Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt–Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available.

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Rising real estate prices along with expensive maintenance costs, and lack of spares during times of instrument failure have become major issues for statutory bodies when dealing with real-time pollution monitoring stations. As a possible solution to these problems, a novel class of hybrid spatio-temporal pollution forecasting networks which are a combination of various widely used temporal forecasting methods and spatial interpolation methods have been proposed in this paper. In addition, a novel multi-site Multi Layer Perception based Ensemble method, capable of improving accuracy by taking exogenous variables into account, has also been proposed. Experimental results based on the multi-site air pollution data of Beijing demonstrate that the proposed class of hybrid networks have been effective in predicting the pollution of unknown locations with great levels of accuracy. Moreover, the proposed novel MLP Ensemble method for spatial interpolation has also been empirically shown to perform equivalently in comparison to commonly used spatial interpolation methods.

The imposition of strict restrictions by the Government of India to restrict the spread of the novel coronavirus has changed the socio-economic landscape like never before. The air quality due to such unprecedented events has undergone drastic changes especially in major metropolitan cities, which serve as important financial and industrial hubs of the country. This study investigates the influence lockdowns had on the pollution scenario of four key cities namely, Delhi, Kolkata, Chennai and Mumbai during both the first (2020) and the second (2021) waves . To evaluate the impact, detailed analysis of ground based pollutant concentration data of PM2.5, NOx, SO2 and O3 from various government set up monitoring stations in the period ranging from April'20 to June'21 is conducted along with the corresponding period during 2019 when business was as usual (BaU). Results show that although PM2.5 and NOx for all cities presented a decrease during the first wave, higher pollutant levels were observed during the second wave. For SO2 and O3, the trend did not show any consistency over all cities as in some cities, the second wave levels showed a significant increase with regard to their BaU counterparts. Out of all the meteorological factors studied over that period, relative humidity was found to have a strong correlation with respect to pollutant levels. Regarding spatial variation within different cities, although stations especially based in industrial areas showed a significant increase in the winter months of October'20 to January'21, second wave and first wave pollutant levels for different stations during the summer months for all cities except Chennai were found to be nearly identical.

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