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  1. Marzuki Ismail, Azrin Suroto, Nurul Ain Ismail, Mohd Talib Latif
    Sains Malaysiana, 2014;43:321-329.
    Surface ozone or tropospheric ozone has been recognized as one of the major factors that can give adverse impact on crops including rice plants. Effects of ozone on rice plants could be seen in decreased of biochemical activities and physiological performance which contribute to yield reduction. In Malaysia, surface ozone is on the rise due to increment anthropogenic sources i.e. urbanization, transportation and also industrialization process. This condition is alarming due to the facts that rice is the major staple food to the majority of Malaysian population. In this study, exceedence of ozone exposure above an hourly threshold concentration of 40 ppb (AOT40) and ozone trends in four major rice growing areas in Malaysia were assessed using time series analysis of ozone data recorded in each area from January 2000 until December 2010 with a total of 132 readings. The results showed a steady increase in exceedence ozone of yearly AOT40 and statistical significant upward trend for ozone concentrations in each rice growing area in Malaysia. This finding was particularly alarming because ozone is able to inhibit production of rice yields. Preventive actions need to be implemented as soon as possible in order to alleviate ozone threat to our national food security agenda.
  2. Marzuki Ismail, Mohd Zamri Ibrahim, Tg. Azmina Ibrahim, Ahmad Makmon Abdullah
    Sains Malaysiana, 2011;40:1179-1186.
    Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). In this study we used Box-Jenkins methodology to build ARIMA model for monthly ozone data taken from an Automatic Air Quality Monitoring System in Kemaman station for the period from 1996 to 2007 with a total of 144 readings. Parametric seasonally adjusted ARIMA (0,1,1) (1,1,2)12 model was successfully applied to predict the long-term trend of ozone concentration. The detection of a steady statistical significant upward trend for ozone concentration in Kemaman is quite alarming. This is likely due to sources of ozone precursors related to industrial activities from nearby areas and the increase in road traffic volume.
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