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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.
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Alan W. Gertler, Ph.D. 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. |
