Urban Climate最新文献

{"title":"New particle formation characteristics and mechanisms of secondary PM2.5 in Seoul and Seosan in the Republic of Korea during 2020–2022","authors":"Yoonkyeong Ha ,&nbsp;Jeongbeen Kim ,&nbsp;Mijung Song ,&nbsp;Ji Yi Lee ,&nbsp;Kyoung-Soon Jang ,&nbsp;Kwangyul Lee ,&nbsp;Junyoung Ahn ,&nbsp;Changhyuk Kim","doi":"10.1016/j.uclim.2025.102565","DOIUrl":"10.1016/j.uclim.2025.102565","url":null,"abstract":"<div><div>Air pollution caused by particulate matter (PM_2.5) adversely affects environmental and human health. A lack of understanding of the characteristics and mechanisms of secondary aerosols has limited the mitigation of atmospheric PM_2.5. This study aims to investigate the spatiotemporal characteristics and mechanisms of new particle formation in the Republic of Korea (ROK). Four intensive monitoring campaigns were conducted at the Seoul (SE, megacity) and Seosan (SS, sub-urban) sites in different seasons during 2020–2022 based on real-time measurements. The mean growth rates at both sites were &gt; 2 times higher during the summer, with a higher concentration of gaseous precursors than during the other seasons. Mean nucleation rates (NRs) were &gt; 2 times higher during the winter at SE due to lower temperatures and condensation/coagulation sinks in this season than the other seasons. However, the mean NRs at SS were &gt; 4 times higher during the summer due to higher sulfuric acid (H₂SO₄) concentrations than during the other seasons. Plotting NRs versus sulfuric acid concentrations revealed that the nucleation mechanisms of secondary PM_2.5 formation were in the middle of the H₂SO₄–ammonia (NH₃)–water and H₂SO₄–dimethylamine–water ternary systems during the summer and closer to the NH₃ ternary system during the other seasons. This seasonal difference may be caused by the decrease in the contribution of NH₃ ternary nucleation at elevated temperatures in the summer.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102565"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of major sports events on air pollution in host cities: A case study of the Nanjing Youth Olympic games","authors":"Ruofei Lin ,&nbsp;Shanlang Lin ,&nbsp;Ziyang Chen ,&nbsp;Tian Yu","doi":"10.1016/j.uclim.2025.102569","DOIUrl":"10.1016/j.uclim.2025.102569","url":null,"abstract":"<div><div>Air pollution not only affects urban productivity and the quality of life for residents, but also poses a threat to human health. The organisation of major sports events will have a far-reaching impact on the ecological environment and many other aspects. Therefore, studying the environmental impacts of major sports events is of great practical significance.This study, based on panel data from 270 prefecture-level cities in China from 2006 to 2021, uses the synthetic control method (SCM) to examine the impact and mechanisms of hosting the Nanjing Youth Olympic Games on air pollution in the host city. The results indicate that hosting the Nanjing Youth Olympic Games significantly reduced the air pollution intensity in the host city. This finding is supported by a number of robustness tests. The mechanism analysis shows that hosting the Nanjing Youth Olympic Games could achieve the reduction of the air pollution intensity of the host city by improving the level of environmental regulation and promoting the innovation of green technology. Further research on the air pollution effects of other events, on the basis of verifying the conclusions of this paper, found that the air pollution improvement effects of sports events were related to the overall planning of the event and the importance attached to environmental protection. This study provides a reference for host cities to achieve a win-win situation of green event organisation and sustainable development.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102569"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the impact of spatial structure on carbon emissions in Chinese urban agglomerations: Insights into polycentric and compact development patterns","authors":"Bin Zhang ,&nbsp;Jian Yin","doi":"10.1016/j.uclim.2025.102557","DOIUrl":"10.1016/j.uclim.2025.102557","url":null,"abstract":"<div><div>Urban agglomerations, as key spatial units, play a pivotal role in fostering economic expansion and reducing carbon emissions (CEs). However, the influence of complex spatial structures within urban agglomerations on CEs remains inadequately explored, thus impeding the progression toward sustainable city. By integrating with multi-source data, this study explores the polycentric and compact spatial structure of urban agglomerations. Applying machine learning approaches, including random forest and SHapley Additive exPlanations model, our research analyzes the heterogeneity, nonlinear characteristics, and interaction effects of spatial structure on CEs. The results indicate that the inverted primacy, effective mesh size, and patch cohesion index are key indicators influencing CEs, and exert heterogeneous impacts on CEs of urban agglomerations with different spatial structure. There are marginal effects of spatial structure on CEs, with thresholds for positive and negative influences varying across different urban agglomerations. When the effective mesh size is less than 500 and the patch cohesion index is at 98 and 99, it can effectively inhibit CEs. Spatial structure indicators interact to influence the intensity and direction of CEs. Our study framework provides new insights into optimizing spatial structures and promoting sustainable development in urban agglomerations.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102557"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing heating and cooling effects of urban spatial structure factors on ambient air temperature: A quantitative synthesis using meta-analysis","authors":"You-Jeong Hong,&nbsp;Mijin Choo,&nbsp;Yeon-Woo Choi,&nbsp;D.K. Yoon","doi":"10.1016/j.uclim.2025.102546","DOIUrl":"10.1016/j.uclim.2025.102546","url":null,"abstract":"<div><div>Numerous studies have examined how urban spatial structures influence ambient air temperature (AAT). However, the evidence on their effects and relative strength remains inconclusive and often conflicting due to varying analytical frameworks and spatiotemporal contexts. This meta-analysis quantitatively synthesizes the impacts of urban spatial structure factors from 34 peer-reviewed publications and draws comprehensive conclusions about their relationships. The analysis encompasses multidimensional factors of urban spatial structure related to land use and land cover (LULC), vegetation, building, and spatial layout. Seasonal and diurnal variations were further examined through subgroup analyses to explore heterogeneity. This study finally aims to compare the relative strength of multiple factors on AAT and discuss spatial planning strategies for urban heat mitigation. The results reveal that high building density and extensive impervious areas exert the strongest heating effects on AAT. Notably, layout-related factors, particularly height-to-width ratio and surface roughness, contribute to heating effects greater than intrinsic building properties, such as volume or height. Conversely, high normalized difference vegetation index (NDVI) and widespread tree areas emerge as the most effective cooling elements, with vegetative characteristics providing stronger cooling effects than natural-based LULC features. Seasonal and diurnal differences observed in several heating and cooling factors suggest that interactions between spatial elements and AAT vary under different meteorological conditions. Especially for urban heat mitigation in summer, expanding tree-based vegetation with improving NDVI is found to be more effective than reducing impervious areas and building density. These findings offer evidence-based guidance on which factors should be prioritized in climate-responsive urban planning.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102546"},"PeriodicalIF":6.9,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decentring our appreciation of the association of factors and cities' mitigation patterns","authors":"Sombol Mokhles ,&nbsp;Kathryn Davidson ,&nbsp;Jason Thompson ,&nbsp;Michele Acuto","doi":"10.1016/j.uclim.2025.102543","DOIUrl":"10.1016/j.uclim.2025.102543","url":null,"abstract":"<div><div>Urban climate governance literature has advanced in understanding how cities address climate change, but most quantitative studies focus disproportionately on large, globally prominent cities in the north, overlooking smaller and Global South cities. This gap limits our understanding of how diverse factors shape cities' mitigation actions and whether Global North and Global South cities follow distinct pathways. This study investigates how networking, political economy, socio-cultural, and environmental factors are associated with cities' mitigation patterns—using sectoral and finance-implementation approaches as proxies for the advancement of climate governance. Drawing on a more representative dataset, we identify that cities with stronger environmental commitment (e.g., emission inventories, risk reporting) tend to implement cross-sectoral mitigation actions, particularly in building and energy systems. National-level wealth and institutional quality (GDP per capita, corruption) also influence sectoral priorities, while finance patterns remain less explained. We further find that C40 membership and globalisation are significantly associated with more ambitious actions in the Global South cities, but not in the Global North, underscoring asymmetric benefits of global networks. Our results reveal that no single factor explains urban climate governance patterns, highlighting how political, economic, and geographic co-dependencies produce divergent climate action pathways. By centering overlooked cities, this study contributes to a more inclusive and context-sensitive understanding of urban climate governance.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102543"},"PeriodicalIF":6.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast estimation and pattern discovery of dynamic vehicle emissions on road networks based on the traffic performance index: A case study of Beijing","authors":"Jinrui Zang ,&nbsp;Xin Hu ,&nbsp;Zhihong Li ,&nbsp;Guohua Song ,&nbsp;Kedi Shi","doi":"10.1016/j.uclim.2025.102537","DOIUrl":"10.1016/j.uclim.2025.102537","url":null,"abstract":"<div><div>The estimation of dynamic emissions from mobile sources such as vehicles presents a major challenge in air quality modeling. Current studies indicate strong positive correlations between traffic congestion and emission levels. However, there is a notable lack of research defining the relationship quantitatively. The dynamic and rapid estimation of total emissions from road networks holds critical importance using real-time Traffic Performance Indices (TPIs). This paper focuses on rapidly estimating and identifying patterns in dynamic vehicle emissions across road networks by leveraging TPIs. At first, relationships between vehicle emissions and TPIs are rigorously examined. Subsequently, a quantitative sine function model is developed to characterize the relationship. Finally, employing the SOM neural network algorithm, temporal emission patterns are investigated, and the impact of the COVID-19 pandemic on emissions is assessed. The results demonstrate: 1) Non-linear positive correlations exist between total emissions and TPIs with emissions escalating with increasing TPI; 2) The proposed sine function model predicts total network emissions with average relative errors of 9.26 % for NO_x, 9.04 % for HC, 8.86 % for CO₂, and 9.02 % for CO; 3) Utilizing the Self-Organizing Map (SOM) neural network clustering algorithm identifies eight emission variation patterns including Holydays, Saturdays, Sundays, Workdays during vacations, Mondays, Fridays, Ordinary workdays, and Congested workdays, which effectively represent 91.54 % of emission scenarios across the network. The findings are expected to broaden TPI applications while providing robust scientific support for integrated policies targeting congestion management and pollution control.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102537"},"PeriodicalIF":6.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the impact of land use and land cover changes on local meteorological conditions and PM2.5 concentrations in Lahore, northeastern Pakistan","authors":"Arshad Abbasi ,&nbsp;Chunsheng Fang ,&nbsp;Ju Wang ,&nbsp;Usman Basharat","doi":"10.1016/j.uclim.2025.102547","DOIUrl":"10.1016/j.uclim.2025.102547","url":null,"abstract":"<div><div>Variations in land use and land cover (LULC) significantly affect the surface energy balance, influencing regional meteorological patterns and air quality. Despite its importance, the extent of this impact has not been thoroughly examined in Lahore, a major urban center in the historically industrial region of Northeastern Pakistan. In this study, based on the WRF-CMAQ model, the LULC2022 (LULC data in 2022) and LULC2010 (LULC data in 2010) scenarios were simulated for January and June 2022 to assess the impact of LULC changes on meteorology and PM_2.5 concentrations in Lahore. Simulations conducted for January and June 2022 revealed elevated daytime sensible heat flux in the urban expansion area (UEA), with maximum values of 153 W/m² and 161 W/m². The latent heat flux declines during the daytime, with maximum values recorded at 22.82 W/m² and 180.73 W/m², respectively. As a result, the 2 m temperature (T2) increased by 4 °C and 3 °C, respectively, and the 10 m wind speed (WS10) increased by 1.06 m/s and 1.60 m/s, respectively. The planetary boundary layer height (PBLH) reached 100 m and 116 m, respectively.</div><div>These changes in meteorological conditions may significantly influence the spatial distribution of air pollutants. Within the UEA, PM₂.₅ concentrations exhibited reductions of 35 μg/m³ and 20 μg/m³ during January and June. The variation in SO2–4 concentrations contributed approximately 25 % to the total PM₂.₅ change, with a decline of 5–6 μg/m³ observed during nighttime in January. Additionally, secondary organic aerosol (SOA) derived from biogenic volatile organic compound (BVOC) precursors (BSOA) showed a slight decrease in cropland areas predominantly covered by green vegetation. Meanwhile, PM₂.₅ around the UEA increased notably in January. CMAQ model analysis reveals that this spatial variability of PM_2.5 is mainly influenced by enhanced transport and diffusion processes occurring in both horizontal and vertical dimensions across the UEA. In January, vertical advection (ZADV) and horizontal advection (HADV) contributed negatively to PM_2.5 levels in the UEA, increasing concentrations by 25 μg/m³ and 40 μg/m³, respectively. In June, the adverse impacts of Vertical diffusion (VDIF) and horizontal advection (HADV) on PM_2.5 were more significant during nighttime, with respective increases of 40 μg/m³ and 31 μg/m³ in the UEA.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102547"},"PeriodicalIF":6.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extreme heatwaves exacerbate canopy urban heat islands: Multi-city observational evidence from eastern China","authors":"Wenkai Wu ,&nbsp;Xiaoshan Yang ,&nbsp;Mingcai Li ,&nbsp;Jingfu Cao ,&nbsp;Jing Zhou ,&nbsp;Weidong Peng","doi":"10.1016/j.uclim.2025.102548","DOIUrl":"10.1016/j.uclim.2025.102548","url":null,"abstract":"<div><div>Heatwaves (HWs) combined with the urban heat island (UHI) effect pose a significant threat to urban systems and public health. Understanding how UHI intensity (UHII) varies during HWs is critical for cities to prepare for extreme heat events. However, existing research has provided inconsistent findings. Some studies reported a substantial increase in UHII during HWs, whereas others observed minimal change or a decrease. To address this discrepancy, we analyzed three years of observational data from 15 cities in Jiangsu Province, China, using a network of dedicated monitoring stations. Our study focused on canopy-layer UHII variations during HWs, defined as periods of at least three consecutive days (May–September) with daily maximum and minimum temperatures exceeding the 90th percentile of historical records. HWs were classified into weak, moderate, and strong intensity based on the average temperature. We used non-heatwave hot summer days (within 15 days before and after a HW event) as a reference baseline for comparison. Our findings reconcile prior contradictions by demonstrating that the UHII is intensity-dependent. The UHII was on average of 0.53 °C lower during weak HWs and 0.61 °C higher during strong HWs than in the reference periods, whereas no significant differences were observed during moderate HWs. Mechanism analysis revealed that strong HWs increased UHII through higher anthropogenic heat release (e.g., air conditioning use) and lower latent heat flux. These findings advance our understanding of the interaction between UHIs and HWs, offering valuable insights into urban heat adaptation and mitigation strategies.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102548"},"PeriodicalIF":6.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stacked deep learning ensemble for time series prediction of PM2.5 levels in Bihar","authors":"Ravi Patel,&nbsp;Aditya Kumar,&nbsp;Jainath Yadav,&nbsp;Mrityunjay Singh","doi":"10.1016/j.uclim.2025.102521","DOIUrl":"10.1016/j.uclim.2025.102521","url":null,"abstract":"<div><div>A major contributor to the deterioration of air quality is Particulate matter (PM_2.5) with a diameter of less than 2.5 micrometers, which makes air pollution among the most pressing environmental concerns in the world. Bihar, one of India’s most densely populated states, has experienced deteriorating air quality over the past decade, particularly in major urban centers like Patna, Gaya, and Muzaffarpur. Given the severe health and environmental implications of high PM_2.5 levels, accurate forecasting models are essential for proactive pollution control measures. This study explores the application of various time series forecasting models for predicting PM_2.5 concentrations, focusing on deep learning ensemble methods. The framework utilizes five base deep learning models: Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Gated Recurrent Unit (GRU), and Bidirectional LSTM (Bi-LSTM), each trained individually to capture different aspects of temporal dependencies in the data. To enhance predictive accuracy, the predictions from these base models are combined using a stacking-based ensemble approach with XGBoost as the meta-learner. This ensemble method refines the final prediction by leveraging the strengths of each model and mitigating their individual weaknesses. The proposed model exhibits outstanding effectiveness in PM_2.5 estimation, attaining an MSE of 33.72, MAE of 2.56, RMSE of 5.80, and an R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.99 for Patna. Similarly, it attains an MSE of 8.90, MAE of 2.12, RMSE of 2.98, and an R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.99 for Gaya, while for Muzaffarpur, the model records an MSE of 11.37, MAE of 2.44, RMSE of 3.37, and an R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.99. These outcomes demonstrate how accurate and dependable the model is at predicting air quality in various places. The findings aim to assist policymakers and environmental agencies in making informed decisions to reduce air pollution and safeguard the general public’s health.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102521"},"PeriodicalIF":6.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microclimate simulation and lichen-based validation analyzing street trees' impact on atmospheric pollutant dispersion at the urban canyon scale","authors":"Carolina Girotti ,&nbsp;Paula Sofia Antunes Matos ,&nbsp;Alessandra R. Prata Shimomura ,&nbsp;Fernando Akira Kurokawa ,&nbsp;Ezequiel Correia ,&nbsp;António Lopes","doi":"10.1016/j.uclim.2025.102549","DOIUrl":"10.1016/j.uclim.2025.102549","url":null,"abstract":"<div><div>This study investigates the impact of street trees on air pollutant concentrations, specifically NO₂ and PM10, in urban environments using computational fluid dynamics (CFD) simulations with ENVI-met software. The study explores how different levels of tree cover influence the dispersion of atmospheric pollutants, focusing on three scenarios: current tree cover, complete removal of street trees, and a 50 % reduction in tree cover. Avenida da Liberdade in Lisbon, known for its high tree density, serves as the study site. To ensure the accuracy of the simulations, the method was validated using air quality data from a local monitoring station, supplemented by an analysis of lichen diversity on 80 trees, a common biomonitor for pollution. The results indicate that both NO₂ and PM10 concentrations are higher under tree canopies, with the greatest increase observed on the windward side of the avenue. Specifically, PM10 levels rose by up to 2.97 %, and NO₂ by up to 25.84 % in the scenario with the highest tree cover. Moreover, the study highlights that street trees have a more significant effect on NO₂ concentrations compared to PM10. The findings suggest that, in this specific case—where there is a high density of trees and low wind speed— reducing tree coverage and improve permeability to the wind, could improve pollution dispersion. This study provides key findings into the complex role of urban trees in air quality and offers a foundation for future research into the modelling of additional pollutants, such as PM2.5 and ozone, to gain a more comprehensive understanding of their impacts on urban air quality.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102549"},"PeriodicalIF":6.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}