Urban Climate最新文献_第5页

Vertical distribution analysis of PM2.5 concentration at urban highway intersections using low-cost sensors and unmanned aerial vehicles

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-19 DOI: 10.1016/j.uclim.2024.102243

Masoud Zarei, Bijan Yeganeh

{"title":"Vertical distribution analysis of PM2.5 concentration at urban highway intersections using low-cost sensors and unmanned aerial vehicles","authors":"Masoud Zarei, Bijan Yeganeh","doi":"10.1016/j.uclim.2024.102243","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102243","url":null,"abstract":"The high level of particulate matter (PM) is a critical issue in megacities and a major environmental challenge in urban management. Currently, the vertical distribution of PM concentration has been overlooked at traffic hot spots in the design and construction of high-rise buildings. This study assessed the vertical profile of PM<ce:inf loc=\"post\">2.5</ce:inf> concentration using low-cost sensors and drones to find the residents' exposure to PM<ce:inf loc=\"post\">2.5</ce:inf> at high-rise buildings. The results showed that the vertical pattern of the PM<ce:inf loc=\"post\">2.5</ce:inf> concentration on highways with lower traffic of light-duty vehicles (LDVs) was affected by height, with a 30 % increase in PM<ce:inf loc=\"post\">2.5</ce:inf> concentration at 15 m above the ground compared to ground-level concentration. In contrast, the concentration of PM<ce:inf loc=\"post\">2.5</ce:inf> on highways with more Heavy-Duty Vehicles (HDVs) traffic at ground levels was about 20 % higher than that at 15 m, gradually decreasing to 23 % at 30 m. The results revealed that PM<ce:inf loc=\"post\">2.5</ce:inf> concentration could increase with height in high-rise buildings near highway intersections instead of dilution, which would adversely affect the health of the residents. The findings of this study can be considered by urban planners and decision-makers to reduce PM<ce:inf loc=\"post\">2.5</ce:inf> exposure before settling the citizens in high-rise buildings.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"13 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867576","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}

引用次数: 0

Association between climate variability and injury-causing road traffic accidents in Singapore – A time-stratified case-crossover study

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-19 DOI: 10.1016/j.uclim.2024.102257

Rae Chua, Yih Yng Ng, Andrew F.W. Ho, Joel Aik

引用次数: 0

A systematic review of justice integration to climate resilience: Current trends and future directions

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-19 DOI: 10.1016/j.uclim.2024.102250

Virginia Pellerey, Sara Torabi Moghadam, Patrizia Lombardi

{"title":"A systematic review of justice integration to climate resilience: Current trends and future directions","authors":"Virginia Pellerey, Sara Torabi Moghadam, Patrizia Lombardi","doi":"10.1016/j.uclim.2024.102250","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102250","url":null,"abstract":"Climate resilience has been adopted as a systematic approach for facing climate change. Although the concept of resilience received criticism for failing to address the issues of power imbalance and conservatism, recent approaches include diverse justice perspectives as pathways to address these concerns. However, the lack of clarity regarding the diverse definitions of justice and their relationship to climate resilience hinders our understanding of how to effectively integrate urban climate resilience and justice. This study offers a systematic review of literature on justice and climate resilience in the urban context with the intent of (i) identifying articles addressing justice and climate resilience and classifying them according to the form of justice and resilience framing, (ii) studying trends in the current literature, (iii) identifying research gaps, and (iv) reflecting on the possibility for integration between justice and resilience in different phases of the resilience-building process and proposing future insights. In particular, the results emphasize the importance of (1) enhancing system thinking using people-centered approaches, (2) focusing on the social implications of climate actions, and (3) evaluating different timeframes. The study concludes by suggesting policymaking and research strategies for facilitating the transition toward just and climate-resilient cities.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"52 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867484","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}

引用次数: 0

Evidence on local climate policies achieving emission reduction targets by 2030

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-19 DOI: 10.1016/j.uclim.2024.102242

Camilo Franco, Giulia Melica, Valentina Palermo, Paolo Bertoldi

{"title":"Evidence on local climate policies achieving emission reduction targets by 2030","authors":"Camilo Franco, Giulia Melica, Valentina Palermo, Paolo Bertoldi","doi":"10.1016/j.uclim.2024.102242","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102242","url":null,"abstract":"Local governments play a crucial role in combating climate change. They directly engage with citizens, impact their daily lives, and implement local policies to meet mitigation goals. This paper focuses on identifying specific policy themes that significantly contribute to achieving 2030 mitigation targets, thereby supporting local governments in developing effective climate action plans. We developed an innovative machine learning methodology to extract policy topics and evaluate their impact on meeting committed mitigation targets. This approach includes a new stopping criterion for Structural Topic Modeling. We applied this methodology to a sample of 744 Global Covenant of Mayors signatories, each committed to reducing a percentage of their baseline emissions by 2030. Our findings reveal that policies addressing building integration and transport modal shift, among others, show a strong positive correlation with the likelihood of meeting emissions reduction targets. By leveraging machine learning techniques, our methodology effectively categorizes diverse individual policies into more cohesive topics, facilitating knowledge sharing among committed cities and enhancing the overall effectiveness of climate action strategies.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"268 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867485","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}

引用次数: 0

Ambient temperature-related attributable risk for emergency asthma hospitalizations and length of stay in Hong Kong

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-18 DOI: 10.1016/j.uclim.2024.102240

Hong Qiu, Shengzhi Sun, Tze-Wai Wong, Xing Qiu, Kin-Fai Ho, Eliza Lai-Yi Wong

{"title":"Ambient temperature-related attributable risk for emergency asthma hospitalizations and length of stay in Hong Kong","authors":"Hong Qiu, Shengzhi Sun, Tze-Wai Wong, Xing Qiu, Kin-Fai Ho, Eliza Lai-Yi Wong","doi":"10.1016/j.uclim.2024.102240","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102240","url":null,"abstract":"We aimed to examine the association of ambient temperature with asthma exacerbations and assess the temperature-attributable disease burden changes. Daily count of asthma emergency hospitalizations and corresponding length of hospital stay, daily mean temperature, relative humidity, and air pollution concentrations from 2004 to 2019 in Hong Kong were collected. Time-series quasi-Poisson model integrated with the distributed-lag-nonlinear model was used to examine the relationships of temperature with asthma hospitalizations and length of stay. Measures of the risk attributable to nonoptimal temperature were calculated to summarize the disease burden and hospital utilization for periods of 2004–2011 and 2012–2019, respectively, and compared the temporal changes. Significantly higher risks at cold/cool temperatures for both admission counts and bed-days were found. Around 19.7 % (95 % CI: 14.1–24.3 %) of hospitalization counts and 22.6 % (95 % CI: 15.5–28.4 %) of bed-days were attributed to ambient temperature, which mainly occurred on cold and cool days. Compared with the early period of 2004–2011, the cold temperature-related attributable fraction in 2012–2019 decreased from 11.0 % to 8.9 % (<ce:italic>p</ce:italic> = 0.005) for admission counts but increased from 10.8 % to 12.6 % (<ce:italic>p</ce:italic> = 0.003) for bed-days. Hospital utilization and expenditure due to the longer hospital stays during cold days would play an adverse role in the healthcare system.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"20 6 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867487","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}

引用次数: 0

Developing local-climate-zone-based logarithmic law wind profile considering urban morphology

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-18 DOI: 10.1016/j.uclim.2024.102246

Shuai Kong, Lin Liu, Junliang Cao

{"title":"Developing local-climate-zone-based logarithmic law wind profile considering urban morphology","authors":"Shuai Kong, Lin Liu, Junliang Cao","doi":"10.1016/j.uclim.2024.102246","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102246","url":null,"abstract":"The spatial heterogeneity of building clusters creates a highly complex urban wind environment, making it difficult for traditional wind profile models to capture the spatial characteristics of urban wind. This study aims to investigate the impact of complex urban morphology on the wind environment characteristics, and thereby to establish an urban wind profile at local scale. Firstly, the Weather Research and Forecasting (WRF) model combined with Local Climate Zone (LCZ) method was used to simulate the urban wind field. Subsequently, the simulated data was validated against wind speed data from meteorological stations and sounding data. Finally, the logarithmic law wind profile parameters were analyzed corresponding to each LCZ type (LCZ1-LCZ6) within varying spatial extent by statistical methods, and thus the optimal parameters were obtained applicable to these types. The results indicated that roughness length and friction velocity were strongly correlated with the LCZ type; based on the spatial layout, the optimal ranges for each LCZ type were determined, with roughness length ranging from 5.70 m to 11.95 m and friction velocity ranging from 0.783 m/s to 0.883 m/s. Logarithmic law wind profiles were established applicable to each LCZ type.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"88 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867486","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}

引用次数: 0

Quantifying urban hydrological processes effects on urban climate: A perspective from a novel parameterization scheme

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-17 DOI: 10.1016/j.uclim.2024.102232

Miao Yu, Jianping Guo, Guiqian Tang

引用次数: 0

Understanding the origins of urban particulate matter pollution based on high-density vehicle-based sensor monitoring and big data analysis

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-14 DOI: 10.1016/j.uclim.2024.102241

Yiheng Liang, Xiaohua Wang, Zhongzhen Dong, Xinfeng Wang, Shidong Wang, Shuchun Si, Jing Wang, Hai-Ying Liu, Qingzhu Zhang, Qiao Wang

{"title":"Understanding the origins of urban particulate matter pollution based on high-density vehicle-based sensor monitoring and big data analysis","authors":"Yiheng Liang, Xiaohua Wang, Zhongzhen Dong, Xinfeng Wang, Shidong Wang, Shuchun Si, Jing Wang, Hai-Ying Liu, Qingzhu Zhang, Qiao Wang","doi":"10.1016/j.uclim.2024.102241","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102241","url":null,"abstract":"This study presents an innovative method for air quality monitoring and identifying pollution sources in Rizhao, a coastal city in northern China, by deploying a network of low-cost sensors mounted on 102 taxis. Over a one-year period, we collected a set of high-resolution PM<ce:inf loc=\"post\">10</ce:inf> and PM<ce:inf loc=\"post\">2.5</ce:inf> data. Using big data analysis, including downwind-calm wind analysis, hotspot detection, and time-series clustering analysis, we traced the pollution back to the urban origins of pollutant. Our extensive study uncovered significant spatial and seasonal variations in PM<ce:inf loc=\"post\">10</ce:inf> and PM<ce:inf loc=\"post\">2.5</ce:inf> concentrations, pinpointing substantial PM<ce:inf loc=\"post\">10</ce:inf> emissions from steel plants and a notable influence of industrial activities on ambient PM<ce:inf loc=\"post\">2.5</ce:inf> concentrations. Through the application of bivariate polar plots and hotspot mapping, we linked major particulate matter sources to industrial activities especially steel plant emissions, and road traffic, which significantly elevated the particulate matter levels in residential and industrial zones. Our time-series clustering analysis further distinguishes traffic and industrial activities as key contributors to particulate matter pollution. This study advances the application of low-cost sensor technologies in urban air quality management and offers a detailed insight into the pollution sources and their diverse impacts on particulate matter levels in urban settings.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"7 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867578","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}

引用次数: 0

Harnessing geographic information system and street view imagery for thermal gradient distribution auditing 利用地理信息系统和街景图像进行热梯度分布审计

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-14 DOI: 10.1016/j.uclim.2024.102248

Lang Zheng, Weisheng Lu, Jianxiang Huang, Fan Xue

{"title":"Harnessing geographic information system and street view imagery for thermal gradient distribution auditing","authors":"Lang Zheng, Weisheng Lu, Jianxiang Huang, Fan Xue","doi":"10.1016/j.uclim.2024.102248","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102248","url":null,"abstract":"Assessing and managing the thermal environment within urban streetscapes is of paramount importance for the health, livability, and ecological sustainability of metropolitan regions. However, due to a scarcity of high-precision historical street thermal environment data for prediction and modeling, existing urban thermal environment classification assessment studies suffer from low resolution (> 30 m) or limited research scope (e.g., community-level), resulting in less accurate and comprehensive insights. This study introduces an innovative framework for constructing large-scale urban street-level thermal gradients using classified samples derived from the spatial structural features of street points. The core of this framework lies in the k-means unsupervised classification algorithm. This approach integrates detailed local geographic information system (GIS) data with street view features, calculated through semantic segmentation of Google Street-View-Panorama using the DeepLabV3 model. This allows for the categorization of a vast array of high-precision street points based on spatial structural similarity, a key factor influencing the similarity of street thermal environment features. By selecting appropriate samples for on-site thermal environment measurements within each category and subsequently extrapolating this knowledge to the thermal environment classification of each category, this framework facilitates the rapid creation of high-precision street-level thermal gradient models across extensive urban areas.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"268 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867587","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}

引用次数: 0

A novel ensemble framework based on intelligent weight optimization and multi-model fusion for air quality index prediction

IF 6.4 2区工程技术

Urban Climate Pub Date : 2024-12-14 DOI: 10.1016/j.uclim.2024.102233

Shijie Qian, Tian Peng, Rui He, Jie Chen, Xuedong Zhang, Muhammad Shahzad Nazir, Chu Zhang

{"title":"A novel ensemble framework based on intelligent weight optimization and multi-model fusion for air quality index prediction","authors":"Shijie Qian, Tian Peng, Rui He, Jie Chen, Xuedong Zhang, Muhammad Shahzad Nazir, Chu Zhang","doi":"10.1016/j.uclim.2024.102233","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102233","url":null,"abstract":"The accuracy of air quality prediction is crucial for public health and environmental management. This paper proposes a hybrid deep learning model based on TimesNet, Crossformer and Modified Honey Badger Algorithm (MHBA) for air quality prediction. First, the original air quality index (AQI) series is decomposed using Seasonal-Trend decomposition based on Loess (STL). Then, the decomposed three components are predicted separately using TimesNet and Crossformer, while the hyperparameters of TimesNet and Crossformer are optimized using the Metis algorithm. In addition, half uniform initialization and Levy flight are added to the original HBA algorithm to make up for its shortcomings of slow optimization search speed and the tendency to fall into local optimal position, and the MHBA algorithm is obtained. Finally, the MHBA algorithm is used to weight the component prediction results of the two models, and compare the advantages and disadvantages of different weighting methods, and select the optimal weighting method to get the final AQI prediction results. The experimental results show that the STL-Metis-MHBA-TC model reduces RMSE, MAE, and MAPE by 19–34 %, 22–38 %, and 22–44 %, respectively, compared to the Transformer model. Therefore, the STL-Metis-MHBA-TC hybrid model proposed in this paper can effectively improve the AQI prediction accuracy.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"24 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867579","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}

引用次数: 0