Urban Climate最新文献

{"title":"Urbanization effects on heat waves characterized by high topographic relief in a typical mountainous urban region","authors":"Chaogui Lei ,&nbsp;Chaoyu Pan ,&nbsp;Yuefeng Wang ,&nbsp;Longfei Han ,&nbsp;Song Song","doi":"10.1016/j.uclim.2025.102538","DOIUrl":"10.1016/j.uclim.2025.102538","url":null,"abstract":"<div><div>Understanding heat wave (HW) changes is essential for effective adaptation to global climate change and mitigation of disaster risks. However, urbanization effects on HW variations are rarely clarified in mountain areas. Taking Sichuan-Chongqing Region (SCR) of China as the study area, this study identified respective changes of HW intensity, frequency and occurrence time with Innovative Trend Analysis (ITA). Based on a dynamical classification of urban and rural stations using annual-varying land use, it innovatively distinguished urbanization effects (UE) on HW in different topographic locations. The results indicate that: (1) from 1970 to 2019, HW events became stronger, longer, and more frequent, with earlier commence and later termination, particularly at highly urbanized stations in middle-east of SCR; (2) In SCR, average urbanization effects respectively for HW magnitude (T_min, HWM and HWA), duration (HWL and HWD) and frequency (HWF) are 0.43 °C, 0.17 and 0.33 days per decade, which became more pronounced in steeper mountains; (3) with a greater topographic relief, the respective urban-rural contrasts of HW change trend and occurrence probability declined, which implies that stronger topographic gradients can partly modulate urbanization effects on HW. Such results unravel the multifaceted behaviors of HW evolution during urbanization in recent 50 years. Moreover, they quantitatively identify the role of topographic variations in urbanization effects on HW. They can provide valuable location-specific suggestion for sustainable urban planning and efficient climate disaster management.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102538"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738190","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":"Extending spatial regression to the analysis of subsurface urban heat islands","authors":"Zhonghao Chu,&nbsp;Alessandro F. Rotta Loria","doi":"10.1016/j.uclim.2025.102554","DOIUrl":"10.1016/j.uclim.2025.102554","url":null,"abstract":"<div><div>Spatial regression models have been widely employed in the analysis of surface urban heat islands to investigate the relationship between urban morphology and surface temperature. In contrast, the employment of such approaches in the study of subsurface urban heat islands, which are a hidden yet central problem in urban climate, remains vastly untapped. This study is the first to apply classical spatial regression approaches to the modeling of subsurface urban heat islands, focusing on the influence of underground heat source density and depth on the subsurface temperature field. Using the Chicago Loop district as a case study, we apply Moran's <em>I</em> coefficient to confirm the presence of spatial autocorrelation in the underground temperature field. We then evaluate six different grid sizes using Pearson correlation coefficients to determine the optimal spatial resolution, identifying 60 m as the most appropriate scale. Based on this resolution, we implement and compare four models to simulate subsurface heat island patterns: the ordinary least squares, the spatial error model, the spatial lag model, and the geographically weighted regression. Results show that geographically weighted regression consistently outperforms other models by capturing spatial heterogeneity, achieving R² values exceeding 0.85 in all scenarios. This work advances the application of spatial analysis in underground urban climate studies and provides a valuable foundation for efficient modeling and harvesting of underground waste heat resources.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102554"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779557","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":"Effects of urbanization on day-to-day temperature variability in China during 1961–2022","authors":"Yujia Shi,&nbsp;Guocan Wu","doi":"10.1016/j.uclim.2025.102560","DOIUrl":"10.1016/j.uclim.2025.102560","url":null,"abstract":"<div><div>Short-term temperature fluctuations have significant impacts on public health, ecosystems, and economic development. This study analyzed the day-to-day temperature variability (DTD) based on the daily mean, maximum, and minimum temperatures across China from 1961 to 2022. The urban and rural stations were identified using high-resolution remote sensing land cover data, and the asymmetric responses of DTDMAX and DTDMIN to urbanization were revealed. The results showed that approximately 88 % of stations had positive ΔDTD values, suggesting greater fluctuations in the daytime than nighttime. In the Songliao and Haihe River basins, urban stations had higher annual mean ΔDTD (0.30 ± 0.02 °C) than rural stations (0.28 ± 0.03 °C), probably due to enhanced daytime heat storage and delayed nighttime heat release driven by rapid urbanization. By contrast, rural stations had higher temperature fluctuations (0.81 ± 0.03 °C and 0.66 ± 0.02 °C) than urban stations (0.40 ± 0.04 °C and 0.17 ± 0.02 °C) in the Southwest and Yangtze River basins, possibly due to the higher atmospheric moisture content in southern areas resulting in smaller daytime temperature variations. The ratios of DTD to the monthly standard deviation were between 0.2 and 0.9 for eight basins except Southeast River basin, indicating that the climate system was mainly characterized by orderliness. The impacts of urbanization were most pronounced in the Yellow River basin and Songliao river basin, where the ΔDTD values reached 0.28 °C/100a and 0.29 °C/100a, respectively. These findings can provide scientific insights for facilitating the formulation of region-specific climate adaptation strategies and for optimizing urban thermal management.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102560"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779556","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":"Numerical study on the climatic effects of building morphology: A case study of Shanghai, China","authors":"Yong Sun ,&nbsp;Ning Zhang","doi":"10.1016/j.uclim.2025.102563","DOIUrl":"10.1016/j.uclim.2025.102563","url":null,"abstract":"<div><div>The impact of urbanization on local climate involves two processes: changes in surface material and changes in spatial morphology. These two processes significantly alter the surface properties of urban areas. Compared to changes in surface material, the changes in urban spatial morphology have a more complex impact on the dynamic and thermal processes in urban areas. This paper examines Shanghai, using the WRF model coupled with urban morphological parameters to conduct simulations of winter and summer from 2016 to 2020. Our analysis focused on the impact of building morphology on near-surface meteorological elements, surface energy balance, boundary layer structure, and summer precipitation characteristics in Shanghai. The results indicate that building morphology affects surface energy balance by influencing urban canopy radiation processes. Buildings cause the city to absorb more radiant energy during the day, increasing both sensible heat and stored heat in the city. At night, this stored heat is released, with the release being more complete during winter. Consequently, Shanghai experiences a notable increase in near-surface air temperature and a rise in boundary layer height. Building morphology also affects summer precipitation in Shanghai, increasing both the amount of precipitation and the number of rainy days, particularly light rain days.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102563"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763887","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":"Corrigendum to’Relationship between upward heat transport and building arrangement in urban districts of Osaka as revealed by large-eddy simulations' [Urban Climate 61 (2025) 102441]","authors":"Seika Tanji,&nbsp;Tetsuya Takemi","doi":"10.1016/j.uclim.2025.102553","DOIUrl":"10.1016/j.uclim.2025.102553","url":null,"abstract":"","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102553"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738191","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":"Regression-based predictive modeling of summer urban microclimate: Quantifying contributions from urban design and urban heat emissions","authors":"Yuan Chen ,&nbsp;Yupeng Wang ,&nbsp;Dian Zhou ,&nbsp;Xilian Luo","doi":"10.1016/j.uclim.2025.102550","DOIUrl":"10.1016/j.uclim.2025.102550","url":null,"abstract":"<div><div>The formation of urban microclimates is a complex process influenced by urban morphology and anthropogenic heat emissions (AHEs). While the combined effects of urban morphology and AHEs remain underexplored. In this study, air temperature (AT), relative humidity and dew point temperature were measured in five representative districts in Xi'an, China, during typical summer days. AHE from buildings (AHEb) was simulated using EnergyPlus, while AHE from traffic (AHEt) was calculated from traffic flow data. Seven urban morphological indices were used to develop partial least squares regression models. Results show that in the average daily AT, the contributions of AHE and two-dimensional morphological indicators are similar, both around 39 %. The contribution of AHEb (20.2 %) is higher than that of AHEt (18.3 %). For the average daytime AT, AHEb contributes less than GCR and SVF. However, during the peak AT hours, AHEb becomes the dominant contributor at 26.7 %. Each 100 W/m² increase in HVAC emissions raises hourly AT by 1.0 °C during the day and 4.8 °C at night. The predictive modeling approach supports microclimate assessment and cooling strategy development in high-density urban areas.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102550"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763886","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":"Observed precipitation enhancement and suppression downwind of a major U.S. city","authors":"Olamiposi Fagunloye,&nbsp;Jeremy E. Diem","doi":"10.1016/j.uclim.2025.102562","DOIUrl":"10.1016/j.uclim.2025.102562","url":null,"abstract":"<div><div>While urban enhancement of precipitation during summer has been heavily studied, there has been little research on urban effects on precipitation during other seasons. Therefore, this study assesses urban effects on precipitation throughout the year in the Atlanta, Georgia USA region during 2014–2023. Precipitation totals and frequencies of precipitation days and heavy-precipitation days from a network of 50 gauges were aggregated for 12 overlapping three-month periods. The two main methods used were the calculations of correlations between precipitation and upwind imperviousness and differences between groups of downwind gauges and control gauges. Urban enhancement and suppression of precipitation totals and heavy-precipitation days occurred east/northeast of Atlanta, primarily in Gwinnett County. Enhancement (suppression) was revealed by significant positive (negative) correlations between precipitation and upwind imperviousness and significantly larger (smaller) precipitation values at downwind gauges compared to control gauges. Precipitation totals and heavy-precipitation days were enhanced by 13 % and 19 %, respectively, during the warm season, and those two variables were suppressed by 5 % and 11 %, respectively, during winter/spring. Precipitation days were enhanced throughout the year, with the largest enhancement (7 %) occurring during summer. The urban heat island and urban aerosols are the likely causes of the urban effects on precipitation.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102562"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763889","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":"Quantifying mutual transport contributions of ozone and its precursors among adjacent county-level cities during typical pollution episodes","authors":"Demian Lai ,&nbsp;Yifei Chen ,&nbsp;Yangjun Wang ,&nbsp;Yan Zhang ,&nbsp;Lin Xu ,&nbsp;Zijia Ma ,&nbsp;Burcak Kaynak ,&nbsp;Shikang Tao ,&nbsp;Yu Shang ,&nbsp;Li Li","doi":"10.1016/j.uclim.2025.102558","DOIUrl":"10.1016/j.uclim.2025.102558","url":null,"abstract":"<div><div>As the demand for continuous improvement in air quality in China grows, county-level cities face growing pressure to mitigate ozone (O₃) pollution, highlighting the urgent need to understand its sources. This study focused on the source apportionment of O₃ in Yucheng and Qihe, two neighboring county-level cities in Dezhou, Shandong Province, which experienced the most severe O₃ pollution during a typical pollution episode from June 22 to 28, 2021. The Community Multiscale Air Quality model, coupled with the Integrated Source Apportionment Method, was employed to quantify the O₃ transport contributions. The results indicate that Qihe's mean contributions to Yucheng's volatile organic compounds (VOCs), nitrogen oxides (NO_x), and Maximum Daily 8-h Average O₃ concentration (O₃_MDA8) were approximately 5.6, 3.3, and 1.9 %, respectively. Their maximum hourly contributions reached approximately 53 ppb, 14, and 21 μg/m³—each higher than the reverse contributions from Yucheng to Qihe. For Yucheng, joint emission control measures with Qihe and Lingcheng have the potential to reduce hourly O₃ concentrations by approximately 56 μg/m³. For Qihe, collaboration with Yucheng and Lingcheng has the potential to reduce hourly O₃ concentrations by approximately 33 μg/m³. In addition, Lingcheng, a neighboring county-level city, contributed significantly to O₃ and its precursors in both Qihe and Yucheng during the pollution period, with concentrations and percentages much higher than those during the clean period. Despite the challenges of joint emission control with distant cities, prioritizing emission reduction strategies among neighboring county-level cities is a feasible approach that can yield significant O₃ reduction potential.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102558"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763888","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":"The impact of urban heat island on snow properties and stratigraphy in the Moscow region","authors":"Anton Komarov ,&nbsp;Alla Turchaninova ,&nbsp;Yuri Seliverstov ,&nbsp;Sergey Sokratov ,&nbsp;Julienne Stroeve","doi":"10.1016/j.uclim.2025.102545","DOIUrl":"10.1016/j.uclim.2025.102545","url":null,"abstract":"<div><div>The urban heat island (UHI) effect is common in large cities during both summer and winter. In winter, heat is not only retained by infrastructure, buildings, and roads, but also actively released during their use, with substantial losses to the surrounding environment. This leads to higher urban temperatures compared to nearby rural areas. While the phenomenon is well documented, its impact on snow cover properties remains understudied.</div><div>In this study, we examine the influence of the UHI on snow cover by comparing snow properties and stratigraphy between an urban site (Moscow) and adjacent rural site (Khotkovo) over the 2014–2022 period. Our methodology included in-situ measurements of snow depth and density, analysis of meteorological station data on snow depth, temperature and precipitation, and satellite-based assessment of land surface temperature using MODIS (MOD11A1) imagery.</div><div>Results show that snow cover duration was shorter at the urban site due to later onset and earlier melt. Despite slightly higher winter precipitation in Moscow, snow depth and snow water equivalent were consistently lower than in Khotkovo. Urban snowpacks had higher average density. Stratigraphic analysis revealed thicker melt-freeze layers in Moscow and thinner layers of faceted crystals and depth hoar compared to the rural site.</div><div>These findings highlight the role of the urban heat island in altering snow cover properties and stratigraphy. They provide valuable insights for improving snowpack modeling and assessing hydrological and ecological conditions in urban environments.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102545"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738273","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":"Positive matrix factorization outperforms machine learning in imputing missing PM2.5 and further identifying spatial patterns in multi-sites without external data","authors":"Youngkwon Kim ,&nbsp;Philip K. Hopke ,&nbsp;Seung-Muk Yi ,&nbsp;Woojoo Lee ,&nbsp;Ho Kim ,&nbsp;JongBae Heo ,&nbsp;Hwajin Kim ,&nbsp;Young Su Lee ,&nbsp;Kwonho Jeon ,&nbsp;Jieun Park","doi":"10.1016/j.uclim.2025.102552","DOIUrl":"10.1016/j.uclim.2025.102552","url":null,"abstract":"<div><div>Missing observations of fine particulate matter (PM_2.5) distort air pollution studies by reducing the available concentration information. While machine learning (ML) and statistical methods are commonly used for imputation, they typically rely on external datasets, limiting reproducibility. This study addresses this gap by evaluating five techniques, including positive matrix factorization (PMF), random forest (RF), denoising autoencoder (DAE), multiple imputation by chained equations (MICE), and k-nearest neighbor (kNN), to impute missing PM_2.5 concentrations from 25 districts in Seoul, South Korea, without external data. First, completely filled dataset was obtained. Then, some observations were artificially masked to mimic the actual missingness rate. Using 5-fold cross-validation, imputation accuracy was assessed via mean absolute percentage error (MAPE). PMF showed the lowest MAPE (19.1 %), outperforming RF (21.3 %), DAE (23.7 %), MICE (24.6 %), and kNN (25.9 %). The imputed concentrations from the PMF analysis were sufficiently accurate to be used in air pollution studies with missing data while considering uncertainties. The highest accuracy of PMF is attributed to its ability to effectively resolve latent factors that represent spatial patterns contributing to PM_2.5 in Seoul and use them to impute missing values. Spatial patterns grouped 25 districts into six areas associated with PM_2.5 concentrations from specific districts that are mainly affected by the same pollution sources. This work demonstrates PMF outperforms ML and statistical methods in accurately imputing missing concentrations and further identifying spatial PM_2.5 patterns in multi-sites without external data. Missing PM_2.5 data in Seoul needs to be imputed using the PMF analysis for reliable air quality investigations.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102552"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763884","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}