Atmospheric Pollution Research最新文献

{"title":"Characterization identification and speciated emission inventory construction of anthropogenic volatile organic compounds (VOCs) in Beijing, China","authors":"Xiaoyu Liu ,&nbsp;Hanyu Zhang ,&nbsp;Zhe Lv ,&nbsp;Huahua Bai ,&nbsp;Guohao Li","doi":"10.1016/j.apr.2025.102452","DOIUrl":"10.1016/j.apr.2025.102452","url":null,"abstract":"<div><div>Volatile organic compounds (VOCs) significantly impact air quality and human health, garnering widespread attention. We conducted a comparative analysis of anthropogenic VOC emissions across different years, established a speciated VOC emission inventory for Beijing in 2020 and assessed the ozone formation potential (OFP). The VOC emissions showed a consistent downward trend, ranging from 10.19 × 10⁴ t to 28.36 × 10⁴ t in 2007–2020. The main sectors shifted from mobile sources (43.42%) and solvent utilization (26.35%) in 2007 to solvent utilization (55.99%) and mobile sources (24.00%) in 2020. The key contributing districts shifted from Fangshan (28.92%), Chaoyang (9.84%), and Daxing (7.33%) in 2013 to Chaoyang (14.29%), Haidian (11.42%), and Fangshan (10.33%) in 2020. The profile dataset encompasses 15 sectors and includes 117 VOC species, with an estimated total of 5.07 × 10⁴ t of VOC emissions in 2020, with alkanes, alkenes, alkyne, aromatics, halocarbons, and OVOCs accounting for 34.51%, 17.84%, 1.01%, 36.20%, 7.37%, and 3.07%, respectively. Significant differences were observed in the proportions of various VOC species across sectoral emissions. This dataset shows substantial deviations from the U.S. SPECIATE database, highlighting the significance of developing VOC source profiles. The uncertainties in VOC emission estimates primarily originate from variations in activity levels, emission factors and spatial distribution of emissions. Some sectors with high OFPs, like automobile manufacturing, barbecue and residential combustion are regarded as critical targets for emission control. Aromatics, alkenes, and OVOCs were identified as the major contributors to OFP, and controlling their emissions is essential for reducing ozone formation in Beijing.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102452"},"PeriodicalIF":3.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inverse modeling of 137Cs during Chernobyl 2020 wildfires without the first guess","authors":"Ondřej Tichý ,&nbsp;Nikolaos Evangeliou ,&nbsp;Anna Selivanova ,&nbsp;Václav Šmídl","doi":"10.1016/j.apr.2025.102419","DOIUrl":"10.1016/j.apr.2025.102419","url":null,"abstract":"<div><div>This study estimates 137Cs emissions from Chernobyl wildfires in April 2020 using inverse modeling. Emissions are resolved with daily resolution by particle sizes (0.4 <span><math><mi>μ</mi></math></span>m, 8 <span><math><mi>μ</mi></math></span>m, 16 <span><math><mi>μ</mi></math></span>m) and altitudes (up to 3 km). The inverse problem’s complexity requires regularization due to its ill-posed nature. One potential way to regularize the problem is the use of the so-called first guess, i.e. emission taken from expert knowledge or previous literature. However, inappropriately chosen first guess may lead to serious bias in results or its availability may be limited for rapid response. We rather follow a Bayesian approach where all model parameters are considered as variables to be estimated from available data. We aim to combine three key principles: modeling of sparsity and smoothness of the emission vector, modeling of bounded ratios between released particle size/altitude fractions, and bias correction of the atmospheric transport model. All these principles proved their significance separately, however, we combine them in one comprehensive method to estimate the 137Cs emissions from the Chernobyl wildfires. The total released activity was estimated to be 458 GBq with uncertainty estimated to be 69 GBq. Our estimates also suggest that most of the activity has been released below a one-kilometer altitude with the more dominant role towards the smallest particle fraction than was considered in other studies. Using our estimate, we calculate the time-integrated volumetric activities of 137Cs over the domain using the JRODOS system and our findings well agrees with previous results.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102419"},"PeriodicalIF":3.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerosol chemical composition and sources during unexpected wintertime haze episodes in 2023 in urban Xuzhou of eastern China","authors":"Xianru Yin ,&nbsp;Yongcai Rao ,&nbsp;Lili Tang ,&nbsp;Yunjiang Zhang","doi":"10.1016/j.apr.2025.102451","DOIUrl":"10.1016/j.apr.2025.102451","url":null,"abstract":"<div><div>Understanding the chemical composition and sources of aerosols during extreme haze episodes is essential for effective air quality management, particularly in rapidly industrializing regions. This study investigates the aerosol chemistry and sources during unexpected winter haze events in December 2023 in Xuzhou, Eastern China. Continuous online monitoring of fine particulate matter (PM_2.5), combined with detailed chemical analysis and concentration weighted trajectory (CWT) analysis, was conducted to elucidate the sources and processes driving these pollution episodes. Positive matrix factorization identified five major PM_2.5 sources: secondary nitrate-rich aerosols, vehicular emissions, industrial activities, dust emissions, and coal combustion. Nitrate was the dominant component during severe haze periods, whereas dust significantly contributed during dust storm episodes. CWT analysis highlighted substantial regional contributions, with industrial and dust-rich areas to the northwest and marine aerosols from coastal regions playing key roles. The findings suggest that nitrate formation and regional dust transport were the primary drivers of severe winter haze in Xuzhou. Effective mitigation strategies should prioritize nitrogen oxides emission control and dust management. This study underscores the necessity of regional collaboration and continuous monitoring to tackle complex air pollution challenges.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102451"},"PeriodicalIF":3.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research of two dust transport pollution in northern China in 2023: Perspectives from LiDAR and multi source data","authors":"Hao Yang ,&nbsp;Xiaomeng Zhu ,&nbsp;Duoyang Qiu ,&nbsp;Zhiyuan Fang ,&nbsp;Yalin Hu ,&nbsp;Xianyang Li","doi":"10.1016/j.apr.2025.102441","DOIUrl":"10.1016/j.apr.2025.102441","url":null,"abstract":"<div><div>In March and April 2023, two dust events occurred in northern China, which had a huge impact on the travel and health of the public in northern China. During the two dust events (the first dust event, FD; the second dust event, SD), the peak PM₁₀ concentrations at the Handan station were 2407 μg/m³ and 829 μg/m³, respectively. According to observations from the Mie scattering LiDAR located in Handan City, during the period of FD and SD, there were differences in depolarization ratio, pollution duration, and spatial distribution. Based on multi-source data including the HYSPLIT model, MODIS sensor data, CALIPSO data, ERA5, CAMS and MERRA-2 reanalysis data, the transport process of two dust events and the causes of pollution were analyzed. The source of the FD dust air mass is twofold, On the one hand, the dust at 3000 m originates from the ground in southern Gansu, On the other hand, the dust at 1000–2000 m comes from the transport and deposition of high-altitude dust in Xinjiang. SD dust originated from deserts and Gobi regions within Mongolia. During the transboundary process, it is shown that part of FD sand dust is blown above the troposphere. When it reached Handan area, the dust above the troposphere settled. The prolonged duration of SD and the occurrence of secondary pollution were caused by calm surface winds and specific high-altitude atmospheric conditions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102441"},"PeriodicalIF":3.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379202","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":"Differential impacts of the 2015–2020 El Niño/El Niño Modoki on seasonal ozone levels across China","authors":"Xiaohong Wang ,&nbsp;Qingheng Lu ,&nbsp;Shiyuan Zhong ,&nbsp;Yinchen Chen ,&nbsp;Zunli Dai ,&nbsp;Lejiang Yu","doi":"10.1016/j.apr.2025.102449","DOIUrl":"10.1016/j.apr.2025.102449","url":null,"abstract":"<div><div>Utilizing daily data from nearly 1500 ground stations within a nationwide air-quality monitoring network spanning 2015 to 2020, this study identifies three primary spatiotemporal modes of ozone (O₃) concentration anomalies and explores the sensitivity of surface O₃ to both Eastern Pacific El Niño and Central Pacific El Niño (El Niño Modoki) across distinct Chinese regions and seasons. Results reveal limited sensitivity during spring and winter, wherein negative (positive) O₃ anomalies predominate across China during the positive (negative) phase of either ENSO type, with spring magnitudes approximately doubling those observed in winter. In contrast, during summer and autumn, O₃ anomalies exhibit significant variations depending on El Niño type and geographical location. Positive anomalies are notably pronounced in summer and autumn, particularly during El Niño Modoki in eastern China, while negative anomalies concentrate in western China during autumn, with a scattered distribution in summer. On average, magnitudes of O₃ anomalies during El Niño Modoki surpass those during El Niño thoroughly, with the most pronounced differences in magnitude during summer for positive anomalies. The differences in O₃ anomalies between El Niño and El Niño Modoki are attributed to variations in anomalous atmospheric circulations, wherein El Niño Modoki stands out with stronger anomalous 850-hPa highs and lows, along with stronger associated anomalous circulations, as well as larger extents and magnitudes of anomalous precipitation and solar radiation. These variations induce changes in low-level wind directions that influence regional O₃ transport, and alterations in solar radiation and precipitation, impacting both O₃ generation and removal processes.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102449"},"PeriodicalIF":3.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419417","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":"Chemical composition, source distribution and health risk assessment of PM2.5 and PM10 in Beijing","authors":"Shaosong Zhen ,&nbsp;Min Luo ,&nbsp;Futao Xin ,&nbsp;Lingling Ma ,&nbsp;Diandou Xu ,&nbsp;Xiaomeng Cheng ,&nbsp;Yang Shao","doi":"10.1016/j.apr.2025.102448","DOIUrl":"10.1016/j.apr.2025.102448","url":null,"abstract":"<div><div>Beijing's air quality has seen significant enhancements owing to the successful implementation of China's emission control measures. However, air pollution incidents continue to occur, and more attention should be paid to continuous air quality monitoring and control. Here, the PM_2.5 and PM₁₀ samples collected during the air pollution days in Beijing in 2022 were analyzed to study the air quality situation and understand the changes in air pollution sources. The average concentration of PM_2.5 was 84.3 μg/m³ and PM₁₀ was 128.7 μg/m³. NO₃⁻, SO₄²⁻ and NH₄⁺ were the primary constituents of water-soluble inorganic ions, with concentrations of 18.8 μg/m³, 7.9 μg/m³, 7.4 μg/m³ in PM_2.5, and 20.5 μg/m³, 9.3 μg/m³, and 8.1 μg/m³ in PM₁₀. The enrichment factor values for Sn, Sb, and Cd in PM_2.5 exceeded 100, indicating severe anthropogenic pollution. Five pollution factors for PM_2.5 and PM₁₀ were obtained from the analysis of PMF model: vehicle emissions and dust, industrial emissions, secondary inorganic aerosols, coal combustion, and electronic manufacturing. The highest contributing factors were vehicle emissions and dust (28 % for PM_2.5 and 34 % for PM₁₀). According to the health risk assessment, Mn presented a non-carcinogenic risk to humans. Cd, As, Ni, and Cr (VI) showed a low level of carcinogenic risk within the acceptable range. The backward trajectory analysis showed that air masses from nearby cities exhibited stronger pollutant capabilities. Combining the potential source contribution function and concentration weight trajectory diagrams, the main potential source areas of Beijing are in Hebei, Tianjin, Shanxi and Henan.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102448"},"PeriodicalIF":3.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331263","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":"3DVAR meteorological data assimilation and aerosol impact on the simulation of heat wave 2022 over Haryana using WRF-Chem","authors":"Paushali Deb ,&nbsp;S.K. Panda ,&nbsp;Unashish Mondal ,&nbsp;Sushil K. Dash ,&nbsp;Devesh Sharma","doi":"10.1016/j.apr.2025.102440","DOIUrl":"10.1016/j.apr.2025.102440","url":null,"abstract":"<div><div>The rising frequency of heat waves in India presents significant risks to public health, agriculture, and the economy. In March 2022, temperatures reached a record-breaking 33.10 °C, the highest in 122 years resulting in two major heat wave events: March 11–21 and March 26–31, which claimed 33 lives. This study delves into the impact of anthropogenic emission/aerosols and meteorological data assimilation on model-predicted surface meteorological variables using “Weather Research and Forecasting (WRF) model” coupled with Chemistry (WRF-Chem). Four distinct simulation scenarios namely, WRF, WRFDA (WRF with meteorological Data Assimilation), WRF-Chem, and WRF-ChemDA (WRF-Chem with meteorological Data Assimilation) were executed across the Haryana domain to assess the sensitivity of model outputs. Analyses within the WRFDA and WRF-ChemDA frameworks utilized a 6-hourly cyclic 3-Dimensional Variational (3DVAR) Data Assimilation (DA) of NCEP ADP Surface Observational Fields. Most critically, the incorporation of aerosols and DA techniques markedly improved forecasts of key meteorological variables, including 2 m Temperature (T2), 2 m Relative Humidity (RH2), Planetary Boundary Layer Height (PBLH), and Outgoing Longwave Radiation (OLR). Skill assessment metrics, including the Heidke Skill Score (HSS ∼ 0.5), Accuracy (ACC &gt;0.9), and Probability of Detection (POD ∼ 1), demonstrate that WRF-ChemDA outperformed other models, especially during heat wave events. Conclusively, this study advocates for the meticulous selection of modeling approaches to accurately simulate heat wave events, ensuring that selected models adeptly capture the intricate dynamics and complexities of extreme temperature phenomena.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102440"},"PeriodicalIF":3.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377854","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":"A novel hybrid model based on dual-layer decomposition and kernel density estimation for VOCs concentration forecasting considering influencing factors","authors":"Fan Yang,&nbsp;Guangqiu Huang,&nbsp;Xin Jiao","doi":"10.1016/j.apr.2025.102439","DOIUrl":"10.1016/j.apr.2025.102439","url":null,"abstract":"<div><div>Accurate VOCs concentration prediction is essential for air pollution control and ecosystem stability. Due to multiple factors such as climatic conditions and photochemical reactions, VOCs monitoring data exhibits high randomness, which poses a challenge for prediction precision. Current decomposition integration models mainly focus on modelling the target variables and pay insufficient attention to the uncertainty of the prediction results. To solve these problems, an innovative VOCs prediction model is proposed by considering multiple external factors and combining dual-layer decomposition and nonlinear integration. Firstly, random forest (RF) is used for feature selection and a dual-layer decomposition method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved variational mode decomposition (IVMD) is proposed to reduce the data complexity. Next, K-means clustering is applied to reconstruct the decomposed subsequences to balance computational efficiency and model complexity, and the reconstructed subsequences is fed into long short-term memory (LSTM) optimized by grey wolf optimization (GWO) for prediction. Then, the predicted values are integrated by support vector regression (SVR) to minimize error accumulation. Finally, construct the prediction intervals based on kernel density estimation (KDE) to capture the fluctuation range of VOCs concentration. In the empirical study with total VOCs concentration data from two monitoring stations, the proposed model exhibits the lowest prediction error, with the root mean square error reduced by a maximum of 85.59% and 86.97%, respectively. The prediction intervals have high coverage and narrow interval width, proving that the proposed model can provide reliable VOCs concentration point and interval prediction.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102439"},"PeriodicalIF":3.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394621","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":"Ability of typical greening tree species to purify NO2 under different environmental factors","authors":"Jiaxing Fang ,&nbsp;Shaoning Li ,&nbsp;Mengxue Wang ,&nbsp;Na Zhao ,&nbsp;Xiaotian Xu ,&nbsp;Bin Li ,&nbsp;Junjie Zhang ,&nbsp;Chen Liu ,&nbsp;Qin Zhang ,&nbsp;Shaowei Lu","doi":"10.1016/j.apr.2024.102357","DOIUrl":"10.1016/j.apr.2024.102357","url":null,"abstract":"<div><div>Trees can uptake nitrogen dioxide(NO₂) and purify atmosphere, but the complex variation of environmental factors affects the ability of trees to purify NO₂. In this study, we conducted a one-time fumigation experiment on four typical greening tree species in China, including Japanese pagoda tree (Styphnolobium japonicum), Ginkgo (Ginkgo biloba), Manchurian red pine (Pinus tabuliformis), and Bunge's pine (Pinus bungeana), to analyze the impact of environmental factors on the ability of the trees to remove NO₂ from the air by quantifying their performance under different conditions of temperature, relative humidity, and wind speed. The results showed the following: (1) Broadleaf trees were more effective at purification compared with coniferous trees. (2) With the increase of temperature, the average purification rate of each tree species showed a decreasing and then increasing trend; except Bunge's pine, the purification amount per unit leaf area of each tree species showed an increasing and then decreasing trend. (3) With increasing relative humidity, the purification of NO₂ per unit leaf area in coniferous trees increased, while the trend in broadleaf trees decreased and then increased; the average purification rate of all the tree species, except Ginkgo, also decreased and then increased. (4) Temperature inhibited plant NO₂ purification capacity, relative humidity promoted plant NO₂ purification capacity, and wind speed had less ability to influence. (5) Multiple linear regression equations were successfully established to predict the ability of trees to purify NO₂ under different environmental factors. The study provides an important reference to purify atmosphere.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102357"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploratory study on the association between air pollution and health problems (ICD-10) with an emphasis on respiratory diseases","authors":"Ronan Adler Tavella ,&nbsp;Julia Oliveira Penteado ,&nbsp;Rodrigo de Lima Brum ,&nbsp;Alicia da Silva Bonifácio ,&nbsp;Meister Coelho San Martin ,&nbsp;Elizabet Saes-Silva ,&nbsp;Aline Neutzling Brum ,&nbsp;Romina Buffarini ,&nbsp;Washington Luiz Félix Correia Filho ,&nbsp;Diana Francisca Adamatti ,&nbsp;Rosália Garcia Neves ,&nbsp;Edmilson Dias de Freitas ,&nbsp;Simone Georges El Khouri Miraglia ,&nbsp;Flavio Manoel Rodrigues da Silva Júnior","doi":"10.1016/j.apr.2024.102377","DOIUrl":"10.1016/j.apr.2024.102377","url":null,"abstract":"<div><div>Air pollution is a growing public health concern, with diverse impacts on human health. This study aimed to conduct an exploratory analysis of the associations between air pollutants (O₃, PM_2.5, and PM₁₀) and health outcomes, using ICD-10 Chapters, across 24 cities with different dimensions in the state of Rio Grande do Sul, Brazil. Three models were developed for both annual and monthly data: one encompassing all 24 cities (Model 1), another with medium and small-sized cities (Model 2), and the last exclusively focusing on small cities (Model 3). Multiple linear regression analyses were conducted with air pollutants and meteorological variables as independent variables, and hospitalization rates within each ICD-10 Chapters and specific respiratory diseases as dependent variables. Our analysis revealed significant positive associations among diverse chapters of the ICD-10 and air pollutants, with Model 3 exhibiting the most robust and significant positive associations with 12 chapters of the ICD-10 (Chapters: II, V, VI, VII, IX, X, XI, XII, XIV, XVI, XVII, XVIII, XIX, and XXI), highlighting the broad impact of pollution on human health beyond traditional respiratory and cardiovascular concerns. Moreover, positive associations were identified with specific respiratory diseases, including asthma, pneumonia, bronchiolitis, and acute bronchitis. Temperature, precipitation, and wind speed emerged as the meteorological factors most frequently associated with multiple health outcomes and ICD chapters. Notably, our findings reveal distinct patterns in associations across cities with different population sizes, highlighting the importance of considering contextual factors, such as city size, when assessing the health impacts of air pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102377"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131201","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}