Toxicity Identification Evaluation (TIE) is a useful method for the classification and identification of toxicants in a composite environment water sample. However, its extension to a larger sample size has been restrained owing to the limited throughput of toxicity bioassays. Here we reported the development of a high-throughput method of TIE Phase I. This newly developed method was assisted by the fluorescence-based cellular oxidation (CO) biosensor fabricated with roGFP2-expressing bacterial cells in 96-well microplate format. The assessment of four river water samples from Langat river basin by this new method demonstrated that the contaminant composition of the four samples can be classified into two distinct groups. The entire toxicity assay consisted of 2338 tests was completed within 12 h with a fluorescence microplate reader. Concurrently, the sample volume for each assay was reduced to 50 μL, which is 600 to 4700 times lesser to compare with conventional bioassays. These imply that the throughput of the CO biosensor-assisted TIE Phase I is now feasible for constructing a large-scale toxicity monitoring system, which would cover a whole watershed scale.
Estuary sediments are one of the important components of coastal ecosystems and have been regarded as a sink for various types of organic pollutants. Organic pollutants such as endocrine disrupting compounds (EDCs) which have been associated with various environmental and human health effects were detected in the estuary sediment at trace level. Considering various interferences that may exist in the estuarine sediment, a sensitive and selective method, capable of detecting multiclass EDC pollutants at the trace levels, needs to be developed and optimized to be applied for environmental analysis. A combination of Soxhlet extraction followed by offline solid phase extraction (SPE) cleaned up with detection based on LC triple quadrupole MS was optimized and validated in this study. The targeted compounds consisted of ten multiclass EDCs, namely, diclofenac, primidone, bisphenol A, estrone (E1), 17β-estradiol (E2), 17α-ethynylestradiol (EE2), 4-octylphenol (4-OP), 4-nonylphenol (4-NP), progesterone, and testosterone. The method showed high extraction efficiency with percentage of recovery from 78% to 108% and excellent sensitivity with detection limit between 0.02ngg-1 and 0.81ngg-1. Excellent linearity from 0.991 to 0.999 was achieved for the developed compounds and the relative standard deviation was less than 18%, an indication of good precision analysis. Evaluation of the matrix effects showed ionization suppression for all the developed compounds. Verification of the method was carried out by analyzing the estuarine sediment collected from Langat River. The analyzed estuarine sediments showed a trace concentration of diclofenac, bisphenol A, progesterone, testosterone, primidone, and E1. However, E2, EE2, 4-OP, and 4-NP were below the method's detection limit. Diclofenac exhibited the highest concentration at 2.67ngg-1 followed by bisphenol A (1.78ngg-1) while E1 showed the lowest concentration at 0.07ngg-1.
The concentration profile, distribution and risk assessment of pharmaceutically active compounds (PhACs) in the coastal surface water from the Klang River estuary were measured. Surface coastal water samples were extracted using offline solid phase, applying polymeric C18 cartridges as extraction sorbent and measuring with liquid chromatography mass spectrometry-mass spectrometry (LC MS-MS) technique. Extraction method was optimized for its recovery, sensitivity and linearity. Excellent recoveries were obtained from the optimized method with percentage of recoveries ranging from 73 to 126%. The optimized analytical method achieved good sensitivity with limit of detection ranging from 0.05 to 0.15 ng L-1, while linearity of targeted compounds in the LC MS-MS system was more than 0.990. The results showed that amoxicillin has the highest concentration (102.31 ng L-1) followed by diclofenac (10.80 ng L-1) and primidone (7.74 ng L-1). The percentage of contribution (% of total concentration) for the targeted PhACs is in the following order; amoxicillin (92.90%) > diclofenac (3.95%) > primidone (1.23%) > dexamethasone (0.75%) > testosterone (0.70%) > sulfamethoxazole (0.33%) > progesterone (0.14%). Environmental risk assessment calculated based on deterministic approach (the RQ method), showed no present risk from the presence of PhACs in the coastal water of Klang River estuary. Nonetheless, this baseline assessment can be used for better understanding on PhACs pollution profile and distribution in the tropical coastal and estuarine ecosystem as well as for future comparative studies.
Influence of deforestation on biodiversity of aquatic organisms was investigated in a stream in the Ulu Sedili Forest Reserve. The stream was monitored five (5) times from December 2011 until December 2012 with 2-month intervals. Sampling of benthic communities was carried out using rectangular dip net while water quality study using a YSI ProPlus meter and the rest were done in the laboratory. Physicochemical parameters and water quality index (WQI) calculation showed no significant difference among the investigated events. WQI classified the Berasau River between Class II (good) to III (moderate) of river water quality. In total, 603 individuals representing 25 taxa that were recorded with Decapods from genus Macrobrabchium were widely distributed. Several intolerant taxa, especially Ephemeroptera and Odonata, were also observed in this river. According to Pearson's correlation analysis, the richness and diversity indices were generally influenced by water quality parameters represented by WQI (P
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.
Metallothionein (MT) concentrations in gill and liver tissues of Oreochromis mossambicus were determined to assess biological response of fish to levels of some metals. Metal concentrations in gill and liver tissues of O. mossambicus ranged from 0.6 to 2.6 for Cd, 16 to 52 for Zn, 0.5 to 17 for Cu and 2 to 67 for T-Hg (all in μg/g wet weight, except for T-Hg in ng/g wet weight). Accumulation of Cd, Zn, Cu and Hg (μg/g wet weight) in the liver and gills of O. mossambicus were in the order of liver > gills. The concentrations of Cd, Zn, Cu and Hg in fish tissues were correlated with MT content. The increases in MT levels from the reference area Puchong Tengah compared to the polluted area Kampung Seri Kenangan were 3.4- and 3.8-fold for gills and livers, respectively. The results indicate that MT concentrations were tissue-specific, with the highest levels in the liver. Therefore, the liver can act as a tissue indicator in O. mossambicus in the study area.
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.
Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.
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.
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.
Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.
This paper describes the concentration of selected heavy metals (Co, Cu, Ni, Pb, and Zn) in the Mamut river sediments and evaluate the degree of contamination of the river polluted by a disused copper mine. Based on the analytical results, copper showed the highest concentration in most of the river samples. A comparison with Interim Canadian Sediment Quality Guidelines (ICSQG) and Germany Sediment Quality Guidelines (GSQG) indicated that the sediment samples in all the sampling stations, except Mamut river control site (M1), exceeded the limit established for Cu, Ni, and Pb. On the contrary, Zn concentrations were reported well below the guidelines limit (ICSQG and GSQG). Mineralogical analysis indicated that the Mamut river sediments were primarily composed of quartz and accessory minerals such as chalcopyrite, pyrite, edenite, kaolinite, mica, and muscovite, reflected by the geological character of the study area. Enrichment factor (EF) and geoaccumulation index (Igeo) were calculated to evaluate the heavy metal pollution in river sediments. Igeo values indicated that all the sites were strongly polluted with the studied metals in most sampling stations, specifically those located along the Mamut main stream. The enrichment factor with value greater than 1.5 suggested that the source of heavy metals was mainly derived from anthropogenic activity such as mining. The degree of metal changes (δfold) revealed that Cu concentration in the river sediments has increased as much as 20 to 38 folds since the preliminary investigation conducted in year 2004.
This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.
The aim of this study was to investigate and apply supported ionic liquid membrane (SILM) in two-phase micro-electrodriven membrane extraction combined with high performance liquid chromatography-ultraviolet detection (HPLC-UV) for pre-concentration and determination of three selected antidepressant drugs in water samples. A thin agarose film impregnated with 1-hexyl-3-methylimidazolium hexafluorophosphate, [C6MIM] [PF6], was prepared and used as supported ionic liquid membrane between aqueous sample solution and acceptor phase for extraction of imipramine, amitriptyline and chlorpromazine. Under the optimized extraction conditions, the method provided good linearity in the range of 1.0-1000μgL(-1), good coefficients of determination (r(2)=0.9974-0.9992) and low limits of detection (0.1-0.4μgL(-1)). The method showed high enrichment factors in the range of 110-150 and high relative recoveries in the range of 88.2-111.4% and 90.9-107.0%, for river water and tap water samples, respectively with RSDs of ≤7.6 (n=3). This method was successfully applied to the determination of the drugs in river and tap water samples. It is envisaged that the SILM improved the perm-selectivity by providing a pathway for targeted analytes which resulted in rapid extraction with high degree of selectivity and high enrichment factor.
A solid phase extraction (SPE) method has been developed using a newly synthesized titanium (IV) butoxide-cyanopropyltriethoxysilane (Ti-CNPrTEOS) sorbent for polar selective extraction of aromatic amines in river water sample. The effect of different parameters on the extraction recovery was studied using the SPE method. The applicability of the sorbents for the extraction of polar aromatic amines by the SPE was extensively studied and evaluated as a function of pH, conditioning solvent, sample loading volume, elution solvent and elution solvent volume. The optimum experimental conditions were sample at pH 7, dichloromethane as conditioning solvent, 10 mL sample loading volume and 5 mL of acetonitrile as the eluting solvent. Under the optimum conditions, the limit of detection (LOD) and limit of quantification (LOQ) for solid phase extraction using Ti-CNPrTEOS SPE sorbent (0.01-0.2; 0.03-0.61 µg L(-1)) were lower compared with those achieved using Si-CN SPE sorbent (0.25-1.50; 1.96-3.59 µg L(-1)) and C18 SPE sorbent (0.37-0.98; 1.87-2.87 µg L(-1)) with higher selectivity towards the extraction of polar aromatic amines. The optimized procedure was successfully applied for the solid phase extraction method of selected aromatic amines in river water, waste water and tap water samples prior to the gas chromatography-flame ionization detector separation.
Polycyclic aromatic hydrocarbons (PAHs) and linear alkylbenzenes (LABs) were used as anthropogenic markers of organic chemical pollution of sediments in the Selangor River, Peninsular Malaysia. This study was conducted on sediment samples from the beginning of the estuary to the upstream river during dry and rainy seasons. The concentrations of ƩPAHs and ƩLABs ranged from 203 to 964 and from 23 to 113 ng g(-1) dry weight (dw), respectively. In particular, the Selangor River was found to have higher sedimentary levels of PAHs and LABs during the wet season than in the dry season, which was primarily associated with the intensity of domestic wastewater discharge and high amounts of urban runoff washing the pollutants from the surrounding area. The concentrations of the toxic contaminants were determined according to the Sediment Quality Guidelines (SQGs). The PAH levels in the Selangor River did not exceed the SQGs, for example, the effects range low (ERL) value, indicating that they cannot exert adverse biological effects.
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.
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.
The environmental reservoirs of sulfonamide (SA) resistome are still poorly understood. We investigated the potential sources and reservoir of SA resistance (SR) in Larut River and Sangga Besar River by measuring the SA residues, sulfamethoxazole resistant (SMXr) in bacteria and their resistance genes (SRGs). The SA residues measured ranged from lower than quantification limits (LOQ) to 33.13 ng L-1 with sulfadiazine (SDZ), sulfadimethoxine (SDM) and SMX as most detected. Hospital wastewater effluent was detected with the highest SA residues concentration followed by the slaughterhouse and zoo wastewater effluents. The wastewater effluents also harbored the highest abundance of SMXr-bacteria (107 CFU mL-1) and SRGs (10-1/16S copies mL-1). Pearson correlation showed only positive correlation between the PO4 and SMXr-bacteria. In conclusion, wastewater effluents from the zoo, hospital and slaughterhouse could serve as important sources of SA residues that could lead to the consequent emergence of SMXr-bacteria and SRGs in the river.