Pub Date : 2026-01-07DOI: 10.1016/j.atmosenv.2026.121789
Swagata Mukhopadhyay , Shantikumar S. Ningombam , Akihiro Uchiyama , Sonam Jorphail , Chiranjeevi G. Vivek , T.S. Shrungeshwara , Sreedevi P. , Tsuneo Matsunaga , Som K. Sharma , Pawan Gupta , Dorje Angchuk , Sridevi Jade
Sun–sky radiometer (model POM-01) is commonly used for studying aerosol optical and physical properties at selected aerosol-specific channels. Although the instrument is equipped with a precipitable water vapor (PWV) channel at 940 nm, the inbuilt software does not provide a tool for estimation of PWV. Hence, the current study adopted a new methodology to estimate PWV from three high-altitude ( 3400 m MSL) sites, Hanle, Merak, and Leh, located in Ladakh, India. The retrieval algorithm focuses on the precise estimation of the calibration constant () and coefficients and using modified Langley plots in two different methods. The estimated average value of is 0.59 ± 0.09 which is very close to those commonly used in global studies. Further, the estimated and values from both methods are found to be similar, which may be due to the advantages of the dry and high-altitude environment, where the annual total column water vapor is typically less than 6 mm. The estimated PWV using observations at selected full clear and stable atmospheric conditions compares well with satellite, AERONET, GPS, reanalysis and empirical model data with correlation coefficient varying from 0.91 to 0.97. Further, the estimated propagated root mean square error (rmse) varies from 0.37 mm to 2.58 mm. These results indicated that sun–sky radiometer derived PWV showed good consistency with the derived PWV from independent data sources at the three sites.
{"title":"Retrieval of precipitable water vapor from sun–sky radiometer (POM-01) at 940 nm absorption band: Calibration, measurement and validation","authors":"Swagata Mukhopadhyay , Shantikumar S. Ningombam , Akihiro Uchiyama , Sonam Jorphail , Chiranjeevi G. Vivek , T.S. Shrungeshwara , Sreedevi P. , Tsuneo Matsunaga , Som K. Sharma , Pawan Gupta , Dorje Angchuk , Sridevi Jade","doi":"10.1016/j.atmosenv.2026.121789","DOIUrl":"10.1016/j.atmosenv.2026.121789","url":null,"abstract":"<div><div>Sun–sky radiometer (model POM-01) is commonly used for studying aerosol optical and physical properties at selected aerosol-specific channels. Although the instrument is equipped with a precipitable water vapor (PWV) channel at 940 nm, the inbuilt software does not provide a tool for estimation of PWV. Hence, the current study adopted a new methodology to estimate PWV from three high-altitude (<span><math><mo>></mo></math></span> 3400 m MSL) sites, Hanle, Merak, and Leh, located in Ladakh, India. The retrieval algorithm focuses on the precise estimation of the calibration constant (<span><math><msub><mrow><mi>V</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) and coefficients <span><math><mi>a</mi></math></span> and <span><math><mi>b</mi></math></span> using modified Langley plots in two different methods. The estimated average value of <span><math><mi>b</mi></math></span> is 0.59 ± 0.09 which is very close to those commonly used in global studies. Further, the estimated <span><math><msub><mrow><mi>V</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> and <span><math><mi>b</mi></math></span> values from both methods are found to be similar, which may be due to the advantages of the dry and high-altitude environment, where the annual total column water vapor is typically less than 6 mm. The estimated PWV using observations at selected full clear and stable atmospheric conditions compares well with satellite, AERONET, GPS, reanalysis and empirical model data with correlation coefficient varying from 0.91 to 0.97. Further, the estimated propagated root mean square error (rmse) varies from 0.37 mm to 2.58 mm. These results indicated that sun–sky radiometer derived PWV showed good consistency with the derived PWV from independent data sources at the three sites.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121789"},"PeriodicalIF":3.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973906","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}
Pub Date : 2026-01-07DOI: 10.1016/j.atmosenv.2025.121767
Shuai Sun , Yuzhong Zhang , Song Liu , Lei Shu , Isabelle De Smedt , Lu Hu , Wade Permar , Dirk Richter , Alan Fried , Lei Zhu
Satellite-derived formaldehyde (HCHO) column densities are commonly used to infer regional emissions of non-methane volatile organic compounds (NMVOCs). However, intercomparison and validation of HCHO retrievals from different satellite sensors remain scarce, especially under fire periods. Here, we use observations from FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality experiment) and WE-CAN (Western Wildfire Experiment for Cloud Chemistry, Aerosol and Nitrogen), two flight campaigns dedicated to investigating smoke plumes during active fire seasons, combined with simulations from the GEOS-Chem to intercompare and validate five HCHO products from four satellites (OMI, OMPS-NPP, OMPS-N20, and TROPOMI). Our analysis suggests that all satellite products consistently capture elevated HCHO signals over the southeastern US and California, but they tend to report lower column values compared to our aircraft-constrained model estimates, with differences ranging from 11.0 % to 56.7 %. Our results imply that while vertical profile shape (reflected in the air mass factor, AMF) plays a role, errors in the slant column retrieval and the treatment of aerosol and cloud scattering effects may be key sources of uncertainty in the satellite HCHO products during fire events. Therefore, future retrieval improvements should prioritize better aerosol and slant column accuracy to reduce biases.
{"title":"Validation of satellite formaldehyde products constrained by aircraft observations over the United States during fire seasons","authors":"Shuai Sun , Yuzhong Zhang , Song Liu , Lei Shu , Isabelle De Smedt , Lu Hu , Wade Permar , Dirk Richter , Alan Fried , Lei Zhu","doi":"10.1016/j.atmosenv.2025.121767","DOIUrl":"10.1016/j.atmosenv.2025.121767","url":null,"abstract":"<div><div>Satellite-derived formaldehyde (HCHO) column densities are commonly used to infer regional emissions of non-methane volatile organic compounds (NMVOCs). However, intercomparison and validation of HCHO retrievals from different satellite sensors remain scarce, especially under fire periods. Here, we use observations from FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality experiment) and WE-CAN (Western Wildfire Experiment for Cloud Chemistry, Aerosol and Nitrogen), two flight campaigns dedicated to investigating smoke plumes during active fire seasons, combined with simulations from the GEOS-Chem to intercompare and validate five HCHO products from four satellites (OMI, OMPS-NPP, OMPS-N20, and TROPOMI). Our analysis suggests that all satellite products consistently capture elevated HCHO signals over the southeastern US and California, but they tend to report lower column values compared to our aircraft-constrained model estimates, with differences ranging from 11.0 % to 56.7 %. Our results imply that while vertical profile shape (reflected in the air mass factor, AMF) plays a role, errors in the slant column retrieval and the treatment of aerosol and cloud scattering effects may be key sources of uncertainty in the satellite HCHO products during fire events. Therefore, future retrieval improvements should prioritize better aerosol and slant column accuracy to reduce biases.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121767"},"PeriodicalIF":3.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973995","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}
Pub Date : 2026-01-06DOI: 10.1016/j.atmosenv.2026.121775
Seonyeong Park , Wonei Choi , Hanlim Lee
The accurate estimation of surface nitrogen dioxide (NO2) from satellite observations is essential for regional air quality management using spaceborne sensors. However, estimation performance can vary significantly under different local and temporal conditions. This study investigates specific conditions causing satellite-based surface NO2 estimators underperformance. We developed a machine-learning model using hourly NO2 vertical column densities from the Geostationary Environment Monitoring Spectrometer (GEMS), along with meteorological and land-use data for December 2021 to November 2022. The model was trained and validated using observations from 614 ground-based monitoring stations across South Korea and demonstrated strong overall performance (R = 0.88). The model accuracy varied by station type and temporal conditions. Higher performance was observed at urban and roadside stations (R = 0.83–0.87). Contrastingly, national background and rural stations exhibited lower correlations (R = 0.66–0.68); and port stations showed moderate performance (R = 0.74). Seasonally, the performance peaked in winter (R = 0.89) and declined in summer (R = 0.82). Diurnally, better performance was observed between 09:45 and 14:45 Korea Standard Time (KST) when higher number of GEMS observations were available. The combined analysis further showed that urban and roadside sites maintained a consistently high performance during winter. These results indicate strong influence of spatial and seasonal factors on model accuracy. Therefore, local NO2 levels, boundary layer data, and land–sea corrections should be considered to improve satellite-based surface NO2 estimation. The findings of this study provide practical guidance on the spatiotemporal limits of geostationary monitoring, which can be adopted, with improvements, in establishing geostationary satellites as reliable tools for operational air quality policies development and public health assessments.
{"title":"Diagnosing the underperformance of satellite-based surface NO2 estimation using geostationary observations: Insights for improvement from station-type and temporal analyses in South Korea","authors":"Seonyeong Park , Wonei Choi , Hanlim Lee","doi":"10.1016/j.atmosenv.2026.121775","DOIUrl":"10.1016/j.atmosenv.2026.121775","url":null,"abstract":"<div><div>The accurate estimation of surface nitrogen dioxide (NO<sub>2</sub>) from satellite observations is essential for regional air quality management using spaceborne sensors. However, estimation performance can vary significantly under different local and temporal conditions. This study investigates specific conditions causing satellite-based surface NO<sub>2</sub> estimators underperformance. We developed a machine-learning model using hourly NO<sub>2</sub> vertical column densities from the Geostationary Environment Monitoring Spectrometer (GEMS), along with meteorological and land-use data for December 2021 to November 2022. The model was trained and validated using observations from 614 ground-based monitoring stations across South Korea and demonstrated strong overall performance (R = 0.88). The model accuracy varied by station type and temporal conditions. Higher performance was observed at urban and roadside stations (R = 0.83–0.87). Contrastingly, national background and rural stations exhibited lower correlations (R = 0.66–0.68); and port stations showed moderate performance (R = 0.74). Seasonally, the performance peaked in winter (R = 0.89) and declined in summer (R = 0.82). Diurnally, better performance was observed between 09:45 and 14:45 Korea Standard Time (KST) when higher number of GEMS observations were available. The combined analysis further showed that urban and roadside sites maintained a consistently high performance during winter. These results indicate strong influence of spatial and seasonal factors on model accuracy. Therefore, local NO<sub>2</sub> levels, boundary layer data, and land–sea corrections should be considered to improve satellite-based surface NO<sub>2</sub> estimation. The findings of this study provide practical guidance on the spatiotemporal limits of geostationary monitoring, which can be adopted, with improvements, in establishing geostationary satellites as reliable tools for operational air quality policies development and public health assessments.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121775"},"PeriodicalIF":3.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923089","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}
Pub Date : 2026-01-05DOI: 10.1016/j.atmosenv.2026.121776
Ashok K. Luhar, David M. Etheridge, Zoë M. Loh, Fabienne Reisen
Atmospheric inverse modelling for source estimation typically relies on hourly-averaged concentration measurements, overlooking valuable extra information contained in real-world high-frequency data, largely due to the absence of suitable modelling frameworks. We present a novel inverse modelling approach that leverages this information by representing the full concentration probability distribution function (PDF) through concentration percentiles derived from a left-shifted clipped-gamma parameterisation, requiring only the mean and variance of concentration, with intermittency parameterised empirically. Mean and variance are modelled using Lagrangian and Eulerian approaches, respectively, within a backward dispersion framework to efficiently compute percentiles for integration within a Bayesian inversion. Applied to a controlled methane release field experiment at a geological carbon capture and storage site, higher-order percentiles improve emission rate estimates and reduce source location uncertainty compared to mean-based inversions, though location accuracy declines slightly; lower-order percentiles worsen performance, likely due to background methane variability. Percentiles inherently encapsulate mean concentration, so combining both offers no added benefit. The primary limitation lies in variance prediction, which is more uncertain and sensitive to turbulence than mean modelling. Exploiting high-frequency data beyond mean values, the proposed percentile-based inverse modelling (PBIM) approach offers a practical path to improved source estimation, warranting further validation with longer-term, spatially extensive datasets or tracer releases with minimal background interference.
{"title":"A percentile-based inverse modelling approach for enhanced source quantification using high-frequency concentration measurements","authors":"Ashok K. Luhar, David M. Etheridge, Zoë M. Loh, Fabienne Reisen","doi":"10.1016/j.atmosenv.2026.121776","DOIUrl":"10.1016/j.atmosenv.2026.121776","url":null,"abstract":"<div><div>Atmospheric inverse modelling for source estimation typically relies on hourly-averaged concentration measurements, overlooking valuable extra information contained in real-world high-frequency data, largely due to the absence of suitable modelling frameworks. We present a novel inverse modelling approach that leverages this information by representing the full concentration probability distribution function (PDF) through concentration percentiles derived from a left-shifted clipped-gamma parameterisation, requiring only the mean and variance of concentration, with intermittency parameterised empirically. Mean and variance are modelled using Lagrangian and Eulerian approaches, respectively, within a backward dispersion framework to efficiently compute percentiles for integration within a Bayesian inversion. Applied to a controlled methane release field experiment at a geological carbon capture and storage site, higher-order percentiles improve emission rate estimates and reduce source location uncertainty compared to mean-based inversions, though location accuracy declines slightly; lower-order percentiles worsen performance, likely due to background methane variability. Percentiles inherently encapsulate mean concentration, so combining both offers no added benefit. The primary limitation lies in variance prediction, which is more uncertain and sensitive to turbulence than mean modelling. Exploiting high-frequency data beyond mean values, the proposed percentile-based inverse modelling (PBIM) approach offers a practical path to improved source estimation, warranting further validation with longer-term, spatially extensive datasets or tracer releases with minimal background interference.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121776"},"PeriodicalIF":3.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973912","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}
Pub Date : 2026-01-03DOI: 10.1016/j.atmosenv.2026.121774
Rodrigo Crespo-Miguel , Carlos Ordóñez , Ricardo García-Herrera , Steven T. Turnock
This study examines the separate roles of emissions and climate on the future evolution of high European ozone events in atmosphere-only simulations of UKESM1 (United Kingdom Earth System Model version 1). These include the historical simulation (ending in 2014), the reference scenario ssp370SST (2014–2099) – with steady increases in near-surface temperature, global population and atmospheric methane concentrations – and several variations thereof. For this purpose, we have identified local ozone extremes, connected them into large episodes (spatiotemporal aggregates with a minimum duration of three days) and calculated the episode sizes as the accumulated areal extents during their life cycles. Despite decreases in precursor emissions over most of Europe under ssp370SST, the number of local extremes and the sizes of ozone episodes would increase across the continent throughout the 21st century because of soaring methane levels. The southeast of Europe and Turkey could experience even larger increases due to rising regional emissions. Mitigation strategies targeting emissions of regional precursors, global methane and, more importantly, the combination of both would effectively decrease ozone pollution below present-day values. On the other hand, climate warming enhances biogenic emissions, reduces dry deposition fluxes and increases atmospheric humidity. Overall, this leads to moderate increases in the occurrence of local ozone extremes, but with some remarkable regional differences and a negligible impact on the sizes of ozone episodes when averaged over the whole continent. Moreover, as future ozone episodes become more common under ssp370SST, the associated circulation anomalies are expected to weaken in the future.
{"title":"Response of future near-surface ozone extremes in Europe to changes in precursor emissions and climate","authors":"Rodrigo Crespo-Miguel , Carlos Ordóñez , Ricardo García-Herrera , Steven T. Turnock","doi":"10.1016/j.atmosenv.2026.121774","DOIUrl":"10.1016/j.atmosenv.2026.121774","url":null,"abstract":"<div><div>This study examines the separate roles of emissions and climate on the future evolution of high European ozone events in atmosphere-only simulations of UKESM1 (United Kingdom Earth System Model version 1). These include the historical simulation (ending in 2014), the reference scenario ssp370SST (2014–2099) – with steady increases in near-surface temperature, global population and atmospheric methane concentrations – and several variations thereof. For this purpose, we have identified local ozone extremes, connected them into large episodes (spatiotemporal aggregates with a minimum duration of three days) and calculated the episode sizes as the accumulated areal extents during their life cycles. Despite decreases in precursor emissions over most of Europe under ssp370SST, the number of local extremes and the sizes of ozone episodes would increase across the continent throughout the 21st century because of soaring methane levels. The southeast of Europe and Turkey could experience even larger increases due to rising regional emissions. Mitigation strategies targeting emissions of regional precursors, global methane and, more importantly, the combination of both would effectively decrease ozone pollution below present-day values. On the other hand, climate warming enhances biogenic emissions, reduces dry deposition fluxes and increases atmospheric humidity. Overall, this leads to moderate increases in the occurrence of local ozone extremes, but with some remarkable regional differences and a negligible impact on the sizes of ozone episodes when averaged over the whole continent. Moreover, as future ozone episodes become more common under ssp370SST, the associated circulation anomalies are expected to weaken in the future.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121774"},"PeriodicalIF":3.7,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145922709","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}
Pub Date : 2026-01-03DOI: 10.1016/j.atmosenv.2026.121773
Ping Jing , Weizhi Deng , Thomas Crabtree , Deborah Chen , Justin Harbison , Mena Whalen , Jun Wang
Wildfire smoke is an increasingly important contributor to urban air pollution and public health risk, especially in ozone (O3) non-attainment areas like Chicago. This study assessed the impact of the 2023 wildfire smoke on ground-level O3 concentrations and associated mortality across Chicago's 77 community areas. We integrated NOAA's Hazard Mapping System smoke classifications, high-resolution downscaled O3 data, and GridMET meteorological data to construct a daily community-level dataset for 2014–2023. We estimated counterfactual O3 levels in the absence of wildfire smoke using matching, linear regression, and machine learning models. We separated the effect of smoke on O3 from those caused by meteorological variability. O3 concentrations increased with smoke density, peaking under medium smoke conditions, while the largest smoke-attributable increase (6.7 ppb) occurred under heavy smoke. Estimated daily all-cause mortality rates attributable to smoke-enhanced O3 followed a similar trend, reaching 0.24 deaths per 100,000 population per day under heavy smoke. Spatial analysis revealed that central, western, and southeastern communities experienced the greatest exposure and health burden, suggesting non-linear interactions between transported smoke and local pollution. These findings highlight how wildfire smoke exacerbates challenges in meeting National Ambient Air Quality Standards and protecting public health in urban environments.
{"title":"Assessing the impact of 2023 wildfire smoke on ozone and public health in Chicago communities","authors":"Ping Jing , Weizhi Deng , Thomas Crabtree , Deborah Chen , Justin Harbison , Mena Whalen , Jun Wang","doi":"10.1016/j.atmosenv.2026.121773","DOIUrl":"10.1016/j.atmosenv.2026.121773","url":null,"abstract":"<div><div>Wildfire smoke is an increasingly important contributor to urban air pollution and public health risk, especially in ozone (O<sub>3</sub>) non-attainment areas like Chicago. This study assessed the impact of the 2023 wildfire smoke on ground-level O<sub>3</sub> concentrations and associated mortality across Chicago's 77 community areas. We integrated NOAA's Hazard Mapping System smoke classifications, high-resolution downscaled O<sub>3</sub> data, and GridMET meteorological data to construct a daily community-level dataset for 2014–2023. We estimated counterfactual O<sub>3</sub> levels in the absence of wildfire smoke using matching, linear regression, and machine learning models. We separated the effect of smoke on O<sub>3</sub> from those caused by meteorological variability. O<sub>3</sub> concentrations increased with smoke density, peaking under medium smoke conditions, while the largest smoke-attributable increase (6.7 ppb) occurred under heavy smoke. Estimated daily all-cause mortality rates attributable to smoke-enhanced O<sub>3</sub> followed a similar trend, reaching 0.24 deaths per 100,000 population per day under heavy smoke. Spatial analysis revealed that central, western, and southeastern communities experienced the greatest exposure and health burden, suggesting non-linear interactions between transported smoke and local pollution. These findings highlight how wildfire smoke exacerbates challenges in meeting National Ambient Air Quality Standards and protecting public health in urban environments.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121773"},"PeriodicalIF":3.7,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145922710","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}
Pub Date : 2026-01-03DOI: 10.1016/j.atmosenv.2026.121772
Zhonghua He , Jie Luo , Congcong Li , Gaofeng Fan , Bin Xu , Lingxiang Wang , Zhao-Cheng Zeng , Dongmei Huang , Yuping Sun , Hao He
Wildfires represent a devastating global natural hazard, with profound ecological, economic, and societal impacts. Accurate prediction of fire behavior is paramount, yet state-of-the-art models like WRF-Fire often neglect the critical meteorological feedbacks induced by fire-emitted aerosols. To address this gap, this study develops a novel modeling framework that integrates WRF-Chem with WRF-Fire to systematically quantify how smoke-radiation interactions influence near-surface meteorology and subsequent fire behavior. Through a series of sensitivity experiments, we demonstrate that fire emissions trigger a complex, time-dependent feedback loop. Initially, aerosols suppress near-surface winds and moderate temperatures in our simulations, leading to a slower Rate of Spread (ROS) and a constrained burned area. However, as the fire persists, the accumulated emissions fundamentally alter the local atmosphere, intensifying the wind field through fire-induced circulations. This results in a dramatic reversal, with the ”With Fire” scenario subsequently exhibiting the most rapid fire front propagation. Our analysis reveals that the integrated radiative effect of all aerosols, rather than the contribution of any single species like black carbon (BC) or organic carbon (OC), is the dominant driver of these meteorological perturbations. This work concludes that incorporating these dynamic, time-evolving aerosol-meteorology-fire feedbacks is not a mere refinement but a crucial necessity for advancing the predictive accuracy of wildfire models, especially for extreme events, thereby informing more effective mitigation and response strategies.
{"title":"Fire emissions modulate wildfire spread through aerosol-meteorology feedbacks: Insights from a novel WRF-Chem/Fire coupled model","authors":"Zhonghua He , Jie Luo , Congcong Li , Gaofeng Fan , Bin Xu , Lingxiang Wang , Zhao-Cheng Zeng , Dongmei Huang , Yuping Sun , Hao He","doi":"10.1016/j.atmosenv.2026.121772","DOIUrl":"10.1016/j.atmosenv.2026.121772","url":null,"abstract":"<div><div>Wildfires represent a devastating global natural hazard, with profound ecological, economic, and societal impacts. Accurate prediction of fire behavior is paramount, yet state-of-the-art models like WRF-Fire often neglect the critical meteorological feedbacks induced by fire-emitted aerosols. To address this gap, this study develops a novel modeling framework that integrates WRF-Chem with WRF-Fire to systematically quantify how smoke-radiation interactions influence near-surface meteorology and subsequent fire behavior. Through a series of sensitivity experiments, we demonstrate that fire emissions trigger a complex, time-dependent feedback loop. Initially, aerosols suppress near-surface winds and moderate temperatures in our simulations, leading to a slower Rate of Spread (ROS) and a constrained burned area. However, as the fire persists, the accumulated emissions fundamentally alter the local atmosphere, intensifying the wind field through fire-induced circulations. This results in a dramatic reversal, with the ”With Fire” scenario subsequently exhibiting the most rapid fire front propagation. Our analysis reveals that the integrated radiative effect of all aerosols, rather than the contribution of any single species like black carbon (BC) or organic carbon (OC), is the dominant driver of these meteorological perturbations. This work concludes that incorporating these dynamic, time-evolving aerosol-meteorology-fire feedbacks is not a mere refinement but a crucial necessity for advancing the predictive accuracy of wildfire models, especially for extreme events, thereby informing more effective mitigation and response strategies.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121772"},"PeriodicalIF":3.7,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145922706","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}
Pub Date : 2026-01-02DOI: 10.1016/j.atmosenv.2025.121770
Se-In Hong , Jeffrey L. Collett Jr. , Young-Ji Han
This study examines the role of volatile organic compounds (VOCs) in ozone (O3) formation in a medium-sized residential city in South Korea. Using continuous measurements of 53 VOC species and photochemically adjusted initial concentrations (PICs), we assessed VOC transformations and their impact on O3 production. Results show that alkenes, particularly isoprene, cis-2-butene, and propene, are dominant contributors to O3 formation, with their influence intensifying under high O3 conditions. PIC of total VOCs (36.1 ppb) were significantly higher than measured concentrations (8.3 ppb), highlighting the extent of photochemical degradation. Ozone formation potential (OFP) analysis showed that metrics based on consumed VOCs better captured O3 variability than those based on measured concentrations, underscoring the importance of accounting for photochemical processing. Positive Matrix Factorization (PMF) identified distinct VOC sources: while LPG/natural gas usage (23.3 %) and coal combustion (22.4 %) dominated measured VOCs, solvent usage (16.2 %) and biogenic emissions (25.7 %) contributed more substantially when PICs were considered. Regional transport also played a key role, suggesting that aged and photochemically processed air masses significantly influence downwind O3 levels. These findings demonstrate the need to prioritize the control of anthropogenic alkenes and aromatic compounds, while also considering the influence of biogenic emissions. Policy measures should incorporate photochemical transformations into source attribution frameworks to support more effective and targeted O3 mitigation strategies.
{"title":"Reconstructing initial VOC concentrations to reveal their role in ozone formation","authors":"Se-In Hong , Jeffrey L. Collett Jr. , Young-Ji Han","doi":"10.1016/j.atmosenv.2025.121770","DOIUrl":"10.1016/j.atmosenv.2025.121770","url":null,"abstract":"<div><div>This study examines the role of volatile organic compounds (VOCs) in ozone (O<sub>3</sub>) formation in a medium-sized residential city in South Korea. Using continuous measurements of 53 VOC species and photochemically adjusted initial concentrations (PICs), we assessed VOC transformations and their impact on O<sub>3</sub> production. Results show that alkenes, particularly isoprene, cis-2-butene, and propene, are dominant contributors to O<sub>3</sub> formation, with their influence intensifying under high O<sub>3</sub> conditions. PIC of total VOCs (36.1 ppb) were significantly higher than measured concentrations (8.3 ppb), highlighting the extent of photochemical degradation. Ozone formation potential (OFP) analysis showed that metrics based on consumed VOCs better captured O<sub>3</sub> variability than those based on measured concentrations, underscoring the importance of accounting for photochemical processing. Positive Matrix Factorization (PMF) identified distinct VOC sources: while LPG/natural gas usage (23.3 %) and coal combustion (22.4 %) dominated measured VOCs, solvent usage (16.2 %) and biogenic emissions (25.7 %) contributed more substantially when PICs were considered. Regional transport also played a key role, suggesting that aged and photochemically processed air masses significantly influence downwind O<sub>3</sub> levels. These findings demonstrate the need to prioritize the control of anthropogenic alkenes and aromatic compounds, while also considering the influence of biogenic emissions. Policy measures should incorporate photochemical transformations into source attribution frameworks to support more effective and targeted O<sub>3</sub> mitigation strategies.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121770"},"PeriodicalIF":3.7,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145922711","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}
Monocotyledonous plants (monocots) are gaining recognition as a major source of biogenic isoprene, but are massively understudied and underrepresented in current isoprene emission inventories. In addition, very scarce leaf observational studies have been conducted to evaluate their emission flux response to environmental variables to date, representing a major gap in current models. Here, we screened isoprene emissions flux from ten monocots growing on the Ryukyu Island, Okinawa, Japan. We further evaluated their leaf-level emission flux response under varying photosynthetic photon flux density (PPFD) and temperature and applied the “Ping-Pong” method to iteratively optimize temperature (CT) and light (CL) parameters of the G93 algorithm. The ten monocots studied were Arenga engleri, Dypsis lutescens, Livistona chinensis, Mascarena verschaffeltii, Phoenix roebelenii, Satakentia liukiuensis, Pandanus boninensis, Arundo donax, Ravenala madagascariensis, and Dracaena fragrans. Eight were found to emit isoprene, and among these, six are being reported for the first time. Plants were treated with increasing PPFD and temperature to a maximum value (ascending phase), followed by a stepwise decrease (descending phase). Their emission response to this treatment resembled a characteristic pattern observed in plants that experienced prolonged hot weather, which is poorly captured by both the default and optimized G93 algorithms. To improve model performance, we applied the separate optimization approach, where the ascending and descending phases were optimized separately. In all species, the optimized CT parameter (CT1) and CL parameter (α) of the ascending phase were higher than that of the default G93 or that of the descending phase. Basal emission rate (BER) at standard conditions (30 °C and 1000 μmol m−2 s−1 PPFD) of A. donax was the highest emitter among the monocots. The BERs of monocots reported here were lower than those of dicots under comparative hot weather conditions. These results add to isoprene emission inventories for monocot species, as well as expand our knowledge of variability in monocot isoprene emission flux response to environmental variables.
{"title":"Isoprene emissions in monocots from Okinawa Island, Japan, and parameterization of the G93 formula","authors":"Hirosuke Oku , Masashi Inafuku , Shigeki Oogai , Ishmael Mutanda","doi":"10.1016/j.atmosenv.2025.121771","DOIUrl":"10.1016/j.atmosenv.2025.121771","url":null,"abstract":"<div><div>Monocotyledonous plants (monocots) are gaining recognition as a major source of biogenic isoprene, but are massively understudied and underrepresented in current isoprene emission inventories. In addition, very scarce leaf observational studies have been conducted to evaluate their emission flux response to environmental variables to date, representing a major gap in current models. Here, we screened isoprene emissions flux from ten monocots growing on the Ryukyu Island, Okinawa, Japan. We further evaluated their leaf-level emission flux response under varying photosynthetic photon flux density (PPFD) and temperature and applied the “Ping-Pong” method to iteratively optimize temperature (<em>C</em><sub><em>T</em></sub>) and light (<em>C</em><sub><em>L</em></sub>) parameters of the G93 algorithm. The ten monocots studied were <em>Arenga engleri</em>, <em>Dypsis lutescens</em>, <em>Livistona chinensis</em>, <em>Mascarena verschaffeltii</em>, <em>Phoenix roebelenii</em>, <em>Satakentia liukiuensis</em>, <em>Pandanus boninensis</em>, <em>Arundo donax</em>, <em>Ravenala madagascariensis</em>, and <em>Dracaena fragrans</em>. Eight were found to emit isoprene, and among these, six are being reported for the first time. Plants were treated with increasing PPFD and temperature to a maximum value (ascending phase), followed by a stepwise decrease (descending phase). Their emission response to this treatment resembled a characteristic pattern observed in plants that experienced prolonged hot weather, which is poorly captured by both the default and optimized G93 algorithms. To improve model performance, we applied the separate optimization approach, where the ascending and descending phases were optimized separately. In all species, the optimized <em>C</em><sub><em>T</em></sub> parameter (<em>C</em><sub><em>T1</em></sub>) and <em>C</em><sub><em>L</em></sub> parameter (α) of the ascending phase were higher than that of the default G93 or that of the descending phase. Basal emission rate (BER) at standard conditions (30 °C and 1000 μmol m<sup>−2</sup> s<sup>−1</sup> PPFD) of <em>A. donax</em> was the highest emitter among the monocots. The BERs of monocots reported here were lower than those of dicots under comparative hot weather conditions. These results add to isoprene emission inventories for monocot species, as well as expand our knowledge of variability in monocot isoprene emission flux response to environmental variables.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121771"},"PeriodicalIF":3.7,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882717","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}
Since the Industrial Revolution, human activities have significantly increased atmospheric black carbon (BC) aerosols, harming air quality, human health, and regional climate. Although the research on the characteristics and sources of atmospheric BC has expanded, understanding of its cross-sea transport remain limited. Zhanjiang city, located on the coast of the northern South China Sea, experiences pronounced monsoon influences and has heavy industries. However, this region maintains good air quality. We hypothesize that monsoon circulation may significantly influence BC pollution. To verify this hypothesis, aerosol samples were continuously collected in Zhanjiang from 2018 to 2019. Results showed clearly seasonal variations in BC concentration and its carbon isotopic values, indicating they are not primarily influenced by local emission sources, as local emissions remain relatively constant. Instead, lower values in summer and higher values in other seasons align closely with monsoon transitions, suggesting that a significant role of monsoon circulation in driving these variations. Backward trajectory model (HYSPLIT) and the potential source contribution factor analysis model (PSCF) further revealed distinctly different air mass pathways between summer (ocean air masses) and other seasons (terrestrial air masses). A Bayesian mixing model indicated that the combustion of fossil fuels (58 %, including 30 % of liquid fuel and 28 % of coal) is the predominant source of BC entering Zhanjiang with air masses. Correlation analyses with water-soluble inorganic ions revealed that BC exhibited significantly weaker associations with terrestrial pollution compared to TC, due to substantial contributions from secondary aerosols to TC during long-range transport. This study suggested that monsoon-driven circulation plays a critical role in aerosol transport and composition in the coastal region, providing new insights into atmosphere–ocean carbon exchange.
{"title":"Monsoon-driven circulation modulates the composition and loading of black carbon aerosol in a tropical coastal city","authors":"Renhao Xu , Qibin Lao , Chunqing Chen , Xin Zhou , Fajin Chen","doi":"10.1016/j.atmosenv.2025.121768","DOIUrl":"10.1016/j.atmosenv.2025.121768","url":null,"abstract":"<div><div>Since the Industrial Revolution, human activities have significantly increased atmospheric black carbon (BC) aerosols, harming air quality, human health, and regional climate. Although the research on the characteristics and sources of atmospheric BC has expanded, understanding of its cross-sea transport remain limited. Zhanjiang city, located on the coast of the northern South China Sea, experiences pronounced monsoon influences and has heavy industries. However, this region maintains good air quality. We hypothesize that monsoon circulation may significantly influence BC pollution. To verify this hypothesis, aerosol samples were continuously collected in Zhanjiang from 2018 to 2019. Results showed clearly seasonal variations in BC concentration and its carbon isotopic values, indicating they are not primarily influenced by local emission sources, as local emissions remain relatively constant. Instead, lower values in summer and higher values in other seasons align closely with monsoon transitions, suggesting that a significant role of monsoon circulation in driving these variations. Backward trajectory model (HYSPLIT) and the potential source contribution factor analysis model (PSCF) further revealed distinctly different air mass pathways between summer (ocean air masses) and other seasons (terrestrial air masses). A Bayesian mixing model indicated that the combustion of fossil fuels (58 %, including 30 % of liquid fuel and 28 % of coal) is the predominant source of BC entering Zhanjiang with air masses. Correlation analyses with water-soluble inorganic ions revealed that BC exhibited significantly weaker associations with terrestrial pollution compared to TC, due to substantial contributions from secondary aerosols to TC during long-range transport. This study suggested that monsoon-driven circulation plays a critical role in aerosol transport and composition in the coastal region, providing new insights into atmosphere–ocean carbon exchange.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121768"},"PeriodicalIF":3.7,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145922712","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}
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