Atmospheric Pollution Research最新文献

{"title":"Air quality impacts of a major wildfire in the UK: Sensitivity to model spatial resolution and transport method","authors":"Benjamin Drummond ,&nbsp;Ailish Graham ,&nbsp;Lucy Neal ,&nbsp;Pedro Molina Jiménez ,&nbsp;Richard J. Pope ,&nbsp;Carly Reddington","doi":"10.1016/j.apr.2025.102795","DOIUrl":"10.1016/j.apr.2025.102795","url":null,"abstract":"<div><div>Wildfires can be important drivers of poor air quality. Numerical atmosphere models are routinely used to estimate pollutant concentrations emitted from a wide range of sources, including from wildfires. Such models often take the Eulerian field or Lagrangian particle method for representing mass and transport in the atmosphere. Using the Saddleworth Moor and Winter Hill fires that occurred in North West England in 2018 as a case study, we compared these two methods consistently within the same model framework. We also explored the impact of model spatial resolution on predicted concentrations and health impacts. In the Eulerian simulations, as the horizontal resolution was made finer (from 12 km to 1 km) the horizontal spread of the downwind wildfire pollution decreased substantially, leading a smaller geographical area and population being impacted by the smoke. The estimated number of people exposed to poor air quality due to wildfire from the 1 km Eulerian simulation was 30% lower than from the 12 km Eulerian simulation. A health impact assessment found a similar relative decrease for the estimated excess mortality due to short-term PM_2.5 exposure when going from 12 km to 1 km horizontal resolution. Estimated air quality impacts were also found to be sensitive to horizontal resolution for the Lagrangian simulations but to a lesser degree (<span><math><mo>∼</mo></math></span>10% decrease from 12 km to 1 km). We recommend that model spatial resolution should be considered as a source of uncertainty for wildfire air quality impact assessments, particularly when an Eulerian model is used.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102795"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and application of a generalized empirical model of BVOC emission (GEMBE) using observations from four Chinese forests","authors":"Jianhui Bai ,&nbsp;Zhixiang Wu ,&nbsp;Chuan Yang ,&nbsp;Alex B. Guenther","doi":"10.1016/j.apr.2025.102814","DOIUrl":"10.1016/j.apr.2025.102814","url":null,"abstract":"<div><div>Based on the measurements of emissions of biogenic volatile organic compounds (BVOCs), solar radiation and meteorological variables in three representative forests from temperate to subtropical zone in China, a primary empirical model of BVOC emissions (EMBE) has been developed. During 2018 and 2019, BVOC emission fluxes were measured in a tropical rubber tree plantation in China, and EMBE was improved to a generalized empirical model of BVOC emissions (GEMBE) and evaluated. This paper presents GEMBE as a fully empirical modeling framework, developed directly from ecosystem-scale flux observations, and compares this approach with MEGAN, a widely-used model that includes both empirical and process-based components. Isoprene emission estimated with GEMBE, using an emission factor based on the average for other sites, overestimated the observation by 34.8 % and monoterpenes by 41.0 %. A summary of the BVOC emission fluxes in the four typical forests in China is reported. With site-specific flux measurements the GEMBE model can be used to calculate BVOC emissions in China. Using GEMBE, the responses of BVOC emissions to their driving factors showed that isoprene and monoterpene emissions were more sensitive to the change in PAR than to changes in other factors. The responses of BVOC emissions to their driving factors were discussed for the four representative forests. The emission factors (EFs) calculated using GEMBE and MEGAN were summarized for the typical forests and grassland. The MEGAN estimated isoprene EF at this rubber plantation is lower, and the monoterpene EF is higher. Both GEMBE and MEGAN showed reasonable agreement in simulations of temporal trends over one year and two years, and well reproduced the evident monthly and seasonal BVOC emissions. As a fully empirical model, GEMBE provides a framework for estimating regional BVOC emissions, and investigating their impact on atmospheric chemistry, with an alternative approach to more complex process-based models that require biophysical parameterization.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102814"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gaseous pollutant emissions from solid fuel combustion: Comparative study of real-world and simulated chamber-based experiments","authors":"Dharini Sahu ,&nbsp;Shamsh Pervez ,&nbsp;Judith C. Chow ,&nbsp;John G. Watson ,&nbsp;Rajan K. Chakrabarty ,&nbsp;Aishwaryashri Tamrakar ,&nbsp;Indrapal Karbhal ,&nbsp;Manas Kanti Deb ,&nbsp;Kamlesh Shrivas ,&nbsp;Yasmeen Fatima Pervez ,&nbsp;Sachchidanand Shukla ,&nbsp;D.P. Bisen","doi":"10.1016/j.apr.2025.102798","DOIUrl":"10.1016/j.apr.2025.102798","url":null,"abstract":"<div><div>This study presents a comparative analysis of gaseous pollutant emissions from solid fuel combustion under real-world household conditions and simulated experimental chamber-based conditions. Focusing on six commonly used domestic fuels in India, fuelwood (FW), dung cake (DC), coal ball (CB), agricultural residue (AR), and two mixed fuel types (M1: CB + DC, M2: FW + DC in a 10:1 ratio), the research quantifies emissions of CO₂, CO, NO, NO₂, SO₂, CH₄, and total volatile organic compounds (TVOCs). Simulated combustion chamber experiments, designed to replicate household stove operation while allowing precise emission monitoring, were conducted alongside field based real-world observations. Emission factors (EFs) and combustion efficiency metrics were assessed to understand pollutant formation mechanisms. Results showed strong correlations between combustion efficiency and emission profiles: higher modified combustion efficiency (MCE) was associated with elevated CO₂ and NO₂ emissions, while lower MCE resulted in higher outputs of incomplete combustion products such as CO, CH₄, and TVOCs. Slightly lower EFs and higher MCEs were observed in field based real-world conditions compared to those found for simulated experimental chamber based conditions. Agricultural residues emitted the highest CH₄ levels, likely due to paddy-origin biomass, whereas mixed fuels showed increased TVOC emissions, linked to their high carbon and moisture content. This comparative study emphasizes the importance of integrating field-based validation with laboratory simulations to accurately assess household air pollution, and supports targeted interventions to promote cleaner combustion practices and reduce public health risks.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102798"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dust microbial contamination in typical indoor environments: Concentration, pathogenic composition and exposure assessment","authors":"Tantan Tan ,&nbsp;Gaoshan Zhang ,&nbsp;Ke Lu ,&nbsp;Yanpeng Li","doi":"10.1016/j.apr.2025.102821","DOIUrl":"10.1016/j.apr.2025.102821","url":null,"abstract":"<div><div>Indoor air quality is critically influenced by microbial contaminants in settled dust, yet existing studies predominantly focus on airborne microorganisms, leaving dust-associated microbial exposure poorly characterized. This study investigated microbial contamination in dust from four university indoor environments (offices, laboratories, dormitories, and classrooms) to assess their concentration, pathogenic composition, and human exposure risks. Dust samples were analyzed via fluorescence staining, high-throughput sequencing, and the dust daily intake model (DDIM). Results revealed significant spatial heterogeneity in microbial concentrations: dormitories exhibited the highest bacterial levels (4068.88 × 10⁴ ± 3386.55 × 10⁴ CFU/g), while offices had the highest fungal concentrations (146.16 × 10⁴ ± 152.93 × 10⁴ CFU/g). Factors such as occupancy density, cleaning frequency, and indoor plant presence are strongly associated with microbial distribution. Three dominant genera (<em>Fusobacterium</em>, <em>Pseudomonas</em>, and <em>Corynebacterium</em>) and four dominant fungal genera (<em>Aspergillus</em>, <em>Penicillium</em>, <em>Fusarium</em>, and <em>Streptomyces</em>) were observed in all indoor dust samples, with a potential risk of association with respiratory diseases and skin infections. Exposure assessment indicated that dust ingestion dominated microbial intake, with dormitories posing the highest bacterial exposure (EDI up to 35,086 CFU/(kg·day)) and offices the highest fungal exposure (EDI up to 1263 CFU/(kg·day)). These findings highlight the urgent need for targeted interventions, such as improved ventilation, regular cleaning, and microbial monitoring, to mitigate health risks in high-exposure indoor environments. This study provides a scientific foundation for refining indoor air quality standards and safeguarding occupants in densely populated educational settings.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102821"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term trends reflecting regulatory impacts on VOCs sources in the New York City metropolitan area","authors":"Lucille Borlaza-Lacoste ,&nbsp;Md. Aynul Bari ,&nbsp;Cheng-Hsuan Lu ,&nbsp;Philip K. Hopke","doi":"10.1016/j.apr.2025.102789","DOIUrl":"10.1016/j.apr.2025.102789","url":null,"abstract":"<div><div>Over the past two decades, shifts in energy use and regulatory policies in New York State have shaped emissions and air quality in the New York City (NYC) metropolitan area, a densely populated and VOC-limited nonattainment region for ozone (O₃). This study analyzed 24-h canister measurements from six sites, Queens, Bronx, Kings, Richmond, Elizabeth, and Chester, spanning the period of 2000–2021. Dispersion-Normalized Positive Matrix Factorization apportioned VOCs sources while accounting for atmospheric dilution, resolving twelve distinct sources dominated by aldehyde-rich factors, vehicle emissions, and industrial activities. Long-term trends from seasonal-trend decomposition and piecewise regression highlighted regulatory- and economy-driven shifts in source contributions. Significant declines in vehicle emissions, MTBE- and MEK-rich factors, and aldehydes aligned with Tier 2 and 3 fuel standards, MTBE phase-out, and MACT regulations. In contrast, natural gas, evaporative, biogenic, and background sources remained stable or increased, reflecting persistent and seasonally modulated emissions. Distinct site- and source-specific patterns in weekday/weekend and seasonal variability were also observed. These results show that while regulations have effectively reduced many anthropogenic VOCs sources, persistent emissions underscore the need for continued monitoring and adaptive control strategies in O₃ nonattainment regions like NYC.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102789"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the agriculture sectors of greenhouse gas emissions prediction in the global scenario: Insights from explainable artificial intelligence (XAI)","authors":"Mantena Sireesha ,&nbsp;Abdul Gaffar Sheik","doi":"10.1016/j.apr.2025.102792","DOIUrl":"10.1016/j.apr.2025.102792","url":null,"abstract":"<div><div>This study explored the potential of four machine learning (ML) models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), and Deep Feedforward Neural Networks (DFNN) or predicting greenhouse gas (GHG) emissions from an agricultural field. It measured carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) over a global scenario spanning 172 years. The rigorous analysis, which included statistical comparisons and cross-validation for predicting CO₂, CH₄, and N₂O fluxes, demonstrated that GRU, CNN, and DFNN models consistently exhibited high prediction accuracy across most sectors. Notably, the GRU model outperformed the others, achieving an R² of 0.9985 and an RMSE of 0.0108 for N₂O emissions in the Waste sector. In contrast to previous studies, this research not only predicts future GHG emissions but also identifies the relationship between these predictions and their influential variables. To achieve this, an interpretable prediction framework was utilized, incorporating explainable artificial intelligence (XAI) methods including SHapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Individual Conditional Expectation (ICE) plots, and Partial Dependence Plots (PDPs) to reveal each GHG’s contribution to overall emissions. The SHAP analysis indicated that CH₄ was the dominant contributor across all sectors, with a high average SHAP value of 10,632.68 in agriculture and 3386.09 in the Waste sector, followed by N₂O and CO₂. Further analyses using ICE and PDP clarified the sector-specific nonlinear interactions, showing that CH4 had the greatest influence on emissions, particularly in synergy with N₂O. These findings illustrate the significant potential of ML models for predicting GHG emissions in the agricultural sector.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102792"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rhizobia inoculation alleviates ozone-induced foliar damage and root biomass loss for Robinia pseudoacacia L.","authors":"Yasutomo Hoshika ,&nbsp;Barbara Baesso Moura ,&nbsp;Robert Haensch ,&nbsp;Jacopo Manzini ,&nbsp;Andrea Viviano ,&nbsp;Elena Marra ,&nbsp;Cesare Garosi ,&nbsp;Matheus Casarini Siqueira ,&nbsp;Ryoji Tanaka ,&nbsp;Bin Hu ,&nbsp;Heinz Rennenberg ,&nbsp;Elena Paoletti","doi":"10.1016/j.apr.2025.102811","DOIUrl":"10.1016/j.apr.2025.102811","url":null,"abstract":"<div><div>Tropospheric ozone (O₃) is an air pollutant with phytotoxic effects on plants. This research aimed to evaluate the impacts of O₃ on <em>Robinia pseudoacacia</em> L., a tree species introduced worldwide due to its ability for symbiotic nitrogen fixation, and to assess if rhizobia root inoculation could alleviate the effects from O₃. Using a free-air O₃ exposure system, plants either inoculated with <em>Mesorhizobium</em> or left uninoculation (Inoculated <em>vs.</em> Control) were subjected to ambient, 1.5 × , and 2 × ambient O₃ levels over a 129-day period. Measurements were made of visible foliar injury (VFI), leaf color index (SPAD), biomass growth and stomatal O₃ uptake. In the presence of elevated O₃, rhizobia inoculation significantly decreased VFI and fine root biomass loss. This protective mechanism was associated with a 22 % reduction in maximum stomatal conductance (<em>g</em>_max), which restricted stomatal O₃ uptake. Dose-response relationships showed that flux-based indices (POD_y) better explained VFI and biomass development than exposure-based indices (AOT40). Accordingly, critical levels (CLs) for O₃ were set at 24.5 mmol m⁻² POD₀ causing the first appearance of VFI, and 7.1 mmol m⁻² POD₄ resulting in a 4 % reduction in biomass. These CLs are higher than those for O₃-sensitive species, suggesting moderate O₃ tolerance of <em>R. pseudoacacia</em> to O₃. Overall, the present results highlight that rhizobia inoculation can increase tree resistance to O₃ stress, possibly by greater limitation of stomatal O₃ uptake and by improving tolerance to oxidative stress. As climate change intensifies abiotic stressors, further research should investigate the combined stress mitigation potential of rhizobial symbioses.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102811"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of the machine learning emission inversion method of the CUACE model on the basis of fused Fengyun observations","authors":"Tao Niu ,&nbsp;Congwu Huang ,&nbsp;Yiliang Jiang ,&nbsp;Qi Jiang ,&nbsp;Bihui Zhang ,&nbsp;Hongli Liu ,&nbsp;Rong Li ,&nbsp;Yuzhan Xie ,&nbsp;Tijian Wang","doi":"10.1016/j.apr.2025.102813","DOIUrl":"10.1016/j.apr.2025.102813","url":null,"abstract":"<div><div>In this study, the maximum correlation coefficient (MCC) method was employed to fuse Fengyun (FY) 3D and 4A satellite aerosol optical depth (AOD) products, and an enhanced particulate matter remote sensing (PMRS) method was subsequently utilised for inverting near-surface fine particulate matter (PM_2.5) and inhalable particle (PM₁₀) concentrations. Comparative experiments with and without assimilation revealed that the combination of the nudging emission inversion method and machine learning increased the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model in predicting the air quality index (AQI), PM_2.5, and PM₁₀ in the China, Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, whereas the positive assimilation effect on emission sources did not decrease rapidly over time. Assimilation using satellite fusion revision data in January 2022 reduced the root mean square errors of the CUACE forecasts of the AQI, PM_2.5, and PM₁₀ by 46 %, 45 %, 36 % compared to nonassimilation forecasts, and 13 %, 13 %, 8 % compared to assimilation using only ground-based observations, respectively. The average threat score (TS) increased by up to 32 % compared with that under the no-assimilation scenario. This demonstrates that the accuracy of the CUACE model for the PM_2.5 and PM₁₀ concentrations can be notably enhanced by employing the nudging emission inversion and machine learning methods developed in this study to assimilate PM_2.5 and PM₁₀ data inverted from FY satellite fused AOD products.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102813"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning–based multiscale quantification of long-term emission contributions to PM2.5 variability in China","authors":"Hongrui Li ,&nbsp;Xiaoyong Liu ,&nbsp;Peng Zhou ,&nbsp;Zijian Liu ,&nbsp;Mengyang Li ,&nbsp;Zhaoxiang Cao","doi":"10.1016/j.apr.2025.102786","DOIUrl":"10.1016/j.apr.2025.102786","url":null,"abstract":"<div><div>Meteorology and anthropogenic emissions jointly drive urban fine particulate matter (PM_2.5), yet their long-term national-scale contributions remain unclear. We combined satellite-derived daily PM_2.5 and ionic species—black carbon (BC), ammonium (NH₄⁺), nitrate (NO₃⁻) and sulfate (SO₄²⁻)—for 372 Chinese cities (2001–2024) with ERA5 meteorology to build a four-step framework of multiscale decomposition, meteorological normalization, spatial-source separation and component attribution. Kolmogorov–Zurbenko filtering partitions daily PM_2.5 variance into short-term (45.2 %), seasonal (34.5 %) and long-term (13.7 %) bands. Random-forest adjustment removes weather effects and reveals that the long-term emission signal (X_EMIS-LT) flipped from a mean + 3 μg/m³ during 2001–2012 to −2 μg/m³ in 2017–2024, except in the Chengdu–Chongqing basin and Fenwei Plain. Empirical orthogonal-function analysis attributes 68 % of X_EMIS-LT to weakened regional transport and 32 % to local abatement, forming an east-coast transport belt versus inland local dominance. Gradient-boosting trees interpreted with SHAP indicate that BC and NH₄⁺ drive local trends, whereas NO₃⁻ and SO₄²⁻ control transport-related changes. Short-term and seasonal modes govern day-to-day and intrayear variability, while the long-term mode tracks policy effectiveness. Coordinated SO₂–NO_x–NH₃ cuts and inter-provincial management are required along the eastern seaboard, while enclosed basins should emphasize primary carbon and agricultural ammonia reductions. The results offer a quantitative, component-resolved basis for region-specific PM_2.5 mitigation across China.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102786"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating IAM-based CO2 projections and traffic demand forecasting for regional CO2 emission mapping in the transport sector","authors":"Hyejung Hu ,&nbsp;Min-Young Choi ,&nbsp;Bomi Kim ,&nbsp;Minje Choi ,&nbsp;Sunggu Kang ,&nbsp;Hyejin Park ,&nbsp;Minwoo Park ,&nbsp;Jinseok Kim ,&nbsp;Jung-Hun Woo","doi":"10.1016/j.apr.2025.102790","DOIUrl":"10.1016/j.apr.2025.102790","url":null,"abstract":"<div><div>Many countries around the world are formulating and implementing policies to reduce greenhouse gas (GHG) emissions based on their Nationally Determined Contributions (NDCs). In this context, accurate assessment of current emissions and reliable projection of future emissions are essential. Since long-term forecasts are typically conducted at the national level, spatial allocation techniques are necessary to enable sub-national or grid-level analysis. In the transport sector, regional disparities in emissions are particularly pronounced, requiring spatial projections to reflect expected changes in societal and infrastructural conditions. This study presents a methodology that utilizes an integrated assessment model (IAM) to forecast national-level energy use and CO₂ emissions, followed by spatial downscaling of transport emissions to administrative and grid levels. To account for future spatial variability in road transport activity, a traffic demand forecasting model is incorporated. The proposed methodology is applied to the case of South Korea. National CO₂ emissions are projected using the MESSAGEix-KR IAM, while future road traffic demand is estimated using the EMME4 model. Based on these projections, spatial allocation coefficients are developed, and transport sector emissions are spatially distributed accordingly. Findings indicate that shifts in road infrastructure and traffic patterns are effectively reflected in the spatial distribution of emissions. The proposed methodology facilitates the development of high-resolution future emissions inventories and spatially explicit CO₂ emission maps. This methodology serves as valuable tools for supporting the formulation of carbon neutrality policies, designing region-specific mitigation strategies, and monitoring progress in emissions reduction efforts.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102790"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}