Page 68 - Special Topic Session (STS) - Volume 2
P. 68

STS459 Norshahida S. et al.
                  photochemical  reactions  of  precursors  such  as  volatile  organic  compound
                  (VOCs) and Nitrogen Oxides (NOx) (Lee et al., 2010). During hot and sunny day,
                  the formation of O3 is greatly enhanced. The variation of O3 is largely depends
                  on meteorology variables such as sunlight, temperature and wind (Ling et al.,
                  2014).
                      Surface or Troposphere O3 contributes to a certain environmental problem
                  including  health  problems,  vegetation  and  materials,  as  well  as  climate
                  changing.  O3  is  defined  as  a  secondary  pollutant  whereby  volatile  organic
                  compounds and NOx from point sources are the precursors of Ozone in the
                  country. Ozone shows strong day-to-day variation and sometimes virtually
                  undetectable  (Ramli  et  al.,  2010).  In  the  Malaysia  environment,  the  main
                  sources of VOCs and NOx emissions are from industries and motor vehicles
                  particularly in urban areas (DOE 2014). In Klang Valley for example, it is found
                  that O3 exceedances pattern is strongly influenced by local pollutant emission
                  and its dispersion characteristics. It is also observed that the variation of O3
                  concentration  is  continuous  in  nature  and  its  severity  behaviour  is  often
                  uncertain (Shaadan et al., 2018). O3 pollution is odourless, the physical form is
                  invisible and the pattern of occurrence is less understood. Thus, the existence
                  of ozone pollution is often unaware.
                      According to Ghazali et al. (2014), Malaysia has not yet reached a critical
                  level of the air pollution as in other metropolitan areas in Asia but its potential
                  increased need to be assessed and predicted. Data monitoring and studies on
                  air quality shows that some of the air pollutants in several large cities such as
                  Johor, Selangor and Sabah are increasing with time and are not always at a
                  satisfied level in comparison to the national standard (Rafia & Ibrahim, 2002).
                  Since Ozone is one of the important pollutants, for the purpose of managing
                  and mitigating air pollution, it is important to study and predict the return
                  period  of  ozone  pollution  in  the  future  period.  Malaysia  weather  is
                  characterized by two main seasons, the Northwest and Southwest monsoons
                  and other two transaction periods. Drier weather condition often recorded
                  during Southwest monsoon and during the second and the first transition
                  period  at  several  regions.  On  the  other  hand,  more  frequent  and  larger
                  amount of rain were recorded during the Northwest monsoon particularly at
                  the east coast region in Peninsular Malaysia. As reported by Siti et al., (2015)),
                  air  pollution  occurrence  and  high  level  of  pollutants  concentration  often
                  recorded during the dry season.
                      There are many statistical methods and models can be used for predicting
                  air  pollutant  level.  However,  the  application  of  probability  models  for
                  predicting high pollutant levels and the exceedance is rarely discussed on O3
                  data in the Malaysia environment. Majority of the studies had discussed on
                  PM10  pollutant since PM10 is reported as a major component of haze. To fill the
                  gap,  this  study  will  focus  on  the  comparison  of  several chosen  probability

                                                                      57 | I S I   W S C   2 0 1 9
   63   64   65   66   67   68   69   70   71   72   73