Atmospheric Environment: X最新文献

{"title":"Atmospheric ammonia concentration measurements in Japanese laying hen buildings and modeling for emission inventory","authors":"T. Yoshida ,&nbsp;G. Katata ,&nbsp;H. Kuroda ,&nbsp;N. Tanaka","doi":"10.1016/j.aeaoa.2026.100422","DOIUrl":"10.1016/j.aeaoa.2026.100422","url":null,"abstract":"<div><div>Atmospheric ammonia (NH₃) from livestock contributes to PM2.5 formation and nitrogen loading in watersheds. Although chemical transport models often adopt the emission factor (<em>EF</em>) and monthly variation coefficient (<em>M</em>_<em>f</em>) from European NH₃ emission inventories due to the limited availability of direct measurement data worldwide, their applicability to Asian countries remains unknown. In this study, we observed the spatial distributions and diurnal changes in NH₃ concentrations within windowless laying hen buildings during May–October of 2022 and 2023. The observed spatial distribution showed that the major drivers of monthly NH₃ emissions were air temperature and ventilation rate. Moreover, NH₃ concentrations changed depending on the number of days after manure removal according to the diurnal observations. A regression analysis was conducted using observed temperature, ventilation rate, and manure removal to develop a statistical model for estimating NH₃ emission rates based on ambient temperature data. Subsequently, model application to the different climatic conditions were performed by using the typical monthly mean air temperature from the surface weather databases at several major poultry production cities in Japan. The results demonstrated that the calculated <em>EF</em> in Japan varied substantially (0.11–0.52 kgNH₃ y⁻¹ head⁻¹) depending on air temperature, whereas that in the Netherlands remained near the lowest values. The summertime <em>M</em>_<em>f</em> was higher in Japan than that in the Netherlands, suggesting differences in climate and manure management systems between Japan and Europe. To improve the accuracy of livestock emission inventories, further observations for NH₃ emission rate estimates are required for other sources, such as manure storage and land application.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100422"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187949","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":"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":"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}