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ABSTRACT
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.
Introduction :
The global human population has risen by more than four
times during the last century [1]. Research show that the major
growth in population is attributed to the metropolitan areas in
the less developed regions around the globe [2, 3]. The consequence of these increased levels of growth in poorly developed
states is low air quality. We note that 80% of global cities [3]
and 98% of cities in middle-income countries surpass the proposed levels of air quality [4, 5]. Increase in air pollution results
in economic losses, reduced visibility, contributes to faster climate change that contribute to extreme weather conditions, millions of premature deaths annually [6]. The major factor in air
pollution is the anthropogenic fine particulate matter (PM); i.e.
PM2.5 (particles with a
an aerodynamic diameter shorter than 2.5
micrometer) [7, 8, 9, 10]. Despite the concentrations of PM2.5
being two to five times higher in developing countries, most of
the air quality considerations and estimations are analyzed for
developed countries
