Model for Forecasting Expressway Fine Particulate Matter and Carbon Monoxide Concentration: Application of Regression and Neural Network Models

Abstract

The Borman Expressway is a heavily traveled 16-mi segment of the Interstate 80/94 freeway through Northwestern Indiana. The Lake and Porter counties through which this expressway passes are designated as particulate matter <2.5 µm (PM_2.5) and ozone 8-hr standard nonattainment areas. The Purdue University air quality group has been collecting PM_2.5, carbon monoxide (CO), wind speed, wind direction, pressure, and temperature data since September 1999. In this work, regression and neural network models were developed for forecasting hourly PM_2.5 and CO concentrations. Time series of PM_2.5 and CO concen trations, traffic data, and meteorological parameters were used for developing the neural network and regression models. The models were compared using a number of statistical quality indicators. Both models had reasonable accuracy in predicting hourly PM_2.5 concentration with coefficient of determination ~0.80, root mean square error (RMSE) <4 µg/m³, and index of agreement (IA) >0.90. For CO prediction, both models showed moderate forecasting performance with a coefficient of determination ~0.55, RMSE ~0.50 ppm, and IA ~0.85. These models are computationally less cumbersome and require less number of predictors as compared with the deterministic models. The availability of real time PM_2.5 and CO forecasts will help highway managers to identify air pollution episodic events beforehand and to determine mitigation strategies.