This study introduces a real-time monitoring and analysis system for industrial heat sources. We employed the K-means algorithm and VIIRS ACF fire point data to identify industrial heat source objects. Additionally, thematic maps based on ARCGIS were generated, and users were notified via email using an SMTP server. By analyzing thematic maps from January to April 2023, we obtained a comprehensive understanding of the quantity and distribution of industrial heat sources in Hangzhou City. Our approach can be extended to other regions, providing a novel method for real-time monitoring that contributes to environmental protection and sustainable development.
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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