An assessment of WRF-urban schemes in simulating local meteorology for heat stress analysis in a tropical sub-Saharan African city, Lagos, Nigeria

Megacities, such as Lagos, Nigeria, face significant challenges due to rapid urbanization and climate change, resulting in a higher intensity of the urban heat island effect, coupled with high population density, making the city fall under the category of moderate to high heat stress/risk. Despite this, very few studies have analyzed the urban impact on heat stress over the coastal city, albeit with poor resolution data. In this study, we assessed the performance of an integrated high-resolution WRF-urban scheme driven by the readily available urban canopy information of the local climate zone (LCZ) to simulate local meteorological data for analyzing the spatiotemporal pattern of heat stress over the megacity. Our results show that the WRF-BEP scheme outperformed the other evaluated urban schemes, reducing the normalized root mean squared error by 25%. Furthermore, using humidex, we found a generally high incidence of intense discomfort in highly urbanized areas and noted the significant influence of urban morphology on the pattern of heat stress, particularly at night due to the combined effect of urban warming and higher relative humidity. The most socioeconomically disadvantaged urban areas, LCZ7, were most affected, with "hot" heat stress conditions observed over 90% of the time. However, during the afternoon, we found reduced heat stress in the core urban areas which might be due to the shading effect and/or cold air advection. Our findings would be relevant in the development of the urgently needed climate/heat adaptation plans for the city and other sub-Saharan African cities.

Land surface and air temperature dynamics: The role of urban form and seasonality

Due to the scarcity of air temperature (Ta) observations, urban heat studies often rely on satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal environment. However, there remains a lack of a quantitative understanding on how LST differs from Ta within urban areas and what are the controlling factors of their interaction. We use crowdsourced air temperature measurements in Sydney, Australia, combined with urban landscape data, Local Climate Zones (LCZ), high-resolution satellite imagery, and machine learning to explore the influence of urban form and fabric on the interaction between Ta and LST. Results show that LST and Ta have distinct spatiotemporal characteristics, and their relationship differs by season, ecological infrastructure, and building morphology. We found greater seasonal variability in LST compared to Ta, along with more pronounced intra-urban spatial variability in LST, particularly in warmer seasons. We also observed a greater temperature difference between LST and Ta in the built environment compared to the natural LCZs, especially during warm days. Natural LCZs (areas with mostly dense and scattered trees) showed stronger LST-Ta relationships compared to built areas. In particular, we observe that built areas with higher building density (where the heat vulnerability is likely more pronounced) show insignificant or negative relationships between LST- Ta in summer. Our results also indicate that surface cover, distance from the ocean, and seasonality significantly influence the distribution of hot and cold spots for LST and Ta. The spatial distribution for Ta hot spots does not always overlap with LST. We find that relying solely on LST as a direct proxy for the urban thermal environment is inappropriate, particularly in densely built-up areas and during warm seasons. These findings provide new perspectives on the relationship between surface and canopy temperatures and how these relate to urban form and fabric.

Exploring the discrepancy between top-down and bottom-up approaches of fine spatio-temporal vehicular CO2 emission in an urban road network

Road transport emissions of high spatial and temporal resolution are useful for greenhouse gas emission assessment in local action plans. However, estimating these high-resolution emissions is not straightforward, and different indirect approaches exist. The main aim of this study is to examine the differences in CO2 emissions obtained with different methods within a street canyon network in Helsinki, Finland, where a mobile laboratory campaign to quantify traffic emissions has been conducted. We compared three aerodynamic resistance based top-down methods (MOST1, MOST2 and BHT) and three activity based bottom-up microscopic emission models (NGM, HBEFAv4.2 and PHEMlight). The resulted CO2 fluxes using different methods could vary a few orders of magnitude. The combination of MOST1 and NGM model leads to the smallest discrepancy (sMAPE = 16.90 %) and the highest correlation coefficient (r = 0.78) among the rest. We evaluated the discrepancies in terms of different spatial (microenvrionments, local climate zones LCZs and grid sizes) and temporal features (seasons and periods of day). Measurements taken in LCZs of open high-rise regions and microenvironments of main road tend to have larger discrepancies between the two approaches. Using a coarser grid would lead to a relatively small discrepancy and high correlation in the wintertime, yet a loss in distinctive spatial variation. The discrepancies were also elevated on winter evenings. Among all explanatory variables, relative humidity shows the strongest relative importance for the discrepancy of the two approaches, followed by LCZs. Therefore, we stress the importance of choosing a suitable model for vehicular CO2 emission calculation based on meteorological conditions and LCZs. Such model comparison made on a local scale directly supports environmental organisations and cities' climate action plans where detailed information of CO2 emissions are needed.

Assessment of green roofs' potential to improve the urban thermal environment: The case of Beijing

Against the backdrop of global warming, rapid urbanization has caused the aggregation of urban building spaces and the heat island effect is becoming increasingly serious, hindering sustainable urban development. In order to investigate the potential and methods of green roofs in different types of neighborhoods to mitigate the urban heat island effect, this study used multivariate data for surface temperature inversion and local climate zone (LCZ), and the potential of green roofs to reduce the heat island effect was evaluated by combining LCZ zoning and ENVI-met prediction model. Finally, a multi-scenario analysis with economic factors was conducted to derive the optimal implementation path for green roofs. The results show that in LCZs 1-9, the green roof can reduce the daytime average air temperature by a maximum of 0.41 °C for 0.5 m of the LCZ8 roof and 0.37 °C for 1.2 m of the LCZ6 pedestrian. Based on the surface cooling efficiency of LCZ green roofs get the best green roof construction order: LCZ3, LCZ6, LCZ8 > LCZ2, LCZ5, LCZ7 > LCZ1, LCZ4, LCZ9. The construction of green roofs for the heat island areas within the fifth ring road of Beijing can reduce the area of high-temperature and sub-high-temperature zones by 52.55% and 29.17%, respectively, compared with the area without green roof construction. The study clarifies the technical methodology system of cooling efficiency of green roofs in different types of neighborhoods and the reduction of the urban-scale heat island effect, which provides a reference for the planning of green roofs for urban buildings.

How can urban green spaces be planned to mitigate urban heat island effect under different climatic backgrounds? A threshold-based perspective

Urban green space (UGS) was widely regarded as an effective nature-based solution to mitigate the urban heat island (UHI) effect, therefore, developing landscape strategies to enhance its cooling intensity (CI) is crucial. However, two main problems prevent the application of results to practical actions: one is the inconsistency of relationships between influencing factors of landscape and the thermal environment; another is the unfeasibility of some common conclusions such as simply increasing the amount of vegetation cover in highly-urbanized areas. This study compared the CIs of UGSs, investigated the influencing factors of CI and identified the absolute threshold of cooling (ToC_abs) of the influencing factors in four Chinese cities with very different climatic backgrounds (Hohhot, Beijing, Shanghai and Haikou). Results demonstrate that local climate condition affects the cooling effect of UGS. The CI of UGS is weaker in cities with humid and hot summer than in cities with dry and hot summer. Patch characteristics (area and shape), the percentage of water bodies within the UGS (Pland_w) and neighboring greenspace (NGP), vegetation abundance (NDVI) and planting structure together can explain a significant proportion (R² = 0.403-0.672, p < 0.001) of the CI variations of UGS. The inclusion of water bodies can ensure effective cooling of UGS, except in the tropical city. Besides, ToC_abs of area (Hohhot, 2.6 ha; Beijing, 5.9 ha; Shanghai, 4.0 and Haikou, 5.3 ha), and NGP (Hohhot, 8.5 %; Beijing, 21.6 %; and Shanghai, 23.5 %), NDVI (Hohhot, 0.31; Beijing, 0.33; and Shanghai, 0.39) were identified and related landscape strategies of cooling were proposed. The identification of ToC_abs values can provide easy-to-use landscape recommendations to UHI mitigation.

Identifying research progress, focuses, and prospects of local climate zone (LCZ) using bibliometrics and critical reviews

The local climate zone (LCZ) has been an important land surface classification used to differentiate urban climate between localities. The general knowledge maps of LCZ studies are needed when LCZ-related research has attracted great attention. This study integrated bibliometrics and critical review to understand the status quo and suggest future research directions. Bibliometrics provided a statistical technique to explore large volumes of article data from the Web of Science, ScienceDirect, and Scopus databases, based on the Co-Occurrence 13.4 (COOC) software. The bibliometric results indicated a rapid increase in LCZ publications and identified the high-frequency keywords which can be clustered into two groups, including a human thermal comfort-related group and the other urban climatology-related one. From 2011 to 2020, the effects of land use and urban morphology on urban climate and heat island effects predominated the LCZ-related research. Since 2021, the research focuses had shifted to the fields of thermal environment and heatwave, due to the growing demand for human thermal comfort and heat risk reduction. Moreover, this study identified 'Land Surface Temperature' and 'Heatwave' as two focuses of LCZ-related research during the last decade. Their critical reviews demonstrated the need for additional in-depth LCZ-heatwave studies that consider the risk of human exposure. This study also recommended incorporating hydrological concerns and social issues into the LCZ plan for a more integrated LCZ research outlook. Overall, this study provides not only a comprehensive understanding of LCZ knowledge networks, but also critical details on research focuses and potential research prospects.

Modeling spatiotemporal carbon emissions for two mega-urban regions in China using urban form and panel data analysis

Spatiotemporal monitoring of urban CO₂ emissions is crucial for developing strategies and actions to mitigate climate change. However, most spatiotemporal inventories do not adopt urban form data and have a coarse resolution of over 1 km, which limits their implications in intra-city planning. This study aims to model the spatiotemporal carbon emissions of the two largest mega-urban regions in China, the Yangtze River Delta and the Pearl River Delta, using urban form data from the Local Climate Zone scheme and landscape metrics, nighttime light images, and a year-fixed effects model at a fine resolution from 2012 to 2016. The panel data model has an R² value of 0.98. This study identifies an overall fall in carbon emissions in both regions since 2012 and a slight elevation of emissions from 2015 to 2016. In addition, urban compaction and integrated natural landscapes are found to be related to low emissions, whereas scattered low-rise buildings are associated with rising carbon emissions. Furthermore, this study more accurately extracts urban areas and can more clearly identify intra-urban variations in carbon emissions than other datasets. The open data supported methodology, regression models, and results can provide accurate and quantifiable evidence at the community level for achieving a carbon-neutral built environment.

Integrating weather observations and local-climate-zone-based landscape patterns for regional hourly air temperature mapping using machine learning

Air temperature is a crucial variable of urban meteorology and is essential to many urban environments, urban climate and climate-change-related studies. However, due to the limited observational records of air temperature and the complex urban morphology and environment, it might not be easy to map the hourly air temperature with a fine resolution at the surface level within and around cities via conventional methods. Thus, this study employed machine learning (ML) algorithms and meteorological and landscape data to develop hourly air temperature mapping techniques and methods at the 1-km resolution over a multi-year warm seasons period. Guangdong Province, China was selected for the case study. Random forest algorithm was employed for the hourly air temperature mapping. The validation results showed that the hourly air temperature maps exhibit good accuracy from 2008 to 2019, with mean R², root mean square error (RMSE) and mean absolute error (MAE) values of 0.8001, 1.4821 °C and 1.0872 °C, respectively. The importance assessment of the driving factors showed that meteorological factors, especially relative humidity, contributed the most to the air temperature mapping. Simultaneously, landscape factors also played a non-negligible role. Further analysis revealed that the maps steadily maintained high accuracy at nighttime (20:00-7:00), which is essential for investigating nighttime urban climate conditions, especially the urban heat island effect. Moreover, a correlation existed between the nighttime air temperature changes and urban morphology represented by the local climate zones. Air temperatures tended to fall more slowly in the core of metropolitan areas than in the urban fringe. Using ML, this study reliably improves the spatial refinement of hourly air temperature mapping and reveals the spatially explicit air temperature patterns in and around cities at different times in a day during the warm seasons. Moreover, it provides a novel valuable and reliable dataset for air-temperature-related implementation and studies.

Analysis of air temperature dynamics in the "local climate zones" of Novi Sad (Serbia) based on long-term database from an urban meteorological network

A comprehensive analysis of air temperature (T_a) dynamics in "local climate zones" (LCZs) of Novi Sad (Serbia) was based on measurements from 17 stations during 3 years. Hourly changes of T_a, cooling rates (CR), heating rates (HR), and urban heat island (UHI) intensity were assessed on seasonal and annual level and during heat wave (HW) and cold wave (CW) periods. Substantial differences are observed for minimum (T_min) and mean temperatures (T_mean) between LCZs. Two-phase nocturnal cooling was recognized with the first cooling phase characterized by intensive LCZ dependent cooling starting at 1-3 h before sunset and lasting until 3-4 h after sunset. The second cooling phase lasts until sunrise and is characterized by less intensive and LCZ nondependent cooling. The most intensive cooling (CR_peak) was observed in first cooling phase of HW and ranged from - 1.6 °C h^-1 in street canyon (LCZ 2) to - 3.9 °C h^-1 in forest (LCZ A). Furthermore, a new cooling indicator (CR_total) was introduced. Due to cooling differences, the most intensive UHI of 5.5 °C was noticed between LCZs 2 and A at sunset + 1 h during HW. Two-phase diurnal heating was also recognized in LCZs with the first heating phase characterized by intensive LCZ dependent heating starting at sunrise and lasting until 4-7 h afterwards. The most intensive heating (HR_peak) ranged from 2.0 °C h^-1 in street canyon to 3.0 °C h^-1 in industrial area (LCZ 8) during HW. The second heating phase lasts until sunset and is characterized by less intensive heating and smaller HR differences between LCZs.

Urban morphology detection and it's linking with land surface temperature: A case study for Tehran Metropolis, Iran

Expansion of urban areas and alteration of natural land cover exacerbate the local climate change. To find out the effect of land cover changes on the local climate, in this study, the Local Climate Zone (LCZ) concept was utilized to detect urban morphology in Tehran Metropolis. LCZ and Land Surface Temperature (LST) can be identified and classified based on available remote sensing products. Firstly, LCZ maps of Tehran metropolis were extracted using Landsat imagery, and secondly, relationships between LCZ and LST were explored for three years (1990, 2004, and 2018). We found that Tehran urban structure has 13 LCZs based on imagery from Landsat 5 and 14 LCZs based on images for Landsat 7 and 8. Overall accuracy and kappa coefficient were estimated at 62% and 0.60, respectively. Results show that built-up classes including compact high-rise, compact mid-rise, and heavy industrial areas tended to increase the surface temperature, while except for bare land, all other land cover types tended to decrease the surface temperature. The findings also suggest that complementary optical and thermal remote sensing data, such as the combination of OLI with TIRS imageries, were sufficient for supervised LCZ and LST classification in a semi-arid region of Tehran metropolis.

Physical and non-physical factors driving urban heat island: Case of Bangkok Metropolitan Administration, Thailand

This study is focused on two aspects of the urban heat island (UHI). Firstly, the study is aimed at examining the difference in temperature between zones that are classified into different built areas and other land cover types instead of using the urban and rural classification, which is prevalent in the existing literature. Secondly, we consider the heat-intensity-related physical structure of the city such as the sky view, building coverage, building height, surface albedo, and pervious and impervious surfaces, as well as non-physical factors such as anthropogenic heat, travel demand, electricity consumption, and air pollutant concentration. The local climate zone (LCZ) is used as an approach for characterizing the landscape and physical structure of the study areas. The Bangkok Metropolitan Administration (BMA) is used as the case study and 2016 as the base year for examination. The LCZ is classified using Landsat data and the training areas are created using Google Earth and Google Street View. The heat intensity is studied by deriving the land surface temperature (LST) from the thermal band of the Landsat satellite images of March 3, April 4, and April 12, in 2016, which represent the summer season in Bangkok. The result shows that the industry building areas have the highest mean LST is 32.41 °C, while the lowest LST is 28.32 °C in areas of water bodies; the temperature difference was approximately 4 °C. The factors significantly influencing the warming in the BMA are pervious and impervious surfaces, the building coverage ratio, and the anthropogenic heat flux, while the sky view factor, vehicular traffic, and air pollutant concentration are the weak drivers of UHI.