Displaying publications 1 - 20 of 204 in total

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  1. Alsaleh M, Chen T, Abdul-Rahim AS
    Environ Technol, 2024 Mar;45(7):1271-1289.
    PMID: 36305514 DOI: 10.1080/09593330.2022.2141662
    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.
    Matched MeSH terms: Water Quality
  2. Chahban M, Akodad M, Skalli A, Gueddari H, El Yousfi Y, Ait Hmeid H, et al.
    Environ Res, 2024 Mar 01;244:117939.
    PMID: 38128604 DOI: 10.1016/j.envres.2023.117939
    The Guerouaou aquifer investigation spanning 280 km2 in Ain Zohra yields promising outcomes, instilling optimism for regional water quality. These analyses were applied to 45 sampling instances from 43 wells, enabling a comprehensive water quality assessment. Groundwater conductivity ranged from medium to high, peaking at 18360 ms/cm2. The conductivity reveals insights about the groundwater's mineralization. Key physiochemical parameters fell within desirable thresholds, bolstering the positive perspective. HCO3- levels spanned 82-420 mg/L, while chloride content ranged from 38 to 5316 mg/L, benefiting water quality. NO3- ions, vital for gauging pollution, ranged from 0 to 260 mg/L, indicating favorable results. Cation concentrations exhibited encouraging variations: Ca2+- 24 to 647 mg/L, Mg2+- 12 to 440 mg/L, Na+- 18 to 2722 mg/L, K+- 1.75 to 28.65 mg/L. These collectively favor water quality. Halite breakdown dominated mineralization, as evidenced by the prevalence of Na-Cl-Na-SO4 facies. Water resource management and local communities need effective management and mitigation strategies to prevent saltwater intrusion.
    Matched MeSH terms: Water Quality
  3. Singh RB, Patra KC, Pradhan B, Samantra A
    J Environ Manage, 2024 Feb 14;352:120091.
    PMID: 38228048 DOI: 10.1016/j.jenvman.2024.120091
    Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity and significant forecasting errors, primarily due to non-linear datasets and hyperparameter settings. To address these challenges, we have developed an innovative HDTO-DeepAR approach for predicting water quality indicators. This proposed approach is compared with standalone algorithms, including DeepAR, BiLSTM, GRU and XGBoost, using performance metrics such as MAE, MSE, MAPE, and NSE. The NSE of the hybrid approach ranges between 0.8 to 0.96. Given the value's proximity to 1, the model appears to be efficient. The PICP values (ranging from 95% to 98%) indicate that the model is highly reliable in forecasting water quality indicators. Experimental results reveal a close resemblance between the model's predictions and actual values, providing valuable insights for predicting future trends. The comparative study shows that the suggested model surpasses all existing, well-known models.
    Matched MeSH terms: Water Quality
  4. Li X, Zhang F, Shi J, Chan NW, Cai Y, Cheng C, et al.
    Environ Sci Pollut Res Int, 2024 Feb;31(6):9333-9346.
    PMID: 38191729 DOI: 10.1007/s11356-023-31702-2
    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.
    Matched MeSH terms: Water Quality
  5. Praveena SM, Aris AZ, Hashim Z, Hashim JH
    J Expo Sci Environ Epidemiol, 2024 Jan;34(1):161-174.
    PMID: 37563210 DOI: 10.1038/s41370-023-00585-3
    BACKGROUND: Like other countries, surface water degradation in Malaysia is linked with common global issues. Although different aspects of drinking water suitability have been examined, the overall understanding of drinking water quality in Malaysia is poor.

    OBJECTIVE: Hence, the present review aims to provide an understanding of drinking water (tap water, groundwater, gravity feed system) quality and its potential implications on policy, human health, and drinking water management law and identification of potential direction of future drinking water research and management needs in Malaysia.

    METHODS: This study utilized a scoping review method. PRISMA Extension for Scoping Reviews was used for search strategy. Relevant studies were screened using the selected keywords and databases.

    RESULTS: A total of 26 drinking water quality studies involving tap water, groundwater, and gravity feed systems have been selected for review. These studies found that the majority of Malaysian Drinking Water and WHO Drinking Water standards have been met. High levels of Cu, Cd, Fe and Pb were attributable to galvanized plumbing and pipe material corrosion. Variation of fluoride in tap water depends on dosage planning and operational processes of the public water supply. Pollutants (nitrate and ammonia) in groundwater and gravity feed system water have been linked to agricultural practices in rural areas. Microbiological quality in tap water is associated with growing biofilms inside the pipelines while in groundwater is caused by shallow surface events. However, only eight studies have reported about the human risks of chemical pollutants in tap water.

    IMPACT STATEMENT: The review discusses the state of drinking water quality in Malaysia and its impact on public health. It suggests that policymakers can use this information to improve the quality of drinking water and enforce restrictions, while also raising public awareness about the importance of safe drinking water. The study can guide future research and initiatives in Malaysia, ultimately contributing to efforts to ensure access to clean and dependable drinking water.

    Matched MeSH terms: Water Quality
  6. Liu B, Lee CW, Bong CW, Wang AJ
    PeerJ, 2024;12:e16556.
    PMID: 38223759 DOI: 10.7717/peerj.16556
    BACKGROUND: Escherichia coli is a commonly used faecal indicator bacterium to assess the level of faecal contamination in aquatic habitats. However, extensive studies have reported that sediment acts as a natural reservoir of E. coli in the extraintestinal environment. E. coli can be released from the sediment, and this may lead to overestimating the level of faecal contamination during water quality surveillance. Thus, we aimed to investigate the effects of E. coli habitat transition from sediment to water on its abundance in the water column.

    METHODS: This study enumerated the abundance of E. coli in the water and sediment at five urban lakes in the Kuala Lumpur-Petaling Jaya area, state of Selangor, Malaysia. We developed a novel method for measuring habitat transition rate of sediment E. coli to the water column, and evaluated the effects of habitat transition on E. coli abundance in the water column after accounting for its decay in the water column.

    RESULTS: The abundance of E. coli in the sediment ranged from below detection to 12,000 cfu g-1, and was about one order higher than in the water column (1 to 2,300 cfu mL-1). The habitat transition rates ranged from 0.03 to 0.41 h-1. In contrast, the E. coli decay rates ranged from 0.02 to 0.16 h-1. In most cases (>80%), the habitat transition rates were higher than the decay rates in our study.

    DISCUSSION: Our study provided a possible explanation for the persistence of E. coli in tropical lakes. To the best of our knowledge, this is the first quantitative study on habitat transition of E. coli from sediments to water column.

    Matched MeSH terms: Water Quality
  7. Zaidi Farouk MIH, Jamil Z, Abdul Latip MF
    Environ Res, 2023 Dec 01;238(Pt 1):117147.
    PMID: 37716398 DOI: 10.1016/j.envres.2023.117147
    The exponential growth of human population and anthropogenic activities have led to the increase of global surface water contamination especially in river, lakes and ocean. Safe and clean surface water sources are crucial to human health and well-being, aquatic ecosystem, environment and economy. Thus, water monitoring is vital to ensure minimal and controllable contamination in the water sources. The conventional surface water monitoring method involves collecting samples on site and then testing them in the laboratory, which is time-consuming and not able to provide real-time water quality data. In addition, it involves many manpower and resources, costly and lack of integration. These make surface water quality monitoring more challenging. The incorporation of Internet of Things (IoT) and smart technology has contributed to the improvement of monitoring system. There are different approaches in the development and implementation of online surface water quality monitoring system to provide real-time data collection with lower operating cost. This paper reviews the sensors and system developed for the online surface water quality monitoring system in the previous studies. The calibration and validation of the sensors, and challenges in the design and development of online surface water quality monitoring system are also discussed.
    Matched MeSH terms: Water Quality*
  8. Bar AR, Mondal I, Das S, Biswas B, Samanta S, Jose F, et al.
    Environ Monit Assess, 2023 Jul 20;195(8):975.
    PMID: 37474709 DOI: 10.1007/s10661-023-11552-8
    The study explores the spatio-temporal variation of water quality parameters in the Hooghly estuary, which is considered an ecologically-stressed shallow estuary and a major distributary for the Ganges River. The estimated parameters are chlorophyll-a, total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM). The Sentinel-3 OLCI remote sensing imageries were analyzed for the duration of October 2018 to February 2019. We observed that the water quality of the Hooghly estuaries is comparatively low-oxygenated, mesotrophic, and phosphate-limited. Ongoing channel dredging for maintaining shipping channel depth keeps the TSM in the estuary at an elevated level, with the highest amount of TSM observed during March of 2019 (41.59g m-3) at station A, upstream point. Since the pre-monsoon season, TSM data shows a decreasing trend towards the mouth of the estuary. Chl-a concentration is higher during pre-monsoon than monsoon and post-monsoon periods, with the highest value observed in April at 1.09 mg m-3 in station D during the pre-monsoon period. The CDOM concentration was high in the middle section (January-February) and gradually decreased towards the estuary's head and mouth. The highest CDOM was found in February at locations C and D during the pre-monsoon period. Every station shows a significant correlation among CDOM, TSM, and Chl-a measured parameters. Based on our satellite data analysis, it is recommended that SNAP C2RCC be regionally used for TSM, Chl-a, and CDOM for water quality product retrieval and in various algorithms for the Hooghly estuary monitoring.
    Matched MeSH terms: Water Quality*
  9. Rathinasamy V, Mohamad ET, Komoo I, Legiman MKA, Romanah NA, Hanapi MNB
    Environ Monit Assess, 2023 Jun 16;195(7):850.
    PMID: 37326879 DOI: 10.1007/s10661-023-11453-w
    Jurong Formation underlies part of Southern Johor Bahru which comprises well cemented and consolidated volcanic-sedimentary rocks. The study aims to assess quality and hydrogeochemistry of rock aquifer in Jurong Formation at Southern Johor Bahru which is mainly overlain by rhyolitic tuff. It also evaluates the differences in quality and hydrogeochemistry of rhyolitic tuff aquifer found in source and floodplain zones of South-West Johor Rivers Basin. In this study, a total of nine samples from four wells, namely TW1-TW4, were collected at foothills of Gunung Pulai (TW1) and Iskandar Puteri (TW2-TW4) in Southern Johor Bahru. The samples were examined for physiochemical parameters. The groundwater in the study area is fresh and non-saline with hardness of soft to hard. The pH of groundwater in source zone is significantly higher than in floodplain zone. Meanwhile, the hardness of groundwater in source zone is significantly lower than in other deep wells in floodplain zone as more calcite mineral is present. The concentration of manganese, iron and zinc is lower at source zone than floodplain zone. Three facies of water types were encountered during the study such as CaNaHCO3 in TW2, CaHCO3 in TW1 and TW3 and CaCl2 in TW4. The deep wells in floodplain zone are susceptible to saline intrusion. Finally, the groundwater quality in the study area is found to control by rock weathering especially silicates and carbonates, rainfall and proximity to seawater. This suggests the major control on groundwater chemistry is due to leaching of volcanic rocks and dissolution on calcite infillings. In conclusion, the groundwater is clean and safe in general although pH value is slightly acidic closer to straits and magnesium's presence in higher concentration at TW2.
    Matched MeSH terms: Water Quality
  10. Ismail W, Niknejad N, Bahari M, Hendradi R, Zaizi NJM, Zulkifli MZ
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71794-71812.
    PMID: 34609681 DOI: 10.1007/s11356-021-16471-0
    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.
    Matched MeSH terms: Water Quality
  11. Mohammed AU, Aris AZ, Ramli MF, Isa NM, Arabi AS, Jabbo JN
    Environ Geochem Health, 2023 Jun;45(6):3891-3906.
    PMID: 36609946 DOI: 10.1007/s10653-022-01468-6
    Multiple interactions of geogenic and anthropogenic activities can trigger groundwater pollution in the tropical savanna watershed. These interactions and resultant contamination have been studied using applied geochemical modeling, conventional hydrochemical plots, and multivariate geochemometric methods, and the results are presented in this paper. The high alkalinity values recorded for the studied groundwater samples might emanate from the leaching of carbonate soil derived from limestone coupled with low rainfall and high temperature in the area. The principal component analysis (PCA) unveils three components with an eigenvalue > 1 and a total dataset variance of 67.37%; this implies that the temporary hardness of the groundwater and water-rock interaction with evaporite minerals (gypsum, halite, calcite, and trona) is the dominant factor affecting groundwater geochemistry. Likewise, the PCA revealed anthropogenic contamination by discharging [Formula: see text] [Formula: see text][Formula: see text] and [Formula: see text] from agricultural activities and probable sewage leakages. Hierarchical cluster analysis (HCA) also revealed three clusters; cluster I reflects the dissolution of gypsum and halite with a high elevated load of [Formula: see text] released by anthropogenic activities. However, cluster II exhibited high [Formula: see text] and [Formula: see text] loading in the groundwater from weathering of bicarbonate and sylvite minerals. Sulfate ([Formula: see text]) dominated cluster III mineralogy resulting from weathering of anhydrite. The three clusters in the Maiganga watershed indicated anhydrite, gypsum, and halite undersaturation. These results suggest that combined anthropogenic and natural processes in the study area are linked with saturation indexes that regulate the modification of groundwater quality.
    Matched MeSH terms: Water Quality
  12. Suresh Raj PR, Mohan Viswanathan P
    Chemosphere, 2023 Mar;316:137838.
    PMID: 36642142 DOI: 10.1016/j.chemosphere.2023.137838
    In this study, estuarine water samples were collected at diverse hot spots in Miri River Estuary, East Malaysia to appraise the geochemical processes, which controls the river water quality. The collected water samples were analysed for various physicochemical parameters (insitu parameters, nutrients, major ions and trace metals), including stable isotopes (oxygen and hydrogen). Suspended solids are also extracted from the water samples and analysed for trace metals. Standard graphs, Piper plot, Gibbs diagram, water quality indices, geochemical modelling and statistical analysis were used for the data analysis. The acquired water quality data was compared with national and international guidelines for the suitability of water for various purposes. Interpretation of data reveals that the estuarine water quality is deemed unsuitable to be used for both drinking and irrigation purposes. Overall, the elemental concentrations are increasing from downstream to river mouth. Based on pollution indices (HEI and Cd), downstream region shows high vulnerability to metal pollution due to anthropogenic disturbance. Isotope values of river water indicate direct atmospheric precipitation with minimal evaporation. Factor analysis reveals that seawater influx, urban pollution, domestic and agricultural discharges at the downstream region are the main controlling factors to the river water quality. It is also deduced that suspended solids play a vital role in the adsorption and desorption of trace metals in the estuarine water. The outcome of this study provides a comprehensive information on pollution status of Miri estuary, which helps the policy makers to practice sustainable management of this water resource for Miri community.
    Matched MeSH terms: Water Quality
  13. Ibrahim A, Ismail A, Juahir H, Iliyasu AB, Wailare BT, Mukhtar M, et al.
    Mar Pollut Bull, 2023 Feb;187:114493.
    PMID: 36566515 DOI: 10.1016/j.marpolbul.2022.114493
    The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.
    Matched MeSH terms: Water Quality*
  14. Jhonson P, Goh HW, Chan DJC, Juiani SF, Zakaria NA
    Environ Sci Pollut Res Int, 2023 Feb;30(9):24562-24574.
    PMID: 36336739 DOI: 10.1007/s11356-022-23605-5
    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.
    Matched MeSH terms: Water Quality
  15. Chen HL, Selvam SB, Ting KN, Gibbins CN
    Environ Monit Assess, 2023 Jan 18;195(2):307.
    PMID: 36652034 DOI: 10.1007/s10661-022-10856-5
    Recent increase in awareness of the extent of microplastic contamination in marine and freshwater systems has heightened concerns over the ecological and human health risks of this ubiquitous material. Assessing risks posed by microplastic in freshwater systems requires sampling to establish contamination levels, but standard sampling protocols have yet to be established. An important question is whether sampling and assessment should focus on microplastic concentrations in the water or the amount deposited on the bed. On three dates, five replicated water and bed sediment samples were collected from each of the eight sites along the upper reach of the Semenyih River, Malaysia. Microplastics were found in all 160 samples, with mean concentrations of 3.12 ± 2.49 particles/L in river water and 6027.39 ± 16,585.87 particles/m2 deposited on the surface of riverbed sediments. Fibres were the dominant type of microplastic in all samples, but fragments made up a greater proportion of the material on the bed than in the water. Within-site variability in microplastic abundance was high for both water and bed sediments, and very often greater than between-site variability. Patterns suggest that microplastic accumulation on the bed is spatially variable, and single samples are therefore inadequate for assessing bed contamination levels at a site. Sites with the highest mean concentrations in samples of water were not those with the highest concentrations on the bed, indicating that monitoring based only on water samples may not provide a good picture of either relative or absolute bed contamination levels, nor the risks posed to benthic organisms.
    Matched MeSH terms: Water Quality
  16. Cao X, Yu ZX, Xie M, Pan K, Tan QG
    Environ Sci Technol, 2023 Jan 17;57(2):1060-1070.
    PMID: 36595456 DOI: 10.1021/acs.est.2c06447
    In coastal waters, particulate metals constitute a substantial fraction of the total metals; however, the prevalent water quality criteria are primarily based on dissolved metals, seemingly neglecting the contribution of particulate metals. Here we developed a method to quantify the toxicity risk of particulate metals, and proposed a way to calculate modifying factors (MFs) for setting site-specific criteria in turbid waters. Specifically, we used a side-by-side experimental design to study copper (Cu) bioaccumulation and toxicity in an estuarine clam, Potamocorbula laevis, under the exposure to "dissolved only" and "dissolved + particulate" 65Cu. A toxicokinetic-toxicodynamic model (TK-TD) was used to quantify the processes of Cu uptake, ingestion, assimilation, egestion, and elimination, and to relate mortality risk to tissue Cu. We find that particulate Cu contributes 40-67% of the Cu bioaccumulation when the suspended particulate matter (SPM) ranges from 12 to 229 mg L-1. The Cu-bearing SPM also increases the sensitivity of organisms to internalized Cu by decreasing the internal threshold concentration (CIT) from 141 to 76.8 μg g-1. MFs were derived based on the TK-TD model to consider the contribution of particulate Cu (in the studied SPM range) for increasing Cu bioaccumulation (MF = 1.3-2.2) and toxicity (MF = 2.3-3.9). Water quality criteria derived from dissolved metal exposure need to be lowered by dividing by an MF to provide adequate protection. Overall, the method we developed provides a scientifically sound framework to manage the risks of metals in turbid waters.
    Matched MeSH terms: Water Quality
  17. Zainurin SN, Wan Ismail WZ, Mahamud SNI, Ismail I, Jamaludin J, Ariffin KNZ, et al.
    Int J Environ Res Public Health, 2022 Oct 28;19(21).
    PMID: 36360992 DOI: 10.3390/ijerph192114080
    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.
    Matched MeSH terms: Water Quality*
  18. Mohd Zebaral Hoque J, Ab Aziz NA, Alelyani S, Mohana M, Hosain M
    Int J Environ Res Public Health, 2022 Oct 21;19(20).
    PMID: 36294286 DOI: 10.3390/ijerph192013702
    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.
    Matched MeSH terms: Water Quality*
  19. S C, M V P, S V, M N, K P, Panda B, et al.
    Environ Res, 2022 03;204(Pt A):111729.
    PMID: 34478727 DOI: 10.1016/j.envres.2021.111729
    This study was focused on identifying the region suitable for agriculture-based, using new irrigation groundwater quality plot and its spatio-temporal variation with fuzzy logic technique in a geographic information system (GIS) platform. Six hundred and eighty groundwater samples were collected during pre, southwest, northeast, and post monsoon periods. A new ternary plot was also attempted to determine the irrigation suitability of water by considering four essential parameters such as sodium adsorption ratio (SAR), permeability index (PI), Sodium percentage (Na %), and electrical conductivity (EC). The derived ternary plot was the most beneficial over other available plots, as it incorporated four parameters, and it differs from the US Salinity Laboratory (USSL) plot, such that the groundwater with higher EC could also be used for irrigation purposes, depending on the Na%. The ternary plot revealed that the groundwater predominantly manifested good to moderate category during post, northeast, and southwest monsoons. The assessment with the amount of fertilizer used during the study period showed that the NPK fertilizers were effectively used for irrigation during monsoon periods. Spatial maps on EC, Kelly's ratio, Mg hazard, Na%, PI, potential salinity (PS), SAR, residual sodium carbonate (RSC), and soluble sodium percentage (SSP) were prepared for each season using fuzzy membership values, integrated for each season. A final suitability map derived by an overlay of all the seasonal outputs has identified that the groundwater in the western and the eastern part of the study area are suitable for agriculture. The study recommends cultivation of groundwater-dependent short-term crops, along the western and northern regions of the study area during the pre-monsoon season.
    Matched MeSH terms: Water Quality
  20. Jamei M, Ahmadianfar I, Karbasi M, Jawad AH, Farooque AA, Yaseen ZM
    J Environ Manage, 2021 Dec 15;300:113774.
    PMID: 34560461 DOI: 10.1016/j.jenvman.2021.113774
    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.
    Matched MeSH terms: Water Quality*
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