  Air pollution is the leading environmental health hazard globally due to
various sources which include factory emissions, car exhaust and cooking
stoves. As a precautionary measure, air pollution forecast serves as the basis
for taking effective pollution control measures, and accurate air pollution
forecasting has become an important task. In this paper, we forecast
fine-grained ambient air quality information for 5 prominent locations in Delhi
based on the historical and real-time ambient air quality and meteorological
data reported by Central Pollution Control board. We present VayuAnukulani
system, a novel end-to-end solution to predict air quality for next 24 hours by
estimating the concentration and level of different air pollutants including
nitrogen dioxide ($NO_2$), particulate matter ($PM_{2.5}$ and $PM_{10}$) for
Delhi. Extensive experiments on data sources obtained in Delhi demonstrate that
the proposed adaptive attention based Bidirectional LSTM Network outperforms
several baselines for classification and regression models. The accuracy of the
proposed adaptive system is $\sim 15 - 20\%$ better than the same offline
trained model. We compare the proposed methodology on several competing
baselines, and show that the network outperforms conventional methods by $\sim
3 - 5 \%$.
