Browsing by Author "Nurulkamal Masseran"

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    Compositional time series analysis for Air Pollution Index data
    (Springer, 2018) Nasr Ahmed AL-Dhurafi; Nurulkamal Masseran; Zamira Hasanah Zamzuri
    The increasing importance of understanding the structure of Air Pollution Index (API) makes it necessary to come out with a compositional of API based on its pollutants. This will be more comprehensible for the public and easier to cooperate with authorities in reducing the causes of air pollution. Since five pollutants contribute in determining the API values, API can be shown as a compositional data. This study is conducted based on the data of API and its pollutants collected from Klang city in Malaysia for the period of January 2005 to December 2014. The proportion of each pollutant in API is considered as a component with five components in a compositional API. The existence of zero components in some pollutants, that have no effect on API, is a serious problem that prevents the application of log-ratio transformation. Thus, the approach of amalgamation has been used to combine the components with zero in order to reduce the number of zeros. Also, a multiplicative replacement has been utilized to eliminate the zero components and replace them with a small value that maintains the ratios of nonzero components. Transforming the compositional data to log-ratio coordinates has been done using the additive log ratio transformation, and the transformed series is then modeled by using a VAR model.
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    Modeling air quality in main cities of Peninsular Malaysia by using a generalized Pareto model
    (Elsevier, 2016) Nurulkamal Masseran; Ahmad Mahir Razali; Kamarulzaman Ibrahim; Mohd Talib Latif
    The air pollution index (API) is an important figure used for measuring the quality of air in the environment. The API is determined based on the highest average value of individual indices for all the variables which include sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and suspended particulate matter (PM10) at a particular hour. API values that exceed the limit of 100 units indicate an unhealthy status for the exposed environment. This study investigates the risk of occurrences of API values greater than 100 units for eight urban areas in Peninsular Malaysia for the period of January 2004 to December 2014.
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    Modeling the stochastic dependence of air pollution index data
    (Springer, 2018) Yousif Alyousifi; Nurulkamal Masseran; Kamarulzaman Ibrahim
    The air pollution index (API) is a common tool, which is often used for determining the quality of air in the environment. In this study, a discrete-time Markov chain model is applied for describing the stochastic behaviour of API data. The study reported in this paper is conducted based on the data collected from Klang city in Malaysia for a period of 3 years (2012-2014)
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    Power-law behaviors of the duration size of unhealthy air pollution events
    (Springer, 2021) Nurulkamal Masseran
    The duration size of air pollution events refers to a state in which air pollution indices reflect unhealthy conditions over an extended period of time. Thus, a large duration size implies prolonged air pollution events. Such events exert negative effects on human health, disrupt economic activities, and deteriorate environmental ecosystems. This study proposed the use of power-law models as a tool for evaluating the behaviors of duration size for extreme and unhealthy air pollution events. Four different power-law models were used to analyze the air pollution data in Klang, Malaysia.
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    Risk assessment of extreme air pollution based on partial duration series: IDF approach
    (Springer, 2020) Nurulkamal Masseran; Muhammad Aslam Mohd Safari
    The occurrences of extreme pollution events have serious effects on human health, environmental ecosystems, and the national economy. To gain a better understanding of this issue, risk assessments on the behavior of these events must be effectively designed to anticipate the likelihood of their occurrence. In this study, we propose using the intensity–duration–frequency (IDF) technique to describe the relationship of pollution intensity (i) to its duration (d) and return period (T).