Statistical modeling approaches for PM₁₀ forecasting at industrial areas of Malaysia

Major industrial areas in Malaysia experience number of unhealthy days because of extreme impermanent PM₁₀ incidents which are detrimental to human and the environment. In order to lessen the threat of acute air pollutant levels, a short-term forecasting algorithms is needed to advise people in general of unsafe air pollution happenings, and additionally to formulate air pollution management strategies. In this approach, statistical models consisting of MLR and PCR are employed for PM predictions at 4 major industrial areas of Seberang Prai, Pasir Gudang, Kemaman and Nilai in Peninsular Malaysia. Gaseous pollutants, meteorological factors and 8 years data of daily PM₁₀ form 2007 till 2014 was applied to predict PM₁₀ concentration levels. Results showed that MLR performed better than PCR and the major primary sources are road traffic and industrial emissions whilst wind speed display inversely proportional relationship with the PM₁₀ concentrations.