| Autor: |
Bakar, Mohd Aftar Abu, Ariff, Noratiqah Mohd, Weki, Sor, Mohd Nadzir, Mohd Shahrul |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3150 Issue 1, p1-12, 12p |
| Abstrakt: |
Deteriorating air quality and uncontrolled air pollution can affect climate change as well as human health. The Air Pollutant Index (API) has been used as an indicator to indicate the status of air quality. The objective of this study is to determine the level of air pollution based on the calculation of API as well as to identify clusters for air quality monitoring stations based on the concentration of air pollutants and API values. This study involved 10 air quality monitoring stations from the urban, sub-urban and industrial areas. The data used in this study are the daily concentration of air pollutants namely carbon monoxide (CO), ozone (O3), fine particles (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) from January until December 2011. Median of the pollutant concentrations were used to represent the monthly API for each station. Urban areas had the highest percentage of moderate air quality levels compared to suburban and industrial areas. O3 was found to be the dominant pollutant in urban areas while PM10 is the dominant pollutant in suburban and industrial areas. Fuzzy k-medoid clustering (FKM) is one of the fuzzy clustering algorithms that has a cluster center known as the medoid and can produce soft clusters. The air quality monitoring stations are assigned to the clusters according to the membership degrees ranging in the interval [0,1] and the data points of the station can belong to more than one cluster at the same time with different membership degrees. The degree of memberships indicate the level of confidence that the data points are in a particular group. Two clusters were obtained in which cluster 1 is characterized as a cluster with higher pollutant concentrations and API values compared to cluster 2. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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