Development of enhanced emission factor equations for paved and unpaved roads using artificial neural network

Abstract Road dust is a primary source of PM that is having a significant impact on human health and air quality. In order to efficiently develop PM control strategies, it is critical to improving the ability to estimate the emission levels of PM resuspended from paved and unpaved roads. The U.S. Environmental Protection Agency (EPA) has developed emission factor equations to quantify the magnitude of PM for paved and unpaved roads based on multiple linear regression (MLR) models. However, the MLR models are not suitable to capture the complex and non-linear mechanisms of PM emissions, thereby limiting the accuracy of the MLR-based PM prediction models. This paper is to present a method to improve the quality of the existing EPA emission factor equations for paved and unpaved roads by employing an artificial neural network (ANN). The presented method consists of the following steps: data processing, ANN model training, and validation of the presented method through data testing. The data utilized for the case study were retrieved from the database used by the EPA to generate PM 10 emission factor equations for the paved and unpaved roads for a fair comparison. The presented method was evaluated by demonstrating its improved performance as shown in the coefficient of determination (R 2 ) and the root mean square error (RMSE) values. The empirical findings of the case study verified that the presented method using the ANN model is capable of improving the quality of the EPA emission factor equations, resulting in higher R 2 and lower RMSE values for both paved and unpaved roads. The expected significance of this paper is that the presented method improves the ability to develop more reliable emission factor equations for predictable PM levels that can help agencies establish enhanced PM control strategies.