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Research on urban thermal environments based on thermal comfort can help formulate effective measures to improve urban thermal and human settlement environments, which is of great significance for improving urban quality, urban climate change adaptation, and sustainable development. Taking 344 municipal administrative districts in China as study areas, the Universal Thermal Climate Index (UTCI) of each city in the last 20 years was calculated to evaluate thermal comfort. We then analyzed the thermal comfort and spatiotemporal heterogeneity of each city during a typical heat wave. Finally, the driving forces of the potential socioeconomic, natural, and landscape factors influencing thermal comfort were analyzed using geographic detectors. The results show that the thermal comfort index had similar spatial patterns and differentiation characteristics in different years, and the interannual variation was not obvious. Cities in the typical heat wave period were mainly distributed in East and Northwest China. The driving factor in the contribution rate of the same index in different years was basically the same and was not affected by the change in years, and the highest contribution rate was the natural factor.

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O 3 , NO 2 , SO 2 , PM 2.5 and PM 10 of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM 2.5 , PM 10 and CO are the main pollutants affecting air quality and fluctuation, followed by SO 2 , NO 2 and O 3 .

From 2000 to 2010 China experienced rapid economic development and urbanization. Many cities in economically developed areas have developed from a single-center status to polycentricity. In this study, we used exploratory spatial data analysis (ESDA) to identify the population centers, which identified 232 cities in China as having urban centers. COMP was used to represent urban agglomeration, and POLYD (representing how far is the city's sub-centers to the main center), POLYC (representing the number of a city's centers), and POLYP (representing the population distributed between the main center and the sub-centers) were used to indicate urban polycentricity. Night light data were used to determine the CO 2 emissions from various cities in China. A mixed model was used to study the impact of urban aggregation and polycentric data on the CO 2 emission efficiency in 2000 and 2010. The study found that cities with higher compactness were distributed in coastal areas, and the cities with higher multicentricity were distributed in the Yangtze River Delta and Shandong Province. The more compact the city was, the less conducive it was to improving CO 2 emission efficiency. Polycentric development of the city was conducive to improving the CO 2 emission efficiency, but the number of urban centers had no significant relationship with the CO 2 emission efficiency. Our research showed that the compactness and multicentricity of the city had an impact on the CO 2 emission efficiency and provided some planning suggestions for the low carbon development of the city., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Fine particulate matter (PM 2.5 ) can pose health problems for humans following urbanization. Because urban vegetation has a large surface area to filter PM 2.5 out of the air, it can be an effective long-term way to mitigate air pollution. Various studies have quantified PM 2.5 removal by vegetation in cities, but the spatial variability of removal within cities and future scenarios have not been well documented. To ensure more reasonable and effective urban tree planting regimes, we used the spatiotemporal i-Tree Eco model combined with the vertical distribution of vegetation in a case study in Shenzhen City, China. The results indicated that the PM 2.5 removal by urban vegetation in 2015 was 1000.1 tons, and the average removal rate by vegetation was 1.6 g m -2  year -1 . A maximum hourly local air quality improvement of up to 3% could be achieved, with an average of 1%, which differed significantly with elevation. In terms of vegetation type, evergreen shrubs, evergreen broadleaved forests, and evergreen needle-leaved forests had the highest removal efficiency within <100, 100-300, and >300 m, respectively. For five future planting scenarios, by increasing vegetation cover by 5% in different elevation zones (<100, 100-300, and >300 m), an annual amount of 1220.6-1308 tones could be achieved. Specifically, it was estimated that an increase in evergreen shrubs cover in the developed area (<100 m) would have the best removal potential., (Copyright © 2019 Elsevier B.V. All rights reserved.)