Suggested Citation:
Delucchi, Mark A. and Donald R. McCubbin (2004) The Contribution of Motor Vehicle and Other Sources to Ambient Air Pollution: Report #16 in the series: The Annualized Social Cost of Motor-Vehicle Use in the United States, based on 1990-1991 Data. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-96-03(16)_rev2

In this Report, we explain how we model the contribution of motor-vehicles and other emissions sources to ambient air pollution.

In Reports 11, 12, and 13 of this social-cost series (see the list at the beginning of this report), we develop dose-response functions that estimate changes in human health, crop production, and visibility as a function of changes in ambient air pollution: ΔE = f(ΔP,O) = f  (PI, PP,O),

where: ∆E = the change in the effect of interest (human health, crop production, or visibility)

∆P = the change in ambient air pollution

O = other variables (such as population or incidence rate)

PI = the initial pollution level

PP = the pollution level after the change in pollution -- in this social-cost analysis, the level after removing all anthropogenic emissions, or 10% or 100% of motor-vehicle related emissions.

The initial pollution level, PI, is the actual ambient air quality in each county in the U.S. These data, and the data for any of the other variables O, such as population, are discussed in Reports 11, 12, and 13. In this report, we discuss how we estimate PP, the pollution level after removing anthropogenic emissions, or 10% or 100% of motor-vehicle related emissions.

Note that, when we estimate the pollution level after removing motor-vehicle related emissions, we estimate the effects of a specific, “marginal” change in pollution: the difference between actual pollution (PI) and, what pollution would have been had motor-vehicle-related emissions been reduced by 10% or 100% (PP). We did consider as an alternative estimating the effect of all anthropogenic air pollution and then assigning a fraction of this total effect to motor vehicles, but for two reasons rejected this alternative. First, some of our dose-response functions (in Reports 11, 12, and 13) are nonlinear, which means that the change in effects (the responses) depends not only on the difference between PI and PP (the “doses”), but on the absolute magnitudes of PI and PP as well. A decrease in pollution from 15 units to 10 units does not necessarily 1 result in the same change in effects as does a decrease from 10 units to 5 units or from 5 units to zero units. If all of the dose-response functions were linear, then effects would be a function only of the difference between PI and PP, and one would have to specify only this difference, and not the absolute values of PI and PP. But as this is not the case, we must specify the absolute magnitudes of PP and PI.

Second, because ozone formation is a nonlinear function of two precursor pollutants, NOx and VOCs, the only way to model the real nonlinear effect on ozone of motor-vehicle ozone-precursor emissions is to model actual ozone levels with and without motor vehicle precursor emissions. It simply is not meaningful to model the elimination of all anthropogenic pollution and then use some ad-hoc rules or “apportioning” factors assign a fraction of this eliminated pollution to motor vehicles.

In short, we perform a “with/without” analysis: we estimate the health, agriculture, or visibility effects of the difference between total air pollution (with motorvehicle-related emissions) and air pollution with 10% or 100% of motor-vehicle-related emissions eliminated. To estimate the difference in pollution due to motor-vehicle emissions, we use data on ambient air quality, a detailed emissions inventory, emissions correction factors, and a simple air-quality dispersion model.