Predicting Air Quality

Trends in air pollutant concentrations can be predicted with simple empirical models based on atmospheric and laboratoiy data. Concentrations of nonreactive pollutants from point sources can be predicted vfith accuracy well within a factor of 2 predictions are more likely to be too high than too low, especially predictions of concentration peaks. Concentrations of reactive pollutants, such as ozone and other photochemical oxidants, can be predicted reasonably well with photochemical-diffusion models when detailed emission, air quality, and meteorolc c measurements are available most such predictions of air pollution in Los Angeles, California, have been accurate to within approximately 50% for ozone. Detailed performance analyses are found elsewhere in this chapter. 

Statistical models based on data correlations and on Markov chains are being actively developed and their fidelity evaluated by several research groups. Photochemical-diffusion models based on deterministic equations are also being developed, but because of their complexity will probably be used only as research tools for some time. 

As expressed in Equation 4-1, the spatial and temporal distributions of pollutants must be known to assess their damage to receptors. Traditionally, monitoring station measurements are used to estimate the concentrations for entire regions. Statutory mandates, however, require 

An air quality model is a method of relating air quality to emission under specific environmental conditions. There are many types of air quality models, and the purposes of this chapter are to describe models that are available for the analysis of photochemical oxidants and to give some information on how well the models perform. 

Because of the dominance of distributed sources over local single sources in the production of photochemical oxidants, point-source models are not discussed here. Related research regarding the measurement of diffusion or the development of atmospheric chemical submodels are not emphasized. Giapter 2 is devoted to the chemical processes that govern atmospheric transformation and removal, and this aspect of the models is not repeated here. 

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One of the purposes of this chapter is to add recent material to that collected in the reviews just described. In contrast with previous reviews, however, this chapter emphasizes the critical evaluation of performance. The sections that follow deal with objectives of models (from research to applied control systems), the elements of schemes for predicting air quality, specific methods of modeling, and the evaluation of prediction techniques. 

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

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

Boundary conditions and thermophysical properties, etc. are clearly specified (e.g., indoor air quality can be predicted only if the location and strength of the sources are known). 

Legislation enacted by both Canada and the United States (see the US-Canada Air Quality Accord, 1991) will, when implemented, reduce the North American emissions of sulphur dioxide by about 50% based upon the 1980 baseline. These projected emission fields have been appplied in the atmospheric source-receptor models that were described above, to provide a projected deposition field for acidic sulphate that would be expected (14). The predicted sulphate deposition fields have then subsequently been appUed in aquatic effects models that provide estimates of regional surface water acidification distributions (50). The regional acidification profiles have then been used in a model of fish species richness (51) that results in an estimate of the expected presence of fish species as compared to that expected in an unacidified case. 

Whether the prediction scheme is a simple chart, a formula, or a complex numerical procedure, there are three basic elements that must be considered meteorology, source emissions, and atmospheric chemical interactions. Despite the diversity of methodologies available for relating emissions to ambient air quality, there are two basic types of models. Those based on a fundamental description of the physics and chemistry occurring in the atmosphere are classified as a priori approaches. Such methods normally incorporate a mathematical treatment of the meteorological and chemical processes and, in addition, utilize information about the distribution of source emissions. Another class of methods involves the use of a posteriori models in which empirical relationships are deduced from laboratory or atmospheric measurements. These models are usually quite simple and typically bear a close relationship to the actual data upon which they are based. The latter feature is a basic weakness. Because the models do not explicitly quantify the causal phenomena, they cannot be reliably extrapolated beyond the bounds of the data from which they were derived. As a result, a posteriori models are not ideally suited to the task of predicting the impacts of substantial changes in emissions. 

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. 

At the heart of the problem of relating improvements in air quality to reductions in pollutant emission is a reliable method of prediction. Only with such a method can there be rational planning for air pollution control through regulation of transportation, indirect sources, and stationary sources. Decision-makers need it as a tool and must specify it in their regulations. Otherwise, their administration of an air quality plan would be based on sheer guesswork tempered by political negotiation. 

ELEMENTS OF AIR QUALITY PREDICTION SCHEMES Stractnrc of Determiiiistic Modeb... 

Before we examine the performance of various models for predicting ambient air quality, it is important to review the criteria for selecting... 

Much of the research work will add content to the model structures, but future applications demand simplifications that are oriented toward the nonspecialist user. One of the laigest obstacles to the effective use of air quality prediction schemes is the resolution of this apparent conflict. At least two steps can be taken by those who produce models to encourage applications and to aid the user ... 

Air quality control r ions (aqcr s), 128 Air quality prediction models, 195, 678-79 airshed photodiemical, 218 balance-equation, 205 box, 213-15, 219 classification of, 200-205 criteria for selecting, 219-20 deterministic, 203 dispersion, 205 explanation of, 1% for episode control, 202 for land-use planning, 201-2 for physiochemical transformations, 208-10... 

Models. See also Air quality prediction models Gas-transport models for assessing oxidant damage to v eta-tion, 554... 

While such calculations can be carried out in principle, they are in fact rarely possible in the detail needed for developing reliable air quality/emission source relationships for particulate pollution. Dispersion modeling however, is necessary to predict the air quality effects of a new source which is to be located in a region where air quality/emission source relationships are poorly understood. 

Using the validated dispersion model as the key tool in the strategy development, efforts turned to Step 5, Control Strategy Development. Table I presents predictions of 1987 particulate air quality based on future year emission data bases, and incorporates all of the identified inventory corrections, and emission growth projections. 

One of the central problems in air pollution research and control is to determine the quantitative relationship between ambient air quality and emission of pollutants from sources. Effective strategies to control pollutants can not be devised without this information. This question has been mainly addressed in the past with source-oriented techniques such as emission inventories and predictive diffusion models with which one traces pollutants from source to receptor. More recently, much effort has been directed toward developing receptor-oriented models that start with the receptor and reconstruct the source contributions. As is the case with much of air pollutant research, improvements in pollutant chemical analysis techniques have greatly enhanced the results of receptor modeling. 

In contrast, at downwind locations where the airshed ozone peaks are more typically encountered, reduction in VOC starting at point B without concurrent NOx control does not lead to the rapid decrease in 03 observed for DTLA indeed, even an 80% reduction in VOC alone is not predicted to reach the U.S. air quality standard of 0.12 ppm 03. In this case, control of NO(. reduces 03 more rapidly than comparable control of VOC. 

The box model is closely related to the more complex airshed models described below in that it is based on the conservation of mass equation and includes chemical submodels that represent the chemistry more accurately than many plume models, for example. However, it is less complex and hence requires less computation time. It has the additional advantage that it does not require the detailed emissions, meteorological, and air quality data needed for input and validation of the airshed models. However, the resulting predictions are... 

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