Abstract
The source identification and apportionment of heavy metals (HMs) is a vital issue for restoring contaminated soil. In this study, qualitative approaches [a finite mixture distribution model (FMDM) and raster-based principal components analysis (RB-PCA)] and a quantitative approach [positive matrix factorization (PMF)] were composed to identify and apportion the sources of five HMs (Cd, Hg, As, Pb, Cr) in Wenzhou City, China, using several crucial auxiliary variables. An initial ecological risk assessment suggested that the ecological risk level in the study area was generally considered low, with the greatest contamination contributions coming from Cd and Hg. The result of the FMDM showed that Cd and Pb fit a single log-normal distribution, Hg fit a double log-normal mixed distribution, and As and Cr presented a triple log-normal distribution. Each element was identified and separated from its natural or anthropogenic sources. A map of RB-PCA combined with an analysis of corresponding auxiliary variables suggested that the three main contribution sources in the entire study area were parental materials, industrial and agricultural mixed pollution, and mining exploration activities. Each element was discussed, using the PMF model, with regard to its quantitative contributions. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) at 89.22%, 7.31%, 35.84%, 84.81% and 27.42%, respectively. Industrial emissions and agricultural inputs mixed pollution contributed 2.94%, 80.77%, 15.93%, 4.79%, and 25.63%, respectively. Mining activities contributed 7.84%,11.92%, 48.23%, 10.40% and 46.95%, respectively, to the five HMs. Such result could be used efficiently to generate scientific decisions and strategies in terms of decision-making on regulating HM pollution in soils.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abanuz, G. Y. (2019). Application of multivariate statistics in the source identification of heavy-metal pollution in roadside soils of Bursa Turkey. Arabian Journal of Geosciences, 12, 382. https://doi.org/10.1007/s12517-019-4545-3
Aminiyan, M. M., Baalousha, M., Mousavi, R., Aminiyan, F. M., Hosseini, H., & Heydariyan, A. (2018). The ecological risk, source identification, and pollution assessment of heavy metals in road dust: a case study in Rafsanjan, SE Iran. Environmental Science and Pollution Research, 25, 13382–13395. https://doi.org/10.1007/s11356-017-8539-y
Brock, J. E. (1953). Variation of coefficients of simultaneous linear equation. Quarterly of Appllied Mathematics, 11, 234–240.
Chen, H. Y., Teng, Y. G., Wang, J. S., Song, L. T., & Zuo, R. (2013). Source apportionment of trace element pollution in surface sediments using positive matrix factorization combined support vector machines: Application to Jinjiang River China. Biology Trace Element Research, 151, 462–470. https://doi.org/10.1007/s12011-012-9576-5
Chen, X. D., & Lu, X. W. (2018). Contamination characteristics and source apportionment of heavy metals in topsoil from an area in Xi’an city, China. Ecotoxicology and Environmental Safety, 151, 153–160. https://doi.org/10.1016/j.ecoenv.2018.01.010
Dai, L. Y., Wang, L. Q., Li, L. F., Liang, T., Zhang, Y. Y., Ma, C. X., et al. (2018). Multivariate geostatistical analysis and source identification of heavy metals in the sediment of Poyang Lake in China. Science of the Total Environment, 621, 1433–1444. https://doi.org/10.1016/j.scitotenv.2017.10.085
Davies, B. E. (1997). Heavy metal contaminated soils in an old industrial area of wales, Great Britain: Source identification through statistical data interpretation. Water, Air & Soil Pollution, 94, 85–87. https://doi.org/10.1023/A:1026478427782
Dong, B., Zhang, R. Z., Gan, Y. D., Cai, L. Q., Freidenreich, A., Wang, K. P., et al. (2019). Multiple methods for the identification of heavy metal sources in cropland soils from a resource-based region. Science of the Total Environment, 651, 3127–3138. https://doi.org/10.1016/j.scitotenv.2018.10.130
Dong, R. Z., Jia, Z. M., & Li, S. Y. (2018). Risk assessment and sources identification of soil heavy metals in a typical county of Chongqing municipality, Southwest China. Process Safety and Environmental Protection, 113, 275–281. https://doi.org/10.1016/j.psep.2017.10.021
Facchinelli, A., Sacchi, E., & Mallen, L. (2001). Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. Environment Pollution, 114, 313–324. https://doi.org/10.1016/S0269-7491(00)00243-8
Glaser, B., Dreyer, A., Bock, M., Fiedler, S., Mehring, M., & Heitmann, T. (2005). Source apportionment or organic pollutants of a highway-traffic-influenced urban area in Bayreuth (Germany) using biomarker and stable carbon isotope signatures. Environmental Science of Technology, 39, 3911–3917. https://doi.org/10.1021/es050002p
Hakason, L. (1980). An ecological risk index for aquatic pollution control: A sediment logical approach. Water Research, 14, 975–1001. https://doi.org/10.1016/0043-1354(80)90143-8
Han, D. M., Cheng, J. P., Hu, X. F., Jiang, Z. Y., Mo, L., Xu, H., Ma, Y., Chen, X., & Wang, H. (2016). Spatial distribution, risk assessment and source identification of heavy metals in sediments of the Yangtze River Estuary China. Marine Pollution Bulletin, 115, 141–148. https://doi.org/10.1016/j.marpolbul.2016.11.062
Hu, B.F., Jia, X.L., Hu, J., Xu, D.Y., Xia, F., & Li, Y. (2017). Assessment of heavy metal pollution and health risks in the soil-plant-human system in the Yangtze River Delta, China. International Journal of. Environmental Research and Public Health, 14(9): 1042. https://doi.org/10.3390/ijerph14091042
Hu, B. F., Shao, S., Fu, Z. Y., Li, Y., Ni, H., Chen, S. C., et al. (2019). Identifying heavy metal pollution hot spots in soil-rice systems: A case study in South of Yangtze River Delta, China. Science of the Total Environment, 658, 614–625. https://doi.org/10.1016/j.scitotenv.2018.12.150
Hu, B. F., Shao, S., Ni, H., Fu, Z. Y., Hu, L. S., Zhou, Y., et al. (2020). Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level. Environmental Pollution. https://doi.org/10.1016/j.envpol.2020.114961
Hu, W. Y., Wang, H. F., Dong, L. R., Huang, B. A., Borggaard, O. K., Hansen, H. C. B., et al. (2018). Source identification of heavy metals in peri-urban agricultural soils of southeast China: An integrated approach. Environment Pollution, 237, 650–661. https://doi.org/10.1016/j.envpol.2018.02.070
Hu, Y. N., & Cheng, H. F. (2013). Application of stochastic models in identification and apportionment of heavy metals pollution sources in the surface soils of a large-scale region. Environmental Science and Technology, 47, 3752–3760. https://doi.org/10.1021/es304310k
Huang, H. (2020). The comparison of interpolation methods and pollution assessments of heavy metals in mini scale block. Environmental Ecology, 2(3), 34–40. (in Chinese).
Huang, Y., Deng, M. H., Wu, S. F., Jan, J., Li, T. Q., Yang, X. E., et al. (2018). A modified receptor model for source apportionment of heavy metal pollution in soil. Journal of Hazard. Materials, 354, 161–169. https://doi.org/10.1016/j.jhazmat.2018.05.006
Huang, Y., Li, T. Q., Wu, C. X., He, Z. L., Japenga, J., Deng, M., et al. (2015). An Integrated approach to assess heavy metal source apportionment in pen-urban agricultural soils. Journal of Hazard Materials, 299, 540–549. https://doi.org/10.1016/j.jhazmat.2015.07.041
Jiang, Y. X., Chao, S. H., Liu, J. W., Yang, Y., Chen, Y. J., Zhang, A. C., et al. (2017). Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China. Chemosphere, 168, 1658–1668. https://doi.org/10.1016/j.chemosphere.2016.11.088
Kashif, A., Said, M., Wajid, A., Ishtiaq, J., & Atta, R. (2020). Occurrence, source identification and potential risk evaluation of heavy metals in sediments of the Hunza River and its tributaries Gilgit-Baltistan. . Environmental Technology & Innovation, 18, 100700. https://doi.org/10.1016/j.eti.2020.100700
Kosharna, S., & Korzhnev, M. (2018). Heavy metals on technogenic objects of mining and processing enterprises of the Kryvyi Rig Basin. Visnyk of Taras Shevchenko National University of Kyiv-Geology., 2, 92–97.
Li, F., Liu, S. Y., Li, Y., & Shi, Z. (2019). Spatiotemporal analysis and source apportionment of heavy metals in soils in a developed industrial city. Environment Science, 40, 0250–3301. (in Chinese).
Li, Y. X., Song, N. N., Yu, Y., Yang, Z. F., & Shen, Z. Y. (2017). Characteristics of PAHs in street dust of Beijing and the annual wash-off load using an improved load calculation method. Science of the Total Environment, 581–582, 328–336. https://doi.org/10.1016/j.scitotenv.2016.12.133
Liang, J., Feng, C. T., Zeng, G. M., Gao, X., Zhong, M. Z., Li, X. D., et al. (2017). Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan, China. Environment Pollution, 225, 681–690. https://doi.org/10.1016/j.envpol.2017.03.057
Liao, S. Y., Jin, G. Q., Muhammad, A. K., Zhu, Y. W., Duan, L., Luo, W. X., Jia, J., et al. (2020). The quantitative source apportionment of heavy metals in peri-urban agricultural soils with UNMIX and input fluxes analysis. Environmental Technology & Innovation. https://doi.org/10.1016/j.eti.2020.101232
Lin, Y. P., & Cheng ShyuChang, B. Y. G. S. T. K. (2009). Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, Central Taiwan. Environment Pollution, 158, 235–244. https://doi.org/10.1016/j.envpol.2009.07.015
Luo, X. S., Xue, Y., Wang, Y. L., Cang, L., Xu, B., & Ding, J. (2015). Source identification and apportionment of heavy metals in urban soil profiles. Chemosphere, 127, 152–157. https://doi.org/10.1016/j.chemosphere.2015.01.048
Lv, J. S., & Liu, Y. (2018). An integrated approach to identify quantitative sources and hazardous areas of heavy metals in soils. Science of the Total Environment, 646, 19–28. https://doi.org/10.1016/j.scitotenv.2018.07.257
Manoli, E., Voutsa, D., & Samara, C. (2002). Chemical characterization and source identification/apportionment of fine and coarse air particles in Thessaloniki, Greece. Atmospheric Environment, 36, 949–961. https://doi.org/10.1016/S1352-2310(01)00486-1
Marchant, B. P., Saby, N. P. A., & Arrouays, D. (2017). A survey of topsoil arsenic and mercury concentrations across France. Chemosphere, 181, 635–644. https://doi.org/10.1016/j.chemosphere.2017.04.106
Marchant, B. P., Saby, N. P. A., Jolivet, C. C., Arrouays, D., & Lark, R. M. (2011). Spatial prediction of soil properties with copulas. Geoderma, 162, 327–334. https://doi.org/10.1016/j.geoderma.2011.03.005
Mehr, M. R., Keshavarzi, B., Moore, F., Reza, S., Lahijanzadeh, A., & Kermani, M. (2017). Distribution, source identification and health risk assessment of soil heavy metals in urban areas of Isfahan province Iran. Journal of African Earth Science, 132, 16–26. https://doi.org/10.1016/j.jafrearsci.2017.04.026
Men, C., Liu, R. M., Wang, Q. R., Guo, L. J., & Shen, Z. Y. (2018). The impact of seasonal varied human activity on characteristics sources of heavy metals in metropolitan road dusts. Science of the Total Environment, 637–638, 844–854. https://doi.org/10.1016/j.scitotenv.2018.05.059
Nabulo, G., Origia, H., & Diamond, M. (2006). Assessment of Lead, Cadmium, and Zinc contamination of road side soils surface films, and vegetables in Kampala City Uganda. Environmental Research, 101(1), 42–52. https://doi.org/10.1016/j.envres.2005.12.016
Nanos, N., & Martín, J. A. R. (2012). Multiscale analysis of heavy metal contents in soils: Spatial variability in the Duero river basin (Spain). Geoderma, 189–190, 554–562. https://doi.org/10.1016/j.geoderma.2012.06.006
Norris, G., & Duvall, R. (2006). EPA Positive Martix Factorization(PMF) 5.0 fundamentals and user guide. Resource document. U.S. Environmental Protection Agency. https://www.epa.gov/air-research/epa-positive-matrix-factorization-50-fundamentals-and-user-guide. Accessed in Apr 2014.
Ogunlaja, A., Ogunlaja, O. O., Okewole, D. M., & Morenkeji, O. A. (2019). Risk assessment and source identification of heavy metal contamination by multivariate and hazard index analyses of a pipeline vandalised area in Lagos State Nigeria. Science of The Total Environment, 651(2), 2943–2952. https://doi.org/10.1016/j.scitotenv.2018.09.386
Pekey, H., & Dogan, G. (2013). Application of positive matrix factorization for the source apportionment of heavy metals in sediments: A comparison with a previous factor analysis study. Microchemical Journal, 106, 233–237. https://doi.org/10.1016/j.microc.2012.07.007
Pirsaheb, M., Almasi, A., Sharafi, K., Jabari, Y., & Haghighi, S. (2016). A comparative study of heavy metals concentration of surface soils at metropolis squares with high traffic: A case study: Kermanshah Iran. Acta Medica Mediterranea, 32, 891–897.
Proshad, R., Kormoker, T., & Islam, S. (2019). Distribution, source identification, ecological and health risks of heavy metals in surface sediments of the Rupsa River. Toxin Reviews. https://doi.org/10.1080/15569543.2018.1564143
Qu, M. K., Li, W. D., Zhang, C. R., Wang, S. Q., Yang, Y., & He, L. Y. (2013). Source apportionment of heavy metals in soils using multivariate statistics and geostatistics. Pedosphere, 23, 437–444. https://doi.org/10.1016/S1002-0160(13)60036-3
Saby, N. P. A., Arrouays, D., Boulonne, L., Jolivet, C., & Pochot, A. (2006). Geostatistical assessment of Pb in soil around Paris France. Science of the Total Environment, 367(1), 212–221. https://doi.org/10.1016/j.scitotenv.2005.11.028
Schneider, A. R., Morvan, X., Saby, N. P. A., Cancès, B., Ponthieu, M., Gommeaux, M., et al. (2016). Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter. Environmental Science and Pollution Research, 23(15), 15164–15174. https://doi.org/10.1007/s11356-016-6624-2
Shao, S., Hu, B. F., Fu, Z. Y., Wang, J. Y., Lou, G., Zhou, Y., et al. (2018). Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China. International Journal of Environment Research and Public Health, 15, 1240. https://doi.org/10.3390/ijerph15061240
Song, H. Y., Hu, K. L., An, Y., Chen, C., & Li, G. D. (2018). Spatial distribution and source apportionment of the heavy metals in the agricultural soil in a regional scale. Journal of Soils and Sediments, 18, 852–862. https://doi.org/10.1007/s11368-017-1795-0
Sungur, A., Soylak, M., & Ozkan, H. (2019). Fractionation, Source Identification and Risk Assessments for Heavy Metals in Soils near a Small-Scale Industrial Area (Çanakkale-Turkey). Soil and Sediment Contamination: An International Journal, 28(2), 213–227. https://doi.org/10.1080/15320383.2018.1564735
Tian, S. H., Liang, T., Li, K. X., & Wang, L. Q. (2018). Source and path identification of metals pollution in a mining area by PMF and rare earth element patterns in road dust. Science of the Total Environment, 633, 958–966. https://doi.org/10.1016/j.scitotenv.2018.03.227
Uría, A. F., Mateo, C. L., Roca, E., & Marcos, M. L. F. (2009). Source identification of heavy metals in pastureland by multivariate analysis in NW Spain. Journal of Hazardous Materials, 165(1–3), 1008–1015. https://doi.org/10.1016/j.jhazmat.2008.10.118
Wang, B., Xia, D. S., Yu, Y., Chen, H., & Jia, J. (2018). Source apportionment of soil-contamination in Baotou City (North China) based on a combined magnetic and geochemical approach. Science of the Total Environment, 642, 95–104. https://doi.org/10.1016/j.scitotenv.2018.06.050
Wang, S. F., Li, R. A., & Ye, Z. J. (2007). Zhejiang soil, second edition. Zhejiang Science and Technology Press.
Wei, B. G., & Yang, L. S. (2010). A Review of heavy metal contaminations in urban soils, urban road dusts and agricultural soils from China. Microchemical Journal, 94(2), 99–107. https://doi.org/10.1016/j.microc.2009.09.014
Xia, F., Hu, B. F., Shao, S., Xu, D. Y., Zhou, Y., Zhou, Y., et al. (2019). Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China. International Journal of Environmental Research and Public Health, 16, 2694. https://doi.org/10.3390/ijerph16152694
Xie, W. S., Peng, C., Wang, H. T., & Chen, W. P. (2018). Bioaccessibility and source identification of heavy metals in agricultural soils contaminated by mining activities. Environment Earth Sciences, 77, 606. https://doi.org/10.1007/s12665-018-7783-x
Yan, S. D. (2008). Source apportionment and spatial heterogeneity of agricultural non-point source pollution in Huangshi, Hubei Province. Transaction of the Chinese Society of Agricultural Engineering, 24, 225–228. (in Chinese).
Zhang, X. W., Wei, S., Sun, Q. Q., Syed, A. W., & Guo, B. L. (2018). Source identification and spatial distribution of arsenic and heavy metals in agricultural soil around Hunan industrial estate by positive matrix factorization model, principle components analysis and geo statistical analysis. Ecotoxicology and Environmental. Safety, 159, 354–362. https://doi.org/10.1016/j.ecoenv.2018.04.072
Zhi, Y. Y., Li, P., Shi, J. C., Zeng, L. Z., & Wu, L. S. (2016). Source identification and apportionment of soil cadmium in cropland of Eastern China: A combined approach of models and geographic information system. Journal of Soils and Sediments, 16, 467–475. https://doi.org/10.1007/s11368-015-1263-7
Zhu, G. X., Guo, Q. J., Xiao, H. Y., Chen, T. B., & 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. Environmental Science and Pollution Research, 24, 14877–14888. https://doi.org/10.1007/s11356-017-9055-9
Zhu, M. J., Tang, L., & Liu, D. Q. (2015). Summary of Monitoring and Evaluation of Soil Heavy Metals along the Traffic Road. China Environment Monitor, 31(03), 84–91. (in Chinese).
Acknowledgments
This work was supported by National Key Research and Development Program (2018YFC1800201). We also acknowledge the support received by Bifeng Hu from the China Scholarship Council (under grant agreement No. 201706320317) for 3 years’ Ph.D. study in INRAE and Orléans University in Orléans, France.
Funding
This work was supported by National Key Research and Development Program (2018YFC1800201).
Author information
Authors and Affiliations
Contributions
SS was involved in conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, and visualization. BH was involved in conceptualization, validation, formal analysis, writing—review & editing, and visualization. YT was involved in software, formal analysis, formal analysis, and visualization. QY was involved in software, investigation, and validation. MH was involved in data curation and funding acquisition. LZ was involved in investigation and project administration. QCh was involved in conceptualization, methodology, formal analysis, writing—review & editing, and supervision. ZS was involved in conceptualization, methodology, validation, writing—review & editing, and funding acquisition.
Corresponding author
Ethics declarations
Conflicts of interests
The authors have no financial or proprietary interests in any material discussed in this article.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Consent to publication
The participant has consented to the submission of the case report to the journal.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shao, S., Hu, B., Tao, Y. et al. Comprehensive source identification and apportionment analysis of five heavy metals in soils in Wenzhou City, China. Environ Geochem Health 44, 579–602 (2022). https://doi.org/10.1007/s10653-021-00881-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10653-021-00881-7