A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database

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 (NO_x) 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 NO_x emissions to AHF, giving a determinant coefficient (R²) 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 10-40Wm^-2 on a 4×4km² grid scale with maximum heat fluxes of 50-140Wm^-2 for major US cities. The AHFs derived from the regression relations between the bottom-up AHF and either CO or NO_x emissions show a small difference of less than 5% (4.7Wm^-2) in city-scale daily mean AHF, and similar R² 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 4×4km² resolution over the entire US based on the combined regression is derived and made publicly available for use in mesoscale numerical modeling.