Atmospheric Pollution Research最新文献

{"title":"Long term (2011–2023) analysis of traffic and biomass burning contributions to black carbon in the third largest metropolitan area of Spain","authors":"Violeta Matos ,&nbsp;Mar Sorribas ,&nbsp;Sara Segura ,&nbsp;María Pilar Utrillas ,&nbsp;Víctor Estellés","doi":"10.1016/j.apr.2025.102527","DOIUrl":"10.1016/j.apr.2025.102527","url":null,"abstract":"<div><div>This work is focused on the temporal characterization of equivalent Black Carbon (eBC) mass concentrations and their sources in a suburban station notably impacted by traffic, located in the metropolitan area of Valencia, Spain (western Mediterranean Sea). The average (<span><math><mo>±</mo></math></span> standard deviation) concentrations of fossil fuel (eBC_ff) and biomass burning (eBC_bb) contributions were 0.9 <span><math><mo>±</mo></math></span> <span><math><mrow><mn>0</mn><mo>.</mo><mn>5</mn></mrow></math></span> <span><math><mrow><mi>μ</mi><msup><mrow><mi>gm</mi></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span> and 0.06 <span><math><mo>±</mo></math></span> <span><math><mrow><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span> <span><math><mrow><mi>μ</mi><msup><mrow><mi>gm</mi></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, respectively. These values represent the anthropogenic character of local aerosols. Both contributions also show a very marked seasonality: higher values in winter and lower in summer, corresponding to the strong dependence of the atmospheric conditions. The eBC_ff concentrations exhibit a daily pattern consistent with the evolution of traffic: a morning peak (around 8 LT) and other in the evening (around 19 LT). The seasonal Mann–Kendall test was applied to identify long-term trends and the Sen slope estimation to quantify the annual variation. Decreasing trends were found for eBC_ff concentrations (<span><math><mrow><mo>−</mo><mn>0</mn><mo>.</mo><mn>023</mn></mrow></math></span> <span><math><mrow><mi>μ</mi><msup><mrow><mi>gm</mi></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>/yr), showing the effectiveness of air quality regulations. Less noticeable trends were found for eBC_bb concentrations. This fact evidences the contribution of biomass burning is not only related to changes in anthropogenic emissions, but also to natural phenomena, making it more difficult to interpret long-term trends.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102527"},"PeriodicalIF":3.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823695","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 atmospheric particulate matters and their removal potential through roadside trees in Chattogram city, Bangladesh","authors":"Nayeem Uddin Emon ,&nbsp;Chinmoy Sarkar Anik ,&nbsp;Forkan Ahamed Rubel ,&nbsp;Sahadeb Chandra Majumder ,&nbsp;Tapan Kumar Nath ,&nbsp;Shyamal Karmakar ,&nbsp;Tarit Kumar Baul","doi":"10.1016/j.apr.2025.102535","DOIUrl":"10.1016/j.apr.2025.102535","url":null,"abstract":"<div><div>Atmospheric particulate matter (PM) affects urban air quality and poses significant health risks. In this study, we measured ambient PM levels and heavy metal concentrations at six vegetated and one non-vegetated (control) roadside locations in Chattogram City, Bangladesh. Using a portable air quality sensor, we assessed ambient PM_0.5 and PM_2.5 concentrations every 15 days over the course of one year and found that the mean concentrations of PM₀.₅ and PM₂.₅ in the control site were significantly higher (<em>p</em> ≤ 0.05) than those at the vegetated roadsides. We also investigated whether roadside trees can effectively remove PM and collected 84 leaf samples from seven tree species each month to quantify PM deposition on the leaves. PM concentrations in the air and on the leaves were higher during the dry season compared to the rainy season. Further analysis of meteorological factors revealed that PM accumulation on the leaves decreased with high temperature, wind speed, and precipitation. These findings suggest that meteorological conditions play a crucial role in PM dynamics, influencing both airborne concentration and accumulation on leaves. Besides, tree species and leaf characteristics play a substantial role in PM accumulation on the leaves. Copper and zinc were in the accumulated PM along all roadsides, indicating the possibility of heavy metal contamination. We propose planting roadside trees with rhomboid, elliptical, rough, and simple leaves to enhance the removal of PM and other contaminants through deposition.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102535"},"PeriodicalIF":3.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816432","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":"Sulfur dioxide concentrations forecasting using a deep learning model in Quintero, Chile","authors":"Patricio Perez ,&nbsp;Francisco Gomez ,&nbsp;Camilo Menares ,&nbsp;Zoë L. Fleming","doi":"10.1016/j.apr.2025.102534","DOIUrl":"10.1016/j.apr.2025.102534","url":null,"abstract":"<div><div>Close to Quintero, a Chilean coastal city, located 160 km northwest of Santiago, a highly concentrated accumulation of industries generate high levels of atmospheric pollution which significantly affects the quality of life of its rural and urban population. The industrial complex, alongside other smaller industries, is home to an oil refinery, a copper foundry and 3 coal power plants. Sulfur dioxide (SO₂) frequently exceeds international and national standards in the area. Episodes of fainting and poisoning associated to high levels of SO₂ have been reported in Quintero. Due to this situation, it is highly relevant to develop a sulfur dioxide forecasting model which may be used as a tool to warn authorities and the local population about unfavorable air quality conditions. Three SO₂ forecasting models for the city of Quintero based on Machine Learning Techniques have been implemented: a Random Forest model, a Deep Learning Feed Forward model (DFFNN) and a Convolutional Long Short Term Memory (LSTM) Deep Learning model. The goal was to forecast the maximum of the hourly average value of SO₂ for the first 12 h of the following day based on information available during the present day. The LSTM model gives the best results with a 78 % accuracy.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102534"},"PeriodicalIF":3.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855409","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":"Integrating spatiotemporal behavior, indoor-outdoor penetration, and ventilation rates to assess prenatal PM2.5 exposure and the association with birth weight","authors":"Dan Long ,&nbsp;Xin Chen ,&nbsp;Maimaitiminjiang Wulayin ,&nbsp;Miaochan Zhu ,&nbsp;Huailin Wang ,&nbsp;Junwei Wu ,&nbsp;Jianyong Lu ,&nbsp;Liecheng Hong ,&nbsp;Qing Wang ,&nbsp;Zhenghong Zhu ,&nbsp;Xiaoxin Zhang ,&nbsp;Cunrui Huang ,&nbsp;Qiong Wang","doi":"10.1016/j.apr.2025.102530","DOIUrl":"10.1016/j.apr.2025.102530","url":null,"abstract":"<div><div>Previous studies that evaluated the association of PM_2.5 with birth outcomes usually assessed personal exposure as outdoor PM_2.5 concentrations of home address (home-based exposure), overlooking factors such as individual spatiotemporal activities, which may result in exposure error. In a prospective birth cohort conducted in Guangzhou, China during 2017–2020, personal PM_2.5 exposure assessment was updated. We incorporated spatiotemporal activities into the exposure assessment by estimating PM_2.5 exposure for each activity based on its specific location and duration. Additionally, an infiltration factor was applied to estimate indoor-outdoor penetration, and ventilation rates (different age groups and activity levels) were used to better adjust individual exposure levels. Logistic regression and distributed lag non-liner model with Cox proportional hazard model were used to assess the associations of prenatal PM_2.5 exposure with low birth weight (LBW) and small for gestational age at a trimester and weekly level, respectively. Updated personal PM_2.5 exposure was lower than the home-based PM_2.5. Per interquartile range increase in PM_2.5 during the third trimester was associated with increased risk of LBW, with ORs (95 % CIs) was 2.17 (1.14–4.14) for updated personal exposure and 2.30 (1.17–4.55) for home-based exposure. Updated personal PM_2.5 in the 6th-7th, home-based PM_2.5 in the 5th-7th, and both PM_2.5 exposure in the 35th week later was associated with LBW. Our findings suggest that spatiotemporal activities, indoor-outdoor penetration, ventilation rate should be taken into account of exposure assessment, otherwise PM_2.5 exposure and the association with adverse birth outcomes may be overestimated.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102530"},"PeriodicalIF":3.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834124","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":"Unveiling the source contributions of fine and coarse particulate matter using PM-bound metals and PMF-AI modeling","authors":"Chin-Yu Hsu ,&nbsp;Akshansha Chauhan ,&nbsp;Yi-Wen Chen ,&nbsp;Meng-Ying Jian ,&nbsp;Kuan-Ting Liu ,&nbsp;Thi Phuong Thao Ho ,&nbsp;Yu-Hsiang Cheng","doi":"10.1016/j.apr.2025.102529","DOIUrl":"10.1016/j.apr.2025.102529","url":null,"abstract":"<div><div>Particle pollution is a critical global concern with significant implications for public health and the environment. Both fine (PM_2.5) and coarse (PM_2.5-10) particles exhibit diverse compositions and origins, leading to distinct health and environmental consequences. In this study, K-means clustering was employed to differentiate between local, regional, and long-range transport (LRT) sources, showing that LRT significantly increases PM_2.5-10 levels, leading to a more than 1.26-fold rise in its annual mean concentration. Using Positive Matrix Factorization (PMF) model, we identified five local and regional source of PM_2.5 and four in case of PM_2.5-10. Further, AutoML model explains up to 70 % and 71 % of the daily variance in PM_2.5 and PM_2.5-10, respectively. The complex relationship of these sources was explained using SHapley Additive ExPlanations (SHAP). Among the five major factors identified, SHAP analysis reveals that oil combustion (24 %), coal burning (18 %), and non-ferrous metal smelting/biomass burning (17 %) are the predominant contributors to PM_2.5. In contrast, ocean spray (28 %) is identified as a significant source of PM_2.5-10 pollution followed by oil, non-ferrous metal smelting/biomass burning (20 %) and traffic related emission (14 %). This study offers a novel and comprehensive methodology for identifying the distinct sources of fine and coarse particulate matter. It provides valuable insights that can inform future policies and regulations, particularly in regions facing challenges related to PM pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102529"},"PeriodicalIF":3.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783807","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":"Estimation and analysis of CO2 column concentrations (XCO2) in the Yangtze River Delta of China based on multi-source data and machine learning","authors":"Chunmei Chen ,&nbsp;Xiaomei Chen ,&nbsp;Qiong Liu ,&nbsp;Weiyu Zhang ,&nbsp;Yonghang Chen ,&nbsp;Yuhuan Ou ,&nbsp;Xin Liu ,&nbsp;Huiyun Yang","doi":"10.1016/j.apr.2025.102528","DOIUrl":"10.1016/j.apr.2025.102528","url":null,"abstract":"<div><div>Carbon dioxide (CO₂) is one of the most significant greenhouse gases in the atmosphere and plays a crucial role in global warming. Currently, the temporal resolution for XCO₂ from the satellite is low, and the ground-based XCO₂ observation station is limited. There is an urgent need for a XCO₂ dataset with high temporal and spatial resolution. Consequently, based on the random forest algorithm, we have developed an optimized model for predicting XCO₂ with a spatial resolution of 0.25° × 0.25° and a temporal resolution of 1 h for the Yangtze River Delta in 2020. The multi-source data, such as the ground-observation XCO₂ from the TCCON, as well as meteorological parameters, aerosols, surface vegetation index, and emission source factors from the ERA5, MERRA-2, MODIS, and MEIC datasets, were used in this study. The results indicate that the random forest model is well-suited for predicting XCO₂. Specifically, the model performs more optimally when utilizing 20 variables, including solar zenith angle, normalized vegetation index, and carbon emission data as input parameters with the prediction RMSE and R² of 1.031 × 10⁻⁶ and 0.940. The MAE for predicted XCO₂ at Xianghe and Hefei stations are 0.628 × 10⁻⁶ and 0.550 × 10⁻⁶, respectively, marking a substantial increase in accuracy compared to GOSAT data. In 2020, daily variations of XCO₂ follow a pattern of higher concentrations at night and lower concentrations during the day, negatively correlating with changes in the atmospheric boundary layer height. The inter-monthly and seasonal variations reveal smaller concentrations in summer and higher concentrations in winter. The minimum concentration occurs in July at 409.64 × 10⁻⁶, while the maximum concentration occurs in November at 413.11 × 10⁻⁶. Spatially, XCO₂ is higher in the northern areas and lower in the southern regions, showing a negative correlation with the NDVI and a positive correlation with anthropogenic carbon emissions. The XCO₂ dataset calculated in this study with continuous spatial and temporal resolutions could address the limitations of satellite products with low temporal resolution and a limited number of ground observation stations.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102528"},"PeriodicalIF":3.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816431","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":"Estimating hourly surface PM2.5 concentrations with full spatiotemporal coverage in China using Himawari-8/9 AOD and a two-stage model","authors":"Shuyang Zhang ,&nbsp;Peng Chen ,&nbsp;Yuchen Zhang ,&nbsp;Chengchang Zhu ,&nbsp;Cheng Zhang ,&nbsp;Jierui Lu ,&nbsp;Mengyan Wu ,&nbsp;Xinyue Yang","doi":"10.1016/j.apr.2025.102519","DOIUrl":"10.1016/j.apr.2025.102519","url":null,"abstract":"<div><div>PM_2.5 (fine particulate matter with an aerodynamic diameter of less than <span><math><mrow><mn>2</mn><mo>.</mo><mn>5</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>) is a significant air pollutant, posing serious risks to both the atmospheric environment and human health. Satellite remote sensing Aerosol Optical Depth (AOD) data are often used to estimate surface PM_2.5 concentrations. However, satellite-derived AOD data are often affected by large-scale data gaps due to cloud contamination and high surface albedo, leading to discontinuities and incompleteness in surface PM_2.5 estimations based on AOD. PM_2.5 is influenced by natural and human activities, both of which show strong diurnal variations. Many previous studies have used AOD data from sun-synchronous orbiting satellites, whose coarser temporal resolution makes it difficult to capture these diurnal PM_2.5 variations. In this study, AOD products from the new generation of geostationary meteorological satellites, Himawari-8/9, are employed to estimate spatiotemporally continuous hourly seamless PM_2.5 grid data using a two-stage Random Forest (RF) model. This model integrates meteorological, surface, and demographic-economic factors. In the first stage, the RF model was used to fill the gaps in the satellite AOD data, achieving a good fit (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>95</mn></mrow></math></span>), with a root mean square error (RMSE) and mean absolute error (MAE) of 0.05 and 0.03, respectively. In the second stage, the model estimates surface PM_2.5 grid data (5<!--> <!-->km <span><math><mo>×</mo></math></span> 5<!--> <!-->km) at hourly intervals during the daytime, based on the gap-filled AOD data, actual PM_2.5 measurements from ground stations, and auxiliary data. The final hourly PM_2.5 estimates were well-fitted to the ground station measurements (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>92</mn></mrow></math></span>), with RMSE and MAE values of 7.14 and <span><math><mrow><mn>4</mn><mo>.</mo><mn>90</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>, respectively. This study provides a valuable approach for estimating complete, hourly-level spatial and temporal distributions of PM_2.5 from incomplete satellite remote sensing AOD data, which is crucial for air quality management and assessing short-term exposure risks.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102519"},"PeriodicalIF":3.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792730","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":"Long-term reconstruction of NO2 photolysis rate coefficients using machine learning and its impact on secondary pollution: A case study in a megacity of the Sichuan Basin, China","authors":"Tong Li ,&nbsp;Song Liu ,&nbsp;Dongyang Chen ,&nbsp;Ruirui Zhang ,&nbsp;Hefan Liu ,&nbsp;Danlin Song ,&nbsp;Qinwen Tan ,&nbsp;Hongbin Jiang ,&nbsp;Li Zhou ,&nbsp;Fumo Yang","doi":"10.1016/j.apr.2025.102526","DOIUrl":"10.1016/j.apr.2025.102526","url":null,"abstract":"<div><div>The NO₂ photolysis rate coefficient (<em>J</em>_<em>NO2</em>) is a critical parameter for assessing the intensity of atmospheric photochemical reactions. However, continuous long-term measurements of <em>J</em>_<em>NO2</em> are scarce. In this study, we developed a machine learning-based method to reconstruct hourly <em>J</em>_<em>NO2</em> values, applying it to a megacity in the Sichuan Basin from 2015 to 2023. The model exhibited strong performance with cross-validation R² = 0.854 and RMSE = 8.15 × 10⁻⁴ s⁻¹. Utilizing the Shapley Additive Explanations (SHAP) method, we identified solar activity and pollutant levels both as significant predictors for <em>J</em>_<em>NO2</em>. Our long-term <em>J</em>_<em>NO2</em> reconstructions indicate a strong correlation between <em>J</em>_<em>NO2</em> and ozone concentration, highlighting its important role in secondary pollution. This study illustrates the effectiveness of machine learning in reconstructing hourly <em>J</em>_<em>NO2</em> values, providing a valuable enhancement to traditional models. The findings are crucial for understanding regional photochemical processes and for analyzing trends and causes of ozone and aerosol pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102526"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759905","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":"Long-term exposure to ambient air pollution and incident nephritis: A prospective cohort study in the UK Biobank","authors":"Qiong Duan ,&nbsp;Cheng Zhou ,&nbsp;Haifeng Chen ,&nbsp;Jie Zhang ,&nbsp;Zhaohui Ruan ,&nbsp;Hongfei Cao ,&nbsp;Zixing Zhang ,&nbsp;Xihai Xu ,&nbsp;Xinyu Fang","doi":"10.1016/j.apr.2025.102524","DOIUrl":"10.1016/j.apr.2025.102524","url":null,"abstract":"<div><div>Substantial studies have highlighted the implications of air pollution in relation to several kidney diseases. However, studies on the relationships of long-term exposure to NO₂, NO_x, PM_2.5, PM_2.5-10, PM₁₀ with the incidence of nephritis are relatively scarce. In our prospective cohort study, 446,626 participants from the UK Biobank who had no kidney diseases at baseline were enrolled. Annual concentrations of particulate matter (PM) with diameters ≤2.5 μm (PM_2.5), between 2.5 and 10 μm (PM_2.5–10), and ≤10 μm (PM₁₀), as well as nitrogen dioxide (NO₂) and nitrogen oxides (NO_x) were gauged by land-use regression models. We employed Cox proportional hazards models to examine the associations of air pollutants with the incidence of nephritis, adjusted for potential covariates. We applied restricted cubic spline (RCS) analysis to find the exposure-response relationship. 3,455 cases were observed through a median follow-up duration of 13.58 years. Our results showed the enhanced risk of nephritis was linked to per interquartile range (IQR) increase in NO₂ (hazard ratio (HR): 1.09, 95 % confidence intervals (95 %CI): 1.04–1.14) and in NO_x (1.05, 1.01–1.08). We found nonlinear relationships between the levels of NO_x, PM_2.5, and PM_2.5-10 and incident nephritis. They all displayed a tendency of initial rapid increase followed by a subsequent gradual growth. We didn't find nonlinear relationships between NO₂ and PM₁₀ concentrations and incident nephritis. Thus, exposure to air pollution may induce the incidence of nephritis, emphasizing the importance of controlling ambient air pollution for its prevention.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102524"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808018","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 COVID-19 pandemic on air pollution and hospitalization risk for cardiovascular and respiratory diseases in Suzhou, China","authors":"Fan Liu ,&nbsp;Xikun Liu ,&nbsp;Shuhua Yu ,&nbsp;Xiang Liu ,&nbsp;Jingguang Li ,&nbsp;Chongyang Zhang ,&nbsp;Chanjuan Sun ,&nbsp;Hua Qian ,&nbsp;Xinyi Zhu","doi":"10.1016/j.apr.2025.102525","DOIUrl":"10.1016/j.apr.2025.102525","url":null,"abstract":"<div><div>During the COVID-19 pandemic in China, a notable reduction in ambient air pollution levels has been documented. The risk of exposure to pollutants for the population is influenced by various factors, including the types of pollutants, seasonal variations, and demographic characteristics. However, it remains unclear whether the effects of these factors differ when comparing the periods before and during the COVID-19 pandemic. This study aims to evaluate the relationships between specific air pollutants (PM_2.5, NO₂ and SO₂) and hospitalization risk for cardiovascular and respiratory diseases in Suzhou, China, both prior to and during the pandemic. A time-series analysis was conducted utilizing a Distributed Lagged Nonlinear Model, incorporating data from 95,235 hospital admissions in Suzhou spanning from 2018 to 2022. The study also accounted for the influences of seasonal variations, gender, and age on these associations. The findings reveal a positive correlation between exposure to air pollution and hospitalization risk, with significant variations based on seasonal factors, gender, and age. Specifically, the risk of hospitalization is markedly increased during cold seasons, while in warm seasons during the pandemic, exposure to NO₂ also contributes to increased risk. Furthermore, female individuals exposed to NO₂ exhibit a higher hospitalization risk compared to males during the pandemic. Notably, elderly individuals aged 65 and above are at a higher risk of hospitalization due to air pollution exposure, highlighting the necessity for careful consideration in the design of environments that are conducive to the well-being of older adults.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102525"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759904","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}