Pub Date : 2026-01-01Epub Date: 2025-12-16DOI: 10.1016/j.aeaoa.2025.100408
Johanna Pedersen , Sasha D. Hafner , Andreas S. Pacholski
This study evaluated technical factors influencing relative ammonia emissions following field application of biogas digestate using different slurry spreading methods. Experiments assessed: (i) slurry distribution uniformity across a trailing hose boom, (ii) the influence of driving speed, (iii) effects of hose spacing, and (iv) the effect of relocating dynamic flux chambers during measurement. Across all tests realistic application rates and representative field conditions were ensured. Results demonstrate that careful equipment setup, particularly hose selection and consistent spacing, minimized variability in measured emissions and dynamic flux chamber relocation elevated measured emissions. These findings provide practical guidance for experimental design and emission mitigation under typical farming conditions.
{"title":"Methodological factors affecting ammonia emission measurement with flux chambers from field-applied biogas digestate slurry (Technical note)","authors":"Johanna Pedersen , Sasha D. Hafner , Andreas S. Pacholski","doi":"10.1016/j.aeaoa.2025.100408","DOIUrl":"10.1016/j.aeaoa.2025.100408","url":null,"abstract":"<div><div>This study evaluated technical factors influencing relative ammonia emissions following field application of biogas digestate using different slurry spreading methods. Experiments assessed: (i) slurry distribution uniformity across a trailing hose boom, (ii) the influence of driving speed, (iii) effects of hose spacing, and (iv) the effect of relocating dynamic flux chambers during measurement. Across all tests realistic application rates and representative field conditions were ensured. Results demonstrate that careful equipment setup, particularly hose selection and consistent spacing, minimized variability in measured emissions and dynamic flux chamber relocation elevated measured emissions. These findings provide practical guidance for experimental design and emission mitigation under typical farming conditions.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100408"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-03-07DOI: 10.1016/j.aeaoa.2026.100435
Yuqing Dai , Juncheng Qian , Jian Zhong , Xiaoming Cai , A. Rob MacKenzie
Air pollution spans metre-scale near-road hotspots to regional and intercontinental transport, yet no single model can represent the full range of processes that control exposure to different pollutants. This review synthesises recent developments in regional-to-local coupling, covering scale-aware regional chemical transport models (CTMs), local dispersion and street-network models and computational fluid dynamics (CFD, from practical RANS to chemistry-enabled LES and emerging GPU/LBM acceleration), and machine learning (ML), including mass-consistent super-resolution, for downscaling and surrogates. One-way “offline” coupling remains the most widely used approach because it is modular and computationally efficient, but its performance depends on how CTM background fields are defined and mapped and on how emission overlap, temporal mismatch and reduced chemistry are handled. Two-way “online” approaches, including Plume-in-Grid (PinG) for point sources and Street-in-Grid (SinG) for dense urban networks, exchange mass during integration, allowing urban plumes, NO–O3 titration and aerosol–radiation interactions to feedback on regional oxidant budgets and meteorology. These benefits require conservative remapping, turbulence and mixing consistency at the canopy–boundary layer interface, and transparent mapping between chemical mechanisms across scales. Persistent challenges include interface turbulence–chemistry interactions, harmonisation of emissions and meteorological inputs, treatment of urban green infrastructure without double counting drag or deposition, computational feasibility for chemistry/aerosol-coupled CFD, ML transferability under regime shifts, and propagation of input uncertainty. Priority directions include regime-based criteria for when two-way coupling is required, routine mass-budget diagnostics, adaptive or variable-resolution strategies, and ML downscales and surrogates that enforce non-negativity and mass consistency for scenario testing.
{"title":"Recent progress, bottlenecks, and outlook of multiscale air quality modelling: a review","authors":"Yuqing Dai , Juncheng Qian , Jian Zhong , Xiaoming Cai , A. Rob MacKenzie","doi":"10.1016/j.aeaoa.2026.100435","DOIUrl":"10.1016/j.aeaoa.2026.100435","url":null,"abstract":"<div><div>Air pollution spans metre-scale near-road hotspots to regional and intercontinental transport, yet no single model can represent the full range of processes that control exposure to different pollutants. This review synthesises recent developments in regional-to-local coupling, covering scale-aware regional chemical transport models (CTMs), local dispersion and street-network models and computational fluid dynamics (CFD, from practical RANS to chemistry-enabled LES and emerging GPU/LBM acceleration), and machine learning (ML), including mass-consistent super-resolution, for downscaling and surrogates. One-way “offline” coupling remains the most widely used approach because it is modular and computationally efficient, but its performance depends on how CTM background fields are defined and mapped and on how emission overlap, temporal mismatch and reduced chemistry are handled. Two-way “online” approaches, including Plume-in-Grid (PinG) for point sources and Street-in-Grid (SinG) for dense urban networks, exchange mass during integration, allowing urban plumes, NO–O<sub>3</sub> titration and aerosol–radiation interactions to feedback on regional oxidant budgets and meteorology. These benefits require conservative remapping, turbulence and mixing consistency at the canopy–boundary layer interface, and transparent mapping between chemical mechanisms across scales. Persistent challenges include interface turbulence–chemistry interactions, harmonisation of emissions and meteorological inputs, treatment of urban green infrastructure without double counting drag or deposition, computational feasibility for chemistry/aerosol-coupled CFD, ML transferability under regime shifts, and propagation of input uncertainty. Priority directions include regime-based criteria for when two-way coupling is required, routine mass-budget diagnostics, adaptive or variable-resolution strategies, and ML downscales and surrogates that enforce non-negativity and mass consistency for scenario testing.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100435"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-05DOI: 10.1016/j.aeaoa.2025.100404
Ian C. Rumsey , Maliha N. Nash , John T. Walker
Animal production has the potential to emit various atmospheric pollutants including ammonia (NH3), which can impact human health, atmospheric visibility and ecosystem health through gaseous NH3 and associated NH4+ particulate matter deposition. Emission estimating methodologies were developed using statistical models to estimate daily NH3 emissions from swine grow-finish barns based on National Air Emissions Monitoring Study (NAEMS) data. Models were developed with variables that represented production, manure management and environmental conditions. Model performance was evaluated for predicting NAEMS and non-NAEMS emissions, consistency of model coefficients and sensitivity to different model input values. Accounting for ease of variable measurement, the best performing models for predicting NAEMS emissions were models 1b and 16a, both of which accounted for the influence of temperature, swine inventory and weight, but used different predictor and response variables. In predicting NAEMS emissions, model 1b had mean error (ME) and mean bias (MB) values of 1.6 kg day−1 (normalized mean error (NME) = 25.9 %) and 0.1 kg day−1 (normalized mean bias (NMB) = 1.2 %), respectively, which were slightly lower than the corresponding values for model 16a (ME/NME = 1.8 kg day−1/25.9 % and MB/NMB = 0.4 kg day−1/5.8 %). Model 1b performed better in predicting non-NAEMS emissions, but model 16a had more reasonable sensitivity when barn live animal weight was >215,000 kg. Models using nitrogen feed intake as a predictor variable also performed well in predicting emissions and although these models have greater uncertainty due to limited NAEMS measurements, they could potentially account for changes in feed practices.
动物生产有可能排放包括氨（NH3）在内的各种大气污染物，这些污染物可以通过气态NH3和相关的NH4+颗粒物沉积影响人类健康、大气能见度和生态系统健康。基于国家空气排放监测研究（NAEMS）数据，采用统计模型估算生猪育肥场每日NH3排放量，开发了排放估算方法。模型采用代表生产、粪肥管理和环境条件的变量。评估了模型预测NAEMS和非NAEMS排放的性能、模型系数的一致性以及对不同模型输入值的敏感性。考虑到变量测量的容易性，预测NAEMS排放的最佳模型是1b和16a模型，这两个模型都考虑了温度、猪存栏和体重的影响，但使用了不同的预测变量和响应变量。在预测NAEMS排放时，模型1b的平均误差（ME）和平均偏差（MB）值分别为1.6 kg day - 1（归一化平均误差(NME) = 25.9%）和0.1 kg day - 1（归一化平均偏差(NMB) = 1.2%），略低于模型16a的相应值（ME/NME = 1.8 kg day - 1/ 25.9%和MB/NMB = 0.4 kg day - 1/ 5.8%）。模型1b对非naems排放的预测效果较好，而模型16a在畜舍活畜体重为21.5万kg时具有更合理的敏感性。使用氮采食量作为预测变量的模型在预测排放方面也表现良好，尽管由于有限的NAEMS测量，这些模型具有更大的不确定性，但它们可能解释饲料实践的变化。
{"title":"Estimating air emissions from animal production in the United States using statistical models: Ammonia emissions from swine grow-finish barns","authors":"Ian C. Rumsey , Maliha N. Nash , John T. Walker","doi":"10.1016/j.aeaoa.2025.100404","DOIUrl":"10.1016/j.aeaoa.2025.100404","url":null,"abstract":"<div><div>Animal production has the potential to emit various atmospheric pollutants including ammonia (NH<sub>3</sub>), which can impact human health, atmospheric visibility and ecosystem health through gaseous NH<sub>3</sub> and associated NH<sub>4</sub><sup>+</sup> particulate matter deposition. Emission estimating methodologies were developed using statistical models to estimate daily NH<sub>3</sub> emissions from swine grow-finish barns based on National Air Emissions Monitoring Study (NAEMS) data. Models were developed with variables that represented production, manure management and environmental conditions. Model performance was evaluated for predicting NAEMS and non-NAEMS emissions, consistency of model coefficients and sensitivity to different model input values. Accounting for ease of variable measurement, the best performing models for predicting NAEMS emissions were models 1b and 16a, both of which accounted for the influence of temperature, swine inventory and weight, but used different predictor and response variables. In predicting NAEMS emissions, model 1b had mean error (ME) and mean bias (MB) values of 1.6 kg day<sup>−1</sup> (normalized mean error (NME) = 25.9 %) and 0.1 kg day<sup>−1</sup> (normalized mean bias (NMB) = 1.2 %), respectively, which were slightly lower than the corresponding values for model 16a (ME/NME = 1.8 kg day<sup>−1</sup>/25.9 % and MB/NMB = 0.4 kg day<sup>−1</sup>/5.8 %). Model 1b performed better in predicting non-NAEMS emissions, but model 16a had more reasonable sensitivity when barn live animal weight was >215,000 kg. Models using nitrogen feed intake as a predictor variable also performed well in predicting emissions and although these models have greater uncertainty due to limited NAEMS measurements, they could potentially account for changes in feed practices.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100404"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The oxidative potential (OP) of particulate matter (PM) reflects its ability to trigger oxidative stress in the respiratory system and is increasingly recognised as a key metric for assessing PM toxicity. Concurrently, PM has gained importance as a health indicator, leading to its inclusion in European regulations. As OP is not routinely monitored at many sites, understanding exposure and related risks remains challenging. While satellite imagery is commonly used to estimate PM mass concentration, its application to OP has not yet been explored. We present a novel deep-learning-based approach employing satellite-based surface features for OP estimation, using both OPAA and OPDTT assays on 24-hour PM10 samples collected over five years in Grenoble (France). We propose OPNet, which consists of two parts: a deep backbone that extracts surface features from one satellite image, and a predictor estimating OPAA and OPDTT using the extracted features combined with contextual variables. The architecture is trained in two stages: in the domain-adaptive task, both are jointly trained to predict daily PM10 concentration, with the backbone initialised from weights from a general classification problem. In the domain-specific task, they are jointly updated to predict either OPAA or OPDTT, with the backbone initialised from the best weights obtained in the first stage. This approach explains up to 75% of the variance in OPAA and 58% in OPDTT when using both satellite imagery and auxiliary data. It offers a cost-effective solution to improve the estimation of OP, with implications for large-scale air quality monitoring and health impact assessments.
{"title":"OPNet: A deep-learning approach for estimating particulate matter’s oxidative potential from satellite imagery","authors":"Alessia Carbone , Ian Hough , Gemine Vivone , Jocelyn Chanussot , Rocco Restaino , Harry Dupont , Jean-Luc Jaffrezo , Gaëlle Uzu","doi":"10.1016/j.aeaoa.2025.100406","DOIUrl":"10.1016/j.aeaoa.2025.100406","url":null,"abstract":"<div><div>The oxidative potential (OP) of particulate matter (PM) reflects its ability to trigger oxidative stress in the respiratory system and is increasingly recognised as a key metric for assessing PM toxicity. Concurrently, PM has gained importance as a health indicator, leading to its inclusion in European regulations. As OP is not routinely monitored at many sites, understanding exposure and related risks remains challenging. While satellite imagery is commonly used to estimate PM mass concentration, its application to OP has not yet been explored. We present a novel deep-learning-based approach employing satellite-based surface features for OP estimation, using both OP<sub>AA</sub> and OP<sub>DTT</sub> assays on 24-hour PM<sub>10</sub> samples collected over five years in Grenoble (France). We propose OPNet, which consists of two parts: a deep backbone that extracts surface features from one satellite image, and a predictor estimating OP<sub>AA</sub> and OP<sub>DTT</sub> using the extracted features combined with contextual variables. The architecture is trained in two stages: in the domain-adaptive task, both are jointly trained to predict daily PM<sub>10</sub> concentration, with the backbone initialised from weights from a general classification problem. In the domain-specific task, they are jointly updated to predict either OP<sub>AA</sub> or OP<sub>DTT</sub>, with the backbone initialised from the best weights obtained in the first stage. This approach explains up to 75% of the variance in OP<sub>AA</sub> and 58% in OP<sub>DTT</sub> when using both satellite imagery and auxiliary data. It offers a cost-effective solution to improve the estimation of OP, with implications for large-scale air quality monitoring and health impact assessments.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"29 ","pages":"Article 100406"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-28DOI: 10.1016/j.aeaoa.2025.100387
Eva Gregorovičová, Jiří Pospíšil
Solid fuel combustion produces harmful particulate matter (PM) and gaseous emissions in the flue gas. To mitigate PM, advanced filtration materials using metal meshes, metal composites, and MOF-polymer composites have been studied. Gaseous emissions can be adsorbed using metal-organic frameworks (MOFs). Metal filters are suitable for high-temperature flue gas filtration (>200 °C) thanks to their high mechanical strength and thermal stability. This review covers research articles (2019–2023) on metal filters and MOF-polymer composites for flue gas filtration. While metal filters are already widely implemented in industrial systems, MOF-polymer composites remain at the research and development stage. Particular emphasis is placed on their filtration performance at high-temperature flue gas and assessment of their potential in combustion systems.
{"title":"Metal filters and MOF-polymer composites for high-temperature flue gas filtration: A review","authors":"Eva Gregorovičová, Jiří Pospíšil","doi":"10.1016/j.aeaoa.2025.100387","DOIUrl":"10.1016/j.aeaoa.2025.100387","url":null,"abstract":"<div><div>Solid fuel combustion produces harmful particulate matter (PM) and gaseous emissions in the flue gas. To mitigate PM, advanced filtration materials using metal meshes, metal composites, and MOF-polymer composites have been studied. Gaseous emissions can be adsorbed using metal-organic frameworks (MOFs). Metal filters are suitable for high-temperature flue gas filtration (>200 °C) thanks to their high mechanical strength and thermal stability. This review covers research articles (2019–2023) on metal filters and MOF-polymer composites for flue gas filtration. While metal filters are already widely implemented in industrial systems, MOF-polymer composites remain at the research and development stage. Particular emphasis is placed on their filtration performance at high-temperature flue gas and assessment of their potential in combustion systems.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100387"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-31DOI: 10.1016/j.aeaoa.2025.100390
Boris Vansevenant , Ashok Singh Vishnoi , Amélie De Filippis , Laetitia Beillon , Guillaume Toïc , Yassine Azizi , Bernard Guiot , Corinne Ferronato , Ludovic Fine , Patrick Tassel , Sophie Serindat , Yao Liu
The use of Natural Gas for Vehicles (NGV) is increasing due to estimated positive impacts on exhaust emissions and carbon footprint. However, significant emissions of unregulated particles in the ultrafine range have been reported in the literature. Significant variations have also been observed, but data is still lacking to thoroughly describe NGV emissions. This is particularly true for some vehicle categories such as light commercial vehicles. A clear need therefore exists to additionally document the NGV emissions, especially unregulated compounds with health or environmental effects such as ultrafine particles and Volatile and Intermediate-Volatility Organic Compounds (VOCs/IVOCs). This study presents the emissions of methane, regulated gases, total particles from 5.6 nm with size distribution, and VOCs/IVOCs. It focuses on NGV, diesel and gasoline passenger cars and light commercial vehicles with comparable technical characteristics. Results show that during regeneration phases, ultrafine particle emissions from gasoline vehicles with particle filters increase by factors 40 to 800. They also show high emissions of ultrafine particles from NGV, with significant shares of sub-23 and sub-10 nm particles (more than 2/3 of the emissions). The organics emitted by the NGV are less volatile than those from diesel or gasoline. Coupled with the significant sub-10 and sub-23 nm particles from NGV, this suggests that the particles could be partly semi-volatile. The NGV particle emissions might thus be underestimated with current normative protocols. In addition to measuring particles from 10 nm in the upcoming Euro 7 norm, this study indicates that quantifying semi-volatile particles could help better describe vehicular emissions, especially for NGV vehicles.
{"title":"Unregulated particle and VOC emissions from comparable diesel, gasoline and natural gas vehicles","authors":"Boris Vansevenant , Ashok Singh Vishnoi , Amélie De Filippis , Laetitia Beillon , Guillaume Toïc , Yassine Azizi , Bernard Guiot , Corinne Ferronato , Ludovic Fine , Patrick Tassel , Sophie Serindat , Yao Liu","doi":"10.1016/j.aeaoa.2025.100390","DOIUrl":"10.1016/j.aeaoa.2025.100390","url":null,"abstract":"<div><div>The use of Natural Gas for Vehicles (NGV) is increasing due to estimated positive impacts on exhaust emissions and carbon footprint. However, significant emissions of unregulated particles in the ultrafine range have been reported in the literature. Significant variations have also been observed, but data is still lacking to thoroughly describe NGV emissions. This is particularly true for some vehicle categories such as light commercial vehicles. A clear need therefore exists to additionally document the NGV emissions, especially unregulated compounds with health or environmental effects such as ultrafine particles and Volatile and Intermediate-Volatility Organic Compounds (VOCs/IVOCs). This study presents the emissions of methane, regulated gases, total particles from 5.6 nm with size distribution, and VOCs/IVOCs. It focuses on NGV, diesel and gasoline passenger cars and light commercial vehicles with comparable technical characteristics. Results show that during regeneration phases, ultrafine particle emissions from gasoline vehicles with particle filters increase by factors 40 to 800. They also show high emissions of ultrafine particles from NGV, with significant shares of sub-23 and sub-10 nm particles (more than 2/3 of the emissions). The organics emitted by the NGV are less volatile than those from diesel or gasoline. Coupled with the significant sub-10 and sub-23 nm particles from NGV, this suggests that the particles could be partly semi-volatile. The NGV particle emissions might thus be underestimated with current normative protocols. In addition to measuring particles from 10 nm in the upcoming Euro 7 norm, this study indicates that quantifying semi-volatile particles could help better describe vehicular emissions, especially for NGV vehicles.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100390"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-27DOI: 10.1016/j.aeaoa.2025.100379
Ashok Singh Vishnoi , Boris Vansevenant , Asma Beji , Mathieu Goriaux , Bernard Guiot , Yassine Azizi , Mélanie Messieux , Patrick Tassel , Sophie Serindat , Nicolas Quennet , Yao Liu
Brake wear contributes significantly to non-exhaust emissions, with poorly documented real-world data on ultrafine particles and gaseous emissions, particularly for heavy vehicles. This study focuses on brake wear ultrafine particles emitted by a school bus in real-world driving conditions, through on-board measurements. Some gaseous compounds were also measured. Tests were conducted on a real-world school pick-up route, as well as an in-service-conformity compliant route. A custom-made stainless-steel emission collection system was designed and placed around the front right disc. Particle and gas measurement instruments were sampled directly from the collection system, which was also equipped with temperature sensors. Results show that brake particle emissions range from 4.1 × 107 #/brake/km to 1.7 × 109 #/brake/km, with a bimodal distribution (first mode around 10 nm and second mode around 200 nm). Emissions were analyzed with regard to energy loss during each braking event, showing it can be critical in estimating brake emissions in most cases. In some cases, particle emissions are poorly correlated with energy in the 10 nm mode, which is due to high-intensity and repeated braking episodes. Concentration peaks were also observed for a few volatile organic compounds such as benzene and toluene. Gaseous emission was also observed for CO, CO2, CH4, NH3, NOx, and SO2.
{"title":"On-board characterization of brake-wear emissions from a heavy-duty vehicle in real-world driving conditions","authors":"Ashok Singh Vishnoi , Boris Vansevenant , Asma Beji , Mathieu Goriaux , Bernard Guiot , Yassine Azizi , Mélanie Messieux , Patrick Tassel , Sophie Serindat , Nicolas Quennet , Yao Liu","doi":"10.1016/j.aeaoa.2025.100379","DOIUrl":"10.1016/j.aeaoa.2025.100379","url":null,"abstract":"<div><div>Brake wear contributes significantly to non-exhaust emissions, with poorly documented real-world data on ultrafine particles and gaseous emissions, particularly for heavy vehicles. This study focuses on brake wear ultrafine particles emitted by a school bus in real-world driving conditions, through on-board measurements. Some gaseous compounds were also measured. Tests were conducted on a real-world school pick-up route, as well as an in-service-conformity compliant route. A custom-made stainless-steel emission collection system was designed and placed around the front right disc. Particle and gas measurement instruments were sampled directly from the collection system, which was also equipped with temperature sensors. Results show that brake particle emissions range from 4.1 × 10<sup>7</sup> #/brake/km to 1.7 × 10<sup>9</sup> #/brake/km, with a bimodal distribution (first mode around 10 nm and second mode around 200 nm). Emissions were analyzed with regard to energy loss during each braking event, showing it can be critical in estimating brake emissions in most cases. In some cases, particle emissions are poorly correlated with energy in the 10 nm mode, which is due to high-intensity and repeated braking episodes. Concentration peaks were also observed for a few volatile organic compounds such as benzene and toluene. Gaseous emission was also observed for CO, CO<sub>2</sub>, CH<sub>4</sub>, NH<sub>3</sub>, NOx, and SO<sub>2</sub>.</div></div><div><h3>Glossary</h3><div>Particle collection system, VOCs, high-intensity braking</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100379"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-31DOI: 10.1016/j.aeaoa.2025.100391
Sergio Harb , Benjamin Cea , Nicolas Karoski , Adrien Dermigny , Vincent Fuvel , Benjamin Cuniasse , Florence Paulus , Isaline Fraboulet
Biomass combustion is the primary source of renewable energy in France. However, it also significantly contributes to outdoor air pollution. This energy sector is undergoing major changes, including evolving regulations, improved fuel types, and enhanced emission control technologies. While the performance of larger installations (>1 MW) is well documented, less is known about biomass boilers with lower capacities. It has become crucial to better understand the operation of smaller biomass boilers, assess their contribution to atmospheric pollution, and identify methods to reduce their emissions.
The ACIBIOQA project investigated emissions from six biomass boilers with nominal power outputs ranging from 150 kW to 1.65 MW across France. Measurements included combustion gas-phase characterization (O2, CO2, CO, NOx, and organic gaseous compounds: OGCs) and particulate-phase characterization, covering total particulate matter (TPM), including both solid (SP) and condensable fractions, PM10, PM2.5, and PM1 by mass, 15 heavy metals, 8 polycyclic aromatic hydrocarbons (PAHs).
Measured concentrations (corrected to 6 vol% O2) varied widely: CO ranged from 153 to 8841 mg Nm−3, NOx from 172 to 395 mg eq. NO2 Nm−3, and organic gaseous compounds from <1 to 634 mg eq. C Nm−3. TPM ranged from <1.5 to 475 mg Nm−3, with SP between <0.3 and 475 mg Nm−3. The condensable fraction contributed 4–26 % to TPM. PM1 dominated the mass size distribution, accounting for 60–100 %. PAHs ranged from 0.1 to 340 μg Nm−3, and heavy metals from 0.03 to 6.7 mg Nm−3.
Emission levels were influenced by boiler nominal power output, combustion load, operating regime, and filtration technology. Particularly, CO, OGCs, and PAHs were highest under unstable and low-load conditions. SP emissions increased with the number of combustion cycles and decreased operating load, while bag filters appeared to reduce SP levels. Higher condensable fractions were observed in boilers with complete on/off cycles and lower nominal outputs.
This study provides new insights into emissions from small biomass boilers and represents a pioneering effort to characterize the condensable particulate fraction at the national level. Emission factors were calculated and compared with literature and national inventory values.
{"title":"Characterization of gaseous and particulate atmospheric emissions, including the condensable particulate fraction, from small biomass boilers (150 kW-1.65 MW) in France","authors":"Sergio Harb , Benjamin Cea , Nicolas Karoski , Adrien Dermigny , Vincent Fuvel , Benjamin Cuniasse , Florence Paulus , Isaline Fraboulet","doi":"10.1016/j.aeaoa.2025.100391","DOIUrl":"10.1016/j.aeaoa.2025.100391","url":null,"abstract":"<div><div>Biomass combustion is the primary source of renewable energy in France. However, it also significantly contributes to outdoor air pollution. This energy sector is undergoing major changes, including evolving regulations, improved fuel types, and enhanced emission control technologies. While the performance of larger installations (>1 MW) is well documented, less is known about biomass boilers with lower capacities. It has become crucial to better understand the operation of smaller biomass boilers, assess their contribution to atmospheric pollution, and identify methods to reduce their emissions.</div><div>The ACIBIOQA project investigated emissions from six biomass boilers with nominal power outputs ranging from 150 kW to 1.65 MW across France. Measurements included combustion gas-phase characterization (O<sub>2</sub>, CO<sub>2</sub>, CO, NOx, and organic gaseous compounds: OGCs) and particulate-phase characterization, covering total particulate matter (TPM), including both solid (SP) and condensable fractions, PM<sub>10</sub>, PM<sub>2.5</sub>, and PM<sub>1</sub> by mass, 15 heavy metals, 8 polycyclic aromatic hydrocarbons (PAHs).</div><div>Measured concentrations (corrected to 6 vol% O<sub>2</sub>) varied widely: CO ranged from 153 to 8841 mg Nm<sup>−3</sup>, NOx from 172 to 395 mg eq. NO<sub>2</sub> Nm<sup>−3</sup>, and organic gaseous compounds from <1 to 634 mg eq. C Nm<sup>−3</sup>. TPM ranged from <1.5 to 475 mg Nm<sup>−3</sup>, with SP between <0.3 and 475 mg Nm<sup>−3</sup>. The condensable fraction contributed 4–26 % to TPM. PM<sub>1</sub> dominated the mass size distribution, accounting for 60–100 %. PAHs ranged from 0.1 to 340 μg Nm<sup>−3</sup>, and heavy metals from 0.03 to 6.7 mg Nm<sup>−3</sup>.</div><div>Emission levels were influenced by boiler nominal power output, combustion load, operating regime, and filtration technology. Particularly, CO, OGCs, and PAHs were highest under unstable and low-load conditions. SP emissions increased with the number of combustion cycles and decreased operating load, while bag filters appeared to reduce SP levels. Higher condensable fractions were observed in boilers with complete on/off cycles and lower nominal outputs.</div><div>This study provides new insights into emissions from small biomass boilers and represents a pioneering effort to characterize the condensable particulate fraction at the national level. Emission factors were calculated and compared with literature and national inventory values.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100391"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-18DOI: 10.1016/j.aeaoa.2025.100370
Demi van Wijk , Ceder R. Raben , Hans J. Erbrink , Dick J.J. Heederik , Wietske Dohmen
Excessive nitrogen deposition is a major problem in nature areas, causing soil acidification and eutrophication, which reduces biodiversity. In the Netherlands, most nitrogen originates from ammonia emissions related to agriculture. This study investigates how various ammonia emission reduction strategies affect spatial patterns of livestock-related ambient ammonia levels, focusing on nature areas near a livestock-dense region. The aim is to provide insights into effects of interventions on environmental exposure levels and efficiency of mitigation strategies. Using dispersion modeling, annual average patterns of ambient ammonia levels were estimated per scenario, considering emissions from approximately 4500 farms. Results indicate that scenarios involving significant reductions in ammonia emissions (54–86 %), achieved through technical or management modifications or farm removal, result in substantial reductions (62–87 %) in ambient ammonia levels within nature areas. Targeted strategies aimed at specific sectors that contribute most to ammonia levels in nature areas achieved relatively modest absolute reductions (8–13 %) but generally higher efficiency compared to more generic approaches. Scenario efficiency, defined as the ratio between emission/concentration reduction, varied considerably from 0.5 to 1.3. This variations underscores the importance of assessing spatial ammonia patterns rather than focusing and relying solely on emission reduction expressed in terms of total mass. The efficiency of reduction strategies depends on the geographical distribution of (sector-specific) farms near nature areas, and emission height from these farms. Therefore, combined strategies explicitly targeting these factors, such as integrating spatially focused measures (e.g., zoning) with generic emission reductions, are expected most effective in reducing ammonia concentrations in nature areas.
{"title":"Effects of different ammonia emission reduction strategies from livestock farming on ambient ammonia concentrations in nature areas: a series of scenario analyses","authors":"Demi van Wijk , Ceder R. Raben , Hans J. Erbrink , Dick J.J. Heederik , Wietske Dohmen","doi":"10.1016/j.aeaoa.2025.100370","DOIUrl":"10.1016/j.aeaoa.2025.100370","url":null,"abstract":"<div><div>Excessive nitrogen deposition is a major problem in nature areas, causing soil acidification and eutrophication, which reduces biodiversity. In the Netherlands, most nitrogen originates from ammonia emissions related to agriculture. This study investigates how various ammonia emission reduction strategies affect spatial patterns of livestock-related ambient ammonia levels, focusing on nature areas near a livestock-dense region. The aim is to provide insights into effects of interventions on environmental exposure levels and efficiency of mitigation strategies. Using dispersion modeling, annual average patterns of ambient ammonia levels were estimated per scenario, considering emissions from approximately 4500 farms. Results indicate that scenarios involving significant reductions in ammonia emissions (54–86 %), achieved through technical or management modifications or farm removal, result in substantial reductions (62–87 %) in ambient ammonia levels within nature areas. Targeted strategies aimed at specific sectors that contribute most to ammonia levels in nature areas achieved relatively modest absolute reductions (8–13 %) but generally higher efficiency compared to more generic approaches. Scenario efficiency, defined as the ratio between emission/concentration reduction, varied considerably from 0.5 to 1.3. This variations underscores the importance of assessing spatial ammonia patterns rather than focusing and relying solely on emission reduction expressed in terms of total mass. The efficiency of reduction strategies depends on the geographical distribution of (sector-specific) farms near nature areas, and emission height from these farms. Therefore, combined strategies explicitly targeting these factors, such as integrating spatially focused measures (e.g., zoning) with generic emission reductions, are expected most effective in reducing ammonia concentrations in nature areas.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100370"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-12DOI: 10.1016/j.aeaoa.2025.100392
Jiading Li , Jianping Huang , Yeqi Huang , Chang Liu , Xuguo Zhang , Yiang Chen , Vincent Tsz Fai Cheung , M.B. Sobnack , Jimmy Fung
Improving model forecasting capability of high surface ozone (O3) concentrations is critical for air quality management. Accurate predictions of O3 and other criteria air pollutants depend heavily on the chemical mechanisms used in models. In this study, the Weather Research Forecast/Community Multiscale Air Quality (WRF/CMAQ) modeling system is utilized to quantify the impact of two chemical mechanisms, the Carbon Bond Mechanism 6 revision 3 (CB6r3) and the State Air Pollution Research Center Version 07 with toluene (T), iodine (I) and chlorine (C) chemistry (SAPRC07) on O3 predictions in the Guangdong-Hong Kong-Macau Greater Bay Area of China (GBA). Three-month simulations were conducted with the two gas-phase mechanisms over the four nested domains in the GBA for July, August, and September 2021. The simulations are evaluated extensively with observations from surface meteorology and air quality monitoring networks, including three volatile organic compounds (VOC) observational sites in Hong Kong. The evaluations show that the SAPRC07 mechanism has a stronger capability than the CB6r3 mechanism in predicting O3 peak concentrations during exceedance events with mean bias (MB) of −2.15 ppbv, correlation coefficient (CR) of 0.86, and hit rate of 0.58, which are higher than CB6r3 with MB of −11.54 ppbv, CR of 0.81, and hit rate of 0.35. The improvement of O3 predictions is largely attributed to the more detailed treatment of VOC species by SAPRC07 mechanisms, as evidenced by our evaluation showing its superior performance in reproducing VOC concentration changes compared to CB6r3 during O3 episodes. The study highlights the potential of implementing detailed VOC chemical mechanisms, such as SAPRC07, in real-time forecast and sensitivity studies to support the development of effective emission reduction strategies, given the rapid advancement of computer technologies.
{"title":"Improving ozone episode predictions in the Great Bay Area: An evaluation of the contribution of gas-phase chemical mechanisms","authors":"Jiading Li , Jianping Huang , Yeqi Huang , Chang Liu , Xuguo Zhang , Yiang Chen , Vincent Tsz Fai Cheung , M.B. Sobnack , Jimmy Fung","doi":"10.1016/j.aeaoa.2025.100392","DOIUrl":"10.1016/j.aeaoa.2025.100392","url":null,"abstract":"<div><div>Improving model forecasting capability of high surface ozone (O<sub>3</sub>) concentrations is critical for air quality management. Accurate predictions of O<sub>3</sub> and other criteria air pollutants depend heavily on the chemical mechanisms used in models. In this study, the Weather Research Forecast/Community Multiscale Air Quality (WRF/CMAQ) modeling system is utilized to quantify the impact of two chemical mechanisms, the Carbon Bond Mechanism 6 revision 3 (CB6r3) and the State Air Pollution Research Center Version 07 with toluene (T), iodine (I) and chlorine (C) chemistry (SAPRC07) on O<sub>3</sub> predictions in the Guangdong-Hong Kong-Macau Greater Bay Area of China (GBA). Three-month simulations were conducted with the two gas-phase mechanisms over the four nested domains in the GBA for July, August, and September 2021. The simulations are evaluated extensively with observations from surface meteorology and air quality monitoring networks, including three volatile organic compounds (VOC) observational sites in Hong Kong. The evaluations show that the SAPRC07 mechanism has a stronger capability than the CB6r3 mechanism in predicting O<sub>3</sub> peak concentrations during exceedance events with mean bias (MB) of −2.15 ppbv, correlation coefficient (CR) of 0.86, and hit rate of 0.58, which are higher than CB6r3 with MB of −11.54 ppbv, CR of 0.81, and hit rate of 0.35. The improvement of O<sub>3</sub> predictions is largely attributed to the more detailed treatment of VOC species by SAPRC07 mechanisms, as evidenced by our evaluation showing its superior performance in reproducing VOC concentration changes compared to CB6r3 during O<sub>3</sub> episodes. The study highlights the potential of implementing detailed VOC chemical mechanisms, such as SAPRC07, in real-time forecast and sensitivity studies to support the development of effective emission reduction strategies, given the rapid advancement of computer technologies.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100392"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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