  Air contamination in urban areas has risen consistently over the past few
years. Due to expanding industrialization and increasing concentration of toxic
gases in the climate, the air is getting more poisonous step by step at an
alarming rate. Since the arrival of the Coronavirus pandemic, it is getting
more critical to lessen air contamination to reduce its impact. The specialists
and environmentalists are making a valiant effort to gauge air contamination
levels. However, its genuinely unpredictable to mimic subatomic communication
in the air, which brings about off base outcomes. There has been an ascent in
using machine learning and deep learning models to foresee the results on time
series data. This study adopts ARIMA, FBProphet, and deep learning models such
as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our
predicted results convey that all adopted methods give comparative outcomes in
terms of average root mean squared error. However, the LSTM outperforms all
other models with reference to mean absolute percentage error.
