Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126034
Joel D. Rindelaub , Jennifer A. Salmond , Wenxia Fan , Gordon M. Miskelly , Kim N. Dirks , Silvia Henning , Thomas Conrath , Frank Stratmann , Guy Coulson
This study quantified airborne microplastic concentrations by mass and number counts using both active and passive sampling at a remote coastal location in Southern New Zealand. Seven polymers were quantified using pyrolysis gas chromatography mass spectrometry (Pyr-GC/MS) in atmospheric samples, finding that plastics comprised at least 0.14 % of total suspended particulate mass at the remote coastal site. Air parcel back trajectories suggest that airborne microplastics at the site, observed at an average concentration of 65 ± 6 ng m−3, have origins from the Southern Ocean. Additionally, the results demonstrate that reporting atmospheric deposition of microplastics by number counts may underestimate the true amount of plastics present in samples, as size limitations associated with microscopic imaging do not allow for quantification of the most abundant sizes and types of environmental microplastics. With current uncertainties related to aerosol formation in the Southern Ocean and the associated impacts on climate forcing, further research is urgently needed on the production of airborne microplastics originating from the Southern Ocean, a possible microplastic reservoir.
{"title":"Aerosol mass concentrations and dry/wet deposition of atmospheric microplastics at a remote coastal location in New Zealand","authors":"Joel D. Rindelaub , Jennifer A. Salmond , Wenxia Fan , Gordon M. Miskelly , Kim N. Dirks , Silvia Henning , Thomas Conrath , Frank Stratmann , Guy Coulson","doi":"10.1016/j.envpol.2025.126034","DOIUrl":"10.1016/j.envpol.2025.126034","url":null,"abstract":"<div><div>This study quantified airborne microplastic concentrations by mass and number counts using both active and passive sampling at a remote coastal location in Southern New Zealand. Seven polymers were quantified using pyrolysis gas chromatography mass spectrometry (Pyr-GC/MS) in atmospheric samples, finding that plastics comprised at least 0.14 % of total suspended particulate mass at the remote coastal site. Air parcel back trajectories suggest that airborne microplastics at the site, observed at an average concentration of 65 ± 6 ng m<sup>−3</sup>, have origins from the Southern Ocean. Additionally, the results demonstrate that reporting atmospheric deposition of microplastics by number counts may underestimate the true amount of plastics present in samples, as size limitations associated with microscopic imaging do not allow for quantification of the most abundant sizes and types of environmental microplastics. With current uncertainties related to aerosol formation in the Southern Ocean and the associated impacts on climate forcing, further research is urgently needed on the production of airborne microplastics originating from the Southern Ocean, a possible microplastic reservoir.</div></div>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"372 ","pages":"Article 126034"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126057
Adeeba Bilquees, Dauood Saleem, Mohd Aamir
This letter acknowledges the valuable contribution of the authors' study, which investigates the associations between long-term air pollution exposure and plasma metabolites in two sub-cohorts of the Rotterdam Study. The use of advanced metabolomics techniques and land use regression models for exposure assessment is praised for strengthening the study's methodology. The identification of enriched metabolic pathways, such as steroid hormone biosynthesis and pyrimidine metabolism, provides key insights into how air pollution affects biological systems. However, the letter highlights the limitation of the study’s cross-sectional design, which hinders causal inference, and suggests that longitudinal studies would offer more definitive conclusions. The mention of "unannotated metabolites" is noted as an intriguing yet underexplored aspect, and further discussion of confounding factors such as diet and socioeconomic status is encouraged. Overall, the letter offers constructive feedback while recognizing the study's important contribution to the field.
{"title":"Addressing Gaps in Integrated Air Quality-Vegetation-Health Impact Analysis: A Call for Broader Implementation and Long-Term Strategies in Industrial Cities","authors":"Adeeba Bilquees, Dauood Saleem, Mohd Aamir","doi":"10.1016/j.envpol.2025.126057","DOIUrl":"https://doi.org/10.1016/j.envpol.2025.126057","url":null,"abstract":"This letter acknowledges the valuable contribution of the authors' study, which investigates the associations between long-term air pollution exposure and plasma metabolites in two sub-cohorts of the Rotterdam Study. The use of advanced metabolomics techniques and land use regression models for exposure assessment is praised for strengthening the study's methodology. The identification of enriched metabolic pathways, such as steroid hormone biosynthesis and pyrimidine metabolism, provides key insights into how air pollution affects biological systems. However, the letter highlights the limitation of the study’s cross-sectional design, which hinders causal inference, and suggests that longitudinal studies would offer more definitive conclusions. The mention of \"unannotated metabolites\" is noted as an intriguing yet underexplored aspect, and further discussion of confounding factors such as diet and socioeconomic status is encouraged. Overall, the letter offers constructive feedback while recognizing the study's important contribution to the field.","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"55 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.125993
Sheen Mclean Cabaneros , Emma Chapman , Mark Hansen , Ben Williams , Jeanette Rotchell
Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of identifying potential particles in micrographs can significantly enhance the research and monitoring of AMPs. Although deep learning has shown substantial promise in microplastics analysis, existing studies have primarily focused on high-resolution images of samples collected from marine and freshwater environments. In contrast, this work introduces a novel approach by employing enhanced U-Net models (Attention U-Net and Dynamic RU-NEXT) along with the Mask Region Convolutional Neural Network (Mask R-CNN) to identify and classify outdoor AMPs in low-resolution micrographs (256 × 256 pixels). A key innovation involves integrating classification directly within the U-Net-based segmentation frameworks, thereby streamlining the workflow and improving computational efficiency. This marks an advancement over previous work where segmentation and classification were performed separately. The enhanced U-Net models attained average classification F1-scores exceeding 85% and segmentation accuracy above 77% on test images. Additionally, the Mask R-CNN model achieved an average bounding box precision of 73.32%, a classification F1-score of 84.29%, and a mask precision of 71.31%. The proposed method provides a faster and more accurate means of identifying AMPs compared to thresholding techniques. It also functions effectively as a pre-screening tool, substantially reducing the number of particles requiring labour-intensive chemical analysis. By integrating advanced deep learning strategies into AMPs research, this study paves the way for more efficient monitoring and characterisation of microplastics.
{"title":"Automatic pre-screening of outdoor airborne microplastics in micrographs using deep learning","authors":"Sheen Mclean Cabaneros , Emma Chapman , Mark Hansen , Ben Williams , Jeanette Rotchell","doi":"10.1016/j.envpol.2025.125993","DOIUrl":"10.1016/j.envpol.2025.125993","url":null,"abstract":"<div><div>Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of identifying potential particles in micrographs can significantly enhance the research and monitoring of AMPs. Although deep learning has shown substantial promise in microplastics analysis, existing studies have primarily focused on high-resolution images of samples collected from marine and freshwater environments. In contrast, this work introduces a novel approach by employing enhanced U-Net models (Attention U-Net and Dynamic RU-NEXT) along with the Mask Region Convolutional Neural Network (Mask R-CNN) to identify and classify outdoor AMPs in low-resolution micrographs (256 × 256 pixels). A key innovation involves integrating classification directly within the U-Net-based segmentation frameworks, thereby streamlining the workflow and improving computational efficiency. This marks an advancement over previous work where segmentation and classification were performed separately. The enhanced U-Net models attained average classification F1-scores exceeding 85% and segmentation accuracy above 77% on test images. Additionally, the Mask R-CNN model achieved an average bounding box precision of 73.32%, a classification F1-score of 84.29%, and a mask precision of 71.31%. The proposed method provides a faster and more accurate means of identifying AMPs compared to thresholding techniques. It also functions effectively as a pre-screening tool, substantially reducing the number of particles requiring labour-intensive chemical analysis. By integrating advanced deep learning strategies into AMPs research, this study paves the way for more efficient monitoring and characterisation of microplastics.</div></div>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"372 ","pages":"Article 125993"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126064
Runtao Wu , Zhenyu Zhu , Wenfeng Xiao , Jiarong Zou , Yaoyao Nie , Ye Yang , Wenxia Zhao , Zhenqiang You , Yingjun Li
Currently, limited research exists on the relationship between osteoarthritis (OA) and Benzophenone-3 (BP-3). This study aims to explore the potential molecular pathways involved, using both in vivo and in vitro biological experiments. In vivo experiments revealed that exposure to BP-3 leads to cartilage damage in the knee joints of rats, suggesting that BP-3 may be a significant risk factor in the development and progression of osteoarthritis. Proteomic sequencing of knee cartilage tissue revealed alterations in multiple inflammatory pathways in the BP-3 group. In vitro cellular experiments further demonstrated the toxic effects of BP-3 on chondrocytes, including inflammatory changes and increased transcriptional levels of IL-6. Cellular transcriptomics sequencing revealed significant changes in multiple intracellular inflammatory pathways, particularly the JAK-STAT pathway. Additional experiments demonstrated that BP-3 enhances STAT3 phosphorylation, promoting the degradation of extracellular matrix (ECM) proteins. Silence of STAT3 alleviated the impaired effects of BP-3 on chondrocytes. Overall, our data suggest that BP-3 exposure may be a significant risk factor for OA development. This study provides substantial evidence and a comprehensive understanding of the impact of BP-3 on OA development.
{"title":"Mechanism of chondrocyte injury induced by Benzophenone-3 through modulation of the IL-6/JAK2/STAT3 pathway","authors":"Runtao Wu , Zhenyu Zhu , Wenfeng Xiao , Jiarong Zou , Yaoyao Nie , Ye Yang , Wenxia Zhao , Zhenqiang You , Yingjun Li","doi":"10.1016/j.envpol.2025.126064","DOIUrl":"10.1016/j.envpol.2025.126064","url":null,"abstract":"<div><div>Currently, limited research exists on the relationship between osteoarthritis (OA) and Benzophenone-3 (BP-3). This study aims to explore the potential molecular pathways involved, using both in vivo and in vitro biological experiments. In vivo experiments revealed that exposure to BP-3 leads to cartilage damage in the knee joints of rats, suggesting that BP-3 may be a significant risk factor in the development and progression of osteoarthritis. Proteomic sequencing of knee cartilage tissue revealed alterations in multiple inflammatory pathways in the BP-3 group. In vitro cellular experiments further demonstrated the toxic effects of BP-3 on chondrocytes, including inflammatory changes and increased transcriptional levels of IL-6. Cellular transcriptomics sequencing revealed significant changes in multiple intracellular inflammatory pathways, particularly the JAK-STAT pathway. Additional experiments demonstrated that BP-3 enhances STAT3 phosphorylation, promoting the degradation of extracellular matrix (ECM) proteins. Silence of STAT3 alleviated the impaired effects of BP-3 on chondrocytes. Overall, our data suggest that BP-3 exposure may be a significant risk factor for OA development. This study provides substantial evidence and a comprehensive understanding of the impact of BP-3 on OA development.</div></div>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"372 ","pages":"Article 126064"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126063
Jason Kent , Jeffrey T. Hutchinson , Vikram Kapoor , Akanksha Matta , Samer Dessouky
In this study, we evaluated the oil and grease concentrations in sediment from roadway runoff in detention basins and swales following rain events. The study was conducted in three swales and two detention basins within the Edwards Aquifer recharge zone in San Antonio, Texas and one swale located in the contributing zone. The Edwards Aquifer is the primary source of municipal water for Bexar and surrounding counties and being a karst aquifer makes it susceptible to pollution. Oil and grease was sampled following eight rain events in detention basin (n = 3) and swales (n = 3) from November 2020 to July 2021 using pitfall traps. Mean oil and grease concentrations in detention basins and swales were 723 mg kg−1 (SE = 41.6) and 667 mg kg−1 (SE = 41.9), respectively, indicating these stormwater management structures are efficient at capturing oil and grease from roadway runoff. Mean sediment weight captured in pitfall traps was 2.6-fold greater in confined detention basins (6475 g m2-1; SE = 1193) compared to swales (2443 g m2-1; SE = 526). Nonmetric Multidimensional Scaling (NMS) indicated that particle sizes of 125, 250, and 500 μm were moderately associated with oil and grease concentrations on axis 1. Detention basins and swales should be promoted for future development in the Edwards Aquifer contributing and recharge zones for management of oil and grease and other pollutants associated with roadway runoff and other impervious structures for protection of groundwater and the associated listed species.
{"title":"Evaluation of oil and grease from roadway runoff in sediment from detention basins and swales within the Edwards Aquifer recharge zone, central Texas, U.S.A.","authors":"Jason Kent , Jeffrey T. Hutchinson , Vikram Kapoor , Akanksha Matta , Samer Dessouky","doi":"10.1016/j.envpol.2025.126063","DOIUrl":"10.1016/j.envpol.2025.126063","url":null,"abstract":"<div><div>In this study, we evaluated the oil and grease concentrations in sediment from roadway runoff in detention basins and swales following rain events. The study was conducted in three swales and two detention basins within the Edwards Aquifer recharge zone in San Antonio, Texas and one swale located in the contributing zone. The Edwards Aquifer is the primary source of municipal water for Bexar and surrounding counties and being a karst aquifer makes it susceptible to pollution. Oil and grease was sampled following eight rain events in detention basin (n = 3) and swales (n = 3) from November 2020 to July 2021 using pitfall traps. Mean oil and grease concentrations in detention basins and swales were 723 mg kg<sup>−1</sup> (SE = 41.6) and 667 mg kg<sup>−1</sup> (SE = 41.9), respectively, indicating these stormwater management structures are efficient at capturing oil and grease from roadway runoff. Mean sediment weight captured in pitfall traps was 2.6-fold greater in confined detention basins (6475 g m<sup>2-1</sup>; SE = 1193) compared to swales (2443 g m<sup>2-1</sup>; SE = 526). Nonmetric Multidimensional Scaling (NMS) indicated that particle sizes of 125, 250, and 500 μm were moderately associated with oil and grease concentrations on axis 1. Detention basins and swales should be promoted for future development in the Edwards Aquifer contributing and recharge zones for management of oil and grease and other pollutants associated with roadway runoff and other impervious structures for protection of groundwater and the associated listed species.</div></div>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"372 ","pages":"Article 126063"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126069
Juan Wen , Shijie Geng , Juan Mu , Junya Wang , Yongmei Dai , Lingmin Hu
Pentachlorophenol (PCP) is a pervasive endocrine-disrupting compound present in the environment. Limited research has explored the effects of PCP exposure on gestational diabetes mellitus (GDM), particularly the metabolites-related mechanism. Our study seeks to characterize the interrelationships between PCP exposure, plasma metabolomic markers, and GDM, aiming to elucidate the metabolomic profile mediating PCP-GDM relationship. From a prospective cohort in Changzhou, China, a nested case-control study was conducted, involving 154 GDM cases and 308 controls. We collected fasting blood samples before 16 weeks of gestation and determined PCP levels by UPLC-MS/MS. Plasma metabolomic markers were identified using untargeted metabolomics. Multivariate logistic regression and mediation analysis were used to examine the relationships among PCP exposure, metabolomic markers, and GDM. Using the Mann-Whitney U test, we found that serum PCP levels were significantly higher in GDM cases (median: 0.43 ng/mL, IQR: 0.28–0.77) compared to controls (median: 0.38 ng/mL, IQR: 0.24–0.64; P = 0.041). In the fully adjusted model, which additionally accounted for dietary patterns, the OR (95 %CI) values for GDM across tertiles of serum PCP were 1 (reference), 1.24 (0.73, 2.11), and 2.17 (1.28, 3.68), respectively, indicating a potential dose-response relationship (P trend = 0.004). Furthermore, 152 differential metabolites were identified between groups (FDR <0.05), implicating 4 metabolic pathways: "Nitrogen metabolism", "Alanine, aspartate and glutamate metabolism", "Glycerophospholipid metabolism", and "Pyrimidine metabolism" (FDR <0.1). Mediation analysis revealed that 5 metabolomic markers (such as N-Acetylalanine and 4-Acetamidobutyric acid) significantly mediated the association between PCP and GDM (FDR <0.05), with mediated proportions ranging from 0.15 to 0.31. Together, pregnant women in Eastern China exhibit widespread PCP exposure, with serum PCP levels positively associated with GDM risk. PCP exposure-related metabolomic changes may partially mediate the link between PCP and GDM.
{"title":"Pentachlorophenol exposure, plasma metabolomic markers, and gestational diabetes mellitus: Association and potential mediation analyses","authors":"Juan Wen , Shijie Geng , Juan Mu , Junya Wang , Yongmei Dai , Lingmin Hu","doi":"10.1016/j.envpol.2025.126069","DOIUrl":"10.1016/j.envpol.2025.126069","url":null,"abstract":"<div><div>Pentachlorophenol (PCP) is a pervasive endocrine-disrupting compound present in the environment. Limited research has explored the effects of PCP exposure on gestational diabetes mellitus (GDM), particularly the metabolites-related mechanism. Our study seeks to characterize the interrelationships between PCP exposure, plasma metabolomic markers, and GDM, aiming to elucidate the metabolomic profile mediating PCP-GDM relationship. From a prospective cohort in Changzhou, China, a nested case-control study was conducted, involving 154 GDM cases and 308 controls. We collected fasting blood samples before 16 weeks of gestation and determined PCP levels by UPLC-MS/MS. Plasma metabolomic markers were identified using untargeted metabolomics. Multivariate logistic regression and mediation analysis were used to examine the relationships among PCP exposure, metabolomic markers, and GDM. Using the Mann-Whitney <em>U</em> test, we found that serum PCP levels were significantly higher in GDM cases (median: 0.43 ng/mL, IQR: 0.28–0.77) compared to controls (median: 0.38 ng/mL, IQR: 0.24–0.64; <em>P</em> = 0.041). In the fully adjusted model, which additionally accounted for dietary patterns, the OR (95 %CI) values for GDM across tertiles of serum PCP were 1 (reference), 1.24 (0.73, 2.11), and 2.17 (1.28, 3.68), respectively, indicating a potential dose-response relationship (<em>P</em> trend = 0.004). Furthermore, 152 differential metabolites were identified between groups (FDR <0.05), implicating 4 metabolic pathways: \"Nitrogen metabolism\", \"Alanine, aspartate and glutamate metabolism\", \"Glycerophospholipid metabolism\", and \"Pyrimidine metabolism\" (FDR <0.1). Mediation analysis revealed that 5 metabolomic markers (such as N-Acetylalanine and 4-Acetamidobutyric acid) significantly mediated the association between PCP and GDM (FDR <0.05), with mediated proportions ranging from 0.15 to 0.31. Together, pregnant women in Eastern China exhibit widespread PCP exposure, with serum PCP levels positively associated with GDM risk. PCP exposure-related metabolomic changes may partially mediate the link between PCP and GDM.</div></div>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"372 ","pages":"Article 126069"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126060
Najeebullah Khan, Shamsuddin Shahid, Kamal Ahmed, Zaher Mundher Yaseen
Air pollution, especially small particulate matter (PM2.5), has emerged as a significant public health crisis in Pakistan, yet its long-term health impacts remain understudied. There is a critical lack of high-resolution spatiotemporal analysis that captures the changing exposure levels and associated mortality trends over extended periods. This study investigates this gap by addressing the spatiotemporal variations in PM2.5 exposure and its associated mortality burden from 2000 to 2021. Additionally, it projects possible spatiotemporal changes in mortality for two scenarios, business-as-usual and PM2.5 mitigation. The Global Exposure Mortality Model (GEMM) was applied on 0.01° resolution gridded PM2.5 and population concentration data to quantify PM2.5-attributed mortality for major diseases: ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lower respiratory infection (LRI) and lung cancer (LC). The results showed escalating levels and expanded geographical spread of PM2.5 and mortality in Pakistan. Population exposure estimates reveal high-risk zones with over 80 μg/m3 concentrations engulfing densely inhabited areas far exceeding the WHO annual limit of 5 μg/m3. The number of PM2.5 -related deaths increased from 57,100 in 2000 to 157,762 in 2021. IHD showed the highest sensitivity to PM2.5, marked by over three times higher hazard ratio at 150 μg/m3 exposure. Spatial mapping revealed IHD and LRI mortality hotspots corresponding to settlers near the Indus River basin. Notably, central parts recorded over 2 μg/m3 annual PM2.5 increase. Future projections based on growth trajectories forecast that the uncontrolled increase in PM2.5 could inflate ischemic heart disease deaths from 14,248 to 142,903 by 2030, leading to a total PM2.5 -related mortality burden exceeding 290,000 deaths. However, stabilizing PM2.5 levels under a mitigation scenario could significantly reduce mortality to 29,062 by 2030. This study provides critical insights into demographic vulnerabilities, high-risk zones, and future mortality trends, emphasizing the urgency for mitigation policies to safeguard millions facing existential risk.
{"title":"Modeling Spatial PM2.5 Risk Dynamics and Projecting Disease Burden in Pakistan","authors":"Najeebullah Khan, Shamsuddin Shahid, Kamal Ahmed, Zaher Mundher Yaseen","doi":"10.1016/j.envpol.2025.126060","DOIUrl":"https://doi.org/10.1016/j.envpol.2025.126060","url":null,"abstract":"Air pollution, especially small particulate matter (PM<sub>2</sub>.<sub>5</sub>), has emerged as a significant public health crisis in Pakistan, yet its long-term health impacts remain understudied. There is a critical lack of high-resolution spatiotemporal analysis that captures the changing exposure levels and associated mortality trends over extended periods. This study investigates this gap by addressing the spatiotemporal variations in PM<sub>2.5</sub> exposure and its associated mortality burden from 2000 to 2021. Additionally, it projects possible spatiotemporal changes in mortality for two scenarios, business-as-usual and PM<sub>2.5</sub> mitigation. The Global Exposure Mortality Model (GEMM) was applied on 0.01° resolution gridded PM<sub>2.5</sub> and population concentration data to quantify PM<sub>2.5</sub>-attributed mortality for major diseases: ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lower respiratory infection (LRI) and lung cancer (LC). The results showed escalating levels and expanded geographical spread of PM<sub>2.5</sub> and mortality in Pakistan. Population exposure estimates reveal high-risk zones with over 80 μg/m<sup>3</sup> concentrations engulfing densely inhabited areas far exceeding the WHO annual limit of 5 μg/m<sup>3</sup>. The number of PM<sub>2.5</sub> -related deaths increased from 57,100 in 2000 to 157,762 in 2021. IHD showed the highest sensitivity to PM<sub>2.5</sub>, marked by over three times higher hazard ratio at 150 μg/m<sup>3</sup> exposure. Spatial mapping revealed IHD and LRI mortality hotspots corresponding to settlers near the Indus River basin. Notably, central parts recorded over 2 μg/m<sup>3</sup> annual PM<sub>2.5</sub> increase. Future projections based on growth trajectories forecast that the uncontrolled increase in PM<sub>2.5</sub> could inflate ischemic heart disease deaths from 14,248 to 142,903 by 2030, leading to a total PM<sub>2.5</sub> -related mortality burden exceeding 290,000 deaths. However, stabilizing PM<sub>2.5</sub> levels under a mitigation scenario could significantly reduce mortality to 29,062 by 2030. This study provides critical insights into demographic vulnerabilities, high-risk zones, and future mortality trends, emphasizing the urgency for mitigation policies to safeguard millions facing existential risk.","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"23 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126048
Haiyan Wu, Shanguo Chen, Yu Deng, Jiahui Shen, Yifei Xu, Tao Wen, Jun Yuan, Qirong Shen, Chao Xue
Although chemical fumigants are widely applied in agriculture to control soil-borne diseases, their influence on soil antibiotic resistance genes (ARGs) remains poorly understood. This study employed metagenomic sequencing to investigate the dynamic response and recovery processes of soil bacterial communities and ARGs after the end of fumigation with Dazomet. The results revealed that the effects of Dazomet were both phased and recoverable. Initially, no significant shifts in bacterial community diversity were observed; however, by day 10 of recovery (Dazomet10), diversity had decreased by 3.1%. By contrast, ARG levels surged by 17.3% and 10.9% on days 10 and 20 (Dazomet20), respectively, before reverting to the baseline by day 50 (Dazomet50). These patterns were corroborated by qPCR data, which showed a 90.8% reduction in 16S rRNA gene abundance, alongside a 4.17- to 4.38-fold increase in the relative abundance of ARGs at Dazomet10 and Dazomet20. Approximately 63% of the variation in ARGs was attributed to bacterial community composition and mobile genetic elements (MGEs). Combined with community analysis and host-tracking analysis, it was found that Streptomyces and Nocardioides were identified as key ARGs hosts. Overall, the microbial communities and resistome required at least 50 days after the end of fumigation to recover to their pre-fumigation state. This study sheds light on the dynamic interactions between bacterial communities and ARGs during recovery from Dazomet fumigation and underscores the critical need for the rational use of fumigants in agricultural practices.
{"title":"Dynamics of antibiotic resistance genes and the bacterial community after stress from a single Dazomet fumigation","authors":"Haiyan Wu, Shanguo Chen, Yu Deng, Jiahui Shen, Yifei Xu, Tao Wen, Jun Yuan, Qirong Shen, Chao Xue","doi":"10.1016/j.envpol.2025.126048","DOIUrl":"https://doi.org/10.1016/j.envpol.2025.126048","url":null,"abstract":"Although chemical fumigants are widely applied in agriculture to control soil-borne diseases, their influence on soil antibiotic resistance genes (ARGs) remains poorly understood. This study employed metagenomic sequencing to investigate the dynamic response and recovery processes of soil bacterial communities and ARGs after the end of fumigation with Dazomet. The results revealed that the effects of Dazomet were both phased and recoverable. Initially, no significant shifts in bacterial community diversity were observed; however, by day 10 of recovery (Dazomet10), diversity had decreased by 3.1%. By contrast, ARG levels surged by 17.3% and 10.9% on days 10 and 20 (Dazomet20), respectively, before reverting to the baseline by day 50 (Dazomet50). These patterns were corroborated by qPCR data, which showed a 90.8% reduction in 16S rRNA gene abundance, alongside a 4.17- to 4.38-fold increase in the relative abundance of ARGs at Dazomet10 and Dazomet20. Approximately 63% of the variation in ARGs was attributed to bacterial community composition and mobile genetic elements (MGEs). Combined with community analysis and host-tracking analysis, it was found that <em>Streptomyces</em> and <em>Nocardioides</em> were identified as key ARGs hosts. Overall, the microbial communities and resistome required at least 50 days after the end of fumigation to recover to their pre-fumigation state. This study sheds light on the dynamic interactions between bacterial communities and ARGs during recovery from Dazomet fumigation and underscores the critical need for the rational use of fumigants in agricultural practices.","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"18 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.envpol.2025.126061
Mahsa Payami, Seyedali Mousavinezhad, Yunsoo Choi, Nima Khorshidian
This study uses an innovative approach by integrating the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model with MOVES (Motor Vehicle Emission Simulator) to evaluate the well-to-wheel (WTW) emissions of key pollutants—carbon monoxide (CO), nitrogen oxides (NOX), particulate matter (PM2.5, PM10), sulfur oxides (SOX), and greenhouse gases (GHG-100, GHG-20)—across four major United States (U.S.) cities: New York City, Los Angeles, Chicago, and Houston, under various electrification scenarios projected for 2035. Using two distinct electricity generation mixes from the U.S. Energy Information Administration (EIA) and the National Renewable Energy Laboratory (NREL), the analysis explores the impact of three electrification scenarios: moderate (MedE), high (HighE), and full electrification (FullE). Results indicate that CO emissions decrease by an average of 15.8% in MedE, 26.5%in HighE, and 99.1% in the FullE scenario across all cities. On average, NOX emissions decline by 5% in MedE, 8.5% in HighE and 97.3% in FullE. GHG-100 and GHG-20 emissions decrease by 9% in MedE, 15.6% in HighE, and 91% in FullE. Reductions in PM2.5 and PM10 are more modest even with full electrification due to the contribution of non-exhaust emissions. However, SOX emissions, which originate primarily from electricity generation, exhibit substantial variability. For example, while New York City, Los Angeles, and Chicago see an average reduction of 79.2% under EIA’s FullE scenario, Houston experiences a 75.1% increase. In contrast, NREL projections show reductions across all cities. These results reaffirm that the benefits of electrification depend on the grid’s carbon intensity.
{"title":"Potential Impact of Vehicle Electrification on Greenhouse Gases and Criteria Pollutants in 2035: A Scenario-Based Well-to-Wheel Analysis for New York City, Los Angeles, Chicago, and Houston Using GREET-MOVES Integration","authors":"Mahsa Payami, Seyedali Mousavinezhad, Yunsoo Choi, Nima Khorshidian","doi":"10.1016/j.envpol.2025.126061","DOIUrl":"https://doi.org/10.1016/j.envpol.2025.126061","url":null,"abstract":"This study uses an innovative approach by integrating the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model with MOVES (Motor Vehicle Emission Simulator) to evaluate the well-to-wheel (WTW) emissions of key pollutants—carbon monoxide (CO), nitrogen oxides (NO<sub>X</sub>), particulate matter (PM<sub>2.5</sub>, PM<sub>10</sub>), sulfur oxides (SO<sub>X</sub>), and greenhouse gases (GHG-100, GHG-20)—across four major United States (U.S.) cities: New York City, Los Angeles, Chicago, and Houston, under various electrification scenarios projected for 2035. Using two distinct electricity generation mixes from the U.S. Energy Information Administration (EIA) and the National Renewable Energy Laboratory (NREL), the analysis explores the impact of three electrification scenarios: moderate (MedE), high (HighE), and full electrification (FullE). Results indicate that CO emissions decrease by an average of 15.8% in MedE, 26.5%in HighE, and 99.1% in the FullE scenario across all cities. On average, NO<sub>X</sub> emissions decline by 5% in MedE, 8.5% in HighE and 97.3% in FullE. GHG-100 and GHG-20 emissions decrease by 9% in MedE, 15.6% in HighE, and 91% in FullE. Reductions in PM<sub>2.5</sub> and PM<sub>10</sub> are more modest even with full electrification due to the contribution of non-exhaust emissions. However, SO<sub>X</sub> emissions, which originate primarily from electricity generation, exhibit substantial variability. For example, while New York City, Los Angeles, and Chicago see an average reduction of 79.2% under EIA’s FullE scenario, Houston experiences a 75.1% increase. In contrast, NREL projections show reductions across all cities. These results reaffirm that the benefits of electrification depend on the grid’s carbon intensity.","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"183 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1016/j.envpol.2025.126055
Man Yue Lam, Reza Ahmadian
Pathogens in nearshore coastal waters have far-reaching public health and economic implications. Faecal indicator organisms (FIOs) are commonly monitored and modelled to indicate pathogen levels in waterbodies. FIO decay modelling is an integral part of numerical hydro-epidemiological models to simulate the die-off of FIOs in the water bodies. This paper identifies the limitations of one of the comprehensive and widely used FIO decay models, developed by Stapleton et al. and enhances the model by remedying the limitations. The identified limitations are: (i) the decay rates for dark or highly irradiated environments are not accurately presented, and (ii) the effect of salinity is not included. Two enhanced models have been developed, namely (i) the ClipStap model, devised by imposing a minimum decay rate to the Stapleton model, and (ii) the RevStap model, devised by extrapolating the decay rate-irradiation slope at a reference irradiation () down to lower irradiation regions. The enhanced models reproduced the literature-reported dark decay rates better and significantly improved the agreement between the modelled and measured decay rate. The enhanced decay models were tested by including them in a hydro-epidemiological model for a data-rich case study, namely Swansea Bay, UK. Results show that the RevStap model improved FIO prediction in some cases. Besides the enhanced models, this research attributes the diurnal variations of FIO to the combined action of riverine FIO inflows, tide action, and FIO decay. These insights on the effect of irradiation and diurnal FIO variations are critical for assessing the impact of water quality on human activities and nearshore ecology.
{"title":"Enhancing transport and decay models for faecal indicator organisms in nearshore coastal waters","authors":"Man Yue Lam, Reza Ahmadian","doi":"10.1016/j.envpol.2025.126055","DOIUrl":"https://doi.org/10.1016/j.envpol.2025.126055","url":null,"abstract":"Pathogens in nearshore coastal waters have far-reaching public health and economic implications. Faecal indicator organisms (FIOs) are commonly monitored and modelled to indicate pathogen levels in waterbodies. FIO decay modelling is an integral part of numerical hydro-epidemiological models to simulate the die-off of FIOs in the water bodies. This paper identifies the limitations of one of the comprehensive and widely used FIO decay models, developed by Stapleton et al. and enhances the model by remedying the limitations. The identified limitations are: (i) the decay rates for dark or highly irradiated environments are not accurately presented, and (ii) the effect of salinity is not included. Two enhanced models have been developed, namely (i) the ClipStap model, devised by imposing a minimum decay rate to the Stapleton model, and (ii) the RevStap model, devised by extrapolating the decay rate-irradiation slope at a reference irradiation (<span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\" />' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"0.24ex\" role=\"img\" style=\"vertical-align: -0.12ex;\" viewbox=\"0 -51.7 0 103.4\" width=\"0\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"></math></span></span><script type=\"math/mml\"><math></math></script></span>) down to lower irradiation regions. The enhanced models reproduced the literature-reported dark decay rates better and significantly improved the agreement between the modelled and measured decay rate. The enhanced decay models were tested by including them in a hydro-epidemiological model for a data-rich case study, namely Swansea Bay, UK. Results show that the RevStap model improved FIO prediction in some cases. Besides the enhanced models, this research attributes the diurnal variations of FIO to the combined action of riverine FIO inflows, tide action, and FIO decay. These insights on the effect of irradiation and diurnal FIO variations are critical for assessing the impact of water quality on human activities and nearshore ecology.","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"68 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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