Urban Climate最新文献

{"title":"Integrating thermal perception and public space use – an experimental outdoor comfort study in cold winter-hot summer zone: Beijing, China","authors":"","doi":"10.1016/j.uclim.2024.102138","DOIUrl":"10.1016/j.uclim.2024.102138","url":null,"abstract":"<div><p>This study aims to unravel the seasonal differences in outdoor thermal comfort (OTC) in a cold winter-hot summer region, as well as the comprehensive impact of meteorological and human factors, along with people's activity patterns in public spaces, on OTC. Questionnaire surveys, microclimate measurements, and photo recording were conducted over 21 daytime sessions in seven public spaces across winter and summer. The results indicated a significant seasonal OTC variance in Beijing. The neutral PET and neutral PET range were 21.9°C and 15.2–28.6°C for summer, and 14.3°C and 6.1–22.5°C for winter. Globe temperature primarily impacted OTC during both seasons, while wind speed was the only additional influential factor in winter. In Beijing, summer cooling is more crucial than winter insulation; however, winter wind protection is prioritized over summer ventilation. Some human factors, including age, activity level, visiting purpose, short-term thermal experience, and environmental stimuli, also influence OTC to some extent. Furthermore, this study identified significant variations in the temporal distribution and age demographics of attendees in various types of public spaces across different seasons, which considerably influence OTC. Our findings provide insights into the relationship between outdoor thermal comfort and public space use, which informs climate adaptive urban design.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233786","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 ambient air pollution and respiratory health in Kampala, Uganda: Implications for policy and practice","authors":"","doi":"10.1016/j.uclim.2024.102128","DOIUrl":"10.1016/j.uclim.2024.102128","url":null,"abstract":"<div><p>Ambient air pollution and weather have an impact on respiratory diseases. This study analysed effects of fine particulate matter (PM_2.5) on monthly respiratory-related healthcare facility visits and admissions (herein referred to as events) in Kampala, whilst considering meteorological factors.</p><p>Monthly healthcare facility events for neonatal pneumonia, asthma, pneumonia and COPD between 2019 and 2020 were retrospectively extracted from the District Health Information Software 2. PM_2.5 and meteorological data were obtained from 45 monitoring stations and 5 weather stations in Kampala. Associations between air pollution and healthcare facility events were examined using generalised additive models (GAM) adjusted for rainfall, temperature and humidity.</p><p>Multivariate analysis showed positive association between monthly PM_2.5 with healthcare facility visits due to asthma (incident rate ratio-IRR = 1.007, 95 % CI: 1.002–1.013; <em>p</em> &lt; 0.05) and admissions due to pneumonia (IRR = 1.007, 95 % CI: 1.001–1.013; <em>p</em> &lt; 05). Healthcare facility events were associated with PM_2.5 (IRR = 1.005; 95 % CI: 1.002–1.009; <em>p</em> &lt; 0.05) and relative humidity (EDF = 1.000; p &lt; 0.05).</p><p>PM_2.5 impacted on healthcare facility events in Kampala and association was influenced by meteorological factors. However, lack of data for daily healthcare facility events could have affected accuracy of the impact therefore further analyses are needed. Our characterisation could support facilities in development of early warning tools for air pollution events.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212095524003250/pdfft?md5=30b31a9cf551201c75e3ad8850c4ee83&pid=1-s2.0-S2212095524003250-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142229685","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":"Estimating the daily flooding probability by the compound effect of rainfall and tides in an Amazonian metropolis","authors":"","doi":"10.1016/j.uclim.2024.102121","DOIUrl":"10.1016/j.uclim.2024.102121","url":null,"abstract":"<div><p>Belém, the capital of the state of Pará, host of the 30th UN Climate Change Conference (COP30), which is located in the Brazilian Amazon, suffers from flooding caused by rainfall and tides. The objective was to estimate the daily flooding probability in Belém based on the two assumptions, i.e., the compound effect of rainfall and tides and rainfall only. In this case, rainfalls were analysed considering intensity and duration. Thus, it was possible to evaluate the flooding probability for each assumption and propose technological solutions to reduce this probability. Rainfall and tide height data for the 2003 to 2021 period were used. The minimum level causing flooding was defined analysing the compound effect (rainfall + tide height). The effects of rainfall and tides were also separately analysed with the aim of isolating the entry of tides into city canals. The daily flooding probability was estimated based on the study of the a priori probability derived from the ratio between the number of flooding events and the number of possible floods. The results showed that certain areas of Belém-PA could be flooded with a minimum value of rainfall plus tide height is 640.4 mm, if the rainfall intensity reaches at least 30 mm/h. Regarding the rainfall duration, the minimum value of rainfall plus tide height is 1398 mm for flooding to occur if rainfall lasts at least two hours. The flooding events were validated by examining local newspaper reports and photographic records describing and capturing flooding, respectively. The highest daily flooding probability occurred during the wettest period in the city, from January to May. The daily flooding probability was greatly reduced when only rainfall was considered. Thus, if city canals encompass floodgate systems, flood damage could be reduced. A floodgate systems project would be an important COP30 legacy for Belém. This system could help reduce the losses suffered by the poorest populations in Belém, who live in floodplain areas.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098732","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":"Analyzing urban footprints over four coastal cities of India and the association with rainfall and temperature using deep learning models","authors":"","doi":"10.1016/j.uclim.2024.102123","DOIUrl":"10.1016/j.uclim.2024.102123","url":null,"abstract":"<div><p>Capabilities of deep-learning models CNN and ConvLSTM are explored for analyzing and projecting future urban growth in four Indian coastal cities, viz., Mumbai, Chennai, Kochi, and Vishakhapatnam. ConvLSTM performed better with higher overall accuracy, Cohen's kappa, macro F1-score (&gt; 95 %), and R² and NSE values (&gt; 0.87). The urbanization trends indicate a higher growth in Kochi (∼90.5 %), and greater projected rate for Mumbai (∼42.5 %). The monthly accumulated rainfall and mean temperature are analyzed and forecasted through the ConvLSTM model. The Mann-Kendall test-based analysis (with <em>p</em>-value &lt;0.05), suggest no significant rainfall pattern for the cities except Mumbai, which exhibits an increasing trend. The projected rainfall trend is unaltered except for Vishakhapatnam, which is expected to increase in the coming years. The mean temperature over all the cities, shows an increasing trend (slope ∼ 0.02). However, in the coming years, the ‘increasing trend’ is expected to change into a ‘no significant trend’ for Mumbai and Vishakhapatnam. The forecasted results indicate a continuous urban expansion, which is expected to have a significant impact on rainfall and temperature trends. The outcome could assist urban planners in defining the timeline for improving drainage and include green spaces in the city development plans.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136905","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":"Multifactorial influences on land surface temperature within local climate zones of typical global cities","authors":"","doi":"10.1016/j.uclim.2024.102130","DOIUrl":"10.1016/j.uclim.2024.102130","url":null,"abstract":"<div><p>The land surface temperature (LST) of most cities is rising steadily due to both human activities and global climate change, affecting the thermal comfort of cities and threatening the physical health of their inhabitants. Investigating surface temperature features and their affecting elements is therefore essential to improve the thermal environment of cities. Due to differences in surface temperature characteristics in different climatic belts. However, there is still a lack of research on how the local climate zones (LCZs) of different climate belts are affected by natural and social causes. According to the Koppen-Geiger climate classification system, we selected three representative cities from each of the four macroclimatic belts and adopted multiple linear stepwise regression and boosted regression trees (BRT) to systematically explore the linear relationships, relative impacts and marginal effects of normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), population density, and road nuclear density within urban LCZs on LST. The results indicate that (1) LCZs with significant differences in LST among the four global climatic belts account for more than 95 % (<em>P</em> &lt; 0.05), demonstrating that LCZs can effectively differentiate LST based on different land surface cover types. This study can delve into the relationships between the four influencing factors and LST based on LCZs. (2) Primary control factors regulating LST differ in different climate belts. The relative effects of NDVI and MNDWI on LST are greater in the arid and temperate belts, with each 0.1 increase in NDVI and MNDWI producing a cooling effect of more than 0.40 °C and 0.92 °C, respectively. Ventilation corridors created by increased road core density produced a cooling effect of 1 °C or more in the cold belt, with the most pronounced cooling effect. (3) When natural factors are higher and social factors are lower, LST cannot be minimized. It was found that LST was minimized when NDVI, MNDWI, population density and road nuclear density were controlled above 0.4, 0.1–0.2, 10,000–50,000 and 1500–2000 respectively. Our study will provide targeted measures for LCZ-based mitigation of urban thermal environments in various macroclimatic belts.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171928","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":"Advancing urban climate adaptation through knowledge exchange: A case study of New York City","authors":"","doi":"10.1016/j.uclim.2024.102100","DOIUrl":"10.1016/j.uclim.2024.102100","url":null,"abstract":"<div><p>Cities are experiencing increasingly extreme and longer-lasting hazards due to climate change. In response, urban decision-makers use climate assessments to inform their adaptation programs. To effectively inform planning, climate assessments must be responsive to community priorities and align with information needs. However, there are few empirical studies evaluating the co-production and exchange of climate information within climate assessment processes. To address this gap, we evaluated the Climate Knowledge Exchange (CKE) piloted in 2020 by the Mayor's Office of Climate and Environmental Justice (then the Mayor's Office of Resiliency) in New York City. The CKE is an ongoing engagement process that asked: What are the research gaps and barriers to adaptation in New York City and how can we design inclusive approaches to knowledge exchange that advance equitable adaptation? To answer this question, we facilitated group discussions, distributed surveys, and reviewed adaptation plans to assess the state of climate knowledge. The findings indicate a need for contextually and culturally sensitive approaches to the co-development of actionable knowledge. Additionally, there is a need to reduce barriers to equitable adaptation and to advance the practice of climate-informed decision-making. Our findings advance understanding of the potential role of climate knowledge exchange in adaptation planning.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098728","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":"Contrasts in the raindrop size distributions of pre-monsoon polluted and non-polluted rainy days over Rourkela, India","authors":"","doi":"10.1016/j.uclim.2024.102111","DOIUrl":"10.1016/j.uclim.2024.102111","url":null,"abstract":"<div><p>Raindrop size distribution (RSD) is crucial for accurate quantitative precipitation estimation and forecasting and is fundamental to precipitation microphysics. This study examines the microphysical characteristics of RSD at Rourkela, Odisha, under polluted and non-polluted conditions. This investigation utilized pre-monsoon in-situ (disdrometer) and reanalysis (ERA5) data for 2018–2021, to identify the pollution impact on precipitation over Rourkela. The polluted rainfall day has been determined based on the air quality index (AQI) value provided by the Central Pollution Control Board (CPCB), Government of India, over the Rourkela region. Classifying the rainfall in polluted and non-polluted days into stratiform and convective types showed that convective rainfall had a higher raindrop concentration and higher mean diameter (<em>D</em>_<em>m</em>) in both polluted and non-polluted days. Convective rainfall has a lower normalized intercept parameter (<em>log</em>_<em>10</em><em>N</em>_<em>w</em>) in polluted rainfall days. The RSD empirical relations (<em>Z</em>-R, <em>μ – λ</em>, <em>D</em>_<em>m</em><em>-R, N</em>_<em>w</em><em>-R</em>) also showed a noteworthy difference between the polluted and non-polluted rainfall days. The results disclosed that non-polluted rainfall has higher concentration of small-diameter raindrops, whereas polluted day rain has higher concentrations of midsize and large-diameter raindrops.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098733","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 changes in LULC and associated impact on urban Heat Islands over Pakistan using geospatial techniques","authors":"","doi":"10.1016/j.uclim.2024.102112","DOIUrl":"10.1016/j.uclim.2024.102112","url":null,"abstract":"<div><p>Urban Heat Islands arise when the natural land cover changes with dense concentrations of pavement, buildings, roads, and other surfaces that absorb and preserve heat. The current study aimed to assess Land Use Land Cover(LULC) changes and associated impact on Urban Heat Island (UHI) from 2000 to 2020 using Geospatial technologies for four metropolitan cities (Islamabad, Lahore, Multan and Muzaffarabad) of Pakistan. A Supervised Classification algorithm was performed on satellite imageries using ArcGIS software for analyzing LULC changes. The study areas were classified into 4 classes such as built-up, barren land, vegetation, and water bodies. Thermal bands of Landsat 4–5 and 8 were used for retrieved Land Surface Temperature (LST) and UHI. The findings of this study indicate that the urban area in all four districts i.e. Islamabad, Lahore, Multan and Muzaffarabad has increased by 21.3 %, 20.9 %, 6.1 % and 15 % respectively during 2000–2020. The built-up and barren land revealed the highest recorded LST, while vegetation and water bodies showed the lowest measured LST values. From 2000 to 2020, the annual maximum LST in Islamabad rose by 1.1 °C, Lahore by 2.1 °C, and Multan and Muzaffarabad by 1.9 °C and 1.5 °C. The expansion of built-up areas and the reduction in vegetation contribute to a favorable contribution to UHI. The study's findings showed that appropriate action plans are required to manage urban heat and promote sustainable city development.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098730","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":"On-site measurement and mediation analysis of physiological parameters and thermal comfort under different outdoor microclimates in a cold region","authors":"","doi":"10.1016/j.uclim.2024.102120","DOIUrl":"10.1016/j.uclim.2024.102120","url":null,"abstract":"<div><p>This study examined the physiological responses and thermal comfort of ten subjects (five females and five males) across five outdoor locations on the campus of Hebei University of Technology during winter. Physiological parameters, including heart rate, LF/HF values from ECG, and α, β, θ values from EEG, were assessed. It revealed significant differences in microclimatic parameters and physiological responses among measurement points. Mean heart rate variations reached up to 5.72 (6.72 %) and mean β values differed by up to 0.05 (26.32 %), 0.08 (47.06 %), and 0.10 (43.48 %) during T1, T2, and T3 periods. HR, α, and θ values positively correlated with TCV, with coefficients of 0.131 (<em>p</em> &lt; 0.01), 0.081 (p &lt; 0.01), and 0.018, while LF/HF and β showed negative correlations, with coefficients of −0.109 (p &lt; 0.01) and − 0.024 (p &lt; 0.01). Changes in TCV from 0 to −2 corresponded to HR, LF/HF, α, β, and θ variations of 6.69 %, −3.5 %, 12.5 %, −9.38 %, and 7.14 %, respectively. Regarding microclimatic parameters' impact on thermal comfort, β in EEG signals showed greater mediating effects than other physiological parameters. This study is useful for providing guidelines for outdoor activities in winter and developing more accurate physiological parameter monitoring and comfort assessment methods.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098731","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":"Are there airborne microbial hotspot areas over Iran's Sistan region?: A spatial analysis of microbe concentrations and relationships with dust","authors":"","doi":"10.1016/j.uclim.2024.102124","DOIUrl":"10.1016/j.uclim.2024.102124","url":null,"abstract":"<div><p>During 18 dust storms from June 2022 to September 2023, bacterial and fungal concentrations were measured in dust collected in different parts of the Sistan region of Iran, including Zabol and Zahak (i.e., urban environments) and Ghorghori, Adimi, and Aliakbar (i.e., non-urban environments). The goal was to assess (a) the spatial distribution of airborne microbes across Sistan to identify hotspots of high levels, (b) airborne microbe concentrations in urban and non-urban environments, and (c) the influence of Hamoun lakes on airborne microbial concentrations. The highest bacterial and fungal concentrations per gram of dust were observed in Zabol (3.5 <span><math><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></math></span> and 1.2 <span><math><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span> cfu g⁻¹) and the lowest in Ghorghori (1.16 <span><math><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span> and 1.1 <span><math><mo>×</mo><msup><mn>10</mn><mn>3</mn></msup></math></span> cfu g⁻¹). The highest bacterial and fungal concentrations per sampled area were found in Aliakbar (4.7 <span><math><mo>×</mo><msup><mn>10</mn><mn>7</mn></msup></math></span> and 8.6 <span><math><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span> cfu g⁻¹) and the lowest in Ghorghori (1.9<span><math><mo>×</mo><msup><mn>10</mn><mn>7</mn></msup></math></span> and 3.2 <span><math><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span> cfu g⁻¹). Aliakbar and Zahak were the most serious airborne microbial hazard sites based on high loads of dust and, consequently, microbes. Hamoun lakes contribute to airborne microbial pollution across Sistan. Management of lakes is suggested as a key path forward to reduce dispersal of microbes.</p></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144486","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}