Atmospheric Environment: X最新文献

{"title":"Improved methane flux estimation from hyper-spectral imagery via log-domain matched filtering and background homogenization","authors":"Fabrizio Masin,&nbsp;Tiziano Maestri,&nbsp;Michele Martinazzo,&nbsp;Giorgia Proietti Pelliccia","doi":"10.1016/j.aeaoa.2026.100417","DOIUrl":"10.1016/j.aeaoa.2026.100417","url":null,"abstract":"<div><div>New satellite hyper-spectral sensors, such as PRISMA of the Italian Space Agency, observe large portions of the Earth’s surface at visible and at shortwave infrared wavelengths with high spectral and spatial resolutions, enabling the investigation of individual molecular species and the localization of emission sources. The ‘Matched Filter’ (MF) methodology, widely exploited in the methane source identification and in the estimation of enhanced concentrations, is discussed in its theoretical foundations, revised and extended within an integrated processing framework. We apply an estimator (termed MF-EVO) operating in the logarithmic radiance-ratio domain, i.e. optical depth space, which allows to overcome the limitations imposed by the linearization assumption of the classical MF and improves robustness across a wide range of methane concentration enhancements. Results from MF-EVO are compared to the traditional algorithm for a set of synthetic PRISMA observations accounting for both homogeneous and heterogeneous background conditions. The MF-EVO algorithm demonstrates superior performance over the MF-Classic method in identifying methane sources across all idealized conditions. Specifically, the estimated identification limit for <span><math><mi>Δ</mi></math></span>XCH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> is approximately 0.05 ppm for MF-EVO, significantly lower than the 0.09 ppm limit for MF-Classic. Furthermore, the MF-EVO consistently outperforms the classic MF in the accurate estimation of concentration enhancements across both small and medium-to-large methane concentration scenarios. Under idealized conditions, MF-EVO achieves an error margin within 5%, which is a substantial improvement compared to the 10%–50% error range observed with the MF-Classic method. To address the challenges posed by real-world scenes, the revised MF formulation is embedded in a processing chain that includes false-positive pixel elimination and scene homogenization through image partitioning into spectrally homogeneous clusters. These steps significantly reduce background-induced artifacts and stabilize methane enhancement retrievals, enabling more reliable plume identification and flux estimation. In the application to the Mumbai metropolitan landfills, the full processing chain reduces the estimated methane fluxes by approximately 40%–55% with respect to the classical MF applied to the full scene, highlighting the impact of background homogenization and false-positive suppression on flux estimation in heterogeneous environments.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100417"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187950","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":"Potential effects of climate change on global air quality and human health","authors":"Racha Samermit ,&nbsp;Thanapat Jansakoo ,&nbsp;Shinichiro Fujimori ,&nbsp;Saritha Sudharmma Vishwanathan","doi":"10.1016/j.aeaoa.2026.100430","DOIUrl":"10.1016/j.aeaoa.2026.100430","url":null,"abstract":"<div><div>Climate change alters air quality and associated health outcomes. Climate-driven meteorological variables such as temperature, precipitation, and relative humidity influence transport, chemical transformation, and removal of air pollutants, particularly fine particulate matter (PM_2.5) and ozone (O₃). Here, we investigated the impacts of climate change on global PM_2.5 and O₃ concentrations via one-way coupling of an atmospheric chemical transport model (CTM) with the outputs of a general circulation model. We examined the impact on future air quality under three climate scenarios: SSP1–2.6, SSP2–4.5, and SSP5–8.5 of the Scenario Model Intercomparison Project (ScenarioMIP) for the mid-century (2040–2049) and the end of the century (2090–2099). To isolate the effect of climate change, anthropogenic and natural emissions were fixed at 2015 levels, enabling quantification of meteorologically driven changes in air quality and mortality. Our results show that climate forcing can trigger substantial regional variations in pollutant levels, with the global mean PM_2.5 concentration changing by −0.01 μg m⁻³ to −0.57 μg m⁻³ and the O₃ level from −0.05 ppbv to −1.20 ppbv. In our experimental framework–where primary and precursor emissions as well as chemical boundary conditions are held constant at 2015 levels–surface PM_2.5 and O₃ concentrations generally decline under future climate conditions due to meteorological shifts. These changes reflect the isolated effects of climate-driven meteorology rather than the combined climate-emission pathways associated with SSP-RCP scenarios. Although mean global pollutant changes appear to be modest, the associated health benefits are not negligible, corresponding to more than 0.2 million deaths avoided from PM_2.5 exposure, and 0.08 million deaths from O₃ exposure, when aggregated across all scenarios. Our results underscore the importance of considering climate–meteorology interactions when assessing future air quality and its public-health impacts.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100430"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394900","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":"Long-term spatiotemporal variability of aerosol optical depth and aerosol size characteristics over the United Arab Emirates from MODIS MAIAC observations (2003–2023)","authors":"Bashayer Alzahmi ,&nbsp;Khalid Hussein ,&nbsp;Abdelgadir Abuelgasim ,&nbsp;Khawla Alhebsi ,&nbsp;Fatima Alkaabi ,&nbsp;Khulood Alshehhi ,&nbsp;Elnazir Ramdan ,&nbsp;Hatim O. Sharif","doi":"10.1016/j.aeaoa.2026.100421","DOIUrl":"10.1016/j.aeaoa.2026.100421","url":null,"abstract":"<div><div>The United Arab Emirates (UAE) is characterized by an arid climate, frequent dust outbreaks, and rapid urban and industrial development, all of which influence atmospheric aerosol levels and their spatial and temporal variability. This study examines the spatiotemporal variability of aerosol optical depth (AOD) and aerosol size characteristics over the UAE from 2003 to 2023 using the MODIS MAIAC MCD19A2 product. Daily AOD at 550 nm (1 km spatial resolution) was aggregated to monthly, seasonal, and annual scales, while aerosol size characteristics were inferred using the Ångström exponent (AE) derived from AOD at 470 nm and 550 nm. The analysis reveals persistent regional differences, with higher AOD over coastal urban areas and lower values over inland desert and mountainous regions, reflecting the combined influence of natural dust activity, coastal circulation patterns, sea-salt aerosols, and anthropogenic emissions. Seasonally, summer is characterized by higher AOD and coarse-mode aerosol dominance associated with intensified dust activity, whereas winter and spring show a relatively greater contribution from fine-mode aerosols, particularly in urban regions. This two-decade, national-scale synthesis of AOD and AE variability provides insight into atmospheric aerosol characteristics in a dust-dominated and rapidly urbanizing environment and supports regional atmospheric modeling and air-quality assessment over the UAE.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100421"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394901","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":"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":"2026-01-01","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":"Airborne measurements of agricultural ammonia emissions: A case study over a livestock farm in Grosseto, Italy","authors":"Lara Noppen ,&nbsp;Lieven Clarisse ,&nbsp;Marie-Thérèse El Kattar ,&nbsp;Frederik Tack ,&nbsp;Mary Langsdale ,&nbsp;Martin Van Damme ,&nbsp;Lorenzo Genesio ,&nbsp;Franco Miglietta ,&nbsp;Valerio Capecchi ,&nbsp;Martin Wooster ,&nbsp;Simon Hook ,&nbsp;Michel Van Roozendael ,&nbsp;Dirk Schuettemeyer ,&nbsp;Pierre Coheur","doi":"10.1016/j.aeaoa.2025.100395","DOIUrl":"10.1016/j.aeaoa.2025.100395","url":null,"abstract":"<div><div>Livestock farming is the dominant source of atmospheric ammonia (NH₃) in large parts of the world. However, its emissions remain difficult to quantify because of the complex and diverse nature of farms, and the technical and practical challenges involved in measuring NH₃. Emission estimates from individual farms are traditionally obtained from in situ measurements, while regional to global distributions are provided by infrared satellite sounders. Airborne hyperspectral infrared imaging can be used to map NH₃ over large areas (<span><math><mrow><mo>&gt;</mo><mn>10</mn><mspace></mspace><msup><mrow><mstyle><mi>k</mi><mi>m</mi></mstyle></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>) and at high spatial resolution (<span><math><mrow><mo>&lt;</mo><mn>5</mn><mspace></mspace><mstyle><mi>m</mi></mstyle></mrow></math></span>), therefore providing measurements at a scale between in situ and satellite data.</div><div>During a joint ESA-NASA funded campaign in the summer of 2023 near Grosseto, Italy, a cattle farm and its surroundings were overflown by a research aircraft 69 times in five days. Airborne hyperspectral longwave infrared imagery was collected using the NASA-JPL Hyperspectral Thermal Emission Spectrometer (HyTES). We developed an efficient lookup table approach to derive NH₃ abundances and associated uncertainties from the HyTES radiance data. The resulting distributions reveal a diversity of small and large NH₃ plumes emanating from the farm. Lagoons and barns were identified as the main emission hotspots. From these distributions and with the help of a box model, total farm fluxes were estimated for each overflight. The emission fluxes range from <span><math><mrow><mn>3</mn><mo>±</mo><mn>1</mn></mrow></math></span> to <span><math><mrow><mn>7</mn><mo>±</mo><mn>5</mn><mspace></mspace><mstyle><mi>g</mi></mstyle><mspace></mspace><msup><mrow><mstyle><mi>h</mi><mi>d</mi></mstyle></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup><mspace></mspace><msup><mrow><mstyle><mi>h</mi></mstyle></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> for the first three days, in line with emission factors reported by other studies. Much larger emissions are seen on the last two days, between <span><math><mrow><mn>13</mn><mo>±</mo><mn>8</mn></mrow></math></span> and <span><math><mrow><mn>59</mn><mo>±</mo><mn>42</mn><mspace></mspace><mstyle><mi>g</mi></mstyle><mspace></mspace><msup><mrow><mstyle><mi>h</mi><mi>d</mi></mstyle></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup><mspace></mspace><msup><mrow><mstyle><mi>h</mi></mstyle></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, likely caused by specific farm activities. Overall, this case study demonstrates that airborne hyperspectral infrared imaging is a valuable complement to existing methods for quantifying NH₃ emissions at the farm scale.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100395"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624999","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":"Recent progress, bottlenecks, and outlook of multiscale air quality modelling: a review","authors":"Yuqing Dai ,&nbsp;Juncheng Qian ,&nbsp;Jian Zhong ,&nbsp;Xiaoming Cai ,&nbsp;A. Rob MacKenzie","doi":"10.1016/j.aeaoa.2026.100435","DOIUrl":"10.1016/j.aeaoa.2026.100435","url":null,"abstract":"<div><div>Air pollution spans metre-scale near-road hotspots to regional and intercontinental transport, yet no single model can represent the full range of processes that control exposure to different pollutants. This review synthesises recent developments in regional-to-local coupling, covering scale-aware regional chemical transport models (CTMs), local dispersion and street-network models and computational fluid dynamics (CFD, from practical RANS to chemistry-enabled LES and emerging GPU/LBM acceleration), and machine learning (ML), including mass-consistent super-resolution, for downscaling and surrogates. One-way “offline” coupling remains the most widely used approach because it is modular and computationally efficient, but its performance depends on how CTM background fields are defined and mapped and on how emission overlap, temporal mismatch and reduced chemistry are handled. Two-way “online” approaches, including Plume-in-Grid (PinG) for point sources and Street-in-Grid (SinG) for dense urban networks, exchange mass during integration, allowing urban plumes, NO–O₃ titration and aerosol–radiation interactions to feedback on regional oxidant budgets and meteorology. These benefits require conservative remapping, turbulence and mixing consistency at the canopy–boundary layer interface, and transparent mapping between chemical mechanisms across scales. Persistent challenges include interface turbulence–chemistry interactions, harmonisation of emissions and meteorological inputs, treatment of urban green infrastructure without double counting drag or deposition, computational feasibility for chemistry/aerosol-coupled CFD, ML transferability under regime shifts, and propagation of input uncertainty. Priority directions include regime-based criteria for when two-way coupling is required, routine mass-budget diagnostics, adaptive or variable-resolution strategies, and ML downscales and surrogates that enforce non-negativity and mass consistency for scenario testing.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100435"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395020","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":"2026-01-01","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}