Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia
Awoke Guadie , Solomon Neway Jida , Jean-François Hetet , Pascal Chesse
DOI:10.1016/j.jes.2020.08.018
Received May 12, 2020,Revised , Accepted August 18, 2020, Available online September 18, 2020
Volume 33,2021,Pages 428-439
Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM2.5) and coarse particle (PM10) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume, weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM2.5 concentration is 13%–144% and 58%–241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM10 results also exceeded the AQI (54%–65%) and WHO (8%–395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM2.5 and PM10 exhaust emission correlation coefficient were predicted to be (R2 = 0.775) and (R2 = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM2.5 (R2 = 0.943) and PM10 (R2 = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.
