Pub Date : 2026-01-14DOI: 10.1016/j.envc.2026.101407
Gabriella Rullander , Roger Herbert , Ann-Margret Strömvall , Jes Vollertsen , Claudia Lorenz , Sebastien Rauch , Amir Saeid Mohammadi , Sahar S. Dalahmeh
Biochar-based filters offer a promising solution for removing pollutants from stormwater, yet their performance under environmental stressors remains insufficiently studied. This study evaluated the efficiency of biochar beds in retaining microplastics (MPs) and metals under prolonged dry conditions and with increased salinity. Results showed that MPs were well retained through entrapment in biochar’s porous structure, with non-polar polypropylene (PP) fragments removed more efficiently (98–99%) than polar polyamide (PA) fragments (83–92%). The MP retention improved over time, highlighting biochar’s long-term filtration potential. However, a five-week dry period lowered effluent pH, consequently increasing metal mobility, while higher salinity events enhanced the dissolution of some metals, reducing their total removal. To simulate real-world conditions, semi-artificial stormwater was created by mixing road dust with deionized water. This mixture, along with virgin MPs, was introduced into biochar beds twice weekly under first-flush conditions. Effluent analysis of metals and MPs via inductively coupled plasma mass spectrometry (ICP-MS) and Fourier transform infrared spectroscopy (µ-FTIR imaging), respectively, confirmed the preferential retention of non-polar MPs and shifts in metal mobility. These findings emphasize the importance of considering environmental conditions and polymer characteristics when assessing biochar’s filtration performance in practical applications.
{"title":"Removal of microplastics and metals in biochar beds for stormwater treatment: Effects of prolonged drying and salinity on pollutant mobility","authors":"Gabriella Rullander , Roger Herbert , Ann-Margret Strömvall , Jes Vollertsen , Claudia Lorenz , Sebastien Rauch , Amir Saeid Mohammadi , Sahar S. Dalahmeh","doi":"10.1016/j.envc.2026.101407","DOIUrl":"10.1016/j.envc.2026.101407","url":null,"abstract":"<div><div>Biochar-based filters offer a promising solution for removing pollutants from stormwater, yet their performance under environmental stressors remains insufficiently studied. This study evaluated the efficiency of biochar beds in retaining microplastics (MPs) and metals under prolonged dry conditions and with increased salinity. Results showed that MPs were well retained through entrapment in biochar’s porous structure, with non-polar polypropylene (PP) fragments removed more efficiently (98–99%) than polar polyamide (PA) fragments (83–92%). The MP retention improved over time, highlighting biochar’s long-term filtration potential. However, a five-week dry period lowered effluent pH, consequently increasing metal mobility, while higher salinity events enhanced the dissolution of some metals, reducing their total removal. To simulate real-world conditions, semi-artificial stormwater was created by mixing road dust with deionized water. This mixture, along with virgin MPs, was introduced into biochar beds twice weekly under first-flush conditions. Effluent analysis of metals and MPs via inductively coupled plasma mass spectrometry (ICP-MS) and Fourier transform infrared spectroscopy (µ-FTIR imaging), respectively, confirmed the preferential retention of non-polar MPs and shifts in metal mobility. These findings emphasize the importance of considering environmental conditions and polymer characteristics when assessing biochar’s filtration performance in practical applications.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101407"},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Mediterranean Sea, a hotspot of biodiversity, is impacted by the urbanisation of its coastlines. Marine Protected Areas (MPAs) play a vital role in safeguarding these urban regions and preserving their marine biodiversity. However, despite the urgent need to protect these populated areas, implementation of MPAs under such conditions remains challenging in terms of policy, financing and governance. Twelve MPAs with different urban scores were assessed in the Western Mediterranean Sea. Our objectives were to compare urban and non-urban MPAs in terms of management performance and ecological effectiveness. We analysed the effect of urbanisation on the management of MPAs and then, the effect of urbanisation and management on the variability of ecological outcomes. Our findings were the following: (1) There is no significant difference in management effectiveness between urban and non-urban MPAs, meaning that MPAs can be effectively managed in both contexts; (2) Urban MPAs are ecologically effective, with at least twice as much fish biomass inside the protected areas as outside; and (3) The implementation of an effective management strategy depends on many different factors, such as staff capacity and access to funding. This study thus provides initial insights into the effectiveness of urban MPAs, showing that they have the potential to be managed effectively, with favourable ecological outcomes. In the context of the 30 × 30 global objective, the framework of urban MPAs can serve as a model for developing new MPAs and promoting policies that encourage action, even in degraded urban ecosystems in need of protection.
{"title":"Can urban Marine Protected Areas (MPAs) generate effective ecological and management outcomes?","authors":"Julie Marty-Gastaldi , Charalampos Dimitriadis , Nathalie Lazaric , Benoit Dérijard","doi":"10.1016/j.envc.2026.101406","DOIUrl":"10.1016/j.envc.2026.101406","url":null,"abstract":"<div><div>The Mediterranean Sea, a hotspot of biodiversity, is impacted by the urbanisation of its coastlines. Marine Protected Areas (MPAs) play a vital role in safeguarding these urban regions and preserving their marine biodiversity. However, despite the urgent need to protect these populated areas, implementation of MPAs under such conditions remains challenging in terms of policy, financing and governance. Twelve MPAs with different urban scores were assessed in the Western Mediterranean Sea. Our objectives were to compare urban and non-urban MPAs in terms of management performance and ecological effectiveness. We analysed the effect of urbanisation on the management of MPAs and then, the effect of urbanisation and management on the variability of ecological outcomes. Our findings were the following: (1) There is no significant difference in management effectiveness between urban and non-urban MPAs, meaning that MPAs can be effectively managed in both contexts; (2) Urban MPAs are ecologically effective, with at least twice as much fish biomass inside the protected areas as outside; and (3) The implementation of an effective management strategy depends on many different factors, such as staff capacity and access to funding. This study thus provides initial insights into the effectiveness of urban MPAs, showing that they have the potential to be managed effectively, with favourable ecological outcomes. In the context of the 30 × 30 global objective, the framework of urban MPAs can serve as a model for developing new MPAs and promoting policies that encourage action, even in degraded urban ecosystems in need of protection.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101406"},"PeriodicalIF":0.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.envc.2026.101405
Alexandria E. West , Paul A. Moore
A prevalent source of sensory pollution in freshwater ecosystems is recreational motorboats, that impacts freshwater fish through several mechanisms. Auditory and visual sensory disturbances are of particular importance as fish use these cues during critical reproductive behaviors such as parental care. To understand how sensory noise disrupts behavior, we conducted a field-based study to examine how auditory noise and visual cues impact smallmouth bass (Micropterus dolomieu) parental care behaviors. Smallmouth bass were exposed to two sequential treatments: auditory noise and visual disturbances. During auditory disturbances, smallmouth bass were exposed to playback noise recordings, while during visual disturbances smallmouth bass were exposed to a solid object moving in their visual field. Each disturbance treatment consisted of three phases: a five-minute pre-disturbance phase, a one-minute disturbance exposure phase and a five-minute post-disturbance phase. Our findings indicate bass were most aggressive during the pre-visual disturbance phase (p = 0.02) and spent more time swimming off the nest after both auditory and visual disturbances (p = 0.017). Further, bass altered their behavioral repertoire. During and immediately following a visual disturbance, smallmouth bass increased percentage of time spent stationary on the nest (37 % and 46 %, respectively) compared to the pre-disturbance phase (32 %). Conversely, when exposed to auditory disturbances, bass increased time spent swimming off the nest (72 %) compared to the pre-disturbance phase (50 %). Our results demonstrate that visual and noise disturbances affect smallmouth bass behaviors differently. This suggests an integrated approach must be considered to truly understand the impact of motorboat activity.
{"title":"Comparative effects of auditory and visual disturbances on parental care behaviors of smallmouth bass","authors":"Alexandria E. West , Paul A. Moore","doi":"10.1016/j.envc.2026.101405","DOIUrl":"10.1016/j.envc.2026.101405","url":null,"abstract":"<div><div>A prevalent source of sensory pollution in freshwater ecosystems is recreational motorboats, that impacts freshwater fish through several mechanisms. Auditory and visual sensory disturbances are of particular importance as fish use these cues during critical reproductive behaviors such as parental care. To understand how sensory noise disrupts behavior, we conducted a field-based study to examine how auditory noise and visual cues impact smallmouth bass (<em>Micropterus dolomieu</em>) parental care behaviors. Smallmouth bass were exposed to two sequential treatments: auditory noise and visual disturbances. During auditory disturbances, smallmouth bass were exposed to playback noise recordings, while during visual disturbances smallmouth bass were exposed to a solid object moving in their visual field. Each disturbance treatment consisted of three phases: a five-minute pre-disturbance phase, a one-minute disturbance exposure phase and a five-minute post-disturbance phase. Our findings indicate bass were most aggressive during the pre-visual disturbance phase (p = 0.02) and spent more time swimming off the nest after both auditory and visual disturbances (p = 0.017). Further, bass altered their behavioral repertoire. During and immediately following a visual disturbance, smallmouth bass increased percentage of time spent stationary on the nest (37 % and 46 %, respectively) compared to the pre-disturbance phase (32 %). Conversely, when exposed to auditory disturbances, bass increased time spent swimming off the nest (72 %) compared to the pre-disturbance phase (50 %). Our results demonstrate that visual and noise disturbances affect smallmouth bass behaviors differently. This suggests an integrated approach must be considered to truly understand the impact of motorboat activity.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101405"},"PeriodicalIF":0.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ecosystems provide vital services for human well-being and economic growth but are increasingly degraded by natural and human activities. Coastal ecosystems, particularly Bangladesh's exposed coast, are experiencing severe soil loss due to erosion, land use alterations from human activities, and increased pressure from climate change-induced hazards and disasters, leading to ecosystem degradation. Thus, this study attempts to identify the spatiotemporal pattern of land-based eco-environmental degradation using remote sensing and multi-criteria evaluation (MCE) analysis from 2000 to 2024. Seven criteria, namely soil loss, spatial severity of impact (SSI), ecological integrity depletion (EID), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land-based carbon emission (LCE), and bio-capacity (BC), are considered to assess the degradation pattern. The analytical hierarchy process (AHP) is used to weigh these criteria, and the weighted sum technique is applied. The findings show that nearly 37 % of the land experienced high to very high degradation, whereas 42 % of the land was affected by moderate degradation. The degradation factors vary by region, with deforestation and aquaculture being the primary drivers in the Sundarbans (western coast), erosion in the Meghna Estuary (central coast), and ecological loss as the source of urban expansion in coastal cities such as Chittagong (eastern shore). The degradation model was successfully validated with a predictive performance that seems high (AUC = 0.94) and indicates robustness of the indicators and methods selected. Although the study mainly used environmental data, social and climate factors may be integrated and will give more precise results in future research. Even so, the findings can help improve land use planning, restoration, and climate action under different national plans. This study aims to develop an innovative approach to achieving spatiotemporal degradation patterns, thereby aiding stakeholders and policymakers in creating a resilient ecosystem.
{"title":"Integrated remote sensing and multi-criteria evaluation to assess coastal ecosystem degradation under climate and human pressures: Insights from Bangladesh","authors":"Md Fuad Hassan , Riffat Mahmood , N.M. Refat Nasher , Sukanta Das , Urme Akter , Nusrat Kona , Nawshin Tabassum , Tazrian Rahman","doi":"10.1016/j.envc.2025.101403","DOIUrl":"10.1016/j.envc.2025.101403","url":null,"abstract":"<div><div>Ecosystems provide vital services for human well-being and economic growth but are increasingly degraded by natural and human activities. Coastal ecosystems, particularly Bangladesh's exposed coast, are experiencing severe soil loss due to erosion, land use alterations from human activities, and increased pressure from climate change-induced hazards and disasters, leading to ecosystem degradation. Thus, this study attempts to identify the spatiotemporal pattern of land-based eco-environmental degradation using remote sensing and multi-criteria evaluation (MCE) analysis from 2000 to 2024. Seven criteria, namely soil loss, spatial severity of impact (SSI), ecological integrity depletion (EID), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land-based carbon emission (LCE), and bio-capacity (BC), are considered to assess the degradation pattern. The analytical hierarchy process (AHP) is used to weigh these criteria, and the weighted sum technique is applied. The findings show that nearly 37 % of the land experienced high to very high degradation, whereas 42 % of the land was affected by moderate degradation. The degradation factors vary by region, with deforestation and aquaculture being the primary drivers in the Sundarbans (western coast), erosion in the Meghna Estuary (central coast), and ecological loss as the source of urban expansion in coastal cities such as Chittagong (eastern shore). The degradation model was successfully validated with a predictive performance that seems high (AUC = 0.94) and indicates robustness of the indicators and methods selected. Although the study mainly used environmental data, social and climate factors may be integrated and will give more precise results in future research. Even so, the findings can help improve land use planning, restoration, and climate action under different national plans. This study aims to develop an innovative approach to achieving spatiotemporal degradation patterns, thereby aiding stakeholders and policymakers in creating a resilient ecosystem.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101403"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article investigates the possibilities of predicting soil quality based on the main agrochemical indicators using machine learning methods. The experimental base consisted of 768 soil samples collected from the territory of the Rozsoshansk community and 192 additional samples from the neighboring territory of the Izyaslav community Khmelnytskyi region, Ukraine, in the autumn of 2022-2023 and spring of 2022-2023. We determined exchangeable acidity, organic carbon, ammonium and nitrate nitrogen, mobile phosphorus, exchangeable calcium, and potassium for each sample. Based on the analyzed indicators, a generalized approach to assessing fertility levels was offered, categorizing soil quality into three classes. Machine learning methods were used to predict soil quality: Gaussian NB, Multinomial NB, Logistic Regression, Ridge Classifier, SGDC, Random Forest, XGBoost, kNN, SVM, and MLP neural network. Random Forest, XGBoost, and MLP demonstrated the highest accuracy on the test dataset. When testing on an independent dataset of 192 new samples, the MLP model preserved the best balance of classification performance metrics. It achieved high G-Mean values of 0.894 for class 1, 0.915 for class 2, and 0.903 for class 3, indicating the model’s effectiveness in both detecting the target class and correctly identifying the remaining classes. In addition, the model demonstrated strong F1-score values of 0.884, 0.921, and 0.773 accordingly. The constructed ROC and Precision–Recall curves further confirmed the high generalization capability of the proposed model. To interpret the operation of the neural network, the SHAP method was applied. Global SHAP analysis identified available phosphorus, soil acidity, and organic carbon as the most influential input features. Local SHAP explanations for sample No. 162 demonstrated physically meaningful and consistent model responses. The conducted SHAP analysis of the MLP neural network made it possible to quantitatively assess the contribution of individual input parameters to the prediction outcomes, which significantly increased the interpretability of the model and the level of confidence in the obtained results. The approach proposed in this study not only improves the accuracy of soil quality classification but also provides an agrochemical interpretation of the results, thereby creating a basis for the development of rational, efficient, and precision land use systems relevant to agronomists, land managers, and farmers.
{"title":"Soil quality classification from chemical composition using machine learning methods with SHAP-based explanation","authors":"Halyna Humeniuk , Dmytro Tymoshchuk , Andrii Sverstiuk","doi":"10.1016/j.envc.2025.101404","DOIUrl":"10.1016/j.envc.2025.101404","url":null,"abstract":"<div><div>The article investigates the possibilities of predicting soil quality based on the main agrochemical indicators using machine learning methods. The experimental base consisted of 768 soil samples collected from the territory of the Rozsoshansk community and 192 additional samples from the neighboring territory of the Izyaslav community Khmelnytskyi region, Ukraine, in the autumn of 2022-2023 and spring of 2022-2023. We determined exchangeable acidity, organic carbon, ammonium and nitrate nitrogen, mobile phosphorus, exchangeable calcium, and potassium for each sample. Based on the analyzed indicators, a generalized approach to assessing fertility levels was offered, categorizing soil quality into three classes. Machine learning methods were used to predict soil quality: Gaussian NB, Multinomial NB, Logistic Regression, Ridge Classifier, SGDC, Random Forest, XGBoost, kNN, SVM, and MLP neural network. Random Forest, XGBoost, and MLP demonstrated the highest accuracy on the test dataset. When testing on an independent dataset of 192 new samples, the MLP model preserved the best balance of classification performance metrics. It achieved high G-Mean values of 0.894 for class 1, 0.915 for class 2, and 0.903 for class 3, indicating the model’s effectiveness in both detecting the target class and correctly identifying the remaining classes. In addition, the model demonstrated strong F1-score values of 0.884, 0.921, and 0.773 accordingly. The constructed ROC and Precision–Recall curves further confirmed the high generalization capability of the proposed model. To interpret the operation of the neural network, the SHAP method was applied. Global SHAP analysis identified available phosphorus, soil acidity, and organic carbon as the most influential input features. Local SHAP explanations for sample No. 162 demonstrated physically meaningful and consistent model responses. The conducted SHAP analysis of the MLP neural network made it possible to quantitatively assess the contribution of individual input parameters to the prediction outcomes, which significantly increased the interpretability of the model and the level of confidence in the obtained results. The approach proposed in this study not only improves the accuracy of soil quality classification but also provides an agrochemical interpretation of the results, thereby creating a basis for the development of rational, efficient, and precision land use systems relevant to agronomists, land managers, and farmers.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101404"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-31DOI: 10.1016/j.envc.2025.101402
Farhad Behzadi, Seied Mehdy Hashemy Shahdany
This study introduces a systematic technical–environmental appraisal framework that links off-farm operation to environmental flow recovery (EFR) potential in watersheds containing multiple irrigation districts (IDs)The analysis focuses on the surface-water distribution infrastructure within the IDs and, specifically, on off-farm agricultural water management. It considers interconnected open-canal networks, a manual operating system, and the associated standard operating procedures (SOPs). The framework is demonstrated in Iran’s arid central plateau, where six IDs operate in a basin with a long record of environmental water-rights violations linked to agricultural withdrawals. To quantify operational losses, integrated hydraulic–operational models were developed by coupling an integrator–delay hydraulic simulation model with IDs’ SOP logic. The models were calibrated and verified for all six IDs. The technical assessment indicates that daily water losses caused by operational failures and inefficiencies vary markedly across operating conditions. Under normal to severe shortage scenarios, these losses account for 6.5–17.3% and 21.5–31.1% of the supplied surface water, respectively. To estimate the EFR recovery potential, the study applied an flow-duration-curve (FDC)-shifting approach together with the Global Environmental Flow Calculator (GEFC). Under the best-case scenario, 61 MCM yr⁻¹ can be recovered from operational losses and returned to the river, which corresponds to 45.16% of the flow deficit required to satisfy class F. Even under the most severe shortage scenario, 13 MCM yr⁻¹ (9.63% of the class F deficit) remains recoverable. Overall, the proposed framework is transferable to similar multi-ID watersheds and provides technical evidence to support licensing decisions for off-farm system automation, enabling basin authorities, licensing agencies, and irrigation-district managers to audit operational losses, define diversion-reduction targets at diversion dams, and operationalize compliance monitoring for environmental-flow releases.
{"title":"Operational losses and environmental flow recovery in a multi-irrigation districts river Basin, evidence from Iran’s arid central plateau","authors":"Farhad Behzadi, Seied Mehdy Hashemy Shahdany","doi":"10.1016/j.envc.2025.101402","DOIUrl":"10.1016/j.envc.2025.101402","url":null,"abstract":"<div><div>This study introduces a systematic technical–environmental appraisal framework that links off-farm operation to environmental flow recovery (EFR) potential in watersheds containing multiple irrigation districts (IDs)The analysis focuses on the surface-water distribution infrastructure within the IDs and, specifically, on off-farm agricultural water management. It considers interconnected open-canal networks, a manual operating system, and the associated standard operating procedures (SOPs). The framework is demonstrated in Iran’s arid central plateau, where six IDs operate in a basin with a long record of environmental water-rights violations linked to agricultural withdrawals. To quantify operational losses, integrated hydraulic–operational models were developed by coupling an integrator–delay hydraulic simulation model with IDs’ SOP logic. The models were calibrated and verified for all six IDs. The technical assessment indicates that daily water losses caused by operational failures and inefficiencies vary markedly across operating conditions. Under normal to severe shortage scenarios, these losses account for 6.5–17.3% and 21.5–31.1% of the supplied surface water, respectively. To estimate the EFR recovery potential, the study applied an flow-duration-curve (FDC)-shifting approach together with the Global Environmental Flow Calculator (GEFC). Under the best-case scenario, 61 MCM yr⁻¹ can be recovered from operational losses and returned to the river, which corresponds to 45.16% of the flow deficit required to satisfy class F. Even under the most severe shortage scenario, 13 MCM yr⁻¹ (9.63% of the class F deficit) remains recoverable. Overall, the proposed framework is transferable to similar multi-ID watersheds and provides technical evidence to support licensing decisions for off-farm system automation, enabling basin authorities, licensing agencies, and irrigation-district managers to audit operational losses, define diversion-reduction targets at diversion dams, and operationalize compliance monitoring for environmental-flow releases.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101402"},"PeriodicalIF":0.0,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-28DOI: 10.1016/j.envc.2025.101401
Lovely Akter , Neaz A. Hasan , Moshiur Rahman , Nasrullah Forajy , Mohammad Mahfujul Haque
Shrimp aquaculture, particularly in South and Southeast Asia, substantially contributes to economic growth and food security. However, the sectors’ heavily reliance on antibiotics together with weak biosecurity – driving the rise of antimicrobial resistance (AMR). This review synthesizes evidence from 2000 to 2025 on antibiotic use, resistance pathways, and the associated environmental (particularly mangrove habitats) and public-health risks. Through the integration of antibiotic management, diagnostic development, and the control of non-antibiotic diseases on the broad One Health platform, this review emphasizes the interdependence of shrimp aquaculture, ecosystem health, and public health. The historical shift from traditional, mangrove-linked practices to intensive, export-oriented systems accelerated the loss of mangroves and increased ecological vulnerability. High disease pressure in intensive farms drove routine, sometimes inappropriate antibiotic use (mostly oxytetracycline, florfenicol, and sufonamides), and the emergence of resistance. Weak regulation and limited diagnostics, along with widespread use of non-approved drugs, enabled persistent selection pressures across production environments, further shaping AMR development. Diverse AMR genes (such tetA, sul1, and blaCTX-M) occur in farm-associated bacteria, raising concerns about movement through aquatic ecosystems and human exposure. Effluents from shrimp farms carry antibiotic residues and resistant microbes into nearby mangroves, where resistance genes persist, spread, and disrupt ecological functions. These pressures diminish shrimp health and productivity, alter microbial nitrogen cycling, suppress diazotrophic taxa, and reduce nitrogenase and functional gene activity compromising mangrove ecosystem services like nutrient cycling, biodiversity, and coastal protection. Public-health risks arise when food chain entry or occupational exposure occurs via either residues or resistant bacteria; these necessitate strong farm-level controls, surveillance, and hygiene practices. AMR mitigation needs tighter antibiotic governance, expanded diagnostic capacity, and wider adoption of non-antibiotic disease-management strategies within a coordinated One Health framework. Future progress depends upon closing knowledge gaps, improving monitoring, and aligning regulations and farm practice for long-term environmental and public-health protection.
{"title":"Antimicrobial resistance in shrimp aquaculture: Pathways, ecosystem risks, and policy responses","authors":"Lovely Akter , Neaz A. Hasan , Moshiur Rahman , Nasrullah Forajy , Mohammad Mahfujul Haque","doi":"10.1016/j.envc.2025.101401","DOIUrl":"10.1016/j.envc.2025.101401","url":null,"abstract":"<div><div>Shrimp aquaculture, particularly in South and Southeast Asia, substantially contributes to economic growth and food security. However, the sectors’ heavily reliance on antibiotics together with weak biosecurity – driving the rise of antimicrobial resistance (AMR). This review synthesizes evidence from 2000 to 2025 on antibiotic use, resistance pathways, and the associated environmental (particularly mangrove habitats) and public-health risks. Through the integration of antibiotic management, diagnostic development, and the control of non-antibiotic diseases on the broad One Health platform, this review emphasizes the interdependence of shrimp aquaculture, ecosystem health, and public health. The historical shift from traditional, mangrove-linked practices to intensive, export-oriented systems accelerated the loss of mangroves and increased ecological vulnerability. High disease pressure in intensive farms drove routine, sometimes inappropriate antibiotic use (mostly oxytetracycline, florfenicol, and sufonamides), and the emergence of resistance. Weak regulation and limited diagnostics, along with widespread use of non-approved drugs, enabled persistent selection pressures across production environments, further shaping AMR development. Diverse AMR genes (such tetA, sul1, and blaCTX-M) occur in farm-associated bacteria, raising concerns about movement through aquatic ecosystems and human exposure. Effluents from shrimp farms carry antibiotic residues and resistant microbes into nearby mangroves, where resistance genes persist, spread, and disrupt ecological functions. These pressures diminish shrimp health and productivity, alter microbial nitrogen cycling, suppress diazotrophic taxa, and reduce nitrogenase and functional gene activity compromising mangrove ecosystem services like nutrient cycling, biodiversity, and coastal protection. Public-health risks arise when food chain entry or occupational exposure occurs via either residues or resistant bacteria; these necessitate strong farm-level controls, surveillance, and hygiene practices. AMR mitigation needs tighter antibiotic governance, expanded diagnostic capacity, and wider adoption of non-antibiotic disease-management strategies within a coordinated One Health framework. Future progress depends upon closing knowledge gaps, improving monitoring, and aligning regulations and farm practice for long-term environmental and public-health protection.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101401"},"PeriodicalIF":0.0,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1016/j.envc.2025.101400
Farhad Hossain , Janifar Hakim Lupin , Md. Mahin Uddin , Md. Yousuf Gazi , Md. Zillur Rahman , A. S. M. Maksud Kamal
Rapid urbanization in developing countries often leads to elevated transportation infrastructure, yet the localized thermal impacts of such linear developments remain understudied. This research provides critical insight into how Dhaka’s newly constructed elevated metro rail (MRT Line-6) disrupts the urban thermal balance, acting as a heat corridor through the city. Using high-resolution satellite data (2015–2023), we quantify a 3–5.5°C rise in Land Surface Temperature (LST) along the metro route, driven by vegetation removal and heat-absorbing concrete structures. Spatio-temporal analysis reveals peak LST (36°C in 2020) during intensive construction, while the Urban Thermal Field Variance Index (UTFVI) shows expansion of extreme Urban Heat Island (UHI) zones from 29.5% (2015) to 33.8% (2023). A reversal in the NDVI-LST relationship from negative (cooling by vegetation) to positive (warming by impervious surfaces) highlights the strong thermal influence of the metro corridor. Climatic data indicate that land-cover modification associated with metro construction played a dominant role in the observed temperature anomalies, while broader urban processes likely contributed to background warming. These findings underscore the need to address linear infrastructure as a distinct contributor to UHI effects. We recommend targeted mitigation strategies (e.g., green roofs, vertical vegetation) to offset thermal impacts. This integrated approach connects the link between rising heat and infrastructure, providing an applied roadmap for designing more sustainable and climate-resilient transport systems in one of the world’s fastest-growing cities.
{"title":"Impact of elevated transportation infrastructure on urban thermal environment in Dhaka Megacity, Bangladesh","authors":"Farhad Hossain , Janifar Hakim Lupin , Md. Mahin Uddin , Md. Yousuf Gazi , Md. Zillur Rahman , A. S. M. Maksud Kamal","doi":"10.1016/j.envc.2025.101400","DOIUrl":"10.1016/j.envc.2025.101400","url":null,"abstract":"<div><div>Rapid urbanization in developing countries often leads to elevated transportation infrastructure, yet the localized thermal impacts of such linear developments remain understudied. This research provides critical insight into how Dhaka’s newly constructed elevated metro rail (MRT Line-6) disrupts the urban thermal balance, acting as a heat corridor through the city. Using high-resolution satellite data (2015–2023), we quantify a 3–5.5°C rise in Land Surface Temperature (LST) along the metro route, driven by vegetation removal and heat-absorbing concrete structures. Spatio-temporal analysis reveals peak LST (36°C in 2020) during intensive construction, while the Urban Thermal Field Variance Index (UTFVI) shows expansion of extreme Urban Heat Island (UHI) zones from 29.5% (2015) to 33.8% (2023). A reversal in the NDVI-LST relationship from negative (cooling by vegetation) to positive (warming by impervious surfaces) highlights the strong thermal influence of the metro corridor. Climatic data indicate that land-cover modification associated with metro construction played a dominant role in the observed temperature anomalies, while broader urban processes likely contributed to background warming. These findings underscore the need to address linear infrastructure as a distinct contributor to UHI effects. We recommend targeted mitigation strategies (e.g., green roofs, vertical vegetation) to offset thermal impacts. This integrated approach connects the link between rising heat and infrastructure, providing an applied roadmap for designing more sustainable and climate-resilient transport systems in one of the world’s fastest-growing cities.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101400"},"PeriodicalIF":0.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1016/j.envc.2025.101399
Enrique Alfonso Retamoza-González , B. Ricardo Eaton-González , Juana Claudia Leyva-Aguilera , Marisa Reyes-Orta , Hector Manuel Arias-Rojo
Land-use and land-cover (LULC) changes are key drivers of vegetation cover loss. Northwestern Mexico hosts the only Mediterranean-climate region in the country, a socio-ecosystem that, due to its distinct socioeconomic and ecological dynamics, simultaneously undergoes processes of anthropization and vegetation recovery, driven by agricultural expansion and land abandonment. In order to identify areas with high recovery and conservation potential within this socio-ecosystem, we evaluated land-cover losses, gains, and rates of change between 2015 and 2020. Using LISA spatial autocorrelation analysis, we identified clusters of anthropization and vegetation recovery, as well as priority areas for conservation actions. Overall, LULC change within the Mexican Mediterranean socio-ecosystem reached 4 %, with coastal shrub being the land-cover type that experienced the greatest loss (64,443 ha), primarily converted to agricultural land, which expanded by 66,203 ha. Anthropization and recovery were the dominant processes in this region. High anthropization clusters were concentrated in mountainous regions and peri-urban areas along the coastal belt, likely associated with agricultural and livestock expansion, whereas recovery was concentrated in the southern portion of the study area, within large agricultural zones, possibly linked to field abandonment due to saline intrusion. Through spatial correlation analysis of change drivers, we identified five zones within the Mexican Mediterranean: Tijuana Coastal Shrubland, Ensenada Coastal Shrubland, Central Coastal Shrubland, Camalú–San Quintín Coastal Rosetophyllous Corridor, and the San Pedro Mártir Boundary Zone, where conservation and restoration efforts should be prioritized through the design and implementation of public policies regulating agricultural expansion at the expense of coastal scrub and other native vegetation types.
{"title":"Identifying priority restoration areas by mapping land-use change drivers","authors":"Enrique Alfonso Retamoza-González , B. Ricardo Eaton-González , Juana Claudia Leyva-Aguilera , Marisa Reyes-Orta , Hector Manuel Arias-Rojo","doi":"10.1016/j.envc.2025.101399","DOIUrl":"10.1016/j.envc.2025.101399","url":null,"abstract":"<div><div>Land-use and land-cover (LULC) changes are key drivers of vegetation cover loss. Northwestern Mexico hosts the only Mediterranean-climate region in the country, a socio-ecosystem that, due to its distinct socioeconomic and ecological dynamics, simultaneously undergoes processes of anthropization and vegetation recovery, driven by agricultural expansion and land abandonment. In order to identify areas with high recovery and conservation potential within this socio-ecosystem, we evaluated land-cover losses, gains, and rates of change between 2015 and 2020. Using LISA spatial autocorrelation analysis, we identified clusters of anthropization and vegetation recovery, as well as priority areas for conservation actions. Overall, LULC change within the Mexican Mediterranean socio-ecosystem reached 4 %, with coastal shrub being the land-cover type that experienced the greatest loss (64,443 ha), primarily converted to agricultural land, which expanded by 66,203 ha. Anthropization and recovery were the dominant processes in this region. High anthropization clusters were concentrated in mountainous regions and peri-urban areas along the coastal belt, likely associated with agricultural and livestock expansion, whereas recovery was concentrated in the southern portion of the study area, within large agricultural zones, possibly linked to field abandonment due to saline intrusion. Through spatial correlation analysis of change drivers, we identified five zones within the Mexican Mediterranean: Tijuana Coastal Shrubland, Ensenada Coastal Shrubland, Central Coastal Shrubland, Camalú–San Quintín Coastal Rosetophyllous Corridor, and the San Pedro Mártir Boundary Zone, where conservation and restoration efforts should be prioritized through the design and implementation of public policies regulating agricultural expansion at the expense of coastal scrub and other native vegetation types.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101399"},"PeriodicalIF":0.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.envc.2025.101397
Ramayanty Bulan , Darwin , Devianti , Agustami Sitorus , Hasanuddin
The development of agricultural machinery for palm trees still faces challenges due to differences in the physical and mechanical properties of fronds and leaves between species. Shredding machines that operate without sensors have difficulty adjusting rotational speed and cutting angle, resulting in decreased performance when faced with variations in raw materials. Therefore, the objective of this study is to classify three types of palm fronds and leaves, including Areca catechu L. (AR), Cocos nucifera (CN), and Elaeis guineensis Jacq. (EG), using a low-cost camera with a resolution of 1920 × 1080 pixels, combined with ensemble machine learning. Samples were prepared under fresh, incubated, and dried conditions, comprising a total of 810 fronds and 972 leaves. Three feature extraction methods were applied, including RGB, Oriented FAST and Rotated BRIEF (ORB), and Lab color space, which were then analyzed using two machine learning ensemble algorithms, including gradient boosting for classification (GBC) and histogram-based gradient boosting classification tree (HGBC). The combination of ORB with HGBC achieved the highest accuracy for fronds (79.6%), while Lab with HGBC was superior for leaves (84.6%). The Lab is the most consistent feature, while ORB is clear for fronds structure. This classification system has the potential to be used as a smart sensor integrated into palm shredding machines, enabling automated operation control and increasing biomass processing efficiency.
由于不同树种棕榈叶的物理力学特性存在差异，棕榈农业机械的发展仍面临挑战。没有传感器的碎纸机难以调节转速和切割角度，导致在面对原材料变化时性能下降。因此，本研究的目的是对三种棕榈叶进行分类，包括arereca catechu L. （AR）、Cocos nucifera （CN）和Elaeis guineensis Jacq。（EG），使用分辨率为1920 × 1080像素的低成本相机，结合集成机器学习。样品在新鲜、孵育和干燥条件下制备，共包括810片叶子和972片叶子。采用RGB、ORB和Lab色彩空间三种特征提取方法，采用梯度增强分类（GBC）和基于直方图的梯度增强分类树（HGBC）两种机器学习集成算法对特征进行分析。ORB联合HGBC对叶片的检测准确率最高（79.6%），而Lab联合HGBC对叶片的检测准确率最高（84.6%）。Lab是最一致的特征，ORB是清晰的叶子结构。这种分类系统有潜力被用作集成到棕榈碎纸机中的智能传感器，实现自动化操作控制，提高生物质处理效率。
{"title":"AI-driven biomass discrimination of palm fronds using low-cost vision sensors for sustainable waste valorization","authors":"Ramayanty Bulan , Darwin , Devianti , Agustami Sitorus , Hasanuddin","doi":"10.1016/j.envc.2025.101397","DOIUrl":"10.1016/j.envc.2025.101397","url":null,"abstract":"<div><div>The development of agricultural machinery for palm trees still faces challenges due to differences in the physical and mechanical properties of fronds and leaves between species. Shredding machines that operate without sensors have difficulty adjusting rotational speed and cutting angle, resulting in decreased performance when faced with variations in raw materials. Therefore, the objective of this study is to classify three types of palm fronds and leaves, including <em>Areca catechu</em> L. (AR), <em>Cocos nucifera</em> (CN), and <em>Elaeis guineensis</em> Jacq. (EG), using a low-cost camera with a resolution of 1920 × 1080 pixels, combined with ensemble machine learning. Samples were prepared under fresh, incubated, and dried conditions, comprising a total of 810 fronds and 972 leaves. Three feature extraction methods were applied, including RGB, Oriented FAST and Rotated BRIEF (ORB), and Lab color space, which were then analyzed using two machine learning ensemble algorithms, including gradient boosting for classification (GBC) and histogram-based gradient boosting classification tree (HGBC). The combination of ORB with HGBC achieved the highest accuracy for fronds (79.6%), while Lab with HGBC was superior for leaves (84.6%). The Lab is the most consistent feature, while ORB is clear for fronds structure. This classification system has the potential to be used as a smart sensor integrated into palm shredding machines, enabling automated operation control and increasing biomass processing efficiency.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101397"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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