7. Air Quality Modeling Laboratory: individual processes Field: system observations Numerical Models: Enable description of complex, interacting, often nonlinear processes involving environmental physics, chemistry and biology

7.1 Models Models are computer replicas of natural system behavior, so that causes and effects (i.e. feedbacks) may be better understood

Air Quality Model typical components: –emissions –atmospheric chemistry –atmospheric physics Goal: prediction of air pollutant concentrations

7.1.1 Potential Uses of Air Quality Models 1. establishment of emission control legislation 2. evaluation of proposed emission control techniques and strategies 3. planning of locations of future sources of air contaminants 4. planning for the control of air pollution episodes 5. assessment of responsibility of air pollution 6. prediction of future air pollutant concentrations and impacts

7.1.2 Types of Models Two broad categories: –1. Physical models –2. Mathematical models

Physical Models Simulate atmospheric processes affecting pollutants via a small-scale representation of the actual air pollution problem –often use a small-scale replica of an urban area in a wind tunnel –physical models are valuable for isolating certain elements of atmospheric behavior –physical models are limited as they are incapable of simulating a variety of meteorological, chemical and emissions conditions (use mathematical models)

Example: ESE Building Air Quality Engineering and Atmospheric Sciences Laboratories: Will snow accumulate on the outdoor instrument platform?

Mathematical Models Two kinds: 1. Models based on statistical analysis of past air monitoring data 2. Models based on fundamental description of atmospheric transport and chemical processes

7.1.3 Temporal Resolution of Models Temporal resolution is a measure of the time period over which predicted concentrations are averaged Range of concern: few minutes to a year Model selection is often based on temporal resolution

Statistical models: Obtain and use several years of measurements at one or more monitoring stations in an airshed Correlate emissions to ambient concentrations Temporal resolution similar to measurements (hourly, daily)

Example: Predict the probability that the 1 hour average concentration at a certain station will exceed a given level if total emissions in that region are at a prescribed value

Input data: Generally only estimates of source emission levels (meteorology and atmospheric transformation and removal processes are implicit in reported concentration data)

Output: Concentrations (or probabilities)

Fate and transport models: Temporal resolution ranging from the order of several minutes to years Based upon continuity equation for each species (i.e. mass balance)

Dynamic models: Models that require the solution in time of a differential equation based on the continuity equation, as they describe the evolution of pollutant concentrations with time Simulate real-time behavior of air pollutants

Input data: –Spatial and temporal distribution of emissions over the region of interest –Spatial and temporal distribution of meteorological variables –Time rate of change of concentrations at a point resulting from transformations and removal processes

Steady State models: Models with simplifications, such as steady: –Source rates –Meteorology –Atmospheric chemistry Capable of predictions spatial distribution airborne pollutant concentrations under time- invariant source emission rates and atmospheric chemistry and physics

7.1.4 Spatial Resolution of Models Spatial resolution is a measure of the area over which predicted concentrations are averaged Spatial resolution is dependent on domain size (i.e. the total area of consideration)

Statistical models: –Predict concentrations at one or more stations (point measurements) or the average of all stations

Fate and transport models: Discrete grid of points Grid size is determined by: 1.spatial detail of emissions 2.spatial detail of meteorological variables 3. atmospheric fate of pollutant 4. computer time

Spatial resolution cannot be smaller than data resolution Spatial resolution is often coupled to temporal resolution Short-lived species have highly variable concentrations spatially Long-lived species can be transported great distances without much change in concentration

Fig. 8.1 Time and source-to-sink spatial scales

Examples: 1. Impact of autos on CO levels around a highway or intersection –Requires resolution of a few meters 2. Photochemical smog formation –Caused by area wide emissions of HC and NO x –Requires a resolution of several kilometers

Fig.8.2 NO 2 concentrations as predicted for the Atlanta area by the Geographical Environmental Modeling System.

–Fig.8.3 The greater Atlanta metropolitan area.