Atmospheric Pollution Research最新文献

{"title":"Soil emissions of HONO and other nitrogen-containing gases: Insights into microbial pathways and moisture effects","authors":"Peng Cheng ,&nbsp;Baobin Han ,&nbsp;Zhilin Tian ,&nbsp;Zhen Liu ,&nbsp;Wenda Yang ,&nbsp;Jianwei Gu ,&nbsp;Xiaofang Yu ,&nbsp;Hongli Wang ,&nbsp;Min Zhou","doi":"10.1016/j.apr.2025.102501","DOIUrl":"10.1016/j.apr.2025.102501","url":null,"abstract":"<div><div>Nitrous acid (HONO) greatly impacts tropospheric chemistry by producing hydroxyl radical (OH) through photolysis, and yet our knowledge about sources of HONO remains elusive. Emissions of nitrogen (N) containing gases from soils have long been a subject of research in biogeochemistry. Soil emissions of HONO have received greater attention recently, helping explain a missing source of observed atmospheric HONO. We conducted laboratory experiments to simultaneously measure emission fluxes of HONO along with other N containing gases including nitric oxide (NO), nitrous oxide (N₂O), and ammonia (NH₃) from lateritic red soil samples, and evaluated the contributions of microbiological processes to HONO emissions by conducting process inhibiting experiments. Results from monitoring emissions during a full wet-drying cycle showed that the emissions of HONO, NO and N₂O have a strong dependance on soil water content, with maximum fluxes for HONO (125 ± 17 ng N m⁻² s⁻¹), NO (115 ± 11 ng N m⁻² s⁻¹) and N₂O (453 ± 100 ng N m⁻² s⁻¹) observed at 17 % (HONO), 42 % (NO) and 94 % (N₂O) water filled pore space (WFPS), respectively, while NH₃ emission remains at ∼16 ng N m⁻² s⁻¹ in majority of the WFPS range. Results from process inhibiting experiments suggested ammonia oxidation to be the dominant pathway for HONO production in the low water-content range, while reduction of nitrate to NO₂⁻ appeared dominant in the high water-content range. Our study demonstrates the feasibility of studying emissions of HONO along with other N containing gases as connected network of processes as a whole.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102501"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679304","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":"How to forecast daily carbon emissions during public health emergencies: A novel self-attention multi-neuron time series model","authors":"Yilong Wang ,&nbsp;Haoran Wang ,&nbsp;Junjie Chen ,&nbsp;Yigang Wei ,&nbsp;Yan Li","doi":"10.1016/j.apr.2025.102502","DOIUrl":"10.1016/j.apr.2025.102502","url":null,"abstract":"<div><div>Affected by numerous uncertainties, climate change is a critical issue linked to carbon emissions that warm the planet. Although scholars have conducted detailed research on carbon emissions and established predictive models for them, there are few models specifically designed for predicting carbon emissions during public health emergencies. With the concentrated outbreak of various uncertain factors, organizations and institutions urgently need a model capable of predicting carbon emissions during public health emergencies. This study introduces a novel self-attention multi-neuron time series (SAMNTS) model to evaluate the previously unexplored impact of public health emergencies on carbon emissions. Specifically, we have designed a more comprehensive deep learning prediction framework that can effectively utilize a large amount of relevant data to conduct detailed reasoning and analysis on the issue of carbon emissions, enabling more accurate predictions of daily carbon emissions. To better test its effectiveness, we used COVID-19 as an example to test the model. The results proved that the model can effectively make predictions and analyze various factors that affect carbon emissions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102502"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725784","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":"How does greenness contribute to reducing lung cancer risks associated with particulate matter exposure?","authors":"Keyu Luo ,&nbsp;Huagui Guo ,&nbsp;Weifeng Li ,&nbsp;Jiansheng Wu","doi":"10.1016/j.apr.2025.102500","DOIUrl":"10.1016/j.apr.2025.102500","url":null,"abstract":"<div><div>The increasing global incidence of lung cancer, which now ranks first among all cancer types, along with the highest risk of lung cancer mortality in East Asia and the narrowing gender gap in incidence since the turn of the century, presents a significant and growing public health concern in Chinese cities. This research investigated how greenness affects the relationships between the incidence of lung cancer and PM₁, PM_2.5 and PM₁₀ concentrations via a linear mixed model (LMM) and a generalized linear mixed model (GLMM). The findings revealed that particulate matter was associated with increased incidence of lung cancer, with the most substantial changes observed for PM₁ (4.92), followed by PM_2.5 (4.57) and PM₁₀ (4.22). Our study also revealed that counties with higher levels of greenness experienced a decrease in the incidence of lung cancer among both males and females compared with counties with lower greenness levels, suggesting a protective effect of greenness against lung cancer. The joint associational analysis of particulate matter and NDVI greenness revealed elevated RRs of lung cancer incidence (male: 33 % for PM₁, 40 % for PM_2.5, 30 % for PM₁₀; female: 43 % for PM₁, 51 % for PM_2.5, 42 % for PM₁₀) in high particulate matter and low greenness (the highest-impacted group) relative to those exposed to low particulate matter and high greenness (the least-impacted group). The moderating role of greenness was stronger in females than in males (PM₁: RERI_female = 0.106; PM_2.5: RERI_female = 0.208, RERI_male = 0.043; and PM₁₀: RERI_female = 0.139, RERI_male = 0.017) and more pronounced in areas with medium greenness than in those with high greenness. These findings remained consistent in the smoking-adjusted and region-adjusted models and with an alternative index of the lung cancer mortality rate and greenness. These findings underscored the importance of urban greenness in the development of healthy cities.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102500"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642123","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":"Atmospheric CO2 and CH4 observations in Hangzhou before, during, and after the 2023 Asian Games: Insights from vehicle-carried and fixed stations","authors":"Tianhao Wang ,&nbsp;Jiansen Wang ,&nbsp;Ning Hu ,&nbsp;Ruonan Li ,&nbsp;Meng Shan ,&nbsp;Qun Lin ,&nbsp;Longlong Chen ,&nbsp;Jun Wang ,&nbsp;Yuxin Jiang ,&nbsp;Zhonghao Yang ,&nbsp;Wei Xiao","doi":"10.1016/j.apr.2025.102499","DOIUrl":"10.1016/j.apr.2025.102499","url":null,"abstract":"<div><div>On the basis of the vehicle-carried mobile observation method, we conducted CO₂ and CH₄ observations in Hangzhou city during different periods before, during and after the Asian Games in autumn 2023. Both the difference between urban and rural monitoring station data and the difference between mobile observation and urban background station data during the period of emission reduction implementation were used as quantitative indicators of policy effectiveness. The differences in the CO₂ and CH₄ concentrations between the mobile observations and the background values exhibited the order of during the Asian Games &lt; before the Asian Games &lt; after the Asian Games, and the differences between the urban and rural observation station values decreased during the Asian Games, indicating the effectiveness of the emission reduction measures. Additionally, the differences in the CO₂ and CH₄ concentrations between the mobile observations and background values revealed different spatial variation characteristics before, during and after the Asian Games. This demonstrates that emission reduction measures do not yield exactly the same effectiveness for greenhouse gases from diverse emission sources. Moreover, the wind speed, wind direction and boundary layer height may negatively affect the effectiveness of emission reduction. To obtain better results, emission reduction measures must continue over a longer period.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102499"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629286","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 military activity on atmospheric aerosol characteristics in Ukraine and Kyiv city","authors":"Xuanyi Wei ,&nbsp;Yuliia Yukhymchuk ,&nbsp;Vassyl Danylevsky ,&nbsp;Gennadi Milinevsky ,&nbsp;Philippe Goloub ,&nbsp;Ihor Fesianov ,&nbsp;Ivan Syniavskyi ,&nbsp;Olena Turos ,&nbsp;Tetiana Maremukha ,&nbsp;Arina Petrosian ,&nbsp;Volodymyr Kyslyi ,&nbsp;Yu Shi","doi":"10.1016/j.apr.2025.102496","DOIUrl":"10.1016/j.apr.2025.102496","url":null,"abstract":"<div><div>We investigate the impact of the Russian invasion and military activities on aerosol parameters and air quality in the atmosphere over Kyiv and Ukraine. This study analyzes changes in pollutants such as black carbon, particulate matter PM_2.5 and PM₁₀, and sulfates (SO₂ and SO₄) using MERRA-2 reanalysis data. Black carbon concentration surged in eastern and western Ukraine during pre-invasion times, attributed to industrial emissions and solid fuel heating. During invasion, black carbon levels decreased overall, except in conflict-affected areas like Kyiv and southeastern regions, reflecting reduced industrial activities in the battle region. Similarly, PM_2.5 levels increased in eastern conflict zones, correlating with military intensity. Shifts in SO₂ and SO₄ concentrations indicated increased emissions in southeastern Ukraine due to military activities and infrastructure damage. Also, this research aims to analyze aerosol properties using AERONET data. Sun photometer observations reveal changes in the annual dynamics of the Ångstrom exponent, with lower values observed in 2022 and a decrease in the fine aerosol fraction. Analysis of the aerosol complex refractive index and single scattering albedo indicate a shift in the dominant aerosol type present in the atmosphere in 2022–2024. According to the GRASP algorithm, a significant increase in the black carbon fraction was registered in 2022. Air contamination in Kyiv through PM_2.5 and PM₁₀ in 2021 and 2022 revealed substantial increases during critical conflict periods attributed to military actions. Despite initial declines just after the invasion, PM levels remained elevated compared to pre-invasion years, indicating ongoing air quality challenges exacerbated by war-related factors.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102496"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679300","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 land use characteristics on air pollutant concentrations considering the spatial range of influence","authors":"Lee Gunwon ,&nbsp;Han Yuhan ,&nbsp;Geunhan Kim","doi":"10.1016/j.apr.2025.102498","DOIUrl":"10.1016/j.apr.2025.102498","url":null,"abstract":"<div><div>Prediction models ranging from statistical probability to machine learning techniques have been employed to improve and manage urban air quality. However, the number of air quality monitoring stations (AQMS) for the collection of air quality information is limited. This study established a model that explains the relationship between six air pollutants–SO₂, CO, O₃, NO₂, PM₁₀, and PM_2.5–measured by approximately 443 AQMS in South Korea and factors, such as the vegetation index, topography, and land cover elements. The model analyzed the impact of land cover changes on air pollutant concentrations and derived scenarios predicting changes in the air quality due to land use changes. Despite the relatively small sample size of approximately 360 AQMS, multiple regression analysis demonstrated higher explanatory power compared with Xtreme Gradient Boosting, a representative machine learning technique. The optimal spatial range for explaining air pollutant concentrations varied for each air pollutant. The highest R² in the multiple regression analysis was 0.34 at a distance of 12,000 m for SO₂; 0.27 at 11,000 m for CO; 0.50 at 6000 m for O₃; 0.70 at 18,000 m for NO₂; 0.49 at 18,000 m for PM₁₀; and 0.48 at 11,000 m for PM_2.5. Certain land cover characteristics were found to significantly affect air quality, whereas small-scale restoration had a minimal impact on air quality improvement, and large-scale development substantially increased pollutant concentrations. This study provides essential information for urban planning and policymaking aimed at improving urban air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102498"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679301","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 physicochemical characteristics of PM2.5 under meteorological influence: A study in South Chungcheong Province, South Korea (2021–2022)","authors":"Kyucheol Hwang ,&nbsp;Sechan Park ,&nbsp;Jeongho Kim ,&nbsp;Jae Young Lee ,&nbsp;Jong-Sung Park ,&nbsp;Kwangyul Lee ,&nbsp;Jungmin Park ,&nbsp;Jong Bum Kim","doi":"10.1016/j.apr.2025.102497","DOIUrl":"10.1016/j.apr.2025.102497","url":null,"abstract":"<div><div>Despite the implementation of various policies worldwide to reduce PM_2.5 concentrations, they have remained sufficiently high and cause serious environmental and health problems. Most studies and policies regarding PM_2.5 in South Korea have primarily focused on the Seoul Metropolitan Area, including Seoul, and there is a lack of research data necessary for implementing PM_2.5 management policies in South Chungcheong Province (SCP). In this study, we used data from the Air Quality Research Center in Seosan, SCP, to conduct a detailed analysis of PM_2.5, focusing on its chemical and physical properties as well as the influence of meteorological factors on PM_2.5 characteristics. The mean PM_2.5 concentrations were 16.7 ± 12.5 μg/m³ in the warm season and 31.1 ± 18.7 μg/m³ in the cold season, showing a twofold increase in the cold season. The ratio of NO₃⁻ in the chemical composition of PM_2.5 was higher in the cold season (19%) compared to the warm season (15%), while SO₄²⁻ was 1.75 times higher in the warm season. Using the atmospheric oxidant (Ox) and analyzing PM_2.5 concentrations under different photochemical conditions, we found that small particles dominated in the warm season, shifting towards smaller particle sizes in the size distribution with higher temperatures due to secondary particle production. In contrast, higher concentrations of PM_2.5 during the cold season were attributed to direct emissions and external influx. Our findings highlight the importance of managing small particles in summer and provide valuable data for South Chungcheong Province, aiding future policy development to reduce PM_2.5 levels.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102497"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629285","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":"Source-specific health risks of PM2.5-bound heavy metals in a Chinese megacity impacted by non-ferrous metal mines","authors":"Yanhong Zhu ,&nbsp;Qiwu Li ,&nbsp;Jian Wu ,&nbsp;Xin Chen ,&nbsp;Junfeng Zhang","doi":"10.1016/j.apr.2025.102485","DOIUrl":"10.1016/j.apr.2025.102485","url":null,"abstract":"<div><div>Non-ferrous metal mining and smelting are considered to be one of the largest sources of heavy metals (HMs) to the atmosphere, posing a serious threat to human health. For this reason, this study addressed the potential impacts in a Chinese megacity affected by non-ferrous metal mines, and explored the characteristics and health risks of HMs in PM_2.5 during summer, autumn, and winter from June 2019 to January 2020. The results showed that the average PM_2.5 concentration and total concentration of 10 associated HMs increased from 25.5 to 48.5 μg m⁻³ and from 51.5 to 133 ng m⁻³, respectively, from summer to winter. Combining methods for health risk assessment of elements and sources, we found that the total carcinogenic risk (CR) of six carcinogenic HMs (As, Cr, Co, Cd, Ni, and Pb) also exhibited a clear increasing trend from summer to winter. However, the total CR (1.12 × 10⁻⁵) in summer still exceeded the minimum acceptable risk level. The main contributors to CR in each of the three seasons were consistently industrial emissions and coal combustion, with their combined contributions exceeding 82.5%. Further analysis indicated that in all three seasons, the CR of industrial emissions mainly resulted from Cr, Co, and Cd, while the CR of coal combustion was primarily due to As, highlighting the significant challenges of controlling Cr-, Co-, and Cd-related industries and As emissions from combustion in areas affected by non-ferrous metal mines in the future.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102485"},"PeriodicalIF":3.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562302","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":"Assessing environmental exposure to phyto-pharmaceutical products in a wine-growing area of Alsace, France: Combined indoor and outdoor air and dust sampling","authors":"Anais Rodrigues ,&nbsp;Olivier Delhomme ,&nbsp;Maurice Millet","doi":"10.1016/j.apr.2024.102362","DOIUrl":"10.1016/j.apr.2024.102362","url":null,"abstract":"<div><div>This study assessed the contamination of ambient air and dust with phyto-pharmaceutical products (PPPs) in homes near agricultural areas, particularly those close to vineyards, to determine the link between local agricultural activities and exposure risks. Residents in such areas face a higher likelihood of PPPs exposure, making it critical to evaluate the impact of agriculture on air quality.</div><div>From March 2018 to December 2019, systematic sampling was conducted in nine houses, including a reference house near vineyards, in an Alsatian village in Bas-Rhin, France. The study monitored 38 molecules in 347 passive air samples and 127 dust samples, using Pressurized Liquid Extraction (PLE), Thermal Desorption (TD), and GC/MSMS for quantification.</div><div>The results showed the presence of various PPPs in air and dust, including several compounds typically used in field crops. The six most frequently detected molecules were cyprodinil (fungicide), diflufenican (herbicide), fenpropidin (fungicide), metamitron (herbicide), and prosulfocarb (herbicide). Metamitron, found in over 50% of both indoor and outdoor air samples and 70% of dust samples, was especially prevalent.</div><div>The study concluded that passive air sampling is an effective method for monitoring PPP contamination, while dust sampling provides valuable complementary data. Regular, frequent sampling is essential to understanding contamination patterns and seasonal variations, emphasizing the need for continuous monitoring in residential areas near agricultural fields.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102362"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578533","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":"Correlating particulate matter and planetary boundary layer dynamics in northwestern South America: A case study of Santiago de Cali","authors":"Diego Arias-Arana ,&nbsp;Elena Montilla-Rosero ,&nbsp;Omar Calderón-Losada ,&nbsp;John H. Reina","doi":"10.1016/j.apr.2024.102352","DOIUrl":"10.1016/j.apr.2024.102352","url":null,"abstract":"<div><div>The relationship between particulate matter (PM) levels and planetary boundary layer height (PBLH) is investigated in Santiago de Cali, a tropical city located above the equator in southwestern Colombia, in the northwestern region of South America. Correlations are studied during both the dry and wet seasons, at different locations of local air quality stations, and under different wind regimes. Hourly estimates of PBL height are derived from Lidar signals using the Extended Kalman Filter (EKF) method, while Bayesian linear regression is used to quantify the PM-PBLH correlation. The results indicate a negative correlation between PM and PBLH, observed during both dry and wet seasons. Furthermore, the location of the observation can influence the relationship between the variables. The correlation is negative for stations located near the western foothills, although the strength of this relationship is reduced at the easternmost air quality station, while a positive correlation was observed at the rural background station. We analyzed the air mass transport regimes for these stations using bivariate plots and cluster analysis, and were able to identify local and distant potential pollution sources that could explain the PM-PBLH correlation behavior. This study advances our understanding of PBL height, its temporal evolution, and its relationship with PM₁₀ and PM_2.5 in a Colombian tropical Andean city characterized by distinct dry and wet seasons and complex topography and wind patterns.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102352"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578515","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}