research on water quality modelling

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• Published: 19 January 2024

Research on water environmental indicators prediction method based on EEMD decomposition with CNN-BiLSTM

• Zhaohua Wang 1 ,
• Longzhen Duan 1 ,
• Dongsheng Shuai 2 &
• Taorong Qiu 1  

Scientific Reports volume  14 , Article number:  1676 ( 2024 ) Cite this article

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• Environmental sciences

Water resources protection is related to the development of the social economy, and the monitoring and prediction of water environmental indicators have important practical significance. In view of the seasonality, periodicity, uncertainty, and nonlinear characteristics of water quality indicators data, traditional prediction models have poor performance. To address this issue, this paper introduces a hybrid water quality index prediction model based on Ensemble Empirical Mode Decomposition (EEMD), combined with Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). We have conducted out experiments to predict dissolved oxygen based on the water quality monitoring indicators of the Liaohe National Control Sanhongcun Village station in Yichun City. The results show that the model proposed in this paper improves the \(R^2\) index by 5%, 7% and 5% compared to the suboptimal model in the 4-h, 1-day and 2-day index predictions, respectively.

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Introduction

In recent years, with the development of socio-economy, water pollution has garnered escalating public attention, leading to water resource protection being widely recognized as a societal consensus. The dynamic monitoring of changes in water quality, coupled with the implementation of water environment indicator predictions, holds profound practical significance for the preservation of water resources.

The prediction of water environment indicators involves the identification of temporal changes in water quality indicators and their correlation with hydrological, meteorological, and other factors within a specified spatiotemporal context 1 . Water environment indicator prediction can be categorized into mechanistic prediction methods and non-mechanistic prediction methods, depending on their underlying theoretical foundations.

Mechanistic prediction methods are holistic approaches grounded in the governing principles and evolving dynamics of the water environment, encompassing diverse disciplines such as hydrodynamics, ecology, and chemistry 2 . These methods typically employ models to encapsulate the intricate interplay among various elements. Commonly utilized models in this category include the Water Quality Analysis Simulation Program (WASP) 3 , QUAL model 4 , MIKE system 5 , Generalized Watershed Loading Function (GWLF) 6 , and others.

In contrast, non-mechanistic prediction methods adopt a ’black box’ approach. These models rely on probabilistic statistical theories and are tailored to specific water environments, demonstrating effective predictive capabilities. Three prevalent non-mechanistic models can be identified: traditional probabilistic statistical models, such as the grey model 7 and Markov chain model 8 ; time series models, such as Exponential Smoothing (ETS) and Auto Regressive Integrated Moving Average (ARIMA); and artificial intelligence models, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), Gate Recurrent Unit (GRU), and Informer, among others.

Surface water is an important type of water environment. Its water quality indicators exhibit characteristics such as seasonality, periodicity, uncertainty, and nonlinearity. There are also complex dependent relationships between the indicators 9 . Traditional probabilistic statistical methods are difficult to model such complex dependent relationships. At present, artificial intelligence methods represented by deep learning have made great progress in the application of surface water environment indicator prediction. Recurrent neural networks (RNNs) are suitable for processing time series data, but they suffer from the problem of gradient disappearance. To solve the problems in RNNs, Hochreiter et al. 10 proposed LSTM networks,which can perform long time series prediction tasks. Hu et al. 11 used LSTM to predict pH and water temperature in water quality indicators, and Zhang Yiting et al. 12 applied LSTM to the prediction of ammonia nitrogen indicators in river water quality. However, a single LSTM model cannot avoid the interference of noise, resulting in unsatisfactory prediction accuracy. To solve the noise interference problem, convolutional neural networks (CNNs) are introduced to extract features from multidimensional time series, such as: Zhang Mingwei et al. 13 employed the CNN-LSTM model to predict the dissolved oxygen index of river water quality, and Wang Zhibo et al. 14 employed CNN-LSTM to predict the dissolved oxygen index of lake water quality. But LSTM can only make predictions based on historical data, while water quality indicators are not only related to historical data, but also related to future data. On the other hand, modal decomposition methods are introduced to eliminate the impact of noise, such as: Yuan Meixue et al. 15 employed wavelet decomposition to denoise water quality data, and then used a hybrid LSTM and Seq2Seq model for prediction. Benjamin et al. 16 applied the Empirical Mode Decomposition (EMD) method to decompose the dissolved oxygen indicator in the water quality time series, effectively isolating the trend and fluctuation components of the data. José et al. 17 employed EMD and LSTM to improve the performance of time series classification. Bai Wenrui et al. 18 first employed Variational Mode Decomposition(VMD) to decompose water quality indicators, and then used LSTM to predict water quality indicators.Wavelet decomposition has defects such as edge effects and difficulty in determining the basis function; while VMD requires higher data stability and linearity.

This paper proposes a CNN-BiLSTM water quality indicator prediction model based on Ensemble Empirical Mode Decomposition (EEMD) decomposition, aiming to overcome the prevalent challenges in deep learning applications for water quality indicator prediction, as well as to address the periodicity, uncertainty, and nonlinearity inherent in water quality monitoring data. EEMD effectively mitigates the issue of mode mixing encountered in EMD and imposes less stringent requirements on data stationarity and linearity compared to VMD. CNN is employed to extract local features from water quality indicator data, while BiLSTM handles sequential dependence modeling within this data, considering the impacts of both forward and backward data. To validate the efficacy of our proposed model, we conducted multivariate and multi-step prediction experiments using water quality data obtained from the national monitoring station in Sanhong Village, Liaohe.

Model and methods

Water environment indicator decomposition.

EEMD was proposed by Wu et al. 19 based on Empirical Mode Decomposition (EMD) to overcome the problem of mode mixing in EMD decomposition.

EEMD is a method that involves adding Gaussian white noise to the original sequence, applying EMD to the sequence multiple times according to a predefined number of experiments, and then taking the average of the decomposition results to eliminate the influence of noise. This methodology imparts properties of uniform distribution and smoothness to the original sequence.The steps for sequence decomposition in EEMD are as follows:

Add white noise of limited amplitude to the original indicator sequence to obtain a new sequence:

where \(X(X \in R^{(m \times n)})\) is the original sequence, \(\varepsilon ^s\) is white noise,and \(X^s\) is the new sequence.

Decompose \(X^s\) into Intrinsic Mode Function(IMF) components using EMD:

where \(C_l^{EMD,s}\) is the intrinsic mode function after EEMD decomposition, r ( t ) is residual.

Repeat the above steps according to the set number of times and calculate the final result:

The process flow of EEMD decomposition for water quality indicators is illustrated in the Fig.  1 .

EEMD decomposition flowchart.

Local correlation feature extraction of water environment indicators

Convolutional neural networks (CNN) are feedforward neural networks that use convolution and pooling operations for feature extraction. It is an important algorithm in deep learning. For time series data, 1D convolutions are often used.

In this paper, a sliding window is employed on the water environment indicator sequence to extract local features. Additional noise filtering is carried out through convolution and pooling operations to achieve enhanced outcomes. The specific formula is as follows:

where w is the convolution kernel, \(*\) denotes convolution, X represents the water quality indicator sequence that has been decomposed by EEMD, and Y is the extracted feature.

Temporal dependence modeling of water environment indicators

This paper chooses BiLSTM to model temporal dependencies. BiLSTM constitutes an advancement over the LSTM neural network. Relevant research 20 indicates that BiLSTM offers noteworthy enhancements in performance compared to LSTM for time series prediction tasks.

where Y represents the vector of target variables to be predicted, H represents the prediction results. BiLSTM consists of two layers of LSTM neural networks that operate in opposing directions.Rather than merely stacking the two LSTM layers, it integrates data features from both forward and backward directions at the present time step for predictive purposes.

Model building

Given the strong coupling and nonlinear characteristics of water environment monitoring data, traditional prediction methods often yield subpar results.Accordingly, this paper introduces a CNN-BiLSTM hybrid model for water environment data prediction based on EEMD decomposition.

Initially, the preprocessed water environment data is decomposed by EEMD, yielding four modes. Each of these modes is subsequently fed into both CNN and BiLSTM for feature extraction. Ultimately, the extracted features are accumulated and reconstructed to derive the predictive outcome.

This hybrid model synergistically integrates EEMD, CNN, and BiLSTM to capitalize on the strengths of each component: EEMD for noise reduction, CNN for capturing local features, and BiLSTM for modeling sequential dependencies. The ensemble methodology has the potential to enhance prediction accuracy. In this experiment, dissolved oxygen is decomposed by EEMD, and then combined with other indicators to form new training data. The model structure is illustrated in the Fig.  2 .

EEMD-CNN-BiLSTM Mixture model Diagram.

Experiments

The research focuses on water quality monitoring data obtained from the national monitoring station in Sanhong Village, Liaohe.Liaohe is the largest tributary of Xiuhe River,which traverses Jing’an County in Yichun City. It holds significance as the primary river in the county and eventually merges into Poyang Lake via the Xiuhe River.

The monitoring dataset spans from November 2020 to December 2022,with measurements taken every four hours, amounting to a total of 4,700 data points. It encompasses nine indicators: water temperature (TEMP),pH,dissolved oxygen (DO),potassium permanganate (PP),ammonia nitrogen (TAN),total phosphorus (TP),total nitrogen (TN),electrical conductivity (EC),and turbidity (TUB).This dataset is obtained from the Environmental Quality Information Release Platform of Jiangxi Province.

In addition, meteorological data from Yichun City covering the same time period was also gathered, encompassing six indicators:temperature,atmospheric pressure,humidity,wind speed,dew point temperature,and precipitation.This data is obtained from the website “Reliable Prognosis”.

Among the various water quality indicators, the concentration of dissolved oxygen serves as a crucial benchmark for assessing water quality 21 . Consequently, this paper focuses on utilizing dissolved oxygen as the target indicator for model prediction.

Through a series of experiments and evaluations, it was determined that ’4’ was the optimal number of modalities, as it demonstrated the best performance and accuracy during model training. In this paper, the EEMD method (4 modes) is employed to decompose the dissolved oxygen indicator through experimental comparison. The waveform diagrams of each mode after decomposition in the validation and test sets are illustrated in Fig.  3 :

Dissolved oxygen index after decomposition of EEMD.

Through autocorrelation experiments, we observed that the three modes: IMF1, IMF2, and IMF3 exhibit evident cyclical characteristics, while IMF4 retains the trend characteristic inherent in the data.

(i) Missing and outlier value handling

During the analysis of the data, it was discovered that certain issues such as missing values and outliers existed due to factors like equipment maintenance or malfunctions that occurred during the data collection process.

For indicators with a significant number of consecutive missing values, linear interpolation is employed to fill in the gaps according to the formula:

where x represents time, \(\varphi \left( x \right)\) represents the estimated value at that specific time x. The coordinates \(x_{0}\) and \(y_{0}\) represent the first known data point, \(x_{1}\) and \(y_{1}\) represent the second known data point.

(ii)Normalization

As water quality indicators possess distinct scales, for optimal model training, each indicator is normalized using the formula:

where x is the original data that needs to be normalized, \(x^{'}\) is the normalized data, and its value range is [0,1], max ( x ) and min ( x ) are the maximum and minimum values in the dataset, respectively.

(iii) Correlation analysis

To investigate the significance of each indicator in the prediction process, correlation analysis is conducted on the data, and a correlation heat map is presented in the figure  4 .

Heat map: ( a ) is correlation between water quality indicators, ( b ) is IMF4 correlation heat map after EEMD decomposition.

It is evident that following EEMD decomposition, the correlations between dissolved oxygen and various indicators such as temperature, electrical conductivity, ammonia nitrogen, and total nitrogen have demonstrated an increase.

Determination of model parameters

In this paper, grid search is employed to optimize the model parameters. Only one parameter is adjusted at a time, and grid search is utilized for fine-tuning. Through iterative execution of the aforementioned steps, the optimized model parameters are presented in Table  1 :

Metrics for experimental evaluation

Mean absolute error (MAE),mean square error (MSE),Mean Absolute Percentage Error (MAPE) and correlation coefficient \((R^2)\) are employed as quantitative metrics to assess the predictive performance of the model.

where y is the true value, \({\hat{y}}\) is the predicted value, and \({\bar{y}}\) is the mean of the indicator. When comparing models, a lower value of MAE, MSE, and MAPE indicates better model performance, while an \(R^2\) value closer to 1 signifies a superior model.

Experimental design

Dissolved oxygen is chosen as the target variable for prediction, and both single-step and multi-step predictions are carried out. Based on data correlation analysis, the following four combinations of data have been designed as described in Table  2 :

Based on the above 4 data combinations,the experiments are designed as follows:

Window size experiment:Verify the impact of window size on results.

Model comparison:Compare with mainstream time series prediction models XGBoost, LSTM, GRU, Informer.

Correlation experiment:Conduct multi-step comparative prediction experiments on four data combinations.

Ablation experiment:Verify the role of each module through ablation experiment.

Experimental results and analysis

In this paper, relevant experiments are conducted in accordance with the aforementioned plan.

(i) Sliding Window Size Experiment: To determine the optimal window size, comparative experiments are performed using window sizes of 8 and 48 for XGBoost, LSTM, GRU, and our proposed model.

Based on the experimental results, it appears that each model demonstrates a low sensitivity to the window size.Taking the \(R^2\) metric as an example,in the XGBoost model, there is only a 2% improvement in prediction results when the window size was increased to 48. However, better prediction results were observed in the other models when the window size was set to 8. Consequently, this paper opts for a window size of 8 in subsequent experiments.

(ii) Popular prediction models commonly used in the field of time series forecasting, namely XGBoost, LSTM, and GRU, are selected for comparison. In the realm of time series forecasting, several popular prediction models are commonly employed for comparative analysis. These models include XGBoost, LSTM, and GRU. In light of the widespread adoption of transformer-based models for time series prediction, Temporal Fusion Transformer (TFT) was introduced by Bryan et al. 22 TFT is capable of learning intricate relationships between different temporal scales within time series data. Building upon this, Jitha et al. 23 leveraged the temporal fusion transformer architecture to model and predict river water quality indicators.

Additionally, Zhou et al. 24 proposed the Informer model for long-term time series prediction. Therefore, we conducted experiments incorporating the Informer model into our comparative analysis.

The comparison experiment is conducted at step sizes of 1 (4 hours), 6 (1 day), 12 (2 days), and 18 (3 days). The results are presented in Table  3 , with the optimal results are in bold.

According to the results, the proposed model in this paper consistently achieves the best prediction performance at step 1, 6 and 12 in Combination 1, with improvements in \(R^2\) of 5%, 7%, 5% compared to the second-best model. And in step 18, the model achieved a second-best result, with a difference of only 0.01 from the optimal value. When meteorological data is introduced (Combination 2), there is a little enhancement in prediction performance observed for any of the models, and the \(R^2\) values remain relatively consistent across different step sizes. Notably, the proposed model continues to deliver optimal results at step sizes of 1, 6, and 12. At the step 18,Informer performed slightly better than our proposed model, proving the advantage of the informer in long-term prediction.

As the prediction step size increases, the forecasting performance of various models tends to decline. However, the proposed model consistently achieves the best results across nearly all step sizes, demonstrating its efficacy in dissolved oxygen prediction.

Comparison of predicting curves.

Examining the 1-step prediction curve, it is evident that the proposed model in this paper provides a better fit to the actual values, with the curves nearly overlapping the true values. The curves are depicted in Fig.  5 .

(iii) Following correlation analysis, the top 4 most strongly correlated indicators are selected and utilized in conjunction with the proposed model for multi-step prediction. The results are presented in Table  4 , with the optimal value are in bold for reference.

It is evident that the prediction accuracy remains relatively consistent even after indicator screening based on correlation analysis. Specifically, Combination 3 achieves the second-best \(R^2\) value in 1-step prediction, while Combination 4 attains the optimal \(R^2\) value in 6-step prediction.

In summary, the selection of indicators that are highly correlated with the target allows for a reduction in data dimensionality without significantly compromising the model’s performance. The proposed model, when incorporated with these correlated indicators, continues to deliver robust multi-step dissolved oxygen forecasting. This approach enables more efficient water quality modeling by utilizing fewer but informative variables, thereby streamlining the modeling process.

(iv) Ablation Experiment: To further substantiate the contributions of individual modules within the proposed model, corresponding ablation experiments have been devised. The results are presented in Table  5 , with the optimal value highlighted by bold for clarity.

It is evident that the inclusion of the CNN module enhances prediction performance at step 1. However, its influence diminishes as the step size escalates. Conversely, the introduction of the EEMD decomposition module leads to marked improvements in prediction performance, attaining the second-best results consistently across all step sizes for both Combinations 1 and 2. This underscores that EEMD contributes more significantly towards enhancing predictions compared to the CNN module.

Discussion and conclusion

Given the seasonal, periodic, uncertain, nonlinear, and intricate interdependencies among indicators within water environmental monitoring data, this paper introduces a hybrid CNN-BiLSTM model integrated with EEMD decomposition for water quality data prediction.

The EEMD decomposition technique is highly effective in mitigating noise interference within the data. Additionally, the four resulting modes from this decomposition process augment the data available for model training, thereby enhancing the training efficacy of the model. The incorporation of CNN enables the model to excel in extracting local features, and its integration with BiLSTM facilitates the utilization of bidirectional data and the acquisition of higher-level features, collectively bolstering prediction performance.

Based on prediction experiments conducted on the dissolved oxygen indicator, the proposed model in this paper demonstrates superior prediction performance compared to existing models. This constitutes a valuable exploration of the practical applications of artificial intelligence technology in the realm of water resource protection. In future, the determination of modal quantity in EEMD, data augmentation for water quality data and and the application of Transformers in long-term water quality data prediction would be beneficial research directions.

In conclusion, the proposed hybrid deep learning approach provides an effective solution for precise multi-step water quality forecasting, capable of addressing the intricate attributes of water environment data. The findings underscore the viability of harnessing advanced AI techniques to enhance environmental modeling and conservation efforts.

Data availibility

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by the university-industry collaboration project “Intelligent Water Environment Monitoring Technology Research”,No.HX202109040001.

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Zhaohua Wang, Longzhen Duan & Taorong Qiu

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T.Q. contributed to the study concept, design, data acquisition/analysis and critical revision. Z.W. contributed to data acquisition, experiments, interpretation, drafting. L.D. contributed to design and critical revision. D.S. contributed to the data acquisition.All authors have read and approved the manuscript.

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Correspondence to Taorong Qiu .

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Wang, Z., Duan, L., Shuai, D. et al. Research on water environmental indicators prediction method based on EEMD decomposition with CNN-BiLSTM. Sci Rep 14 , 1676 (2024). https://doi.org/10.1038/s41598-024-51936-5

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Water Quality Modeling (WQM)

Research Scholar (WAT)

Yoshihide Wada

Principal Research Scholar (WAT)

Water quality modeling for water availability/scarcity assessment, water-energy-land-environment nexus analysis and identification of cost-effective solutions under long-term changes.

Water quality issues pose increasing risks to human health, water security and ecosystem functioning worldwide. Water quality is an important consideration in both water supply and environmental quality. 

In this context, water quality modeling is added as a new component into IIASA’s Water Program to assess the long-term impacts of future changing socio-economic and climatic conditions on water quality and water resources, and to identify potential solution options. 

• The nutrient export model MARINA is soft-linked to other IIASA models to explore basin-scale nexus solutions.
• A global gridded water quality model is being developed (currently for nutrients, next steps including sediment transport and salinity).
• The model is intended to be open source, modular and will be coupled with existing IIASA models, including CWATM and ECHO

Two main lines of ongoing activities:

1. Water quality modeling using the  MARINA  model (Strokal et al., 2016), mainly in collaboration with the  Water Systems and Global Change  Group, Wageningen University & Research, Netherlands.

MARINA was originally developed for China to quantify nutrient export to seas (Strokal et al., 2016) and recently up-scaled to the world for multiple pollutants in rivers from point sources. As part of on-going IIASA projects, the MARINA model is used to quantify current and future nutrient export to coastal waters for selected large river basins (e.g., Zambezi, Indus, Yangtze) under different socioeconomic development and climate change pathways. To this end, the model is soft-linked to other IIASA models ( CWATM ,  ECHO ,  GLOBIOM ,  EPIC , etc.) to explore basin-scale nexus solutions. The model linkages ( Figure 1 ) and some example outputs ( Figure 2 ) for the Zambezi river basin are shown below. 

2. Development of a global gridded water quality model (currently for nutrients, next steps including sediment transport and salinity) The model is intended to be open source and will be coupled with  CWATM  and  ECHO . To facilitate the coupling with these models, it is designed as a flexible modular process-based parsimonious model with a mixture of empirical or mechanistic process descriptions. This is part of our efforts to develop a next-generation global hydro-economic modeling framework that can explore the economic trade-offs among different water management options, encompassing both water infrastructure and management of water demand and water resources.

Figure 1 The MARINA model is soft-linked to CWATM and ECHO at IIASA’s Water Program to explore economically-optimal water management solutions for selected river basins.

Figure 2 Illustrative example of the MARINA output for annual river export of total dissolved nitrogen by source (TDN, kg/km2/yr) for the Zambezi river basin. The figure illustrates the increase in river export of TDN to sea between 2010 and 2050.

Relevant publications:

Strokal M, Kroeze C, Wang M, Bai Z, Ma L, (2016). The MARINA model (Model to Assess River Inputs of Nutrients to seAs): Model description and results for China.  Sci. Total Environ . 562, 869–888. DOI:  10.1016/j.scitotenv.2016.04.071

Tang T , Strokal M, van Vliet MTH, Seuntjens P,  Burek P , Kroeze C,  Langan S , &  Wada Y  (2019).  Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide.   Current Opinion in Environmental Sustainability  36: 39-48. DOI: 10.1016/j.cosust.2018.10.004 .

Tang T , Strokal M,  Wada Y ,  Burek P ,  Kroeze C ,  van Vliet M , &  Langan S  (2018). Sources and export of nutrients in the Zambezi River basin: status and future trend.  In:  International Conference Water Science for Impact , 16-18 October 2018, Wageningen, Netherlands.

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1. Becoming Uncultured: Daily Recreational Water Quality Monitoring and Public Notification at Chicago Beaches Using qPCR

Beach water monitoring and notification is vital for protecting the health of beachgoers. Traditionally, beach monitoring has relied on culturing fecal indicator bacteria using methods that require 18-24 hours to generate results, thus providing outdated information for current beach management decisions. In the 2012 Recreational Water Quality Criteria (RWQC), the USEPA endorsed a qPCR method for rapidly measuring enterococci. Since 2015, through a partnership between Chicago Park District (CPD) and the University of Illinois Chicago School of Public Health (UIC SPH), Chicagoans have daily beach monitoring notifications typically within four hours. Having a well-trained team and consistent daily schedule proved essential for delivering timely and high-quality qPCR results to guide same-day beach management decisions. Given that culture-based results from the previous day can often lead to erroneous beach management decisions, the rapid molecular method should be regarded as the benchmark for public health protection. In this presentation, the discussion will focus on how, following a two-year pilot program of rapid molecular testing of beach water samples, in 2017, Chicago became the first large U.S. city to issue same-day water-quality warnings for all its public recreational beaches and has successfully done so every year after that. The CPD and UIC SPH partnership illustrates that true daily beach monitoring using same-day water quality results is an achievable goal.

Presenter: Abhilasha Shrestha, PhD, University of Illinois Chicago Abhilasha is a Research Assistant Professor within the Environmental and Occupational Health Sciences Department at the University of Illinois Chicago School of Public Health (Chicago, IL). Dr. Shrestha conducts research primarily focused on water quality and its implications for public health. Her research interests include investigating various indicator targets and genes to rapidly assess infectious agents in water. Since 2015, she has overseen the management and operation of Chicago’s Lake Michigan beach monitoring project, conducted in collaboration with the Chicago Park District. In addition to her work on beach monitoring, her research also focuses on identifying and mitigating different bacterial sources of pollution at public Lake Michigan beaches in Chicago, utilizing microbial source tracking. Furthermore, Dr. Shrestha spearheaded a wastewater surveillance project for SARS-CoV-2 in Kisumu, Kenya, from 2022 to 2023. She is engaged in ongoing global health water research projects in Kenya and Nepal, contributing significantly to the understanding and management of waterborne health risks on an international scale.

2. Standard Control Material for Quantitative Real-Time PCR Recreational Water Quality Monitoring

Fecal pollution remains a significant challenge for recreational water quality managers. The use of quantitative real-time PCR (qPCR) methods is increasing, allowing for same day beach water quality public notification and identification of key fecal pollution sources. However, widespread implementation requires access to a high-quality standard control material. This presentation describes a collaboration between the U.S. Environmental Protection Agency and the National Institute of Standards and Technology to develop Standard Reference Material® 2917 (SRM 2917), SRM 2917 “fit for purpose” performance assessment, and implications for qPCR recreational water monitoring implementation.

Presenter: Orin Shanks, PhD, EPA Office of Research and Development Orin is a senior scientist with the EPA’s Center for Environmental Measurement and Modeling. His current research activities focus on the development and implementation of quantitative nucleic acid-based fecal pollution diagnostic tools, advances in molecular method data analyses and visualization, as well as the persistence of genetic material in environmental scenarios.  In addition to research activities, he also provides technical assistance to EPA program offices and regions, states, tribes, and other local groups with an interest in clean and safe waters.

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Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation

Many agricultural watersheds rely on the voluntary use of management practices (MPs) to reduce nonpoint source nutrient and sediment loads; however, the water-quality effects of MPs are uncertain. We interpreted water-quality responses from as early as 1985 through 2020 in three agricultural Chesapeake Bay watersheds that were prioritized for MP implementation, namely, the Smith Creek (Virginia), Upper Chester River (Maryland), and Conewago Creek (Pennsylvania) watersheds. We synthesized patterns in MPs, climate, land use, and nutrient inputs to better understand factors affecting nutrient and sediment loads. Relations between MPs and expected water-quality improvements were not consistently identifiable. The number of MPs increased in all watersheds since the early 2010s, but most monitored nutrient and sediment loads did not decrease. Nutrient and sediment loads increased or remained stable in Smith Creek and the Upper Chester River. Sediment loads and some nutrient loads decreased in Conewago Creek. In Smith Creek, a 36-year time-series model suggests that changes in manure affected flow-normalized total nitrogen loads. We hypothesize that increases in nutrient applications may overshadow some expected MP effects. MPs might have stemmed further water-quality degradation, but improvements in nutrient loads may rely on reducing manure and fertilizer applications. Our results highlight the importance of assessing MP performance with long-term monitoring-based studies.

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Water Quality Portal

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service ( https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do ) and the Integrated Taxonomic Information System ( https://www.itis.gov/ ) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system.

The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/ ), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP.

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The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs.

The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service.

The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse.

The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community.

The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources.

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The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.

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• Environmental sciences
• Groundwater quality processes and contaminated land assessment
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Applicability of water quality models around the world—a review

• Review Article
• Published: 23 November 2019
• Volume 26 , pages 36141–36162, ( 2019 )

Cite this article

• Cássia Monteiro da Silva Burigato Costa 1 ,
• Leidiane da Silva Marques 1 ,
• Aleska Kaufmann Almeida 1 ,
• Izabel Rodrigues Leite 1 &
• Isabel Kaufmann de Almeida   ORCID: orcid.org/0000-0002-8609-2991 1  

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Water quality models are important tools used in the management of water resources. The models are usually developed for specific regions, with particular climates and physical characteristics. Thus, applying these models in regions other than those they were designed for can generate large simulation errors. With consideration to these discrepancies, the goal of this study is to identify the models employed in different countries and assist researchers in the selection of the most appropriate models for management purposes. Published studies from the last 21 years (1997–2017) that discuss the application of water quality models were selected from three engineering databases: SpringerLink, Web of Science, and Scopus. Seven models for water quality simulations have been widely applied around the world: AQUATOX, CE-QUAL-W2, EFDC, QUALs, SWAT, SPARROW, and WASP. The countries most frequently applying water quality models are the USA, followed by China, and South Korea. SWAT was the most used model, followed by the QUAL group and CE-QUAL-W2. This study provides the opportunity for researchers, who wish to study countries with fewer cases of applied water quality models, to easily identify the work from that region. Furthermore, this work collated central themes of interest and the most simulated parameters for the seven countries that most frequently employed the water quality models.

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Acknowledgments

The authors are grateful to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES, to the Programa de Apoio à Pós-graduação—PROAP and to the Federal University of Mato Grosso do Sul—UFMS for their support in the development of this work.

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Burigato Costa, C.M.d., da Silva Marques, L., Almeida, A.K. et al. Applicability of water quality models around the world—a review. Environ Sci Pollut Res 26 , 36141–36162 (2019). https://doi.org/10.1007/s11356-019-06637-2

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Received : 19 March 2019

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DOI : https://doi.org/10.1007/s11356-019-06637-2

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