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Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China

文献类型：期刊论文

作者Kong, Xiangyi3; Liu, Yang2; Duan, Zongqi1; Lv, Jianshu3
刊名JOURNAL OF HAZARDOUS MATERIALS
出版日期2024-09-05
卷号476页码:13
关键词Source apportionment Spatial distribution Potentially toxic elements Bayesian receptor model Convolutional neural network
ISSN号0304-3894
DOI10.1016/j.jhazmat.2024.135184
英文摘要Determining sources and spatial distributions of potentially toxic elements (PTEs) is a crucial issue of soil pollution survey. However, uncertainty estimation for source contributions remains lack, and accurate spatial prediction is still challenging. Robust Bayesian multivariate receptor model (RBMRM) was applied to the soil dataset of Qingzhou City (8 PTEs in 429 samples), to calculate source contributions with uncertainties. Multi-task convolutional neural network (MTCNN) was proposed to predict spatial distributions of soil PTEs. RBMRM afforded three sources, consistent with US-EPA positive matrix factorization. Natural source dominated As, Cr, Cu, and Ni contents (78.5 %similar to 86.1 %), and contributed 37.1 %, 61.0 %, and 65.9 % of Cd, Pb, and Zn, exhibiting low uncertainties with uncertainty index (UI) < 26.7 %. Industrial, traffic, and agricultural sources had significant influences on Cd, Pb, and Zn (30.2 %similar to 61.9 %), with UI < 39.3 %. Hg originated dominantly from atmosphere deposition (99.1 %), with relatively high uncertainties (UI=87.7 %). MTCNN acquired satisfactory accuracies, with R-2 of 0.357-0.896 and nRMSE of 0.092-0.366. Spatial distributions of As, Cd, Cr, Cu, Ni, Pb, and Zn were influenced by parent materials. Cd, Hg, Pb, and Zn showed significant hotspot in urban area. This work conducted a new approach exploration, and practical implications for soil pollution regulation were proposed.
WOS关键词SEQUENTIAL GAUSSIAN SIMULATION ; HEAVY-METALS ; AGRICULTURAL SOILS ; MERCURY EMISSIONS ; SOURCE IDENTIFICATION ; SOURCE APPORTIONMENT ; ATMOSPHERIC MERCURY ; GREENHOUSE SOILS ; AREAS ; PMF
资助项目National Natural Science Foundation of China[42271083] ; Natural Science Foundation of Shandong Province[ZR2020YQ31]
WOS研究方向Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001274978500001
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province
源URL[http://ir.igsnrr.ac.cn/handle/311030/206990]  
专题科技平台与基建处_地理学报编辑部
通讯作者Lv, Jianshu
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Jinan, Business Sch, Jinan 250022, Peoples R China
3.Shandong Normal Univ, Coll Geog & Environm, Jinan 250014, Peoples R China
推荐引用方式
GB/T 7714		 Kong, Xiangyi,Liu, Yang,Duan, Zongqi,et al. Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China[J]. JOURNAL OF HAZARDOUS MATERIALS,2024,476:13.
APA Kong, Xiangyi,Liu, Yang,Duan, Zongqi,&Lv, Jianshu.(2024).Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China.JOURNAL OF HAZARDOUS MATERIALS,476,13.
MLA Kong, Xiangyi,et al."Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China".JOURNAL OF HAZARDOUS MATERIALS 476(2024):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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