Predicting particulate matter for assessing air quality in Delhi using meteorological features

Abstract

Air pollution is one of the biggest threats to the environment. According to statistics of World Health Organization, more than 80% of people living in urban areas inhale poor air quality levels. Hence assessing air quality is important especially in urban areas where people suffer more health problems due to poor air quality. Data mining techniques can serve to be very useful for analyzing the air quality data. In the past, several research works were done for various developing countries of the world, except a few for developing countries, like India. Specifically for Delhi, where high concentrations of Oxides of Nitrogen, Oxides of Sulphur, Benzene, Toluene, Particulate Matter etc. are reported in its atmosphere. The presence of certain meteorological conditions in the atmosphere can be very helpful to identify the presence of such pollutants. Particulate matter with a diameter of 2.5 µm or less (PM2.5) is focused upon in this work. Data mining techniques like multivariate linear regression model and regression trees etc. to identify the relationship between meteorological features and air quality are deployed. Further, the use of ensemble techniques such as random forests are also given in the present research work. Evaluation is done over root mean square error metrics and results are found to be promising. © Springer International Publishing AG, part of Springer Nature 2018.