Nurulkamal Masseran

Modeling air quality in main cities of Peninsular Malaysia by using a generalized Pareto model

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. An extreme value model, known as the generalized Pareto distribution (GPD), has been fitted to the API values found. Based on the fitted model, return period for describing the occurrences of API exceeding 100 in the different cities has been computed as the indicator of risk. The results obtained indicated that most of the urban areas considered have a very small risk of occurrence of the unhealthy events, except for Kuala Lumpur, Malacca, and Klang. However, among these three cities, it is found that Klang has the highest risk. Based on all the results obtained, the air quality standard in urban areas of Peninsular Malaysia falls within healthy limits to human beings.

Modeling the Air Pollution Index based on its structure and descriptive status

Accurate air pollution modeling is essential for estimating the Air Pollution Index (API) effectively. The air quality assessment relies on the ability of the selected probability density function (PDF) to describe the observed air pollution data. This study characterizes the API data in Klang, Malaysia, for the period of January 2005 to December 2014. The study proposed three different approaches in modeling API characteristics, including conventional models, API structure models, and descriptive status models. The first approach is the conventional models, which are the most common distributions used for modeling the API and its pollutants. The fitted distributions of the observed and generated API data are used for comparisons to other proposed models. In addition, the selected distributions of pollutants were used as a basis in the construction of API structure models. The second approach is the API structure models, which involve a mixture of distributions for the critical pollutants. Finally, the third approach was based on the descriptive status of the API. The results show that the healthy status is able to be described using the conventional fitted models, while the generalized Pareto distribution (GPD) is found to be a good fitted model for the unhealthy status. In fact, based on the selection criteria, it was found that the API structure models are superior for modeling the API data. In addition, the API descriptive status models are useful for evaluating the unhealthy API return level. In summary, we conclude that the mixture distribution of the API components should be considered as a better method for simulating the API data.

The probability distribution model of air pollution index and its dominants in Kuala Lumpur

This paper focuses on the statistical modeling for the distributions of air pollution index (API) and its sub-indexes data observed at Kuala Lumpur in Malaysia. Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulate matter (PM10). Four probability distributions are considered, namely log-normal, exponential, Gamma and Weibull in search for the best fit distribution to the Malaysian air pollutants data. In order to determine the best distribution for describing the air pollutants data, five goodness-of-fit criteria’s are applied. This will help in minimizing the uncertainty in pollution resource estimates and improving the assessment phase of planning. The conflict in criterion results for selecting the best distribution was overcome by using the weight of ranks method. We found that the Gamma distribution is the best distribution for the majority of air pollutants data in Kuala Lumpur.

Fitting a circular distribution based on nonnegative trigonometric sums for wind direction in Malaysia

Wind direction has a substantial effect on the environment and human lives. As examples, the wind direction influences the dispersion of particulate matter in the air and affects the construction of engineering structures, such as towers, bridges, and tall buildings. Therefore, a statistical analysis of the wind direction provides important information about the wind regime at a particular location. In addition, knowledge of the wind direction and wind speed can be used to derive information about the energy potential. This study investigated the characteristics of the wind regime of Mersing, Malaysia. A circular distribution based on Nonnegative Trigonometric Sums (NNTS) was fitted to a histogram of the average hourly wind direction data. The Newton-like manifold algorithm was used to estimate the parameter of each component of the NNTS model. Next, the suitability of each NNTS model was judged based on a graphical representation and Akaike’s Information Criteria. The study found that the NNTS model with...

Outliers detection for Pareto distributed data

This study aims to examine the presence of outliers in the upper tail of Malaysian income distribution under the assumption that the data follow Pareto model. For this purpose, three types of boxplot: standard boxplot, adjusted boxplot and generalized boxplot are considered. The performance of these boxplots is determined by a simulation study. In this study, the data were simulated from Pareto distribution, P(1, α = 2, 3, 4), then the simulated data were contaminated by replacing a proportion e (3%, 5%, 10%) of randomly selected data. It is found that the generalized boxplot gives higher power value compared to the standard and adjusted boxplots. Therefore, the generalized boxplot was used for determining the presence of outliers in the upper tail of income distribution, while the threshold for Pareto tail modelling was determined by using Van Kerm’s formula. The results showed that 0.4%, 0.4%, 0.9% and 1.2% outliers were detected by the generalized boxplot in the household income data that exceeded the threshold for the years of 2007, 2009, 2012 and 2014.This study aims to examine the presence of outliers in the upper tail of Malaysian income distribution under the assumption that the data follow Pareto model. For this purpose, three types of boxplot: standard boxplot, adjusted boxplot and generalized boxplot are considered. The performance of these boxplots is determined by a simulation study. In this study, the data were simulated from Pareto distribution, P(1, α = 2, 3, 4), then the simulated data were contaminated by replacing a proportion e (3%, 5%, 10%) of randomly selected data. It is found that the generalized boxplot gives higher power value compared to the standard and adjusted boxplots. Therefore, the generalized boxplot was used for determining the presence of outliers in the upper tail of income distribution, while the threshold for Pareto tail modelling was determined by using Van Kerm’s formula. The results showed that 0.4%, 0.4%, 0.9% and 1.2% outliers were detected by the generalized boxplot in the household income data that exceeded the ...

Public acceptance on stand-alone renewable energy project in rural area of Malaysia

This paper investigates the rural public acceptance on stand-alone Renewable Energy (RE) project for the utilization of the Pusat Penyelidikan Ekosistem Marin (EKOMAR) which is located at Kampung Tanjung Resang, Mersing. Two surveys were conducted which are before and after the installation of a hybrid wind-solar renewable energy. The main result showed that 54.7% and 96.2 % of respondents were aware about the RE technologies such as wind and solar energy in before and after the project. In overall, the rural residents were generally supportive toward the RE project at EKOMAR and as well as RE technologies.

The impact of wind technology among rural community in Mersing Malaysia

The renewable technology is very important in our day lives. The impact of renewable technology are improving lifestyle, meet the basic electricity for rural need and the impact to green environment. Currently the community in Mersing use grid power source supply by Tenaga Nasional Berhad (TNB). Abundant and consistent of wind speed in Mersing are potential enough to develop wind turbine. This will generate renewable energy (RE). However, the drawbacks of device utilization gives burden to the community. To initiate the project, Solar Energy Research Institute (SERI), Universiti Kebangsaan Malaysia (UKM) has developed wind turbine in Kampung Tanjung Resang, Mersing, Johor, Malaysia. This study presents the acceptance and the impact of wind energy of the people in Tanjung Resang. The procedure of this research is done based on qualitative method through survey sampling and non-directive interviews to the villager. From the survey and interview, they were completely relying with the benefit yield from wind technology. Based on survey sampling, we classified the factor that can give the impact to the people in Tanjung Resang. In long term, wind energy can improve the quality of life for the people in this area.