Real-Time Air Quality Estimation from Station Data Using Extended Fractional Kalman Filter

Abstract

Air, soil and water pollutions have the greatest risk factors for human health. There are different types of air pollutants which are emitted from human activities. One of these pollutants is nitrogen dioxide (NO2) which is produced from fossil fuel-based energy and use of motor vehicles. Since India is facing deteriorated air quality due to economic development, air quality management is becoming a real challenge. In 2015, an emission inventory (EI) was developed for India with 2015 as the base year. This EI is developed on an engineering model approach which is based on a technology-linked energy emission modeling approach. Accurate EI is important for future air quality modeling and air quality management. Since EI has uncertainties in data, some kind of estimation is essential. Estimation through extended fractional Kalman filter (EFKF) is considered in the present paper, and its performance is found to be superior as compared to a standard extended Kalman filter (EKF).