Industrial Heat Sources Research Articles

A dataset of high energy-consuming industrial products in the ASEAN during 2012–2021

The uneven energy distribution and economic development levels of the countries in the ASEAN region have led to the uncoordinated distribution of high-energy-consuming industries in the region. It is extremely important to timely obtain the spatial distribution pattern of high energy-intensive industries. In this paper, on the basis of the 375m high spatial and temporal resolution NPP-VIIRS active fire point data and vector data density segmentation algorithms, we produced a dataset of high energy-consuming industrial products in the ASEAN during 2012–2021. This dataset covers valuable information, such as latitude and longitude of industrial heat sources data, administrative region to which they belong, and the year of operation start and end, etc. It can provide scientific data support for energy distribution and economic development in the region, as well as decision-making support for its cooperation with China for further development.

Annual dynamics of global remote industrial heat sources dataset from 2012 to 2021

The spatiotemporal distribution of industrial heat sources (IHS) is an important indicator for assessing levels of energy consumption and air pollution. Continuous, comprehensive, dynamic monitoring and publicly available datasets of global IHS (GIHS) are lacking and urgently needed. In this study, we built the first long-term (2012–2021) GIHS dataset based on the density-based spatiotemporal clustering method using multi-sources remote sensing data. A total of 25,544 IHS objects with 19 characteristics are identified and validated individually using high-resolution remote sensing images and point of interest (POI) data. The results show that the user’s accuracy of the GIHS dataset ranges from 90.95% to 93.46%, surpassing other global IHS products in terms of accuracy, omission rates, and granularity. This long-term GIHS dataset serves as a valuable resource for understanding global environmental changes and making informed policy decisions. Its availability contributes to filling the gap in GIHS data and enhances our knowledge of global-scale industrial heat sources.

An industrial heat source dataset based on remotely sensed active fire/hotspot detection in China from 2012 to 2021

An industrial heat source dataset based on remotely sensed active fire/hotspot detection in China from 2012 to 2021

Source and variability of formaldehyde in the Fenwei Plain: An integrated multi-source satellite and emission inventory study

Formaldehyde (HCHO) is a high-yield product of the oxidation of volatile organic compounds (VOCs) released by anthropogenic activities, fires, and vegetations. Hence, we examined the spatiotemporal variation trends in HCHO columns observed using the Ozone Monitoring Instrument (OMI) during 2005–2021 across the Fenwei Plain (FWP) and analysed the source and variability of HCHO using multi-source data, such as thermal anomalies. The spatial distribution of the annual mean HCHO in the FWP increased from northwest to southeast during 2005–2021, and the high-value aggregation areas contracted and gradually clustered, forming a belt-shaped distribution area from Xi'an to Baoji, north of the Qinling Mountains. The annual mean HCHO concentration generally showed a two-step increase over the 17 years. Fires showed a single-peak trend in March and a double-peak M-shaped trend in March and October, whereas urban thermal anomalies (UTAs) showed an inverted U-shaped trend over 17 years, with peaks occurring in May. The HCHO peaks are mainly caused by the alternating contributions of fires and UTAs. The fires and UTAs (predominantly industrial heat sources) played a role in controlling the background level of HCHO in the FWP. Precipitation and temperature were also important influencing variables for seasonal variations, and the influence of plant sources on HCHO concentrations had significant regional characteristics and contributions. In addition, the FWP has poor dispersion conditions and is an aggregated area for the long-range transport of air pollutants.

Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager

Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

Technologies for Heat Hazard Governance and Thermal Energy Recovery in Deep Mines

As the depth of mines continues to increase, the problems of high temperature and potential heat damage become more prominent. In this study, the characteristics of natural and industrial heat sources in mines were reviewed, and then mainstream heat hazard governance technologies and corresponding utilization methods were discussed and compared. The first category of technologies comprises the optimization of ventilation systems, the insulation of roc heat, and artificial refrigeration. These cooling approaches are limited because the heat resources cannot be recovered. The second category is the utilization of waste industrial heat in mines, including the use of waste heat from the air compressors, drainage water, and foul airflow, but the current applications of these approaches have limited effectiveness in cooling the underground space. The third category is the application of geo-structures to recover natural heat in mines. Based on the principles of the chiller/heat pump cycle and the characteristics of heat sources and sinks in mines, the potential and constraints of each technology were discussed and summarized. This study provides a scientific reference for the selection of suitable heat governance and utilization technologies.

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

Industrial Heat Source-Related PM2.5 Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing–Tianjin–Hebei Region

The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this pollution. Effectively managing emissions from these sources is pivotal for achieving air pollution control goals in the region. A new three-stage model using multi-source long-term data was proposed to estimate atmospheric, delicate particulate matter (PM2.5) concentrations caused by IHS. In the first stage, a region-growing algorithm was used to identify the IHS radiation areas. In the second and third stages, based on a seasonal trend decomposition procedure based on Loess (STL), multiple linear regression, and U-convLSTM models, IHS-related PM2.5 concentrations caused by meteorological and anthropogenic conditions were removed using long-term data from 2012 to 2021. Finally, this study analyzed the spatial and temporal variations in IHS-related PM2.5 concentrations in the BTH region. The findings reveal that PM2.5 concentrations in IHS radiation areas were higher than in background areas, with approximately 33.16% attributable to IHS activities. A decreasing trend in IHS-related PM2.5 concentrations was observed. Seasonal and spatial analyses indicated higher concentrations in the industrially dense southern region, particularly during autumn and winter. Moreover, a case study in Handan’s She County demonstrated dynamic fluctuations in IHS-related PM2.5 concentrations, with notable reductions during periods of industrial inactivity. Our results aligned closely with previous studies and actual IHS operations, showing strong positive correlations with related industrial indices. This study’s outcomes are theoretically and practically significant for understanding and addressing the regional air quality caused by IHSs, contributing positively to regional environmental quality improvement and sustainable industrial development.

Satellite-based ANN Identification and Spatiotemporal Evolution Analysis of Industrial Heat Sources Coupled with Temperature Characteristics

Satellite-based ANN Identification and Spatiotemporal Evolution Analysis of Industrial Heat Sources Coupled with Temperature Characteristics

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

Utilisation of waste heat from PEM electrolysers – Unlocking local optimisation

Recovery of heat from electrolysers is potentially interesting to increase the total system efficiency, reduce CO2 emissions, and increase the economic feasibility of both hydrogen and heat production. This study examines different designs for the utilisation of (waste) heat from a 2.5 MWel polymer electrolyte membrane (PEM) electrolyser. Redundancy is important in the design, to ensure safe operation regardless of the heat demand of the heat consumer. We analysed cases with local heat consumption (with/without a heat pump) and coupling with a district heating network (DHN). Overall, 14–15% of the electricity input to the stack can be utilised by a heat consumer, increasing the total system efficiency to 90% (HHV) with CO2-savings of 0.08 (DHN)-0.28 (direct use) tonne CO2/MWhheat, used. We performed a first-order techno-economic analysis showing that the levelized costs of the electrolyser heat (8.4–36.9 €/MWh) fall within the range of other industrial heat sources and below lower-temperature heat sources.

Visual supervision of large‐scope heat source factories based on knowledge graph

AbstractAs a result of limited resources and economic development acting as dual bottleneck constraints, optimizing industrial layout structures has been a general trend in heavy industry. The visual supervision of heat source factories based on integrated multidimensional information is important for optimizing an industrial layout. Based on Visible Infrared Imaging Radiometer Suite (VIIRS) I‐band 375‐m active fire product (VNP14IMG) data, point of interest data, enterprise attribute information and other data, combined with clustering regression, knowledge graphs (KGs), 3D geographic information systems, and other technical methods, the temporal and spatial variations in China's heat source industries are macroscopically analyzed, and a visual supervision platform for heat source industries with functions such as visualization, time‐series analysis, and knowledge discovery is established. The results show that: (1) overall, heat source factories exhibit a spatial pattern of dense in the east and sparse in the west, and the number of industrial heat source objects and the number of industrial fire hotspots decreased from 2013 to 2021, with rates of decline of 22.0 and 27.3%, respectively; (2) the enterprise KG, which contains basic enterprise information, dynamic enterprise risk information and enterprise equity structure information, can provide users with accurate and reliable enterprise knowledge; and (3) the remote sensing monitoring information platform for heat source factories performs well in terms of the discovery and management of heat source factories at large scale. In general, the platform constructed in this study can support the rapid monitoring and positioning of industrial heat sources over large areas to improve supervision in terms of finding problems and preventing risks and to provide a necessary reference for optimizing industrial spatial patterns and environmental governance.

A dataset of in-operation industrial heat source objects in Beijing-Tianjin-Hebei Region during 2012-2020

There is a direct correlation between the level of industrial development and the rate of economic growth in China, and it is important to grasp timely the objective data on the spatial distribution and operation of high-energy-consuming industrial plants for national economic growth and industrial structure reform. In this paper, based our study on the NPP-VIIRS active fire/hotpot data (NPP-VIIRS active thermal anomaly data) with high spatial and temporal resolution of 375m and overlaid with POI data and high-resolution remote sensing data, we used the vector data density segmentation algorithm to produce a dataset of in-operation industrial heat source objects in Beijing-Tianjin-Hebei Region during 2012-2020. This dataset provides information, such as latitude and longitude of industrial heat sources data, category code, administrative region to which they belong, and the start and end years of operation. This dataset can provide data support for industrial reform results and future reform directions.

Classification of Industrial Heat Source Objects Based on Active Fire Point Density Segmentation and Spatial Topological Correlation Analysis in the Beijing–Tianjin–Hebei Region

The development of industrial infrastructure in the Beijing–Tianjin–Hebei(BTH) region has been accompanied by a disorderly expansion of industrial zones and other inappropriate development. Accurate industrial heat source classification data become important to evaluate the policies of industrial restructuring and air quality improvement. In this study, a new classification of industrial heat source objects model based on active fire point density segmentation and spatial topological correlation analysis in the BTH Region was proposed. First, industrial heat source objects were detected with an active fire point density segmentation method using NPP-VIIRS active fire/hotspot data. Then, industrial heat source objects were classified into five categories based on a spatial topological correlation analysis method using POI data. Then, identification and classification results were manually validated based on Google Earth imagery. Finally, we evaluated the factors influencing the number of industrial heat sources based on an OLS regression model. A total of 493 industrial heat source objects were identified in this study with an identification accuracy of 96.14%(474/493). Compared with results for nighttime fires, the number of industrial heat source objects that were identified was higher, and the spatial coverage was greater; the minimum size of the detected objects was also smaller. Based on the function of the identified industrial heat source objects, the objects in the BTH region were then divided into five categories: cement plants (21.73%), steel plants (53.80%), coal and chemical industry (12.66%), oil and gas developments (7.81%), and other (4.01%). An analysis of their operations showed that the number of industrial heat source objects in operation in the BTH region tended to first rise and then decline during the 2012–2021 period, with the peak being reached in 2013. The results of this study will aid the rationalization of industrial infrastructure in the BTH region and, by extension, in China as a whole.

Identification and spatiotemporal distribution analysis of global biomass burning based on Suomi-NPP VIIRS Nightfire data

Biomass burning refers to the combustion of various organisms formed through photosynthesis, including natural and man-made biomass burning. Biomass combustion has great impacts on nature and human health. Biomass combustion and industrial heat sources overlap in temperature range, it is difficult to distinguish them with traditional methods. In order to explore the distribution of biomass combustion, the influence of industrial heat sources must be eliminated. This paper used Suomi-NPP (Suomi National Polar-orbiting Partnership) VIIRS (Visible Infrared Imaging Radiometer Suite) Nightfire data to extract 1,457,930 global biomass combustion objects from 2012 to 2018 based on the spatial distribution, occurrence frequency and duration characteristics of biomass combustion. The extracted results were verified using industrial directory Point of Interest (POI) data obtained online from high-resolution remote sensing images and place name maps, and the results showed that 98.37% of the industrial heat sources were successfully removed. The spatial and temporal characteristics of biomass combustion in regions with varying levels of economic development at the intercontinental, international and global levels from 2012 to 2018 were revealed in this paper. It also compares the temporal and spatial differences in biomass burning among different climate zones and land cover types from 2012 to 2018. Overall, global biomass burning showed an upward trend from 2012 to 2018 and increased significantly from 2015 to 2016. Global biomass combustion was mostly distributed in Africa, followed by Oceania and South America. Australia, Brazil, Russia, the United States and Canada were the five countries with the most biomass burning. The proportions of biomass burning in different climatic zones and land cover types also showed obvious regularity. Biomass combustion mostly occurred in low-latitude and high-temperature areas and in areas where the land cover types included shrublands, grasslands and farmlands.

Estimating the Carbon Emissions of Remotely Sensed Energy-Intensive Industries Using VIIRS Thermal Anomaly-Derived Industrial Heat Sources and Auxiliary Data

The energy-intensive industrial sector (EIIS) occupies a majority of global CO2 emissions, but spatially monitoring the spatiotemporal dynamics of these emissions remains challenging. In this study, we used the Chinese province with the largest carbon emissions, Shandong Province, as an example to investigate the capacity of remotely sensed thermal anomaly products to identify annual industrial heat source (IHS) patterns at a 1 km resolution and estimated the carbon emissions of these sources using auxiliary datasets and the boosting regression tree (BRT) model. The IHS identification accuracy was evaluated based on two IHS references and further attributed according to corporate inventory data. We followed a bottom-up approach to estimate carbon emissions for each IHS object and conducted model fitting using the explanatory strength of the annual population density, nighttime light (NTL), and relevant thermal characteristic information derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). We generated a time series of IHS distributions from 2012 to 2020 containing a total of over 3700 IHS pixels exhibiting better alignment with the reference data than that obtained in previous work. The results indicated that the identified IHSs mostly belonged to the EIIS, such as energy-related industries (e.g., thermal power plants) and heavy manufacturing industries (e.g., chemistry and cement plants), that primarily use coal and coke as fuel sources. The BRT model exhibited a good performance, explaining 61.9% of the variance in the inventory-based carbon emissions and possessing an index of agreement (IOA) of 0.83, suggesting a feasible goodness of fit of the model when simulating carbon emissions. Explanatory variables such as the population density, thermal power radiation, NTL, and remotely sensed thermal anomaly durations were found to be important factors for improving carbon emissions modeling. The method proposed in this study is useful to aid management agencies and policymakers in tracking the carbon footprint of the EIIS and regulating high-emission corporations to achieve carbon neutrality.

GIS-based approach to identifying potential heat sources for heat pumps and chillers providing district heating and cooling

Geographic information system (GIS) software has been essential for visualising and determining heating and cooling requirements, sources of industrial excess heat, natural bodies of water, and municipalities. Policymakers highly encourage the use of GIS software at all administrative levels. It is expected that the heating and cooling demand will continue to increase. For a reliable heat and cooling supply, we must identify heat sources that can be used to provide heat or for removing surplus heat. We propose a method for identifying possible heat sources for large heat pumps and chillers that combines geospatial data from administrative units, industrial facilities, and natural bodies of water. Temperatures, capacities, heat source availability, as well as their proximity to areas with high demand density for heating and cooling were considered. This method was used for Estonia, Latvia and Lithuania. Excess heat from heat generation plants and industries, sewage water treatment plants, and natural heat sources such as rivers, lakes and seawater were included. The study’s findings provide an overview of possible industrial and natural heat sources, as well as their characteristics. The potential of the heat sources was analysed, quantified, and then compared to the areas of heating and cooling demand.

A Model for Expressing Industrial Information Based on Object-Oriented Industrial Heat Sources Detected Using Multi-Source Thermal Anomaly Data in China

Industrial heat sources have made a great contribution to Chinese economic development. However, it has also been found that emissions from industrial heat sources are the main contribution to regional air pollution. Therefore, the detection of industrial heat sources and the expression of related information is becoming important. In this paper, the detection of industrial heat sources was used to express industrial information, thus that the accuracy of the detection of industrial thermal anomalies could be improved and the problems of noise and missing parameters addressed. A model for expressing industrial information based on object-oriented industrial heat sources and using multi-source thermal anomaly data in China was, therefore, proposed. It was a new real-time, objective, and real way to describe the production operation status of industrial heat sources on a large-scale area. First, 4340 working industrial heat sources in mainland China were detected by applying an adaptive k-means algorithm to ACF (NPP VIIRS 375-m active fire/hotspot data) data from the period 19 January 2012 to 31 December 2020. Secondly, several features of working industrial heat sources were extracted from NPP VIIRS 375-m active fire/hotspot data (ACF), VIIRS Nightfire data (VNF), and the Fires product based on Landsat-8 AIRCAS (L8F) data. Areas containing working industrial heat sources were then identified based on these different types of fire data. Light, land-surface temperature, and CO2 and N2O emissions data related to the working industrial heat sources were also extracted. The results show that feature parameters extracted from the multi-source thermal anomaly data mostly have a good positive correlation with the other parameters.

Understanding China's resumption of work and production during the critical period of COVlD‐19 based on multi‐source data

AbstractMonitoring the resumption of work and production (RWP) is of great significance to the restoration of social order and the economy after the coronavirus disease 2019 (COVID‐19) epidemic. In this study, the most critical period of epidemic prevention and control and economic recovery was divided into four phases according to the changes in emergency response levels during the epidemic in China, and based on VIIRS I‐Band 375‐m active fire data (VNP14IMG), NO2 tropospheric vertical column density (TVCD) data, and intracity travel intensity data, the density‐based spatial clustering of applications with the noise logistic regression industrial heat source identification model, NO2 difference model, and urban relative recovery index were constructed to analyze work resumption at different phases. The results indicate that: (1) the production intensity of China's heat source enterprises decreased first and then exceeded the epidemic period compared with the same period in history. From phases 1 to 3, the heat source enterprise production intensity was lower than the same period in 2019 (the production intensities were 19,562.79, 22,462.57, and 38,466.89, respectively), and the reduction rates of production intensity were 27.5, 26.1, and 22.7%, respectively. In phase 4, enterprise production intensity achieved an increase of 7%. (2) The process of the RWP in China is closely related to NO2 TVCD. In the first three phases, significantly decreased emission sources of NO2 reduced the total value of NO2 by varying degrees in most provinces of the country. Correspondingly, in phase 4, the NO2 TVCD in some provinces recovered to the value of the same period in 2019, including typical industrial bases (Shandong, Hebei, Shanxi, etc.) and economically developed coastal areas (Jiangsu, Guangdong, Zhejiang, etc.). (3) In phase 3, the intracity travel intensity in all cities improved, and the city relative recovery index (RRI) rose across the board. In particular, the western cities were better than the eastern cities. Among them, the RRIs of most cities in southwest China (Sichuan, Yunnan) and northwest China (Gansu) were more than 90%. Notably, the recovery situation exhibited an obvious “siege phenomenon,” and the recovery of core cities required a longer period. Overall, the sudden COVID‐19 epidemic seriously affected the production of Chinese enterprises. However, benefitting from rapid and effective epidemic prevention and control measures, the resumption of work and production has been a success. Our study provides guidance for economic recovery strategies during post‐pandemic stages.

Spatial-temporal analysis of industrial heat and productivity in China

China is currently in a key period of transformation and upgrading for its industrial infrastructure, and it is important to investigate the quantity, spatial distribution and production status of heat source industries to ensure effective supervision by environmental protection departments and to optimize the layout for regional industrial planning. In this study, based on the 2013–2019 Visible Infrared Imaging Radiometer Suite (VIIRS) I-band 375-m active fire data (VNP14IMG) product, a model combining density-based spatial clustering of applications with noise (DBSCAN) and logistic regression (LR) was adopted to identify industrial heat source objects in China, and the spatiotemporal patterns and trends of industrial objects at different levels were analyzed. The results indicate the following: (1) the VNP14IMG-based DBSCAN-LR identification model feasibly identifies industrial heat sources, and the accuracy of cross-validation is 93% and that of typical cities reaches as high as 92%; (2) The number of industrial heat source objects (NIHSO) in China from 2013 to 2019 exhibits a general downward trend, with the NIHSO values from 2013 to 2019 being 2673, 2608, 2374, 2273, 2284, 2317, and 2269, respectively; (3) the spatial pattern of regional industrial development is extremely unbalanced and can be characterized as being "dense in the north and sparse in the south". The industrial focus has shifted southwest, reflecting advances in the central and western regions achieved through industrial transfer; (4) the global spatial autocorrelation of industrial distribution is positive, and the correlation increases over time. The local correlation shows that the geographic concentration characteristics of “high-high” agglomeration in the north and “low-low” agglomeration in the west.