Air Quality Atmosphere and Health最新文献

{"title":"Ambient air pollution and type 2 diabetes mellitus: an updated global systematic review, meta-analysis, and cross-country comparative analysis","authors":"Ferial Ahmadi,&nbsp;Maryam Karimi,&nbsp;Rouzbeh Nazari,&nbsp;Mohammad Reza Nikoo","doi":"10.1007/s11869-026-02114-1","DOIUrl":"10.1007/s11869-026-02114-1","url":null,"abstract":"<div><p>This study integrated systematic review, meta-analysis, and cross-country ecological comparisons to evaluate associations between ambient air pollutants and T2DM prevalence and incidence. Observational studies published between 2010 and 2025 were analyzed. Meta-analyses included subgroup analyses by geographic region and age group, while cross-country analysis contextualized regional differences in T2DM burden. All pooled estimates are expressed per 10 µg/m³ increase in pollutant concentration. PM₂.₅ exposure was significantly associated with T2DM prevalence (OR = 1.08; 95% CI: 1.06–1.11) and incidence (HR = 1.42; 95% CI: 1.31–1.55). NO₂ showed significant associations with prevalence (OR = 1.27; 95% CI: 1.15–1.40) and incidence (HR = 1.16; 95% CI: 1.09–1.23). PM₁₀ was also significantly associated with prevalence (OR = 1.09; 95% CI: 1.04–1.15) and incidence (HR = 1.79; 95% CI: 1.14–2.83). O₃ was significantly associated with incidence (HR = 1.06; 95% CI: 1.01–1.12), but not prevalence (OR = 1.03; 95% CI: 0.99–1.08). Subgroup analyses showed stronger associations among older adults and Asian populations. Cross-country comparisons showed inverse ecological correlations between T2DM prevalence and environmental performance, food security, education, healthcare expenditure, diabetes treatment coverage, and walkability. Overall, PM₂.₅, NO₂, and PM₁₀ were positively associated with T2DM prevalence and incidence, while O₃ findings were less consistent. The strongest and most consistent evidence was observed for PM₂.₅.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02114-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132389","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":"Reframing urban green infrastructure for particulate matter mitigation: a systematic review and functional planning framework for high-density cities","authors":"Jeong-Hee Eum,&nbsp;Jeong-Min Son,&nbsp;Ju-Hyeon Park,&nbsp;Uk-Je Sung,&nbsp;Ju-Eun Kim,&nbsp;Alex Guenther,&nbsp;Jung-Hun Woo","doi":"10.1007/s11869-026-02099-x","DOIUrl":"10.1007/s11869-026-02099-x","url":null,"abstract":"<div><p>Rapid urbanization and increasing particulate matter (PM) pollution have heightened public health risks in high-density urban environments, highlighting the need for effective nature-based solutions. Although urban green infrastructure (UGI) is widely recognized for its potential to improve air quality, existing classification systems, particularly those based on development purposes, remain inadequate for capturing the functional mechanisms necessary for effective PM mitigation. This limitation hinders the development of context-sensitive planning strategies that account for spatial conditions, emission sources, and human exposure. To address this gap, this study develops a PM-oriented functional planning framework for high-density cities by reclassifying UGI according to PM mitigation functions rather than conventional purpose-based categories. Drawing on 8 Korean institutional/policy documents and a systematic review of 139 academic studies (124 international and 15 Korean), four functional classification criteria, namely development purpose, proximity to emission sources, receptor vulnerability, and physical structure, were identified and used to establish an evidence-informed typology comprising ten PM-oriented UGI types. These types were contextualized and operationalized within the Korean urban system as a representative model for complex, high-density urban development. The findings indicate that type-specific strategies emphasizing deposition, blocking, and dispersion are essential for effective PM mitigation. Structurally diverse vegetation can enhance particle deposition in residential areas, whereas optimized vegetation porosity improves performance in roadside environments. The proposed framework offers a decision-support tool for policymakers and urban planners; however, further empirical validation is required to assess its effectiveness and applicability across diverse urban contexts.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02099-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133190","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":"An intelligent AQI prediction method using transfer learning and an IASO-optimized DCM-DESN under multi-source data: A case study of four Chinese cities","authors":"Yuanhao Du,&nbsp;Xiuli Geng,&nbsp;Sheng Cheng,&nbsp;Hongliu Zhang","doi":"10.1007/s11869-026-02109-y","DOIUrl":"10.1007/s11869-026-02109-y","url":null,"abstract":"<div><p>Accurately understanding the real-time evolution of the Air Quality Index (AQI) is essential for atmospheric pollution prevention and urban public health management. Although scholars have proposed numerous AQI prediction models, most have not fully considered AQI data's noise characteristics. Moreover, previous models mainly focus on a single region and do not effectively address cross-regional prediction issues. To tackle these challenges, this paper proposes an intelligent AQI monitoring framework: using transfer learning and an improved atomic search optimization (ASO) algorithm optimized deep echo state network with a dynamic activation mechanism (DCM-DESN) under multi-source data. Firstly, a data processing method combining complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and phase space reconstruction (PSR) is designed for the collected multi-source AQI data. CEEMDAN is applied to adaptively decompose the data, significantly reducing noise effects, followed by PSR on the denoised data to identify the dynamic characteristics of the AQI time series data. Secondly, we design a deep learning model of DCM-DESN for AQI prediction. By adjusting the nonlinear connection mode of DESN through a dynamic activation mechanism, it reduces the model's dependence on the number of reservoir neurons. Meanwhile, an improved ASO (IASO) is proposed to optimize the network weights of DCM-DESN. Finally, daily data from Xi’an, Wuhan, Changsha, and Shenzhen covering the period from January 1, 2021 to March 24, 2024 were collected to construct multiple cross-city transfer-learning tasks. Historical pollutant concentrations and related environmental variables available up to the current day were used to directly predict the AQI one day ahead. For each task, the IASO-DCM-DESN is first pretrained on the source-city data. The pretrained reservoir-based model is then transferred to the target city and frozen, while the output weights are fine-tuned using a limited target-domain adaptation set. The proposed model’s performance across various tasks is validated using MAE, RMSE, and R² metrics, demonstrating excellent performance.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133207","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":"Age-specific lagged effects of air pollution on asthma hospital admissions in Sakarya, Türkiye","authors":"Hilal Arslan","doi":"10.1007/s11869-026-02113-2","DOIUrl":"10.1007/s11869-026-02113-2","url":null,"abstract":"<div><p>Ambient air pollution remains a major public health concern, yet age-specific short-term associations with respiratory morbidity in rapidly urbanizing settings such as Sakarya, Türkiye, remain insufficiently characterized. This study examined the short-term association between exposure to particulate matter (PM_2.5, PM₁₀) and gaseous pollutants (SO₂, NO₂) and asthma hospital admissions in Sakarya during 2014–2018. Daily time-series analyses were conducted using a quasi-Poisson generalized additive model combined with distributed lag non-linear models to estimate relative risks (RRs) per 10 µg/m³ increment in pollutant concentration over lags 0–7 days, stratified by age group (15–34, 35–64, and &gt; 64 years) to evaluate effect modification. PM₁₀, PM_2.5, and NO₂ concentrations frequently exceeded the 2021 WHO air quality guideline values, with the highest concentrations observed during winter months. Particulate matter showed small but statistically significant associations with asthma hospital admissions among young adults aged 15–34 years, with increased risks observed for PM_2.5 at lags 3–4 and for PM₁₀ at lags 2–3. NO₂ was positively associated with asthma hospital admissions in the 35–64-year group at lags 2–3, whereas SO₂ showed no consistent lag-specific association across age groups. Short-term air pollution exposure in Sakarya showed modest, pollutant-specific, and age-specific associations with asthma hospital admissions, with distinct lag structures across pollutants. The frequent exceedance of WHO guideline levels highlights the need for targeted interventions, particularly to reduce wintertime heating emissions and traffic-related pollution. Future studies should integrate regulatory monitoring data with satellite-derived estimates and land-use regression models to better characterize within-city exposure variability and refine risk estimates.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132588","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":"Seasonal variation in particulate emissions, particle size distribution, and worker exposure at a sandstone-carving workplace in Rajasthan","authors":"Shubham Sharma,&nbsp;Nivedita Kaul,&nbsp;Sumit Khandelwal","doi":"10.1007/s11869-026-02110-5","DOIUrl":"10.1007/s11869-026-02110-5","url":null,"abstract":"<div><p>Stone carving generates substantial airborne particulate matter (PM), but seasonal variation and size-resolved characteristics of particulate emissions and worker exposure at sandstone-carving workplaces remain insufficiently characterized. This study examined seasonal variation in PM exposure, characterized particle size distributions, and evaluated associations between particulate concentrations and meteorological conditions at a sandstone-carving unit in Rajasthan, India. Personal respirable dust samples and real-time measurements of PM₁₀, PM₄, PM₂.₅, and PM₁ were collected during spring, summer, and winter, together with 31-channel aerodynamic particle-size distribution and concurrent meteorological measurements. Mean personal respirable dust exposure increased from spring to summer to winter (1.00, 1.35, and 2.95 mg/m³, respectively), with winter showing the highest exposure and PM concentration (PM₂.₅ – 109.3 µg/m³). Seasonal particle-size ratios indicated relatively stable PM₁/PM₂.₅ contributions but greater variability in PM₂.₅/PM₄, while size distributions showed enhanced submicron particle accumulation during winter and stronger coarse-mode contributions during spring and summer. Strong correlations among PM fractions (<i>r</i> &gt; 0.90) indicated common source characteristics. To address multicollinearity among nested PM fractions, principal component regression was applied. The models explained 90.4–97.7% of the variation in PM₁ concentrations, with seasonally varying associations with particle-size and meteorological components. The findings provide a characterization of seasonal particulate emissions, particle-size distributions, and worker exposure at the monitored sandstone-carving workplace. The observed seasonal patterns highlight periods of elevated particulate exposure and support the need for targeted dust-control and exposure-management measures, while larger multi-site and longer-duration investigations are required to establish their broader applicability to the stone-carving sector.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132972","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":"Retraction Note: Exposure to polycyclic aromatic hydrocarbons and liver function: a systematic review of observational studies","authors":"Efriza Efriza,&nbsp;Shadia Hamoud Alshahrani,&nbsp;Angelina O. Zekiy,&nbsp;Ghaidaa Raheem Lateef Al‑Awsi,&nbsp;Satish Kumar Sharma,&nbsp;Andrés Alexis Ramírez‑Coronel,&nbsp;Nasir Shakeel,&nbsp;Yassine Riadi,&nbsp;Zafar Aminov,&nbsp;Rahman S. Zabibah,&nbsp;Yasser Fakri Mustafa,&nbsp;Moslem Lari Najafi","doi":"10.1007/s11869-026-02101-6","DOIUrl":"10.1007/s11869-026-02101-6","url":null,"abstract":"","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035070","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":"Association between mean temperature and hospital admissions for acute ischemic stroke: a time-series study","authors":"Xiaoxiao Song,&nbsp;Wei Chen,&nbsp;Xiaolan Huang,&nbsp;Zhiling Cai,&nbsp;Xiaodong Wen,&nbsp;Jialei Yang,&nbsp;Lian Gu","doi":"10.1007/s11869-026-02102-5","DOIUrl":"10.1007/s11869-026-02102-5","url":null,"abstract":"<div><p>Ambient temperature has been associated with cardiovascular and cerebrovascular outcomes, but its short-term relationship with acute ischemic stroke (AIS) admissions remains heterogeneous across regions. This study evaluated the association between mean temperature (MT) and AIS admissions in a subtropical city in China. Daily AIS admissions from July 1, 2017, to June 30, 2020, were collected from a tertiary hospital in Nanning, China. A quasi-Poisson generalized linear model combined with a distributed lag nonlinear model was used to assess the nonlinear and lagged associations between MT and AIS admissions over lag days 0–7. Sensitivity analyses were conducted by varying temporal adjustment, air-pollutant adjustment, maximum lag periods, excluding 2020, and adjusting for the Spring Festival window. Among 2,382 AIS admissions, the mean daily number of admissions was 2.17. Compared with the median MT of 23.30 °C, higher MT (29.60 °C) was associated with increased AIS admissions over early cumulative lag periods, with relative risks of 1.347 (95% CI: 1.058–1.714) for lag0-1 and 1.396 (95% CI: 1.085–1.797) for lag0-2. The association attenuated over longer lag periods. Lower MT (10.50 °C) showed a lower same-day estimate but less consistent cumulative associations. The main findings were generally robust in sensitivity analyses. Higher MT was associated with a short-term increase in AIS admissions in this subtropical setting, with the association concentrated within the early lag period. The low-MT association was less consistent and requires cautious interpretation.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035286","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":"A retrieval-augmented historical knowledge-base framework with residual correction for station-level ozone forecasting across urban agglomerations","authors":"Mingkun Zhu,&nbsp;Xiaoxia Han,&nbsp;Jinde Wu,&nbsp;Haonan Zhu,&nbsp;Wenxin Chai","doi":"10.1007/s11869-026-02105-2","DOIUrl":"10.1007/s11869-026-02105-2","url":null,"abstract":"<div><p>Accurate station-level ozone forecasting across urban agglomerations remains challenging because ozone evolution is affected by nonlinear pollutant–meteorology interactions, regional transport, local heterogeneity, and interannual distribution shifts. Most data-driven models encode historical patterns implicitly in trained parameters, limiting their ability to explicitly reuse recurring pollution episodes. We propose OzoneKBNet, a retrieval-augmented historical knowledge-base framework that forecasts station-level ozone concentrations over the next 48 h from the preceding 96 h observations of <span>(O_3)</span>, <span>(NO_2)</span>, <span>(PM_{2.5})</span>, temperature, and relative humidity. It retrieves multi-scale cross-station analogs, re-ranks them by representation similarity and local-trend consistency, and uses their future trajectories as bounded, reliability-aware corrections to a lightweight LSTM forecast. To prevent temporal leakage, observations from 2023 are used for normalization, retrieval-encoder pretraining, and knowledge-base construction, data from 2024 for supervised training, and fixed samples from 2025 for testing. Across seven Chinese urban agglomerations, OzoneKBNet achieves macro-average MAE and RMSE values of 20.06 and 24.45 <span>(mu textrm{g},textrm{m}^{-3})</span>, respectively, and the lowest regional MAE in six of the seven regions. Relative to station-specific LSTM, it reduces MAE and RMSE by 1.6% and 1.2%, respectively. These results indicate that explicit regional historical evidence can complement parametric forecasting under the evaluated cross-year setting and may support short-term ozone-pollution episode warning.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035287","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":"Association of air pollutants and PM2.5 components with pediatric outpatient visits for acute respiratory infections in Baoshan District, Shanghai","authors":"Duo Wang,&nbsp;Chunyang Dong,&nbsp;Cheng Yang,&nbsp;Yewen Shi,&nbsp;Feier Chen,&nbsp;Zheng Wu,&nbsp;Mengshuang Liu,&nbsp;Xianbiao Shen,&nbsp;Tian Chen,&nbsp;Jianghua Zhang","doi":"10.1007/s11869-026-02106-1","DOIUrl":"10.1007/s11869-026-02106-1","url":null,"abstract":"<div><p>Although ambient air pollution has been associated with increased outpatient visits for respiratory diseases, the health effects of PM_2.5 chemical constituents on pediatric outpatient visits for acute upper respiratory infections (AURI) and acute lower respiratory infections (ALRI) remain insufficiently characterized. This time-series study, conducted in Shanghai from 2018 to 2019, employed generalized additive models to examine the associations of air pollutants (SO₂, NO₂, CO, O₃, PM₁₀, PM_2.5) and PM_2.5 components with outpatient visits for total respiratory diseases (TPRD), AURI, and ALRI. Sensitivity analyses were conducted to assess the robustness of the findings. Statistically significant positive associations were observed between NO₂ and CO and outpatient visits for TPRD and AURI. PM₁₀ was significantly associated with outpatient visits for both AURI and ALRI, whereas PM_2.5 was significantly associated only with AURI. In analyses based on a limited, non‑continuous sampling framework comprising 174 sampling days, the water-soluble ions NO₃⁻ and NH₄⁺ were significantly associated with outpatient visits for TPRD and ALRI, while SO₄²⁻ was significantly associated with ALRI. No statistically significant associations were detected for the measured metal/metalloid components. Overall, these findings support the consideration of PM_2.5 chemical composition, in addition to PM_2.5 mass concentrations and gaseous pollutants, when evaluating the short-term respiratory health effects of ambient air pollution in children.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034507","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":"Indoor exposure to microplastics in dust: polymeric composition and health risks in a tropical savanna climatic region","authors":"Raja Sibhi M,&nbsp;Lekshmi Mohan V,&nbsp;Amit Passi","doi":"10.1007/s11869-026-02107-0","DOIUrl":"10.1007/s11869-026-02107-0","url":null,"abstract":"<div><p>Indoor dust bound microplastics (MPs) can be inadvertently inhaled or ingested, especially by children and infants. This study investigated MPs in indoor dust collected from an academic office and two residential dwellings in Tiruchirappalli, South India, representing contrasting urban microenvironments (occupational, traffic and industrial-influenced). Polymer identification using micro-Fourier infrared (µ-FTIR) spectroscopy revealed the dominance of polymers such as, polyethylene terephthalate (PET), nylon, polystyrene (PS), and polyethylene (PE) in indoor dust across all sites. MPs deposition differed significantly among the sites (Kruskal-Wallis, <i>p</i> = 0.027), with highest mean deposition recorded at academic office (Site A; 241 ± 23 particles/m²/day), followed by residence located near busy traffic area (Site C; 151 ± 24 particles/m²/day) and residence located near a small-scale industrial site with lesser traffic (Site B; 54 ± 3 particles/m²/day). The particle size also varied significantly among sites (<i>p</i> &lt; 0.001), with Site A showing significantly smaller MPs (18.9 ± 11.2 μm) than Sites B and C (33–36 μm), likely due to greater occupant activity, lesser frequency of cleaning, and source diversity in the office environment. Site A recorded highest potential polyurethane (PU)- and polyvinyl chloride (PVC)- associated exposure risk (1750 hazard units), and Site C recorded a potential PU-associated hazard ranking (1500 hazard units), while Site B exhibited negligible polymer-associated potential hazard score, indicating that site classification (traffic vs. industrial) does not align with exposure risk. This study emphasizes the need for a comprehensive understanding and characterization of the sources of indoor MPs, as mere count alone cannot determine risk and inform targeted mitigation strategies.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034449","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}