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A platform for research: civil engineering, architecture and urbanism
A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database
https://orcid.org/0000-0003-1720-8214
Abstract A statistical regression method is presented for estimating hourly anthropogenic heat flux (AHF) using an anthropogenic pollutant emission inventory for use in mesoscale meteorological and air-quality modeling. Based on bottom-up AHF estimated from detailed energy consumption data and anthropogenic pollutant emissions of carbon monoxide (CO) and nitrogen oxides (NOx) in the US National Emission Inventory year 2005 (NEI-2005), a robust regression relation between the AHF and the pollutant emissions is obtained for Houston. This relation is a combination of two power functions (Y = aX b) relating CO and NOx emissions to AHF, giving a determinant coefficient (R 2) of 0.72. The AHF for Houston derived from the regression relation has high temporal (R = 0.91) and spatial (R = 0.83) correlations with the bottom-up AHF. Hourly AHF for the whole US in summer is estimated by applying the regression relation to the NEI-2005 summer pollutant emissions with a high spatial resolution of 4-km. The summer daily mean AHF range 10–40 W m−2 on a 4 × 4 km2 grid scale with maximum heat fluxes of 50–140 W m−2 for major US cities. The AHFs derived from the regression relations between the bottom-up AHF and either CO or NOx emissions show a small difference of less than 5% (4.7 W m−2) in city-scale daily mean AHF, and similar R 2 statistics, compared to results from their combination. Thus, emissions of either species can be used to estimate AHF in the US cities. An hourly AHF inventory at 4 × 4 km2 resolution over the entire US based on the combined regression is derived and made publicly available for use in mesoscale numerical modeling.
Highlights A new methodology for anthropogenic heat flux (AHF) estimation is suggested. The AHF over the entire US regions is estimated at 4-km and 1-h resolution. The gridded AHF dataset is publicly available via a ftp site.
A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database
https://orcid.org/0000-0003-1720-8214
Abstract A statistical regression method is presented for estimating hourly anthropogenic heat flux (AHF) using an anthropogenic pollutant emission inventory for use in mesoscale meteorological and air-quality modeling. Based on bottom-up AHF estimated from detailed energy consumption data and anthropogenic pollutant emissions of carbon monoxide (CO) and nitrogen oxides (NOx) in the US National Emission Inventory year 2005 (NEI-2005), a robust regression relation between the AHF and the pollutant emissions is obtained for Houston. This relation is a combination of two power functions (Y = aX b) relating CO and NOx emissions to AHF, giving a determinant coefficient (R 2) of 0.72. The AHF for Houston derived from the regression relation has high temporal (R = 0.91) and spatial (R = 0.83) correlations with the bottom-up AHF. Hourly AHF for the whole US in summer is estimated by applying the regression relation to the NEI-2005 summer pollutant emissions with a high spatial resolution of 4-km. The summer daily mean AHF range 10–40 W m−2 on a 4 × 4 km2 grid scale with maximum heat fluxes of 50–140 W m−2 for major US cities. The AHFs derived from the regression relations between the bottom-up AHF and either CO or NOx emissions show a small difference of less than 5% (4.7 W m−2) in city-scale daily mean AHF, and similar R 2 statistics, compared to results from their combination. Thus, emissions of either species can be used to estimate AHF in the US cities. An hourly AHF inventory at 4 × 4 km2 resolution over the entire US based on the combined regression is derived and made publicly available for use in mesoscale numerical modeling.
Highlights A new methodology for anthropogenic heat flux (AHF) estimation is suggested. The AHF over the entire US regions is estimated at 4-km and 1-h resolution. The gridded AHF dataset is publicly available via a ftp site.
A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database
https://orcid.org/0000-0003-1720-8214
Abstract A statistical regression method is presented for estimating hourly anthropogenic heat flux (AHF) using an anthropogenic pollutant emission inventory for use in mesoscale meteorological and air-quality modeling. Based on bottom-up AHF estimated from detailed energy consumption data and anthropogenic pollutant emissions of carbon monoxide (CO) and nitrogen oxides (NOx) in the US National Emission Inventory year 2005 (NEI-2005), a robust regression relation between the AHF and the pollutant emissions is obtained for Houston. This relation is a combination of two power functions (Y = aX b) relating CO and NOx emissions to AHF, giving a determinant coefficient (R 2) of 0.72. The AHF for Houston derived from the regression relation has high temporal (R = 0.91) and spatial (R = 0.83) correlations with the bottom-up AHF. Hourly AHF for the whole US in summer is estimated by applying the regression relation to the NEI-2005 summer pollutant emissions with a high spatial resolution of 4-km. The summer daily mean AHF range 10–40 W m−2 on a 4 × 4 km2 grid scale with maximum heat fluxes of 50–140 W m−2 for major US cities. The AHFs derived from the regression relations between the bottom-up AHF and either CO or NOx emissions show a small difference of less than 5% (4.7 W m−2) in city-scale daily mean AHF, and similar R 2 statistics, compared to results from their combination. Thus, emissions of either species can be used to estimate AHF in the US cities. An hourly AHF inventory at 4 × 4 km2 resolution over the entire US based on the combined regression is derived and made publicly available for use in mesoscale numerical modeling.
Highlights A new methodology for anthropogenic heat flux (AHF) estimation is suggested. The AHF over the entire US regions is estimated at 4-km and 1-h resolution. The gridded AHF dataset is publicly available via a ftp site.
A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database
Lee, Sang-Hyun (author) / McKeen, Stuart A. (author) / Sailor, David J. (author)
Atmospheric Environment ; 95 ; 629-633
2014-07-02
5 pages
Article (Journal)
Electronic Resource
English
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