Abstract
Air pollution, a major environmental concern, negatively affects human health and the environment. In Malaysia, urban areas such as Klang in Selangor have suffered from increased air pollution due to heavy industrial activities, dense populations, and high vehicular traffic, which are particularly vulnerable. This study identified Selangor’s air quality trends using the Department of Environment (DOE) data. The monitoring station in Klang was selected based on six air pollutants (sulphur dioxide (SO2), particulate matter below 10 microns (PM10), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter below 2.5 microns (PM2.5) during three years from January 1, 2020, to December 31, 2022. This study aims to use the principal component analysis (PCA) method to classify the variables that predict the air pollution index (API) in Klang, Selangor. PCA was used to reduce dimensionality and identify significant pollutant predictors, with Bartlett’s test and KMO measure supporting data suitability and rotating PCA reducing predictor variables. As a result, two principal components that highlighted PM10, PM2.5 and O3 as the most important pollutants in Klang were revealed after the PCA was rotated using varimax rotation. The finding could assist DOE management in identifying the types of pollutants responsible for air pollution.
Metadata
| Item Type: | Book Section |
|---|---|
| Creators: | Creators Email / ID Num. Norazman, Nur Afrina Syamimi UNSPECIFIED Ab Malek, Isnewati UNSPECIFIED |
| Subjects: | Q Science > Q Science (General) > General. Including nature conservation, geographical distribution Q Science > Q Science (General) > Study and teaching |
| Divisions: | Universiti Teknologi MARA, Negeri Sembilan > Seremban Campus |
| Page Range: | pp. 17-22 |
| Keywords: | Air pollution index, Principal Component Analysis (PCA), Klang |
| Date: | 2025 |
| URI: | https://ir.uitm.edu.my/id/eprint/137152 |
