Modeling Indoor Particulate Matter and Small Ion Concentration Relationship—A Comparison of a Balance Equation Approach and Data Driven Approach

Abstract

In this work we explore the relationship between particulate matter (PM) and small ion (SI) concentration in a typical indoor elementary school environment. A range of important air quality parameters (radon, PM, SI, temperature, humidity) were measured in two elementary schools located in urban background and suburban area in Belgrade city, Serbia. We focus on an interplay between concentrations of radon, small ions (SI) and particulate matter (PM) and for this purpose, we utilize two approaches. The first approach is based on a balance equation which is used to derive approximate relation between concentration of small ions and particulate matter. The form of the obtained relation suggests physics based linear regression modelling. The second approach is more data driven and utilizes machine learning techniques, and in this approach, we develop a more complex statistical model. This paper attempts to put together these two methods into a practical statistical modelling approach that would be more useful than either approach alone. The artificial neural network model enabled prediction of small ion concentration based on radon and particulate matter measurements. Models achieved median absolute error of about 40 ions/cm3 and explained variance of about 0.7. This could potentially enable more simple measurement campaigns, where a smaller number of parameters would be measured, but still allowing for similar insights.