Existing Water Quality Research Articles

Review of Climate Change Impacts on Water Quantity and Quality in the Murray–Darling Basin, Australia

Climate change is a global phenomenon that significantly affects water quality and quantity, with implications observed across various regions worldwide. In the Murray–Darling Basin (MDB), Australia’s largest and most vital river system, climate change is exacerbating environmental and public health challenges, particularly through its impact on water resources. This review assesses the historical, current, and potential future impacts of climate change on both water quantity and quality in the MDB. The study involved a systematic review of 126 reputable sources, including peer-reviewed journals, government reports, and relevant books. A particular focus was given to the increasing frequency of blue-green algae (BGA) and blackwater events, which are key indicators of climate change’s impact on the basin’s water systems. The findings underscore the critical importance of integrating climate adaptation measures into existing water quality management policies to mitigate these adverse effects. The review concludes that proactive adaptation measures are essential for enhancing the resilience of the MDB’s water resources against the ongoing and future impacts of climate change, with lessons that may be applicable to other regions facing similar challenges.

Evaluating the water quality of the rice–fish co-culture pattern based on the modified NSF water quality index model

The rice–fish co-culture pattern, a sustainable agricultural production method, plays a significant role in enhancing farmland ecological and economic benefits. However, few studies have evaluated the water quality of this system using the water quality index (WQI). In this study, improvements were made to the United States National Sanitation Foundation WQI (NSFWQI), including indicator selection, weight calculation, and sub-indexing, using principal component analysis (PCA), a minimum data set (MDS), linear function equations, and a comprehensive water quality assessment of the double-cropping rice–fish (DRF) co-culture and (DR) rice patterns. The results showed that using PCA; pH, dissolved oxygen (DO), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), and the Shannon–Wiener index were selected from 11 water quality indicators to construct the MDS for WQI calculation. Based on the WQI results, the DRF had a negative impact on water quality, with the Shannon–Wiener index the greatest contributor to the WQI (22.9 %), consistently ranging between 1 and 3. Although total phosphorus (TP) and chemical oxygen demand (COD) were not included in the MDS for the WQI calculation, their significant increase during the late rice season in the DRF indicated water quality deterioration, further validating the accuracy of the improved NSFWQI. The improved NSFWQI model provides a more objective analysis of water quality under the rice–fish co-culture pattern. Compared with existing water quality evaluation methods, this approach offers advantages such as comprehensiveness, low cost, and standardization, and presents an effective alternative for water quality assessment.

Research on the Index Calculation Method for the Impact of Drought on Water Quality in the Nakdong River, Korea

The impact of drought is intensifying due to climate change, leading to significant environmental consequences, particularly concerning river water quality. While drought is typically classified as meteorological or hydrological, studies assessing its environmental impacts remain limited. Drought-induced hydrological alterations in rivers often degrade water quality, necessitating the development of an environmental drought index. This study introduces a novel methodology for calculating an index to evaluate the effects of drought on river water quality, specifically applied to tributaries of the Nakdong River in South Korea. The index was constructed by reviewing existing water quality and drought indices, selecting relevant parameters, and weighting each factor following the National Sanitation Foundation Water Quality Index (NSFWQI) methodology. Factors integrated into the index encompass both meteorological and hydrological indicators, with priority given to variables measurable in real time. Real-time parameters—such as flow rate, cumulative precipitation, days without rainfall, and sensor-based metrics (pH, electrical conductivity [EC], dissolved oxygen [DO], and total organic carbon [TOC])—were incorporated. Additionally, for rivers with upstream dams, dam discharge data were included to reflect its influence on flow conditions. The applicability of the calculated index was assessed by comparing index values to observed water quality data. A class interval structure was implemented to enhance the index’s usability across diverse riverine conditions. Furthermore, the utility of the index was validated by comparing it to the basin’s target water quality, thereby assessing its sensitivity to drought-induced water quality deterioration. The environmental drought index proposed in this study enables the proactive and real-time monitoring of water quality under drought conditions. When applied to 10 tributaries of the Nakdong River, the index demonstrated a clear correlation between drought conditions and water quality deterioration. This index provides a practical tool for river management, facilitating early response strategies to mitigate water quality impacts associated with environmental drought.

A novel hybrid model for long-term water quality prediction with the ‘decomposition–inputs–prediction’ hierarchical optimization framework

ABSTRACT Accurate, stable, and long-term water quality predictions are essential for water pollution warning and efficient water environment management. In this study, a hierarchical water quality prediction (HWQP) model was developed based on ‘data decomposition–predictor screening–efficient prediction’ via wavelet decomposition, Spearman correlation analysis, and long short-term memory network, respectively. The observed data from 14 stations in the Huaihe River–Hongze Lake system, including ammonia nitrogen (AN) and chemical oxygen demand (COD), were used to make long-term water quality predictions. The results suggested that, compared to existing water quality prediction models, the HWQP model has higher accuracy, with the root mean square errors of 6 and 17% for simulating AN and COD, respectively. The AN and COD concentrations will range from 0 to 1 mg/l and from 3 to 5 mg/l at 12 stations, respectively, and the COD concentrations will exceed the water quality target at Stations 4 and 5. The established model has great potential to address the challenges associated with the water environment.

Asymptomatic bacteriuria screening for developing countries using a modified water quality test kit.

Between 2% and 15% of pregnant women unknowingly experience asymptomatic bacteriuria (ASB), defined as ≥105 CFU per milliliter of urine in the absence of symptoms. ASB increases the risk of adverse pregnancy outcomes including pyelonephritis, preterm labor, and low-birth weight infants. While pregnant women in the United States are routinely screened for ASB, those in developing countries with limited resources and funding lack an accurate mechanism for ASB screening. Aquagenx water quality test kits detect Escherichia coli, the most common causative agent of ASB, and total coliform bacteria in drinking water via colorimetric and fluorescent indicators. We found that the Aquagenx system is compatible with human urine and then proceeded to develop an ASB screening protocol using disposable inoculating loops. Our protocol diagnosed artificial ASB- samples (104 CFU/mL E. coli) with a false positive (FP) rate of 33% (n = 18) and ASB+ (105 CFU/mL E. coli) with a false negative (FN) rate of 5.6% (n = 18). Clinical sample testing with our protocol revealed a FP rate of 0% in ASB- samples (n = 28) and a FN rate of 0% in ASB+ samples caused by coliforms (n = 13). Aquagenx did not detect ASB in nine clinical samples with non-coliform etiological agents due to the limitations of the technology. However, with very high accuracy for detection of E. coli and other causative agents that collectively account for 90.1% of ASB cases, these kits could be used as a diagnostic ASB screening tool in developing countries in which there is currently no alternative to urine culture.IMPORTANCEAsymptomatic bacteriuria (ASB) affects 2%-15% of pregnant women and can result in adverse maternal and fetal outcomes if left undetected and untreated. In the United States and other developed nations, pregnant women are regularly screened for ASB via urine culture. However, in low-resource countries where bacterial culture is not available, dipstick testing is used. Although accurate in cases of symptomatic bacteriuria, dipstick detection is ineffective for detecting ASB. Here, we made use of an existing water quality field test for ASB urine screening, which would be readily deployable in low-resource settings. We optimized a dilution protocol for sampling patient urine within the detection limits of the Aquagenx kit technology. Overall, we were able to detect ASB samples with Gram-negative pathogens that collectively account for 90% of all ASB cases. Utilization of this repurposed technology for proactive medical screening may help prevent adverse pregnancy and birth outcomes due to ASB.

Adapting to PFAS contamination of private drinking water wells near a PFAS production facility in the US Atlantic Coastal Plain of North Carolina.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widespread groundwater contaminants and are present in over 7000 drinking water wells near a North Carolina (NC) PFAS plant (Chemours). To understand options available to affected residents, we used new and previously existing water quality data to investigate deeper aquifers as alternate drinking water supplies and compared the regulatory responses near Chemours and three other PFAS production facilities with nearby contaminated wells. Data from >100 wells show that GenX concentrations decrease with increasing depth through the four aquifers in the study area: surficial, Black Creek, Upper Cape Fear, and bedrock. This illustrates the extent of vertical PFAS penetration through the aquifer sequence following roughly 40 years of atmospheric emissions. Detailed data on 143 water quality parameters in nine deep wells (two Upper Cape Fear, seven bedrock) revealed only eight exceedances of drinking water standards (one each for arsenic, perfluorooctanoic acid [PFOA], iron, chloride, and gross alpha, and three for manganese) and nine exceedances of health advisories (all for sodium). Regulatory responses to PFAS contamination of wells in four states included mention of deeper wells as an alternate water source only for nonresidential users in NC and residential users in Vermont. The bedrock aquifer is currently used by some residents and may be a viable alternative to shallower groundwater, though arsenic treatment may be beneficial at some deep wells and long-term sustainability of the aquifer should be evaluated. PRACTITIONER POINTS: GenX concentrations decreased with increasing depth in four aquifers near a PFAS plant. A few exceedances of drinking water standards and health advisories were found in deep bedrock wells. New bedrock wells could be part of the response to PFAS issues in shallower wells. In the long term, deep bedrock wells might be a less expensive option for some residents.

Deep learning-based water quality index classification using stacked ensemble variational mode decomposition

Water is crucial to human survival in general, and determining the WQI (water quality index) is one of the primary aspects. The existing water quality classification models are facing various challenges and gaps that are impeding their effectiveness. These challenges include limited data availability, the intricate nature of water systems, spatial and temporal variability, non-linear relationships, sensor noise, and error, interpretability, and explainability. It is imperative to address these challenges to improve the accuracy and efficacy of the models and to ensure that they continue to serve as reliable tools for monitoring and safeguarding water quality. To solve the issues, this paper proposes a Stacked Ensemble efficient long short-term memory (StackEL) model for an efficient water quality index classification. At first, the raw input data is pre-processed to rescale the input data using data normalization and one-hot encoding. After that, the process known as variational mode decomposition (VMD) is applied to get at the intrinsic mode functions (IMFs). Consequently, feature selection is performed using an extended coati optimization (EX-CoA) algorithm to select the most significant attributes from the feature selection. Here, publicly available datasets, namely the water quality dataset from Kaggle, are used for classification and performed using are used to perform the Stacked Ensemble efficient long short-term memory (StackEL) classification process effectively. To further perfect the proposed prediction model, the Dwarf Mongoose optimization (DMO) method is implemented. Several measures of effectiveness are examined. When compared to other existing models, the suggested model can achieve a high accuracy of 98.85% of the water quality dataset.

Water Quality Analysis Through Satellite Images

This project report presents a comprehensive study on water quality analysis using satellite images. The increasing global demand for water resources and the growing impact of human activities on water ecosystems underscore the need for efficient and scalable methods for water quality assessment. Leveraging remote sensing technology, particularly satellite imagery, provides a promising solution for monitoring large water bodies at a regional and global scale. The project focuses on the development and implementation of a robust methodology for water quality analysis through the utilization of satellite images. Key objectives include the extraction of relevant water quality indicators such as turbidity, chlorophyll-a concentration, and dissolved organic matter. Image processing techniques, including spectral analysis and machine learning algorithms, are employed to derive quantitative information from satellite data. The study employs a multi-sensor approach, utilizing data from various satellite platforms, to enhance the accuracy and reliability of the water quality assessments. Additionally, ground truth data collected from in situ measurements and laboratory analysis are integrated into the analysis to validate and calibrate the remote sensing results. The project contributes to the advancement of remote sensing applications in environmental monitoring, providing a cost-effective and timely method for water quality assessment. The findings of this research have implications for water resource management, environmental conservation, and policy formulation. Furthermore, the developed methodology can be applied to monitor changes in water quality over time, supporting sustainable water resource management practices. The report concludes with recommendations for further research and the potential integration of the developed methodology into existing water quality monitoring systems. Overall, the project highlights the significance of satellite-based approaches in addressing contemporary challenges related to water quality on a global scale.

Geological and anthropogenic contributions of metal(loid)s in the artisanal and small-scale mining-impacted Ocoña watershed of Arequipa, Peru

Strategies to resolve conflicts around water supply and quality in mining-impacted communities include a better understanding of the role and mitigation of mining activities proximal to surface water bodies. The Ocoña watershed of Arequipa (Peru) is one such region where there are conflicts between artisanal miners, fishers, and agriculture over local water resources. The aim of this study is to develop a workflow that leverages existing water quality monitoring and geological mapping to identify major processes that adversely impact water quality with a focus on the relative contribution of mining-related activities on the Ocoña watershed. This study integrates a statistical evaluation of water chemistry data, concentration-discharge relationships, and detailed mineralogy with geochemical modeling to identify sources and mechanisms of contaminant contributions to surface waters in the Ocoña watershed. This revealed that primary deposit mineralogy and seasonal variations exert direct effects on water quality in this mining-active watershed. Multivariate statistical analysis reveals two distinct hydrogeochemical signatures in Ocoña River water that likely represent natural geothermal activity in the northern portion and acid mine drainage contribution in the southern portion of the watershed. In both cases, anthropogenic activities such as mining and agricultural may increase contaminant mobilization. Interestingly, despite perceived challenges, water quality in the Ocoña watershed is better than that of neighbors potentially due to pH-controlled precipitation of Fe and resultant sorption of metals onto precipitants. However, copper released from acid mine drainage, and in particular peak concentrations during the high flow season, exert a potential for adverse ecotoxicological effects. This workflow could potentially be extrapolated to other regions with similar available data to better identify the relative contributions of natural and anthropogenic pressures to the release of toxic compounds in watersheds of concern.

Numerical simulation model of water quality evolution in urban rivers considering adsorption–desorption process of nitrogen and phosphorus in sediments

In order to more realistically simulate the river channel water quality considering the characteristics of sediment release, it is necessary to couple the simulation model of sediment release to the hydrology-hydrodynamics-water quality model to quantify the complexities involved in the nitrogen and phosphorus cycle in river channel sediments. However, in the existing studies, most of the water–sediment models constructed in the analysis of single pollutants such as NH3-N or BOD, but fail to consider the interaction between multiple pollutants, and the model software related to water quality simulation fails to establish complex river sediment pollution simulations, there are still challenges in the application of the existing water quality module of the MIKE simulation software to the nitrogen and phosphorus cycle of sediments. In this study, based on the new sediment nitrogen and phosphorus release mechanism, we rewrite the water quality module ECOLab of the MIKE 21 model. The laboratory test results and the measured data are applied to the calibration of the model parameters. The new water quality module considering the adsorption–desorption process of sediments was embedded into the MIKE 21 model. The new module couples the MIKE model and the SWWM model to establish a 2D water quality model of river channel hydrology-hydrodynamics-water quality-sediment release coupling. This model targeted applies in urban river channels with sediment releasion as the main pollution. The results show that the new model has significantly improved the simulation accuracy of BOD and NH3-N, and has an optimized effect on the simulation of TP. The R, NSE, and RMSE values of the model for NH3-N simulation are 0.904, 0.845, and 0.612. This model is more suitable for numerical simulation of urban river water quality where sediment release is the main cause of endogenous pollution.

Improving incomplete mixing modeling for junctions of water distribution networks

AbstractMost of the existing water quality models for water distribution networks assume complete mixing at junctions. Albeit few models offer the possibility to consider incomplete mixing (IM) at junctions, most of them were developed under laboratory conditions and for equal pipe size junctions. In real-world distribution networks, however, cross junctions of 150 × 100 × 150 × 100 mm or 100 × 150 × 150 × 150 mm are common, yet no model has been developed for these configurations. This paper presents a new equation to compute concentrations in cross junction outlets while considering IM for six cross junction configurations, including unequal pipe sizes and 150 mm pipes. For each cross junction configuration, mixing was studied under 25 flow scenarios in the laboratory and 40 simulated flow scenarios using OpenFOAM software. Two new flow rate ratios were selected as independent variables to compute different outlet concentrations. For two specific cross junctions with equal pipe sizes, the root-mean-squared error between the observed and simulated concentrations of the newly developed model was 0.02, while it was 0.05 and 0.07, respectively, for the AZRED IM model and the Shao et al. IM model.

Evaluation of Drinking Water Quality Using Fuzzy Logic in Sulaimani City, Kurdistan, Iraq

Water quality assessment is crucial for maintaining the health and well-being of communities. In this study, Fuzzy Logic Water Quality Index (FWQI) utilized as a tool to assess the water quality in Sulaimani City, Kurdistan, Iraq. FWQI combines the flexibility and interpretability of fuzzy logic with the ability to handle imprecise and uncertain data, making it suitable for assessing complex systems such as water quality. Daily data of eleven water parameters from 14-water tanks location across Sulaimani city were evaluated for the duration of 2011 to 2021. The identified parameters includes pH, TDS, EC, TA, TH, Ca, Mg, Cl, Na, K, and SO4 . Fuzzy logic membership functions used to represent the linguistic variables associated with each parameter, allowing for the conversion of numerical data into fuzzy sets. A set of fuzzy rules established to map the fuzzy sets of the input parameters to an overall water quality index. The formulated rules were based on expert knowledge and existing water quality guidelines. The Fuzzy Logic Inference Index (FLII) outcomes were validated by comparing them with conventional water quality indices and expert judgments, demonstrating the effectiveness and accuracy of the proposed approach. The FWQI values of the study area showed that the drinking water quality of all selected tanks are suitable for drinking. The quality classes of water ranges between acceptable to desirable.

Pengaruh Penggunaan Media Filtrasi Terhadap Kualitas Air Sumur Gali di Kota Sorong

Sorong City is a city in Southwest Papua Province, with an area of 1,105 km2. The need for clean water is not directly proportional to the mapping of clean water services provided by the government so far it has not reached 30% of the total population in the Sorong City Region. The aims of the study were to analyze the existing water quality in Sorong City, to analyze the effect of the filter media on the planned filtration device on the water quality of the research sample, and to analyze the cost of the planned filtration device. The method used is a qualitative method that is carried out is the monitoring and interpretation of water quality data including physical and chemical quality. Observations of water from dug wells in the City of Sorong have the quality or nature of water that smells, has a salty taste, and is slightly brown in color. Of the 3 samples analyzed according to the existing location, only 2 samples were quality standards, the effect of the filtration media on water quality from 3 samples. Filtration equipment with filtration media 1 has an average per parameter of sample 1 of 25.78%, sample 2 of 36.59%, and sample 3 of 21.86%. The filtration device with 2 filtration media has an average sample 1 of 34.24%, sample 2 of 49.51%, and sample 3 of 25.44%. The filtration device with 3 filtration media has an average sample 1 of 22.32%, sample 2 of 30.6%, and sample 3 of 20.87%. and the cost of making a filtration device with filtration media 1 is IDR 300,000.00, filtration media 2 is IDR 295,000.00, filtration media 3 is IDR 290,000.00. The recommended filtration device is one that uses 2 filtration media.

Water Quality Assesment Based on Government Regulation Standard in Sangkalami River, North Kalimantan, Indonesia.

Sangkalami river was located in, Tanjung Palas Tengah, Bulungan, North Kalimantan. Main issues that occured in Sangkalami River is there was an active open pit coal mine nearby and currently using Sangkalami River as the main outlet of Acid Mine Drainage as a result of mining activities. The goal of this research is to be able provide information regarding potential contamination in Sangkalami River due to coal mine activities nearby. Sample water from the river will be taken to be tested in laboratory for extracting information regarding existing water quality. Government Regulation of Indonesia Number 22 of 2021 will be used as water quality standard to compare the level of pH, Mn, H2S, DO, COD, and BOD in Sangkalami River to the government implemented standard regarding water quality. Main goal of the study is to be able to provide information about contamination level that occured in Sangkalami River.

Comparing Drivers of Spatial Variability in U.S. Lake and Stream Phosphorus Concentrations.

Decision makers need to know the drivers of surface water phosphorus (P) concentrations, the environmental factors that mediate P loading in freshwater systems, and where pollution sources and mediating factors are co-located to inform water quality restoration efforts. To provide this information, publicly available spatial data sets of P pollution sources and relevant environmental variables, like temperature, precipitation, and agricultural soil erodibility, were matched with >7,000 stream and lake total P observations throughout the conterminous United States. Using three statistical approaches, consisting of (a) correlation, (b) regression, and (c) machine learning techniques, we identified likely drivers of P concentrations. Surface water concentrations in streams were more strongly correlated and effectively predicted by annual fertilizer and manure input rates and agricultural legacy sources compared to that of lakes. This observation suggests that streams may be more immediately responsive to improvements in agricultural nutrient management. In contrast, lake concentrations, though still positively associated with agricultural input and surplus variables, may be more influenced by historic erosional inputs, internal lake recycling, and other environmental factors. Thus, lake TP concentrations may not be as immediately responsive as streams to improvements in phosphorus management. Both stream and lake P concentrations will potentially increase because of warming temperatures and forest recovering from past acidification, putting even further pressure on existing water quality restoration efforts to meet nutrient loading reduction targets. The identified spatial data sets and relationships elucidated in this effort can inform the placement and development of watershed restoration strategies to reduce excess P in aquatic systems.

Mapping Irish Water Bodies: Comparison of Platforms, Indices and Water Body Type

Accurate monitoring of water bodies is essential for the management and regulation of water resources. Traditional methods for measuring water quality are always time-consuming and expensive; furthermore, it can be very difficult capture the full spatiotemporal variations across regions. Many studies have shown the possibility of remote-sensing-based water monitoring work in many areas, especially for water quality monitoring. However, the use of optical remotely sensed imagery depends on several factors, including weather, quality of images and the size of water bodies. Hence, in this study, the feasibility of optical remote sensing for water quality monitoring in the Republic of Ireland was investigated. To assess the value of remote sensing for water quality monitoring, it is critical to know how well water bodies and the existing in situ monitoring stations are mapped. In this study, two satellite platforms (Sentinel-2 MSI and Landsat-8 OLI) and four indices for separating water and land pixel (Normalized Difference Vegetation Index—NDVI; Normalized Difference Water Index—NDWI; Modified Normalized Difference Water Index—MNDWI; and Automated Water Extraction Index—AWEI) have been used to create water masks for two scenarios. In the first scenario (Scenario 1), we included all pixels classified as water, while for the second scenario (Scenario 2) accounts for potential land contamination and only used water pixels that were completed surround by other water pixels. The water masks for the different scenarios and combinations of platforms and indices were then compared with the existing water quality monitoring station and to the shapefile of the river network, lakes and coastal and transitional water bodies. We found that both platforms had potential for water quality monitoring in the Republic of Ireland, with Sentinel-2 outperforming Landsat due to its finer spatial resolution. Overall, Sentinel-2 was able to map ~25% of the existing monitoring station, while Landsat-8 could only map ~21%. These percentages were heavily impacted by the large number of river monitoring stations that were difficult to map with either satellite due to their location on smaller rivers. Our results showed the importance of testing several indices. No index performed the best across the different platforms. AWEInsh (Automated Water Extraction Index—no shadow) and Sentinel-2 outperformed all other combinations and was able to map over 80% of the area of all non-river water bodies across the Republic of Ireland. While MNDWI was the best index for Landsat-8, it was the worst performer for Sentinel-2. This study showed that optical remote sensing has potential for water monitoring in the Republic of Ireland, especially for larger rivers, lakes and transitional and coastal water bodies.

Fuzzy-based global water quality assessment and water quality cells identification using satellite data

The water environmental impact assessment and management programs increasingly rely on accurate and quantitative estimates of water quality parameters through remote sensing, owing to the limitation of the time-consuming field-based approaches. Numerous studies have utilised the remote-derived water-quality products and existing water quality index WQI models, but they are typically site-specific and yield significant errors for the accurate assessment and monitoring of coastal and inland water bodies. This study presents a generalized WQI model that incorporates a flexible number of parameters, simplifying them to produce comprehensive water quality index values with the fuzzy logic approach. To derive these index values, three major water quality parameters such as Chl, TSS and aCDOM443 were estimated using new remote-sensing models, and the corresponding indices Trophic State Index (TSI), Total Suspended Solids Index (TSSI) and CDOM Index (CI) were produced by a generalized index model. Finally, WQI products were derived based on the Mamdani-based Fuzzy Inference System (FIS) and individual contribution of the water quality parameters to WQI was analysed to establish ‘Water Quality Cells’ WQcells, which are represented by the dominant WQ parameter. The new models were tested on MODIS-Aqua and Sentinel-3 OLCI data in different regional and global oceanic waters. Further, a time series analysis was performed in regional coastal oceanic waters (along the Indian coast) to study the seasonal variations of individual water quality parameters and WQI over the period from 2011 to 2020. The results demonstrated that the FIS is efficient in handling the parameters with varying units and their relative importance. The water quality cells were identified in the bloom-dominated (Arabian Sea), TSS-dominated (Point Calimere, India and Yangtze River estuary, China) and CDOM-dominated (South Carolina coast, USA) regions. The time series analysis revealed that the water quality of the Indian coast exhibits cyclic seasonal variations due to the annual occurrence of the south-west and north-east monsoons. These results are critical for monitoring and assessing the quality of surface waters in coastal and inland environments and enabling water resources managers to formulate and implement management plans for a variety of water bodies cost-effectively.

Research on a multiparameter water quality prediction method based on a hybrid model

Watershed water quality monitoring is of great significance in the protection and management of water environments. Because existing water quality prediction algorithms cannot achieve high-precision multiparameter analysis and usually require a large amount of data, this paper proposes the VARLST hybrid water quality prediction model. The proposed model combines the traditional statistical vector autoregressive moving average model and the bidirectional long short-term memory neural network to achieve multiparameter water quality data prediction on small data samples, and the model data processing is simple and highly efficient. This model is used to analyze the characteristics of water quality parameters in the Inner Mongolian section of the Yellow River Basin in northern China. The average error and fitting accuracy of the prediction results are 0.0015 and 99.869%, respectively; hence, the model achieves high-accuracy prediction of multiparameter indicators using less data, outperforming single models in terms of feasibility and accuracy.

Water quality assessment of deep learning-improved comprehensive pollution index: a case study of Dagu River, Jiaozhou Bay, China

In the past few decades, with the country's rapid development, water pollution has become a significant problem many countries face. Most of the existing water quality evaluation uses a single time-invariant model to simulate the evolution process, which cannot directly describe the complex behavior of long-term water quality evolution. In addition, the traditional comprehensive index method, fuzzy comprehensive evaluation, and gray pattern recognition have more subjective factors. It can lead to an inevitable subjectivity of the results and weak applicability. Given these shortcomings, this paper proposes a deep learning-improved comprehensive pollution index method to predict future water quality development. As a first processing step, the historical data is normalized. Three deep learning models, multilayer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM), are used to train historical data. The optimal data prediction model is selected through simulation and comparative analysis of relevant measured data, and the improved entropy weight comprehensive pollution index method is applied to evaluate future water quality changes. Compared with the traditional time-invariant evaluation model, the feature of this model is that it can effectively reflect the development of water quality in the future. Moreover, the entropy weight method is introduced to balance the errors caused by subjective weight. The result shows that LSTM performs well in accurately identifying and predicting water quality. And the deep learning-improved comprehensive pollution index method can provide helpful information and enlightenment for water quality change, which can help improve the water quality prediction and scientific management of coastal water resources.

Water Quality Assessment and Monitoring in Pakistan: A Comprehensive Review

Water quality has been a major problem in Pakistan owing to a mix of factors such as population expansion, industrial units in urban areas, and agricultural activities. The purpose of this research is to conduct a comprehensive evaluation of water quality monitoring and assessment in Pakistan. The article begins by examining the water sources of Pakistan (i.e., surface water, groundwater, and rainwater). The paper then discusses the methods used by researchers in Pakistan for water quality monitoring and assessment, including chemical, physical, and biological methods. It has been determined that in certain regions in Pakistan, the concentration of arsenic present in the groundwater exceeds the national and international prescribed maximum limits. The range of arsenic concentrations in the Punjab province can vary from 10 to 200 μg/L, while higher concentrations of up to 1400 μg/L have been recorded in Sindh. In the Punjab province, fluoride concentrations vary from 0.5 to 30 mg/L, while in Sindh, the levels can reach up to 18 mg/L. In addition, some of the research has talked about bacteria. A 2017 study found that the fecal coliform concentrations in certain water in different cities of Pakistan surpassed limits and were as high as 1100 CFU/100 mL. Additionally, natural factors such as geological formations and high salinity in some areas contribute to the contamination of water. The effect of water pollution on public health has the potential to cause harm. It is critical to investigate creative strategies for improving water quality, and it is necessary to make investments in research and development, which could include the implementation of sophisticated technologies and the conception of new treatment processes. The review performed in this paper facilitates an understanding of the current water quality in Pakistan, including the types and magnitudes of contaminants present in the water sources. Subsequently, the assessment emphasizes deficiencies and challenges in the existing water quality monitoring frameworks and provides suggestions for improving them. This review is also of significant benefit to all the stakeholders involved in ensuring clean and safe water for human consumption and other purposes in Pakistan, such as policymakers, water managers, researchers, and other stakeholders.