Atmospheric Environment: X最新文献

{"title":"The aqueous film formed on aerosol surfaces enhances the absorption of urban locally emitted free amino acids during the dust event","authors":"NengJian Zheng ,&nbsp;Ren-Guo Zhu ,&nbsp;Meiju Yin ,&nbsp;Yaping Li ,&nbsp;Yujun Ma ,&nbsp;Yuanyuan Zhu ,&nbsp;LingShuang Deng ,&nbsp;Hao Xiao ,&nbsp;Yichen Zou ,&nbsp;Hua-Yun Xiao","doi":"10.1016/j.aeaoa.2025.100407","DOIUrl":"10.1016/j.aeaoa.2025.100407","url":null,"abstract":"<div><div>Free amino acids (FAAs) are essential bioavailable organic nitrogen compounds, yet their atmospheric concentrations, distributions, sources, and secondary formation processes during dust events remain poorly understood. Here, we investigated FAA concentrations, molecular distributions, and δ¹⁵N signatures in PM_2.5 collected from four cities in the Beijing-Tianjin-Hebei air pollution transport channel during spring 2018 to investigate the impact of a dust event on the concentrations, sources and formation mechanism of FAAs in PM_2.5. During the dust event, total FAA (TFAA) concentrations across all sites were substantially higher than those environments not influenced by the dust events and exhibited a strong linear correlation with PM₁₀ (<em>r</em> = 0.6, <em>p</em> &lt; 0.01), indicating that dust events significantly enhance FAA levels. We observed regional variability in the average TFAA concentration during the dust event. The concentrations in Beijing, Tianjin, Shijiazhuang and Taiyuan increased by factors of 1.05, 1.36, 1.55 and 1.63, respectively. Among these four sites, Taiyuan exhibited the most pronounced increase, with its TFAA concentration rising from 0.24 ± 0.07 μg m⁻³ during the non-dust period to 0.39 ± 0.003 μg m⁻³ during the dust event (<em>p</em> &lt; 0.05). These regional differences underscore the influence of local sources and atmospheric quality parameters. The consistent FAA composition profiles and δ¹⁵N values of TFAA and glycine between dust and non-dust periods across all sites further supported that local sources, rather than long-range transport from the Gobi Desert, was the major contributor to atmospheric FAA pool even during the dust event. In Taiyuan, temporal variations of aerosol liquid water (ALW) were highly correlated with total and subgrouped FAAs (r &gt; 0.8, <em>p</em> &lt; 0.01), identifying ALW as a key factor controlling FAA concentrations. It suggested that even under the low relative humidity conditions of dust events, hygroscopic aerosol growth can facilitate the partitioning of FAAs into the particle phase, leading to higher observed FAA concentrations. Our findings provide new insights into the impact of dust events on FAAs in polluted urban environments and provide a basis for assessing the ecological effects of atmospheric dust transport.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100407"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An update of emission factors for nitric oxide emissions from croplands and grasslands","authors":"Gokul Prasad Mathivanan ,&nbsp;Andreas Gattinger ,&nbsp;Roland Fuß","doi":"10.1016/j.aeaoa.2025.100410","DOIUrl":"10.1016/j.aeaoa.2025.100410","url":null,"abstract":"<div><div>Nitric Oxide (NO), an intermediate product in the soil nitrogen cycle, is also an air pollutant causing adverse health effects, including contribution towards the formation of fine particulate matter and tropospheric ozone. Accurate quantification of NO emissions is necessary to mitigate these impacts, but current emission factors used in national greenhouse gas and air pollutant inventories are associated with substantial uncertainty, necessitating an updated assessment based on recent field measurements. In this study, we compiled a comprehensive dataset of 692 field observations of NO measurements from 128 studies conducted globally. Using Bayesian generalised linear mixed-effects models, we derived new emission factors that account for synthetic and organic fertiliser inputs, climate zones and crop groups. The new global emission factor (expressed as mass fraction of nitrogen) for synthetic nitrogen inputs to common crops and grasslands was 0.0042 kg NO-N (kg N)⁻¹ (95 % credible interval: 0.0035–0.0049), while organic nitrogen inputs exhibited a negligible fertiliser induced emission factor of 0.0001 kg NO-N (kg N)⁻¹ (95 % CI: 0.0003 – 0.0005). The emission factors varied markedly upon stratification by climate zones and crop groups, with higher emission factors from synthetic fertilisers in the tropical rainforest regions, while paddy rice cultivation exhibited negligible fertiliser-induced NO emissions and tea plantations showed exceptionally high emissions with a factor of 0.0155 kg NO-N (kg N)⁻¹ (95 % CI: 0.0101–0.0225). The new emission factors are considerably lower than the Tier 1 value of 0.0133 (0.0015–0.0317) kg NO-N (kg N)⁻¹), currently recommended in the EMEP/EEA guidelines. Application to nitrogen fertiliser input data from EU member states' 2025 Informative Inventory Reports indicates that, adopting the new emission factors would result in reported emissions from synthetic fertilisers being lower by approximately 65 %. These findings highlight the need for refined inventory methodologies, replacing the default emission factor with climate and crop specific values, to better inform strategies for nitrogen management and air quality monitoring.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100410"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilinear regression analysis of PM2.5 in Kampala and Fort Portal cities: Effects of meteorological factors and lagged pollution","authors":"Fidel Raja Wabinyai ,&nbsp;Richard Sserunjogi ,&nbsp;Gideon Lubisia ,&nbsp;Deo Okure ,&nbsp;Edwin Akugizibwe ,&nbsp;Jennifer Kutesakwe ,&nbsp;Angela Nshimye ,&nbsp;Alex Ndyabakira ,&nbsp;Engineer Bainomugisha","doi":"10.1016/j.aeaoa.2025.100411","DOIUrl":"10.1016/j.aeaoa.2025.100411","url":null,"abstract":"<div><div>Rapid urbanization across Sub-Saharan Africa intensifies fine particulate matter (PM_2.5) pollution, yet the combined effects of meteorology and pollution persistence remain poorly understood. This study investigates the spatiotemporal variability of PM_2.5 in Kampala (urban) and Fort Portal (semi-urban), Uganda, using daily observations from October 2021 to January 2024. Calibrated low-cost AirQo sensor data were integrated with meteorological parameters, including temperature, humidity, wind speed, wind direction, and precipitation, as well as one-day lagged PM_2.5, to develop enhanced multilinear regression (MLR) models. Results revealed strong seasonal contrasts, with mean dry-season concentrations in Kampala (38.3 μgm⁻³) and Fort Portal (32.9 μgm⁻³) exceeding World Health Organization and NEMA-Uganda limits. Model performance varied by city, explaining up to 57 % of daily PM_2.5 variability in Kampala and 80 % in Fort Portal. The inclusion of lagged PM_2.5 significantly improved model accuracy, highlighting persistence effects under stagnant meteorological conditions. Wind rose analysis showed that southerly and westerly winds enhanced pollutant transport, particularly during dry months, suggesting potential transboundary contributions to Fort Portal's pollution burden. Although the models performed well during dry seasons, predictive power declined in wet seasons due to rainfall-induced washout effects not fully captured by linear formulations. These findings emphasize the importance of meteorological drivers and pollution persistence in shaping urban air quality and support data-driven interventions such as emission control, traffic management, biomass burning reduction, and regional cooperation to protect public health in rapidly urbanizing African cities.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100411"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of contributors to airborne PAHs and heavy metals in PM10 using temporal, spatial, traffic and heating data in explainable machine learning models","authors":"Nikolina Račić ,&nbsp;Stanko Ružičić ,&nbsp;Valentino Petrić ,&nbsp;Teo Terzić ,&nbsp;Mario Antunović ,&nbsp;Ivan Škaro ,&nbsp;Gordana Pehnec ,&nbsp;Ivan Bešlić ,&nbsp;Ivana Jakovljević ,&nbsp;Zdravka Sever Štrukil ,&nbsp;Jasmina Rinkovec ,&nbsp;Silva Žužul ,&nbsp;Mario Lovrić","doi":"10.1016/j.aeaoa.2026.100413","DOIUrl":"10.1016/j.aeaoa.2026.100413","url":null,"abstract":"<div><div>Air pollution in urban areas originates from multiple interacting sources and is strongly influenced by meteorology, yet direct emission data are often incomplete. This study quantifies how meteorological conditions, station location, and proxy indicators of traffic and residential heating affect PM₁₀-bound polycyclic aromatic hydrocarbons (PAHs) and metals in Zagreb, Croatia. Daily concentrations of PM₁₀, selected PAHs, metals and NO₂ from four monitoring stations (2017–2020) were combined with local and ERA5 meteorology, highway traffic counts and gas consumption as emission proxies. Non-negative Matrix Factorization (NMF) was applied separately to PAHs and metals to identify dominant source-related patterns, while Random Forest regression and SHapley Additive Explanations (SHAP) were used to evaluate the influence of temporal, spatial, meteorological, traffic and heating predictors. NMF separated a heating-related PAH component dominated by Pyr and Flu from a traffic-related component characterised by BaA, Chry and BkF, and indicated enrichment of As and Pb at traffic- and industry-affected stations. Random Forest models showed higher predictive skill for PAHs (R² ≈ 0.60–0.68) than for metals (R² ≈ 0.24–0.42). Temperature and solar radiation were the main predictors for PAHs, whereas PM₁₀, NO₂ and station indicators dominated the prediction of metals. These results demonstrate that integrating proxy emission indicators with explainable machine learning provides an efficient framework for characterising sources and supports season- and location-specific air quality management in data-limited urban environments.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100413"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of the area-based assimilative capacity for sustainability management of air toxic emission from petroleum and petrochemical industrial complex","authors":"Peemapat Jookjantra ,&nbsp;Sarawut Thepanondh ,&nbsp;Kiyoung Lee ,&nbsp;Jutarat Keawboonchu ,&nbsp;Wissawa Malakan","doi":"10.1016/j.aeaoa.2025.100409","DOIUrl":"10.1016/j.aeaoa.2025.100409","url":null,"abstract":"<div><div>This study explored benzene and 1,3-butadiene emissions from a petroleum and petrochemical industrial estate in Rayong, Thailand, using a comprehensive, multi-step approach. The research combined detailed emission inventories, air dispersion modeling with AERMOD which is appropriate for assessing primary, non-reactive pollutants at near-field distances from industrial sources, and evaluations of the area's capacity to absorb pollutants. The objective was to identify emission patterns, assess environmental impacts, and pinpoint the main sources influencing pollutant levels. Results showed that storage tanks were the primary driver of benzene emissions (54 %) and wastewater treatment systems were the main source of 1,3-butadiene emissions (63 %), with source analysis confirming that benzene levels were dominated by storage tanks while 1,3-butadiene concentrations were closely tied to wastewater treatment facilities. Although most predicted ground-level concentrations complied with national ambient air quality standards, elevated levels were detected near emission sources. The assimilative capacity assessment indicated that most monitoring sites could accommodate additional emissions without exceeding regulatory limits; however, one site located beside a busy road showed a negative capacity for both pollutants, highlighting the significant impact of vehicle emissions in areas with dense industrial and traffic activities. By integrating emission inventories, dispersion modeling, and environmental thresholds, this study offers valuable insights relevant locally and transferable to other industrial regions. It stresses the importance of emission control strategies targeting both industrial processes and traffic sources. The combined methodology provides practical guidance for environmental planners and policymakers seeking to implement effective, site-specific air quality management aligned with sustainable development goals.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100409"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OPNet: A deep-learning approach for estimating particulate matter’s oxidative potential from satellite imagery","authors":"Alessia Carbone ,&nbsp;Ian Hough ,&nbsp;Gemine Vivone ,&nbsp;Jocelyn Chanussot ,&nbsp;Rocco Restaino ,&nbsp;Harry Dupont ,&nbsp;Jean-Luc Jaffrezo ,&nbsp;Gaëlle Uzu","doi":"10.1016/j.aeaoa.2025.100406","DOIUrl":"10.1016/j.aeaoa.2025.100406","url":null,"abstract":"<div><div>The oxidative potential (OP) of particulate matter (PM) reflects its ability to trigger oxidative stress in the respiratory system and is increasingly recognised as a key metric for assessing PM toxicity. Concurrently, PM has gained importance as a health indicator, leading to its inclusion in European regulations. As OP is not routinely monitored at many sites, understanding exposure and related risks remains challenging. While satellite imagery is commonly used to estimate PM mass concentration, its application to OP has not yet been explored. We present a novel deep-learning-based approach employing satellite-based surface features for OP estimation, using both OP_AA and OP_DTT assays on 24-hour PM₁₀ samples collected over five years in Grenoble (France). We propose OPNet, which consists of two parts: a deep backbone that extracts surface features from one satellite image, and a predictor estimating OP_AA and OP_DTT using the extracted features combined with contextual variables. The architecture is trained in two stages: in the domain-adaptive task, both are jointly trained to predict daily PM₁₀ concentration, with the backbone initialised from weights from a general classification problem. In the domain-specific task, they are jointly updated to predict either OP_AA or OP_DTT, with the backbone initialised from the best weights obtained in the first stage. This approach explains up to 75% of the variance in OP_AA and 58% in OP_DTT when using both satellite imagery and auxiliary data. It offers a cost-effective solution to improve the estimation of OP, with implications for large-scale air quality monitoring and health impact assessments.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100406"},"PeriodicalIF":3.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological factors affecting ammonia emission measurement with flux chambers from field-applied biogas digestate slurry (Technical note)","authors":"Johanna Pedersen ,&nbsp;Sasha D. Hafner ,&nbsp;Andreas S. Pacholski","doi":"10.1016/j.aeaoa.2025.100408","DOIUrl":"10.1016/j.aeaoa.2025.100408","url":null,"abstract":"<div><div>This study evaluated technical factors influencing relative ammonia emissions following field application of biogas digestate using different slurry spreading methods. Experiments assessed: (i) slurry distribution uniformity across a trailing hose boom, (ii) the influence of driving speed, (iii) effects of hose spacing, and (iv) the effect of relocating dynamic flux chambers during measurement. Across all tests realistic application rates and representative field conditions were ensured. Results demonstrate that careful equipment setup, particularly hose selection and consistent spacing, minimized variability in measured emissions and dynamic flux chamber relocation elevated measured emissions. These findings provide practical guidance for experimental design and emission mitigation under typical farming conditions.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100408"},"PeriodicalIF":3.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using low-cost sensors for source attribution and health assessment: An air quality study in Brownsville, Texas","authors":"Sai Deepak Pinakana ,&nbsp;Kabir Bahadur Shah ,&nbsp;Daniel Jaffe ,&nbsp;Juan L. Gonzalez ,&nbsp;Owen Temby ,&nbsp;Gabriel Ibarra-Mejia ,&nbsp;Amit U. Raysoni","doi":"10.1016/j.aeaoa.2025.100405","DOIUrl":"10.1016/j.aeaoa.2025.100405","url":null,"abstract":"<div><div>Air quality monitoring remains a challenge in areas lacking or having sparse federal monitoring infrastructure, posing significant barriers to public health research. This study demonstrates the usage of low-cost sensors in addressing gaps in air quality monitoring, source attribution, and health risk assessment in a Brownsville, TX neighborhood impacted by emissions from a barite and celestite mineral processing unit. PM_2.5 concentrations were measured using PurpleAir sensors deployed across three residential locations, with the site nearest to the processing unit recording a 24-h averaged PM_2.5 concentration of 25.12 μg/m³—approximately 2.79 times higher than the nearest Texas Commission of Environmental Quality (TCEQ) CAMS (Continuous Ambient Monitoring Station) site. Indoor air quality was also evaluated in two of the residential units to characterize the influence of outdoor pollution on indoor microenvironment. The local wind data was used to conduct source attribution, and the results suggested that the mineral processing entity located south of the neighborhood was the likely source of particulate pollution in this middle-income neighborhood. A health risk assessment for PM_2.5 exposure was conducted, and the results indicate a hazard quotient level below unity, suggesting low-risk non-carcinogenic effects on the community. This study underscores the pivotal role of low-cost sensors in generating localized air quality data, and their potential to support ameliorative evidence-based interventions.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100405"},"PeriodicalIF":3.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating air emissions from animal production in the United States using statistical models: Ammonia emissions from swine grow-finish barns","authors":"Ian C. Rumsey ,&nbsp;Maliha N. Nash ,&nbsp;John T. Walker","doi":"10.1016/j.aeaoa.2025.100404","DOIUrl":"10.1016/j.aeaoa.2025.100404","url":null,"abstract":"<div><div>Animal production has the potential to emit various atmospheric pollutants including ammonia (NH₃), which can impact human health, atmospheric visibility and ecosystem health through gaseous NH₃ and associated NH₄⁺ particulate matter deposition. Emission estimating methodologies were developed using statistical models to estimate daily NH₃ emissions from swine grow-finish barns based on National Air Emissions Monitoring Study (NAEMS) data. Models were developed with variables that represented production, manure management and environmental conditions. Model performance was evaluated for predicting NAEMS and non-NAEMS emissions, consistency of model coefficients and sensitivity to different model input values. Accounting for ease of variable measurement, the best performing models for predicting NAEMS emissions were models 1b and 16a, both of which accounted for the influence of temperature, swine inventory and weight, but used different predictor and response variables. In predicting NAEMS emissions, model 1b had mean error (ME) and mean bias (MB) values of 1.6 kg day⁻¹ (normalized mean error (NME) = 25.9 %) and 0.1 kg day⁻¹ (normalized mean bias (NMB) = 1.2 %), respectively, which were slightly lower than the corresponding values for model 16a (ME/NME = 1.8 kg day⁻¹/25.9 % and MB/NMB = 0.4 kg day⁻¹/5.8 %). Model 1b performed better in predicting non-NAEMS emissions, but model 16a had more reasonable sensitivity when barn live animal weight was &gt;215,000 kg. Models using nitrogen feed intake as a predictor variable also performed well in predicting emissions and although these models have greater uncertainty due to limited NAEMS measurements, they could potentially account for changes in feed practices.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100404"},"PeriodicalIF":3.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial heterogeneity and sources of atmospheric carbonyls during ozone episodes in the Pearl River Delta","authors":"Lei Wei ,&nbsp;Daocheng Gong ,&nbsp;Chengliang Zhang ,&nbsp;Zhuo Yan ,&nbsp;Yu Wang ,&nbsp;Qinqin Li ,&nbsp;Chunlin Zhang ,&nbsp;Shuo Deng ,&nbsp;Yunfeng Liu ,&nbsp;Yiming Zhao ,&nbsp;Guanghui Li ,&nbsp;Xujun Mo ,&nbsp;Ruili Yang ,&nbsp;Hao Wang ,&nbsp;Boguang Wang","doi":"10.1016/j.aeaoa.2025.100400","DOIUrl":"10.1016/j.aeaoa.2025.100400","url":null,"abstract":"<div><div>Carbonyls play critical roles in tropospheric photochemistry, significantly influencing radical budgets and ozone (O₃) formation. Despite frequent O₃ pollution episodes in the Pearl River Delta (PRD), the spatial heterogeneity and sources of carbonyls, particularly long-chain aliphatic saturated aldehydes (≥C6), remain poorly characterized. This study conducted large-scale grid-based sampling analysis of 23 carbonyls across 35 sites in the PRD during spring and autumn O₃ pollution episodes in 2021. Higher concentrations were observed in the eastern PRD and Pearl River Estuary compared to the western PRD, with formaldehyde, acetaldehyde, acetone, and 2-butanone dominating the carbonyl profile. Short-chain carbonyls (C1 ∼ C5) exhibited strong correlations with industrial density, confirming anthropogenic dominance. Long-chain aldehydes showed non-biogenic characteristics, with significant contributions from cooking, shipping, and industrial processes involving fatty acids. Ozone formation potential analysis revealed that formaldehyde and acetaldehyde remained the principal contributors to O₃ formation. However, the contribution of long-chain aldehydes was substantial at specific local sites (notably some rural locations), in some cases exceeding that of short-chain aldehydes. Our findings underscore the need for targeted control strategies addressing both short-chain and long-chain carbonyls, particularly from industrial and cooking-related sources.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100400"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}