Estimation of anthropogenic heat emission over South Korea using a statistical regression method
Anthropogenic heating by human activity is one of the key contributing factors in forming urban heat islands, thus inclusion of the heat source plays an important role in urban meteorological and environmental modeling. In this study, gridded anthropogenic heat flux (AHF) with high spatial (1-km) an...
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| Published in | Asia-Pacific journal of atmospheric sciences Vol. 51; no. 2; pp. 157 - 166 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
Korean Meteorological Society
01.05.2015
한국기상학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1976-7633 1976-7951 |
| DOI | 10.1007/s13143-015-0065-6 |
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| Abstract | Anthropogenic heating by human activity is one of the key contributing factors in forming urban heat islands, thus inclusion of the heat source plays an important role in urban meteorological and environmental modeling. In this study, gridded anthropogenic heat flux (AHF) with high spatial (1-km) and temporal (1-hr) resolution is estimated for the whole South Korea region in year 2010 using a statistical regression method which derives based on similarity of anthropogenic air pollutant emissions and AHF in their emission inventories. The bottom-up anthropogenic pollutant emissions required for the regression method are produced using the intensive Korean air pollutants emission inventories. The calculated regression-based AHF compares well with the inventory-based AHF estimation for the Gyeong-In region, demonstrating that the statistical regression method can reasonably represent spatio-temporal variation of the AHF within the region. The estimated AHF shows that for major Korean cities (Seoul, Busan, Daegu, Gwangju, Daejeon, and Ulsan) the annual mean AHF range 10–50 Wm
−2
on a grid scale and 20–30W m
−2
on a city-scale. The winter AHF are larger by about 22% than that in summer, while the weekday AHF are larger by 4–5% than the weekend AHF in the major Korean cities. The gridded AHF data estimated in this study can be used in mesoscale meteorological and environmental modeling for the South Korea region. |
|---|---|
| AbstractList | Anthropogenic heating by human activity is one of the key contributing factors in forming urban heat islands, thus inclusion of the heat source plays an important role in urban meteorological and environmental modeling. In this study, gridded anthropogenic heat flux (AHF) with high spatial (1-km) and temporal (1-hr) resolution is estimated for the whole South Korea region in year 2010 using a statistical regression method which derives based on similarity of anthropogenic air pollutant emissions and AHF in their emission inventories. The bottom-up anthropogenic pollutant emissions required for the regression method are produced using the intensive Korean air pollutants emission inventories. The calculated regression-based AHF compares well with the inventory-based AHF estimation for the Gyeong-In region, demonstrating that the statistical regression method can reasonably represent spatio-temporal variation of the AHF within the region. The estimated AHF shows that for major Korean cities (Seoul, Busan, Daegu, Gwangju, Daejeon, and Ulsan) the annual mean AHF range 10-50 W m−2 on a grid scale and 20-30 W m−2 on a city-scale. The winter AHF are larger by about 22% than that in summer, while the weekday AHF are larger by 45% than the weekend AHF in the major Korean cities. The gridded AHF data estimated in this study can be used in mesoscale meteorological and environmental modeling for the South Korea region. KCI Citation Count: 6 Anthropogenic heating by human activity is one of the key contributing factors in forming urban heat islands, thus inclusion of the heat source plays an important role in urban meteorological and environmental modeling. In this study, gridded anthropogenic heat flux (AHF) with high spatial (1-km) and temporal (1-hr) resolution is estimated for the whole South Korea region in year 2010 using a statistical regression method which derives based on similarity of anthropogenic air pollutant emissions and AHF in their emission inventories. The bottom-up anthropogenic pollutant emissions required for the regression method are produced using the intensive Korean air pollutants emission inventories. The calculated regression-based AHF compares well with the inventory-based AHF estimation for the Gyeong-In region, demonstrating that the statistical regression method can reasonably represent spatio-temporal variation of the AHF within the region. The estimated AHF shows that for major Korean cities (Seoul, Busan, Daegu, Gwangju, Daejeon, and Ulsan) the annual mean AHF range 10-50 Wm super(-2) on a grid scale and 20-30W m super(-2) on a city-scale. The winter AHF are larger by about 22% than that in summer, while the weekday AHF are larger by 4-5% than the weekend AHF in the major Korean cities. The gridded AHF data estimated in this study can be used in mesoscale meteorological and environmental modeling for the South Korea region. Anthropogenic heating by human activity is one of the key contributing factors in forming urban heat islands, thus inclusion of the heat source plays an important role in urban meteorological and environmental modeling. In this study, gridded anthropogenic heat flux (AHF) with high spatial (1-km) and temporal (1-hr) resolution is estimated for the whole South Korea region in year 2010 using a statistical regression method which derives based on similarity of anthropogenic air pollutant emissions and AHF in their emission inventories. The bottom-up anthropogenic pollutant emissions required for the regression method are produced using the intensive Korean air pollutants emission inventories. The calculated regression-based AHF compares well with the inventory-based AHF estimation for the Gyeong-In region, demonstrating that the statistical regression method can reasonably represent spatio-temporal variation of the AHF within the region. The estimated AHF shows that for major Korean cities (Seoul, Busan, Daegu, Gwangju, Daejeon, and Ulsan) the annual mean AHF range 10–50 Wm −2 on a grid scale and 20–30W m −2 on a city-scale. The winter AHF are larger by about 22% than that in summer, while the weekday AHF are larger by 4–5% than the weekend AHF in the major Korean cities. The gridded AHF data estimated in this study can be used in mesoscale meteorological and environmental modeling for the South Korea region. |
| Author | Kim, Soon-Tae Lee, Sang-Hyun |
| Author_xml | – sequence: 1 givenname: Sang-Hyun surname: Lee fullname: Lee, Sang-Hyun email: sanghyun@kongju.ac.kr organization: Department of Atmospheric Science, Kongju National University – sequence: 2 givenname: Soon-Tae surname: Kim fullname: Kim, Soon-Tae organization: Department of Environmental Engineering, Ajou University |
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| CitedBy_id | crossref_primary_10_1002_joc_6463 crossref_primary_10_1016_j_envpol_2016_04_055 crossref_primary_10_1016_j_apenergy_2020_115566 crossref_primary_10_1016_j_buildenv_2021_107772 crossref_primary_10_1016_j_atmosenv_2019_117012 crossref_primary_10_1016_j_rser_2024_114802 crossref_primary_10_1016_j_envpol_2019_03_113 crossref_primary_10_53898_josse2024426 crossref_primary_10_1016_j_uclim_2021_101044 crossref_primary_10_1007_s00704_020_03214_3 crossref_primary_10_1016_j_uclim_2022_101186 crossref_primary_10_1016_j_scs_2023_105125 crossref_primary_10_1175_JAMC_D_15_0321_1 crossref_primary_10_1016_j_resconrec_2021_105682 crossref_primary_10_1016_j_jclepro_2017_02_010 crossref_primary_10_1016_j_rse_2020_111751 crossref_primary_10_1002_qj_2847 |
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| Keywords | surface energy balance statistical regression method urban heat island Anthropogenic heat anthropogenic pollutants waste heat |
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| References_xml | – reference: HeipleS.SailorD. J.Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profilesEnerg. Buildings2008401426143610.1016/j.enbuild.2008.01.005 – reference: LeeS.-H.McKeenS. A.SailorD.A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission databaseAtmos. Environ.20149562963310.1016/j.atmosenv.2014.07.009 – reference: SailorD. J.A review of methods for estimating anthropogenic heat and moisture emissions in the urban environmentInt. J. Climatol.20113118919910.1002/joc.2106 – reference: MakarP.GravelS.ChirkovV.StrawbridgeK.FroudeF.ArnoldJ.BrookJ.Heat flux, urban properties, and regional weatherAtmos. Environ.2006402750276610.1016/j.atmosenv.2005.11.061 – reference: Allen, L., F. Lindberg, and C. S. B. Grimmond, 2010: Global to city scale urban anthropogenic heat flux: model and variability. Int. J. Climatol., 31, doi:10.1002/joc.2210. – reference: CoatsC. J.High performance algorithms in the sparse matrix operator kernel emissions (SMOKE) modeling systemProceedings of the Ninth AMS Joint Conference on Applications of Air Pollution Meteorology with AWMA, American Meteorological Society, Atlanta, GA (1996)1996584588 – reference: SailorD. J.LuL.A top-down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areasAtmos. Environ.2004382737274810.1016/j.atmosenv.2004.01.034 – reference: RyuY.-H.BaikJ.-J.LeeS.-H.Effects of anthropogenic heat on ozone air quality in a megacityAtmos. Environ.201380203010.1016/j.atmosenv.2013.07.053 – reference: SonE.-H.KimY.-K.HongJ.-H.Study on estimation of urban anthropogenic heat generationJ. Korean Soc. Atmos. Environ.2000163747 – reference: IchinoseT.ShimodozonoK.HanakiK.Impact of anthropogenic heat on urban climate in TokyoAtmos. Environ.1999333897390910.1016/S1352-2310(99)00132-6 – reference: FanH.SailorD. 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