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Zha Y, Yang Y. Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China. Sci Rep 2024; 14:16505. [PMID: 39019919 PMCID: PMC11255285 DOI: 10.1038/s41598-024-67175-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/09/2024] [Indexed: 07/19/2024] Open
Abstract
Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network (GNN) model, Multi-Scale Attention-based Graph Neural Network for Heavy Metal Prediction (MSA-GNN-HMP). The model integrates multi-scale graph convolutional network (MS-GCN) and attention-based GNN (AGNN) to capture spatial relationships. Using surface soil samples from the Pearl River Basin, we evaluate the MSA-GNN-HMP model against four other models. The experimental results show that the MSA-GNN-HMP model has the best predictive performance for Cd and Pb, with a coefficient of determination (R2) of 0.841 for Cd and 0.886 for Pb, and the lowest mean absolute error (MAE) of 0.403 mg kg-1 for Cd and 0.670 mg kg-1 for Pb, as well as the lowest root mean square error (RMSE) of 0.563 mg kg-1for Cd and 0.898 mg kg-1 for Pb. In feature importance analysis, latitude and longitude emerged as key factors influencing the heavy metal content. The spatial distribution prediction trend of heavy metal elements by different prediction methods is basically consistent, with the high-value areas of Cd and Pb respectively distributed in the northwest and northeast of the basin center. However, the MSA-GNN-HMP model demonstrates superior detail representation in spatial prediction. MSA-GNN-HMP model has excellent spatial information representation capabilities and can more accurately predict heavy metal content and spatial distribution, providing a new theoretical basis for monitoring, assessing, and managing soil pollution.
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Affiliation(s)
- Yannan Zha
- Guangzhou Institute of Technology, Guangzhou, Computer Simulation Research and Development Center, 465 Huanshi East Road, Guangzhou, 510075, China.
| | - Yao Yang
- Guangdong Provincial Key Laboratory of Agricultural & Rural Pollution Abatement and Environmental Safety, College of Natural Resources and Environment, Joint Institute for Environment & Education, South China Agricultural University, 483 Wushan St., Guangzhou, 510642, China
- Key Laboratory of Arable Land Conservation (South China), Ministry of Agriculture, Guangzhou, 510642, China
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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.
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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.
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Lv S, Zhu Y, Cheng L, Zhang J, Shen W, Li X. Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172223. [PMID: 38588737 DOI: 10.1016/j.scitotenv.2024.172223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/06/2024] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.
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Affiliation(s)
- Songjian Lv
- Center for Health Geology & Carbon Peak and Carbon Neutrality of Lanzhou University, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Ying Zhu
- Center for Health Geology & Carbon Peak and Carbon Neutrality of Lanzhou University, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Li Cheng
- Center for Health Geology & Carbon Peak and Carbon Neutrality of Lanzhou University, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Jingru Zhang
- Center for Health Geology & Carbon Peak and Carbon Neutrality of Lanzhou University, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; Guangdong Province Academic of Environmental Science, Guangzhou 510045, China
| | - Wenjie Shen
- School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519000, China
| | - Xingyuan Li
- Center for Health Geology & Carbon Peak and Carbon Neutrality of Lanzhou University, Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.
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Zou Z, Wang Q, Wu Q, Li M, Zhen J, Yuan D, Zhou M, Xu C, Wang Y, Zhao Y, Yin S, Xu L. Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 355:120503. [PMID: 38457894 DOI: 10.1016/j.jenvman.2024.120503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/16/2024] [Accepted: 02/25/2024] [Indexed: 03/10/2024]
Abstract
The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.
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Affiliation(s)
- Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Menghua Li
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Dongyu Yuan
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Man Zhou
- College of Food Science, Sichuan Agricultural University, Ya'an, 625014, China
| | - Chong Xu
- Ruijie Networks Co., Ltd., Chengdu, 610000, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Shutao Yin
- Institute of Modern Agricultural Industry, China Agricultural University, Chengdu, Sichuan, 611430, China.
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China.
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Yaseen ZM, Melini Wan Mohtar WH, Homod RZ, Alawi OA, Abba SI, Oudah AY, Togun H, Goliatt L, Ul Hassan Kazmi SS, Tao H. Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm. CHEMOSPHERE 2024; 352:141329. [PMID: 38296204 DOI: 10.1016/j.chemosphere.2024.141329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/09/2024] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.
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Affiliation(s)
- Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Wan Hanna Melini Wan Mohtar
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM, Bangi, Selangor, Malaysia; Environmental Management Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| | - Raad Z Homod
- Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basra, Iraq.
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Malaysia.
| | - Sani I Abba
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Atheer Y Oudah
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, 64001, Iraq; Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, 64001, Iraq.
| | - Hussein Togun
- Department of Mechanical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq.
| | - Leonardo Goliatt
- Computational and Applied Mechanics Department, Federal University of Juiz de Fora, 36036-900, Brazil.
| | - Syed Shabi Ul Hassan Kazmi
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, 515063, China.
| | - Hai Tao
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Institute of Big Data Application and Artificial Intelligence, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Faculty of Data Science and Information Technology, INTI International University, 71800, Malaysia.
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Niezgoda M, Dziubanek G, Rogala D, Niesler A. Health Risks for Consumers of Forest Ground Cover Produce Contaminated with Heavy Metals. TOXICS 2024; 12:101. [PMID: 38393196 PMCID: PMC10892603 DOI: 10.3390/toxics12020101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/08/2024] [Accepted: 01/21/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND The activity of heavy metal (HM) mining and processing industries causes soils contamination with HM. The metals could be transferred from contaminated soils to edible plants and fungi. This study aimed to assess the content of Cd, Pb, Hg, As, and Ni in berries and edible mushrooms collected in the forests located near Miasteczko Slaskie zinc smelter and in the Lubliniec region, in the context of consumers' health risk. METHODS The ET-AAS method was used to determine the content of Cd, Pb, Ni, and As. Mercury concentration was determined using the CV-AFS method. RESULTS The studies showed high levels of Cd and Pb in the examined products. A statistically significant impact of the distance from the smelter on the Cd concentration in the berries was observed. Total non-cancer health risk from the combined exposure of adults to all HM in mushrooms and berries was significant when consuming the most heavily contaminated produce. The risk to children was significant, even when consuming products with moderate levels of the metals. Ingestion of Cd by children with mushrooms was related to a high cancer risk. The uncertainty of the results was determined. CONCLUSIONS It is recommended to take action to increase awareness among residents of the areas adjacent to the forests regarding the existing health risk and to take possible measures to reduce exposure to HM.
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Affiliation(s)
- Magdalena Niezgoda
- School of Public Health in Bytom, Medical University of Silesia in Katowice (Poland), ul. Piekarska 18, 42-902 Bytom, Poland
| | - Grzegorz Dziubanek
- Department of Environmental Health Risk Factors, School of Public Health in Bytom, Medical University of Silesia in Katowice (Poland), ul. Piekarska 18, 42-902 Bytom, Poland;
| | - Danuta Rogala
- Department of Environmental Health, School of Public Health in Bytom, Medical University of Silesia in Katowice (Poland), ul. Piekarska 18, 42-902 Bytom, Poland;
| | - Anna Niesler
- Department of Environmental Health, School of Public Health in Bytom, Medical University of Silesia in Katowice (Poland), ul. Piekarska 18, 42-902 Bytom, Poland;
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Zhang Y, Zhang Q, Chen W, Shi W, Cui Y, Chen L, Shao J. Source apportionment and migration characteristics of heavy metal(loid)s in soil and groundwater of contaminated site. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 338:122584. [PMID: 37739256 DOI: 10.1016/j.envpol.2023.122584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
The rapid industrial growth has generated heavy metal(loid)s contamination in the soil, which poses a serious threat to the ecology and human health. In this study, 580 samples were collected in Henan Province, China, for source apportionment, migration characterization and health risk evaluation using self-organizing map, positive matrix factorization and multivariate risk assessment methods. The results showed that samples were classified into four groups and pollution sources included chromium slag dump, soil parent rock and abandoned factory. The contents of Cr, Pb, As and Hg were low in Group 1. Group 2 was characterized by total Cr, Cr(Ⅵ) and pH. The enrichment of total Cr and Cr(Ⅵ) in soil was mainly attributed to chromium slag dump, accounting for more than 84.0%. Group 3 was dominated by Hg and Pb. Hg and Pb were primarily attributed to abandoned factory, accounting for 84.7% and 70.0%, respectively. Group 4 was characterized by As. The occurrence of As was not limited to one individual region. The contribution of soil parent rock reached 83.0%. Furthermore, the vertical migration of As, Hg, Pb and Cr(Ⅵ) in soil was mainly influenced by medium permeability, pH and organic matter content. The trends of As, Pb, and Hg with depth were basically consistent with the trends of organic matter with depth, and were negatively correlated with the change in pH with depth. The trends of Cr(Ⅵ) with depth were basically consistent with the changes in pH with the depth. The content of Cr(Ⅵ) in the deep soil did not exceed the detection limits and Cr(Ⅵ) contamination occurred in the deep aquifer, suggesting that Cr(Ⅵ) in the deep groundwater originated from the leakage of shallow groundwater. The assessment indicated that the non-carcinogenic and carcinogenic risks for children and adults could not be neglected. Moreover, children were more susceptible than adults.
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Affiliation(s)
- Yaobin Zhang
- Ministry of Education Key Laboratory of Groundwater Circulation and Environmental Evolution, China University of Geosciences (Beijing), Beijing, 100083, China; School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083, China; MNR Key Laboratory of Shallow Geothermal Energy, China University of Geosciences (Beijing), Beijing, 100083, China
| | - Qiulan Zhang
- Ministry of Education Key Laboratory of Groundwater Circulation and Environmental Evolution, China University of Geosciences (Beijing), Beijing, 100083, China; School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083, China; MNR Key Laboratory of Shallow Geothermal Energy, China University of Geosciences (Beijing), Beijing, 100083, China.
| | - Wenfang Chen
- The First Institute of Geo-environment Survey of Henan, Zhengzhou, 450045, China
| | - Weiwei Shi
- The First Institute of Geo-environment Survey of Henan, Zhengzhou, 450045, China
| | - Yali Cui
- Ministry of Education Key Laboratory of Groundwater Circulation and Environmental Evolution, China University of Geosciences (Beijing), Beijing, 100083, China; School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083, China; MNR Key Laboratory of Shallow Geothermal Energy, China University of Geosciences (Beijing), Beijing, 100083, China
| | - Leilei Chen
- The First Institute of Geo-environment Survey of Henan, Zhengzhou, 450045, China
| | - Jingli Shao
- Ministry of Education Key Laboratory of Groundwater Circulation and Environmental Evolution, China University of Geosciences (Beijing), Beijing, 100083, China; School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083, China; MNR Key Laboratory of Shallow Geothermal Energy, China University of Geosciences (Beijing), Beijing, 100083, China
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Janga JK, Reddy KR, Raviteja KVNS. Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review. CHEMOSPHERE 2023; 345:140476. [PMID: 37866497 DOI: 10.1016/j.chemosphere.2023.140476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/24/2023]
Abstract
The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.
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Affiliation(s)
- Jagadeesh Kumar Janga
- University of Illinois Chicago, Department of Civil, Materials, and Environmental Engineering, 842 West Taylor Street, Chicago, IL 60607, USA.
| | - Krishna R Reddy
- University of Illinois Chicago, Department of Civil, Materials, and Environmental Engineering, 842 West Taylor Street, Chicago, IL 60607, USA.
| | - K V N S Raviteja
- SRM University AP, Department of Civil Engineering, Guntur, Andhra Pradesh 522503, India.
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Fang S, Fang Z, Hua C, Zhu M, Tian Y, Yong X, Yang J, Ren L. Distribution, sources, and risk analysis of heavy metals in sediments of Xiaoqing River basin, Shandong province, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:112445-112461. [PMID: 37831261 DOI: 10.1007/s11356-023-30239-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/29/2023] [Indexed: 10/14/2023]
Abstract
The accumulation of heavy metals in river sediment poses a major threat to ecological safety. The Xiaoqing River originates in western Jinan, with higher population density and per capita gross domestic product (GDP) in its basin compared to the Shandong province average. This study analyzed the spatial characteristics, ecological risk, human health risk, and contamination sources of heavy metals by collecting sediment samples from Xiaoqing River. We use the methods such as geo-accumulation index (Igeo), ecological risk assessment based on the interval number sorting method, and health risk assessment to evaluate the risk of heavy metals in sediments. The research finding suggests heavy metals including Pb, As, Ni, and Cr are low ecological risks, while Hg and Cd have reached high and extreme ecological risks. Correlation analysis and principal component analysis were used to analyze the correlation and sources of different heavy metals. The six heavy metals were categorized into three groups. Factor 1, comprising Hg, Cr, and Pb, was identified as a mixed source with a contribution rate of 37.76%. Factor 2 is an agricultural source and comprises Ni, Cd, and As with a contribution rate of 27.05%. Factor 3 includes Pb and Ni contributing to 15.30% as a natural source. This study offers valuable insights for the prevention of heavy metal pollution, as well as promoting sustainable urban development.
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Affiliation(s)
- Shumin Fang
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China
| | - Zhaotong Fang
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China
| | - Chunyu Hua
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China
| | - Mengyuan Zhu
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China
| | - Yueru Tian
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China
| | - Xian Yong
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China
| | - Jiaying Yang
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China
| | - Lijun Ren
- School of Environmental Science and Engineering, Shandong University, Shandong Province, 72# Binhai Road, Jimo, 266235, People's Republic of China.
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Fei X, Lou Z, Lv X, Ren Z, Xiao R. Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory. JOURNAL OF HAZARDOUS MATERIALS 2023; 452:131231. [PMID: 36934631 DOI: 10.1016/j.jhazmat.2023.131231] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/13/2023] [Accepted: 03/15/2023] [Indexed: 06/18/2023]
Abstract
Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.39, 84.29, 47.84, 49.75, 28.95, 18.01, 0.49 and 0.16 mg/kg, respectively. The thresholds were consistently greater than the Zhejiang Province backgrounds but lower than the national risk screening values, except for Cd. Approximately 27.9% of the samples were classified as contaminated at various levels, and they were distributed in the northern, northwestern and eastern regions of the study area. Additionally, 3.73%, 5.34% and 8.22% of the total area were classified as at-risk areas under confidence levels of 95%, 90% and 75%, respectively, through BME theory. The findings provide a reasonable classification system and suggestions for heavy metal pollution management and control.
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Affiliation(s)
- Xufeng Fei
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Zhaohan Lou
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xiaonan Lv
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Zhouqiao Ren
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China.
| | - Rui Xiao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
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Shiyi Y, Xiaonuo L, Weiping C. High-resolution risk mapping of heavy metals in soil with an integrated static-dynamic interaction model: A case study in an industrial agglomeration area in China. JOURNAL OF HAZARDOUS MATERIALS 2023; 455:131650. [PMID: 37229828 DOI: 10.1016/j.jhazmat.2023.131650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/17/2023] [Accepted: 05/14/2023] [Indexed: 05/27/2023]
Abstract
Heavy metal pollution of soils in industrial agglomeration areas is an increasing concern worldwide. In this study, we traced the sources of heavy metal emissions using a positive matrix factorization (PMF) model. Accordingly, we proposed a novel static-dynamic risk interaction model incorporating multiple risk-related factors to quantify the spatial interaction of emission sources and the probability of accumulation of heavy metals on a large scale. This model was further classified using the Jenks optimization technique to predict the spatial distribution of high-risk hotspots. Our results determined four primary emission sources of heavy metals: industrial (35.01 %), natural (28.61 %), agricultural (26.07 %), and traffic (10.31 %) sources. Five levels were classified by the integrated risk coefficient (IRC), namely, from extremely high to extremely low risk. The extremely high- and high-risk hotspots constituting 41.52 % of the total area of the Zhenhai District, with IRC values ranging from 0.221 to 0.413, were mainly generated by multiple sources linked to PMF-based factors. This quantitative evaluation framework can generate a high-resolution spatially distributed pollution risk map at the grid scale (1 km), which can provide a relatively precise basis for policymaking for point-to-point soil pollution management.
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Affiliation(s)
- Yi Shiyi
- Laboratory of Soil Environmental Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Xiaonuo
- Laboratory of Soil Environmental Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
| | - Chen Weiping
- Laboratory of Soil Environmental Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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