Predicting PM2.5 levels over Indian metropolitan cities using Recurrent Neural Networks

Air pollution, particularly ambient particulate matter with aerodynamic diameter less than 2.5 mu m (PM2.5), has emerged as a significant global concern due to its adverse impact on public health and the environment. Rapid urbanization, industrialization, and the increased number of automobiles in the cities have led to a significant enhancement in the PM2.5 concentrations to their hazardous level, which indicates the requirement for early warning systems to reduce exposure. Artificial Intelligence and Machine Learning (AI/ML) have come forth as highly sought-after tools widely utilized for air quality (AQ) forecasting. A deep learning based Recurrent Neural Network (RNN) models are highly being used due to their performance in predicting the AQ from the time series data. The present study evaluated three types of RNNs, namely SimpleRNN, Gradient Recurrent Units (GRU) and Long Short-Term Memory (LSTM) to forecast the PM2.5 in the four major Indian metropolitan cities. This research utilizes the daily in-situ PM2.5 data from national AQ monitoring agency in India, known as Central Pollution Control Board (CPCB) for the period 2018 to 2023. Various atmospheric gases and dispersion factors were employed to train model for the prediction of PM2.5 over the cities of Chennai, Delhi, Hyderabad and Kolkata. The ability of the each RNN model is evaluated and compared with observed data using various statistical parameters such as root mean squared error, mean absolute error, and mean absolute percentage error, coefficient of determination and correlation coefficient. Our findings indicate that all three neural networks can capture future PM2.5 trends consistently, albeit with some uncertainty. GRU was the most proficient in estimating PM2.5 levels in all the cities, followed by LSTM and SimpleRNN. The highest accuracy score was observed over Hyderabad followed by Kolkata, Chennai and Delhi.