Pub Date : 2025-11-07DOI: 10.1016/j.aeaoa.2025.100393
Betsy Sandoval Guzmán, Mathias Huber, Philippe Zimmermann, Barbara Zeps, Christian Bach, Miriam Elser
Plug-in Hybrid Electric Vehicles (PHEVs) play an important role in the transition to sustainable transportation, but their real-world performance often deviates from type-approval values. These deviations stem from factors not captured in standard test procedures, including driving dynamics, ambient temperature, and use of auxiliary systems. As a result, real-world fuel consumption for PHEVs in Europe is, on average, up to five times higher than certified values. To address this gap, the present study provides an empirical evaluation of the performance of eight PHEV passenger cars using controlled test-bench measurements under various conditions. The analyses focus on the influence of low ambient temperature, dynamic driving, and auxiliary heating on electric range, energy consumption (fuel and electric energy demand), and carbon dioxide and key pollutants emissions. Results show a substantial reduction in electric range under demanding conditions, increasing reliance on the combustion engine and leading to higher fuel consumption and emissions. These findings underscore the limitations of current type-approval procedures and the importance of considering real-world usage conditions in environmental assessments.
{"title":"Environmental performance of plug-in hybrid electric vehicles: Impacts of driving cycles, ambient temperature, and auxiliary loads","authors":"Betsy Sandoval Guzmán, Mathias Huber, Philippe Zimmermann, Barbara Zeps, Christian Bach, Miriam Elser","doi":"10.1016/j.aeaoa.2025.100393","DOIUrl":"10.1016/j.aeaoa.2025.100393","url":null,"abstract":"<div><div>Plug-in Hybrid Electric Vehicles (PHEVs) play an important role in the transition to sustainable transportation, but their real-world performance often deviates from type-approval values. These deviations stem from factors not captured in standard test procedures, including driving dynamics, ambient temperature, and use of auxiliary systems. As a result, real-world fuel consumption for PHEVs in Europe is, on average, up to five times higher than certified values. To address this gap, the present study provides an empirical evaluation of the performance of eight PHEV passenger cars using controlled test-bench measurements under various conditions. The analyses focus on the influence of low ambient temperature, dynamic driving, and auxiliary heating on electric range, energy consumption (fuel and electric energy demand), and carbon dioxide and key pollutants emissions. Results show a substantial reduction in electric range under demanding conditions, increasing reliance on the combustion engine and leading to higher fuel consumption and emissions. These findings underscore the limitations of current type-approval procedures and the importance of considering real-world usage conditions in environmental assessments.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100393"},"PeriodicalIF":3.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520098","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}
Phthalate acid esters are semi-volatile organic compounds widely used as plasticizers in consumer and industrial products. This review compares indoor PAE concentrations across countries and evaluates sampling techniques, analytical methods, and exposure risks. China accounts for the largest share of studies (37 %), followed by the United States, Japan, and other countries, with higher indoor levels in China likely linked to extensive plastic production. Active air sampling was employed in 88 % of studies, mainly using vacuum cleaners (45 %), polyurethane foam disks (10.6 %), brushes (9.1 %), low-volume pumps (7.5 %), and tubes (4.5 %), while 18 % used passive techniques. Active methods allow controlled air collection but are limited by equipment cost and energy demand, whereas passive sampling supports longer-term monitoring with minimal environmental dependency. Sampling durations ranged from under 8 to over 72 h for active methods and several hours to weeks for passive ones. Indoor relative humidity typically ranged from 31 % to 50 %, with higher humidity enhancing PAE release from plastics. PAE concentrations varied across indoor microenvironments: dust levels ranged from 312 μg/g in schools to 3893 μg/g in apartments, and air levels from 786 ng/m3 in homes to 10,204 ng/m3 in hospitals. DEHP concentrations were higher in dust than air due to low volatility. DEHP and DnBP consistently pose the highest hazards for adults and children, with oral ingestion as the primary exposure route for children. Overall, higher PAE levels were observed in homes, offices, and hospitals due to building materials and furnishings. Risk assessments highlight DEHP as the greatest potential hazard, particularly for children. Indoor PAE contamination is influenced by geography, sampling approach, humidity, and material composition, reflecting widespread plastic use, differences in ventilation and humidity control, and variations in lifestyle and product consumption across regions.
{"title":"Phthalate acid esters in indoor environments: Concentrations, sampling techniques, and health risk assessment","authors":"Afsaneh Esmaeili Nasrabadi , Narges Babaei , Fateme Kabirinia , Ziaeddin Bonyadi","doi":"10.1016/j.aeaoa.2025.100394","DOIUrl":"10.1016/j.aeaoa.2025.100394","url":null,"abstract":"<div><div>Phthalate acid esters are semi-volatile organic compounds widely used as plasticizers in consumer and industrial products. This review compares indoor PAE concentrations across countries and evaluates sampling techniques, analytical methods, and exposure risks. China accounts for the largest share of studies (37 %), followed by the United States, Japan, and other countries, with higher indoor levels in China likely linked to extensive plastic production. Active air sampling was employed in 88 % of studies, mainly using vacuum cleaners (45 %), polyurethane foam disks (10.6 %), brushes (9.1 %), low-volume pumps (7.5 %), and tubes (4.5 %), while 18 % used passive techniques. Active methods allow controlled air collection but are limited by equipment cost and energy demand, whereas passive sampling supports longer-term monitoring with minimal environmental dependency. Sampling durations ranged from under 8 to over 72 h for active methods and several hours to weeks for passive ones. Indoor relative humidity typically ranged from 31 % to 50 %, with higher humidity enhancing PAE release from plastics. PAE concentrations varied across indoor microenvironments: dust levels ranged from 312 μg/g in schools to 3893 μg/g in apartments, and air levels from 786 ng/m<sup>3</sup> in homes to 10,204 ng/m<sup>3</sup> in hospitals. DEHP concentrations were higher in dust than air due to low volatility. DEHP and DnBP consistently pose the highest hazards for adults and children, with oral ingestion as the primary exposure route for children. Overall, higher PAE levels were observed in homes, offices, and hospitals due to building materials and furnishings. Risk assessments highlight DEHP as the greatest potential hazard, particularly for children. Indoor PAE contamination is influenced by geography, sampling approach, humidity, and material composition, reflecting widespread plastic use, differences in ventilation and humidity control, and variations in lifestyle and product consumption across regions.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100394"},"PeriodicalIF":3.4,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467009","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 : 2025-11-01DOI: 10.1016/j.aeaoa.2025.100389
Simon Gant , Joseph Chang , Rory Hetherington , Steven Hanna , Gemma Tickle , Tom Spicer , Sun McMasters , Shannon Fox , Ron Meris , Scott Bradley , Sean Miner , Matthew King , Steven Simpson , Thomas Mazzola , Alison McGillivray , Harvey Tucker , Oscar Björnham , Bertrand Carissimo , Luciano Fabbri , Maureen Wood , Ailo Aasen
This paper presents the findings of an international model inter-comparison exercise that was undertaken in the period 2021–2024 to assess the performance of atmospheric dispersion models for simulating releases of pressure-liquefied ammonia. The exercise used data from ammonia field trials dating from the 1980s and 1990s: the Desert Tortoise and the FLADIS trials. Concentration data from two arcs of sensors in the Desert Tortoise trials and three arcs of sensors in the FLADIS trials were used. Twenty-one independent modelling teams from North America and Europe participated in the exercise and provided in total twenty-seven sets of results from a range of different models, including empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and Computational Fluid Dynamics (CFD) models. The work is novel in presenting the results from such a large cohort of models, examining specifically the dispersion behaviour of ammonia. This is particularly relevant at the current time, given the growing international interest in using ammonia as a clean energy vector and shipping fuel.
The study found that the agreement between model predictions and measurements (as determined by performance measures such as geometric mean bias and geometric variance) varied between different models. At any downwind distance, the range in predicted plume arc-max concentrations spanned a range of up to one or two orders of magnitude about the measurements. Several modelling teams used the same models and, in most cases, their predictions differed. Given appropriate inputs, most models generally predicted concentrations that agreed with the data within commonly-used model acceptance criteria. There was no single class of model that provided superior predictions to others; predictions from several empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and CFD models were all in close agreement with the data (as defined by the model acceptance criteria).
The findings of the exercise are being used to help plan a programme of future ammonia experiments in the USA, called the Jack Rabbit III trials. The results are also useful for assessing the performance of models that may be applied to assess risks at ammonia facilities, and for emergency planning and response.
{"title":"Pressure-liquefied ammonia jet dispersion: Multi-model intercomparison using Desert Tortoise and FLADIS field data","authors":"Simon Gant , Joseph Chang , Rory Hetherington , Steven Hanna , Gemma Tickle , Tom Spicer , Sun McMasters , Shannon Fox , Ron Meris , Scott Bradley , Sean Miner , Matthew King , Steven Simpson , Thomas Mazzola , Alison McGillivray , Harvey Tucker , Oscar Björnham , Bertrand Carissimo , Luciano Fabbri , Maureen Wood , Ailo Aasen","doi":"10.1016/j.aeaoa.2025.100389","DOIUrl":"10.1016/j.aeaoa.2025.100389","url":null,"abstract":"<div><div>This paper presents the findings of an international model inter-comparison exercise that was undertaken in the period 2021–2024 to assess the performance of atmospheric dispersion models for simulating releases of pressure-liquefied ammonia. The exercise used data from ammonia field trials dating from the 1980s and 1990s: the Desert Tortoise and the FLADIS trials. Concentration data from two arcs of sensors in the Desert Tortoise trials and three arcs of sensors in the FLADIS trials were used. Twenty-one independent modelling teams from North America and Europe participated in the exercise and provided in total twenty-seven sets of results from a range of different models, including empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and Computational Fluid Dynamics (CFD) models. The work is novel in presenting the results from such a large cohort of models, examining specifically the dispersion behaviour of ammonia. This is particularly relevant at the current time, given the growing international interest in using ammonia as a clean energy vector and shipping fuel.</div><div>The study found that the agreement between model predictions and measurements (as determined by performance measures such as geometric mean bias and geometric variance) varied between different models. At any downwind distance, the range in predicted plume arc-max concentrations spanned a range of up to one or two orders of magnitude about the measurements. Several modelling teams used the same models and, in most cases, their predictions differed. Given appropriate inputs, most models generally predicted concentrations that agreed with the data within commonly-used model acceptance criteria. There was no single class of model that provided superior predictions to others; predictions from several empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and CFD models were all in close agreement with the data (as defined by the model acceptance criteria).</div><div>The findings of the exercise are being used to help plan a programme of future ammonia experiments in the USA, called the Jack Rabbit III trials. The results are also useful for assessing the performance of models that may be applied to assess risks at ammonia facilities, and for emergency planning and response.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100389"},"PeriodicalIF":3.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568314","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 : 2025-10-31DOI: 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":"2025-10-31","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 : 2025-10-31DOI: 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":"2025-10-31","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 : 2025-10-28DOI: 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":"2025-10-28","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 : 2025-10-28DOI: 10.1016/j.aeaoa.2025.100386
Anuva Bhowmick , Louise Olsen-Kettle , Yali Li , Suwanna Kitpati Boontanon , Narin Boontanon
Air pollution poses a significant threat to public health in rapidly developing countries like Bangladesh, necessitating robust and cost-effective monitoring solutions. This study validates the performance of the CUPI-G device, a low-cost air quality monitoring device, in Dhaka, Bangladesh. The CUPI-G, equipped with electrochemical sensors for PM2.5, CO, NO, NO2, and O3, was deployed across multiple sites representing diverse urban environments, including residential, near-road, and educational areas. Data from the CUPI-G was validated with a collocated reference instrument using statistical (single and multiple linear regression) and machine learning (random forest, RF) approaches. The RF model, particularly when incorporating relative humidity, demonstrated superior performance in predicting pollutant concentrations, with high correlation coefficients (e.g., O3: R=0.798) and low error metrics (RMSE=3.594 ppb, MAPE=4.812 %). However, model accuracy decreased when applied outside the training humidity range, highlighting the need for broader validation datasets. Despite this, the CUPI-G device was validated without using the relative humidity as a factor and was found to still perform adequately. A two-month spatial analysis across three different areas revealed that the hourly average of PM2.5 and O3 concentrations peaked in the near roadways as 89 g/m and 66.50 ppb, respectively. NO2 levels were highest in the residential area at 63.49 ppb. The results demonstrate that the CUPI-G device provides a reliable and cost-effective solution for expanding air quality monitoring networks, offering detailed spatial and temporal data essential for public health advisories and policy interventions, particularly in resource-limited settings.
{"title":"Performance assessment and deployment of a low-cost device for urban air quality monitoring in a developing country","authors":"Anuva Bhowmick , Louise Olsen-Kettle , Yali Li , Suwanna Kitpati Boontanon , Narin Boontanon","doi":"10.1016/j.aeaoa.2025.100386","DOIUrl":"10.1016/j.aeaoa.2025.100386","url":null,"abstract":"<div><div>Air pollution poses a significant threat to public health in rapidly developing countries like Bangladesh, necessitating robust and cost-effective monitoring solutions. This study validates the performance of the CUPI-G device, a low-cost air quality monitoring device, in Dhaka, Bangladesh. The CUPI-G, equipped with electrochemical sensors for PM<sub>2.5</sub>, CO, NO, NO<sub>2</sub>, and O<sub>3</sub>, was deployed across multiple sites representing diverse urban environments, including residential, near-road, and educational areas. Data from the CUPI-G was validated with a collocated reference instrument using statistical (single and multiple linear regression) and machine learning (random forest, RF) approaches. The RF model, particularly when incorporating relative humidity, demonstrated superior performance in predicting pollutant concentrations, with high correlation coefficients (e.g., O<sub>3</sub>: R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.798) and low error metrics (RMSE=3.594 ppb, MAPE=4.812 %). However, model accuracy decreased when applied outside the training humidity range, highlighting the need for broader validation datasets. Despite this, the CUPI-G device was validated without using the relative humidity as a factor and was found to still perform adequately. A two-month spatial analysis across three different areas revealed that the hourly average of PM<sub>2.5</sub> and O<sub>3</sub> concentrations peaked in the near roadways as 89 <span><math><mi>μ</mi></math></span>g/m<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span> and 66.50 ppb, respectively. NO<sub>2</sub> levels were highest in the residential area at 63.49 ppb. The results demonstrate that the CUPI-G device provides a reliable and cost-effective solution for expanding air quality monitoring networks, offering detailed spatial and temporal data essential for public health advisories and policy interventions, particularly in resource-limited settings.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100386"},"PeriodicalIF":3.4,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417603","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}
Daily PM10 samples were collected at Ny Ålesund (Svalbard Islands, Norway) from February to October 2015 and analyzed using a multi-technique approach, including ion chromatography (IC), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Particle Induced X-ray Emission (PIXE), and thermo-optical analysis (TOA). This dataset allowed for the characterization of a wide range of chemical species, including major ions, organic and elemental carbon, and over 40 elements, despite the extremely low atmospheric concentrations typical of this remote Arctic site (daily PM10 rarely exceeded 6 μg m−3, with an overall maximum of 16 μg m−3). The high temporal resolution and the large number of samples allowed the investigation of seasonal patterns and the identification of aerosol sources using the Positive Matrix Factorization (PMF) receptor model and the Potential Source Contribution Function (PSCF) to analyze of the back-trajectories. Seven distinct sources were identified: biogenic, sulphate, sea salt, combustion, nitrate, crustal, and anthropogenic. It was seen that the sampling site is mainly affected by the local natural sources; marine biogenic emissions show a strong seasonal signal linked to sunlight availability; anthropogenic sources, although less frequent, were significant during the Arctic Haze period and included long-range transported pollution from lower latitudes and the formation of secondary aerosols; episodic but intense contributions from biomass burning originating from wildfires in North America or Siberia occasionally became the dominant component of Arctic aerosol. These findings highlight the complex interplay between local natural emissions and long-range transported pollution in shaping the Arctic aerosol composition.
2015年2月至10月在Ny Ålesund（挪威斯瓦尔巴群岛）收集每日PM10样品，并使用多种技术方法进行分析，包括离子色谱（IC），电感耦合等离子体质谱（ICP-MS），粒子诱导x射线发射（PIXE）和热光学分析（TOA）。尽管这个偏远的北极地区典型的大气浓度极低（每日PM10很少超过6 μg m - 3，总体最大值为16 μg m - 3），但该数据集允许对广泛的化学物种进行表征，包括主要离子、有机碳和元素碳，以及40多种元素。高时间分辨率和大量样本使得季节模式调查和气溶胶源识别成为可能，使用正矩阵分解（PMF）受体模型和潜在源贡献函数（PSCF）分析反轨迹。确定了七种不同的来源：生物源、硫酸盐、海盐、燃烧、硝酸盐、地壳和人为。结果表明，采样点主要受当地自然污染源的影响；海洋生物排放显示出与阳光可用性有关的强烈季节性信号；人为来源虽然频率较低，但在北极雾霾期间仍很重要，包括来自低纬度的远距离输送污染和次生气溶胶的形成；北美或西伯利亚野火产生的生物质燃烧偶尔会成为北极气溶胶的主要组成部分，这种贡献虽不明显，但强度很大。这些发现突出表明，在形成北极气溶胶组成的过程中，当地自然排放和远距离输送污染之间存在复杂的相互作用。
{"title":"Tracing natural, anthropogenic, and biomass burning contributions to Arctic aerosol combining daily chemical characterization and receptor modeling analysis","authors":"Fabio Giardi , Giulia Calzolai , Silvia Nava , Massimo Chiari , Franco Lucarelli , Cosimo Fratticioli , Laura Caiazzo , David Cappelletti , Stefano Crocchianti , Silvia Becagli , Mirko Severi , Rita Traversi","doi":"10.1016/j.aeaoa.2025.100388","DOIUrl":"10.1016/j.aeaoa.2025.100388","url":null,"abstract":"<div><div>Daily PM<sub>10</sub> samples were collected at Ny Ålesund (Svalbard Islands, Norway) from February to October 2015 and analyzed using a multi-technique approach, including ion chromatography (IC), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Particle Induced X-ray Emission (PIXE), and thermo-optical analysis (TOA). This dataset allowed for the characterization of a wide range of chemical species, including major ions, organic and elemental carbon, and over 40 elements, despite the extremely low atmospheric concentrations typical of this remote Arctic site (daily PM<sub>10</sub> rarely exceeded 6 μg m<sup>−3</sup>, with an overall maximum of 16 μg m<sup>−3</sup>). The high temporal resolution and the large number of samples allowed the investigation of seasonal patterns and the identification of aerosol sources using the Positive Matrix Factorization (PMF) receptor model and the Potential Source Contribution Function (PSCF) to analyze of the back-trajectories. Seven distinct sources were identified: biogenic, sulphate, sea salt, combustion, nitrate, crustal, and anthropogenic. It was seen that the sampling site is mainly affected by the local natural sources; marine biogenic emissions show a strong seasonal signal linked to sunlight availability; anthropogenic sources, although less frequent, were significant during the Arctic Haze period and included long-range transported pollution from lower latitudes and the formation of secondary aerosols; episodic but intense contributions from biomass burning originating from wildfires in North America or Siberia occasionally became the dominant component of Arctic aerosol. These findings highlight the complex interplay between local natural emissions and long-range transported pollution in shaping the Arctic aerosol composition.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100388"},"PeriodicalIF":3.4,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417605","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 : 2025-10-24DOI: 10.1016/j.aeaoa.2025.100384
Ceres A. Woolley Maisch , Rebecca E. Fisher , James L. France , David Lowry , Mathias Lanoisellé , Thomas Röckmann , Carina van der Veen , Euan G. Nisbet
<div><div>Field campaigns in Jersey, Channel Islands (Crown Dependency of British Isles), were carried out to understand the distribution and scale of agricultural methane (CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>) emissions. We used vehicle-mounted spectrometers and isotope analysis to fingerprint and map methane sources on Jersey dairy farms to test whether mobile dual-isotope surveys can quantitatively separate enteric and manure CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> sources on a regional-farm scale. Peak barn CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> mixing ratios (<span><math><mo>≤</mo></math></span> 500 ppm), observed from continuous overnight monitoring in a confined cattle barn, fall within concentration windows targeted by catalytic-oxidation prototypes, suggesting potential for the successful implementation of removal techniques, subject to ventilation-rate and cost studies. CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>, CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> (carbon dioxide) and <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>13</mn></mrow></msup></math></span>C<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> were mapped across the 120 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> island of Jersey, visiting 11 dairy farms and one wastewater treatment works. Methane emissions from different sources at each farm were isolated in order to determine <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>13</mn></mrow></msup></math></span>C<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> and <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>H<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> source signatures and also typical CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>:CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> ratios proximal to cattle in barns, expressed as relative excess over background. Excess CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>:CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> ratios around 8–12 can be considered a cow barn signature. 138 grab samples were collected during two island-wide campaigns (November 2021, June 2023) and analysed for <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>13</mn></mrow></msup></math></span>C<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> and <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>H<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>. Isotopic source signatures were determined using Keeling plot analysis for 61
{"title":"Characterising methane emissions from dairy farm sources using mobile and dual-isotope measurements in Jersey, Channel Islands","authors":"Ceres A. Woolley Maisch , Rebecca E. Fisher , James L. France , David Lowry , Mathias Lanoisellé , Thomas Röckmann , Carina van der Veen , Euan G. Nisbet","doi":"10.1016/j.aeaoa.2025.100384","DOIUrl":"10.1016/j.aeaoa.2025.100384","url":null,"abstract":"<div><div>Field campaigns in Jersey, Channel Islands (Crown Dependency of British Isles), were carried out to understand the distribution and scale of agricultural methane (CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>) emissions. We used vehicle-mounted spectrometers and isotope analysis to fingerprint and map methane sources on Jersey dairy farms to test whether mobile dual-isotope surveys can quantitatively separate enteric and manure CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> sources on a regional-farm scale. Peak barn CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> mixing ratios (<span><math><mo>≤</mo></math></span> 500 ppm), observed from continuous overnight monitoring in a confined cattle barn, fall within concentration windows targeted by catalytic-oxidation prototypes, suggesting potential for the successful implementation of removal techniques, subject to ventilation-rate and cost studies. CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>, CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> (carbon dioxide) and <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>13</mn></mrow></msup></math></span>C<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> were mapped across the 120 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> island of Jersey, visiting 11 dairy farms and one wastewater treatment works. Methane emissions from different sources at each farm were isolated in order to determine <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>13</mn></mrow></msup></math></span>C<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> and <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>H<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> source signatures and also typical CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>:CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> ratios proximal to cattle in barns, expressed as relative excess over background. Excess CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>:CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> ratios around 8–12 can be considered a cow barn signature. 138 grab samples were collected during two island-wide campaigns (November 2021, June 2023) and analysed for <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>13</mn></mrow></msup></math></span>C<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> and <span><math><msup><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>H<img>CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>. Isotopic source signatures were determined using Keeling plot analysis for 61","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100384"},"PeriodicalIF":3.4,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417602","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 : 2025-10-21DOI: 10.1016/j.aeaoa.2025.100385
Amanuel Gebisa Aga, Alemayehu Nigussie Arsedi, Alemayehu Wakjira Huluka
This study investigates Addis Ababa's passenger car growth after 2005 and its effects on the environment between 2018 and 2024. Predictive Linear Regression and Artificial Neural Networks (ANN) machine learning techniques were used in the study to forecast the growth of the fleet of passenger cars to determine related emissions and energy usage. The study incorporates environmental factors, vehicle activity patterns, and local vehicle registration data into the COPERT model. Vehicle classifications by fuel type (diesel and petrol) and Euro 1 to Euro 6 criteria were taken into account in the analysis. The overall number of passenger vehicles has increased by more than twentyfold during the last 20 years, according to the results. Pollutant emissions have increased as a result, especially from older Euro 1 to Euro 3 vehicles, and include CO2, NOx, CO, and VOC. Due to limited adoption of low-emission vehicle technology and growing travel demand, the energy consumption for passenger cars also exhibited steady growth. The results highlighted how policy changes stopped the increase of passenger vehicle emissions after 2024 due to the banning of internal combustion-based passenger vehicles. By offering a data-driven methodology for assessing vehicle-related emissions and guiding mitigation methods, this study supports sustainable transportation planning at the local and national levels.
{"title":"Predictive analysis of passenger vehicle emissions and fuel consumption in Addis Ababa, Ethiopia using COPERT based on vehicle growth forecasting","authors":"Amanuel Gebisa Aga, Alemayehu Nigussie Arsedi, Alemayehu Wakjira Huluka","doi":"10.1016/j.aeaoa.2025.100385","DOIUrl":"10.1016/j.aeaoa.2025.100385","url":null,"abstract":"<div><div>This study investigates Addis Ababa's passenger car growth after 2005 and its effects on the environment between 2018 and 2024. Predictive Linear Regression and Artificial Neural Networks (ANN) machine learning techniques were used in the study to forecast the growth of the fleet of passenger cars to determine related emissions and energy usage. The study incorporates environmental factors, vehicle activity patterns, and local vehicle registration data into the COPERT model. Vehicle classifications by fuel type (diesel and petrol) and Euro 1 to Euro 6 criteria were taken into account in the analysis. The overall number of passenger vehicles has increased by more than twentyfold during the last 20 years, according to the results. Pollutant emissions have increased as a result, especially from older Euro 1 to Euro 3 vehicles, and include CO<sub>2</sub>, NO<sub>x</sub>, CO, and VOC. Due to limited adoption of low-emission vehicle technology and growing travel demand, the energy consumption for passenger cars also exhibited steady growth. The results highlighted how policy changes stopped the increase of passenger vehicle emissions after 2024 due to the banning of internal combustion-based passenger vehicles. By offering a data-driven methodology for assessing vehicle-related emissions and guiding mitigation methods, this study supports sustainable transportation planning at the local and national levels.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100385"},"PeriodicalIF":3.4,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363640","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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