Table of Contents
International Journal of Atmospheric Sciences
Volume 2014, Article ID 141923, 12 pages
http://dx.doi.org/10.1155/2014/141923
Research Article

Predictive Ability of Improved Neural Network Models to Simulate Pollutant Dispersion

Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, Canada M5B 2K3

Received 28 February 2014; Accepted 7 May 2014; Published 26 June 2014

Academic Editor: Prodromos Zanis

Copyright © 2014 Khandaker M. A. Hossain. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper describes the ability of artificial neural network (ANN) models to simulate the pollutant dispersion characteristics in varying urban atmospheres at different regions. ANN models are developed based on twelve meteorological (including rainfall/precipitation) and six traffic parameters/variables that have significant influence on emission/pollutant dispersion. The models are trained to predict concentration of carbon monoxide and particulate matters in urban atmospheres using field meteorological and traffic data. Training, validation, and testing of ANN models are conducted using data from the Dhaka city of Bangladesh. The models are used to simulate concentration of pollutants as well as the effect of rainfall on emission dispersion throughout the year and inversion condition during the night. The predicting ability and robustness of the models are then determined by using data of the coastal cities of Chittagong and Dhaka. ANN models based on both meteorological and traffic variables exhibit the best performance and are capable of resolving patterns of pollutant dispersion to the atmosphere for different cities.