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Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
Published on: January 7, 2019
Monsoonal differences and probability distribution of PM(10) concentration
Noor Faizah Fitri Md Yusof1, Nor Azam Ramli, Ahmad Shukri Yahaya
1Clean Air Research Group, Environmental and Sustainable Development Section, School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, Pulau Pinang, Malaysia. myfaizah@hotmail.com
Environmental Monitoring and Assessment
|April 15, 2009
Summary
Particulate Matter (PM10) concentrations in Seberang Perai peak during the dry season (south west monsoon). This study modeled PM10 using lognormal and Weibull distributions, finding seasonal variations in model performance.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Statistical Modeling
Background:
- Particulate Matter (PM10) significantly impacts air quality and public health.
- Seasonal weather patterns, specifically monsoons, influence atmospheric pollutant concentrations.
Purpose of the Study:
- To analyze PM10 concentration variations between Malaysia's wet (north east) and dry (south west) monsoons.
- To model PM10 concentrations using lognormal and Weibull distributions.
- To evaluate the predictive performance of chosen statistical models.
Main Methods:
- Collected PM10 data from Seberang Perai, Malaysia (2000-2004).
- Analyzed PM10 concentrations against monsoon seasons (north east and south west).
- Applied lognormal and Weibull probability distributions for data modeling.
- Assessed model performance using established indicators.
Main Results:
- Highest PM10 concentrations were recorded during the dry season (south west monsoon).
- Lognormal distribution provided a better fit for PM10 data in 2000-2002.
- Weibull distribution showed superior performance for PM10 data in 2003-2004.
- Statistical models successfully estimated exceedances and predicted return periods.
Conclusions:
- The south west monsoon significantly contributes to elevated PM10 levels in Seberang Perai.
- The optimal statistical model for PM10 prediction varies annually.
- Probability distributions are effective tools for air quality management and risk assessment.
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