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.