Effects of urban form on the urban heat island effect based on spatial regression model

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina

Evaluating the forthcoming impacts of climate change is important for formulating efficient and flexible approaches to water resource management. General Circulation Models (GCMs) are primary tools that enable scientists to study both past and potential future climate changes, as well as their impacts on policies and actions. In this work, we quantify the future projected impacts of hydroclimatic extremes on the coastal, risk-prone Tar-Pamlico River basin in North Carolina using GCMs from the Sixth International Coupled Model Intercomparison Project (CMIP6). These models incorporate projected future societal development scenarios (Shared Socioeconomic Pathways, SSPs) as defined in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Specifically, we have utilized historical residential expansion data, the Soil and Water Assessment Tool Plus (SWAT+), the Standardized Precipitation Index (SPI), and the Interquartile Range (IQR) method for analyzing extremes from 2024 to 2100. Our findings include: (1) a trend toward wetter conditions is identified with an increase in flood events toward 2100; (2) projected increases in the severity of flood peaks are found, quantified by a rise of 21% compared to the 2000-2020 period; (3) downstream regions are forecast to experience severe droughts up to 2044; and (4) low-lying and coastal regions are found as particularly susceptible to higher flood peaks and more frequent drought events between 2045 and 2100. This work provides valuable insights into the anticipated shifts in natural disaster patterns and supports decision-makers and authorities in promoting adaptive strategies and sustainable policies to address challenges posed by future climate changes in the Tar-Pamlico region and throughout the state of North Carolina, United States.

Enhancing human resilience against climate change: Assessment of hydroclimatic extremes and sea level rise impacts on the Eastern Shore of Virginia, United States

Coastal regions face climate-induced threats that have likely increased over the past four decades. In this work, we quantify the future climate impacts on hydroclimatic extremes in the risk-prone, 15-m-above-sea-level Eastern Shore of Virginia (ESVA) region, utilizing the Sixth International Coupled Model Intercomparison Project (CMIP6) Assessment Report 6 (AR6) and General Circulation Models (GCMs). We incorporate historical data on demographics and disasters, land use land cover (LULC), Landsat imagery, and sea level rise (SLR) to better understand and highlight the correlation between hydroclimatic extremes and societal components in this region. The hydrological model Soil and Water Assessment Tool (SWAT), Standardized Precipitation Index (SPI), Normalized Difference Water Index (NDWI), and Interquartile Range (IQR) method have been used to evaluate the intensity and frequency of projected climate extremes, in which SLR projections under different greenhouse gas emission pathways are temporally and spatially quantified. Our findings include (1) a trend towards wetter conditions is found with an increase in the number of flood events and up to an 8.9 % rise in the severity of flood peaks compared to the 2003-2020 period; (2) current coastal high-risk regions, identified using historical data of natural disasters, demographics, and LULC, are projected to be more susceptible to future climate impacts; and (3) low-lying coastal towns and regions are identified as currently vulnerable to coastal and SLR-induced flooding and are projected to become even more susceptible by 2100. This is the first effort that provides a valuable scientific basis for anticipated shifts in future climate patterns, essential for natural hazard prevention in ESVA. It highlights the need for authorities and decision-makers to plan and implement adaptive strategies and sustainable policies for the ESVA region and other coastal areas across the United States.

MSPA-based green space morphological pattern and its spatiotemporal influence on land surface temperature

The frequent occurrence of extreme heat events has notably affected human's living environment, and a considerable number of studies have reported that green space is an efficient measure by investigating the correlation between green space and land surface temperature (LST). However, spatiotemporal effects of green space on LST still remain unclear. In this study, green space patterns (e.g., core, islet, perforation, edge, loop, bridge, and branch) were identified through morphological spatial pattern analysis (MSPA). Moreover, the effects of green space pattern on LST in three periods were investigated through three kinds of models. As indicated by the results: (1) the geographically and temporally weighted regression model exhibited the optimal performance compared with other two models. (2) in general, the core, the edge, the bridge, and the branch significantly contributed to cooling, and the islet hindered cooling. However, the perforation and the loop exerted significant dual nature effects with the similar quantity of the negative and positive coefficients, showing relatively complex impact mechanism. (3) the intensity of the effect of the respective MSPA class varied across the study area. The core had the most substantial effect, which distributed in the south and middle corners. (4) the result suggested that a neighborhood scale in China, which was 960 m in this study, served as a basic unit in green space management. The spatiotemporal non-stationarity of the effects of green space morphological patterns on LST provided important insights into urban thermal environment improvement through urban green space planning and design.

Demographic disparity in diurnal surface urban Heat Island exposure across local climate zones: A case study of Chongqing, China

Surface urban heat island (SUHI) exposure significantly harms human health during rapid urbanization. Identifying the areas and demographic groups under high SUHI exposure is critical for mitigating heat-related hazards. However, despite broad concern in US-European countries, rare studies discuss the diurnal SUHI exposure of demographic subgroups across Local Climate Zones (LCZs) in Chinese cities. Therefore, taking Chongqing as the case study, we measured the diurnal SUHI exposure of demographic subgroups (e.g., gender, age, and income) across different LCZs (compact, open, and sparsely-built zones) by coupling the ECOSTRESS data and mobile phone signaling data. The results indicated that Chongqing's compact high/middle-rise zones suffered a higher SUHI exposure due to high land surface temperature (LST) and a larger size of population than open zones. Despite a relatively low population density, extremely high LST in compact low-rise zones (e.g., industrial parks) contributes to considerable accumulated SUHI exposure. The SUHI exposure risk exhibited the differences between daytime and nighttime, resulting from SUHI variation and population flow. The demographic analysis showed that Chongqing's demographic subgroups are exposed disproportionately to SUHI. Elderly groups suffered relatively high exposure in compact high-rise zones. Low-incomers witnessed a high exposure in open zones. These findings call for alleviating SUHI exposure risk by targeting vulnerable groups and high-intensity exposure areas.

Exploring the combined cooling effect of street canyon geometry and the surrounding built environment

Exploring the impact of complex urban morphology on the urban heat island (UHI) effect is essential for sustainable environmental management and enhancing human well-being. This study explored the combined cooling effect of street canyon geometry and the surrounding built environment using a CatBoost model and the Shapley method. The findings indicated that in streets with low building height and density, a high proportion of sky and vegetation and a flatter skyline are conductive to mitigate UHI effect. In streets with high building height and density, a lower proportion of sky and vegetation, and a well-proportioned skyline, can effectively mitigate UHI effect. Regardless of the building density and height around the street, street trees are the optimal choice for greening construction and improvement of large and medium-sized cities in China, given their high controllability and the current urban stock background. Therefore, reasonable control and allocation of street trees can effectively adjust the street canyon geometry, providing suitable cooling strategies for streets with different surrounding built environments. This study proposed a method to mitigate the UHI effect through street canyon geometry, which can be extended to other high-density urban thermal environment studies and guide policymakers on street construction and urban design.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Greenness and chronic respiratory health issues: a systematic review and meta-analysis

Introduction

The number of chronic respiratory disease (CRD) individuals worldwide has been continuously increasing. Numerous studies have shown that greenness can improve chronic respiratory health issues through different mechanisms, with inconsistent evidence. By quantitatively summarizing existing studies, our purpose is to determine the connection between greenness exposure and various chronic respiratory health.

Methods

We conducted a comprehensive search on PubMed, EMBASE, and Web of Science core databases to identify relevant studies on the correlation between greenness exposure and chronic respiratory health issues. Studies published up to January 2023 were included in the search. The study used the most frequent indicator (normalized difference vegetation index [NDVI]) as the definition of greenness exposure.

Results

We finally identified 35 studies for meta-analysis. We calculated pooled effects across studies using a random-effects model and conducted a subgroup analysis by age and buffer zones to discuss the effects on chronic respiratory health issues. This study showed that 0.1 increments in NDVI were significantly related to lower rates of asthma incidence, lung cancer incidence, and chronic obstructive pulmonary disease (COPD) mortality risk; the pooled RRs were 0.92 (95% CI: 0.85-0.98), 0.62 (95% CI: 0.40-0.95), and 0.95 (95% CI: 0.92- 0.99), respectively. For the age subgroup, the higher greenness exposure level was related to the incidence rate of asthma among teenagers aged 13-18years (RR: 0.91; 95% CI: 0.83-0.99). For the buffer subgroup, a positive relationship with greenness exposure and asthma incidence/prevalence at 200-300m and 800- 1000m buffers, as well as the COPD mortality at 800-1000m buffer, the pooled RRs were 0.92 (95% CI: 0.86-0.98), 0.87 (95% CI: 0.81-0.93), and 0.93 (95% CI: 0.88- 0.98), respectively. Evidence of publication bias was not detected in this study.

Discussion

Our study is the first global meta-analysis between greenness and various CRDs to report an inverse association. Further research is needed in order to determine the effect of greenness exposure on different CRDs. Therefore, when planning for green development, more consideration must be given to public health and green management as intervention measures.

https://www.crd.york.ac.uk/PROSPEROFILES/384029_STRATEGY_20230116.pdf.

Spatially Explicit Modeling of Anthropogenic Heat Intensity in Beijing Center Area: An Investigation of Driving Factors with Urban Spatial Forms

The escalation of anthropogenic heat emissions poses a significant threat to the urban thermal environment as cities continue to develop. However, the impact of urban spatial form on anthropogenic heat flux (AHF) in different urban functional zones (UFZ) has received limited attention. In this study, we employed the energy inventory method and remotely sensed technology to estimate AHF in Beijing's central area and utilized the random forest algorithm for UFZ classification. Subsequently, linear fitting models were developed to analyze the relationship between AHF and urban spatial form indicators across diverse UFZ. The results show that the overall accuracy of the classification was determined to be 87.2%, with a Kappa coefficient of 0.8377, indicating a high level of agreement with the actual situation. The business/commercial zone exhibited the highest average AHF value of 33.13 W m-2 and the maximum AHF value of 338.07 W m-2 among the six land functional zones, indicating that business and commercial areas are the primary sources of anthropogenic heat emissions. The findings reveal substantial variations in the influence of urban spatial form on AHF across different UFZ. Consequently, distinct spatial form control requirements and tailored design strategies are essential for each UFZ. This research highlights the significance of considering urban spatial form in mitigating anthropogenic heat emissions and emphasizes the need for customized planning and renewal approaches in diverse UFZ.

Categorizing relative water use perception bias using household surveys and monthly water bills

We introduce the concept of relative water use perception bias to highlight the role of human relationships, social cues, and the built environment in household water consumption. Although previous studies have explored actual water use, it is also important to understand how people perceive their relative behaviors because humans are social animals and act in relation to each other. We combine household survey responses and water utility bills in a large sample of households to quantify the degree of over- and under-estimation bias in perceived relative household water use. We then use multi-level nested regression models to investigate four categories of potential influence: sociodemographic characteristics, perceived social norms, neighborhood characteristics, and water bill information. Results show that most households tended to view themselves as 'better than average' water users when they actually used more water compared to neighbors. Respondents in high-income households and those who are more concerned about water shortages were more likely to underestimate their relative water use (using comparatively more than they thought). However, in more suburbanized neighborhood environments, households were more likely to overestimate their relative water use (using comparatively less than they thought). We call the inaccuracy in assessing water usage compared to their neighbors' relative water use perception bias. We propose that a better understanding of this bias can aid the design of policy initiatives like neighborhood planning, better water bill design, targeted messaging, and social signaling. By bringing a relational lens to bear on water conservation studies, understanding relative water use perception bias sheds new light on the complex drivers of household water consumption.

Uneven impacts of COVID-19 on residents' utilization of urban parks: A case study of Guangzhou, China

As COVID-19 increased people's dependency on urban parks for physical and psychological well-being, it also has uncertain impacts on park utilization. Understanding these impacts and how the pandemic has contributed to them is an issue that warrants urgent attention. We used multi-source spatio-temporal data to examine urban park use before and during COVID-19 in Guangzhou, China, and constructed a set of regression models to evaluate the associated factors. We found that COVID-19 has significantly reduced the overall utilization of urban parks while also exacerbating spatial unevenness. This was due to residents' limited movement distance, and the diminished role of urban transportation affecting the efficient citywide use of parks. Meanwhile, residents' increased demand for nearby parks amplified the importance of community parks, which exacerbated the consequences caused by the uneven distribution of park resources. We propose that city administrators improve the efficiency of existing parks and prioritize the adequate placement of community parks at urban fringes to improve access. Furthermore, cities with similar layouts as Guangzhou should plan for urban parks from a multi-perspective and consider the sub-city level differences to address unevenness during the current pandemic and in the future.

Differences in urban heat island and its driving factors between central and new urban areas of Wuhan, China

Urban heat island (UHI) is one of the important effects of urbanization on built environment. Land surface temperature data was taken from moderate-resolution imaging spectroradiometer (MODIS) to investigate the long-term spatiotemporal patterns of UHI in Wuhan during 2001~2018 and, the UHI intensity changes of built-up land in 13 administrative regions in Wuhan were analyzed. Furthermore, 34 spatial error models and 34 ordinary least squares models were established and compared. Spatial error models showed good fitting effect, which were used to determine the influence of normalized difference vegetation index (NDVI), normalized difference building index (NDBI), and social-economic factors (population and nighttime light) on UHI intensity in central urban area and new urban area. The explanatory power changes of these four indicators during 2001~2018 were explored as well. The average UHI intensity in 2014~2018 has increased by about 0.45 °C compared to that in 2001~2005. NDBI is the most dominant factor contributing to the increase in temperature. The impact of NDVI on UHI intensity changes from negative to positive, and the impact of NDBI on UHI intensity in central urban area is weakened during 2001-2018. Social-economic factors have a greater impact on new urban area than on central urban area. These findings show the effects and the explanatory power changes of driving factors during 18 years, which can provide a better understanding of the formation and development of UHI and support for the future urban planning of Wuhan.

Poultry manure gleaned antibiotic residues in soil environment: A perspective of spatial variability and influencing factors

The antibiotics released by human and animals end up in the environmental sinks like soil and water to cause contamination and induce resistance in the microflora. The knowledge of fate and behavior of antibiotics in diverse geographical, climatological, and physicochemical is limited. Therefore, present study investigated the spatial distribution of antibiotics and their relationship with various factors and the source-sink relationship between soil and poultry manure. This was achieved by employing spatially constrained hierarchical clustering, global and local spatial autocorrelation, and spatial regression techniques. Most of the antibiotics co-occurred in both soil and poultry manure matrices, however antibiotic concentration in soil (1.20 μg kg^-1 < antibiotics ≤21.38 μg kg^-1) was lower than that in the poultry manure (7.05 μg kg^-1< antibiotics ≤60.2 μg kg^-1). Majority of the antibiotics showed spatial independence in both poultry manure and soil, except for sulfadiazine, sulfanilamide and sulfapyridine with Moran's I > - 0.111. Local indicator of spatial association indicated localized spatial clustering and outlier behavior of antibiotics. The underlying reasons for spatial heterogeneity of antibiotics resolved by spatial regression models indicated elevation, S%, C%, pH and mean annual temperature as the major factors. The influence of antibiotic concentration in poultry manure as a source was significant but marginal compared to the other predictors of spatial heterogeneity.

Study on the mechanism of urban morphology on river cooling effect in severe cold regions

In the context of global warming, urban climate problems such as heat waves, urban heat islands and air pollution are becoming increasingly prominent, and the cooling effect of rivers is an effective way to mitigate urban hot climate. This study investigates the surrounding urban area of the Hun River in Shenyang, a severe cold region of China, by calculating satellite inversion surface temperature and urban morphology data, and explores the cooling effect of rivers using linear regression models and spatial regression models. The results show that (1) water bodies have a cooling effect on the surrounding environment, with the farthest cooling distance being 4,000 m, but the optimal cooling distance being 2,500 m. (2) In the results of the spatial regression model analysis, the R ² value stays above 0.7 in the range of 0-4,000 m, indicating that urban morphological factors are closely related to LST (land surface temperature). The negative correlation is most pronounced for NVDI (normalized vegetation index), with a peak of -14.8075 calculated by the regression model, and the positive correlation is most pronounced for BD (building density), with a peak of 8.5526. (3) The urban thermal environment can be improved and the heat island effect mitigated through measures such as increasing urban vegetation cover and reducing building density, and these findings can provide data references and case studies to support urban planning and development departments.

Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns

The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.

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.

Influence of view factors on intra-urban air temperature and thermal comfort variability in a temperate city

Urban geometry is known to be one of the major factors in explaining the intra-urban temperature variations. A commonly used indicator to describe the urban geometry is the sky view factor. However, the existing studies have shown that the relationship between SVF and urban temperature is quite contradictory. This suggests that a single SVF cannot accurately quantify the urban geometry. For comparison, we here propose to use view factors, including sky, building and tree view factors (SVF, BVF, and TVF, respectively), to accurately quantify the three-dimensional urban geometry. Based on microclimate measurements conducted in Beijing Olympic Park and its surrounding urban environment in Beijing, China, the impact of view factors on intra-urban air temperature and thermal comfort was evaluated. Measurements were conducted along a selected path during hot summer days with clear skies and light winds by mobile traverses. The obtained results showed that SVF was positively correlated with air temperature during the day but negatively correlated with air temperature at night. BVF mainly played a warming role in both daytime and nighttime. Especially at night, BVF was the main geometric warming factor. TVF had a significant cooling effect during the day but did not have a negative effect at night due to reduced SVF. There was a strong point-to-point correlation between SVF and outdoor thermal comfort in the daytime. The mean differences in Mean Radiant Temperature and Physiologically Equivalent Temperature between shaded and unshaded sites were 12.0 °C and 6.8 °C, respectively, which suggested that providing effective shading is extremely important for improving outdoor daytime thermal comfort.

Noise levels associated with urban land use types in Kigali, Rwanda

Noise pollution poses a serious threat to public health and continues to grow in extent, frequency, and severity due to the rapid population growth and urbanization, and this is of particular concern in developing countries such as Rwanda. However, data on noise pollution levels, noise laws and regulations are, however, lacking in Rwanda. We assessed the effect of land-use type during a two-month period at nine sites: three commercial sites, three passenger-car parking sites, two road junction sites, and one reference site (Car-Free Zone) in Rwanda. We collected data on noise pollution during weekdays (Monday, Wednesday, and Friday) and Weekends (Saturday and Sunday) in the morning (7h00-10h00), around noon (11h00-14h00), and in the evening (15h00-18h00). The mean noise levels were higher during weekdays (60-80) A-weighted decibels (dB) (A)) than during weekends (50-70 dB (A)). We recorded the lowest noise level at Kigali car-free zone in the morning (34.4 dB (A)) and the highest noise level at Nyabugogo passenger-car parks in the evening (111.2dB (A)). Spatial variation of noise levels interpolated for Kigali City shows higher noise levels (hotspot) in the outskirts of Kigali, Remera and Kimironko. Noise levels recorded in Kigali exceeded the World Health Organization permissible daytime limits during both weekdays and weekends at all land-use types except the car-free zone site. Our results indicate that Kigali residents are exposed to high levels of noise, and urgent development of noise pollution monitoring programs and control measures in Rwanda is required.

Effects of residential building height, density, and floor area ratios on indoor thermal environment in Singapore

Building height, building density, and floor area ratio are the three key parameters in urban planning. However, little is known about their impact on indoor thermal environments as compared with outdoor thermal environments. The study aimed to investigate their impact on indoor air temperatures in Singapore. Singapore's residential buildings were reviewed from the perspective of the three parameters, and the Envi-met model was employed for simulation after its accuracy was confirmed by field experiments. Indoor air temperatures under 18 scenarios were simulated and analyzed. The analytical results revealed that among the three parameters, the building density was the most influential. An increase in building density reduced the indoor temperature. In Singapore, the building density increases from 0.0625 to 0.766, which reduced the mean indoor temperature by 4.7 °C. The indoor temperature decreased slightly with an increase in building height. An increase in building height from 12 to 72 m produced an indoor temperature decrease of approximately 1.7 °C. The influence of floor area ratio on indoor air temperature was the most complex. For a fixed floor area ratio of 2, the indoor temperature first increased and then decreased with an increase in building density, which resulted in an indoor temperature difference of 2.1 °C when the building density increased from 0.141 to 0.766.

Evaluation of the Thermal Environmental Effects of Urban Ecological Networks—A Case Study of Xuzhou City, China

Urban heat islands (UHIs) constitute an important ecological problem in cities. Ecological space has a positive effect on UHI mitigation, which can be effectively organized in the form of ecological networks. In this study, the framework for structural UHI improvement based on ecological networks considering the source-corridor model is proposed to examine the spatial threshold of the thermal effect of ecological network factors. Additionally, the cooling mechanism of each constituent element in the ecological network context is further explored. The results demonstrate that (1) an obvious cold and heat island spatial aggregation distribution exists in the Xuzhou main urban area, and land of the same land use type exhibits the dual thermal environmental properties of cold and heat islands through its spatial distribution and characteristics. Ecological space is the main bearing area of cold islands. (2) The ecological network in the main urban area of Xuzhou city occurs at a moderately complex level, and the overall network efficiency is acceptable; the network connectivity is low, while the network loop distribution is uneven. (3) Ecological networks represent an effective spatial means to improve overall UHI patterns. The ecological source area cooling threshold is 300 m, and the optimal threshold is 100 m, while the ecological corridor width threshold is 500 m and 60 m, respectively. (4) Within the optimal threshold in the context of ecological networks, the temperature of ecological sources in category G land is influenced by NDBI and FVC; ecological corridors are mainly influenced by NDBI. The results can provide a quantitative basis for urban ecological network planning considering UHI improvement and a reference for urban thermal environment research within different ecological substrates and planning and control systems in other countries and regions worldwide.

Evaluating the Annual Effect of the Sky View Factor on the Indoor Thermal Environment of Residential Buildings by Envi-met

The effect of the sky view factor (SVF) on outdoor thermal comfort has been extensively explored, while its impact on the indoor thermal environment is ignored. This research combined Envi-met and kriging models to explore the annual effect of the sky view factor on the indoor thermal environment. Different from previous studies, this study explored the effect of the sky view factor on indoor temperature rather than outdoor temperature, and from the perspective of a full year instead of a typical summer day. The analytical results reveal that an increase in the sky view factor raised the indoor air temperature every month. Although a low sky view factor was beneficial to the insulation of the built environment at night, it was proven that in Chenzhou city, the indoor air temperature was still higher in a built area with a high sky view factor than with a low sky view factor. In addition, the sky view factor was shown to have a nonlinear relationship with indoor thermal comfort throughout the year. When the sky view factor increased from 0.05 to 0.45, the indoor temperature increased by around 10 °C at 16:00 and increased by about 4 °C throughout the night for each month, and from the view of the annual cycle, the cooling demand duration increased by 1611.6 h (18.4%), and the heating demand duration decreased by 1192.3 h (13.61%).

Influences of landform and urban form factors on urban heat island: Comparative case study between Chengdu and Chongqing

Although the degraded urban thermal environment has been widely concerned, whether monocentric or polycentric urban development can alleviate urban heat island (UHI) is still debatable, particularly considering different types of cities in plain and mountainous settings. To fill the gap, this study chose the cases of Chengdu on alluvial flatlands and Chongqing on mountainous landforms in China for comparison. Adapting to natural landforms, Chengdu has a dominated monocentric urban form, whereas Chongqing has a naturally polycentric urban form. This study found that areas with high UHI intensity were located in the urban center and peripheral suburbs in Chengdu, while those were located in the peripheral industrial zones in Chongqing. The spatial error model and random forest regression results showed that natural elements such as vegetation and water had similar influences and contributions to UHI. However, distinct landforms and urban forms played critical roles in UHI differences among the two cities. Impervious surface and building density were the dominant variables to UHI of Chengdu, while sky view factor and industrial zones were the main contributors to UHI of Chongqing. The findings called for corresponding policy strategies to optimize urban form and improve the urban thermal environment.

Multi-Time Scale Analysis of Urbanization in Urban Thermal Environment in Major Function-Oriented Zones at Landsat-Scale: A Case Study of Hefei City, China

Urbanization and increasing demand for natural resources and land have affected the urban thermal environment. This is an important hot topic in urban climate research. In this study, we obtained multi-time scale land surface temperatures (LST) at the Landsat scale in Hefei, China, from 2011 to 2020. The evolution of the surface urban heat island (SUHI) was analyzed, and the contribution index (CI), urban thermal field variation index (UTFVI), and landscape pattern were evaluated to analyze the thermal environment mechanism of a major function-oriented zone (MFOZ). In addition, we explored the role and mechanism of different MFOZs in a thermal environment. Our results show that the multi-time scale differences in the SUHI were obvious, with the phenomenon of heat islands being concentrated in the main city zone. There are significant multi-time scale differences in the CI of different landscapes under the MFOZ. The UTFVI analysis of the MFOZ shows that the livability of the cities in the core optimization zone (COZ) and modern urbanization and industrialization cluster development zone (IDZ) is poor. MFOZ planning moderately alleviated the urban thermal environment of the entire study area, especially in the agricultural development zone (ADZ) and ecological conservation zone (ECZ). This study can guide the planning of the MFOZ and guide decision-makers in selecting governance zones when planning policies or dividing the key restoration areas of the thermal environment.

The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China

Urban forms are closely related to the urban environment, providing great potential to analyze human socioeconomic activities. However, limited studies have investigated the impacts of three-dimensional (3-D) urban forms on socioeconomic activities across cities. In this paper, we explored the relationship between urban form and socioeconomic activities using 3-D building height data from 38 cities in China. First, we aggregated the building footprint data and calculated three building indicators at the grid scale, based on which the spatial patterns of building height and road density were analyzed. Then, we examined the capacities of two-dimensional (2D)/3D urban forms in characterizing socioeconomic activities using satellite-derived nighttime light (NTL) data. Finally, we analyzed the relationship between road density distributions and building heights across 38 cities in China. Our results suggest that the building height information can improve the correlation between urban form and NTL. Different patterns of road distribution were revealed according to the distribution of road density change from the building hotspots, showing the capacity of 3-D building height data in helping characterize socioeconomic activities. Our study indicates that the 3-D building height information is of great potential to support a variety of studies in urban domains, such as population distribution and carbon emissions, with significantly improved capacities.

Exploring Adaptive UHI Mitigation Solutions by Spatial Heterogeneity of Land Surface Temperature and Its Relationship to Urban Morphology in Historical Downtown Blocks, Beijing

Heat stress brought on by the intensification of urban heat island (UHI) has caused many negative effects on human beings, which were found to be more severe in highly urbanized old towns. With the inconsistent findings on how urban spatial morphological characteristics influence land surface temperature (LST) and gaps between design practices being found, we chose Beijing Old Town (BOT) as the study area and took the basic planning implementation module “block” as a study to reveal the spatial heterogeneity of LST and its relationship to multiple urban morphological characteristics with higher spatial resolution calculated via WorldView3. Our results have shown that (1) UHI effect was significant and spatially heterogeneous in BOT, and significant hot areas with high LST value and small LST differences were found, as cold areas were the exact opposite. (2) The proportion of vegetated area, water, impervious surface, and urban spatial structure indicators i.e., building coverage ratio, mean height, highest building index, height fluctuation degree, space crowd degree and sky view factor were identified as significantly affecting the LST of blocks in BOT. (3) The effects of GBI components and configuration on LST varied within different block types; generally, blocks with GBI with larger patches that were more complex in shape, more aggregated, and less fragmented were associated with lower LST. Finally, in the context of integrating our study results with relevant planning and design guidelines, a strategy sample of adaptive GBI planning and vegetation design for blocks with different morphological features was provided for urban planners and managers to make a decision on UHI mitigation in the renewal process of BOT.

Estimation of the Urban Heat Island Effect in a Reformed Urban District: A Scenario-Based Study in Hong Kong

Urban heat island (UHI), a phenomenon in which land surface temperatures (LSTs) in an urban area are notably higher than that in the surrounding rural area, has made the living environment thermally uncomfortable, endangered public health, and increased the energy consumption on indoor air cooling. To develop a liveable and sustainable city, it is crucial to provide an accurate estimation of the UHI effect for urban planners when an area is transformed from bare lands to a high density of buildings. With this objective, the study develops multivariate spatial regression models based on LSTs retrieved from Landsat-8 thermal images to estimate the distribution of urban heat magnitudes (i.e., UHMs, relative temperatures referenced to rural temperature), by considering four types of causative factors that include land use and land cover, urban morphology, heat source, and local climate zones. Partial correlation analysis is performed to determine explainable variables and R2 is used to evaluate the models. Based on the constructed models and a master plan of buildings in Kowloon East, Hong Kong, the future UHM distributions are forecasted on four representative days in different seasons. Results show that the UHI effect will be mitigated significantly when the new buildings are built, suggesting appropriate urban planning regarding the urban thermal environment. We found that the considered factors can largely explain the daytime UHIs in both the built-up areas and land-cover areas. The proposed method can also be used to optimize the urban design for creating a more thermo-friendly urban environment.

Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China

The urban heat island (UHI) phenomenon caused by rapid urbanization has become an important global ecological and environmental problem that cannot be ignored. In this study, the UHI effect was quantified using Landsat 8 image inversion land surface temperatures (LSTs). With the spatial scale of street units in Fuzhou City, China, using ordinary least squares (OLS) regression, geographically weighted regression (GWR) models, and multi-scale geographically weighted regression (MGWR), we explored the spatial heterogeneities of the influencing factors and LST. The results indicated that, compared with traditional OLS models, GWR improved the model fit by considering spatial heterogeneity, whereas MGWR outperformed OLS and GWR in terms of goodness of fit by considering the effects of different bandwidths on LST. Building density (BD), normalized difference impervious surface index (NDISI), and the sky view factor (SVF) were important influences on elevated LST, while building height (BH), forest land percentage (Forest_per), and waterbody percentage (Water_per) were negatively correlated with LST. In addition, built-up percentage (Built_per) and population density (Pop_Den) showed significant spatial non-stationary characteristics. These findings suggest the need to consider spatial heterogeneity in analyses of impact factors. This study can be used to provide guidance on mitigation strategies for UHIs in different regions.

Modeling the Relationships Between Historical Redlining, Urban Heat, and Heat-Related Emergency Department Visits: An Examination of 11 Texas Cities

Place-based structural inequalities can have critical implications for the health of vulnerable populations. Historical urban policies, such as redlining, have contributed to current inequalities in exposure to intra-urban heat. However, it is unknown whether these spatial inequalities are associated with disparities in heat-related health outcomes. The aim of this study is to determine the relationships between historical redlining, intra-urban heat conditions, and heat-related emergency department visits using data from eleven Texas cities. At the zip code level, the proportion of historical redlining was determined, and heat exposure was measured using daytime and nighttime land surface temperature (LST). Heat-related inpatient and outpatient rates were calculated based on emergency department visit data that included ten categories of heat-related diseases between 2016 and 2019. Regression or spatial error/lag models revealed significant associations between higher proportions of redlined areas in the neighborhood and higher LST (Coef. = 0.0122, 95% CI = 0.0039 - 0.0205). After adjusting for indicators of social vulnerability, neighborhoods with higher proportions of redlining showed significantly elevated heat-related outpatient visit rate (Coef. = 0.0036, 95% CI = 0.0007-0.0066) and inpatient admission rate (Coef. = 0.0018, 95% CI = 0.0001-0.0035). These results highlight the role of historical discriminatory policies on the disparities of heat-related illness and suggest a need for equity-based urban heat planning and management strategies.

Dynamic Expansion of Urban Land in China’s Coastal Zone since 2000

Although a major region with strong urbanization, there is not yet a systematic and comprehensive understanding of urban expansion during the last 20 years for China’s coastal zone. In this paper, based on remote sensing techniques, and using indicators such as new urban land proportion, annual urban increase, and annual growth rate, as well as a landscape expansion index reflecting the urban expansion type (e.g., edge-expansion, infilling, and outlying), we measured the dynamic expansion of urban land in China’s coastal zone since 2000. The results indicated that: (1) China’s coastal zone experienced rapid urbanization from 2000 to 2020, with the new urban land and annual urban growth rate at 17,979.72 km2 and 4.83%, respectively. The new urban land was mainly concentrated in economically advanced regions, such as Bohai Rim, Shandong Peninsula, the Yangtze River delta, and the Pearl River delta. (2) The urban growth rates of coastal cities in Liaoning, Hebei, Shandong, southeast Fujian, and Taiwan became slower over time, with a sharp decline during 2015–2020. In the mid and south of China’s coastal zone, such as coastal cities in Jiangsu, Guangxi, and Hainan, there was slow urbanization before 2015, and urban land expanded dramatically during 2015–2020. (3) The urban expansion of China’s coastal zone was dominated by edge-expansion after 2000, but it went through a low-speed and intensive development stage during 2010–2015, with an increase in urban land less than 50% of that in the other three five-year periods, and the most significant filling of urban space compared with the other three five-year periods, which was probably caused by the global financial crisis. (4) The spatial-temporal differences in the urbanization process in China’s coastal zone were largely consequent on national economic development strategies and regional development plans implemented in China’s coastal zone.

Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions

Urban environments lie at the confluence of social, cultural, and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures. The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island (UHI) effect, referring to the higher temperature in cities compared to their natural surroundings. Besides the UHI effect and heat waves, urbanization also impacts atmospheric moisture, wind, boundary layer structure, cloud formation, dispersion of air pollutants, precipitation, and storms. In this review article, we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies. We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.

Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images

Roadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses-shrubs-trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation.

How Does Urban Green Space Impact Residents' Mental Health: A Literature Review of Mediators

In recent years, the interest in the relationship between urban green space and residents’ mental health has gradually risen. A number of researchers have investigated the causal relationship and possible mediators between the two, although few have summarized these mediators. For this reason, we searched for relevant studies and filtered them by criteria and quality score, and analyzed the mediators and paths of the impact of urban green space on residents’ mental health. The mediators can be divided into environmental factors, outdoor activity, and social cohesion. From the perspective of heterogeneity, both individual characteristics (e.g., age and gender) and group characteristics (e.g., level of urban development and urban density) of residents are considered to be the cause of various mediating effects. Types of urban green space tend to affect residents’ mental health through different paths. Furthermore, this review discusses the details of each part under the influence paths. Finally, the policy implications for urban green space planning from three mediator levels are put forward based on an analysis of the situation in different countries.

Procedure for the selection and evaluation of prefabricated housing buildings for the implementation of green roofs in the context of Urban Heat Island mitigation. The example of Wrocław, Poland

The assessment of the suitability of existing buildings for implementation of green roofs is an important research issue, especially in the context of Urban Heat Island (UHI), the negative impacts of which are locally exacerbated by the global warming. The studies carried out so far have covered a variety of buildings and have taken into account a range of different conditions. Relatively little attention has been paid to the possibilities of greening the roofs of prefabricated apartment blocks from the second half of the 20th century in the context of the potential climate effect. Yet, these buildings are found in many cities around the world, and seem in fact attractive for greening. In view of the above, we proposed a three-stage investigatory procedure to: (I) identify and classify buildings based on the number of floors and the rooftop available area; (II) select buildings by designating priority areas depending on the highest UHI intensity and roof density; (III) analyse the roof load capacity to develop retrofit scenarios. The procedure was applied to prefabricated housing estates built in the 1970s and 1980s in Wrocław, Poland. The research shows that there are 1962 buildings of different heights and roof area of 722405 m2, of which 480 buildings with a roof area of 122749.1 m2 were selected for greening within priority areas. The structure of the studied roofs was not designed to carry additional loads, which requires the application of complementary solutions. Scenario 1 assumes extensive greening provided that the existing ventilated roof is strengthened, scenario 2 -semi-intensive greening, which however requires the conversion of the ventilated roof to a non-ventilated one. The presented procedure can be applied in any other city with prefabricated apartment blocks and available UHI data, and serve to support the decision to implement green roofs to mitigate UHI.

Effects of Land Use on Land Surface Temperature: A Case Study of Wuhan, China

In this study, we aim to understand the impact of land use on the urban heat island (UHI) effect across an urban area. Considering the case study of Wuhan, China, land use factors and land surface temperatures (LSTs) of 589 planning management units were quantified in order to identify the spatial autocorrelation of LST, which indicated that a traditional regression would be invalid. By investigating the relationships between land use factors and the LST in summer, based on spatial regression models including the spatial lag model and the spatial error model, four conclusions were derived. First, the spatial error model effectively explains the relationships between LST and land use factors. Second, the impact on LST of the percentage of industrial areas is significant even though the impacts of land cover and building-group morphology indicators are combined, indicating that anthropogenic heat emission of industrial production contributes to high LSTs. Third, the relationship between the percentage of commercial area and LST is significant in the Pearson correlation analysis and traditional regression models, while not significant in spatial error model, suggesting that the urban heat environment of a commercial area is determined by the land use factors of the surrounding area. Fourth, the UHI effect in industrial and commercial areas could be precisely mitigated by not locating industrial areas beside residential areas, and setting up buffer zones between commercial areas and surrounding traditional residential areas. Overall, the results of this study innovatively deepen the understanding of the impact of the percentage of different urban land use types on the urban heat environment at the scale of planning management units, which is conducive to formulating precise regulation measures for mitigating UHI effects and improving public health.

Spatial Analysis of Citizens' Environmental Complaints in China: Implications in Environmental Monitoring and Governance

Citizen environmental complaints play a key role in China’s current environmental monitoring network and environmental governance system. Based on 5796 cases of environmental complaints lodged by citizens via hotline and the internet to the MEP of China, we examined the spatial characteristics and influencing factors of citizen complaints for the period of 2013–2017 using spatial analysis methods and spatial econometric models. The roles of citizen complaints in the two systems were then reevaluated. The results show that, among all cases, 75.88% of cases were identified as verified complaints, while nearly a 25% noisy rate directed large amounts of inspection resources to be utilized in response to nonverified cases. Air pollution received the most attention by citizens in China, accounting for 67.22% of total cases. The hotspots of citizen complaints were mostly distributed in the three major national urban agglomerations in China. We found that industrial wastewater and SO₂ were positively associated with the likelihood of citizens filing complaints, while the effect of industrial soot/dust emission was insignificant. Citizen complaints might be triggered by certain, but not all, forms of pollutants, even though highly visible particulate pollutants did not necessarily induce corresponding complaints. Moreover, the negative relationship between citizen complaints and per capita GDP revealed the unbalanced geographical pattern between economical development and environmental quality. The proliferation of the internet greatly facilitated citizens lodging complaints through various ways. The synergy mechanism between citizen environmental complaints and other parts in China’s environmental monitoring and governance system should be established in the future.

Variations in the Effects of Landscape Patterns on the Urban Thermal Environment during Rapid Urbanization (1990–2020) in Megacities

Deterioration of the urban thermal environment, especially in megacities with intensive populations and high densities of impervious surfaces, is a global issue resulting from rapid urbanization. The effects of landscape patterns on the urban thermal environment within a single area or single period have been well documented. Few studies, however, have explored whether the effects can be adapted to various cities at different urbanization stages. This paper investigated the variations of these effects in the five largest and highly urbanized megacities of China from 1990 to 2020 using various geospatial approaches, including concentric buffer analysis, correlation analysis, and hierarchical ridge regression models. The results indicated that the effects of landscape patterns on the urban thermal environment were greatly variable at different urbanization stages. Although landscape composition was more important than landscape configuration in determining the urban thermal environment, the standard coefficients of composition metrics continuously decreased from 1990 to 2020. However, configuration metrics, such as patch density, edge density, and shape complexity, could affect the land surface temperature (LST) to a larger extent at the highly urbanized stage. The urbanization process could also affect the cooling effect of urban green space. At the initial stage of rapid urban expansion in approximately 2000, urban green space explained the most variation in LST, with a value as high as 10%. To maximize the cooling effect, the spatial arrangement of urban green space should be highlighted in the region that was 10–15 km from the city center, where the mean LST experienced a significant decline. These results may provide deeper insights into improving the urban thermal environment by targeted strategies in optimizing landscape patterns for areas at different urbanization stages.

Impact of urban morphology on the spatial and temporal distribution of PM2.5 concentration: A numerical simulation with WRF/CMAQ model in Wuhan, China

The urban morphology can significantly change the urban microclimate, which in turn affects the diffusion of air pollutants. Urban planning is the most important means of shaping urban morphology. Therefore, this study takes Wuhan as an example and uses the method of WRF/CMAQ coupled UCM model to analyze the spatial and temporal distribution characteristics of PM_2.5 in the Wuhan metropolitan area in winter 2015. The six most important urban morphological indicators in urban planning: the floor area ratio and building height, building density and building width, vegetation coverage ratio, and urban fraction, are selected and classified into three groups. Studying their impact on the spatial and temporal distribution of PM_2.5 concentration provides support for urban planners to improve air quality. The results show that the maximum value of PM_2.5 concentration in Wuhan urban area occurs in the morning rush hour, and PM_2.5 is distributed concentrically in the downtown of the city (within the second ring highway) according to the highways around the city. The PM_2.5 concentration in the downtown area with the most extensive urban morphological index is the highest, and it decreases with increasing distance from the downtown. Among the six indicators, building density and urban fraction have the most significant impact on PM_2.5 concentration because they have the greatest impact on the wind speed at 10 m. The height of the planetary boundary layer is the key factors affect the vertical and horizontal diffusion of air pollutants. Except for the vegetation coverage ratio, the increase of other urban morphological indicators will lead to a decrease of PM_2.5 concentration in Wuhan urban area at night. During the daytime, increasing the floor area ratio and building height will cause an increasing of PM_2.5 concentration, but other indicators have the opposite effects.

Impacts of urban form and urban heat island on the outdoor thermal comfort: a pilot study on Mashhad

There is an increasing demand for cooling cities because of its importance on human health and the quality of life in outdoor urban spaces. However, the development of methods in improving outdoor thermal comfort and zoning cities based on outdoor thermal comfort is still challenging. In this work, we propose a new approach to cities zoning from the lens of outdoor thermal comfort in the arid climate of the city of Mashhad, Iran, and investigate the impacts of urban form characteristics on pedestrian thermal comfort. The effects of complex urban form parameters including height to width (H/W) ratio, canyon orientation, tree canopy cover, and building surface materials on the thermal comfort of pedestrians were studied in the arid climate of Mashhad. Microclimate simulation and analysis is conducted in ENVI-met software, and ArcMap is used to calculate Mashhad urban heat islands. Path analysis in SPSS presents an urban form formulation, which predicts approximate outdoor thermal comfort condition in current and future urban context of Mashhad and other cities with the same climate. We finally demonstrate the use of our research method as an alternative method for all cities: urban heat island (UHI) zoning can be used as a substitute for urban form zonings based on outdoor thermal comfort, especially in large cities where data collection on urban form can be difficult due to limited time and resources.

Impacts of the evolving urban development on intra-urban surface thermal environment: Evidence from 323 Chinese cities

Urban development has significantly modified the surface thermal environment in urban areas. This study provides the first attempt to characterize the urban development imprint on surface thermal environment for 323 cities across the entire country of China, using an intra-urban perspective. Specifically, it investigates the variation of surface thermal environment in terms of land surface temperature (LST) difference triggered by significant urban evolution of intra-urban division containing two primary classes: old urban areas developed by 1992 and new ones expanded in the 1992-2015 period. Under this "old-new" dichotomy, the relationship between urban development and the LST difference is explored through Multi-scale Geographically Weighted Regression (MGWR). Results reveal that urban development is closely related to the difference in LST between old and new urban areas in 2015, which varies from -2.66 °C to 2.46 °C, up to -6.27 °C in western China. 264 cities manifest relatively "cooler" urban environments in the generally larger-sized new urban areas. The seven selected urban development indicators can explain 75% of the variance in the LST difference through MGWR. Among them, the old-new elevation difference, the normalized difference vegetation index (NDVI) difference, and Gini coefficient are found to influence the LST difference in various spatially varying manners. The elevation difference, a generally underestimated nature-driven indicator, is found dominant in explaining the LST difference for 252 cities, among which 216 cities demonstrate higher LSTs in the urban areas with lower elevations. Overall, this study provides valuable information of human-environment interaction across many cities in a generalized way, which complements similar studies at local level, and helps to depict a complete picture of environmental impacts of urban development. The integrated workflow can also be promoted to other periods or other countries to examine the corresponding urbanization imprint on intra-urban surface warming.

Exploring diurnal cycles of surface urban heat island intensity in Boston with land surface temperature data derived from GOES-R geostationary satellites

The surface urban heat island (SUHI) is one of the most significant human-induced alterations to the Earth's surface climate and can aggravate health risks for city dwellers during heat waves. Although the SUHI effect has received growing attention, its diurnal cycles (i.e., the variations over the full 24 h within the diel cycle) are poorly understood because polar-orbiting satellites (e.g., Landsat Series, Sentinel, Terra, Aqua) only provide one or two observations over each repeat cycle (e.g., 16 days) with constant overpass time for the same area. Geostationary satellites provide high-frequency land surface temperature (LST) observations throughout the day and the night, and thereby offer unprecedented opportunities for exploring the diurnal cycles of SUHI. Here we examined how the SUHI intensity varied over the course of the diurnal cycle in the Boston Metropolitan Area using LST observations from the NOAA's latest generation of Geostationary Operational Environmental Satellites (GOES-R). GOES-R LST was strongly correlated with MODIS LST (R² = 0.98, p < 0.0001) across urban core, suburban, and rural areas. We calculated the SUHI intensity at an hourly time step for both the urban core and suburban areas using GOES-R LST data. The maximum SUHI intensity for the urban core occurred near noon, and was +3.0 °C (12:00), +5.4 °C (12:00), +4.9 °C (11:00), and +3.7 °C (12:00) in winter, spring, summer, and autumn, respectively. The maximum intensity for the suburban area was about 3.0 °C lower in spring and summer and 2.0 °C lower in autumn and winter than that of the urban-core area. The minimum SUHI intensity occurred at nighttime, and ranged from -1.0 °C to +1.0 °C. The difference in the nighttime SUHI intensity between urban core and suburban area was insignificant for all seasons except the summer. The SUHI intensity showed similar diurnal variations across the seasons. Throughout the year, the maximum SUHI intensity (+2.7-+5.8 °C) at the urban core occurred at 11:00-14:00 (local time), while the minimum SUHI intensity (-0.6-+0.9 °C) was commonly observed at 00:00-07:00 and 17:00-23:00. We also found different relationships between SUHI intensity and potential drivers within a diurnal cycle, characterized by the strongest correlation with impervious surface area and population size during the middle of the day, and with tree canopy cover at night. Our research highlights the great potential of the new-generation geostationary satellites in revealing the detailed diurnal variations of SUHI. Our findings have implications for informing urban planning and public health risk management.

The Challenge of the Urban Compact Form: Three-Dimensional Index Construction and Urban Land Surface Temperature Impacts

Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization.

Spatial Interconnections of Land Surface Temperatures with Land Cover/Use: A Case Study of Tokyo

As one of the most populated metropolitan areas in the world, the Tokyo Metropolitan Area (TMA) has experienced severe climatic modifications and pressure due to densified human activities and urban expansion. The surface urban heat island (SUHI) phenomenon particularly constitutes a significant threat to human comfort and geo-environmental health in TMA. This study aimed to profile the spatial interconnections between land surface temperature (LST) and land cover/use in TMA from 2001 to 2015 using multi-source spatial data. To this end, the thermal gradients between the urban and non-urban fabric areas in TMA were examined by joint analysis of land cover/use and LST. The spatiotemporal aggregation patterns, variations, and movement trajectories of SUHI intensity in TMA were identified and delineated. The spatial relationship between SUHI and the potential driving forces in TMA was clarified using geographically weighted regression (GWR) analysis. The results show that the thermal environment of TMA exhibited a polynucleated spatial structure with multiple thermal island cores. Overall, the magnitude and extent of SUHI in TMA increased and expanded from 2001 to 2015. During that time, SUHIs clustered in the compact residential quarters and redevelopment/renovation areas rather than downtown. The GWR models showed better performance than ordinary least squares (OLS) models, with Adj R2 > 0.9, indicating that the magnitude of SUHI significantly depended on its neighboring geographical setting, including land cover composition and configuration, population size, and terrain. We suggest that UHI mitigation in Tokyo should be focused on alleviating the magnitude of persistent thermal cores and controlling unstable SUHI occurrence based on partitioned or location-specific landscape design. This study’s findings have immense implications for SUHI mitigation in metropolitan areas situated in bay regions.

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

Urban Form Resilience: A Comparative Analysis of Traditional, Semi-Planned, and Planned Neighborhoods in Shiraz, Iran

As cities are exposed to a portfolio of risks, the concept of resilience has risen to prominence over the past two decades. Consequently, a large volume of research has been published on different aspects of urban resilience. However, urban form resilience is still relatively understudied. As a step toward filling this gap, this study examines resilience of nine selected neighborhoods from Shiraz, an old Iranian city. The selected cases represent three different urban form patterns, namely, traditional, semi-planned, and planned. Different indicators related to the physical configuration of lots, blocks, open and green spaces, and street networks are used to examine resilience of each neighborhood to three major stressors, namely, earthquakes, extreme heat events, and floods. Additionally, a combination of Shannon entropy and the VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian) method is used to rank the resilience of each neighborhood to each of the three stressors. Results show that, overall, the physical form of the planned neighborhoods is more conducive to urban resilience. In contrast, the urban form of traditional neighborhoods was found to be less resilient. There were, however, some variations depending on the type of stressor considered. The paper concludes by emphasizing the need to consider social and economic factors in future studies of urban form resilience.

Dimensions of Thermal Inequity: Neighborhood Social Demographics and Urban Heat in the Southwestern U.S

Exposure to heat is a growing public health concern as climate change accelerates worldwide. Different socioeconomic and racial groups often face unequal exposure to heat as well as increased heat-related sickness, mortality, and energy costs. We provide new insight into thermal inequities by analyzing 20 Southwestern U.S. metropolitan regions at the census block group scale for three temperature scenarios (average summer heat, extreme summer heat, and average summer nighttime heat). We first compared average temperatures for top and bottom decile block groups according to demographic variables. Then we used spatial regression models to investigate the extent to which exposure to heat (measured by land surface temperature) varies according to income and race. Large thermal inequities exist within all the regions studied. On average, the poorest 10% of neighborhoods in an urban region were 2.2 °C (4 °F) hotter than the wealthiest 10% on both extreme heat days and average summer days. The difference was as high as 3.3-3.7 °C (6-7 °F) in California metro areas such as Palm Springs and the Inland Empire. A similar pattern held for Latinx neighborhoods. Temperature disparities at night were much smaller (usually ~1 °F). Disparities for Black neighborhoods were also lower, perhaps because Black populations are small in most of these cities. California urban regions show stronger thermal disparities than those in other Southwestern states, perhaps because inexpensive water has led to more extensive vegetation in affluent neighborhoods. Our findings provide new details about urban thermal inequities and reinforce the need for programs to reduce the disproportionate heat experienced by disadvantaged communities.

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

Review on Urban Heat Island in China: Methods, Its Impact on Buildings Energy Demand and Mitigation Strategies

High population density, dense high-rise buildings, and impervious pavements increase the vulnerability of cities, which aggravate the urban climate environment characterized by the urban heat island (UHI) effect. Cities in China provide unique information on the UHI phenomenon because they have experienced rapid urbanization and dramatic economic development, which have had a great influence on the climate in recent decades. This paper provides a review of recent research on the methods and impacts of UHI on building energy consumption, and the practical techniques that can be used to mitigate the adverse effects of UHI in China. The impact of UHI on building energy consumption depends largely on the local microclimate, the urban area features where the building is located, and the type and characteristics of the building. In the urban areas dominated by air conditioning, UHI could result in an approximately 10–16% increase in cooling energy consumption. Besides, the potential negative effects of UHI can be prevented from China in many ways, such as urban greening, cool material, water bodies, urban ventilation, etc. These strategies could have a substantial impact on the overall urban thermal environment if they can be used in the project design stage of urban planning and implemented on a large scale. Therefore, this study is useful to deepen the understanding of the physical mechanisms of UHI and provide practical approaches to fight the UHI for the urban planners, public health officials, and city decision-makers in China.

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones

Local warming induced by rapid urbanization has been threatening residents' health, raising significant concerns among urban planners. Local climate zone (LCZ), a widely accepted approach to reclassify the urban area, which is helpful to propose planning strategies for mitigating local warming, has been well documented in recent years. Based on the LCZ framework, many scholars have carried out diversified extensions in urban zoning research in recent years, in which urban functional zone (UFZ) is a typical perspective because it directly takes into account the impacts of human activities. UFZs, widely used in urban planning and management, were chosen as the basic unit of this study to explore the spatial heterogeneity in the relationship between landscape composition, urban morphology, urban functions, and land surface temperature (LST). Global regression including ordinary least square regression (OLS) and random forest regression (RF) were used to model the landscape-LST correlations to screen indicators to participate in following spatial regression. The spatial regression including semi-parametric geographically weighted regression (SGWR) and multiscale geographically weighted regression (MGWR) were applied to investigate the spatial heterogeneity in landscape-LST among different types of UFZ and within each UFZ. Urban two-dimensional (2D) morphology indicators including building density (BD); three-dimensional (3D) morphology indicators including building height (BH), building volume density (BVD), and sky view factor (SVF); and other indicators including albedo and normalized difference vegetation index (NDVI) and impervious surface fraction (ISF) were used as potential landscape drivers for LST. The results show significant spatial heterogeneity in the Landscape-LST relationship across UFZs, but the spatial heterogeneity is not obvious within specific UFZs. The significant impact of urban morphology on LST was observed in six types of UFZs representing urban built up areas including Residential (R), Urban village (UV), Administration and Public Services (APS), Commercial and Business Facilities (CBF), Industrial and Manufacturing (IM), and Logistics and Warehouse (LW). Specifically, a significant correlation between urban 3D morphology indicators and LST in CBF was discovered. Based on the results, we propose different planning strategies to settle the local warming problems for each UFZ. In general, this research reveals UFZs to be an appropriate operational scale for analyzing LST on an urban scale.

Urban Morphological Controls on Surface Thermal Dynamics: A Comparative Assessment of Major European Cities with a Focus on Athens, Greece

Variations in urban form lead to the development of distinctive intra-urban surface thermal patterns. Previous assessment of the relation between urban structure and satellite-based Land Surface Temperature (LST) has generally been limited to single-city cases. Here, examining 25 European cities (June–August 2017), we estimated the statistical association between surface parameters—the impervious fraction (λimp), the building fraction (λb), and the building height (H)—and the neighborhood scale (1000 × 1000 m) LST variations, as captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Correlation analysis, multiple linear regression, and spatial regression were used. As expected, λimp had a consistent positive influence on LSTs. In contrast, the relation of LST with λb and H was generally weaker or negative in the daytime, whereas at night it shifted to a robust positive effect. In particular, daytime LSTs of densely built, high-rise European districts tended to have lower values. This was especially the case for the city of Athens, Greece, where a more focused analysis was conducted, using further surface parameters and the Local Climate Zone (LCZ) scheme. For the urban core of the city, the canyon aspect ratio H/W had a statistically significant (p <0.01) negative relationship with LST by day (Spearman’s rho = −0.68) and positive during nighttime (rho = 0.45). The prevailing intra-urban surface thermal variability in Athens was well reproduced by a 5-day numerical experiment using the meteorological Weather Research and Forecasting Model (WRF) model and a modified urban parameterization scheme. Although the simulation resulted in some systematic errors, the overall accuracy of the model was adequate, regarding the surface temperature (RMSE = 2.4 K) and the near-surface air temperature (RMSE = 1.7 K) estimations.