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The aim of the study was to evaluate spatial connectivity and socioeconomic status of African cities using street network datasets and geospatial methods. The drivable street network was collected from OpenStreetMap, and spatial connectivity has developed at the cityscape level and central business districts (CBD). At the cityscape level, almost all studied cities have minimum spatial connectivity as illustrated by metrics like betweenness centrality, average node average and intersection density metrics where maximum values were 0.11, 6.28 and 359 nodes/km2 respectively. The spatial connectivity of CBD was higher compared cityscape level, which indicated the availability unbalanced growth of drivable street network in the sample cities. Moreover, the study has also founded relationship between spatial connectivity and socioeconomic status of cities which in turn have implications to the sustainability of urban areas. [ABSTRACT FROM AUTHOR]

Distributed solar photovoltaic (PV) harvesting is an effective way to collect solar energy in existing metropolitan cities. To facilitate the installation of PV modules at solar abundant locations, an accurate estimation of solar PV spatial potential is indispensable. Solar energy could be reflected on high-albedo building surfaces inside the urban canyon. However, using conventional ways to construct albedo datasets for different building surfaces is extremely labor-intense. In this work, we proposed to use semantic segmentation to identify façade materials from street view images. Due to the distinguishable features between materials in terms of the subtle texture and patterns rather than just their shapes and colors, identification requires more details from images, which makes multi-scale inference structure a promising solution. Compared with existing methods combining scales features at pixel-level, we proposed a novel Multi-Scale Contextual Attention Network (MSCA), using a Multi-Scale Object-Contextual Representation (OCR) block to exploit and combine contextual information from different scales in high dimensional layers. The experimental results show that the proposed model significantly outperforms the existing models, achieving a mean Intersection over Union (mIOU) of 70.23%. The results indicate that the MSCA can effectively obtain the materials information from street views and can be a reliable solution to providing urban albedo information for solar estimation. [ABSTRACT FROM AUTHOR]

Analyzing long-term variations of aerosol optical depth (AOD) is beneficial for determining high-pollutant-risk areas and formulating mitigation policies. In this study, multi-spatiotemporal trends and periodicity of AOD, as well as the persistence over the Guangdong-Hong Kong-Macao Greater Bay Area from 2001 to 2021, were investigated by the extreme-point symmetric mode decomposition (ESMD), Theil-Sen Median trend analysis and Hurst exponent. The results elucidate that AOD exhibits fluctuant variations during the 21-year period with the year 2012 as the turning point. There is a slight upward tendency (0.009 year−1) in the pre-2012 period but a pronounced downward trend (− 0.03 year−1) in the post-2012 period, suggesting an overall declining trend in the study area. The northern cities in the area present an increasing-stable-decreasing trend of monthly average AOD, whereas other cities have an increasing-fluctuating-decreasing trend over the study period. The decreasing rate in the western parts is higher than that in the eastern parts, like Zhaoqing, Jiangmen and Foshan city. A continuous decline of AOD is dominated over the study area, whereas an anti-persistence tendency is accumulated in the northeastern parts. Additionally, elevated AOD can be observed in unused land, water bodies and construction land, while grassland, cropland and woodland have lower AOD. The decreasing rate is larger when land-use types with high AOD are converted to those with low AOD; otherwise, the decreasing rate is smaller. The results have a great significance for improving the understanding of long-term variations of AOD, as well as providing a scientific basis to formulate environmental protection and mitigation practices. [ABSTRACT FROM AUTHOR]

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

Modeling thematic and spatial dynamic behaviors of urban heat islands (UHIs) over time is important for understanding the evolution of this phenomenon to mitigate the warming effect in urban areas. Although previous studies conceptualized that a UHI only has a single life cycle with spatial behaviors, a UHI can be detected to appear and disappear several times periodically in terms of thematic and spatial integrated behaviors. Such multiple behaviors have not yet been illustrated with proof or evidence. This study conceptualizes a UHI as an object which has thematic and spatial behaviors simultaneously and proposes several graphs to depict periodic life‐cycle transitions triggered by behaviors. The conceptualized behaviors have been modeled and implemented in an object‐relational database management system and temperature readings collected from numerous weather stations were interpolated as temperature images per hour. The results of this study indicate that the model could track the spatial and thematic evolution of UHIs continuously and reveal their periodical patterns and abnormal cases. [ABSTRACT FROM AUTHOR]

This study presents a Single Channel Method using Urban Exitance Model (UEM-SCM) to retrieve land surface temperature (LST) from satellite data in an urbanized city, and evaluates the geometry effect on land surface temperature retrieval using single channel method and split-window algorithm. The UEM-SCM incorporates the effect of urban geometry and considers both reflection caused by the target pixel and its neighboring pixels. In order to evaluate the geometry effect, the retrieved LSTs with and without geometry effect were studied. Results show that the LSTs without geometry effect are generally higher than the LSTs with geometry effect. The temperature difference occurs because the material emissivity is always lower than the effective emissivity caused by multiple scattering and reflection in urban areas (cavity effect). The LST without geometry effect also cannot fully capture the variability and complexity of urban thermal patterns. The temperature difference between with and without the geometry effect can reach 2 K in built-up areas. A comparison was also conducted between LST retrieved by split-window algorithm with and without geometry effect. Results show that the LST retrieved by split-window algorithm without geometry effect has generally higher values than the one with the geometry effect, e.g. 1.1 K on average and 1.5–2 K in built-up areas. The geometry effect will be removed and mis-deemed as atmospheric effect when the split-window algorithm without geometry effect is applied in urban areas. The split-window algorithm with the geometry effect can be used to distinguish between geometry and atmospheric effect in further study. [ABSTRACT FROM AUTHOR]

Surface emissivity is a critical parameter for studying city-, meso-, and micro-scale climate and energy balance. The emissivity of complex surfaces e.g. a forest or an urban canopy is an effective surface property since it depends on both surface materials and geometry. This study presents a novel methodology for estimating effective emissivity using sky view factor retrieved from airborne Lidar data, building GIS data, and land use and land cover classification data. First, a high correlation between the effective emissivity retrieved from ASTER TIR bands 10–14 and the sky view factor was observed ( r 2 = 0.93, 0.99, 0.99, 0.97, 0.97). When the sky view factor decreases, the effective emissivity tends to increase, which is mainly due to multiple scattering (cavity effect), thus increases the effective emissivity. A simplified model which assumes that reflection and scattering only occurs within a single pixel was developed. Results showed that the correlations between the modeled and the spectral (band) emissivity retrieved from the ASTER multispectral TIR data (five spectral bands) are high ( r 2 = 0.93, 0.99, 0.98, 0.93, 0.97), and with low RMSE (0.019, 0.016, 0.012, 0.003 and 0.004 from band 10–14 respectively). The emissivity derived from this simplified model, however, tends to be overestimated in band 10–12. Thus, a refined urban emissivity model based on sky view factor (UEM-SVF) which considers the scattering and reflection from adjacent pixels was developed in this study. Results show a good agreement with ASTER spectral (band) emissivity: r 2 = 0.90, 0.98, 0.96, 0.94 and 0.96, and very low RMSE (0.006, 0.003, 0.004, 0.002 and 0.004). This study illustrates that the UEM-SVF can be useful for estimation of land surface emissivity of complex surfaces, and can further be used for accurate land surface temperature retrieval. [ABSTRACT FROM AUTHOR]

Aerosol retrieval using MODerate resolution Imaging Spectroradiometer (MODIS) has been well researched over the past decade. However, the application is limited to global- and regional-scale studies, which may not be applicable for urban areas due to its low spatial resolution. To overcome the limitation, this paper proposed an improved aerosol retrieval algorithm for Landsat images (ImAero-Landsat) at spatial resolution of 30 m. This ImAero-Landsat algorithm has been improved in the following two aspects: (i) it does not require a comprehensive look up table and thus it is more efficient in AOT retrieval; and (ii) it can be operated in both bright and dark surfaces. The derived aerosol optical thickness (AOT) images were validated with AErosol RObotic NETwork (AERONET) measurements as well as MODIS MOD04 AOT products. Small root mean square errors (RMSEs) of 0.11 and 0.14 and mean absolute difference (MAD) of 0.07 and 0.11 between ImAero-Landsat AOT, with MODIS MOD04 and AERONET products were observed. By correlating with ground based PM 10 concentrations, the ImAero-Landsat method outperforms (r 2 = 0.32) than MOD04 AOT products (r 2 = 0.23). In addition, the accuracy of estimating PM 10 can be improved to r 2 = 0.55 when the derived AOT was integrated with meteorological parameters. The accuracy is similar to the results derived from AERONET AOT (r 2 = 0.62). This study offers a simple and accurate method to investigate aerosol optical thickness at detailed city-scale. Environmental authorities may use the derived methods for deriving aerosol distribution maps and pinpointing the sources of pollutants in urban areas. [ABSTRACT FROM AUTHOR]

Abstract: Environmental quality is an abstract concept resulting from both human and natural factors operating at different spatial scales. In urban areas the local scale is dominated by individual buildings, streets and trees, but regional scale influences may include the whole city and beyond. This paper demonstrates the ability of current satellite-based sensing systems to depict parameters of urban environmental quality over large areas at detailed level, using 3D Virtual Reality models. A method is described for increasing the spatial detail and spectral accuracy of Landsat ETM+ thermal data, by fusion with an IKONOS image representing vegetation. Additionally, by depicting the complete radiating surface involved in energy exchange between the surface and atmosphere, including vertical walls, as well as the horizontal surfaces ‘seen’ by the satellite, a more accurate representation of the urban thermal environment is obtained. The models permit 3D visualization and fly-through animation to represent urban environmental quality, based on quantifiable image parameters, and assist the understanding of the complex and dynamic factors controlling urban environmental quality. [Copyright &y& Elsevier]

This paper aims to analyze how the associations of built environment and usage of urban space changed due to the social restrictions during COVID-19 pandemic in the United States. A total of 57,185 census block groups (CBGs) in 459 cities were analyzed. Built environment elements including road network, green space, and land use associated with usage of urban space (measured by over two billion mobile phone data) before/during COVID-19 were evaluated at both CBG and city level. Our results showed drastic decreases of urban space usage, ranging from 0.25 % to 76.35 %, among cities during COVID-19 social restriction. Specifically, CBG-level road length-node ratio, road intersection index and land use densities contributed less to usage of urban space during COVID-19. Meanwhile, the heterogeneity of city-level road network, commercial, public places, as well as hospitals and health cares exhibited stronger relationship with usage of urban space. The heterogeneity of green space at both CBG and city level showed less associations with usage of urban space during COVID-19. The discrepancies of the built environment effects on usage of urban space between CBG and city level suggest the importance of varying-scale analysis. Future interventions should consider space usages and health inequality from a space-varying perspective for post-pandemic period. • The changing associations of built environment with usage of urban space during COVID-19 were investigated • The changing associations were evaluated based on the census-block-group (CBG) level and city level in 459 U.S. cities • CBG-level road complexity and urban facility density contributed less on the usage of urban space during social restriction • City-level built environment exhibited stronger relationship with usage of urban space during social restriction [ABSTRACT FROM AUTHOR]

Climate change influences the vulnerability of urban populations worldwide. To improve their adaptive capacity, the implementation of nature-based solutions (NBS) in urban areas has been identified as an appropriate action, giving urban planning and development an important role towards climate change adaptation/mitigation and risk management and resilience. However, the importance of extensively applying NBS is still underestimated, especially regarding its potential to induce significantly positive environmental and socioeconomic impacts across cities. Concerning environmental impacts, monitoring and evaluation is an important step of NBS management, where earth observation (EO) can contribute. EO is known for providing valuable disaggregated data to assess the modifications caused by NBS implementation in terms of land cover, whereas the potential of EO to uncover the role of NBS in urban metabolism modifications (e.g., energy, water, and carbon fluxes and balances) still remains underexplored. This study reviews the EO potential in the monitoring and evaluation of NBS implementation in cities, indicating that satellite observations combined with data from complementary sources may provide an evidence-based approach in terms of NBS adaptive management. EO-based tools can be applied to assess NBS' impacts on urban energy, water, and carbon balances, further improving our understanding of urban systems dynamics and supporting sustainable urbanization. [ABSTRACT FROM AUTHOR]

Environmental deprivation significantly influences urban livability. Previous studies applied spatial data to evaluate environmental deprivation across various neighborhoods, and the identified deprived areas can be directly used in urban planning as areas that need to be addressed. However, perceptions of oneself regarding the local urban environment can influence sense of wellbeing, mental health, and social behavior of this individual; and any adverse feelings from the subjective environmental status can further influence perceived environmental deprivation. This perceived environmental deprivation can be different from the estimation of "objective" environmental deprivation, and perception itself can vary among subpopulations. Absence of consideration of variation in perceived environmental deprivation can lead to a failure of sustainable planning to support all oppressed people affected by urban development. Therefore, we combined citizens' perceptions with remote-sensed and administrative data to characterize perceived environmental deprivation among subpopulations, based on a questionnaire with ranks of specific environmental issues under a "city as a whole" concept. Generally, perceived environmental deprivation among subpopulations was driven by different facts. Based on the spatial comparison, self-identified urban residents and people aged>=30 have faced higher environmental deprivation across the whole city than self-identified rural residents and younger ages. Females, lower income population, and indoor workers have faced with higher environmental deprivation across urban areas than males and higher income population and outdoor workers. These implied that perceived environmental deprivation may be driven by social behaviors of individuals because of social inequality, while planning protocols should be targeted to specific populations to provide comprehensive community support and equity. • "City as a whole" concept to estimate accumulation of environmental experiences. • Subjective (self-rated) data with objective (spatial) data to estimate deprivation. • Urban residents and aged>=30: high environmental deprivation across the whole city. • Females, low income, indoor workers: high environmental deprivation across urban area. • Perceived environmental deprivation driven by social behaviors of local population. [ABSTRACT FROM AUTHOR]

Traffic-related emissions are well-known factors in urban environment which may have adverse implication on human health. Estimating vehicular emissions in urban areas provides an understanding of the air pollution caused by traffic. However, existing microscopic approaches cannot simulate the traffic flows and emissions for an entire city and most of the macroscopic approaches are usually highly complex and require priori knowledge about vehicles' route options. This study, therefore, proposes a straightforward and robust approach to simulate vehicular flows and estimated transport emissions at a city scale via a deterministic approach and by applying the Cell Transmission Model (CTM) to simplify the modeling of vehicles' route selections. Under a space-time integrated framework, we firstly simulate a time-dependent distribution of urban vehicular flows and then estimate pollutant emissions of Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Violate Organic Compounds (VOC) for traffic flows on weekday and weekend. Finally, the spatiotemporal patterns of traffic flows as well as traffic emissions were visualized and illustrated under a space-time integrated framework. With accuracies of around 67.4% to 70%, the results demonstrated the feasibility of the proposed approach for estimating city-scale traffic flows and emissions from road transport. • This work presents a straightforward and robust approach to simulate the time-dependent distribution of urban vehicular flows. • It adopts a deterministic approach and the Cell Transmission Model to simplify the modeling of vehicles route selections. • Various pollutant emissions are estimated at the city level, with accuracies of around 78.6% to 70%. • Spatio-temporal patterns of traffic flows and emissions are visualized and analyzed under a space-time integrated framework. [ABSTRACT FROM AUTHOR]

The impact of socio-economic restriction measures in the United States showed a significant reduction in nitrogen dioxide (NO 2) emissions due to the changes in commuting patterns, travel restrictions, and reduced social and economic activities. The data from 60 observation stations across different states representing of different urban settings were used to assess the impacts of dynamic changes in human mobility and static emission sources on NO 2 concentrations. Two predictive models, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) and Long Short-Term Memory (LSTM), were used to forecast NO 2 concentrations under a counterfactual scenario in the assumption of no lockdown was implemented. The predicted concentrations were then compared to the observed concentrations to evaluate the difference between the two scenarios, during lockdown and after lockdown by using the last five-years (2015–2019) average as baseline. Results showed that reduction in NO 2 varied across stations due to a combination of factors such as human mobility, population density, income, local climate, and stationary sources of NO 2 emissions in the neighbourhood. The stations located in high-income regions with higher population density experienced a greater reduction in mobility, resulting in a greater variation in NO 2 levels during both periods. In contrast, stations in densely populated low-income regions experienced lower variation in mobility and NO 2 levels during the lockdown period but showed recovery during the post-lockdown period. Stations located outside the city with very low population density experienced no significant reduction in NO 2 emissions. The study highlights the disparities in NO 2 reduction and income inequality exposed by the pandemic, which may have scientific impacts on air quality and health disparities. • Dynamic human mobility and static emissions sources found major NO 2 contributors. • NO 2 has disproportionate variation depending on the income and population density. • Populous low-income areas showed less mobility reduction & faster NO 2 recovery. • Populous high-income areas showed greater human mobility and NO 2 reductions. • Marine and mixed-humid climate zones showed a greater reduction in NO 2. [ABSTRACT FROM AUTHOR]

Abstract: This study demonstrates that thermal satellite images combined with ‘in situ’ ground data can be used to examine models of heat island genesis and thus identify the main causes of urban heat islands (UHIs). The models, although proposed over 30years ago, have not been thoroughly evaluated due to a combination of inadequate ground data and the low resolution of thermal satellite data. Also there has been limited understanding of the relevance of satellite-derived surface temperatures to local and regional scale air temperatures. A cloud-free ASTER thermal image of urban and rural areas of Hong Kong was obtained on a winter night with a well-developed heat island, accompanied by a 148km vehicle traverse of air temperatures. Over the whole traverse a high R 2 of 0.80 was observed between surface and air temperatures, with the two datasets showing a similar amplitude and general trend, but with the surface exhibiting much higher local variability than air temperature. Gradients in both surface and air temperature could be related to differences in land cover, with little evidence of large scale advection, thus supporting the population/physical structure model of UHI causation, rather than the advection model. However, the much higher surface and air temperatures observed over the largest urban area, Kowloon, than over any smaller urban centre with similar physical structure in the New Territories, would seem more indicative of the advection model. The image and ground data suggest that Kowloon''s urban canopy layer climate is mainly influenced by local city structure, but it is also modified by a strongly developed, regional scale urban boundary layer which has developed over the largest urban centre of Kowloon, and reinforces heating from both above and below. [Copyright &y& Elsevier]