Air Quality Atmosphere and Health最新文献

{"title":"Study on correlation between atmospheric pollutants and meteorological factors during extreme weather in Mexico based on GTWR model","authors":"Tianzhen Ju,&nbsp;Lanzhi Wang,&nbsp;Bingnan Li,&nbsp;Zhichao Lv,&nbsp;Zhenrong Gu","doi":"10.1007/s11869-025-01724-5","DOIUrl":"10.1007/s11869-025-01724-5","url":null,"abstract":"<div><p>In recent years, with the ongoing rise in surface temperatures, heat waves, a frequently occurring extreme weather phenomenon, have manifested within the context of global warming. To investigate the variation of atmospheric pollutants ozone (O₃), formaldehyde (HCHO), and nitrogen dioxide (NO₂) and their relationship with meteorological factors during normal weather conditions from March to July 2018–2022, as well as during extreme weather conditions in the Mexican region from March to July 2023. This study utilizes Python, ArcGIS 10.8, and other software to investigate pollutants during two time periods by employing the Geographical Time-Weighted Regression model (GTWR), Random Forest Regression model, Backward Trajectory model, and Ozone Generation Sensitivity Analysis based on daily data from the OMI satellite and meteorological factors. The findings suggest that: (1) O₃ concentrations exhibit an increase, while HCHO concentrations demonstrate a decrease during extreme weather events in the study area; however, NO₂ concentrations do not exhibit significant changes. (2) Extreme weather conditions induce alterations in the correlation between atmospheric pollutants and meteorological factors within the Mexican region. (3) A minor portion of the study area undergoes a shift in Ozone Generation Sensitivity during occurrences of extreme weather.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1489 - 1505"},"PeriodicalIF":2.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Hybrid graph convolutional LSTM model for spatio-temporal air quality transfer learning","authors":"Sooraj Raj,&nbsp;Jim Smith,&nbsp;Enda Hayes","doi":"10.1007/s11869-025-01713-8","DOIUrl":"10.1007/s11869-025-01713-8","url":null,"abstract":"<div><p>The short-term air quality forecasting models serve as an early warning system for local agencies, aiding in preparing mitigation strategies against severe pollution episodes. This paper explores the application of Transfer Learning to enhance short-term air quality forecasting model accuracy when labelled data is limited or missing, as often occurs with newly installed monitoring stations or due to sensor malfunctions. These monitoring stations are typically installed in areas of high exposure, like roads or urban/industrial areas, due to recurrent peak episodes or to monitor background pollutant levels generally. Forecasts with greater reliability, even when there is limited historical data available due to the recent installation of the monitoring station for example, are expected to enable the swift implementation of proactive measures to prevent significant pollution episodes from happening. The proposed method leverages knowledge from spatially neighbouring air quality monitoring stations to achieve the multi-modal spatial-temporal transfer learning to the target station, exploring multivariate time series data available from neighbouring monitoring stations. This study employed historical air quality data from spatially adjacent monitoring stations identified in South Wales, UK. The study evaluates the predictive capabilities of four base models and their corresponding transfer learning variants for estimating NO₂ and PM₁₀ pollutant levels, which are the most difficult pollutants to meet objectives and limit values in the UK’s air quality strategy. The paper highlights the importance of capturing spatial patterns from different monitoring stations along with temporal trends when it comes to air quality prediction. Our experiments demonstrate that transfer learning models outperform models trained from scratch on air quality multivariate time series prediction problems in a low data environment. The proposed hybrid Graph Convolutional-LSTM model, making use of a novel Granger causality-based adjacency matrix for the new site, has significantly outperformed other baseline models in predicting pollutants, achieving notable improvements in prediction accuracy of approximately 8% for PM₁₀ and 7% for NO₂ values, as reflected in the RMSE values. It has also demonstrated the potential for data-efficient approaches in spatial transfer learning by reducing the need for large datasets by incorporating prior causal information.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1425 - 1445"},"PeriodicalIF":2.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01713-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity analysis for odour dispersion modelling: LAPMOD evaluation and comparison with CALPUFF","authors":"Francesca Tagliaferri,&nbsp;Alessandra Rota,&nbsp;Marzio Invernizzi","doi":"10.1007/s11869-025-01721-8","DOIUrl":"10.1007/s11869-025-01721-8","url":null,"abstract":"<div><p>Accurate dispersion modelling of odour emissions is essential for assessing their environmental impact on citizens. In this context, the sensitivity analysis of dispersion models is crucial for identifying the factors that most influence their predictions, thereby improving accuracy and reliability in environmental assessments. This study aims to perform a sensitivity analysis of the Lagrangian particle model LAPMOD, focusing on some key parameters including turbulent parametrization, meteorological data interpolation, plume rise algorithms, and concentration prediction kernels. It also compares LAPMOD results with CALPUFF results, one of the most widely applied models for regulatory purposes and odour impact assessments, to evaluate dissimilarities in odour impact predictions for both area and point sources. The analysis reveals that the choice of concentration estimation kernel has a significant impact on LAPMOD's predictions, with the Gaussian Kernel yielding the most consistent results. All other investigated input parameters show minimal influence, leading to variations in the results always below 15%. Concerning the comparison between models, while both models show quite consistent trends for point sources, LAPMOD tends to estimate significantly lower odour impacts from area sources compared to CALPUFF, with estimated separation distances differing up to a factor of 4 between the two models.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1447 - 1461"},"PeriodicalIF":2.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01721-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating dynamic population activities in modeling exposure to urban air pollution: insights from COVID-19 lockdowns in three European cities","authors":"Martin Otto Paul Ramacher","doi":"10.1007/s11869-025-01707-6","DOIUrl":"10.1007/s11869-025-01707-6","url":null,"abstract":"<div><p>The COVID-19 pandemic in 2020 led to global lockdown measures, significantly changing population activity patterns and providing an unprecedented situation to study their effects on air quality. Previous studies primarily focused on pollutant concentration changes, often neglecting the influence of modified population activities on exposure estimates. This study aims to evaluate the impact of changes in time-activity patterns on population exposures to NO₂, O₃, and PM_2.5 in three urban European areas during the first lockdowns in March and April of 2020. A comprehensive hybrid exposure model was used, integrating urban-scale air pollutant dispersion data with diurnal population activity, accounting for both concentration and population activity changes due to lockdown measures. Population-weighted exposures and total time-integrated exposure levels were assessed for Hamburg, Germany, Liège, Belgium, and Marseille, France. The lockdown measures led to significant reductions in NO₂ and PM_2.5 concentrations while increasing O₃ concentrations. Adjusting for population activity changes showed additional hourly population weighted exposure reductions for NO₂ by up to 6% and for O₃ and PM_2.5 by up to 7%, while total time-integrated exposure was additionally reduced for NO₂ (up to 3%), O₃ (up to 8%) and PM_2.5 (up to 7%). These findings highlight the importance of incorporating dynamic population activity data for more accurate exposure and health impact assessments, especially in urban areas. The study highlights that exposure estimated at residential addresses likely underestimate exposure and related health effects.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1507 - 1526"},"PeriodicalIF":2.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01707-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the kinetics and product characteristics of aqueous-phase oxidation of Eugenol and acetosyringone upon 254 nm/313 nm irradiation","authors":"Zixuan Wang,&nbsp;Zhaolian Ye,&nbsp;Dandan Hu,&nbsp;Hui Wang,&nbsp;Xinlei Ge","doi":"10.1007/s11869-025-01717-4","DOIUrl":"10.1007/s11869-025-01717-4","url":null,"abstract":"<div><p>Phenolic compounds emitted from biomass burning are important precursors for the formation of aqueous secondary organic aerosols (aqSOA), but there is limited research on the formation mechanisms, product characteristics, and related kinetics, especially on comparative studies under different wavelengths of ultraviolet (UV) light. Therefore, this study selected eugenol (Eug) and acetosyringone (AS) as precursors to investigate the kinetics of aqueous-phase oxidation reactions under two UV light sources (254 nm/313 nm). The results showed that the pseudo-first-order rate constant, <i>k</i>_obs was highly dependent on pH value, initial phenolic compounds concentration and added H₂O₂ dosages. The ranking of <i>k</i>_obs were <i>k</i>_O2 &gt; <i>k</i>_air &gt; <i>k</i>_N2, indicating that oxygen is beneficial for degradation. Scavenging experiments determined the relative contributions of reactive oxygen species (ROS) and the results showed that both •OH and O₂^•− played significant roles in the photodecay of Eug and AS, with O₂^•− being more prominent. Light-absorbance at 365 nm of products increased for the first 7 h, and then decreased at the later stage indicated the formation of light-absorbing substances and subsequent photobleaching. Combining the changes in organic acids, oxidative characteristics, and product composition during the reaction, it is speculated that the products of the OH aqueous-phase oxidation of the two phenolic compounds undergo functionalization followed by fragmentation, leading to an increase in oxidation degree. Organic acids are formed through dehydrogenation or ring-opening, further impacting climate change and air quality. This study for the first time compared the reaction rates of the two phenols under 313 nm UV light source and comprehensively analyze the products characteristics and mechanisms, which was significant for expanding the atmospheric kinetics database and understanding the chemical activity of phenolic substances in the atmosphere.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1407 - 1424"},"PeriodicalIF":2.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Effects of seasons and weather on occupational exposure to BTEX concentrations in a changing climate","authors":"Amir Hossein Khoshakhlagh,&nbsp;Saeid Yazdanirad,&nbsp;Philip K. Hopke","doi":"10.1007/s11869-025-01715-6","DOIUrl":"10.1007/s11869-025-01715-6","url":null,"abstract":"<div><p>Climatic variations in the workplace may change concentration patterns. The objective of this study was to investigate the impact of seasons and weather on the occupational concentrations of BTEX compounds. A systematic review was conducted in five digital bibliographic databases. Adhering to PRISMA protocols, an algorithm in the search strategy was employed that incorporated four groups of search terms. The pooled values were also computed using a random-effect model. The results of the qualitative analysis showed that the values of occupational exposure concentrations for BTEX were higher in the winter and summer seasons compared to other seasons. In the non-industrial settings, the pooled values of exposure concentration (μg/m³) to benzene, toluene, ethyl benzene, and xylene(s) were calculated as 6.74, 28.84, 3.64, and 6.20 in summer and 15.95, 33.09, 6.30, and 8.23 in winter, respectively. In the industrial settings, the pooled values of exposure concentration (μg/m³) to benzene, toluene, ethyl benzene, and xylene(s) were measured as 9.36, 555.51, 42.09, and 290.54 in summer and 77.44, 2254.84, 1024.21, and 2733.98 in winter, respectively. Occupational exposure to BTEX compounds increases during the winter followed by the summer season. These results might be a recommendation for introducing probable hazardous seasons of exposure for human health.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1597 - 1613"},"PeriodicalIF":2.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145170862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 investigation into NO2 column concentrations in the Sahara Desert region: relationships with other pollutants","authors":"Zhenrong Gu,&nbsp;Tianzhen Ju,&nbsp;Bingnan Li,&nbsp;Lanzhi Wang,&nbsp;Zhichao Lv","doi":"10.1007/s11869-025-01723-6","DOIUrl":"10.1007/s11869-025-01723-6","url":null,"abstract":"<div><p>The investigation of atmospheric complex pollutant relationships in the Sahara Desert, the largest desert located in northern Africa, constitutes a critical component of global environmental research. This study analyzed the intricate interactions among NO₂, HCHO, and O₃ in the Sahara Desert region (encompassing Algeria, Libya, and Egypt) using temporal and spatial distributions derived from OMI sensor data, the HYSPLIT model, and ozone-sensitive control areas. The analysis revealed their spatial and temporal distributions, trans-regional transport pathways, and the dominance of ozone as a pollutant. Results indicated that the annual changes in the spatial distribution of NO₂, HCHO, and O₃ exhibited an increasing trend from south to north. Temporally, NO₂ showed a wavy pattern with a peak in 2017, HCHO displayed an inverted V pattern peaking in 2018, and O₃ followed a Z pattern with a peak in 2017. Monthly variations showed that both NO₂ and O₃ were highest in summer, followed by spring, autumn, and winter, while HCHO peaked in winter, followed by autumn, summer, and spring. Exogenous atmospheric transport significantly influences NO₂ pollution in the Sahara Desert, as demonstrated by the HYSPLIT model’s analysis of backward trajectories and potential source areas over a decade of rainy and dry seasons. High-pollution areas are primarily affected by the southeastern Mediterranean monsoon and the southeastern Algiers winds. Annual spatial variations within ozone-sensitive control areas indicate that ozone is predominantly influenced by VOCs and NOx synergistic control. Monthly spatial variations show that NOx control dominates in autumn and winter, while VOCs control prevails in summer and fall.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1391 - 1406"},"PeriodicalIF":2.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145170863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Influence of urban forest size and form on PM2.5 and O3 concentrations: A perspective of size threshold","authors":"Xin Chen,&nbsp;Fang Wei","doi":"10.1007/s11869-025-01722-7","DOIUrl":"10.1007/s11869-025-01722-7","url":null,"abstract":"<p>Urban forest (UF) is an effective means of mitigating air pollution. At the stage where PM_2.5-O₃ composite pollution is prevalent and UF expansion reaches a bottleneck, optimizing UF form based on its area size becomes a superior management strategy. However, existing research has not fully explored the threshold effects of UF size. Using the panel threshold regression model, this study examines relationships between air pollution and UF (size and form) in 305 counties within the Yangtze River Delta (YRD) from 2014 to 2022. The results show (1) UF size exhibits a double threshold effect in three relationships: between patch density (PD) and PM_2.5, between splitting index (SPLIT) and PM_2.5, and between SPLIT and O₃. Meanwhile, a single threshold effect of UF size is observed in relationships between other UF form metrics and pollutant concentrations. (2) When UF size plays the threshold effect in relationships between UF form and pollutant concentrations, its threshold values usually lie around 3%, 14%, and 45%. UF form metrics’ correlations with PM_2.5 are generally opposite to those with O₃. (3) When UF size is below 45%, PM_2.5 reduction favors complex-shaped and dispersed UF patches, while O₃ reduction benefits from more regular and concentrated UF patches. Once UF size exceeds 45%, a continuous regional UF network can potentially address PM_2.5-O₃ composite pollution. This study aims to provide insights into atmospheric governance and UF planning.</p>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1377 - 1390"},"PeriodicalIF":2.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Integrated air quality prediction and mitigation strategies for sustainable mining operations in India","authors":"Swades Kumar Chaulya,&nbsp;Shailendra Kumar Singh,&nbsp;Siddharth Singh,&nbsp;Gautam Chandra Mondal,&nbsp;Ranjeet Kumar Singh,&nbsp;Sameer Shekhar","doi":"10.1007/s11869-025-01720-9","DOIUrl":"10.1007/s11869-025-01720-9","url":null,"abstract":"<div><p>The study focuses on dual purpose of forecasting air pollution levels and implementing eco-friendly dust suppression methods for a planned expansion of a lignite mine in India, aligning with the goal of achieving sustainable mining practices. The main objective is to predict the highest concentration of dust emissions from the mine, both with and without the implementation of mitigation measures. The study determined the baseline levels of PM₁₀, PM_2.5, SO₂, and NO_x in the surrounding of the mine site. The recorded values ranged from 53.1–79.5, 20.2–43.2, 16.6–31.2, and 21.2–50.1 µg m^–3, correspondingly. The concentrations detected were below the allowable thresholds of 100, 60, 80, and 80 µg m^–3, correspondingly. Air quality modelling was conducted to forecast the air quality in the vicinity of the lignite mine, both with and without the implementation of control measures during the project's expansion phase. This was achieved by measuring background air pollutants level, assessing emission sources, determining activity-specific emission rates, analysing micro-meteorological parameters, and identifying receptor locations. Without the application of control measures, the projected levels of PM₁₀, PM_2.5, SO₂, and NO_x are assessed to range from 73.9–97.1, 31.9–44.2, 11.46–21.09, and 15.27–28.40 µg m^–3, respectively. However, by employing mitigation measures during the mine's expansion operation, it is expected that the amounts of PM₁₀, PM_2.5, SO₂, and NO_x will be within the range of 73.5–82.5, 31.8–43.8, 9.38–20.14, and 13.17–25.45 µg m^–3, respectively. Accordingly, it is anticipated that the air pollutants will persist lower than the allowable limits in the surrounding buffer zone with control measures. Hence, the study also proposes effective measures to control dust pollution, together with a comprehensive description of the developed intelligent dust suppression systems those can be utilized at different dust-emission sources within the opencast mine for ecofriendly and clean mining.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1615 - 1633"},"PeriodicalIF":2.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Multi-group organic pollutants in urban and suburban atmospheric particulate matter (PM2.5): Temporal variation, meteorological impact, and sources","authors":"Ebru Koçak,&nbsp;Seda Aslan,&nbsp;İpek İmamoğlu,&nbsp;Gürdal Tuncel","doi":"10.1007/s11869-025-01716-5","DOIUrl":"10.1007/s11869-025-01716-5","url":null,"abstract":"<div><p>Several adverse health impacts have been attributed to particulate matter-PM_2.5, defined as having a diameter of less than 2.5 µm. The World Health Organization has determined that 5 µg m⁻³ is the 24-h limit threshold. PM_2.5 comes from various primary sources and is also created by secondary atmospheric processes. Finding responsible sources can help regulate by focusing on the biological processes that underlie the observed health impacts. Determining the chemical composition of PM_2.5 is the first phase in allocating PM_2.5 to various sources. This study outlines the procedure for organic speciation of PM_2.5—solvent-extractable polycyclic aromatic hydrocarbons (PAHs), n-alkanes, n-alkanoic acids, and levoglucosan. Daily PM_2.5 aerosol samples were collected between July 2014 and September 2015 in Ankara, Turkey. Seasonal average concentrations of measured species ranged from 13.51 to 65.04 ng m⁻³ for PAHs, 36 to 150 ng m⁻³ for n-alkanes, 24 to 47 ng m⁻³ for n-alkanoic acids, 0.44 to 3.6 ng m⁻³ for levoglucosan. n-Alkanes are the most abundant group at both urban and suburban sites. Concentrations of all groups were higher during winter, which is associated with emissions from space heating and lower mixing height in winter months. The diagnostic ratios between specific atmospheric concentrations of tracers depicted that the particulate organic compounds are mainly from anthropogenic sources like vehicular emission, biogenic combustion, and food cooking.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1359 - 1376"},"PeriodicalIF":2.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}