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.

自过去几十年以来，解决空气污染问题已变得至关重要。到目前为止，已经提出了不同的统计方法和深度学习方法，但很少用于预测未来的长期污染趋势。预测未来的长期污染趋势对于全球政府机构来说非常重要，因为它们有助于制定有效的环境政策。本文对各种统计和深度学习方法进行了比较研究，以预测 PM2.5 和 PM10 这两类最重要的颗粒物 (PM) 的长期污染趋势。该研究基于印度东部的主要城市加尔各答。从加尔各答政府设立的监测站收集的历史污染数据用于借助各种时间序列分析技术分析潜在模式，然后使用不同的统计和深度来生成未来两年的预测学习方法。研究结果表明，自回归 (AR)、季节性自回归积分移动平均 (SARIMA) 和 Holt-Winters 等统计方法优于堆叠、双向、自编码器和卷积长短期等深度学习方法基于有限可用数据的记忆网络。