Air Quality Atmosphere and Health最新文献

{"title":"Four-dimensional spatiotemporal analysis of PM2.5 and seasonal climate interactions across Pakistan’s topographic gradient using 4D-GTWR","authors":"Atia Elahi,&nbsp;Kamran Khan,&nbsp;Abdul Jameel Khan,&nbsp;Saqib Ur-Rehman","doi":"10.1007/s11869-026-02055-9","DOIUrl":"10.1007/s11869-026-02055-9","url":null,"abstract":"<div><p>The fine particulate matter (PM2.5) is a serious environmental and public health problem in Pakistan as it is determined by multiple interactions between meteorological conditions, seasonal variations, and topographic heterogeneities. In this study, a four-dimensional (spatial, temporal and altitudinal) geographically and temporally weighted regression (4D-GTWR) framework is used to explore the spatial, temporal and altitudinal variations of PM2.5 concentrations with respect to precipitation, temperature, relative humidity, wind speed, surface pressure and aerosol optical depth (AOD) in 161 districts of Pakistan for the period 1998–2021. The proposed approach differs from traditional GTWR in that it explicitly accounts for the elevation effect as part of the spatiotemporal weighting function to reflect the variation in local regression relationships with elevation. The outcomes indicate significant seasonal and regional variation in the PM2.5 and meteorological relationships, with different responses in the plains, mountainous and monsoon influenced parts of Pakistan. Model validation results showed that 4D-GTWR was able to capture the seasonal R² of 70.28%, 80.70%, 80.87% and 72.41% for DJF, MAM, JJA and SON seasons, respectively and had a lower level of residual error as well as spatial autocorrelation (Moran’s I) compared to both OLS and GTWR models. The results show that the use of elevation increases the ability to capture the spatiotemporal variability in PM2.5, and offers a more comprehensive framework for understanding the seasonal variability in air pollution in Pakistan. The proposed framework provides an important framework in support of air quality management and mitigation for different geographic regions in various seasons.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458845","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 novel LSTM-Kriging model for spatiotemporal interpolation of air pollution","authors":"Rui Xu,&nbsp;Jiaming Zou,&nbsp;Yuanfeng Sun,&nbsp;Jian Li,&nbsp;Liyi Guo,&nbsp;Shiming Shen,&nbsp;Hang Wan","doi":"10.1007/s11869-026-02053-x","DOIUrl":"10.1007/s11869-026-02053-x","url":null,"abstract":"<div><p>While the Kriging method is widely employed for the spatial interpolation of air pollution, its original design lies in geostatistics, focusing solely on the impact of distance on the target values. In the spatial interpolation of air pollution, factors such as wind speed, wind direction, and underlying surface parameters exert a greater influence on the spatial distribution of pollutants compared to distance alone. To account for these impacts, this study proposed an LSTM-Kriging model that replaced the semi-variance function fitting process with the time-series deep learning long short-term memory (LSTM) model. This model preserved the structure of the Kriging model while considering the physical mechanism and time-series characteristics of regional air pollution using the proposed LSTM-based optimization method. The proposed LSTM-Kriging model was validated using time-series meteorological and PM_2.5 data from 61 atmospheric monitoring stations in Guilin, China. The model employs a sliding window of 12 h and selects the 13 nearest neighboring stations. With 80% of the temporal data used for training and 20% for validation. The results indicated that replacing the kernel function with the LSTM model significantly improved prediction accuracy, with average errors of PM_2.5 concentration prediction being 6.268–15.903%, compared to 14.022–23.625% for the traditional Kriging method. More importantly, the established method has the capability to characterize the influence of wind direction on the dispersion and distribution of air pollutants. The proposed LSTM-Kriging model with Root Mean Square Error, Mean Absolute Error, and Symmetric Mean Absolute Percentage Error values of 7.151, 5.388, and 19.762% respectively, outperforms other models, providing more accurate and robust estimation performance. The proposed model considers the characteristics of the application domain within the core calculation of Kriging, enhancing its interpolation accuracy and enabling generalization to other spatial evaluation domains.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458714","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":"Spatio-temporal analysis of land surface temperature, urban heat island intensity, and urban thermal field variance index in Faisalabad, Pakistan","authors":"Muhammad Naveed,&nbsp;Bilal Hussain,&nbsp;Awais Ali,&nbsp;Ayesha Mariam,&nbsp;Abdul Ghafoor,&nbsp;Fozia Allah Ditta,&nbsp;Basharat Ali","doi":"10.1007/s11869-026-02047-9","DOIUrl":"10.1007/s11869-026-02047-9","url":null,"abstract":"<div><p>Rapid industrial growth and development created serious environmental issues due to urbanization and global warming. It has resulted a rise in ground temperature, thus intensifying the Urban Heat Island effect and generating ecological damage. This study analyzed Land Surface Temperature (LST), Urban Heat Island (UHI) intensity, and Urban Thermal Field Variance Index (UTFVI) using remote sensing data during 2013, 2018, and 2023 in district Faisalabad, Punjab, Pakistan. Results showed that mean LST values increased from 21.99 °C to 28.64 °C during 2013–2023 due to urban expansion, industrial expansion, and climate change effects. The total area of vegetation decreased from 5075 km² to 4988 km² between 2013 and 2023. However, built-up areas increased by 90 km², resulting in more heat stress conditions. During this period, the UHI zones experienced substantial growth because high zones expanded from 11.11% to 17.68%, while very high zones developed from 3.74% to 12.35%. UTFVI analysis revealed that increased extreme heat areas have worsened ecological stability and mild temperatures in the study area. This study demonstrates the immediate requirement for urban development planning that integrates water management green infrastructure and sustainable practices to protect Faisalabad from heat rise while building its resilience.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465903","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-world and workload-based characterization of VOCs emissions from construction machinery in highway subgrade construction","authors":"Yimeng Cui,&nbsp;Liu Yang,&nbsp;Jiarong Zhou,&nbsp;Xiaohan Miao,&nbsp;Dongping Guo,&nbsp;Songyan Gao,&nbsp;Zhiwen Jiang,&nbsp;Weihua Qin,&nbsp;Jianjun Xiao,&nbsp;Xiuyan Wang,&nbsp;Taosheng Jin","doi":"10.1007/s11869-026-02049-7","DOIUrl":"10.1007/s11869-026-02049-7","url":null,"abstract":"<div><p>Construction machinery is a significant source of volatile organic compounds (VOCs), which contribute to both ozone and particulate matter. In this study, VOCs emission factors for typical machinery used in highway subgrade construction were quantified under real-world operating conditions using both conventional fuel-based and workload-based normalization approaches. Component analysis of six types of machinery identified oxygenated VOCs (OVOCs) and alkanes as the predominant constituents, accounting for 47.3% and 25.7% of total emissions, respectively. Acetaldehyde and dodecane together contributed 17% of VOCs emissions, with more than 50% of species falling within the C₂–C₄ carbon-number range. Compound-specific assessment revealed that OVOCs and alkenes exhibited high ozone formation potential (OFP), with acetaldehyde and ethylene emerging as key reactive species. At the project scale, workload-normalized analysis identified dump trucks as the dominant VOCs emission source, contributing 38% of total emissions, and a reduction of 1 km in haul distance was estimated to decrease total project VOCs emissions by approximately 7.2%. Overall, this study provides a comprehensive evaluation of fuel-based emission factors, project-level emission contributions, dominant chemical components, and ozone-forming reactivity of VOCs from construction machinery in subgrade engineering.</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 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465897","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":"Particulate matter exposure in school environments near traffic corridors and associated health risks in school-aged children: a systematic review","authors":"Rochitra Keisham,&nbsp;Nongthombam Premananda Singh","doi":"10.1007/s11869-026-02048-8","DOIUrl":"10.1007/s11869-026-02048-8","url":null,"abstract":"<div><p>Air pollution in and around the schools may affect children’s daily exposure as they spend a larger part of the day in school. Particulate matter, particularly PM_2.5 and PM₁₀ is important because they are related to respiratory and other child health effects. This paper systematically brings together studies on exposure to particulate matter in school settings and its health effects in school-age children. A comprehensive literature review was conducted using Google Scholar, Scopus and the Web of Science according to PRISMA 2020. 40 studies are included in the qualitative synthesis after screening. The studies that were reviewed were carried out in different geographical settings like those of India, Malaysia, Palestine, Kuwait, Rwanda, the UK, and so on. Different studies find variations in PM concentration. For selected indoor locations, PM_2.5 and PM₁₀ concentration was 10 ± 2 µg/m³ and 29 ± 20 µg/m³ in London classrooms. On the other hand, the naturally ventilated classrooms in Gaza show much higher average values of 103.96 ± 84.96 µg/m³ and 349.49 ± 196.57 µg/m³ respectively. Reported PM concentrations at numerous schools were above the guideline values of 15 µg/m3 (for PM_2.5) and 45 µg/m3 (for PM₁₀) set out by the WHO in 2021. It was generally seen that higher levels of PM were observed in schools in traffic corridors, densely populated areas, dust-prone areas and naturally ventilated classrooms. The health evidence showed mainly associations with respiratory symptoms, asthma-related outcomes, inflammatory biomarkers and reduced lung function. Some studies also found limited evidence of possible effects on cognitive development and school performance. In conclusion, the review highlights that schools are important exposure microenvironments for children. Many under-studied regions are in need of long-term local monitoring, source apportionment and personal exposure assessment as well as source-specific locally applicable mitigation measures.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465148","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":"Digital FDI, ICT development and environmental performance: a comparison between G7 and BRICS economies","authors":"Eugene Ray Atsi,&nbsp;Decai Tang,&nbsp;Michael Provide Fumey,&nbsp;Gabriel Mordzifa Sackitey,&nbsp;Valentina Boamah","doi":"10.1007/s11869-026-02046-w","DOIUrl":"10.1007/s11869-026-02046-w","url":null,"abstract":"<div><p>Digitalization is known to improve ecological sustainability. However, the rise in ICT infrastructure, such as data centers, cloud computing, and network servers, continues to accelerate greenhouse gas emissions. Foreign direct investment (FDI) can, therefore, help reduce the environmental risks of digitalization by fostering green technology transfer, sustainable manufacturing, and clean energy. This study conducts a comparative examination to determine how FDI inflows in digitalization, particularly ICT trade and internet penetration, can ensure environmental pollution (CO₂ and N₂O emissions) mitigation in the BRICS and G7 economies. In addition, for the 2000-2023 dataset, the study uses Panel-corrected Standard Errors (PCSE) and Driscoll-Kraay Standard Errors (DKSE) in its analysis. The findings indicate that, when considered in isolation, ICT trade leads to an increase in emissions at the country level for G7 countries and has no effect for BRICS nations. However, FDI in ICT trade reduces CO₂ emissions across BRICS economies. Internet access also largely offsets emissions in both blocs, and when integrated with FDI, it decreases CO₂ emissions in the G7 and mitigates N₂O emissions across BRICS nations. This research helps policymakers and investors understand how foreign investment can support green technologies and internet infrastructure, thereby increasing long-term benefits and ensuring a sustainable environment.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465077","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":"Green economy and artificial intelligence: pathways to better air quality and health in China","authors":"Syed Tauseef Hassan,&nbsp;Wang Long,&nbsp;Cheng Fei,&nbsp;Kan Wu,&nbsp;Shahid Ali","doi":"10.1007/s11869-026-02041-1","DOIUrl":"10.1007/s11869-026-02041-1","url":null,"abstract":"<div><p>China is under growing pressure from rising healthcare costs, persistent air-pollution exposure, rapid urbanization, and fast digital transformation. While previous studies have examined how pollution, energy production, and technological change affect health expenditure, most rely on average-based methods that overlook how these relationships differ when healthcare spending is low, moderate, or high. This study addresses that gap by treating health expenditure as a nonlinear, distribution-sensitive outcome shaped by artificial intelligence, green growth, energy production, and air quality. The study applies quantile ADF and quantile KPSS stationarity tests, and multivariate quantile-on-quantile regression, with conventional quantile regression used for robustness. The findings show clear nonlinear patterns, stable conditional distributions, and strong quantile-specific differences. Artificial intelligence has a mixed effect: it may increase spending through digital infrastructure and diagnostic investment, but may also reduce cost pressure through efficiency, early detection, and better resource allocation. For long-term policy, China’s health expenditure reflects a complex technology–energy–environment nexus, requiring coordinated policies on AI, green growth, clean energy, urban planning, and air quality improvement.</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 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465085","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 long-term ambient exposure to air pollution with frailty progression and mortality: A multi-state cohort prospective study","authors":"Xingyue Dai,&nbsp;Yanqin Zhu,&nbsp;Zhaoliang Wang,&nbsp;Wenchen He,&nbsp;Zihan Wang,&nbsp;Yuting Zhang,&nbsp;Ni Sang,&nbsp;Yisheng He,&nbsp;Guocui Wu","doi":"10.1007/s11869-026-02045-x","DOIUrl":"10.1007/s11869-026-02045-x","url":null,"abstract":"<div><p>In an aging society, frailty represents a dynamic senile syndrome. Previous studies have linked air pollutants such as nitrogen oxides (NO_x), nitrogen dioxide (NO₂), particulate matter 2.5 (PM_2.5), and particulate matter 10 (PM₁₀) to frailty. However, the relationship between air pollution, frailty progression, and all-cause mortality remains inadequately elucidated. This prospective cohort study utilized the UK Biobank data from three waves (2006–2010, 2012–2013, 2019). Frailty was defined by five phenotypes, classifying participants as non-frail, pre-frail, or frail. Death was ascertained up to January 2022. Exposure to air pollutants—PM_2.5 (≤ 2.5 μm), PM₁₀ (≤ 10 μm), nitrogen dioxide (NO₂), and nitrogen oxide (NO_x)—was estimated using land-use regression models. A Discrete-time Markov multi-state model was employed to analyze transitions between non-frail, pre-frail, frail, and death. Among 27,710 participants with a mean age of 59 ± 7.7 years, during a mean follow-up of 3.45 years, a total of 32,265 status transitions were observed. Multivariable-status results showed that the HRs (95%CI) for the transition from non-frail to death were 1.06 (1.01, 1.12) for NO₂, 1.05 (1.00, 1.11) for NO_x, 1.07 (1.02, 1.13) for PM_2.5, and 1.06 (1.01, 1.12) for PM₁₀, respectively. For the transition from pre-frail to death, the HR (95% CI) were 1.13 (1.08, 1.19) for NO₂, 1.12 (1.07, 1.17) for NO_x, 1.08 (1.03, 1.13) for PM_2.5, and 1.11 (1.06, 1.16) for PM₁₀. In addition, for the air pollution score, HR (95%CI) for the transitions from non-frail to death and from pre-frail to death were 1.07 (1.02, 1.13) and 1.13 (1.08, 1.19), respectively. This study confirms that air pollution is a key variable associated with an increased risk of mortality in individuals without frailty or those in the pre-frail state.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380369","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":"PM2.5-attributable chronic disease burden in BRICS–plus countries, 1990–2021: temporal trends and health benefits from optimal control","authors":"Jiachang Wei,&nbsp;Mengfei Yang,&nbsp;Yiyang Sun,&nbsp;Quanxi Ge,&nbsp;Sha Yang,&nbsp;Guanran Wang,&nbsp;Jing Ding,&nbsp;Sihan Sun,&nbsp;Li Li","doi":"10.1007/s11869-026-02044-y","DOIUrl":"10.1007/s11869-026-02044-y","url":null,"abstract":"<div><h3>Background</h3><p>This study aims to assess PM_2.5-attributable five chronic diseases burden in BRICS-plus over 1990–2021, identify key driving factors, and provide evidence for targeted air quality and public health policies.</p><h3>Methods</h3><p>We analyzed the PM_2.5-attributable burden of ischemic heart disease, stroke, type 2 diabetes mellitus (T2DM), chronic obstructive pulmonary disease (COPD), and tracheal/bronchus/lung cancer for both ambient PM_2.5 and household air pollution from solid fuel combustion (HAP). Decomposition analysis (aging, population, epidemiological change) and population attributable fractions (PAFs) were used to explore burden drivers and preventable disease burden.</p><h3>Results</h3><p>Ambient PM_2.5-attributable chronic disease burdens showed an upward trend in most BRICS-plus, particularly of T2DM burden in China (from 14.10 [6.17,25.82] to 134.47 [72.81,213.96] from 1990 to 2021) and India (from 15.41 [7.58,27.09] to 102.53 [53.86,161.27] from 1990 to 2021). HAP-attributable showed increases trend in India and Ethiopia. Population has made a consistent positive contribution to the evolving disease burdens. Ethiopia showing the highest HAP-PAFs (COPD: 60.83%, stroke: 39.58%). Egypt and Saudi Arabia showed highest ambient PM_2.5-PAF.</p><h3>Conclusions</h3><p>Overall, ambient PM_2.5 has become an important contributor to chronic disease burdens (notably in China and India), while HAP-attributable burdens generally declined except in India and Ethiopia. Population contributed positively to five disease burdens, aging and epidemiological changes notable country differences. Reducing PM_2.5 and HAP to TMREL significantly alleviated disease burdens (particularly COPD). BRICS-plus countries should address ambient PM_2.5and HAP, population growth and protect vulnerable groups to mitigate the long‑term PM_2.5-related disease burdens.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380434","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":"Unraveling the short-duration Delhi dust storm: insights from In-Situ, satellite, and reanalysis datasets","authors":"Krishna Kumar Shukla,&nbsp;Sukhwinder Kaur,&nbsp;Gajendra Kumar,&nbsp;Kondapalli Niranjan Kumar,&nbsp;Anoop Kumar Mishra,&nbsp;Raju Attada,&nbsp;Brajesh Kumar Kanaujiya","doi":"10.1007/s11869-026-02043-z","DOIUrl":"10.1007/s11869-026-02043-z","url":null,"abstract":"<div><p>Short-lived dust storms (DS) pose significant challenges due to their sudden onset, rapid intensification, and severe impacts on air quality, visibility, transportation, and public health. Unlike long-duration dust storms, these short-duration events remain poorly understood, largely because of limitations in observations and forecasting capabilities. The dust storm that occurred over Delhi on 11 April 2025 provides a valuable case to examine the meteorological processes driving such rapid-onset events. ERA5 fields reveal the presence of a low-pressure system over northwest India, accompanied by strong westerly surface winds exceeding 12 m s⁻¹, which facilitated dust transport from nearby arid regions. Radiosonde derived convective available potential energy shows a sharp increase (~ 2300 J kg⁻¹) on the dust storm day, compared to ~ 700 J kg⁻¹ on the preceding day, indicating a highly unstable atmosphere conducive to deep convection and dust uplift. HYSPLIT back-trajectory analysis further confirms the dust transport from source regions. AERONET and satellite observations show enhanced aerosol loading, with aerosol optical depth peaking at ~ 0.73 and 0.6 during the event, respectively. An abrupt rise in PM₁₀ and PM₂.₅ concentrations is noticed ~ 950 µg m⁻³ and ~ 200 µg m⁻³, respectively, highlighting the dominance of coarse-mode dust aerosols. This abundance of dust aerosols reduced visibility from ~ 3500 m to ~ 500 m and led to a surface cooling of ~ 3 °C due to reduced incoming solar radiation. The results highlight the urgent need to strengthen short-range forecasting of rapid-onset DS by integrating high-frequency observations and improving dust parameterizations, thereby enhancing public health preparedness, aviation safety, and urban resilience.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380433","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}