排序方式： 共有5条查询结果，搜索用时 234 毫秒
1
1.
Nasr?Ahmed?AL-DhurafiEmail author Nurulkamal?Masseran Zamira?Hasanah?Zamzuri 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(10):2903-2911
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. Four criteria are used to determine the number of lags p of VAR(p) and these are: the Akaike Information, the Schwartz, the Hannan–Quinn and the Final Prediction Error criteria. Based on the results, A VAR (1) model with no constants or trend is considered as the best fitted model and it is used to forecast 12 months ahead. In addition, API values are mainly determined by PM10 that has a proportion close to one most of the time during study period. Therefore, authorities and researchers need to study the sources of PM10 and provide the public with useful information and alternatives in term of reducing the air pollution. 相似文献
2.
Natural Hazards - The severity level of air pollution refers to the cumulative effect of unhealthy air pollutant index (API) values during certain air pollution events. High severity levels... 相似文献
3.
Safari Muhammad Aslam Mohd Masseran Nurulkamal Majid Muhammad Hilmi Abdul 《Theoretical and Applied Climatology》2022,148(3-4):1085-1110
Theoretical and Applied Climatology - In this study, the Weibull distribution with various numerical estimation methods is utilized for the assessment of wind energy potential in Mersing and Port... 相似文献
4.
Modeling the stochastic dependence of air pollution index data 总被引：1，自引：1，他引：0
Yousif?AlyousifiEmail author Nurulkamal?Masseran Kamarulzaman?Ibrahim 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(6):1603-1611
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). Based on the API data, we considered a five-state Markov chain for depicting the five different states of the air pollution. We identified the Markov chain is an ergodic Markov chain and determined the limiting distribution for each state of the air pollution. In addition, we have identified the mean first passage time from one state to another. Based on the limiting distribution and the mean return time, we found that the risk of occurrences for unhealthy events is small. However, the risk remains notably troubling. Therefore, the standard of air quality in Klang falls within a margin that is considered healthy for human beings. 相似文献
5.
Nurulkamal Masseran Muhammad Aslam Mohd Safari 《Stochastic Environmental Research and Risk Assessment (SERRA)》2020,34(3):545-559
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). As a case study, we used data from the city of Klang, Malaysia. The construction of IDF curves involves a process of determining a partial duration series of an extreme pollution event. Based on PDS data, a generalized Pareto distribution (GPD) is used to represent its probabilistic behaviors. The estimated return period and IDF curves for pollution intensities corresponding to various return periods are determined based on the fitted GPD model. The results reveal that pollution intensities in Klang tend to increase with increases in the length of time between return periods. Although the IDF curves show different magnitudes for different return periods, all the curves show similar increasing trends. In fact, longer return periods are associated with higher estimates of pollution intensity. Based on the study results, we can conclude that the IDF approach provides a good basis for decision-makers to evaluate the expected risk of future extreme pollution events. 相似文献
1