Prediction of hourly PM10 concentration through a hybrid deep learning-based method

Abstract

Air pollution can have detrimental effects on human health as well as the environment. Particulate Matter (PM), as a global issue, is a type of air pollution that consists of small particles suspended in the air. Therefore, it is crucial to estimate and monitor levels of PM in the air in order to protect public health and the environment. This study proposed a novel hybrid method to apply the capability of two various deep learning models, namely, the encoder-decoder convolutional neural network and the Long Short-Term Memory (LSTM) model for PM10 prediction. The first model was utilized as a data argumentation method to enhance dataset diversity, and the LSTM model employed meteorological parameters and spatiotemporal factors to estimate the PM10 levels. The proposed technique achieved performance resulting in a coefficient of determination value of 0.88 and a mean absolute error value of 7.24. The results confirm that the developed hybrid method as an effective tool of PM prediction can be used to inform decision-making about policies and actions to reduce PM levels.