Monsoonal differences and probability distribution of PM₁₀ concentration

There are many factors that influence PM₁₀ concentration in the atmosphere. This paper will look at the PM₁₀ concentration in relation with the wet season (north east monsoon) and dry season (south west monsoon) in Seberang Perai, Malaysia from the year 2000 to 2004. It is expected that PM₁₀ will re...

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Bibliographic Details
Published in:Environmental monitoring and assessment 2010-04, Vol.163 (1-4), p.655-667
Main Authors: Md Yusof, Noor Faizah Fitri, Ramli, Nor Azam, Yahaya, Ahmad Shukri, Sansuddin, Nurulilyana, Ghazali, Nurul Adyani, al Madhoun, Wesam
Format: Article
Language:English
Subjects:
Air pollution
Applied sciences
Atmospheric pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Climate
Dry season
Earth and Environmental Science
Ecology
Ecotoxicology
Emissions
Environment
Environmental Management
Environmental monitoring
Exact sciences and technology
Exceedences
Humidity
Lognormal distribution
Monitoring/Environmental Analysis
Monsoons
Outdoor air quality
Particulate matter
Pollutants
Pollution
Population
Probability distribution
Rain
Rainy season
Random variables
Return period
Seasons
State government
Studies
Time series
Weibull distribution
Wind
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Summary:There are many factors that influence PM₁₀ concentration in the atmosphere. This paper will look at the PM₁₀ concentration in relation with the wet season (north east monsoon) and dry season (south west monsoon) in Seberang Perai, Malaysia from the year 2000 to 2004. It is expected that PM₁₀ will reach the peak during south west monsoon as the weather during this season becomes dry and this study has proved that the highest PM₁₀ concentrations in 2000 to 2004 were recorded in this monsoon. Two probability distributions using Weibull and lognormal were used to model the PM₁₀ concentration. The best model used for prediction was selected based on performance indicators. Lognormal distribution represents the data better than Weibull distribution model for 2000, 2001, and 2002. However, for 2003 and 2004, Weibull distribution represents better than the lognormal distribution. The proposed distributions were successfully used for estimation of exceedences and predicting the return periods of the sequence year.
ISSN:0167-6369
1573-2959
DOI:10.1007/s10661-009-0866-0