Background: Air pollution affects both the health of living being and the materialistic resources.
Amid the development of anthropogenic methods and machines, atmosphere has turned as a
sink for foul gases and disorderly energy. Air pollutant discharges have deteriorated the natural composition
of air in atmosphere at spatial manner.
Method: The resulting losses from air pollutants can be minimized either by control at source (through
air pollution control devices) or by diverting the receptor from pollution prone area. The second option
requires an exact estimation of air pollutant’s concentration, which can be done by use of air pollution
forecast models. There are many simulation models for forecasting the air pollutant concentration at any
place. However, the results of these models are questioned many times because of inappropriate forecasting.
In this paper author proposes a comprehensive multi-criteria process based upon statistical formulae
for the evaluation of the performance of Air Pollution Forecast Models.
Results: The performance of Air Pollution Models can be judged by error analysis, comparison of forecasted
and observational data, use of statistical performance measurements and criteria. All these techniques
are discussed for their suitability and guiding the modeler to select the optimal criteria for best
Conclusion: The selection and use of specific evaluation criteria and their ability to interpret the results
varies with the study area, prevailing meteorological conditions, formulation of model structure and behaviour
of observed data and simulation results. A suitably selected performance criteria help in designing
the best deliverable air pollution forecast model.