Atmospheric Pollution Research最新文献

{"title":"Observations of surface CO2 at an urban station in Wuhan, Central China: temporal variations, sources, and sinks","authors":"Wei Liu ,&nbsp;Huang Zheng ,&nbsp;Feng Ding ,&nbsp;Junying Zhang ,&nbsp;Yongchun Zhao ,&nbsp;Zhuo Xiong ,&nbsp;Qian Wu ,&nbsp;Linjun Li","doi":"10.1016/j.apr.2025.102614","DOIUrl":"10.1016/j.apr.2025.102614","url":null,"abstract":"<div><div>Observation of atmospheric carbon dioxide (CO₂) is important to understand its temporal variations, sources, and sinks, which ultimately help mitigate its climate effects. While most CO₂ observations are conducted in background or remote regions, fewer studies focus on urban areas. This study reported a one-year continuous observation of CO₂ at an urban site in Wuhan, Central China. In 2023, the average CO₂ concentration at this site was 459 ± 23.5 ppm, which was 5.53 % higher than mean values collected from other urban stations. Using the moving average filtering method, the CO₂ concentrations were filtered as a pollution source, background, and absorption sink with mean concentrations of 494 ± 21.1 ppm, 455 ± 16.1 ppm, and 434 ± 9.61 ppm, respectively. Temporal variations of these components showed similar monthly trends but different diurnal patterns, reflecting the influences of terrestrial ecosystems and human activities. Model simulations from Carbon Tracker indicated that fossil fuel combustion was the primary source, increasing CO₂ levels by 125 ppm. The ocean and biosphere acted as CO₂ sinks, reducing concentrations by 31.6 ppm and 31.2 ppm, respectively. The conditional bivariate probability function results suggested that urban CO₂ levels were influenced by local emissions and regional transports. Combined with backward trajectory analysis and CO₂ emission inventory, the local and regional contributions were further quantified with percentages of 81.9 % and 18.1 %, respectively. This study enhances our understanding of greenhouse gas behaviors and contributes to efforts to achieve carbon neutrality in urban areas.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102614"},"PeriodicalIF":3.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of Russia-Ukraine War on air pollution over the Black Sea Region due to changes in maritime transport","authors":"Muhittin Gunes Onay ,&nbsp;Serra Saracoglu ,&nbsp;Elif Pehlivanoglu ,&nbsp;Burcak Kaynak","doi":"10.1016/j.apr.2025.102613","DOIUrl":"10.1016/j.apr.2025.102613","url":null,"abstract":"<div><div>The Russia-Ukraine war, which began on February 24, 2022, has affected maritime transport in the Black Sea, leading to significant changes in air pollution levels in addition to other social and environmental impacts. This study examines the impact of reduced maritime activity on air pollution levels in the Black Sea by analyzing TROPOMI NO₂ and SO₂ as well as COBRA algorithm SO₂ retrievals from two years: 2019 (pre-war) and 2022 (war) along with 2019–2022 interval. To determine spatio-temporal variations, retrievals were spatially and temporally matched with EMODnet route density (RD) data. The pollution levels in the study region, over major shipping routes, and ports were examined.</div><div>The results indicated that maritime traffic declined sharply around Ukrainian ports (Odessa, Kherson, and Yuzhny), while it increased in the eastern Black Sea, particularly near Russian ports (Novorossiysk and Tuapse). This shift in shipping patterns influenced NO₂ concentrations, with significant increases in the eastern regions where maritime activity intensified and decreases in the northwestern regions. In contrast, SO₂ levels showed a more complex response due to additional influences and uncertainties on SO₂ retrievals. These results highlighted the importance of satellite-based measurements in evaluating air quality impacts at offshore maritime regions with no ground-based monitoring. The study showed the complex effects of the Russia-Ukraine war on regional air pollution, demonstrating that while reduced maritime traffic led to lower pollutant concentrations in some areas, alternative shipping routes, military activities, and other factors contributed to increased pollution in others.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102613"},"PeriodicalIF":3.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of sea-land breezes on a severe ozone pollution episode over the western Pearl River Estuary","authors":"Jianhua Mai ,&nbsp;Lingling Yu ,&nbsp;Tao Deng ,&nbsp;Yiheng Li ,&nbsp;Shenxiao Zhi ,&nbsp;Chengman Cai","doi":"10.1016/j.apr.2025.102611","DOIUrl":"10.1016/j.apr.2025.102611","url":null,"abstract":"<div><div>In this paper, the role of sea-land breezes in a severe ozone (O₃) pollution episode was studied. The analysis revealed that under the control of the periphery of Typhoon Nanmadol, Zhongshan City on the western coast of the Pearl River Estuary experienced severe O₃ pollution on 16 September 2022, with the daily maximum 8-h average O₃ concentration reaching 274 μg m⁻³. From morning to noon, rapid O₃ accumulation under intense solar radiation and weak northerly winds produced the first peak. During early evening hours, sea breezes occurred and initiated convergence with northerly synoptic winds over central Zhongshan. The recirculation factor of the V-component of ground winds decreased from 1.0 to 0.52, accompanied by an 86 % increase in net O₃ influx compared to the midday level. Concurrently, ventilation index below 1000 m altitude dropped by 58 %, driving secondary O₃ peaks at both ground level and boundary layer heights. After the occurrence of sea breezes, dominant updrafts generated negative vertical O₃ flux at ground. However, the vertical flux removal accounted for merely 4–13 % of horizontal O₃ influx, leading to the accumulation of ground O₃. Concurrent upward transport caused significant O₃ increases at 300 m and 400 m altitude by 49 μg m⁻³ and 45 μg m⁻³ respectively, markedly exceeding the concentration variations at 500–800 m layers.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102611"},"PeriodicalIF":3.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of machine learning algorithms in predicting indoor residential PM2.5 concentrations","authors":"Renato Camilleri ,&nbsp;Roy M. Harrison ,&nbsp;Noel J. Aquilina","doi":"10.1016/j.apr.2025.102609","DOIUrl":"10.1016/j.apr.2025.102609","url":null,"abstract":"<div><div>Recently Machine Learning (ML) has been amply used in environmental research for prediction purposes, but only a limited number of studies have been employed to predict indoor residential fine particulate matter, PM_2.5 concentrations. PM_2.5 can penetrate deep into the lungs and has been linked to respiratory and cardiovascular problems, with long term exposure associated with increased morbidity and mortality. The use of ML can provide a better estimate of residential PM_2.5 concentrations which usually is a significant contributor to personal exposure, especially for the elderly and those with pre-existing health conditions who tend to spend most of their time inside their homes. This study used ML algorithms (General Linear Model (GLM) with Lasso regularisation and Tree-based algorithms, RF and XGBoost) to predict indoor PM_2.5 concentrations at six-hourly averages in the Maltese Islands using outdoor residential PM concentrations and several meteorological parameters. Continuous PM sampling using aerosol spectrometers was carried out at six non-smoking residences in Malta and Gozo. A repeated 10-fold cross-validation was carried out on the training dataset, with hyperparameter tuning using grid search. Hyperparameter tuning used the Root Mean Square Error (RMSE) as the evaluation metric. Five sampling sites showed low indoor PM contributions and the GLM for these sites showed good performance indicators for the testing data, but serial correlation at lag-1 was recorded. For these sites, RF and XGBoost showed very good performance indicators with an Index of Agreement (IOA) of 0.92 and 0.93, respectively, with the most important predictor variable being the outdoor PM₁ fraction. The RF regression model gave the lowest RMSE (30.65 μg m⁻³) and the highest index of agreement (IOA) (0.66) when the models were tested with the data from all sampling sites, which included a site with a PM_2.5 I/O ratio of 5.2, where the high indoor PM generation was primarily associated with emissions from cooking and the indoor relative humidity was suggested as a good predictor variable for such a scenario. This study showed the significant impact of outdoor PM₁ on indoor PM_2.5 levels at sites with limited indoor fine PM sources. At sites with significant indoor generation from cooking, indoor PM_2.5 was 3.6 times the short-term (24-h) AQG of the WHO, indicating that regulations on extraction systems for domestic kitchens would minimise very high exposures of home dwellers to indoor fine PM.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102609"},"PeriodicalIF":3.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air pollution prediction with advanced preprocessing and deep ensemble learning","authors":"Gaurav Narkhede ,&nbsp;Mustafa Poonawala ,&nbsp;Atharva Sonawane ,&nbsp;Anil Hiwale ,&nbsp;Arvind R. Singh","doi":"10.1016/j.apr.2025.102610","DOIUrl":"10.1016/j.apr.2025.102610","url":null,"abstract":"<div><div>The research presented investigates the impact of model selection and preprocessing techniques on air pollution prediction performance, particularly pertaining to achieving Sustainable Development Goals (SDGs). Accurate training of predictive models necessitates effective handling of missing or null values in environmental datasets. To address this challenge, Probabilistic Principal Component Analysis (PPCA) and the Extra Tree Regressor for data imputation are employed, followed by scaling using Robust Scaler, Min-Max Scaler, and Standard Scaler. A thorough comparison of these preprocessing methods revealed that PPCA is the most suitable choice for imputing missing values in air quality datasets, while the Robust Scaler provided the most reliable and accurate scaling. Additionally, Stochastic Gradient Descent (SGD) is integrated as an optimization technique to enhance model performance. The Weighted Average ensemble method, combining PPCA imputation and Robust Scaler, demonstrated superior predictive capabilities. This research highlights the potential for further improvements through additional ensemble techniques and model optimization strategies, opening avenues for future research focused on improving prediction precision and advancing the achievement of Sustainable Development Goals linked to environmental sustainability.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102610"},"PeriodicalIF":3.9,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemical characteristics and source apportionment of water-soluble ions in precipitation in Harbin, China","authors":"Pengyu Chen ,&nbsp;Xiaoyu Qu ,&nbsp;Zihan Song ,&nbsp;Zhaoyang Jia ,&nbsp;Fuxiang Zhang ,&nbsp;Song Cui","doi":"10.1016/j.apr.2025.102604","DOIUrl":"10.1016/j.apr.2025.102604","url":null,"abstract":"<div><div>Precipitation can effectively scavenge atmospheric pollutants, making precipitation chemistry a crucial indicator for tracing anthropogenic impact on air quality. In this study, we collected a total of 99 precipitation samples in Harbin, a megacity in Northeast China, spanning the period from November 2021 to October 2023, and analyzed precipitation pH as well as concentrations of elemental carbon (EC) and major water-soluble ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, NH₄⁺, NO₃⁻, SO₄²⁻, Cl⁻, and F⁻). Our findings revealed that the equivalent concentrations of total water-soluble ions ranged from 40.0 to 1225.2 μeq·L⁻¹, with a volume weighted mean (VWM) concentration of 264.9 μeq·L⁻¹. NO₃⁻, Ca²⁺, NH₄⁺, and SO₄²⁻ were identified the dominant ions in precipitation, collectively accounting for 86.7 % of the total water-soluble ions. The concentrations of most ions in precipitation were elevated by a factor of 1.2–3.6 during the heating period compared to those in the non-heating period. The sources of water-soluble ions were determined through combined analyses involving enrichment factors, positive matrix factorization (PMF), and backward trajectories, which revealed that Ca²⁺, K⁺, and Mg²⁺ were primarily originated from crustal sources, Na⁺ and Cl⁻ from marine sources, and SO₄²⁻ and NO₃⁻ from anthropogenic sources. Furthermore, contributions to water-soluble ion levels in precipitation were attributed to crustal dust deposition (29.8 %), biomass combustion and agricultural activities (26.0 %), as well as fossil fuel combustion (22.0 %). The potential source regions of water-soluble ions in precipitation were identified as cities and regions located northwest, southwest, and south of Harbin. The knowledge gained from this study provides critical information necessary for formulating future pollution control policies.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102604"},"PeriodicalIF":3.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between air pollution and obstructive sleep apnea in terms of respiratory event frequency and lung-to-finger circulation time","authors":"Rachel Chien ,&nbsp;Zhihe Chen ,&nbsp;Peizheng Wang ,&nbsp;Wen-Te Liu ,&nbsp;Ying-Ying Chen ,&nbsp;Yen-Ling Chen ,&nbsp;Arnab Majumdar ,&nbsp;Jiunn-Horng Kang ,&nbsp;Kang-Yun Lee ,&nbsp;Wun-Hao Cheng ,&nbsp;Yi-Chih Lin ,&nbsp;Cheng-Jung Wu ,&nbsp;Yi-Chun Kuan ,&nbsp;Hsin-Chien Lee ,&nbsp;Cheng-Yu Tsai","doi":"10.1016/j.apr.2025.102603","DOIUrl":"10.1016/j.apr.2025.102603","url":null,"abstract":"<div><div>Air pollutant exposure has been shown to exacerbate obstructive sleep apnea (OSA) manifestations, such as increased respiratory episodes and intermittent oxygen desaturation. However, most studies have focused on episode frequency rather than duration. This study investigated the associations between air pollution and OSA manifestations using both frequency-based indices and the duration-based marker lung-to-finger circulation time (LFCT). Polysomnography data from 820 individuals with suspected OSA (mild-to-moderate: n = 224; severe: n = 596) were analyzed. Sleep disorder indices included the apnea–hypopnea index (AHI), oxygen desaturation index (ODI), arousal index, and mean LFCT. A novel composite metric, respiratory event response time area (RERTA), defined as the square root of the product of LFCT and AHI, was introduced to evaluate the interplay between frequency and duration. Air pollutant exposure was estimated using data from monitoring stations within 4 km of the registered residences of the study cohort. Multivariable Poisson regression models were employed to assess short-term (1-month) and long-term (1-year) exposure effects. Short-term PM_2.5 and NO₂ exposure were significantly associated with increased AHI, ODI, arousal index, and reduced LFCT. Long-term exposure to PM_2.5, PM₁₀, and NO₂ was associated with increased AHI and ODI, while PM_2.5 and PM₁₀ were linked to reduced LFCT. Both short- and long-term O₃ exposure were associated with increased AHI and ODI. PM_2.5 and O₃ (short-term) were positively associated with RERTA. These findings suggest that air pollution exacerbates OSA by increasing episode frequency and accelerating compensatory responses, thereby elevating cardiopulmonary burden as reflected by RERTA.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102603"},"PeriodicalIF":3.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating size-resolved levels, characteristics, and processes of carbonaceous aerosols (CA) in an urban atmosphere","authors":"Yasarapu Sathish ,&nbsp;Abhishek Chakraborty ,&nbsp;Shruti Tripathi","doi":"10.1016/j.apr.2025.102602","DOIUrl":"10.1016/j.apr.2025.102602","url":null,"abstract":"<div><div>An Anderson Cascade Impactor (ACI) was utilised in an urban coastal area of Mumbai to collect size-resolved particulate matter (PM) during winter and summer. This study aims to provide insights into size-resolved composition, characteristics and processes of organic aerosols in a coastal and tropical environment, which is scarce in the available literature. Collected samples were analysed for organic (total and soluble) and elemental carbon levels, and volatility using thermal-optical and total carbon analyzers. OC peaked at 0.65–1.1 μm in both seasons, indicating formation via gas-to-particle condensation. EC showed a peak at 0.43–0.65 μm, characteristic of vehicular emissions. Peak OC concentrations were 12.22 ± 3.65 μg/m³ in winter and 3.14 ± 2.87 μg/m³ in summer. For EC, peak concentrations were 3.39 ± 1.14 μg/m³ in winter and 3.14 ± 1.87 μg/m³ in summer. Most samples showed OC/EC ratios &gt;2, indicating significant secondary organic carbon (SOC) contribution to OC. The SOC/OC ratio in PM2.1 was higher in summer (9.84 ± 3.14) than in winter (4.21 ± 1.45), likely due to enhanced photochemical activity and ozone levels. The most abundant volatility-based OC-EC fractions were OC2, OC4 and EC1, indicating major contribution of vehicular emissions to PM mass. Char-EC/soot-EC ratios varied significantly (1–16) based on size and seasons indicating influence of various sources. Anthropogenic VOCs showed good correlation with SOC at certain sizes and seasons. This study shows that size-resolved carbonaceous aerosol (CA = EC + OM) contributes significantly to all PM sizes and to improve air quality, targeted approach based on various contributing processes and precursors of CA are needed.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102602"},"PeriodicalIF":3.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neighborhood ozone estimation in Busan, South Korea: A comparative study of proximity-based ensemble clustering and machine-learning models","authors":"Ahmad Daudsyah Imami ,&nbsp;Jurng-Jae Yee","doi":"10.1016/j.apr.2025.102601","DOIUrl":"10.1016/j.apr.2025.102601","url":null,"abstract":"<div><div>Busan is one of the southernmost metropolitan areas with the highest ozone pollution levels influenced by urban development and specific coastal meteorological conditions. The study present Cluster-Based Ensemble Regression (CBER), which consist of two-stage workflow which are benchmarking stage (Pre-CBER) and operational stage (CBER). During Pre-CBER, six machine-learning algorithms were compared, and multiple unsupervised-learning techniques were evaluated in parallel to cluster stations with similar meteorological characteristics and ozone patterns. Hyper-parameter-tuned XGBoost emerged as the most accurate regressor (RMSE = 3.69 ppb, R² = 0.95). Nine clustering scenarios were assessed with the Silhouette score, ultimately retaining solutions based on both centroid based and density based clustering. In the CBER phase, XGBoost models were trained within each shortlisted cluster scenario and validated through leave-one-station-out tests. KNN based Meteorological Regionalization preserved fine-scale variability, sustaining R² &gt; 0.90 and RMSE &lt;7 ppb in 12 of 14 clusters, while still achieving R² averagely 0.75–0.80 in the emissions-intensive port and mountainous northeast. SHAP interpretation ranked nitrogen dioxide, temperature, solar radiation, and diurnal timing as dominant predictors. The computationally light, transparent pipeline thus converts sparse monitoring into hourly subdistrict ozone maps, providing actionable decision for Busan and other coastal cities with limited AQMS networks.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102601"},"PeriodicalIF":3.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on vehicle evaporative emission characteristics and purge strategy based on China VI measurement methods","authors":"Zihan Chen ,&nbsp;Xin Zhang ,&nbsp;Qiang Chen ,&nbsp;Ren He","doi":"10.1016/j.apr.2025.102608","DOIUrl":"10.1016/j.apr.2025.102608","url":null,"abstract":"<div><div>In this study, several experiments were conducted based on China VI measurement methods to analyze the vehicle evaporative emission characteristics and purge strategy. The results showed that the diurnal breathing emissions from the atmospheric fuel system were 157.2 mg, with 88.5 % of the carbon canister bleeding emissions, and the main species emitted were alkanes. The diurnal breathing emissions from the sealed fuel system were 82.2 mg, with 97.1 % of the permeation emissions, and the main species emitted were aromatic and alkane hydrocarbons. There is a significant negative correlation between the vehicle refueling emission and the adsorption capacity of the carbon canister, but no significant correlation with the purge volume. The carbon canister refueling working capacity (ORVR BWC) is only 53 %–72 % of the regular working capacity (BWC). Tires, interior and exterior trims are the main background emission sources of the vehicle, accounting for 29 % and 25.1 % respectively. Baking pretreatment is an effective way to reduce background emissions. Compared to gasoline vehicles, hybrid electric vehicles (HEVs) equipped with atmospheric fuel systems compensate for the lack of purge time by increasing the purge rate. Vehicles equipped with sealed fuel systems can be freed from reliance on the purge strategy. It is a technological trend for vehicles to be equipped with sealed fuel systems, and future evaporative emission control should focus on permeation emissions, refueling emissions and background emissions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 10","pages":"Article 102608"},"PeriodicalIF":3.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}