Sharifah Mastura Syed Abdullah

Comprehensive assessment of PM2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

A comprehensive assessment of fine particulate matter (PM2.5) compositions during the Southeast Asia dry season is presented. Samples of PM2.5 were collected between 24 June and 14 September 2014 using a high-volume sampler. Water-soluble ions, trace species, rare earth elements, and a range of elemental carbon (EC) and organic carbon were analyzed. The characterization and source apportionment of PM2.5 were investigated. The results showed that the 24 h PM2.5 concentration ranged from 6.64 to 68.2 µg m−3. Meteorological driving factors strongly governed the diurnal concentration of aerosol, while the traffic in the morning and evening rush hours coincided with higher levels of CO and NO2. The correlation analysis for non sea-salt K+-EC showed that EC is potentially associated with biomass burning events, while the formation of secondary organic carbon had a moderate association with motor vehicle emissions. Positive matrix factorization (PMF) version 5.0 identified the sources of PM2.5: (i) biomass burning coupled with sea salt [I] (7%), (ii) aged sea salt and mixed industrial emissions (5%), (iii) road dust and fuel oil combustion (7%), (iv) coal-fired combustion (25%), (v) mineral dust (8%), (vi) secondary inorganic aerosol (SIA) coupled with F− (15%), and (vii) motor vehicle emissions coupled with sea salt [II] (24%). Motor vehicle emissions, SIA, and coal-fired power plant are the predominant sources contributing to PM2.5. The response of the potential source contribution function and Hybrid Single-Particle Lagrangian Integrated Trajectory backward trajectory model suggest that the outline of source regions were consistent to the sources by PMF 5.0.

Energy consumption and emission projection for the road transport sector in Malaysia: an application of the LEAP model

This study has attempted to estimate the energy consumption and emission of pollutants namely carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx) and non-methane volatile organic compounds (NMVOC) from the road transport sector in Malaysia from the year 2012 till 2040. This was done using the long-range energy alternatives planning (LEAP) model. Estimates of energy consumption and emissions were evaluated and analysed under a business-as-usual scenario and three other alternative fuel policy scenarios of biodiesel vehicles (BIO), natural gas vehicles (NGV) and hybrid electric vehicles (HEV). The aim of this study has been to identify the potential alternative fuel policies that would be effective in reducing the future growth of road transport energy consumption and emission in Malaysia. Results indicate that the NGV scenario contributes towards the highest reduction in road transport energy consumption followed by BIO and HEV. The NGV scenario also achieves highest mitigation of emission of all the four pollutants. In the case of CO2 emission, BIO scenario attains second highest mitigation, whereas in the event of CO, NOx and NMVOC emission, HEV scenario achieves second highest mitigation.

Dynamic impact of household consumption on its CO 2 emissions in Malaysia

This article aims to measure the dynamic impact of household consumption (final household consumption expenditure, LHC) on CO2 emission from household’s energy consumption in Malaysia from 1971 to 2010. The estimation of autoregressive distributed lag (ARDL) bounds test confirms a non-monotonic relationship between LHC and residential CO2 emission. In the long run, there is a positive relationship between LHC and CO2 emission as well as a negative relationship between quadratic forms of LHC and CO2 emission which indicates the existence of an inverted U-shaped relationship between these two variables. The analysis also found a similar relationship in both the short and long run. To confirm the non-monotonous relationship, the U test of Sasabuchi–Lind–Mehlum (2010) approach has followed to obtain the sufficient conditions for the existence of inverted U relationship. Moreover, the U test of Sasabuchi–Lind–Mehlum (2010) found that CO2 emission increases with increasing LHC up to 6.5 units, but it declines with an additional increase of LHC which is also found by the ARDL model. However, the existence of environmental Kuznets curve implies that in the long run, household CO2 emission declines with the additional increase of household consumption in the Malaysian economy.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Prediction of fatigue crack growth rate using rule-based systems

Fatigue crack propagation life has been estimated based on established empirical equations. In the present work, fatigue crack rates of ASTM A533 alloy under the different load histories was predicted by adaptive neuro fuzzy approach (ANFIS). A novel soft-computing application, suitable for non-linear, noisy and complex problems like fatigue. The features and usefulness of our method are to initial the fatigue crack growth rate with the number of cycles relationship for each case study. In this research, an automatic prediction methodology has been adopted to estimate the constant amplitude loading fatigue life under the above condition by applying neuro fuzzy approach (ANFIS). Soft-computing methods show great potential for predicting the fatigue crack growth rate, especially with really data. The predicted results are found to be in perfect agreement with the experimental findings when tested on ASTM A533 alloy.

Physicochemical factors and their potential sources inferred from long-term rainfall measurements at an urban and a remote rural site in tropical areas

Air pollution can be detected through rainwater composition. In this study, long-term measurements (2000-2014) of wet deposition were made to evaluate the physicochemical interaction and the potential sources of pollution due to changes of land use. The rainwater samples were obtained from an urban site in Kuala Lumpur and a highland-rural site in the middle of Peninsular Malaysia. The compositions of rainwater were obtained from the Malaysian Meteorological Department. The results showed that the urban site experienced more acidity in rainwater (avg=277mm, range of 13.8 to 841mm; pH=4.37) than the rural background site (avg=245mm, range of 2.90 to 598mm; pH=4.97) due to higher anthropogenic input of acid precursors. The enrichment factor (EF) analysis showed that at both sites, SO42-, Ca2+ and K+ were less sensitive to seawater but were greatly influenced by soil dust. NH4+ and Ca2+ can neutralise a larger fraction of the available acid ions in the rainwater at the urban and rural background sites. However, acidifying potential was dominant at urban site compared to rural site. Source-receptor relationship via positive matrix factorisation (PMF 5.0) revealed four similar major sources at both sites with a large variation of the contribution proportions. For urban, the major sources influence on the rainwater chemistry were in the order of secondary nitrates and sulfates>ammonium-rich/agricultural farming>soil components>marine sea salt and biomass burning, while at the background site the order was secondary nitrates and sulfates>marine sea salt and biomass burning=soil components>ammonia-rich/agricultural farming. The long-term trend showed that anthropogenic activities and land use changes have greatly altered the rainwater compositions in the urban environment while the seasonality strongly affected the contribution of sources in the background environment.

Pattern discovery algorithm for weather prediction problem

This paper proposes two pattern discovery algorithms for weather prediction problem. The episodes mining algorithm is introduced to find frequent episodes in rainfall sequences and sequential pattern mining algorithm to find relationship of patterns between weather stations. Real data are collected from ten rainfall stations of Selangor State, Malaysia. The sequential pattern algorithm is applied to extract the relationship between ten rainfall stations in 33 years periods of time. The proposed study produces valuable patterns of weather and preserves important knowledge for weather prediction.

A GIS-ANN-Based Approach for Enhancing the Effect of Slope in the Modified Green-Ampt Model

Most infiltration models survey infiltration in large scale regions using an assumption that the slope of the ground is equal to zero. The Modified Green and Ampt model is one of a few infiltration models that considers slope as an input parameter in its formulation. Here, using artificial neural networks in a raster-based design, basic research is presented regarding the effect of surface slope on infiltration. For the investigation, three catchments with different areas and slopes were selected as case studies, based on existing runoff stations in the upstream region of the Johor River Basin in southern Malaysia. In this research, the efficiency of six different functions was studied in order to determine the best performer for slope in the Modified Green and Ampt model. We also sought to find the most suitable ANN transfer function for infiltration calculations. By calculating runoff for each pixel, accumulation maps were used for corroborating the suitability of the obtained results. The results indicated that the Log-sigmoid was the most appropriate transfer function. We also determined that using the exponential form for the slope in the Modified Green and Ampt model formulation was more accurate, as compared to the original linear shape.

Spatial and temporal analysis of deforestation and forest degradation in Selangor: Implication to carbon stock above ground

This paper aims to develop an operational methodology for monitoring spatial and temporal changes due to deforestation in Selangor over a 22 year period. The driving forces determining the changes were also analysed. Overall, the results show that the causes of deforestation were the economic factors, namely agriculture intensification, and population dynamics, related to the process of urbanization. However, deforestation statistics shows only a total of 10 percent decrease; it is the degradation of the remaining forest that is the major concern. Knowledge on deforestation and its driving forces in Selangor is very important as it provides the basis for the calculation of the total amount of carbon stock above ground. It also gives insight into the appropriate intervention measures that can be taken to increase carbon stock, thus reducing the release of carbon dioxide emission to the atmosphere.

Discovering frequent serial episodes in symbolic sequences for rainfall dataset

Serial episode is a type of temporal frequent pattern in time series. Many different algorithms have been proposed to discover different types of episodes for different applications. In this paper we propose an algorithm for discovering frequent episodes from processed rain fall data. The algorithm is based on three main steps. (1) The rainfall data is first represented in symbolic representation (2) Then numbers of events are detected by applying sliding window for segmentation and CBR for classification. (3)Finally the processed rain fall data is passed through mining phase. Frequent algorithm is used to discover frequent episodes with fixed width. The experiment shows that many frequent episodes with different structure in different years are extracted.