Atmospheric Pollution Research最新文献

{"title":"Characterizing on-road particle number emissions from container diesel trucks using interpretable machine learning","authors":"Rui-min Hao ,&nbsp;Hong-di He ,&nbsp;Hong-mei Zhao ,&nbsp;Wen-jian Jia ,&nbsp;Zhong-ren Peng ,&nbsp;Hui Wang","doi":"10.1016/j.apr.2025.102838","DOIUrl":"10.1016/j.apr.2025.102838","url":null,"abstract":"<div><div>The rising number of containerized diesel trucks (CDTs) significantly increases particulate matter emissions. However, research on number concentration of particulate matter from CDTs under real-world driving conditions remains limited. This study combines field measurements with interpretable machine learning to systematically analyze the particle number (PN) emission patterns of CDTs. The study collected emission data from a China V CDT on real-world roads in Shanghai using a Portable Emission Measurement System (PEMS) and extracted multidimensional features—encompassing environmental, engine, and driving characteristics—for preliminary analysis. This initial analysis found that high PN emissions are concentrated during the acceleration phase. And PN emissions exhibit a second-order polynomial relationship with vehicle specific power (VSP). Subsequently, among multiple machine learning models, the eXtreme Gradient Boosting (XGBoost) model demonstrated optimal performance in predicting PN emissions for different road scenarios. The model identified engine power, air-fuel ratio, and VSP as the three core influencing factors, with their importance varying by road type: engine power was dominant on highways (43 %), while VSP was primary on non-highways (53 %). The study also used partial dependence plots to analyze the non-linear effects of these key factors. Additionally, the PN emission factor for non-highways (1.91 × 10¹² #/km) is more than twice that of highways (9.80 × 10¹¹ #/km).</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102838"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584589","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":"Emission characteristics and size distribution of aerosols from biomass combustion in the Yangtze River Delta of China","authors":"Fengwei Liu ,&nbsp;Yufei Ling ,&nbsp;Keqiang Wang ,&nbsp;Lei Tong ,&nbsp;Kun Wu ,&nbsp;Chunli Liu ,&nbsp;Hang Xiao ,&nbsp;Cenyan Huang","doi":"10.1016/j.apr.2025.102829","DOIUrl":"10.1016/j.apr.2025.102829","url":null,"abstract":"<div><div>The open burning of crop straws is a major source of air pollution, posing serious environmental and health risks. However, the size distribution and chemical composition of particulate matter (PM) emitted from biomass combustion remain poorly understood, which limits our understanding of its source characteristics. This study investigated the emission characteristics of PM and its chemical components in the Yangtze River Delta, China. Emission factors across nine particle size ranges were determined through laboratory burning simulations using six typical crop straws. Results showed that the highest emission factors of PM and its chemical components were mainly concentrated in the 1.1–3.3 μm size range. The highest emission factors of organic carbon (OC), elemental carbon (EC), and water-soluble ions (K⁺, NH₄⁺, Cl⁻, NO₃⁻, and SO₄²⁻) were all observed in this range. Among the tested crops, wheat exhibited the highest emission factor, followed by corn and sorghum. Rice and soybeans contributed the highest emission factors of OC and EC, respectively. The OC/EC ratio increased significantly in particles of size &lt;1.1 μm, indicating that organic matter was more concentrated in submicron particles. Heavy metal emissions, by contrast, were mainly concentrated in coarse particles. From 2013 to 2022, the proportions of wheat (46.26 %), rice (28.75 %), and corn (23.21 %) were dominant in the total emissions in the Yangtze River Delta. Overall, the findings provide more evidence that biomass combustion contributes to the formation of haze pollution during the harvest period.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102829"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584590","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":"PM2.5 seasonal variations at an economic development zone site in southwest karst plateau city: Chemical compositions and source apportionment","authors":"Jixue Jia ,&nbsp;Peng Xu ,&nbsp;Jiaqun Gui ,&nbsp;Yunwu Li ,&nbsp;Xianqin Wang ,&nbsp;Guangxuan Yan ,&nbsp;Shouyang He ,&nbsp;Xuehai Fei ,&nbsp;Wenkang Gao ,&nbsp;Zirui Liu ,&nbsp;Bo Hu ,&nbsp;Aijiang Yang","doi":"10.1016/j.apr.2025.102864","DOIUrl":"10.1016/j.apr.2025.102864","url":null,"abstract":"&lt;div&gt;&lt;div&gt;To investigate the seasonal variation characteristics and sources of PM&lt;sub&gt;2.5&lt;/sub&gt; chemical components in an economic development zone within a karst plateau city in southwestern China, PM&lt;sub&gt;2.5&lt;/sub&gt; samples were collected in 2022 around a national air quality monitoring station in the Guiyang Economic and Technological Development Zone. Laboratory analyses and modeling approaches were employed to examine the seasonal variations of carbonaceous components, water-soluble inorganic ions, and trace metal elements in PM&lt;sub&gt;2.5&lt;/sub&gt;, as well as to apportion its sources. The results demonstrate that the average concentrations of OC and EC were 7.0 μg m&lt;sup&gt;−3&lt;/sup&gt; and 3.5 μg m&lt;sup&gt;−3&lt;/sup&gt;, accounting for 13.9 % and 7.4 % of PM&lt;sub&gt;2.5&lt;/sub&gt;, respectively. The proportions of SOC in PM&lt;sub&gt;2.5&lt;/sub&gt; were 5.1 %, 12.0 %, 10.9 %, and 7.9 % across the four seasons. SOC was strongly correlated with OC (R&lt;sup&gt;2&lt;/sup&gt; = 0.87) and increased with the enhancement of atmospheric oxidation capacity (Ox level), while OC showed a weak correlation with EC (R&lt;sup&gt;2&lt;/sup&gt; = 0.47). The annual average mass concentration of water-soluble ions (WSIIs) was (17.2 ± 10.0 μg m&lt;sup&gt;−3&lt;/sup&gt;), with SNA accounting for 30.1 % of the water-soluble ions in PM&lt;sub&gt;2.5&lt;/sub&gt;. The overall characteristics showed sulfate &gt; nitrate &gt; ammonium, with an average NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;−&lt;/sup&gt;/SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt; ratio of 0.4, indicating that stationary sources contributed significantly to atmospheric pollution at this sampling site. Furthermore, ammonium salts mainly existed as (NH&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt; and NH&lt;sub&gt;4&lt;/sub&gt;HSO&lt;sub&gt;4&lt;/sub&gt;. The neutralization carriers of nitrate showed seasonal differences: in spring, summer, and autumn, NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;−&lt;/sup&gt; was significantly positively correlated with NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt; (r = 0.67–0.81) and mainly present as NH&lt;sub&gt;4&lt;/sub&gt;NO&lt;sub&gt;3&lt;/sub&gt;. It was strongly positively correlated with Ca&lt;sup&gt;2+&lt;/sup&gt; (r = 0.94) and dominated by metal nitrates such as Ca(NO&lt;sub&gt;3&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt; in winter. Among the 14 trace metals, Zn was the dominant element with an average concentration of 53.54 ng m&lt;sup&gt;−3&lt;/sup&gt; (accounting for 33.99 %) and the highest concentration in spring (63.50 ng m&lt;sup&gt;−3&lt;/sup&gt;). The concentrations of Cr (17.37 ng m&lt;sup&gt;−3&lt;/sup&gt;), Mn (16.77 ng m&lt;sup&gt;−3&lt;/sup&gt;), and As (12.32 ng m&lt;sup&gt;−3&lt;/sup&gt;) all exceeded the relevant standard limits, such as those specified in GB 3095-2012. During the sampling period, the contributions of PM&lt;sub&gt;2.5&lt;/sub&gt; components were as follows: organic matter (OM, 31.3 %), sulfate (SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt;, 23.9 %), nitrate (NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;−&lt;/sup&gt;, 9.4 %), ammonium (NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;, 7.1 %), trace metals (0.3 %), and mineral dust (18.1 %). Positive matrix factorization (PMF) revealed that the sources of PM&lt;sub&gt;2.5&lt;/sub&gt; were dust (6.0 %), vehicle emissions (13.0 %), secondary nitrate (29.4 %), Industrial sources (10.4 %), biomass burning (2","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102864"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584763","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":"Exploring AI potential in optical analysis of organic and elemental carbons: A review of emerging applications in airborne particulate characterization","authors":"Akmaral Agibayeva ,&nbsp;Aset Muratuly ,&nbsp;Olga P. Ibragimova ,&nbsp;Kazbek Tursun ,&nbsp;Azamat Mukhamediya ,&nbsp;Amin Zollanvari ,&nbsp;Akhan Almagambetov ,&nbsp;Serpil Yenisoy ,&nbsp;Nassiba Baimatova ,&nbsp;Ferhat Karaca","doi":"10.1016/j.apr.2025.102839","DOIUrl":"10.1016/j.apr.2025.102839","url":null,"abstract":"<div><div>Carbonaceous aerosols, comprising organic carbon (OC) and elemental carbon (EC), constitute up to 70% of atmospheric particulate matter (PM) and significantly impact human health, climate forcing, and air quality. Despite their significance, accurate characterization of OC and EC fractions remains challenging due to aerosols’ complex and variable composition and the constraints in conventional analytical techniques. This review aims to assess how artificial intelligence (AI) can advance OC/EC characterization by enhancing data interpretation, automation, and measurement reliability. Recent developments in photo-optical methods for OC/EC quantification, including real-time monitoring, destructive, and non-destructive techniques, are comprehensively analyzed. The review further examines emerging efforts to integrate AI into conventional analytical frameworks, critically evaluating the potential of semi-supervised learning, active learning, incremental learning, TinyML, explainable AI, and large language models (LLMs) for improving OC/EC analysis. Notably, the underexplored potential of these AI approaches in source apportionment and real-time monitoring is emphasized, alongside a proposed roadmap for implementing AI-driven strategies in carbonaceous aerosol analysis and air quality assessments. AI integration with existing OC/EC analytical techniques can minimize measurement uncertainties and enable real-time, cost-effective monitoring systems. This approach facilitates high-resolution source apportionment with broad applications in environmental monitoring research, ultimately improving urban air quality diagnostics and evidence-based pollution control strategies.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102839"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584768","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 characterization and source apportionment of PM10 in the Sistan basin under high dust conditions","authors":"D.G. Kaskaoutis ,&nbsp;F.-A. Kozonaki ,&nbsp;S.K. Garas ,&nbsp;R. Dehmardeh Behrooz ,&nbsp;M. Tsagkaraki ,&nbsp;U.C. Dumka ,&nbsp;M. Ganjali ,&nbsp;G. Grivas ,&nbsp;N. Mihalopoulos","doi":"10.1016/j.apr.2025.102849","DOIUrl":"10.1016/j.apr.2025.102849","url":null,"abstract":"<div><div>This study analyzes the chemical composition of airborne PM₁₀ samples collected in the Sistan Basin, eastern Iran, during the summer dust period of 2021. The chemical speciation emphasized carbonaceous aerosols (organic, elemental and carbonate carbon) examined for the first time in Sistan, water-soluble inorganic species (WSIS) and trace elements. A high mean PM₁₀ concentration of 308.9 μg m⁻³ (range: 62.5–1791 μg m⁻³) was observed, while the OC and EC concentrations were 6.95 μg m⁻³ and 0.56 μg m⁻³, respectively, with even higher levels of carbonate carbon (7.45 μg m⁻³). WSIS were dominated by Ca²⁺ (20.03 μg m⁻³), followed by SO₄²⁻ (5.32 μg m⁻³), Cl⁻, NO₃⁻ and Na⁺. The anion deficit in the ion chromatography analysis was highly correlated to Ca²⁺ (R² = 0.99), revealing the presence of carbonates, while the high concentrations of Na⁺ indicated halite presence in the dust from the desiccated Hamoun lakes. Major and trace elements mainly comprised crustal particle markers (Ca &gt; Al &gt; Fe &gt; Mg). In contrast, the sum of anthropogenic trace elements averaged lower than 200 ng m⁻³ (with contributions mainly by Mn and Zn). The strong correlations between most ionic species and trace elements suggest common area sources, mostly related to mineral emissions and secondarily to urban activities in Zabol City. The mass closure analysis revealed a dust contribution of 73 % to PM₁₀, followed by carbonaceous aerosols (17 %). In addition, the first-time Positive Matrix Factorization (PMF) analysis in Sistan highlighted dust as the major source (63.5 %), followed by regional/secondary aerosols (29.2 %) and fossil-fuel combustion (7.4 %).</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102849"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584676","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":"CO2 capture performance of MEA/AMP blended absorbents in real flue gas at different blending ratios","authors":"Qingzhu Tian ,&nbsp;Zhenxing Shen ,&nbsp;Xuefeng He ,&nbsp;Hao Fan ,&nbsp;Qin Peng ,&nbsp;Bo Song","doi":"10.1016/j.apr.2025.102857","DOIUrl":"10.1016/j.apr.2025.102857","url":null,"abstract":"<div><div>Research into blended solvent formulations under real combustion conditions has become essential for developing carbon capture technologies for coal-fired power plants. Single-solvent systems, such as those based on monoethanolamine (MEA), perform suboptimally in capturing carbon from flue gas. In this study, the CO₂ capture performance of four MEA/AMP (2-amino-2-methyl-1-propanol) blended absorbents with different concentration ratios was experimentally evaluated using real coal-derived flue gas. These blended absorbents were evaluated in terms of their absorption capacity, desorption performance, and change in pH level. An analytic hierarchy process methodology was used to quantify overall CO₂ capture performance. The inhibitory effects of contaminant gases on absorption efficacy were also investigated. An MEA/AMP blend at a 2:3 M ratio outperformed pure MEA in CO₂ capture, with a 31.8 % higher absorption capacity and a 28.3 % improvement in cyclic performance. However, an 8.9 % decrease in solution alkalinity caused by flue gas impurities (mainly SO₂ and NO_x) led to quantifiable declines in the capture performance of the MEA/AMP blend—including a 3.8 % decrease in CO₂ uptake, a 7.1 % decline in regeneration efficiency, and a 1.3 % increase in regeneration energy. These empirical results can serve as a guide for improving amine-based carbon capture systems for use in large-scale industrial settings.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102857"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584681","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":"Trifluoroacetic acid production from trifluoroacetyl peroxynitrate degradation in the presence of water","authors":"O.J. Nielsen ,&nbsp;M.P. Sulbaek Andersen","doi":"10.1016/j.apr.2025.102828","DOIUrl":"10.1016/j.apr.2025.102828","url":null,"abstract":"<div><div>The possibility of formation of trifluoroacetic acid (TFA) from the gas phase reaction of trifluoroacetyl peroxynitrate (FPAN) with water was investigated using a large photochemical chamber and FTIR spectroscopy. After 298 h of exposure of FPAN to 180 mTorr water approximately 31 % of FPAN had decayed away. The formed TFA constituted (15 ± 1)% of the consumed FPAN. As a control experiment, FPAN was left in the chamber without water present and the results indicated some TFA production. Assuming that the formation of TFA formation in our experiments is due only to the gas phase reaction CF₃C(O)O₂NO₂ + H₂O → CF₃C(O)OH + HOONO₂, the observed decay rate of FPAN indicates an upper limit for the rate coefficient for the reaction of 3.3 × 10⁻²³ cm³ molecule⁻¹s⁻¹. In the atmosphere, formation of TFA from the gaseous reaction of FPAN with water is expected to be of little importance relative to known sources of TFA.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102828"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584769","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":"Anomalous PM2.5 spikes during short rain events in urban environment","authors":"S. Talukdar,&nbsp;M. Venkat Ratnam","doi":"10.1016/j.apr.2025.102816","DOIUrl":"10.1016/j.apr.2025.102816","url":null,"abstract":"<div><div>Rainfall over a given location generally leads to a reduction in atmospheric aerosol concentrations. However, the present study reveals an anomalous behaviour in the diurnal variation of PM_2.5 over Kolkata (22.34° E, 88.22° N), a major metropolitan area in eastern India, during rain events. The diurnal pattern of PM_2.5 in Kolkata exhibits pronounced variability, with concentrations from April to September remaining substantially lower than those recorded between October to March. Furthermore, the typical night-time enhancement in PM_2.5 levels is often absent during the summer and monsoons months. Contrary to the expected pattern of low concentrations from April to September and even lower levels at night, certain days show rise in PM_2.5 concentrations during the late evening or early night time hours, particularly in association with short rainfall events. As rainfall generally scavenges aerosols, this behaviour appears counterintuitive. This study investigates this underlying cause of this anomalous increase in aerosol loading during rain events over Kolkata using comprehensive in-situ observations. The analysis indicates that short-duration rainfall in urban environments removes only a small fraction of PM_2.5 and the subsequent reduction in wind speed and ventilation coefficient supresses aerosol dispersion. This, combined with the persistence of local emission sources such as vehicular traffic, results in a localized accumulation of PM_2.5 following such rain events.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102816"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584770","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":"Volatile organic compounds (VOCs) emission profiles of diesel vehicles based on cluster analysis","authors":"Chang Wang ,&nbsp;Dongwei Liu ,&nbsp;Yimeng Cui ,&nbsp;Xueyao Li ,&nbsp;Jingyuan Li ,&nbsp;Zhentao Liu ,&nbsp;Yong Li ,&nbsp;Xiuyan Wang ,&nbsp;Taosheng Jin","doi":"10.1016/j.apr.2025.102833","DOIUrl":"10.1016/j.apr.2025.102833","url":null,"abstract":"<div><div>Diesel vehicle VOC emissions are increasingly recognized as key contributors to ambient ozone (O₃) and secondary organic aerosol (SOA) formation. Although recent studies have focused on total volatile organic compound (VOC) quantities, detailed chemical profiles remain poorly characterized due to regional, fuel-type, and vehicle-technology heterogeneity. This study aims to address these uncertainties by compiling the dataset compiled from multiple countries and regions and employing clustering analysis to elucidate the emission characteristics of diesel vehicles. Our findings reveal that oxygenated volatile organic compounds (OVOCs) and alkynes collectively accounted for approximately 60 % of total emissions in China, with methanol, formaldehyde, and acetaldehyde being among the major OVOC species identified in China, accounting for approximately 60 % of the total emitted together with alkynes, with methanol alone contributing 14.2 % of the OVOCs. Additionally, acetone and toluene were identified as characteristic typical components, contributing 1.6 % and 3.0 % to total diesel VOC emissions, respectively. Clustering results further revealed that light-duty vehicles exhibited lower proportions of these compounds, while heavy-duty vehicles, particularly China VI trucks, showed significantly higher levels. Distinct regional differences were observed, with aromatic hydrocarbons dominating non-Chinese profiles. The findings support the refinement of VOC emission inventories and the development of targeted control strategies, particularly in differentiating regulatory priorities between regions. Enhanced understanding of chemical profiles is essential for accurate source apportionment and more effective mitigation of ozone and SOA pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102833"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584584","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":"Top-down constraint on CO emissions by incorporating emission heights: insights from COVID-19 activity changes in Nanchang city, China","authors":"Cheng Hu ,&nbsp;Xuejing Shi ,&nbsp;Lingjun Xia ,&nbsp;Qitao Xiao ,&nbsp;Guoqiang Bai ,&nbsp;Fan Sun ,&nbsp;Yifan Zhang ,&nbsp;Yu Cui","doi":"10.1016/j.apr.2025.102848","DOIUrl":"10.1016/j.apr.2025.102848","url":null,"abstract":"<div><div>Carbon monoxide (CO) is recognized as a major atmospheric pollutant that poses significant risks to both ecosystems and human health, and also contributes to global climate change through atmospheric chemical interactions. Timely and accurate monitoring of CO emission and its temporal variations are therefore crucial for effective environmental policymaking and emission mitigation strategies. Although tower-based concentration measurements and atmospheric inversion techniques have been widely applied to constrain greenhouse gas emissions, their application to CO emissions remains limited. Moreover, nearly all previous top-down studies on emissions assumed emission sources at ground level without incorporating emission height information, such assumption will introduce substantial biases in concentration simulation and <em>posterior</em> emission estimates. To address these limitations, we first revised the WRF-STILT(Weather Research and Forecasting Model-Stochastic Time-Inverted Lagrangian Transport) model by integrating emission height information, and also conducted continuous, high-precision 50 m tower-based CO observations in Nanchang, China, covering the period from January 1 to April 30 in 2019–2021. The above whole observation period spans the pre-, during-, and post-COVID-19 pandemic phases, offering a unique opportunity to assess the impact of abrupt anthropogenic activity changes on CO emissions, and to evaluate the performance of the revised WRF-STILT model. The main findings are as follows: (1) Incorporating diurnal variability and emission height into the inversion framework significantly improved the accuracy of simulated CO concentrations, especially during nighttime hours. (2) The <em>prior</em> EDGAR emission inventory considerably underestimated anthropogenic CO emissions. After applying the scaling factors derived from observational constraints, <em>posterior</em> simulations showed obvious improvements over <em>prior</em> simulations, with <em>posterior</em> CO emissions increasing by 30.4 %–103.1 % across different periods. (3) CO emissions in 2020 were substantially lower than in 2019 for Nanchang city. During the Level 1 and Level 2–3 periods, CO emissions were 3.69 × 10⁶ kg and 4.58 × 10⁶ kg, representing reductions of 30.4 % and 21.2 %, respectively, relative to 2019. In 2021, emissions rebounded by 29.8 % and 10.5 %, but still remained below pre-pandemic levels, indicating a gradual recovery of industrial and transportation activities. Our study highlights the necessary of using diurnal varied emission inventories and incorporating emission height in the emission inversion studies, which can better represent concentration variations within planetary boundary layer.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102848"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584675","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}