | 28.61 * | |
8–20 | 466 | 71.59 * | 0.198 | 0.355 * | 6.70 | 36.58 * | 111.96 * | 31.15 * | |
>20 | 482 | 76.63 * | 0.208 | 0.426 * | 6.67 | 40.39 * | 122.69 * | 31.43 * | |
Other | 0–4 | 1606 | 65.56 * | 0.195 | 0.251 | 6.52 * | 33.38 | 108.02 * | 28.77 |
4–8 | 277 | 73.55 * | 0.208 | 0.379 | 6.63 * | 39.41 | 118.76 * | 30.11 | |
8–20 | 152 | 78.29 * | 0.204 | 0.502 | 7.09 * | 40.54 | 122.51 * | 31.83 | |
>20 | 16 | 82.68 * | 0.199 | 0.458 | 8.01 * | 38.86 | 123.74 * | 34.09 | |
Total | 0–4 | 562 | 57.53 * | 0.188 * | 0.146 * | 6.36 * | 30.37 * | 102.31 * | 26.30 * |
4–8 | 259 | 66.47 * | 0.193 * | 0.230 * | 6.66 * | 32.59 * | 107.50 * | 28.20 * | |
8–20 | 446 | 68.04 * | 0.199 * | 0.283 * | 6.57 * | 33.33 * | 107.50 * | 29.92 * | |
>20 | 784 | 75.25 * | 0.204 * | 0.413 * | 6.73 * | 39.47 * | 119.50 * | 31.24 * |
Soil Parental Materials Type | Sample Quantity | Cr | Pb | Cd | Hg | As | Cu | Zn | Ni | |
---|---|---|---|---|---|---|---|---|---|---|
Flooding parental material | Flood alluvial face | 102 | 51.84 | 50.38 * | 0.212 * | 0.213 | 4.86 | 29.25 | 111.35 * | 23.58 |
Plain fluvial face | 23 | 67.73 * | 35.07 | 0.190 | 0.236 | 6.37 | 42.25 * | 120.90 * | 29.70 * | |
Estuary alluvial sediment | Modern estuary face | 149 | 61.79 | 34.00 | 0.199 | 0.210 | 6.21 | 31.19 | 90.98 | 28.23 |
Stumpy parental material | Granite type residual slope face | 25 | 30.36 | 46.67 * | 0.205 | 0.108 | 3.77 | 20.64 | 99.54 | 12.09 |
Quaternary Pleistocene Laterite face | 6 | 62.30 | 48.40 * | 0.475 * | 0.171 | 3.67 | 28.50 | 95.32 | 13.75 | |
Rhyolitic, tuffaceous residual slope face | 586 | 59.41 | 43.93 * | 0.185 | 0.194 | 6.64 * | 31.86 | 109.53 | 26.48 | |
Purple sandstone residual slope face | 9 | 46.80 | 31.54 | 0.112 | 0.116 | 4.82 | 25.53 | 88.18 | 17.26 | |
Lacustrine parent material | Lagoon face | 655 | 77.10 * | 51.15 * | 0.206 * | 0.496 * | 6.40 | 38.99 * | 118.80 * | 30.60 * |
Coastal deposition parental material | Silica sand face | 168 | 63.44 | 28.64 | 0.196 | 0.136 | 6.27 | 30.73 | 94.28 | 29.20 |
Aleurite and silt face | 293 | 76.58 * | 35.19 | 0.194 | 0.209 | 7.97 * | 38.04 * | 114.21 * | 35.12 * | |
Silt face | 35 | 82.07 * | 37.39 | 0.188 | 0.145 | 8.76 * | 36.67 | 117.93 * | 38.02 * |
Soil Types | Sample Quantity | Cr | Cd | Hg | As | Cu | Zn | Ni |
---|---|---|---|---|---|---|---|---|
Coastal saline soil | 118 | 70.40 * | 0.168 | 0.100 | 8.71 * | 36.39 * | 104.53 | 34.39 * |
Fluvo-aquic soil | 370 | 67.07 | 0.195 | 0.155 | 6.83 * | 32.98 | 97.36 | 31.20 * |
Skeletal soil | 155 | 60.31 | 0.186 | 0.458 * | 5.74 | 34.22 | 109.54 | 24.08 |
Red soil | 278 | 54.68 | 0.201 * | 0.254 | 5.39 | 31.72 | 107.54 | 22.92 |
Yellow soil | 8 | 57.71 | 0.188 | 0.206 | 5.17 | 17.18 | 90.86 | 16.07 |
Paddy soil | 1099 | 72.08 * | 0.203 * | 0.341 * | 6.72 * | 36.32 * | 117.30 * | 30.42 * |
Purple soil | 5 | 61.26 | 0.121 | 0.117 | 6.09 | 27.44 | 87.14 | 22.53 |
Other | 18 | 68.72 * | 0.159 | 0.212 | 6.18 | 28.13 | 92.38 | 30.18 * |
Element | ≤ 1 | 1 < ≤ 2 | 2 < ≤ 3 | > 3 | ||||
---|---|---|---|---|---|---|---|---|
Sample Number | Proportion | Sample Number | Proportion | Sample Number | Proportion | Sample Number | Proportion | |
Cr | 2036 | 99.27% | 14 | 0.68% | 1 | 0.05% | 0 | 0% |
Cd | 1917 | 93.47% | 124 | 6.05% | 7 | 0.34% | 3 | 0.15% |
Hg | 1421 | 69.28% | 389 | 18.97% | 124 | 6.05% | 117 | 5.70% |
As | 2051 | 100% | 0 | 0% | 0 | 0% | 0 | 0% |
Cu | 1975 | 96.29% | 73 | 3.56% | 2 | 0.10% | 1 | 0.05% |
Zn | 2023 | 98.63% | 25 | 1.22% | 2 | 0.10% | 1 | 0.05% |
Ni | 1897 | 92.49% | 137 | 6.68% | 7 | 0.34% | 10 | 0.49% |
Element | Component Matrix | Rotated Component Matrix | ||||
---|---|---|---|---|---|---|
PC1 | PC2 | PC3 | PC1 (30%) | PC2 (25%) | PC3 (15%) | |
Cr | 0.82 | −0.39 | 0.06 | 0.88 | 0.18 | 0.15 |
Cd | 0.34 | 0.73 | −0.23 | −0.20 | 0.81 | 0.04 |
Hg | 0.40 | 0.28 | 0.53 | 0.12 | 0.26 | 0.67 |
As | 0.57 | −0.47 | 0.03 | 0.73 | −0.01 | 0.04 |
Cu | 0.81 | 0.27 | −0.14 | 0.45 | 0.72 | 0.12 |
Zn | 0.72 | 0.44 | −0.20 | 0.28 | 0.82 | 0.10 |
Ni | 0.75 | −0.46 | −0.10 | 0.87 | 0.14 | −0.03 |
Elements | Class | Proportion% | Mean (mg/kg) | STD (mg/kg) | Freedom | χ2 | p | Cutoff Value (mg/kg) | Background Values |
---|---|---|---|---|---|---|---|---|---|
Cr | 2 | 35.79% | 49.51 | 1231.02 | 52 | 51.47 | 0.15 | 52.49 | 68.7 |
64.21% | 77.54 | 226.39 | |||||||
Cd | 1 | 100% | 0.18 | 0.006 | 45 | 48.06 | 0.35 | - | 0.157 |
Hg | 3 | 45.35% | 0.09 | 0.001 | 20 | 16.27 | 0.7 | 0.156 | 0.11 |
41.21% | 0.30 | 0.002 | 0.566 | ||||||
13.44% | 0.91 | 0.12 | |||||||
As | 2 | 7.28% | 2.74 | 0.75 | 41 | 51.81 | 0.12 | 2.85 | 6.23 |
92.72% | 6.85 | 6.05 | |||||||
Cu | 1 | 100% | 34.65 | 206.12 | 55 | 59.05 | 0.33 | - | 29.2 |
Zn | 1 | 100% | 127.80 | 3857.11 | 68 | 53.54 | 0.09 | - | 89.9 |
Ni | 2 | 38.51% | 21.8 | 290.52 | 30 | 28.76 | 0.53 | 22.77 | 26.3 |
61.49% | 31.29 | 39.94 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shao, S.; Hu, B.; Fu, Z.; Wang, J.; Lou, G.; Zhou, Y.; Jin, B.; Li, Y.; Shi, Z. Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China. Int. J. Environ. Res. Public Health 2018, 15, 1240. https://doi.org/10.3390/ijerph15061240
Shao S, Hu B, Fu Z, Wang J, Lou G, Zhou Y, Jin B, Li Y, Shi Z. Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China. International Journal of Environmental Research and Public Health. 2018; 15(6):1240. https://doi.org/10.3390/ijerph15061240
Chicago/Turabian StyleShao, Shuai, Bifeng Hu, Zhiyi Fu, Jiayu Wang, Ge Lou, Yue Zhou, Bin Jin, Yan Li, and Zhou Shi. 2018. "Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China" International Journal of Environmental Research and Public Health 15, no. 6: 1240. https://doi.org/10.3390/ijerph15061240