Atmospheric Pollution Research最新文献

{"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}

{"title":"Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning","authors":"Boya Liu ,&nbsp;Yuanyuan Li ,&nbsp;Lili Wang ,&nbsp;Lei Zhang ,&nbsp;Fengxue Qiao ,&nbsp;Peifan Nan ,&nbsp;Dan Ji ,&nbsp;Bo Hu ,&nbsp;Zheng Xia ,&nbsp;Zhengang Lou","doi":"10.1016/j.apr.2024.102354","DOIUrl":"10.1016/j.apr.2024.102354","url":null,"abstract":"<div><div>Systematically understanding the impact of meteorological conditions on regional ozone pollution helps to retrieve ozone dataset and evaluate emission changes on ozone variation. Here, more air-quality observation sites were collected, and Random Forest algorithm was applied to retrieve daily maximum 8 h average (MDA8) O₃ concentrations at 1 km resolution in 2019–2020 in China. The region-season model and whole-retrieved model were established to compare the contributions of meteorological variables to ozone variations on spatiotemporal scale. The former model outperformed the latter for retrieval capability, but the predictive ability of the latter was slight stronger. This may be associated with the spatiotemporal heterogeneity of meteorological influence. Daily ozone variability in China was mainly influenced by meteorology, especially in North China Plain in autumn. The key factors were temperature, ultraviolet radiation and relative humidity over the whole country, but varied significantly in different regions and seasons. The effects of meteorology and emission sources on ozone were separated by weather normalization technique. Meteorological conditions were particularly favorable for increasing ozone concentrations in spring, but particularly unfavorable in winter. During the COVID-19 lockdown, the increases in ozone in spring and winter of 2020 were the contribution of the combination of meteorology and emissions; while the decreases in ozone in summer and autumn of 2020 were mainly due to the changes of emission sources, although meteorological conditions were unfavorable to ozone mitigation in heavily polluted areas. Our findings provide scientific basis for the prevention and control of ozone pollution for the regional scale in China.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102354"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578513","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":"The effect of atmospheric boundary layer meteorology in climate-related gases and VOCs concentrations over 3 European cities using an aerial platform","authors":"Thomas Maggos ,&nbsp;Helena Flocas ,&nbsp;Themistoklis Soulos ,&nbsp;Panagiotis Panagopoulos ,&nbsp;Dikaia E. Saraga ,&nbsp;Dimosthenis Sarigiannis","doi":"10.1016/j.apr.2024.102335","DOIUrl":"10.1016/j.apr.2024.102335","url":null,"abstract":"<div><div>In the frame of the EU Horizon2020 ICARUS project, a N.A.S.A Awarded Light Manned Aircraft equipped with high-tech scientific instrumentation was used to perform an aerial mapping over Athens, Thessaloniki and Ljubljana greater areas. This study aimed to evaluate the effect of Atmospheric Boundary Layer (ABL) on Green House Gases (GHGs) and Volatile Organic Compounds (VOCs) concentrations over urban and rural areas of the above cities. Simultaneous ground-based measurements were performed in the respective regions. Air samples were pressurized with a stainless-steel bellows compressor into electropolished stainless steel canisters and analyzed by the use of a novel gas chromatographic system. The estimation of the mixing height (ΜΗ) of the ABL was based on the synoptic scale atmospheric circulation and the prevailing background wind. It was found that the MH variation, following the prevailing meteorological conditions, results in different concentration profiles in the lower troposphere over the examined regions. The pollutants concentrations were generally decreasing with altitude in the ABL. Under certain meteorological conditions, vertical mixing plus horizontal transport can cause a high pollution level at the top of ABL. The pollutants concentrations were low over less industrialized and upwind regions, suggesting that local emission sources play significant role on the GHGs and VOCs levels over the regions. However, due to the large scale of sampling area that the aircraft covers the above gradients in concentrations are relative low. AQ modelling activities for simulating the cases studied in the current or any relative future work could reduce operating costs and allow projections of potential impacts.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 3","pages":"Article 102335"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578514","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":"Inhalation risk assessment on 14 organic chemicals in children's masks based on the Monte Carlo stochastic modeling method","authors":"Yunbo Wang ,&nbsp;Guixiao Li ,&nbsp;Lihua Yu ,&nbsp;Xiangke He ,&nbsp;Xiaofeng Wei ,&nbsp;Zhongxian Liu ,&nbsp;Guiqin Zhang ,&nbsp;Cuiling Gao","doi":"10.1016/j.apr.2025.102486","DOIUrl":"10.1016/j.apr.2025.102486","url":null,"abstract":"<div><div>Children are a relatively vulnerable group, and their health has received widespread attention. However, understanding of the types of pollutants in children's products and the associated health risks remains limited. In this study, the release concentrations of 14 inhalable organics in children's masks made of polyvinyl chloride, ethylene-vinyl acetate co-polymer, and polyvinyl acetate materials were measured by air bag method, and the health risk assessment of these substances was carried out based on Monte Carlo method. The results indicated that cyclohexanone, p/m-xylene, o-xylene and ethylbenzene were the several pollutants with the highest release concentrations, the average release concentration of cyclohexanone in polyvinyl chloride, ethylene-vinyl acetate co-polymer and polyvinyl acetate were 5.76 mg m⁻³, 0.14 mg m⁻³, 4.60 mg m⁻³, respectively, p/m-xylene concentrations were 1.32 mg m⁻³, 0.67 mg m⁻³, 1.84 mg m⁻³, respectively, o-xylene concentrations were 1.91 mg m⁻³, 0.56 mg m⁻³, 2.37 mg m⁻³, respectively, ethylbenzene concentrations were 1.26 mg m⁻³, 0.84 mg m⁻³, 2.10 mg m⁻³, respectively. The acute risks of children's masks were assessed and none were found to pose an acute risk. The overall carcinogenic risk and the carcinogenic risks of benzene and carbon tetrachloride did not exceed the limit. However, the overall non-carcinogenic risks of the three materials of children's masks (HQ: 10.22–20.38) exceed the limit value, which will cause harm to children's health. Specifically, xylene, 1-butanol and 1, 3, 5-trimethylbenzene in polyvinyl chloride and polyvinyl acetate materials (HQ: 1.86–18.07), as well as xylene (HQ:6.35) and 1, 3, 5-trimethylbenzene (HQ:4.84) ethylene-vinyl acetate co-polymer material.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102486"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550950","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":"Dynamicity of carbon emission and its relationship with heat extreme and green spaces in a global south tropical mega-city region","authors":"Manob Das ,&nbsp;Arijit Das","doi":"10.1016/j.apr.2025.102484","DOIUrl":"10.1016/j.apr.2025.102484","url":null,"abstract":"<div><div>Assessing the dynamicity of carbon emissions (CE) and their relationship with heat extremes and green spaces in cities is crucial for sustainable urban planning and climate resilience. CE contributes to the urban heat island (UHI) effect, intensifying heat extremes that pose health risks and increase energy consumption. This research aims to examine the dynamics of CE and its relationship with heat extreme (UHI) and green spaces in Kolkata Metropolitan Area (KMA), India from 2000 to 2020 using land-use carbon emission (LCE) model. The influence of green space and extreme heat on CE was analyzed using spearman correlation analysis. The study revealed that a) green spaces decreased by 60%, while built-up area increased by 185.9% b) the mean land surface temperature (LST) rose by 28%, and areas with high and very high UHI intensity expanded by 3.9% c) CE increased by approximately 188.2%, with an average annual increase of 9.4% with the highest increase from built-up areas d) UHI intensity had a positive impact on CE whereas green space was found to have a negative impact on CE (significant at p &lt; 0.001). Thus, green spaces play a vital role in reducing carbon levels by acting as carbon sinks and regulating urban temperatures. By analyzing their impact, cities can optimize green infrastructure to mitigate heat stress and improve air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102484"},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579996","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":"The soil fugitive dust emission assessment using satellite data: A case study in Beijing-Tianjin-Hebei and its surrounding areas (BTHSA)","authors":"Yanyu Li ,&nbsp;Qizhong Wu ,&nbsp;Huaqiong Cheng ,&nbsp;Yiming Sun ,&nbsp;Jieying He ,&nbsp;Jie Li","doi":"10.1016/j.apr.2025.102482","DOIUrl":"10.1016/j.apr.2025.102482","url":null,"abstract":"<div><div>Soil fugitive dust (SFD) emission is a vital to environmental supervision in Beijing-Tianjin-Hebei and its Surrounding Areas (BTHSA). However, SFD emission inventory is updated slowly and has great uncertainty for air quality models. In this study, the Google Earth Engine (GEE) cloud platform is used for image acquisition, data preprocessing, and index calculation to rapidly produce bare soil maps, and a dynamic method of developing SFD emission inventory via bare soil maps is developed. The results showed that the BTHSA is susceptible to wind erosion and that the total bare soil area reached 1.05 × 10⁵ km², and the SFD PM_2.5 emission was 1.2 × 10⁵ tons in 2020 according to the wind erosion model. SFD PM_2.5 emission is higher in plains areas than in mountainous areas in the BTHSA. The Community Multiscale Air Quality (CMAQ) modeling system is used to validate the SFD emissions with ground-based observational data. SFD emission generates greatly increase PM_2.5 in simulations and significantly alleviates 57.9% of the negative biases in PM_2.5 in the BTHSA. Identifying the spatiotemporal characteristics of SFD emissions is crucial for controlling air pollution in cities.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102482"},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562303","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}