Assessment of Ambient Air Quality in Hulu Langat District, Selangor, Malaysia

Mohd Saudi, A.S.; Abdullah, A.; Shafii, N.Z.; Khairuddin, M.Z.F.; Ismail, M.F.; Mejía, P.A.C.; Kadir, K.A.; Saudi, M.M.; Kamarudin, M.K.A.; Muhammad-Sukki, F.; Ali, M.H.; Bani, N.A.

Abstract

Air pollution is one of the most pressing environmental issues in the world. The district of Hulu Langat, bordering the capital city of Kuala Lumpur, is the focus area of the surrounding population. The district experienced a decline in air quality index due to population density, congested traffic flow, and vibrant industrial activities. This study aims to investigate the variation patterns for the ambient air quality in Hulu Langat, Selangor. A five-year secondary data of the Hulu Langat air quality (2014-2018) was obtained from the Air Quality Division of the Department of Environment of Malaysia (DOE). The database consisted the Air Pollutant Index (API) data and five major air pollutant parameters, including carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), and particulate matter with a diameter of 10 microns or less (PM10). The data were analysed using the chemometrics technique. The findings showed that PM10 had the highest correlation affecting the API readings, followed by SO2 and NO2. The Statistical Control Process (SPC) revealed that some data of PM10 exceeded the national guidelines and control limits. The air quality prediction model through the Artificial Neural Network (ANN) method showed high accuracy with the actual data (R2 = 0.9). This study found a significant association in the air quality data in Hulu Langat. Continuous monitoring and extensive collaboration across environmental departments and relevant agencies should be implemented for effective air quality management to attain a sustainable environment in the future.

• Presentation Conference Type: Presentation / Talk
• Conference Name: The 3rd International Conference on Medical Science Technology
• Start Date: Nov 23, 2022
• End Date: Nov 24, 2022
• Deposit Date: Feb 24, 2023
• Keywords: air quality, artificial neural network, chemometrics, correlation, principal component analysis, statistical process control
• Related Public URLs: https://icmst.unikl.edu.my/wp-content/uploads/2022/11/program-book-icmst-2022-final.pdf