Pub Date : 2026-01-31DOI: 10.1016/j.jenvman.2026.128762
Yuanli Qin , Honghong Ma , Chengrui Wang , Min Peng , Fugui Zhang , Zheng Yang , Yu Zhao , Zhenyu Chen , Lingling Ma , Yang Shao , Chen Zhao , Hangxin Cheng
Soil salinization threatens agricultural sustainability and economic welfare, challenging global food production and sustainable agricultural development. Understanding the interactions between soil salinity and covariates is essential for advancing the understanding of salinization processes and informing land management decisions in salt-affected regions. This study examines the middle and lower reaches of the Tarim River Basin (TRB) in southern Xinjiang, leveraging machine learning-based data to identify key factors contributing to salinization evolution, while quantifying interactions among variables using the SHapley Additive exPlanations (SHAP) algorithm and analyzing causal relationships through structural equation model (SEM). The results reveal significant spatial heterogeneity in soil salinity across the middle and lower reaches of the TRB, increasing from northwest to southeast. Key driving factors include Silica-Alumina ratio (Sa), SiO2, Fe2O3, annual potential evapotranspiration (Pet), elevation (DEM), Normalized Difference Vegetation Index (NDVI), land use (LU) and soil texture (Texture), with their complex interactions contributing to this heterogeneity. What's more, this paper leverages SHAP dependency plots to elucidate interactions among factors influencing soil salinity from the perspectives of soil formation, climate and environment. To better understand the relationships between soil salinity and the influencing variables, the causal relationships between these factors and soil salinity are quantified in the SEM. The findings can provide valuable insights for managing soil salinization and promoting sustainable agricultural development in arid regions.
{"title":"Divergent key driving factors of soil salinity in Tarim River Basin, Northwest China","authors":"Yuanli Qin , Honghong Ma , Chengrui Wang , Min Peng , Fugui Zhang , Zheng Yang , Yu Zhao , Zhenyu Chen , Lingling Ma , Yang Shao , Chen Zhao , Hangxin Cheng","doi":"10.1016/j.jenvman.2026.128762","DOIUrl":"10.1016/j.jenvman.2026.128762","url":null,"abstract":"<div><div>Soil salinization threatens agricultural sustainability and economic welfare, challenging global food production and sustainable agricultural development. Understanding the interactions between soil salinity and covariates is essential for advancing the understanding of salinization processes and informing land management decisions in salt-affected regions. This study examines the middle and lower reaches of the Tarim River Basin (TRB) in southern Xinjiang, leveraging machine learning-based data to identify key factors contributing to salinization evolution, while quantifying interactions among variables using the SHapley Additive exPlanations (SHAP) algorithm and analyzing causal relationships through structural equation model (SEM). The results reveal significant spatial heterogeneity in soil salinity across the middle and lower reaches of the TRB, increasing from northwest to southeast. Key driving factors include Silica-Alumina ratio (Sa), SiO<sub>2</sub>, Fe<sub>2</sub>O<sub>3</sub>, annual potential evapotranspiration (Pet), elevation (DEM), Normalized Difference Vegetation Index (NDVI), land use (LU) and soil texture (Texture), with their complex interactions contributing to this heterogeneity. What's more, this paper leverages SHAP dependency plots to elucidate interactions among factors influencing soil salinity from the perspectives of soil formation, climate and environment. To better understand the relationships between soil salinity and the influencing variables, the causal relationships between these factors and soil salinity are quantified in the SEM. The findings can provide valuable insights for managing soil salinization and promoting sustainable agricultural development in arid regions.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128762"},"PeriodicalIF":8.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.jenvman.2026.128783
Wenlu Li, Fanping Meng, Wenjia Sun
This study investigated the removal efficiency and pathways of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) at environmentally relevant concentrations by marine diatom Chaetoceros calcitrans MZB-1, with a particular emphasis on kinetic analysis and optimization of environmental factors. In presence of alga MZB-1, the removal efficiencies of PFOA and PFOS at an initial concentration of 100 μg/L were 24.65 % and 29.35 %, respectively, within 15 d. The removal kinetics followed first-order models, with removal rate constants (k) of 0.014-0.019 d-1 for PFOA and 0.023-0.042 d-1 for PFOS, corresponding to the half-lives (t1/2) of 36.48-49.51 d and 16.50-30.14 d, respectively. By moderately increasing the microalgal inoculation density, the removal efficiencies of PFOA and PFOS could be improved, and the optimal initial inoculation density was 105 cells/mL. They were mainly removed through biodegradation processes, followed by biosorption and bioaccumulation. Single-factor experiments showed that the optimal growth conditions for the alga were temperature 25 °C, salinity 15 psμ, pH 8, and light intensity 60 μmol/(m2·s), while the maximum removal of PFOA and PFOS occurred at 25 °C, salinity 30 psμ, pH 8, and light intensity 200 μmol/(m2·s). The findings contributed to understanding the removal ability and pathways of marine microalgae towards per- and polyfluoroalkyl substances (PFASs), providing support for the application of microalgae in the bioremediation of PFASs-contaminated seawater.
{"title":"Biodegradation of perfluorooctanoic acid and perfluorooctanesulfonic acid by a marine microalga Chaetoceros calcitrans MZB-1: kinetic analysis, removal pathways, and effects of environmental factors.","authors":"Wenlu Li, Fanping Meng, Wenjia Sun","doi":"10.1016/j.jenvman.2026.128783","DOIUrl":"https://doi.org/10.1016/j.jenvman.2026.128783","url":null,"abstract":"<p><p>This study investigated the removal efficiency and pathways of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) at environmentally relevant concentrations by marine diatom Chaetoceros calcitrans MZB-1, with a particular emphasis on kinetic analysis and optimization of environmental factors. In presence of alga MZB-1, the removal efficiencies of PFOA and PFOS at an initial concentration of 100 μg/L were 24.65 % and 29.35 %, respectively, within 15 d. The removal kinetics followed first-order models, with removal rate constants (k) of 0.014-0.019 d<sup>-1</sup> for PFOA and 0.023-0.042 d<sup>-1</sup> for PFOS, corresponding to the half-lives (t<sub>1/2</sub>) of 36.48-49.51 d and 16.50-30.14 d, respectively. By moderately increasing the microalgal inoculation density, the removal efficiencies of PFOA and PFOS could be improved, and the optimal initial inoculation density was 10<sup>5</sup> cells/mL. They were mainly removed through biodegradation processes, followed by biosorption and bioaccumulation. Single-factor experiments showed that the optimal growth conditions for the alga were temperature 25 °C, salinity 15 psμ, pH 8, and light intensity 60 μmol/(m<sup>2</sup>·s), while the maximum removal of PFOA and PFOS occurred at 25 °C, salinity 30 psμ, pH 8, and light intensity 200 μmol/(m<sup>2</sup>·s). The findings contributed to understanding the removal ability and pathways of marine microalgae towards per- and polyfluoroalkyl substances (PFASs), providing support for the application of microalgae in the bioremediation of PFASs-contaminated seawater.</p>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"128783"},"PeriodicalIF":8.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the context of climate change, Romania is actively advancing its transition to renewable energy, contributing to the European Green Deal goals of zero greenhouse gas emissions by 2050 and 45 % renewable energy share by 2030. Romania's onshore wind potential is estimated at 14,000 MW, with an existing installed capacity of around 3000 MW, concentrated mainly in the Dobrogea region. The National Energy Strategy 2022–2030 forecasts an additional 2302 MW capacity increase. This study develops a spatial suitability model for wind farm siting using the Fuzzy Analytic Hierarchy Process (FAHP) combined with GIS-based analysis. Eight criteria were evaluated and weighted based on expert judgment. Factors considered include wind speed, slope, altitude, land use, distance to transmission lines, settlements, roads and protected areas. Using GIS Weighted Overlay analysis, land was classified into five suitability classes. Results show that 51.1 % of Romania's territory is suitable for wind energy projects, especially in the eastern and southeastern counties of Tulcea, Constanța, Brăila, Galați, Călăraşi, and Vaslui. Validation against existing wind farm locations showed a 69.9 % spatial match within areas classified a ‘Very High’ suitability, and over 80 % within the combined ‘High’ and ‘Very High’ classes. Discrepancies are mainly due to protected area zoning and spatial data resolution. The findings deliver quantitative evidence to optimize wind farm siting and support data-driven decisions for policymakers and investors in line with EU decarbonization objectives.
{"title":"Land suitability assessment for wind farm development in Romania: Policy and GIS–AHP integration","authors":"Odelin Talabă , Alexandra Vrînceanu , Monica Dumitraşcu , Ines Grigorescu , Bianca Mitrică","doi":"10.1016/j.jenvman.2026.128777","DOIUrl":"10.1016/j.jenvman.2026.128777","url":null,"abstract":"<div><div>In the context of climate change, Romania is actively advancing its transition to renewable energy, contributing to the European Green Deal goals of zero greenhouse gas emissions by 2050 and 45 % renewable energy share by 2030. Romania's onshore wind potential is estimated at 14,000 MW, with an existing installed capacity of around 3000 MW, concentrated mainly in the Dobrogea region. The National Energy Strategy 2022–2030 forecasts an additional 2302 MW capacity increase. This study develops a spatial suitability model for wind farm siting using the Fuzzy Analytic Hierarchy Process (FAHP) combined with GIS-based analysis. Eight criteria were evaluated and weighted based on expert judgment. Factors considered include wind speed, slope, altitude, land use, distance to transmission lines, settlements, roads and protected areas. Using GIS Weighted Overlay analysis, land was classified into five suitability classes. Results show that 51.1 % of Romania's territory is suitable for wind energy projects, especially in the eastern and southeastern counties of Tulcea, Constanța, Brăila, Galați, Călăraşi, and Vaslui. Validation against existing wind farm locations showed a 69.9 % spatial match within areas classified a ‘Very High’ suitability, and over 80 % within the combined ‘High’ and ‘Very High’ classes. Discrepancies are mainly due to protected area zoning and spatial data resolution. The findings deliver quantitative evidence to optimize wind farm siting and support data-driven decisions for policymakers and investors in line with EU decarbonization objectives.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128777"},"PeriodicalIF":8.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.jenvman.2026.128721
Ansa Rebi , Guan Wang , Tao Yang , Irsa Ejaz , Adnan Mustafa , Jasper Kanomanyanga , Mohsin Mahmood , Xinglei Cui , Jinxing Zhou
Clip-and-burn is a forestry and land management technique common in China and other tropical regions worldwide to clear land for agriculture or to manage vegetation. However, the dynamics of carbon and nitrogen in karst regions following clip-and-burn practices remain largely unknown. Here, we evaluate the impact of clip-and-burn management practices on carbon and nitrogen cycling in the Karst region of China. The treatments included high-intensity fire (HIF), low-intensity fire (LIF), clipping and fire (CF), clipping (CP), and control (CK). Soil samples were collected one year after treatment application. Clipping-and-burning significantly influenced soil organic carbon (SOC), total nitrogen (TN), nitrate (NO3−), and ammonium (NH4+) levels compared to the control. Clipping-and-burning treatments significantly (p < 0.05) reduced SOC, soil organic matter, TN, and NO3- compared to CK. In contrast, NH4+ content increased substantially under clip-and-burn treatments compared to CK, suggesting it potentially affects soil fertility and the recovery of plant life in the post-fire environment. Clipping-and-burning treatments showed higher Acidobacteria and Proteobacteria, likely driving C and N cycling, with Proteobacteria involved in N transformations. The higher bacterial diversity in LIF indicates more active nutrient cycling, with Actinobacteria indicating the importance of organic matter availability and nutrient balance. Moreover, CP significantly (p < 0.05) enhanced the metabolic potential for various N cycle processes, including N fixation, N uptake, and assimilatory NO3- reduction. On the other hand, CP also showed higher gene levels for N fixation than other treatments, indicating its effectiveness in promoting N assimilation in the soil. In addition, microbial biomass C, microbial biomass N, and microbial biomass P were significantly (p < 0.05) increased (by 10.70 %, 11.90 %, 25.60 % respectively) under CP treatment compared to CK. The changes in bacterial composition and diversity could have important implications for soil fertility, nutrient availability, and ecosystem functioning, particularly in terms of C sequestration and N availability in plants in the karst landscape. It suggests that careful fire management and selective clipping could enhance soil health and ecosystem productivity.
{"title":"Clipping-and-burning alters carbon and nitrogen cycling through bacterial fixation pathways in the key Chinese karst region","authors":"Ansa Rebi , Guan Wang , Tao Yang , Irsa Ejaz , Adnan Mustafa , Jasper Kanomanyanga , Mohsin Mahmood , Xinglei Cui , Jinxing Zhou","doi":"10.1016/j.jenvman.2026.128721","DOIUrl":"10.1016/j.jenvman.2026.128721","url":null,"abstract":"<div><div>Clip-and-burn is a forestry and land management technique common in China and other tropical regions worldwide to clear land for agriculture or to manage vegetation. However, the dynamics of carbon and nitrogen in karst regions following clip-and-burn practices remain largely unknown. Here, we evaluate the impact of clip-and-burn management practices on carbon and nitrogen cycling in the Karst region of China. The treatments included high-intensity fire (HIF), low-intensity fire (LIF), clipping and fire (CF), clipping (CP), and control (CK). Soil samples were collected one year after treatment application. Clipping-and-burning significantly influenced soil organic carbon (SOC), total nitrogen (TN), nitrate (NO<sub>3</sub><sup>−</sup>), and ammonium (NH<sub>4</sub><sup>+</sup>) levels compared to the control. Clipping-and-burning treatments significantly (<em>p</em> < 0.05) reduced SOC, soil organic matter, TN, and NO<sub>3</sub><sup>-</sup> compared to CK. In contrast, NH<sub>4</sub><sup>+</sup> content increased substantially under clip-and-burn treatments compared to CK, suggesting it potentially affects soil fertility and the recovery of plant life in the post-fire environment. Clipping-and-burning treatments showed higher <em>Acidobacteria</em> and <em>Proteobacteria,</em> likely driving C and N cycling, with <em>Proteobacteria</em> involved in N transformations. The higher bacterial diversity in LIF indicates more active nutrient cycling, with <em>Actinobacteria</em> indicating the importance of organic matter availability and nutrient balance. Moreover, CP significantly (<em>p</em> < 0.05) enhanced the metabolic potential for various N cycle processes, including N fixation, N uptake, and assimilatory NO<sub>3</sub><sub><sup>-</sup></sub> reduction. On the other hand, CP also showed higher gene levels for N fixation than other treatments, indicating its effectiveness in promoting N assimilation in the soil. In addition, microbial biomass C, microbial biomass N, and microbial biomass P were significantly (<em>p</em> < 0.05) increased (by 10.70 %, 11.90 %, 25.60 % respectively) under CP treatment compared to CK. The changes in bacterial composition and diversity could have important implications for soil fertility, nutrient availability, and ecosystem functioning, particularly in terms of C sequestration and N availability in plants in the karst landscape. It suggests that careful fire management and selective clipping could enhance soil health and ecosystem productivity.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128721"},"PeriodicalIF":8.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Co-smouldering is a promising way to improve the value of products and smouldering performance, while the detailed synergistic effects between various feedstocks are still unknown. This study provides novel insights into the synergistic effects on reaction characteristics and gas production during the co-smouldering of food waste and gasification slag, which have contrasting volatile and fixed carbon contents. The results demonstrated that blending 30 % gasification slag with food waste increased the maximum allowable moisture content to 75 %. For feedstocks with comparable low heating value (6.67-6.85 MJ/kg), high fixed carbon to volatile matter ratio significantly elevated the average peak temperature from 691.0 °C to 992.6 °C and increased the CO yield from 1.00 % to 6.87 %. Volatile-derived radicals from food waste promoted the formation of disordered carbon structures and C-O functional groups on the surface of gasification slag, leading to the enhancement formation of CO and the reduction of heavy condensable components (e.g., heterocyclic compounds). Additionally, catalytically active calcium species from food waste formed oxygen-containing complexes (e.g., C-O-M) with the gasification slag char matrix, which facilitated the gasification reactions (e.g., the Boudouard reaction) and increased smouldering velocity by 24.46 %-64.23 % compared to gasification slag alone. These findings provide valuable insights for developing efficient waste-to-energy strategies, highlighting the potential of co-smouldering for fuel gas production.
{"title":"Co-smouldering combustion of food waste and coal gasification slag: Synergistic effects on reaction characteristics and fuel gas properties.","authors":"Zhen Hu, Jingchun Huang, Yuhao Zhong, Xiaolong An, Pengfei Qin, Maolong Zhang, Qianshi Song, Yu Qiao","doi":"10.1016/j.jenvman.2026.128790","DOIUrl":"https://doi.org/10.1016/j.jenvman.2026.128790","url":null,"abstract":"<p><p>Co-smouldering is a promising way to improve the value of products and smouldering performance, while the detailed synergistic effects between various feedstocks are still unknown. This study provides novel insights into the synergistic effects on reaction characteristics and gas production during the co-smouldering of food waste and gasification slag, which have contrasting volatile and fixed carbon contents. The results demonstrated that blending 30 % gasification slag with food waste increased the maximum allowable moisture content to 75 %. For feedstocks with comparable low heating value (6.67-6.85 MJ/kg), high fixed carbon to volatile matter ratio significantly elevated the average peak temperature from 691.0 °C to 992.6 °C and increased the CO yield from 1.00 % to 6.87 %. Volatile-derived radicals from food waste promoted the formation of disordered carbon structures and C-O functional groups on the surface of gasification slag, leading to the enhancement formation of CO and the reduction of heavy condensable components (e.g., heterocyclic compounds). Additionally, catalytically active calcium species from food waste formed oxygen-containing complexes (e.g., C-O-M) with the gasification slag char matrix, which facilitated the gasification reactions (e.g., the Boudouard reaction) and increased smouldering velocity by 24.46 %-64.23 % compared to gasification slag alone. These findings provide valuable insights for developing efficient waste-to-energy strategies, highlighting the potential of co-smouldering for fuel gas production.</p>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"128790"},"PeriodicalIF":8.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146099781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.jenvman.2026.128766
Jina Yin , Xinyao Hu , Tongchao Nan , Chunhui Lu
Large-scale groundwater prediction inherently involves data characterized by strong spatial and temporal heterogeneity as well as high dimensionality. Conventional data-driven approaches often struggle to effectively capture the complex and nonlinear groundwater dynamics, leading to great challenges in terms of accuracy and reliability. To achieve transparent and trustworthy predictions, this study proposes advanced hybrid deep learning modeling methods that integrate groundwater prediction, attribution analysis and uncertainty quantification within a cohesive framework. Two hybrid models with distinct architectures are constructed to effectively capture spatial patterns and temporal dependencies: CNN-Attention-LSTM (CAL) and Transformer-LSTM (TL). Intrinsic contributors are interpreted through SHapley Additive exPlanations. Stein Variational Gradient Descent (SVGD) is incorporated to quantify uncertainty and produce probabilistic predictions by capturing complex posterior distributions. Effectiveness of the methodology is well demonstrated through its application to groundwater storage anomaly (GWSA) prediction across the Yangtze River basin, China. Results show that both CAL and TL models deliver accurate GWSA distributions of spatiotemporal heterogeneity over the basin, with average R2 above 0.90. The CAL model outperforms in localized GWSA prediction. Meteorological factors predominantly contribute to GWSA, accounting for 80.66 % in the middle and lower basin. Furthermore, interactions among features and their synergistic impact on GWSA behave sensitive to feature ranges. SVGD significantly enhances predictive reliability, with major observations falling within 95 % confidence intervals. The modeling signifies a promising advancement for accurate GWSA prediction and risk-informed decision-making, while improving interpretability of outcomes. Our method has broad applicability and scalability by flexibly updating feature data in other environmental tasks.
{"title":"Spatiotemporal prediction and attribution of groundwater storage anomaly using enhanced hybrid deep learning modeling with uncertainty quantification","authors":"Jina Yin , Xinyao Hu , Tongchao Nan , Chunhui Lu","doi":"10.1016/j.jenvman.2026.128766","DOIUrl":"10.1016/j.jenvman.2026.128766","url":null,"abstract":"<div><div>Large-scale groundwater prediction inherently involves data characterized by strong spatial and temporal heterogeneity as well as high dimensionality. Conventional data-driven approaches often struggle to effectively capture the complex and nonlinear groundwater dynamics, leading to great challenges in terms of accuracy and reliability. To achieve transparent and trustworthy predictions, this study proposes advanced hybrid deep learning modeling methods that integrate groundwater prediction, attribution analysis and uncertainty quantification within a cohesive framework. Two hybrid models with distinct architectures are constructed to effectively capture spatial patterns and temporal dependencies: CNN-Attention-LSTM (CAL) and Transformer-LSTM (TL). Intrinsic contributors are interpreted through SHapley Additive exPlanations. Stein Variational Gradient Descent (SVGD) is incorporated to quantify uncertainty and produce probabilistic predictions by capturing complex posterior distributions. Effectiveness of the methodology is well demonstrated through its application to groundwater storage anomaly (GWSA) prediction across the Yangtze River basin, China. Results show that both CAL and TL models deliver accurate GWSA distributions of spatiotemporal heterogeneity over the basin, with average R<sup>2</sup> above 0.90. The CAL model outperforms in localized GWSA prediction. Meteorological factors predominantly contribute to GWSA, accounting for 80.66 % in the middle and lower basin. Furthermore, interactions among features and their synergistic impact on GWSA behave sensitive to feature ranges. SVGD significantly enhances predictive reliability, with major observations falling within 95 % confidence intervals. The modeling signifies a promising advancement for accurate GWSA prediction and risk-informed decision-making, while improving interpretability of outcomes. Our method has broad applicability and scalability by flexibly updating feature data in other environmental tasks.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128766"},"PeriodicalIF":8.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.jenvman.2026.128763
Manyu Jin , Yijiang Zheng , Yun Ge , Baojian Ma , Jiangyu Zhang , Rui An , Jianjun Cheng
Porosity is a critical factor affecting the windbreak and sand-fixing performance of upright reed sand fences. However, achieving consistent porosity control is challenging due to variations in reed bundle morphology and manufacturing complexities. This paper presents an intelligent system that integrates deep learning with real-time control to accurately regulate porosity. A lightweight instance segmentation model, Reed-YOLOv8n-SSAS, was developed for machine vision-based monitoring. Compared to the baseline YOLOv8n-seg, it achieved 98.6 % precision and 98.7 % [email protected], while reducing the model size, parameter count, and computational cost by 51.5 %, 55.4 %, and 40.0 %, respectively. A mask relocation algorithm was proposed to estimate the equivalent feed diameter during the transition from dispersed to bundled reeds. This enables real-time adjustment of the forming machine's parameters via a closed-loop control strategy. Field tests demonstrated that the system reliably maintained porosity near the target 45 %. Validation along the Xinjiang He-Ruo Railway showed that mechanically produced fences exhibited high structural integrity (straightness deviation ≤3°) and consistent porosity (44.8 ± 1.2 %), with significantly higher production efficiency compared to manual methods. This study provides a viable technical pathway for the green, efficient, and intelligent manufacturing of materials for desertification control.
{"title":"Towards sustainable desertification control: A manufacturing method for porosity-controlled upright reed sand fences","authors":"Manyu Jin , Yijiang Zheng , Yun Ge , Baojian Ma , Jiangyu Zhang , Rui An , Jianjun Cheng","doi":"10.1016/j.jenvman.2026.128763","DOIUrl":"10.1016/j.jenvman.2026.128763","url":null,"abstract":"<div><div>Porosity is a critical factor affecting the windbreak and sand-fixing performance of upright reed sand fences. However, achieving consistent porosity control is challenging due to variations in reed bundle morphology and manufacturing complexities. This paper presents an intelligent system that integrates deep learning with real-time control to accurately regulate porosity. A lightweight instance segmentation model, Reed-YOLOv8n-SSAS, was developed for machine vision-based monitoring. Compared to the baseline YOLOv8n-seg, it achieved 98.6 % precision and 98.7 % [email protected], while reducing the model size, parameter count, and computational cost by 51.5 %, 55.4 %, and 40.0 %, respectively. A mask relocation algorithm was proposed to estimate the equivalent feed diameter during the transition from dispersed to bundled reeds. This enables real-time adjustment of the forming machine's parameters via a closed-loop control strategy. Field tests demonstrated that the system reliably maintained porosity near the target 45 %. Validation along the Xinjiang He-Ruo Railway showed that mechanically produced fences exhibited high structural integrity (straightness deviation ≤3°) and consistent porosity (44.8 ± 1.2 %), with significantly higher production efficiency compared to manual methods. This study provides a viable technical pathway for the green, efficient, and intelligent manufacturing of materials for desertification control.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128763"},"PeriodicalIF":8.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.jenvman.2026.128770
Augusto D. Alvarenga, Marcos R.V. Lanza
Carbon-based materials, such as carbon black, have emerged as prominent candidates for the cathodic electrosynthesis of H2O2 due to their low cost, durability, and ease of chemical modification. While recent advances in catalyst design and parameter optimization have improved the electroasynthesis efficiency, the large number of interdependent features introduces complexity and unpredictability in the development of the system. In this context, machine learning (ML) algorithms have demonstrated great potential when used in interpreting complex datasets and deriving meaningful insights in diverse areas, including electrochemistry. While the selectivity and efficiency of metal-free carbon-based catalysts are strongly influenced by their surface functional groups, having a precise control over these functional groups remains a challenging task, and this affects the interpretation of experimental results. In this study, the XGBoost algorithm was used to analyze literature data on experimental conditions for H2O2 electrosynthesis, physicochemical characterizations, and the application of materials and their influence on H2O2 production efficiency. XPS analysis, Raman spectroscopy, BET surface area analysis, contact angle measurements, and electrical property analyses were used to characterize the electrocatalysts. The developed model demonstrated strong generalization capabilities, even when tested on external datasets not included in the training program. Furthermore, hyperparameter optimization analysis was used to refine the database, where the most relevant catalyst features were identified. This analysis enabled the creation of a more efficient model with reduced computational demands. Finally, thorough discussions were put forth regarding the challenges involving the construction of a robust database from fragmented experimental literature. This work provides useful contributions and insights into the application of ML in H2O2 electrosynthesis from experimental data, the construction of a structured database of carbonaceous catalysts, and the systematic interpretation of modeling results, paving the way for the rational design of new materials.
{"title":"Application of XGBoost using data from the oxygen reduction reaction of carbonaceous materials for H2O2 electrosynthesis","authors":"Augusto D. Alvarenga, Marcos R.V. Lanza","doi":"10.1016/j.jenvman.2026.128770","DOIUrl":"10.1016/j.jenvman.2026.128770","url":null,"abstract":"<div><div>Carbon-based materials, such as carbon black, have emerged as prominent candidates for the cathodic electrosynthesis of H<sub>2</sub>O<sub>2</sub> due to their low cost, durability, and ease of chemical modification. While recent advances in catalyst design and parameter optimization have improved the electroasynthesis efficiency, the large number of interdependent features introduces complexity and unpredictability in the development of the system. In this context, machine learning (ML) algorithms have demonstrated great potential when used in interpreting complex datasets and deriving meaningful insights in diverse areas, including electrochemistry. While the selectivity and efficiency of metal-free carbon-based catalysts are strongly influenced by their surface functional groups, having a precise control over these functional groups remains a challenging task, and this affects the interpretation of experimental results. In this study, the XGBoost algorithm was used to analyze literature data on experimental conditions for H<sub>2</sub>O<sub>2</sub> electrosynthesis, physicochemical characterizations, and the application of materials and their influence on H<sub>2</sub>O<sub>2</sub> production efficiency. XPS analysis, Raman spectroscopy, BET surface area analysis, contact angle measurements, and electrical property analyses were used to characterize the electrocatalysts. The developed model demonstrated strong generalization capabilities, even when tested on external datasets not included in the training program. Furthermore, hyperparameter optimization analysis was used to refine the database, where the most relevant catalyst features were identified. This analysis enabled the creation of a more efficient model with reduced computational demands. Finally, thorough discussions were put forth regarding the challenges involving the construction of a robust database from fragmented experimental literature. This work provides useful contributions and insights into the application of ML in H<sub>2</sub>O<sub>2</sub> electrosynthesis from experimental data, the construction of a structured database of carbonaceous catalysts, and the systematic interpretation of modeling results, paving the way for the rational design of new materials.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128770"},"PeriodicalIF":8.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.jenvman.2026.128769
Zhen He , Jinghao Hei , Zhiqiang Wu , Otthein Herzog , Lan Li , Ting Yang , Xiang Li
Increases in precipitation extremes and rapid urbanization are globally reshaping urban flood risks. However, few studies jointly assess within-city exposure inequalities and green-gray infrastructure capacities at national scale. A multistage framework integrating statistical modeling with machine learning is employed to analyze flood exposure of over 30,000 towns across 303 Chinese cities in 2000–2018. Flood hazard is derived from satellite-based inundation footprints, while population, development, greenness, and a gray-infrastructure proxy are compiled from remote-sensing and statistical sources. First, two within-city flood-exposure patterns are identified, the High-Development-skewed exposure pattern (HD) and the Low-Development-skewed exposure pattern (LD), with risk ratios around 1.4 for both skews. These patterns cluster spatially and show divergent town- and city-level drivers across socioeconomic and green-gray indicators. Secondly, a comparison of green-gray infrastructure capacity deficits reveals that green under-capacity is pattern-invariant, indicating a baseline requirement across cities. Gray infrastructure shortfalls are more prevalent under LD, by about 6 %. Thirdly, hazard, exposure, and vulnerability are integrated into a compound city-level risk index and modulated by an inequality factor, capturing both physical risk and distributional burden. Across China, risk hotspots align along four belts, the Sichuan-Chongqing-Yunnan corridor, the southeastern seaboard, the middle-lower Yangtze corridor, and parts of Northeast China. This result reflects hazard clustering in the humid south and southwest, concentrated exposure along coastal and inland corridors, and an eastward increasing vulnerability. This integrated framework provides an equity-aware, interpretable risk decomposition that supports transferable, resource-efficient flood mitigation prioritization in China and other regions worldwide.
{"title":"Socioeconomic inequality in flood exposure and compound urban flood risk: A multistage assessment across 30,000 towns in China","authors":"Zhen He , Jinghao Hei , Zhiqiang Wu , Otthein Herzog , Lan Li , Ting Yang , Xiang Li","doi":"10.1016/j.jenvman.2026.128769","DOIUrl":"10.1016/j.jenvman.2026.128769","url":null,"abstract":"<div><div>Increases in precipitation extremes and rapid urbanization are globally reshaping urban flood risks. However, few studies jointly assess within-city exposure inequalities and green-gray infrastructure capacities at national scale. A multistage framework integrating statistical modeling with machine learning is employed to analyze flood exposure of over 30,000 towns across 303 Chinese cities in 2000–2018. Flood hazard is derived from satellite-based inundation footprints, while population, development, greenness, and a gray-infrastructure proxy are compiled from remote-sensing and statistical sources. First, two within-city flood-exposure patterns are identified, the High-Development-skewed exposure pattern (HD) and the Low-Development-skewed exposure pattern (LD), with risk ratios around 1.4 for both skews. These patterns cluster spatially and show divergent town- and city-level drivers across socioeconomic and green-gray indicators. Secondly, a comparison of green-gray infrastructure capacity deficits reveals that green under-capacity is pattern-invariant, indicating a baseline requirement across cities. Gray infrastructure shortfalls are more prevalent under LD, by about 6 %. Thirdly, hazard, exposure, and vulnerability are integrated into a compound city-level risk index and modulated by an inequality factor, capturing both physical risk and distributional burden. Across China, risk hotspots align along four belts, the Sichuan-Chongqing-Yunnan corridor, the southeastern seaboard, the middle-lower Yangtze corridor, and parts of Northeast China. This result reflects hazard clustering in the humid south and southwest, concentrated exposure along coastal and inland corridors, and an eastward increasing vulnerability. This integrated framework provides an equity-aware, interpretable risk decomposition that supports transferable, resource-efficient flood mitigation prioritization in China and other regions worldwide.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128769"},"PeriodicalIF":8.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.jenvman.2026.128758
Dan Wang , Le Zhang , Wenqin Wu , Hui Cai , Yuanlong Sun , Xiaoning Liu , Zhaowei Zhang , Zhi-Quan Tian
Microplastic (MP) pollution in aquatic organisms poses a critical threat to aquatic ecosystem health and human food safety, however, the MP abundance, composition, and toxicological effects in cold fish from the Tibetan Plateau remain poorly understood. Microplastic pollution has emerged as a potential ecological threat to native fish species inhabiting the Tibetan Plateau. Field surveys revealed widespread MP ingestion, with a higher occurrence in farmed fish (61.9 %, 1.50–3.88 MPs/individual) compared to wild populations (44.4 %, 1.33–7.60 MPs/individual). The 30-day exposure experiment revealed dose-dependent PS-MPs accumulation in S. davidi (up to 1.37 μg/mg), accompanied by oxidative stress responses. Although glutathione levels increased as a compensatory defense, lipid peroxidation occurred at the highest exposure concentration (250 μg/L), with malondialdehyde levels reaching 2.1-fold of controls. Physical swimming ability was reduced, with a 45 % increase in induced velocity and a 37 % reduction in critical swimming speed. Chronic PS-MPs exposure also caused pronounced dysbiosis of gut microbiota, including a 5267 % proliferation of Cetobacterium and resurgence of pathogenic Aeromonas. Collectively, these effects resulted in a 28–37 % decline in migratory swimming capacity and multiple physiological disruptions. The results indicated that PS-MPs induced the oxidative stress, neurobehavioral impairment, and gut microbial imbalance, thereby threatening the survival and migratory fitness of Tibetan Plateau fish.
{"title":"Distribution characteristics of microplastics in fish of the Tibetan plateau and its physiological effects on Schizothorax davidi","authors":"Dan Wang , Le Zhang , Wenqin Wu , Hui Cai , Yuanlong Sun , Xiaoning Liu , Zhaowei Zhang , Zhi-Quan Tian","doi":"10.1016/j.jenvman.2026.128758","DOIUrl":"10.1016/j.jenvman.2026.128758","url":null,"abstract":"<div><div>Microplastic (MP) pollution in aquatic organisms poses a critical threat to aquatic ecosystem health and human food safety, however, the MP abundance, composition, and toxicological effects in cold fish from the Tibetan Plateau remain poorly understood. Microplastic pollution has emerged as a potential ecological threat to native fish species inhabiting the Tibetan Plateau. Field surveys revealed widespread MP ingestion, with a higher occurrence in farmed fish (61.9 %, 1.50–3.88 MPs/individual) compared to wild populations (44.4 %, 1.33–7.60 MPs/individual). The 30-day exposure experiment revealed dose-dependent PS-MPs accumulation in <em>S. davidi</em> (up to 1.37 μg/mg), accompanied by oxidative stress responses. Although glutathione levels increased as a compensatory defense, lipid peroxidation occurred at the highest exposure concentration (250 μg/L), with malondialdehyde levels reaching 2.1-fold of controls. Physical swimming ability was reduced, with a 45 % increase in induced velocity and a 37 % reduction in critical swimming speed. Chronic PS-MPs exposure also caused pronounced dysbiosis of gut microbiota, including a 5267 % proliferation of <em>Cetobacterium</em> and resurgence of pathogenic <em>Aeromonas</em>. Collectively, these effects resulted in a 28–37 % decline in migratory swimming capacity and multiple physiological disruptions. The results indicated that PS-MPs induced the oxidative stress, neurobehavioral impairment, and gut microbial imbalance, thereby threatening the survival and migratory fitness of Tibetan Plateau fish.</div></div>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"400 ","pages":"Article 128758"},"PeriodicalIF":8.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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