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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 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. 19 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] 20 Results: Field Data Results Regression Modeling 21 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 23 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