Machine Learning Techniques for Air Quality Forecasting and Study on Real-Time Air Quality Monitoring

Abstract

Air Pollution has become major, serious problem worldwide. Because of its close relation to human health, it has gained a lot of attention of many researchers. People are becoming more cautious about better ways of monitoring air quality information and has become important to protect human health from serious health problems caused by air pollution. Many researchers are working on real-time air quality monitoring and forecasting for getting accurate results which will help in implementing various government policies related to the environment or air pollution and for taking crucial decisions. There are many recent advancements in the air quality forecasting and monitoring techniques. Most of the techniques are Machine Learning (ML) based as it has become popular analysis tool because of its various distinctive features. This paper summarizes air quality forecasting models as well as realtime monitoring tools and techniques based on real-time and historical data. It has discussed the merits and demerits of every methodology used for air quality forecasting and monitoring used in recent research along with their comparative analysis, limitations, and challenges. This paper will be useful to understand current status, past work done and future research questions which needs to be addressed.