Spatial-temporal analysis of industrial heat and productivity in China

Lai, Jianbo; Zhu, Jun; Chai, Jinchuan; Xu, Bo

doi:10.1016/j.apgeog.2021.102618

Cited by 5 publications

(7 citation statements)

References 32 publications

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“…VNP14IMG data provide a high sensitivity to fires in small regions, high performance, and increased detection efficiency compared to products with a lower resolution (1 km). The data include the spatial position, timing, and fire radiative power of thermal anomaly objects. ,, We used MATLAB version 2021 to calculate the nearest distance between each grid to the fire location for each day in degrees.…”

Section: Methodsmentioning

confidence: 99%

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Addressing Biases in Ambient PM_2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Katoch,

Kumar,

Imam

et al. 2023

Environ. Sci. Technol.

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Ambient PM 2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM 2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM 2.5 in recent years (2017−2022) over India, which showed fivefold cross-validation R 2 of 0.92 and root mean square error (RMSE) of 11.8 μg m −3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638− 110,597) during these years. With the gap-filled data, we found that the rising ambient PM 2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM 2.

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Section: Methodsmentioning

confidence: 99%

“…The data include the spatial position, timing, and fire radiative power of thermal anomaly objects. 18,22,23 We used MATLAB version 2021 to calculate the nearest distance between each grid to the fire location for each day in degrees.…”

Section: Fire Count (Fc)mentioning

confidence: 99%

Addressing Biases in Ambient PM_2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Katoch,

Kumar,

Imam

et al. 2023

Environ. Sci. Technol.

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“…Recently, high-resolution optical [19] and thermal remote sensing data have been widely utilized to detect and monitor changes in IHSs in large-scale areas [20][21][22]. Chen et al [19] identified key equipment by using high-resolution optical images obtained from Worldview and then monitored the illegal and irregular construction of steel firms in Shijiazhuang.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to detect whether firms are working or not, or even to estimate their heat emissions. As a result of its sensitivity to information regarding surface thermal radiation, thermal infrared remote sensing is commonly employed to monitor changes in surface temperatures [20,[22][23][24] using the VIIRS night-fire product (750 m) in conjunction with spatial and temporal features to detect global heat sources. To detect IHSs in small areas by using an adaptive K-means algorithm, VIIRS active fire point data (ACF, 375 m) with a greater spatial resolution were introduced [21].…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Xie,

et al. 2024

Applied Sciences

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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.

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“…3 | CONSTRUCTION OF THE HEAT SOURCE FACTORY ENTITY DATABASE3.1 | Industrial heat source object identificationSince industrial heat sources usually operate continuously in time and the location of factories is relatively fixed in space, industrial heat sources can be distinguished from natural (fire) or man-made (straw incineration) thermal anomalies with considering and seasonality based on the temporal and spatial features of the factories. In our previous research(Lai, Zhu, Chai, et al 2022), we combined the DBSCAN and LR methods to construct an industrial heat source identification model and achieved good accuracy. Therefore, this study uses the DBSCAN-LR industrial heat source object identification model to identify industrial heat source objects from 2013 to 2021 fire point data.…”

mentioning

confidence: 99%

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

Lai

Zhu

Luo

et al. 2023

Transactions in GIS

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As 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.

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Spatial-temporal analysis of industrial heat and productivity in China

Cited by 5 publications

References 32 publications

Addressing Biases in Ambient PM_2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Addressing Biases in Ambient PM_2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

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

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

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Spatial-temporal analysis of industrial heat and productivity in China

Cited by 5 publications

References 32 publications

Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

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

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

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Product

Resources

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Addressing Biases in Ambient PM_2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Addressing Biases in Ambient PM_2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India