ARB Research Seminar

This page updated July 26, 2013

Development and Validation of a Predictive Model to Assess the Impact of Coastal Zone Emissions on Urban Scale Air Quality

Alan W. Gertler, Ph.D., Desert Research Institute, Nevada

January 12, 2006
Cal EPA Headquarters, 1001 "I" Street, Sacramento, CA



Many of the urban areas classified as non-attainment for ambient air pollutants are located in coastal areas (e.g., Los Angeles and San Diego). Much of the uncertainty in developing an understanding of the causes of reduced air quality in these areas is due to uncertainty in the emissions inventories; however, in coastal zones the situation is confounded by the complex meteorology associated with the land/sea interface.

Given the difficulty of predicting pollutant transport and dispersion in coastal environments, the primary objective of our current SERDP sponsored study was to develop and validate a prognostic modeling system capable of assessing the impact of coastal DoD operations (e.g., emissions from ships, aircraft, training, etc.) on air quality. This included the determination of primary and secondary pollutant concentrations, as well as their spatial and temporal variation.

Traditional approaches to address this need have included the application of statistical, Gaussian dispersion, single chemical box, Lagrangian, and Eulerian models. Each of these models has distinct advantages and disadvantages. As part of our study, we have developed a meteorological air quality model with in-line chemistry that combines the advantages of the Eulerian and Lagrangian models. A Lagrangian model has the potential advantage in that it may calculate more accurately the advection and dispersion from various sources and allow more complete characterization of the impact of turbulence on the transport of air pollutants. An Eulerian model allows for the treatment of the chemical interactions of all air parcels within a grid square.

Thus, in order to achieve the objective of predicting pollutant transport, dispersion, and transformation in complex environments, the hybrid modeling system is composed of four components:

  • An emissions processing system that allows for area sources, stationary sources, and mobile sources (vehicles, hips, airplanes, etc.).
  • A prognostic meteorological component that uses operationally available forecasts for the domain of interest.
  • A prognostic hybrid Lagrangian random particle dispersion component-model that uses output from the meteorological component to simulate the transport and dispersion of pollutants emanating from specified emission sources.
  • An Eulerian chemical component using the RADM mechanism to evaluate chemical transformations occurring during the period of pollutant transport and dispersion.

This seminar describes the development of the hybrid model and, as part of the model validation task, presents simulation results for a case study in the San Diego area.

Speaker Biography

Alan W. Gertler is a Research Professor at the Desert Research Institute, Reno, NV. His research includes studies of atmospheric chemistry with particular emphasis on the impact of mobile sources on the environment and the development of new methods to attribute observed pollutant levels to specific sources. He has investigated discrepancies between observed and predicted automotive emission factors, measuring CO, CO2, NOx, speciated NMHC, dioxins and furans, and organic and inorganic speciated PM2.5 and PM10 emissions from on-road vehicles, and assessing the impact of mobile source emissions on ambient particulate levels. Other research at has included investigating the sources and composition of ambient particulates, characterization of factors affecting the rate and mechanism of SO2 and NOx, wet and dry deposition processes, studies of chemical processes leading to "gas-to-particle" conversion, and the effects of acids and their precursors on materials.

Dr. Gertler was the recipient of the "Hope for the Future of a Sustainable World 2001" award from the International Union of Air Pollution Prevention & Environmental Protection Agencies and the International Academy of Science.

He received a B.S. in Chemistry from SUNY Albany and a Ph.D. in Physical Chemistry from UCLA.

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