Clean Air Research Group, Environmental and Sustainable Development Section, School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, Pulau Pinang, Malaysia.
Environ Monit Assess. 2010 Apr;163(1-4):655-67. doi: 10.1007/s10661-009-0866-0. Epub 2009 Apr 14.
There are many factors that influence PM(10) concentration in the atmosphere. This paper will look at the PM(10) 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 PM(10) will reach the peak during south west monsoon as the weather during this season becomes dry and this study has proved that the highest PM(10) concentrations in 2000 to 2004 were recorded in this monsoon. Two probability distributions using Weibull and lognormal were used to model the PM(10) 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.
有许多因素会影响大气中的 PM(10)浓度。本文将探讨马来西亚槟城 2000 年至 2004 年期间与湿季(东北季风)和干季(西南季风)相关的 PM(10)浓度。预计在西南季风期间 PM(10)浓度将达到峰值,因为该季节的天气干燥,研究表明,2000 年至 2004 年期间记录到的 PM(10)浓度最高。使用威布尔和对数正态分布两种概率分布来对 PM(10)浓度进行建模。基于性能指标选择用于预测的最佳模型。对于 2000 年、2001 年和 2002 年,对数正态分布模型比威布尔分布模型更能代表数据。然而,对于 2003 年和 2004 年,威布尔分布模型比对数正态分布模型更能代表数据。所提出的分布成功地用于估计超过值和预测序列年份的重现期。