A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings

A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings

The 24 h and 14-day relationship between indoor and outdoor PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, relative humidity, and temperature were assessed for an elementary school (site 1), a laboratory (site 2), and a residential unit (site 3) in Gainesville...

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Bibliographic Details
Main Authors: He Zhang, Ravi Srinivasan
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Buildings
Subjects:
air pollution
indoor air quality
principal component analysis
biplot
Online Access:https://www.mdpi.com/2075-5309/11/5/218
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spelling doaj-1ae821ec46e048948cf13c29da633c612021-06-01T00:38:02ZengMDPI AGBuildings2075-53092021-05-011121821810.3390/buildings11050218A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study BuildingsHe Zhang0Ravi Srinivasan1UrbSys (Urban Building Energy, Sensing, Controls, Big Data Analysis, and Visualization) Laboratory, M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32603, USAUrbSys (Urban Building Energy, Sensing, Controls, Big Data Analysis, and Visualization) Laboratory, M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32603, USAThe 24 h and 14-day relationship between indoor and outdoor PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, relative humidity, and temperature were assessed for an elementary school (site 1), a laboratory (site 2), and a residential unit (site 3) in Gainesville city, Florida. The primary aim of this study was to introduce a biplot-based PCA approach to visualize and validate the correlation among indoor and outdoor air quality data. The Spearman coefficients showed a stronger correlation among these target environmental measurements on site 1 and site 2, while it showed a weaker correlation on site 3. The biplot-based PCA regression performed higher dependency for site 1 and site 2 (<i>p</i> < 0.001) when compared to the correlation values and showed a lower dependency for site 3. The results displayed a mismatch between the biplot-based PCA and correlation analysis for site 3. The method utilized in this paper can be implemented in studies and analyzes high volumes of multiple building environmental measurements along with optimized visualization.https://www.mdpi.com/2075-5309/11/5/218air pollutionindoor air qualityprincipal component analysisbiplot
collection DOAJ
language English
format Article
sources DOAJ
author He Zhang
Ravi Srinivasan
spellingShingle He Zhang
Ravi Srinivasan
A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings
Buildings
air pollution
indoor air quality
principal component analysis
biplot
author_facet He Zhang
Ravi Srinivasan
author_sort He Zhang
title A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings
title_short A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings
title_full A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings
title_fullStr A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings
title_full_unstemmed A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings
title_sort biplot-based pca approach to study the relations between indoor and outdoor air pollutants using case study buildings
publisher MDPI AG
series Buildings
issn 2075-5309
publishDate 2021-05-01
description The 24 h and 14-day relationship between indoor and outdoor PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, relative humidity, and temperature were assessed for an elementary school (site 1), a laboratory (site 2), and a residential unit (site 3) in Gainesville city, Florida. The primary aim of this study was to introduce a biplot-based PCA approach to visualize and validate the correlation among indoor and outdoor air quality data. The Spearman coefficients showed a stronger correlation among these target environmental measurements on site 1 and site 2, while it showed a weaker correlation on site 3. The biplot-based PCA regression performed higher dependency for site 1 and site 2 (<i>p</i> < 0.001) when compared to the correlation values and showed a lower dependency for site 3. The results displayed a mismatch between the biplot-based PCA and correlation analysis for site 3. The method utilized in this paper can be implemented in studies and analyzes high volumes of multiple building environmental measurements along with optimized visualization.
topic air pollution
indoor air quality
principal component analysis
biplot
url https://www.mdpi.com/2075-5309/11/5/218
work_keys_str_mv AT hezhang abiplotbasedpcaapproachtostudytherelationsbetweenindoorandoutdoorairpollutantsusingcasestudybuildings
AT ravisrinivasan abiplotbasedpcaapproachtostudytherelationsbetweenindoorandoutdoorairpollutantsusingcasestudybuildings
AT hezhang biplotbasedpcaapproachtostudytherelationsbetweenindoorandoutdoorairpollutantsusingcasestudybuildings
AT ravisrinivasan biplotbasedpcaapproachtostudytherelationsbetweenindoorandoutdoorairpollutantsusingcasestudybuildings
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