Urban Climate最新文献

{"title":"Modeling of urban lake breeze circulation: Implications for the mitigation of urban overheating","authors":"Qilong Zhong ,&nbsp;Jiyun Song ,&nbsp;Xiaoxue Wang ,&nbsp;Huanhuan Wang ,&nbsp;Dachuan Shi ,&nbsp;Yuguo Li","doi":"10.1016/j.uclim.2025.102295","DOIUrl":"10.1016/j.uclim.2025.102295","url":null,"abstract":"<div><div>The urban overheating phenomenon, induced by both global warming and the urban heat island (UHI) effect, has been exacerbated by worsened urban wind environments. Urban lakes may aid in mitigating urban overheating through the process of lake breeze circulation (LBC). In this study, we developed a multi-scale water-energy coupled CFD model to simulate the transport of heat and moisture between lake surfaces and built-up areas and to resolve dynamics of atmospheric temperature, humidity, and wind at both street canyon scale (1m) and city scale (50km) with relatively low computational costs. Based on this model, we conducted sensitivity analyses to study the impact of urban, lake, and atmospheric parameters on thermal comfort conditions in the city. Our results show that the cooling capacity of LBC is more evident in hotter and denser cities. Furthermore, in the process of urban expansion, horizontal sprawl (i.e., increasing city size) is more advantageous than vertical growth (i.e., increasing building height) and infilling growth (i.e., increasing building density), considering the cooling potential of LBC. Our results can provide significant references for urban planning and city design for the sake of the mitigation of urban overheating via lake breezes.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102295"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072695","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":"Temperature-related mortality and future health risks from climate change in a middle eastern Metropolis","authors":"Peran Riahi,&nbsp;Babak Khorsandi","doi":"10.1016/j.uclim.2025.102327","DOIUrl":"10.1016/j.uclim.2025.102327","url":null,"abstract":"<div><div>This study investigates the effect of past and future temperatures on five distinct causes of mortality, including non-accidental causes, as well as diseases related to the cardiovascular, respiratory, nervous systems, and diabetes in Tehran, Iran.</div><div>Data on meteorological conditions and mortality were obtained for 7 years. To create a model for the association between temperature and death from the diseases, a time-series quasi-Poisson regression in combination with a distributed lag non-linear model was used. Extreme cold and hot temperatures were assessed to determine relative risks (RRs) at the 2.5th and 97.5th percentiles of the temperature distribution. To quantify the future risks, projections of average temperature for 2090–2099 from General Circulation Models under Representative Concentration Pathways scenarios downscaled by LARS-WG were used.</div><div>Our results revealed that cold temperatures are more harmful to mortality than hot temperatures. The RRs for non-accidental deaths at extreme cold for the group of elderlies (aged≥65) is significant (1.20, %95 CI: 1.02–1.41, <em>p</em>-value&lt;0.05). Elderlies are also at a higher risk of dying from respiratory ailments. The RRs for nervous system diseases and diabetes are significant (<em>p</em>-value&lt;0.05) for extreme cold temperatures. The effects of projected temperature changes on causes of mortality except non-accidental causes and cardiovascular diseases are not statistically significant. However, the RRs of respiratory mortality rises with colder temperatures.</div><div>The findings of this research can lead to the identification of sensitive populations exposed to non-optimal temperatures. Lastly, this research provides decision-makers with important information for planning preventive measures to reduce temperature-related mortality.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102327"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172050","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":"Analysis of ozone pollution causes under influence of extreme high temperatures and power rationing measures: Chengdu, China","authors":"Lijuan Lei ,&nbsp;Miao Feng ,&nbsp;Yi Zhang ,&nbsp;Wei Li ,&nbsp;Shishi Yang ,&nbsp;Danlin Song ,&nbsp;Yang Chen ,&nbsp;Junjie Wang","doi":"10.1016/j.uclim.2024.102239","DOIUrl":"10.1016/j.uclim.2024.102239","url":null,"abstract":"<div><div>Ozone(O₃) pollution has become increasingly severe and persistent in recent years. A persistent O₃ pollution event occurred in Chengdu in 2022 during a period of extreme high-temperature weather. In response, the government implemented power rationing measures to restrict industrial electricity consumption. Based on the observational data, an observation based model (OBM) and probability mass function (PMF) are used to study the effect of power rationing on the O₃ formation mechanism. After power rationing, the temperature increased by 1.7 °C,despite the conditions for O₃ generation were more favorable, the O₃ concentration was reduced. This is mainly because the power rationing measures reduced non-methane hydrocarbons (NMHCs) by about 22.71 % and reduced the generation of organic peroxide radicals (RO₂), thereby suppressing O₃ generation. The reduced NMHCs primarily came from solvent use sources. In recent years, extreme high-temperature days have occurred frequently, and the O₃ pollution situation has become grim. This study seeks to understand the influence of human activities on O₃ in Chengdu under extreme high-temperature conditions, emphasizes that O₃ pollution prevention and control measures in Chengdu should consider addressing solvent use sources, reducing the NMHC, and the adopting of regional joint preventive and control measures, and provides references for other regions.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102239"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816599","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":"Association between climate variability and injury-causing road traffic accidents in Singapore – A time-stratified case-crossover study","authors":"Rae Chua ,&nbsp;Yih Yng Ng ,&nbsp;Andrew F.W. Ho ,&nbsp;Joel Aik","doi":"10.1016/j.uclim.2024.102257","DOIUrl":"10.1016/j.uclim.2024.102257","url":null,"abstract":"<div><h3>Introduction</h3><div>Studies examining the association between weather exposures and the likelihood of road traffic accidents (RTAs) have widely been conducted in temperate settings. However, evidence on such associations in tropical urban settings where the climate differs is limited.</div></div><div><h3>Methods</h3><div>National level data on road locations with injury-causing RTAs in Singapore was obtained. We linked the accident data to weather exposure measures from the nearest weather station. We used a time-stratified, case-crossover analytical study design, the Distributed Lag Non-Linear Modelling framework and conditional logistic regression to assess the association between weather exposures and the odds of injury-causing traffic accidents on roads.</div></div><div><h3>Results</h3><div>There were 42,989 injury-causing RTAs from January-2016 to November-2021. Cool temperatures (adjusted OR₂₅°_C: 1.087, 95 % CI: 1.023 to 1.156) and higher rainfall (adjusted OR_38mm: 1.168, 95 % CI: 1.129 to 1.208) were positively associated with increased odds of injury-causing traffic accidents. These effects were greater on expressways compared to normal roads, suggesting potential effect modification by road type.</div></div><div><h3>Conclusion</h3><div>Lower ambient temperature and higher rainfall were positively associated with injury-causing RTAs in Singapore. Prevention measures ought to be timed with anticipated cooler and wetter days to maximise their uptake and effectiveness among motorists, especially those who use expressways frequently.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102257"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867483","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":"Mapping flood risk using a workflow including deep learning and MCDM– Application to southern Iran","authors":"Hamid Gholami ,&nbsp;Aliakbar Mohammadifar ,&nbsp;Shahram Golzari ,&nbsp;Reza Torkamandi ,&nbsp;Elahe Moayedi ,&nbsp;Maryam Zare Reshkooeiyeh ,&nbsp;Yougui Song ,&nbsp;Christian Zeeden","doi":"10.1016/j.uclim.2024.102272","DOIUrl":"10.1016/j.uclim.2024.102272","url":null,"abstract":"<div><div>Floods - an important risk that threatens many people worldwide – affect both the environment and human-made structures, and can cause loss of agricultural activities and life, economic challenges such as the destruction of infrastructure. Therefore, spatial maps of flooding probability can be useful to identify regions with high risk, these can be used to mitigate its negative consequences. Here, we developed a methodology to map flood risk in a catchment in southern Iran by combining a hazard map produced by a bidirectional long short-term memory (bLSTM) deep learning (DL) model, and a flood vulnerability map produced by a complex proportional assessment (COPRAS) model as a multi-criteria decision making (MCDM) model. Different environmental variables as lithology, vegetation cover, land use were mapped spatially, and a GrootCV was employed to identifying the most important variables controlling flood risk. Among various variables explored as controls flood risk, the variables extracted from a digital elevation model (DEM) (e.g., topographic wetness index (TWI), river density, topographic position index (TPI), stream power index (SPI), slope, elevation and distance to river) were recognized as the most effective features controlling the flood risk. Finally, a bLSTM model was employed to map the flood hazard. Its performance was assessed by the cumulative gain and Kolmogorov Smirnov (KS) tests. To map flood vulnerability, seven socio-economic variables were mapped as key controls, and then, analytical hierarchy process (AHP) and COPRAS models were employed to determine the weights of variables to map flood vulnerability. Finally, a flood risk model was generated by integration of the bLSTM and COPRAS. The results revealed that 23.2 %, 27.7 %, 18.7 %, 15.8 % and 14.6 % of the total study area are classified as very low to very high risk classes, respectively. Overall, our methodology based on DL and MCDM can employ to map flood risk and another disasters (e.g., landslide, land subsidence, soil erosion, etc.) in different climatic regions worldwide.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102272"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888663","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":"Disparities in the impact of urban heat island effect on particulate pollutants at different pollution stages - A case study of the “2 + 36” cities","authors":"Chang Yinghui ,&nbsp;Guo Xiaomin","doi":"10.1016/j.uclim.2024.102273","DOIUrl":"10.1016/j.uclim.2024.102273","url":null,"abstract":"<div><div>Urban heat island (UHI) and atmospheric pollution are critical ecological challenges in urban areas. China's air quality presents a stage-specific “polluting first and cleaning up later” model. However, the differential impact of UHI on particulate matter (PM) pollution across various stages remains unclear. This study utilizes land surface temperature (LST) data between 2000 and 2021 to calculate the surface urban heat island intensity (SUHII) across the “2 + 36” cities, a key pollution prevention and control area. The inflection points of PM pollution in each city were identified through piecewise linear regression, dividing the study period into distinct pollution stage. The geographical and temporal weighted regression (GTWR) model was employed to analyze the varying impacts of SUHII on PM pollution across different stages. The result indicate that UHI persisted significantly during the study period. The impact of SUHII on PM pollution exhibits pronounced temporal and regional characteristics, with an increasing influence of Nighttine SUHII (N_SUHII) on pollution. This finding highlights a crucial entry point for future pollution control measures. Moreover, under the influence of SUHII, ecological factors such as enhanced vegetation index and precipitation may paradoxically exacerbate PM pollution at certain times. These findings reveal the multifaceted causes and dynamics of urban PM pollution in developing countries, validate the necessity of implementing targeted management measures in the context of future climate change, and are important for the formulation of strategies to promote sustainable urban development.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102273"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918077","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":"Spatial bias in placement of citizen and conventional weather stations and their impact on urban climate research: A case study of the Urban Heat Island effect in Canada","authors":"Priyadharshini Sakthivel ,&nbsp;Raja Sengupta","doi":"10.1016/j.uclim.2024.102280","DOIUrl":"10.1016/j.uclim.2024.102280","url":null,"abstract":"<div><div>Citizen Weather Stations (CWS) are a source of Crowdsourced Geographic Information for urban climate research, which can provide extensive datasets in areas where data are scarce or unavailable. In this article, we explore the efficacy of using meteorological data from CWS in studying the Urban Heat Island (UHI) effect across Canada during late spring and summer of 2022. In particular, we evaluate the distribution of CWS before relying on them for UHI intensity estimates, since potential spatial biases in placement of these sensors can greatly affect canopy-level measurements. We compared the spatial distribution of Netatmo CWS with conventional weather stations from Environment and Climate Change Canada (ECCC), and found that ECCC sensors are more numerous in rural areas, while Netatmo sensors are present in greater numbers in urban areas. We then computed UHI intensity using urban temperature from Netatmo sensors and peri-urban temperature from ECCC sensors. The resulting intensity values were higher than those estimated using either the Netatmo or the ECCC sensors individually, thus highlighting the influence of sensor distribution in estimating UHI magnitude. Overall, our research explores the distribution of both ECCC and CWS sensors, and highlights their potential complementarity in urban climate research.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102280"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibrating the UTCI scale for hot and humid climates through comprehensive year-round field surveys to improve the adaptability","authors":"Wei-An Chen ,&nbsp;Pei-Lun Fang ,&nbsp;Ruey-Lung Hwang","doi":"10.1016/j.uclim.2024.102267","DOIUrl":"10.1016/j.uclim.2024.102267","url":null,"abstract":"<div><div>The Universal Thermal Climate Index (UTCI), a recent advancement in outdoor thermal comfort modeling, requires calibration for hot-humid climates, as its original scale may not suit all climate conditions. This study conducted a comprehensive survey in Taichung City, central Taiwan, to develop a UTCI scale tailored for subtropical regions. Recognizing the limitations of symmetrical results in traditional regression methods, this study applied logistic regression to capture asymmetries in thermal sensation. This approach revealed that individuals in hot-humid regions tolerate warmth better than cold, leading to a calibrated UTCI scale. Our findings indicate that the revised UTCI scale for Taiwan displays higher ranges across thermal stress categories than the original scale, with the “no thermal stress” range extending from 21.6 to 30.9 °C UTCI. This adjusted scale is also higher than those for Mediterranean cities like Athens and Tehran, underscoring the influence of local climates and regional differences in thermal perception. Overall, our novel and more precise approach to evaluating thermal comfort addresses the limitations of the original scale for subtropical climates. Linear regression confirms a warming trend with potential impacts on local thermal stress, providing valuable insights for urban planning in hot-humid regions.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102267"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917984","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":"Impact of drought on cooling capacity and carbon sequestration in urban green area","authors":"Gabriele Guidolotti ,&nbsp;Terenzio Zenone ,&nbsp;Theodore Endreny ,&nbsp;Rocco Pace ,&nbsp;Marco Ciolfi ,&nbsp;Michele Mattioni ,&nbsp;Emanuele Pallozzi ,&nbsp;Negar Rezaie ,&nbsp;Teresa Bertolini ,&nbsp;Chiara Corradi ,&nbsp;Carlo Calfapietra","doi":"10.1016/j.uclim.2024.102244","DOIUrl":"10.1016/j.uclim.2024.102244","url":null,"abstract":"<div><div>Through experimental observations of carbon and energy exchange in Urban Green Areas (UGA) using the Eddy Covariance technique we show that the vegetation air cooling capacity and carbon uptake are influenced by water availability: multivariate analysis shows that solar radiation (Rg) was the primary control factor in latent heat (LE) and CO₂ fluxes followed by the air temperature (T_air) and Vapor pressure deficit (VPD). The relative importance of VPD was higher in correspondence of the year characterized by drought condition. The mature UGA investigated was a net carbon sink only during the summer season from June to September while on annual basis was a carbon source for all the years investigated. The summer average daytime UGA air temperature cooling potential ranged from 3.02 °C (± 1.4) during the wet season, to 2.1 °C (± 1.3) during the dry season. The city-scale application of the i-Tree model highlighted the reduced cooling potential effect of the UGA during dry periods, relative to those with better soil water supply from precipitation and indicates that, in case an emergency irrigation would be possible, about 50 L m⁻² during the drought period would compensate the water shortage.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102244"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting environmental pollutants in the apartment public space: Evaluating the impact of spatial enclosure and monitoring locations","authors":"Yang Lv ,&nbsp;Xiaodong Wang ,&nbsp;Dan Liu","doi":"10.1016/j.uclim.2024.102277","DOIUrl":"10.1016/j.uclim.2024.102277","url":null,"abstract":"<div><div>To effectively reduce monitoring costs for pollutants in complex apartment public spaces and ensure a healthy living environment for residents, this study explores the feasibility of predicting environmental pollution in apartment public spaces through experimental and data analysis methods. By adjusting the spatial enclosure (e.g., window opening or closing) and monitoring locations on the first-floor public space, this research utilizes Spearman rank correlation coefficients and exploratory data analysis (including five linear models, nonlinear models, three tree-based models, one nearest-neighbor model, and one neural network model) to assess how these adjustments impact pollutant correlations and model performance. Results indicate that tree-based models, particularly Decision Tree Regression, consistently outperform other models, demonstrating reliable predictive capabilities across varying enclosure and location conditions. Time granularity and wind direction significantly influence correlations, while pollutants like PM and ozone exhibit unidirectional correlations across different locations. The study also finds that changes in spatial enclosure alter indoor airflow and pollutant diffusion patterns, thereby affecting predictive accuracy. Additionally, this research elucidates the feasibility of spatially predicting environmental pollutants under different conditions, offering practical guidance for low-cost monitoring of apartment public spaces. These findings support sustainable building management, effective pollution control, and enhanced health protection for residents.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102277"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939741","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}