Abstract
Particulate matter is one of the key contributors of air pollution and climate change. Long-term exposure to constituents of air pollutants has exerted serious health implications in both humans and plants leading to a detrimental impact on economy. Among the pollutants contributing to air quality determination, particulate matter has been linked to serious health implications causing pulmonary complications, cardiovascular diseases, growth retardation and ultimately death. In agriculture, crop yield is also negatively impacted by the deposition of particulate matter on stomata of the plant which is alarming and can cause food security concerns. The deleterious impact of air pollutants on human health, agricultural and economic well-being highlights the importance of quantifying and forecasting particulate matter. Several deterministic and deep learning models have been employed in the recent years to forecast the concentration of particulate matter. Among them, deep learning models have shown promising results when it comes to modeling time series data and forecasting it. We have explored recurrent neural networks with LSTM model which shows potential to predict the particulate matter (PM2.5) based on multi-step multi-variate data of two of the most polluted regions of South Asia, Beijing, China and Punjab, Pakistan effectively. The LSTM model is tuned using Bayesian optimization technique to employ the appropriate hyper-parameters and weight initialization strategies based on the dataset. The model was able to predict PM2.5 for the next hour with root-mean-square error (RMSE) of 0.1913 (91.5% accuracy) and this error gradually increases with the number of time steps with next 24 hours steps prediction having RMSE of 0.7290. While in case of Punjab dataset with data recorded once a day, the RMSE for the next day forecast is 0.2192. These multi-step short-term forecasts would play a pivotal role in establishing an early warning system based on the air quality index (AQI) calculated and enable the government in enacting policies to contain it.