On the linkage between urban heat island and urban pollution island: Three-decade literature review towards a conceptual framework

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.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

Association between fine particulate matter and fecundability in Henan, China: A prospective cohort study

OBJECTIVE

To investigate the relationship between ambient fine particulate matter (PM2.5) exposure and fecundability.

METHODS

This study included 751,270 female residents from Henan Province who participated in the National Free Pre-conception Check-up Projects during 2015-2017. Ambient cycle-specific PM2.5 exposure was assessed at the county level for each participant using satellite-based PM2.5 concentration data at 1-km resolution. Cox proportional hazards models with time-varying exposure were used to estimate the association between fecundability and PM2.5 exposure, adjusted for potential individual risk factors.

RESULTS

During the study period, 568,713 participants were pregnant, monthly mean PM2.5 concentrations varied from 25.5 to 114.0 µg/m3 across study areas. For each 10 µg/m3 increase in cycle-specific PM2.5, the hazard ratio for fecundability was 0.951 (95 % confidence interval: 0.950-0.953). The association was more pronounced in women who were older, with urban household registration, history of pregnancy, higher body mass index (BMI), hypertension, without exposure to tobacco, or whose male partners were older, with higher BMI, or hypertension.

CONCLUSION

In this population-based prospective cohort, ambient cycle-specific PM2.5 exposure was associated with reduced fecundability. These findings may support the adverse implications of severe air pollution on reproductive health.

Evolutionary approach for pollution study: The case of ionizing radiation

Estimating the consequences of environmental changes, specifically in a global change context, is essential for conservation issues. In the case of pollutants, the interest in using an evolutionary approach to investigate their consequences has been emphasized since the 2000s, but these studies remain rare compared to the characterization of direct effects on individual features. We focused on the study case of anthropogenic ionizing radiation because, despite its potential strong impact on evolution, the scarcity of evolutionary approaches to study the biological consequences of this stressor is particularly true. In this study, by investigating some particular features of the biological effects of this stressor, and by reviewing existing studies on evolution under ionizing radiation, we suggest that evolutionary approach may help provide an integrative view on the biological consequences of ionizing radiation. We focused on three topics: (i) the mutagenic properties of ionizing radiation and its disruption of evolutionary processes, (ii) exposures at different time scales, leading to an interaction between past and contemporary evolution, and (iii) the special features of contaminated areas called exclusion zones and how evolution could match field and laboratory observed effects. This approach can contribute to answering several key issues in radioecology: to explain species differences in the sensitivity to ionizing radiation, to improve our estimation of the impacts of ionizing radiation on populations, and to help identify the environmental features impacting organisms (e.g., interaction with other pollution, migration of populations, anthropogenic environmental changes). Evolutionary approach would benefit from being integrated to the ecological risk assessment process.

Land surface temperature and transboundary air pollution: a case of Bangkok Metropolitan Region

In a rapidly urbanizing world, heavy air pollution and increasing surface temperature pose significant threats to human health and lives, especially in densely populated cities. In this study, we took an information theory perspective to investigate the causal relationship between diel land surface temperature (LST) and transboundary air pollution (TAP) from 2003 to 2020 in the Bangkok Metropolitan Region (BMR), which includes Bangkok Metropolis and its five adjacent provinces. We found an overall increasing trend of LST over the study region, with the mean daytime LST rising faster than nighttime LST. Evident seasonal variations showed high aerosol optical depth (AOD) loadings during the dry period and low loadings at the beginning of the rainy season. Our study revealed that TAP affected diel surface temperature in Bangkok Metropolis significantly. Causality tests show that air pollutants of two adjacent provinces west of Bangkok, i.e., Nakhon Pathom and Samut Sakhon, have a greater influence on the LST of Bangkok than other provinces. Also, the bidirectional relationship indicates that air pollution has a greater impact on daytime LST than nighttime LST. While LST has an insignificant influence on AOD during the daytime, it influences AOD significantly at night. Our study offers a new approach to understanding the causal impact of TAP and can help policymakers to identify the most relevant locations that cause pollution, leading to appropriate planning and management.

A Matter of Climate Justice: Heat and Air Pollution Combine to Worsen Effects of Homelessness

Few studies on these concurrent health risks account for individuals without housing, yet they often experience greater exposure than other people-along with exacerbation of existing health issues.

Using individual approach to examine the association between urban heat island and preterm birth: A nationwide cohort study in China

BACKGROUND

Evidence suggests that maternal exposure to heat might increase the risk of preterm birth (PTB), but no study has investigated the effect from urban heat island (UHI) at individual level.

AIMS

Our study aimed to investigate the association between individual UHI exposure and PTB.

METHODS

We utilized data from the ongoing China Birth Cohort Study (CBCS), encompassing 103,040 birth records up to December 2020. UHI exposure was estimated for each participant using a novel individual assessment method based on temperature data and satellite-derived land cover data. We used generalized linear mixed-effects models to estimate the association between UHI exposure and PTB, adjusting for potential confounders including maternal characteristics and environmental factors.

RESULTS

Consistent and statistically significant associations between UHI exposure and PTB were observed up to 21 days before birth. A 5 °C increment in UHI exposure was associated with 27 % higher risk (OR = 1.27, 95 % confident interval: 1.20, 1.34) of preterm birth in lagged day 1. Stratified analysis indicated that the associations were more pronounced in participants who were older, had higher pre-pregnancy body mass index level, of higher socioeconomic status and living in greener areas.

CONCLUSION

Maternal exposure to UHI was associated with increased risk of PTB. These findings have implications for developing targeted interventions for susceptible subgroups of pregnant women. More research is needed to validate our findings of increased risk of preterm birth due to UHI exposure among pregnant women.

Hysteresis response of groundwater depth on the influencing factors using an explainable learning model framework with Shapley values

Machine learning has been widely used for groundwater prediction. However, the hysteresis response of groundwater depth (GD) to input features has not been fully investigated. This study uses an interpretation method to reveal the interplay between climate, human activity, and GD while considering the response of groundwater to multiple factors. Six factors [precipitation (P), wind speed (WS), temperature (T), population (POP), gross domestic product (GDP), and effective irrigated area (EIA)] were selected to analyze the hysteresis response of GD in terms of the lag correlation coefficient and lag time. The correlation between climatic variables and GD was weaker than that of anthropogenic variables. The lag time between variables and different types of GD was less than four months at most sites, except for EIA and WS in deep groundwater. The SVM model achieved satisfactory performance in 89 % of the sites. If there were sharp changes in GD during the testing period or significant variations in its seasonal patterns at different times, the SVM model performed poorly. The model was interpreted using the Shapley additive explanation method. The impact of POP and GDP on deep groundwater in irrigated areas was higher than that of shallow groundwater. In urban areas with intensive human activities, anthropogenic variables were the main factors affecting shallow groundwater while the impact of climate was gradually increasing in the suburbs. The influence of precipitation on shallow groundwater was decreased after water transfer from the South-to-North Water Diversion project. Furthermore, this study proposed a multifactor-driven conceptual model that can provide recommendations for analyzing groundwater dynamics in similar areas.

China's public health initiatives for climate change adaptation

China's health gains over the past decades face potential reversals if climate change adaptation is not prioritized. China's temperature rise surpasses the global average due to urban heat islands and ecological changes, and demands urgent actions to safeguard public health. Effective adaptation need to consider China's urbanization trends, underlying non-communicable diseases, an aging population, and future pandemic threats. Climate change adaptation initiatives and strategies include urban green space, healthy indoor environments, spatial planning for cities, advance location-specific early warning systems for extreme weather events, and a holistic approach for linking carbon neutrality to health co-benefits. Innovation and technology uptake is a crucial opportunity. China's successful climate adaptation can foster international collaboration regionally and beyond.

Spatial spillover and threshold effect of green development efficiency on urban human settlement resilience in Yangtze River Delta urban agglomeration

Green development is the necessary path and fundamental way for urban development. Exploring the influence mechanism and spatial effect of green development on the urban human settlement resilience is conducive to promoting high-quality and sustainable urban development. We used the entropy method, super-efficient SBM model, spatial econometric model and threshold model to analyze the spatial spillover effect of green development efficiency on urban human settlement resilience and its nonlinear impact in the Yangtze River Delta(YRD) urban agglomeration. The results indicated that During the study period, the level of green development efficiency and urban settlement resilience was on the rise, and the spatial differences between different regions was significant. The green development efficiency of each city in the YRD urban agglomeration has a significant contribution to urban human settlement resilience in the region, but has a negative spatial effect on the level of urban human settlement resilience in the neighboring region. At different population density levels, the effect of green development efficiency on urban human settlement resilience shows a significant "V" non-linear characteristic. Furthermore, the influence of green development efficiency on urban human settlement resilience increases in a stepwise manner under different industrial structure distribution. The results of this study can help provide a reference basis for the creation of high-level, high-quality green and livable resilient cities in the YRD urban agglomeration under the concept of green development, and provide relevant experience for the construction of livable cities in other regions of China.

Main Challenges Expected from the Impact of Climate Change on Microbial Biodiversity of Table Olives: Current Status and Trends

Climate change is a global emergency that is affecting agriculture in Mediterranean countries, notably the production and the characteristics of the final products. This is the case of olive cultivars, a source of olive oil and table olives. Table olives are the most important fermented vegetables in the Mediterranean area, whose world production exceeds 3 million tons/year. Lactic acid bacteria and yeast are the main microorganisms responsible for the fermentation of this product. The microbial diversity and population dynamics during the fermentation process are influenced by several factors, such as the content of sugars and phenols, all of which together influence the quality and safety of the table olives. The composition of fruits is in turn influenced by environmental conditions, such as rainfall, temperature, radiation, and the concentration of minerals in the soil, among others. In this review, we discuss the effect of climate change on the microbial diversity of table olives, with special emphasis on Spanish and Portuguese cultivars. The alterations expected to occur in climate change scenario(s) include changes in the microbial populations, their succession, diversity, and growth kinetics, which may impact the safety and quality of the table olives. Mitigation and adaptation measures are proposed to safeguard the authenticity and sensorial features of this valuable fermented food while ensuring food safety requirements.

The unrelenting global expansion of the urban heat island over the last century

The past century has seen dramatic increases in global temperatures and mounting urbanization. As a result of these events, the urban heat island (UHI) effect has received growing attention in scientific research worldwide. A global search was initially conducted using a scientific literature database to collect all available relevant publications to understand how the UHI has been expanding worldwide and affecting more cities across different latitudes and altitudes. Subsequently, a semantic analysis was performed to extract city names. The literature search and analysis combined resulted in 6078 publications in which UHI was investigated in 1726 cities worldwide in the 1901 to 2022 time period. The cities were grouped into 'first appearance' and 'recurrent appearance'. Results show that UHI was studied in only 134 cities during the 90-year period from 1901 to 1992, with a remarkable growth over time in the number of cities where interest in UHI increased. Interestingly, the number of first appearances was always notably higher than the number of recurrent appearances. The Shannon evenness index was employed to identify the spatial locations (hotspots) across the globe where UHI-related research has been concentrated in multiple cities over the last 120 years. Finally, Europe was selected as a testbed for conducting an analysis to shed light on how economic, demographic, and environmental factors can impact UHI. Our study is unique for having demonstrated not only the rapid growth of cities affected by UHI globally but also the increasing and unrelenting expansion of UHI occurrences across different latitudes and altitudes over time. These novel findings will undoubtedly be of interest to scientists investigating the UHI phenomenon and its trends. Stakeholders will acquire a broader perspective and deeper understanding of UHI in order to engage in more effective urban planning to offset and mitigate the phenomenon's adverse effects in the context of increasing climate change and urbanization.

Impact of Human Activities on Disease-Spreading Mosquitoes in Urban Areas

Urbanization is one of the leading global trends of the twenty-first century that has a significant impact on health. Among health challenges caused by urbanization, the relationship of urbanization between emergence and the spread of mosquito-borne infectious diseases (MBIDs) is a great public health concern. Urbanization processes encompass social, economic, and environmental changes that directly impact the biology of mosquito species. In particular, urbanized areas experience higher temperatures and pollution levels than outlying areas but also favor the development of infrastructures and objects that are favorable to mosquito development. All these modifications may influence mosquito life history traits and their ability to transmit diseases. This review aimed to summarize the impact of urbanization on mosquito spreading in urban areas and the risk associated with the emergence of MBIDs. Moreover, mosquitoes are considered as holobionts, as evidenced by numerous studies highlighting the role of mosquito-microbiota interactions in mosquito biology. Taking into account this new paradigm, this review also represents an initial synthesis on how human-driven transformations impact microbial communities in larval habitats and further interfere with mosquito behavior and life cycle in urban areas.

Synergistic interactions of fine particles and radiative effects in modulating urban heat islands during winter haze event in a cold megacity of Northeast China

Severe air pollution and urban heat islands (UHI) intensity (UHII) are two challenging problems that have attracted wide attention in populated cities. However, previous studies mostly focused on the relationship between fine particulate matter (PM_2.5) and UHII, but how UHII responds to the interactions between radiative effects (direct effect (DE), indirect effect (IDE) with slope and shading effects (SSE)) and PM_2.5 during heavy pollution is still unclear, especially in the cold region. Therefore, this study explores the synergistic interactions between PM_2.5 and radiative effects in influencing UHII during a heavy pollution event in the cold-megacity of Harbin-China. Hence, we designed four scenarios: non-aerosol radiative feedback (NARF), DE, IDE, and combined effects (DE + IDE + SSE) in December 2018 (clear-episode) and December 2019 (heavy-haze-episode) using numerical modeling. The results showed that the radiative effects influenced the spatial distribution of PM_2.5 concentration leading to a mean drop in 2-m air-temperature by approximately 0.67 °C (downtown) and 1.48 °C (satellite-town) between the episodes. The diurnal-temporal variations revealed that the daytime and nighttime UHIIs were strengthened in the downtown during the heavy-haze-episode, while a reverse effect was observed in the satellite-town. Interestingly, during the heavy-haze-episode, the considerable difference between excellent and heavily polluted PM_2.5 levels showed a decrease in UHIIs (1.32 °C, 1.32 °C, 1.27 °C, and 1.20 °C) due to the radiative effects (NARF, DE, IDE, and (DE + IDE + SSE)), respectively. In assessing other pollutants' interactions with the radiative effects, PM₁₀ and NOx had a considerable impact on the UHII during the heavy-haze episode while O₃ and SO₂ were discovered to be very low in both episodes. Moreover, the SSE has uniquely influenced UHII, especially during the heavy-haze-episode. Therefore, insight from this study provides an understanding of how UHII responds uniquely in the cold region, which in turn could help to formulate effective policies and co-mitigation strategies for air pollution and UHI problems.

Spatiotemporal analysis of the surface urban heat island (SUHI), air pollution and disease pattern: an applied study on the city of Granada (Spain)

There is worldwide concern about how climate change -which involves rising temperatures- may increase the risk of contracting and developing diseases, reducing the quality of life. This study provides new research that takes into account parameters such as land surface temperature (LST), surface urban heat island (SUHI), urban hotspot (UHS), air pollution (SO₂, NO₂, CO, O₃ and aerosols), the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI) and the proportion of vegetation (PV) that allows evaluating environmental quality and establishes mitigation measures in future urban developments that could improve the quality of life of a given population. With the help of Sentinel 3 and 5P satellite images, we studied these variables in the context of Granada (Spain) during the year 2021 to assess how they may affect the risk of developing diseases (stomach, colorectal, lung, prostate and bladder cancer, dementia, cerebrovascular disease, liver disease and suicide). The results, corroborated by the statistical analysis using the Data Panel technique, indicate that the variables LST, SUHI and daytime UHS, NO₂, SO₂ and NDBI have important positive correlations above 99% (p value: 0.000) with an excess risk of developing these diseases. Hence, the importance of this study for the formulation of healthy policies in cities and future research that minimizes the excess risk of diseases.

Statistically Validated Urban Heat Island Risk Indicators for UHI Susceptibility Assessment

This research proposes a collection of urban heat island (UHI) risk indicators under four UHI risk components: hazard, exposure, sensitivity, and adaptive capacity. There are 46 UHI risk indicators linked to three pillars of sustainability: social equity, economic viability, and environmental protection. In this study, the UHI risk indicators were first validated by experts to determine their relevancy and subsequently applied to randomly sampled dwellers of Thailand's capital Bangkok. The UHI indicators were further validated with confirmatory factor analysis to determine the factor loadings (0-1) and reliability. Under the hazard component, the percentage of days when the daily minimum temperature is less than the 10th percentile exhibited the highest indicator-level factor loading (0.915). Vehicular traffic was the UHI exposure indicator with the highest factor loading (0.923), and the proportion of green space to build environment was the UHI sensitivity indicator with the highest factor loading (0.910). For the UHI adaptive capacity component, the highest factor loading (0.910) belonged to government policy and action. To effectively mitigate UHI impacts, greater emphasis should be placed on the indicators with highest factor loadings. Essentially, this research is the first to use statistical structural equation modeling to validate UHI indicators.

The mixed layer modified radionuclide atmospheric diffusion based on Gaussian model

Background

Atmospheric diffusion is often accompanied by complex meteorological conditions of inversion temperature.

Methods

In response to the emergency needs for rapid consequence assessment of nuclear accidents under these complex meteorological conditions, a Gaussian diffusion-based model of radionuclide is developed with mixed layer modification. The inhibition effect of the inversion temperature on the diffusion of radionuclides is modified in the vertical direction. The intensity of the radionuclide source is modified by the decay constant.

Results

The results indicate that the enhancement effect of the mixed layer on the concentration of radionuclides is reflected. The shorter the half-life of the radionuclide, the greater the effect of reducing the diffusion concentration. The Kincaid dataset validation in the Model Validation Kit (MVK) shows that, compared to the non-modified model, predictions of the modified model have an enhancement effect beyond 5 km, modulating the prediction values to be closer to the observation values.

Conclusions

This development is consistent with the modification effects of the mixed layer. The statistical indicators show that the criteria of the modified model meet the criteria of the recommended model.

The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning

Increasing attention has been given to the impact of PM2.5 concentration on human health. Exploring the influential factors of PM2.5 is conducive to improving air quality. Most existing studies explore the factors that influence the PM2.5 concentration from the perspective of cities or urban agglomerations, while few studies are conducted from the perspective of climate zones. We used the standard deviation ellipse and spatial autocorrelation analysis to explore the spatial-temporal evolution of the PM2.5 concentration in different climate zones in China during 2000-2018. We used differentiated EKC to construct panel regression models to explore the differences in the influential factors of the PM2.5 concentration in three climate zones. The number of cities with PM2.5 concentration less than 35 μg/m³ increased in the different climate zones. The center of gravity of the PM2.5 concentration has remained at the junction of the temperate and subtropical monsoon climate zones. The PM2.5 concentration had a high positive spatial autocorrelation in the different climate zones. The high-high clustering areas were located in the south of the temperate monsoon climate zone and the north of the subtropical monsoon climate zone. There was an inverted "U-shaped" curve between the PM2.5 concentration and economic development in China that varied in different climate zones. Identifying the differences in the influential factors of PM2.5 concentration in different climate zones will help to accelerate the implementation of the EKC inflection point.

Determination of optimal height of barriers to reduce the amount of pollution in the viaduct settings in an idealized urban canyon: a numerical study

In this work, we numerically investigate the process of atmospheric air pollution in idealized urban canyons along the road in the presence of a viaduct, taking into account different height of barriers. To solve this problem, the 3D Reynolds-averaged Navier-Stokes equations (RANS) were used. The closure of this system of equations was achieved by using various turbulent models. The verification of the mathematical model and the numerical algorithm was carried out using a test problem. The obtained results using various turbulent models were compared with experimental data and calculated results of other authors. The main problem considered in this work is characterized as follows: assessment of emissions of pollutants between buildings using barriers of various types in the presence of a viaduct. Computational results have shown that the barrier viaduct plays a large role in improving air quality in urban canyons. So, for example, a barrier erected on a viaduct with a height of 2 m reduces the concentration value to a cross-section x = 84 by more than 2 times in comparison with the case of a complete absence of protective barriers. A similar situation was observed with barriers erected above the earth's surface: located along the road, they also significantly reduce the value of the concentration of pollutants. Thus, the presence of barriers in both cases is necessary to prevent the dispersion and deposition of pollutants.

Dominance of Influencing Factors on Cooling Effect of Urban Parks in Different Climatic Regions

The enhancement of the park cooling effect (PCE) is one method used to alleviate the urban heat island (UHI). The cooling effect is affected by park factors; however, the importance of these factors in the case of the PCE is still unclear. Optimizing or planning urban parks according to the importance of the influencing factors can effectively enhance the PCE. Herein, we selected 502 urban parks in 29 cities in China with three different climatic regions and quantified the PCE based on the park cooling intensity (PCI) and park cooling area (PCA). Subsequently, the relative importance of the influencing factors for the PCE was compared to identify the main factors. Consequently, certain park planning suggestions were proposed to enhance the cooling effect. The results show that: (1) the PCE increased in the order of arid/semi-arid, semi-humid, and humid regions. (2) The main factors of the PCI differed significantly in different climatic regions; however, the waterbody within a park significantly affected the PCI in all three climates. However, for the PCA, park patch characteristics were the dominant factor, contributing approximately 80% in the three climates regions. (3) In arid/semi-arid and semi-humid regions, the optimal area proportion of waterbody and vegetation within the park were approximately 1:2 and 1:1, respectively, and the threshold value of the park area was 16 ha. In contrast, in the humid region, the addition of a waterbody area within the park, to the best extent possible, enhanced the PCI, and the threshold value of the park area was 19 ha. The unique results of this study are expected to function as a guide to future urban park planning on a regional scale to maximize ecological benefits while mitigating the UHI.

Exploring surface urban heat island (SUHI) intensity and its implications based on urban 3D neighborhood metrics: An investigation of 57 Chinese cities

Excessive urban temperature exerts a substantially negative impact on urban sustainability. Three-dimensional (3D) landscapes have a great impact on urban thermal environments, while their heat conditions and driving factors still remain unclear. This study mapped urban 3D neighborhoods and their associated SUHI (surface urban heat island) intensities in summer daytime across 57 Chinese cities, and then explored their relationships, driving factors as well as implications. Nine categories of urban 3D neighborhoods existed in Chinese cities and the 3D neighborhood of High Density & Medium Rise (HDMR) contributed the largest share of urban areas. The distribution of 3D neighborhoods varied among cities due to their distinct natural and economic traits. The average SUHI intensity can amount to 4.27 °C across all Chinese 3D neighborhoods. High Density & Low Rise (HDLR) and HDMR presented higher SUHI intensities than other 3D neighborhoods in China. Urban green space (UGI) and building height (BH) had great influences on SUHI intensities. The relative contribution of UGI decreased with the increase of building density and building height, but BH presented the opposite trend. The interaction of urban 3D landscapes and function zones led to highly complicated urban thermal environments, with higher SUHI intensities in industrial zones. Besides, the SUHI intensities of 3D neighborhoods presented great diurnal and seasonal variations, with higher SUHI intensities in HDHR and HDMR at nighttime in winter and summer. What's more, urban residents may suffer unequal heat risk inside cities due to the deviations of SUHI intensities among different 3D neighborhoods. It could be a highly effective way to mitigate SUHI effects in cities by increasing urban greening and improving urban ventilation.

Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature

Many cities of the world suffer from air pollution because of poor planning and design and heavy traffic in rapidly expanding urban environments. These conditions are exacerbated due to the Urban Heat Island (UHI) effect. While there have been studies linking the built environment and air pollution with health, they have ignored the aggravating role of UHI. The past urban planning literature in this field has also ignored the science of materials, vehicles and air pollution, and technological solutions for reducing cumulative health impacts of air pollution and UHI. Air Pollution, built environment and human health are complex discussion factors that involve several different fields. The built environment is linked with human health through opportunities of physical activity and air quality. Recent planning literature focuses on creating compact and walkable urban areas dotted with green infrastructure to promote physical activity and to reduce vehicle emission-related air pollution. Reduced car use leading to reduced air pollution and UHI is implied in the literature. The literature from technology fields speaks to the issue of air pollution directly. Zero emission cars, green infrastructure and building materials that absorb air pollutants and reduce UHI fall within this category. This paper identifies main themes in the two streams of urban air pollution and UHI that impact human health and presents a systematic review of the academic papers, policy documents, reports and features in print media published in the last 10–20 years.

Dynamic and Heterogeneity of Urban Heat Island: A Theoretical Framework in the Context of Urban Ecology

The dynamic and heterogeneity of the urban heat island (UHI) is the result of the interactions between biotic, physical, social, and built components. Urban ecology as a transdisciplinary science can provide a context to understand the complex social–biophysical issues such as the thermal environment in cities. This study aimed at developing a theoretical framework to elucidate the interactions between the social–biophysical patterns and processes mediating UHI. To do it, we conducted a theoretical review to delineate UHI complexity using the concept of dynamic heterogeneity of pattern, process, and function in UHI phenomenon. Furthermore, a hypothetical heterogeneity spiral (i.e., driver-outcome spiral) related to the UHI was conceived as a model template. The adopted theoretical framework can provide a holistic vision of the UHI, contributing to a better understanding of UHI’s spatial variations in long-term studies. Through the developed framework, we can devise appropriate methodological approaches (i.e., statistic-based techniques) to develop prediction models of UHI’s spatial heterogeneity.

Evaluation of the Relationship between Fractional Exhaled Nitric Oxide (FeNO) with Indoor PM10, PM2.5 and NO2 in Suburban and Urban Schools

Numerous epidemiological studies have evaluated the association of fractional exhaled nitric oxide (FeNO) and indoor air pollutants, but limited information available of the risks between schools located in suburban and urban areas. We therefore investigated the association of FeNO levels with indoor particulate matter (PM10 and PM2.5), and nitrogen dioxide (NO2) exposure in suburban and urban school areas. A comparative cross-sectional study was undertaken among secondary school students in eight schools located in the suburban and urban areas in the district of Hulu Langat, Selangor, Malaysia. A total of 470 school children (aged 14 years old) were randomly selected, their FeNO levels were measured, and allergic skin prick tests were conducted. The PM2.5, PM10, NO2, and carbon dioxide (CO2), temperature, and relative humidity were measured inside the classrooms. We found that the median of FeNO in the school children from urban areas (22.0 ppb, IQR = 32.0) were slightly higher as compared to the suburban group (19.5 ppb, IQR = 24.0). After adjustment of potential confounders, the two-level hierarchical multiple logistic regression models showed that the concentrations of PM2.5 were significantly associated with elevated of FeNO (>20 ppb) in school children from suburban (OR = 1.42, 95% CI = 1.17−1.72) and urban (OR = 1.30, 95% CI = 1.10−1.91) areas. Despite the concentrations of NO2 being below the local and international recommendation guidelines, NO2 was found to be significantly associated with the elevated FeNO levels among school children from suburban areas (OR = 1.11, 95% CI = 1.06−1.17). The findings of this study support the evidence of indoor pollutants in the school micro-environment associated with FeNO levels among school children from suburban and urban areas.

A citizen centred urban network for weather and air quality in Australian schools

High-quality, standardized urban canopy layer observations are a worldwide necessity for urban climate and air quality research and monitoring. The Schools Weather and Air Quality (SWAQ) network was developed and distributed across the Greater Sydney region with a view to establish a citizen-centred network for investigation of the intra-urban heterogeneity and inter-parameter dependency of all major urban climate and air quality metrics. The network comprises a matrix of eleven automatic weather stations, nested with a web of six automatic air quality stations, stretched across 2779 km², with average spacing of 10.2 km. Six meteorological parameters and six air pollutants are recorded. The network has a focus on Sydney’s western suburbs of rapid urbanization, but also extends to many eastern coastal sites where there are gaps in existing regulatory networks. Observations and metadata are available from September 2019 and undergo routine quality control, quality assurance and publication. Metadata, original datasets and quality-controlled datasets are open-source and available for extended academic and non-academic use.

Measurement(s)	temperature of air • relative humidity • pressure of air • atmospheric wind speed • atmospheric wind direction • rainfall rate • sulphur dioxide • carbon monoxide • nitrogen dioxide • tropospheric ozone • pm10 • pm2.5
Technology Type(s)	weather station • air quality station
Sample Characteristic - Environment	urban weather and pollution
Sample Characteristic - Location	Sydney, Australia

Meteorological influence on forecasting urban pollutants: Long-term predictability versus extreme events in a spatially heterogeneous urban ecosystem

With the escalation of heat- and pollution-related threats in cities across the globe, timely counteractions and emergency procedures are vital, which calls for accurate co-prediction of urban heat and air quality under both standard conditions and under extreme events. In this study, we used historical hourly data recorded at 9 sites across the Sydney metropolitan area to test the performance of long short-term memory (LSTM) forecasting architectures in predicting 5 urban pollutants based on different combinations of meteorological inputs and considering standard, bushfire, and pandemic lockdown conditions. We demonstrate that, in most cases and even in a fast-growing city, there is no significant benefit achieved by including extra predictors to temperature and humidity, when adequate forecasting techniques capable of learning long-term dependencies are used. Further, in agreement with previous studies, we provide evidence of ozone's higher responsiveness to all weather parameters and thus enhanced predictability and PM₁₀'s lower predictability as compared to all other considered urban pollutants. The prediction accuracy tends to be comparable between standard conditions and bushfire events. However, the predictability significantly declines under anomalies in anthropogenic patterns and urban metabolic rates as those recorded during the pandemic. The inclusion of local emission sources and anthropogenic factors in the input dataset is considered necessary for NO and PM₁₀ to properly predict urban air quality, especially under human-related extreme conditions.

Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island

Greater Cairo, Egypt, which lies in the apex of the Nile Delta, is one of the most populated regions in the world. Air pollution is a profound environmental issue prevailing in the urban/rural landscapes of this crowded megacity. The objectives of the present study were to utilize remotely sensed data in order to address the seasonal variations of the nocturnal surface urban heat island intensity (SUHII) as extracted from the American Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and the related seasonal distribution of selected air pollutants, including nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and carbon monoxide (CO) as extracted from the European TROPOspheric Monitoring Instrument (TROPOMI) for the period from 2018 to 2021. It is observed that there is clear nocturnal urban heat island over Greater Cairo, particularly at the administrative districts dominated by urban land use with high density of population and at the industrial and power generation locations. The highest SUHII is observed during winter. On the other hand, the selected pollutants also represent an urban pollution island (UPI) capping the regions of high SUHII. At the seasonal level, the highest NO₂ correlation with the SUHII occurs during spring (R² = 0.59), while the CO correlates maximum during winter (R² = 0.51). Nonetheless, the seasonal SO₂ distribution is poorly related to the SUHII as this specific pollutant is significantly associated with the industrial land use. Climatic and topographic factors could intensify the distribution of air pollution in the study area. Results of this study demonstrate the significance of geospatial technology tools in the subtle analysis and addressing regional air pollution. The outputs are also of a paramount implication on the management of urban environment and the adaptation of urban air quality.

Long-Term Impacts of COVID-19 Lockdown on the NO2 Concentrations and Urban Thermal Environment: Evidence from the Five Largest Urban Agglomerations in China

Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area; changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable; the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.

Perceived impacts of Urban Heat Island phenomenon in a tropical metropolitan city: Perspectives from stakeholder dialogue sessions

The stakeholders' perceptions on the impacts of Urban Heat Island (UHI) are critical for reducing exposure and influencing their response to interventions that are aimed at encouraging a behaviour change. A proper understanding of the UHI impacts on the society, economy and environment is deemed an essential motivating factor for the stakeholders to work towards UHI mitigations in the local context. This study adopted an inductive qualitative approach using Stakeholder Dialogue Sessions (SDSs) to assess the perceived impacts of UHI among various stakeholders, comprising policy makers, academicians, developers and Non-Governmental Organizations (NGO), in a tropical metropolitan city. The results revealed five themes such as deterioration of public health, acceleration of urban migration patterns and spending time in cooler areas, reduction of workers' productivity, increased energy consumption by the households and deterioration of environmental quality and natural resources that were categorized into social, economic and environmental impacts. Although most of the stakeholders were quite unfamiliar with the term UHI, they still display a good understanding of the potential impacts of UHI due to their posteriori knowledge and ability to rationalize the physical condition of the environment in which they live. The findings provide useful insights and valuable information to the local authorities to tailor necessary actions and educational campaigns to increase UHI awareness among the stakeholders. Being among the earlier studies to use a qualitative approach to attain the aforementioned objective, the findings are crucial to determine the level of understanding of the stakeholders on the impact of UHI. Through this study, the authors have highlighted the gaps and needs for knowledge improvements aimed at behaviour change among the stakeholders.

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.

Numerical Simulations of Air Flow and Traffic–Related Air Pollution Distribution in a Real Urban Area

With increasing urbanization, urban air pollutants are becoming more and more relevant to human health. Here, combined with meteorological observation data, a numerical simulation of typical urban blocks in Shanghai was carried out to understand the spread of air pollutants caused by road traffic sources (ground–level and viaduct–level). Firstly, we analyzed the wind environment characteristics. Then, we quantitatively analyzed the pollutant distribution profiles and the contributions of two pollutant sources (PSV). Finally, we analyzed seven urban morphological parameters based on ventilation efficiency indices. Results revealed the following. (1) Ventilation patterns within the architectural complex are determined by local geometry; (2) Pollutants released at ground level were dominant when the Z–plane < 8 m high, and pollutants released from the viaduct source were 0.8–6.1% higher when the Z–plane ≥ 8 m high; (3) From ground level to a height of 60 m, the spatially–averaged normalized concentration (C*) tended to decrease gradually with distance from the source. C* increased irregularly with an increase in distance between 60 m and 86 m. Above 86 m, C* tended to increase linearly; (4) Vertical profiles of C* around buildings were building–specific, and their rate of change was inconsistent with height increases. In general, the correlations between C* and VRw, and between C* and KEturb were larger on the windward side of PSV upstream buildings than on the leeward side. Buildings downstream of the PSV showed the opposite situation; (5) At pedestrian level, the seven urban morphological parameters had no significant correlation with VRw, Cir*, and Czs*.

Fixed and Mobile Low-Cost Sensing Approaches for Microclimate Monitoring in Urban Areas: A Preliminary Study in the City of Bolzano (Italy)

In the current scenario of massive urbanization and global climate change, an intelligent monitoring of the environmental variables is becoming fundamental to ensure good living conditions in cities. Indeed, the acquisition of data with high spatiotemporal resolution can enable the assessment of environmental vulnerabilities in urban areas towards the definition of responsive adaptation and mitigation strategies. In this context, the current work presents a two-fold approach based on low-cost cloud-connected sensors for (i) fixed and (ii) mobile monitoring of several environmental parameters. This paper, which focuses on the measurement aspects of the urban micro-climate, describes in detail the hardware and software components of both approaches, and how to exploit them for setting up a field campaign. The methods were tested in the city of Bolzano (Italy), demonstrating their suitability for identifying the spatial variability of the microclimate in relation to the urban morphology, and for highlighting the presence of the urban heat island and estimating its intensity.

Analysis of two-decade meteorological and air quality trends in Rome (Italy)

This study presents a trend analysis of a two-decade (2000-2020) series of surface meteorological and air quality data measured in Rome (Italy). Data series are collected at three sites in Rome downtown and its coastal surroundings. This paper fills the gap due to missing or incomplete recent information about the meteorological and atmospheric composition tendencies in the Rome area. Datasets are subjected to in-depth quality control and to statistical analysis to verify the data homogeneity, whilst trend analysis is performed using the Seasonal Kendall test. The results show a statistically significant positive trend for average air temperature (0.07 °C year^-1 in urban and coastal sites), whilst maximum and minimum temperatures increase more in urban (0.10 °C year^-1) than in coastal (0.01 °C year^-1) environment. The water vapour mixing ratio trend is higher in the city (0.10 g kg^-1 year^-1) than along the coast (0.03 g kg^-1 year^-1). The heat index tendency is more pronounced in the Rome centre (0.11 °C year^-1) than in the coast (0.06 °C year^-1). The monthly cumulative precipitations do not show statistically significant trends. On the other hand, air quality generally improved: surface pollutant concentrations show a significant decrease thanks to the reduction of local emissions (C₆H₆, - 0.12 µg m^-3 year^-1; SO₂, - 0.09 µg m^-3 year^-1; CO, - 0.02 mg m^-3 year^-1; NO_x, - 1.28 µg m^-3 year^-1; NO, - 0.38 µg m^-3 year^-1; NO₂, - 0.60 µg m^-3 year^-1, PM₁₀, - 0.35 µg m^-3 year^-1). Only O₃ shows a statistically significant positive trend (0.15 µg m^-3 year^-1), in agreement with wider scale studies. The outcomes suggest that whilst local and national policies contribute to the improvement of air quality, the rising of temperature-as likely consequence of climate change-may be an emerging cause of concern for human thermal discomfort and potential effect on the conservation of tangible immovable heritage.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00704-022-04047-y.

Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.

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.

Local synergies and antagonisms between meteorological factors and air pollution: A 15-year comprehensive study in the Sydney region

Associated with rapid urbanization and escalation of bushfire events, Sydney has experienced significant air quality degradation in the XXI century. In this study, we present a 15-year retrospective analysis on the influence of individual meteorological factors on major air pollutants (NO₂, O₃, PM₁₀ and PM_2.5) at 14 different sites in Greater Sydney and Illawarra. By applying a newly developed "zooming in" approach to long-term ground-based data, we disclose general, seasonal, daily and hourly patterns while increasing the level of spatial associativity. We provide evidence on the pivotal role played by urbanization, sprawling dynamics, global warming and bushfires on local meteorology and air pollution. We strike associations between temperature and O₃, both as average trends and extremes, on account of increasing heat island effects. The role of wind in a coastal-basin environment, influenced by a vast desert biome inland, is investigated. A steady trend towards stagnation is outlined, boosted by enhanced urban roughness and intensified heat island circulation. Relative humidity is also crucial in the modulation between NO₂ and O₃. With a sharp tendency towards drier and hotter microclimates, NO₂ levels dropped by approximately 50% over the years at all locations, while O₃'s median levels almost doubled in the last 10 years. Further, O₃ and PMs shifted towards more frequent extreme events, strongly associated with the exacerbation of bushfire events. Such results suggest an urgent need to prioritize emission control, building air tightness improvement and urban heat mitigation, towards a future-proof governance in Sydney and similar regions in the world.

Detecting urban landscape factors controlling seasonal land surface temperature: from the perspective of urban function zones

Understanding the impact on the thermal effect by urbanization is of great significance for urban thermal regulation and is essential for determining the relationship between the urban heat island (UHI) effect and the complexities of urban function and landscape structure. For this purpose, we conducted case research in the metropolitan region of Beijing, China, and nearly 5000 urban blocks assigned different urban function zones (UFZs) were identified as the basic spatial analysis units. The seasonal land surface temperature (LST) retrieved from remote sensing data was used to represent the UHI characteristics of the study area, and the surface biophysical parameters, building forms, and filtered landscape pattern metrics were selected as the urban landscape factors. Then, the effects of urban function and landscape structure on the UHI effect were examined based on the optimal results of the ordinary least squares and geographically weighted regression models. The results indicated that (1) Significant spatiotemporal heterogeneity of the LST was found in the study area, and there was an obvious temperature gradient with "working-living-resting" UFZs. (2) All types of urban landscape factors showed a significant contribution to the seasonal LST, in the order of surface biophysical factors > building forms > landscape factors; however, their contributions varied in different seasons. (3) The major contributing factors showed a certain difference due to the variation of urban function and landscape complexity. This study expands the understanding on the complex relationship among urban landscape, function, and thermal environment, which could benefit urban landscape planning for UHI alleviation.

A review of strategies for mitigating roadside air pollution in urban street canyons

Urban street canyons formed by high-rise buildings restrict the dispersion of vehicle emissions, which pose severe health risks to the public by aggravating roadside air quality. However, this issue is often overlooked in city planning. This paper reviews the mechanisms controlling vehicle emission dispersion in urban street canyons and the strategies for managing roadside air pollution. Studies have shown that air pollution hotspots are not all attributed to heavy traffic and proper urban design can mitigate air pollution. The key factors include traffic conditions, canyon geometry, weather conditions and chemical reactions. Two categories of mitigation strategies are identified, namely traffic interventions and city planning. Popular traffic interventions for street canyons include low emission zones and congestion charges which can moderately improve roadside air quality. In comparison, city planning in terms of building geometry can significantly promote pollutant dispersion in street canyons. General design guidelines, such as lower canyon aspect ratio, alignment between streets and prevailing winds, non-uniform building heights and ground-level building porosity, may be encompassed in new development. Concurrently, in-street barriers are widely applicable to rectify the poor roadside air quality in existing street canyons. They are broadly classified into porous (e.g. trees and hedges) and solid (e.g. kerbside parked cars, noise fences and viaducts) barriers that utilize their aerodynamic advantages to ease roadside air pollution. Post-evaluations are needed to review these strategies by real-world field experiments and more detailed modelling in the practical perspective.

Combined Effect of Hot Weather and Outdoor Air Pollution on Respiratory Health: Literature Review

Association between short-term exposure to ambient air pollution and respiratory health is well documented. At the same time, it is widely known that extreme weather events intrinsically exacerbate air pollution impact. Particularly, hot weather and extreme temperatures during heat waves (HW) significantly affect human health, increasing risks of respiratory mortality and morbidity. Concurrently, a synergistic effect of air pollution and high temperatures can be combined with weather–air pollution interaction during wildfires. The purpose of the current review is to summarize literature on interplay of hot weather, air pollution, and respiratory health consequences worldwide, with the ultimate goal of identifying the most dangerous pollution agents and vulnerable population groups. A literature search was conducted using electronic databases Web of Science, Pubmed, Science Direct, and Scopus, focusing only on peer-reviewed journal articles published in English from 2000 to 2021. The main findings demonstrate that the increased level of PM10 and O3 results in significantly higher rates of respiratory and cardiopulmonary mortality. Increments in PM2.5 and PM10, O3, CO, and NO2 concentrations during high temperature episodes are dramatically associated with higher admissions to hospital in patients with chronic obstructive pulmonary disease, daily hospital emergency transports for asthma, acute and chronic bronchitis, and premature mortality caused by respiratory disease. Excessive respiratory health risk is more pronounced in elderly cohorts and small children. Both heat waves and outdoor air pollution are synergistically linked and are expected to be more serious in the future due to greater climate instability, being a crucial threat to global public health that requires the responsible involvement of researchers at all levels. Sustainable urban planning and smart city design could significantly reduce both urban heat islands effect and air pollution.

Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges

Low-cost sensors (LCSs) are widely acknowledged for bringing a paradigm shift in supplemental traditional air monitoring by air regulatory agencies. However, there is concern regarding its data quality and performance stability, which has greatly restricted its large-scale applications. Knowing the recent techniques, progress, and challenges of LCS calibration is of immense significance to promote the field of environmental monitoring. By summarizing the published evidence, this review shows that the global sensor market is rapidly expanding due to the surging needs, but the calibration efforts have been focused on a limited selection of sensors. Relative humidity correction, regression, and machine learning are the three mainstream calibration techniques. Although there is no one-size-fits-all solution, a feature of the latest research tendency is machine learning. The duration of calibration is largely neglected in the experiment design, but it is found to affect the performance of different calibration methods, especially those that are data-driven. Geographically, China and the United States gained the most research attention in the sensor calibration field, but the spatial mismatch between particulate matter hotspots and calibration sites is quite evident for the rest of the world. Incomplete and unevenly distributed research footprints could limit the large-scale test of method generalizability, as well as diminish the monitoring capacity in underserved areas that suffer greater environmental justice crises. In general, model performance is enhanced by including the key influencing factors, but the degree of improvement is not evidently related to the number of explanatory variables. Overall, studies prove the critical importance of field calibration before sensor deployment, but more studies are needed to establish experiment protocols that can be customized to specific needs.

Empirical evidence on the impact of urban overheating on building cooling and heating energy consumption

A primary contributor to urban overheating is the urban heat island (UHI) formed due to increased urbanization. The adverse effects of UHI on building energy use are substantial and well documented. However, such effects are typically demonstrated through numerical simulations which are susceptible to modeling uncertainties and lack of validation resulting in a pressing research gap. Here, for the first time, we conduct a large-scale assessment to demonstrate the devastating impact of UHI on building energy consumption using real building energy use data. We find empirical evidence correlating UHI with building energy use; changes in average UHI intensity of 0.5 K correspond to an increase in monthly cooling energy consumption in a range of 0.17 kWh/m²–1.84 kWh/m². The study validates theoretical evidence on the impact of UHI on building energy and proposes a highly innovative methodology to assess the impact of overheating on the energy balance of cities.

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Real energy data used to study the effect of local climates on building energy use.

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Cooling energy rises between 0.17 and 1.84 kWh/m² due to 0.5 K urban overheating.

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Heating energy drops between 0.06 and 1.19 kWh/m² due to 0.5 K urban overheating.

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Urban overheating effects are more exacerbated during the cooling period.

Applied Machine Learning Algorithms for Courtyards Thermal Patterns Accurate Prediction

Currently, there is a lack of accurate simulation tools for the thermal performance modeling of courtyards due to their intricate thermodynamics. Machine Learning (ML) models have previously been used to predict and evaluate the structural performance of buildings as a means of solving complex mathematical problems. Nevertheless, the microclimatic conditions of the building surroundings have not been as thoroughly addressed by these methodologies. To this end, in this paper, the adaptation of ML techniques as a more comprehensive methodology to fill this research gap, covering not only the prediction of the courtyard microclimate but also the interpretation of experimental data and pattern recognition, is proposed. Accordingly, based on the climate zoning and aspect ratios of 32 monitored case studies located in the South of Spain, the Support Vector Regression (SVR) method was applied to predict the measured temperature inside the courtyard. The results provided by this strategy showed good accuracy when compared to monitored data. In particular, for two representative case studies, if the daytime slot with the highest urban overheating is considered, the relative error is almost below 0.05%. Additionally, values for statistical parameters are in good agreement with other studies in the literature, which use more computationally expensive CFD models and show more accuracy than existing commercial tools.

COVID-19 lockdowns induced land surface temperature variability in mega urban agglomerations in India

The COVID-19 pandemic forced a nationwide lockdown in India for months when close to 1.3 billion people were confined to their homes. An abrupt halt in the majority of the urban activities reduced the generation of anthropogenic heat which often exacerbates the Urban Heat Island (UHI) effect in the urban pockets of the country. We studied the lockdown impact on seven highly populated and polluted mega urban agglomerations across India, namely Delhi, Ahmedabad, Hyderabad, Kolkata, Mumbai, Bengaluru and Chennai, using near-anniversary Landsat 8 data. The results revealed that the lockdowns have improved the air quality and reduced the Land Surface Temperature (LST) and hence the UHI effect over these cities. Each of the cities experienced an improved Air Quality Index (AQI) ranging from 18 to 151 units except Chennai (with a marginal 8 units increase in AQI), a decrease in mean LST in the range of 0.27 °C to 7.06 °C except Kolkata which showed an increment by ∼4 °C, and a reduction in daily averaged air temperature ranging from 0.3 °C to 10.88 °C except Hyderabad which witnessed an increase of 0.09 °C during the lockdown (April 2020) compared to the previous years (April 2019 and 2018). Delhi exhibited the maximum positive impact of the lockdown in all aspects with two-fold improved air quality, and Ahmedabad showed the least improvement. In addition to the variations in regional land use and land cover and proportion of essential industries that remained operational throughout the lockdown, the geographic location, topography, local meteorology and climate were some of the other factors also responsible for either aiding or overcompensating the large scale LST variabilities observed in these cities. These results hint at an unprecedented opportunity to evaluate the effectiveness of periodic planned lockdowns as a possible mitigating measure to reduce LST spikes and degraded air quality in urban areas in the future.

Cooling Effect of Different Land Cover Types: A Case Study in Xi’an and Xianyang, China

One of the climate problems caused by rapid urbanization is the urban heat island effect, which directly threatens the human survival environment. In general, some land cover types, such as vegetation and water, are generally considered to alleviate the urban heat island effect, because these landscapes can significantly reduce the temperature of the surrounding environment, known as the cold island effect. However, this phenomenon varies over different geographical locations, climates, and other environmental factors. Therefore, how to reasonably configure these land cover types with the cooling effect from the perspective of urban planning is a great challenge, and it is necessary to find the regularity of this effect by designing experiments in more cities. In this study, land cover (LC) classification and land surface temperature (LST) of Xi’an, Xianyang and its surrounding areas were obtained by Landsat-8 images. The land types with cooling effect were identified and their ideal configuration was discussed through grid analysis, distance analysis, landscape index analysis and correlation analysis. The results showed that an obvious cooling effect occurred in both woodland and water at different spatial scales. The cooling distance of woodland is 330 m, much more than that of water (180 m), but the land surface temperature around water decreased more than that around the woodland within the cooling distance. In the specific urban planning cases, woodland can be designed with a complex shape, high tree planting density and large planting areas while water bodies with large patch areas to cool the densely built-up areas. The results of this study have utility for researchers, urban planners and urban designers seeking how to efficiently and reasonably rearrange landscapes with cooling effect and in urban land design, which is of great significance to improve urban heat island problem.

Urban Overheating and Cooling Potential in Australia: An Evidence-Based Review

Cities in Australia are experiencing unprecedented levels of urban overheating, which has caused a significant impact on the country’s socioeconomic environment. This article provides a comprehensive review on urban overheating, its impact on health, energy, economy, and the heat mitigation potential of a series of strategies in Australia. Existing studies show that the average urban heat island (UHI) intensity ranges from 1.0 °C to 13.0 °C. The magnitude of urban overheating phenomenon in Australia is determined by a combination of UHI effects and dualistic atmospheric circulation systems (cool sea breeze and hot desert winds). The strong relation between multiple characteristics contribute to dramatic fluctuations and high spatiotemporal variabilities in urban overheating. In addition, urban overheating contributes to serious impacts on human health, energy costs, thermal comfort, labour productivity, and social behaviour. Evidence suggest that cool materials, green roofs, vertical gardens, urban greenery, and water-based technologies can significantly alleviate the UHI effect, cool the ambient air, and create thermally balanced cities. Urban greenery, especially trees, has a high potential for mitigation. Trees and hedges can reduce the average maximum UHI by 1.0 °C. The average maximum mitigation performance values of green roofs and green walls are 0.2 °C and 0.1 °C, respectively. Reflective roofs and pavements can reduce the average maximum UHI by 0.3 °C. In dry areas, water has a high cooling potential. The average maximum cooling potential using only one technology is 0.4 °C. When two or more technologies are used at the same time, the average maximum UHI drop is 1.5 °C. The mitigation strategies identified in this article can help the governments and other stakeholders manage urban heating in the natural and built environment, and save health, energy, and economic costs.

Concentrations of Four Major Air Pollutants among Ecological Functional Zones in Shenyang, Northeast China

Air pollution is a critical urban environmental issue in China; however, the relationships between air pollutants and ecological functional zones in urban areas are poorly understood. Therefore, we analyzed the spatiotemporal characteristics of four major air pollutants (particulate matter less than or equal to 2.5 µm (PM2.5) and 10 µm (PM10) in diameter, SO2, and NO2) concentrations over five ecological functional zones in Shenyang, Liaoning Province, at hourly, seasonal, and annual scales using data collected from 11 monitoring stations over 2 years. We further assessed the relationships between these pollutants and meteorological conditions and land-use types at the local scale. Peaks in PM, SO2, and NO2 concentrations occurred at 08:00–09:00 and 23:00 in all five zones. Daytime PM concentrations were highest in the industrial zone, and those of SO2 and NO2 were highest in residential areas. All four air pollutants reached their highest concentrations in winter and lowest in summer. The highest mean seasonal PM concentrations were found in the industrial zone, and the highest SO2 and NO2 concentrations were found in residential areas. The mean annual PM and SO2 concentrations decreased in 2017 in all zones, while that of NO2 increased in all zones excluding the cultural zone. The natural reserve zone had the lowest concentrations of all pollutants at all temporal scales. Pollutant concentrations of PM2.5, PM10, SO2, and NO2 were correlated with visibility, and their correlation coefficients are 0.675, 0.579, 0.475, and 0.477. Land coverage with buildings and natural vegetation negatively and positively influence air pollutant concentrations, respectively.