Search

Rapid urbanization has brought about many problems in urban environments, including the deterioration of the thermal environment, which greatly affects the sustainable development of cities and the health of urban residents. Therefore, a comprehensive and thorough study of the thermal environment is necessary. In this study, quantitative and qualitative analyses of the interannual and seasonal variation of the thermal environment in the urban area of Chongqing were conducted using a combination of equal sector analysis, mathematical statistics, and principal component analysis. In addition, the mechanism by which multiple integrated human and natural factors affect the urban thermal environment in different seasons was explored. The thermal environment of the land surface has a clear spatial correlation with built-up areas. From 2010 to 2020, the Liangjiang New Area and the western high-tech zone were the main areas of thermal environment area expansion. From 2010 to 2020, a decrease in the area of low-temperature zones and an increase in the area of high-temperature zones were the main trends. In particular, during the summer of 2020, the low-temperature zones almost disappeared, and the area of high-temperature zones was nearly twice as large as in the other seasons. The combined influence of multiple factors on surface temperature has certain seasonal characteristics. The slope, elevation, normalized water body index, fractional vegetation cover, normalized imperviousness index, and nighttime light intensity were the main factors affecting the surface temperature, whereas landscape pattern indicators, as well as the population and points of interest (POI) density, had a low influence. The strength of the combined influence of these multiple factors of the different seasons exhibited the following order: winter > spring > summer > autumn. [ABSTRACT FROM AUTHOR]

Urban impervious surfaces area (ISA) and green space (GS), two primary components of urban environment, are pivotal in detecting urban environmental quality and addressing global environmental change issues. However, the current global mapping of ISA and GS is not effective enough to accurately delineate in urban areas due to the mosaicked and complex structure. To address the issue, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban ISA and GS fractions for the years 2015 and circa 2020. We use random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine. The correlation coefficients of global urban ISA and GS fractions were all higher than 0.9 for 2015 and circa 2020. Our results show global urban ISA and GS areas in circa 2020 were 31.19 × 104 km2 and 17.16 × 104 km2, respectively. The novel ISA and GS fractions product can show potential applications in assessing the effects of urbanization on climate, ecology, and urban sustainability. [ABSTRACT FROM AUTHOR]

As a primary air pollutant, fine particulate matter (PM 2.5) is increasingly attracting attention. Crowdsourcing observations based methods are thought to be the best solutions for identifying the spatio-temporal distribution of PM 2.5 in intra-urban areas. However, inconsistent timing in the collection of crowdsourced data has typically been ignored in previous studies. To address this issue, a temporally calibrated method (TCM) was introduced in this study. By interpolating TCM-estimated observations using the inverse distance weighted (IDW) method, variations of PM 2.5 concentrations across the urban areas of Changsha City were captured. The results demonstrate that TCM can efficiently resolve the inconsistent timing defects of raw crowdsourcing observations (R 2 was 0.73 and the RMSE was 7.65 μg/m3). Furthermore, PM 2.5 distributions developed using TCM-based interpolations are of a finer spatial scale than those developed from raw observations at crowdsourcing locations. With a lack of funds to build sufficient stationary monitoring sites, developing crowdsourcing observation-based technology is the most promising solution for revealing intra-urban PM 2.5 variations at a higher spatio-temporal- resolution. Image 1 • A temporal calibration method was introduced for crowdsourcing air quality data. • TCM can eliminate time inconsistence of original crowdsourcing based PM 2.5 concentrations. • Intra-urban variations of PM 2.5 concentrations were revealed by densely observation. • Crowdsourcing observation is effective for fine scale air quality mapping in urban area. [ABSTRACT FROM AUTHOR]