Atmospheric Environment: X最新文献

{"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,&nbsp;Alemayehu Nigussie Arsedi,&nbsp;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 CO₂, NO_x, 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":"2025-12-01","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}

{"title":"Re-evaluation of particulate matter emission rates and variability of livestock houses in the Netherlands","authors":"Marien Korevaar ,&nbsp;Nico Ogink ,&nbsp;Johanna Rebel ,&nbsp;Albert Winkel","doi":"10.1016/j.aeaoa.2025.100369","DOIUrl":"10.1016/j.aeaoa.2025.100369","url":null,"abstract":"","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100369"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098798","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}

{"title":"Characterising methane emissions from dairy farm sources using mobile and dual-isotope measurements in Jersey, Channel Islands","authors":"Ceres A. Woolley Maisch ,&nbsp;Rebecca E. Fisher ,&nbsp;James L. France ,&nbsp;David Lowry ,&nbsp;Mathias Lanoisellé ,&nbsp;Thomas Röckmann ,&nbsp;Carina van der Veen ,&nbsp;Euan G. Nisbet","doi":"10.1016/j.aeaoa.2025.100384","DOIUrl":"10.1016/j.aeaoa.2025.100384","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Field campaigns in Jersey, Channel Islands (Crown Dependency of British Isles), were carried out to understand the distribution and scale of agricultural methane (CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) 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&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; sources on a regional-farm scale. Peak barn CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; mixing ratios (&lt;span&gt;&lt;math&gt;&lt;mo&gt;≤&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt; 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&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, CO&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; (carbon dioxide) and &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;δ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;13&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;C&lt;img&gt;CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; were mapped across the 120 km&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; 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 &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;δ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;13&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;C&lt;img&gt;CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;δ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;H&lt;img&gt;CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; source signatures and also typical CO&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;:CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; ratios proximal to cattle in barns, expressed as relative excess over background. Excess CO&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;:CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; 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 &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;δ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;13&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;C&lt;img&gt;CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;δ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;H&lt;img&gt;CH&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;. 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":"2025-12-01","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}

{"title":"Tracing natural, anthropogenic, and biomass burning contributions to Arctic aerosol combining daily chemical characterization and receptor modeling analysis","authors":"Fabio Giardi ,&nbsp;Giulia Calzolai ,&nbsp;Silvia Nava ,&nbsp;Massimo Chiari ,&nbsp;Franco Lucarelli ,&nbsp;Cosimo Fratticioli ,&nbsp;Laura Caiazzo ,&nbsp;David Cappelletti ,&nbsp;Stefano Crocchianti ,&nbsp;Silvia Becagli ,&nbsp;Mirko Severi ,&nbsp;Rita Traversi","doi":"10.1016/j.aeaoa.2025.100388","DOIUrl":"10.1016/j.aeaoa.2025.100388","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100388"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","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}

{"title":"Performance assessment and deployment of a low-cost device for urban air quality monitoring in a developing country","authors":"Anuva Bhowmick ,&nbsp;Louise Olsen-Kettle ,&nbsp;Yali Li ,&nbsp;Suwanna Kitpati Boontanon ,&nbsp;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_2.5, 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<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_2.5 and O₃ 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. 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.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100386"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","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}

{"title":"Volatility of secondary organic aerosol and sulphate particles formed in ship engine emission","authors":"Oskari Kangasniemi ,&nbsp;Pauli Simonen ,&nbsp;Panu Karjalainen ,&nbsp;Luis M.F. Barreira ,&nbsp;Jana Moldanová ,&nbsp;Hilkka Timonen ,&nbsp;Barbara D’Anna ,&nbsp;Jorma Keskinen ,&nbsp;Miikka Dal Maso","doi":"10.1016/j.aeaoa.2025.100376","DOIUrl":"10.1016/j.aeaoa.2025.100376","url":null,"abstract":"<div><div>The secondary organic aerosol formation potential of a ship engine emission is assumed to be significant since ship engines are known to emit large amounts semi- and intermediate volatility organic compounds capable of forming secondary organic mass in the atmosphere. However, this is poorly studied in real-world conditions. Here, oxidation reactor was used to simulate atmospheric aging of an exhaust emission aboard a ship in real-world conditions. The samples were also heat-treated to gain information on the volatility of the aged emission. Genetic optimization algorithm was combined with a volatility model to study the volatility distribution of the emission and partitioning of the emission was calculated in different dilution scenarios. Aging of the ship exhaust emission was seen to produce significant amounts of secondary organic mass and quite volatile particle phase sulphate. Most of the secondary organic aerosol was in semi- and intermediate volatility range. This volatility range in particle phase means that care has to be taken when diluting the samples. The gas–particle phase partitioning of volatile material can significantly change the particle phase concentrations in addition to just dilution.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100376"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222169","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}

{"title":"Severe health risks from ambient volatile organic compounds (VOCs) in a Central Asian city: Source attribution and probabilistic risk assessment","authors":"Alisher Alibekov ,&nbsp;Yingkar Bahetnur ,&nbsp;Kadisha Yessenbayeva ,&nbsp;Nassiba Baimatova ,&nbsp;Woojin Lee","doi":"10.1016/j.aeaoa.2025.100378","DOIUrl":"10.1016/j.aeaoa.2025.100378","url":null,"abstract":"<div><div>Volatile organic compounds (VOCs) significantly contribute to ambient air pollution and pose serious health threats, particularly in rapidly urbanizing regions. This study comprehensively assessed ambient VOC concentrations, identified potential emission sources, and conducted a stochastic human health risk assessment in Almaty, Kazakhstan – a metropolitan Central Asian city characterized by intense traffic, extensive coal combustion, and frequent temperature inversions. Ambient air samples were collected seasonally at multiple elevation points across the city and analyzed for 23 VOC species. Their concentrations were notably elevated during the heating season, especially in the lower city, with benzene, toluene, ethylbenzene, xylenes (BTEX), and naphthalene exhibiting alarming levels compared to other urban settings worldwide. Principal component and BTEX ratio analyses identified coal combustion, vehicle emissions, and industrial activities as the primary VOC sources, with persistent impacts observed even during non-heating seasons due to pollutant resuspension and revolatilization. The stochastic health risk assessment revealed median non-carcinogenic hazard indices generally within acceptable limits but highlighted substantial exceedances (HI &gt; 1) at the 95th percentile, driven mainly by benzene and naphthalene. Carcinogenic risks consistently exceeded acceptable thresholds (10⁻⁶), with benzene being the predominant contributor, which raised urgent public health concerns. Almaty's population faces significantly higher cancer risks than North American and European cities, highlighting the critical need for targeted regulatory measures to mitigate VOC emissions and protect public health.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100378"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222168","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}

{"title":"Phthalate acid esters in indoor environments: Concentrations, sampling techniques, and health risk assessment","authors":"Afsaneh Esmaeili Nasrabadi ,&nbsp;Narges Babaei ,&nbsp;Fateme Kabirinia ,&nbsp;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/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.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100394"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","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}

{"title":"Characterisation of rare earth elements in natural and exhaust gas samples: SEM-microscopy and EDX-analysis for source identifications","authors":"Margot Bruneau ,&nbsp;Mathieu Goriaux ,&nbsp;Liliane Jean-Soro ,&nbsp;Yao Liu ,&nbsp;Patrick Tassel ,&nbsp;Béatrice Béchet","doi":"10.1016/j.aeaoa.2025.100381","DOIUrl":"10.1016/j.aeaoa.2025.100381","url":null,"abstract":"<div><div>Natural materials and exhaust gas emissions are sources of Rare Earth Elements (REEs) in the road environment. A methodology based on SEM-EDX analyses is proposed to: 1) provide a morphological and chemical characterisation of REEs particles in natural materials and exhaust gas; and 2) identify indicators that can distinguish the sources of REEs. The chemical composition of various washcoats was evaluated. Natural materials, exhaust gases, and ceramic monoliths from catalytic converters were described using a scanning electron microscope (SEM) and analysed with an Energy Dispersive X-ray analyser (EDX). The results indicated that REEs natural particles predominantly exhibited sharp corners in contrast to the spherical shapes of REEs particles within exhaust gases. Exhaust gas particles were smaller (0.07–1.22 μm) than those observed in natural materials (0.64–25.42 μm). REEs particles were associated with mineral carrier phase compounds (e.g., Al, Si, P) and, in some instances, with natural source fingerprints (e.g., Rb, Sr, Th). REEs particles in exhaust gases were embedded in organic combustion particles composed of C, Fe, S or in washcoat detected through Zr, Ti, Pd. La/Ce ratio of natural particles (0.20–0.63) is higher than that for exhaust gas particles (0–0.25). Hence, La/Ce ratio could be used as an indicator for exhaust gas particles in environmental samples. To go further, this study provides information on the physical and chemical speciation of REEs particles necessary to assess the transfer of particles from emission to the environment.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100381"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325289","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}

{"title":"Winter emission factors of ultrafine total and solid particle numbers and PM2.5 near a major arterial road in Tokyo","authors":"Hiroyuki Hagino","doi":"10.1016/j.aeaoa.2025.100382","DOIUrl":"10.1016/j.aeaoa.2025.100382","url":null,"abstract":"<div><div>Road-traffic particulate emissions are regulated by limits expressed as emission factors per driving distance. However, these limits are difficult to compare directly to atmospheric concentrations. This study measured particulate and gaseous pollutant concentrations simultaneously in winter of 2023 at two locations near a major ring road in Tokyo, one roadside and the other 100 m back. Traffic-related emissions of PM_2.5 (fine particulate matter, particle size ≤2.5 μm), total particle number (TPN_{10–100 nm}), and solid particle number (SPN_{10–100 nm}) were not correlated with traffic volume. Marked differences at the two sites confirmed that traffic-related emissions affected the roadside atmospheric environment, although this impact was evident only in hourly data. Emission factors for TPN_{10–100 nm} and SPN_{10–100 nm}, estimated using concentration differences between sites and CO₂/CO and NO<em>x</em>/CO ratios, yielded factors of 1.61 × 10¹³ ± 4.5 × 10¹² and 4.78 × 10¹² ± 2.2 × 10¹² #/km/veh., respectively. Barium in PM_2.5 was used to estimate brake particle emission factors (2.05 × 10⁹ ± 6.6 × 10⁸ #/km/veh., 0.01 % of TPN_{10–100 nm}; 1.64 × 10⁹ ± 5.2 × 10⁸ #/km/veh., 0.03 % of SPN_{10–100 nm}), revealing only a small contribution to total traffic-derived particle emissions. The traffic-derived PM_2.5 emission factor was 20.1 ± 6.5 mg/km/veh., with brake particle–derived PM_2.5 contributing only 0.94 mg/km/veh. (4.7 %). These findings support previous studies showing no correlation between roadside PM_2.5 and traffic volume, and highlight the importance of high-resolution, simultaneous roadside and background measurements for evaluating traffic-derived particulate emissions.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"28 ","pages":"Article 100382"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325291","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}