Displaying publications 1 - 20 of 204 in total

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  1. Fulltext Temporal aspects of surface water quality variation using robust statistical tools
    Mustapha A, Aris AZ, Ramli MF, Juahir H
    ScientificWorldJournal, 2012;2012:294540.
    PMID: 22919302 DOI: 10.1100/2012/294540
    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.
    Matched MeSH terms: Water Quality*
  2. Fulltext Sustainability of rainwater harvesting system in terms of water quality
    Rahman S, Khan MT, Akib S, Din NB, Biswas SK, Shirazi SM
    ScientificWorldJournal, 2014;2014:721357.
    PMID: 24701186 DOI: 10.1155/2014/721357
    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.
    Matched MeSH terms: Water Quality*
  3. Water quality modeling in sewer networks: Review and future research directions
    Jia Y, Zheng F, Maier HR, Ostfeld A, Creaco E, Savic D, et al.
    Water Res, 2021 Sep 01;202:117419.
    PMID: 34274902 DOI: 10.1016/j.watres.2021.117419
    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.
    Matched MeSH terms: Water Quality*
  4. Fulltext Improving Water Quality Index Prediction Using Regression Learning Models
    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*
  5. Fulltext Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review
    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*
  6. Water quality modelling using principal component analysis and artificial neural network
    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*
  7. Mapping of tide-dominated Hooghly estuary water quality parameters using Sentinel-3 OLCI time-series data
    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*
  8. Towards online surface water quality monitoring technology: A review
    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*
  9. Physicochemical and biological status of Aghlagan river, Iran: effects of seasonal changes and point source pollution
    Asadi Sharif E, Yahyavi B, Bayrami A, Rahim Pouran S, Atazadeh E, Singh R, et al.
    Environ Sci Pollut Res Int, 2021 Mar;28(12):15339-15349.
    PMID: 33236302 DOI: 10.1007/s11356-020-11660-9
    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.
    Matched MeSH terms: Water Quality*
  10. Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index
    Abba SI, Pham QB, Saini G, Linh NTT, Ahmed AN, Mohajane M, et al.
    Environ Sci Pollut Res Int, 2020 Nov;27(33):41524-41539.
    PMID: 32686045 DOI: 10.1007/s11356-020-09689-x
    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.
    Matched MeSH terms: Water Quality*
  11. Water quality assessment and analysis of spatial patterns and temporal trends
    Gazzaz NM, Yusoff MK, Juahir H, Ramli MF, Aris AZ
    Water Environ Res, 2013 Aug;85(8):751-66.
    PMID: 24003601
    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.
    Matched MeSH terms: Water Quality*
  12. Comparison among different ASEAN water quality indices for the assessment of the spatial variation of surface water quality in the Selangor river basin, Malaysia
    Wong YJ, Shimizu Y, He K, Nik Sulaiman NM
    Environ Monit Assess, 2020 Sep 16;192(10):644.
    PMID: 32935203 DOI: 10.1007/s10661-020-08543-4
    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.
    Matched MeSH terms: Water Quality*
  13. Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones
    Samsudin MS, Azid A, Khalit SI, Sani MSA, Lananan F
    Mar Pollut Bull, 2019 Apr;141:472-481.
    PMID: 30955758 DOI: 10.1016/j.marpolbul.2019.02.045
    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.
    Matched MeSH terms: Water Quality*
  14. Fulltext Master-Leader-Slave Cuckoo Search with Parameter Control for ANN Optimization and Its Real-World Application to Water Quality Prediction
    Jaddi NS, Abdullah S, Abdul Malek M
    PLoS One, 2017;12(1):e0170372.
    PMID: 28125609 DOI: 10.1371/journal.pone.0170372
    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.
    Matched MeSH terms: Water Quality/standards*
  15. The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4-2 surface water quality
    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*
  16. From tap to table: An assessment of drinking water quality in Perak, Malaysia
    Ibrahim TNBT, Feisal NAS, Azmi NM, Nazli SN, Salehuddin ASM, Nasir NICM, et al.
    Med J Malaysia, 2024 Mar;79(Suppl 1):14-22.
    PMID: 38555880
    INTRODUCTION: A study on the quality of drinking water was conducted at Air Kuning Treatment Plant In Perak, Malaysia, based on a sanitary survey in 14 sampling points stations from the intake area to the auxiliary points. This was to ensure the continuous supply of clean and safe drinking water to the consumers for public health protection. The objective was to examine the physical, microbiological, and chemical parameters of the water, classification at each site based on National Drinking Water Standards (NDWQS) and to understand the spatial variation using environmetric technique; principal component analysis (PCA).

    MATERIALS AND METHODS: Water samples were subjected to in situ and laboratory water quality analyses and focused on pH, turbidity, chlorine, Escherichia coli, total coliform, total hardness, iron (Fe), aluminium (Al), zinc (Zn), magnesium (Mg) and sodium (Na). All procedures followed the American Public Health Association (APHA) testing procedures.

    RESULTS: Based on the results obtained, the values of each parameter were found to be within the safe limits set by the NDWQS except for total coliform and iron (Fe). PCA has indicated that turbidity, total coliform, E. coli, Na, and Al were the major factors that contributed to the drinking water contamination in river water intake.

    CONCLUSION: Overall, the water from all sampling point stations after undergoing water treatment process was found to be safe as drinking water. It is important to evaluate the drinking water quality of the treatment plant to ensure that consumers have access to safe and clean drinking water as well as community awareness on drinking water quality is essential to promote public health and environmental protection.

    Matched MeSH terms: Water Quality*
  17. Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach
    Rimba AB, Mohan G, Chapagain SK, Arumansawang A, Payus C, Fukushi K, et al.
    Environ Sci Pollut Res Int, 2021 May;28(20):25920-25938.
    PMID: 33475923 DOI: 10.1007/s11356-020-12285-8
    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.
    Matched MeSH terms: Water Quality*
  18. Permeable pavement and stormwater management systems: a review
    Imran HM, Akib S, Karim MR
    Environ Technol, 2013 Sep-Oct;34(17-20):2649-56.
    PMID: 24527626
    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.
    Matched MeSH terms: Water Quality
  19. Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
    Wong YJ, Shimizu Y, Kamiya A, Maneechot L, Bharambe KP, Fong CS, et al.
    Environ Monit Assess, 2021 Jun 22;193(7):438.
    PMID: 34159431 DOI: 10.1007/s10661-021-09202-y
    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.
    Matched MeSH terms: Water Quality
  20. Perspectives on the use of algae as biological indicators for monitoring and protecting aquatic environments, with special reference to malaysian freshwater ecosystems
    Omar WM
    Trop Life Sci Res, 2010 Dec;21(2):51-67.
    PMID: 24575199
    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.
    Matched MeSH terms: Water Quality
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