NSF PROJECT SUMMARY REPORT
Empirical Understanding of Traffic Data Influencing Roadway PM2.5 Emission Estimate
Submitted To
The 2012 Academic Year NSF AY-REU Program
Part of
NSF Type 1 STEP Grant
Sponsored By
The National Science Foundation
Grant ID No.: DUE-0756921
College of Engineering and Applied Science
University of Cincinnati
Cincinnati, Ohio
Prepared By
Justin Cox, Sr., Electrical Engineering,
Zachary D. Johnson, Sr., Mechanical Engineering
Faculty Advisor:
Dr. Heng Wei
REU Faculty Mentors:
Mr. Zhuo Yao
Mr. Hao Liu
Mr. Qingyi Ai
University of Cincinnati
September 17 – December 6, 2012
The main goal of this research project was to gain insights on how dynamic traffic operating
conditions affect the PM2.5 emission estimation. Other goals include: gain concept and experience to
experiment design and field data collection and to develop a regression model from field data that was
collected. This was done through meeting objectives that include researching project relevant articles
covering traffic operation and emission characteristics, becoming experienced with the data collection
instruments, and by modeling the data using VEVID, REMCAN, and MOVES traffic modeling software.
These objectives were further broken down into several tasks for a final result.
Understanding basic traffic flow fundamentals and emission characterization was the first task
accomplished during this project. Different literature was read for better understanding of the subject
matter. A lot of new things were discovered in this task of our research project. Some of which will be
talked about in the following paragraphs.
Particulate matter (PM) in general (also called fine particles) is a mixture of either tiny solid particles
or liquid particles or a mixture of both. PM2.5 is particulate matter that is less than or equal to a diameter of
2.5 μm (micro-meters). Particularly because of its very small size, this pollutant can be inhaled through
the respiratory tract and cause adverse health impacts. Besides traffic conditions, wind speed,
temperature, and wind direction also affect near-road PM2.5 concentrations. Other contributions in traffic
air pollution include the traffic volume, fleet composition, as well as factors such as the presence of a
noise barrier.
Depending on how much an individual is exposed to the emission of PM 2.5 is the basis for how much
it can affect someone’s health. Long-term exposure to concentrations of PM2.5 increases the risk of acute
and chronic respiratory infection, lung cancer, arteriosclerosis, and other cardiovascular diseases. Short
term exposure can make worse existing problems that are related to pulmonary and cardiovascular
diseases.
Tests for PM2.5 emissions have been going on for many years now in a variety of different locations
because traffic conditions continue to get more complex and models continue to be made which can
make up for the complexity. Also, studies are often conducted for specific local traffic situations over short
periods of time (hours/days compared to the necessary weeks or months) so studies only partially
validate traffic emission models. Therefore, information from single studies has not been able to be used
for an overall evaluation of PM2.5. Ultimately, the goal in researching this topic is to get a sufficient
correlation between traffic conditions and the emission of the particulate matter.
Due to an increasing number of vehicle types and fuel types, models for monitoring the emission of
PM2.5 are becoming more comprehensive over time and they also are made to show results for a higher
quantity of vehicle (thousands as compared to previous models used before the 1980s which were meant
for ~100). Many households (approximately 11%) are located within 100 meters of 4-lane highways,
where vehicle emissions are the major source of PM2.5.
After all of the background information in the literature review was collected for this research project,
the next task was to design and plan field data collection. The main location for the field data collection
site was at a highway located on interstate 275. This location was selected particularly because of access
to a loop detector (AKA automatic traffic recorder). The Ohio department of transportation sponsors this
ongoing UC project and supplies the loop detector. It measures how many cars pass, the speed, and the
classification of the car to name a few things. This location was also convenient because of the traffic flow
and air quality concerns in the area. Other equipment used for the design and plan of field collection data
include a meteorology station (shown in Figure 1), video cameras, and air samplers on each side of the
highway. The meteorology station has a weather tracker that measures wind speed, temperature,
direction of wind, and humidity. Video cameras were used to record the incoming and outgoing traffic.
The video cameras are able to stay on for so long (14 hour data collecting periods) because of a
generator which is fueled with gasoline. Lastly, an air sampler, which also samples carbon dioxide, is
what actually samples the PM2.5.
Task three includes the actual field participation in the data collection. Figure 2 Research team
standing next to PM2.5 sampler at the data collection site. A time span of three weeks was spent
collecting data. The activity required included setting up and tearing down the equipment, making sure
the equipment is collecting correctly, keeping the equipment secure, and recording any significant traffic
events. Figure 3 is an example of the team learning new information about the PM 2.5 sampler at the data
collection site.
Figure 1 Meteorology Station
Figure 2 Research team standing next to PM 2.5 sampler
Figure 3 Graduate Mentors explaining how PM 2.5 sampler works
The fourth task of this research project involved preparing camcorder data and also cutting the videos
into chapters. Preparing the camcorder data was done with the help of Mr. Zhuo Yao, who is a graduate
mentor on the team. The first step to this task was to find days where sufficient field data was collected.
This was achieved with a combination of camcorder results and air sampler results. The goal was to find
days where good quality video was recorded for the hours of 16:30 to 18:30 (4:30 – 6:30 p.m.) This time
was convenient because this is a time where typically a lot of cars are on the road and therefore a higher
emission rate might be recorded. It was a requirement that adequate PM 2.5 results from the air sampler
were recorded as well for these times of the day. After the videos were observed and two days were
found that met this criteria using Microsoft Excel, video editing software was used to cut the videos into
smaller pieces. Two days were used (October 3 and October 9) and these videos were initially cut into
two hour periods from 16:30 to 18:30. Video results were used for traffic flow going east and west, so a
total of four two hour videos were edited. After this, the two hour videos were cut into five minute sections
which gave the videos better quality and these videos were used in REMCAN. One of the five minute
videos from each day for both directions were then cut into one minute videos for the convenience of
extracting data using VEVID. Figure 4 shows VEVID in use, which is a type of software developed by the
Civil Engineering department at UC.
The fifth task included the data analysis and modeling. Methods of data analysis and modeling for this
task were chose based on the traffic condition where data was collected. Because monitoring PM 2.5
concentrations cannot be done for all of these near-road regions, use of appropriate models with vehicle
and meteorological information to estimate near-road PM2.5 concentrations is important. There are
advantages and disadvantages of different models (such as CALINE4 and CAL3QHC), but for this
particular research project, VEVID, REMCAN, and MOVES were the appropriate models to use for data
analysis due to the rapid data input attribute. VEVID calculates the speed of the vehicle as well as their
classification, REMCAN is a rapid traffic emission and energy consumption analysis system, and MOVES
(Motor Vehicle Emission Simulator) generates emission rates.
The data gathered is analyzed using several applications, the first being VEVID. Graduate students
designed this program and it is a graphical user interface which uses distance markings on the highway
to calculate the speed of vehicles. Classification of the vehicle is determined by VEVID as well.
Figure 4 Field data collected using VEVID
The next application for the gathered data was for REMCAN. REMCAN stands for the computer
vision-based Rapid traffic Emission and Energy Consumption Analysis. REMCAN is an automated
computer vision-based computer system for ground truth (which allows image data to be related to real
features and materials on the ground) vehicle detection and tracking. This video acquisition module
enhances, splits, and resizes raw video data into a common standard size and length so MOVES can
read. It uses VEVID data to extract data for the purpose of vehicle speed calibration and validation. A
possible error for REMCAN might be that the angle at which vehicles are recorded can cause phantom
cars in other lanes. Also, the sun can cause shadows in other lanes as well.
With the data collected from REMCAN which includes operation mode and number of vehicles, the
MOVES input generation/conversion module generates the operating mode distribution. Volume
describes the total amount of vehicle traffic flow through the recorded data. Link Source allows the
MOVES program to identify the amount of car versus truck vehicles moving through the recorded data.
Operation mode is found initially in REMCAN with the use of Vehicle Specific Power (VSP). VSP reveals
the impact of vehicle operation conditions on emission and energy consumption estimates that are
dependent upon the speed roadway grade and acceleration or deceleration on the basis of the secondby-second cycles.
With the combinations of speed and VSP representing real-world vehicle operating mode, MOVES
adapted the 23 operating mode bins, plus additional operating modes for starts and evaporative
emissions.
Age distribution, meteorological data, fuel type, fuel supply, and formulation are the other inputs
MOVES uses in addition to the global inputs to project level emission analysis. MOVES results are based
on EPA standards. Further, a regression model was also found using REMCAN data. The significance of
a regression model is to predict the PM2.5 emission under different traffic conditions.
In conclusion, tolerances for linear regression model include error from the modeling software,
meteorological differences between the place of collection and the place of experimentation, and
averaging errors. Reduction in error is attempted using linear regression. Linear, quadratic, and
polynomial linearization techniques were used and the techniques indicate that the greater the amount of
terms in the model, the greater the accuracy is.
A collection of meteorological and PM2.5 emissions data was collected and modeled using VEVID,
REMCAN and MOVES traffic modeling software in order to gain insights on the relationship between
PM2.5 emissions and varying traffic levels. “MOVES” is maintained by the Environmental Protection
Agency and is used to predict emissions in the United States of America. The method of PM2.5 capture
instituted in this study successfully models an increase in PM2.5 pollutants as traffic increases. This can
be verified by the MOVES results displaying the same behavior, thus validating a linear relationship
between PM2.5 emissions and traffic flow, but also that MOVES and the E-BAM PM2.5 collector yield
similar results, but on different scales. The field results are 6 orders of magnitude (10 6) less than MOVES
results because MOVES measures along 1 mile, while our data is collected at one point.
Organic carbon (hydrocarbons) accounts for the greatest of the PM 2.5 pollutants. The carbons
represented 3 and 4 times the amounts of other pollutants (Brake wear and Tire wear) and this identifies
a key pollutant particulate that would be the most beneficial in reducing. The reason organic carbon is
the most prevalent is hypothesized to be due to the large amount of un-burnt fuel being emitted by each
vehicle.
Finally, within the MOVES vehicle speed inputs, vehicles traveling above 50mph are placed into the
same Operating Mode as vehicles traveling 100mph. This difference in gas being burnt is substantial and
therefore reduces the accuracy in the MOVES software when this difference is neglected due to the bin
limit. Further research into traffic and emissions models should include whether a bin limit reduces
MOVES software’s accuracy as well as repeating the use of the REMCAN at an arterial roadway data
collection site in order to validate REMCAN and the data collection techniques utilized in this study.