Predictive Ability of Improved Neural Network Models to Simulate Pollutant Dispersion
Open Access
- 26 June 2014
- journal article
- research article
- Published by Hindawi Limited in International Journal of Atmospheric Sciences
- Vol. 2014, 1-12
- https://doi.org/10.1155/2014/141923
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.Keywords
Funding Information
- Tradescan Group, Bangladesh
This publication has 21 references indexed in Scilit:
- Spatial and diurnal variations in concentration of atmospheric NOx along urban-rural roadways in Nanjing, Southeastern ChinaInternational Journal of Environment and Pollution, 2008
- Temporal variations and spatial distribution of ambient PM2.2 and PM10 concentrations in Dhaka, BangladeshScience of The Total Environment, 2006
- Air-pollution modelling in an urban area: Correlating turbulent diffusion coefficients by means of an artificial neural network approachAtmospheric Environment, 2006
- Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, ChileAtmospheric Environment, 2000
- Statistical surface ozone models: an improved methodology to account for non-linear behaviourAtmospheric Environment, 2000
- Effects of Particulate and Gaseous Air Pollution on Cardiorespiratory HospitalizationsArchives of environmental health, 1999
- Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in LondonAtmospheric Environment, 1999
- Characteristics of the air pollution in the city of Dhaka, Bangladesh in winterAtmospheric Environment, 1998
- Air quality modelling: a technical review of mathematical approachesMeteorlogical Applications, 1997
- Effects of traffic-generated turbulence on near-field dispersionAtmospheric Environment (1967), 1981