Application of artificial neural networks in the prediction of PM10 levels in thewinter months: A case study in the Tricity Agglomeration, Poland

Abstract

Poor urban air quality due to high concentrations of particulate matter (PM) remains a major public health problem worldwide. Therefore, research efforts are being made to forecast ambient PM concentrations. In this study, artificial neural networks (ANNs) were employed to generate models forecasting hourly PM10 concentrations 1-6 h ahead, involving 3 measurement locations in the Tricity Agglomeration, Poland. In Poland, the majority of high PM concentration cases occurs in winter due to coal combustion being the main energy carrier. For this reason, the present study covers only the periods of the winter calendar (December, January, February) in the period 2002/2003-2016/2017. Inputs to the models were the values of hourly PM10 concentrations and meteorological factors such as air temperature, relative humidity, air pressure, and wind speed. The results of the neural network models were satisfactory and the values of the coefficient of determination (R2) for the independent test set for three sites ranged from 0.452 to 0.848. The values of the index of agreement (IA) were from 0.693 to 0.957, the fractional mean bias (FB) values were 0 or close to 0 and the root mean square error (RMSE) values varied from 8.80 to 23.56. It is concluded that ANNs have been proven to be effective in the prediction of air pollution levels based on the measured air monitoring data.