empirical understanding of traffic data influencing roadway pm 2.5 emission estimate

Empirical Understanding of Traffic Data Influencing Roadway PM2.5 Emission Estimate

NSF-UC 2012-2013 Academic-Year REU Program

Faculty Mentor

Heng Wei, Ph.D., P.E.Associate Professor Director, ART-Engines LabSchool of Advanced StructuresUniversity of Cincinnati

GRA Mentors

Mr. Zhuo YaoMr. Hao LiuMr. Qingyi Ai

Undergraduate Researchers

Mr. Zachary Johnson (Sr. M.E.)Mr. Charles Justin Cox (Sr. E.E.)

2

BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results- Field Data- Regression Modeling

Conclusions

3

What is PM2.5?

Background[1]

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PM2.5, Current Models & MethodsPM2.5

• 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

5

BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results- Field Data- Regression Modeling

Conclusions

Problem Statement• Regional Air Quality Index Concerns• Cincinnati and PM2.5

• Contribution of On-road Transportation Activity to PM2.5 Emission:

6Problem Statement

Current Location

[2]

7

BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results - Field Data- Regression Modeling

Conclusions

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

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Design and Plan of Field Data Collection

Goals & Objectives

10

11

BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results - Field Data- Regression Modeling

Conclusions

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Methodology

Methodology

13

BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results - Field Data- Regression Modeling

Conclusions

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Data Attained Through Field Collection

Results: PM2.5 Results

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Results: PM2.5 Results

Data Attained Through MOVES

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MOVES and Field Data Comparison

Results: PM2.5 Results

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BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results - Field Data- Regression Modeling

Conclusions

Vehicle Traffic on October 3rd and October 9th

18Results: Field Data

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Pollutant Emissions and Meteorological Results

Results: Field Data

Arrow direction denotes the direction in which wind is moving.

90 Degrees: North180 Degrees: West270 Degrees: South0/360 Degrees: East

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Operating Mode Distribution Results

Results: Field Data

[2]

𝑉𝑆𝑃 =v x [1.1a + 9.81 x grade(%)+ 0.132]+ 0.000302 x v3

Cars

VSP = v x [a + 9.81 x grade(%) + 0.09199] + 0.000169 x v3

Trucks

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BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results- Field Data- Regression Modeling

Conclusions

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Regression Modeling

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).

Basic Regression Equation Example

Our Regression Equation Example

Results: Regression Modeling

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Results: Regression Modeling

Comparing Linear, Quadratic, and Polynomial Linearization Results

𝒀 (𝒎𝒊𝒄𝒓𝒐𝒈𝒓𝒂𝒎𝒔𝒐𝒇 𝑷𝑴𝟐 .𝟓)=0.054−0.000015∗ 𝐴𝑙𝑙 h𝑉𝑒 𝑖𝑐𝑙𝑒𝑠+0.000016∗𝐶𝑎𝑟𝑠+0.000015∗𝑇𝑟𝑢𝑐𝑘𝑠−0.0000267∗𝑊𝑖𝑛𝑑𝑆𝑝𝑒𝑒𝑑 ( h𝑚𝑝 )−0.000106∗𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (𝐹 )−0.000163∗𝑊𝑖𝑛𝑑𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑖𝑛𝑅𝑎𝑑𝑖𝑎𝑛𝑠−6.127∗𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒𝐻𝑢𝑚𝑖𝑑𝑖𝑡𝑦−0.0403∗𝑊𝑖𝑛𝑑𝐷𝑒𝑛𝑠𝑖𝑡𝑦 (𝑘𝑔𝑚3 )Variable P-ValueAll Vehicles 0.72

Cars 0.72

Trucks 0.68

Wind Speed 0.36

Outside Temperature (°F) 0.24

Wind Direction (radians) 0.10

Relative Humidity 0.08

Wind Density (kg/m3)  0.45

Regression Type R-squared Terms

Linear

Quadratic

Polynomial

0.107

0.59

0.863

8

45

165

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BackgroundProblem StatementGoals and ObjectivesMethodologyResults

- PM2.5 Results- Field Data- Regression Modeling

Conclusions

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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

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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.

Thankyou.

Dr.HengWeiZhuoYaoHaoLiuQingyiAiKristenStromingerDr.UrmilaGhiaDr.KirtiGhiaDr.DariaNarmoneva …and to the REU-program