Instrumentation and Data Analysis Design for Evaluating Mechanic Performance in Decentralized I/M Programs

Significant reductions in motor vehicle emissions are possible through the implementation of inspection and maintenance (I/M) programs. However, the potential benefits of I/M are obviously not achieved when specific inspection requirements are ignored or improperly performed. In addition, I/M benefits may be substantially reduced when improper repair procedures are used on vehicles which fail the test.

In order for the “theoretical” benefits of I/M to be achieved, certain program design and enforcement procedures are necessary. The use of instrumentation and data analysis methods capable of identifying individuals who are improperly performing inspections and repairs is critical. Under the I/M programs developed for use in the state of Alaska, data analysis techniques are employed which identify mechanics who are reporting invalid information about the vehicles they inspect, failing to repair an excessive number of vehicles, or reporting visual inspection failure rates that are much higher or much lower than the norm. Similar data analysis methods are being used under the California I/M program.

Based on analyses of currently operating programs, it is clear that modified instrumentation and data analysis techniques can further enhance the ability to identify inspectors and mechanics who need additional instruction or other remedial action. Progress in this area will significantly affect the air quality benefits of I/M.