Robust statistical tools were applied on the water quality datasets with the aim of determining the most significance parameters and their contribution towards temporal water quality variation. Surface water samples were collected from four different sampling points during dry and wet seasons and analyzed for their physicochemical constituents. Discriminant analysis (DA) provided better results with great discriminatory ability by using five parameters with (P < 0.05) for dry season affording more than 96% correct assignation and used five and six parameters for forward and backward stepwise in wet season data with P-value (P < 0.05) affording 68.20% and 82%, respectively. Partial correlation results revealed that there are strong (r(p) = 0.829) and moderate (r(p) = 0.614) relationships between five-day biochemical oxygen demand (BOD(5)) and chemical oxygen demand (COD), total solids (TS) and dissolved solids (DS) controlling for the linear effect of nitrogen in the form of ammonia (NH(3)) and conductivity for dry and wet seasons, respectively. Multiple linear regression identified the contribution of each variable with significant values r = 0.988, R(2) = 0.976 and r = 0.970, R(2) = 0.942 (P < 0.05) for dry and wet seasons, respectively. Repeated measure t-test confirmed that the surface water quality varies significantly between the seasons with significant value P < 0.05.
Water is considered an everlasting free source that can be acquired naturally. Demand for processed supply water is growing higher due to an increasing population. Sustainable use of water could maintain a balance between its demand and supply. Rainwater harvesting (RWH) is the most traditional and sustainable method, which could be easily used for potable and nonpotable purposes both in residential and commercial buildings. This could reduce the pressure on processed supply water which enhances the green living. This paper ensures the sustainability of this system through assessing several water-quality parameters of collected rainwater with respect to allowable limits. A number of parameters were included in the analysis: pH, fecal coliform, total coliform, total dissolved solids, turbidity, NH3-N, lead, BOD5, and so forth. The study reveals that the overall quality of water is quite satisfactory as per Bangladesh standards. RWH system offers sufficient amount of water and energy savings through lower consumption. Moreover, considering the cost for installation and maintenance expenses, the system is effective and economical.
Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.
Although the macroinvertebrates have been widely used as bio-indicator for river water quality assessment in developed countries, its application is new in Iran and data on the health status of the most ecologically important rivers in Iran is scarce. The present study aimed at monitoring and assessing the ecological quality of Aghlagan river, northwest of Iran, using integrated physicochemical-biological approaches. A total of 14,423 samplings were carried out from the headwater to downstream sites at four stations (S1, 2, 3, 4) by a Surber sampler (30 cm × 30 cm) from June 2018 to April 2019. The results obtained from macroinvertebrate biotic index revealed that the genera of Gammarus (Amphipoda) and Baetis (Ephemeroptera) were the most abundant in all seasons. The PAST software was applied to analyze the diversity indices (Shannon-Weiner diversity, Evenness, and Simpson indices). Based on the cluster analysis, S3 established the least similarity to other stations. The average frequency of each macroinvertebrate species was determined by one-factor analysis of similarities (ANOSIM). In accordance with canonical correspondence analysis (CCA), temperature and phosphate were found as the dominant factors effecting the macroinvertebrate assemblage and distribution. Moreover, the results obtained from the biological indices concluded very good quality of S4 by Helsinhoff and EPT indices and fair quality using BMWP index. The data on the macrobenthos assemblage and dynamics in the Aghlagan river across a hydraulic gradient provided useful information on water management efforts that assist us to find sustainable solutions for the enhanced quality of the river by balancing environmental and human values.
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.
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.
Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results.
The assessment of surface water quality is often laborious, expensive and tedious, as well as impractical, especially for the developing and middle-income countries in the ASEAN region. The application of the water quality index (WQI), which depends on several independent key parameters, has great potential and is a useful tool in this region. Therefore, this study aims to find out the spatial variability of various water quality parameters in geographical information system (GIS) environment and perform a comparative study among the ASEAN WQI systems. At present, there are four ASEAN countries which have implemented the WQI system to evaluate their surface water quality, which are (i) Own WQI system-Malaysia, Thailand and Vietnam-and (ii) Adopted WQI system: Indonesia. A spatial distribution of 12 water quality parameters in the Selangor river basin, Malaysia, was plotted and then applied into the different ASEAN WQI systems. The WQI values obtained from the different WQI systems have an appreciable difference, even for the same water samples due to the disparity in the parameter selection and the standards among them. WQI systems which consider all biophysicochemical parameters provide a consistent evaluation (Very Poor), but the system which either considers physicochemical or biochemical parameters gives a relatively lenient evaluation (Fair-Poor). The Selangor river basin is stressed and impacted by all physical, biological and chemical parameters caused by both the aridity of the climate and anthropogenic activities. Therefore, it is crucial to include all these aspects into the evaluation and corresponding actions should be taken.
The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.
This paper aims to assess the influence of land use and land cover (LULC) indicators and population density on water quality parameters during dry and rainy seasons in a tourism area in Indonesia. This study applies least squares regression (OLS) and Pearson correlation analysis to see the relationship among factors, and all LULC and population density were significantly correlated with most of water quality parameter with P values of 0.01 and 0.05. For example, DO shows high correlation with population density, farm, and built-up in dry season; however, each observation point has different percentages of LULC and population density. The concentration value should be different over space since watershed characteristics and pollutions sources are not the same in the diverse locations. The geographically weighted regression (GWR) analyze the spatially varying relationships among population density, LULC categories (i.e., built-up areas, rice fields, farms, and forests), and 11 water quality indicators across three selected rivers (Ayung, Badung, and Mati) with different levels of tourism urbanization in Bali Province, Indonesia. The results explore that compared with OLS estimates, GWR performed well in terms of their R2 values and the Akaike information criterion (AIC) in all the parameters and seasons. Further, the findings exhibit population density as a critical indicator having a highly significant association with BOD and E. Coli parameters. Moreover, the built-up area has correlated positively to the water quality parameters (Ni, Pb, KMnO4 and TSS). The parameter DO is associated negatively with the built-up area, which indicates increasing built-up area tends to deteriorate the water quality. Hence, our findings can be used as input to provide a reference to the local governments and stakeholders for issuing policy on water and LULC for achieving a sustainable water environment in this region.
The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
Uncontrolled stormwater runoff not only creates drainage problems and flash floods but also presents a considerable threat to water quality and the environment. These problems can, to a large extent, be reduced by a type of stormwater management approach employing permeable pavement systems (PPS) in urban, industrial and commercial areas, where frequent problems are caused by intense undrained stormwater. PPS could be an efficient solution for sustainable drainage systems, and control water security as well as renewable energy in certain cases. Considerable research has been conducted on the function of PPS and their improvement to ensure sustainable drainage systems and water quality. This paper presents a review of the use of permeable pavement for different purposes. The paper focuses on drainage systems and stormwater runoff quality from roads, driveways, rooftops and parking lots. PPS are very effective for stormwater management and water reuse. Moreover, geotextiles provide additional facilities to reduce the pollutants from infiltrate runoff into the ground, creating a suitable environment for the biodegradation process. Furthermore, recently, ground source heat pumps and PPS have been found to be an excellent combination for sustainable renewable energy. In addition, this study has identified several gaps in the present state of knowledge on PPS and indicates some research needs for future consideration.
In this paper, stormwater runoff from a residential catchment located in Miri, Sarawak, was characterized to determine the pollutant concentrations and loading. The observed average event mean concentrations were 116 mg/L for TSS, 115 mg/L for COD, 1.5 mg/L for NH3-N, and 0.23 mg/L for Pb. Based on Interim National Water Quality Standards (INWQS) for Malaysia, the average event mean concentration, EMC value for TSS exceeded class II (50 mg/L), exceeded class V (>100 mg/L) for COD, and exceeded class III (0.9 mg/L) for NH3-N. All four water quality parameters exhibited first flush characteristic but to varying magnitude which was influenced by the storm characteristics.
Water filters are being increasingly promoted and used in the home. There are many types of commercial water jilters available for domestic use but almost all of them employ a physical filter media and an activated substance. The study showed that water filters effectively removed suspended solids and residual chlorine. However, as far as removing colhform bacteria is concerned, in ZZ .5% of the cases, bacteria were in fact introduced into the water. And in 20% ofthe cases, the amount of bacteria introduced was “too numerous to count (TNTC)". Furthermore, water hlters can lose their ability to filter bacteria without losing their ability to filter suspended solids and residual chlorine. This highlights the necessity of some authorized body looking into the claims made by these water filter manufacturers and impose certain standards to ensure that at the very least, the water quality ofthe hltered water is not worse than the unfiltered water.
Anthropogenic pressures are causing substantial degradation to the freshwater ecosystems globally and Malaysia has not escaped such a bleak scenario. Prompted by the predicament, this study's objective was to pioneer a river assessment system that can be readily adopted to monitor, manage and drive improvement in a wholesome manner. Three sets of a priori metrics were selected to form the Ichthyofaunal Quality Index (IQI: biological), Water Quality Index (WQI: chemical) and River Physical Quality Index (RPQI: physical). These indices were further integrated on equal weighting to construct a novel Malaysian River Integrity Index (MyRII). To test its robustness, the MyRII protocol was field tested in four eco-hydrological zones located in the Kampar River water basin for 18 months to reveal its strengths, weaknesses, and establish the "excellent", "good", "average", "poor" and "impaired" thresholds based on the "best performer" reference site in an empirical manner. The resultant MyRII showed a clear trend that corresponded with different levels of river impairment. Test site zone A which was a reference site with minimal disturbance achieved the highest MyRII (88.95 ± 4.29), followed by partially disturbed zone B (61.95 ± 5.90) and heavily disturbed zone C (50.00 ± 4.29). However, the MyRII in zone D (59.9 ± 6.39), which was a heavily disturbed wetland that was disjointed from the river, did not conform to such trend. Also unveiled and recognized, however, are some unexpected nuances, limitations and challenges that emerged from this study. These are critically discussed as precautions when interpreting and implementing the MyRII protocol. This study adds to the mounting body of evidence that water resource stakeholders and policymakers must look at the big picture and adopt the "balanced ecosystem" mind-set when assessing, restoring and managing the rivers as a freshwater resource.
Multicollinearity that may exist among explanatory variables in a regression model can make the regression coefficients insignificant and difficult to interpret. Principal component regression (PCR) is an effective way for solving multicollinearity in regression analysis. The existence of multicollinearity mayor may not be induced by the presence of influential observations. This paper discusses some diagnostic methods for identifying influential observations in the PCR. A data set on water quality of New York Rivers was considered to illustrate the methods.
Multikolinearan yang wujud di kalangan pembolehubah penerang dalam model regresi boleh menyebabkan pekali regresi tidak bererti dan sukar untuk ditafsirkan. Regresi komponen utama (PCR) merupakan cara yang berkesan bagi menyelesaikan masalah multikolinearan dalam analisis regresi. Kewujudan multikolinearan mungkin disebabkan oleh data terpencil yang berpengaruh. Kertas ini membincangkan beberapa kaedah pengecaman bagi mengenalpasti data berpengaruh dalam PCR. Data tentang kualiti air di beberapa batang sungai di New York digunakan untuk memperihalkan kaedah pengecaman yang disarankan.
A study of spatial and temporal variations on water quality and trophic status was conducted twice a month from December
2012 to January 2014 in four sampling stations at Bukit Merah Reservoir (BMR). The concentration of dissolved oxygen
(DO), water temperature, conductivity, total dissolved solids (TDS), total phosphorous (TP), PO4
net primary productivity had significant differences temporally (p<0.05) except for pH, total suspended solids (TSS)
and chlorophyll-a. Based on correlation analysis, the amount of rainfall and rain days has negatively correlated with
secchi depth and chlorophyll-a (p<0.01). The water level has significantly decreased the value of the temperature, pH,
conductivity, TP and NO2
but it has positive correlation with NO3
+. Discharged from Sungai Kurau increased
the value of conductivity, TSS, TP and NO2
as a result from runoff and erosion, thus decreasing the secchi depth values,
+. The water quality of BMR is classified in Class II and TSI indicates that the BMR has an intermediate level
of productivity (mesotrophic) and meets the objective of this reservoir which was to provide water for paddy irrigation.
A study on the chironomids (Diptera:Chironomidae) diversity at pristine ecosystem was carried out at upstream of Sungai Langat, Selangor. The study determines chironomids distribution and composition at 7 streams and river within the upstream site of Langat Catchment. Chironomid was sampled using Surber net and water quality was measured based on Malaysia WQI. The result indicated that Chironomidae was represented by three subfamilies namely Chironominae, Orthocladiinae and Tanypodinae, which comprises of 2502 individuals. Chironominae was the most dominant subfamily (1619 individuals) followed by Orthocladinae (629 individuals) and Tanypodinae (254 individuals). Polypedilum (subfamily: Chironominae) is the most dominant genus found followed by Rheocricotopus (subfamily: Orthocladiinae), Microtendipes and Cryptochironomus. Polypedilum was abundant in all stations except Sg. Langat 3 which was dominated by Rheocricotopus. Sungai Langat 3 has the highest number of individual (1113) which is (44.5%) from total chironomid individual and followed by Sg. Lolo with 468 individuals that were dominated by Polypedilum.
Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.
Algal communities possess many attributes as biological indicators of spatial and temporal environmental changes. Algal parameters, especially the community structural and functional variables that have been used in biological monitoring programs, are highlighted in this document. Biological indicators like algae have only recently been included in water quality assessments in some areas of Malaysia. The use of algal parameters in identifying various types of water degradation is essential and complementary to other environmental indicators.