Pub Date : 2026-03-01Epub Date: 2025-12-20DOI: 10.1016/j.atmosenv.2025.121754
Chiranjit Das , Ravi Kumar Kunchala , Prabir K. Patra , Naveen Chandra , Kentaro Ishijima , Toshinobu Machida
Estimation of accurate CO2 fluxes remains challenging because of limited high-quality data and inaccuracies in atmospheric chemistry-transport models (ACTMs). While satellite observations of total-columns (XCO2) have improved global data coverage, integration of co-located CO2 observations from multiple platforms and consistent methodologies are yet to be fully developed for altitude-wise model evaluations. In our study, we used MIROC4-ACTM simulations, surface and aircraft observations (ATom, Amazon, and CONTRAIL projects - considered as ground truth), and Orbiting Carbon Observatory-2 (OCO-2) XCO2 covering 2015–2021. MIROC4-ACTM and ATom profiles show mean differences of −0.1 ± 0.48 and 0.01 ± 0.3 ppm over land and ocean, respectively (p < 0.05), and those are −0.34 ± 1.07 and −0.2 ± 0.51 ppm for OCO-2 XCO2 sampled at ATom profile locations. Height-wise analysis shows that CO2 differences are concentrated in the lower troposphere (0–2 km), where model simulation are strongly influenced by surface fluxes. In Amazon, MIROC4-ACTM inversion does not have CO2 observation sites and limited vertical coverage of aircraft profiles right above the forest canopy (∼0.15 km) to 4.4 km, leading to poor ACTM–OCO-2 (−0.88 ± 1.02 ppm) and ACTM-aircraft (−0.105 ± 2.58 ppm) agreements mainly due to lower troposphere. Over the airports in Asian megacities (i.e., emission hotspots), the model shows a higher difference with CONTRAIL (−1.06 ± 0.58 ppm) than OCO-2 (−0.15 ± 0.53 ppm). The larger ACTM–CONTRAIL difference reflects ACTM's coarse resolution (approx. 2.8° × 2.8°), which limits its ability to resolve smaller scale urban fossil fuel emissions, while the smaller ACTM–OCO-2 difference likely also results from OCO-2's limited sensitivity below the boundary layer.
{"title":"An assessment of a CO2 transport model simulations using surface, aircraft and satellite data (2015–2021)","authors":"Chiranjit Das , Ravi Kumar Kunchala , Prabir K. Patra , Naveen Chandra , Kentaro Ishijima , Toshinobu Machida","doi":"10.1016/j.atmosenv.2025.121754","DOIUrl":"10.1016/j.atmosenv.2025.121754","url":null,"abstract":"<div><div>Estimation of accurate CO<sub>2</sub> fluxes remains challenging because of limited high-quality data and inaccuracies in atmospheric chemistry-transport models (ACTMs). While satellite observations of total-columns (XCO<sub>2</sub>) have improved global data coverage, integration of co-located CO<sub>2</sub> observations from multiple platforms and consistent methodologies are yet to be fully developed for altitude-wise model evaluations. In our study, we used MIROC4-ACTM simulations, surface and aircraft observations (ATom, Amazon, and CONTRAIL projects - considered as ground truth), and Orbiting Carbon Observatory-2 (OCO-2) XCO<sub>2</sub> covering 2015–2021. MIROC4-ACTM and ATom profiles show mean differences of −0.1 ± 0.48 and 0.01 ± 0.3 ppm over land and ocean, respectively (p < 0.05), and those are −0.34 ± 1.07 and −0.2 ± 0.51 ppm for OCO-2 XCO<sub>2</sub> sampled at ATom profile locations. Height-wise analysis shows that CO<sub>2</sub> differences are concentrated in the lower troposphere (0–2 km), where model simulation are strongly influenced by surface fluxes. In Amazon, MIROC4-ACTM inversion does not have CO<sub>2</sub> observation sites and limited vertical coverage of aircraft profiles right above the forest canopy (∼0.15 km) to 4.4 km, leading to poor ACTM–OCO-2 (−0.88 ± 1.02 ppm) and ACTM-aircraft (−0.105 ± 2.58 ppm) agreements mainly due to lower troposphere. Over the airports in Asian megacities (i.e., emission hotspots), the model shows a higher difference with CONTRAIL (−1.06 ± 0.58 ppm) than OCO-2 (−0.15 ± 0.53 ppm). The larger ACTM–CONTRAIL difference reflects ACTM's coarse resolution (approx. 2.8° × 2.8°), which limits its ability to resolve smaller scale urban fossil fuel emissions, while the smaller ACTM–OCO-2 difference likely also results from OCO-2's limited sensitivity below the boundary layer.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121754"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145922707","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 : 2026-03-01Epub Date: 2025-12-30DOI: 10.1016/j.atmosenv.2025.121764
Yi Zhang , Lin Zang , Yuqing Su , Jingru Yang , Ying Yang , Feiyue Mao
Near-surface nitrogen dioxide (NO2) plays a critical role in the formation of acid rain, photochemical smog, and aerosol particles, posing serious risks to public health. Polar-orbiting satellites are conventionally used for large-scale monitoring of NO2. However, satellite-based NO2 retrieval techniques predominantly rely on gas absorption within the ultraviolet and visible spectral regions, and thus are unable to capture nighttime NO2 concentrations. Moreover, deriving NO2 values in regions blurred by cloud cover or data missing remains a significant unresolved challenge, further complicating efforts to achieve spatially and temporally consistent atmospheric analyses. This study utilizes the thermal infrared absorption band of NO2 from the Advanced Himawari Imager (AHI) aboard Himawari-8, alongside various auxiliary datasets, to estimate both diurnal and nocturnal near-surface NO2 concentrations across central and eastern China. To address data missing caused by cloud coverage, a NO2 inpainting algorithm based on satellite-ground data fusion is proposed. Three kinds of 5-fold cross-validation demonstrate strong agreement between derived NO2 estimates and in-situ measurements, achieving R2 up to 0.85. The retrieval data reveal significant spatial and temporal heterogeneity in ground NO2 levels across China. Urban centers, particularly large metropolitan areas, display a distinct "urban island effect". Diurnal patterns show two distinct peaks around 09:00 and 20:00 local time. Seasonal fluctuations are also evident, with summer recording the lowest NO2 concentration and winter showing the highest. This study offers new insights into hourly dissolved 24-h cycle surface NO2 dynamics, potentially advancing real-time pollution monitoring and public health protection.
{"title":"Comprehensive diurnal and nocturnal surface NO2 concentration retrieval with seamless temporal and spatial coverage","authors":"Yi Zhang , Lin Zang , Yuqing Su , Jingru Yang , Ying Yang , Feiyue Mao","doi":"10.1016/j.atmosenv.2025.121764","DOIUrl":"10.1016/j.atmosenv.2025.121764","url":null,"abstract":"<div><div>Near-surface nitrogen dioxide (NO<sub>2</sub>) plays a critical role in the formation of acid rain, photochemical smog, and aerosol particles, posing serious risks to public health. Polar-orbiting satellites are conventionally used for large-scale monitoring of NO<sub>2</sub>. However, satellite-based NO<sub>2</sub> retrieval techniques predominantly rely on gas absorption within the ultraviolet and visible spectral regions, and thus are unable to capture nighttime NO<sub>2</sub> concentrations. Moreover, deriving NO<sub>2</sub> values in regions blurred by cloud cover or data missing remains a significant unresolved challenge, further complicating efforts to achieve spatially and temporally consistent atmospheric analyses. This study utilizes the thermal infrared absorption band of NO<sub>2</sub> from the Advanced Himawari Imager (AHI) aboard Himawari-8, alongside various auxiliary datasets, to estimate both diurnal and nocturnal near-surface NO<sub>2</sub> concentrations across central and eastern China. To address data missing caused by cloud coverage, a NO<sub>2</sub> inpainting algorithm based on satellite-ground data fusion is proposed. Three kinds of 5-fold cross-validation demonstrate strong agreement between derived NO<sub>2</sub> estimates and in-situ measurements, achieving R<sup>2</sup> up to 0.85. The retrieval data reveal significant spatial and temporal heterogeneity in ground NO<sub>2</sub> levels across China. Urban centers, particularly large metropolitan areas, display a distinct \"urban island effect\". Diurnal patterns show two distinct peaks around 09:00 and 20:00 local time. Seasonal fluctuations are also evident, with summer recording the lowest NO<sub>2</sub> concentration and winter showing the highest. This study offers new insights into hourly dissolved 24-h cycle surface NO<sub>2</sub> dynamics, potentially advancing real-time pollution monitoring and public health protection.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121764"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923091","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 : 2026-03-01Epub 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":"2026-03-01","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 : 2026-03-01Epub Date: 2026-01-10DOI: 10.1016/j.atmosenv.2026.121795
Jiaxin Cao, Xingfeng Wang, Anqi Wang, Zibing Yuan
As a common precursor to both fine particulates (PM2.5) and O3, ambient volatile organic compounds (VOCs) require accurate source tracing to enable targeted emission reductions. This study developed an enhanced source tracing framework integrating four key components: (1) gridded speciated sampling at 80 stations across the Pearl River Delta (PRD), China, conducted from 2021 to 2023; (2) an optimized Positive Matrix Factorization model with source-specific species pairs and their initial ratios determined from a localized database to account for VOC photochemical aging; (3) a multi-site spatial aggregation weighted potential source contribution function for tracking emission hotspots; and (4) a novel method to quantitatively assess the influences of emission and meteorological condition on inter-campaign variability in VOC levels. Nine major VOC sources were identified, with solvent use (30.2 %), gasoline vehicle exhaust (16.7 %), and industrial process (12.3 %) being the dominant anthropogenic sources. Regionally, emission reductions were the primary driver of the declining contribution from gasoline vehicle exhaust. In contrast, meteorological conditions exhibited location- and campaign-specific impacts, and even counteracted emission-driven trends in some cases, especially in the coastal area such as Hong Kong. The surge in solvent use contributions across the PRD during Campaign 4 was mainly meteorology-driven, whereas Dongguan's spike was primarily emission-driven. Industrial process contributions showed a meteorology-driven bimodal pattern, exemplified by Hong Kong where upwind transport led to a 17.7-fold concentration increase during Campaign 2. This source tracing framework enables high-resolution mapping of VOC emission variations, thereby providing robust scientific support for formulating dynamic, location-specific VOC mitigation strategies.
{"title":"Assessment of emission and meteorological influences on the ambient volatile organic compounds based on spatially resolved source tracing","authors":"Jiaxin Cao, Xingfeng Wang, Anqi Wang, Zibing Yuan","doi":"10.1016/j.atmosenv.2026.121795","DOIUrl":"10.1016/j.atmosenv.2026.121795","url":null,"abstract":"<div><div>As a common precursor to both fine particulates (PM<sub>2.5</sub>) and O<sub>3</sub>, ambient volatile organic compounds (VOCs) require accurate source tracing to enable targeted emission reductions. This study developed an enhanced source tracing framework integrating four key components: (1) gridded speciated sampling at 80 stations across the Pearl River Delta (PRD), China, conducted from 2021 to 2023; (2) an optimized Positive Matrix Factorization model with source-specific species pairs and their initial ratios determined from a localized database to account for VOC photochemical aging; (3) a multi-site spatial aggregation weighted potential source contribution function for tracking emission hotspots; and (4) a novel method to quantitatively assess the influences of emission and meteorological condition on inter-campaign variability in VOC levels. Nine major VOC sources were identified, with solvent use (30.2 %), gasoline vehicle exhaust (16.7 %), and industrial process (12.3 %) being the dominant anthropogenic sources. Regionally, emission reductions were the primary driver of the declining contribution from gasoline vehicle exhaust. In contrast, meteorological conditions exhibited location- and campaign-specific impacts, and even counteracted emission-driven trends in some cases, especially in the coastal area such as Hong Kong. The surge in solvent use contributions across the PRD during Campaign 4 was mainly meteorology-driven, whereas Dongguan's spike was primarily emission-driven. Industrial process contributions showed a meteorology-driven bimodal pattern, exemplified by Hong Kong where upwind transport led to a 17.7-fold concentration increase during Campaign 2. This source tracing framework enables high-resolution mapping of VOC emission variations, thereby providing robust scientific support for formulating dynamic, location-specific VOC mitigation strategies.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121795"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973907","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 : 2026-03-01Epub Date: 2026-01-13DOI: 10.1016/j.atmosenv.2026.121802
Sofia Eirini Chatoutsidou, Eleftheria Chalvatzaki, Mihalis Lazaridis
Cardiovascular exercise is a popular activity that aims to improve physical fitness and overall health, however practicing outdoors enhances pollutant inhalation. The main objective was to estimate the dose received by inhalation of airborne particles during cardiovascular exercise in urban environments. Dosimetry simulations used particle mass concentrations (PM2.5, PM2.5-10) to estimate the deposited dose in the human respiratory tract that assumed a young and healthy adult male and female train at variable activity intensities. Hourly dose rates were substantially increased with activity intensity due to increased inhaled volumes, with a 9.5-fold increase from rest (60 bpm) to high-intensity exercise (170 bpm). PM levels played also a crucial role as increased concentrations were linked with increased deposition rates. Heating, and Sahara events comprised the most burdened cases with unfavorable conditions for exercise. Higher % nasal contribution for female trainees was the reason for higher deposition in the anterior nose compared to male trainees. Linking these results with a health risk showed that females have an increased risk related to a health outcome in the upper respiratory tract whereas male trainees have increased risk for a health impact in the lungs. Overall, health risk analysis verified the negative impact of elevated PM concentrations and the enhanced risk accompanied by increased intensity for experienced trainees. To prevent negative health outcomes, trainees are recommended to practice in areas with reduced particulate pollution (e.g suburban areas) and during times of the day where concentrations are expected to be lower.
{"title":"Personal dose during cardiovascular exercise: Links between PM2.5/PM10 concentration levels, activity intensity and health risk","authors":"Sofia Eirini Chatoutsidou, Eleftheria Chalvatzaki, Mihalis Lazaridis","doi":"10.1016/j.atmosenv.2026.121802","DOIUrl":"10.1016/j.atmosenv.2026.121802","url":null,"abstract":"<div><div>Cardiovascular exercise is a popular activity that aims to improve physical fitness and overall health, however practicing outdoors enhances pollutant inhalation. The main objective was to estimate the dose received by inhalation of airborne particles during cardiovascular exercise in urban environments. Dosimetry simulations used particle mass concentrations (PM<sub>2.5</sub>, PM<sub>2.5-10</sub>) to estimate the deposited dose in the human respiratory tract that assumed a young and healthy adult male and female train at variable activity intensities. Hourly dose rates were substantially increased with activity intensity due to increased inhaled volumes, with a 9.5-fold increase from rest (60 bpm) to high-intensity exercise (170 bpm). PM levels played also a crucial role as increased concentrations were linked with increased deposition rates. Heating, and Sahara events comprised the most burdened cases with unfavorable conditions for exercise. Higher % nasal contribution for female trainees was the reason for higher deposition in the anterior nose compared to male trainees. Linking these results with a health risk showed that females have an increased risk related to a health outcome in the upper respiratory tract whereas male trainees have increased risk for a health impact in the lungs. Overall, health risk analysis verified the negative impact of elevated PM concentrations and the enhanced risk accompanied by increased intensity for experienced trainees. To prevent negative health outcomes, trainees are recommended to practice in areas with reduced particulate pollution (e.g suburban areas) and during times of the day where concentrations are expected to be lower.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121802"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973908","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 : 2026-03-01Epub 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":"2026-03-01","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 : 2026-03-01Epub Date: 2026-01-12DOI: 10.1016/j.atmosenv.2026.121798
Lihang Fan , Guangjian Wu , Jing Gao , Ju Huang , Gebanruo Chen
The vertical distribution of aerosols is crucial for constraining their sources and transport mechanisms. However, high-precision in situ aerosol measurements remain limited in high-altitude regions. Here, we present the first high-resolution in situ vertical profiles of aerosol number concentration (Na) obtained from a tethered balloon at the Qomolangma (Mt. Everest) Station (QOMS) from May 14 to 26, 2022. Fourteen vertical profiles were categorized into three types using K-means clustering analysis: (I) high-altitude single-peak profiles, (II) linearly decreasing profiles, and (III) surface-accumulation profiles. Type I was associated with long-range transport of aerosols from North Africa and South Asia via the westerly jet stream and the South Asian monsoon, respectively, with a peak aerosol number concentration reaching 110 cm−3. Types II and III were related to the diurnal evolution of planetary boundary layer, dominated by local emissions. In Type III, the combined effects of a shallow planetary boundary layer and strong temperature inversions (inversion layer intensity: 0.26 °C per 100 m) led to the accumulation of aerosols near the surface, resulting in a surface aerosol number concentration of 203 cm−3. Analyses of effective diameter and backward trajectory models revealed a clear vertical stratification of aerosol sources, with ∼6 km a.s.l. acting as a critical boundary: Below this altitude, aerosols were mainly originated from local and regional (South Asia) sources, exhibiting a bimodal particle volume size distributions with peaks at 0.45 μm and 2 μm, while above it, they were dominated by remote sources from North Africa and the Middle East, showing a unimodal particle volume size distributions. These results advance understanding of vertical aerosols distributions over the southern Tibetan Plateau, and highlight the importance of altitude-dependent source and transport processes in assessing aerosol–climate interactions.
{"title":"Vertical distribution characteristics of aerosol particles at Mt. Qomolangma (Everest) using the tethered balloon","authors":"Lihang Fan , Guangjian Wu , Jing Gao , Ju Huang , Gebanruo Chen","doi":"10.1016/j.atmosenv.2026.121798","DOIUrl":"10.1016/j.atmosenv.2026.121798","url":null,"abstract":"<div><div>The vertical distribution of aerosols is crucial for constraining their sources and transport mechanisms. However, high-precision in situ aerosol measurements remain limited in high-altitude regions. Here, we present the first high-resolution in situ vertical profiles of aerosol number concentration (N<sub>a</sub>) obtained from a tethered balloon at the Qomolangma (Mt. Everest) Station (QOMS) from May 14 to 26, 2022. Fourteen vertical profiles were categorized into three types using K-means clustering analysis: (I) high-altitude single-peak profiles, (II) linearly decreasing profiles, and (III) surface-accumulation profiles. Type I was associated with long-range transport of aerosols from North Africa and South Asia via the westerly jet stream and the South Asian monsoon, respectively, with a peak aerosol number concentration reaching 110 cm<sup>−3</sup>. Types II and III were related to the diurnal evolution of planetary boundary layer, dominated by local emissions. In Type III, the combined effects of a shallow planetary boundary layer and strong temperature inversions (inversion layer intensity: 0.26 °C per 100 m) led to the accumulation of aerosols near the surface, resulting in a surface aerosol number concentration of 203 cm<sup>−3</sup>. Analyses of effective diameter and backward trajectory models revealed a clear vertical stratification of aerosol sources, with ∼6 km a.s.l. acting as a critical boundary: Below this altitude, aerosols were mainly originated from local and regional (South Asia) sources, exhibiting a bimodal particle volume size distributions with peaks at 0.45 μm and 2 μm, while above it, they were dominated by remote sources from North Africa and the Middle East, showing a unimodal particle volume size distributions. These results advance understanding of vertical aerosols distributions over the southern Tibetan Plateau, and highlight the importance of altitude-dependent source and transport processes in assessing aerosol–climate interactions.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121798"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023321","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 : 2026-03-01Epub Date: 2025-12-31DOI: 10.1016/j.atmosenv.2025.121761
Evangelia Siouti , Ksakousti Skyllakou , David Patoulias , Eleni Athanasopoulou , Jeroen Kuenen , Marta Via , Marjan Savadkoohi , María Cruz Minguillón , Marco Pandolfi , Andrés Alastuey , Xavier Querol , Spyros N. Pandis
Particulate matter (PM) significantly impacts urban air quality and public health, making the quantification of its source contributions crucial for effective air quality management. In this work, we investigate the origins of organic aerosol (OA) and elemental carbon (EC) in an urban environment by synthesizing results from in situ observational analyses (receptor modeling) and chemical transport modeling. This study focused on the city of Barcelona, Spain, during a summer and a winter period in 2019, using measurement data from an aerosol chemical speciation monitor (ACSM), an Aethalometer, and analyses of filter samples along with source-resolved predictions from the chemical transport model (CTM) PMCAMx. Results refer to PM1 (PM finer than 1 μm). Oxygenated OA (OOA) was the dominant source of OA during both periods with contributions ranging from 63 % of PM1 OA in winter to 80 % in summer. During summer, most of it originated from sources outside Barcelona such as wildfires, biogenic sources, as well as sources outside Europe. PMCAMx significantly underpredicted OOA during wintertime, suggesting that the model is lacking both processes that produce secondary OA (SOA) during periods of low photochemical activity and the corresponding emissions of organic pollutants. Biomass burning OA (BBOA) emitted far away from the city and its conversion to SOA either due to nighttime or aqueous chemistry could explain part of the missing OOA. Hydrocarbon-like OA (HOA) ranged from 8 to 14 % of the OA in both periods, peaking during morning and evening rush hours. The primary OA (POA) emissions from transportation during winter may be underestimated in the emission inventory. Cooking OA (COA) was also a significant source (11 % of total PM1 OA) and it needs to be added to the current European emission inventories. Fresh BBOA was a small component of OA during summer and higher during winter. The PM1 EC levels were found to be dominated by local sources during both seasons. Among these sources, fossil fuel combustion was the most important contributor, accounting for approximately 74 % of the total EC. This highlights the strong influence of traffic and other fossil fuel-related activities on EC concentrations in Barcelona, regardless of season.
This study demonstrates the value of integrating observational data (and receptor modelling) with chemical transport modeling to more accurately identify the sources of carbonaceous PM in urban environments. Such combined approaches are essential for developing effective mitigation strategies tailored to seasonal and local emission patterns, ultimately supporting improved air quality management.
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Pub Date : 2026-03-01Epub Date: 2025-12-31DOI: 10.1016/j.atmosenv.2025.121769
Chengkai Fang , Zhe Jiang , Min Wang , Xiaokang Chen , Weichao Han , Tai-Long He , Yanan Shen
Surface observations are crucial for understanding atmospheric pollutant sources and variability. However, interpreting these observations, particularly when comparing them to chemical transport model (CTM) simulations, remains challenging. A key difficulty is the spatial mismatch between model resolutions and the point measurements from surface stations. To mitigate this issue, we developed a deep learning (DL)-based method to quantify systematic discrepancies between surface carbon monoxide (CO) observations and GEOS-Chem model simulations across China during 2015–2022. Our method generated daily correction factors for adjusting modeled CO concentrations. Validation demonstrated good consistency between the observed-to-modeled (Obs/GC) concentration ratios and the derived correction factors, with correlation coefficients (R) ranging from 0.85 to 0.93. Our analysis reveals broadly uniform negative correlations between correction factors and observed CO concentrations across eastern China, suggesting that systematic discrepancies decrease with increasing local emissions. In contrast, positive correlations prevail in western China. Furthermore, significant temporal variability in systematic discrepancies was identified at seasonal scales, emphasizing the need for time-dependent dynamic corrections. Applying the DL-based correction approach to GEOS-Chem-simulated surface CO concentrations for 2015–2022 led to a significant improvement in model-observation agreement: R values increased from 0.30–0.43 to 0.63–0.70 (spatial consistency) and from 0.15–0.49 to 0.62–0.81 (temporal consistency). This work provides a novel data-driven approach for correcting systematic discrepancies in model/observation comparisons, which is important for more accurate interpretation of surface observations.
{"title":"Quantifying and correcting systematic discrepancies in the comparison between surface CO observations and simulations","authors":"Chengkai Fang , Zhe Jiang , Min Wang , Xiaokang Chen , Weichao Han , Tai-Long He , Yanan Shen","doi":"10.1016/j.atmosenv.2025.121769","DOIUrl":"10.1016/j.atmosenv.2025.121769","url":null,"abstract":"<div><div>Surface observations are crucial for understanding atmospheric pollutant sources and variability. However, interpreting these observations, particularly when comparing them to chemical transport model (CTM) simulations, remains challenging. A key difficulty is the spatial mismatch between model resolutions and the point measurements from surface stations. To mitigate this issue, we developed a deep learning (DL)-based method to quantify systematic discrepancies between surface carbon monoxide (CO) observations and GEOS-Chem model simulations across China during 2015–2022. Our method generated daily correction factors for adjusting modeled CO concentrations. Validation demonstrated good consistency between the observed-to-modeled (Obs/GC) concentration ratios and the derived correction factors, with correlation coefficients (<em>R</em>) ranging from 0.85 to 0.93. Our analysis reveals broadly uniform negative correlations between correction factors and observed CO concentrations across eastern China, suggesting that systematic discrepancies decrease with increasing local emissions. In contrast, positive correlations prevail in western China. Furthermore, significant temporal variability in systematic discrepancies was identified at seasonal scales, emphasizing the need for time-dependent dynamic corrections. Applying the DL-based correction approach to GEOS-Chem-simulated surface CO concentrations for 2015–2022 led to a significant improvement in model-observation agreement: <em>R</em> values increased from 0.30–0.43 to 0.63–0.70 (spatial consistency) and from 0.15–0.49 to 0.62–0.81 (temporal consistency). This work provides a novel data-driven approach for correcting systematic discrepancies in model/observation comparisons, which is important for more accurate interpretation of surface observations.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121769"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923088","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 : 2026-03-01Epub Date: 2026-01-08DOI: 10.1016/j.atmosenv.2026.121793
Jingqi Bai, Libin Wu, Qingzi Zhao, Ke Xin, Wei Hu, Junjun Deng, Pingqing Fu
Stable nitrogen isotope analysis serves as a powerful tool for tracing the sources and transformation pathways of nitrogen-containing aerosols, which significantly influence atmospheric chemistry, climate change, and environmental processes. This review comprehensively synthesizes the application of stable nitrogen isotopes for the analysis of various forms of nitrogen in atmospheric aerosols, including ammonium, nitrate, and organic nitrogen, as well as atmospheric nitrous acid (HONO). We summarize key measurement techniques and analytical frameworks, with a particular emphasis on stable isotope mixing models-especially Bayesian methods-for source apportionment and the evaluation of isotope fractionation. The review delineates distinct stable nitrogen isotope signatures of major sources (e.g., fossil fuel combustion, agriculture, biomass burning) and elucidates the spatiotemporal variability of isotopic compositions driven by anthropogenic activities and natural processes. Despite advances in the stable isotope analysis of nitrogen-containing aerosols, several challenges remain, particularly concerning nitrogen isotope fractionation processes and the complexity of organic nitrogen species. Finally, we propose that future studies should refine the database of isotope characteristics from various sources, enhance the analytical precision of measurement techniques, and integrate multi-method approaches to better understand nitrogen cycles and mitigate the environmental impacts of nitrogen-containing aerosols.
{"title":"Use of stable nitrogen isotopes in atmospheric aerosol research","authors":"Jingqi Bai, Libin Wu, Qingzi Zhao, Ke Xin, Wei Hu, Junjun Deng, Pingqing Fu","doi":"10.1016/j.atmosenv.2026.121793","DOIUrl":"10.1016/j.atmosenv.2026.121793","url":null,"abstract":"<div><div>Stable nitrogen isotope analysis serves as a powerful tool for tracing the sources and transformation pathways of nitrogen-containing aerosols, which significantly influence atmospheric chemistry, climate change, and environmental processes. This review comprehensively synthesizes the application of stable nitrogen isotopes for the analysis of various forms of nitrogen in atmospheric aerosols, including ammonium, nitrate, and organic nitrogen, as well as atmospheric nitrous acid (HONO). We summarize key measurement techniques and analytical frameworks, with a particular emphasis on stable isotope mixing models-especially Bayesian methods-for source apportionment and the evaluation of isotope fractionation. The review delineates distinct stable nitrogen isotope signatures of major sources (e.g., fossil fuel combustion, agriculture, biomass burning) and elucidates the spatiotemporal variability of isotopic compositions driven by anthropogenic activities and natural processes. Despite advances in the stable isotope analysis of nitrogen-containing aerosols, several challenges remain, particularly concerning nitrogen isotope fractionation processes and the complexity of organic nitrogen species. Finally, we propose that future studies should refine the database of isotope characteristics from various sources, enhance the analytical precision of measurement techniques, and integrate multi-method approaches to better understand nitrogen cycles and mitigate the environmental impacts of nitrogen-containing aerosols.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"368 ","pages":"Article 121793"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973943","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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