Atmospheric Pollution Research最新文献

{"title":"Effects of temperature and humidity on water-soluble organic pollutants in the kitchen environment and health risks of lung diseases","authors":"Mingxin Luo ,&nbsp;Hongwei Lou ,&nbsp;Xiaofang Zhang ,&nbsp;Hai Xiao ,&nbsp;Qin Yang","doi":"10.1016/j.apr.2025.102800","DOIUrl":"10.1016/j.apr.2025.102800","url":null,"abstract":"<div><div>Kitchens are significant sources of volatile organic compounds (VOCs), yet water-soluble VOCs (W-VOCs) remain understudied despite their heightened bioavailability and health risks. This study investigates how temperature and relative humidity (RH) influence W-VOC profiles in kitchen environments and assesses associated pulmonary health risks. Simulating real cooking conditions, W-VOCs were collected using color-changing absorbent silica gel across 20 temperature (10–35 °C) and RH (30–≥90 %) scenarios. Gas chromatography–mass spectrometry (GC-MS) identified 65 W-VOCs, including 58 previously unreported compounds, predominantly aldehydes (e.g., nonanal, hexanal, heptanal). Humidity critically impacted W-VOC diversity: species count increased with rising RH across all temperatures, peaking at 25–30 °C. Aldehydes consistently dominated the relative composition. Using molecular probes, W-VOCs significantly enhanced activities of lactate dehydrogenase (LDH), angiotensin-converting enzyme (ACE), and glutathione reductase (GR) (p &lt; 0.05), while inhibiting catalase (CAT). Humidity amplified these effects: higher RH intensified LDH/ACE/GR activation and CAT suppression. Temperature exhibited no clear pattern on enzyme modulation. These findings suggest that W-VOCs may represent a key class of overlooked pollutants, with humidity as a critical modulator of both pollutant diversity and lung injury biomarkers. This study provides pollutant sampling and analysis for W-VOCs and their toxicity screening, supporting targeted kitchen air quality interventions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102800"},"PeriodicalIF":3.5,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135621","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":"Characteristics and source apportionments of WSIIs under different pollution conditions in Beijing from 2014 to 2017","authors":"Fangwei Zuo ,&nbsp;Qing Duan ,&nbsp;Honglei Wang ,&nbsp;Tianliang Zhao ,&nbsp;Zihan Wang ,&nbsp;Kun Cui ,&nbsp;Deyu Liu ,&nbsp;Yue Chen","doi":"10.1016/j.apr.2025.102799","DOIUrl":"10.1016/j.apr.2025.102799","url":null,"abstract":"<div><div>This study analyzed the temporal variations of water-soluble inorganic ions (WSIIs) in Beijing from 2014 to 2017, focusing on their characteristics and source apportionments under varying pollution conditions. Between these years, notable changes in pollution patterns were observed: the number of clean days increased by 22.2 %, with PM_2.5 pollution days decreasing by 51.0 % and O₃ pollution days increasing by 27.7 %. Concurrently, NH₄⁺, Cl⁻, NO₃⁻ and SO₄²⁻ concentrations declined by 51.7 %, 68.1 %, 48.7 % and 57.5 %, respectively, whereas K⁺, Mg²⁺ and Ca²⁺ inversely increased by 39–75 %. Under worsening PM₁₀ pollution, dust components increased to 30 %, whereas chemical components remained stable despite PM_2.5 intensification. During PM_2.5-PM₁₀ co-pollution, the proportion of dust components decreased from 8 % (slight) to 3 % (serious), while secondary ions increased proportionally; additionally, as O₃ pollution worsened from moderate to heavy, secondary ion concentrations declined by 12 %and this reduction expanded further to 48 % under PM_2.5-O₃ co-pollution. WSIIs exhibited a diurnal unimodal pattern (peaking at 09:00) during moderate O₃ pollution and slight-to-moderate PM_2.5-O₃ co-pollution. The 2014–2016 primary source (mixed coal combustion-vehicle emissions) saw 8.6 % lower concentration but annual rising contribution. In 2014, its contribution increased as PM_2.5 pollution worsened but declined under aggravated O₃ pollution; the situation was the opposite in 2016. The secondary contribution consistently exhibited inverse trends to the coal-vehicle mixed sources under aggravated pollution episodes. Industry contributed 22–86 % more to PM_2.5 pollution to O₃ pollution, while dust played a more significant role on clean days. Photochemical maintained a stable contribution (20–30 %) under slight-to-moderate O₃ pollution, significantly higher than under PM_2.5 pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102799"},"PeriodicalIF":3.5,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135685","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":"Understanding the agriculture sectors of greenhouse gas emissions prediction in the global scenario: Insights from explainable artificial intelligence (XAI)","authors":"Mantena Sireesha ,&nbsp;Abdul Gaffar Sheik","doi":"10.1016/j.apr.2025.102792","DOIUrl":"10.1016/j.apr.2025.102792","url":null,"abstract":"<div><div>This study explored the potential of four machine learning (ML) models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), and Deep Feedforward Neural Networks (DFNN) or predicting greenhouse gas (GHG) emissions from an agricultural field. It measured carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) over a global scenario spanning 172 years. The rigorous analysis, which included statistical comparisons and cross-validation for predicting CO₂, CH₄, and N₂O fluxes, demonstrated that GRU, CNN, and DFNN models consistently exhibited high prediction accuracy across most sectors. Notably, the GRU model outperformed the others, achieving an R² of 0.9985 and an RMSE of 0.0108 for N₂O emissions in the Waste sector. In contrast to previous studies, this research not only predicts future GHG emissions but also identifies the relationship between these predictions and their influential variables. To achieve this, an interpretable prediction framework was utilized, incorporating explainable artificial intelligence (XAI) methods including SHapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Individual Conditional Expectation (ICE) plots, and Partial Dependence Plots (PDPs) to reveal each GHG’s contribution to overall emissions. The SHAP analysis indicated that CH₄ was the dominant contributor across all sectors, with a high average SHAP value of 10,632.68 in agriculture and 3386.09 in the Waste sector, followed by N₂O and CO₂. Further analyses using ICE and PDP clarified the sector-specific nonlinear interactions, showing that CH4 had the greatest influence on emissions, particularly in synergy with N₂O. These findings illustrate the significant potential of ML models for predicting GHG emissions in the agricultural sector.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102792"},"PeriodicalIF":3.5,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135730","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":"Integrating IAM-based CO2 projections and traffic demand forecasting for regional CO2 emission mapping in the transport sector","authors":"Hyejung Hu ,&nbsp;Min-Young Choi ,&nbsp;Bomi Kim ,&nbsp;Minje Choi ,&nbsp;Sunggu Kang ,&nbsp;Hyejin Park ,&nbsp;Minwoo Park ,&nbsp;Jinseok Kim ,&nbsp;Jung-Hun Woo","doi":"10.1016/j.apr.2025.102790","DOIUrl":"10.1016/j.apr.2025.102790","url":null,"abstract":"<div><div>Many countries around the world are formulating and implementing policies to reduce greenhouse gas (GHG) emissions based on their Nationally Determined Contributions (NDCs). In this context, accurate assessment of current emissions and reliable projection of future emissions are essential. Since long-term forecasts are typically conducted at the national level, spatial allocation techniques are necessary to enable sub-national or grid-level analysis. In the transport sector, regional disparities in emissions are particularly pronounced, requiring spatial projections to reflect expected changes in societal and infrastructural conditions. This study presents a methodology that utilizes an integrated assessment model (IAM) to forecast national-level energy use and CO₂ emissions, followed by spatial downscaling of transport emissions to administrative and grid levels. To account for future spatial variability in road transport activity, a traffic demand forecasting model is incorporated. The proposed methodology is applied to the case of South Korea. National CO₂ emissions are projected using the MESSAGEix-KR IAM, while future road traffic demand is estimated using the EMME4 model. Based on these projections, spatial allocation coefficients are developed, and transport sector emissions are spatially distributed accordingly. Findings indicate that shifts in road infrastructure and traffic patterns are effectively reflected in the spatial distribution of emissions. The proposed methodology facilitates the development of high-resolution future emissions inventories and spatially explicit CO₂ emission maps. This methodology serves as valuable tools for supporting the formulation of carbon neutrality policies, designing region-specific mitigation strategies, and monitoring progress in emissions reduction efforts.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102790"},"PeriodicalIF":3.5,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135728","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":"Long-term seasonal variability in the water-soluble inorganic composition of coarse and fine aerosols over the northeast Arabian sea","authors":"Garima Shukla ,&nbsp;Ashwini Kumar ,&nbsp;Ankush Kaushik","doi":"10.1016/j.apr.2025.102794","DOIUrl":"10.1016/j.apr.2025.102794","url":null,"abstract":"<div><div>The chemical composition of atmospheric aerosols plays a crucial role in understanding air quality, climate interactions, and oceanic biogeochemical cycles. This study presents a comprehensive long-term seasonal analysis (December 2017–May 2022) of water-soluble inorganic composition (WSIC) in coarse (PM₁₀) and fine (PM_2.5) aerosols over a tropical coastal site located in the northeast Arabian Sea (Goa, India). A total of 583 aerosol samples (290 p.m.₁₀ and 293 p.m._2.5) were simultaneously collected and analysed for major cations (Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺) and anions (Cl⁻, NO₃⁻, SO₄²⁻). The seasonal variability in WSIC was found to be strongly influenced by continental outflows, monsoonal dynamics, and marine sources. The highest WSIC concentrations were observed during winter (23.1 μg m⁻³ for PM₁₀ and 16.2 μg m⁻³ for PM_2.5) season which was attributed to anthropogenic emissions and secondary inorganic aerosol formation, while lower values were observed during summer months mainly due to dilution effects caused by strong marine air intrusion. Non-sea-salt sulphate (nss-SO₄^2-) dominated both aerosol fractions (&gt;98 %) while nitrate (NO₃⁻) was dominant particularly in summer due to its interaction with long-range transported mineral dust. Ammonium (NH₄⁺) correlated strongly with sulphate (r &gt; 0.9) in winter and post-monsoon, indicating ammonium sulphate as a major component of secondary aerosols. Crustal elements (Ca²⁺, Mg²⁺) were more prominent in PM₁₀, peaking in summer due to long-range dust transport. Biomass burning tracers (nss-K⁺) were significantly higher (p &lt; 0.05) in PM_2.5 during winter, emphasizing regional agricultural residue burning as a key source from the northern regions in the Indo-Gangetic Plains. Global reanalysis datasets (MERRA-2, CAMS) effectively captured sulphate trends but overestimated sea salt concentrations. These findings provide critical insights into aerosol chemistry in a tropical coastal environment and its implications for air quality and climate modeling. Regional scale chemical characterisation of aerosols is important for their better parameterization in chemical transport models.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102794"},"PeriodicalIF":3.5,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135714","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 in the urban built environment: A comprehensive evaluation","authors":"Elisavet Tsekeri,&nbsp;Aikaterini Lilli,&nbsp;Mihalis Lazaridis,&nbsp;Dionysia Kolokotsa","doi":"10.1016/j.apr.2025.102797","DOIUrl":"10.1016/j.apr.2025.102797","url":null,"abstract":"<div><div>This study assesses air pollution levels in the city of Chania, Greece, utilizing a combination of bike-mounted sensors and stationary monitoring stations to analyze the spatial and temporal variability of microclimate conditions and key pollutants, including PM_2.5, PM₁₀, SO₂, CO, and NO₂. The data analysis reveals significant seasonal variations in air pollution levels, with concentrations peaking during winter, primarily due to increased emissions from heating-related combustion and reduced atmospheric dispersion. In contrast, summer months exhibit lower pollution levels, as favorable meteorological conditions enhance pollutant dispersion. In spring, periodic dust episodes contribute to elevated PM concentrations, further influencing seasonal air quality patterns. Weekday pollution levels are generally higher than those on weekends, primarily due to traffic emissions and daily commuting patterns. However, in spring and summer, this trend becomes less consistent, as increased leisure activities and tourism-related transport led to elevated pollutant concentrations on certain weekends. Spatially, the highest pollution concentrations are observed in the city center, where dense traffic and urban structures contribute to pollutant accumulation. Conversely, coastal areas record lower pollution levels, benefiting from natural ventilation and reduced vehicular activity. These findings underscore the need for integrated air quality assessments in urban planning and policy development. Strengthening public transportation networks, enforcing emission control measures, expanding urban green infrastructure, and enhancing real-time air quality monitoring are recommended strategies to mitigate air pollution. By implementing these measures, cities can enhance air quality, public health, and environmental resilience, fostering more sustainable and equitable urban development.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102797"},"PeriodicalIF":3.5,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135623","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 quality impacts of a major wildfire in the UK: Sensitivity to model spatial resolution and transport method","authors":"Benjamin Drummond ,&nbsp;Ailish Graham ,&nbsp;Lucy Neal ,&nbsp;Pedro Molina Jiménez ,&nbsp;Richard J. Pope ,&nbsp;Carly Reddington","doi":"10.1016/j.apr.2025.102795","DOIUrl":"10.1016/j.apr.2025.102795","url":null,"abstract":"<div><div>Wildfires can be important drivers of poor air quality. Numerical atmosphere models are routinely used to estimate pollutant concentrations emitted from a wide range of sources, including from wildfires. Such models often take the Eulerian field or Lagrangian particle method for representing mass and transport in the atmosphere. Using the Saddleworth Moor and Winter Hill fires that occurred in North West England in 2018 as a case study, we compared these two methods consistently within the same model framework. We also explored the impact of model spatial resolution on predicted concentrations and health impacts. In the Eulerian simulations, as the horizontal resolution was made finer (from 12 km to 1 km) the horizontal spread of the downwind wildfire pollution decreased substantially, leading a smaller geographical area and population being impacted by the smoke. The estimated number of people exposed to poor air quality due to wildfire from the 1 km Eulerian simulation was 30% lower than from the 12 km Eulerian simulation. A health impact assessment found a similar relative decrease for the estimated excess mortality due to short-term PM_2.5 exposure when going from 12 km to 1 km horizontal resolution. Estimated air quality impacts were also found to be sensitive to horizontal resolution for the Lagrangian simulations but to a lesser degree (<span><math><mo>∼</mo></math></span>10% decrease from 12 km to 1 km). We recommend that model spatial resolution should be considered as a source of uncertainty for wildfire air quality impact assessments, particularly when an Eulerian model is used.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102795"},"PeriodicalIF":3.5,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135715","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":"Gaseous pollutant emissions from solid fuel combustion: Comparative study of real-world and simulated chamber-based experiments","authors":"Dharini Sahu ,&nbsp;Shamsh Pervez ,&nbsp;Judith C. Chow ,&nbsp;John G. Watson ,&nbsp;Rajan K. Chakrabarty ,&nbsp;Aishwaryashri Tamrakar ,&nbsp;Indrapal Karbhal ,&nbsp;Manas Kanti Deb ,&nbsp;Kamlesh Shrivas ,&nbsp;Yasmeen Fatima Pervez ,&nbsp;Sachchidanand Shukla ,&nbsp;D.P. Bisen","doi":"10.1016/j.apr.2025.102798","DOIUrl":"10.1016/j.apr.2025.102798","url":null,"abstract":"<div><div>This study presents a comparative analysis of gaseous pollutant emissions from solid fuel combustion under real-world household conditions and simulated experimental chamber-based conditions. Focusing on six commonly used domestic fuels in India, fuelwood (FW), dung cake (DC), coal ball (CB), agricultural residue (AR), and two mixed fuel types (M1: CB + DC, M2: FW + DC in a 10:1 ratio), the research quantifies emissions of CO₂, CO, NO, NO₂, SO₂, CH₄, and total volatile organic compounds (TVOCs). Simulated combustion chamber experiments, designed to replicate household stove operation while allowing precise emission monitoring, were conducted alongside field based real-world observations. Emission factors (EFs) and combustion efficiency metrics were assessed to understand pollutant formation mechanisms. Results showed strong correlations between combustion efficiency and emission profiles: higher modified combustion efficiency (MCE) was associated with elevated CO₂ and NO₂ emissions, while lower MCE resulted in higher outputs of incomplete combustion products such as CO, CH₄, and TVOCs. Slightly lower EFs and higher MCEs were observed in field based real-world conditions compared to those found for simulated experimental chamber based conditions. Agricultural residues emitted the highest CH₄ levels, likely due to paddy-origin biomass, whereas mixed fuels showed increased TVOC emissions, linked to their high carbon and moisture content. This comparative study emphasizes the importance of integrating field-based validation with laboratory simulations to accurately assess household air pollution, and supports targeted interventions to promote cleaner combustion practices and reduce public health risks.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102798"},"PeriodicalIF":3.5,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135684","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":"Long-term trends reflecting regulatory impacts on VOCs sources in the New York City metropolitan area","authors":"Lucille Borlaza-Lacoste ,&nbsp;Md. Aynul Bari ,&nbsp;Cheng-Hsuan Lu ,&nbsp;Philip K. Hopke","doi":"10.1016/j.apr.2025.102789","DOIUrl":"10.1016/j.apr.2025.102789","url":null,"abstract":"<div><div>Over the past two decades, shifts in energy use and regulatory policies in New York State have shaped emissions and air quality in the New York City (NYC) metropolitan area, a densely populated and VOC-limited nonattainment region for ozone (O₃). This study analyzed 24-h canister measurements from six sites, Queens, Bronx, Kings, Richmond, Elizabeth, and Chester, spanning the period of 2000–2021. Dispersion-Normalized Positive Matrix Factorization apportioned VOCs sources while accounting for atmospheric dilution, resolving twelve distinct sources dominated by aldehyde-rich factors, vehicle emissions, and industrial activities. Long-term trends from seasonal-trend decomposition and piecewise regression highlighted regulatory- and economy-driven shifts in source contributions. Significant declines in vehicle emissions, MTBE- and MEK-rich factors, and aldehydes aligned with Tier 2 and 3 fuel standards, MTBE phase-out, and MACT regulations. In contrast, natural gas, evaporative, biogenic, and background sources remained stable or increased, reflecting persistent and seasonally modulated emissions. Distinct site- and source-specific patterns in weekday/weekend and seasonal variability were also observed. These results show that while regulations have effectively reduced many anthropogenic VOCs sources, persistent emissions underscore the need for continued monitoring and adaptive control strategies in O₃ nonattainment regions like NYC.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102789"},"PeriodicalIF":3.5,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135727","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":"Ozone pollution in Taiyuan Basin during summer: the impact of atmospheric boundary layer structure and synoptic patterns","authors":"Shutong Liu ,&nbsp;Yan Yan ,&nbsp;Xuhui Cai ,&nbsp;Yu Song ,&nbsp;Hongsheng Zhang ,&nbsp;Xiaobin Wang","doi":"10.1016/j.apr.2025.102791","DOIUrl":"10.1016/j.apr.2025.102791","url":null,"abstract":"<div><div>Taiyuan Basin has attracted much attention due to its serious ozone (O₃) pollution in summer. However, the combined effects of synoptic patterns and atmospheric boundary layer processes on ozone pollution remain unclear. In this study, the high-resolution (1 km) ozone concentration distribution datasets during the summer seasons from 2015 to 2020 were analyzed, together with the Weather Research and Forecasting (WRF) model simulations. Four dominant synoptic patterns were identified based on the obliquely rotated T-mode Principal Component Analysis (T-PCA). Results revealed three key dynamic mechanisms regulating ozone pollution within Taiyuan Basin by synoptic patterns and boundary layer processes. 1) Easterly winds partially alleviated urban pollution through the air-flushing process while advecting ozone air mass southward. 2) Southwesterly flows facilitated northward transport of industrial emissions to urban areas, combined with favorable photochemical conditions (e.g., elevated temperatures and reduced relative humidity) to amplify ozone production. 3) Strong northwest winds invaded the whole Taiyuan Basin and enhanced atmospheric ventilation capacity, achieving effective pollutant removal while entrapping residual ozone in southern piedmont regions. Furthermore, under weak synoptic conditions, terrain-induced mountain-valley circulation emerged and generated subsidence compensation flows that interacted with organized descending motions from upper large-scale weather systems. This multi-scale vertical coupling mechanism significantly enhanced surface ozone accumulation. This study provided insight for designing localized ozone control strategies, which may apply to other cities with complex terrain worldwide.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102791"},"PeriodicalIF":3.5,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135729","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}