Empirical Understanding of Traffic Data Influencing Roadway PM 2.5 Emission Estimate

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

Progress Report Presentation

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. Hao LiuMr. Zhuo YaoMr. Qingyi Ai

Undergraduate Students

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

Problem Statement• Regional Air Quality Concerns from PM2.5

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

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= Minimal Concern

= No Concern= Moderate Concern

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Goals & Objectives

Goals:• Gain insights on how dynamic traffic operating conditions affect the PM2.5

emission estimation;• Gain concept and experience to experiment design and field data collection.

Objectives:• To read the 3 article about the basic traffic operational parameters and emission

characteristics;• To familiarize with the field data collection equipment/instruments by attending a

data collection demonstration; • Learn how to use USEPA recommended emission and dispersion models using

MOVES and AIRMOD modeling software data successfully• Enhance analytical and modeling skills by creating and presenting our final report.

Tasks & Tentative Schedule1. Understanding basic traffic flow fundamentals and emission characterization;

2. Designing and planning of field data collection;

3. Participating in the field data collection;

4. Data acquisition and processing;

5. Data analysis and modeling;

6. Final presentation, report and summary

Tasks Wk1 (Sept. 24)

Wk2 Wk3Oct.08

Wk4 Wk5 Wk6 Wk7 Wk8 Wk9 Wk10 Wk11(Dec. 6)

Task 1

Task 2

Task 3

Task 4

Task 5

Task 6

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Understanding basic traffic flow fundamentals and emission characterization (Task 1)

WHAT IS PM2.5?• Particulate matter less than 2.5 micrometers in diameter• Air pollutant

LONG TERM VS. SHORT TERM AFFECTS

WHY ARE TEST CONTINUOUSLY MADE WITH NO ACTION?• Increasing complexity of traffic conditions

MONITORING PM2.5• AERMOD & MOVES

Designing and Planning of Field Data Collection –Task 2

• Loop Detectors

• Distance Markings

Participating in Field Data Collection - Task 3

• Amount of data• Greater

understanding• Noting the traffic

events

Illustrate what you plans are, based on your current understanding

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CITATIONS1. Chen, Hao., Bai, Song., Eisinger, Douglas., Niemeier, Deb., and Claggett, Michael. (2009). “Predicting Near-Road PM2.5 Concentrations Comparative Assessment of CALINE4, CAL3QHC, and AERMOD”. Environment 2009, pp. 26-37.

2. Karner, Lexa., Eisinger, Douglass., and Niemeier, Deba. (2010). “Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data”. ENVIRONMENTAL SCIENCE & TECHNOLOGY, /VOL. 44, NO. 14, pp. 5334-5343.

3. Smit, Robin., Ntziachristos, Leonidas., Boulter, Paul. (2010). “Validation of road vehicle and traffic emission models e A review and meta-analysis). Atmospheric Environment, 44 pp. 2943-2953.

4. Riediker, Michael. (2007). “Cardiovascular Effects of Fine Particulate Matter Components in Highway Patrol Officers.” Inhalation Toxicology, Supplement 1, Vol. 19, p99-105.

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Thank You:

Faculty Advisor:

Heng Wei, Ph.D., P.E.

Graduate Research Assistants– Mr. Hao Liu– Mr. Zhuo Yao– Mr. Qingyi Ai

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Comments or Questions?