Modeling Indoor Particulate Matter and Small Ion Concentration Relationship—A Comparison of a Balance Equation Approach and Data Driven Approach
| Autor: | Predrag Kolarž, Milena Jovasevic-Stojanovic, Zoran Ristovski, Rastko Jovanović, Milena Davidović, Miloš D. Davidović |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
chemistry.chemical_element Radon 010501 environmental sciences 01 natural sciences lcsh:Technology lcsh:Chemistry Indoor air quality Approximation error 11. Sustainability Linear regression General Materials Science Statistical physics Instrumentation Air quality index lcsh:QH301-705.5 0105 earth and related environmental sciences Fluid Flow and Transfer Processes particulate matter small ions Artificial neural networks lcsh:T Process Chemistry and Technology General Engineering Statistical model radon Particulates Small ions lcsh:QC1-999 Computer Science Applications chemistry lcsh:Biology (General) lcsh:QD1-999 13. Climate action lcsh:TA1-2040 Balance equation linear regression Environmental science Particulate matter lcsh:Engineering (General). Civil engineering (General) artificial neural networks lcsh:Physics indoor air quality |
| Zdroj: | Applied Sciences, Vol 10, Iss 5939, p 5939 (2020) Applied Sciences APPLIED SCIENCES-BASEL Volume 10 Issue 17 |
| ISSN: | 2076-3417 |
| Popis: | 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. © 2020 by the authors. |
| Databáze: | OpenAIRE |
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