Air Quality Atmosphere and Health最新文献

{"title":"Air pollution observation—bridging spaceborne to unmanned airborne remote sensing: a systematic review and meta-analysis","authors":"Farzaneh Dadrass Javan,&nbsp;Farhad Samadzadegan,&nbsp;Ahmad Toosi","doi":"10.1007/s11869-025-01771-y","DOIUrl":"10.1007/s11869-025-01771-y","url":null,"abstract":"<div><p>Air pollution is one of the most critical environmental concerns affecting human health and ecosystem sustainability. This comprehensive review analyzes the evolution and current state of Remote Sensing (RS) methods for air pollution monitoring, examining over 241 relevant papers from the Scopus database using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study systematically evaluates three main approaches: spaceborne, Manned Aerial Vehicle (MAV)-borne, and Unmanned Aerial Vehicle (UAV)-borne RS. Our analysis reveals significant technological advancements in sensors, platforms, and data processing methods. Spaceborne monitoring demonstrates enhanced spatial resolution (from 10 km to sub-kilometer) and temporal frequency (from monthly to near-real-time). MAV-based systems show superior regional mapping capabilities but face operational constraints. UAVs emerge as promising solutions for local-scale monitoring, particularly in hazardous environments, offering operational flexibility, cost-effectiveness, and the ability to capture high-resolution spatial data. The Internet of Things (IoT) has enhanced data collection networks, while integration of Artificial Intelligence (AI), specifically deep learning, has revolutionized data processing capabilities. Cloud computing platforms, particularly Google Earth Engine (GEE), have further transformed the scale and efficiency of big data analysis for air quality. The meta-analysis of COVID-19 lockdown impacts shows significant pollution reductions, with an overall average decrease of 28% across major pollutants (NO2, PM2.5, PM10, SO2, CO), though individual pollutants showed varying responses, with O3 notably demonstrating increases due to atmospheric chemistry dynamics. The review identifies current limitations and future directions, emphasizing the need for improved multi-platform and multi-sensor RS data integration, sensor miniaturization, and regulatory frameworks. This comprehensive analysis provides valuable insights for researchers, policymakers, and practitioners in environmental monitoring and public health.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2481 - 2549"},"PeriodicalIF":2.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01771-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Comparative analysis of the impact of PM2.5 components on visibility, extinction and oxidative potential in beijing, China","authors":"Wei Wen,&nbsp;N. D. Tharaka W. A.,&nbsp;Lei Liu,&nbsp;Xin Ma,&nbsp;Liyao Shen,&nbsp;Zifan Deng","doi":"10.1007/s11869-025-01766-9","DOIUrl":"10.1007/s11869-025-01766-9","url":null,"abstract":"<div><p>The implementation of policies such as the “Clean Air Action Plan” has contributed to the ongoing enhancement of air quality in Beijing. This study investigates the characteristics of visibility, PM_2.5 concentration, and its components, as well as their effects on extinction, and oxidative potential (OPv) through observational data and the WRF-Chem model within the context of these emission reduction strategies. Analysis of data collected during the spring and summer of 2019 revealed that PM_2.5 concentrations were significantly higher in spring (48 µg/m³) compared to summer (35 µg/m³), primarily due to emissions related to heating and unfavorable meteorological conditions. While average visibility showed slight variation between the seasons (21.53 km in spring versus 20.99 km in summer), OPv was markedly elevated in summer. The chemical composition of PM_2.5 was found to exert a more substantial influence on visibility and OPv than mass concentration alone. Visibility appeared to be strongly influenced by relative humidity (RH), mainly when RH ≤ 60%. Conversely, PM_2.5 concentrations significantly affected visibility within the range of 40% ≤ RH &lt; 60%. Nitrate contributed the most to the extinction coefficient (37%), followed by organic carbon (24%) and sulfate (15%). The contribution rates of various chemical components to atmospheric extinction were significantly altered depending on visibility levels. Secondary components were identified as the principal source of atmospheric extinction, comprising 41% of total extinction, and were also the predominant contributors to OPv. The findings of this study lay the groundwork for the development of more effective air quality management strategies in Beijing.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2343 - 2356"},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Assessing indoor concentrations of termiticides and their risk to humans","authors":"Kazushi Noro,&nbsp;Kasumi Yamaguchi,&nbsp;Qi Wang,&nbsp;Yuichi Miyake,&nbsp;Takashi Amagai","doi":"10.1007/s11869-025-01767-8","DOIUrl":"10.1007/s11869-025-01767-8","url":null,"abstract":"<div>\u0000 \u0000 <p>Termiticides are widely used to protect wooden houses from termites. Dieldrin, chlordane, heptachlor, and chlorpyrifos, which are effective termiticides, have been banned because of their high toxicity. Neonicotinoids, pyrethroids, phenyl pyrazoles, and triazoles have been used as alternatives to termiticides in indoor environments. However, despite numerous studies showing that farm-applied pesticides contaminate house dust, the health risks to humans from indoor termiticides remain unclear. We collected house dust and indoor air samples from 37 and 7 houses, respectively, to investigate the indoor termiticide contamination levels. The minimum margin of exposure to fipronil was 173, indicating that fipronil posed the highest risk among the targeted 28 compounds in indoor environment. The mean concentrations of alternative termiticides in house dust and air samples ranged from 1,126 ng g^− 1 (cyproconazole) to 5,356 ng g^− 1 (MGK-264) in thirty-seven houses and 0.08 ng m^− 3 (acetamiprid) to 34 ng m^− 3 (MGK-264) in seven houses, respectively. These results are comparable to the pesticide concentrations in houses close to farms where pesticides were applied, and are higher than atmospheric pesticide concentrations in oceans. Therefore, houses sprayed with termiticides may be as contaminated as agricultural environments where farmers apply substantial quantities of pesticides. The main route of exposure was air inhalation for fipronil, and both air inhalation and house dust ingestion for triazoles and potentiators. Establishment of regulations and development of decontamination methods are needed for indoor contamination of termiticides. Floor cleaning may be effective to remove termiticides that are ingested mainly through the house dust pathway.</p>\u0000 </div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2333 - 2341"},"PeriodicalIF":2.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"PM10 mass concentration characteristics in a coastal mediterranean site: a yearly study of seasonality and sources with short term elemental analysis","authors":"Sofia Eirini Chatoutsidou,&nbsp;Amalia Bali,&nbsp;Mihalis Lazaridis","doi":"10.1007/s11869-025-01763-y","DOIUrl":"10.1007/s11869-025-01763-y","url":null,"abstract":"<div>\u0000 \u0000 <p>PM₁₀ mass concentrations were measured online for one year at the Akrotiri monitoring station in Chania (Greece). PM₁₀ concentrations ranged from 7.3 to 287.5 µg m^− 3 with highest concentrations measured in April, due to intense dust episodes. Overall, a total of 50 events days were identified with most episodes taking place during the first semester of the year. The majority of PM₁₀ exceedances (80%), using the World Health Organization air quality guidelines (45 µg m⁻³), were mainly due to dust episodes and secondarily due to anthropogenic activities. Increased concentrations of PM₁₀ in winter were due to domestic heating, in spring due to frequent and intense dust episodes, in summer due to touristic activities and in autumn due to vehicular traffic and road dust resuspension. Diurnal analysis demonstrated higher concentrations of PM₁₀ during morning and midday on weekdays and during afternoon and evening on weekends, all associated with peak hours. Gravimetric sampling of PM₁₀ for one month has verified previous observations at the study site. Particularly, PM₁₀ particles were enriched withNa, Cl and Br when windblown arrived from the sea whilst crustal elements such as Mg, Si, Ca, K and Al, were significantly increased during dust episodes. On the other hand, heavy metals such as V and Ni were linked with shipping activities from the nearby port Zn was associated with non-exhaust vehicle emissions and Pb with engine related sources.</p>\u0000 </div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2317 - 2332"},"PeriodicalIF":2.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01763-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Estimation of real-time PM exposure and associated health risk of HEMM operators using low-cost sensors in a highly mechanised opencast coal mine","authors":"Dhruti Sundar Pradhan,&nbsp;Aditya Kumar Patra,&nbsp;Abhishek Penchala,&nbsp;Samrat Santra","doi":"10.1007/s11869-025-01769-6","DOIUrl":"10.1007/s11869-025-01769-6","url":null,"abstract":"<div>\u0000 \u0000 <p>The working environment of the operators of heavy earth moving machineries (HEMMs) expose them to high airborne particulate matter (PM) concentration as a part of their occupation. Using a set of low-cost sensors, the present study investigated the in-cabin PM exposure of dumper, shovel and drill machine operators and compared it with the cabin outside PM concentration in a large opencast mine where a large number of HEMMs are deployed. The results revealed that the drill operators were exposed to the highest in-cabin PM concentration (PM₁ = 190.98 ± 30.3 µg m⁻³, PM_2.5 = 281 ± 52.85 µg m⁻³, PM₁₀ = 1475.23 ± 915.42 µg m⁻³) followed by the exposures of shovel (PM₁ = 45.62 ± 24.28 µg m⁻³, PM_2.5 = 97.85 ± 51.10 µg m⁻³, PM₁₀ = 354.38 ± 219.69 µg m⁻³) and dumper operators (PM₁ = 42.08 ± 18.25 µg m⁻³, PM_2.5 = 90.38 ± 44.55 µg m⁻³, PM₁₀ = 331.05 ± 225.65 µg m⁻³). The exposure in the evening shift was 10% higher than exposure during the morning shift. The in-cabin PM concentrations while the dumper travelled on main haul road were ~ 2 times of the corresponding values when it travelled on the internal haul road. In addition to the outside concentration, the in-cabin PM levels are influenced by cabin ventilation (AC vs. non-AC), structural leakage, and door/window operation. AC cabins could reduce the PM exposure up to 40% (10% for non-AC cabins) than ambient mine environments. GLM explained 65–89% of PM concentration variability, with HEMM type and meteorological parameters as significant predictors. The health risk assessment indicates that all operators are exposed to non-carcinogenic health risks, as the Risk Quotient (RQ) values for each exceed the threshold of 1 (RQ &gt; 1). Notably, the drill operator is subjected to the highest non-carcinogenic risk, with an RQ value of 8. While, the Excess Lifetime Cancer Risk (ELCR) assessment for PM_2.5 exposure reveals potential carcinogenic risks among operators in two age groups. For the 18-year age group, the ELCR values range from 6.15 × 10⁻⁴ to 1.92 × 10⁻³, whereas for the 21 years and above age group, the values range from 5.13 × 10⁻⁴ to 1.60 × 10⁻³, indicating elevated cancer risk among 18 + age group of operators. This research highlights the occupational health risk of the HEMM operators working in opencast mines and important role of low-cost sensors in real-time PM exposure assessment in mining environments.</p>\u0000 </div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2277 - 2302"},"PeriodicalIF":2.9,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Study on the emission reduction effect of emergency management and control measures in Tangshan city, Hebei province, China","authors":"Ruting Zhang,&nbsp;Chuanmin Chen,&nbsp;Songtao Liu,&nbsp;Peng Li,&nbsp;Huacheng Wu,&nbsp;Weiqing Zhou","doi":"10.1007/s11869-025-01772-x","DOIUrl":"10.1007/s11869-025-01772-x","url":null,"abstract":"<div>\u0000 \u0000 <p>Tangshan Air Heavy Pollution Emergency Command issued four orange alerts for air heavy pollution at 08:00 on January 4, 2018, 12:00 on January 26, 2018, 12:00 on December 10, 2018 and 12:00 on January 8, 2019, respectively. To evaluate the effectiveness of the four orange alert emergency measures in mitigating severe air pollution, this study employed the WRF-CMAQ air quality model to conduct numerical simulations. These simulations were based on the differences in industrial pollutant emissions under emergency measures. The analysis focused on air pollutant concentrations, including PM_2.5 and other pollutants, along with meteorological conditions, weather patterns, and air mass transport during the orange alert periods. Additionally, the study examined how emission reduction measures in Tangshan’s industrial affected PM_2.5 and other pollutant concentrations, and it assessed the effectiveness of the orange alert emission reductions. The results showed that the average PM_2.5 concentration was 0.74–5.01 times higher than that after the alert. During the four orange alerts, the influence of weather conditions and regional transport on pollution was different. The orange emergency management reduced the concentrations of PM_2.5, PM₁₀, SO₂, NO₂ and CO by 0.7 − 2.9%, 0.5 − 2.1%, 1.4 − 6.9%, 2.1 − 5.2%, and 5.6 − 10.1%, respectively. The orange alert suppresses the deterioration of severe air pollution, achieves the effect of pollutant peak reduction and deceleration, and slows down the accumulation rate of PM_2.5.</p>\u0000 </div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2303 - 2316"},"PeriodicalIF":2.9,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Real-traffic emissions of CO, NOx, CO2, and PM2.5 from vehicles using a portable emission measurement system","authors":"Chunying Lu,&nbsp;Shuwei Dong,&nbsp;Shanqian Huang,&nbsp;Shuohan Gao,&nbsp;Jinsheng Fu,&nbsp;Xinyi Tian,&nbsp;Sha Lin,&nbsp;Xinzhu Xiong,&nbsp;Xinjun Wang,&nbsp;Xinfeng Wang","doi":"10.1007/s11869-025-01765-w","DOIUrl":"10.1007/s11869-025-01765-w","url":null,"abstract":"<div>\u0000 \u0000 <p>Vehicle emissions are one of the important sources of air pollution in urban areas. This study analyzed emissions of carbon monoxide (CO), nitrogen oxides (NO_x), carbon dioxide (CO₂), and fine particulate matter (PM_2.5) from gasoline, diesel, and compressed natural gas (CNG)-powered vehicles, including passenger cars, trucks, and taxis under real-traffic conditions. These pollutants and greenhouse gas were collected and measured through on-road experiments using a specifically designed portable emission measurement system (PEMS) on two testing routes. The results indicated significant variations in emission factors across vehicle and fuel types. Gasoline vehicles exhibited higher CO and CO₂ emission factors than their diesel and CNG counterparts. However, diesel vehicles produced significantly more NO_x and PM_2.5 emissions. Among all vehicle categories, light-duty gasoline trucks (LDGTs) and medium-duty gasoline passenger vehicles (MDGVs) had the highest CO emissions (10.3 g km^− 1 and 8.0 g km^− 1, respectively). Heavy-duty diesel passenger vehicles (HDDVs) emitted the most NO_x (10.1 g km^− 1) and CO₂ (1109.4 g km^− 1). Light-duty diesel trucks (LDDTs) and medium-duty diesel trucks (MDDTs) contributed the highest PM_2.5 emissions (295.2 mg km^− 1 and 769.4 mg km^− 1, respectively). Compressed natural gas vehicles were the cleanest, with emission values of 3.4 g km^− 1 (CO), 0.76 g km^− 1 (NO_x), 321.6 g km^− 1 (CO₂), and 1.4 mg km^− 1 (PM_2.5). Furthermore, while stricter emission standards help reduce pollutants and greenhouse gas emissions, increasing mileage is a major contributor to increased emission levels. Additionally, an estimation of CO, NO_x, CO₂, and PM_2.5 emissions from vehicle exhaust across 31 Chinese provinces in 2021 recognized high-emission vehicle types and revealed substantial regional variations in vehicle exhaust emissions. The findings from these on-road emission experiments provide essential insights for designing effective pollution control strategies and mitigating urban air pollution.</p>\u0000 </div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2265 - 2276"},"PeriodicalIF":2.9,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Does Ambient and Indoor Air Pollution Drive Hypertension in Young and Middle-Aged (30–49 years) Women in India? A Cross-Sectional Analysis","authors":"Mriganka Dolui,&nbsp;Sanjit Sarkar","doi":"10.1007/s11869-025-01770-z","DOIUrl":"10.1007/s11869-025-01770-z","url":null,"abstract":"<div><p>Air pollution, both ambient and indoor, is a significant contributor to cardiovascular oxidative stress, inflammation and elevated blood pressure, a key mechanism for hypertension. Hypertension has emerged as a significant public health concern in low- and middle-income countries (LMICs), including India. Previous studies have linked air pollution to hypertension in older adults, while the remaining young and middle-aged (YAMA) women (30–49 years) are also increasingly affected by hypertension due to prolonged exposure to environmental risks, particularly from cooking fuels and proximity to pollution sources. Therefore, this study investigates the association between ambient and household air pollution (HAP) with hypertension among YAMA women in India. We analysed data from the National Family Health Survey-five (NFHS-5) integrated with the PM_2.5 Grids database of NASA. Descriptive statistics and bivariate and multivariable statistical analyses were included. The results showed that the mean PM_2.5 level was 64.34 μg/m³, with a 21.91% prevalence of hypertension among women in India. The logistic regression model revealed that exposure to PM_2.5 and HAP increased the likelihood of hypertension by 1.036 times (<i>p</i> &lt; 0.001) and 1.025 times (<i>p</i> &lt; 0.001), respectively. Other risk factors, including women’s age, education, religion, caste, wealth index, self-smoking behaviour, body mass index, and diabetes, were also significantly associated with hypertension. These findings underscore the urgent need to address social and environmental determinants of hypertension burden among women. Public health policies should prioritize cleaner household energy sources and enforce stricter ambient air quality regulations in reducing the burden of hypertension and preventing cardiovascular diseases among women in India.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2247 - 2264"},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Long-term PM2.5 exposure in Bangladesh: identification of pollution hotspots, trends, sources and health risk assessment","authors":"Md. Arfan Ali,&nbsp;Mazen E. Assiri,&nbsp;Muhammad Bilal,&nbsp;Salman Tariq,&nbsp;Gerrit de Leeuw,&nbsp;M. Nazrul Islam,&nbsp;Yu Wang,&nbsp;Lama Alamri,&nbsp;Ayman S. Ghulam,&nbsp;Shamsuddin Shahid","doi":"10.1007/s11869-025-01768-7","DOIUrl":"10.1007/s11869-025-01768-7","url":null,"abstract":"<div><p>Fine particulate matter (PM_2.5) represents a significant global challenge due to its severe effects on human health, climate, and the environment, and is identified as the leading cause of premature mortality and morbidity worldwide. The limited availability and distribution of ground-based measurements hinder long-term studies on the impacts of air pollution in Bangladesh. Therefore, in this study, global estimates of monthly PM2.5 developed by Washington University were used to analyze the spatiotemporal distribution and variability of PM2.5, trends, health risk (HR), control zones, and potential source contribution function (PSCF) in Bangladesh from 2001 to 2020. The 20-year average spatial distribution shows PM_2.5 hotspots across Bangladesh, particularly in the urban areas of Dhaka, Mymensingh, Chittagong, Barisal, Khulna, Rajshahi, and Rangpur, with higher pollution in the winter than in other seasons. In winter, PM_2.5 ranged from 55.12 to 159.42 µg/m³ across 64 cities in Bangladesh, which is 11 to 32 times higher than the World Health Organization Air Quality Standards (WHOAQS; annual mean: ≤ 5 µg/m³) and 4 to 11 times higher than the Bangladesh National Ambient Air Quality Standards (BNAAQS; annual mean: ≤ 15 µg/m³). Moreover, PM₂.₅ levels significantly increased in 63 cities across Bangladesh, with rates ranging from 0.54 to 1.38 µg/m³/year between 2000 and 2020. PM₂.₅ components in Bangladesh show an increasing trend for Sea Salt (SS), Organic Carbon (OC), Sulfate (SO₄), Black Carbon (BC), and Nitrate, except for Dust, which exhibits a negligible decreasing trend. Weather conditions, industrial emissions, vehicle exhaust, and biomass burning significantly influence PM₂.₅ concentrations. The HR assessment showed that the percentage of extremely high-risk areas in Bangladesh rose significantly from 14.57 to 39.29% between 2001 and 2020. Finally, PSCF analysis shows that air quality in Bangladesh is mainly affected by external sources of PM_2.5 originating from the Indo-Gangetic Plain (IGB), India, Nepal, Bay of Bengal, Sri Lanka, Gulf of Mannar, Arabian Sea, and Laccadive Sea, with the strongest impact in the winter, followed by spring, autumn, and summer. It is recommended that policymakers utilize the findings of this study to implement targeted strategies for reducing PM_2.5 concentrations and improving air quality across Bangladesh.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2229 - 2246"},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Microplastics in the indoor air of subway station in Istanbul","authors":"Melike Servin Coşgun,&nbsp;Sedat Gündoğdu,&nbsp;Ülkü Alver Şahin,&nbsp;Burcu Uzun Ayvaz,&nbsp;Burcu Onat,&nbsp;Coşkun Ayvaz","doi":"10.1007/s11869-025-01764-x","DOIUrl":"10.1007/s11869-025-01764-x","url":null,"abstract":"<div>\u0000 \u0000 <p>Microplastics (MPs) have emerged as significant airborne pollutants, yet their presence in enclosed public transport environments remains understudied. This research investigates the occurrence, characteristics, and potential human exposure to MPs in the indoor air of a subway station in Istanbul. Airborne MPs were collected using both passive deposition and active air sampling methods for deposited and airborne MPs, respectively over seven days at a high-traffic metro station. Deposited MP concentrations ranged from 278,571 to 865,714 MPs/m²/day, while airborne MP concentrations varied between 0.4 and 2.93 MPs/m³. Inhalation exposure estimates indicate that passengers in different age groups may inhale between 0.24 and 0.30 MPs during a 15-minute wait on the platform. Microscopic and Raman spectroscopy analyses revealed that over 70% of MPs were fibers, predominantly composed of polyethylene (PE, 25%) and polyethylene terephthalate (PET, 21%). The majority of airborne MPs measured &lt; 100 μm, whereas deposited MPs were predominantly 200–400 μm in size. These findings underscore the presence of MPs in enclosed transport systems and their potential contribution to indoor air pollution. Given the widespread public reliance on subway transport, further studies are necessary to assess the health risks associated with chronic inhalation of MPs and to develop mitigation strategies for reducing indoor MP exposure.</p>\u0000 </div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 8","pages":"2213 - 2227"},"PeriodicalIF":2.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}