Air quality modelling

In another review, Hoffert discussed the social motivations for modeling air quality for predictive purposes and elucidated the components of a model. Meteorologic factors were summarized in terms of windfields and atmospheric stability as they are traditionally represented mathematically. The species-balance equation was discussed, and several solutions of the equation for constant-diffusion coefficient and concentrated sources were suggested. Gaussian plume and puff results were related to the problems of developing multiple-source urban-dispersion models. Numerical solutions and box models were then considered. The review concluded with a brief outline of the atmospheric chemical effects that influence the concentration of pollutants by transformation. 

Vardoulakis S, Fisher BRA, Pericleous K, Gonzalez-Flesca N (2003) Modelling air quality in street canyons a review. Atmos Environ 37 155-182... 

Guideline on Air Quality Models, OAQPS Guideline Series,U. i. Environmental Protection Agency, Research Triangle Park, N.C., 1980. 

Fig. 3. Schematic diagram of a deterministic air quality model, showing the model components and interactions (1) where each of the boxes involves a large...

K. L. Demerjian, "Photochemical Diffusion Models for Air QuaUty Simulation Current Status," in Proceedings of the Conference on the State of the Art of Assessing Transportation-Kelated Air Quality Impacts, Oct. 22—24, 1975, Transportation Research Board, Washington, D.C., 1975. 

A. C. Lloyd and co-workers. Development of the EESTAR Photochemical Air Quality Simulation Model and Its Evaluation Relative to the EARPP Data Base, Environmental Research and Technology Report, No. P-5287-500, West Lake Village, Calif., 1979. 

Air pollution meteorology came of age and, by 1980, mathematical models of the pollution of the atmosphere were being energetically developed. A start had been made in elucidating the photochemistry of air pollution. Air quality monitoring systems became operational throughout the world. A wide variety of measuring instruments became available. 

Sloane, C. S., and Tesche, T. W., "Atmospheric Chemistry Models and Predictions for Climate and Air Quality." Lewis Publishers, Chelsea, Ml, 1991. 

The initial direction of transport of pollutants from their source is determined by the wind direction at the source. Air pollutant concentrations from point sources are probably more sensitive to wind direction than any other parameter. If the wind is blowing directly toward a receptor (a location receiving transported pollutants), a shift in direction of as little as 5° (the approximate accuracy of a wind direction measurement) causes concentrations at the receptor to drop about 10% under unstable conditions, about 50% under neutral conditions, and about 90% under stable conditions. The direction of plume transport is very important in source impact assessment where there are sensitive receptors or two or more sources and in trying to assess the performance of a model through comparison of measured air quality with model estimates. 

Larsen (18-21) has developed averaging time models for use in analysis and interpretation of air quality data. For urban areas where concentrations for a given averaging time tend to be lognormally distributed, that is, where a plot of the log of concentration versus the cumulative frequency of occurrence on a normal frequency distribution scale is nearly linear,... 

In order to build new facilities or expand existing ones without harming the environment, it is desirable to assess the air pollution impact of a facility prior to its construction, rather than construct and monitor to determine the impact and whether it is necessary to retrofit additional controls. Potential air pollution impact is usually estimated through the use of air quality simulation models. A wide variety of models is available. They are usually distinguished by type of source, pollutant, transformations and removal, distance of transport, and averaging time. No attempt will be made here to list aU the models in existence at the time of this writing. 

Modeling a single parcel of air as it is being moved along allows the chemical reactions in the parcel to be modeled. A further advantage of trajectory models is that only one trajectory is required to estimate the concentration at a given endpoint. This minimizes calculation because concentrations at only a limited number of points are required, such as at stations where air quality is routinely monitored. Since wind speed and direction at the top and the bottom of the column are different, the column is skewed from the vertical. However, for computational purposes, the column is usually assumed to remain vertical and to be moved at the wind speed and direction near the surface. This is acceptable for urban application in the daytime, when winds are relatively uniform throughout the lower atmosphere. 

In time series of measurements of air quality and estimates of atmospheric concentration made by a model, residuals d can be computed for each location. The residual d is the difference between values paired timewise. 

The use of various statistical techniques has been discussed (46) for two situations. For standard air quality networks with an extensive period of record, analysis of residuals, visual inspection of scatter diagrams, and comparison of cumulative frequency distributions are quite useful techniques for assessing model performance. For tracer studies the spatial coverage is better, so that identification of meiximum measured concentrations during each test is more feasible. However, temporal coverage is more limited with a specific number of tests not continuous in time. 

For the air quality manager to place model estimates in the proper perspective to aid in making decisions, it is becoming increasingly important to place error bounds about model estimates. In order to do this effectively, a history of model performance under circumstances similar to those of common model use must be established for the various models. It is anticipated that performance standards will eventually be set for models. 

Seeing the success of the UNAMAP BBS, EPA s Office of Air Quality Planning and Standards started a BBS for information on regulatory models in June 1989. This has expanded to a BBS called TTN, Technology Transfer Network. This BBS, in Durham, NC, is reached on (919) 541-5742 and the system operator on (919) 541-5384. A part of this BBS called SCRAM, Support Center for Regulatory Air Models, contains model FORTRAN codes, model executable codes for use on personal computers, meteorological data, and in some cases model user s guides. Much of the information is downloaded in "packed" form, and software to unpack the files must also be downloaded from the bulletin board. 

Schere, K. L., and Demerjian, K. L., A photochemical box model for urban air quality simulation, in "Proceedings of the Fourth Joint Conference on Sensing of Environmental Pollutants." American Chemical Society, Washington, DC, 1978, pp. 427-433. 

Demerjian, K. L., and Schere, K. L., Application of a photochemical box model for O3 air quality in Houston, TX, in "Proceedings of Ozone/Oxidants Interactions with the Total Environment II." Air Pollution Control Association, Pittsburgh, 1979, pp. 329-352. 

Bowne, N. E., Validation and performance criteria for air quality models, in "Conference Papers, Second Joint Conference on Applications of Air Pollution Meteorology." American Meteorological Society, Boston, 1980, pp. 614-626. 

Shreffler, J. H., and Schere, K. L., "Evaluation of Four Urban-Scale Photochemical Air Quality Simulation Models." U.S. Environmental Protection Agency Pub. EPA-600/3-82-043. Research Triangle Park, NC, 1982. 

U.S. Environmental Protection Agency, Guideline on Ait Quality Models (Revised). EPA-450/4-80-023R. Office of Air Quality Planning and Standards. Research Triangle Park, NC, 1986. (NTIS Accession Number PB86-245 248.)... 

Szepesi, D. J., "Compendium of Regulatory Air Quality Simulation Models." Akademiai lOadd es Nyomda Vdllalat, Budapest, 1989. 

On a prospective basis, an agency can project its source composition and location and their emissions into the future and by the use of mathematical models and statishcal techniques determine what control steps have to be taken now to establish future air quality levels. Since the future involves a mix of existing and new sources, decisions must be made about the control levels required for both categories and whether these levels should be the same or different. 

Filing of applicants plans, specifications, air quality monitoring data, and mathematical model predictions. 

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