1
|
Duan D, Wang P, Rao X, Zhong J, Xiao M, Huang F, Xiao R. Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173284. [PMID: 38768726 DOI: 10.1016/j.scitotenv.2024.173284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/22/2024]
Abstract
The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.
Collapse
Affiliation(s)
- Deyu Duan
- School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Peng Wang
- School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
| | - Xin Rao
- School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510420, China
| | - Junhong Zhong
- School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China
| | - Meihong Xiao
- School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Fei Huang
- School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Rongbo Xiao
- School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
| |
Collapse
|
2
|
Chandra K, Proshad R, Islam M, Idris AM. An integrated overview of metals contamination, source-specific risks investigation in coal mining vicinity soils. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2023; 45:7425-7458. [PMID: 37452259 DOI: 10.1007/s10653-023-01672-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023]
Abstract
Heavy metals in soil are harmful to natural biodiversity and human health, and it is difficult to estimate the effects accurately. To reduce pollution and manage risk in coal-mining regions, it is essential to evaluate risks for heavy metals in soil. The present study reviews the levels of 21 metals (Nb, Zr, Ag, Ni, Na, K, Mg, Rb, Zn, Ca, Sr, As, Cr, Fe, Pb, Cd, Co, Hg, Cu, Mn and Ti) in soils around Barapukuria coal-mining vicinity, Bangladesh which were reported in literature. An integrated approach for risk assessments with the positive matrix factorization (PMF) model, source-oriented ecological and health hazards were applied for the study. The contents of Rb, Ca, Zn, Pb, As, Ti, Mn, Co, Ag, Zr, and Nb were 1.63, 1.10, 1.97, 14.12, 1.20, 3.13, 1.22, 3.05, 3.85, 5.48, and 7.21 times greater than shale value. About 37%, 67%, 12%, and 85% of sampling sites posed higher risks according to the modified contamination factor, Nemerow pollution index, Nemerow integrated risk index, and mean effect range median quotient, respectively. Five probable metal sources were computed, including industrial activities to coal mining (17%), agricultural activities (33%), atmospheric deposition (19%), traffic emission (16%), and natural sources (15%). Modified Nemerow integrated risk index reported that agricultural activities, industrial coal mining activities, and atmospheric deposition showed moderate risk. Health hazards revealed that cancer risk values computed by the PMF-HHR model with identified sources were higher than the standard value (1.0E-04) for children, adult male, and female. Agricultural activities showed higher cancer risks to adult male (39%) and children (32%) whereas traffic emission contributed to female (25%). These findings highlight the ecological and health issues connected to potential sources of metal contamination and provide useful information to policymakers on how to reduce such risks.
Collapse
Affiliation(s)
- Krishno Chandra
- Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet, 3100, Bangladesh
| | - Ram Proshad
- Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, Sichuan, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Maksudul Islam
- Department of Environmental Science, Patuakhali Science and Technology University, Dumki, Patuakhali, 8602, Bangladesh
| | - Abubakr M Idris
- Department of Chemistry, College of Science, King Khalid University, Abha, 62529, Saudi Arabia
- Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, 62529, Saudi Arabia
| |
Collapse
|
3
|
Qiu Y, Zhou S, Zhang C, Qin W, Lv C, Zou M. Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 877:162891. [PMID: 36940748 DOI: 10.1016/j.scitotenv.2023.162891] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/10/2023] [Accepted: 03/12/2023] [Indexed: 05/06/2023]
Abstract
Soil microplastic (MP) pollution has recently become increasingly aggravated, with severe consequences being generated. Understanding the spatial distribution characteristics of soil MPs is an important prerequisite for protecting and controlling soil pollution. However, determining the spatial distribution of soil MPs through a large number of soil field sampling and laboratory test analyses is unrealistic. In this study, we compared the accuracy and applicability of different machine learning models for predicting the spatial distribution of soil MPs. The support vector machine regression model with radial basis function (RBF) as kernel function (SVR-RBF) has a high prediction accuracy (R2 = 0.8934). Among the six ensemble models, random forest (R2 = 0.9007) could better explain the significance of source and sink factors affecting the occurrence of soil MPs. Soil texture, population density, and MPs point of interest (MPs-POI) were the main source-sink factors affecting the occurrence of soil MPs. Furthermore, the accumulation of MPs in soil was significantly affected by human activity. The spatial distribution map of soil MP pollution in the study area was drawn based on the bivariate local Moran's I model of soil MP pollution and the normalized difference vegetation index (NDVI) variation trend. A total of 48.74 km2 of soil was in an area of serious MP pollution, mainly concentrated in urban soil. This study provides a hybrid framework that includes spatial distribution prediction of MPs, source-sink analysis, and pollution risk area identification, providing scientific and systematic methods and techniques for pollution management in other soil environments.
Collapse
Affiliation(s)
- Yifei Qiu
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210024, China
| | - Shenglu Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210024, China.
| | - Chuchu Zhang
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China
| | - Wendong Qin
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210024, China
| | - Chengxiang Lv
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210024, China
| | - Mengmeng Zou
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210024, China
| |
Collapse
|
4
|
Xiao M, Xu S, Yang B, Zeng G, Qian L, Huang H, Ren S. Contamination, Source Apportionment, and Health Risk Assessment of Heavy Metals in Farmland Soils Surrounding a Typical Copper Tailings Pond. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192114264. [PMID: 36361145 PMCID: PMC9656670 DOI: 10.3390/ijerph192114264] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/23/2022] [Accepted: 10/26/2022] [Indexed: 06/05/2023]
Abstract
Tailings resulting from mining and smelting activities may cause soil heavy-metal pollution and harm human health. To evaluate the environmental impact of heavy metals from tailings on farmland soils in the surrounding area, heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in tailings and farmland soils in the vicinity of a typical copper tailings pond were analyzed. Contamination status, potential sources, and health risks for farmland soils were investigated. The results showed that the tailings contained a high concentration of Cu (1136.23 mg/kg). The concentrations of Cd and Cu in the farmland soils exceeded the soil quality standard. The geoaccumulation index (Igeo) indicated that the soils were moderately polluted by Cu and Cd, and slightly polluted by Ni, Cr, and Zn. The absolute principal component scores-multiple linear regression (APCS-MLR) model was applied for source apportionment. The results showed that tailings release is the main source of soil heavy-metals contamination, accounting for 35.81%, followed by agricultural activities (19.41%) and traffic emission (16.31%). The health risk assessment suggested that the children in the study region were exposed to non-carcinogenic risks caused by As, while the non-carcinogenic risk to adults and the carcinogenic risk to both adults and children were at acceptable levels. It is necessary to take effective measures to control heavy-metal contamination from tailings releases to protect humans, especially children, from adverse health risks.
Collapse
Affiliation(s)
- Minsi Xiao
- Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi University of Science and Technology, Ganzhou 341400, China
| | - Shitong Xu
- Jiangxi Key Laboratory of Mining Engineering, Jiangxi University of Science and Technology, Ganzhou 341400, China
| | - Bing Yang
- Jiangxi Key Laboratory of Mining Engineering, Jiangxi University of Science and Technology, Ganzhou 341400, China
| | - Guangcong Zeng
- Jiangxi Key Laboratory of Mining Engineering, Jiangxi University of Science and Technology, Ganzhou 341400, China
| | - Lidan Qian
- Jiangxi Key Laboratory of Mining Engineering, Jiangxi University of Science and Technology, Ganzhou 341400, China
| | - Haiwei Huang
- Jiangxi Key Laboratory of Mining Engineering, Jiangxi University of Science and Technology, Ganzhou 341400, China
| | - Sili Ren
- Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi University of Science and Technology, Ganzhou 341400, China
- Jiangxi Key Laboratory of Mining Engineering, Jiangxi University of Science and Technology, Ganzhou 341400, China
| |
Collapse
|
5
|
Yin G, Chen X, Zhu H, Chen Z, Su C, He Z, Qiu J, Wang T. A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 825:153948. [PMID: 35219652 DOI: 10.1016/j.scitotenv.2022.153948] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/13/2022] [Accepted: 02/13/2022] [Indexed: 06/14/2023]
Abstract
To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.
Collapse
Affiliation(s)
- Guangcai Yin
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Xingling Chen
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Hanghai Zhu
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhiliang Chen
- Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China
| | - Chuanghong Su
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China
| | - Zechen He
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Jinrong Qiu
- Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China
| | - Tieyu Wang
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China.
| |
Collapse
|
6
|
Zhang B, Guo B, Zou B, Wei W, Lei Y, Li T. Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 300:118981. [PMID: 35150799 DOI: 10.1016/j.envpol.2022.118981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/21/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Soil heavy metals pollution has been becoming one of the severely environmental issues globally. Previous studies reported laboratory-measured spectra could be used to infer soil heavy metals concentrations to some extent. However, using field-obtained spectra to estimate soil heavy metals concentrations is still a great challenge due to the low precision and weak efficiency at large scales. The present study collected 110 topsoil samples from an Opencast Coal Mine of Ordos, Inner Mongolia, China. Then, the spectra and soil heavy metals concentrations of samples were measured under laboratory conditions. The direct standardization (DS) algorithm was introduced to calibrate the Gaofen-5 (GF-5) hyperspectral image based on the measured spectra of samples. The spectral reflectance of the GF-5 hyperspectral image was reconstructed using continuous wavelet transform (CWT) at different scales. The characteristic bands of GF-5 for estimating heavy metals concentrations were selected by the Boruta algorithm. Finally, the random forest (RF), the extreme learning machine (ELM), the support vector machine (SVM), and the back-propagation neural network (BPNN) algorithms were used to predict the heavy metals concentrations. Some findings were achieved. First, CWT can effectively eliminate the noise of satellite hyperspectral data. The characteristic bands of Zn (480-677, 827-1029, 1241-1334, 1435-1797, and 1949-2500 nm), Ni (514-630, 835-985, 1258-1325, 1460-1578, and 1949-2319 nm), and Cu (822-831; 1029-1300, 1486-1595, and 1730-2294 nm) can be effectively retrieved via the Boruta algorithm. Second, the estimation accuracy was significantly improved by using the DS algorithm. For zinc (Zn), nickel (Ni), and copper (Cu), the determination coefficients of the validation dataset (Rv2) were 0.77 (RF), 0.62 (RF), and 0.56 (ELM), respectively. Third, the distribution trends of heavy metals were almost consistent with the results of actual ground measurements. This paper revealed that the GF-5 can be one of the reliable satellite hyperspectral imagery for mapping soil heavy metals.
Collapse
Affiliation(s)
- Bo Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China
| | - Wei Wei
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yongzhi Lei
- China Power Construction Group Northwest Survey, Design and Research Institute Co, Ltd, Xi'an, 710065, China
| | - Tianqi Li
- China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, 100083, China
| |
Collapse
|