Several research efforts have been conducted to monitor and analyze the impact of environmental factors on the heavy metal concentrations and physicochemical properties of water bodies (lakes and rivers) in different countries worldwide. This article provides a general overview of the previous works that have been completed in monitoring and analyzing heavy metals. The intention of this review is to introduce the historical studies to distinguish and understand the previous challenges faced by researchers in analyzing heavy metal accumulation. In addition, this review introduces a survey on the importance of time increment sampling (monthly and/or seasonally) to comprehend and determine the rate of change of different parameters on a monthly and seasonal basis. Furthermore, suggestions are made for future research to achieve more understandable figures on heavy metal accumulation by considering climate conditions. Thus, the intent of the current study is the provision of reliable models for predicting future heavy metal accumulation in water bodies in different climates and pollution conditions so that water management can be achieved using intelligent proactive strategies and artificial neural network (ANN) techniques.
Effects of aquaculture activities on the environmental parameters and phytoplankton community structure were investigated in a semi-enclosed lagoon located at Semerak River, Malaysia. Elevated concentrations of phosphate and ammonia were observed at the aquaculture area and the inner lagoon. Relatively low dissolved oxygen, high total chlorophyll a, and high phytoplankton abundances but low species richness were recorded. Chaetoceros, Pseudo-nitzschia brasiliana, Blixaea quinquecornis, and Skeletonema blooms were observed, and some were associated with anoxia condition. Eutrophication level assessed by UNTRIX suggests that the water quality in the lagoon is deteriorating. Dissolved inorganic phosphorus and nitrogen at the impacted area were 15 and 12 times higher than the reference sites, respectively. Such trophic status indices could provide a useful guideline for optimal aquaculture management plan to reduce the environmental impact caused by aquaculture.
Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.
The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.
A study was carried out to determine the level of rare earth elements (REEs) in water and sediment samples from ex-mining lakes and River in Kinta Valley, Perak, Malaysia. Surface water and sediments from an ex-mining lake and Kinta River water samples were analyzed for REEs by inductively coupled plasma mass spectrometry. The total concentration of REEs in the ex-mining lake water samples and sediments were found to be 3685 mg/l and 14159 mg/kg, respectively, while the total concentration of REEs in Kinta River water sample was found to be 1224 mg/l. REEs in mining lake water were found to be within 2.42 mg/l (Tb) to 46.50 mg/l (Ce), while for the Kinta River, it was 1.33 mg/l (Ho) to 29.95 mg/l (Ce). Sediment samples were also found with REEs from 9.81 mg/kg (Ho) to 765.84 mg/kg (Ce). Ce showed the highest average concentrations for mining lake (3.88 to 49.08 mg/l) and Kinta River (4.44 to 33.15 mg/l) water samples, while the concentration of La was the highest (11.59 to 771.61 mg/kg) in the mining lake sediment. Lu was shown to have the highest enrichment of REEs in ex-mining lake sediments (107.3). Multivariate statistical analyses such as factor analysis and principal component analysis indicated that REEs were associated and controlled by mixed origin, with similar contributions from anthropogenic and geogenic sources. The speciation study of REEs in ex-tin mining sediments using a modified five-stage sequential extraction procedure indicated that yttrium (Y), gadolinium (Gd), and lanthanum (La) were obtained at higher percentages from the adsorbed/exchanged/carbonate fraction. The average potential mobility of the REEs was arranged in a descending order: Yb > Gd > Y = Dy > Pr > Er > Tm > Eu > Nd > Tb > Sc > Lu > Ce > La, implying that under favorable conditions, these REEs could be released and subsequently pollute the environment.
The present study aimed to assess the effects of anthropogenic activities on the heavy metal levels in the Langat River by transplantation of Corbicula javanica. In addition, potential ecological risk indexes (PERI) of heavy metals in the surface sediments of the river were also investigated. The correlation analysis revealed that eight metals (As, Co, Cr, Fe, Mn, Ni, Pb and Zn) in total soft tissue (TST) while five metals (As, Cd, Cr, Fe and Mn) in shell have positively and significantly correlation with respective metal concentration in sediment, indicating the clams is a good biomonitor of the metal levels. Based on clustering patterns, the discharge of dam impoundment, agricultural activities and urban domestic waste were identified as three major contributors of the metals in Pangsun, Semenyih and Dusun Tua, and Kajang, respectively. Various geochemical indexes for a single metal pollutant (geoaccumulation index (I geo), enrichment factors (EF), contamination factor (C f) and ecological risk (Er)) all agreed that Cd, Co, Cr, Cu, Fe, Mn, Ni and Zn are not likely to cause adverse effect to the river ecosystem, but As and Pb could pose a potential ecological risk to the river ecosystem. All indexes (degree of contamination (C d), combined pollution index (CPI) and PERI) showed that overall metal concentrations in the tropical river are still within safe limit. River metal pollution was investigated. Anthropogenic activities were contributors of the metal pollution. Geochemical indexes showed that metals are within the safe limit.
The effect of metals on environmental health is well documented and monitoring these and other pollutants is considered an important part of environmental management. Developing countries are yet to fully appreciate the direct impacts of pollution on aquatic ecosystems and as such, information on pollution dynamics is scant. Here, we assessed the temporal and spatial dynamics of stream sediment metal and nutrient concentrations using contaminant indices (e.g. enrichment factors, pollution load and toxic risk indices) in an arid temperate environment over the wet and dry seasons. The mean sediment nutrient, organic matter and metal concentration were highest during the dry season, with high values being observed for the urban environment. Sediment contaminant assessment scores indicated that during the wet season, the sediment quality was acceptable, but not so during the dry season. The dry season had low to moderate levels of enrichment for metals B, Cu, Cr, Fe, Mg, K and Zn. Overall, applying the sediment pollution load index highlighted poor quality river sediment along the length of the river. Toxic risk index indicated that most sites posed no toxic risk. The results of this study highlighted that river discharge plays a major role in structuring temporal differences in sediment quality. It was also evident that infrastructure degradation was likely contributing to the observed state of the river quality. The study contributes to our understanding of pollution dynamics in arid temperate landscapes where vast temporal differences in base flow characterise the riverscape. Such information is further useful for contrasting sediment pollution dynamics in aquatic environments with other climatic regions.
Allium cepa assay was carried out in this study to evaluate genotoxic effects of raw and treated water samples from Perak River in Perak state, Malaysia. Samples were collected from three surface water treatment plants along the river, namely WTPP, WTPS, and WTPK. Initially, triplicates of equal size Allium cepa (onions) bulbs, 25-30 mm in diameter and average weight of 20 g, were set up in distilled water for 24 h at 20 ± 2 °C and protected from direct sunlight, to let the roots to grow. After germination of roots (0.5-1.0 cm in length), bulbs were transferred to collected water samples each for a 96-h period of exposure. The root physical deformations were observed. Genotoxicity quantification was based on mitotic index and genotoxicity level. Statistical analysis using cross-correlation function for replicates from treated water showed that root length has inverse correlation with mitotic indices (r = - 0.969) and frequencies of cell aberrations (r = - 0.976) at lag 1. Mitotic indices and cell aberrations of replicates from raw water have shown positive correlation at lag 1 (r = 0.946). Genotoxicity levels obtained were 23.4 ± 1.98 (WTPP), 26.68 ± 0.34 (WTPS), and 30.4 ± 1.13 (WTPK) for treated water and 17.8 ± 0.18 (WTPP), 37.15 ± 0.17 (WTPS), and 47.2 ± 0.48 (WTPK) for raw water. The observed cell aberrations were adherence, chromosome delay, C-metaphase, chromosome loss, chromosome bridge, chromosome breaks, binucleated cell, mini cell, and lobulated nuclei. The morphogenetic deformations obtained were likely due to genotoxic substances presence in collected water samples. Thus, water treatment in Malaysia does not remove genotoxic compounds.
The present paper aims to assess the phytoremediation performance based on pollution removal efficiency of the highly polluted region of Alur Ilmu urban river for its applicability of on-site treatment. Thirteen stations along Alur Ilmu were selected to produce thematic maps through spatial distribution analysis based on six water quality parameters of Malaysia's Water Quality Index (WQI) for dry and raining seasons. The maps generated were used to identify the highly polluted region for phytoremediation applicability assessment. Four free-floating plants were tested in treating water samples from the highly polluted region under three different conditions, namely controlled, aerated and normal treatments. The selected free-floating plants were water hyacinth (Eichhornia crassipes), water lettuce (Pistia stratiotes), rose water lettuce (Pistia sp.) and pennywort (Centella asiatica). The results showed that Alur Ilmu was more polluted during dry season compared to raining season based on the water quality analysis. During dry season, four parameters were marked as polluted along Alur Ilmu, namely dissolve oxygen (DO), 4.72 mg/L (class III); ammoniacal nitrogen (NH3-N), 0.85 mg/L (class IV); total suspended solid (TSS), 402 mg/L (class V) and biological oxygen demand (BOD), 3.89 mg/L (class III), whereas, two parameters were classed as polluted during raining season, namely total suspended solid (TSS), 571 mg/L (class V) and biological oxygen demand (BOD), 4.01 mg/L (class III). The thematic maps generated from spatial distribution analysis using Kriging gridding method showed that the highly polluted region was recorded at station AL 5. Hence, water samples were taken from this station for pollution removal analysis. All the free-floating plants were able to reduce TSS and COD in less than 14 days. However, water hyacinth showed the least detrimental effect from the phytoremediation process compared to other free-floating plants, thus made it a suitable free-floating plants to be used for on-site treatment.
Land exploitation for mining sector may leave a series of environmental impacts on our ecosystem if not appropriately managed. Therefore, the present study attempts to evaluate the various environmental aspects due to abandoned metal mining including former iron ore, bauxite, and tin mining lands in view of its hydrogeochemical behavior. Mine-impacted waters and sediments were ascertained from former mining ponds, mine tailings, and impacted streams for interpretation of aqueous and sediment geochemistry, major and trace elements, hydrochemical facies, chemical weathering rate and CO2 consumption, and water quality classification. Results indicated that the environmental impact of the long-abandoned iron ore mine was still evident with some high concentration of metals and acidic pH. Higher concentrations of Fe and Mn in water were noticeable in some areas while other trace elements (Pb, Zn, As, Cd, Cr, and Cu) were found below the recommended guideline values. Sediment quality reflected the trend of water quality variables mainly associated with metal(loid) elements, resulting in potential ecological risk, classified as having low to moderate risk. There were variations in terms of hydrochemical facies of the waters suggesting the influence of minerals in water. The chemical weathering rate suggests that contribution of carbonate mineral weathering was more important (up to 60%) than silicate weathering. The resulting CO2 consumption by mineral weathering was estimated to be in the range of 1.7-9.8 × 107 mol/year (former bauxite and tin mining areas can act as temporary sinks for CO2). Water quality classifications according to several chemical indices (Kelly's ratio, sodium absorption ratio, soluble sodium percentage, residual sodium carbonate, magnesium absorption ratio, and permeability index) were also discussed in regards to mine water reuse for irrigation purpose. The findings suggest that a holistic approach that integrates all important hydrogeochemical aspects is essential for a thorough evaluation of the implication of medium- to long-term mining exploitation on its surrounding ecosystems. This would be beneficial in light of restoration potential of degraded mining land so as for future mitigation strategies in the mining sector.
The Bernam River is one of the most important rivers in Malaysia in that it provides water for industries and agriculture located along its banks. The present study was conducted to assess the level of contamination of heavy metals (Cd, Ni, Cr, Sn, and Fe) in surface sediments in the Bernam River. Nine surface sediment samples were collected from the lower, middle, and upper courses of the river. The results indicated that the concentrations of the metals decreased in the order of Sn > Cr > Ni > Fe > Cd (56.35, 14.90, 5.3, 4.6, and 0.62 μg/g(1) dry weight). Bernam River sediments have moderate to severe enrichment for Sn, moderate for Cd, and no enrichment for Cr, Ni, and Fe. The contamination factor (CF) results demonstrated that Cd and Sn are responsible for the high contamination. The pollution load index (PLI), for all the sampling sites, suggests that the sampling stations were generally unpolluted with the exception of the Bagan Tepi Sungai, Sabak Bernam, and Tanjom Malim stations. Multivariate techniques including Pearson's correlation and hierarchical cluster analysis were used to apportion the various sources of the metals. The results suggested that the sediment samples collected from the upper course of the river had lower metal concentrations, while sediments in the middle and lower courses of the river had higher metal concentrations. Therefore, our results can be useful as a baseline data for government bodies to adopt corrective measure on the issues related to heavy metal pollution in the Bernam River in the future.
This is the first extensive report on linear alkylbenzenes (LABs) as sewage molecular markers in surface sediments collected from the Perlis, Kedah, Merbok, Prai, and Perak Rivers and Estuaries in the west of Peninsular Malaysia. Sediment samples were extracted, fractionated, and analyzed using gas chromatography mass spectrometry (GC-MS). The concentrations of total LABs ranged from 68 to 154 (Perlis River), 103 to 314 (Kedah River), 242 to 1062 (Merbok River), 1985 to 2910 (Prai River), and 217 to 329 ng g(-1) (Perak River) dry weight (dw). The highest levels of LABs were found at PI3 (Prai Estuary) due to the rapid industrialization and population growth in this region, while the lowest concentrations of LABs were found at PS1 (upstream of Perlis River). The LABs ratio of internal to external isomers (I/E) in this study ranged from 0.56 at KH1 (upstream of Kedah River) to 1.35 at MK3 (Merbok Estuary) indicating that the rivers receive raw sewage and primary treatment effluents in the study area. In general, the results of this paper highlighted the necessity of continuation of water treatment system improvement in Malaysia.
A comprehensive study of the chemical composition of rainwater was carried out from October 2016 to September 2017 in the equatorial tropical rainforest region of northwestern Borneo. Monthly cumulative rainwater samples were collected from different locations in the Limbang River Basin (LRB) and were later categorized into seasonal samples representing northeast monsoon (NEM), southwest monsoon (SWM), and inter-monsoon (IM) periods. Physical parameters (pH, EC, TDS, DO, and turbidity), major ions (HCO3-, Cl-, Ca2+, Mg2+, Na+, and K+) and trace metals (Co, Ni, Cd, Fe, Mn, Pb, Zn, and Cu) were analyzed from collected rainwater samples. Rainwater is slightly alkaline with mean pH higher than 5.8. Chloride and bicarbonate are the most abundant ions, and the concentration of major ions in seasonal rainwater has shown slight variation which follows a descending order of HCO3-> Cl-> Na+ > Ca2+ > Mg2+ > K+ in NEM and Cl- > HCO3- > Na+ > Ca2+ > K+ > Mg2+ in SWM and Cl- > HCO3- > Na+ > Ca2+ > Mg2+ > K+ in IM period. Trace metals such as Fe and Ni have shown dominance in seasonal rainwater samples, and all the metals have shown variation in concentration in different seasons. Variation in chemical characteristic of seasonal rainwater samples identified through piper diagram indicates dominance of Ca2+-Mg2+-HCO3- and mixed Ca2+-Mg2+-Cl- facies during NEM, SWM, and IM periods. Statistical analysis of the results through two-way ANOVA and Pearson's correlation also indicates significant variation in physico-chemical characteristics. This suggests a variation in contributing sources during the monsoon seasons. Factor analysis confirmed the source variation by explaining the total variance of 79.80%, 90.72%, and 90.52% with three factor components in NEM, SWM, and IM rainwater samples with different loading of parameters. Enrichment factor analysis revealed a combined contribution of marine and crustal sources except K+ which was solely from crustal sources. Sample analysis of backward air mass trajectory supports all these findings by explaining seasonal variation in the source of pollutants reaching the study area. Overall, the results show that the chemical composition of seasonal rainwater samples in LRB was significantly influenced by natural as well as anthropogenic processes. These include (long-range and local) industrial activities, fossil fuel combustion, forest burning, transportation activities including road transport and shipping activities, and land-derived soil dust along with chemical constituents carried by seasonal wind.
Increasing toxic metal content in aquatic products has become a universal burden due to the risks to aquatic organisms and human health associated with the consumption of these products. In this study, toxic metal distribution and accumulation in the organs of fish and bivalve species of economic and culinary importance from the lower reaches of the Yangtze River are examined, and the corresponding health risks are also investigated. In general, the viscera and gill show higher concentration of metals than other tissues. The order of the accumulation sequence of metals in muscle tissue of fish and bivalve is Zn > Cu > Mn > Cr > As > Hg > Pb > Cd and Mn > Zn > Cu > As > Cr > Pb > Cd > Hg respectively. Maximum accumulation of Mn (507.50 μg g-1) and Pb (0.51 μg g-1) in the gill tissues indicates the major uptake of these metals from the water column. According to the Hazard Index (HI) calculations (based on USEPA), the analyzed metals will not cause any harmful health effects to individuals for both normal and habitual fish consumers, except for Hg and As in habitual consumers, if these species are consumed at a larger amount. Compared to the Chinese Food Health Criterion and other international standards (WHO/FAO), metal concentrations in the edible muscle tissues of the studied species are lesser than the acceptable levels and found to be fit for human consumption.
In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.
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.
Field and laboratory studies were conducted to estimate concentration of potential contaminants from landfill in the underlying groundwater, leachate, and surface water. Samples collected in the vicinity of the landfill were analyzed for physiochemical parameters, organic contaminants, and toxic heavy metals. Water quality results obtained were compared from published data and reports. The results indicate serious groundwater and surface water contamination in and around the waste disposal site. Analysis of the organic samples revealed that the site contains polychlorinated biphenyls and other organo-chlorine chemicals, principally chloro-benzenes. Although the amount of PCB concentration discovered was not extreme, their presence indicates a potentially serious environmental threat. Elevated concentrations of lead, copper, nickel, manganese, cadmium, and cobalt at the downgradient indicate that the contamination plume migrated further from the site, and the distribution of metals and metals containing wastes in the site is nonhomogeneous. These results clearly indicate that materials are poorly contained and are at risk of entering the environment. Therefore, full characterization of the dump contents and the integrity of the site are necessary to evaluate the scope of the problem and to identify suitable remediation options.
In this study, the distributions and sources of sediment-associated polycyclic aromatic hydrocarbons (PAHs) and hopanes in the Malaysian rivers and estuaries were evaluated. The concentrations of 16 USEPA PAHs varied from 225.5 to 293.9 (Perlis River), 195.2 to 481.2 (Kedah River), 791.2 to 1995.4 (Merbok River), 231.2 to 426.7 (Perak River), and 3803.2 to 7442.7 ng g(-1) (Klang River) dry weight. PAHs can be classified as moderate in the Perlis, Kedah, and Perak Rivers, moderate to high in the Merbok River, and high to very high in the Klang River. The comparison of PAHs with sediment quality guidelines (SQGs) indicates that occasionally adverse biological effects may occur from total PAHs, low molecular weight (LMW), and high molecular weight (HMW) PAHs at stations 1, 2, and 3 of the Klang River and from total PAHs at station 2 of the Merbok River. The diagnostic ratios of individual PAHs indicate both petrogenic and pyrogenic origin PAHs with significant dominance of pyrogenic sources in the study areas. The results suggest that Malaysian sediments had hopane ratios (C29/C30) similar to MECO suggesting MECO as a major source of the petroleum hydrocarbons found in the sediments, which is consistent with results reported in previous studies. These findings demonstrate that effective and improved environmental regulations in Malaysia have shifted the source of petroleum hydrocarbons from petrogenic to pyrogenic origin.
We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.
An aquatic biomonitoring of Giardia cysts and Cryptosporidium oocysts in river water corresponding to five villages situated in three states in peninsular Malaysia was determined. There were 51.3% (20/39) and 23.1% (9/39) samples positive for Giardia and Cryptosporidium (oo)cysts, respectively. Overall mean concentration between villages for Giardia cysts ranged from 0.10 to 25.80 cysts/l whilst Cryptosporidium oocysts ranged from 0.10 to 0.90 oocysts/l. Detailed results of the river samples from five villages indicated that Kuala Pangsun 100% (9/9), Kemensah 77.8% (7/9), Pos Piah 33.3% (3/9) and Paya Lebar 33.3% (1/3) were contaminated with Giardia cysts whilst Cryptosporidium (oo)cysts were only detected in Kemensah (100 %; 9/9) and Kuala Pangsun (66.6%; 6/9). However, the water samples from Bentong were all negative for these waterborne parasites. Samples were collected from lower point, midpoint and upper point. Midpoint refers to the section of the river where the studied communities are highly populated. Meanwhile, the position of the lower point is at least 2 km southward of the midpoint and upper point is at least 2 km northward of the midpoint. The highest mean concentration for (oo)cysts was found at the lower points [3.15 ± 6.09 (oo)cysts/l], followed by midpoints [0.66 ± 1.10 (oo)cysts/l] and upper points [0.66 ± 0.92 (oo)cysts/l]. The mean concentration of Giardia cysts was highest at Kuala Pangsun (i.e. 5.97 ± 7.0 cysts/l), followed by Kemensah (0.83 ± 0.81 cysts/l), Pos Piah (0.20 ± 0.35 cysts/l) and Paya Lebar (0.10 ± 0.19 cysts/l). On the other hand, the mean concentration of Cryptosporidium oocysts was higher at Kemensah (0.31 ± 0.19 cysts/l) compared to Kuala Pangsun (0.03 ± 0.03cysts/l). All the physical and chemical parameters did not show significant correlation with both protozoa. In future, viability status and molecular characterisation of Giardia and Cryptosporidium should be applied to identify species and genotypes/subgenotypes for better understanding of the epidemiology of these waterborne parasites.