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Zhang F, Zhou S, Jia Z, Xie X, Xu M, Wu S. A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:455. [PMID: 39320603 DOI: 10.1007/s10653-024-02246-2] [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: 07/03/2024] [Accepted: 09/20/2024] [Indexed: 09/26/2024]
Abstract
The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.
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Affiliation(s)
- Feng Zhang
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | - Shenglu Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China
| | - Zhenyi Jia
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China.
- Key Laboratory of Watershed Earth Surface Processes and Ecological Security, Zhejiang Normal University, Jinhua, 321004, Zhejiang Province, China.
| | - Xuefeng Xie
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | - Mingxing Xu
- Zhejiang Institute of Geosciences, Hangzhou, 310007, China
| | - Shaohua Wu
- School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou, 310018, China
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Bi Z, Sun J, Xie Y, Gu Y, Zhang H, Zheng B, Ou R, Liu G, Li L, Peng X, Gao X, Wei N. Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:135109. [PMID: 38972204 DOI: 10.1016/j.jhazmat.2024.135109] [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: 04/01/2024] [Revised: 06/07/2024] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.
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Affiliation(s)
- Zihan Bi
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China
| | - Jian Sun
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China; School of Public Policy and Administration, Chongqing University, Chongqing 400045, China
| | - Yutong Xie
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China
| | - Yilu Gu
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China
| | - Hongzhen Zhang
- Center for Soil Protection and Landscape Design, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Bowen Zheng
- School of Engineering, Hong Kong University of Science and Technology, Clear water bay, Sai Kung, New Territories, Hong Kong 999077, China
| | - Rongtao Ou
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China
| | - Gaoyuan Liu
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China
| | - Lei Li
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China
| | - Xuya Peng
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China
| | - Xiaofeng Gao
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China.
| | - Nan Wei
- Center for Soil Protection and Landscape Design, Chinese Academy of Environmental Planning, Beijing 100041, China.
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Wang L, Tang M, Gong J, Malik K, Liu J, Kong X, Chen X, Chen L, Tang R, Zheng R, Wang J, Yi Y. Variations of soil metal content, soil enzyme activity and soil bacterial community in Rhododendron delavayi natural shrub forest at different elevations. BMC Microbiol 2024; 24:300. [PMID: 39135165 PMCID: PMC11318175 DOI: 10.1186/s12866-024-03455-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 08/05/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Rhododendron delavayi is a natural shrub that is distributed at different elevations in the karst region of Bijie, China, and that has an important role in preventing land degradation in this region. In this study, we determined the soil mineral element contents and soil enzyme activities. The composition of the soil bacterial community of R. delavayi at three elevations (1448 m, 1643 m, and 1821 m) was analyzed by high-throughput sequencing, and the interrelationships among the soil bacterial communities, mineral elements, and enzyme activities were determined. RESULTS The Shannon index of the soil bacterial community increased and then decreased with increasing elevation and was highest at 1643 m. Elevations increased the number of total nodes and edges of the soil bacterial community network, and more positive correlations at 1821 m suggested stronger intraspecific cooperation. Acidobacteria, Actinobacteria and Proteobacteria were the dominant phyla at all three elevations. The Mantel test and correlation analysis showed that Fe and soil urease significantly affected bacterial communities at 1448 m; interestingly, Chloroflexi was positively related to soil urease at 1448 m, and Actinobacteria was positively correlated with Ni and Zn at 1821 m. Fe and soil urease significantly influenced the bacterial communities at lower elevations, and high elevation (1821 m) enhanced the positive interactions of the soil bacteria, which might be a strategy for R. delavayi to adapt to high elevation environments. CONCLUSION Elevation significantly influenced the composition of soil bacterial communities by affecting the content of soil mineral elements and soil enzyme activity.
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Affiliation(s)
- Li Wang
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China
| | - Ming Tang
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China
| | - Jiyi Gong
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China
| | - Kamran Malik
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University; Center for Grassland Microbiome, Lanzhou University; Key Laboratory of Grassland Livestock Industry Innovation; Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry; Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730000, China
| | - Jie Liu
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China
| | - Xin Kong
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China
| | - Xianlei Chen
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China
| | - Lanlan Chen
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China
| | - Rong Tang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University; Center for Grassland Microbiome, Lanzhou University; Key Laboratory of Grassland Livestock Industry Innovation; Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry; Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730000, China
| | - Rong Zheng
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University; Center for Grassland Microbiome, Lanzhou University; Key Laboratory of Grassland Livestock Industry Innovation; Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry; Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730000, China
| | - Jianfeng Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University; Center for Grassland Microbiome, Lanzhou University; Key Laboratory of Grassland Livestock Industry Innovation; Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry; Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730000, China.
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai Academy of Animal and Veterinary Sciences, Qinghai University, Xining, 810016, China.
| | - Yin Yi
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountains Areas of Southwest China, Key Laboratory of Plant Physiology and Developmental Regulation, School of Life Science, Guizhou Normal University, Guiyang, 550001, China.
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Jibrin AM, Abba SI, Usman J, Al-Suwaiyan M, Aldrees A, Dan'azumi S, Yassin MA, Wakili AA, Usman AG. Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:53219-53236. [PMID: 39180658 DOI: 10.1007/s11356-024-34716-6] [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: 05/20/2024] [Accepted: 08/12/2024] [Indexed: 08/26/2024]
Abstract
The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam leads to significant risks to public health and environmental sustainability, challenging compliance with Environmental Protection Agency (EPA) guidelines, World Health Organization (WHO) standards, and Sustainable Development Goals (SDGs) related to clean water and life on land. This study developed the predictive-based monitoring of HM concentrations, including cadmium (Cd), chromium (Cr), and mercury (Hg) in the coastal aquifers of Dammam, influenced by industrial, agricultural, and urban activities. For this purpose, dynamic system identification and machine learning (ML) models integrated with three ensemble techniques, namely, simple averaging (SAE), weighted averaging (WAE), and neuro-ensemble (N-ESB), were employed to enhance the accuracy, reliability, and efficiency of environmental monitoring systems. The experimental data were calibrated and validated in addition to k-fold cross-validation to ensure the predictive skills of the models. The methodology integrates extensive data collection across varied land uses in Dammam and accurate model calibration and validation phases to develop highly accurate predictive models. The findings proved that the N-ESB and Hammerstein-Wiener (HW) models surpassed other models in predicting the concentrations of all HM. For Cd, the N-ESB model achieved a root mean square error (RMSE = 0.0010 mg/kg). Similarly, Cr demonstrated superior performance (RMSE = 0.0179 mg/kg). Further numerical results indicated that the HW algorithm proved the most effective for Hg, with RMSE = 0.0000 mg/kg. The quantitative comparison suggested that the N-ESB model's consistently high performance and low error rates make it an optimal choice for real-time, precise monitoring and management of HM pollution in coastal aquifers. The outcomes of this research highlighted the importance of integrating advanced predictive modeling techniques in environmental science, providing significant and practical implications for policymaking and ecological management.
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Affiliation(s)
- Abdulhayat M Jibrin
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
| | - Sani I Abba
- Interdisciplinary Research Centre for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
| | - Jamilu Usman
- Interdisciplinary Research Centre for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
| | - Mohammad Al-Suwaiyan
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
| | - Ali Aldrees
- Department of Civil Engineering, College of Engineering in Al-Kharaj, Prince Sattam Bin Abdulaziz University, Al-Kharaj, 11942, Saudi Arabia
| | - Salisu Dan'azumi
- Department of Civil Engineering, College of Engineering in Al-Kharaj, Prince Sattam Bin Abdulaziz University, Al-Kharaj, 11942, Saudi Arabia
| | - Mohamed A Yassin
- Interdisciplinary Research Centre for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
| | - Almustapha A Wakili
- Department of Computer and Information Sciences, Towson University, Towson, MD, USA
| | - Abdullahi G Usman
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138, Nicosia, Turkey
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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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Li D, Deng Y, Liu L, Wang J, Huang Z, Zhang X. Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:146. [PMID: 38578375 DOI: 10.1007/s10653-024-01928-1] [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: 08/09/2023] [Accepted: 02/20/2024] [Indexed: 04/06/2024]
Abstract
With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.
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Affiliation(s)
- De'an Li
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Yirong Deng
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China.
| | - LiLi Liu
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Jun Wang
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Zaoquan Huang
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Xiaolu Zhang
- Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
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8
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Chen H, Qiao S, Li C, Wu Y, Li D, Li L, Liu J. Source-oriented risk assessment of heavy metal(loid)s in agricultural soils around a multimetal smelting area near the Yellow River, China. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:59. [PMID: 38280129 DOI: 10.1007/s10653-023-01849-5] [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: 10/16/2023] [Accepted: 12/27/2023] [Indexed: 01/29/2024]
Abstract
Heavy metal(loid) (HM) contamination in agricultural soils, particularly in areas severely impacted by smelting industries, has attracted worldwide attention. In this study, agricultural soils were collected in a flourishing multimetal smelting area near the Yellow River in central China. By an integrated approach encompassing the positive matrix factorization model, ordinary kriging interpolation and hierarchical clustering analysis (PMF-OK-HC), a total of four major sources and their mass contributions were identified, namely, soil parent material (56.6%), industrial waste and Mo smelting (24.0%), metal smelting and traffic emissions (12.8%), and coal combustion (6.7%). On this basis, the health risk of HMs was evaluated by Monte Carlo simulations and showed that a higher risk, with a higher proportion of exceeding-thresholds risk, was observed for children than for adults in terms of both noncarcinogenic and carcinogenic risks. Exposure pathways of oral ingestion in children could result in a higher attributed risk than other pathways. Furthermore, source-oriented risk assessment (SORA) revealed that the sources of coal combustion, industrial waste and Mo smelting had the highest contributions to noncarcinogenic and carcinogenic risks. Overall, for effective environmental management in agricultural soil, the framework of SORA was verified as an effective tool in the identification of the priority control of HMs and their sources.
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Affiliation(s)
- Hui Chen
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China
| | - Shuo Qiao
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China
| | - Chang Li
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China
| | - Yong Wu
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China
| | - Donghao Li
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China
| | - Ling Li
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China
| | - Jianwei Liu
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China.
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Lei K, Li Y, Zhang Y, Wang S, Yu E, Li F, Xiao F, Xia F. Development of a new method framework to estimate the nonlinear and interaction relationship between environmental factors and soil heavy metals. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167133. [PMID: 37730041 DOI: 10.1016/j.scitotenv.2023.167133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 09/22/2023]
Abstract
The intricate and multifaceted nature of soil system profoundly influences the highly complex and often nonlinear changes that soil heavy metals (HM) undergo. Spatial heterogeneity, location and scale variability, and the interaction and superposition among environmental drivers challenged researchers to determine the sophisticated nature of soil HMs changes at the regional scale. This study aims to develop a new method framework and selects Ningbo as the case study to apportion the environmental factors responsible for soil HMs pollution that include Cd, Cr, Pb, Hg, As, Cu, Zn and Ni, focusing on nonlinearity and interaction. We harnessed the Random Forest model to apportion the environmental drivers of soil HM change. The directionality and shape of the nonlinear relationship between HMs and their individual contributors were derived by Partial Dependence Plots. The interactions of multiple drivers were quantitatively assessed by the Conditional Inference Tree. Our results demonstrated that soil HMs in the study area varied spatially. Soil HMs pollution was mitigated by natural factors and anthropogenic factors. The main influencing factors were pH, soil parent material type, enterprise activities, and agricultural application. The effects of some factors on soil HMs showed a monotonic linear trend, but some have apparent threshold effects. The direction of influence on soil HMs will shift when pH and phosphate fertilizer reach a specific value. The addition of enterprises in the area would rarely have an impact on the HMs pollution once it reached around 2 per km2 because of the industrial agglomeration. Soil HM concentrations were mainly from multi-pollutants and were governed by a combination of environmental factors. Our study provided managers and policymakers with site-specific and definite guidelines for preventing and controlling soil HM pollution.
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Affiliation(s)
- Kaige Lei
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Yan Li
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China.
| | - Yanbin Zhang
- Zhejiang Land Consolidation and Rehabilitation Center, Hangzhou 310007, China
| | - Shiyi Wang
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Er Yu
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Feng Li
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Fen Xiao
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Fang Xia
- College of Economics and Management, Zhejiang A&F University, Hangzhou 311302, China
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Chen X, Wong CUI, Zhang H. Analysis and pollution evaluation of heavy metal content in soil of the Yellow River Wetland Reserve in Henan. PeerJ 2023; 11:e16454. [PMID: 38107560 PMCID: PMC10725677 DOI: 10.7717/peerj.16454] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/23/2023] [Indexed: 12/19/2023] Open
Abstract
Objective This study aims to assess the contamination levels of six heavy metals, namely arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), and lead (Pb), in the soil of the Henan Yellow River Wetland Reserve. It seeks to reveal the spatial distribution and trends of heavy metal pollution, providing a scientific basis for the rational utilization and effective protection of soil. Additionally, it aims to propose targeted management and remediation recommendations to mitigate or prevent soil pollution. Method A total of 706 soil samples were collected in this area in combination with the land use type map. As and Hg were determined by atomic fluorescence spectrometry, and Cr, Cu, Pb and Cd were determined by inductively coupled plasma mass spectrometry. Taking the soil pollution risk screening value of agricultural land (GB15618-2018) as a reference value, the sample data were statistically analyzed, and the Nemerow comprehensive pollution index method combined with ArcGIS technology was used to evaluate the soil environmental quality. Result The comprehensive pollution index of the soil in the Yellow River Wetland Reserve was 0.42, ranging from 0.17 to 2.38, which was safe and not polluted (I grade). Out of 706 sampling locations, 674 remained uncontaminated, while 26 exhibited cleanliness. Although they were in the warning line, they did not exceed the standard, accounting for 3.68% of the total number of sampling points. Five sample points were slightly polluted, accounting for 0.71% of the total sample points, and one sample point was moderately polluted, accounting for 0.14% of the total sample points. It can be seen that there are few agricultural land pollution points in the Yellow River Wetland Reserve, and the soil environment quality is generally good. Conclusion The soil in the Yellow River Wetland Reserve in Henan has a very small amount of mild and moderate pollution, and there is no severe pollution. The cleanliness is currently high.
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Affiliation(s)
- Xiaolong Chen
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China
- Department of Management, Henan Institute of Technology, Xinxiang, China
| | - Cora Un In Wong
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China
| | - Hongfeng Zhang
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China
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Zhou Y, Wang T, Zou M, Yin Q, Jia Z, Su B, Zhang Q, Chen L, Zhou S. Trends in the occurrence and accumulation of microplastics in urban soil of Nanjing and their policy implications. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166144. [PMID: 37572915 DOI: 10.1016/j.scitotenv.2023.166144] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/03/2023] [Accepted: 08/06/2023] [Indexed: 08/14/2023]
Abstract
Urban soil is an important sink of terrestrial microplastics (MPs), and understanding their distribution over time is essential for effective pollution management. Here, based on soil MP data from Nanjing, a typical megacity in eastern China, this study analyzed MP accumulation trends using decision tree and time series network based on soil attributes, POI (point of interest), and human activity factors such as urban industrial structure, transportation, water use. We also evaluated the impact of plastic policy interventions. In the past 15 years, MPs in urban soil in Nanjing have gradually increased, and highly polluted areas have also grown. From 2010 to 2020, the concentration of MPs in urban soil increased from 326.7 items/kg to 480.9 items/kg, with high pollution areas expanding from only 2.0 km2 (0.7 %) to 48.7 km2 (14.9 %). The accumulation of MPs was also influenced by changing factors due to urbanization. In the early 21st century, residential areas had the largest effect, while in the later period, public passenger transport and domestic water consumption were the dominant factors. The scenarios simulation suggests recent plastic intervention policies have helped alleviate this rate of increase, but MP source management (e.g., laundry fibers, tire wear) still needs improvement. By the proposed method, the past trend of microplastics in urban soil and their relationship with soil properties and human activities can be accurately revealed, which will be helpful for the formulation of countermeasures to mitigate regional soil MP pollution.
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Affiliation(s)
- Yujie Zhou
- School of Geographic Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China
| | - Teng Wang
- College of Oceanography, Hohai University, Nanjing 210098, China
| | - Mengmeng Zou
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China
| | - Qiqi Yin
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China
| | - Zhenyi Jia
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
| | - Bo Su
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China
| | - Qi Zhang
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China
| | - Long Chen
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China
| | - Shenglu Zhou
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China.
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Lei K, Li Y, Zhang Y, Wang S, Yu E, Li F, Xiao F, Shi Z, Xia F. Machine learning combined with Geodetector quantifies the synergistic effect of environmental factors on soil heavy metal pollution. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:126148-126164. [PMID: 38008833 DOI: 10.1007/s11356-023-31131-1] [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: 06/14/2023] [Accepted: 11/16/2023] [Indexed: 11/28/2023]
Abstract
The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type.
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Affiliation(s)
- Kaige Lei
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Yan Li
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou, 310058, China.
| | - Yanbin Zhang
- Zhejiang Land Consolidation and Rehabilitation Center, Hangzhou, 310007, China
| | - Shiyi Wang
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Er Yu
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Feng Li
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Fen Xiao
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Zhou Shi
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Fang Xia
- College of Economics and Management, Zhejiang A&F University, Hangzhou, 311302, China
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13
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Hao H, Li P, Jiao W, Ge D, Hu C, Li J, Lv Y, Chen W. Ensemble learning-based applied research on heavy metals prediction in a soil-rice system. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 898:165456. [PMID: 37451444 DOI: 10.1016/j.scitotenv.2023.165456] [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: 03/27/2023] [Revised: 07/06/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
Accurate prediction of heavy metal accumulation in soil ecosystems is crucial for maintaining healthy soil environments and ensuring high-quality agricultural products, as well as a challenging scientific task. In this study, we constructed a dataset containing 490 sets of multidimensional environmental covariate data and proposed prediction models for heavy metal concentrations (HMC) in a soil-rice system, EL-HMC (including RF-HMC and GBM-HMC), based on Random Forest (RF) and Gradient Boosting Machine (GBM) ensemble learning (EL) techniques. To reasonably evaluate the effectiveness of each model, Multiple linear and Bayesian regressions were selected as benchmark models (BM), and mean absolute error (MAE), root mean square error (RMSE), and determination coefficient R2 were selected as evaluation indicators. In addition, sensitivity and spatial autocorrelation (SAC) analyses were used to examine the robustness of the model. The results showed that the R2 values of RF-HMC and GBM-HMC for modeling available cadmium (Cd) concentrations in soil were 0.654 and 0.690, respectively, with an average increase of 48.0 % compared to the BMs. The R2 values of RF-HMC and GBM-HMC for predicting Cd, lead (Pb), chromium (Cr), and mercury (Hg) concentrations in rice ranged from 0.618 to 0.824 and 0.645 to 0.850, respectively, with an average increase of 58.2 % compared with the BMs. The corresponding MAEs and RMSEs of RF-HMC and GBM-HMC had low error levels. Sensitivity analysis of the input features and the SAC of the prediction bias showed that the EL-HMC models have excellent robustness. Therefore, the EL technology-based prediction models for HMCs proposed herein are practical and feasible, demonstrating better accuracy and stability than the traditional model. This study verifies the application potential of EL technology in pollution ecology and provides a new perspective and solution for sustainable management and precise prevention of heavy metal pollution in farmland soil at the regional scale.
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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, PLA Strategic Support Force Characteristic Medical Center, Beijing 100101, PR China.
| | - Wentao Jiao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Dabing Ge
- College of Resources and Environment, Hunan Agricultural University, Changsha 410128, PR China
| | - Chengwei Hu
- Information Centre, PLA Strategic Support Force Characteristic Medical Center, Beijing 100101, PR China
| | - Jing Li
- Department of Oncology, Huludao Central Hospital, Huludao 125001, 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
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14
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Wu H, Sun J, Zhou W, Hashem IA, Cai J, Xiao N. Crop effect and mechanism of amino acid-modified biomass ash in remediation of cadmium-contaminated soil. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:101026-101034. [PMID: 37644271 DOI: 10.1007/s11356-023-29466-w] [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: 05/13/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023]
Abstract
Lignocellulosic biomass ash (BA) has certain adsorption and passivation effects on heavy metals, but its function is generally weak. Amino acid salt can facilitate the leaching of heavy metals in soil. Therefore, modification of BA with amino acid salt may realize a higher leaching rate and better passivation of heavy metals in soil. In this study, BA was modified by amino acid hydrolysate obtained from the hydrolysis of chicken feathers by sulfuric acid. The physicochemical properties of BA and modified BA (MBA), their effects on Chinese cabbage (CC) yield and nutritional quality, and passivation effects on soil cadmium (Cd) were compared, and the related mechanisms were investigated. SEM-EDS, XRD, and FTIR demonstrated that BA was a CaCO3-type soil amendment, while MBA was a CaSO4-type soil amendment with the loading of amino acid. Compared with BA, MBA significantly increased the fresh weight, soluble sugar, vitamin C (Vc), and protein contents of CC in both non-Cd contaminated soil and Cd contaminated soil, and obviously decreased the nitrate content and Cd uptake of CC in Cd-contaminated soil. After the application of MBA, cadmium species in potted soil were transformed from higher plant availability, representing by exchangeable and carbonate-bound, into lower plant availability, representing by iron-manganese oxide bound, which was identified as the key reason for the significant reduction of Cd content in CC under MBA application.
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Affiliation(s)
- Haopeng Wu
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, 430070, China
- Lab of Ecological and Environmental Engineering, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jiamei Sun
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, 430070, China
- Lab of Ecological and Environmental Engineering, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenbing Zhou
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, 430070, China.
- Lab of Ecological and Environmental Engineering, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Inas A Hashem
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, 430070, China
- Lab of Ecological and Environmental Engineering, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China
- Department of Soils and Water Science, Faculty of Agriculture, Benha University, Benha, Qalyubia, Arab Republic of Egypt
| | - Jianbo Cai
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, 430070, China
- Lab of Ecological and Environmental Engineering, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China
| | - Naidong Xiao
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, 430070, China
- Lab of Ecological and Environmental Engineering, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China
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Jiang Z, Yang S, Luo S. Source analysis and health risk assessment of heavy metals in agricultural land of multi-mineral mining and smelting area in the Karst region - a case study of Jichangpo Town, Southwest China. Heliyon 2023; 9:e17246. [PMID: 37456041 PMCID: PMC10338313 DOI: 10.1016/j.heliyon.2023.e17246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
In the Karst region of Southwest China, the content of soil heavy metals is generally high because of the geological background. Moreover, Southwest China is rich in mineral resources. A large number of mining and smelting activities discharge heavy metals into surrounding soil and cause superimposed pollution, which has drawn widespread concern. Due to the large variation coefficients of soil heavy metals in the Karst region, it is particularly essential to select appropriate analysis methods. In this paper, Jichangpo in Puding County, a Karst area with multi-mineral mining and smelting, is selected as the research object. A total of 368 pieces of agricultural topsoil in the study area are collected. The pollution level of heavy metals in agricultural soil is evaluated by the geological accumulation index (Igeo) and enrichment factor (EF). Absolute Factor Score/Multiple Linear Regression (APCS/MLR), geographic information system (GIS), self-organizing mapping (SOM), and random forest (RF) are used for the source allocation of soil heavy metals. Finally, the combination of APCS/MLR and health risk assessment model is adopted to evaluate the risks of heavy metal sources and determine the priority-control source. The results show that the average values of soil heavy metals in the study area (Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni) exceed the background values of corresponding elements in Guizhou Province. Three sources of heavy metals are identified by combining APCS/MLR, GIS, SOM, and RF. Zn (63.47%), Pb (55.77%), Cd (58.98%), Hg (32.17%), Cu (14.41%), and As (5.99%) are related to lead-zinc mining and smelting; Cr (98.14%), Ni (90.64%), Cu (76.93%), Pb (43.02%), Zn (35.22%), Cd (28.97%), Hg (22.44%), and As (5.84%) are mixed sources (natural and agricultural sources); As (88.17%), Hg (45.39%), Cd (12.04%), Cu (8.66%), and Ni (6.72%) are related to the mining and smelting of coal and iron. The results of health risk assessment show that only As poses a non-carcinogenic risk to human health. 3.31% of the sampling points of As have non-carcinogenic risks to adults and 10.22% to children. In terms of carcinogenic risks, As, Pb, and Cr pose carcinogenic risks to adults and children. Combined with APCS/MLR and the health risk assessment model, the mining and smelting of coal and iron is the priority-control pollution source. This paper provides a comprehensive method for studying the distribution of heavy metal sources in areas with large variation coefficients of soil heavy metals in the Karst region. Furthermore, it offers a theoretical basis for the management and assessment of heavy metal pollution in agricultural land in the study area, which is helpful for researchers to make strategic decisions on food security when selecting agricultural land.
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Affiliation(s)
- Zaiju Jiang
- Guizhou Coal Mine Geological Engineering Advisory and Geological Environment Monitoring Center, Guiyang, 550081, China
| | - Shaozhang Yang
- Guizhou Coal Mine Geological Engineering Advisory and Geological Environment Monitoring Center, Guiyang, 550081, China
- Guizhou Rongyuan Environmental Protection Technology Co. LTD, Guiyang, 550081, China
| | - Sha Luo
- Guizhou Coal Mine Geological Engineering Advisory and Geological Environment Monitoring Center, Guiyang, 550081, China
- Guizhou Rongyuan Environmental Protection Technology Co. LTD, Guiyang, 550081, China
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Lu X, Du J, Zheng L, Wang G, Li X, Sun L, Huang X. Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 259:115052. [PMID: 37224784 DOI: 10.1016/j.ecoenv.2023.115052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 05/26/2023]
Abstract
Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%- 72.9% and 72.0%- 74.7%, which were 2.1%- 2.5% and 0.3%- 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%- 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17-0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS.
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Affiliation(s)
- Xiaosong Lu
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Junyang Du
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Liping Zheng
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Guoqing Wang
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China.
| | - Xuzhi Li
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Li Sun
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
| | - Xinghua Huang
- College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, China
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Source apportionment and source-specific risk evaluation of potential toxic elements in oasis agricultural soils of Tarim River Basin. Sci Rep 2023; 13:2980. [PMID: 36806786 PMCID: PMC9941508 DOI: 10.1038/s41598-023-29911-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/13/2023] [Indexed: 02/22/2023] Open
Abstract
As rapidly developing area of intensive agriculture during the past half century, the oases in the source region of the Tarim River have encountered serious environmental challenges. Therefore, a comparative analysis of soil pollution characteristics and source-specific risks in different oases is an important measure to prevent and control soil pollution and provide guidance for extensive resource management in this area. In this study, the concentration of potential toxic elements (PTEs) was analyzed by collecting soil samples from the four oases in the source region of the Tarim River. The cumulative frequency curve method, pollution index method, positive matrix factorization (PMF) model, geographical detector method and health risk assessment model were used to analyze the pollution status and source-specific risk of potential toxic elements in different oases. The results showed that Cd was the most prominent PTE in the oasis agricultural soil in the source region of the Tarim River. Especially in Hotan Oasis, where 81.25% of the soil samples were moderately contaminated and 18.75% were highly contaminated with Cd. The PTEs in the Hotan Oasis corresponded to a moderate level of risk to the ecological environment, and the noncarcinogenic risk of soil PTEs in the four oases to local children exceeded the threshold (TH > 1), while the carcinogenic risk to local residents was acceptable (1E-06 < TCR < 1E-04). The research results suggested that the Hotan Oasis should be the key area for soil pollution control in the source region of the Tarim River, and agricultural activities and natural sources, industrial sources, and atmospheric dust fall are the priority sources that should be controlled in the Aksu Oasis, Kashgar Oasis and Yarkant River Oasis, respectively. The results of this study provide important decision-making support for the protection and management of regional agricultural soil and the environment.
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