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

Sang Hyun Lee, Stuart A. McKeen, David Sailor

Research output: Contribution to journalArticle

10 Citations (Scopus)

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=aXb) relating CO and NOx 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 NOx 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.

Original languageEnglish (US)
Pages (from-to)629-633
Number of pages5
JournalAtmospheric Environment
Volume95
DOIs
StatePublished - 2014
Externally publishedYes

Fingerprint

heat flux
emission inventory
nitrogen oxides
carbon monoxide
pollutant emission
air pollutant
summer
modeling
air quality
spatial resolution

Keywords

  • Anthropogenic heat
  • Energy balance
  • Urban heat islands
  • Urban modeling
  • Waste heat

ASJC Scopus subject areas

  • Atmospheric Science
  • Environmental Science(all)

Cite this

A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database. / Lee, Sang Hyun; McKeen, Stuart A.; Sailor, David.

In: Atmospheric Environment, Vol. 95, 2014, p. 629-633.

Research output: Contribution to journalArticle

@article{ef045450b1304cada309a7862939a1cc,
title = "A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database",
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=aXb) relating CO and NOx 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 NOx 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.",
keywords = "Anthropogenic heat, Energy balance, Urban heat islands, Urban modeling, Waste heat",
author = "Lee, {Sang Hyun} and McKeen, {Stuart A.} and David Sailor",
year = "2014",
doi = "10.1016/j.atmosenv.2014.07.009",
language = "English (US)",
volume = "95",
pages = "629--633",
journal = "Atmospheric Environment",
issn = "0004-6981",
publisher = "Pergamon Press Ltd.",

}

TY - JOUR

T1 - A regression approach for estimation of anthropogenic heat flux based on a bottom-up air pollutant emission database

AU - Lee, Sang Hyun

AU - McKeen, Stuart A.

AU - Sailor, David

PY - 2014

Y1 - 2014

N2 - 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=aXb) relating CO and NOx 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 NOx 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.

AB - 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=aXb) relating CO and NOx 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 NOx 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.

KW - Anthropogenic heat

KW - Energy balance

KW - Urban heat islands

KW - Urban modeling

KW - Waste heat

UR - http://www.scopus.com/inward/record.url?scp=84904122993&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904122993&partnerID=8YFLogxK

U2 - 10.1016/j.atmosenv.2014.07.009

DO - 10.1016/j.atmosenv.2014.07.009

M3 - Article

VL - 95

SP - 629

EP - 633

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 0004-6981

ER -