Spatial variations of drinking water quality monitoring in water treatment plant using environmetric techniques

This research investigates the relationship between the physicochemical levels and the drinking water quality in Kuala Kubu Bharu, Selangor, Malaysia based on three different classes of drinking water. The environmetric techniques such as the discriminant analysis (DA), the principal component analysis (PCA) and the factor analysis (FA) were applied to analyze the spatial variation of the most significant physicochemical parameters of the drinking water quality and to determine the source of pollution. Seven physicochemical variables were analyzed. The forward and backward stepwise DA managed to discriminate six and two variables, respectively from the original seven variables. PCA and FA (varimax functionality) were to identify the origin of each water quality variable based on the three different drinking water classes. This study shows that environmetric method is the ideal way into provide meaningful information on the spatial variability of sophisticated drinking water quality data

Spatial assessment of water quality affected by the land-use changes along Kuantan river basin

This study addresses the effects of development on water quality in the Kuantan River Basin from 2003 to 2008. Chemometrics analysis namely MLR, HACA, DA and PCA was utilised as part of the methods for this study. From the result, MLR was irrefutably proven as an efficient predicting method for missing data. HACA classified seven stations as Low Polluted Stations (LPS), six stations as Moderate Polluted Stations (MPS) and two stations as High Polluted Stations (HPS). DA result depicted the accuracy rate for all reclassified data was 83.61 % respectively, while the constituting parameters namely Dissolved Oxygen (DO), Escherichia coli (E. coli), pH, Phosphate (PO4), Chemical Oxygen Demand (COD), and Chloride (Cl), gave the biggest impacts towards water quality by means of forward and backward stepwise methods. The PCA result after varimax rotation indicated that five varimax factors have presented strong parameter coefficient exceeding 0.7 by E. coli, coliform, Dissolved Solids (DS), Total Solids (TS), Chlorine (Cl), Ammonical Nitrogen (NH3NL), nitrate and pH. The relationship between land use and water quality denoted that after applying Spearman correlation based on 90 % interval population distribution, aspects influencing the rate of DO was successfully identified

Marine water quality index trend from eight-year study of Klang Estuary

In the context of marine water quality monitoring, detailed information concerning the marine water quality index is importance. The paper presents the analysis of 8-year period trend (2010-2017) marine water quality index and the other marine water quality parameters fluctuations in the Klang estuary, which is have the famous Port Klang, the one of the largest and busiest ports in peninsular Malaysia. The 2010–2017 data employed in this study entailed 12 marine water quality parameters. In order to investigate the trend analysis, the nonparametric Mann-Kendall statistical test has been used. The result shows the upward trends for MWQI, Salinity, COND, TEMP, DO and O&G and downward trends for pH, TUR, TSS, coliform, PO4, NH3N and NO3. in 8-year period in Klang estuary. The results indicated Klang Estuary has experienced a mild pollution trend due to anthropogenic influence from domestic activities in the vicinity of the estuary

The Effectiveness of the Modular Enrichment Activities Based on Gardner Multiple Intelligences and Sternberg Thinking Skills

This study aimed to test the effectiveness of the modular enrichment activities which were developed based on multiple intelligences and thinking skills. The adoption of the four types of intelligence as introduced by Gardner (1983) consisted of verbal linguistic, logical mathematical, visual and kinesthetic intelligences. While the analytical, practical and creative thinking skills as raised by Sternberg (1985) in the triarchic theory of intelligence (Sternberg Triarchic Theory). A quasi-experimental research design was used which involved academically-gifted students from two Mara Junior Science Colleges (MRSMs) in Malaysia. Two instruments were used in this study, the Sternberg Triarchic Ability Test (STAT) and multiple intelligence test. ANCOVA analysis and Kruskal-Wallis H test were then employed to analyze the data. The findings suggested that the integration of Gardner's multiple intelligence and Sternberg’s thinking skills through the modular enrichment activities stimulated the multiple intelligence profiles and the levels of the thinking skills of the treatment group significantly. Keywords: Multiple intelligences, Thinking skills, Enrichment activities

Laboratory Assessment of Cadmium, Copper, and Zinc Phytoaccumulation by Ipomoea aquatica, Peltandra virginica, and Salvinia molesta for Phytoremediation Potential

Heavy metal contamination in aquatic ecosystems poses significant risks to biodiversity and human health. Conventional remediation methods, while effective, are often expensive and inefficient. This study explores phytoremediation—a sustainable and cost-effective alternative that uses plants to remove pollutants—as a potential solution. We assessed the accumulation capacities of cadmium (Cd), copper (Cu), and zinc (Zn) by three aquatic plant species: Ipomoea aquatica (water spinach), Peltandra virginica (arrow arum), and Salvinia molesta (giant salvinia). Plants were acclimatized for seven days before exposure to metal solutions at concentrations of 5 mg/L, 10 mg/L, and 15 mg/L over a 20-day period under controlled laboratory conditions. Sampling was performed every four days, and metal accumulation was quantified using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). One-way Analysis of Variance (ANOVA) was used to determine statistically significant differences in uptake among species. I. aquatica exhibited the highest Cd accumulation (13.77 mg/L) at 15 mg/L concentration, with a removal efficiency of 89.7%. S. molesta showed the greatest Cu (57.3%) and Zn (92.6%) removal efficiencies at 15 mg/L and 10 mg/L, respectively. Statistically significant differences in metal uptake were observed at higher concentrations. These findings suggest that I. aquatica and S. molesta are potential plant for phytoremediation applications in contaminated aquatic environments. Further research is recommended to enhance their uptake mechanisms and evaluate scalability under field conditions

A preliminary study of marine water quality status using principal component analysis at three selected mangrove estuaries in east coast Peninsular Malaysia

This research presents marine water quality status in three different mangrove estuaries. The objective of this study is to evaluate the surface water quality of three estuaries in east coast Peninsular Malaysia. The parameters measured were Dissolved Oxygen (DO), pH, Biochemical Oxygen Demand (BOD), total dissolved solid (TDS), ammonium (NH4-N), turbidity (TUR), total suspended solid (TSS) and coliform. Monthly sampling was performed during the dry season, from June 2016 until September 2016. Data were analysed using principal component analysis (PCA). PCA yielded two PCs where VF1 forms strong factor loadings for pH, NH4-N, SAL, and TDS signifying saltwater intrusion in mangrove area. VF2 designed strong factors of BOD, TUR and Coliform and strong negative loading of DO indicating anthropogenic pollutions in the area. This study output will be a baseline setting for future studies in mangrove estuary marine water quality. Mangrove marine water samples of future monitoring studies in mangrove estuary will benefit by enabling understanding of pollution loading and coastal water quality. It is essential to plan a workable water quality modelling as powerful tool to simulate marine water quality and forecast future consequences to facilitate mangrove biodiversity conservation

Spatial and temporal air quality pattern recognition using environmetric techniques : a case study in Malaysia.

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000–December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations

Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones

The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011–2015 data em- ployed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods