Air Quality Atmosphere and Health最新文献

{"title":"Identifying PM2.5 flexion points for environmental diseases in susceptible and vulnerable groups: A nationwide cohort in South Korea","authors":"You Hyun Park,&nbsp;Se Hwa Hong,&nbsp;Yeonjae Park,&nbsp;Yong Whi Jeong,&nbsp;Ji Ye Jung,&nbsp;Yong Jin Lee,&nbsp;Dae Ryong Kang","doi":"10.1007/s11869-026-02056-8","DOIUrl":"10.1007/s11869-026-02056-8","url":null,"abstract":"<div><p>To evaluate the health impacts of PM_2.5 exposure on sensitive and vulnerable populations in South Korea and to identify flexion points—concentration levels at which the risk of environmental diseases increases significantly. A retrospective cohort study using nationwide health insurance claims data linked with environmental monitoring data. Statistical analysis included Cox proportional hazards regression and piecewise linear regression to estimate disease risks and identify flexion points. South Korea, using data from the National Health Insurance Service–National Sample Cohort (NHIS-NSC) and national air quality monitoring stations. A total of 1,031,517 individuals aged 0–79 years who were covered by the national health insurance system between 2017 and 2019 and had no prior diagnosis of the target diseases during the 2016 washout period. Primary outcomes were the incidence and exacerbation of four environmental diseases: chronic obstructive pulmonary disease (COPD), asthma, stroke, and heart failure. Exacerbation was defined as hospitalization or emergency department visits due to these diseases. Higher PM_2.5 exposure was significantly associated with increased incidence and exacerbation of all four diseases. Flexion points were identified at 30 µg/m³ for incidence and between 24 and 26 µg/m³ for exacerbation. Subgroup analyses showed lower flexion points among elderly participants. The findings suggest that current PM_2.5 standards in South Korea may not sufficiently protect vulnerable populations. Operational flexion points identified in this study may serve as evidence-based thresholds to guide targeted public health interventions and air quality regulations.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02056-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751097","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":"Decoding urban PM2.5 dynamics and air pollution-health relationships using machine learning: Evidence from Northern Thailand","authors":"Pattarakun Khammisawang,&nbsp;Phakphum Paluang,&nbsp;Masami Furuuchi,&nbsp;Worradorn Phairuang","doi":"10.1007/s11869-026-02074-6","DOIUrl":"10.1007/s11869-026-02074-6","url":null,"abstract":"<div><p>This work developed and tested Random Forest Regression (RFR) and Long Short-Term Memory (LSTM)models to predict PM_2.5 concentrations and respiratory illness cases in Phrae Province, northern Thailand. In 2020–2023, daily atmospheric pollutants and meteorological parameters were analyzed alongside monthly hospital data on asthma, bronchitis, COPD, and lung cancer to examine the relationship between air pollution and respiratory health and to evaluate machine learning (ML) models. PM_2.5 correlated positively with nitrogen dioxide (NO₂) and ozone (O₃) and negatively with relative humidity, especially during the rainy season. PM_2.5 was strongly linked to bronchitis and asthma, especially during the dry season, when biomass-burning activities led to the highest PM_2.5 concentrations. The best predictive model for PM_2.5 concentrations and respiratory illness cases was RFR, which found no statistically significant short-term link between PM_2.5 and lung cancer. This suggests that long-term cumulative exposure and other risk factors are more strongly linked to lung cancer than short-term variations in air pollution. LSTM detected episodic PM_2.5 peaks. However, the limited monthly dataset (48 observations) and considerable temporal variability of patient records hindered LSTM’s respiratory illness prediction. So, consider these data exploratory rather than conclusive. Results suggest a hierarchy of decision support: RFR for normal PM_2.5 forecasting, LSTM for peak event early warning when longer, continuous time-series data are available, and dry-season public health interventions for bronchitis surveillance and prevention. Local air quality management and respiratory health planning can be integrated with ML-based environmental forecasts using the framework to assist evidence-based pollution reduction and public health preparedness decisions.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751096","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":"Experimental and numerical analysis of outdoor PM2.5 in indoor microenvironments using the indoor air quality and inhalation exposure (IAQX) model","authors":"Saeed Shojaee Barjoee","doi":"10.1007/s11869-026-02004-6","DOIUrl":"10.1007/s11869-026-02004-6","url":null,"abstract":"<div><p>In urban environments, almost 90% of the population is exposed to indoor levels of fine particulate matter (PM_2.5) that surpass the World Health Organization’s (WHO) recommended annual air quality guideline (AQG) of 5 µg/m³. Accurately estimating PM_2.5 levels in indoor microenvironments and identifying the contribution of outdoor sources are critical for assessing human exposure and informing mitigation strategies. This study evaluated indoor and outdoor PM_2.5 concentrations in a student residence housing approximately 1,500 individuals in Saint Petersburg, Russia, during the summer of 2023. Indoor PM_2.5 concentrations were measured directly using a portable sensor, while outdoor PM_2.5 concentrations were obtained from the Ventusky platform based on the system for integrated modeling of atmospheric composition (SILAM) Eulerian chemical transport model and temporally aligned with indoor measurements. The Indoor Air Quality and Inhalation Exposure (IAQX) model was employed to simulate the contribution of outdoor PM_2.5 to indoor concentrations. The modeled data were used to estimate PM_2.5 burdens across microenvironments and to examine the influence of five key input parameters—zone volume, airflow rate, sink surface area, filtration efficiency, and outdoor PM_2.5 concentration—using a polynomial regression-based sensitivity analysis. The average simulated and measured indoor PM_2.5 concentrations were 12 µg/m³ and 38.20 µg/m³, respectively, while the mean outdoor concentration during the monitoring period was 7.08 µg/m³. Indoor zones with direct exposure to the outdoor environment exhibited higher infiltration rates, indicating a stronger influence of ambient PM_2.5 sources. Sensitivity analysis revealed that airflow rate was the most significant factor affecting modeled indoor PM_2.5 concentrations (<i>p</i> &lt; 0.0001). Notable secondary interactions included: outdoor PM_2.5 concentration and zone volume (<i>p</i> = 0.0010), outdoor PM_2.5 concentration and airflow rate (<i>p</i> = 0.0001), and zone volume and airflow rate (<i>p</i> = 0.0005). These results highlight the critical role of ventilation dynamics and building configuration in shaping indoor air quality in high-occupancy urban residences.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751016","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":"Human health risk assessment of heavy metal pollution in airborne dust from Zakho City, Iraq","authors":"Berivan Hadi Mahdi,&nbsp;Dilsouz Dakhil Hassan,&nbsp;Myasar Kh. Ibrahim,&nbsp;Dalshad Azeez Darwesh,&nbsp;Jasim M. Rajab,&nbsp;Ali M. Al-Salihi,&nbsp;Hwee San Lim","doi":"10.1007/s11869-026-02075-5","DOIUrl":"10.1007/s11869-026-02075-5","url":null,"abstract":"<div><p>Airborne dust is a significant source of toxic metals in urban settings, but there is a lack of integrated evaluations of the level of pollution, ecological risk, and human health risk in semi-arid urban settings. This study examined Cd, Pb, Cu, Cr, and Zn concentrations in airborne dust collected from four locations in Zakho City during the wet and dry seasons. The sampling represented seasonally accumulated deposited/fallout airborne dust rather than event-specific dust-storm samples. The measured metal concentrations generally followed the order Zn &gt; Cu &gt; Pb &gt; Cr &gt; Cd. Due to the limited unreplicated sampling design, the observed spatial and seasonal variations should be interpreted as preliminary descriptive trends rather than definitive statistical patterns. The pollution indices (CF, EF, I_geo, and PLI) showed that the anthropogenic enrichment of Zn (CF up to 4.7; EF &gt; 3) and Cd (CF up to 2.9) was localized, whereas Pb, Cu, and Cr were in low contamination ranges. The pollution load index values at each site were less than 1, indicating a low total multi-metal pollution burden. Within the limitations of this screening-level assessment, Cd showed the highest contribution to the ecological risk indices among the analyzed metals, while the potential ecological risk index remained below 150 at all locations. However, the inhalation-only screening assessment showed HQ and HI values many-fold lower than threshold levels, indicating no apparent non-carcinogenic risk through the inhalation pathway under the applied assumptions.</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 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751309","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":"Investigating PM2.5 transport pathways and potential source contributions in severely polluted hotspots of Beijing-Tianjin-Hebei region during three key policy years","authors":"Aifang Gao,&nbsp;Yitu Liu,&nbsp;Yansen Yang,&nbsp;Xi You,&nbsp;Chenglong Liao,&nbsp;Aibin Kang,&nbsp;Hongliang Zhang","doi":"10.1007/s11869-026-02061-x","DOIUrl":"10.1007/s11869-026-02061-x","url":null,"abstract":"<div><p>To explore the transport pathways and potential source regions of fine particulate matter (PM_2.5) in the severe polluted hotspots of Beijing-Tianjin-Hebei region (Handan and Xingtai), 72-hour backward trajectories were simulated for 2017, 2020, and 2023 using Meteoinfo software. Trajectory cluster analysis, weighted potential source contribution function (WPSCF) and weighted concentration-weighted trajectory (WCWT) analysis were applied. Analysis of the vertical distribution of air pollution transport pathways in Handan showed that near-surface transport was predominantly short-range. Transport pathway analysis indicated that during winter, the trajectory with the highest PM_2.5 concentration for Handan (113.5 µg/m³) originated from Shijiazhuang, while that for Xingtai (113.6 µg/m³) originated from Cangzhou, both passing through the Hebei-Shandong border area. WPSCF and WCWT analysis revealed the spatial extent of major PM_2.5 source regions was largest in 2017, decreased in 2020, but expanded again in 2023. The WPSCF/WCWT results of PM_2.5 demonstrated that the largest potential areas were identified in winter and were mainly distributed at the junction of Hebei, Henan, and Shandong Provinces, and a larger area covering multiple provinces (Hebei, Henan, Shandong, and Shanxi Provinces) for Xingtai. The PM_2.5 pollution in Handan and Xingtai exhibited high homogeneity and mutual influence. These findings underscore the necessity of implementing regional joint prevention and control measures to mitigate PM_2.5 pollution in Handan and Xingtai.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751421","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":"Network study of particulate matter distribution within the urban canopy layer using magnetic biomonitoring – assessing of CAR-FMI model performance and representativeness of a monitoring station","authors":"Jukka Limo,&nbsp;Mari Kauhaniemi,&nbsp;Leena Kangas,&nbsp;Petriina Paturi,&nbsp;Joni Mäkinen,&nbsp;Ari Karppinen","doi":"10.1007/s11869-026-02059-5","DOIUrl":"10.1007/s11869-026-02059-5","url":null,"abstract":"<div><p>The distribution of air pollution in urban environments is greatly influenced by its source and the complexity of the urban environment, which determine the pollution concentrations in individual locations. Traffic is one of the main pollution sources in cities and, especially particulate matter (PM) poses a serious risk for public health. This study applies magnetic biomonitoring using moss bags to establish an extensive urban canopy layer monitoring network (<i>n</i> = 52) to study the distributions and concentrations of magnetic PM. Moss bags were placed at street and roof levels within the grid plan area of Turku, Finland, for 62 days in late autumn. Samples were analysed for mass-specific magnetic susceptibility (χ), hysteresis parameters and elemental components. At street level, traffic was the primary source of magnetic PM and related elements. At higher altitudes, these concentrations were reduced and mainly replaced by crustal elements. Magnetic measurements were also applied to test for equivalence with the CAR-FMI dispersion model and to assess the representativeness of an air quality monitoring station in the city centre. Inconsistencies were observed between the model and magnetic PM concentrations at street level. At rooftop level, the model estimates were higher than indicated by the magnetic PM. The magnetic measurements complemented measurements derived from supplementary monitoring sensors and Turku monitoring station. The representativeness of the monitoring station should be re-evaluated, and recategorizing should be considered. Magnetic biomonitoring using moss bags is an unparalleled tool for monitoring airborne PM pollution and a reliable tool for evaluating monitoring stations and dispersion model performance.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02059-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750937","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":"Characterization of drivers and impacts of dust storms in Cameroon to strengthen early warning systems : case studies of February 2021 and 2023","authors":"E. L. Ndichout Molou,&nbsp;G. M. Guenang,&nbsp;A. J. Komkoua Mbienda,&nbsp;Derbetini A. Vondou,&nbsp;P. Igri Moudi","doi":"10.1007/s11869-026-02052-y","DOIUrl":"10.1007/s11869-026-02052-y","url":null,"abstract":"<div><p>This study examines two major dust storm events over Cameroon, in February 2021 and February 2023, using a multi-dataset framework combining ERA5 reanalysis, CAMS atmospheric products, GPCP and MODIS satellite observations to characterize their meteorological drivers and transport pathways. The analysis shows that both events were associated with suppressed precipitation, low relative humidity, strong northeasterly Harmattan winds, and high surface temperatures, creating favorable conditions for dust uplift and long-range transport. Spatial patterns reveal a coherent southward extension of dust plumes from northern Cameroon toward central and southern regions, with the 2023 event exhibiting a stronger and more persistent aerosol signal than the 2021 event. The results also show that aerosol loading evolves progressively across the domain, confirming a transported dust plume rather than isolated local emission. The main precursors of dust outbreaks were detectable about 24-48 hours before peak aerosol loading, suggesting a useful predictability window for threshold-based early warning, and emphasizing the urgent need for enhanced early warning systems, improved air quality monitoring, sustainable land management, and public awareness programs to mitigate adverse effects of dust storms.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750853","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":"The effect of renewable and non-renewable energy consumption on carbon dioxide emissions in OECD countries: A panel quantile regression approach","authors":"Mehmet Songur,&nbsp;Erginbay Uğurlu,&nbsp;Devran Şanlı,&nbsp;Yusuf Muratoğlu","doi":"10.1007/s11869-026-02051-z","DOIUrl":"10.1007/s11869-026-02051-z","url":null,"abstract":"<div><p>The objective of this study is to examine the impact of renewable and non-renewable energy consumption, income, internet use and urbanization on CO₂ emissions in 38 OECD countries during over the period 1995–2022 using the heterogeneous panel quantile regression technique. The study addresses the existing gap in the literature by analyzing the relationship between income and renewable energy, as well as the Environmental Kuznets Curve (EKC) at specified quantile levels. The findings show that the EKC is more dominant in countries with low CO₂ emissions. Furthermore, the negative relationship between renewable energy consumption and CO₂ emissions becomes considerably more significant in quantiles with higher CO₂ emissions. The research clearly showed that while urbanisation is an important positive factor in increasing CO₂ emissions, the use of the internet as an indicator of digitalization has a decreasing impact on CO₂ emissions. Our study provides valuable insights for policymakers to identify effective strategies for renewable energy adoption, digitalization, and urbanization policies to ensure sustainable development while addressing environmental degradation. The results of this study indicate that in order to achieve green economic development, it is essential for governments to prioritize the utilization of renewable energy sources and the advancement of sustainable urbanization practices. Additional robustness checks based on MMQR and SIVQR estimations confirm the reliability of the empirical findings across alternative quantile estimation techniques.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750875","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":"Spatiotemporal assessment of population exposure and premature mortality using models for estimation of PM2.5 concentrations","authors":"Jihun Shin,&nbsp;Jaemin Woo,&nbsp;Youngtae Choe,&nbsp;Gihong Min,&nbsp;Wonho Yang","doi":"10.1007/s11869-026-02072-8","DOIUrl":"10.1007/s11869-026-02072-8","url":null,"abstract":"<p>PM_2.5, a fine particle, is sufficiently dangerous for classification as a Class-1 carcinogen, and it remarkably contributes to premature death. This study aimed to assess population exposure to PM_2.5 in Seoul, South Korea, using data from air quality monitoring stations (AQMSs) and sensor-based monitoring instruments (SAMIs). Five AQMSs were verified using co-location tests, and population distribution data were segmented by sex and age in hourly grids. The population-weighted average concentration (PWAC) was calculated using occupancy rates and indoor-to-outdoor PM_2.5 concentration ratios. Premature death was estimated using a concentration–risk function and compared to those of the WHO (World Health Organization) guidelines. In addition, machine-learning models, including random forest (RF) and a support vector machine (SVM), were used for PM_2.5 estimation, achieving high accuracy (R² =0.83, RMSE = 10.1 µg/m³). The PWAC was found to be approximately 8% higher in spring than in fall, with the average seasonal PM_2.5 levels exceeding the WHO annual standard of 15 µg/m³. Reducing PM_2.5 concentrations to the WHO recommended level of 5 µg/m³ could lower premature deaths by over 70%. A SAMI, when integrated with an AQMS, could enhance real-time PM_2.5 monitoring and forecasting, thereby reducing public-health risks by enabling timely interventions.</p>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750666","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":"GreenAirOps: production-ready MLOps for real-time air quality index prediction","authors":"Sahil Goyal,&nbsp;Vaibhav Sharma,&nbsp;Vivek Hotchandani,&nbsp;Girish Mahale,&nbsp;Mayur Gaikwad,&nbsp;Aniket K. Shahade","doi":"10.1007/s11869-026-02068-4","DOIUrl":"10.1007/s11869-026-02068-4","url":null,"abstract":"<div><p>Pollution is a growing problem with dire effects on public health; therefore, accurate prediction of Air Quality Index (AQI) is paramount for both urban development and public health strategy. Here, we present GreenAirOps, a production-ready MLOps system that ingests multithsource environmental data, automatizes preprocessing and feature extraction, and combines Random For- est and XGBoost with ensemble learning for prediction of low-latency, near real-time AQI. This production pipeline has full MLOps pipeline capabilities: Data versioning with DVC; experiment tracking and model registration with MLflow; automated retraining and deployment using GitHub Actions. The deployment infrastructure utilizes Docker containers run on an AWS environ- ment and is designed for production grade. The specific contributions of this work are: an optimized ensemble learning system for low-latency AQI prediction; an end-to-end MLOps system which guarantee reproducibility and operational capability; and a set of production grade functions for auto-retraining, model health monitoring and auto-rollback.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02068-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750489","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}