Atmospheric Pollution Research最新文献

{"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}

{"title":"A self-designed pyrolysis-gasification-combustion pilot plant for rural solid waste disposal: The elucidation of emission factors","authors":"Wei Jiang ,&nbsp;Dian Li ,&nbsp;Hui Cai ,&nbsp;Jiahui Yan ,&nbsp;Yuanyao Ye ,&nbsp;Jianxiong Kang ,&nbsp;Qian Tang ,&nbsp;Yongzheng Ren ,&nbsp;Songlin Wang ,&nbsp;Dongqi Liu ,&nbsp;Zizheng Liu ,&nbsp;Yiqun Chen","doi":"10.1016/j.apr.2024.102372","DOIUrl":"10.1016/j.apr.2024.102372","url":null,"abstract":"<div><div>The research group developed a pyrolysis-gasification-combustion pilot plant (PGCP) to treat rural solid waste (RSW). Based on the study of previous operating conditions, this study further explored the characteristics of potential secondary pollutants in the plant, such as slag, fly ash and flue gas. In the present study, X-ray fluorescence (XRF), X-ray diffraction (XRD) and scanning electron microscope (SEM) were used to analyze slag and fly ash. Besides, the leaching toxicity, content and morphology distribution of heavy metals (e.g., Cu, Pb, Cd, Zn and Cr) in the slag of PGCP were also studied as well as the emission characteristics of various pollutants in flue gas. Results show that slag mainly contains CaCO₃ and SiO₂, while CaCO₃ is mainly contained in fly ash. Moreover, the leaching toxicity of heavy metals in the slag did not exceed the limit value of the standard in China. The present study could provide technical support and data for optimizing the operation of plant-scale pyrolysis of RSW.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102372"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131202","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":"Unraveling the complexities of ozone and PM2.5 pollution in the Pearl River Delta region: Impacts of precursors emissions and meteorological factors, and effective mitigation strategies","authors":"Yating Chen ,&nbsp;Liye Zhu ,&nbsp;Sihui Wang ,&nbsp;Daven K. Henze ,&nbsp;Tzung-May Fu ,&nbsp;Lin Zhang ,&nbsp;Xiaoling Wang","doi":"10.1016/j.apr.2024.102368","DOIUrl":"10.1016/j.apr.2024.102368","url":null,"abstract":"<div><div>Ozone (O₃) and fine particulate matter (PM_2.5) are known to be interconnected due to shared precursor compounds. While numerous studies have examined the impact of precursors and meteorological factors on compound pollution events, few have proposed effective mitigation strategies tailored to specific regions. In this study, we conducted simulations of two types of O₃ and PM_2.5 pollution events in the Pearl River Delta (PRD) region during 2018 using the GEOS-Chem model. We applied a multiple linear regression model to quantify and distinguish the contributions of precursor emissions and meteorological factors to these events. Our findings highlight the predominant role of precursor emission factors in driving these pollution events. Notably, reducing NO_x emissions in the Pearl River Estuary (PRE) region was found to exacerbate O₃ pollution during specific periods, while reducing emissions of C4 alkanes (ALK4), lumped C3 alkenes (PRPE) and NH₃ in proportion to their respective contributions emerged as an effective strategy to mitigate combined O₃ and PM_2.5 pollution. This research elucidates the mechanisms underlying O₃ and PM_2.5 compound pollution in the PRD region and presents a practical and significant approach to managing air pollution in this area.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102368"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131206","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":"Linking sand/dust storms hotspots and land use over Iran","authors":"Mahdi Boroughani ,&nbsp;Rahman Zandi ,&nbsp;Sima Pourhashemi ,&nbsp;Hamid Gholami ,&nbsp;Dimitris G. Kaskaoutis","doi":"10.1016/j.apr.2024.102380","DOIUrl":"10.1016/j.apr.2024.102380","url":null,"abstract":"<div><div>Sand and dust storms (SDS), as a direct consequence of land degradation and wind erosion, is an important environmental challenge in the last two decades, especially in arid and semi-arid areas. Land use changes due to human intervention and soil's susceptibility to erosion are among the most important factors influencing the SDS hotspots. This study aims to explore possible linkage between land use changes and SDS hotspots in Iran during a 20-years period (2001–2022). In this scope, four dust characterization indices based on MODIS observations (BTD₃₁₃₂, BTD₂₉₃₁, NDDI, and D) were employed to identify the SDS hotspots. Then, the land use – land cover (LULC) changes over Iran were mapped using MODIS images, aiming to identify the areas exhibiting large LULC changes and tendency to become SDS hotspots. Finally, the LULC changes were analyzed with respect to SDS hotspots. The results revealed 618 SDS hotspots in the whole Iranian territory, with the largest number of them located in non-vegetated lands, scattered shrubs and rangelands. In addition, Zabol in east Iran presented the highest frequency of SDS, while southwest Iran faced also a large number of SDS. The highest number of SDS was recorded in 2008 in most of the country's stations, following the dust-regime shift in the Middle East due to prolonged drought. Current methodology links SDS hotspots and LULC changes very well and can be helpful for developing mitigation strategies for the consequences of human and climate-induced LULC changes, wind erosion and SDS in arid environments.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102380"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131207","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":"Comparative study on the dispersion and removal efficiency of indoor aerosol particles under various displacement ventilation modes","authors":"Wencheng Liu,&nbsp;Qianfeng Liang,&nbsp;Cheng Yao,&nbsp;Bin Li,&nbsp;Jie Ji,&nbsp;Xianzhuo Wang,&nbsp;Yang Luo,&nbsp;Yuandong Huang","doi":"10.1016/j.apr.2024.102397","DOIUrl":"10.1016/j.apr.2024.102397","url":null,"abstract":"<div><div>The removal of indoor aerosols through ventilation is a critical area of research in indoor air quality management. This study utilized the validated Re-Normalization Group (RNG) <em>k-ε</em> model to simulate the effect of different ventilation methods on the diffusion and distribution of indoor aerosol particles. The analysis encompassed variations in temperature, airflow dynamics, and aerosol dispersion characteristics under both summer and winter conditions. The results indicate significant differences in indoor airflow structures among different ventilation modes, influencing the thermal comfort of indoor occupants distinctly. Furthermore, these ventilation modes had varying impacts on the diffusion of particles of different sizes under different seasonal conditions. In summer, the ceiling-supply and side-return ventilation mode demonstrated outstanding particle removal efficiency, achieving a remarkable 99.2% removal rate after just 50 s of ventilation. Conversely, winter conditions posed challenges for efficient indoor aerosol particle removal, even with similar ventilation modes. During winter, the most effective ventilation mode was the left-upper supply and right-lower return mode, which achieved a particle removal efficiency of 98% at 100 s. These findings highlight the importance of understanding seasonal variations in indoor aerosol distribution and the effectiveness of diverse ventilation methods in improving indoor air quality. Such insights are invaluable for optimizing ventilation systems and the continually improving indoor air quality management.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102397"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131624","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":"Identification of aerosol and meteorological parameters threshold for visibility conditions over Delhi city","authors":"Prachi Goyal,&nbsp;Chinmay Jena,&nbsp;Anikender Kumar,&nbsp;V.K. Soni,&nbsp;Mrutyunjay Mohapatra","doi":"10.1016/j.apr.2024.102373","DOIUrl":"10.1016/j.apr.2024.102373","url":null,"abstract":"<div><div>The quantitative assessment of air pollution induced visibility impairment is a prevalent concern across Indian sub-continent. In present study, efforts have been made to identify thresholds of aerosol (PM_2.5), meteorological variables (relative humidity, temperature at 2 m, wind speed at 10 m) and boundary layer for shallow (Category (CAT) I: 550–1000 m), moderate (CAT II: 300–550 m), dense (CAT IIIA: 175–300 m &amp; CAT IIIB: 50–175 m) and very dense (CAT IIIC: &lt;50 m) fog categories. A 5-year dataset from 2018 to 2023 for wintertime (December–February) is used to determine thresholds using statistical methods. A unique two-way rolling window correlation analysis is performed for linear, inverse and logarithmic functions considering described visibility classes, PM_2.5, RH and temperature. Different sliding windows and subsequent step sizes are taken to ascertain interrelatedness based on significant correlation coefficients. Further, a frequency distribution based averaging method is used for wind speed and boundary layer thresholds. Based on the findings, identified thresholds for PM_2.5, RH, temperature, wind speed and boundary layer are &gt;270 μg/m³, &gt;70%, &lt;13 °C, &lt;1.5 m/s, &lt;100 m for CAT I; &gt;250 μg/m³, &gt;70%, &lt;13 °C, &lt;1.2 m/s, &lt;80 m for CAT II; &gt;250 μg/m³, &gt;70%, &lt;11 °C, calm winds, &lt;80 m for CAT IIIA; &gt;220 μg/m³, &gt;80%, &lt;11 °C, calm winds, &lt;70 m for CAT IIIB while 180 μg/m³, &gt;90%, &lt;9 °C, calm winds and &lt;60 m for CAT IIIC. The determined thresholds have been validated using 2023–2024 data based on which shallow, moderate, dense and very dense categories are found to be 65%, 77%, 88%, 90% and 93% compliant with the thresholds of CAT I, CAT II, CAT IIIA, CAT IIIB and CAT IIIC. Trajectory clustering is also included for ascertaining the potential pollution source regions. The study can aid policymakers in predicting fog events. Moreover, appropriate policy interventions can be formulated in wake of the early warning system and information dissemination.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102373"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131198","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 pollution sources and health risks of black carbon aerosols in industrial cities of Northwestern China using light absorption observations","authors":"Kai Cheng ,&nbsp;Yuting Zhong ,&nbsp;Xia Li ,&nbsp;Shuting Li ,&nbsp;Maulen Ayitken ,&nbsp;Wang Zhang ,&nbsp;Xinchun Liu","doi":"10.1016/j.apr.2024.102381","DOIUrl":"10.1016/j.apr.2024.102381","url":null,"abstract":"<div><div>Black carbon (BC) aerosols have notably negative impacts on climate, environment, and public health. However, current knowledge of the sources and health hazards of BC remains limited. Equivalent black carbon (eBC) measurements were conducted using an AE33 aethalometer in Urumqi, China, from July 2022 to April 2023. The temporal variability, potential pollution areas and health hazards of fossil fuel (eBC_ff) and biomass burning (eBC_bb) sources were analysed. The average concentrations of eBC_ff and eBC_bb were 1.3 μg m⁻³ and 0.5 μg m⁻³, respectively, highlighting the dominant role of fossil fuel combustion. In winter, due to increased coal heating and industrial emissions under adverse weather conditions, the contribution rate of eBC_ff surged to 80.3% (2.44 μg m⁻³). The highest concentrations of eBC_bb were observed in spring (0.71 μg m⁻³), influenced by pre-sowing burning of agricultural residues. The concentrations of eBC_ff were higher during the day than at night, with a morning peak due to vehicular emissions. High concentrations of eBC_ff and eBC_bb mainly originate from pollution sources around the observation site, along with residential and agricultural areas to the southeastern. Regional transport between cities on the northern slopes of the Tianshan Mountains and long-range drift from Central Asian countries also had important effects on eBC_ff and eBC_bb. Health risk assessments revealed that carcinogenic risks (CR) from eBC exceeded acceptable levels by two orders of magnitude, with passive smoking equivalents (PSC) four times higher in winter than in summer. Our findings highlight the significant environmental and public health benefits of reducing fossil fuel consumption.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 2","pages":"Article 102381"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131618","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}