Discover the Desirable Landscape Structure of Urban Parks for Mitigating Urban Heat: A High Spatial Resolution Study Using a Forest City, Luoyang, China as a Lens.

Urban parks can mitigate the urban heat island (UHI) and effectively improve the urban microclimate. In addition, quantifying the park land surface temperature (LST) and its relationship with park characteristics is crucial for guiding park design in practical urban planning. The study's primary purpose is to investigate the relationship between LST and landscape features in different park categories based on high-resolution data. In this study, we identified the land cover types of 123 parks in Luoyang using WorldView-2 data and selected 26 landscape pattern indicators to quantify the park landscape characteristics. The result shows that the parks can alleviate UHI in most seasons, but some can increase it in winter. While the percentage of bare land, PD, and PAFRAC have a positive impact on LST, AREA_MN has a significant negative impact. However, to deal with the current urban warming trend, a compact, clustered landscape configuration is required. This study provides an understanding of the major factors affecting the mitigation of thermal effects in urban parks (UP) and establishes a practical and feasible urban park renewal method under the idea of climate adaptive design, which provides valuable inspiration for urban park planning and design.

Understanding Anthropogenic PM2.5 Concentrations and Their Drivers in China during 1998-2016.

Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM2.5 pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM2.5 concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM2.5 concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM2.5 concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen's slope estimator and the Mann-Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM2.5 concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM2.5 in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 µg/m3 from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM2.5 concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 µg/m3) gradually decreased and gradient 3 (≥35 µg/m3) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM2.5 concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM2.5 emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM2.5 concentrations and the mechanisms influencing anthropogenic PM2.5 concentrations, which can help the Chinese government develop effective abatement strategies.