Dissolved organic matter (DOM) was characterised in water samples sampled in the Lower Kinabatangan River Catchment, Sabah, Malaysia between October 2009 and May 2010. This study aims at: (i) distinguishing between the quality of DOM in waters draining palm oil plantations (OP), secondary forests (SF) and coastal swamps (CS) and, (ii) identifying the seasonal variability of DOM quantity and quality. Surface waters were sampled during fieldwork campaigns that spanned the wet and dry seasons. DOM was characterised optically by using the fluorescence Excitation Emission Matrix (EEM), the absorption coefficient at 340 nm and the spectral slope coefficient (S). Parallel Factor Analysis (PARAFAC) was undertaken to assess the DOM composition from EEM spectra and five terrestrial derived components were identified: (C1, C2, C3, C4 and C5). Components C1 and C4 contributed the most to DOM fluorescence in all study areas during both the wet and dry seasons. The results suggest that component C4 could be a significant (and common) PARAFAC signal found in similar catchments. Peak M (C2 and C3) was dominant in all samples collected during wet and dry seasons, which could be anthropogenic in origin given the active land use change in the study area. In conclusion, there were significant seasonal and spatial variations in DOM which demonstrated the effects of land use cover and precipitation amounts in the Kinabatangan catchment.
Matched MeSH terms: Water Pollution/statistics & numerical data
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.
Matched MeSH terms: Water Pollution/statistics & numerical data
Water quality degradation in the Citarum river will increase from the year to year due to increasing pollutant loads when released particularly from Bandung region of the upstream areas into the river without treatment. This will be facing the problems on water quality status to use for multi-purposes in the downstream areas. The water quality evaluation system is used to evaluate the available water condition that distinguishes into two categories, i.e., the water quality index (WQI) and water quality aptitude (WQA). The assessment of water quality for the Citarum river from 10 selected stations was found that the WQI situates in the bad category generally and the WQA ranges from the suitable quality for agriculture and livestock watering uses to the unsuitable for biological potential function, drinking water production, and leisure activities and sports in the upstream areas of Saguling dam generally.
Matched MeSH terms: Water Pollution/statistics & numerical data
A size and distance-based technique was used to assess the distribution, abundance and composition of floating marine debris in the northeast Indian Ocean. Densities of floating litter (>1 cm) were greater and more variable in the Straits of Malacca (578±219 items km(-2)) than in oceanic waters of the Bay of Bengal (8.8±1.4 items km(-2)). The density of debris in the Straits was correlated with terrestrial vegetation, and peaked close to urban centres, indicating the predominance of land-based sources. In the Bay of Bengal, debris density increased north of 17°N mainly due to small fragments probably carried in run-off from the Ganges Delta. The low densities in the Bay of Bengal relative to model predictions may result from biofouling-induced sinking and wind-driven export of debris items. Standardised data collection protocols are needed for counts of floating debris, particularly as regards the size classes used, to facilitate comparisons among studies.
Matched MeSH terms: Water Pollution/statistics & numerical data
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.
Matched MeSH terms: Water Pollution/statistics & numerical data*
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.
Matched MeSH terms: Water Pollution/statistics & numerical data
A sacred ritual well with continuously discharging of methane gas through its water body was studied for physicochemical and microbiological quality in three seasons and during ritual mass bathing. Most of the physicochemical parameters showed significant seasonal variations (P<0.05) and a sharp fluctuation during mass bathing. Dissolved oxygen (DO) was found negatively correlated with temperature (r=-0.384, P<0.05), biochemical oxygen demand (BOD) (r=-0.58, P<0.001) and ammonia (r=-0.738, P<0.001), while BOD showed positive correlation with chemical oxygen demand (COD) (r=0.762, P<0.001) and ammonia (r=0.83, P<0.001). Simple regression analysis also yielded significant linear relationship in DO vs. temperature (r(2)=0.147, P<0.05), DO vs. ammonia (r(2)=0.544, P<0.001) and BOD vs. DO (r(2)=0.336, P<0.001). A total of eight microbial indicators were studied and found that all of them increased unusually during mass bathing comparing with their respective seasonal values. Total coliforms (TC) were found positively correlated with fecal coliforms (FC) (r=0.971), FC with Escherichia coli (EC) (r=0.952), EC with intestinal enterococci (IE) (r=0.921), fecal streptococci (FS) with IE (r=0.953) and Staphylococcus aureus (SA) with Pseudomonas aeruginosa (PA) (r=0.946), which were significant at P<0.001. Some regression models showed significant linear relationship at P<0.001 with r(2) value of 0.943 for FC vs. TC, 0.907 for EC vs. FC, 0.869 for FS vs. FC, 0.848 for IE vs. EC and 0.909 for IE vs. FS. The overall results found in this study revealed that well water is suitable for bathing purpose but the religious activity considerably worsen its quality.
Matched MeSH terms: Water Pollution/statistics & numerical data
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.
Matched MeSH terms: Water Pollution/statistics & numerical data*
In recent times, surface water resource in the Western Region of Ghana has been found to be inadequate in supply and polluted by various anthropogenic activities. As a result of these problems, the demand for groundwater by the human populations in the peri-urban communities for domestic, municipal and irrigation purposes has increased without prior knowledge of its water quality. Water samples were collected from 14 public hand-dug wells during the rainy season in 2013 and investigated for total coliforms, Escherichia coli, mercury (Hg), arsenic (As), cadmium (Cd) and physicochemical parameters. Multivariate statistical analysis of the dataset and a linear stoichiometric plot of major ions were applied to group the water samples and to identify the main factors and sources of contamination. Hierarchal cluster analysis revealed four clusters from the hydrochemical variables (R-mode) and three clusters in the case of water samples (Q-mode) after z score standardization. Principal component analysis after a varimax rotation of the dataset indicated that the four factors extracted explained 93.3 % of the total variance, which highlighted salinity, toxic elements and hardness pollution as the dominant factors affecting groundwater quality. Cation exchange, mineral dissolution and silicate weathering influenced groundwater quality. The ranking order of major ions was Na(+) > Ca(2+) > K(+) > Mg(2+) and Cl(-) > SO4 (2-) > HCO3 (-). Based on piper plot and the hydrogeology of the study area, sodium chloride (86 %), sodium hydrogen carbonate and sodium carbonate (14 %) water types were identified. Although E. coli were absent in the water samples, 36 % of the wells contained total coliforms (Enterobacter species) which exceeded the WHO guidelines limit of zero colony-forming unit (CFU)/100 mL of drinking water. With the exception of Hg, the concentration of As and Cd in 79 and 43 % of the water samples exceeded the WHO guideline limits of 10 and 3 μg/L for drinking water, respectively. Reported values in some areas in Nigeria, Malaysia and USA indicated that the maximum concentration of Cd was low and As was high in this study. Health risk assessment of Cd, As and Hg based on average daily dose, hazard quotient and cancer risk was determined. In conclusion, multiple natural processes and anthropogenic activities from non-point sources contributed significantly to groundwater salinization, hardness, toxic element and microbiological contamination of the study area. The outcome of this study can be used as a baseline data to prioritize areas for future sustainable development of public wells.
Matched MeSH terms: Water Pollution/statistics & numerical data*