Atmospheric Environment: X最新文献

{"title":"In-situ detection of illegal waste burning based on flue gas parameters","authors":"András Hoffer,&nbsp;Ádám Tóth,&nbsp;Beatrix Jancsek-Turóczi,&nbsp;Tibor Ajtai,&nbsp;Szabolcs Hodovány,&nbsp;Alex Kummer,&nbsp;András Gelencsér","doi":"10.1016/j.aeaoa.2026.100491","DOIUrl":"10.1016/j.aeaoa.2026.100491","url":null,"abstract":"<div><div>Large amounts of aerosols containing harmful components are released into the atmosphere during the burning of household waste. This activity greatly impairs air quality on a local scale, sometimes making it unbearable near the sources, which often leading to public complaints. The available methods to detect waste burning are based on the analysis of the metal content in the remaining ash sometimes well after the activity in question. In this study, we present the development and the principles of a method for real-time detection of waste burning from the flue gas at the sources, intended as a proof of concept. In the laboratory experiments, different types of waste were co-combusted with firewood in a stove, with waste doses added at different stages of the firewood burning process to simulate the variable combustion conditions present under real-world burning scenarios. Numerous flue gas parameters were measured during the experiments from which the most representative ones were selected to enable the identification of waste burning on site. In the 3D spaces defined by different selected parameters, we identified the region that is characteristic of waste burning. Random forest models using nine parameters were also optimized, trained and tested to identify the burning of different fuel types. The co-burning of plastic and plastic containing wastes (PET, PE, PUR, PP, OILYRAG, PVC and PS), their mixtures, and shoes with firewood was identified as waste burning with high accuracy (83–99%). The flaming combustion phase of composite wood panels (furniture panels and oriented strand boards) and the flaming phase of painted wood burning were also correctly recognised as waste-related combustion within this accuracy range (97-98%). The burning of wastes containing natural materials was identified with accuracies of 75% for rags and 57% for Tetra Pak. Firewood combustion was reliably recognised, with an accuracy of 83-84%. Overall, the results demonstrate that the model can reliably distinguish between authorized and waste-containing fuels based on flue gas parameters, under the investigated combustion conditions. The developed method provides a basis for the future development of a cost-effective real-time detection approach for identifying domestic waste burning, especially where conventional tracers cannot be applied and rapid determination is required.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100491"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148642227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial modeling of ambient PM2.5 and population exposure in Addis Ababa using land use regression","authors":"Desalew Meseret Moges,&nbsp;Per-Ola Olsson,&nbsp;Ebba Malmqvist,&nbsp;Christina Isaxon,&nbsp;Muluneh Getachew Garedew,&nbsp;Araya Asfaw,&nbsp;Sina Hasheminassab,&nbsp;Tesfaye Mamo,&nbsp;Kedir Teji Roba,&nbsp;Gudina Terefe Tucho,&nbsp;Masresha Tessema,&nbsp;Eleni Papadopoulou,&nbsp;Kristoffer Mattisson","doi":"10.1016/j.aeaoa.2026.100493","DOIUrl":"10.1016/j.aeaoa.2026.100493","url":null,"abstract":"<div><div>Rapid urban growth in Africa is worsening air pollution in many cities, yet limited air-quality monitoring infrastructure hinders accurate assessment of residents’ exposure. In this study, we applied a land-use regression (LUR) model to estimate annual ambient fine particulate matter (PM₂.₅) concentrations and assess population exposure in Addis Ababa, Ethiopia. Estimated annual PM₂.₅ concentrations showed substantial spatial variability, ranging from 5.9 to 47.9 μg/m³, with a citywide average of 17.3 μg/m³. No part of the city met the World Health Organization (WHO) annual air quality guideline of 5 μg/m³, and 77% of the area exceeded Interim Target 3 (IT-3; 15 μg/m³). The highest concentrations, exceeding the lowest threshold (IT-1, 35 μg/m³), were observed in the sub-cities of Arada, Addis Ketema, Lideta, and Kirkos, where dense urban development and major transportation corridors are concentrated. The LUR model demonstrated strong performance, with an adjusted R² of 0.74 and a root mean square error (RMSE) of 1.85 μg/m³. Key predictors included proximity to waste dump sites, green space coverage, and the lengths of primary and secondary roads, reflecting major pollution sources and land-use patterns associated with reduced emissions or improved pollutant dispersion. Model diagnostics showed good internal validity, with low multicollinearity among predictors and no evidence of spatial autocorrelation in the residuals. We further quantified population exposure to PM₂.₅ by calculating population-weighted concentrations at both city and kebele (the lowest administrative unit) levels. The population-weighted citywide mean concentration was 21.3 μg/m³, while kebele-level exposures ranged from 13 to 30 μg/m³, highlighting significant disparities in pollution burden across the city. Overall, ambient PM₂.₅ concentrations substantially exceeded WHO guideline levels, underscoring the urgent need for stronger air quality policies, targeted emission reductions, and urban planning strategies to promote healthier, more equitable living environments.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100493"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148642228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation analysis of CO2 and air pollutants (CO, NO2, SO2, PM2.5, PM10, and O3) at selected monitoring sites in Europe, North America, and China","authors":"Lu Guan,&nbsp;Zengliang Zang,&nbsp;Yiwen Hu,&nbsp;Lang Liu,&nbsp;Yi Li,&nbsp;Ning Liu,&nbsp;Gen Zhang,&nbsp;Chong Wei,&nbsp;Xiaoying Huang,&nbsp;Chao Zhang","doi":"10.1016/j.aeaoa.2026.100495","DOIUrl":"10.1016/j.aeaoa.2026.100495","url":null,"abstract":"<div><div>Global climate change and air pollution are closely linked environmental challenges. As the most abundant greenhouse gas, CO₂ requires further investigation in relation to co-occurring air pollutants. Based on hourly observations from 16 monitoring-site groups across Europe, the United States, China, and Canada, this study examines the temporal and seasonal variability of CO₂ and six air pollutants, including CO, NO₂, SO₂, PM_2.5, PM₁₀, and O₃. The study periods differ among site groups and are defined by the common valid observation periods of CO₂ and the six pollutants, with an overall temporal coverage from 2014 to 2025. Pearson correlation analysis quantified the relationships between CO₂ and air pollutants, hierarchical clustering classified monitoring sites according to correlation patterns, and stepwise multiple regression assessed the effects of time scale and pollutant concentrations on CO₂ variability. Results show that CO₂ exhibits a clear seasonal cycle, with higher concentrations in winter and lower in summer. CO and NO₂ show seasonal variations consistent with CO₂, whereas O₃ displays an opposite pattern (r: -0.83<span><math><mo>∼</mo></math></span>-0.09). The coupling between particulate matter (PM) and CO₂ strengthens in winter but weakens in summer. CO₂ is positively correlated with most pollutants except O₃ in winter, while correlations decline substantially in summer. Cluster analysis separates the sites into anthropogenic emission-dominated and natural source-regulated groups. Stepwise regression (the maximum <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> is 0.92) indicates positive within-period linear tendencies and seasonal variability in CO₂ at most sites, although model performance is limited where geographical and vegetation-related factors are excluded.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100495"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148737326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty quantification in aviation climate impact: A stochastic approach to mission-specific CO2 and non-CO2 Effects","authors":"Marcus Wiegand,&nbsp;Lily Koops,&nbsp;Ronald Mailach,&nbsp;Rafael Balderas-Xicohtencatl","doi":"10.1016/j.aeaoa.2026.100502","DOIUrl":"10.1016/j.aeaoa.2026.100502","url":null,"abstract":"<div><div>The aviation industry significantly contributes to global climate change through both <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> and non-<span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> effects. This study introduces a probabilistic framework that integrates flight mission, engine performance, emission, and simplified climate response models to quantify aviation’s climate impact while accounting for uncertainties in emission and climate model parameters. In particular, uncertainties in emission indices and the contrail forcing factor are updated. Using Monte Carlo simulations, the framework provides probabilistic predictions of key climate metrics, offering insights into the sensitivity of predicted climate impacts to modeling uncertainties. A case study of a representative flight mission illustrates the framework’s application and highlights the contributions of <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span>, <span><math><msub><mrow><mtext>NO</mtext></mrow><mrow><mtext>x</mtext></mrow></msub></math></span>, and contrails to mission-level climate forcing, revealing that uncertainties in non-<span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> effects, particularly <span><math><msub><mrow><mtext>NO</mtext></mrow><mrow><mtext>x</mtext></mrow></msub></math></span> emissions and contrails, dominate the total uncertainty. The findings emphasize the need for improved modeling of non-<span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> effects and demonstrate the framework’s utility in evaluating current and future propulsion systems. By advancing uncertainty quantification in aviation climate impact assessments, this study provides a robust tool to inform sustainable aviation technologies, flight optimization strategies, and evidence-based policymaking.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100502"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variation trends of air quality and pollution characteristics in Baoji City from 2017 to 2025","authors":"Wang Xujing,&nbsp;Cui Ying,&nbsp;Sun Wei,&nbsp;Niu Xiaowei,&nbsp;Li Songbo,&nbsp;Zheng Jingxu,&nbsp;Shao Jing","doi":"10.1016/j.aeaoa.2026.100500","DOIUrl":"10.1016/j.aeaoa.2026.100500","url":null,"abstract":"<div><div>Ambient air pollution remains a leading environmental risk factor globally. Over the past decade, China has achieved marked reductions in particulate matter (PM) following the 2013 Air Pollution Prevention and Control Action Plan; however, surface ozone (O₃) has increased in multiple urban agglomerations, signaling a transition from single-pollutant to complex multi-pollutant regimes. At the western terminus of the Fenwei Plain—a national key region designated in 2018—systematic, county-level assessments of long-term air-quality evolution are lacking for Baoji City. This study presents a nine-year (2017–2025) analysis of six criteria pollutants (SO₂, NO₂, CO, O₃, PM₂.₅, and PM₁₀) across all 12 administrative divisions (four districts and eight counties) of Baoji, using continuous monthly data from 16 national and provincial monitoring stations (n = 108 months). Spearman rank correlation, Mann–Kendall trend tests, and Sen's slope estimators were applied to (i) quantify the significance of inter-annual concentration trends, (ii) characterize the spatial decoupling between particulate matter and ozone at the county level, and (iii) document the timing and magnitude of the regime shift in dominant pollution types. CO, NO₂, PM₁₀, and PM₂.₅ exhibited statistically significant downward trends at &gt;90% of monitoring sites (p &lt; 0.05), with cumulative reduction rates exceeding 45%, 46%, 51%, and 53%, respectively. Annual mean SO₂, NO₂, and CO continuously met the National Grade-II Standard from 2020 onward, while PM₂.₅ achieved stable compliance from 2023 to 2025. The 90th percentile of daily maximum 8-h O₃ (O₃-8 h-90th) remained below the Grade-II limit of 160 μg/m³ throughout the study period, despite a phased rebound during 2022–2024. Spatially, PM₂.₅ and PM₁₀ followed a west-low/center-east-high distribution with a continuously shrinking regional gradient, whereas O₃ maintained a stable east-high/south-low pattern, reflecting persistent photochemical pressure in the eastern plain counties. The proportion of O₃ as the primary pollutant rose from 15% in 2017 to 28% in 2025, marking a regime shift from PM-dominated to O₃–PM₂.₅ co-dominated air pollution. These findings provide empirical evidence for synergistic pollution–carbon governance and inform seasonally differentiated, location-specific control strategies in Baoji and analogous industrial basin cities.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100500"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Re-evaluation of particulate matter emission rates and variability of livestock houses in the Netherlands” [Atmos. Environ.: X (2025) 100369]","authors":"Marien Korevaar,&nbsp;Nico Ogink,&nbsp;Johanna Rebel,&nbsp;Albert Winkel","doi":"10.1016/j.aeaoa.2026.100508","DOIUrl":"10.1016/j.aeaoa.2026.100508","url":null,"abstract":"","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100508"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149004069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ten-meter resolution spatial allocation of CO2 emissions in tourist city Guilin using GOSAT observations and key factor attribution","authors":"Hao Song,&nbsp;Shuqin Wu,&nbsp;Huidong Wang","doi":"10.1016/j.aeaoa.2026.100507","DOIUrl":"10.1016/j.aeaoa.2026.100507","url":null,"abstract":"<div><div>This study takes Guilin, a typical tourist city, to explore its carbon emission characteristics. It integrates multi-source remote sensing data (satellite CO₂ column concentration, NO₂, nightlight data, etc.), social statistics, geographic information of the road network, and constructs a 10-m high-resolution spatial allocation product of CO₂ emissions for Guilin (2019-2024). The Logarithmic Mean Divisia Index (LMDI) is then adopted to quantify tourism-related driving factors.</div><div>Under the most stringent buffered block cross-validation, the CatBoost model achieved an RMSE of 0.0010 kg CO₂ m⁻² s⁻¹, an MAE of 0.0006 kg CO₂ m⁻² s⁻¹, and an R² of 0.8225, while showing comparatively stable error performance across the spatially explicit validation schemes. Spatially, elevated predicted CO₂ emission flux density was concentrated around major tourism and transportation activity areas, including Liangjiang International Airport and the Lijiang River tourism corridor. Relative to ODIAC, the 10 m spatial allocation relocated the principal airport-related hotspot toward the actual airport area.</div><div>COVID-19 mobility restrictions coincided with a decrease in mean CO₂ emission flux density of 2.31 × 10⁻⁵ kg CO₂ m⁻² s⁻¹ in 2019–2020. The LMDI decomposition indicates that contraction of the tourism-scale effect was the dominant emission-reducing contribution during this interval. A modest decrease in mean CO₂ emission flux density was observed after 2023; however, the limited time series does not support attribution of this change to any specific policy intervention.</div><div>The LMDI decomposition shows that industrial structure upgrading (cumulative contribution: −1.98 × 10⁻⁴ kg CO₂ m⁻² s⁻¹) was the largest emission-reducing effect, whereas tourism-scale expansion (+8.60 × 10⁻⁵ kg CO₂ m⁻² s⁻¹) partially offset this reduction. These contributions represent changes in the mean CO₂ emission flux density over the study area. The results highlight a “scale–structure” trade-off in tourism-related carbon emissions.</div><div>This spatial allocation product can identify dispersed emission hotspots in tourism cities and provide methodological support for fine-scale spatial analysis. The results provide evidence for considering zone-specific emission management and differentiated low-carbon transition strategies in tourism-oriented cities, while the specific policy instruments require further feasibility assessment.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100507"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PM2.5 trends, mortality risks, and health benefits under air quality improvement scenarios in Northern Thailand","authors":"Alongkot Prasansri,&nbsp;Pantitcha Thanatrakolsri,&nbsp;Katiya Ivanovitch","doi":"10.1016/j.aeaoa.2026.100490","DOIUrl":"10.1016/j.aeaoa.2026.100490","url":null,"abstract":"<div><div>Fine particulate matter (PM_2.5) pollution remains a persistent air quality problem in northern Thailand, where seasonal biomass burning and regional meteorological conditions frequently lead to elevated concentrations. This study quantified exposure to PM_2.5 and associated health risks in the upper northern region of Thailand and evaluated potential health benefits under different PM_2.5 reduction scenarios. Daily PM_2.5 concentrations from 14 air quality monitoring stations during 2020–2024 were analyzed to derive annual mean exposure levels. Health impacts among adults aged ≥30 years were estimated using established concentration–response relationships to calculate relative risk (RR) and attributable fraction (AF) for cardiopulmonary and lung cancer mortality. The regional annual mean PM_2.5 concentration was 40.42 μg m⁻³, substantially exceeding international air quality guideline levels. Under baseline exposure conditions, estimated RR values were 1.44 (95% CI: 1.14 - 1.81) for cardiopulmonary mortality and 1.72 (95% CI: 1.22 - 2.42) for lung cancer mortality. Corresponding AF estimates indicated that 30.56% (95% CI: 12.28 - 44.75) and 41.86% (95% CI: 18.03 - 58.68) of cardiopulmonary and lung cancer mortality risk, respectively, were attributable to PM_2.5 exposure. Scenario analyses showed progressive reductions in RR and AF with decreasing PM_2.5 concentrations, with the most stringent reduction scenario yielding mean AF reductions exceeding 20% for both health outcomes. These results indicate a consistent pattern between PM_2.5 concentration scenarios and mortality outcomes and suggest potential reductions in estimated health risks associated with lower PM_2.5 concentrations.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100490"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148642229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinct transformation mechanisms of proteinaceous materials to low-molecular-weight NOCs during combustion of different biomass fuels","authors":"Yaping Li,&nbsp;Ren-Guo Zhu,&nbsp;Yuanyuan Zhu,&nbsp;Ling-shuang Deng,&nbsp;Fanping Zeng,&nbsp;Han-Bin Chen,&nbsp;Binyu Lu","doi":"10.1016/j.aeaoa.2026.100497","DOIUrl":"10.1016/j.aeaoa.2026.100497","url":null,"abstract":"<div><div>Low-molecular-weight nitrogen-containing organic compounds (LMW NOCs) from biomass burning (BB) significantly influence aerosol chemistry, yet their fuel-dependent molecular signatures and formation pathways remain poorly understood. Here, we combined molecular characterization with compound-specific nitrogen isotope analysis of amino acids and LMW NOCs in raw biofuels and combustion-derived aerosols from herbaceous, softwood, and hardwood fuels burned in a traditional household stove. Herbaceous-burning aerosols showed higher semi-quantitative relative abundances of six-membered N-heterocycles, especially 3,6-dimethyl-2,5-hydroxypyrazine, whereas wood-burning aerosols were characterized by higher relative abundances of aminophenols, particularly 2-aminophenol.Strong correlations between amino-acid pools and LMW NOCs, together with Rayleigh-type δ¹⁵N enrichment of free Gly (y = 3.0 – 8.6×ln(1-f), r² = 0.41, <em>p</em> &lt; 0.05) indicate a stronger linkage between proteinaceous-material thermal transformation and LMW NOC formation during herbaceous combustion, whereas wood smoke likely involves additional or alternative pathways. Furthermore, similar δ¹⁵N values of adenine in raw fuels and aerosols are consistent with partial direct transfer or survival during combustion. These findings reveal distinct fuel-dependent LMW NOC signatures and provide molecular and isotopic constraints on nitrogen transformation in fresh BB aerosols.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100497"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148737370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Munich CO2 emission inventory for sectors public power and commercial and residential combustion for the years 2019–2024","authors":"Patrick Aigner,&nbsp;Jia Chen,&nbsp;Thomas Althammer,&nbsp;Hugo Denier van der Gon,&nbsp;Hannes Harter,&nbsp;Maik Günther,&nbsp;Daniel Kühbacher,&nbsp;Michael Suhendra,&nbsp;Ingrid Super,&nbsp;Beyza Yirtar,&nbsp;Daniel Zinsmeister","doi":"10.1016/j.aeaoa.2026.100498","DOIUrl":"10.1016/j.aeaoa.2026.100498","url":null,"abstract":"<div><div>Cities play a central role in climate mitigation, but often lack emission inventories with sufficient spatial and temporal resolution for detailed monitoring and policy evaluation. Existing inventories are frequently based on downscaled national statistics and generic temporal profiles, limiting their ability to capture city-specific activity patterns and local temporal variability.</div><div>In this study, we present a high-resolution bottom-up emission inventory for the city of Munich, Germany, covering public power and commercial and residential combustion for the years 2019–2024. <span><math><mrow><mi>C</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> emissions for fossil fuel and biofuel are quantified at 100 m spatial resolution and hourly temporal resolution.</div><div>A building-level heat-demand proxy is calculated and used to spatially distribute reported fuel consumption. Hourly district heating heat-load data are used to train a machine-learning model to derive temporal profiles. For power plants, hourly fuel-input data enable temporal emission profiles, while total emissions are based on annual environmental reports.</div><div>The inventory is evaluated by comparing it with national downscaled inventories and by assessing temporal profiles against energy production data and meteorological indicators. Spatial uncertainty is analyzed by comparing it with an alternative building-level model and a non-public model from the local operator.</div><div>The inventory reveals interannual emission changes between 2019 and 2024 and shows the influence of external drivers such as the energy crisis and variations in mean annual temperature. We highlight the impact of using local data but also show that local inventories are constrained by the quality and availability of the input data. The inventory is released as open datasets.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"31 ","pages":"Article 100498"},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}