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Empirical Understanding of Traffic Data
Influencing Roadway PM2.5 Emission Estimate
NSF-UC 2012-2013
Academic-Year REU Program
Faculty Mentor
GRA Mentors
Heng Wei, Ph.D., P.E.
Mr. Zhuo Yao
Associate Professor
Mr. Hao Liu
Director, ART-Engines Lab Mr. Qingyi Ai
School of Advanced
Structures
University of Cincinnati
Undergraduate Researchers
Mr. Zachary Johnson (Sr. M.E.)
Mr. Charles Justin Cox (Sr.
E.E.)
Background
2
What is PM2.5?
Background
[1]
3
PM2.5, Current Models & Methods
PM2.5
• Long term vs short term effects
Complexity of modeling pollutants
• Number of models (CALINE4,CAL3QHC,etc.)
• Rapidly changing traffic conditions
• Difficulty getting accurate traffic data into MOVES
Modeling methods used
• Vehicle Video-Capture Data Collector (VEVID)
• Rapid Traffic Emission and Energy Consumption
Analysis (REMCAN)
• Motor Vehicle Emission Simulator (MOVES)
Background
4
Problem Statement
5
Problem Statement
• Regional Air Quality Index Concerns
• Cincinnati and PM2.5
• Contribution of On-road Transportation Activity to PM2.5 Emission:
Current Location
[2]
Problem Statement
6
Goals and Objectives
7
Goals & Objectives
Goal:
• Gain insights on how dynamic traffic operating conditions
affect the PM2.5 emission estimation;
Objectives:
• Design and plan to collect traffic and PM2.5;
• Model data using VEVID, and REMCAN then compare
results to the EPA’s MOVES model.
• Develop regression model to predict the emission of
PM2.5;
4
Goals & Objectives
Design and Plan of Field Data Collection
9
Goals & Objectives
10
Methodology
11
Methodology
12
Methodology
Results
PM2.5 Results
13
PM2.5 Data Attained Through Field Collection
14
Results: PM2.5 Results
PM2.5 Data Attained Through MOVES
15
Results: PM2.5 Results
MOVES and Field Data Comparison
16
Results: PM2.5 Results
Results
Field Data
17
Vehicle Traffic on October 3rd and October 9th
Results: Field Data
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Pollutant Emissions and Meteorological Results
90 Degrees: North
180 Degrees: West
270 Degrees: South
0/360 Degrees: East
Arrow direction
denotes the direction
in which wind is
moving.
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Results: Field Data
Operating Mode Distribution Results
Cars
𝑉𝑆𝑃 =v x [1.1a + 9.81 x
grade(%)+ 0.132]+
0.000302 x v3
Trucks
VSP = v x [a + 9.81 x
grade(%) + 0.09199] +
0.000169 x v3
[2]
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Results: Field Data
Results
Regression Modeling
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Regression Modeling
Basic Regression Equation Example
Our Regression Equation Example
PM2.5 = intercept+ X1*All Vehicles +
X2*Cars + X3*Trucks +
X4*WindSpeed(mph) + X5*Outside
Temperature (F) +X6*Wind + Direction in
Radians + X7*Relative Humidity +
X8*Wind Density (kg/m3).
22
Results: Regression Modeling
Comparing Linear, Quadratic, and Polynomial
Linearization Results
Regression Type
R-squared
Terms
Linear
0.107
8
Quadratic
0.59
45
Polynomial
0.863
165
𝒀(𝒎𝒊𝒄𝒓𝒐𝒈𝒓𝒂𝒎𝒔 𝒐𝒇 𝑷𝑴𝟐.𝟓 )
= 0.054 − 0.000015 ∗ 𝐴𝑙𝑙 𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 + 0.000016 ∗ 𝐶𝑎𝑟𝑠 + 0.000015
∗ 𝑇𝑟𝑢𝑐𝑘𝑠 − 0.0000267 ∗ 𝑊𝑖𝑛𝑑𝑆𝑝𝑒𝑒𝑑 𝑚𝑝ℎ − 0.000106
∗ 𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝐹 − 0.000163 ∗ 𝑊𝑖𝑛𝑑𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑖𝑛 𝑅𝑎𝑑𝑖𝑎𝑛𝑠
𝑘𝑔
− 6.127 ∗ 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐻𝑢𝑚𝑖𝑑𝑖𝑡𝑦 − 0.0403 ∗ 𝑊𝑖𝑛𝑑 𝐷𝑒𝑛𝑠𝑖𝑡𝑦
Variable
P-Value
𝑚3
All Vehicles
0.72
Results: Regression Modeling
Cars
Trucks
Wind Speed
Outside Temperature (°F)
Wind Direction (radians)
Relative Humidity
0.72
Wind Density (kg/m3)
0.45
0.68
0.36
0.24
0.10
0.08
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Conclusions
24
Conclusions
– Our method of PM2.5 capture successfully models an
increase in PM2.5 pollutants as traffic increases.
– Our field results are 6 orders of magnitude (106) less
than MOVES results. MOVES measures along 1 mile,
while our data is collected at one point.
– Organic Carbon (hydrocarbons) accounts for the
greatest of the PM2.5 pollutants.
– Vehicle speeds above 50mph are placed into the
same Operating Mode and therefore reducing
accuracy with higher speeds.
Conclusions
25
Citations
1.
“Basic Information” EPA. Environmental Protection Agency, n.d. Web. 03 Dec. 2012.
http://www.epa.gov/pm/basic.html.
2.
"Air Quality Index Forecasts." Air Quality Index Forecasts. N.p., n.d. Web. 06 Dec. 2012.
3.
Yao, Zhuo, Heng Wei, Tao Ma, Qingyi Ai, and Hao Liu. Developing Operating Mode
Distribution Inputs for MOVES Using Computer. Tech. no. 13-4899. N.p.: n.p., n.d. Web. 3
Dec. 2012.
26
Thankyou.
Dr.HengWeiZhuoYaoHao
LiuQingyiAiKristenStrominge
rDr.UrmilaGhiaDr.KirtiGhia
Dr.DariaNarmoneva
…and to the REU-program