Using Google Earth Engine (GEE), Landsat 8 and Sentinel-2 data were analyzed to invert surface temperature and identify land cover types through a random forest algorithm. By analyzing data, investigated the impact of different types of land cover on the urban thermal environment. The results indicated that although the average temperature did not change significantly from 2018 to 2020, the range between the average maximum and minimum temperatures widened, leading to an increasingly evident urban heat environment. At the same time, lower temperature zones were mainly distributed in farmland, forest land, and other areas with high vegetation coverage and near water bodies. In contrast, moderate, high, and higher- temperature zones were predominantly found in built-up and unused lands with low vegetation coverage. In recent years, Xuzhou has transitioned from a mining-dependent city to a renewable-resource city, with most abandoned mines being reclaimed as ecological parks, farmland, etc. Therefore, the area of cultivated land increased by 21.9% from 2018 to 2020. Through our research, it is suggested that future efforts could be intensified to reclaim bare lands, such as abandoned mines, into vegetated lands, in order to alleviate the urban heat phenomenon in Xuzhou. These research findings can serve as a scientific basis for improving the urban thermal environment and provide a reference for Xuzhou to formulate eco-city construction strategies.
Understanding the spatiotemporal dynamics of PM2.5 emissions from industrial heat sources is crucial for industrial reform and air pollution control. This study utilizes PM2.5 concentration data from 2012 to 2021 in the Beijing-Tianjin-Hebei region (BTH), employing spatial statistical analysis and correlation analysis. Evaluation indices, including total PM2.5 load and average concentration, were used to dynamically assess the ten-year spatiotemporal variation of PM2.5 emissions at the surface of industrial heat sources. Further analyses by category and pollution level were conducted. Results indicate that: (1) the central and southern in BTH have higher total PM2.5 loads from industrial heat sources, with the south having a higher average PM2.5 concentration. (2) Steel industries contribute the highest total PM2.5 load, while oil and gas development industries have the highest average PM2.5 concentration. (3) PM2.5 concentrations at the surface of industrial heat sources are relatively even, mainly clustering within the 60-80μg/m³ range. (4) Over the ten years, the PM2.5 concentration at the surface of industrial heat sources exhibits a fluctuating declining trend with a pronounced seasonal distribution: concentrations are highest in winter and lowest in summer, the highest concentration occurred in January 2014, reaching 124.76μg/m³.
This study examines the impact of war conflict and political instability on industrial heat sources in Ukraine, considering the contrasting backdrop of abundant mineral resources and lagging economic development. Utilizing long time series remote sensing satellite data, the research enhances statistical efficiency compared to traditional labor-intensive methods of data collection. An innovative approach combining spatio-temporal density partitioning and machine learning is employed to identify energy-consuming industrial heat sources. By analyzing the spatio-temporal distribution of these sources in Ukraine from 2012 to 2022, the study reveals a significant decline in the number of industrial heat sources during the post-war period in the region, highlighting the significant impact of the war on its heavy industry.
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