  Air pollution has a wide range of implications on agriculture, economy, road
accidents, and health. In this paper, we use novel deep learning methods for
short-term (multi-step-ahead) air-quality prediction in selected parts of
Delhi, India. Our deep learning methods comprise of long short-term memory
(LSTM) network models which also include some recent versions such as
bidirectional-LSTM and encoder-decoder LSTM models. We use a multivariate time
series approach that attempts to predict air quality for 10 prediction horizons
covering total of 80 hours and provide a long-term (one month ahead) forecast
with uncertainties quantified. Our results show that the multivariate
bidirectional-LSTM model provides best predictions despite COVID-19 impact on
the air-quality during full and partial lockdown periods. The effect of
COVID-19 on the air quality has been significant during full lockdown; however,
there was unprecedented growth of poor air quality afterwards.
