Title: Two-parameter central fitting distribution to predict the concentration of ground level ozone: Case study in industrial area
Abstract: The decline in air quality can have a significant impact, particularly on human health. The elderly, children and people with asthma are among the most affected if faced with low air quality level. In Malaysia, the Department of Environment is responsible for monitoring and recording air quality and at this point, there are a total of 52 air quality monitoring stations operated by Alam Sekitar Malaysia Berhad (ASMA). The parameters monitored are airborne particles smaller than 10 micrometers, ground level ozone, sulphur dioxide, nitrogen dioxide, carbon monoxide, ambient temperature, relative humidity and wind speed. This research will focus on the ground level ozone since this pollutant is one of the main contributor to Air Pollution Index (API) in Malaysia. It is important to do air pollution prediction to help the management of air quality by the local authorities. This research will consider two central fitting probability distribution which is lognormal and gamma distribution to search for the best models for improving the prediction accuracy. The main objective of this research is to determine the best distribution to fit the actual monitoring records of ground level ozone and to predict the exceedences of ground level ozone and its return period. Results show that gamma distribution fit the data better compared to lognormal distribution.
Publication Year: 2018
Publication Date: 2018-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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