The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution.

The benefit per ton ($/ton) of reducing PM(2.5) varies by the location of the emission reduction, the type of source emitting the precursor, and the specific precursor controlled. This paper examines how each of these factors influences the magnitude of the $/ton estimate. We employ a reduced-form air quality model to predict changes in ambient PM(2.5) resulting from an array of emission control scenarios affecting 12 different combinations of sources emitting carbonaceous particles, NO(x), SO(x), NH(3), and volatile organic compounds. We perform this modeling for each of nine urban areas and one nationwide area. Upon modeling the air quality change, we then divide the total monetized health benefits by the PM(2.5) precursor emission reductions to generate $/ton metrics. The resulting $/ton estimates exhibit the greatest variability across certain precursors and sources such as area source SO(x), point source SO(x), and mobile source NH(3). Certain $/ton estimates, including mobile source NO(x), exhibit significant variability across urban areas. Reductions in carbonaceous particles generate the largest $/ton across all locations.