Michael St. Denis, D.Env., Joe Roeschen - Revecorp Inc. I/M Solutions May 20 – 24, 2012 Sacramento, California

 To estimate the cost effectiveness of a high evaporative emitter identification and repair program in Denver by combining remote sensing data (used for high emitter identification), evaporative emission rates from MOVES, on-road hydrocarbon emissions distributions and repair data Revecorp Inc.

 The field data used in this study came from a cooperative effort of US EPA, Colorado Department of Public Health and Environment, Eastern Research Group, Envirotest, Applied Analysis, many others  This analysis was sponsored by Envirotest  This presentation has not be reviewed by any of the above and is not endorsed by any of the above  The opinions expressed are solely those of Revecorp Revecorp Inc.

 It is estimated that approximately half of all mobile source hydrocarbon emissions are from evaporative sources  Few I/M programs test for evaporative emissions (besides gas cap and OBDII) and therefore may not identify gross leakers  Current evaporative testing methods (purge and pressure testing) are difficult, intrusive, expensive  Few I/M programs performing these checks, failure rates are low for pressure testing in CA and not performed on vehicles  Can you find and repair these vehicles cost effectively?  Identify – Test – Repair - Retest Revecorp Inc.

 Study data are from work conducted over two summers in Colorado at an Air Care Colorado test station and on an adjacent highway  Remote Sensing was used to screen vehicles for high evaporative emissions Revecorp Inc.

 Study data are from work conducted over two summers in Colorado at an Air Care Colorado test station and on an adjacent highway  Remote Sensing was used to screen vehicles for high evaporative emissions  A representative sample of screened vehicles were tested for evaporative emissions in a portable SHED (g/15 min) Revecorp Inc.

 Study data are from work conducted over two summers in Colorado at an Air Care Colorado test station and on an adjacent highway  Remote Sensing was used to screen vehicles for high evaporative emissions  A representative sample of screened vehicles were tested for evaporative emissions in a portable SHED (g/15 min)  Remote Sensing performed ▪ Two measurements each on road at approximately 34 and 55 mph ▪ Three measurements at entrance gate at approximately 12 mph  Higher emitters were sent out for repair: ▪ Year 1 – Local repair shops performed repairs ▪ Year 2 – CDPHE performed repairs and additional SHED testing  Retest on-road and in P-SHED Revecorp Inc.

 Repairs made by local shops and CDPHE had nearly identical repair reductions and repair costs  Difference in repair effectiveness between 1997-/1998+  In general - repairs were inexpensive and effective Revecorp Inc.

 Average remote sensing speed in Colorado is 44 mph  Averaged capture rates for 34 and 55 mph for gross liquid leakers ~ 66% Revecorp Inc.

 Used MOVES to model evaporative emissions for the metro Denver fleet, by model year for June 2010 – average emissions by model year  Used 9 million on-road remote sensing measurements from Denver to determine the distribution of on-road fleet population by model year (1981 to 2010) and on-road HC emissions by model year (100 bins for each model year) Revecorp Inc.

 Used MOVES to model evaporative emissions for the metro Denver fleet, by model year for June 2010 – average emissions by model year  Used 9 million on-road remote sensing measurements from Denver to determine the distribution of on-road fleet population by model year (1981 to 2010) and on-road HC emissions by model year (100 bins for each model year)  Normalized each model years emissions rates Revecorp Inc.

 Used MOVES to model evaporative emissions for the metro Denver fleet, by model year for June 2010 – average emissions by model year  Used 9 million on-road remote sensing measurements from Denver to determine the distribution of on-road fleet population by model year (1981 to 2010) and on-road HC emissions by model year (100 bins for each model year)  Normalized each model years emissions rates  Applied the average modeled emission rate for each model year to the distributions of on-road emissions by model year to develop by model year distributions of evaporative HC emissions  Multiplied the emissions rates by the population for each 1/3000 of the fleet to get it’s overall emissions contribution: Average emission rate x fraction x population = contribution Revecorp Inc.

 Determined the emissions for the whole fleet – tons/year  Determined the emissions of highest emitters - top 0.5%, 1%, 2%, 4%, 6%, 8%, 10% of the fleet = E X%  Multiplied by the identification rate = 66%  Multiplied by the repair effectiveness rate by model year group (1997 and older = 88.2%, 1998 and newer = 98.7%) = E XReduced  Multiplied the number of vehicles “repaired” by the average cost of repair = $353  Cost effectiveness = Total cost / E XReduced Revecorp Inc.

 Assume repairs last three years  In Colorado, identification is no cost since there is an existing Remote Sensing Program which clean screens vehicles – revenue could be used to pay for repairs  The use of Remote Sensing to identify high evaporative emitters for repair and the repair of these vehicles appears to be a cost effective emissions control strategy Revecorp Inc.

Contact: Revecorp Inc. Michael St. Denis 5732 Lonetree Blvd Rocklin, CA (916) Revecorp Inc.