Investigating major sources of air pollution and improving spatiotemporal forecast accuracy using supervised machine learning and a proxy

Abstract

Because weather can’t be controlled, air pollution can only be managed by planning and controlling the sources. Therefore, mathematical models that do not relate the source to the air quality do not offer guidance on how to regulate the air quality. This is often due to a lack of information about sources or a limited budget to measure emissions from sources that are expected to have a major contribution. A proxy for emission rate was used in modeling to improve the prediction of pollutant concentration and to investigate major sources. The daily average air concentration of NO2 and SO2 were predicted at five different locations using supervised machine learning methods, including random forests and support vector machines regression. Three models were constructed using different combinations of features. In model I, only features relating to the weather were used. Model II used only features of the types and quantities of fossil fuels consumed by power plants, which serve as a proxy for emission rates. In model III, all features from models I and II were used. Model III was found to be the most accurate. The average improvement in R2 for NO2 and SO2 regressions for all sites and using both methods were between 61% and 33%, respectively. This improvement in the prediction indicates that power plants are major sources and contribute significantly to the ambient level of SO2 and NO2 levels. The relative importance of all fuel consumption features was found to be comparable to the value of all weather features. The relative importance values of the fuel consumption features show an opportunity to improve air quality by burning a quantity of fuel in a power plant that has a smaller relative importance value instead. However, whether the receptors are located in rural or urban areas must be considered to maximize the net benefits.