Atmospheric Environment最新文献

{"title":"Aqueous photochemistry of brown carbon: H2O2 formation, enhanced SO2 oxidation, and the real role of excited triplets","authors":"Beibei Liu ,&nbsp;Lingdong Kong ,&nbsp;Yixuan An ,&nbsp;Yu Lu ,&nbsp;Yuwen Wang ,&nbsp;Jie Tan ,&nbsp;Xingfu Tang ,&nbsp;Lin Wang","doi":"10.1016/j.atmosenv.2025.121366","DOIUrl":"10.1016/j.atmosenv.2025.121366","url":null,"abstract":"<div><div>Field observations highlight high H₂O₂ level and enhanced sulfate production during haze episodes in China, but so far, the known H₂O₂ sources and traditional secondary sulfate formation mechanisms cannot elucidate these phenomena. In recent years, the atmospheric photosensitized multiphase oxidation of SO₂ to trigger sulfate production has attracted great attention. However, few reports on the contributions of triplet excited states and its secondary reactive oxygen species (ROS) formed in photosensitization reaction to sulfate formation. In this study, the aqueous photochemistry of water-soluble humic-like substances (HULIS) proxy was further investigated. It was found that hydroxyl radicals and peroxyl radicals were produced after 313 nm UV irradiation, accompanied by H₂O₂ production. The effect of HULIS photosensitization on SO₂ oxidation was also investigated. The results showed that the triplet excited states of HULIS (³HULIS∗) cannot directly and effectively oxidize SO₂ to form sulfate, but the formation of sulfate was realized by the oxidation of ROS generated by the secondary reaction of ³HULIS∗ in the presence of O₂ and hydrogen atom donors such as HULIS itself. These findings clarify the misunderstood role of the ³HULIS∗ in the photosensitized oxidation of SO₂, and the study helps better understand atmospheric photosensitization processes.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"358 ","pages":"Article 121366"},"PeriodicalIF":4.2,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Prediction of elemental carbon and organic carbon using a hybrid deep learning model integrated temporal dependencies and meteorological features","authors":"Tatenda Makoni ,&nbsp;Shu-Qing Ding ,&nbsp;Hong-Di He ,&nbsp;Chun-Xia Lu ,&nbsp;Wei-guo Wu","doi":"10.1016/j.atmosenv.2025.121371","DOIUrl":"10.1016/j.atmosenv.2025.121371","url":null,"abstract":"<div><div>Elemental Carbon (EC) and Organic Carbon (OC) are critical components of PM_2.5, with significant implications for air quality and public health. Traditional prediction models often fail to capture the non-linear dynamics of EC and OC, particularly in urban environments with high traffic and industrial emissions. This study addresses this gap by proposing a novel hybrid deep learning model that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with attention mechanisms to account for temporal dependencies, meteorological factors, and co-pollutants (PM_2.5, O₃). Using comprehensive air quality and meteorological data from Changshu City, we identify distinct diurnal and seasonal patterns of EC and OC, driven by traffic emissions, weather conditions, and secondary aerosol formation. The proposed model significantly outperforms traditional methods, achieving high prediction accuracy for both EC and OC concentrations. Key innovations include the integration of attention mechanisms to prioritize critical time steps and the incorporation of meteorological features and co-pollutants, which enhance the model's ability to capture complex pollutant interactions. The results demonstrate the model's robustness in real-time air quality forecasting, providing actionable insights for urban planning and pollution mitigation strategies. This research contributes to the field by offering a scalable and accurate tool for predicting <span>EC</span> and <span>OC</span> in dynamic urban environments, ultimately supporting efforts to improve public health and sustainable urban development.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"359 ","pages":"Article 121371"},"PeriodicalIF":4.2,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Modification effects of regional temperature and humidity on the relationship between daily mortality and short-term coexposure to fine particulate matter and ozone","authors":"Jing Zeng ,&nbsp;Jian Qian ,&nbsp;Jiayi Chen ,&nbsp;Sheng Li ,&nbsp;Junyu Wang ,&nbsp;Menghan Yao ,&nbsp;Qianqian Du ,&nbsp;Na Yang ,&nbsp;Tao Zhang ,&nbsp;Fei Yin ,&nbsp;Chenglin Tao ,&nbsp;Xinyin Xu ,&nbsp;Nan Wang ,&nbsp;Menglu Jiang ,&nbsp;Xingyu Zhang ,&nbsp;Ying Deng ,&nbsp;Yue Ma","doi":"10.1016/j.atmosenv.2025.121369","DOIUrl":"10.1016/j.atmosenv.2025.121369","url":null,"abstract":"<div><div>Previous studies assessing the synergistic impact of PM_2.5 and O₃ on mortality have demonstrated regional heterogeneity in results. A time series study based on 130 Chinese counties aimed to investigate the modification of regional temperature and humidity on this synergy effect. Synergy indices and analyses stratified by coexposure level were applied to characterize the synergistic impacts of PM_2.5 and O₃ on mortality. Modification effects of temperature and relative humidity were evaluated by stratified analyses. Deaths attributable to PM_2.5 and O₃ were calculated both with and without considering their synergy, as well as the influences of regional temperature and relative humidity. The increases in cardiorespiratory mortality every 10 <span><math><mrow><mi>μ</mi></mrow></math></span> g/m³ rise in PM_2.5 and O₃ are significantly greater under high O₃ (2 %) and PM_2.5 (1.10 %) levels than under low O₃ (0.53 %) and PM_2.5 (0.86 %) levels. The synergistic effects were more pronounced in low-temperature areas for cardiovascular mortality and in low-humidity areas for respiratory mortality. Ignoring the synergistic impact of PM_2.5 and O₃ may lead to inaccurate estimates of attributable deaths, with the extent of inaccuracy influenced by temperature and relative humidity. Our study underscores the importance of considering meteorological factors when assessing the synergistic impact of PM_2.5 and O₃ to improve the accuracy of disease burden estimates and inform air pollution control policies.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"358 ","pages":"Article 121369"},"PeriodicalIF":4.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Assessing the efficiency of different mitigation strategies to reduce shipping related air pollution levels and exposure in the Mediterranean coastal region – An ensemble modelling approach","authors":"Evangelia Fragkou ,&nbsp;George Tsegas ,&nbsp;Ummugulsum Alyuz ,&nbsp;Risto Hänninen ,&nbsp;Jana Moldanova ,&nbsp;Sara Jutterström ,&nbsp;Elisa Majamäki ,&nbsp;Jukka-Pekka Jalkanen ,&nbsp;Ranjeet S. Sokhi ,&nbsp;Jaakko Kukkonen ,&nbsp;Mikhail Sofiev ,&nbsp;Leonidas Ntziachristos","doi":"10.1016/j.atmosenv.2025.121347","DOIUrl":"10.1016/j.atmosenv.2025.121347","url":null,"abstract":"<div><div>The air quality in the Mediterranean coastal region is adversely affected by rising shipping emissions in the area. The present study demonstrates the results of an ensemble modelling approach for assessing the impact of shipping emissions on current and future air quality concentrations and exposure in the coastal Mediterranean region.</div><div>Ensemble model means were calculated based on the results of three regional or global dispersion models (EMEP, SILAM and CMAQ) to simulate annual and seasonal atmospheric pollutant dispersion patterns in the Mediterranean region, under the baseline 2018 scenario and two 2050 future scenarios developed within the EMERGE (Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions) project. The selected scenarios examine the impact of a) the designation of a Mediterranean Sulfur/Nitrogen Emission Control Area (SECA/NECA) and the extensive use of open-loop scrubbers, Exhaust Gas Cleaning Systems and Selective Catalytic Reduction systems on future shipping emissions (Scenario 3 - S3) and b) the increased use of methane and methanol as alternative fuels in 2050 (Scenario 8 - S8).</div><div>Ensemble model results were combined with population data for calculating population-weighted concentrations of legislated atmospheric pollutants for 30 Mediterranean coastal cities, representing exposure of city inhabitants to current and predicted future air pollution levels.</div><div>The findings of the present study reveal significant annual city average reductions of SO₂ and PM_2.5 shipping related exposure for both S3 (89.78 % reduction) and S8 (96.22 % reduction), while shipping induced NO₂ city-average exposure levels were efficiently controlled in S3 (42.74 % reduction) but not in S8 (4.39 % reduction). The reported results need to be specifically considered in the development of policy recommendations on shipping emission control for the Mediterranean region.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121347"},"PeriodicalIF":4.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhanced continuous aerosol optical depth (AOD) estimation using geostationary satellite data: focusing on nighttime AOD over East Asia","authors":"Sanghyeon Song ,&nbsp;Yoojin Kang ,&nbsp;Jungho Im ,&nbsp;Sang Seo Park","doi":"10.1016/j.atmosenv.2025.121365","DOIUrl":"10.1016/j.atmosenv.2025.121365","url":null,"abstract":"<div><div>Continuous aerosol monitoring in East Asia is essential due to the massive aerosol emissions from natural and anthropogenic sources. Geostationary satellites enable continuous aerosol monitoring; however, the observation is limited to the daytime. This study proposed machine learning-based models to estimate daytime and nighttime aerosol optical depth (AOD) in East Asia using a geostationary satellite, Geo-KOMPSAT-2A (GK-2A). The input variables for the machine learning models include the brightness temperature (BT) and top-of-atmosphere (TOA) reflectance from GK-2A, meteorological and geographical data, and auxiliary variables. The two models that used different combinations of GK-2A variables were proposed and compared: the all-day BT model, which estimates AOD during both day and night using BT variables, and the daytime TOA model, which estimates AOD during the day using TOA reflectance variables as well. The estimated AODs by the models were validated with ground-based AOD data from the Aerosol Robotic Network (AERONET) by 10-fold cross-validation and hold-out validation methods. The performance of the daytime TOA model was slightly higher than the all-day BT model during the day (R² = 0.80–0.82, root mean square error (RMSE) = 0.107–0.116 for the all-day BT model, R² = 0.83, RMSE = 0.098 for the daytime TOA model). The SHapley Additive exPlanations (SHAP) analysis showed that total precipitable water content and seasonality contributed the most for both proposed models. BT differences and TOA reflectance variables were identified as the next most contributing variables for the all-day BT and daytime TOA models. The spatiotemporal distributions of estimated AODs from the proposed models show similar patterns compared with other AOD products. A time series comparison at a test station demonstrated that the estimated AOD of the proposed models was consistent with the AERONET AOD.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"358 ","pages":"Article 121365"},"PeriodicalIF":4.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-enabled estimation and high-resolution forecasting of atmospheric VOCs","authors":"Bingqing Lu ,&nbsp;Shuyue Zhang ,&nbsp;Chao Liu ,&nbsp;Gantuya Ganbat ,&nbsp;Hartmut Herrmann ,&nbsp;Xiang Li ,&nbsp;Yang Zhao","doi":"10.1016/j.atmosenv.2025.121364","DOIUrl":"10.1016/j.atmosenv.2025.121364","url":null,"abstract":"<div><div>Atmospheric volatile organic compounds (VOCs) are crucial to reducing air pollution, which has adverse effects on human health. However, VOCs estimation and forecasting has been limited by insufficient observational data and complex interactions with other pollutants. Here, we developed machine learning models to estimate regional VOCs distributions and produce hourly distribution maps for the next 24 h with a 1 km resolution. Combining VOCs observations from monitoring sites, along with meteorological, emission, geographical and other related variables, we successfully employed a space-time LightGBM model to estimate VOCs concentrations from 2019 to 2021 and created a high-resolution uninterrupted VOCs dataset in Shanghai. Using this dataset, we evaluated the forecasting performance of three machine learning models and one deep learning model, finding that LightGBM outperformed other models. The models demonstrated substantial efficacy, with R² values ranging from 0.527 to 0.938 and MAE values between 41.5 and 126 ppb, indicating significant performance in both temporal and spatial scales. With developed models, we provided first high-resolution hourly VOCs prediction maps in Shanghai, providing valuable insights for control strategy formulation in advance. This study also offers a novel tool for VOCs forecasting, with the developed model being adaptable to other regions experiencing high VOCs levels.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"358 ","pages":"Article 121364"},"PeriodicalIF":4.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Corn-straw-processed fuels in residential use: Combustion characteristics, kinetics, and pollutant formation mechanisms","authors":"Yue Zhang ,&nbsp;Fei Gao ,&nbsp;Zhenxing Shen ,&nbsp;Lei Cao ,&nbsp;Haonan Li ,&nbsp;Bin Li ,&nbsp;Ke Zhang ,&nbsp;Jian Sun ,&nbsp;Bin Zhang","doi":"10.1016/j.atmosenv.2025.121372","DOIUrl":"10.1016/j.atmosenv.2025.121372","url":null,"abstract":"<div><div>This study explored the combustion characteristics, kinetics, pollutant formation, and reduction mechanisms of corn straw (CS)-processed fuels in residential applications. CS was collected to prepare CS briquettes (CSB) and CS charcoal (CSC). Derivative thermogravimetric (DTG) peaks implied that the combustion of CS, CSB, and CSC was dominated by pyrolysis, pyrolysis and char combustion, char combustion, respectively. Briquetting considerably decreased the devolatilization index (<em>D</em>) (66.4 %) and the maximum weight loss rate (<em>DTG</em>_<em>max</em>) (55.0 %); therefore, the CSB exhibited controlled pyrolysis and lower pollutant emissions than CS. Moreover, carbonization led to a considerable reduction in volatile matter (VM) content; thus, CSC exhibited lower pollutant emissions than CS. Both CSB and CSC successfully achieved significant emission reductions compared to CS for SO₂, NO_x, PM_2.5 and its sub-factions include organic carbon (OC), element carbon (EC), polycyclic aromatic hydrocarbons (PAHs), inorganic ions and metals. CSC is a little more effective in most pollutants' reduction, and much more effective in EC reduction, yet less effective in ions reduction compared to CSB. EC emissions positively correlate with VM content (R² = 0.989), which lead to the more remarkable EC reduction in CSC for CSC's much lowered VM content. Overall, A well-designed briquette factory location as well as encouragement of other users besides residents would make it sustainable for the large-scale utilization of corn straw processed fuels.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"358 ","pages":"Article 121372"},"PeriodicalIF":4.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Real-time contributions of different types of VOCs to O3 formation in a typical industrial park: capturing features at hourly resolution","authors":"Haijun Zhang ,&nbsp;Mengmeng Zhao ,&nbsp;Chih-Rung Chen ,&nbsp;Hongbin Jiang ,&nbsp;Chunqiong Liu ,&nbsp;Kai Shi","doi":"10.1016/j.atmosenv.2025.121368","DOIUrl":"10.1016/j.atmosenv.2025.121368","url":null,"abstract":"<div><div>Owing to a high nonlinear dynamic relationship between the precursors of NO_x-VOCs and O₃ formation in actual atmospheric conditions, traditional approaches—such as empirical kinetic models and air quality models—struggle to accurately quantify the hour-scale relative contributions of VOCs to O₃ production. Chemical industrial parks have become a critical challenge for ozone pollution control due to their high variability in emission intensities and types. This study aims to elucidate the real-time, hourly-scale relative contributions of alkanes, alkenes, aromatic hydrocarbons, and OVOCs to O₃ formation. By integrating Coupled Detrended Fluctuation Analysis (CDFA) with the Ozone Formation Potential (OFP) model, this research analyzes 12 months of VOC composition data collected in 2023 from a chemical industrial park. OFP analysis reveals that the contributions to O₃ formation followed an order of aromatics &gt; OVOCs &gt; alkanes &gt; alkenes during non-O₃ pollution periods, which was consistent with the results from O₃ pollution days. A different trend was found from the results of CDFA analysis, with an order of aromatics &gt; OVOCs &gt; alkenes &gt; alkanes. Importantly, evidence from the OFP not only verified the high effectiveness of the CDFA method, but also successfully revealed the spatial and temporal evolution characteristics of VOC contributions with different types. This study pioneers a novel application of CDFA, enabling precise quantification of the real-time contributions of various VOC species to O₃ formation. By significantly enhancing the resolution of VOCs relative contributions to O₃, this research offers an effective strategy for mitigating complex air pollution. The findings provide a robust framework for improving pollution control measures.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"359 ","pages":"Article 121368"},"PeriodicalIF":4.2,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Influence of atmospheric ammonia on secondary inorganic aerosol formation in PM2.5 during spring 2024 in the Hongseong area, Republic of Korea","authors":"Jun-Hyeok Jang ,&nbsp;Jinhyeok Hong ,&nbsp;Jong Bum Kim ,&nbsp;Sechan Park ,&nbsp;Kyucheol Hwang ,&nbsp;Jeongho Kim ,&nbsp;Jin Young Kim ,&nbsp;Gwi-Nam Bae ,&nbsp;Seongheon Kim ,&nbsp;Kyung Hwan Kim","doi":"10.1016/j.atmosenv.2025.121363","DOIUrl":"10.1016/j.atmosenv.2025.121363","url":null,"abstract":"<div><div>Nitrate and organic components have been considered key contributors to high PM_2.5 concentrations in Korea. This study examines the chemical evolution of aerosol species influenced by atmospheric ammonia (NH₃) in Hongseong county, Chungcheongnam-do, Republic of Korea, during late spring 2024 (May 20 – June 18), a period characterized by intensive agricultural activity. Using an aerosol chemical speciation monitor and ammonia analyzer, an ammonium-rich atmospheric state (average NH₃ concentration: 26.7 ± 12.4 ppb) was observed, with notable contributions from ammonium (NH₄⁺), nitrate (NO₃⁻), and sulfate (SO₄²⁻), especially under high-humidity conditions. The relative contributions of nitrate to PM_2.5 for low (0–15 μg/m³), medium (16–30 μg/m³), and high (over 30 μg/m³) concentration intervals were 13.8 %, 23.4 %, and 29.6 %, respectively. Elevated relative humidity (RH), averaging 63.1 % (low), 82.3 % (medium), and 91.7 % (high), played a significant role in nitrate formation for those concentration intervals. Elevated nighttime ammonium conversion ratios (NHR) highlighted the importance of abundant atmospheric NH₃ under high RH conditions, facilitating effective heterogeneous uptake and subsequent particulate ammonium formation. Source tracking using the conditional bivariate probability function (CBPF) model identified agricultural fields, power plants, and industrial complexes as major sources of precursor emissions. These results emphasize the need to control emissions of NH₃, SO₂, and NOx in rural areas to mitigate PM_2.5 pollution and improve air quality.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"358 ","pages":"Article 121363"},"PeriodicalIF":4.2,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Evaluation of black carbon emission inventories over Indian subcontinent: Role of open biomass burning and its representation in emission fluxes","authors":"Akanksha Arora ,&nbsp;H. Gadhavi ,&nbsp;S. Ramachandran ,&nbsp;T.A. Rajesh","doi":"10.1016/j.atmosenv.2025.121367","DOIUrl":"10.1016/j.atmosenv.2025.121367","url":null,"abstract":"<div><div>Black carbon (BC) aerosols play an important role in air pollution, environment, and climate. Emission inventories of BC are key inputs for atmospheric models that assess the impact of BC emissions on health and the environment. However, estimated BC emission fluxes are highly uncertain. In this study, we evaluated three emission inventories — the Community Emission Data System (CEDS), the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants (ECLIPSE) and Global Fire Emission Database (GFED) — using the Lagrangian dispersion model FLEXPART, along with BC observations from two locations in India viz. Ahmedabad (urban) and Gadanki (rural). The modeled BC concentrations using ECLIPSE and CEDS were comparable to each other; however, the modeled BC concentrations were underestimated by a factor of 2 using these emission inventories. The annual mean biases between model and observation (observation-model) are approximately 3 μg/m³ and 1 μg/m³ at Ahmedabad (urban) and Gadanki (rural), respectively. Adding the contribution of biomass-burning sources from the GFED inventory (less than 0.01 μg/m³) did not significantly improve the bias. Open biomass burning (OBB) emissions, resulting from large-scale burning of biomass in agricultural fields, forests, and wastelands, is the largest contributor to BC concentrations globally and are highly uncertain. To investigate the underestimation of BC by the model and the role of OBB emissions in this discrepancy, a novel rank-based statistical framework is developed. This framework integrates satellite-detected fire hotspots, FLEXPART model, and ground-based observations to evaluate relative contribution of OBB emissions to BC concentrations. We find that OBB emissions are not a significant source of BC concentrations over Ahmedabad (23.03°N, 72.55°E). In contrast, BC emissions from OBB are underestimated at Gadanki (13.48°N, 79.18°E). The framework used to evaluate cause-effect relation between OBB and BC can help constrain better the sources of BC. This framework can be readily extended globally to evaluate other sectors where emission activity data can be observed using satellites.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"359 ","pages":"Article 121367"},"PeriodicalIF":4.2,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}