In this work, the DRASTIC and GALDIT models were employed to determine the groundwater vulnerability to contamination from anthropogenic activities and seawater intrusion in Kapas Island. In addition, the work also utilized sensitivity analysis to evaluate the influence of each individual parameter used in developing the final models. Based on these effects and variation indices of the said parameters, new effective weights were determined and were used to create modified DRASTIC and GALDIT models. The final DRASTIC model classified the island into five vulnerability classes: no risk (110-140), low (140-160), moderate (160-180), high (180-200), and very high (>200), covering 4, 26, 59, 4, and 7 % of the island, respectively. Likewise, for seawater intrusion, the modified GALDIT model delineates the island into four vulnerability classes: very low (<90), low (90-110), moderate (110-130), and high (>130) covering 39, 33, 18, and 9 % of the island, respectively. Both models show that the areas that are likely to be affected by anthropogenic pollution and seawater intrusion are within the alluvial deposit at the western part of the island. Pearson correlation was used to verify the reliability of the two models in predicting their respective contaminants. The correlation matrix showed a good relationship between DRASTIC model and nitrate (r = 0.58). In a similar development, the correlation also reveals a very strong negative relationship between GALDIT model and seawater contaminant indicator (resistivity Ωm) values (r = -0.86) suggesting that the model predicts more than 86 % of seawater intrusion. In order to facilitate management strategy, suitable areas for artificial recharge were identified through modeling. The result suggested some areas within the alluvial deposit at the western part of the island as suitable for artificial recharge. This work can serve as a guide for a full vulnerability assessment to anthropogenic pollution and seawater intrusion in small islands and will help policy maker and manager with understanding needed to ensure sustainability of the island's aquifer.
In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol-Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75% of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.
17α-ethynylestradiol (EE2) is a synthetic hormone, which is a derivative of the natural hormone, estradiol (E2). EE2 is an orally bio-active estrogen, and is one of the most commonly used medications for humans as well as livestock and aquaculture activity. EE2 has become a widespread problem in the environment due to its high resistance to the process of degradation and its tendency to (i) absorb organic matter, (ii) accumulate in sediment and (iii) concentrate in biota. Numerous studies have reported the ability of EE2 to alter sex determination, delay sexual maturity, and decrease the secondary sexual characteristics of exposed organisms even at a low concentration (ng/L) by mimicking its natural analogue, 17β-estradiol (E2). Thus, the aim of this review is to provide an overview of the science regarding EE2, the concentration levels in the environment (water, sediment and biota) and summarize the effects of this compound on exposed biota at various concentrations, stage life, sex, and species. The challenges in respect of EE2 include the extension of the limited database on the EE2 pollution profile in the environment, its fate and transport mechanism, as well as the exposure level of EE2 for better prediction and definition revision of EE2 toxicity end points, notably for the purpose of environmental risk assessment.
This work presents the structural and adsorption properties of the CaCO3(-)-rich Corbicula fluminea shell as a natural and economic adsorbent to remove Cd ions from aqueous solutions under batch studies. Experiments were conducted with different contact times, various initial concentrations, initial solution pH and serial biosorbent dosage to examine the dynamic characterization of the adsorption and its influence on Cd uptake capacity. The characterization of the C. fluminea shell using SEM/EDX revealed that the adsorbent surface is mostly impregnated by small particles of potentially calcium salts. The dominant Cd adsorption mechanism is strongly pH and concentration dependent. A maximum Cd removal efficiency of 96.20% was obtained at pH 7 while the optimum adsorbent dosage was observed as 5 g/L. The Langmuir isotherm was discovered to be more suitable to represent the experimental equilibrium isotherm results with higher correlation coefficients (R(2) > 0.98) than Freundlich (R(2)
The existing knowledge regarding seawater intrusion and particularly upconing, in which both problems are linked to pumping, entirely relies on theoretical assumptions. Therefore, in this paper, an attempt is made to capture the effects of pumping on seawater intrusion and upconing using 2D resistivity measurement. For this work, two positions, one perpendicular and the other parallel to the sea, were chosen as profile line for resistivity measurement in the coastal area near the pumping wells of Kapas Island, Malaysia. Subsequently, water was pumped out of two pumping wells simultaneously for about five straight hours. Then, immediately after the pumping stopped, resistivity measurements were taken along the two stationed profile lines. This was followed by additional measurements after four and eight hours. The results showed an upconing with low resistivity of about 1-10 Ωm just beneath the pumping well along the first profile line that was taken just after the pumping stopped. The resistivity image also shows an intrusion of saline water (water enriched with diluted salt) from the sea coming towards the pumping well with resistivity values ranging between 10 and 25 Ωm. The subsequent measurements show the recovery of freshwater in the aquifer and how the saline water is gradually diluted or pushed out of the aquifer. Similarly the line parallel to the sea (L2) reveals almost the same result as the first line. However, in the second and third measurements, there were some significant variations which were contrary to the expectation that the freshwater may completely flush out the saline water from the aquifer. These two time series lines show that as the areas with the lowest resistivity (1 Ωm) shrink with time, the low resistivity (10 Ωm) tends to take over almost the entire area implying that the freshwater-saltwater equilibrium zone has already been altered. These results have clearly enhanced our current understanding and add more scientific weight to the theoretical assumptions on the effects of pumping on seawater intrusion and upconing.
Hydrogeochemical investigations had been carried out at the Amol-Babol Plain in the north of Iran. Geochemical processes and factors controlling the groundwater chemistry are identified based on the combination of classic geochemical methods with geographic information system (GIS) and geostatistical techniques. The results of the ionic ratios and Gibbs plots show that water rock interaction mechanisms, followed by cation exchange, and dissolution of carbonate and silicate minerals have influenced the groundwater chemistry in the study area. The hydrogeochemical characteristics of groundwater show a shift from low mineralized Ca-HCO3, Ca-Na-HCO3, and Ca-Cl water types to high mineralized Na-Cl water type. Three classes, namely, C1, C2, and C3, have been classified using cluster analysis. The spatial distribution maps of Na(+)/Cl(-), Mg(2+)/Ca(2+), and Cl(-)/HCO3 (-) ratios and electrical conductivity values indicate that the carbonate and weathering of silicate minerals played a significant role in the groundwater chemistry on the southern and western sides of the plain. However, salinization process had increased due to the influence of the evaporation-precipitation process towards the north-eastern side of the study area.
In this study, geophysics, geochemistry, and geostatistical techniques were integrated to assess seawater intrusion in Kapas Island due to its geological complexity and multiple contamination sources. Five resistivity profiles were measured using an electric resistivity technique. The results reveal very low resistivity <1 Ωm, suggesting either marine clay deposit or seawater intrusion or both along the majority of the resistivity images. As a result, geochemistry was further employed to verify the resistivity evidence. The Chadha and Stiff diagrams classify the island groundwater into Ca-HCO3, Ca-Na-HCO3, Na-HCO3, and Na-Cl water types, with Ca-HCO3 as the dominant. The Mg(2+)/Mg(2+)+Ca(2+), HCO3 (-)/anion, Cl(-)/HCO3 (-), Na(+)/Cl(-), and SO4 (2-)/Cl(-) ratios show that some sampling sites are affected by seawater intrusion; these sampling sites fall within the same areas that show low-resistivity values. The resulting ratios and resistivity values were then used in the geographical information system (GIS) environment to create the geostatistical map of individual indicators. These maps were then overlaid to create the final map showing seawater-affected areas. The final map successfully delineates the area that is actually undergoing seawater intrusion. The proposed technique is not area specific, and hence, it can work in any place with similar completed characteristics or under the influence of multiple contaminants so as to distinguish the area that is truly affected by any targeted pollutants from the rest. This information would provide managers and policy makers with the knowledge of the current situation and will serve as a guide and standard in water research for sustainable management plan.
The status report on metal pollution in tropical estuaries and coastal waters is important to understand potential environmental health hazards. Detailed baseline measurements were made on physicochemical parameters (pH, temperature, redox potential, electrical conductivity, salinity, dissolved oxygen, total dissolved solid), major ions (Na, Ca, Mg, K, HCO3, Cl, SO4 and NO3) and metals concentrations ((27)Al, (75)As, (138)Ba, (9)Be, (111)Cd, (59)Co, (63)Cu, (52)Cr, (57)Fe, (55)Mn, (60)Ni, (208)Pb, (80)Se, (66)Zn) at estuaries and coastal waters along the Straits of Malacca. Principal component analysis (PCA) was employed to reveal potential pollution sources. Seven principal components were extracted with relation to pollution contribution from minerals-related parameters, natural and anthropogenic sources. The output from this study will generate a profound understanding on the metal pollution status and pollution risk of the estuaries and coastal system.
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.
This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non-linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3-N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3-N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.
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.
Groundwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (PCA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island's hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island's water types into Ca-HCO3 water type, Na-HCO3 water type, Na-SO4-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.
This study investigated polycyclic aromatic hydrocarbons (PAHs) pollution in surface sediments within aquaculture areas in Peninsular Malaysia using chemometric techniques, forensics and univariate methods. The samples were analysed using soxhlet extraction, silica gel column clean-up and gas chromatography mass spectrometry. The total PAH concentrations ranged from 20 to 1841 ng/g with a mean of 363 ng/g dw. The application of chemometric techniques enabled clustering and discrimination of the aquaculture sediments into four groups according to the contamination levels. A combination of chemometric and molecular indices was used to identify the sources of PAHs, which could be attributed to vehicle emissions, oil combustion and biomass combustion. Source apportionment using absolute principle component scores-multiple linear regression showed that the main sources of PAHs are vehicle emissions 54%, oil 37% and biomass combustion 9%. Land-based pollution from vehicle emissions is the predominant contributor of PAHs in the aquaculture sediments of Peninsular Malaysia.
Tidal variation in tropical coastal water plays an important role on physicochemical characteristics and nutrients concentration. Baseline measurements were made for nutrients concentration and physicochemical properties of coastal water, Port Dickson, Malaysia. pH, temperature, oxidation reduction potential, salinity and electrical conductivity have high values at high tides. Principal Components Analysis (PCA) was used to understand spatial variation of nutrients and physicochemical pattern of Port Dickson coastal water at high and low tide. Four principal components of PCA were extracted at low and high tides. Positively loaded nutrients with negative loadings of DO, pH and ORP in PCA outputs indicated nutrients contribution related with pollution sources. This study output will be a baseline frame for future studies in Port Dickson involving water and sediment samples. Water and sediment samples of future monitoring studies in Port Dickson coastal water will help in understanding of coastal water chemistry and pollution sources.
An investigative study was carried out in Langat River to determine the heavy metal pollution in the sediment with 22 sampling stations selected for the collection of sediment samples. The sediment samples were digested and analyzed for extractable metal ((48)Cd, (29)Cu, (30)Zn, (33)As, (82)Pb) using the Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Parameters, such as pH, Eh, electrical conductivity (EC), salinity, cation exchange capacity (CEC) and loss on ignition (LOI) were also determined. The assessment of heavy metal pollution was derived using the enrichment factors (EF) and geoaccumulation index (I(geo)). This study revealed that the sediment is predominantly by As > Cd > Pb > Zn > Cu. As recorded the highest EF value at 187.45 followed by Cd (100.59), Pb (20.32), Zn (12.42) and Cu (3.46). This is similar to the I(geo), which indicates that the highest level goes to As (2.2), exhibits moderately polluted. Meanwhile, Cd recorded 1.8 and Pb (0.23), which illustrates that both of these elements vary from unpolluted to moderately polluted. The Cu and Zn levels are below 0, which demonstrates background concentrations. The findings are expected to update the current status of the heavy metal pollution as well as creating awareness concerning the security of the river water as a drinking water source.
Small islands are susceptible to anthropogenic and natural activities, especially in respect of their freshwater supply. The freshwater supply in small islands may be threatened by the encroachment of seawater into freshwater aquifers, usually caused by over pumping. This study focused on the hydrochemistry of the Kapas Island aquifer, which controls the groundwater composition. Groundwater samples were taken from six constructed boreholes for the analysis and measurement of its in-situ and major ions. The experimental results show a positive and significant correlation between Na-Cl (r=0.907; p<0.01), which can be defined as the effect of salinization. The mechanisms involved in groundwater chemistry changes were ion exchange and mineralization. These processes can be demonstrated using Piper's diagram in which the water type has shifted into a Na-HCO(3) water type from a Ca-HCO(3) water type. Saturation indices have been calculated in order to determine the saturation condition related to dissolution or the precipitation state of the aquifer bedrock. About 76% of collected data (n=108) were found to be in the dissolution process of carbonate minerals. Moreover, the correlation between total CEC and Ca shows a positive and strong relationship (r=0.995; p<0.01). This indicates that the major mineral component in Kapas Island is Ca ion, which contributes to the groundwater chemical composition. The output of this research explains the chemical mechanism attributed to the groundwater condition of the Kapas Island aquifer.
This study was designed as the first to assess the trace metal (Cd, Cu, Fe, Mn, Ni and Zn) in coral skeleton in relation to metal availabilities and sampling locations in Sabah. The study also aims to determine the differential abilities of Scleractinian coral species as a bioindicator of environmental conditions. Skeletons of Scleractinian coral (Hydnophora microconos, Favia speciosa and Porites lobata) showed concentrations of Fe, Mn and Ni relatively higher than Cd and Zn in the skeletons. Statistical analyses outputs showed significant relationships between trace metal concentrations in coral species and those in seawater and sediment. The highest bioaccumulation factors among three Scleractinian coral species investigated was for Zn followed by Mn, Ni, Fe, Cd and Cu can provide a sign about pollution levels. However, metal tolerance, coral structure and morphology as well as multispecies monitoring are factors that need to be a focus in future studies.
This article describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI)(1) for Kinta River (Malaysia). The modeling efforts showed that the optimal network architecture was 23-34-1 and that the best WQI predictions were associated with the quick propagation (QP) training algorithm; a learning rate of 0.06; and a QP coefficient of 1.75. The WQI predictions of this model had significant, positive, very high correlation (r=0.977, p<0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values. The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value, or range of values, of the constituent water quality variables.
Principal component analysis (PCA) is capable of handling large sets of data. However, lack of consistent method in data pre-treatment and its importance are the limitations in PCA applications. This study examined pre-treatments methods (log (x + 1) transformation, outlier removal, and granulometric and geochemical normalization) on dataset of Mengkabong Lagoon, Sabah, mangrove surface sediment at high and low tides. The study revealed that geochemical normalization using Al with outliers removal resulted in a better classification of the mangrove surface sediment than that outliers removal, granulometric normalization using clay and log (x + 1) transformation. PCA output using geochemical normalization with outliers removal demonstrated associations between environmental variables and tides of mangrove surface sediment, Mengkabong Lagoon, Sabah. The PCA outputs at high and low tides also provided to better interpret information about the sediment and its controlling factors in the intertidal zone. The study showed data pre-treatment method to be a useful procedure to standardize the datasets and reducing the influence of outliers.
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.