Air pollution radiative forcing from specific emissions sectors at 2030

Unger, N., D. Shindell, D. Koch, and D. Streets (2008), Air pollution radiative forcing from specific emissions sectors at 2030, J. Geophys. Res., 113, D02306, doi:10.1029/2007JD008683.
Abstract: 

Reduction of short-lived air pollutants can contribute to mitigate global warming in the near-term with ancillary benefits to human health. However, the radiative forcings of short-lived air pollutants depend on the location and source type of the precursor emissions. We apply the Goddard Institute for Space Studies atmospheric compositionclimate model to quantify near-future (2030 A1B) global annual mean radiative forcing by ozone (O3) and sulfate from six emissions sectors in seven geographic regions. At 2030 the net forcings from O3, sulfate, black and organic carbon, and indirect CH4 effects for each emission sector are (in mWm-2) biomass burning, +95; domestic, +68; transportation, +67; industry, -131; and power, -224. Biomass burning emissions in East Asia and central and southern Africa, domestic biofuel emissions in East Asia, south Asia, and central and southern Africa, and transportation emissions in Europe and North America have large net positive forcings and are therefore attractive targets to counter global warming. Power and industry emissions from East Asia, south Asia, and north Africa and the Middle East have large net negative forcings. Therefore air quality control measures that affect these regional sectors require offsetting climate measures to avoid a warming impact. Linear relationships exist between O3 forcing and biomass burning and domestic biofuel CO precursor emissions independent of region with sensitivity of +0.2 mWm-2/TgCO. Similarly, linear relationships exist between sulfate forcing and SO2 precursor emissions that depend upon region but are independent of sector with sensitivities ranging from -3 to -12 mWm-2/TgS.

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Research Program: 
Atmospheric Chemistry Modeling and Analysis Program (ACMAP)
Modeling Analysis and Prediction Program (MAP)