Interpreting relationships between pollutants and carbon dioxide emitted into air from industries in Serbia

Abstract

The focus was on the pollution problem in Serbia and the relationships between CO2 emitted into air from industries and air quality indicators such as particulate matters (PM2.5, PM10), nitrogen and sulfur oxides (NOx, SOx), and volatile organic compounds were analyzed. To identify the dependencies, both parametric and nonparametric statistical learning-based evaluation algorithms were taken into consideration. Both the model structures produced satisfactory estimations with high accuracy levels. As a result of the model interpretation, PM2.5 has been recorded as the main indicator to explore the variability in CO2 concentrations. The implementations exhibited that interpretable machine learning can provide meta-data and sufficient information for making blackbox air quality system more explainable. Thus, the practiced modelling tools, the provided interrelationships as well as the new information could be considered by the national authorities within a computational environmental management strategy.