Predicting the Air Quality Index of Industrial Areas in an Industrialized City in India Using Adopting Markov Chain Model

Abstract

Introduction: The rapid urbanization coupled with industrial development in Indian cities has led to air pollution that causes adverse effects on the health of human beings. So, it is crucial to track the quality of air in industrial areas of a city to insulate the public from harmful air pollutants. The present study examined and predicted air quality index levels in industrial areas located in Hyderabad, India. Materials and Methods: Markov chain model was developed to predict the air quality index levels in three industrial areas of Hyderabad city. The secondary data pertaining to the air quality index was analyzed from January, 2016 to December 2019 by developing Markov chain model. The state transition probabilities were used to find the predicted probability for the next 4 years. The study also analyzed the mean return time for specific states. Results: According to the findings, the highest frequency observed for transition in a month to the next month was 31 for the second industrial area in moderate state. The longest time required to repeat the state was 23.585 months and 23.259 months for the industrial area 3. Conclusions: Air quality index varies in industrial areas depending on the nature of industries and type of emissions. The prediction of air quality index is useful for the local authorities to implement measures to minimize the impact of pollutants on human health.