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

Miloš Davidović,Predrag Kolarž,Milena Jovašević-Stojanović,Milena Davidović,Zoran Ristovski,Rastko Jovanović

doi:10.3390/app10175939

Miloš Davidović, Predrag Kolarž + Show 4 more

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https://doi.org/10.3390/app10175939

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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.

Highlights

Our health and wellbeing is a complex and multifaceted phenomenon, but clean air can be with certainty regarded as one of its most critical components
In this paper we focus on a smaller subset of indoor air pollution phenomena with a focus on an interplay between primary pollutants radon and particulate matter (PM) [8] and small ions (SI)
We investigate the hypothesis that small ion concentration can be predicted based on radon and particulate matter measurements predictors, by using artificial neural network model

Summary

Introduction

Our health and wellbeing is a complex and multifaceted phenomenon, but clean air can be with certainty regarded as one of its most critical components. It has been shown that air pollution is the single largest environmental health risk in Europe [1]. While level of concentration of air pollutants can widely vary even locally, the reactions people may have in response to exposure even. Sci. 2020, 10, 5939 to the same level of air pollutants concentration can vary due to the breathing volume (e.g., because of different age and levels of physical fitness and activity) and duration of exposure (e.g., large amount of time spent indoors, as a commuter, etc.). Some age groups have behavioral patterns that may affect their exposure in a negative manner, such as elderly or young children, and result in various negative health effects including asthma, allergies, and other [2,3]

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