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Siti Aisyah Zakaria
Preferred name
Siti Aisyah Zakaria
Official Name
Siti Aisyah, Zakaria
Alternative Name
Zakaria, S.
Zakaria, S.A.
S.A., Zakaria
Main Affiliation
Scopus Author ID
56442195200
Researcher ID
EII-3879-2022
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PublicationSpatial max-stable with cyclic generalizes extreme value model for extreme ground level ozoneThe high level of ground-level ozone (GLO) concentration has serious adverse effects on health problems and affects the environment. This study integrates the spatial max-stable processes with the cyclic generalized extreme value (GEV) model to analyze and forecast extreme GLO levels. Spatial extreme provides a framework for analyzing and modelling the behaviour of rare events considering the extreme data pattern and the characteristics of several stations. One key component in this approach is selecting an appropriate GEV marginal distribution based on the data structure of each monitoring station. The choice of marginal distribution depends on whether there is an apparent trend in the extreme data series. In cases where the data exhibit strong seasonal variation, a stationary model may not be appropriate. This study acknowledges the seasonal variation in GLO data at various monitoring stations, influenced by the interchange of monsoons. Therefore, a seasonal variation model is considered as the marginal distribution for the spatial extreme model. Additionally, the study extends the non-stationary model from univariate cases to the spatial extreme model. This model incorporates a cyclic pattern in location parameters to complement the GEV distribution as a new marginal distribution within the max-stable process, a standard dependency model for spatial extreme observed at different locations. The clustering process using hierarchical cluster analysis (HCA) found that all the stations can be grouped into two clusters depending on the same characteristic of the weekly maxima data. The extremal coefficient between 1 to 2 indicates that the stations are dependent on each other’s within the cluster. Validation of the developed model is crucial for accurate predictions. Synthetic data approximating real data characteristics are generated to validate the model and facilitate predictions of future extreme cases based on return values for specific return periods. Return levels, indicating the average amount of extreme events within a specified return period, are used to predict GLO concentration levels across different locations, enhancing the understanding of GLO concentration patterns based on location categories. The presentation of return level results in return level mapping further aids in visualizing and interpreting the predictions for all monitoring stations. The main finding of this study indicates that the return level of GLO concentration increased as the return period increased. The results show that most of the return levels exceed the guideline of MAAQG for 8-hour average that is 0.06 ppm. Notably, the station in Kota Bahru (CA22) stands out with the lowest return levels, while the Shah Alam (CA25) station exhibits the highest estimated values. This high GLO concentration in Shah Alam may be attributed to its urban location, marked by high traffic density, industrial operations, and diverse meteorological influences. In conclusion, this study is highly significant as it offers valuable insights that can be applied in the fields of environmental and climatology, specifically regarding GLO in peninsular Malaysia. The methodology detailed in this study can be adapted for the analysis of other extreme datasets.