TEMPORAL ANALYSIS AND PREDICTIVE MODELING OF AMBIENT AIR QUALITY IN HULU LANGAT DISTRICT, SELANGOR, MALAYSIA: A CHEMOMETRIC APPROACH
DOI:
https://doi.org/10.21837/pm.v22i30.1448Keywords:
Air quality, Artificial neural network, Chemometrics, Correlation, Principal component analysisAbstract
One of the most important environmental problems facing the globe today is air pollution. The centre area for the local populace is the Hulu Langat district, which borders Kuala Lumpur, the capital. The purpose of this study is to look at how the ambient air quality varies in Hulu Langat, Selangor. The Air Quality Division of the Malaysian Department of Environment provided five years' worth of secondary data on the air quality at Hulu Langat. The database included five primary air pollutant characteristics sulphur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter with a diameter of 10 microns or less (PM10), in addition to data from the Air Pollutant Index (API). Chemometric analysis was used to examine the results. According to the results, SO2, NO2 and PM10 had the greatest correlations with API readings. A statistical process known as statistical control (SPC) showed that certain PM10 values were over national recommendations and control limits. The artificial neural network method's air quality prediction model demonstrated good accuracy with real data (R2 = 0.9). The results of this investigation indicated a strong correlation between the Hulu Langat air quality data. In order to achieve sustainable environmental practices in the future, it is imperative to engage in extensive collaboration across environmental departments and relevant authorities and engage in continuous monitoring of air quality.
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