Assessing Indoor Air Quality Using Chemometric Models
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Faculty Bioresources and Food Industry, Universiti Sultan Zainal Abidin, Besut Campus, Terengganu, Malaysia
East Coast Environmental Research Institute, Universiti Sultan Zainal Abidin, Gong Badak Campus, Terengganu, Malaysia
Kulliyyah of Science, International Islamic University Malaysia, Pahang, Malaysia
Institute of Medical Science and Technology, University of Kuala Lumpur, Selangor, Malaysia
Faculty of Health Science, Universiti Sultan Zainal Abidin, Gong Badak Campus, Terengganu, Malaysia
Submission date: 2017-04-06
Final revision date: 2017-09-29
Acceptance date: 2017-10-01
Online publication date: 2018-05-24
Publication date: 2018-07-09
Corresponding author
Azman Azid   

Universiti Sultan Zainal Abidin, Faculty Bioresources and Food Industry, Besut Campus, 22200 Kuala Terengganu, Malaysia
Pol. J. Environ. Stud. 2018;27(6):2443–2450
The objectives of this study are to identify the significant variables and to verify the best statistical method for determining the effect of indoor air quality (IAQ) at 7 different locations in Universiti Sultan Zainal Abidin, Terengganu, Malaysia. The IAQ data were collected using in-situ measurement. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discrimination analysis (LDA), and agglomerative hierarchical clustering (AHC) were used to classify the significant variables as well as to compare the best method for determining IAQ levels. PCA verifies only 4 out of 9 parameters (PM10, PM2.5, PM1.0, and O3) and is the significant variable in IAQ. The PLS-DA model classifies 89.05% correct of the IAQ variables in each station compared to LDA with only 66.67% correct. AHC identifies three cluster groups, which are highly polluted concentration (HPC), moderately polluted concentration (MPC), and low-polluted concentration (LPC) area. PLS-DA verifies the groups produced by AHC by identifying the variables that affect the quality at each station without being affected by redundancy. In conclusion, PLS-DA is a promising procedure for differentiating the group classes and determining the correct percentage of variables for IAQ.