Quantify coupling effects between outdoor microclimate and building energy consumption for BIPV buildings in different local climate zones

<p>A new 10-type urban Local Climate Zone (LCZ) classification with 100-m resolution was developed, following the guidelines of the World Urban Database and Access Portal Tools (WUDAPT) over the Greater Bay Area (GBA). This LCZ dataset was incorporated into the Building Environment Parameterization (BEP)-Building Energy Model (BEM) multi-layer urban canopy scheme used by the Weather Research and Forecasting (WRF) model, with key parameters (such as fraction of impervious surface, building height/width, road width, air conditioning usage) determined from local building morphology and energy consumption patterns. The impacts of using such detailed 10-type LCZ, as compared to using remapped 3-type LCZ and using default WRF 1-type urban land cover were assessed, based on parallel integrations of the WRF system at 1-km resolution for a historical hot-and-polluted event over the GBA. It was found that the model surface temperature, air temperature, humidity and wind speed in the 10-type LCZ run were in closer agreement with in-situ observations, demonstrating the value of detailed urban LCZ data in improving the model performance. Smaller diurnal temperature range and higher nighttime temperature were found in the 10-type LCZ run compared to the 3-type LCZ and 1-type runs. Increased building height in the 10-type LCZ setting also reduces positive bias of wind speed in the lower planetary boundary layer at urban locations. The cold and dry biases over the non-urban areas in the 10-type LCZ run could be further reduced through considering updated land cover, soil type, soil hydraulic/thermal parameters, soil moisture/temperature. Owing to the improvement in capturing the urban meteorology, incorporating more detailed LCZ classification might also improve air-quality simulations. These findings should be relevant to the development of comprehensive, high-resolution earth system models, which are an indispensable tool for mitigation of and adaption to regional environmental and climate changes.</p>

Urban climate monitoring system (UCMS) was established in Novi Sad (Serbia) in 2014 based on the Local Climate Zones (LCZs) classification system, GIS model calculations and field work. Seven built and two land cover LCZ types were delineated and 27 stations equipped with air temperature and relative humidity sensors were distributed across all LCZs. Suitability of the developed monitoring system for human outdoor thermal comfort research in different LCZs of the city and its surroundings was investigated during a heat wave period using Physiologically Equivalent Temperature (PET) index. During the daytime (night-time) the highest thermal loads are present in open midrise (compact midrise) LCZ, while the most comfortable is LCZ A (dense trees) during the whole day. In general, the highest thermal loads are obtained in midrise, followed by low-rise, sparsely built, low plants and dense trees LCZs. All LCZs (except LCZ A - dense trees) had higher PET when compared to LCZ D (LCZ D - low plants) during evening and nocturnal hours with maximum difference of 7.1 °C (00 UTC) between LCZ 2 (compact midrise) and LCZ D (low plants). Contrary to this, LCZ D (low plants) had higher PET compared to the majority of LCZs during the daytime with maximum difference of 8.5 °C (9 UTC) when compared to LCZ A (dense trees). Furthermore, the smallest thermal comfort differences during heat wave occurred between LCZs with similar structure (i.e. open low-rise and large low-rise, compact midrise and compact low-rise) and cover (i.e. sparsely built and low plants).

The local climate zone (LCZ) scheme is now used to investigate urban heat islands, which provides additional reference for energy consumption simulation. Based on the LCZ scheme, a LCZ mapping of Shenyang, a city in northeast China, was first constructed using the World Urban Database and Access Portal Tools (WUDAPT) Level 0 method. Subsequently, DeST-h was considered to simulate the energy consumption of urban buildings with concentration areas. The results show that with Shenyang being a severely cold area, the annual energy consumption of heating is approximately twice that of refrigeration for an individual building. The total energy consumption of open-distributed single buildings is higher than that of compact-distributed single buildings. Consequently, the unit cumulative energy consumption in compact-distributed buildings is higher than that in openly distributed building areas. The compact high-rise buildings (LCZ 1) have the highest energy consumption, with a unit annual energy consumption of 123,771.150 MW·h, which is equivalent to 41,257 tons of standard coal combustion power generation. Considering the energy consumption of residential buildings, the central high-rise buildings group and the compact centralized middle-rise buildings in the downtown area are high energy consumption areas. For future urban planning, design strategies such as energy-saving transformation and energy planning should be considered. The research results can provide a scientific basis and theoretical support for reducing building energy consumption, alleviating the urban heat island effect, and the development of modern urban planning.

Impact of LCZs spatial pattern on urban heat island: A case study in Wuhan, China

Urban landscape has important effects on urban climate, and the local climate zone (LCZ) framework has been widely applied in related studies. However, few studies have compared the relative contributions of LCZ on the urban thermal environment across different cities. Therefore, Beijing, Shanghai, and Shenzhen in China were selected to conduct a comparative study to explore the relationship between LCZ and land surface temperature (LST). The results showed that (1) both the composition and spatial configuration of LCZ had obvious differences among the three cities. Beijing had a higher area proportion of compact mid-rise and low-rise LCZ types. The spatial pattern of LCZ in Shenzhen was especially quite different from those of Beijing and Shanghai. (2) Shenzhen had the strongest summer surface urban heat island (UHI) intensity and the largest UHI region area. However, the proportion of urban cooling island areas was still the highest in Shenzhen. (3) Different LCZs showed significant LST differences. The largest LST difference between the LCZs reached 5.57 °C, 4.50 °C, and 12.08 °C in Beijing, Shanghai, and Shenzhen, respectively. Built-up LCZs had higher LSTs than other LCZ types. (4) The dominant driving LCZs on LST were different among these cities. The LST in Beijing was easily influenced by built-up LCZ types, while the cooling effects generated by LCZ G(water) were much stronger than built-up LCZs’ warming effects in Shanghai. These results indicated that the effect of the LCZ on LST had significant differences among LCZ types and across cities, and the dominant LCZs should be given more priority in future urban planning.

An investigation into heat storage by adopting local climate zones and nocturnal-diurnal urban heat island differences in the Tokyo Prefecture

The deterioration of the urban thermal environment has seriously affected the quality of life of urban residents, and studying the optimal cooling landscape combination and configuration based on local climate zones (LCZs) is crucial for mitigating the thermal environment. In this study, the LCZ system was combined to analyze the spatial and temporal changes to the thermal environment in the central area of Fuzhou, and the 159 blocks in the core area were selected to derive the optimal LCZ combination and configuration. The conclusions are as follows: (1) From 2013 to 2021, the building layout of the study area became more open and the building height gradually increased. The high-temperature areas were mainly clustered in the core area; (2) The LSTs for low-rise buildings (LCZ 3 (41.67 °C), LCZ 7 (40.10 °C), LCZ 8 (42.61 °C), and LCZ 10 (41.85 °C)) were higher than the LSTs for high-rise buildings (LCZ 1 (38.58 °C) and LCZ 4 (38.50 °C)); (3) The thermal contribution index for low building types was higher for dense buildings (LCZ 3 (0.4331), LCZ 8 (0.3149), and LCZ 10 (0.2325)) than for open buildings (LCZ 6 (0.0247) and LCZ 9 (0.0317)); (4) Blocks with an average LST of 36 °C had the most cost-effective cooling, and the combination and configuration of LCZs within such blocks were optimal. Our results can be used to better guide urban planners in managing LCZ combinations and configurations within blocks (the smallest planning unit) at an earlier phase of thermal environment design, and for appropriately adapting existing block layouts, providing a new perspective on urban thermal environment research with important implications for climate-friendly city and neighborhood planning.

Studying air Urban Heat Islands (AUHI) in African cities is limited by building height data scarcity and sparse air temperature (Tair) networks, leading to classification confusion and gaps in Tair data. Satellite imagery used in surface UHI (SUHI) applications overcomes the gaps which befall AUHI, thus making it the primary focus of UHI studies in areas with limited Tair stations. Consequently, we used Landsat 30 m imagery to analyse SUHI patterns using Land Surface Temperature (LST) data. Local climate zones (LCZ) as a UHI study tool have been documented to not result in distinct thermal environments at the surface level per LCZ class. The goal in this study was thus to explore relationships between LCZs and LST patterns, aiming to create a building height (BH)-independent LCZ framework capable of creating distinct thermal environments to study SUHI in African cities where LiDAR data are scarce. Random forests (RF) classified LCZ in R, and the Single Channel Algorithm (SCA) extracted LST via the Google Earth Engine. Statistical analyses, including ANOVA and Tukey’s HSD, assessed thermal distinctiveness, using a 95% confidence interval and 1 °C threshold for practical significance. Semi-Automated Agglomerative Clustering (SAAC) and Automated Divisive Clustering (ADC) grouped LCZs into thermally distinct clusters based on physical characteristics and LST data internal patterns. Built LCZs (1–9) had higher mean LSTs; LCZ 8 reached 37.6 °C in Spring, with a smaller interquartile range (IQR) (34–36 °C) and standard deviation (SD) (1.85 °C), compared to natural classes (A–G) with LCZ 11 (A–B) at 14.9 °C/LST, 17–25 °C/IQR, and 4.2 °C SD. Compact LCZs (2, 3) and open LCZs (5, 6), as well as similar LCZs in composition and density, did not show distinct thermal environments even with building height included. The SAAC and ADC clustered the 14 LCZs into six thermally distinct clusters, with the smallest LST difference being 1.19 °C, above the 1 °C threshold. This clustering approach provides an optimal LCZ framework for SUHI studies, transferable to different urban areas without relying on BH, making it more suitable than the full LCZ typology, particularly for the African context. This clustered framework ensures a thermal distinction between clusters large enough to have practical significance, which is more useful in urban planning than statistical significance.

The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs.

This study aims to investigate spatial and temporal dynamics and relationship between air temperature and five air humidity parameters (relative humidity, water vapor pressure, absolute humidity, specific humidity, and vapor pressure deficit) in Novi Sad, Serbia, based on two-year data (December 2015–December 2017). The analysis includes different urban areas of Novi Sad, which are delineated in five built (urban) types of local climate zones (LCZ) (LCZ 2, LCZ 5, LCZ 6, LCZ 8, and LCZ 9), and one land cover (natural) local climate zone (LCZ A) located outside the urban area. Temporal analysis included annual, seasonal, and monthly dynamics of air temperature and air humidity parameters, as well as their patterns during the extreme periods (heat and cold wave). The results showed that urban dry island (UDI) occurs in densely urbanized LCZ 2 from February to October, unlike other urban LCZs. The analysis of the air humidity dynamics during the heat wave shows that UDI intensity is most pronounced during the daytime, but also in the evening (approximately until midnight) in LCZ 2. However, lower UDI intensity is observed in the afternoon, in other urban LCZs (LCZ 6, LCZ 8, and LCZ 9) and occasionally in the later afternoon in LCZ 5. Regression analysis confirms the relationship between air temperature and each of the analyzed air humidity parameters.

Urban heat islands (UHIs) are a key topic in urban climate studies. However, systematic criteria for UHI comparisons were lacking prior to 2012, when the concept of Local Climate Zones (LCZs) was introduced. By relating remotely sensed land surface temperature (LST) to LCZs, we explored the applicability of LCZs in surface urban heat island (SUHI) investigations and compared the LST variation within and among LCZs for the Austin, San Antonio, and Dallas-Fort Worth metropolitan areas in Texas, United States. Landsat 8 images from one summer and one winter day in 2015 were used to obtain LSTs to measure and model SUHIs. LCZs that were characterized by different land covers had the greatest LST variations, and LCZs that were further characterized by various urban morphological properties (including building density and height of roughness) also showed significant LST differences. Moreover, LCZ 9 (sparsely built), LCZ 10 (heavy industry), LCZ D (low plants), LCZ E (bare rock or paved), and LCZ F (bare soil or sand) tended to contribute to contradictory heating/cooling effects in different metropolitan areas, primarily due to the spatial arrangement and geographic locations of LCZs. The close association between LCZs and LST demonstrates that LCZs are valuable for comparative analysis of the SUHI phenomenon between different cities and can be helpful for examination of the evolution of SUHIs over time. Our findings further suggest that understanding the spatial distribution of LCZs can benefit the development of mitigation strategies for SUHIs.

Heat risk assessment and response to green infrastructure based on local climate zones

Urban heat island effect of a polynuclear megacity Delhi – Compactness and thermal evaluation of four sub-cities

Understanding the urban thermal environment is vital for improving urban planning and strategy development when mitigating urban heat islands. However, urban thermal characteristics of local climate zones (LCZ) are different within cities and most studies lack regional perspective. This study explored surface thermal performances of cities in three urban agglomerations (Jing-Jin-Ji, Yangtze River Delta and Pearl River Delta) in China using MODIS land surface temperature (LST). Besides that, the diurnal and seasonal LST variations of LCZs are also studied. Moreover, the optimal LCZs for better urban cooling are also investigated in this study. Although the thermal distributions of LCZs are different in China, there are still some similar features. Our four key findings were as follows. (1) LCZs in China are well classified, with average overall accuracy of 82% being higher than that in some previous studies. (2) The LST of mid-rise (LCZ 2, 5) is higher than that of high- and low-rise buildings (LCZ 1, 3, 4, 6); and compact buildings are warmer than open buildings (LCZ 1–3 > LST 4–6) in summer of China. That shows both mid-rise and compact buildings are not beneficial to cool urban. In addition, LST variations at daytime and in summer are more significant than nighttime and other seasons. (3) LST differences within LCZs are significant at p < 0.05, and are most significant in Jing-Jin-Ji (JJJ). The LST difference within built types (LCZ 1–10) is more significant than within natural types (LCZ A–G), showing that built types alteration will be more effective for thermal environmental improvement. (4) Under the current population and urban area, increasing greenness and water area in compact high-rise buildings are the most effective strategies for urban cooling in all three urban agglomerations, with the largest reduction in LST of 4.11 K in JJJ. These findings will provide support for thermal environment mitigation, urban planning and sustainable urban development.

The Local Climate Zone (LCZ) classification system is used in this study to analyze the impacts of urban morphology on a surface urban heat island (SUHI). Our study involved a comparative analysis of SUHI effects in two Japanese cities, Sapporo and Hiroshima, between 2000 to 2022. We used geographical-information-system (GIS) mapping techniques to measure temporal LST changes using Landsat 7 and 8 images during the summer’s hottest month (August) and classified the study area into LCZ classes using The World Urban Database and Access Portal Tools (WUDAPT) method with Google Earth Pro. The urban thermal field variance index (UTFVI) is used to examine each LCZ’s thermal comfort level, and the SUHI heat spots (HS) in each LCZ classes are identified. The research findings indicate that the mean LST in Sapporo only experienced a 0.5 °C increase over the time, while the mean LST increased by 1.8 °C in Hiroshima City between 2000 and 2022. In 2000, open low-rise (LCZ 6) areas in Sapporo were the hottest, but by 2022, heavy industry (LCZ 10) became the hottest. In Hiroshima, compact mid-rise (LCZ 2) areas were the hottest in 2000, but by 2022, heavy-industry areas took the lead. The study found that LCZ 10, LCZ 8, LCZ E, and LCZ 3 areas in both Dfa and Cfa climate classifications had unfavorable UTFVI conditions. This was attributed to factors such as a high concentration of heat-absorbing materials, impervious surfaces, and limited green spaces. The majority of the SUHI HS and areas with the highest surface temperatures were situated near industrial zones and large low-rise urban forms in both cities. The study offers valuable insights into the potential long-term effects of various urban forms on the SUHI phenomenon.