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Ding Y, Li Y, You T, Liu S, Wang S, Zeng X, Jia Y. Effects of denitrification on speciation and redistribution of arsenic in estuarine sediments. WATER RESEARCH 2024; 258:121766. [PMID: 38759285 DOI: 10.1016/j.watres.2024.121766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/22/2024] [Accepted: 05/09/2024] [Indexed: 05/19/2024]
Abstract
Microbially-mediated redox processes involving arsenic (As) and its host minerals significantly contribute to the mobilization of As in estuarine sediments. Despite its significance, the coupling between As dynamics and denitrification processes in these sediments is not well understood. This study employed sequential sediment extractions and simultaneous monitoring of dissolved iron (Fe), nitrogen (N), and sulfur (S) to investigate the impact of nitrate (NO3-) on the speciation and redistribution of As, alongside changes in microbial community composition. Our results indicated that NO3- additions significantly enhance anaerobic arsenite (As(III)) oxidation, facilitating its immobilization by increased adsorption onto sediment matrices in As-contaminated estuarine settings. Furthermore, NO3- promoted the conversion of As bound to troilite (FeS) and pyrite (FeS2) into forms associated with Fe oxides, challenging the previously assumed stability of FeS/FeS2-bound As in such environments. Continuous NO3- additions ensured As and Fe oxidation, thereby preventing their reductive dissolution and stabilizing the process that reduces As mobility. Changes in the abundance of bacterial communities and correlation analyses revealed that uncultured Anaerolineaceae and Thioalkalispira may be the main genus involved in these transformations. This study underscores the critical role of NO3- availability in modulating the biogeochemical cycle of As in estuarine sediments, offering profound insights for enhancing As immobilization techniques and informing environmental management and remediation strategies in As-contaminated coastal regions.
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Affiliation(s)
- Yu Ding
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education, China), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yongbin Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education, China), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
| | - Tingting You
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education, China), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Shichao Liu
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education, China), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Shaofeng Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education, China), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xiangfeng Zeng
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
| | - Yongfeng Jia
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
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Thanh NN, Chotpantarat S, Ngu NH, Thunyawatcharakul P, Kaewdum N. Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand. ENVIRONMENTAL RESEARCH 2024; 252:118952. [PMID: 38636644 DOI: 10.1016/j.envres.2024.118952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 03/10/2024] [Accepted: 04/14/2024] [Indexed: 04/20/2024]
Abstract
Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.
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Affiliation(s)
- Nguyen Ngoc Thanh
- University of Agriculture and Forestry, Hue University, 102 Phung Hung Str, Hue City, Thua Thien Hue, 53000, Viet Nam
| | - Srilert Chotpantarat
- Department of Geology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand; Center of Excellence in Environmental Innovation and Management of Metals (EnvIMM), Environmental Research Institute, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand.
| | - Nguyen Huu Ngu
- University of Agriculture and Forestry, Hue University, 102 Phung Hung Str, Hue City, Thua Thien Hue, 53000, Viet Nam
| | - Pongsathorn Thunyawatcharakul
- International Postgraduate Program in Hazardous Substance and Environmental Management, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Narongsak Kaewdum
- Geoscience Program, Mahidol University Kanchanaburi Campus, Kanchanaburi, 71150, Thailand
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Uddin MG, Imran MH, Sajib AM, Hasan MA, Diganta MTM, Dabrowski T, Olbert AI, Moniruzzaman M. Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches. JOURNAL OF CONTAMINANT HYDROLOGY 2024; 261:104307. [PMID: 38278020 DOI: 10.1016/j.jconhyd.2024.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/10/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024]
Abstract
The Rooppur Nuclear Power Plant (RNPP) at Ishwardi, Bangladesh is planning to go into operation within 2024 and therefore, adjacent areas of RNPP is gaining adequate attention from the scientific community for environmental monitoring purposes especially for water resources management. However, there is a substantial lack of literature as well as environmental datasets for earlier years since very little was done at the beginning of the RNPP's construction phase. Therefore, this study was conducted to assess the potential toxic elements (PTEs) contamination in the groundwater and its associated health risk for residents at the adjacent part of the RNPP during the year of 2014-2015. For the purposes of achieving the aim of the study, groundwater samples were collected seasonally (dry and wet season) from nine sampling sites and afterwards analyzed for water quality indicators such as temperature (Temp.), pH, electrical conductivity (EC), total dissolved solid (TDS), total hardness (TH) and for PTEs including Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), Chromium (Cr), Cadmium (Cd) and Arsenic (As). This study adopted the newly developed Root Mean Square water quality index (RMS-WQI) model to assess the scenario of contamination from PTEs in groundwater whereas the human health risk assessment model was utilized to quantify the risk of toxicity from PTEs. In most of the sampling sites, PTEs concentration was found higher during the wet season than the dry season and Fe, Mn, Cd and As exceeded the guideline limit for drinking water. The RMS score mostly classified the groundwater in terms of PTEs contamination into "Fair" condition. The non-carcinogenic risks (expressed as Hazard Index-HI) revealed that around 44% and 89% of samples for adults and 67% and 100% of samples for children exceeded the threshold limit set by USEPA (HI > 1) and possessed risks through the oral pathway during dry and wet season, respectively. Furthermore, the calculated cumulative HI score was found higher for children than the adults throughout the study period. In terms of carcinogenic risk (CR) from PTEs, the magnitude of risk decreased following the pattern of Cr > As > Cd. Although the current study is based on old dataset, the findings might serve as a baseline for monitoring purposes to reduce future hazardous impact from the power plant.
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Affiliation(s)
- Md Galal Uddin
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland; Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh.
| | - Md Hasan Imran
- Department of Environmental Science and Resource Management, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
| | - Abdul Majed Sajib
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | - Md Abu Hasan
- Bangladesh Reference institute for Chemical Measurements (BRiCM), Dr. Qudrat-e-Khuda Road, Dhanmondi, Dhaka 1205, Bangladesh
| | - Mir Talas Mahammad Diganta
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | | | - Agnieszka I Olbert
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | - Md Moniruzzaman
- Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh
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Zhang X, Long T, Deng S, Chen Q, Chen S, Luo M, Yu R, Zhu X. Machine Learning Modeling Based on Microbial Community for Prediction of Natural Attenuation in Groundwater. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21212-21223. [PMID: 38064381 DOI: 10.1021/acs.est.3c05667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Natural attenuation is widely adopted as a remediation strategy, and the attenuation potential is crucial to evaluate whether remediation goals can be achieved within the specified time. In this work, long-term monitoring of indigenous microbial communities as well as benzene, toluene, ethylbenzene, and xylene (BTEX) and chlorinated aliphatic hydrocarbons (CAHs) in groundwater was conducted at a historic pesticide manufacturing site. A machine learning approach for natural attenuation prediction was developed with random forest classification (RFC) followed by either random forest regression (RFR) or artificial neural networks (ANNs), utilizing microbiological information and contaminant attenuation rates for model training and cross-validation. Results showed that the RFC could accurately predict the feasibility of natural attenuation for both BTEX and CAHs, and it could successfully identify the key genera. The RFR model was sufficient for the BTEX natural attenuation rate prediction but unreliable for CAHs. The ANN model showed better performance in the prediction of the attenuation rates for both BTEX and CAHs. Based on the assessments, a composite modeling method of RFC and ANN was proposed, which could reduce the mean absolute percentage errors. This study reveals that the combined machine learning approach under the synergistic use of field microbial data has promising potential for predicting natural attenuation.
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Affiliation(s)
- Xiaodong Zhang
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
- Department of Environmental Science and Engineering, School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu, China
| | - Tao Long
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
| | - Shaopo Deng
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
| | - Qiang Chen
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
| | - Sheng Chen
- Geo-engineering Investigation Institute of Jiangsu Province, Nanjing 210041, Jiangsu, China
| | - Moye Luo
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
- Department of Environmental Science and Engineering, School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu, China
| | - Ran Yu
- Department of Environmental Science and Engineering, School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu, China
| | - Xin Zhu
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
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Thanh NN, Chotpantarat S, Ha NT, Trung NH. Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2023:10.1007/s10653-023-01512-z. [PMID: 36881245 DOI: 10.1007/s10653-023-01512-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 02/10/2023] [Indexed: 05/17/2023]
Abstract
Groundwater pollution from nickel (Ni) has been a severe concern in Kanchanaburi Province, Thailand. Recent assessments revealed that the Ni concentration in groundwater, particularly in urban areas, often exceeded the permissible limit. The challenge for groundwater agencies is therefore to delineate regions with high susceptibility to Ni contamination. In this study, a novel modeling approach was applied to a dataset of 117 groundwater samples collected from Kanchanaburi Province between April and July 2021. Twenty site-specific initial variables were considered as influencing factors to Ni contamination. The Random Forest (RF) algorithm with Recursive Feature Elimination (RFE) function was used to select the fourteen most influencing variables. These variables were then used as input features to train a ME model to delineate the Ni contamination susceptibility at a high confidence (Area Under the Curve (AUC) validation value of 0.845). Ten input variables of the altitude, geology, land use, slope, soil type, distance to industrial areas, distance to mining areas, electric conductivity, oxidation-reduction potential, and groundwater depth were discovered in the most explaining the variation of spatial Ni contamination at very high (95.47 km2) and high (86.65 km2) susceptibility. This study devises the novel machine learning approach to identify the conditioning factors and map Ni contamination susceptibility in the groundwater, which provides a baseline dataset and reliable methods for the development of a sustainable groundwater management strategy.
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Affiliation(s)
- Nguyen Ngoc Thanh
- Interdisciplinary Program in Environmental Science, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand
- University of Agriculture and Forestry, Hue University, 102 Phung Hung Str, Hue City, Thua Thien Hue, 53000, Vietnam
| | - Srilert Chotpantarat
- Department of Geology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
- Center of Excellence in Environmental Innovation and Management of Metals (EnvIMM), Environmental Research Institute, Chulalongkorn University (ERIC), Bangkok, 10330, Thailand.
| | - Nam-Thang Ha
- University of Agriculture and Forestry, Hue University, 102 Phung Hung Str, Hue City, Thua Thien Hue, 53000, Vietnam
| | - Nguyen H Trung
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, 2 George St, Brisbane, QLD, 4000, Australia
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