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(AMD) ANALYSIS OF AIR QUALITY DATA NEAR ROADWAYS USING A DISPERSION MODEL
Citation:
VENKATRAM, A., V. ISAKOV, E. D. THOMA, AND R. W. BALDAUF. (AMD) ANALYSIS OF AIR QUALITY DATA NEAR ROADWAYS USING A DISPERSION MODEL. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 41(40):9481-9497, (2007).
Impact/Purpose:
The objective of this task is to improve EPA's ability to accurately predict the concentrations and deposition of air pollutants in the atmosphere that are known or suspected to cause cancer or other serious health effects to humans, or adverse environmental effects. It is an essential component of EPA's National Air Toxics Assessment (NATA), which seeks to identify and quantify the concentrations and sources of those hazardous air pollutants which are of greatest potential concern, in terms of contribution to population risk. It is a major contributor to NERL's Air Toxics Research Program.
"Air toxics" or "hazardous air pollutants" (HAPs) is a category that covers a large variety of chemicals, which range from relatively non reactive to extremely reactive; can exist in the gas, aqueous, and/or particle phases; display a large range of volatilities; experience varying deposition velocities, including in some cases revolatilization; and are emitted from a wide variety of sources at a large variety of different scales. In addition, concentrations of air toxics are needed by regulators for both short (days) as well as long (up to a year) time scales. These requirements challenge our current capabilities in air quality models far beyond the needs for other pollutants, such as ozone. The specific work being done under this task involves 1.) developing and testing chemical mechanisms which are appropriate for describing the chemistry of air toxics; 2.) incorporating these chemical and physical mechanisms into EPA's CMAQ modeling system and applying the model at a variety of scales; and 3.) developing the methods for using models to predict HAPs concentrations at subgrid or neighborhood scales; and 4.) using these tools to assess the magnitude and variability of concentrations to which urban populations are exposed.
Description:
We used a dispersion model to analyze measurements made during a field study conducted by the U.S. EPA in July-August 2006, to estimate the impact of traffic emissions on air quality at distances of tens of meters from an 8 lane highway located in Raleigh, North Carolina. The air quality measurements consisted of long path optical measurements of NO at distances of 7 m and 17 m from the edge of the highway. Sonic anemometers were used to measure wind speed and turbulent velocities at 6 m and 20 m from the highway. Traffic flow rates were monitored using traffic surveillance cameras. The dispersion model (Venkatram, 2004) explained over 60% of the variance of the observed path averaged NO concentrations, and over 90% of the observed concentrations were within a factor of two of the model estimates. Sensitivity tests conducted with the model indicated that the traffic flow rate made the largest contribution to the variance of the observed NO concentrations. The meteorological variable that had the largest impact on the near road NO concentrations was the standard deviation of the vertical velocity fluctuations, w σ . Wind speed had a relatively minor effect on concentrations. Furthermore, as long as the wind direction was within plus or minus 45º from the normal to the road, wind speed had little impact on near road concentrations. The measurements did not allow us to draw conclusions on the impact of traffic induced turbulence on dispersion. The analysis of air quality and meteorological observations resulted in plausible estimates of on-road emission factors for NO. The performance of the dispersion model in explaining NO observations suggests that appropriate air quality measurements coupled with dispersion modeling can be used to estimate on-road emission factors of other species, such as VOCs.