Atmospheric Environment: X最新文献

{"title":"Acute exposure to fine ambient particulate matter and pulmonary exacerbations in cystic fibrosis: A case-crossover study","authors":"Stephen P. Colegate ,&nbsp;Erika Rasnick Manning ,&nbsp;Andrew Vancil ,&nbsp;Ziyun Wang ,&nbsp;Marepalli Rao ,&nbsp;Emrah Gecili ,&nbsp;Anushka Palipana ,&nbsp;Patrick Ryan ,&nbsp;Rhonda D. Szczesniak ,&nbsp;Cole Brokamp","doi":"10.1016/j.aeaoa.2026.100414","DOIUrl":"10.1016/j.aeaoa.2026.100414","url":null,"abstract":"<div><div>In cystic fibrosis (CF) lung disease, increased exposure to PM_2.5 (particulate matter &lt;2.5 μm in diameter) has been linked to more frequent pulmonary exacerbations, yet the timing of exposure that presents the greatest exacerbation risk remains uncertain. Our study investigated whether short-term increases in ambient fine particulate matter (PM_2.5) contribute to exacerbation risk in individuals with CF. We analyzed data from 108 participants across 17 U.S. clinical sites enrolled in the Early Intervention in Cystic Fibrosis Exacerbation study (October 2011–July 2015), who recorded forced expiratory volume in 1 s (FEV₁) measurements at least twice weekly. Exacerbation cases were identified via the FEV₁ indicated exacerbation signal (FIES), defined as a ≥10 % predicted decline in FEV₁. Daily 24-h average PM_2.5 concentrations were estimated at the residential ZIP code using a high-resolution spatiotemporal exposure model. A time-stratified case-crossover design modeled exacerbation onset and PM_2.5 exposure, controlled for temperature, relative humidity, and holiday impacts. We analyzed 6936 FEV₁ observations yielding 1432 exacerbation cases, averaging 7.7 cases per person-year, matched to 4794 control dates in the same year, month, and day of the week. Increased exacerbation risk occurred one (OR: 1.18; 95 % CI: 1.02–1.36), two (OR: 1.20; 95 % CI: 1.04–1.40), and three (OR: 1.23; 95 % CI: 1.05–1.43) days after PM_2.5 exposure, but not on the day of onset (OR: 1.07; 95 % CI: 0.92–1.25). Integrating home-monitored lung function with hyperlocal exposure estimates provides improved temporal resolution for detecting acute environmental triggers and may inform early intervention strategies in CF care.</div><div>e-ICE clinical trial registered with <span><span>www.clinicaltrials.gov</span><svg><path></path></svg></span> (NCT01104402).</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100414"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187948","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":"2026-01-01","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":"Effect of filter rod structure on aerosol particle size distribution in electrically heated cigarettes","authors":"Jinfeng Wu ,&nbsp;Yihan Gao ,&nbsp;Huaquan Sheng ,&nbsp;Changguo Wang ,&nbsp;Ting Fei ,&nbsp;Cheng Liu ,&nbsp;Jianfeng Guo ,&nbsp;Lijun Zhu ,&nbsp;Peicai Cui","doi":"10.1016/j.aeaoa.2025.100412","DOIUrl":"10.1016/j.aeaoa.2025.100412","url":null,"abstract":"<div><div>Non-uniform and unstable particle size distributions in heated cigarette aerosols compromise the reliability of risk assessment outcomes and the quality of the smoke. This study utilized an electrically heated cigarette model to clarify how filter rod structure governs aerosol particle size distribution, which then addressed a fundamental gap in understanding how structural parameters affect aerosol evolution. Employing the SCS-DMS500 system and a controlled variable approach, the particle size distribution of aerosols from heated tobacco products was tested across varying filter segment lengths (8–24 mm) and three cooling segment structures (hollow, Collins, folded paper). Results revealed a clear competition between interception and coalescence mechanisms: within the filter segments, increasing length progressively elevated the count median diameter (CMD) while reducing number concentration (NC) and volume concentration (VC) by 48 % and 18.8 %, respectively, due to enhanced adsorptive capture. By contrast, cooling segment geometry exerted a fundamentally different form of control: Collins and folded paper filter rods yielded substantially smaller particles with higher number concentrations compared to hollow-core designs. A distinctive length-dependent reduction in CMD was observed specifically with folded paper filter rods. This study establishes the first mechanistic framework for regulating aerosol particle size through composite filter design, offering theoretical support for employing low-retention, low-coagulation control strategies aimed at reducing respiratory exposure risks.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100412"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187952","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":"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":"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":"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":"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":"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}