Article citationsMore>>

Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam, C. N. C. et al. (2014). Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: A Case Study in Malaysia. Water, Air, & Soil Pollution, 225, Article No. 2063.
https://doi.org/10.1007/s11270-014-2063-1

has been cited by the following article:

• TITLE: Spatio-Temporal Air Pollutant Characterization for Urban Areas

  AUTHORS: Nurainshafika Sahak, Arnis Asmat, Noor Zaitun Yahaya

  KEYWORDS: Urban Pollution, Air Pollution Index (API), Spatio-Temporal Characterization, Source of Pollutants, Inverse Distance Weighted (IDW), Principal Component Analysis (PCA)

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.10 No.1, January 29, 2022

  ABSTRACT: Urban pollution has now become increasingly recognized as an important determinant of air pollution in developed countries. The effect of urban air pollution in developing countries, on the other hand, has not been adequately addressed in the data Spatio-temporal time series. Thus, this study was intended to characterize the effect of urbanization on air pollution for an urbanized Klang Valley, Malaysia using Spatio-temporal data from 2008 to 2017. The Air Pollution Index (API) data and local pollutant concentration were employed to establish the links between urban air pollution. The analysis will be supported by determining the source of pollutants during the study period using Principal Component Analysis (PCA). The study identified that Carbon monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3) are the major air pollution that has contributed to degrading air quality in the Klang Valley due to the vehicles, combustion process, and industries.