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1

Air pollution index trend analysis in malaysia, 2010-15

100%
Rani N. L. A., Azid A., Khalit S. I., Juahir H., Samsudin M. S.
Polish Journal of Environmental Studies
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2018
|
tom 27
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nr 2
Air pollution index (API) is used in Malaysia to determine the level of air quality. API is based on the calculation consist of pollutants PM₁₀, O₃, CO₂, SO₂, and NO₂. Unhealthy air quality can harm human health and the environment as well as property. In view of this fact, a study of air pollution trend analysis in Malaysia from 2010 to 2015 was performed with the objective of determining the API trend in Malaysia from 2010 to 2015. A dataset of API value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). In this study, 19,872 datasets for all Malaysian air quality monitoring stations that had API value greater than 100 and a total of 52,584 datasets for Muar District in Johor were used. XLSTAT add-in 2014 was used to analyze the API hourly reading. Analysis shows that the air monitoring station at Sekolah Menengah Teknik Muar in Johor shows the highest value of API reading with 663 on 23 June 2013 (emergency level), where on that day Malaysia faced its worst air quality due to haze episodes. Other locations also show the worst air quality with API registering at unhealthy, very unhealthy, and hazardous levels.
2

Assessing indoor air quality using chemometric models

72%
Azid A., Azizi Amran M., Saiful Samsudin M., Abd Rani N. L., Khalit S. I., Barzani Gasim M., Yunus K., Mohd Saudi A. S., Amin S. N. S., Ku Yusof K. M. K.
Polish Journal of Environmental Studies
|
2018
|
tom 27
|
nr 6
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.
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