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Empirical Understanding of Traffic Data Influencing Roadway PM 2.5 Emission Estimate

Empirical Understanding of Traffic Data Influencing Roadway PM 2.5 Emission Estimate. NSF-UC 2012-2013 Academic-Year REU Program. GRA Mentors. Faculty Mentor. Undergraduate Researchers. Heng Wei, Ph.D., P.E. Associate Professor Director, ART-Engines Lab School of Advanced Structures

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Empirical Understanding of Traffic Data Influencing Roadway PM 2.5 Emission Estimate

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  1. Empirical Understanding of Traffic Data Influencing Roadway PM2.5 Emission Estimate NSF-UC 2012-2013 Academic-Year REU Program GRA Mentors Faculty Mentor Undergraduate Researchers Heng Wei, Ph.D., P.E. Associate Professor Director, ART-Engines Lab School of Advanced Structures University of Cincinnati Mr. Zhuo Yao Mr. Hao Liu Mr. Qingyi Ai Mr. Zachary Johnson (Sr. M.E.) Mr. Charles Justin Cox (Sr. E.E.)

  2. Background Problem Statement Goals and Objectives Methodology Results - PM2.5Results - Field Data - Regression Modeling Conclusions

  3. What is PM2.5? [1] Background

  4. 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

  5. Background Problem Statement Goals and Objectives Methodology Results - PM2.5 Results - Field Data - Regression Modeling Conclusions

  6. Problem Statement Current Location • Regional Air Quality Index Concerns • Cincinnati and PM2.5 • Contribution of On-road Transportation Activity to PM2.5 Emission: [2] Problem Statement

  7. Background Problem Statement Goals and Objectives Methodology Results - PM2.5 Results - Field Data - Regression Modeling Conclusions

  8. 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; Goals & Objectives

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

  10. Background Problem Statement Goals and Objectives Methodology Results - PM2.5 Results - Field Data - Regression Modeling Conclusions

  11. Methodology Methodology

  12. Background Problem Statement Goals and Objectives Methodology Results - PM2.5Results - Field Data - Regression Modeling Conclusions

  13. Data Attained Through Field Collection Results: PM2.5 Results

  14. Data Attained Through MOVES Results: PM2.5 Results

  15. MOVES and Field Data Comparison Results: PM2.5 Results

  16. Background Problem Statement Goals and Objectives Methodology Results - PM2.5 Results - Field Data - Regression Modeling Conclusions

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

  18. 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. Results: Field Data

  19. 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] Results: Field Data

  20. Background Problem Statement Goals and Objectives Methodology Results - PM2.5Results - Field Data - Regression Modeling Conclusions

  21. Regression Modeling Basic 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). Our Regression Equation Example Results: Regression Modeling

  22. Comparing Linear, Quadratic, and Polynomial Linearization Results Results: Regression Modeling

  23. Background Problem Statement Goals and Objectives Methodology Results - PM2.5 Results - Field Data - Regression Modeling 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 • “Basic Information” EPA. Environmental Protection Agency, n.d. Web. 03 Dec. 2012. http://www.epa.gov/pm/basic.html. • "Air Quality Index Forecasts." Air Quality Index Forecasts. N.p., n.d. Web. 06 Dec. 2012. • 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.HengWeiZhuoYaoHaoLiuQingyiAiKristenStromingerDr.UrmilaGhiaDr.KirtiGhiaDr.DariaNarmoneva …and to the REU-program

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