Poor water quality is a serious problem in the world which threatens human health, ecosystems, and plant/animal life. Prediction of surface water quality is a main concern in water resource and environmental systems. In this research, the support vector machine and two methods of artificial neural networks (ANNs), namely feed forward back propagation (FFBP) and radial basis function (RBF), were used to predict the water quality index (WQI) in a free constructed wetland. Seventeen points of the wetland were monitored twice a month over a period of 14 months, and an extensive dataset was collected for 11 water quality variables. A detailed comparison of the overall performance showed that prediction of the support vector machine (SVM) model with coefficient of correlation (R(2)) = 0.9984 and mean absolute error (MAE) = 0.0052 was either better or comparable with neural networks. This research highlights that the SVM and FFBP can be successfully employed for the prediction of water quality in a free surface constructed wetland environment. These methods simplify the calculation of the WQI and reduce substantial efforts and time by optimizing the computations.
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.
Bioretention systems are among the most popular stormwater best management practices (BMPs) for urban runoff treatment. Studies on plant performance using bioretention systems have been conducted, especially in developed countries with a temperate climate, such as the USA and Australia. However, these results might not be applicable in developing countries with tropical climates due to the different rainfall regimes and the strength of runoff pollutants. Thus, this study focuses on the performance of tropical plants in treating urban runoff polluted with greywater using a bioretention system. Ten different tropical plant species were triplicated and planted in 30 mesocosms with two control mesocosms without vegetation. One-way ANOVA was used to analyze the performance of plants, which were then ranked based on their performance in removing pollutants using the total score obtained for each water quality test. Results showed that vetiver topped the table with 86.4% of total nitrogen (TN) removal, 93.5% of total phosphorus (TP) removal, 89.8% of biological oxygen demand (BOD) removal, 90% of total suspended solids (TSS) removal, and 92.5% of chemical oxygen demand (COD) removal followed by blue porterweed, Hibiscus, golden trumpet, and tall sedge which can be recommended to be employed in future bioretention studies.
As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.
As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is -0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.
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.
A set of methodological tools was tested to assess the sensitivity of several ecological and biological indices to eutrophication while at the same time attempting to explore a linkage among pressures, classification assessment and drivers. Industrial discharges, harbor activities, natural interactions and river discharges are the pressures most related to the eutrophication process in tropical coastal water bodies. Among the eutrophication indices used, TRIX and operational indicators overestimated the eutrophication status in the study area, but EI and chl-a seems to be a rather responsive index to reflect the first stage of eutrophication. It is noteworthy that EI and chl-a showed better overall agreement with the ecological quality status (EcoQ) showing that probably it reflects the indirect relation of macrobenthic with water eutrophication in a better way. An ecological boundary of EI and chl-a from moderate to poor may be needed in order to explain the poor status of relatively eutrophic Klang Strait coastal sites.
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.
This study's main goal is to evaluate how the research will look at the impact of geothermal energy production on the quality of the subterranean in the 27 European nations from 1990 to 2021. A considerable decline in the subterranean water supply can occur in EU14 emerging nations employing geothermal energy growth compared to EU13 emerging economies, according to research that uses the autoregressive distributed lag (ARDL). Fossil fuel use, population growth, and economic expansion are some factors that have a more detrimental effect on the subterranean water supply in EU14 emerging economies than in EU13 emerging nations. In contrast, the study's findings indicate that EU13 emerging nations may be better able to enhance their underground water supply than EU14 emerging economies because of more effective institutional qualities. The findings so indicate that increasing the amount of geothermal energy generation among the 27 European Union countries can accelerate subsurface water degradation at a high capacity and help achieve unionism's 2030 energy-related goals. When this is achieved, climate change will be put to check, as pollution of the environment. All calculations projected were seen to be of a good level of validity, and this is ascertained through three estimators considered in this study.
In this study, vegetative cell suspensions of two Bacillus subtilis strains, L10 and G1 in equal proportions, was administered at two different doses 10(5) (BM5) and 10(8) (BM8) CFU ml(-1) in the rearing water of shrimp (Litopenaeus vannamei) for eight weeks. Both probiotic groups showed a significant reduction of ammonia, nitrite and nitrate ions under in vitro and in vivo conditions. In comparison to untreated control group, final weight, weight gain, specific growth rate (SGR), food conversion ratio (FCR) and digestive enzymatic activity were significantly greater in the BM5 and BM8 groups. Significant differences for survival were recorded in the BM8 group as compared to the control. Eight weeks after the start of experiment, shrimp were challenged with Vibrio harveyi. Statistical analysis revealed significant differences in shrimp survival between probiotic and control groups. Cumulative mortality of the control group was 80%, whereas cumulative mortality of the shrimp that had been given probiotics was 36.7% with MB8 and 50% with MB5. Subsequently, real-time RT-PCR was employed to determine the mRNA levels of prophenoloxidase (proPO), peroxinectin (PE), lipopolysaccharide- and β-1,3-glucan- binding protein (LGBP) and serine protein (SP). The expression of all immune-related genes studied was only significantly up-regulated in the BM5 group compared to the BM8 and control groups. These results suggest that administration of B. subtilis strains in the rearing water confers beneficial effects for shrimp aquaculture, considering water quality, growth performance, digestive enzymatic activity, immune response and disease resistance.
Microalgae can use either ammonium or nitrate for its growth and vitality. However, at a certain level of concentration, ammonium nitrogen exhibits toxicity which consequently can inhibit microalgae productivity. Therefore, this study is aimed to investigate the tolerance of Tetraselmis tetrathele to high ammonium nitrogen concentrations and its effects on growth rate, photosynthetic efficiency (F v /F m ), pigment contents (chlorophyll a, lutein, neoxanthin, and β-carotene), and fatty acids production. Experiments were performed at different ammonium nitrogen concentrations (0.31-0.87 gL-1) for 6 days under a light source with an intensity of 300 μmol photons m-2 s-1 and nitrate-nitrogen source as the experimental control. The findings indicated no apparent enhancement of photosynthetic efficiency (Fv/Fm) at high levels of ammonium nitrogen (
NH
4
+
-N) for T. tetrathele within 24 h. However, after 24 h, the photosynthetic efficiency of T. tetrathele increased significantly (p < 0.05) in high concentration of
NH
4
+
-N. Chlorophyll a content in T. tetrathele grown in all of the different
NH
4
+
-N levels increased significantly compared to nitrate-nitrogen (NO3-N) treatment (p < 0.05); which supported that this microalgal could grow even in high level of
NH
4
+
-N concentrations. The findings also indicated that T. tetrathele is highly resistant to high ammonium nitrogen which suggests T. tetrathele to be used in the aquaculture industry for bioremediation purpose to remove ammonium nitrogen, thus reducing the production cost while improving the water quality.
Water constitutes a major environmental and public health concerns worldwide. A large proportion of global water consumption is sourced from surface water. The dependency level on surface water is higher in developing countries, especially in rural-to-semi-urban areas, where subsurface water is not accessible. Presented in this paper is a spatiotemporal and hydrochemical quality assessment of the spring-originated Landzun Stream in Bida, Nigeria; which is usually consumed in its untreated state. Water samples were systematically collected in eighteen locations along the stream channel in both rainy and dry seasons at an equidistance interval of 500m. On-site and laboratory measurement of important physical and hydrochemical parameters were carried out using standard procedures. Water temperature in the rainy season (34-37 °C) slightly exceeds measured values in the dry season (29-33 °C). 72.22% (rainy) and 83.33% (dry) of collected samples did not meet the odourless requirement for drinking water. Similarly, estimated percentages of 66.67 and 94.44 of collected samples in rainy and dry seasons respectively have a taste. Contrary to data in the rainy season, 89%, 11%, 67% and 56% of the dry season's samples were enriched in magnesium (Mg), lead (Pb), potassium (K) and iron (Fe) respectively above the 2018 World Health Organisation guidelines for drinking water. This study further established that seasonal variation plays a major role in altering the aesthetic surface water quality. The intake of untreated surface water is a vehicle for potential water-borne diseases and allergies, hence alternative sources of drinking water for the populace dependent on the Landzun Stream is recommended to reduce risks and possible dangers of consuming the stream water.
The previous research showed that slow sand filtration (SSF) can remove the total coli by approximately 99% because of the schmutzecke layer in the filter. The presented study aimed to complete the previous research on SSF, especially on the schmuztdecke layer mechanism, to remove total coli. Total coli is a parameter of water quality standard in Indonesia, and the behavior of schmutzdecke affects the total coli removal. In the present study, the raw water from Amprong River was treated using horizontal roughing filter (HRF) and SSF. The variations in SSF rate used were 0.2 and 0.4 m/h. Total coliforms were analyzed using the most probable number test, and schmutzdecke visualization was conducted through scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDX). The best coliform concentration in water treated by the combination of HRF and SSF was 4,386 colonies per 100 mL of sample using the filtration rate of 0.2 m/h, and its removal efficiency was 99.60%. However, the quality of water treated by the combination of HRF and SSF did not meet the drinking water quality standard because the removal of total coli must be 100%. The SEM-EDX visualization results in schmutzdecke showed that the average bacteria in the schmutzdecke layer were small, white, opaque, and circular, with entire edge and flat elevation. The Gram test results showed that the schmutzdecke bacteria consisted of Gram-positive and Gram-negative bacteria with basil as the common cell form.
Access to clean and safe drinking water that is free from pathogenic protozoan parasites, especially Cryptosporidium parvum and Giardia lamblia that cause gastrointestinal illness in humans, is still an issue in Southeast Asia (SEA). This study is the first attempt to detect the aforementioned protozoan parasites in water samples from countries in SEA, using real-time polymerase chain reaction (qPCR) assays.
Background: The public health issue of consuming groundwater is a major concern because people often extract groundwater directly from the aquifers either through wells or boreholes without treating it with any form of filtration system or chlorine disinfection. Based on the Malaysian National Drinking Water guidelines the current study was designed to provide a better understanding on the variable factors that are influencing the quality of well-water in an urbanised village in Malaysia. Well water quality assessment of heavy metals, chemicals, microbial and physical parameters were carried out for Sungai Buloh Village in the Klang Valley to ensure it was safe for human consumption.
Materials and Methods: Water samples were collected from wells at four sites (Sites A,B,C,D), a river and a tap inside a house in Sungai Buloh village. Soil was sampled from the riverbed and area surrounding the wells. Samples were collected every two months over a one year duration from all sites. The water samples were processed and examined for viruses, coliforms and protozoa as well as for heavy metal contaminants.
Results: The turbidity and colour ranged in the average of 0.57-0.13 Nephelometric Turbidity (NTU) and 4.16-5.00 Total Conjunctive Use (TCU) respectively for all sites except Site C. At Site C the turbidity level was 2.56 ± 1.38 NTU. The well-water was polluted with coliforms (1.2 to 2.4 x 103 CFU/100 ml) in all sites, E. coli (0.12 - 4 x 102 CFU/100 ml CFU/ 100 ml) and Cryptosporidium oocysts (0.4 cysts/100 ml). All the heavy metals and chemical parameters were within the Malaysian Guidelines’ limits except manganese. The average pH ranged from 5.44 - 6.62 and the temperature was 28 ºC.
Conclusion: In summary, the well water at Sungai Buloh is considered unsafe for consumption due to pollution. Therefore the major thrust will be to provide better quality of drinking water to the residents of the village.
Groundwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (PCA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island's hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island's water types into Ca-HCO3 water type, Na-HCO3 water type, Na-SO4-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.
The aim of this study was to propose a groundwater quality index (GWQI) that presents water quality data as a single number and represents the water quality level. The development of the GWQI in agricultural areas is vital as the groundwater considered as an alternative water source for domestic purposes. The insufficiency of the groundwater quality standard in Malaysia revealed the importance of the GWQI development in determining the quality of groundwater. Groundwater samples were collected from thirteen groundwater wells in the Northern Kuala Langat and the Southern Kuala Langat regions from February 2018 to January 2019. Thirty-four parameters that embodied physicochemical characteristics, aggregate indicator, major ions, and trace elements were considered in the development of the GWQI. Multivariate analysis has been used to finalize the important parameters by using principal component analysis (PCA). Notably, seven parameters-electrical conductivity, chemical oxygen demand (COD), magnesium, calcium, potassium, sodium, and chloride were chosen to evaluate the quality of groundwater. The GWQI was then verified by comparing the groundwater quality in Kota Bharu, Kelantan. A sensitivity analysis was performed on this index to verify its reliability. The sensitivity GWQI has been analyzed and showed high sensitivity to any changes of the pollutant parameters. The development of GWQI should be beneficial to the public, practitioners, and industries. From another angle, this index can help to detect any form of pollution which ultimately could be minimized by controlling the sources of pollutants.
Rapid urbanization and industrial development in the Langat Basin has disturbed the groundwater quality. The populations' reliance on groundwater sources may induce possible risks to human health such as cancer and endocrine dysfunction. This study aims to determine the groundwater quality of an urbanized basin through 24 studied hydrochemical parameters from 45 groundwater samples obtained from 15 different sampling stations by employing integrated multivariate analysis. The abundance of the major ions was in the following order: bicarbonate (HCO3-) > chloride (Cl-) > sodium (Na+) > sulphate (SO42-) > calcium (Ca2+) > potassium (K+) > magnesium (Mg2+). Heavy metal dominance was in the following order: Fe > Mn > Zn > As > Hg > Pb > Ni > Cu > Cd > Se > Sr. Classification of the groundwater facies indicated that the studied groundwater belongs to the Na-Cl with saline water type and Na-HCO3 with mix water type characteristics. The saline water type characteristics are derived from agricultural activities, while the mixed water types occur from water-rock interaction. Multivariate analysis performance suggests that industrial, agricultural, and weathering activities have contributed to groundwater contamination. The study will help in the understanding of the groundwater quality issue and serve as a reference for other basins with similar characteristics.
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.
Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.