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

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

Assessing the Country-Level Excess All-Cause Mortality and the Impacts of Air Pollution and Human Activity during the COVID-19 Epidemic.

The impact of Coronavirus Disease 2019 (COVID-19) on cause-specific mortality has been investigated on a global scale. However, less is known about the excess all-cause mortality and air pollution-human activity responses. This study estimated the weekly excess all-cause mortality during COVID-19 and evaluated the impacts of air pollution and human activities on mortality variations during the 10th to 52nd weeks of 2020 among sixteen countries. A SARIMA model was adopted to estimate the mortality benchmark based on short-term mortality during 2015-2019 and calculate excess mortality. A quasi-likelihood Poisson-based GAM model was further applied for air pollution/human activity response evaluation, namely ground-level NO2 and PM2.5 and the visit frequencies of parks and workplaces. The findings showed that, compared with COVID-19 mortality (i.e., cause-specific mortality), excess all-cause mortality changed from -26.52% to 373.60% during the 10th to 52nd weeks across the sixteen countries examined, revealing higher excess all-cause mortality than COVID-19 mortality in most countries. For the impact of air pollution and human activities, the average country-level relative risk showed that one unit increase in weekly NO2, PM2.5, park visits and workplace visits was associated with approximately 1.54% increase and 0.19%, 0.23%, and 0.23% decrease in excess all-cause mortality, respectively. Moreover, compared with the impact on COVID-19 mortality, the relative risks of weekly NO2 and PM2.5 were lower, and the relative risks of weekly park and workplace visits were higher for excess all-cause mortality. These results suggest that the estimation based on excess all-cause mortality reduced the potential impact of air pollution and enhanced the influence of human activities compared with the estimation based on COVID-19 mortality.

Effects of urban functional fragmentation on nitrogen dioxide (NO2) variation with anthropogenic-emission restriction in China.

Urban functional fragmentation plays an important role in assessing Nitrogen Dioxide (NO2) emissions and variations. While the mediated impact of anthropogenic-emission restriction has not been comprehensively discussed, the lockdown response to the novel coronavirus disease 2019 (COVID-19) provides an unprecedented opportunity to meet this goal. This study proposes a new idea to explore the effects of urban functional fragmentation on NO2 variation with anthropogenic-emission restriction in China. First, NO2 variations are quantified by an Autoregressive Integrated Moving Average with external variables-Dynamic Time Warping (SARIMAX-DTW)-based model. Then, urban functional fragmentation indices including industrial/public Edge Density (ED) and Landscape Shape Index (LSI), urban functional Aggregation Index (AI) and Number of Patches (NP) are developed. Finally, the mediated impacts of anthropogenic-emission restriction are assessed by evaluating the fragmentation-NO2 variation association before and during the lockdown during COVID-19. The findings reveal negative effects of industrial ED, public LSI, urban functional AI and NP and positive effects of public ED and industrial LSI on NO2 variation based on the restricted anthropogenic emissions. By comparing the association analysis before and during lockdown, the mediated impact of anthropogenic-emission restriction is revealed to partially increase the effect of industrial ED, industrial LSI, public LSI, urban functional AI and NP and decrease the effect of public ED on NO2 variation. This study provides scientific findings for redesigning the urban environment in related to the urban functional configuration to mitigating the air pollution, ultimately developing sustainable societies.

Regional differences in socioeconomic trends: The spatiotemporal evolution from individual cities to a megacity region over a long time series.

Regional differences in socioeconomic factors are important for assessing the regional development of an area. While much research has focused on the overall patterns of regional differences within independent cities and areas, the hierarchical spatiotemporal structures of megacity regions have seldom been discussed. To fill this gap, this paper investigates the multilevel regional differences within megacity regions. Employing GDP, population and total retail sales as socioeconomic indicators, the spatiotemporal patterns of socioeconomic trends are identified. A hierarchical clustering approach that utilizes socioeconomic similarities is proposed for the identification of the spatiotemporal patterns of individual cities. At the megacity regional level, gravity centers and pathways are constructed to evaluate spatial imbalances and temporal change intensities. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as its study area, this research produces results that show diverse spatiotemporal patterns among the individual cities, revealing high/low starting point and high/low growth rate modes in terms of city interactions. From the perspective of the entire GBA, the spatial imbalance of GDP is the highest of the factors, followed by the spatial imbalance of the total retail sales of the region and, finally, by that of its population. Total retail sales exhibit the highest level of temporal change intensity, followed by GDP and population. In terms of the contribution of the various cities to the overall regional changes, Guangzhou, Shenzhen and Hong Kong dominate the spatiotemporal changes in the gravity centers, while Foshan and Dongguan show significant potential to contribute to these socioeconomic patterns. These results provide effective guidance for the sustainable development of megacity regions.

Understanding the changes in spatial fairness of urban greenery using time-series remote sensing images: A case study of Guangdong-Hong Kong-Macao Greater Bay.

Urban greenery is essential to the living environment of humans. Objectively assessing the rationality of the spatial distribution of green space resources will contribute to regional greening plans, thereby reducing social injustice. However, it is difficult to propose a reasonable greening policy aimed at the coordinated development of an urban agglomeration due to a lack of baseline information. This study investigated the changes in spatial fairness of the greenery surrounding residents in Guangdong-Hong Kong-Macao Greater Bay by examining time-series remote sensing images from 1997 to 2017. With the substitution of impervious, artificial surfaces for universal areas of human activities, we quantified the amount of surrounding greenery from the perspective of human activities at the pixel level by utilizing a nested buffer. The Gini coefficient was further calculated for each city to quantify the spatial fairness of the surrounding greenery to people. The results indicated that areas with less greenery surrounding them decreased during 1997 and 2017 in Guangdong-Hong Kong-Macao Greater Bay. The spatial fairness did not tend to increase with the improvements in the overall greening level. The spatial fairness of 4 cities had an increasing trend, and the Gini coefficients of 5 cities were still over 0.6 in 2017. We further proposed different greening policy suggestions for different cities based on the amount of greenery surrounding people and the trend in fairness. The results and the conclusion of this research will help to improve future regional greening policies and to reduce environmental injustice.