Urban Climate最新文献_第7页

Machine learning-driven analysis of agro-climatic data for temperature modeling and forecasting in Philippine urban areas

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-21 DOI: 10.1016/j.uclim.2025.102339

Jamlech Iram Gojo Cruz , Jose Maria Lorenzo de Vera , Karl Ezra Pilario

{"title":"Machine learning-driven analysis of agro-climatic data for temperature modeling and forecasting in Philippine urban areas","authors":"Jamlech Iram Gojo Cruz , Jose Maria Lorenzo de Vera , Karl Ezra Pilario","doi":"10.1016/j.uclim.2025.102339","DOIUrl":"10.1016/j.uclim.2025.102339","url":null,"abstract":"<div><div>The effects of climate change in the Philippines call for effective strategies to improve resilience, especially in urban areas. Machine learning models are now being used to provide data-driven insights for climate action, in particular, to address urban overheating. In this context, this paper developed machine learning models by using agro-climatological data to predict the maximum temperature at 2 m (in °C) in Manila and Dagupan, Philippines, with 32 predictors. A pipeline of standard scaling, principal component analysis, regression models, and time-series models were used for forecasting. It was found that the multilayer perceptron (MLP) regressor had the best test forecast performance in the Manila data, with an <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of 0.8128 and MSE of 0.9334, even without autoregressive information. Meanwhile, Long Short-Term Memory was found to have comparatively decent performance with a test <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of 0.6452 for the case of univariate autoregressive forecasting. We also prove that the models are location-specific since the model trained at Manila data yields inaccurate results when transferred to the Dagupan data. With more accurate forecasts of maximum temperature, policymakers can make more informed decisions toward a more sustainable living in Philippine cities.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102339"},"PeriodicalIF":6.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453587","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

Exploring factors influencing the resilience of flood disaster response networks for old communities in China through an enhanced DNA-ISM framework

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-19 DOI: 10.1016/j.uclim.2025.102336

Tiantian Gu , Wenxiu Chang , Yang Li , Yongchao Wang

{"title":"Exploring factors influencing the resilience of flood disaster response networks for old communities in China through an enhanced DNA-ISM framework","authors":"Tiantian Gu , Wenxiu Chang , Yang Li , Yongchao Wang","doi":"10.1016/j.uclim.2025.102336","DOIUrl":"10.1016/j.uclim.2025.102336","url":null,"abstract":"<div><div>As the intensity of flood disasters in old communities continues to escalate, the resilience of flood disaster response networks (FDRNs) is under growing threat. Addressing this pressing challenge, this study develops an enhanced framework integrating Dynamic Network Analysis (DNA) and Interpretive Structural Model (ISM) to systematically elucidate the factors and mechanisms that influence FDRN resilience. This framework first employed DNA to conceptualize the complex flood disaster response as the ‘A-T-R-I' dynamic network, delineating interrelationships among stakeholders, tasks, resources, and information. Subsequently, ISM was utilized to analyze the mechanisms that affect FDRN resilience. Validation of this framework through a case study of the Y community in Xuzhou City of China led to the development of a five-level ISM model, clarifying the interconnections between 10 critical FDRN nodes and 12 determinants of the FDRN resilience. Moreover, strategies for enhancing FDRNs resilience were provided, and the advantages of the enhanced DNA-ISM framework were highlighted. Overall, this study not only enriches the knowledge system of community resilience but also offers actionable guidance for decision-makers to develop resilient flood response networks, highlighting a globally applicable framework to enhance community resilience against flood disasters.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102336"},"PeriodicalIF":6.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444410","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

Retraction notice to “Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption” [Urban Climate 51 (2023) 101647]

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-18 DOI: 10.1016/j.uclim.2025.102315

Leren Qian , Zhongsheng Chen , Yiqian Huang , Russell J. Stanford

引用次数: 0

Variability of greenhouse gases in Lagos: CO2, CH4, N2O and halocarbons in a developing Western Africa megacity

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-17 DOI: 10.1016/j.uclim.2025.102337

Adebola Odu-Onikosi , Ganiyu Oke , Philip K. Hopke , Paul A. Solomon

{"title":"Variability of greenhouse gases in Lagos: CO2, CH4, N2O and halocarbons in a developing Western Africa megacity","authors":"Adebola Odu-Onikosi , Ganiyu Oke , Philip K. Hopke , Paul A. Solomon","doi":"10.1016/j.uclim.2025.102337","DOIUrl":"10.1016/j.uclim.2025.102337","url":null,"abstract":"<div><div>Greenhouse gas (GHG) emissions significantly affect climate change, public health, and the environment. Lagos, a developing megacity with a bustling economy and growing population, contributes substantially to GHG concentrations through intensive energy consumption and material use. This study investigated the temporal and spatial distributions of key ambient GHGs across Lagos, Nigeria, from August 2020 to July 2021, including carbon dioxide (CO₂), nitrous oxide (N₂O), methane (CH₄), and halocarbons (chlorofluorocarbons, hydrochlorofluorocarbons, and hydrofluorocarbons). Using data from a network of six sampling stations, the relationships among these GHGs and their concentrations in relation to urban emissions were analyzed. Our findings identified distinct seasonal variability in GHG concentrations driven by urban activities and meteorological conditions. CO₂ concentrations ranged from 455 to 484 ppmv, with peak values at the IKO and NCF stations. CH₄ concentrations reached a maximum of 2210 ppbv, primarily attributed to waste management activities, while N₂O showed minimal variations suggesting limited local sources. Halocarbon concentrations peaked during the dry season due to increased air conditioning use and industrial activities. Alternatively, CO₂, N₂O, and CH₄ were highest during the wet season (April to July), influenced by increased vehicular emissions and enhanced waste decomposition in waterlogged landfills. The study identified transportation, waste management, and refrigeration as the primary GHG sources in Lagos. The observed correlations between halocarbons and other GHGs underscore the interconnected nature of urban emissions sources. These findings provide valuable insights for policymakers and stakeholders to develop targeted mitigation strategies for reducing GHG emissions in rapidly urbanizing megacities.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102337"},"PeriodicalIF":6.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420578","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

Multiscale weather forecasting sensitivities to urban characteristics and atmospheric conditions during a cold front passage over the Dallas-Fort Worth metroplex

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-17 DOI: 10.1016/j.uclim.2025.102334

Domingo Muñoz-Esparza, Jeremy A. Sauer, Pedro A. Jiménez, Jennifer Boehnert, David Hahn, Matthias Steiner

{"title":"Multiscale weather forecasting sensitivities to urban characteristics and atmospheric conditions during a cold front passage over the Dallas-Fort Worth metroplex","authors":"Domingo Muñoz-Esparza, Jeremy A. Sauer, Pedro A. Jiménez, Jennifer Boehnert, David Hahn, Matthias Steiner","doi":"10.1016/j.uclim.2025.102334","DOIUrl":"10.1016/j.uclim.2025.102334","url":null,"abstract":"<div><div>Sensitivities of microscale weather modeling to atmospheric conditions and urban layout are investigated utilizing a combination of automated surface observing systems (ASOS) data, 1-km mesoscale numerical weather prediction (NWP), and 5-m nested large-eddy simulation (LES) modeled conditions. The 1-km mesoscale predictions in analysis mode satisfactorily reproduce the observed spatiotemporal evolution of the frontal boundary in terms of wind speed, wind direction, and temperature. The 5-m nested LES simulations follow the large-scale forcing trends while improving wind speed predictions due to explicitly resolving turbulence and building interactions. Moreover, 5-min averaged nested LES results reveal improved temporal variability particularly during the stronger wind and turbulence post-frontal conditions. The skill of the 1-km mesoscale NWP model prediction is compared to coarse-grained LES fields. Probability distributions extracted from the 5-m nested LES predictions exhibit the largest sensitivity to the contrasting meteorological conditions. In contrast, cumulative distributions of TKE additionally expose a marked dependency on the unique distribution of building heights, urban density and clustering in a given area. For the first time, an ensemble forecast methodological design at building-resolving grid spacing is explored. A larger microscale ensemble spread is found for TKE than for wind speed, decreasing with height and modulated by weather conditions.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102334"},"PeriodicalIF":6.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420066","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}

引用次数: 0

Modeling PM2.5 urbane pollution using hybrid models incorporating decomposition and multiple factors

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-15 DOI: 10.1016/j.uclim.2025.102338

Somayeh Mirzaei , Ting Lun Liao , Chin-Yu Hsu

{"title":"Modeling PM2.5 urbane pollution using hybrid models incorporating decomposition and multiple factors","authors":"Somayeh Mirzaei , Ting Lun Liao , Chin-Yu Hsu","doi":"10.1016/j.uclim.2025.102338","DOIUrl":"10.1016/j.uclim.2025.102338","url":null,"abstract":"<div><div>PM<sub>2.5</sub> negatively impacts air quality, human health, and the environment. Modeling PM<sub>2.5</sub> concentrations is helpful for understanding pollution dynamics and supporting government emergency responses and preventive measures. This study introduces a novel method to develop hybrid models that enhance PM<sub>2.5</sub> concentration modeling and evaluate source contributions. We applied empirical mode decomposition (EMD)-based models—EMD-LSTM, EMD-Bi-LSTM, EMD-GRU, EMD-CNN, and EMD-CNN-LSTM— to model hourly PM<sub>2.5</sub> concentrations using a 4-year dataset. PM<sub>2.5</sub> concentration data from the target and nine neighboring stations, combined with EMD and time lag functions, as well as other air pollutants and meteorological inputs, were used to develop models. We adopted a Shapley additive explanations analyzer-based LSTM model to identify pivotal features. Among all models, EMD-Bi-LSTM emerged as the top performer, achieving up to 89.5 % model accuracy (<em>R</em><sup>2</sup>). PM<sub>2.5</sub> concentration at the target station from the previous 1 h was identified as a key contributor in the model. Other influencing factors included PM<sub>2.5</sub> concentrations at neighboring stations, PM<sub>10</sub>, CO, O<sub>3</sub>, total hydrocarbon compounds, and wind direction.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102338"},"PeriodicalIF":6.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420579","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 量化城市水文过程对城市气候的影响：一种新的参数化方案的视角

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2024.102232

Miao Yu , Jianping Guo , Guiqian Tang

引用次数: 0

Vertical distribution analysis of PM2.5 concentration at urban highway intersections using low-cost sensors and unmanned aerial vehicles 基于低成本传感器和无人机的城市公路交叉口PM2.5浓度垂直分布分析

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 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":"10.1016/j.uclim.2024.102243","url":null,"abstract":"<div><div>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<sub>2.5</sub> concentration using low-cost sensors and drones to find the residents' exposure to PM<sub>2.5</sub> at high-rise buildings. The results showed that the vertical pattern of the PM<sub>2.5</sub> concentration on highways with lower traffic of light-duty vehicles (LDVs) was affected by height, with a 30 % increase in PM<sub>2.5</sub> concentration at 15 m above the ground compared to ground-level concentration. In contrast, the concentration of PM<sub>2.5</sub> 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<sub>2.5</sub> 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<sub>2.5</sub> exposure before settling the citizens in high-rise buildings.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102243"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","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

Understanding the origins of urban particulate matter pollution based on high-density vehicle-based sensor monitoring and big data analysis 基于高密度车载传感器监测和大数据分析，了解城市颗粒物污染源头

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 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":"10.1016/j.uclim.2024.102241","url":null,"abstract":"<div><div>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<sub>10</sub> and PM<sub>2.5</sub> 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<sub>10</sub> and PM<sub>2.5</sub> concentrations, pinpointing substantial PM<sub>10</sub> emissions from steel plants and a notable influence of industrial activities on ambient PM<sub>2.5</sub> 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.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102241"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","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

Integrating seasonal climate variability and spatial accessibility in ecosystem service value assessment for optimized NbS allocation

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2025.102314

Yoonshin Kwak , Si Chen

{"title":"Integrating seasonal climate variability and spatial accessibility in ecosystem service value assessment for optimized NbS allocation","authors":"Yoonshin Kwak , Si Chen","doi":"10.1016/j.uclim.2025.102314","DOIUrl":"10.1016/j.uclim.2025.102314","url":null,"abstract":"<div><div>Nature-based Solutions (NbS) efficiently manage ecosystem services (ESs) to address socio-environmental challenges. Strategic NbS planning must consider ecological and social benefits, especially in urban areas with limited resources. However, systemic planning approaches remain lacking in terms of urban climate variability. This study proposes a novel method to optimize ES benefits from NbS allocation, emphasizing ES dynamics under seasonal climate change in an urbanizing Korean city. We estimate seasonal impacts on ecosystem service value (ESV) using dynamic corrections. The Gravity-based Two-Step Floating Catchment Area (G2SFCA) method identifies priority NbS allocation areas based on fine-scale distributions of ecological supply and social demand under different scenarios. Our findings show that water bodies sensitive to seasonal climate changes need strategic management. For NbS planning, prioritizing highly populated areas with low ES access, and deprioritizing sparsely populated areas with limited accessibility due to lower demand, promotes efficient and equitable ES distribution. Considering both the contribution and sensitivity of ESs in terms of supply and demand leads to optimized NbS allocations, maximizing benefits. The study emphasizes the urgent need for integrated natural and social assessments in NbS planning, proposing a framework to guide planners in enhancing NbS implementation efficiency for sustainable urban environments.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102314"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027312","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