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Traffic Data and Its Use in Air Quality Analysis
Citation:
kimbrough, s. AND J. Hirt. Traffic Data and Its Use in Air Quality Analysis. To be Presented at North American Travel Monitoring Exposition and Conference (NATMEC): Improving Traffic Data Collection, Analysis, and Use, Dallas, TX, July 04 - 07, 2012.
Impact/Purpose:
Present research results at NATMEC
Description:
A study was conducted in Las Vegas, NV from mid-December 2008 to mid-December 2009 to characterize near road emissions and associated ambient concentrations at various distances from a major roadway as a function of meteorology and traffic. The air emissions and meteorological data were collected by the U.S. Environmental Protection Agency's National Risk Management Research Laboratory and the traffic data was recorded by the Nevada Department of Transportation (DOT). In an effort to determine how well actual measurements compare with model results, data from the study was input into two of the many models available for air quality analyses, the Motor Vehicle Emission Simulator (MOVES) and the AMSIEPA Regulatory Model (AERMOD). Both of these models are EPA approved models for mobile source emissions inventories and air quality dispersion modeling. This monitor-to-model comparison brought to light traffic data issues not easily resolved. This presentation focuses on the use of the traffic data for air quality analysis and modeling as well as the lessons learned. For the typical air quality analyst or modeler that uses traffic data in an air quality analysis, issues of concern are (1) Quantity: a single annual average daily traffic value versus IS-minute data or some other highly time resolved data set; (2) Quality: automated methods abound but how good are the devices and how often must they be calibrated; (3) Resolution: vehicle classification (technology) is problematic and assumptions must be made with regard to gasoline vehicles versus diesel vehicles; (4) Gaps: data gaps may be the result of a failed sensor(s), construction issues, vehicular drive cycle changes, communications connectivity, or some other issue; and (5) Data limitations: use and misuse of traffic data. For example, the quality of air quality measurements can be quantified in a variety of ways including method detection limit, calibration of the device at recommended intervals, and data quality objectives to name just a few. Similar quantifications are needed with regard to meteorological and traffic data in order that the air quality analyst or modeler may better understand these limitations as they relate to comparing measured values with modeled values.