The probability distribution model of air pollution index and its dominants in Kuala Lumpur

This paper focuses on the statistical modeling for the distributions of air pollution index (API) and its sub-indexes data observed at Kuala Lumpur in Malaysia. Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulat...

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Bibliographic Details
Main Authors: AL-Dhurafi, Nasr Ahmed, Razali, Ahmad Mahir, Masseran, Nurulkamal, Zamzuri, Zamira Hasanah
Format: Conference Proceeding
Language:English
Subjects:
Air pollution
Carbon monoxide
Goodness of fit
Nitrogen dioxide
Particulate emissions
Pollutants
Probability distribution functions
Statistical analysis
Statistical models
Sulfur dioxide
Weight
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Summary:This paper focuses on the statistical modeling for the distributions of air pollution index (API) and its sub-indexes data observed at Kuala Lumpur in Malaysia. Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulate matter (PM10). Four probability distributions are considered, namely log-normal, exponential, Gamma and Weibull in search for the best fit distribution to the Malaysian air pollutants data. In order to determine the best distribution for describing the air pollutants data, five goodness-of-fit criteria’s are applied. This will help in minimizing the uncertainty in pollution resource estimates and improving the assessment phase of planning. The conflict in criterion results for selecting the best distribution was overcome by using the weight of ranks method. We found that the Gamma distribution is the best distribution for the majority of air pollutants data in Kuala Lumpur.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4966829