The EPA is initiating a systematic data collection designed to improve the methods and tools used by the Agency to estimate exhaust emissions as vehicles age. Data to be collected include vehicle type, vehicle characteristics, measurements of tailpipe exhaust emissions and measurements of typical driving behavior.
One of the main issues in the study of vehicle emissions is the difficulty in acquiring representative results. Major challenges include the diversity of technology, the highly variable nature of emissions, the complexity and expense of measurement, difficulty in acquiring and retaining engines or vehicles, and the array of external variables that influence emissions, ranging from temperature to driver behavior. In combination, these factors tend to limit the numbers of vehicles that can be included in a given study. Limited sample sizes in combination with high variability make emissions data challenging to interpret.
The collection is a survey, to be conducted by the Office of Transportation and Air Quality (OTAQ) in the Office of Air and Radiation (OAR). This study will be designed to develop and test novel screening, sampling and measurement procedures. These approaches promise to substantially reduce the cost of exhaust emissions measurement as well as to improve the accuracy of resulting estimates.
An innovative feature of this project will be the use of roadside remote-sensing measurements to construct a pool of vehicles from which vehicles can be sampled for purposes of recruitment and measurement using portable emissions measurement systems (PEMS) and portable activity measurement systems (PAMS). The acquisition of remote-sensing measurements for hydrocarbons, carbon-monoxide, and oxides of nitrogen will provide an index of emissions for all vehicles prior to sampling and recruitment for more intensive measurement. The index is expected to facilitate recruitment of vehicles with an emphasis on rare high-emitting vehicles, and provide a means to appropriately relate measured vehicles to the overall fleet.
Research questions for the project include: (1) can remote-sensing be used as a reliable index of emissions across the range of emissions? (2) is it feasible to measure start emissions using portable instruments?, (3) can the emissions index used for recruitment also serve as a means to estimate potential non-response bias? and (4) how do numbers of vehicle starts differ between the work week and the weekend?
We plan to collect remote-sensing measurements on approximately 30,000 vehicles, and from this pool, to recruit approximately 250 vehicles for measurement. Tailpipe emissions will be measured over two days under various driving conditions, and vehicle activity under typical conditions over a period of three months. Participation in the program will be voluntary. The target population for the project will include light-duty cars and trucks certified to Tier 2 (Bin 5) or equivalent LEV-II standards (LEV).
The information collection will involve 850 respondents, requiring 1,213 hours to complete at a total cost to those respondents of $32,247. For the agency, the collection will require 5,578 hours to complete at a total cost of $641,809.
The EPA is initiating a systematic data collection designed to improve the methods and tools used by the Agency to estimate exhaust emissions as vehicles age. This is a new collection.
On behalf of this Federal agency, I certify that the collection of information encompassed by this request complies with 5 CFR 1320.9 and the related provisions of 5 CFR 1320.8(b)(3).
The following is a summary of the topics, regarding the proposed collection of information, that the certification covers:
(i) Why the information is being collected;
(ii) Use of information;
(iii) Burden estimate;
(iv) Nature of response (voluntary, required for a benefit, or mandatory);
(v) Nature and extent of confidentiality; and
(vi) Need to display currently valid OMB control number;
If you are unable to certify compliance with any of these provisions, identify the item by leaving the box unchecked and explain the reason in the Supporting Statement.