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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.
Ecotoxicol Environ Saf. 2018 Sep 15; 159:354-362.EE

Abstract

Characterizing the distribution and defining potential sources of arsenic and heavy metals are the basic preconditions for reducing the contamination of heavy metals and metalloids. 71 topsoil samples and 61 subsoil samples were collected by grid method to measure the concentration of cadmium (Cd), arsenic (As), lead (Pb), copper (Cu), zinc (Zn), nickel (Ni) and chromium (Cr). Principle components analysis (PCA), GIS-based geo-statistical methods and Positive Matrix Factorization (PMF) were applied. The results showed that the mean concentrations were 9.59 mg kg-1, 51.28 mg kg-1, 202.07 mg kg-1, 81.32 mg kg-1 and 771.22 mg kg-1 for Cd, As, Pb, Cu and Zn, respectively, higher than the guideline values of Chinese Environmental Quality Standard for Soils; while the concentrations of Ni and Cr were very close to recommended value (50 mg kg-1, 200 mg kg-1), and some site were higher than guideline values. The soil was polluted by As and heavy metals in different degree, which had harmful impact on human health. The results from principle components analysis methods extracted three components, namely industrial sources (Cd, Zn and Pb), agricultural sources (As and Cu) and nature sources (Cr and Ni). GIS-based geo-statistical combined with local conditions further apportioned the sources of these trace elements. To better identify pollution sources of As and heavy metals in soil, the PMF was applied. The results of PMF demonstrated that the enrichment of Zn, Cd and Pb were attributed to industrial activities and their contribution was 24.9%; As was closely related to agricultural activities and its contribution was 19.1%; Cr, a part of Cu and Ni were related to subsoil and their contribution was 30.1%; Cu and Pb came from industry and traffic emission and their contribution was 25.9%.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Zhang X
Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences/ Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture, Beijing 100193, PR China.
Wei S
Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences/ Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture, Beijing 100193, PR China.
Sun Q
Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences/ Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture, Beijing 100193, PR China.
Wadood SA
Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences/ Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture, Beijing 100193, PR China.
Guo B
Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences/ Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture, Beijing 100193, PR China. Electronic address: guoboli2007@126.com.

MeSH

AgricultureArsenicChinaData Interpretation, StatisticalEnvironmental MonitoringIndustryMetals, HeavyPrincipal Component AnalysisSoil PollutantsTrace Elements

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29778047

Citation

Zhang, Xiaowen, et al. "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, vol. 159, 2018, pp. 354-362.
Zhang X, Wei S, Sun Q, et al. 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. Ecotoxicol Environ Saf. 2018;159:354-362.
Zhang, X., Wei, S., Sun, Q., Wadood, S. A., & Guo, B. (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
Zhang X, et al. 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. Ecotoxicol Environ Saf. 2018 Sep 15;159:354-362. PubMed PMID: 29778047.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - 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. AU - Zhang,Xiaowen, AU - Wei,Shuai, AU - Sun,Qianqian, AU - Wadood,Syed Abdul, AU - Guo,Boli, Y1 - 2018/05/21/ PY - 2018/01/19/received PY - 2018/04/20/revised PY - 2018/04/29/accepted PY - 2018/5/20/pubmed PY - 2018/9/21/medline PY - 2018/5/20/entrez KW - Geo-statistical analysis KW - Heavy metals and metalloids KW - PCA KW - PMF KW - Source identification SP - 354 EP - 362 JF - Ecotoxicology and environmental safety JO - Ecotoxicol Environ Saf VL - 159 N2 - Characterizing the distribution and defining potential sources of arsenic and heavy metals are the basic preconditions for reducing the contamination of heavy metals and metalloids. 71 topsoil samples and 61 subsoil samples were collected by grid method to measure the concentration of cadmium (Cd), arsenic (As), lead (Pb), copper (Cu), zinc (Zn), nickel (Ni) and chromium (Cr). Principle components analysis (PCA), GIS-based geo-statistical methods and Positive Matrix Factorization (PMF) were applied. The results showed that the mean concentrations were 9.59 mg kg-1, 51.28 mg kg-1, 202.07 mg kg-1, 81.32 mg kg-1 and 771.22 mg kg-1 for Cd, As, Pb, Cu and Zn, respectively, higher than the guideline values of Chinese Environmental Quality Standard for Soils; while the concentrations of Ni and Cr were very close to recommended value (50 mg kg-1, 200 mg kg-1), and some site were higher than guideline values. The soil was polluted by As and heavy metals in different degree, which had harmful impact on human health. The results from principle components analysis methods extracted three components, namely industrial sources (Cd, Zn and Pb), agricultural sources (As and Cu) and nature sources (Cr and Ni). GIS-based geo-statistical combined with local conditions further apportioned the sources of these trace elements. To better identify pollution sources of As and heavy metals in soil, the PMF was applied. The results of PMF demonstrated that the enrichment of Zn, Cd and Pb were attributed to industrial activities and their contribution was 24.9%; As was closely related to agricultural activities and its contribution was 19.1%; Cr, a part of Cu and Ni were related to subsoil and their contribution was 30.1%; Cu and Pb came from industry and traffic emission and their contribution was 25.9%. SN - 1090-2414 UR - https://www.unboundmedicine.com/medline/citation/29778047/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_ DB - PRIME DP - Unbound Medicine ER -