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Assessing climate change projections in the Volta Basin using the CORDEX-Africa climate simulations and statistical bias-correction

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environmental Challenges 2022年 6卷

作者： Yeboah, Kofi A. Akpoti, Komlavi Kabo-bah, Amos T. Ofosu, Eric A. Siabi, Ebenezer K. Mortey, Eric M. Okyereh, Samuel A. Department of Civil and Environmental Engineering University of Energy and Natural Resources P. O. Box 214 Sunyani Ghana Regional Center for Energy and Environmental Sustainability (RCEES) University of Energy and Natural Resources (UENR) P. O. Box 214 Sunyani Ghana Earth Observation Research and Innovation Center (EORIC) University of Energy and Natural Resources P. O. Box 214 Sunyani Ghana Faculty of Science and Techniques Doctoral Research Program in Climate Change and Energy (DRP-CCE) of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) Université Abdou Moumouni P.O. Box 10662 Niamey 8000 Niger

Climate change potential impacts are evaluated through the changes in the local and regional climate. However, Global and Regional Climate simulated outputs do not often capture these changes well, hampering their direct applicability. Impact studies using coarse resolution data require bias-correction of climate variables, a process that minimizes the discrepancy between observed and simulated climate variables. This study assessed climate projections in the Volta Basin using an ensemble of 4 Regional Climate Models under the Representative Concentration Pathways-RCPs 4.5 and 8.5 scenarios in the CORDEX-Africa datasets. These datasets were bias-corrected using the Climate Model data for hydrologic modeling tool (CMhyd) and 27-years of Climate Forecast System Reanalysis data. The performances of the ensemble bias-corrected precipitation ranged from 97-99%, 93-99%, 70-485mm, and -9-5% for R², NSE, RMSE, and PBIAS respectively. TMAX bias-correction performances ranged from 65-99%, 27-99%, 0-4°C and -2-7% for R², NSE, RMSE and PBIAS respectively. For TMIN, the performances ranged from 91–99%, 91–99%, 0-1°C and 0-1% for R², NSE, RMSE and PBIAS respectively. The annual projected change in precipitation under RCP4.5 and 8.5 indicated a decrease in precipitation for the near (the 2020s), the mid-century (2050s), and the end far (2080s) with a relative increase from late November to January, a period currently part of dry season period in the Volta Basin. This suggests that the basin could expect a potential shift in the rainy season. The 12-month standard precipitation index suggests more frequent and longer drought periods in the future. Changes in annual mean monthly maximum temperature revealed an increase under all scenarios and throughout the century with an intensified increase by the end of the century under the higher CO₂ concentration scenario (RCP 8.5). The study showed that under RCP 4.5 and 8.5 scenarios, the Volta Ba

关键词： Bias-correction Climate change CMhyd CMIP5 simulations RCPs scenarios Volta Basin

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Machine Learning Modeling for Hydrolysis Recycling of Pet Waste

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SSRN 2025年

作者： Li, Jie Pan, Lanjia Abedsoltan, Hossein Wang, Hailong Liu, Taiyang Yuan, Xiangzhou Ok, Yong Sik Wang, Yin State Key Laboratory for Ecological Security of Regions and Cities Institute of Urban Environment Chinese Academy of Sciences Xiamen361021 China State Key Laboratory of Advanced Environmental Technology Institute of Urban Environment Chinese Academy of Sciences Xiamen361021 China University of Chinese Academy of Sciences Beijing100049 China Sustainable Waste Management Program Division of Environmental Science and Ecological Engineering Korea University Seoul02841 Korea Republic of Ministry of Education of Key Laboratory of Energy Thermal Conversion and Control School of Energy and Environment Southeast University Nanjing210096 China Department of Chemical Engineering The University of Toledo ToledoOH43607 United States Linda and Bipin Doshi Department of Chemical and Biochemical Engineering Missouri University of Science and Technology RollaMO65401 United States

Hydrolysis of polyethylene terephthalate (PET) into terephthalic acid (TPA) is one key approach for waste PET recycling. However, achieving an in-depth understanding in PET hydrolysis remains challenging. Herein, we employed machine learning (ML) methods to interpret the PET conversion with a dataset compiled, containing 942 data points and rich hydrolysis factors of PET-related information, heating type, acid/base catalyst (ABC), organic solvent (OS), co-solvent, phase transfer catalyst (PTC), and operational condition. A Neural Network model was found to be the best for predicting PET conversion, with a testing R2 and RMSE of 0.93 and 8.45%. The model-based interpretation suggested that operational conditions accounted for 45% of importance, followed by ABC (19%), OS (12%), and PTC (12%). Furthermore, hydrolysis time, temperature, and ABC concentration were identified as the top-three factors positively influencing PET conversion. This work facilitates the catalyst and process optimization for PET hydrolysis to promote the plastic circular economy. © 2025, The Authors. All rights reserved.

关键词： Transfer learning

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Retraction notice to "Organic composts as A vehicle for the entry of microplastics into the environment: A comprehensive review" [Sci. Total Environ. 892 (2023) 164758]

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The science of the total environment 2024年 957卷 177731页

作者： Van-Re Le Minh-Ky Nguyen Hoang-Lam Nguyen Chitsan Lin Md Refat Jahan Rakib Van-Anh Thai Van-Giang Le Guilherme Malafaia Abubakr M Idris Ho Chi Minh City University of Food Industry (HUFI) 140 Le Trong Tan Street Tan Phu District Ho Chi Minh City 700000 Viet Nam. Faculty of Environment and Natural Resources Nong Lam University of Ho Chi Minh City Hamlet 6 Linh Trung Ward Thu Duc City Ho Chi Minh City 700000 Viet Nam Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. Department of Civil Engineering McGill University Montreal Canada. Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. Department of Fisheries and Marine Science Faculty of Science Noakhali Science and Technology University Noakhali 3814 Bangladesh. Institute of Aquatic Science and Technology National Kaohsiung University of Science and Technology Kaohsiung City 81157 Taiwan. Central Institute for Natural Resources and Environmental Studies Vietnam National University Hanoi 111000 Viet Nam. Post-Graduation Program in Ecology Conservation and Biodiversity Federal University of Uberlândia Uberlândia MG Brazil Post-Graduation Program in Biotechnology and Biodiversity Federal University of Goiás Goiânia GO Brazil Post-Graduation Program in Conservation of Cerrado Natural Resources Goiano Federal Institute Urutaí GO Brazil. Department of Chemistry College of Science King Khalid University 61431 Abha Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Abha 61421 Saudi Arabia.

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Microbial Phylogenetic Divergence between Surface-Water and Sedimentary Ecosystems Drove the Resistome Profiles

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SSRN 2023年

作者： Ohore, Okugbe Ebiotubo Zhang, Jingli Ifon, Binessi Edouard Kumwimba, Mathieu Nsenga Mu, Xiaoying Kuang, Dai Wang, Zhen Gu, Ji-Dong Yang, Guojing Key Laboratory of Tropical Diseases Control National Health Commission Key Laboratory of Tropical Translational Medicine of Ministry of Education School of Tropical Medicine Hainan Medical University Hainan Haikou571199 China Key Laboratory of Tropical Translational Medicine of Ministry of Education School of Tropical Medicine Hainan Medical University Hainan Haikou571199 China The First Affiliated Hospital of Hainan Medical University Hainan Haikou China Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou515063 China Department of Clinical Medicine Shantou University Medical College Guangdong Shantou515041 China Department of Civil and Environmental Engineering Shantou University Shantou515063 China Laboratory of Physical Chemistry University of Abomey-Calavi Republic of Benin Cotonou01 BP 4521 Benin State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China Center for Water and Ecology State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Environmental Science and Engineering Program Guangdong Technion - Israel Institute of Technology 241 Daxue Road Jinping District Guangdong Shantou515063 China Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion Guangdong Technion Israel Institute of Technology 241 Daxue Road Jinping District Guangdong Shantou515063 China

Antibiotic pollution and the evolution of antibiotic resistance genes (ARGs) are critical issues of global significance. This study investigated the fate of antibiotics in sedimentary and aqueous substrates and the impact of ecosystem shift between water and sedimentary phases on resistome profiles. The total concentration of antibiotics in the surface water (49.5%) was significantly higher than suspended particulate matter (31.4%), and sedimental phases (19.1%). However, the relative abundance of ARGs in surface water and sediment was disproportionate to the abundance of antibiotics concentration, and sediments were the predominant ARGs reservoirs. Phylogenetic divergence of the microbial communities between the surface water and the sedimentary ecosystems resulted in a significant shift in the ARGs profiles between the two distinctive surface-water and sediments. ARGs of Clinical importance;blaGES, MCR-7.1, ermB, tet(34), tet36, tetG-01, and sul2 were significantly increased in the surface water, while blaCTX-M-01, blaTEM, blaOXA10-01, blaVIM, tet(W/N/W), tetM02, and ermX were amplified in the sediments. cfxA was an endemic ARG in surface-water ecosystems while the endemic ARGs of the sedimentary ecosystems included aacC4, aadA9-02, blaCTX-M-04, blaIMP-01, blaIMP-02, bla-L1, penA, erm(36), ermC, ermT-01, msrA-01, pikR2, vgb-01, mexA, oprD, ttgB, and aac. These findings provide crucial information for the identification of ARGs-specific high-risk reservoirs © 2023, The Authors. All rights reserved.

关键词： Antibiotics

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Prevalence of Neonicotinoid Insecticides in Paired Private-Well Tap Water and Human Urine Samples in a Region of Intense Agriculture Overlying Vulnerable Aquifers in Eastern Iowa

SSRN

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SSRN 2022年

作者： Thompson, Darrin A. Kolpin, Dana W. Hladik, Michelle L. Lehmler, Hans-Joachim Meppelink, Shannon M. Poch, Matthew C. Vargo, John D. Soupene, Victor A. Irfan, Nafis Md. Robinson, Morgan Kannan, Kurunthachalam Beane Freeman, Laura E. Hofmann, Jonathan N. Cwiertny, David M. Field, R. William University of Iowa College of Public Health Iowa CityIA United States University of Iowa Center for Health Effects of Environmental Contamination Iowa CityIA United States U.S. Geological Survey Central Midwest Water Science Center Iowa CityIA United States U.S. Geological Survey California Water Science Center SacramentoCA United States State Hygienic Laboratory The University of Iowa Iowa CityIA United States Interdisciplinary Graduate Program in Human Toxicology University of Iowa Iowa CityIA United States University of Iowa Department of Internal Medicine Iowa CityIA United States University of Dhaka Institute of Nutrition and Food Science Dhaka Bangladesh Department of Pediatrics New York University School of Medicine New YorkNY United States Department of Environmental Medicine New York University School of Medicine New YorkNY United States Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute RockvilleMD United States Department of Civil and Environmental Engineering University of Iowa Iowa CityIA United States Public Policy Center University of Iowa Iowa CityIA United States

A pilot study among farming households in eastern Iowa was conducted between December 2018 and February 2019 to assess human exposure to neonicotinoids. The study area was in a hydrologic setting where groundwater is vulnerable to surface-applied contaminants, and in a region of intense crop and livestock production. In addition to paired outdoor (hydrant) water and indoor (tap) water samples where private wells are being used as the source of drinking water, urine samples were collected before bedtime, during the night, and first morning void from 47 adult male pesticide applicators. Neonicotinoids (NEOs) were detected in 53% of outdoor and 55% of indoor samples, with two or more NEOs in 13% of samples. Clothianidin was the most frequently detected neonicotinoid (outdoor: 53%, maximum (max): 570.6 nanograms per liter [ng/L];indoor: 55%, max: 140.5 ng/L), followed by imidacloprid (outdoor: 13%, max: 3.0 ng/L;indoor: 11%, 29.3 ng/L), and thiamethoxam (outdoor: 6%, max: 16.1 ng/L;indoor: 6%, max: 13.1 ng/L). Human exposure was widely detected in urine samples. A median of 10 different NEOs and/or transformation products (TPs) were detected in urine, with five NEOs detected in all 115 urine samples analyzed, including clothianidin (max: 4.7 micrograms per gram [µg/g]), nitenpyram (max: 1.1 µg/g), thiamethoxam (max: 3.4 µg/g), 6-chloronicotinic acid (max: 7.0 µg/g), and thiacloprid amide (max: 1.5 µg/g). Dinotefuran (max: 2.9 µg/g), imidaclothiz (max: 15.3 µg/g), acetamiprid-N-desmethyl (max: 16.9 µg/g), and N-desmethyl thiamethoxam (max: 3.3 µg/g) were found in > 70% of urine samples. The study results indicate that while the consumption of private well tap water provides a human exposure pathway, the companion urine results provide evidence that diet and/or other exposure pathways (e.g., occupational, house dust) may contribute to exposure more than water contamination. © 2022, The Authors. All rights reserved.

关键词： Aquifers

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Evaluation of the characteristics of recycled aggregates produced in campinas-SP/Brazil

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International Journal of Sustainable Development and Planning 2020年第4期15卷 451-459页

作者： Martins, Valter Pimentel, Lia Lorena Postgraduate Program in Science and Environmental Engineering Federal University of Alfenas 39 João de Moraes Sobrinho Itapira SP13976-153 Brazil Postgraduate Program in Urban Infraestrutura Systems Pontifical Catholic University of Campinas 1516 Professor Dr. Euryclides de Jesus Zerbini Campinas SP13087-571 Brazil

To use the aggregate generated by recycling plants in the production of concrete, it is essential to know its characteristics. This paper aims to evaluate samples of recycled aggregates, both small and large, produced at two recycling plants located in the city of Campinas, SP, Brazil, comparing the results with parameters established by NBR 15116:2004 and with the characteristics of the natural aggregates used in the production of concrete. For the development of the study, samples of aggregates, small and large, both natural and recycled were collected, which had their characteristics determined and evaluated according to the specifications of NBRs 7211:2009 and 15116:2004. The results indicate that even the recycled aggregates showing some variability throughout the tests and unfulfilled with some normative specifications, it is concluded that their use in concrete is close to becoming feasible. It was verified that simple corrections in the recycling plants or additions of a certain amount of natural aggregate, would probably already be enough for the total standardization of the recycled aggregate. Comparing the recycled aggregates with the natural ones it was verified that the recycled ones present a higher index of fines and mainly greater absorption of water, which reflects in smaller values of specific mass, apparent specific mass and crushing resistance. However, these are not limiting factors in the use of recycled materials in concretes without structural function, since they will not be so mechanically required. © 2020 WITPress. All rights reserved.

关键词： Specifications

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Correction to: A mini‑review on plasticrusts: occurrence, current trends, potential threats, and recommendations for coastal sustainability

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environmental monitoring and assessment 2024年第7期196卷 629页

作者： Minh-Ky Nguyen Md Refat Jahan Rakib Hoang-Lam Nguyen Chitsan Lin Guilherme Malafaia Abubakr M Idris Faculty of Environment and Natural Resources Nong Lam University of Ho Chi Minh City Hamlet 6 Linh Trung Ward Thu Duc City Ho Chi Minh City 700000 Vietnam. Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. Department of Fisheries and Marine Science Faculty of Science Noakhali Science and Technology University Sonapur Noakhali 3814 Bangladesh. Department of Civil Engineering McGill University Montreal Canada. Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. ctlin@nkust.edu.tw. Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. ctlin@nkust.edu.tw. Post‑Graduation Program in Ecology Conservation and Biodiversity Federal University of Uberlandia Uberlandia MG Brazil. guilhermeifgoiano@***. Post‑Graduation Program in Biotechnology and Biodiversity Federal University of Goias Goiania GO Brazil. guilhermeifgoiano@***. Post‑Graduation Program in Conservation of Cerrado Natural Resources Goiano Federal Institute Urutai GO Brazil. guilhermeifgoiano@***. Department of Chemistry College of Science King Khalid University 62529 Abha Saudi Arabia. Research Center for Advanced Materials Science (RCAMS) King Khalid University 62529 Abha Saudi Arabia.

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Comparative analysis of SWAT and SWAT coupled with XGBoost model using Optuna hyperparameter optimization for nutrient simulation: A case study in the Upper Nan River basin, Thailand

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Journal of environmental Management 2025年 388卷 126053页

作者： Pinichka, Chayut Chotpantarat, Srilert Cho, Kyung Hwa Siriwong, Wattasit Chulalongkorn University Bangkok Thailand International Health Policy Program Ministry of Public Health Nonthaburi Thailand Department of Geology Faculty of Science Chulalongkorn University Bangkok Thailand Sustainable Research Institute Chulalongkorn University Thailand School of Civil Environmental and Architectural Engineering Korea University Seoul02841 Korea Republic of College of Public Health Sciences Chulalongkorn University Bangkok Thailand

Agricultural runoff leading to nitrate (NO3-N) and orthophosphate (PO4-P) contamination poses significant environmental and public health risks. This study integrates the Soil and Water Assessment Tool (SWAT) with eXtreme Gradient Boosting (XGBoost), optimized using Optuna hyperparameter tuning, to enhance predictions of nutrient concentrations in water bodies. The hybrid SWAT-XGBoost model combines physical hydrological processes with machine learning for improved accuracy. The optimized model demonstrated significant improvements over SWAT alone, with R2 values increasing by up to 50 % and RMSE decreasing by up to 75 % across datasets. These results highlight the enhanced predictive capabilities of the hybrid approach in capturing nutrient transport dynamics. SHAP analysis further identified key factors, such as sediment dynamics and nutrient mineralization, as dominant drivers of contamination, providing actionable insights for effective watershed management. By integrating SHAP analysis, the study identified key processes influencing nutrient transport, such as sediment dynamics and nutrient mineralization, offering deeper insights into pollution pathways. This approach provides a scalable and adaptable framework for improving watershed management, supporting sustainable agricultural practices, and mitigating contamination risks. The findings highlight an approach that combines physical modeling with machine learning to effectively address complex environmental challenges. © 2025 Elsevier Ltd

关键词： Health risks

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A worldwide aerosol phenomenology: Elemental and organic carbon in PM 2.5 and PM 10 .

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Atmospheric Environment 2025年

作者： Jean-Philippe Putaud Fabrizia Cavalli Karl Espen Yttri Judith C. Chow John G. Watson Baerbel Sinha Chandra Venkataraman Fumikazu Ikemori Jean-Luc Jaffrezo Gaelle Uzu Isabel Moreno Radovan Krejci Paolo Laj Tarun Gupta Min Hu Sang-Woo Kim Olga Mayol-Bracero Patricia Quinn Wenche Aas Andres Alastuey Kajal Yadav European Commission Joint Research Centre (JRC) Ispra Italy NILU Kjeller Norway Division of Atmospheric Sciences Desert Research Institute Reno (NV) USA Department of Earth and Environmental Sciences Indian Institute of Science Education and Research Mohali Sector 81 S.A.S Nagar India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Maharashtra India Nagoya city Institute for Environmental Sciences Nagoya Graduate School of Integrated Science and Technology Nagasaki University Nagasaki Japan Institut des Géosciences de l'Environnement Université Grenoble Alpes CNRS IRD Grenoble INP France Laboratorio de Física de la Atmósfera Instituto de Investigaciones Físicas Universidad Mayor de San Andrés La Paz Bolivia Department of Environmental Science & Bolin Centre for Climate Research Stockholm Sweden World Meteorological Organization (WMO) Geneva Switzerland Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur India State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University) College of Environmental Sciences and Engineering Peking University Beijing China School of Earth and Environmental Sciences Seoul National University Seoul South Korea Brookhaven National Laboratory Upton New York USA National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) Seattle (WA) USA Institute of Environmental Assessment and Water Research (IDAEA-CSIC) Barcelona Spain Department of Earth and Environmental Sciences Indian Institute of Science Education and Research Bhopal Bhopal India

Elemental carbon (EC), organic carbon (OC), and particulate matter (PM) concentrations in the inhalable (PM 10 ) and fine (PM 2.5 ) size fractions are measured worldwide, albeit with different analytical methods. These measurements from many researchers were collected and analysed for Africa, America, Asia, and Europe for 2012 - 2019. EC/PM, OC/PM, and OC/EC ratios were examined based on region, site type, and season to infer potential sources and impacts. These analyses demonstrate that carbonaceous materials are important PM constituents throughout the world. Mean EC/PM ratios were lowest in PM 10 in Sahelian Africa and Europe (∼ 0.01), highest (> 0.07) in PM 2.5 at urban sites in North America, South America, and Japan. Mean OC/PM ratios were lowest in PM 10 in the Sahel (∼ 0.06) and in PM 2.5 in China and Thailand (0.10), and highest in central and eastern Europe (∼ 0.3) and North America (∼ 0.4). OC/EC ratios were elevated in western and northern Europe, and at regional background sites in North America. EC/PM increased with PM 10 in Thailand, while OC/PM increased with higher PM mass in Thailand, India, and North America, highlighting the specific contribution of carbonaceous aerosols to PM pollution in these regions. At European and North American background sites, OC/EC ratios increased with PM mass. Higher OC/EC ratios in dry periods indicate influence of wildfires, prescribed burns, and secondary aerosol formation. Elevated wintertime EC/PM ratios coincide with residential heating in temperate climate zones.

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Explainable Machine Learning for Anaerobic Digestion: A Critical Review

SSRN

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SSRN 2022年

作者： Li, Wangliang Gupta, Rohit Zhang, Le Liu, Hongtao Zhang, Zhikai Hou, Jiayi You, Siming Ok, Yong Sik James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Nanoengineered Systems Laboratory UCL Mechanical Engineering University College London LondonWC1E 7JE United Kingdom Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London LondonW1W 7TS United Kingdom Department of Resources and Environment School of Agriculture and Biology Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Institute of Geographic Science and Natural Resources Research Chinese Academy of Sciences Beijing100101 China CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China School of Water Resources and Environment Hebei GEO University Hebei Shijiazhuang050031 China Korea Biochar Research Center APRU Sustainable Waste Management Program Division of Environmental Science and Ecological Engineering Korea University Seoul02841 Korea Republic of

Anaerobic digestion (AD) is a traditional process to achieve energy recovery from a variety of organic waste streams. However, the AD performance in terms of biogas production and process stability relies on the cooperation of the functional microbial communities and the operating parameters. The latest progress in machine learning for AD processes including optimization, parametric prediction, perturbation detection, and long-term monitoring were critically reviewed. The explainable data-driven models including black-box machine learning (ML) models and nature-inspired optimization techniques were compared, while summarizing their applications. The review also highlighted the challenges, perspectives, and opportunities for future research of applying ML for AD processes. © 2022, The Authors. All rights reserved.

关键词： Optimization

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