Analysis of the Source Apportionment and Pathways of Heavy Metals in Soil in a Coal Mining Area Based on Machine Learning and an APCS-MLR Model

被引:0
|
作者
Chen, Yeyu [1 ]
Zhao, Jiyang [1 ]
Chen, Xing [1 ,2 ]
Zheng, Liugen [1 ]
机构
[1] Anhui Univ, Anhui Prov Engn Lab Mine Ecol Remediat, Hefei 230601, Peoples R China
[2] Anhui Jianzhu Univ, Sch Environm & Energy Engn, Hefei 230601, Peoples R China
基金
中国国家自然科学基金;
关键词
coal mining area; heavy metals; APCS-MLR; source apportionment; pollution pathways; RISK-ASSESSMENT; POLLUTION;
D O I
10.3390/min14010054
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Long-term coal mining activities have led to severe heavy metal pollution in the soil environment of coal mining areas, posing significant threats to both ecological environments and human health. In this study, surface soil samples were collected from the overlying soil of coal gangue and the surrounding areas of the Panyi coal mine in Huainan. The concentrations of Cd, Zn, Cu, Ni, and Pb elements were determined. A self-organizing map (SOM) and an absolute principal component score multiple linear regression (APCS-MLR) receptor model were employed for the quantitative analysis of the soil's heavy metal pollution sources and contributions. Additionally, this study focused on the analysis of the pathways of the relatively serious pollution of Cd. The results revealed that the average concentrations of heavy metals (Cd, Pb, Zn, Cu, Cr, and Ni) in the study area were 4.55, 0.59, 1.54, 0.69, 0.59, and 0.71 times the local soil background values, respectively. The concentrations of Cd and Zn exceeded the risk screening values at some sampling points, with exceedance rates of 44% and 8%, respectively, indicating a relatively serious Cd contamination. The sources of heavy metals in the soil in the study area were classified into four categories: mining activities, agricultural activities, weathering of natural matrices, and other unknown sources, with average contributions of 55.48 percent, 24.44 percent, 8.91 percent and 11.86 percent, respectively. Based on the spatial distribution of Cd, it was inferred that atmospheric deposition is one of the important pollution pathways of Cd in the study area. Cd profile distribution patterns and a surface water pollution survey showed that the farmland areas were affected by the irrigation water pathway to some extent. The vertical distribution of heavy metal content in the forest area showed a strong disorder, which was related to the absorption function of plant roots. The results of this study can help to improve the environmental management of heavy metal pollution so as to protect the ecological environment and human health.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model
    Liu, Hong
    Ma, Jiawei
    Taj, Raheela
    Xu, Meizhen
    Lou, Fei
    Liu, Wenbin
    Xu, Yan
    Xu, Jingwen
    Xu, Yaonan
    Liu, Dan
    [J]. Environmental Science and Pollution Research, 2024, 31 (24) : 34953 - 34961
  • [22] Contamination Characteristics and Source Apportionment of Potentially Toxic Elements in Soil around the Coal-fired Power Plant Using APCS-MLR and PMF Models
    Li J.
    Li X.
    Gao S.-G.
    Li K.-M.
    Jiao L.
    Zang F.
    Pan W.-H.
    Tai X.-S.
    [J]. Huanjing Kexue/Environmental Science, 2023, 44 (10): : 5689 - 5703
  • [23] Environmental Risk Assessment of Heavy Metals in Cultivated Soils Around Industrial Area and Source Identification under APCS-MLR Model: a Case of Suzhou in Northern China
    Cheng, Chen
    Xu, Zhimin
    Feng, Songbao
    Wang, Ling
    [J]. POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2023, 32 (05): : 3989 - 3998
  • [24] Source Apportionment of Heavy Metals in Farmland Soils Around Mining Area Based on UNMIX Model
    Lu X.
    Hu W.-Y.
    Huang B.
    Li Y.
    Zu Y.-Q.
    Zhan F.-D.
    Kuang R.-X.
    [J]. Huanjing Kexue/Environmental Science, 2018, 39 (03): : 1421 - 1429
  • [25] Source identification and driving factor apportionment for soil potentially toxic elements via combining APCS-MLR, UNMIX, PMF and GDM
    Gong, Cang
    Xia, Xiang
    Lan, Mingguo
    Shi, Youchang
    Lu, Haichuan
    Wang, Shunxiang
    Chen, Ying
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [26] APCS-MLR Combined with PMF Model to Analyze the Source of Metals in Sediment of Xinglin Bay Suburban Watershed, Xiamen
    Shen C.-Y.
    Yan Y.
    Yu R.-L.
    Hu G.-R.
    Cui J.-Y.
    Yan Y.
    Huang H.-B.
    [J]. Huanjing Kexue/Environmental Science, 2022, 43 (05): : 2476 - 2488
  • [27] Source Analysis of Heavy Metals in Typical Farmland Soils Based on PCA-APCS-MLR and Geostatistics
    Wang M.-H.
    [J]. Huanjing Kexue/Environmental Science, 2023, 44 (06): : 3509 - 3519
  • [28] Risk Assessment of Heavy Metals in Dust Fall Around Reservoirs in Typical Ecologically Fragile Areas and Traceability Based on APCS-MLR Model
    Shi Z.-Y.
    Lu J.-P.
    Liu T.-X.
    Zhang X.-J.
    Jia Y.-Q.
    Wang Y.
    Liu Y.-H.
    [J]. Huanjing Kexue/Environmental Science, 2023, 44 (10): : 5344 - 5355
  • [29] Spatiotemporal Distribution and Source Analysis of Heavy Metals in Surface Sediments in Lake Ulansuhai Based on PCA-APCS-MLR Model
    Cui Z.-M.
    Shi X.-H.
    Zhao S.-N.
    Lu J.-P.
    Zhang H.
    Liu Y.-H.
    Guo X.
    Wang Y.-J.
    [J]. Huanjing Kexue/Environmental Science, 2024, 45 (03): : 1415 - 1427
  • [30] Source Apportionment of Agricultural Soil Heavy Metals Based on PMF Model and Multivariate Statistical Analysis
    Liu, Hong
    Anwar, Shazma
    Fang, Liqiang
    Chen, Linhua
    Xu, Weijie
    Xiao, Linlin
    Zhong, Bin
    Liu, Dan
    [J]. ENVIRONMENTAL FORENSICS, 2024, 25 (1-2) : 40 - 48