1. Empirical Understanding of Traffic Data Influencing Roadway PM 2.5 Emission Estimate NSF-UC 2012-2013 Academic-Year REU Program Faculty Mentor Heng Wei, Ph.D., P.E. Associate Professor Director, ART-Engines Lab School of Advanced Structures University of Cincinnati GRA Mentors Mr. Zhuo Yao Mr. Hao Liu Mr. Qingyi Ai Undergraduate Researchers Mr. Zachary Johnson (Sr. M.E.) Mr. Charles Justin Cox (Sr. E.E.)

2. Background Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 2

3. What is PM 2.5 ? 3 Background [1]

4. PM 2.5, Current Models & Methods PM 2.5 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) 4 Background

5. Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 5

6. Problem Statement Regional Air Quality Index Concerns Cincinnati and PM 2.5 Contribution of On-road Transportation Activity to PM 2.5 Emission: 6 Problem Statement Current Location [2]

7. Background Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 7

8. Goals & Objectives Goal: Gain insights on how dynamic traffic operating conditions affect the PM 2.5 emission estimation; Objectives: Design and plan to collect traffic and PM 2.5 ; Model data using VEVID, and REMCAN then compare results to the EPA’s MOVES model. Develop regression model to predict the emission of PM 2.5 ; 4 Goals & Objectives

9. Design and Plan of Field Data Collection 9 Goals & Objectives

10. 10

11. Background Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 11

12. Methodology 12 Methodology

13. Background Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 13

14. 14 Results: PM 2.5 Results

15. 15 Results: PM 2.5 Results

16. 16 MOVES and Field Data Comparison Results: PM 2.5 Results

17. Background Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 17

18. Vehicle Traffic on October 3 rd and October 9 th 18 Results: Field Data

19. 19 Pollutant Emissions and Meteorological Results Results: Field Data Arrow direction denotes the direction in which wind is moving. 90 Degrees: North 180 Degrees: West 270 Degrees: South 0/360 Degrees: East

20. 20 Operating Mode Distribution Results Results: Field Data [2] =v x [1.1a + 9.81 x grade(%)+ 0.132]+ 0.000302 x v 3 Cars VSP = v x [a + 9.81 x grade(%) + 0.09199] + 0.000169 x v 3 Trucks

21. Background Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 21

22. Regression Modeling PM 2.5 = 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 Basic Regression Equation Example Our Regression Equation Example Results: Regression Modeling

23. 23 Results: Regression Modeling Comparing Linear, Quadratic, and Polynomial Linearization Results VariableP-Value All Vehicles0.72 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/m 3 ) 0.45 Regression TypeR-squaredTerms Linear Quadratic Polynomial 0.107 0.59 0.863 8 45 165

24. Background Problem Statement Goals and Objectives Methodology Results - PM 2.5 Results - Field Data - Regression Modeling Conclusions 24

25. Conclusions –Our method of PM 2.5 capture successfully models an increase in PM 2.5 pollutants as traffic increases. –Our field results are 6 orders of magnitude (10 6 ) 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 PM 2.5 pollutants. –Vehicle speeds above 50mph are placed into the same Operating Mode and therefore reducing accuracy with higher speeds. 25 Conclusions

26. Citations 1.“Basic Information” EPA. Environmental Protection Agency, n.d. Web. 03 Dec. 2012. http://www.epa.gov/pm/basic.html. 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

27. Thankyou. Dr.HengWeiZhuoYaoHao LiuQingyiAiKristenStrominge rDr.UrmilaGhiaDr.KirtiGhia Dr.DariaNarmoneva …and to the REU-program