ABSTRACT
The research presented in this paper employs the Step Method of Inspection/Maintenance (I/M) program evaluation to estimate the emissions reduction for an Atlanta I/M program. Stedman et al. (Stedman, D. H.; Bishop, G. A.; Aldrete, P.; Slott, R. S. Environ. Sci. Technol. 1997, 31, 927-931) introduced the Step Method of evaluation when they presented the results of a 1995 Denver I/M program evaluation. The research presented here replicates the original Denver Step Method analysis for a 1997 Atlanta I/M program. This evaluation was conducted separately for the nine outlying Atlanta counties and the four counties that are closest to the center of the city. The results of the analysis are similar to those found by Stedman et al. in Denver. While the Denver carbon monoxide (CO) weighted program benefit was 6.9%, the Atlanta area CO weighted program benefit is found to be 11.5% and 4.9% for the nine-county and four-county Atlanta areas, respectively. We conclude that the 1997 I/M program change in Atlanta yielded a noteworthy and observable change in fleet emissions.
Subject(s)
Carbon Monoxide/analysis , Environmental Monitoring/methods , Hydrocarbons/analysis , Motor Vehicles/standards , Vehicle Emissions/prevention & control , Colorado , Georgia , Maintenance , Program Evaluation , United States , United States Environmental Protection Agency , Vehicle Emissions/analysisABSTRACT
On-road remote sensing data is an increasingly popular source of evaluation information for vehicle inspection/maintenance (I/M) programs. This article conducts one such remote sensing data evaluation for the Atlanta, Georgia, I/M program. The reference method involves comparing emissions differences in I/M and non-I/M fleet vehicles with those predicted by a regulatory computer model. Assuming that on-road emissions differences represent observed effectiveness and model-predicted emissions differences represent effectiveness goals, the Atlanta enhanced I/M program appears to be achieving 83% of its targeted emissions reductions. The method compares favorably with other remote sensing evaluation methods in its ability to be applied over time and its relatively small sample size requirement. The chief limitation to the approach is its reliance on a representative non-I/M fleet, which may differ in characteristics for which controls are difficult to locate. Such potential confounding factors include discrepancies in maintenance trends, socioeconomic conditions, and vehicle quality.