Atmospheric Pollution Research最新文献

{"title":"Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China","authors":"Jianli Yang ,&nbsp;Chaolong Wang ,&nbsp;Yisheng Zhang ,&nbsp;Sufan Zhang ,&nbsp;Xing Peng ,&nbsp;Xiaofei Qin ,&nbsp;Jianhui Bai ,&nbsp;Lian Xue ,&nbsp;Guan Wang ,&nbsp;Shanshan Cui ,&nbsp;Wenxin Tao ,&nbsp;Jinhua Du ,&nbsp;Dasa Gu ,&nbsp;Xiaohan Su","doi":"10.1016/j.apr.2025.102461","DOIUrl":"10.1016/j.apr.2025.102461","url":null,"abstract":"<div><div>This study investigates the seasonal variations in O₃ levels in Qingdao, a typical coastal city, and quantifies the effects of key photolysis rate constants (<em>J</em>[O¹D] and <em>J</em>[NO₂]), meteorological parameters (RH, TEMP, and SF), and pollutants (ΔCO, PM_2.5, and NO₂) on O₃ levels across different seasons using machine learning. Additionally, the summer months, when photochemical reactions are most active, were analyzed in detail. The results indicate that the factors contributing to summer O₃ levels in order of importance, were RH, ΔCO, SF, PM_2.5, <em>J</em>[O¹D], NO₂, TEMP, WS, and <em>J</em>[NO₂]. RH was the most significant factor, with high humidity levels (&gt;75%) inhibiting O₃ formation. ΔCO, representing regional transport, was the second most influential, suggesting that direct O₃ transport and the delivery of high concentrations of precursors significantly promoted local O₃ production and accumulation. While <em>J</em>[O¹D] and <em>J</em>[NO₂] had different roles in O₃ promotion and depletion, <em>J</em>[O¹D] had a greater impact overall. The temperature in the range of 26 °C–32 °C inhibits O₃ production, When RH exceeded 90%, <em>J</em>[O¹D] accelerates while other photolysis rate constants decline, further suppressing the production of O₃. For comparison, multiple linear regression models were used to develop empirical equations for calculating hourly O₃ concentrations across the four seasons. The results showed that these factors explained 50%, 64%, 61%, and 63% of the O₃ sources in Qingdao for spring, summer, autumn, and winter, respectively. Sensitivity tests on factors influencing summer O₃ concentrations found that MLR could not quantify their contributions to O₃ levels.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102461"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419419","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":"Evaluating the spatiotemporal variations in atmospheric CO2 concentrations in China and identifying factors contributing to its increase","authors":"Weixin Zhu ,&nbsp;Hong Zhang ,&nbsp;Xiaoyu Zhang ,&nbsp;Haohao Guo ,&nbsp;Yong Liu","doi":"10.1016/j.apr.2025.102458","DOIUrl":"10.1016/j.apr.2025.102458","url":null,"abstract":"<div><div>Understanding the patterns and trends of atmospheric carbon dioxide (CO₂) is essential for comprehending the global carbon cycle and making accurate future climate predictions. CO₂ levels are influenced by complex and often interrelated factors, requiring innovative approaches that can tie place-specific factors with CO₂ concentrations. This study utilized the Orbiting Carbon Observatory-2 (OCO-2) data to explore the changes of CO₂ concentrations in China over the past decade. Additionally, climate parameters, vegetation cover, and anthropogenic activities were combined to explain temporal and spatial changes in CO₂ concentrations, using Geodetector and Multiscale Geographically Weighted Regression (MGWR) model. The results revealed a consistent increase (2.54 ppm/yr) and significant spatial agglomeration (High-High cluster in the east, Low-Low cluster in the west) of CO₂ concentrations in China. The spatial location (<em>q</em> = 0.68) emerged as the primary determinant of CO₂ levels, with population variable (<em>q</em> = 0.55) representing the secondary influencing factor. The interactions among natural elements and anthropogenic activities had substantially elevated CO₂ levels. Compared to the Geographically Weighted Regression (GWR), and Ordinary Least Squares (OLS) models, the MGWR model demonstrated superior capability in revealing the varying spatial scales of influence among different variables, making it more suitable for investigating the impacts of multiple factors on atmospheric CO₂ concentrations. The MGWR revealed significant variations in the optimal bandwidths among different explanatory variables, with temperature, precipitation, and LAI operating at much smaller scales. The findings are expected to provide valuable insights into regional processes influencing CO₂ concentrations and the development of targeted interventions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102458"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474202","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":"An integrated feature selection and machine learning framework for PM10 concentration prediction","authors":"Elham Kalantari ,&nbsp;Hamid Gholami ,&nbsp;Hossein Malakooti ,&nbsp;Dimitris G. Kaskaoutis ,&nbsp;Poorya Saneei","doi":"10.1016/j.apr.2025.102456","DOIUrl":"10.1016/j.apr.2025.102456","url":null,"abstract":"<div><div>The Sistan Basin, east Iran is a major dust source, presenting significant atmospheric, ecological, socio-economic, and health challenges. This study employed machine learning (ML) algorithms, including Random Forest (RF), K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Support Vector Regression (SVR), and Least Absolute Shrinkage and Selection Operator (LASSO), to model and predict PM₁₀ concentrations in Zabol City (2013–2022), utilizing independent meteorological variables such as temperature, relative humidity, wind speed and direction. Feature selection methods — Filter (Information Gain, F-Test, Correlation Coefficient), Wrapper (Recursive Feature Elimination, Sequential Forward/Backward Selection), and Embedded (LASSO, Elastic Net, Ridge Regression, RF Importance) — were applied to identify significant predictors, with embedded methods providing the best balance of simplicity, accuracy, and cost-efficiency. Among the models, RF demonstrated the highest seasonal performance (R² = 0.75) during summer. RF's prediction R² values for PM₁₀ remained above 0.5 in all seasons, consistently outperformed the other models. The WKNN model performed reasonably well across all seasons, ranking second among the models, while the LASSO model demonstrated weaker performance. The SVR model showed satisfactory performance in specific seasons, such as summer and autumn. A common feature of all models was their better performance during summer. Importantly, the models relied solely on readily available meteorological data, enabling accurate predictions of PM₁₀ in this arid region of eastern Iran. The findings highlight the potential of ML techniques for developing air pollution prediction and warning systems, offering valuable support to policymakers in the design of effective pollution control strategies and safeguarding public health.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102456"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479230","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 and PM10 concentrations in urban and peri-urban environments of two Pacific Island Countries","authors":"J.J. Hilly ,&nbsp;J. Sinha ,&nbsp;F.S. Mani ,&nbsp;A. Turagabeci ,&nbsp;P. Jagals ,&nbsp;D.S.G. Thomas ,&nbsp;G.F.S. Wiggs ,&nbsp;L. Morawska ,&nbsp;K. Singh ,&nbsp;J. Gucake ,&nbsp;M. Ashworth ,&nbsp;M. Mataki ,&nbsp;D. Hiba ,&nbsp;D. Bainivalu ,&nbsp;L.D. Knibbs ,&nbsp;R.M. Stuetz ,&nbsp;A.P. Dansie","doi":"10.1016/j.apr.2025.102454","DOIUrl":"10.1016/j.apr.2025.102454","url":null,"abstract":"<div><div>Air quality monitoring in most Pacific Island Countries, Territories, and States (PICTS) is minimal, with notable exceptions in Hawai'i and New Caledonia. However, air quality issues are increasingly significant in the region. Existing data on air quality, particularly regarding PM_2.5 and PM₁₀, are limited, with studies focusing on Fiji and New Caledonia. Our research provides the first continuous and comparative air quality monitoring in urban and peri-urban areas of Fiji and the Solomon Islands, and it is the first assessment since the introduction of the 2021 World Health Organization (WHO) Air Quality Guidelines (AQG). This study assesses health risks and air pollution trends to inform governmental recommendations. We collected PM_2.5, PM₁₀, and weather data from Honiara, Solomon Islands (February 2020–August 2023), and Suva, Fiji (April 2021–August 2023). In Honiara, PM_2.5 levels exceeded WHO AQG on 75% of days in urban areas and 51% in peri-urban areas, while PM₁₀ levels surpassed guidelines on 2% of days in both areas. In Suva, urban areas had a 10% exceedance of PM_2.5 guidelines, compared to 13% in peri-urban areas. Annual PM_2.5 averages exceeded WHO guidelines every year, with levels in Suva and Honiara exceeding guidelines by 2–4 times. PM₁₀ levels were 1.5 times higher than WHO AQG in urban Honiara and 1.2 times higher in peri-urban areas. These findings highlight the urgent need for governmental action to establish robust air quality standards and long-term monitoring programs in Fiji and the Solomon Islands to mitigate health risks from poor air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102454"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the horizontal and vertical distribution of a dust weather event in the Tarim Basin based on multi-source observational datasets","authors":"Hong Jiang ,&nbsp;Qing He ,&nbsp;Ruqi Li ,&nbsp;Hao Tang ,&nbsp;Quanwei Zhao ,&nbsp;Hailiang Zhang ,&nbsp;Jinglong Li ,&nbsp;Yongkang Li ,&nbsp;Jingjing Li","doi":"10.1016/j.apr.2025.102455","DOIUrl":"10.1016/j.apr.2025.102455","url":null,"abstract":"<div><div>The study employed multi-source observation data from unmanned aerial vehicles (UAVs), satellites, and LiDAR conduct an observational study on a dust weather event that occurred in the Tarim Basin, China, from May 2 to 4, 2023. The results showed that FY-4A dust storm detection and MODIS aerosol optical depth (AOD) products could effectively observe the horizontal distribution of dust. Dust areas and intensities were identified at the AOD threshold range of 0.54–3.50. The convolutional neural network algorithm dust mask could identify dust structures with more precision compared to traditional FY-4 dust storm detection. Moreover, vertical particulate matter (PM) concentration changes determined by UAVs were analyzed at different altitudes, with low PM concentrations observed at higher altitudes. The dust area obtained through the CALIPSO vertical feature mask product was consistent with the PM concentration changes observed by the UAV. When the visibility value was below 1 km, the ground-based LiDAR 532 nm extinction coefficient (EC), backscatter coefficient (BC), and depolarization ratio (DR) values reached 3.42 km⁻¹, 0.057 km⁻¹sr⁻¹, and 0.47, respectively. The vertical profile changes of EC, BC, and DR were in strong agreement with the vertical profile changes of the PM concentrations by the UAV.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102455"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419418","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":"Burn parameters affect PAH emissions at conditions relevant for prescribed fires","authors":"Karl Töpperwien ,&nbsp;Guillaume Vignat ,&nbsp;Alexandra J. Feinberg ,&nbsp;Conner Daube ,&nbsp;Mitchell W. Alton ,&nbsp;Edward C. Fortner ,&nbsp;Manjula R. Canagaratna ,&nbsp;Matthias F. Kling ,&nbsp;Mary Johnson ,&nbsp;Kari Nadeau ,&nbsp;Scott Herndon ,&nbsp;John T. Jayne ,&nbsp;Matthias Ihme","doi":"10.1016/j.apr.2025.102438","DOIUrl":"10.1016/j.apr.2025.102438","url":null,"abstract":"<div><div>Wildfire smoke is a health hazard as it contains carcinogenic volatile compounds and fine particulate matter. In particular, exposure to polycyclic aromatic hydrocarbons (PAHs) is a major concern, since these compounds have been recognized as important contributors to the overall carcinogenic risk. In this work, gas and particle-phase PAH emissions from combustion of Eastern White Pine (<em>Pinus strobus</em>) were quantified using time-of-flight mass spectrometry over a range of burn conditions representative of wildfires and prescribed fires, including fuel moisture, heat flux, and oxygen concentration. We found that changing the burn environment lead to a variability of up to 77% in phenanthrene/anthracene emissions. This could explain a large part of the variability in PAH emission factors from biomass combustion reported in the literature. We found that optimal conditions for fuel moisture content of 20–30<span><math><mtext>%</mtext></math></span>, sample heat load of <span><math><mrow><mn>60</mn><mo>−</mo><mtext>70</mtext><mspace></mspace><mtext>kW</mtext><mspace></mspace><mtext>m</mtext><msup><mrow></mrow><mrow><mi>−2</mi></mrow></msup></mrow></math></span>, and oxygen concentrations of 5–15<span><math><mtext>%</mtext></math></span> can significantly reduce the emissions of heavy molar weight PAHs.</div><div>Our analysis showed that the relative carcinogenic risk from PAH exposure can be reduced by more than 50% under optimal conditions. In light of the increasing use of prescribed fire for forest management, the relationship between emissions and burn conditions that we have established provides a guidance for assessing the expected health impact from prescription burns, and can inform strategies to reduce PAH emissions from prescribed fire activities.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102438"},"PeriodicalIF":3.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430083","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":"Experimental and kinetic analysis of Hg0 removal by CoFe2O4 nanoparticles as an efficient activator of persulfate","authors":"Qianqian Zhang,&nbsp;Haixing Du,&nbsp;Anchao Zhang,&nbsp;Hongyu Zheng,&nbsp;Haixia Li,&nbsp;Weiwei Zhang,&nbsp;Zhijun Sun","doi":"10.1016/j.apr.2025.102453","DOIUrl":"10.1016/j.apr.2025.102453","url":null,"abstract":"<div><div>The sulfate radical (SO₄^•−) and hydroxyl radical (^•OH), derived from the oxidation of persulfate (PS), are significant active substances in the treatment of pollution. In this study, magnetic CoFe₂O₄ nanoparticles (CFO NPs) were synthesized by a hydrothermal method and applied to activate PS for Hg⁰ removal from the simulated flue gas. The results exhibited that the Hg⁰ removal efficiency can reach as high as 99.5% within 60 min under the optimal condition of 6 mM of PS, 0.8 g/L of CFO dose, 20 °C of reaction temperature and 7 of initial pH. The characterizations demonstrated that the large surface area and coexistence of Co/Fe mixed valence were generated after the formation of CFO nanostructure, improving the amount of active sites and facilitating the adsorption and activation of PS. Scavenging tests indicated that SO₄^•− and ^•OH were the main active radicals on Hg⁰ removal, where the ^•OH radicals primarily originated from the conversion of SO₄^•−. Moreover, the circulation of ≡Co(III)/≡Co(II) and ≡Fe(III)/≡Fe(II) resulted in a superior Hg⁰ removal activity. Based on the experiments and characterization analysis, the reaction mechanism was proposed. In addition, the kinetic model for Hg⁰ removal was systematically analyzed, and the role of SO₄^•− and ^•OH was further verified. This study provided new insights toward efficient activation of persulfate for removal of Hg⁰ from coal-fired flue gas.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102453"},"PeriodicalIF":3.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403231","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":"Characterization identification and speciated emission inventory construction of anthropogenic volatile organic compounds (VOCs) in Beijing, China","authors":"Xiaoyu Liu ,&nbsp;Hanyu Zhang ,&nbsp;Zhe Lv ,&nbsp;Huahua Bai ,&nbsp;Guohao Li","doi":"10.1016/j.apr.2025.102452","DOIUrl":"10.1016/j.apr.2025.102452","url":null,"abstract":"<div><div>Volatile organic compounds (VOCs) significantly impact air quality and human health, garnering widespread attention. We conducted a comparative analysis of anthropogenic VOC emissions across different years, established a speciated VOC emission inventory for Beijing in 2020 and assessed the ozone formation potential (OFP). The VOC emissions showed a consistent downward trend, ranging from 10.19 × 10⁴ t to 28.36 × 10⁴ t in 2007–2020. The main sectors shifted from mobile sources (43.42%) and solvent utilization (26.35%) in 2007 to solvent utilization (55.99%) and mobile sources (24.00%) in 2020. The key contributing districts shifted from Fangshan (28.92%), Chaoyang (9.84%), and Daxing (7.33%) in 2013 to Chaoyang (14.29%), Haidian (11.42%), and Fangshan (10.33%) in 2020. The profile dataset encompasses 15 sectors and includes 117 VOC species, with an estimated total of 5.07 × 10⁴ t of VOC emissions in 2020, with alkanes, alkenes, alkyne, aromatics, halocarbons, and OVOCs accounting for 34.51%, 17.84%, 1.01%, 36.20%, 7.37%, and 3.07%, respectively. Significant differences were observed in the proportions of various VOC species across sectoral emissions. This dataset shows substantial deviations from the U.S. SPECIATE database, highlighting the significance of developing VOC source profiles. The uncertainties in VOC emission estimates primarily originate from variations in activity levels, emission factors and spatial distribution of emissions. Some sectors with high OFPs, like automobile manufacturing, barbecue and residential combustion are regarded as critical targets for emission control. Aromatics, alkenes, and OVOCs were identified as the major contributors to OFP, and controlling their emissions is essential for reducing ozone formation in Beijing.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102452"},"PeriodicalIF":3.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388031","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":"Inverse modeling of 137Cs during Chernobyl 2020 wildfires without the first guess","authors":"Ondřej Tichý ,&nbsp;Nikolaos Evangeliou ,&nbsp;Anna Selivanova ,&nbsp;Václav Šmídl","doi":"10.1016/j.apr.2025.102419","DOIUrl":"10.1016/j.apr.2025.102419","url":null,"abstract":"<div><div>This study estimates 137Cs emissions from Chernobyl wildfires in April 2020 using inverse modeling. Emissions are resolved with daily resolution by particle sizes (0.4 <span><math><mi>μ</mi></math></span>m, 8 <span><math><mi>μ</mi></math></span>m, 16 <span><math><mi>μ</mi></math></span>m) and altitudes (up to 3 km). The inverse problem’s complexity requires regularization due to its ill-posed nature. One potential way to regularize the problem is the use of the so-called first guess, i.e. emission taken from expert knowledge or previous literature. However, inappropriately chosen first guess may lead to serious bias in results or its availability may be limited for rapid response. We rather follow a Bayesian approach where all model parameters are considered as variables to be estimated from available data. We aim to combine three key principles: modeling of sparsity and smoothness of the emission vector, modeling of bounded ratios between released particle size/altitude fractions, and bias correction of the atmospheric transport model. All these principles proved their significance separately, however, we combine them in one comprehensive method to estimate the 137Cs emissions from the Chernobyl wildfires. The total released activity was estimated to be 458 GBq with uncertainty estimated to be 69 GBq. Our estimates also suggest that most of the activity has been released below a one-kilometer altitude with the more dominant role towards the smallest particle fraction than was considered in other studies. Using our estimate, we calculate the time-integrated volumetric activities of 137Cs over the domain using the JRODOS system and our findings well agrees with previous results.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102419"},"PeriodicalIF":3.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377795","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":"Aerosol chemical composition and sources during unexpected wintertime haze episodes in 2023 in urban Xuzhou of eastern China","authors":"Xianru Yin ,&nbsp;Yongcai Rao ,&nbsp;Lili Tang ,&nbsp;Yunjiang Zhang","doi":"10.1016/j.apr.2025.102451","DOIUrl":"10.1016/j.apr.2025.102451","url":null,"abstract":"<div><div>Understanding the chemical composition and sources of aerosols during extreme haze episodes is essential for effective air quality management, particularly in rapidly industrializing regions. This study investigates the aerosol chemistry and sources during unexpected winter haze events in December 2023 in Xuzhou, Eastern China. Continuous online monitoring of fine particulate matter (PM_2.5), combined with detailed chemical analysis and concentration weighted trajectory (CWT) analysis, was conducted to elucidate the sources and processes driving these pollution episodes. Positive matrix factorization identified five major PM_2.5 sources: secondary nitrate-rich aerosols, vehicular emissions, industrial activities, dust emissions, and coal combustion. Nitrate was the dominant component during severe haze periods, whereas dust significantly contributed during dust storm episodes. CWT analysis highlighted substantial regional contributions, with industrial and dust-rich areas to the northwest and marine aerosols from coastal regions playing key roles. The findings suggest that nitrate formation and regional dust transport were the primary drivers of severe winter haze in Xuzhou. Effective mitigation strategies should prioritize nitrogen oxides emission control and dust management. This study underscores the necessity of regional collaboration and continuous monitoring to tackle complex air pollution challenges.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102451"},"PeriodicalIF":3.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453109","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}