In this study, factor analysis (FA) was applied to extract the hidden factors responsible for water quality variations during both wet and dry seasons. Water samples were collected from six sampling stations (St. 1 Lalang River, St. 2 Semeling River, St. 3 Jagung River, St. 4 Teluk Wang River, St. 5 Gelam River and St. 6 Derhaka River) in the Merbok estuary, Malaysia from January to December 2011; the samples were further analysed in the laboratory. Correlation analysis of the data sets showed strong correlations between the parameters. Nutrients such as nitrate (NO3 (-)), nitrite (NO2 (-)), ammonia (NH3) and phosphate (PO4 (3-)) were determined to be critical indicators of water quality throughout the year. Influential water quality parameters during the wet season were conductivity, salinity, biochemical oxygen demand (BOD), dissolved oxygen (DO) and chlorophyll a (Chla), whereas total suspended solid (TSS) and pH were critical water quality indicators during the dry season. The Kruskal-Wallis H test showed that water quality parameters were significantly different among the sampling months and stations (p<0.05), and Mann-Whitney U tests further revealed that the significantly different parameters were temperature, pH, DO, TSS, NO2 (-) and BOD (p<0.01), whereas salinity, conductivity, NO3 (-), PO4 (3-), NH3 and Chla were not significantly different (p>0.05). Water quality parameters in the estuary varied on both temporal and spatial scales and these results may serve as baseline information for estuary management, specifically for the Merbok estuary.
Pulau Tioman is a famous tourist island off Peninsular Malaysia with beautiful coral reefs. This study aims to assess the health of the coral reefs surrounding Pulau Tioman based on the application of the Foraminifera in Reef Assessment and Monitoring Index (FI). Ten sampling sites around Pulau Tioman were studied with a total of 30 samples. Eight orders, 41 families, 80 genera, and 161 species of benthic foraminifera were identified. The agglutinated type of foraminifera constituted 2-8% of the total assemblages. Calcareous hyaline and porcelaneous groups represented 79% and 19% of the total assemblages, respectively. Symbiont-bearing taxa were the most common foraminifera. The results indicate that most of the sampling sites are conducive for coral reef growth with good recoverability from future stress to the ecosystem. However, several areas with higher coastal development and tourism have reduced water and sediment quality. Therefore, the limit on the number of visitors and tourists should be revised to enable coral growth and health. The FI values in this study showed a positive correlation with good water qualities and a negative correlation with organic matter enrichment. The FI is a good measure to assess the health of a coral reef and can be applied to other reef ecosystems around Malaysia.
A computer-aided multivariate water quality index is developed based on partial least squares (PLS) regression. The index is termed as the partial least squares water quality index (PLS-WQI). Briefly, a training set was computationally generated based on the guideline of National Water Quality Standards for Malaysia (NWQS) to predict the water quality. The index is benchmarked with the well-established index developed by the Department of Environment, Malaysia (DOE-WQI). The PLS-WQI is a continuous variable with the value closer to I indicating good water quality and closer to V indicating poor water quality. Unlike other conventional indexing methods, the algorithm calculates the index in a multivariate manner. The algorithm allows rapid processing of a large dataset without tedious calculation; it can be an efficient tool for spatial and temporal routine monitoring of water quality. Although the algorithm is designed based on the guideline of NWQS, it can be easily adapted to accommodate other guidelines. The algorithm was evaluated and demonstrated on the simulated and real datasets. Results indicate that the algorithm is robust and reliable. Based on six parameters, the overall ratings derived are inversely correlated to DOE-WQI. When the number of parameter is increased, the overall ratings appear to provide better insights into the water quality.
Multiple interactions of geogenic and anthropogenic activities can trigger groundwater pollution in the tropical savanna watershed. These interactions and resultant contamination have been studied using applied geochemical modeling, conventional hydrochemical plots, and multivariate geochemometric methods, and the results are presented in this paper. The high alkalinity values recorded for the studied groundwater samples might emanate from the leaching of carbonate soil derived from limestone coupled with low rainfall and high temperature in the area. The principal component analysis (PCA) unveils three components with an eigenvalue > 1 and a total dataset variance of 67.37%; this implies that the temporary hardness of the groundwater and water-rock interaction with evaporite minerals (gypsum, halite, calcite, and trona) is the dominant factor affecting groundwater geochemistry. Likewise, the PCA revealed anthropogenic contamination by discharging [Formula: see text] [Formula: see text][Formula: see text] and [Formula: see text] from agricultural activities and probable sewage leakages. Hierarchical cluster analysis (HCA) also revealed three clusters; cluster I reflects the dissolution of gypsum and halite with a high elevated load of [Formula: see text] released by anthropogenic activities. However, cluster II exhibited high [Formula: see text] and [Formula: see text] loading in the groundwater from weathering of bicarbonate and sylvite minerals. Sulfate ([Formula: see text]) dominated cluster III mineralogy resulting from weathering of anhydrite. The three clusters in the Maiganga watershed indicated anhydrite, gypsum, and halite undersaturation. These results suggest that combined anthropogenic and natural processes in the study area are linked with saturation indexes that regulate the modification of groundwater quality.
This study aimed to document the fish diversity and water quality at Semariang mangrove area, Kuching, Sarawak, which is located at the eastern part of Kuching Wetland National Park. Field samplings were carried out in 2009 during the construction of the flood mitigation channel at the eastern part of the park. A total of 21 families represented by 37 species of fish were caught from the area. The six dominant families in terms of the number of individuals caught were Mugilidae (16%), Leiognathidae (16%), Ambassidae (11%), Ariidae (9%), Lutjanidae (8%) and Plotosidae (6%). In terms of the percentage of six dominant genera based on the number of individuals caught, 16% was represented by Valamugil, 11% by Ambassis, 10% by Gazza, 9% by Arius, 8% by Lutjanus and 6% by Plotosus. The values of diversity and richness indices were lower at stations located close to the flood mitigation channel. Similarly, the concentrations of dissolved oxygen were lower and total suspended solids were significantly higher at stations close to the channel and sand mining area. Therefore, fish fauna and water quality at Semariang mangrove area were affected during the construction of the flood mitigation channel.
Robust statistical tools were applied on the water quality datasets with the aim of determining the most significance parameters and their contribution towards temporal water quality variation. Surface water samples were collected from four different sampling points during dry and wet seasons and analyzed for their physicochemical constituents. Discriminant analysis (DA) provided better results with great discriminatory ability by using five parameters with (P < 0.05) for dry season affording more than 96% correct assignation and used five and six parameters for forward and backward stepwise in wet season data with P-value (P < 0.05) affording 68.20% and 82%, respectively. Partial correlation results revealed that there are strong (r(p) = 0.829) and moderate (r(p) = 0.614) relationships between five-day biochemical oxygen demand (BOD(5)) and chemical oxygen demand (COD), total solids (TS) and dissolved solids (DS) controlling for the linear effect of nitrogen in the form of ammonia (NH(3)) and conductivity for dry and wet seasons, respectively. Multiple linear regression identified the contribution of each variable with significant values r = 0.988, R(2) = 0.976 and r = 0.970, R(2) = 0.942 (P < 0.05) for dry and wet seasons, respectively. Repeated measure t-test confirmed that the surface water quality varies significantly between the seasons with significant value P < 0.05.
The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH(3)), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO(3)), chloride (Cl) and phosphates (PO(3-)(4)) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearson's correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD(5), NH(3), COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.
This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta River's WQ and accentuated the roles of weathering and surface runoff in determining the river's WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.
Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.
The aim of this study was to propose a groundwater quality index (GWQI) that presents water quality data as a single number and represents the water quality level. The development of the GWQI in agricultural areas is vital as the groundwater considered as an alternative water source for domestic purposes. The insufficiency of the groundwater quality standard in Malaysia revealed the importance of the GWQI development in determining the quality of groundwater. Groundwater samples were collected from thirteen groundwater wells in the Northern Kuala Langat and the Southern Kuala Langat regions from February 2018 to January 2019. Thirty-four parameters that embodied physicochemical characteristics, aggregate indicator, major ions, and trace elements were considered in the development of the GWQI. Multivariate analysis has been used to finalize the important parameters by using principal component analysis (PCA). Notably, seven parameters-electrical conductivity, chemical oxygen demand (COD), magnesium, calcium, potassium, sodium, and chloride were chosen to evaluate the quality of groundwater. The GWQI was then verified by comparing the groundwater quality in Kota Bharu, Kelantan. A sensitivity analysis was performed on this index to verify its reliability. The sensitivity GWQI has been analyzed and showed high sensitivity to any changes of the pollutant parameters. The development of GWQI should be beneficial to the public, practitioners, and industries. From another angle, this index can help to detect any form of pollution which ultimately could be minimized by controlling the sources of pollutants.
Uncontrolled stormwater runoff not only creates drainage problems and flash floods but also presents a considerable threat to water quality and the environment. These problems can, to a large extent, be reduced by a type of stormwater management approach employing permeable pavement systems (PPS) in urban, industrial and commercial areas, where frequent problems are caused by intense undrained stormwater. PPS could be an efficient solution for sustainable drainage systems, and control water security as well as renewable energy in certain cases. Considerable research has been conducted on the function of PPS and their improvement to ensure sustainable drainage systems and water quality. This paper presents a review of the use of permeable pavement for different purposes. The paper focuses on drainage systems and stormwater runoff quality from roads, driveways, rooftops and parking lots. PPS are very effective for stormwater management and water reuse. Moreover, geotextiles provide additional facilities to reduce the pollutants from infiltrate runoff into the ground, creating a suitable environment for the biodegradation process. Furthermore, recently, ground source heat pumps and PPS have been found to be an excellent combination for sustainable renewable energy. In addition, this study has identified several gaps in the present state of knowledge on PPS and indicates some research needs for future consideration.
A set of methodological tools was tested to assess the sensitivity of several ecological and biological indices to eutrophication while at the same time attempting to explore a linkage among pressures, classification assessment and drivers. Industrial discharges, harbor activities, natural interactions and river discharges are the pressures most related to the eutrophication process in tropical coastal water bodies. Among the eutrophication indices used, TRIX and operational indicators overestimated the eutrophication status in the study area, but EI and chl-a seems to be a rather responsive index to reflect the first stage of eutrophication. It is noteworthy that EI and chl-a showed better overall agreement with the ecological quality status (EcoQ) showing that probably it reflects the indirect relation of macrobenthic with water eutrophication in a better way. An ecological boundary of EI and chl-a from moderate to poor may be needed in order to explain the poor status of relatively eutrophic Klang Strait coastal sites.
Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.
Characterization of hydrochemistry changes in Johor Straits within 5 years of monitoring works was successfully carried out. Water quality data sets (27 stations and 19 parameters) collected in this area were interpreted subject to multivariate statistical analysis. Cluster analysis grouped all the stations into four clusters ((Dlink/Dmax) × 100<90) and two clusters ((Dlink/Dmax) × 100<80) for site and period similarities. Principal component analysis rendered six significant components (eigenvalue>1) that explained 82.6% of the total variance of the data set. Classification matrix of discriminant analysis assigned 88.9-92.6% and 83.3-100% correctness in spatial and temporal variability, respectively. Times series analysis then confirmed that only four parameters were not significant over time change. Therefore, it is imperative that the environmental impact of reclamation and dredging works, municipal or industrial discharge, marine aquaculture and shipping activities in this area be effectively controlled and managed.
Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors.
El Niño and Southern Oscillation (ENSO) is a natural forcing that affects global climate patterns, thereon influencing freshwater quality and security. In the advent of a strong El Niño warming event in 2016 which induced an extreme dry weather in Malaysia, water quality variation was investigated in Kampar River which supplies potable water to a population of 92,850. Sampling points were stratified into four ecohydrological units and 144 water samples were examined from October 2015 to March 2017. The Malaysian Water Quality Index (WQI) and some supplementary parameters were analysed in the context of reduced precipitation. Data shows that prolonged dry weather, episodic and sporadic pollution incidents have caused some anomalies in dissolved oxygen (DO), total suspended solids (TSS), turbidity and ammoniacal nitrogen (AN) values recorded and the possible factors are discussed. The month of March and August 2016 recorded the lowest precipitation, but the overall resultant WQI remained acceptable. Since the occurrence of a strong El Niño event is infrequent and far between in decadal time scale, this paper gives some rare insights that may be central to monitoring and managing freshwater resource that has a crucial impact to the mass population in the region of Southeast Asia.
Anthropogenic pressures are causing substantial degradation to the freshwater ecosystems globally and Malaysia has not escaped such a bleak scenario. Prompted by the predicament, this study's objective was to pioneer a river assessment system that can be readily adopted to monitor, manage and drive improvement in a wholesome manner. Three sets of a priori metrics were selected to form the Ichthyofaunal Quality Index (IQI: biological), Water Quality Index (WQI: chemical) and River Physical Quality Index (RPQI: physical). These indices were further integrated on equal weighting to construct a novel Malaysian River Integrity Index (MyRII). To test its robustness, the MyRII protocol was field tested in four eco-hydrological zones located in the Kampar River water basin for 18 months to reveal its strengths, weaknesses, and establish the "excellent", "good", "average", "poor" and "impaired" thresholds based on the "best performer" reference site in an empirical manner. The resultant MyRII showed a clear trend that corresponded with different levels of river impairment. Test site zone A which was a reference site with minimal disturbance achieved the highest MyRII (88.95 ± 4.29), followed by partially disturbed zone B (61.95 ± 5.90) and heavily disturbed zone C (50.00 ± 4.29). However, the MyRII in zone D (59.9 ± 6.39), which was a heavily disturbed wetland that was disjointed from the river, did not conform to such trend. Also unveiled and recognized, however, are some unexpected nuances, limitations and challenges that emerged from this study. These are critically discussed as precautions when interpreting and implementing the MyRII protocol. This study adds to the mounting body of evidence that water resource stakeholders and policymakers must look at the big picture and adopt the "balanced ecosystem" mind-set when assessing, restoring and managing the rivers as a freshwater resource.
Rapid urbanization and industrial development in the Langat Basin has disturbed the groundwater quality. The populations' reliance on groundwater sources may induce possible risks to human health such as cancer and endocrine dysfunction. This study aims to determine the groundwater quality of an urbanized basin through 24 studied hydrochemical parameters from 45 groundwater samples obtained from 15 different sampling stations by employing integrated multivariate analysis. The abundance of the major ions was in the following order: bicarbonate (HCO3-) > chloride (Cl-) > sodium (Na+) > sulphate (SO42-) > calcium (Ca2+) > potassium (K+) > magnesium (Mg2+). Heavy metal dominance was in the following order: Fe > Mn > Zn > As > Hg > Pb > Ni > Cu > Cd > Se > Sr. Classification of the groundwater facies indicated that the studied groundwater belongs to the Na-Cl with saline water type and Na-HCO3 with mix water type characteristics. The saline water type characteristics are derived from agricultural activities, while the mixed water types occur from water-rock interaction. Multivariate analysis performance suggests that industrial, agricultural, and weathering activities have contributed to groundwater contamination. The study will help in the understanding of the groundwater quality issue and serve as a reference for other basins with similar characteristics.
Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.
The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed 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.