As climate change accelerates, extreme heat events have become one of the most pervasive and dangerous threats to urban populations worldwide, disproportionately affecting vulnerable communities. This study investigates household awareness, experiences, and governance responses to extreme heat in the Greater Accra Metropolitan Area, Ghana. A mixed-methods approach, involving household surveys (n = 413) and focus group discussions (n = 3), was used to assess three neighbourhoods: Dansoman, Osu, and Ashaley Botwe. The findings show high levels of awareness of extreme heat across all neighbourhoods, but Ashaley Botwe reported the greatest disruption to daily life, driven by rapid urbanisation and economic vulnerability. Health concerns, discomfort, and sleep disruptions emerged as the most common impacts. Further analyses revealed that age, generation group, and income significantly influenced household awareness and adaptive responses to extreme heat. Older residents and higher-income households were more likely to invest in cooling systems, while education positively correlated with increased awareness of extreme heat risks. Despite the clear recognition of extreme heat as a major issue, government-led strategies and local engagement in heat governance were found to be largely absent, highlighting a governance gap. This study highlights the necessity for targeted, community-specific climate resilience strategies that consider demographic and socio-economic vulnerabilities. The findings advocate for the integration of localized climate adaptation measures into urban planning frameworks to mitigate the adverse effects of extreme heat in fast-growing cities like Accra.
{"title":"\"It is getting too hot lately\": Urban households' knowledge, experiences and governance of extreme heat events in Accra, Ghana","authors":"Yaw Agyeman Boafo, Ebenezer Forkuo Amankwaa, Catalina Spataru, Priscila Carvalho","doi":"10.1016/j.uclim.2025.102287","DOIUrl":"https://doi.org/10.1016/j.uclim.2025.102287","url":null,"abstract":"As climate change accelerates, extreme heat events have become one of the most pervasive and dangerous threats to urban populations worldwide, disproportionately affecting vulnerable communities. This study investigates household awareness, experiences, and governance responses to extreme heat in the Greater Accra Metropolitan Area, Ghana. A mixed-methods approach, involving household surveys (<ce:italic>n</ce:italic> = 413) and focus group discussions (<ce:italic>n</ce:italic> = 3), was used to assess three neighbourhoods: Dansoman, Osu, and Ashaley Botwe. The findings show high levels of awareness of extreme heat across all neighbourhoods, but Ashaley Botwe reported the greatest disruption to daily life, driven by rapid urbanisation and economic vulnerability. Health concerns, discomfort, and sleep disruptions emerged as the most common impacts. Further analyses revealed that age, generation group, and income significantly influenced household awareness and adaptive responses to extreme heat. Older residents and higher-income households were more likely to invest in cooling systems, while education positively correlated with increased awareness of extreme heat risks. Despite the clear recognition of extreme heat as a major issue, government-led strategies and local engagement in heat governance were found to be largely absent, highlighting a governance gap. This study highlights the necessity for targeted, community-specific climate resilience strategies that consider demographic and socio-economic vulnerabilities. The findings advocate for the integration of localized climate adaptation measures into urban planning frameworks to mitigate the adverse effects of extreme heat in fast-growing cities like Accra.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"30 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988428","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}
Pub Date : 2025-01-16DOI: 10.1016/j.uclim.2025.102290
Mohammad Fereshtehpour, Mohammad Reza Najafi
Rapid urbanization combined with increasing extreme precipitation driven by climate change poses significant challenges to urban infrastructure. This study analyzes stormwater management practices across 11 cities in North America, Europe, and Australia, emphasizing strategies for climate change adaptation. Drawing on a review of published documents and interviews with city officials, we assess regulatory frameworks, policies, and design guidelines. This review identifies a critical gap in integrating stormwater management with emission reduction policies, essential for synergistic co-benefits and addressing both mitigation and adaptation challenges. This study examines the policies through the lens of blue-green infrastructure (BGI), identifying challenges such as adapting multifunctional designs to local contexts and establishing effective governance frameworks to maximize their potential. From a funding perspective, stormwater fees offer a transparent way to finance climate-resilient initiatives, with affordability and public acceptance addressed through incentives like stormwater credits. Regular updates to design storm criteria, guided by advancing climate science, are vital for long-term resilience. However, design storms should be a starting point, focusing more on adaptive, multifunctional structures based on the safe-to-fail paradigm. This study highlights the urgent need for holistic, integrated stormwater management approaches to enhance urban resilience and sustainability in a changing climate.
{"title":"Urban stormwater resilience: Global insights and strategies for climate adaptation","authors":"Mohammad Fereshtehpour, Mohammad Reza Najafi","doi":"10.1016/j.uclim.2025.102290","DOIUrl":"https://doi.org/10.1016/j.uclim.2025.102290","url":null,"abstract":"Rapid urbanization combined with increasing extreme precipitation driven by climate change poses significant challenges to urban infrastructure. This study analyzes stormwater management practices across 11 cities in North America, Europe, and Australia, emphasizing strategies for climate change adaptation. Drawing on a review of published documents and interviews with city officials, we assess regulatory frameworks, policies, and design guidelines. This review identifies a critical gap in integrating stormwater management with emission reduction policies, essential for synergistic co-benefits and addressing both mitigation and adaptation challenges. This study examines the policies through the lens of blue-green infrastructure (BGI), identifying challenges such as adapting multifunctional designs to local contexts and establishing effective governance frameworks to maximize their potential. From a funding perspective, stormwater fees offer a transparent way to finance climate-resilient initiatives, with affordability and public acceptance addressed through incentives like stormwater credits. Regular updates to design storm criteria, guided by advancing climate science, are vital for long-term resilience. However, design storms should be a starting point, focusing more on adaptive, multifunctional structures based on the safe-to-fail paradigm. This study highlights the urgent need for holistic, integrated stormwater management approaches to enhance urban resilience and sustainability in a changing climate.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"21 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988094","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}
Pub Date : 2025-01-16DOI: 10.1016/j.uclim.2025.102289
Zikang Feng, Lina Zheng, Bilin Ren
Low-cost particle sensors enable dense, geospatially distributed networks that enhance the spatial and temporal resolution of urban air quality monitoring. However, field interference in complex urban systems challenges the reliability of sensor data. Robust evaluation and calibration are essential to address this issue. In this study, a low-cost sensor system was deployed near standard monitoring stations from March 1 to May 30, 2024, recording PM2.5 concentration, PM10 concentration, particle counts in six different size channels, and ambient temperature and humidity. The results revealed systematic overestimation and interactions with environmental factors in the sensor data. To address these challenges, a physics-based calibration model, leveraging sensor-reported particle size information, was developed and compared with traditional empirical and machine learning models. These calibration models were embedded into the sensor system, followed by a second field campaign from June 1 to 30. While the machine learning model achieved the best performance during the first campaign (R2 > 0.90, RMSE <10 μg/m3 for PM2.5 and PM10), its generalization ability was limited. The physics-based model, however, excelled on a new dataset from the second campaign, demonstrating robust performance and strong generalization across urban conditions. These findings highlight the potential of the physics-based calibration model to improve the reliability and sustainability of urban air quality monitoring by integrating it into the embedded systems of low-cost sensors. This approach offers enhanced stability and applicability in complex urban environments, providing a more effective calibration method for urban environmental systems.
{"title":"In-situ validation of embedded physics-based calibration in low-cost particulate matter sensor for urban air quality monitoring","authors":"Zikang Feng, Lina Zheng, Bilin Ren","doi":"10.1016/j.uclim.2025.102289","DOIUrl":"https://doi.org/10.1016/j.uclim.2025.102289","url":null,"abstract":"Low-cost particle sensors enable dense, geospatially distributed networks that enhance the spatial and temporal resolution of urban air quality monitoring. However, field interference in complex urban systems challenges the reliability of sensor data. Robust evaluation and calibration are essential to address this issue. In this study, a low-cost sensor system was deployed near standard monitoring stations from March 1 to May 30, 2024, recording PM<ce:inf loc=\"post\">2.5</ce:inf> concentration, PM<ce:inf loc=\"post\">10</ce:inf> concentration, particle counts in six different size channels, and ambient temperature and humidity. The results revealed systematic overestimation and interactions with environmental factors in the sensor data. To address these challenges, a physics-based calibration model, leveraging sensor-reported particle size information, was developed and compared with traditional empirical and machine learning models. These calibration models were embedded into the sensor system, followed by a second field campaign from June 1 to 30. While the machine learning model achieved the best performance during the first campaign (R<ce:sup loc=\"post\">2</ce:sup> > 0.90, RMSE <10 μg/m<ce:sup loc=\"post\">3</ce:sup> for PM<ce:inf loc=\"post\">2.5</ce:inf> and PM<ce:inf loc=\"post\">10</ce:inf>), its generalization ability was limited. The physics-based model, however, excelled on a new dataset from the second campaign, demonstrating robust performance and strong generalization across urban conditions. These findings highlight the potential of the physics-based calibration model to improve the reliability and sustainability of urban air quality monitoring by integrating it into the embedded systems of low-cost sensors. This approach offers enhanced stability and applicability in complex urban environments, providing a more effective calibration method for urban environmental systems.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"45 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988095","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}
Heating buildings is a significant contributor to CO2 emissions in cities located at mid- and high-latitudes. This study aims to enhance our understanding of the average daily cycle and interseasonal variability of CO2 emissions from space heating. To achieve this goal, we have developed a methodology solely relying on observations to identify the contribution of space heating to CO2 fluxes measured in the urban inertial sublayer. This method offers two main advantages. Firstly, it allows for the estimation of space heating contribution with high frequency, facilitating the analysis of its daily cycle. Secondly, our estimation is independent of other methods that do not rely on observations, such as modeling or fuel-consumption based approaches.
{"title":"Estimation of space heating CO2 emissions based only on CO2 fluxes observations","authors":"Marine Goret, Valéry Masson, Marie-Pierre Moine, William Maurel, Dominique Legain, Grégoire Pigeon","doi":"10.1016/j.uclim.2024.102255","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102255","url":null,"abstract":"Heating buildings is a significant contributor to CO<ce:inf loc=\"post\">2</ce:inf> emissions in cities located at mid- and high-latitudes. This study aims to enhance our understanding of the average daily cycle and interseasonal variability of CO<ce:inf loc=\"post\">2</ce:inf> emissions from space heating. To achieve this goal, we have developed a methodology solely relying on observations to identify the contribution of space heating to CO<ce:inf loc=\"post\">2</ce:inf> fluxes measured in the urban inertial sublayer. This method offers two main advantages. Firstly, it allows for the estimation of space heating contribution with high frequency, facilitating the analysis of its daily cycle. Secondly, our estimation is independent of other methods that do not rely on observations, such as modeling or fuel-consumption based approaches.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"44 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988429","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}
The study aims to assess the hydrological behavioural changes of the Noyyal watershed, using the SWAT model. The SWAT model was used to stimulate a total of 15 years' of information on factors including rainfall, temperature, relative humidity, wind speed, and solar radiation. It was chose to study the Uncertainty Fitting procedure (SUFI-2) as the model for sensitivity analysis, calibration, and validation. The hydrological activity models were used with DEM, LULC data, soil, and climatological data for both types of sensitivity analyses, such as one-at-a-time and global sensitivity analysis. In this study, stream flow and sediment yield were calibrated and validated on a monthly basis. Calibration began over a 12-year period from 2003 to 2014, while validation actually occurred over a four-year period from 2011 to 2014. PBIAS, NSE, PSR, and R2 statistical indices show the model performs “excellently” at simulating hydrology. In comparison to the various automatic calibration techniques, SUFI-2 was observed to be very acceptable and simple to use. The hydrological behaviour of the Noyyal watershed has changed dramatically over the last two decades.
{"title":"Assessment of hydrological behavioural changes of Noyyal watershed in Coimbatore district, India by using SWAT model","authors":"Thangavelu Arumugam, Sapna Kinattinkara, Sampathkumar Velusamy, Manoj Shanmugamoorthy, Senthilkumar Veerasamy","doi":"10.1016/j.uclim.2025.102285","DOIUrl":"https://doi.org/10.1016/j.uclim.2025.102285","url":null,"abstract":"The study aims to assess the hydrological behavioural changes of the Noyyal watershed, using the SWAT model. The SWAT model was used to stimulate a total of 15 years' of information on factors including rainfall, temperature, relative humidity, wind speed, and solar radiation. It was chose to study the Uncertainty Fitting procedure (SUFI-2) as the model for sensitivity analysis, calibration, and validation. The hydrological activity models were used with DEM, LULC data, soil, and climatological data for both types of sensitivity analyses, such as one-at-a-time and global sensitivity analysis. In this study, stream flow and sediment yield were calibrated and validated on a monthly basis. Calibration began over a 12-year period from 2003 to 2014, while validation actually occurred over a four-year period from 2011 to 2014. PBIAS, NSE, PSR, and R<ce:sup loc=\"post\">2</ce:sup> statistical indices show the model performs “excellently” at simulating hydrology. In comparison to the various automatic calibration techniques, SUFI-2 was observed to be very acceptable and simple to use. The hydrological behaviour of the Noyyal watershed has changed dramatically over the last two decades.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"55 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988096","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}
Urban greening plays a crucial role in mitigating urban heat islands (UHIs) and improving building energy efficiency. However, the effects of urban vegetation on microclimate regulation and building energy consumption (BEC) in cold-climate regions remain underexplored. This study aims to evaluate the cooling impacts of urban greening and its influence on BEC in Harbin, a representative severe cold-climate city, by using a coupled simulation approach. The methodology integrates the ENVI-met microclimate model and EnergyPlus building energy simulation, incorporating real-world parameters such as urban morphology, vegetation characteristics, and meteorological data. The results demonstrate that urban vegetation significantly reduces surface and air temperatures during summer, with cooling effects reaching up to 1.27 °C. The incorporation of greening also reduces building cooling loads by 14.56 %, highlighting its potential for energy savings. Furthermore, the findings reveal spatial heterogeneity in the cooling effects, with tree-dominated areas outperforming grass-covered spaces. This study fills a gap in previous research, which often neglects the unique climatic and morphological conditions of cold regions, by providing a comprehensive evaluation framework for urban greening strategies. These results offer practical insights for urban planners and policymakers to optimize greening strategies in cold-climate cities, aiming to enhance thermal comfort and achieve sustainable energy use. This work emphasizes the need for region-specific studies and integrated approaches to address the dual challenges of urban climate adaptation and energy efficiency.
{"title":"Cooling effects and energy-saving potential of urban vegetation in cold-climate cities: A comparative study using regression and coupled simulation models","authors":"Dongliang Han, Mingqi Wang, Jiayi Li, Tiantian Zhang, Xuedan Zhang, Jing Liu, Yufei Tan","doi":"10.1016/j.uclim.2024.102268","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102268","url":null,"abstract":"Urban greening plays a crucial role in mitigating urban heat islands (UHIs) and improving building energy efficiency. However, the effects of urban vegetation on microclimate regulation and building energy consumption (BEC) in cold-climate regions remain underexplored. This study aims to evaluate the cooling impacts of urban greening and its influence on BEC in Harbin, a representative severe cold-climate city, by using a coupled simulation approach. The methodology integrates the ENVI-met microclimate model and EnergyPlus building energy simulation, incorporating real-world parameters such as urban morphology, vegetation characteristics, and meteorological data. The results demonstrate that urban vegetation significantly reduces surface and air temperatures during summer, with cooling effects reaching up to 1.27 °C. The incorporation of greening also reduces building cooling loads by 14.56 %, highlighting its potential for energy savings. Furthermore, the findings reveal spatial heterogeneity in the cooling effects, with tree-dominated areas outperforming grass-covered spaces. This study fills a gap in previous research, which often neglects the unique climatic and morphological conditions of cold regions, by providing a comprehensive evaluation framework for urban greening strategies. These results offer practical insights for urban planners and policymakers to optimize greening strategies in cold-climate cities, aiming to enhance thermal comfort and achieve sustainable energy use. This work emphasizes the need for region-specific studies and integrated approaches to address the dual challenges of urban climate adaptation and energy efficiency.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"30 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988098","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}
Pub Date : 2025-01-10DOI: 10.1016/j.uclim.2024.102274
Rui Liu, Yuxiang Wang, Yu Zhang, Zhixing Peng, Hankai Chen, Xiang Li, Hang Li, Weiyue Li
Urban wind corridors can improve the air exchange and ventilation within the city center and mitigate high UHIs. The current study simplified the complex underlying surface using a GIS clustering analysis model to establish the 3D digital model for CFD simulation, the Reynolds-averaged Navier–Stokes (RANS) model was used to simulate the city-scale wind environment with a horizontal resolution of 30 m. Machine learning methods were then employed to predict how ventilation potential could be improved within the identified wind corridors. The results are summarized as follows: (1) Building coverage ratio (BCRz) and floor area ratio (FAR) exerted significant influences on both horizontal and vertical wind fields, leading to variations in wind speed and direction. (2) BCRz and FAR have negative correlations with the average wind speed (Umean) at the heights ranging from 5 to 50 m above the ground, while NDVI and green plot ratio (GR) show positive correlations. (3) 14 potential ventilation corridors were detected based on the CFD simulations. Targeted recommendations to enhance urban ventilation were validated by machine learning methods, emphasizing adjustments to urban morphology and landscape types. These findings provide actionable insights for urban planning and design strategies aimed at improving ventilation in high-density metropolitan areas.
{"title":"Analysis of the city-scale wind environment and detection of ventilation corridors in high-density metropolitan areas based on CFD method","authors":"Rui Liu, Yuxiang Wang, Yu Zhang, Zhixing Peng, Hankai Chen, Xiang Li, Hang Li, Weiyue Li","doi":"10.1016/j.uclim.2024.102274","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102274","url":null,"abstract":"Urban wind corridors can improve the air exchange and ventilation within the city center and mitigate high UHIs. The current study simplified the complex underlying surface using a GIS clustering analysis model to establish the 3D digital model for CFD simulation, the Reynolds-averaged Navier–Stokes (RANS) model was used to simulate the city-scale wind environment with a horizontal resolution of 30 m. Machine learning methods were then employed to predict how ventilation potential could be improved within the identified wind corridors. The results are summarized as follows: (1) Building coverage ratio (<ce:italic>BCR</ce:italic><ce:inf loc=\"post\"><ce:italic>z</ce:italic></ce:inf>) and floor area ratio (<ce:italic>FAR</ce:italic>) exerted significant influences on both horizontal and vertical wind fields, leading to variations in wind speed and direction. (2) <ce:italic>BCR</ce:italic><ce:inf loc=\"post\"><ce:italic>z</ce:italic></ce:inf> and <ce:italic>FAR</ce:italic> have negative correlations with the average wind speed (<ce:italic>U</ce:italic><ce:inf loc=\"post\"><ce:italic>mean</ce:italic></ce:inf>) at the heights ranging from 5 to 50 m above the ground, while <ce:italic>NDVI</ce:italic> and green plot ratio (<ce:italic>GR</ce:italic>) show positive correlations. (3) 14 potential ventilation corridors were detected based on the CFD simulations. Targeted recommendations to enhance urban ventilation were validated by machine learning methods, emphasizing adjustments to urban morphology and landscape types. These findings provide actionable insights for urban planning and design strategies aimed at improving ventilation in high-density metropolitan areas.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"17 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988099","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}
Pub Date : 2025-01-09DOI: 10.1016/j.uclim.2024.102263
Rajsree Das Tuli MSc, Kazi Jihadur Rashid MSc, Md. Mezbahul Islam MSc, Masrur Sobhan, Sheikh Tawhidul Islam PhD, Krishna Prosad Mondal MEng, Byomkesh Talukder PhD, Ananda Mohan Mondal PhD
This study analyzed the spatiotemporal distribution of atmospheric contaminants in seven distinct climatic subzones of Bangladesh and their interactions with meteorological variables. Monthly time series data for air pollutants and meteorological variables were collected using the Google Earth Engine platform. Pearson's correlation analysis assessed the relationship between pollutant concentrations and meteorological variables at each site. The key findings reveal a consistent nationwide increase in pollution levels, with the highest levels observed in winter and pre-monsoon seasons, gradually decreasing by the end of the pre-monsoon period, and reaching their lowest concentrations during the monsoon season. Pollutant concentrations started to rise again during the post-monsoon. Precipitation exhibited an inverse correlation with NO2 and CO concentrations across various climatic regions and seasons. In contrast, O3 displayed a moderate to strong positive correlation with rainfall and humidity. Temperature generally had a positive relationship with AAI, CO, and NO2 concentrations, while wind speed showed a positive relationship with NO2 and SO2 concentrations. These findings provide valuable insights into the country's air quality and the intricate relationship between weather patterns and pollutant concentration.
{"title":"Impact of climatic parameters on spatiotemporal variation of air pollutants across Bangladesh","authors":"Rajsree Das Tuli MSc, Kazi Jihadur Rashid MSc, Md. Mezbahul Islam MSc, Masrur Sobhan, Sheikh Tawhidul Islam PhD, Krishna Prosad Mondal MEng, Byomkesh Talukder PhD, Ananda Mohan Mondal PhD","doi":"10.1016/j.uclim.2024.102263","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102263","url":null,"abstract":"This study analyzed the spatiotemporal distribution of atmospheric contaminants in seven distinct climatic subzones of Bangladesh and their interactions with meteorological variables. Monthly time series data for air pollutants and meteorological variables were collected using the Google Earth Engine platform. Pearson's correlation analysis assessed the relationship between pollutant concentrations and meteorological variables at each site. The key findings reveal a consistent nationwide increase in pollution levels, with the highest levels observed in winter and pre-monsoon seasons, gradually decreasing by the end of the pre-monsoon period, and reaching their lowest concentrations during the monsoon season. Pollutant concentrations started to rise again during the post-monsoon. Precipitation exhibited an inverse correlation with NO<ce:inf loc=\"post\">2</ce:inf> and CO concentrations across various climatic regions and seasons. In contrast, O<ce:inf loc=\"post\">3</ce:inf> displayed a moderate to strong positive correlation with rainfall and humidity. Temperature generally had a positive relationship with AAI, CO, and NO<ce:inf loc=\"post\">2</ce:inf> concentrations, while wind speed showed a positive relationship with NO<ce:inf loc=\"post\">2</ce:inf> and SO<ce:inf loc=\"post\">2</ce:inf> concentrations. These findings provide valuable insights into the country's air quality and the intricate relationship between weather patterns and pollutant concentration.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"26 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939707","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}
Pub Date : 2025-01-09DOI: 10.1016/j.uclim.2024.102276
Alamin Molla, David J. Sailor, Aaron B. Flores
This study investigates the dynamics of heat exposure in Maricopa County, Arizona, employing a multidimensional approach. Utilizing the Extreme Gradient Boosting machine learning model, we predict census block group (CBG) level air temperatures, revealing the significant influences of land surface temperature (LST) (23 % importance ‘Gain’) and elevation (28 % importance ‘Weight’) on air temperature variability. Even though LST is an important predictor of air temperature variation, for ∼90.0 % of CBGs, LST variations are not associated with air temperature variation in a statistically significant way; rather other relevant factors such as impervious surface, and vegetation played significant roles. Among the minority populations, the Hispanic/Latinx populations are highly exposed to elevated air temperature. There are 181 CBGs with positive association between Hispanic/Latinx and air temperature, based on the local statistical significance test from Geographically Weighted Regression modeling. The study demonstrates the importance of considering local topography, and land use/land cover patterns to characterize UHI and considering socio-demographic characteristics in assessing spatial variation of heat exposure. By addressing socio-demographic disparities in heat exposure, this research contributes valuable insights for urban planning, public health interventions, and climate resilience efforts in Maricopa County, with methods and findings that are widely transferable.
{"title":"Exploring air temperature variability and socio-demographic inequalities in heat exposure through machine learning: A case study of Maricopa County, Arizona","authors":"Alamin Molla, David J. Sailor, Aaron B. Flores","doi":"10.1016/j.uclim.2024.102276","DOIUrl":"https://doi.org/10.1016/j.uclim.2024.102276","url":null,"abstract":"This study investigates the dynamics of heat exposure in Maricopa County, Arizona, employing a multidimensional approach. Utilizing the Extreme Gradient Boosting machine learning model, we predict census block group (CBG) level air temperatures, revealing the significant influences of land surface temperature (LST) (23 % importance ‘Gain’) and elevation (28 % importance ‘Weight’) on air temperature variability. Even though LST is an important predictor of air temperature variation, for ∼90.0 % of CBGs, LST variations are not associated with air temperature variation in a statistically significant way; rather other relevant factors such as impervious surface, and vegetation played significant roles. Among the minority populations, the Hispanic/Latinx populations are highly exposed to elevated air temperature. There are 181 CBGs with positive association between Hispanic/Latinx and air temperature, based on the local statistical significance test from Geographically Weighted Regression modeling. The study demonstrates the importance of considering local topography, and land use/land cover patterns to characterize UHI and considering socio-demographic characteristics in assessing spatial variation of heat exposure. By addressing socio-demographic disparities in heat exposure, this research contributes valuable insights for urban planning, public health interventions, and climate resilience efforts in Maricopa County, with methods and findings that are widely transferable.","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"1 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939743","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}
Pub Date : 2025-01-08DOI: 10.1016/j.uclim.2024.102281
David Hidalgo-García, Dimitra Founda, Hamed Rezapouraghdam
There is a close relationship between tourism and climate, the latter being one of the most important factors influencing the choice of destination. Today, rising temperatures and extreme weather events pose significant risks to the tourism sector by affecting the safety and well-being of visitors. Urban tourism is particularly vulnerable due to the additive effect of the urban heat islands which exacerbate heat-related risk in cities. This research aims to examine the spatio-temporal variation of thermal conditions at Seville, a popular tourist destination in Spain, where the tourism sector represents 25 % of the gross domestic product. To this end, the Universal Thermal Climate Index (UTCI) and Landsat 8 images have been used, corresponding to August 2017, when the city experienced two heat waves and decreased number of visitors. Our results showed high variability of the UTCI between 28 and 39 °C corresponding to strong thermal stress that increased in the afternoon hours. During heat waves, this variability intensified by 9.77 %, reaching values between 32 and 42 °C corresponding to very strong thermal stress. Our findings show that adverse thermal conditions negatively affect tourist arrivals, which could lead to significant economic repercussions. Also, our results point to the urgent need for mitigation and resilience measures including the use of Blue Infrastructure (BI), new green areas, naturalizing streets, and use of green facades and roofs. These results will allow the development of adaptation and urban planning policies together with the development of resilience measures that improve the environmental comfort conditions of the historic center and therefore the visitors' experience.
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