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Proshad R, Rahim MA, Rahman M, Asif MR, Dey HC, Khurram D, Al MA, Islam M, Idris AM. Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175746. [PMID: 39182771 DOI: 10.1016/j.scitotenv.2024.175746] [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: 03/27/2024] [Revised: 07/24/2024] [Accepted: 08/22/2024] [Indexed: 08/27/2024]
Abstract
The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.
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Affiliation(s)
- Ram Proshad
- State Key Laboratory of Mountain Hazards and Engineering Safety, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, Sichuan, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Md Abdur Rahim
- State Key Laboratory of Mountain Hazards and Engineering Safety, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, Sichuan, China; University of Chinese Academy of Sciences, Beijing 100049, China; Department of Disaster Resilience and Engineering, Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Mahfuzur Rahman
- Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh; Renewable Energy Research Institute, Kunsan National University, 558 Daehakro, Gunsan, Jeollabugdo, 54150, Republic of Korea
| | - Maksudur Rahman Asif
- College of Environmental Science & Engineering, Taiyuan University of Technology, Jinzhong City, China
| | - Hridoy Chandra Dey
- Department of Agronomy, Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Dil Khurram
- State Key Laboratory of Mountain Hazards and Engineering Safety, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, Sichuan, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mamun Abdullah Al
- Environmental Microbiomics Research Center, School of Environmental Science and Engineering, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou 510275, China; Aquatic Eco-Health Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, 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.
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Hou Y, Ding W, Xie T, Chen W. Prediction of soil heavy metal contents in urban residential areas and the strength of deep learning: A case study of Beijing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175133. [PMID: 39084356 DOI: 10.1016/j.scitotenv.2024.175133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/23/2024] [Accepted: 07/27/2024] [Indexed: 08/02/2024]
Abstract
Predicting soil heavy metal (SHM) content is crucial for understanding SHM pollution levels in urban residential areas and guide efforts to reduce pollution. However, current research indicates low SHM prediction accuracy in urban areas. Therefore, we employed a deep learning method (fully connected deep neural network) alongside four other methods (muti-layer perceptron, radial basis function neural network, multiple stepwise linear regression, and Kriging interpolation) to predict SHM content in the urban residential areas of Beijing and demonstrated the strength of deep learning in improving prediction accuracy. We found the contents of the evaluated heavy metals (Cd, Cu, Pb, and Zn) exhibited significant correlations with numerous other soil physicochemical properties and environmental factors. The prediction accuracy for Cu, Pb, and Zn contents was relatively high across different methods. Notably, deep learning showed considerable strength in predicting the contents of the four heavy metals, with the R2 for the test set of the model ranging from 0.75 to 0.91. Compared to other methods, deep learning achieved markedly higher prediction accuracy according to different accuracy evaluation indicators (e.g., deep learning showed increases in the cumulative R2 of the four heavy metals ranging from 53.16 % to 187.36 % compared to other methods). Our study indicates that deep learning can significantly improve SHM content prediction accuracy in urban areas and is highly applicable in urban residential areas with complex environmental influences.
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Affiliation(s)
- Ying Hou
- 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
| | - Wenhao Ding
- 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
| | - Tian Xie
- 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
| | - Weiping Chen
- 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.
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Li B, Zhang L, Cheng M, Chen L, Fang W, Liu S, Zhou T, Zhao Y, Cen Q, Qian W, Mei X, Liu Z. Evaluation of fluoride emissions and pollution from an electrolytic aluminum plant located in Yunnan province. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135500. [PMID: 39141941 DOI: 10.1016/j.jhazmat.2024.135500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 08/05/2024] [Accepted: 08/11/2024] [Indexed: 08/16/2024]
Abstract
The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na3AlF6, AlF3, CaF2, and SiF4. Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 μg/m3 and 9.0 μg/m3, respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry.
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Affiliation(s)
- Bin Li
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Liping Zhang
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Mingqian Cheng
- Yunnan Land Resources Vocational College, Kunming 652501, China
| | - Ling Chen
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Wei Fang
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Shuai Liu
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Appraisal Center for Ecological and Environmental Engineering, Kunming 650228, China
| | - Tao Zhou
- The State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Youcai Zhao
- The State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Qihong Cen
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Wenmin Qian
- Yunnan Appraisal Center for Ecological and Environmental Engineering, Kunming 650228, China
| | - Xiangyang Mei
- Yunnan Appraisal Center for Ecological and Environmental Engineering, Kunming 650228, China.
| | - Zewei Liu
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Land Resources Vocational College, Kunming 652501, China; The State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
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Ma X, Guan DX, Zhang C, Yu T, Li C, Wu Z, Li B, Geng W, Wu T, Yang Z. Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135407. [PMID: 39116745 DOI: 10.1016/j.jhazmat.2024.135407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 08/10/2024]
Abstract
The accurate spatial mapping of heavy metal levels in agricultural soils is crucial for environmental management and food security. However, the inherent limitations of traditional interpolation methods and emerging machine-learning techniques restrict their spatial prediction accuracy. This study aimed to refine the spatial prediction of heavy metal distributions in Guangxi, China, by integrating machine learning models and spatial regionalization indices (SRIs). The results demonstrated that random forest (RF) models incorporating SRIs outperformed artificial neural network and support vector regression models, achieving R2 values exceeding 0.96 for eight heavy metals on the test data. Hierarchical clustering for feature selection further improved the model performance. The optimized RF models accurately predicted the heavy metal distributions in agricultural soils across the province, revealing higher levels in the central-western regions and lower levels in the north and south. Notably, the models identified that 25.78 % of agricultural soils constitute hotspots with multiple co-occurring heavy metals, and over 6.41 million people are exposed to excessive soil heavy metal levels. Our findings provide valuable insights for the development of targeted strategies for soil pollution control and agricultural soil management to safeguard food security and public health.
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Affiliation(s)
- Xudong Ma
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Dong-Xing Guan
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaosheng Zhang
- International Network for Environment and Health, School of Geography, Archaeology and Irish Studies, University of Galway, Ireland
| | - Tao Yu
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Cheng Li
- Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin, Guangxi 541004, China
| | - Zhiliang Wu
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Bo Li
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Wenda Geng
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Tiansheng Wu
- Guangxi Institute of Geological Survey, Nanning 530023, China
| | - Zhongfang Yang
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China.
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Li K, Guo G, Zhang D, Lei M, Wang Y. Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135454. [PMID: 39151355 DOI: 10.1016/j.jhazmat.2024.135454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/04/2024] [Accepted: 08/06/2024] [Indexed: 08/19/2024]
Abstract
Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution of soil PTEs at a large scale using conventional methods presents significant challenges. In this study, machine learning (ML) models, specially artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), were used to predict spatial distribution of soil PTEs and identify associated key factors in mining and smelting area located in Yunnan Province, China, under the three scenarios: (1) natural + socioeconomic + spatial datasets (NS), (2) NS + irrigation pollution index (IPI) datasets, (3) NS + IPI + deposition (DEPO) datasets. The results highlighted the combination of NS+IPI+DEPO yielded the highest predictive accuracy across ML models. Particularly, XGB exhibited the highest performance for As (R2 =0.7939), Cd (R2 =0.6679), Cu (R2 =0.8519), Pb (R2 =0.8317), and Zn (R2 =0.7669), whereas RF performed the best for Ni (R2 =0.7146). The feature importance and Shapley additive explanation (SHAP) analysis revealed that DEPO and IPI were the pivotal factors influencing the distribution of soil PTEs. Our findings highlighted the important role of DEPO in spatial distribution prediction of soil PTEs, which has often been ignored in previous studies.
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Affiliation(s)
- Kai Li
- Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China,; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guanghui Guo
- Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China,; University of Chinese Academy of Sciences, Beijing 100049, China.
| | | | - Mei Lei
- Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China,; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingying Wang
- Sichuan Eco-environmental Monitoring Station, Chengdu 610091, China
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Wang M, Gou Z, Zhao W, Qu Y, Chen Y, Sun Y, Cai Y, Ma J. Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations. JOURNAL OF HAZARDOUS MATERIALS 2024; 477:135393. [PMID: 39106722 DOI: 10.1016/j.jhazmat.2024.135393] [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: 04/13/2024] [Revised: 06/27/2024] [Accepted: 07/30/2024] [Indexed: 08/09/2024]
Abstract
Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas stations were collected in Beijing to explore the impact of gas stations on PTE accumulation. The analysis revealed that the average Pollution Index (PI) values for Cd, Hg, Pb, Cu, and Zn in the soil samples all exceeded 1. The random forest (RF) model, achieving an AUC score of 0.95, was employed to predict PTE pollution at 372 unsampled gas stations. Additionally, a Positive Matrix Factorization (PMF) model indicated that gas station operations and vehicle emissions were responsible for 70 % of the lead (Pb) enrichment. Probabilistic health risk assessments showed that the carcinogenic risk (CR) and noncarcinogenic risk (NCR) for PTE pollution to adult females were the highest, at 0.451 and 1.61E-05 respectively, but still within acceptable levels. For adult males at contaminated sites, the Pb-associated CR and NCR were approximately twice as high as those at uncontaminated sites, with increases of 107 % and 81 %, respectively. This study provides new insights for managing pollution caused by gas stations.
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Affiliation(s)
- Meiying Wang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zilun Gou
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Wenhao Zhao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yajing Qu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Ying Chen
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yi Sun
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yuxuan Cai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Jin Ma
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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Liu W, Chen J, Wang H, Fu Z, Peijnenburg WJGM, Hong H. Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39226136 DOI: 10.1021/acs.est.4c03088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.
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Affiliation(s)
- Wenjia Liu
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Haobo Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhiqiang Fu
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences (CML), Leiden University, Leiden 2300 RA, The Netherlands
- Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), Bilthoven 3720 BA, The Netherlands
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
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Zhu G, Zhu G, Tong B, Zhang D, Wu J, Zhai Y, Chen H. Spatial heterogeneity: Necessary and feasible for revealing soil trace elements pollution, sources, risks, and their links. JOURNAL OF HAZARDOUS MATERIALS 2024; 479:135698. [PMID: 39217934 DOI: 10.1016/j.jhazmat.2024.135698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 08/13/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
The source diversity and health risk of trace elements (TEs) in soil make it necessary to reveal the relationship between pollution, source, and risk. However, neglect of spatial heterogeneity restricts the reliability of existing identification methods. In this study, spatial heterogeneity is proposed as a necessary and feasible factor for accurately dissecting the pollution-source-risk link of soil TEs. A comprehensive framework is developed by integrating positive matrix factorization, Geodetector, and risk evaluation tools, and successfully applied in a mining-intensive city in northern China. Overall, the TEs are derived from natural background (28.5 %), atmospheric deposition (25.6 %), coal mining (21.8 %), and metal industry (24.1 %). The formation mechanism of heterogeneity for high-variance TEs (Se, Hg, Cd) is first systematically deciphered by revealing the heterogeneous source-sink relationship. Specifically, Se is dominated (76.5 %) by heterogeneous coal mining (q=0.187), Hg is determined (92.6 %) by the heterogeneity of metal mining (q=0.183) and smelting (q=0.363), and Cd is caused (50.9 %) by heterogeneous atmospheric deposition (q>0.254) co-influenced by the terrains and soil properties. Highly heterogeneous sources are also noteworthy for their potential to pose extreme risks (THI=1.122) in local areas. This study highlights the necessity of integrating spatial heterogeneity in pollution and risk assessment of soil TEs.
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Affiliation(s)
- Guanhua Zhu
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Ganghui Zhu
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment of the People's Republic of China, Beijing 100012, China
| | - Baocai Tong
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Dasheng Zhang
- Hebei Institute of Water Science, Shijiazhuang 050051, China
| | - Jin Wu
- Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
| | - Yuanzheng Zhai
- College of Water Sciences, Beijing Normal University, Beijing 100875, China.
| | - Haiyang Chen
- College of Water Sciences, Beijing Normal University, Beijing 100875, China.
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Jin H, Kong F, Li X, Shen J. Artificial intelligence in microplastic detection and pollution control. ENVIRONMENTAL RESEARCH 2024; 262:119812. [PMID: 39155042 DOI: 10.1016/j.envres.2024.119812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/04/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for advanced detection and mitigation strategies. This review examines the integration of artificial intelligence (AI) with environmental science to improve microplastic detection. Focusing on image processing, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and hyperspectral imaging (HSI), the review highlights how AI enhances the efficiency and accuracy of these techniques. AI-driven image processing automates the identification and quantification of MPs, significantly reducing the need for manual analysis. FTIR and Raman spectroscopy accurately distinguish MP types by analyzing their unique spectral features, while HSI captures extensive spatial and spectral data, facilitating detection in complex environmental matrices. Furthermore, AI algorithms integrate data from these methods, enabling real-time monitoring, traceability prediction, and pollution hotspot identification. The synergy between AI and spectral imaging technologies represents a transformative approach to environmental monitoring and emphasizes the need to adopt innovative tools for protecting ecosystem health.
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Affiliation(s)
- Hui Jin
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Fanhao Kong
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xiangyu Li
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Jie Shen
- College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China.
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Yang Y, Lu X, Yu B, Wang Z, Wang L, Lei K, Zuo L, Fan P, Liang T. Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model. JOURNAL OF HAZARDOUS MATERIALS 2024; 473:134708. [PMID: 38795490 DOI: 10.1016/j.jhazmat.2024.134708] [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: 04/03/2024] [Revised: 05/13/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
The environmental pollution caused by mineral exploitation and energy consumption poses a serious threat to ecological security and human health, particularly in resource-based cities. To address this issue, a comprehensive investigation was conducted on potentially toxic elements (PTEs) in road dust from different seasons to assess the environmental risks and influencing factors faced by Datong City. Multivariate statistical analysis and absolute principal component score were employed for source identification and quantitative allocation. The geo-accumulation index and improved Nemerow index were utilized to evaluate the pollution levels of PTEs. Monte Carlo simulation was employed to assess the ecological-health risks associated with PTEs content and source orientation. Furthermore, geo-detector and random forest analysis were conducted to examine the key environmental variables and driving factors contributing to the spatiotemporal variation in PTEs content. In all PTEs, Cd, Hg, and Zn exhibited higher levels of content, with an average content/background value of 3.65 to 4.91, 2.53 to 3.34, and 2.15 to 2.89 times, respectively. Seasonal disparities were evident in PTEs contents, with average levels generally showing a pattern of spring (winter) > summer (autumn). PTEs in fine road dust (FRD) were primarily influenced by traffic, natural factors, coal-related industrial activities, and metallurgical activities, contributing 14.9-33.9 %, 41.4-47.5 %, 4.4-8.3 %, and 14.2-29.4 % to the total contents, respectively. The overall pollution and ecological risk of PTEs were categorized as moderate and high, respectively, with the winter season exhibiting the most severe conditions, primarily driven by Hg emissions from coal-related industries. Non-carcinogenic risk of PTEs for adults was within the safe limit, yet children still faced a probability of 4.1 %-16.4 % of unacceptable risks, particularly in summer. Carcinogenic risks were evident across all demographics, with children at the highest risk, mainly due to Cr and smelting industrial sources. Geo-detector and random forest model indicated that spatial disparities in prioritized control elements (Cr and Hg) were primarily influenced by particulate matter (PM10) and anthropogenic activities (industrial and socio-economic factors); variations in particulate matter (PM10 and PM2.5) and meteorological factors (wind speed and precipitation) were the primary controllers of seasonal disparities of Cr and Hg.
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Affiliation(s)
- Yufan Yang
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Xinwei Lu
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China.
| | - Bo Yu
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Zhenze Wang
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Lingqing Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Kai Lei
- School of Biological and Environmental Engineering, Xi'an University, Xi'an 710065, China
| | - Ling Zuo
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Peng Fan
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Tao Liang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Li C, Jiang Z, Li W, Yu T, Wu X, Hu Z, Yang Y, Yang Z, Xu H, Zhang W, Zhang W, Ye Z. Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:315. [PMID: 39001912 DOI: 10.1007/s10653-024-02087-z] [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: 05/05/2024] [Accepted: 06/18/2024] [Indexed: 07/15/2024]
Abstract
Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.
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Affiliation(s)
- Cheng Li
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Zhongcheng Jiang
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Wenli Li
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Tao Yu
- School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, People's Republic of China
| | - Xiangke Wu
- Mineral Resource Reservoir Evaluation Center of Guangxi, Nanning, 530023, People's Republic of China
| | - Zhaoxin Hu
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Yeyu Yang
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Zhongfang Yang
- School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, People's Republic of China.
| | - Haofan Xu
- School of Environmental and Chemical Engineering, Foshan University, Foshan, 528000, Guangdong, People's Republic of China
| | - Wenping Zhang
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst Under the Auspices of UNESCO, Guilin, 541004, Guangxi, People's Republic of China
- Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, 531406, Guangxi, People's Republic of China
| | - Wenjie Zhang
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
| | - Zongda Ye
- Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning, 530028, People's Republic of China
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Hao H, Li P, Li K, Shan Y, Liu F, Hu N, Zhang B, Li M, Sang X, Xu X, Lv Y, Chen W, Jiao W. A novel prediction approach driven by graph representation learning for heavy metal concentrations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174713. [PMID: 38997020 DOI: 10.1016/j.scitotenv.2024.174713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/14/2024] [Accepted: 07/09/2024] [Indexed: 07/14/2024]
Abstract
The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental factors into consideration, which negatively affects the accuracy of the prediction model and the interpretability of intrinsic mechanisms. Graph representation learning (GraRL) can simultaneously learn the attribute relationships between environmental factors and graph structural information. Herein, we developed the GraRL-HM method to predict the HM concentrations in soil-rice systems. The method consists of two modules, which are PeTPG and GCN-HM. In PeTPG, a graphic structure was generated using graph representation and communitization technology to explore the correlations and transmission paths of different environmental factors. Subsequently, the GCN-HM model based on the graph convolutional neural network (GCN) was used to predict the HM concentrations. The GraRL-HM method was validated by 2295 sets of data covering 21 environmental factors. The results indicated that the PeTPG model simplified correlation paths between factor nodes from 396 to 184, reducing by 53.5 % graph scale by eliminating the invalid paths. The concise and efficient graph structure enhanced the learning efficiency and representation accuracy of downstream prediction models. The GCN-HM model was superior to the four benchmark models in predicting the HM concentration in the crop, improving R2 by 36.1 %. This study develops a novel approach to improve the prediction accuracy of pollutant accumulation and provides valuable insights into intelligent regulation and planting guidance for heavy metal pollution control.
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Affiliation(s)
- Huijuan Hao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Panpan Li
- Information Centre, Strategic Support Force Medical Center, 9 Anxiang North Lane, Chaoyang District, Beijing 100101, PR China
| | - Ke Li
- Strategic Support Force Medical Center, Beijing 100101, PR China
| | - Yongping Shan
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Feng Liu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Naiwen Hu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Bo Zhang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Man Li
- Shandong Provincial Soil Pollution Prevention and Control Centre, Jinan 250012, PR China
| | - Xudong Sang
- Strategic Support Force Medical Center, Beijing 100101, PR China
| | - Xiaotong Xu
- Strategic Support Force Medical Center, Beijing 100101, PR China
| | - Yuntao Lv
- Risk Assessment Laboratory for Environmental Factors of Agro-product Quality Safety, Ministry of Agriculture and Villages, Changsha 410005, PR China
| | - Wanming Chen
- Risk Assessment Laboratory for Environmental Factors of Agro-product Quality Safety, Ministry of Agriculture and Villages, Changsha 410005, PR China
| | - Wentao Jiao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, PR China.
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13
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Wang H, Zhao M, Huang X, Song X, Cai B, Tang R, Sun J, Han Z, Yang J, Liu Y, Fan Z. Improving prediction of soil heavy metal(loid) concentration by developing a combined Co-kriging and geographically and temporally weighted regression (GTWR) model. JOURNAL OF HAZARDOUS MATERIALS 2024; 468:133745. [PMID: 38401211 DOI: 10.1016/j.jhazmat.2024.133745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/26/2024]
Abstract
The study of heavy metal(loid) (HM) contamination in soil using extensive data obtained from published literature is an economical and convenient method. However, the uneven distribution of these data in time and space limits their direct applicability. Therefore, based on the concentration data obtained from the published literature (2000-2020), we investigated the relationship between soil HM accumulation and various anthropogenic activities, developed a hybrid model to predict soil HM concentrations, and then evaluated their ecological risks. The results demonstrated that various anthropogenic activities were the main cause of soil HM accumulation using Geographically and temporally weighted regression (GTWR) model. The hybrid Co-kriging + GTWR model, which incorporates two of the most influential auxiliary variables, can improve the accuracy and reliability of predicting HM concentrations. The predicted concentrations of eight HMs all exceeded the background values for soil environment in China. The results of the ecological risk assessment revealed that five HMs accounted for more than 90% of the area at the "High risk" level (RQ ≥ 1), with the descending order of Ni (100%) = Cu (100%) > As (98.73%) > Zn (95.50%) > Pb (94.90%). This study provides a novel approach to environmental pollution research using the published data.
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Affiliation(s)
- Huijuan Wang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; School of Resoureces and Environment, Anqing Normal University, Anqing 246133, China
| | - Menglu Zhao
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xinmiao Huang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xiaoyong Song
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Boya Cai
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Rui Tang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jiaxun Sun
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Department of Geographical Sciences, University of Maryland, College Park 20742, the United States
| | - Zilin Han
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jing Yang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510530, China
| | - Yafeng Liu
- School of Resoureces and Environment, Anqing Normal University, Anqing 246133, China.
| | - Zhengqiu Fan
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
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Yu J, Liu X, Yang B, Li X, Wang P, Yuan B, Wang M, Liang T, Shi P, Li R, Cheng H, Li F. Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 274:116231. [PMID: 38503102 DOI: 10.1016/j.ecoenv.2024.116231] [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: 11/16/2023] [Revised: 02/08/2024] [Accepted: 03/14/2024] [Indexed: 03/21/2024]
Abstract
Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02-15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.
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Affiliation(s)
- Jingjing Yu
- College of Water Sciences, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Xiaoyang Liu
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China.
| | - Bin Yang
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China
| | - Xiaodong Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Panpan Wang
- College of Water Sciences, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Bei Yuan
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China
| | - Minghao Wang
- China Metallurgical Industry Planning and Research Institute, Beijing 100013, China
| | - Tian Liang
- College of Water Sciences, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Pengfei Shi
- College of Water Sciences, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Renyou Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; School of Ecology and Environment, Inner Mongolia University, Inner Mongolia, 010020, China
| | - Hongguang Cheng
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Fasheng Li
- College of Water Sciences, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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Hu X, Qu Y, Yao L, Zhang Z, Tan G, Bai C. Boosted simultaneous removal of chlortetracycline and Cu (II) by Litchi Leaves Biochar: Influence of pH, ionic strength, and background electrolyte ions. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:10430-10442. [PMID: 38196041 DOI: 10.1007/s11356-023-31770-4] [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: 09/14/2023] [Accepted: 12/25/2023] [Indexed: 01/11/2024]
Abstract
The coexistence of heavy metals and antibiotics in the environment always results in greater toxicity compared to the individual precursors. Therefore, efficient and economic technology for the simultaneous removal of antibiotics and heavy metals is essential. Herein, litchi leaves biochar carbonized at 550 °C (L550) demonstrated high efficiency in co-removal of CTC (1838.1 mmol/kg) and Cu (II) (1212.9 mmol/kg) within wide range of pH (pH 4-7). Ionic strength obviously enhanced the Cu (II) removal but showed no significant effect on CTC removal. Although Al3+ and HPO42- decreased the adsorption capacities of CTC and Cu (II) on L550, the coexistence of Na+, K+, Mg2+, Cl-, NO3-, CO32- and SO42- showed a negligible effect on the simultaneous removal of CTC and Cu (II). Moreover, the adsorption capacities of CTC and Cu (II) on L550 were excellent in the river water, tap water, and lake water. In addition to electrostatic interactions, ion exchange governed Cu (II) adsorption, while surface complexation played a key role in CTC adsorption on L550. Our results demonstrated that litchi leaves biochar could be a promising adsorbent for remediating multi-contaminated environments.
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Affiliation(s)
- Xian Hu
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agricultural and Rural Pollution Abatement and Environmental Safety, Guangzhou, 510642, China
| | - Yifan Qu
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agricultural and Rural Pollution Abatement and Environmental Safety, Guangzhou, 510642, China
| | - Lixian Yao
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agricultural and Rural Pollution Abatement and Environmental Safety, Guangzhou, 510642, China
| | - Zhilin Zhang
- Hubei Key Laboratory of Quality Control of Characteristic Fruits and Vegetables, College of Life Science and Technology, Hubei Engineering University, Xiaogan, 432000, China
| | - Guangcai Tan
- CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026, China
| | - Cuihua Bai
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agricultural and Rural Pollution Abatement and Environmental Safety, Guangzhou, 510642, China.
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