Atmospheric Pollution Research最新文献

{"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}

{"title":"Exposure of city-dwellers to particulate matters during commuting trips in the metropolitan area of Karachi","authors":"Muhammad Kamran Khan ,&nbsp;Haider A. Khwaja ,&nbsp;Sumayya Saied ,&nbsp;Mirza M. Hussain ,&nbsp;Saiyada Shadiah Masood ,&nbsp;Rija Zehra","doi":"10.1016/j.apr.2024.102355","DOIUrl":"10.1016/j.apr.2024.102355","url":null,"abstract":"<div><div>The brisk and persistent surge in the population, urbanization, automobiles, and industries fused with climate change and geogenic conditions have materialized in acute ambient air pollution problems in the mega city Karachi with profound health impacts. To evaluate the extent of personal exposure and quantification of the particulate matter (PM) concentrations, we organized the mobile size-segregated PM (TSP, PM₁₀, PM₇, PM_2.5, and PM₁) monitoring campaign in Karachi. Seven in-vehicle tracks in Karachi's diverse industrial/commercial/residential regions were investigated. High spatial variability in PM concentrations was observed along each track. Results demonstrate that commuters in Karachi were exposed to a significantly higher level of PM than several cities in high-income countries. Mean concentrations across the seven tracks were: PM₁ (8.7 ± 8.0 μg/m³), PM_2.5 (51.9 ± 48.0 μg/m³), PM₇ (386 ± 538 μg/m³), PM₁₀ (527 ± 646 μg/m³), and TSP (685 ± 769 μg/m³). The carcinogenic risks of PM_2.5 were found to be outside the acceptable range (10⁻⁶ - 10⁻⁴). Therefore, better insight into PM pollution exposure and its determinants in Karachi should influence the development of more appropriate exposure reduction strategies and have major public health effects.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102355"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578534","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":"Assessment of eco-driving strategies on carbon emissions for hybrid vehicles through portable emissions measurement systems","authors":"Bo-wen Li ,&nbsp;Zhi-heng Chen ,&nbsp;Xing-hang Zhu ,&nbsp;Zhe Zhang ,&nbsp;Zhong-ren Peng ,&nbsp;Hong-mei Zhao ,&nbsp;Hong-di He","doi":"10.1016/j.apr.2024.102365","DOIUrl":"10.1016/j.apr.2024.102365","url":null,"abstract":"<div><div>Eco-driving is considered a cost-effective way to reduce fuel consumption and carbon emissions. However, eco-driving strategies for hybrid electric vehicles (HEVs) are understudied. Therefore, this study analyzed extensive road test data to assess HEV carbon reduction under different driving behaviors and to identify optimal eco-driving conditions. Firstly, the portable emissions measurement system (PEMS) was used to characterize the real-world emissions from two vehicles, one conventional vehicle (CV) and the other HEV. The results indicate that HEVs reduce average CO₂ emissions by 24.5%–54.7% compared to CVs. Secondly, based on the measured data, the impact of driving behavior on emission was investigated. It demonstrated that driving behavior was closely linked to engine operating state in HEVs, which in turn significantly affects carbon emissions. Notably, the emission reduction advantage of HEVs diminishes when considering only the engine-on state. At cruising speeds below 10 m/s, HEVs emit approximately 68% more CO₂ than CVs due to frequent start-stop cycles occurring. Finally, an eXtreme Gradient Boosting (XGBoost) model was proposed to predict engine operating status based on driving behavior and external traffic conditions. Combined with the Local Interpretable Model-Agnostic Explanation (LIME) algorithm, this model provides insights into the factors influencing engine state predictions, thus offering real-time eco-driving strategies for HEVs’ drivers. These findings reveal the carbon emission characteristics of HEVs under microscopic driving behavior and enhance the carbon reduction potential of HEVs in combination with eco-driving.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102365"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578512","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":"Spatiotemporal estimation of surface NO2 concentrations in the Pearl River Delta region based on TROPOMI data and machine learning","authors":"Qunlan Wei ,&nbsp;Weiwei Song ,&nbsp;Bolan Dai ,&nbsp;Hongling Wu ,&nbsp;Xiaoqing Zuo ,&nbsp;Jinxia Wang ,&nbsp;Jianglong Chen ,&nbsp;Jiahao Li ,&nbsp;Siyuan Li ,&nbsp;Zhiyu Chen","doi":"10.1016/j.apr.2024.102353","DOIUrl":"10.1016/j.apr.2024.102353","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Nitrogen dioxide (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) is a major air pollutant, and its concentration data are crucial for the study of air pollution and its impact on the environment. Although satellite data provide an effective method for estimating surface concentrations on a large scale through integrated modeling, the estimation of surface &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; concentrations is hampered by the substantial amount of missing satellite data. This restricts in-depth studies of surface &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; pollution. This study aims to reconstruct the missing data on tropospheric &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; vertical column density from the TROPOspheric Monitoring Instrument (TROPOMI &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;). Subsequently, the reconstructed TROPOMI &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; data and other predictor variables were utilized to estimate the daily surface &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; concentrations at a 1 km resolution for the Pearl River Delta (PRD) region. The TROPOMI &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; reconstruction models and the surface &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; estimation model were both developed using the Extreme Gradient Boosting (XGBoost) algorithm. Additionally, comparative experiments were conducted between the XGBoost model and other traditional machine learning models, and the performances of the XGBoost model were evaluated through 10-fold cross-validation (CV) sample-based and site-based evaluations. The results indicate that the sample-based and site-based CV R&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; values were 0.873 and 0.709, respectively. The feature importance scores indicate that TROPOMI &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; was the most significant variable contributing to the estimation model. This indicates that the reconstruction of TROPOMI &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; data and the development of an XGBoost model are suitable for the spatiotemporal estimation of surface &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; concentrations in the PRD region, effectively reflecting the spatiotemporal distribution and evolution of surface &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mtext&gt;NO&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; concentrations in t","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102353"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578536","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":"Examining the structural properties of hydrophilic and hydrophobic organic aerosols using 1H NMR: Diurnal variations and source apportionment","authors":"Avik Kumar Sam ,&nbsp;Shreya Dubey ,&nbsp;Ujjawal Arora ,&nbsp;Shweta Chandrashekhar Archana Sakpal ,&nbsp;Chimurkar Navinya ,&nbsp;Ashutosh Kumar ,&nbsp;Harish C. Phuleria","doi":"10.1016/j.apr.2024.102363","DOIUrl":"10.1016/j.apr.2024.102363","url":null,"abstract":"<div><div>The behaviour of the carbonaceous aerosols during the rainy season and the diurnal variations in their structural groups have not been thoroughly examined. The present study aims to understand the structural composition of hydrophilic and hydrophobic organic aerosols (OA) at an urban background location in Mumbai, India. The carbonaceous fractions, i.e., Elemental (EC) and Organic (OC) Carbon, accounted for 14–34% of the total PM₁₀ (Particulate matter with aerodynamic diameter ≤10 μm). The PM₁₀ and EC were maximum in the morning, while OC was the highest in the evening. The aliphatic structural groups were more concentrated in the total fraction, contributing 53–62% of the total resonances. The total concentrations of the structural groups in both hydrophilic (29.2 ± 9.8 μmol/m³) and total (197.8 ± 154.3 μmol/m³) fractions were highest in the morning. Traffic emissions impacted the morning and evening aerosols, as suggested by the broad aliphatic and sharp aromatic resonances observed in the total fraction. This is further corroborated by the variability in EC and OC, their significant correlations with Volatile OC and Nitrogen oxides, and their contribution to regression models and principal components. The afternoon aerosols demonstrated characteristics of Secondary OA. Our work extends the present understanding of the diurnal variability and the heterogeneity of the hydrophilic and hydrophobic structural groups in organic aerosols.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102363"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578537","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":"Can “fine scale” data on air pollution be an evaluation tool for public health professionals?","authors":"F. Nisticò ,&nbsp;G. Messina ,&nbsp;C. Quercioli ,&nbsp;S. Errico ,&nbsp;E. Fanti ,&nbsp;E. Frilli ,&nbsp;M. Postiglione ,&nbsp;A. De Luca ,&nbsp;A. D'Urso ,&nbsp;N. Nante","doi":"10.1016/j.apr.2025.102487","DOIUrl":"10.1016/j.apr.2025.102487","url":null,"abstract":"<div><div>Air pollution is one of the greatest environmental risks to health and mainly made up of Particulate Matter (PM or PM10 and PM2.5). The PM2.5 value is a good proxy of air pollution. This paper aims to analyse the possible use by health professionals of \"fine scale\" satellite data as regards PM derived from the EPISAT study to monitor air pollutants the population may be exposed to. Through the Open-Source GIS, EPISAT data was analysed to provide high spatial (1 km²) resolution estimations.</div><div>Differences between domestic and industrial pollution was carried out by Regional Agency for Environmental Protection database. From 2013 to 2019, the trend of the annual average concentration of PM2.5 in the territory of Local Health Authority of South East Tuscany was examined. In 2015 a peak in PM2.5 values was registered. From the 2019 data, the percentage of cells in which recommended PM2.5 values were exceeded, percentage of population affected by the exceedances and population weighted exposure (PWE) or the annual weighted average exposure for the population residing in each individual cell were calculated. The highest PM2.5 values were concentrated in the provincial capitals and in the Valdarno area. Maximum annual average PM2.5 values were recorded in the city center area of Arezzo (14.91 μg/m³) while the lowest values were recorded in countryside areas. In 2019, all cells studied recorded levels exceeding the WHO limit value; 3.2% of the cells had double the value recommended and the exposed population turned out to be 47.4% of the total studied. Analyzing data on the municipality of Arezzo showed that there is a statically significant difference between the exposure of citizens living in the center compared to those in the suburbs. PWE values (11.6 μg/m³) turned out to be about 25% higher than average air concentration values (9.4 μg/m³). The fine scale data due to high precision and high resolution, have shown how average air PM2.5 concentrations in a given territory may lead to a clear underestimation of the population's exposure, especially in areas with extreme geographical, anthropic and economic heterogeneity. The use of this data can be particularly helpful for monitoring the exposure to air pollutants of a local population and characterizing a territory for direct programming and planning policies in areas with an industrial vocation and to give a thought to environmental justice.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102487"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579998","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":"Adaptive graph-generating jump network for air quality prediction based on improved graph convolutional network","authors":"Qiaolin Zeng ,&nbsp;Honghui Zeng ,&nbsp;Meng Fan ,&nbsp;Liangfu Chen ,&nbsp;Jinhua Tao ,&nbsp;Ying Zhang ,&nbsp;Hao Zhu ,&nbsp;Sizhu Liu ,&nbsp;Yuanyuan Zhu","doi":"10.1016/j.apr.2025.102488","DOIUrl":"10.1016/j.apr.2025.102488","url":null,"abstract":"<div><div>Long-term exposure to PM_2.5 is harmful to human health, and it is important and necessary for accurate PM_2.5 forecasts. However, complex spatial correlations make air quality prediction challenging, and some studies are limited still by the priori knowledge and may lead to incomplete information transmission between different sites. To address this issue, this study proposes a Dynamic Adaptive Graph Generating Jump Network (DAGJN) to predict PM_2.5. Specifically, in terms of spatial modelling, this study is the first to treat the graph structure as a learnable part, which can continuously optimize the weights with the training to better captures the potential spatial correlations among sites. A jump graph convolutional network that uses channel attention to weight features for selection of graph signals at different depths to utilize spatial information and mitigate the over-smoothing problem. A multiple self-attention mechanism is used to capture the global temporal correlation in time series data. Lastly, a spatial-temporal fusion layer can dynamically fuse spatial-temporal information based on global and local features. Meanwhile, extensive experiments were conducted on air quality datasets from Beijing and Chongqing with R² of 0.514 (0.770), and RMSE of 62.284 μg/m³ (12.814 μg/m³) in 1–24 h prediction. The differences of PM_2.5 prediction is compared with seasonal scales and shows that the DAGJN model outperforms other models. This study contributes significantly to the field of PM_2.5 prediction, and these results illustrate the potential of the DAGJN model for PM_2.5 prediction.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102488"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550946","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}