Applying artificial neural networks to short-term PM2.5 forecasting modeling

Abstract

Air pollution with suspended particles from PM2.5 fraction represents an important factor to increasing atmospheric pollution degree in urban areas, with a significant potential effect on the health of vulnerable people such as children and elderly. PM2.5 air pollutant concentration continuous monitoring represents an efficient solution for the environment management if it is implemented as a real time forecasting system which can detect the PM2.5 air pollution trends and provide early warning or alerting to persons whose health might be affected by PM2.5 air pollution episodes. The forecasting methods for PM concentration use mainly statistical and artificial intelligence-based models. This paper presents a model based protocol, MBP – PM2.5 forecasting protocol, for the selection of the best ANN model and a case study with two artificial neural network (ANN) models for real time short-term PM2.5 forecasting.