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 : 2025-12-28DOI: 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}
Pub Date : 2025-12-18DOI: 10.1016/j.envc.2025.101398
Shungudzemwoyo P. Garaba
Plastics are pivotal in extensive agricultural activities contributing towards the targets of the United Nations Sustainable Development Goal 2 (UN SDG 2). However, there are rising concerns about biodiversity changes and waste management challenges when plastics are used in agriculture that affect the targets proposed in the UN SDGs 12 and 15. Over the years, the general mapping of plastic greenhouses has been achieved using high spatial and multispectral resolution satellite missions. However, multispectral missions have limited information content and are prone to spectral shape ambiguities that limit the definitive identification of plastic greenhouses in natural environments with many heterogenous optically active targets. To this end, the current study proposes a verifiable workflow for a diagnostic spectral shape-based identification of plastic greenhouses utilising open access hyperspectral imagery from ASI PRISMA, DLR EnMAP and NASA EMIT missions. A feasibility exercise was conducted in the Spanish province of Granada where the validation of observations including spectral characterisation of the greenhouses was achieved by proximal laboratory and airborne measurements. Polymer type of the fragments from the plastic greenhouses and harvested waste was revealed to be Low Density Polyethylene (LDPE). Identification algorithms for the LDPE plastic greenhouses were based on the diagnostic absorption features (∼1215, ∼1730, ∼2312 nm) found in the measured and continuum removed reflectance. Thematic maps and diagnostic optical features of the evaluated unique targets indicated the bottom-of-atmosphere reflectance analysis ready data from the three satellite missions possessed consistent spectral shape similarities in the discrete images from 2021 to 2025. Matches in the generated maps suggested the algorithms were interoperable among the tested hyperspectral satellite imagery. The transferability potential of the proposed methods to other environmental scenarios or geographic regions (i.e., Italy, The Netherlands, Tunisia, Türkiye) was examined through a spectral-based inference approach. Insights were also presented on the added-value of having hyperspectral data as a way to mitigate the likely spectral ambiguities in algorithms based on the multispectral Sentinel-2 observations. The experimental findings also echo the benefits of exploring secondary applications and new variables from hyperspectral missions leveraging the vast information content that can be deciphered in the recorded big data.
{"title":"Towards advanced mapping of plastic greenhouses from EMIT, EnMAP and PRISMA hyperspectral missions","authors":"Shungudzemwoyo P. Garaba","doi":"10.1016/j.envc.2025.101398","DOIUrl":"10.1016/j.envc.2025.101398","url":null,"abstract":"<div><div>Plastics are pivotal in extensive agricultural activities contributing towards the targets of the United Nations Sustainable Development Goal 2 (UN SDG 2). However, there are rising concerns about biodiversity changes and waste management challenges when plastics are used in agriculture that affect the targets proposed in the UN SDGs 12 and 15. Over the years, the general mapping of plastic greenhouses has been achieved using high spatial and multispectral resolution satellite missions. However, multispectral missions have limited information content and are prone to spectral shape ambiguities that limit the definitive identification of plastic greenhouses in natural environments with many heterogenous optically active targets. To this end, the current study proposes a verifiable workflow for a diagnostic spectral shape-based identification of plastic greenhouses utilising open access hyperspectral imagery from ASI PRISMA, DLR EnMAP and NASA EMIT missions. A feasibility exercise was conducted in the Spanish province of Granada where the validation of observations including spectral characterisation of the greenhouses was achieved by proximal laboratory and airborne measurements. Polymer type of the fragments from the plastic greenhouses and harvested waste was revealed to be Low Density Polyethylene (LDPE). Identification algorithms for the LDPE plastic greenhouses were based on the diagnostic absorption features (∼1215, ∼1730, ∼2312 nm) found in the measured and continuum removed reflectance. Thematic maps and diagnostic optical features of the evaluated unique targets indicated the bottom-of-atmosphere reflectance analysis ready data from the three satellite missions possessed consistent spectral shape similarities in the discrete images from 2021 to 2025. Matches in the generated maps suggested the algorithms were interoperable among the tested hyperspectral satellite imagery. The transferability potential of the proposed methods to other environmental scenarios or geographic regions (i.e., Italy, The Netherlands, Tunisia, Türkiye) was examined through a spectral-based inference approach. Insights were also presented on the added-value of having hyperspectral data as a way to mitigate the likely spectral ambiguities in algorithms based on the multispectral Sentinel-2 observations. The experimental findings also echo the benefits of exploring secondary applications and new variables from hyperspectral missions leveraging the vast information content that can be deciphered in the recorded big data.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101398"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939101","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 : 2025-12-17DOI: 10.1016/j.envc.2025.101396
Eleonora Crenna , Roland Hischier , Thijs Defraeye , Daniel Onwude
Global demand for fruits and vegetables is rising, intensifying pressures on land, water, and energy, and driving post-harvest losses that waste ∼30% of annual production. Such losses, together with energy-intensive cold chains, amplify greenhouse gas emissions. Amidst these concerns, the environmental impact of the fruit and vegetable value chain, particularly the transcontinental cold chain, is gaining attention but remains largely unexplored. Here, we quantify the environmental impacts of the intercontinental citrus supply chain from South Africa to the Netherlands using life cycle assessment. By evaluating 16 impact indicators, including water use, land use, freshwater ecotoxicity, and marine eutrophication, we capture hidden burdens typically overlooked in carbon-focused studies. Cultivation dominates water-use impacts (99%), exacerbating risks in water-scarce regions, and accounts for 68% of freshwater ecotoxicity due to chemical inputs. In the post-harvest stages, overseas shipment contributes 62% to the impact of photochemical ozone formation and 52% to the impact of marine eutrophication, highlighting the need for low-carbon transport solutions. Cardboard box production for transport ranks as the second-highest post-harvest contributor to environmental impacts. Aggregated into a weighted single score, pre-harvest activities contribute 56% of total impacts, primarily from irrigation and agrochemicals. These findings pinpoint the ecological hotspots in global fruit trade and underscore the urgency of sustainable irrigation, low-carbon logistics, and material efficiency. Our holistic approach not only identifies ecological hotspots across a real-world, global fruit chain but also establishes citrus as a model system for assessing the sustainability of perishable, globally traded commodities. Our results provide a robust evidence base for policy, supply chain optimisation, and digital tools that support sustainable intercontinental food systems.
{"title":"Ecological hotspots across the global citrus supply chain: A comprehensive life cycle assessment","authors":"Eleonora Crenna , Roland Hischier , Thijs Defraeye , Daniel Onwude","doi":"10.1016/j.envc.2025.101396","DOIUrl":"10.1016/j.envc.2025.101396","url":null,"abstract":"<div><div>Global demand for fruits and vegetables is rising, intensifying pressures on land, water, and energy, and driving post-harvest losses that waste ∼30% of annual production. Such losses, together with energy-intensive cold chains, amplify greenhouse gas emissions. Amidst these concerns, the environmental impact of the fruit and vegetable value chain, particularly the transcontinental cold chain, is gaining attention but remains largely unexplored. Here, we quantify the environmental impacts of the intercontinental citrus supply chain from South Africa to the Netherlands using life cycle assessment. By evaluating 16 impact indicators, including water use, land use, freshwater ecotoxicity, and marine eutrophication, we capture hidden burdens typically overlooked in carbon-focused studies. Cultivation dominates water-use impacts (99%), exacerbating risks in water-scarce regions, and accounts for 68% of freshwater ecotoxicity due to chemical inputs. In the post-harvest stages, overseas shipment contributes 62% to the impact of photochemical ozone formation and 52% to the impact of marine eutrophication, highlighting the need for low-carbon transport solutions. Cardboard box production for transport ranks as the second-highest post-harvest contributor to environmental impacts. Aggregated into a weighted single score, pre-harvest activities contribute 56% of total impacts, primarily from irrigation and agrochemicals. These findings pinpoint the ecological hotspots in global fruit trade and underscore the urgency of sustainable irrigation, low-carbon logistics, and material efficiency. Our holistic approach not only identifies ecological hotspots across a real-world, global fruit chain but also establishes citrus as a model system for assessing the sustainability of perishable, globally traded commodities. Our results provide a robust evidence base for policy, supply chain optimisation, and digital tools that support sustainable intercontinental food systems.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"22 ","pages":"Article 101396"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939100","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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