Statistical modeling for locational differences and prediction of temporal PM10 concentration

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dc.contributor.authorSansuddin, Nurulilyana
dc.date.accessioned2015-07-30T01:14:13Z
dc.date.available2015-07-30T01:14:13Z
dc.date.issued2010
dc.description.abstractThe aim for this research is to model and predict the PM10 concentrations using the probability distributions and time series models to help curb the adverse impact of PM10 on human health. Ten monitoring stations with five years PM10 monitoring records from 2000 to 2004 were used in this research. Four distributions namely gamma, log-normal, Weibull and inverse Gaussian distributions were used to fit hourly average of PM10 observation records. Based on the five types of performance indicator values, the gamma distribution is chosen as the best distribution to fitting Johor Bharu, Jerantut, Kangar and Nilai while, log-normal distribution was fitted to Kota Kinabalu, Kuantan, Kuching, Manjung, Melaka and Seberang Perai. Predicted PM10 concentrations which exceeds the threshold limit in unit of days were estimated using the best distributions and were compared to the actual monitoring records. In order to calibrate the monitoring records from E-sampler and Beta Attenuation Mass (BAM), the most appropriate k-factor given by Kuching station was used. In addition, the daily average of PM 10 concentrations was used to find the best time series model. Three types of time series models were used named autoregressive (AR), moving-average (MA) and autoregressive moving-average (ARMA). The AR(l) is identified as the best model to represent all stations except for J erantut which is represented by the ARMA( 1, 1 ).en_US
dc.identifier.urihttp://hdl.handle.net/123456789/895
dc.language.isoenen_US
dc.subjectModeling for temporalen_US
dc.subjectPM10 consentrationen_US
dc.titleStatistical modeling for locational differences and prediction of temporal PM10 concentrationen_US
dc.typeThesisen_US
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