- freely available
Int. J. Environ. Res. Public Health 2014, 11(4), 4066-4077; https://doi.org/10.3390/ijerph110404066
2. Data and Methods
2.1. Multi-Temporal Dataset of DMSP-OLS
2.2. Statistical Data
2.3. Methods for Calculating AHF
- Since larger DMSP-OLS digital number (DN) represents more active economic activities, areas where DN ≥ 12 were classed as urban areas while the remainder are classed as rural regions.
- We divide energy consumption (petroleum, coal and natural gas) into two parts: energy used in urban areas and in rural areas. We use the 2009 consumption ratio from IEA (87% coal, 77% oil, and 81% gas for urban energy consumption). This ratio may change annually, but should not change dramatically during the study period.
- There exists a strong exponential relationship between urban area and energy consumption (R2 = 0.9) from the available statistical data (Figure 2). Based on this relationship, energy consumption in the urban area obtained from step (2) is exploited in the urban map obtained in step (1). In China, most large factories or refineries are located in urban areas.Therefore, the energy consumed in urban area not only include the energy consumption for domestic use but also industrial usage. Human metabolism makes up a small share of the total heat released by human activies, and we exclude it here. We note that statistical errors in urban energy consumption are a function of urban area and energy consumption. In order to alleviate their effects, we can allocate total urban energy into patches based on percent-normalized urban energy from Figure 2.
- Finally, AHF was calculated using energy data obtained for each urban patch in step (3) according to the percent-normalized DMSP-OLS data in every urban patch and rural region. As data of energy consumption allocated to each grid cell are total energy consumption for corresponding location, the sum consumption is converted to heat flux in each grid (Wm−2) by dividing by elapsed time.
3.1. AHF Temporal and Spatial Patterns
3.2.1. Comparison of Data for China
3.2.2. Comparison of Province-Level AHF Data
3.2.3. Comparison of Single-City AHF Data
|Regions||Flanner (Wm−2)||Chen (Wm−2)||Our Data (Wm−2)||AHF of Single City (Wm−2)||Estimating Methods of Single City|
|Beijing||-||5.81||24.67||94.9 ||Heat Emission Inventory|
|Guangzhou||7.99||12||15.1||41 ||Energy Based|
|Hangzhou||-||7.84||17.36||50 ||Energy Based|
|Xiaoshan||-||4.62||14.9||40 ||Energy Based|
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