Urban Climate最新文献

{"title":"Employing the spray system to improve the regional thermal environment in outdoor open space","authors":"Yi Gao, Liming Ge, Xi Meng","doi":"10.1016/j.uclim.2024.102266","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102266","url":null,"abstract":"Rapid urbanization has led to a significant urban heat island effect. When the spray cooling system is applied to outdoor public space, it is helpful to improve the microclimate. While a reasonable spray system is an important guarantee to improve the thermal comfort of the human body in the spray space. In this study, the combination of experimental measurement and questionnaire survey was used to explore the thermal comfort index suitable for evaluating natural and spray space to modify the heat stress scale. The accuracy of thermal sensation prediction based on the original thermal comfort index and the corresponding heat stress scale was compared between the two methods of thermal sensation regression and thermal unacceptable percentage prediction. A heat stress scale based on natural and spray space in Qingdao was proposed. The results showed that compared with direct exposure to sunlight, the spray space could effectively improve thermal sensation (−1.07) and thermal comfort (+0.80), and inhibit the rate of skin temperature rise (+0.553 ∼ −0.155 °C/min). The thermal unacceptable percentage method can be used to predict the thermal sensation in the natural space, and the prediction rate (88 %) of the universal thermal climate index (UTCI) was the highest. The thermal sensation regression method can be used to predict the thermal sensation in the spray space, and the standard effective temperature (SET<ce:sup loc=\"post\">⁎</ce:sup>) prediction rate (60 %) was the highest. In the assessment of outdoor thermal risk, when the UTCI exceeded 38 °C in Qingdao, the heat risk reached the range of strong heat stress, and it was necessary to start the spray device to improve the thermal environment. When the SET<ce:sup loc=\"post\">⁎</ce:sup> in the spray space exceeded 41 °C, the spray still cannot improve the thermal health status, and it was recommended to reduce outdoor activities.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"15 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918076","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 role of urban forests in mitigation of particulate air pollution: Evidence from ground observations in South Korea","authors":"Jihoon Seo, Hye-Ryun Oh, Doo-Sun R. Park, Jin Young Kim, Dong Yeong Chang, Chan Ryul Park, Hong-Duck Sou, Sujong Jeong","doi":"10.1016/j.uclim.2024.102264","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102264","url":null,"abstract":"The effects of forests in mitigating urban air pollution have not been fully evaluated due to limited observational data. This study assesses the role of urban forests in reducing particulate matter (PM) using data from the recently installed ground PM observation network across South Korean forests, known as the Asian Initiative for Clean Air Networks (AICAN). Results show that urban forests are more effective at reducing coarse PM (with PM decrease efficiencies of 4.5 % to 24.4 % for PM<ce:inf loc=\"post\">2.5–10</ce:inf>) than fine PM (1.5 % to 11.4 % for PM<ce:inf loc=\"post\">0.25–1.0</ce:inf>), particularly in summer due to increased biomass growth. The diminished removal effects observed for fine PM may result from elevated physiological activities within forests, which can enhance the accumulation of fine PM. Buffering forests, planted as belts between pollution sources and residential areas, effectively mitigate PM pollution only when sufficiently wide (at least 200 m) and aligned with the prevailing wind direction. Tower measurements reveal PM deposition onto the forest canopy, while fine PM increases through turbulent diffusion under the canopy. This study underscores the importance of urban planning and reforestation strategies in reducing PM levels and highlights the significant role of urban forests in mitigating air pollution.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"2 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918078","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":"Explainable artificial intelligence models for proposing mitigation strategies to combat urbanization impact on land surface temperature dynamics in Saudi Arabia","authors":"Javed Mallick, Saeed Alqadhi","doi":"10.1016/j.uclim.2024.102259","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102259","url":null,"abstract":"Urbanization in Saudi Arabia has significantly altered land use and land cover (LULC), driving notable changes in land surface temperature (LST) dynamics. This study aims to analyze spatio-temporal LULC changes from 2002 to 2022 and their impact on LST, employing optimized machine learning models like Random Forest, Gradient Boosting, XGBoost, and LightGBM, enhanced by explainable artificial intelligence (XAI). Results show a complete loss of water bodies (from 99.25 km<ce:sup loc=\"post\">2</ce:sup> to 0 km<ce:sup loc=\"post\">2</ce:sup>), urban expansion (from 1344.38 km<ce:sup loc=\"post\">2</ce:sup> to 1377.12 km<ce:sup loc=\"post\">2</ce:sup>), and a decline in sparse vegetation (from 231,430.12 km<ce:sup loc=\"post\">2</ce:sup> to 230,454.50 km<ce:sup loc=\"post\">2</ce:sup>). Concurrently, LST increased, with temperatures rising from 25.08 °C–54.42 °C in 2018 to 26.08 °C–56.31 °C in 2022. The LightGBM model demonstrated the highest predictive accuracy with the lowest mean absolute error (MAE). SHAP analysis revealed that higher aerosol concentrations, air temperatures, and pollutants (CO, NO<ce:inf loc=\"post\">2</ce:inf>, SO<ce:inf loc=\"post\">2</ce:inf>) increase LST, while vegetation (NDVI) and water presence (NDWI) mitigate it. The study emphasizes the significant environmental impact of urbanization on LST and highlights the importance of integrated environmental management strategies, such as enhancing vegetation cover, optimizing water management, and minimizing pollution, to mitigate urban heat island effects and foster sustainable development in Saudi Arabia.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"300 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918082","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":"Global warming and urbanization triggering the record-breaking heat event in summer 2023 over Beijing-Tianjin-Hebei urban agglomeration, China","authors":"Yujie Wang, Jisong Chen, Xin Lin, Lianchun Song","doi":"10.1016/j.uclim.2024.102271","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102271","url":null,"abstract":"The summer of 2023 was characterized by record-breaking widespread heat with extensive impacts on agriculture, water supply, transportation and the socio-economy over Beijing-Tianjin-Hebei (BTH) urban agglomeration. Here, we present a timely, comprehensive, multi-faceted analysis of the 2023 extreme heat event based on six extreme heat indices(EHIs). Our findings indicate that the synergistic effect of global warming, urbanization and anomalous atmospheric circulation is the main cause for the occurrence of the record-breaking event. The EHIs have significantly increased during 1971–2023 over the urban agglomeration, especially since 21st century. The increasing magnitudes of EHIs in megacities (i.e., Beijing, Tianjin and Shijiazhuang) were much greater than in surrounding areas. The Urban Heat Island (UHI) effects contribute more than a half to the increase of EHIs. In addition, the intensified continental high-pressure and Western Pacific Subtropical High are favor to the occurrences of extreme heat event in the era of rapid global warming. With such record-breaking heat events becoming more frequent and intense under the rapid global warming and urbanization, we suggest that greening urban areas, designing cool rooftop and planning ventilation corridor could be the practical approaches to mitigate the UHI effect for climate change adaptation strategy.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"28 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918007","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":"Spatiotemporal characteristics and influencing factors of heterogeneity in human dynamic exposure risk to particulate matters","authors":"Chuyi Zhang, Yuanman Hu, Rencang Bu, Zaiping Xiong, Miao Liu, Binglun Li, Lujia Zhao, Yu Song, Chunlin Li","doi":"10.1016/j.uclim.2024.102261","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102261","url":null,"abstract":"Urban residents face serious health issues owing to air pollution, especially from particulate matter (PM). The dynamic exposure risk of PM exhibits intricate spatiotemporal fluctuations influenced by resident activity and urban patterns. Therefore, high spatiotemporal resolution assessments and researches are needed. In this study, high-resolution dynamic exposure risk was assessed using mobile monitoring of three types of PM (PM<ce:inf loc=\"post\">1</ce:inf>, PM<ce:inf loc=\"post\">2.5</ce:inf>, and PM<ce:inf loc=\"post\">10</ce:inf>) and cell phone signaling data in the center of Shenyang, China, combined with geographically weighted regression model and dynamic exposure risk model. And influencing factors of dynamic exposure risks were explored by boosted regression tree model. The results showed that high-risk areas were concentrated along the main roads. Residents suffered greater risks during the morning peak than evening peak, and weekday than weekend. The dynamic exposure risk was significantly affected by the speed of population mobility (relative influence&gt;55.49), surpassing the effect of POI (Point of Interest) density (relative influence&lt;36.55), except during the weekday morning peak. POI density more pronounced affected on dynamic exposure risk of PM<ce:inf loc=\"post\">2.5</ce:inf>, except during the weekend evening peak. Leveraging diverse data with model simulations to independently analyses based on human activity enables a cost-effective assessment and better understanding of the spatiotemporal variability of dynamic exposure risks.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"19 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918085","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":"Decoding prediction of PM2.5 against jointly street-tree canopy size and running vehicle density using big data in streetscapes","authors":"Yifeng Liu, Zhanhua Cao, Hongxu Wei, Peng Guo","doi":"10.1016/j.uclim.2024.102282","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102282","url":null,"abstract":"Running vehicles are one of major sources of PM<ce:inf loc=\"post\">2.5</ce:inf> emission, and street-tree canopy can function as a green veil to retard diffusion. To decode joint contributions to PM<ce:inf loc=\"post\">2.5</ce:inf> of host city is a practical approach to predict accurate PM<ce:inf loc=\"post\">2.5</ce:inf> pollution across results given by different models. In this study, a total of 153 Chinese cities were randomly selected from China where ∼0.3 million of streetscapes were extracted from digital maps in 2023 for analyzing green view index (GVI) and running vehicle density (RVD). Roads were categorized into four classes as coefficients in host cities at varied levels of population urbanization. Air PM<ce:inf loc=\"post\">2.5</ce:inf> concentration occurred at higher levels (&gt; 60 μg m<ce:sup loc=\"post\">−3</ce:sup>) in northwestern cities and low in southwestern ones (∼10 μg m<ce:sup loc=\"post\">−3</ce:sup>). GVI and RVD showed negative relationships with each other in all road classes in most cities except for five medium sized cities (&lt;1000 thousand population). Multivariate regression models indicated that GVI showed a negative contribution to PM<ce:inf loc=\"post\">2.5</ce:inf> while RVD contributed positively. City-level PM<ce:inf loc=\"post\">2.5</ce:inf> was modeled against GVI and RVD using multivariate linear regression, which can be optimized using random forest algorithm (<ce:italic>R</ce:italic><ce:sup loc=\"post\">2</ce:sup> = 0.3062 and 0.9231, accuracy = 71.02 % and 88.17 %, MSE = 90.0327 and 20.4885, MAE = 8.0788 and 3.7173, respectively). GVI was weighted with a higher feature importance than RVD for predicting PM<ce:inf loc=\"post\">2.5</ce:inf>. It was predicted that cities in the centre and along the west edge of mainland China were agglomerated as hotspots with high PM<ce:inf loc=\"post\">2.5</ce:inf> contamination risks.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"71 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918081","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":"Forecasting the air pollution concentration with neural networks","authors":"Jarosław Bernacki","doi":"10.1016/j.uclim.2024.102262","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102262","url":null,"abstract":"Air pollution is a global problem, which has a major impact on human health. Every year concentrations of many air pollutants cause a large number of deaths. In Europe, particularly Poland, there is poor air quality. In this paper, we deal with forecasting the concentration of air pollutants. We propose four deep learning-based methods for forecasting, which include temporal convolutional network (TCN), Kolmogorov-Arnold Network (KAN), fully convolutional network (FCN), and gated recurrent unit (GRU). Each of the methods is used in three different configurations. We generate predictions for eight air pollutants, from eight cities during a heating season in Poland. Extensive twofold experimental evaluation, combining statistical hypotheses verification and error measures (MAE, MAPE, RMSE) for more than 740 forecast models confirmed high prediction accuracy. Moreover, experiments revealed the advantage of the proposed methods over several state-of-the-art algorithms from the literature.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"28 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918088","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":"Exploration of improvement schemes for simulating LST in Beijing, China over multiple years based on LCZ","authors":"Hanguang Yu, Chunxiao Zhang, Le Yu","doi":"10.1016/j.uclim.2024.102275","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102275","url":null,"abstract":"In response to the intensification of the urban heat island (UHI) effect caused by the rapid development of global cities in recent years, this study coupled the Urban Canopy Model (UCM) of the Weather Forecast and Research (WRF) model with the Local Climate Zone (LCZ), and developed various different schemes to simulate the land surface temperatures (LST) of Beijing in 2014, 2017, and 2020, to explore the impact of different levels of urban development on the improvement of LST simulation by LCZ, revealing the evolution of the heat island effect. By comparing the observation data from meteorological stations with the default underlying surface, it can be concluded that the improvement effect of LCZ on simulation increases with the growth of years under clear weather conditions, and modifying the building parameters within LCZ offers a more pragmatic approach compared to alterations in the physical parameters. In addition, for the enhancement of the UHI in recent years, LCZ can provide more precise division on both spatial and temporal dimensions, effectively capturing the distribution and changes of urban thermal environment.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"34 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918084","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, Aliakbar Mohammadifar, Shahram Golzari, Reza Torkamandi, Elahe Moayedi, Maryam Zare Reshkooeiyeh, Yougui Song, Christian Zeeden","doi":"10.1016/j.uclim.2024.102272","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102272","url":null,"abstract":"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.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"5 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-27","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":"Calibrating the UTCI scale for hot and humid climates through comprehensive year-round field surveys to improve the adaptability","authors":"Wei-An Chen, Pei-Lun Fang, Ruey-Lung Hwang","doi":"10.1016/j.uclim.2024.102267","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102267","url":null,"abstract":"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.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"13 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-27","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}