Exploratory multivariate modeling and prediction of the physico-chemical properties of surface water and groundwater

NASA Astrophysics Data System (ADS)

Ayoko, Godwin A.; Singh, Kirpal; Balerea, Steven; Kokot, Serge

2007-03-01

SummaryPhysico-chemical properties of surface water and groundwater samples from some developing countries have been subjected to multivariate analyses by the non-parametric multi-criteria decision-making methods, PROMETHEE and GAIA. Complete ranking information necessary to select one source of water in preference to all others was obtained, and this enabled relationships between the physico-chemical properties and water quality to be assessed. Thus, the ranking of the quality of the water bodies was found to be strongly dependent on the total dissolved solid, phosphate, sulfate, ammonia-nitrogen, calcium, iron, chloride, magnesium, zinc, nitrate and fluoride contents of the waters. However, potassium, manganese and zinc composition showed the least influence in differentiating the water bodies. To model and predict the water quality influencing parameters, partial least squares analyses were carried out on a matrix made up of the results of water quality assessment studies carried out in Nigeria, Papua New Guinea, Egypt, Thailand and India/Pakistan. The results showed that the total dissolved solid, calcium, sulfate, sodium and chloride contents can be used to predict a wide range of physico-chemical characteristics of water. The potential implications of these observations on the financial and opportunity costs associated with elaborate water quality monitoring are discussed.

Predicting Nitrogen Transport From Individual Sewage Disposal Systems for a Proposed Development in Adams County, Colorado

NASA Astrophysics Data System (ADS)

Heatwole, K. K.; McCray, J.; Lowe, K.

2005-12-01

Individual sewage disposal systems (ISDS) have demonstrated the capability to be an effective method of treatment for domestic wastewater. They also are advantageous from a water resources standpoint because there is little water leaving the local hydrologic system. However, if unfavorable settings exist, ISDS can have a detrimental effect on local water-quality. This presentation will focus on assessing the potential impacts of a large housing development to area water quality. The residential development plans to utilize ISDS to accommodate all domestic wastewater generated within the development. The area of interest is located just west of Brighton, Colorado, on the northwestern margin of the Denver Basin. Efforts of this research will focus on impacts of ISDS to local groundwater and surface water systems. The Arapahoe Aquifer, which exists at relatively shallow depths in the area of proposed development, is suspected to be vulnerable to contamination from ISDS. Additionally, the local water quality of the Arapahoe Aquifer was not well known at the start of the study. As a result, nitrate was selected as a fo-cus water quality parameter because it is easily produced through nitrification of septic tank effluent and because of the previous agricultural practices that could be another potential source of nitrate. Several different predictive tools were used to attempt to predict the potential impacts of ISDS to water quality in the Arapahoe Aquifer. The objectives of these tools were to 1) assess the vulnerability of the Arapahoe Aquifer to ni-trate contamination, 2) predict the nitrate load to the aquifer, and 3) determine the sensitivity of different parameter inputs and the overall prediction uncertainty. These predictive tools began with very simple mass-loading calcula-tions and progressed to more complex, vadose-zone numerical contaminant transport modeling.

Tracing the influence of land-use change on water quality and coral reefs using a Bayesian model.

PubMed

Brown, Christopher J; Jupiter, Stacy D; Albert, Simon; Klein, Carissa J; Mangubhai, Sangeeta; Maina, Joseph M; Mumby, Peter; Olley, Jon; Stewart-Koster, Ben; Tulloch, Vivitskaia; Wenger, Amelia

2017-07-06

Coastal ecosystems can be degraded by poor water quality. Tracing the causes of poor water quality back to land-use change is necessary to target catchment management for coastal zone management. However, existing models for tracing the sources of pollution require extensive data-sets which are not available for many of the world's coral reef regions that may have severe water quality issues. Here we develop a hierarchical Bayesian model that uses freely available satellite data to infer the connection between land-uses in catchments and water clarity in coastal oceans. We apply the model to estimate the influence of land-use change on water clarity in Fiji. We tested the model's predictions against underwater surveys, finding that predictions of poor water quality are consistent with observations of high siltation and low coverage of sediment-sensitive coral genera. The model thus provides a means to link land-use change to declines in coastal water quality.

The European water framework directive: water quality classification and implications to engineering planning.

PubMed

Achleitner, Stefan; De Toffol, Sara; Engelhard, Carolina; Rauch, Wolfgang

2005-04-01

The European Water framework directive (WFD) is probably the most important environmental management directive that has been enacted over the last decade in the European Union. The directive aims at achieving an overall good ecological status in all European water bodies. In this article, we discuss the implementation steps of the WFD and their implications for environmental engineering practice while focusing on rivers as the main receiving waters. Arising challenges for engineers and scientists are seen in the quantitative assessment of water quality, where standardized systems are needed to estimate the biological status. This is equally of concern in engineering planning, where the prediction of ecological impacts is required. Studies dealing with both classification and prediction of the ecological water quality are reviewed. Further, the combined emission-water quality approach is discussed. Common understanding of this combined approach is to apply the most stringent of either water quality or emission standard to a certain case. In contrast, for example, the Austrian water act enables the application of only the water quality based approach--at least on a temporary basis.

Predicting non-stationary algal dynamics following changes in hydrometeorological conditions using data assimilation techniques

NASA Astrophysics Data System (ADS)

Kim, S.; Seo, D. J.

2017-12-01

When water temperature (TW) increases due to changes in hydrometeorological conditions, the overall ecological conditions change in the aquatic system. The changes can be harmful to human health and potentially fatal to fish habitat. Therefore, it is important to assess the impacts of thermal disturbances on in-stream processes of water quality variables and be able to predict effectiveness of possible actions that may be taken for water quality protection. For skillful prediction of in-stream water quality processes, it is necessary for the watershed water quality models to be able to reflect such changes. Most of the currently available models, however, assume static parameters for the biophysiochemical processes and hence are not able to capture nonstationaries seen in water quality observations. In this work, we assess the performance of the Hydrological Simulation Program-Fortran (HSPF) in predicting algal dynamics following TW increase. The study area is located in the Republic of Korea where waterway change due to weir construction and drought concurrently occurred around 2012. In this work we use data assimilation (DA) techniques to update model parameters as well as the initial condition of selected state variables for in-stream processes relevant to algal growth. For assessment of model performance and characterization of temporal variability, various goodness-of-fit measures and wavelet analysis are used.

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

PubMed

Zhang, Lei; Zou, Zhihong; Shan, Wei

2017-06-01

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO 4 and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction. Copyright © 2016. Published by Elsevier B.V.

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

PubMed

Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

2015-09-15

The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

Impact of climate change on water quality of an impaired New Mexico river

USDA-ARS?s Scientific Manuscript database

Climate change is predicted to advance runoff timing in snowmelt basins and decrease available water, particularly in arid and semi-arid regions. Researchers have suggested that the impacts of climate change will degrade water quality by reducing dilution. We use coupled snowmelt and water quality m...

Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao Reservoir, China.

PubMed

Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang

2015-01-01

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.

Nonstarch polysaccharides in wheat flour wire-cut cookie making.

PubMed

Guttieri, Mary J; Souza, Edward J; Sneller, Clay

2008-11-26

Nonstarch polysaccharides in wheat flour have significant capacity to affect the processing quality of wheat flour dough and the finished quality of wheat flour products. Most research has focused on the effects of arabinoxylans (AX) in bread making. This study found that water-extractable AX and arabinogalactan peptides can predict variation in pastry wheat quality as captured by the wire-cut cookie model system. The sum of water-extractable AX plus arabinogalactan was highly predictive of cookie spread factor. The combination of cookie spread factor and the ratio of water-extractable arabinose to xylose predicted peak force of the three-point bend test of cookie texture.

Predicting Near-Term Water Quality from Satellite Observations of Watershed Conditions

NASA Astrophysics Data System (ADS)

Weiss, W. J.; Wang, L.; Hoffman, K.; West, D.; Mehta, A. V.; Lee, C.

2017-12-01

Despite the strong influence of watershed conditions on source water quality, most water utilities and water resource agencies do not currently have the capability to monitor watershed sources of contamination with great temporal or spatial detail. Typically, knowledge of source water quality is limited to periodic grab sampling; automated monitoring of a limited number of parameters at a few select locations; and/or monitoring relevant constituents at a treatment plant intake. While important, such observations are not sufficient to inform proactive watershed or source water management at a monthly or seasonal scale. Satellite remote sensing data on the other hand can provide a snapshot of an entire watershed at regular, sub-monthly intervals, helping analysts characterize watershed conditions and identify trends that could signal changes in source water quality. Accordingly, the authors are investigating correlations between satellite remote sensing observations of watersheds and source water quality, at a variety of spatial and temporal scales and lags. While correlations between remote sensing observations and direct in situ measurements of water quality have been well described in the literature, there are few studies that link remote sensing observations across a watershed with near-term predictions of water quality. In this presentation, the authors will describe results of statistical analyses and discuss how these results are being used to inform development of a desktop decision support tool to support predictive application of remote sensing data. Predictor variables under evaluation include parameters that describe vegetative conditions; parameters that describe climate/weather conditions; and non-remote sensing, in situ measurements. Water quality parameters under investigation include nitrogen, phosphorus, organic carbon, chlorophyll-a, and turbidity.

Identify the dominant variables to predict stream water temperature

NASA Astrophysics Data System (ADS)

Chien, H.; Flagler, J.

2016-12-01

Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

High adherence is necessary to realize health gains from water quality interventions.

PubMed

Brown, Joe; Clasen, Thomas

2012-01-01

Safe drinking water is critical for health. Household water treatment (HWT) has been recommended for improving access to potable water where existing sources are unsafe. Reports of low adherence to HWT may limit the usefulness of this approach, however. We constructed a quantitative microbial risk model to predict gains in health attributable to water quality interventions based on a range of assumptions about pre-treatment water quality; treatment effectiveness in reducing bacteria, viruses, and protozoan parasites; adherence to treatment interventions; volume of water consumed per person per day; and other variables. According to mean estimates, greater than 500 DALYs may be averted per 100,000 person-years with increased access to safe water, assuming moderately poor pre-treatment water quality that is a source of risk and high treatment adherence (>90% of water consumed is treated). A decline in adherence from 100% to 90% reduces predicted health gains by up to 96%, with sharpest declines when pre-treatment water quality is of higher risk. Results suggest that high adherence is essential in order to realize potential health gains from HWT.

High Adherence Is Necessary to Realize Health Gains from Water Quality Interventions

PubMed Central

Brown, Joe; Clasen, Thomas

2012-01-01

Background Safe drinking water is critical for health. Household water treatment (HWT) has been recommended for improving access to potable water where existing sources are unsafe. Reports of low adherence to HWT may limit the usefulness of this approach, however. Methods and Findings We constructed a quantitative microbial risk model to predict gains in health attributable to water quality interventions based on a range of assumptions about pre-treatment water quality; treatment effectiveness in reducing bacteria, viruses, and protozoan parasites; adherence to treatment interventions; volume of water consumed per person per day; and other variables. According to mean estimates, greater than 500 DALYs may be averted per 100,000 person-years with increased access to safe water, assuming moderately poor pre-treatment water quality that is a source of risk and high treatment adherence (>90% of water consumed is treated). A decline in adherence from 100% to 90% reduces predicted health gains by up to 96%, with sharpest declines when pre-treatment water quality is of higher risk. Conclusions Results suggest that high adherence is essential in order to realize potential health gains from HWT. PMID:22586491

Remote sensing of effects of land-use practices on water quality. [environmental surveys using Landsat satellites

NASA Technical Reports Server (NTRS)

Graves, D. H.

1975-01-01

Research efforts are presented for the use of remote sensing in environmental surveys in Kentucky. Ground truth parameters were established that represent the vegetative cover of disturbed and undisturbed watersheds in the Cumberland Plateau of eastern Kentucky. Several water quality parameters were monitored of the watersheds utilized in the establishment of ground truth data. The capabilities of multistage-multispectral aerial photography and satellite imagery were evaluated in detecting various land use practices. The use of photographic signatures of known land use areas utilizing manually-operated spot densitometers was studied. The correlation of imagery signature data to water quality data was examined. Potential water quality predictions were developed from forested and nonforested watersheds based upon the above correlations. The cost effectiveness of predicting water quality values was evaluated using multistage and satellite imagery sampling techniques.

Environmental effects of dredging. Documentation of the efqual module for ADDAMS: Comparison of predicted effluent water quality with standards. Technical notes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Palermo, M.R.; Schroeder, P.R.

This technical note describes a technique for comparison of the predicted quality of effluent discharged from confined dredged material disposal areas with applicable water quality standards. This note also serves as documentation of a computer program called EFQUAL written for that purpose as part of the Automated Dredging and Disposal Alternatives Management System (ADDAMS).

Predicting water quality by relating secchi-disk transparency and chlorophyll a measurements to Landsat satellite imagery for Michigan inland lakes, 2001-2006

USGS Publications Warehouse

Fuller, L.M.; Minnerick, R.J.

2007-01-01

The State of Michigan has more than 11,000 inland lakes; approximately 3,500 of these lakes are greater than 25 acres. The USGS, in cooperation with the Michigan Department of Environmental Quality (MDEQ), has been monitoring the quality of inland lakes in Michigan through the Lake Water Quality Assessment monitoring program. Approximately 100 inland lakes will be sampled per year from 2001 to 2015. Volunteers coordinated by MDEQ started sampling lakes in 1974, and continue to sample to date approximately 250 inland lakes each year through the Cooperative Lakes Monitoring Program (CLMP), Michigan’s volunteer lakes monitoring program. Despite this sampling effort, it is still impossible to physically collect the necessary water-quality measurements for all 3,500 Michigan inland lakes. Therefore, a technique was used by USGS, modeled after Olmanson and others (2001), in cooperation with MDEQ that uses satellite remote sensing to predict water quality in unsampled inland lakes greater than 25 acres. Water-quality characteristics that are associated with water clarity can be predicted for Michigan inland lakes by relating sampled measurements of secchi-disk transparency (SDT) and chlorophyll a concentrations (Chl-a), to satellite imagery. The trophic state index (TSI) which is an indicator of the biological productivity can be calculated based on SDT measurements, Chl-a concentrations, and total phosphorus (TP) concentrations measured near the lake’s surface. Through this process, unsampled inland lakes within the fourteen Landsat satellite scenes encompassing Michigan can be translated into estimated TSI from either predicted SDT or Chl-a (fig. 1).

Predicting the Effect of Changing Precipitation Extremes and Land Cover Change on Urban Water Quality

NASA Astrophysics Data System (ADS)

SUN, N.; Yearsley, J. R.; Lettenmaier, D. P.

2013-12-01

Recent research shows that precipitation extremes in many of the largest U.S. urban areas have increased over the last 60 years. These changes have important implications for stormwater runoff and water quality, which in urban areas are dominated by the most extreme precipitation events. We assess the potential implications of changes in extreme precipitation and changing land cover in urban and urbanizing watersheds at the regional scale using a combination of hydrology and water quality models. Specifically, we describe the integration of a spatially distributed hydrological model - the Distributed Hydrology Soil Vegetation Model (DHSVM), the urban water quality model in EPA's Storm Water Management Model (SWMM), the semi-Lagrangian stream temperature model RBM10, and dynamical and statistical downscaling methods applied to global climate predictions. Key output water quality parameters include total suspended solids (TSS), toal nitrogen, total phosphorous, fecal coliform bacteria and stream temperature. We have evaluated the performance of the modeling system in the highly urbanized Mercer Creek watershed in the rapidly growing Bellevue urban area in WA, USA. The results suggest that the model is able to (1) produce reasonable streamflow predictions at fine temporal and spatial scales; (2) provide spatially distributed water temperature predictions that mostly agree with observations throughout a complex stream network, and characterize impacts of climate, landscape, near-stream vegetation change on stream temperature at local and regional scales; and (3) capture plausibly the response of water quality constituents to varying magnitude of precipitation events in urban environments. Next we will extend the scope of the study from the Mercer Creek watershed to include the entire Puget Sound Basin, WA, USA.

A Mass-balance nitrate model for predicting the effects of land use on ground-water quality in municipal wellhead-protection areas

USGS Publications Warehouse

Frimpter, M.H.; Donohue, J.J.; Rapacz, M.V.; Beye, H.G.

1990-01-01

A mass-balance accounting model can be used to guide the management of septic systems and fertilizers to control the degradation of groundwater quality in zones of an aquifer that contributes water to public supply wells. The nitrate nitrogen concentration of the mixture in the well can be predicted for steady-state conditions by calculating the concentration that results from the total weight of nitrogen and total volume of water entering the zone of contribution to the well. These calculations will allow water-quality managers to predict the nitrate concentrations that would be produced by different types and levels of development, and to plan development accordingly. Computations for different development schemes provide a technical basis for planners and managers to compare water quality effects and to select alternatives that limit nitrate concentration in wells. Appendix A contains tables of nitrate loads and water volumes from common sources for use with the accounting model. Appendix B describes the preparation of a spreadsheet for the nitrate loading calculations with a software package generally available for desktop computers. (USGS)

Documentation of the runqual module for ADDAMS: Comparison of predicted runoff water quality with standards. Environmental effects of dredging. Technical notes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schroeder, P.R.; Gibson, A.C.; Dardeau, E.A.

This technical note has a twofold purpose: to describe a technique for comparing the predicted quality of surface runoff from confined dredged material disposal areas with applicable water quality standards and to document a computer program called RUNQUAL, written for that purpose as a part of the Automated Dredging and Disposal Alternatives Management System (ADDAMS).

Surrogate Analysis and Index Developer (SAID) tool

USGS Publications Warehouse

Domanski, Marian M.; Straub, Timothy D.; Landers, Mark N.

2015-10-01

The regression models created in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water quality and associated engineering and ecological management decisions.

Assessing and Predicting the Effectiveness of Stormwater BMPs on Water Quality, Flow, Thermal Regime, and Substrate Integrity in the Delaware River Basin

EPA Science Inventory

Assessments of the effectiveness of stormwater best management practices (BMPs) have focused on measurement of load or concentration reductions, which can be translated to predict biological impacts based on chemical water quality criteria. However, many of the impacts of develo...

Predicting synoptic water quality indicators of wadeable streams in the U.S. using National Soil Database

EPA Science Inventory

Nationwide assessment of water quality is a goal of the United States Environmental Protection Agency (USEPA), and the EPA’s Wadeable Stream Assessment (WSA) was developed in response to that goal. The observed chemical, physical, and biological water quality indicators (WQI) fro...

Scale effects of STATSGO and SSURGO databases on flow and water quality predictions

USDA-ARS?s Scientific Manuscript database

Soil information is one of the crucial inputs needed to assess the impacts of existing and alternative agricultural management practices on water quality. Therefore, it is important to understand the effects of spatial scale at which soil databases are developed on water quality evaluations. In the ...

Predicting synoptic water quality indicators of wadeable streams in the U.S. using national soil database - Shirazi

EPA Science Inventory

Nationwide assessment of water quality is a goal of the United States Environmental Protection Agency (USEPA), and the EPA’s Wadeable Stream Assessment (WSA) was developed in response to that goal. The observed chemical, physical, and biological water quality indicators (WQI) fro...

Predicting Risk from Radon in Source Waters from Water Quality Parameters

EPA Science Inventory

Overall, 47 groundwater samples were collected from 45 small community water systems (CWSs) and analyzed for radon and other water quality constituents. In general, groundwater from unconsolidated deposits and sedimentary rocks had lower average radon levels (ranging from 223 to...

Maps showing predicted probabilities for selected dissolved oxygen and dissolved manganese threshold events in depth zones used by the domestic and public drinking water supply wells, Central Valley, California

USGS Publications Warehouse

Rosecrans, Celia Z.; Nolan, Bernard T.; Gronberg, JoAnn M.

2018-01-31

The purpose of the prediction grids for selected redox constituents—dissolved oxygen and dissolved manganese—are intended to provide an understanding of groundwater-quality conditions at the domestic and public-supply drinking water depths. The chemical quality of groundwater and the fate of many contaminants is influenced by redox processes in all aquifers, and understanding the redox conditions horizontally and vertically is critical in evaluating groundwater quality. The redox condition of groundwater—whether oxic (oxygen present) or anoxic (oxygen absent)—strongly influences the oxidation state of a chemical in groundwater. The anoxic dissolved oxygen thresholds of <0.5 milligram per liter (mg/L), <1.0 mg/L, and <2.0 mg/L were selected to apply broadly to regional groundwater-quality investigations. Although the presence of dissolved manganese in groundwater indicates strongly reducing (anoxic) groundwater conditions, it is also considered a “nuisance” constituent in drinking water, making drinking water undesirable with respect to taste, staining, or scaling. Three dissolved manganese thresholds, <50 micrograms per liter (µg/L), <150 µg/L, and <300 µg/L, were selected to create predicted probabilities of exceedances in depth zones used by domestic and public-supply water wells. The 50 µg/L event threshold represents the secondary maximum contaminant level (SMCL) benchmark for manganese (U.S. Environmental Protection Agency, 2017; California Division of Drinking Water, 2014), whereas the 300 µg/L event threshold represents the U.S. Geological Survey (USGS) health-based screening level (HBSL) benchmark, used to put measured concentrations of drinking-water contaminants into a human-health context (Toccalino and others, 2014). The 150 µg/L event threshold represents one-half the USGS HBSL. The resultant dissolved oxygen and dissolved manganese prediction grids may be of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Prediction grids for selected redox constituents and thresholds were created by the USGS National Water-Quality Assessment (NAWQA) modeling and mapping team.

Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14

USGS Publications Warehouse

Francy, Donna S.; Graham, Jennifer L.; Stelzer, Erin A.; Ecker, Christopher D.; Brady, Amie M. G.; Pam Struffolino,; Loftin, Keith A.

2015-11-06

The results of this study showed that water-quality and environmental variables are promising for use in site-specific daily or long-term predictive models. In order to develop more accurate models to predict toxin concentrations at freshwater lake sites, data need to be collected more frequently and for consecutive days in future studies.

GIS ANALYSIS FOR EPIDEMIOLOGIC RECREATIONAL WATER SUTDIES

EPA Science Inventory

Introduction: The Beaches Act of 2000 requires that the Agency develop new rapid method water quality indicators (2 hours or less) that predict whether or not coastal water is safe for swimming. This new set of water quality indicators must be validated through the epidemiologi...

Evaluating and Predicting the Effectiveness of Green Infrastructure on a Small Watershed Scale - Emphasis on Water Quality, Flow, Thermal Regime, Substrate Integrity, and Biological Condition

EPA Science Inventory

Assessments of the effectiveness of stormwater best management practices (BMPs) have focused on measurement of load or concentration reductions, which can be translated to predict biological impacts based on chemical water quality criteria. However, many of the impacts of develo...

Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.

Ground-water models for water resource planning

USGS Publications Warehouse

Moore, J.E.

1983-01-01

In the past decade hydrogeologists have emphasized the development of computer-based mathematical models to aid in the understanding of flow, the transport of solutes, transport of heat, and deformation in the ground-water system. These models have been used to provide information and predictions for water managers. Too frequently, ground-water was neglected in water resource planning because managers believed that it could not be adequately evaluated in terms of availability, quality, and effect of development on surface-water supplies. Now, however, with newly developed digital ground-water models, effects of development can be predicted. Such models have been used to predict hydrologic and quality changes under different stresses. These models have grown in complexity over the last ten years from simple one-layer models to three-dimensional simulations of ground-water flow, which may include solute transport, heat transport, effects of land subsidence, and encroachment of saltwater. Case histories illustrate how predictive ground-water models have provided the information needed for the sound planning and management of water resources in the USA. ?? 1983 D. Reidel Publishing Company.

Assessing Cumulative Impacts of Coal Bed Methane Development on Surface Water Quality and its Suitability for Irrigation in the Powder River Basin

NASA Astrophysics Data System (ADS)

Dawson, H. E.

2003-12-01

This paper presents a mass balance approach to assessing the cumulative impacts of discharge from Coal Bed Methane (CBM) wells on surface water quality and its suitability for irrigation in the Powder River Basin. Key water quality parameters for predicting potential effects of CBM development on irrigated agriculture are sodicity, expressed as sodium adsorption ratio (SAR) and salinity, expressed as electrical conductivity (EC). The assessment was performed with the aid of a spreadsheet model, which was designed to estimate steady-state SAR and EC at gauged stream locations after mixing with CBM produced water. Model input included ambient stream water quality and flow, CBM produced water quality and discharge rates, conveyance loss (quantity of water loss that may occur between the discharge point and the receiving streams), beneficial uses, regulatory thresholds, and discharge allocation at state-line boundaries. Historical USGS data were used to establish ambient stream water quality and flow conditions. The resultant water quality predicted for each stream station included the cumulative discharge of CBM produced water in all reaches upstream of the station. Model output was presented in both tabular and graphical formats, and indicated the suitability of pre- and post-mixing water quality for irrigation. Advantages and disadvantages of the spreadsheet model are discussed. This approach was used by federal agencies to support the development of the January 2003 Environmental Impact Statements (EIS) for the Wyoming and Montana portions of the Powder River Basin.

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

PubMed

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Predicting fire effects on water quality: a perspective and future needs

NASA Astrophysics Data System (ADS)

Smith, Hugh; Sheridan, Gary; Nyman, Petter; Langhans, Christoph; Noske, Philip; Lane, Patrick

2017-04-01

Forest environments are a globally significant source of drinking water. Fire presents a credible threat to the supply of high quality water in many forested regions. The post-fire risk to water supplies depends on storm event characteristics, vegetation cover and fire-related changes in soil infiltration and erodibility modulated by landscape position. The resulting magnitude of runoff generation, erosion and constituent flux to streams and reservoirs determines the severity of water quality impacts in combination with the physical and chemical composition of the entrained material. Research to date suggests that most post-fire water quality impacts are due to large increases in the supply of particulates (fine-grained sediment and ash) and particle-associated chemical constituents. The largest water quality impacts result from high magnitude erosion events, including debris flow processes, which typically occur in response to short duration, high intensity storm events during the recovery period. Most research to date focuses on impacts on water quality after fire. However, information on potential water quality impacts is required prior to fire events for risk planning. Moreover, changes in climate and forest management (e.g. prescribed burning) that affect fire regimes may alter water quality risks. Therefore, prediction requires spatial-temporal representation of fire and rainfall regimes coupled with information on fire-related changes to soil hydrologic parameters. Recent work has applied such an approach by combining a fire spread model with historic fire weather data in a Monte Carlo simulation to quantify probabilities associated with fire and storm events generating debris flows and fine sediment influx to a reservoir located in Victoria, Australia. Prediction of fire effects on water quality would benefit from further research in several areas. First, more work on regional-scale stochastic modelling of intersecting fire and storm events with landscape zones of erosion vulnerability is required to support quantitative evaluation of water quality risk and the effect of future changes in climate and land management. Second, we underscore previous calls for characterisation of landscape-scale domains to support regionalisation of parameter sets derived from empirical studies. Recent examples include work identifying aridity as a control of hydro-geomorphic response to fire and the use of spectral-based indices to predict spatial heterogeneity in ash loadings. Third, information on post-fire erosion from colluvial or alluvial stores is needed to determine their significance as both sediment-contaminant sinks and sources. Such sediment stores may require explicit spatial representation in risk models for some environments and sediment tracing can be used to determine their relative importance as secondary sources. Fourth, increased dating of sediment archives could provide regional datasets of fire-related erosion event frequency. Presently, the lack of such data hinders evaluation of risk models linking fire and storm events to erosion and water quality impacts.

Ground-water models for water resources planning

USGS Publications Warehouse

Moore, John E.

1980-01-01

In the past decade hydrologists have emphasized the development of computer-based mathematical models to aid in the understanding of flow, the transport of solutes, transport of heat, and deformation in the groundwater system. These models have been used to provide information and predictions for water managers. Too frequently, groundwater was neglected in water-resource planning because managers believed that it could not be adequately evaluated in terms of availability, quality, and effect of development on surface water supplies. Now, however, with newly developed digital groundwater models, effects of development can be predicted. Such models have been used to predict hydrologic and quality changes under different stresses. These models have grown in complexity over the last 10 years from simple one-layer flow models to three-dimensional simulations of groundwater flow which may include solute transport, heat transport, effects of land subsidence, and encroachment of salt water. This paper illustrates, through case histories, how predictive groundwater models have provided the information needed for the sound planning and management of water resources in the United States. (USGS)

Table Rock Lake Water-Clarity Assessment Using Landsat Thematic Mapper Satellite Data

USGS Publications Warehouse

Krizanich, Gary; Finn, Michael P.

2009-01-01

Water quality of Table Rock Lake in southwestern Missouri is assessed using Landsat Thematic Mapper satellite data. A pilot study uses multidate satellite image scenes in conjunction with physical measurements of secchi disk transparency collected by the Lakes of Missouri Volunteer Program to construct a regression model used to estimate water clarity. The natural log of secchi disk transparency is the dependent variable in the regression and the independent variables are Thematic Mapper band 1 (blue) reflectance and a ratio of the band 1 and band 3 (red) reflectance. The regression model can be used to reliably predict water clarity anywhere within the lake. A pixel-level lake map of predicted water clarity or computed trophic state can be produced from the model output. Information derived from this model can be used by water-resource managers to assess water quality and evaluate effects of changes in the watershed on water quality.

Environmental and Water Quality Operational Studies: Proceedings of the DeGray Lake Symposium Held in Arkadelphia, Arkansas.

DTIC Science & Technology

1987-03-01

statistics for storm water quality variables and fractions of phosphorus, solids, and carbon are presented in Tables 7 and 8, respectively. The correlation...matrix and factor analysis (same method as used for baseflow) of storm water quality variables suggested three groups: Group I - TMG, TCA, TNA, TSI...models to predict storm water quality . The 11 static and 3 dynamic storm variables were used as potential dependent variables. All independent and

A statistical model for water quality predictions from a river discharge using coastal observations

NASA Astrophysics Data System (ADS)

Kim, S.; Terrill, E. J.

2007-12-01

Understanding and predicting coastal ocean water quality has benefits for reducing human health risks, protecting the environment, and improving local economies which depend on clean beaches. Continuous observations of coastal physical oceanography increase the understanding of the processes which control the fate and transport of a riverine plume which potentially contains high levels of contaminants from the upstream watershed. A data-driven model of the fate and transport of river plume water from the Tijuana River has been developed using surface current observations provided by a network of HF radar operated as part of a local coastal observatory that has been in place since 2002. The model outputs are compared with water quality sampling of shoreline indicator bacteria, and the skill of an alarm for low water quality is evaluated using the receiver operating characteristic (ROC) curve. In addition, statistical analysis of beach closures in comparison with environmental variables is also discussed.

RECREATIONAL WATER QUALITY AND SWIMMING ASSOCIATED HEALTH EFFECTS

EPA Science Inventory

The U.S. EPA's National Epidemiological and Environmental Assessment of Recreational Water study is currently underway with the goal of determining if new rapid methods for measuring water quality can be used to predict illness in swimmers. This lecture will provide a historical...

Water-quality data and Escherichia coli predictions for selected karst catchments of the upper Duck River watershed in central Tennessee, 2007–10

USGS Publications Warehouse

Murphy, Jennifer C.; Farmer, James; Layton, Alice

2016-06-13

The U.S. Geological Survey, in cooperation with the Tennessee Duck River Development Agency, monitored water quality at several locations in the upper Duck River watershed between October 2007 and September 2010. Discrete water samples collected at 24 sites in the watershed were analyzed for water quality, and Escherichia coli (E. coli) and enterococci concentrations. Additional analyses, including the determination of anthropogenic-organic compounds, bacterial concentration of resuspended sediment, and bacterial-source tracking, were performed at a subset of sites. Continuous monitoring of streamflow, turbidity, and specific conductance was conducted at seven sites; a subset of sites also was monitored for water temperature and dissolved oxygen concentration. Multiple-regression models were developed to predict instantaneous E. coli concentrations and loads at sites with continuous monitoring. This data collection effort, along with the E. coli models and predictions, support analyses of the relations among land use, bacteria source and transport, and basin hydrology in the upper Duck River watershed.

Assessing the Challenges Associated with Developing an Integrated Modeling Approach for Predicting and Managing Water Quality and Quantity from the Watershed through the Drinking Water Treatment System

EPA Science Inventory

Natural and Engineered water systems interact throughout watersheds (e.g., at water intakes, wastewater outfalls and water pipe breaks of all kinds), and while there is clearly a link between watershed activities and the quality of water entering the engineered environment, surfa...

Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

USGS Publications Warehouse

Francy, Donna S.; Brady, Amie M.G.; Carvin, Rebecca B.; Corsi, Steven R.; Fuller, Lori M.; Harrison, John H.; Hayhurst, Brett A.; Lant, Jeremiah; Nevers, Meredith B.; Terrio, Paul J.; Zimmerman, Tammy M.

2013-01-01

Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.

PREDICTING CHLORINE RESIDUAL LOSSES IN UNLINED METALIC PIPES

EPA Science Inventory

There is substantial evidence that as water moves through a water distribution system its quality can deteriorate through interactions between the bulk phase and the pipe wall. One of the most serious aspects of water quality deterioration, in a network, is the loss of disinfect...

Quality assessment of butter cookies applying multispectral imaging

PubMed Central

Andresen, Mette S; Dissing, Bjørn S; Løje, Hanne

2013-01-01

A method for characterization of butter cookie quality by assessing the surface browning and water content using multispectral images is presented. Based on evaluations of the browning of butter cookies, cookies were manually divided into groups. From this categorization, reference values were calculated for a statistical prediction model correlating multispectral images with a browning score. The browning score is calculated as a function of oven temperature and baking time. It is presented as a quadratic response surface. The investigated process window was the intervals 4–16 min and 160–200°C in a forced convection electrically heated oven. In addition to the browning score, a model for predicting the average water content based on the same images is presented. This shows how multispectral images of butter cookies may be used for the assessment of different quality parameters. Statistical analysis showed that the most significant wavelengths for browning predictions were in the interval 400–700 nm and the wavelengths significant for water prediction were primarily located in the near-infrared spectrum. The water prediction model was found to correctly estimate the average water content with an absolute error of 0.22%. From the images it was also possible to follow the browning and drying propagation from the cookie edge toward the center. PMID:24804036

Spectral Band Characterization for Hyperspectral Monitoring of Water Quality

NASA Technical Reports Server (NTRS)

Vermillion, Stephanie C.; Raqueno, Rolando; Simmons, Rulon

2001-01-01

A method for selecting the set of spectral characteristics that provides the smallest increase in prediction error is of interest to those using hyperspectral imaging (HSI) to monitor water quality. The spectral characteristics of interest to these applications are spectral bandwidth and location. Three water quality constituents of interest that are detectable via remote sensing are chlorophyll (CHL), total suspended solids (TSS), and colored dissolved organic matter (CDOM). Hyperspectral data provides a rich source of information regarding the content and composition of these materials, but often provides more data than an analyst can manage. This study addresses the spectral characteristics need for water quality monitoring for two reasons. First, determination of the greatest contribution of these spectral characteristics would greatly improve computational ease and efficiency. Second, understanding the spectral capabilities of different spectral resolutions and specific regions is an essential part of future system development and characterization. As new systems are developed and tested, water quality managers will be asked to determine sensor specifications that provide the most accurate and efficient water quality measurements. We address these issues using data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and a set of models to predict constituent concentrations.

PREDICTING CHLORINE RESIDUAL LOSSES IN UNLINED METALLIC PIPES (PRESENTATION)

EPA Science Inventory

There is substantial evidence that as water moves through a water distribution system its quality can deteriorate through interactions between the bulk phase and the pipe wall. One of the most serious aspects of water quality deterioration, in a network, is the loss of disinfecta...

PREDICTING CHLORINE RESIDUAL LOSSES IN UNLINED METALLIC PIPES (POSTER)

EPA Science Inventory

There is substantial evidence that as water moves through a water distribution system its quality can deteriorate through interactions between the bulk phase and the pipe wall. One of the most serious aspects of water quality deterioration, in a network, is the loss of disinfect...

PREDICTIVE UNCERTAINTY IN HYDROLOGIC AND WATER QUALITY MODELING: APPROACHES, APPLICATION TO ENVIRONMENTAL MANAGEMENT, AND FUTURE CHALLENGES (PRESENTATION)

EPA Science Inventory

Extant process-based hydrologic and water quality models are indispensable to water resources planning and environmental management. However, models are only approximations of real systems and often calibrated with incomplete and uncertain data. Reliable estimates, or perhaps f...

PREDICTIVE UNCERTAINTY IN HYDROLOGIC AND WATER QUALITY MODELING: APPROACHES, APPLICATION TO ENVIRONMENTAL MANAGEMENT, AND FUTURE CHALLENGES

EPA Science Inventory

Extant process-based hydrologic and water quality models are indispensable to water resources planning and environmental management. However, models are only approximations of real systems and often calibrated with incomplete and uncertain data. Reliable estimates, or perhaps f...

Contaminant Permeation in the Ionomer-Membrane Water Processor (IWP) System

NASA Technical Reports Server (NTRS)

Kelsey, Laura K.; Finger, Barry W.; Pasadilla, Patrick; Perry, Jay

2016-01-01

The Ionomer-membrane Water Processor (IWP) is a patented membrane-distillation based urine brine water recovery system. The unique properties of the IWP membrane pair limit contaminant permeation from the brine to the recovered water and purge gas. A paper study was conducted to predict volatile trace contaminant permeation in the IWP system. Testing of a large-scale IWP Engineering Development Unit (EDU) with urine brine pretreated with the International Space Station (ISS) pretreatment formulation was then conducted to collect air and water samples for quality analysis. Distillate water quality and purge air GC-MS results are presented and compared to predictions, along with implications for the IWP brine processing system.

Modeling the Effects of Conservation Tillage on Water Quality at the Field Scale

USDA-ARS?s Scientific Manuscript database

The development and application of predictive tools to quantitatively assess the effects of tillage and related management activities should be carefully tested against high quality field data. This study reports on: 1) the calibration and validation of the Root Zone Water Quality Model (RZWQM) to a...

Development of Software Sensors for Determining Total Phosphorus and Total Nitrogen in Waters

PubMed Central

Lee, Eunhyoung; Han, Sanghoon; Kim, Hyunook

2013-01-01

Total nitrogen (TN) and total phosphorus (TP) concentrations are important parameters to assess the quality of water bodies and are used as criteria to regulate the water quality of the effluent from a wastewater treatment plant (WWTP) in Korea. Therefore, continuous monitoring of TN and TP using in situ instruments is conducted nationwide in Korea. However, most in situ instruments in the market are expensive and require a time-consuming sample pretreatment step, which hinders the widespread use of in situ TN and TP monitoring. In this study, therefore, software sensors based on multiple-regression with a few easily in situ measurable water quality parameters were applied to estimate the TN and TP concentrations in a stream, a lake, combined sewer overflows (CSOs), and WWTP effluent. In general, the developed software sensors predicted TN and TP concentrations of the WWTP effluent and CSOs reasonably well. However, they showed relatively lower predictability for TN and TP concentrations of stream and lake waters, possibly because the water quality of stream and lake waters is more variable than that of WWTP effluent or CSOs. PMID:23307350

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Kim, Chan Moon; Parnichkun, Manukid

2017-11-01

Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

Spatial and temporal variation of fecal indicator organisms in two creeks in Beltsville, Maryland

USDA-ARS?s Scientific Manuscript database

Evaluation of microbial water quality is commonly achieved by monitoring populations of indicator bacteria such as E. coli and enterococci. Monitoring data are utilized by water managers to predict potential fecal contaminations as well as a decision tool to improve microbial water quality. Both te...

Three-dimensional numerical modeling of water quality and sediment-associated processes in natural lakes

USDA-ARS?s Scientific Manuscript database

This chapter presents the development and application of a three-dimensional water quality model for predicting the distributions of nutrients, phytoplankton, dissolved oxygen, etc., in natural lakes. In this model, the computational domain was divided into two parts: the water column and the bed se...

Modeling Benthic Sediment Processes to Predict Water Quality and Ecology in Narragansett Bay

EPA Science Inventory

The benthic sediment acts as a huge reservoir of particulate and dissolved material (within interstitial water) which can contribute to loading of contaminants and nutrients to the water column. A benthic sediment model is presented in this report to predict spatial and temporal ...

Advanced Water Quality Modelling in Marine Systems: Application to the Wadden Sea, the Netherlands

NASA Astrophysics Data System (ADS)

Boon, J.; Smits, J. G.

2006-12-01

There is an increasing demand for knowledge and models that arise from water management in relation to water quality, sediment quality (ecology) and sediment accumulation (ecomorphology). Recently, models for sediment diagenesis and erosion developed or incorporated by Delft Hydraulics integrates the relevant physical, (bio)chemical and biological processes for the sediment-water exchange of substances. The aim of the diagenesis models is the prediction of both sediment quality and the return fluxes of substances such as nutrients and micropollutants to the overlying water. The resulting so-called DELWAQ-G model is a new, generic version of the water and sediment quality model of the DELFT3D framework. One set of generic water quality process formulations is used to calculate process rates in both water and sediment compartments. DELWAQ-G involves the explicit simulation of sediment layers in the water quality model with state-of-the-art process kinetics. The local conditions in a water layer or sediment layer such as the dissolved oxygen concentration determine if and how individual processes come to expression. New processes were added for sulphate, sulphide, methane and the distribution of the electron-acceptor demand over dissolved oxygen, nitrate, sulphate and carbon dioxide. DELWAQ-G also includes the dispersive and advective transport processes in the sediment and across the sediment-water interface. DELWAQ-G has been applied for the Wadden Sea. A very dynamic tidal and ecologically active estuary with a complex hydrodynamic behaviour located at the north of the Netherlands. The predicted profiles in the sediment reflect the typical interactions of diagenesis processes.

Predicting Bacteria Removal by Enhanced Stormwater Control Measures (SCMs) at the Watershed Scale

NASA Astrophysics Data System (ADS)

Wolfand, J.; Bell, C. D.; Boehm, A. B.; Hogue, T. S.; Luthy, R. G.

2017-12-01

Urban stormwater is a major cause of water quality impairment, resulting in surface waters that fail to meet water quality standards and support their designated uses. Fecal indicator bacteria are present in high concentrations in stormwater and are strictly regulated in receiving waters; yet, their fate and transport in urban stormwater is poorly understood. Stormwater control measures (SCMs) are often used to treat, infiltrate, and release urban runoff, but field measurements show that the removal of bacteria by these structural solutions is limited (median log removal = 0.24, n = 370). Researchers have therefore looked to improve bacterial removal by enhancing SCMs through alterations in flow regimes or adding geomedia such as biochar. The present research seeks to develop a model to predict removal of fecal indicator bacteria by enhanced SCMs at the watershed scale in a semi-arid climate. Using the highly developed Ballona Creek watershed (290 km2) located in Los Angeles County as a case study, a hydrologic model is coupled with a stochastic water quality model to predict E. coli concentration near the outfall of the Ballona Creek, Santa Monica Bay. A hydrologic model was developed using EPA SWMM, calibrated for flow from water year 1998-2006 (NSE = 0.94; R2 = 0.94), and validated from water year 2007-2015 (NSE = 0.90; R2 = 0.93). This bacterial loading model was then linked to EPA SUSTAIN and a SCM bacterial removal script to simulate log removal of bacteria by various SCMs and predict bacterial concentrations in Ballona Creek. Preliminary results suggest small enhancements to SCMs that improve bacterial removal (<0.5 log removal) may offer large benefits to surface water quality and enable communities such as Los Angeles to meet their regulatory requirements.

Implications of Modeling Uncertainty for Water Quality Decision Making

NASA Astrophysics Data System (ADS)

Shabman, L.

2002-05-01

The report, National Academy of Sciences report, "Assessing the TMDL Approach to Water Quality Management" endorsed the "watershed" and "ambient water quality focused" approach" to water quality management called for in the TMDL program. The committee felt that available data and models were adequate to move such a program forward, if the EPA and all stakeholders better understood the nature of the scientific enterprise and its application to the TMDL program. Specifically, the report called for a greater acknowledgement of model prediction uncertinaity in making and implementing TMDL plans. To assure that such uncertinaity was addressed in water quality decision making the committee called for a commitment to "adaptive implementation" of water quality management plans. The committee found that the number and complexity of the interactions of multiple stressors, combined with model prediction uncertinaity means that we need to avoid the temptation to make assurances that specific actions will result in attainment of particular water quality standards. Until the work on solving a water quality problem begins, analysts and decision makers cannot be sure what the correct solutions are, or even what water quality goals a community should be seeking. In complex systems we need to act in order to learn; adaptive implementation is a concurrent process of action and learning. Learning requires (1) continued monitoring of the waterbody to determine how it responds to the actions taken and (2) carefully designed experiments in the watershed. If we do not design learning into what we attempt we are not doing adaptive implementation. Therefore, there needs to be an increased commitment to monitoring and experiments in watersheds that will lead to learning. This presentation will 1) explain the logic for adaptive implementation; 2) discuss the ways that water quality modelers could characterize and explain model uncertinaity to decision makers; 3) speculate on the implications of the adaptive implementation for setting of water quality standards, for design of watershed monitoring programs and for the regulatory rules governing the TMDL program implementation.

Spatial and temporal variations in the relationship between lake water surface temperatures and water quality - A case study of Dianchi Lake.

PubMed

Yang, Kun; Yu, Zhenyu; Luo, Yi; Yang, Yang; Zhao, Lei; Zhou, Xiaolu

2018-05-15

Global warming and rapid urbanization in China have caused a series of ecological problems. One consequence has involved the degradation of lake water environments. Lake surface water temperatures (LSWTs) significantly shape water ecological environments and are highly correlated with the watershed ecosystem features and biodiversity levels. Analysing and predicting spatiotemporal changes in LSWT and exploring the corresponding impacts on water quality is essential for controlling and improving the ecological water environment of watersheds. In this study, Dianchi Lake was examined through an analysis of 54 water quality indicators from 10 water quality monitoring sites from 2005 to 2016. Support vector regression (SVR), Principal Component Analysis (PCA) and Back Propagation Artificial Neural Network (BPANN) methods were applied to form a hybrid forecasting model. A geospatial analysis was conducted to observe historical LSWTs and water quality changes for Dianchi Lake from 2005 to 2016. Based on the constructed model, LSWTs and changes in water quality were simulated for 2017 to 2020. The relationship between LSWTs and water quality thresholds was studied. The results show limited errors and highly generalized levels of predictive performance. In addition, a spatial visualization analysis shows that from 2005 to 2020, the chlorophyll-a (Chla), chemical oxygen demand (COD) and total nitrogen (TN) diffused from north to south and that ammonia nitrogen (NH 3 -N) and total phosphorus (TP) levels are increases in the northern part of Dianchi Lake, where the LSWT levels exceed 17°C. The LSWT threshold is 17.6-18.53°C, which falls within the threshold for nutritional water quality, but COD and TN levels fall below V class water quality standards. Transparency (Trans), COD, biochemical oxygen demand (BOD) and Chla levels present a close relationship with LSWT, and LSWTs are found to fundamentally affect lake cyanobacterial blooms. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset

USGS Publications Warehouse

Harris, Ted D.; Graham, Jennifer L.

2017-01-01

Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.

Use of watershed factors to predict consumer surfactant risk, water quality, and habitat quality in the upper Trinity River, Texas.

PubMed

Atkinson, S F; Johnson, D R; Venables, B J; Slye, J L; Kennedy, J R; Dyer, S D; Price, B B; Ciarlo, M; Stanton, K; Sanderson, H; Nielsen, A

2009-06-15

Surfactants are high production volume chemicals that are used in a wide assortment of "down-the-drain" consumer products. Wastewater treatment plants (WWTPs) generally remove 85 to more than 99% of all surfactants from influents, but residual concentrations are discharged into receiving waters via wastewater treatment plant effluents. The Trinity River that flows through the Dallas-Fort Worth metropolitan area, Texas, is an ideal study site for surfactants due to the high ratio of wastewater treatment plant effluent to river flow (>95%) during late summer months, providing an interesting scenario for surfactant loading into the environment. The objective of this project was to determine whether surfactant concentrations, expressed as toxic units, in-stream water quality, and aquatic habitat in the upper Trinity River could be predicted based on easily accessible watershed characteristics. Surface water and pore water samples were collected in late summer 2005 at 11 sites on the Trinity River in and around the Dallas-Fort Worth metropolitan area. Effluents of 4 major waste water treatment plants that discharge effluents into the Trinity River were also sampled. General chemistries and individual surfactant concentrations were determined, and total surfactant toxic units were calculated. GIS models of geospatial, anthropogenic factors (e.g., population density) and natural factors (e.g., soil organic matter) were collected and analyzed according to subwatersheds. Multiple regression analyses using the stepwise maximum R(2) improvement method were performed to develop prediction models of surfactant risk, water quality, and aquatic habitat (dependent variables) using the geospatial parameters (independent variables) that characterized the upper Trinity River watershed. We show that GIS modeling has the potential to be a reliable and inexpensive method of predicting water and habitat quality in the upper Trinity River watershed and perhaps other highly urbanized watersheds in semi-arid regions.

A Web-Based Decision Support System for Assessing Regional Water-Quality Conditions and Management Actions

NASA Astrophysics Data System (ADS)

Booth, N. L.; Everman, E.; Kuo, I.; Sprague, L.; Murphy, L.

2011-12-01

A new web-based decision support system has been developed as part of the U.S. Geological Survey (USGS) National Water Quality Assessment Program's (NAWQA) effort to provide ready access to Spatially Referenced Regressions On Watershed attributes (SPARROW) results of stream water-quality conditions and to offer sophisticated scenario testing capabilities for research and water-quality planning via an intuitive graphical user interface with a map-based display. The SPARROW Decision Support System (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water-quality conditions, distribution of nutrient sources, nutrient delivery to downstream waterbodies, and simulations of altered nutrient inputs including atmospheric and agricultural sources. The DSS offers other features for analysis including various background map layers, model output exports, and the ability to save and share prediction scenarios. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000-scale River Reach File (RF1) and 1:100,000-scale National Hydrography Dataset (medium-resolution, NHDPlus) stream networks. The underlying modeling framework and server infrastructure illustrate innovations in the information technology and geosciences fields for delivering SPARROW model predictions over the web by performing intensive model computations and map visualizations of the predicted conditions within the stream network.

Use of EDTA in modified kinetic testing for contaminated drainage prediction from waste rocks: case of the Lac Tio mine.

PubMed

Plante, Benoît; Benzaazoua, Mostafa; Bussière, Bruno; Kandji, El-Hadji-Babacar; Chopard, Aurélie; Bouzahzah, Hassan

2015-05-01

The tools developed for acid mine drainage (AMD) prediction were proven unsuccessful to predict the geochemical behavior of mine waste rocks having a significant chemical sorption capacity, which delays the onset of contaminated neutral drainage (CND). The present work was performed in order to test a new approach of water quality prediction, by using a chelating agent solution (0.03 M EDTA, or ethylenediaminetetraacetic acid) in kinetic testing used for the prediction of the geochemical behavior of geologic material. The hypothesis underlying the proposed approach is that the EDTA solution should chelate the metals as soon as they are released by sulfide oxidation, inhibiting their sorption or secondary precipitation, and therefore reproduce a worst-case scenario where very low metal attenuation mechanisms are present in the drainage waters. Fresh and weathered waste rocks from the Lac Tio mine (Rio tinto, Iron and Titanium), which are known to generate Ni-CND at the field scale, were submitted to small-scale humidity cells in control tests (using deionized water) and using an EDTA solution. Results show that EDTA effectively prevents the metals to be sorbed or to precipitate as secondary minerals, therefore enabling to bypass the delay associated with metal sorption in the prediction of water quality from these materials. This work shows that the use of a chelating agent solution is a promising novel approach of water quality prediction and provides general guidelines to be used in further studies, which will help both practitioners and regulators to plan more efficient management and disposal strategies of mine wastes.

Quantification of Water Quality Parameters for the Wabash River Using Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Tan, J.; Cherkauer, K. A.; Chaubey, I.

2011-12-01

Increasingly impaired water bodies in the agriculturally dominated Midwestern United States pose a risk to water supplies, aquatic ecology and contribute to the eutrophication of the Gulf of Mexico. Improving regional water quality calls for new techniques for monitoring and managing water quality over large river systems. Optical indicators of water quality enable a timely and cost-effective method for observing and quantifying water quality conditions by remote sensing. Compared to broad spectral sensors such as Landsat, which observe reflectance over limited spectral bands, hyperspectral sensors should have significant advantages in their ability to estimate water quality parameters because they are designed to split the spectral signature into hundreds of very narrow spectral bands increasing their ability to resolve optically sensitive water quality indicators. Two airborne hyperspectral images were acquired over the Wabash River using a ProSpecTIR-VS2 sensor system on May 15th, 2010. These images were analyzed together with concurrent in-stream water quality data collected to assess our ability to extract optically sensitive constituents. Utilizing the correlation between in-stream data and reflectance from the hyperspectral images, models were developed to estimate the concentrations of chlorophyll a, dissolved organic carbon and total suspended solids. Models were developed using the full array of hyperspectral bands, as well as Landsat bands synthesized by averaging hyperspectral bands within the Landsat spectral range. Higher R2 and lower RMSE values were found for the models taking full advantage of the hyperspectral sensor, supporting the conclusion that the hyperspectral sensor was better at predicting the in-stream concentrations of chlorophyll a, dissolved organic carbon and total suspended solids in the Wabash River. Results also suggest that predictive models may not be the same for the Wabash River as for its tributaries.

Landscape-scale modeling of water quality in Lake Superior and Lake Michigan watersheds: How useful are forest-based indicators? Journal of Great Lakes Research

Treesearch

Titus S. Seilheimer; Patrick L. Zimmerman; Kirk M. Stueve; Charles H. Perry

2013-01-01

The Great Lakes watersheds have an important influence on the water quality of the nearshore environment, therefore, watershed characteristics can be used to predict what will be observed in the streams. We used novel landscape information describing the forest cover change, along with forest census data and established land cover data to predict total phosphorus and...

Stressor-response modeling using the 2D water quality model and regression trees to predict chlorophyll-a in a reservoir system

USDA-ARS?s Scientific Manuscript database

In order to control algal blooms, stressor-response relationships between water quality metrics, environmental variables, and algal growth should be understood and modeled. Machine-learning methods were suggested to express stressor-response relationships found by application of mechanistic water qu...

The impact of water quality in Narragansett Bay on housing prices

NASA Astrophysics Data System (ADS)

Liu, Tingting; Opaluch, James J.; Uchida, Emi

2017-08-01

We examine the impact of water quality in Narragansett Bay on housing prices in coastal towns and cities using a hedonic housing-price model. Unlike other hedonic studies of water quality, we test whether housing market responds to average water quality or more to extreme events. We also test the spatial and temporal extent of effects of water quality on housing prices. We find that poor coastal water quality, measured in terms of the concentration of chlorophyll, has a negative impact on housing prices that diminishes with distance from the shoreline. Furthermore, our finding suggests that housing prices are most influenced by the extreme environmental conditions, which may be accompanied by unpleasant odors, discoloration, and even fish kills. We further predict potential increases in home values associated under water quality improvement scenarios and find an increase in the values of homes in coastal communities along Narragansett Bay of about 18 million up to 136 million.

Escherichia coli and fecal-coliform bacteria as indicators of recreational water quality

USGS Publications Warehouse

Francy, D.S.; Myers, Donna N.; Metzker, K.D.

1993-01-01

In 1986, the U.S. Environmental Protection Agency (USEPA) recommended that Escherichia coli (E. coli) be used in place of fecal-coliform bacteria in State recreational water-quality standards as an indicator of fecal contamination. This announcement followed an epidemiological study in which E. coli concentration was shown to be a better predictor of swimming-associated gastrointestinal illness than fecal-coliform concentration. Water-resource managers from Ohio have decided to collect information specific to their waters and decide whether to use E. coli or fecal-coliform bacteria as the basis for State recreational water-quality standards. If one indicator is a better predictor of recreational water quality than the other and if the relation between the two indicators is variable, then the indicator providing the most accurate measure of recreational water quality should be used in water-quality standards. Water-quality studies of the variability of concentrations of E. coli to fecal-coliform bacteria have shown that (1) concentrations of the two indicators are positively correlated, (2) E. coli to fecal-coliform ratios differ considerably from site to site, and (3) the E. coli criteria recommended by USEPA may be more difficult to meet than current (1992) fecal-coliform standards. In this study, a statistical analysis was done on concentrations of E. coli and fecal-coliform bacteria in water samples collected by two government agencies in Ohio-- the U.S. Geological Survey (USGS) and the Ohio River Valley Water Sanitation Commission (ORSANCO). Data were organized initially into five data sets for statistical analysis: (1) Cuyahoga River, (2) Olentangy River, (3) Scioto River, (4) Ohio River at Anderson Ferry, and (5) Ohio River at Cincinnati Water Works and Tanners Creek. The USGS collected the data in sets 1, 2, and 3, whereas ORSANCO collected the data in sets 4 and 5. The relation of E. coli to fecal-coliform concentration was investigated by use of linear-regression analysis and analysis of covariance. Log-transformed E. coli and fecal-coliform concentrations were highly correlated in all data sets (r-values ranged from 0.929 to 0.984). Linear regression analysis on USGS and ORSANCO data sets showed that concentration of E. coli could be predicted from fecal-coliform concentration (coefficients of determination (R2) ranged from 0.863 to 0.970). Results of analysis of covariance (ANCOVA) indicated that the predictive equations among the three USGS data sets and two ORSANCO data sets were not significantly different and that the data could be pooled into two large data sets, one for USGS data and one for ORSANCO data. However, results of ANCOVA indicated that USGS and ORSANCO data could not be pooled into one large data set. Predictions of E. coli concentrations calculated for USGS And ORSANCO regression relations, based on fecal-coliform concentrations set to equal Ohio water-quality standards, further showed the differences in E. coli to fecal-coliform relations among data sets. For USGS data, a predicted geometric mean of 176 col/100 mL (number of colonies per 100 milliliters) was greater than the current geometric-mean E. coli standard for bathing water of 126 col/100mL. In contrast, for ORSANCO data, the predicted geometric mean of 101 col/100 mL was less than the current E. coli standard. The risk of illness associated with predicted E. coli concentrations for USGS and ORSANCO data was evaluated by use of the USEPA regression equation that predicts swimming-related gastroenteritis rates from E. coli concentrations.1 The predicted geometric-mean E. coli concentrations for bathing water of 176 col/100 mL for USGS data and 101 col/100 mL for ORSANCO data would allow 9.4 and 7.1 gastrointestinal illnesses per 1,000 swimmers, respectively. This prediction compares well with the illness rate of 8 individuals per 1,000 swimmers estimated by the USEPA for an E. coli concentration of 126 col/100 mL. Therefore, the

Klang River water quality modelling using music

NASA Astrophysics Data System (ADS)

Zahari, Nazirul Mubin; Zawawi, Mohd Hafiz; Muda, Zakaria Che; Sidek, Lariyah Mohd; Fauzi, Nurfazila Mohd; Othman, Mohd Edzham Fareez; Ahmad, Zulkepply

2017-09-01

Water is an essential resource that sustains life on earth; changes in the natural quality and distribution of water have ecological impacts that can sometimes be devastating. Recently, Malaysia is facing many environmental issues regarding water pollution. The main causes of river pollution are rapid urbanization, arising from the development of residential, commercial, industrial sites, infrastructural facilities and others. The purpose of the study was to predict the water quality of the Connaught Bridge Power Station (CBPS), Klang River. Besides that, affects to the low tide and high tide and. to forecast the pollutant concentrations of the Biochemical Oxygen Demand (BOD) and Total Suspended Solid (TSS) for existing land use of the catchment area through water quality modeling (by using the MUSIC software). Besides that, to identifying an integrated urban stormwater treatment system (Best Management Practice or BMPs) to achieve optimal performance in improving the water quality of the catchment using the MUSIC software in catchment areas having tropical climates. Result from MUSIC Model such as BOD5 at station 1 can be reduce the concentration from Class IV to become Class III. Whereas, for TSS concentration from Class III to become Class II at the station 1. The model predicted a mean TSS reduction of 0.17%, TP reduction of 0.14%, TN reduction of 0.48% and BOD5 reduction of 0.31% for Station 1 Thus, from the result after purposed BMPs the water quality is safe to use because basically water quality monitoring is important due to threat such as activities are harmful to aquatic organisms and public health.

Predicting effects of environmental change on river inflows to ...

EPA Pesticide Factsheets

Estuarine river watersheds provide valued ecosystem services to their surrounding communities including drinking water, fish habitat, and regulation of estuarine water quality. However, the provisioning of these services can be affected by changes in the quantity and quality of river water, such as those caused by altered landscapes or shifting temperatures or precipitation. We used the ecohydrology model, VELMA, in the Trask River watershed to simulate the effects of environmental change scenarios on estuarine river inputs to Tillamook Bay (OR) estuary. The Trask River watershed is 453 km2 and contains extensive agriculture, silviculture, urban, and wetland areas. VELMA was parameterized using existing spatial datasets of elevation, soil type, land use, air temperature, precipitation, river flow, and water quality. Simulated land use change scenarios included alterations in the distribution of the nitrogen-fixing tree species Alnus rubra, and comparisons of varying timber harvest plans. Scenarios involving spatial and temporal shifts in air temperature and precipitation trends were also simulated. Our research demonstrates the utility of ecohydrology models such as VELMA to aid in watershed management decision-making. Model outputs of river water flow, temperature, and nutrient concentrations can be used to predict effects on drinking water quality, salmonid populations, and estuarine water quality. This modeling effort is part of a larger framework of

Relations Between Environmental and Water-Quality Variables and Escherichia coli in the Cuyahoga River With Emphasis on Turbidity as a Predictor of Recreational Water Quality, Cuyahoga Valley National Park, Ohio, 2008

USGS Publications Warehouse

Brady, Amie M.G.; Plona, Meg B.

2009-01-01

During the recreational season of 2008 (May through August), a regression model relating turbidity to concentrations of Escherichia coli (E. coli) was used to predict recreational water quality in the Cuyahoga River at the historical community of Jaite, within the present city of Brecksville, Ohio, a site centrally located within Cuyahoga Valley National Park. Samples were collected three days per week at Jaite and at three other sites on the river. Concentrations of E. coli were determined and compared to environmental and water-quality measures and to concentrations predicted with a regression model. Linear relations between E. coli concentrations and turbidity, gage height, and rainfall were statistically significant for Jaite. Relations between E. coli concentrations and turbidity were statistically significant for the three additional sites, and relations between E. coli concentrations and gage height were significant at the two sites where gage-height data were available. The turbidity model correctly predicted concentrations of E. coli above or below Ohio's single-sample standard for primary-contact recreation for 77 percent of samples collected at Jaite.

Cost-effective water quality assessment through the integration of monitoring data and modeling results

NASA Astrophysics Data System (ADS)

Lobuglio, Joseph N.; Characklis, Gregory W.; Serre, Marc L.

2007-03-01

Sparse monitoring data and error inherent in water quality models make the identification of waters not meeting regulatory standards uncertain. Additional monitoring can be implemented to reduce this uncertainty, but it is often expensive. These costs are currently a major concern, since developing total maximum daily loads, as mandated by the Clean Water Act, will require assessing tens of thousands of water bodies across the United States. This work uses the Bayesian maximum entropy (BME) method of modern geostatistics to integrate water quality monitoring data together with model predictions to provide improved estimates of water quality in a cost-effective manner. This information includes estimates of uncertainty and can be used to aid probabilistic-based decisions concerning the status of a water (i.e., impaired or not impaired) and the level of monitoring needed to characterize the water for regulatory purposes. This approach is applied to the Catawba River reservoir system in western North Carolina as a means of estimating seasonal chlorophyll a concentration. Mean concentration and confidence intervals for chlorophyll a are estimated for 66 reservoir segments over an 11-year period (726 values) based on 219 measured seasonal averages and 54 model predictions. Although the model predictions had a high degree of uncertainty, integration of modeling results via BME methods reduced the uncertainty associated with chlorophyll estimates compared with estimates made solely with information from monitoring efforts. Probabilistic predictions of future chlorophyll levels on one reservoir are used to illustrate the cost savings that can be achieved by less extensive and rigorous monitoring methods within the BME framework. While BME methods have been applied in several environmental contexts, employing these methods as a means of integrating monitoring and modeling results, as well as application of this approach to the assessment of surface water monitoring networks, represent unexplored areas of research.

Predicting Trihalomethanes (THMs) in the New York City Water Supply

NASA Astrophysics Data System (ADS)

Mukundan, R.; Van Dreason, R.

2013-12-01

Chlorine, a commonly used disinfectant in most water supply systems, can combine with organic carbon to form disinfectant byproducts including carcinogenic trihalomethanes (THMs). We used water quality data from 24 monitoring sites within the New York City (NYC) water supply distribution system, measured between January 2009 and April 2012, to develop site-specific empirical models for predicting total trihalomethane (TTHM) levels. Terms in the model included various combinations of the following water quality parameters: total organic carbon, pH, specific conductivity, and water temperature. Reasonable estimates of TTHM levels were achieved with overall R2 of about 0.87 and predicted values within 5 μg/L of measured values. The relative importance of factors affecting TTHM formation was estimated by ranking the model regression coefficients. Site-specific models showed improved model performance statistics compared to a single model for the entire system most likely because the single model did not consider locational differences in the water treatment process. Although never out of compliance in 2011, the TTHM levels in the water supply increased following tropical storms Irene and Lee with 45% of the samples exceeding the 80 μg/L Maximum Contaminant Level (MCL) in October and November. This increase was explained by changes in water quality parameters, particularly by the increase in total organic carbon concentration and pH during this period.

Predicting effects of environmental change on river inflows to Tillamook Bay, Oregon

EPA Science Inventory

Estuarine river watersheds provide valued ecosystem services to their surrounding communities including drinking water, fish habitat, and regulation of estuarine water quality. However, the provisioning of these services can be affected by changes in the quantity and quality of ...

Predicting the Effects of Water Quality on the Growth of Thalassia testudinum in Tampa Bay with a Dynamic Simile-Based Model Tool

EPA Science Inventory

We describe a seagrass growth (SGG) model that is coupled to a water quality (WQ) model that includes the effects of phytoplankton (chlorophyll), colored dissolved organic matter (CDOM) and suspended solids (TSS) on water clarity. Phytoplankton growth was adjusted daily for PAR (...

Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Keith M. Reynolds; Timothy J. Sullivan; Todd C. McDonnell; R. Brion Salter

2013-01-01

In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially...

Data assimilation in optimizing and integrating soil and water quality water model predictions at different scales

USDA-ARS?s Scientific Manuscript database

Relevant data about subsurface water flow and solute transport at relatively large scales that are of interest to the public are inherently laborious and in most cases simply impossible to obtain. Upscaling in which fine-scale models and data are used to predict changes at the coarser scales is the...

From microbes to water districts: Linking observations across scales to uncover the implications of riparian and channel management on water quality in an irrigated agricultural landscape

NASA Astrophysics Data System (ADS)

Webster, A.; Cadenasso, M. L.

2016-12-01

Interactions among runoff, riparian and stream ecosystems, and water quality remain uncertain in many settings, particularly those heavily impacted by human activities. For example, waterways in the irrigated agricultural landscape of California's Central Valley are seasonally disconnected from groundwater tables and are extensively modified by infrastructure and management. These conditions make the impact of riparian and channel management difficult to predict across scales, which hinders efforts to promote best management practices to improve water quality. We seek to link observations across catchment, reach, and patch scales to understand patterns of nitrate and turbidity in waterways draining irrigated cropland. Data was collected on 80 reaches spanning two water management districts. At the catchment scale, water districts implemented waterway and riparian management differently: one water district had a decentralized approach, allowing individual land owners to manage their waterway channels and banks, while the other had a centralized approach, in which land owners defer management to a district-run program. At the reach scale, riparian and waterway vegetation, geomorphic complexity, and flow conditions were quantified. Reach-scale management such as riparian planting projects and channel dredging frequency were also considered. At the patch scale, denitrification potential and organic matter were measured in riparian toe-slope soils and channel sediments, along with associated vegetation and geomorphic features. All factors were tested for their ability to predict water quality using generalized linear mixed effects models and the consistency of predictors within and across scales was evaluated. A hierarchy of predictors emerges: catchment-scale management regimes predict reach-scale geomorphic and vegetation complexity, which in turn predicts sediment denitrification potential - the patch-scale factor most associated with low nitrate. Similarly, turbidity conveyance was most associated with reach-scale factors. These findings suggest that, in the absence of other regulations, a decentralized management approach to riparian zones and waterways allows reach-scale complexity to arise, which in turn promotes ecosystem function and improved water quality.

Accounting for and predicting the influence of spatial autocorrelation in water quality modeling

NASA Astrophysics Data System (ADS)

Miralha, L.; Kim, D.

2017-12-01

Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of SAC in response variables can predict improvements in models even before performing spatial regression approaches. We also recognize the constraints of this research and suggest that further studies focus on better ways of defining spatial neighborhoods, considering the differences among stations set in tributaries near to each other and in upstream areas.

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.

PubMed

Najah, A; El-Shafie, A; Karim, O A; El-Shafie, Amr H

2014-02-01

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

Stochastic modeling for river pollution of Sungai Perlis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yunus, Nurul Izzaty Mohd.; Rahman, Haliza Abd.; Bahar, Arifah

2015-02-03

River pollution has been recognized as a contributor to a wide range of health problems and disorders in human. It can pose health dangers to humans who come into contact with it, either directly or indirectly. Therefore, it is most important to measure the concentration of Biochemical Oxygen Demand (BOD) as a water quality parameter since the parameter has long been the basic means for determining the degree of water pollution in rivers. In this study, BOD is used as a parameter to estimate the water quality at Sungai Perlis. It has been observed that Sungai Perlis is polluted duemore » to lack of management and improper use of resources. Therefore, it is of importance to model the Sungai Perlis water quality in order to describe and predict the water quality systems. The BOD concentration secondary data set is used which was extracted from the Drainage and Irrigation Department Perlis State website. The first order differential equation from Streeter – Phelps model was utilized as a deterministic model. Then, the model was developed into a stochastic model. Results from this study shows that the stochastic model is more adequate to describe and predict the BOD concentration and the water quality systems in Sungai Perlis by having smaller value of mean squared error (MSE)« less

Modelling the impacts of agricultural management practices on river water quality in Eastern England.

PubMed

Taylor, Sam D; He, Yi; Hiscock, Kevin M

2016-09-15

Agricultural diffuse water pollution remains a notable global pressure on water quality, posing risks to aquatic ecosystems, human health and water resources and as a result legislation has been introduced in many parts of the world to protect water bodies. Due to their efficiency and cost-effectiveness, water quality models have been increasingly applied to catchments as Decision Support Tools (DSTs) to identify mitigation options that can be introduced to reduce agricultural diffuse water pollution and improve water quality. In this study, the Soil and Water Assessment Tool (SWAT) was applied to the River Wensum catchment in eastern England with the aim of quantifying the long-term impacts of potential changes to agricultural management practices on river water quality. Calibration and validation were successfully performed at a daily time-step against observations of discharge, nitrate and total phosphorus obtained from high-frequency water quality monitoring within the Blackwater sub-catchment, covering an area of 19.6 km(2). A variety of mitigation options were identified and modelled, both singly and in combination, and their long-term effects on nitrate and total phosphorus losses were quantified together with the 95% uncertainty range of model predictions. Results showed that introducing a red clover cover crop to the crop rotation scheme applied within the catchment reduced nitrate losses by 19.6%. Buffer strips of 2 m and 6 m width represented the most effective options to reduce total phosphorus losses, achieving reductions of 12.2% and 16.9%, respectively. This is one of the first studies to quantify the impacts of agricultural mitigation options on long-term water quality for nitrate and total phosphorus at a daily resolution, in addition to providing an estimate of the uncertainties of those impacts. The results highlighted the need to consider multiple pollutants, the degree of uncertainty associated with model predictions and the risk of unintended pollutant impacts when evaluating the effectiveness of mitigation options, and showed that high-frequency water quality datasets can be applied to robustly calibrate water quality models, creating DSTs that are more effective and reliable. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Improving the Accuracy of Extracting Surface Water Quality Levels (SWQLs) Using Remote Sensing and Artificial Neural Network: a Case Study in the Saint John River, Canada

NASA Astrophysics Data System (ADS)

Sammartano, G.; Spanò, A.

2017-09-01

Delineating accurate surface water quality levels (SWQLs) always presents a great challenge to researchers. Existing methods of assessing surface water quality only provide individual concentrations of monitoring stations without providing the overall SWQLs. Therefore, the results of existing methods are usually difficult to be understood by decision-makers. Conversely, the water quality index (WQI) can simplify surface water quality assessment process to be accessible to decision-makers. However, in most cases, the WQI reflects inaccurate SWQLs due to the lack of representative water samples. It is very challenging to provide representative water samples because this process is costly and time consuming. To solve this problem, we introduce a cost-effective method which combines the Landsat-8 imagery and artificial intelligence to develop models to derive representative water samples by correlating concentrations of ground truth water samples to satellite spectral information. Our method was validated and the correlation between concentrations of ground truth water samples and predicted concentrations from the developed models reached a high level of coefficient of determination (R2) > 0.80, which is trustworthy. Afterwards, the predicted concentrations over each pixel of the study area were used as an input to the WQI developed by the Canadian Council of Ministers of the Environment to extract accurate SWQLs, for drinking purposes, in the Saint John River. The results indicated that SWQL was observed as 67 (Fair) and 59 (Marginal) for the lower and middle basins of the river, respectively. These findings demonstrate the potential of using our approach in surface water quality management.

Predicting microbial water quality with models: over-arching questions for managing risk in agricultural catchments

USDA-ARS?s Scientific Manuscript database

Determining the microbial quality of recreational, irrigation and shellfish-harvesting waters is important to ensure compliance with health-related standards and associated legislation. Animal faeces represent a significant human health risk, and concentrations of fecal indicator organisms (FIOs) pr...

APPLICATION OF A WATER QUALITY ASSESSMENT MODELING SYSTEM AT A SUPERFUND SITE

EPA Science Inventory

Water quality modeling and related exposure assessments at a Superfund site, Silver Bow Creek-Clark Fork River in Montana, demonstrate the capability to predict the fate of mining waste pollutants in the environment. inked assessment system--consisting of hydrology and erosion, r...

A neighborhood statistics model for predicting stream pathogen indicator levels.

PubMed

Pandey, Pramod K; Pasternack, Gregory B; Majumder, Mahbubul; Soupir, Michelle L; Kaiser, Mark S

2015-03-01

Because elevated levels of water-borne Escherichia coli in streams are a leading cause of water quality impairments in the U.S., water-quality managers need tools for predicting aqueous E. coli levels. Presently, E. coli levels may be predicted using complex mechanistic models that have a high degree of unchecked uncertainty or simpler statistical models. To assess spatio-temporal patterns of instream E. coli levels, herein we measured E. coli, a pathogen indicator, at 16 sites (at four different times) within the Squaw Creek watershed, Iowa, and subsequently, the Markov Random Field model was exploited to develop a neighborhood statistics model for predicting instream E. coli levels. Two observed covariates, local water temperature (degrees Celsius) and mean cross-sectional depth (meters), were used as inputs to the model. Predictions of E. coli levels in the water column were compared with independent observational data collected from 16 in-stream locations. The results revealed that spatio-temporal averages of predicted and observed E. coli levels were extremely close. Approximately 66 % of individual predicted E. coli concentrations were within a factor of 2 of the observed values. In only one event, the difference between prediction and observation was beyond one order of magnitude. The mean of all predicted values at 16 locations was approximately 1 % higher than the mean of the observed values. The approach presented here will be useful while assessing instream contaminations such as pathogen/pathogen indicator levels at the watershed scale.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Artificial neural networks for defining the water quality determinants of groundwater abstraction in coastal aquifer

NASA Astrophysics Data System (ADS)

Lallahem, S.; Hani, A.

2017-02-01

Water sustainability in the lower Seybouse River basin, eastern Algeria, must take into account the importance of water quantity and quality integration. So, there is a need for a better knowledge and understanding of the water quality determinants of groundwater abstraction to meet the municipal and agricultural uses. In this paper, the artificial neural network (ANN) models were used to model and predict the relationship between groundwater abstraction and water quality determinants in the lower Seybouse River basin. The study area chosen is the lower Seybouse River basin and real data were collected from forty five wells for reference year 2006. Results indicate that the feed-forward multilayer perceptron models with back-propagation are useful tools to define and prioritize the important water quality parameters of groundwater abstraction and use. The model evaluation shows that the correlation coefficients are more than 95% for training, verification and testing data. The model aims to link the water quantity and quality with the objective to strengthen the Integrated Water Resources Management approach. It assists water planners and managers to better assess the water quality parameters and progress towards the provision of appropriate quantities of water of suitable quality.

Development and implementation of a regression model for predicting recreational water quality in the Cuyahoga River, Cuyahoga Valley National Park, Ohio 2009-11

USGS Publications Warehouse

Brady, Amie M.G.; Plona, Meg B.

2012-01-01

The Cuyahoga River within Cuyahoga Valley National Park (CVNP) is at times impaired for recreational use due to elevated concentrations of Escherichia coli (E. coli), a fecal-indicator bacterium. During the recreational seasons of mid-May through September during 2009–11, samples were collected 4 days per week and analyzed for E. coli concentrations at two sites within CVNP. Other water-quality and environ-mental data, including turbidity, rainfall, and streamflow, were measured and (or) tabulated for analysis. Regression models developed to predict recreational water quality in the river were implemented during the recreational seasons of 2009–11 for one site within CVNP–Jaite. For the 2009 and 2010 seasons, the regression models were better at predicting exceedances of Ohio's single-sample standard for primary-contact recreation compared to the traditional method of using the previous day's E. coli concentration. During 2009, the regression model was based on data collected during 2005 through 2008, excluding available 2004 data. The resulting model for 2009 did not perform as well as expected (based on the calibration data set) and tended to overestimate concentrations (correct responses at 69 percent). During 2010, the regression model was based on data collected during 2004 through 2009, including all of the available data. The 2010 model performed well, correctly predicting 89 percent of the samples above or below the single-sample standard, even though the predictions tended to be lower than actual sample concentrations. During 2011, the regression model was based on data collected during 2004 through 2010 and tended to overestimate concentrations. The 2011 model did not perform as well as the traditional method or as expected, based on the calibration dataset (correct responses at 56 percent). At a second site—Lock 29, approximately 5 river miles upstream from Jaite, a regression model based on data collected at the site during the recreational seasons of 2008–10 also did not perform as well as the traditional method or as well as expected (correct responses at 60 percent). Above normal precipitation in the region and a delayed start to the 2011 sampling season (sampling began mid-June) may have affected how well the 2011 models performed. With these new data, however, updated regression models may be better able to predict recreational water quality conditions due to the increased amount of diverse water quality conditions included in the calibration data. Daily recreational water-quality predictions for Jaite were made available on the Ohio Nowcast Web site at www.ohionowcast.info. Other public outreach included signage at trailheads in the park, articles in the park's quarterly-published schedule of events and volunteer newsletters. A U.S. Geological Survey Fact Sheet was also published to bring attention to water-quality issues in the park.

A model to predict stream water temperature across the conterminous USA

Treesearch

Catalina Segura; Peter Caldwell; Ge Sun; Steve McNulty; Yang Zhang

2014-01-01

Stream water temperature (ts) is a critical water quality parameter for aquatic ecosystems. However, ts records are sparse or nonexistent in many river systems. In this work, we present an empirical model to predict ts at the site scale across the USA. The model, derived using data from 171 reference sites selected from the Geospatial Attributes of Gages for Evaluating...

Marine waters contaminated with domestic sewage: nonenteric illnesses associated with bather exposure in the United Kingdom.

PubMed Central

Fleisher, J M; Kay, D; Salmon, R L; Jones, F; Wyer, M D; Godfree, A F

1996-01-01

OBJECTIVES: This study identified possible dose-response relationships among bathers exposed to marine waters contaminated with domestic sewage and subsequent risk of nonenteric illness. METHODS: Four intervention follow-up studies were conducted within the United Kingdom. Healthy volunteers (n = 1273) were randomized into bather and nonbather groups. Intensive water-quality monitoring was used to assign five bacteriological indices of water quality to individual bathers. Illnesses studied were acute febrile respiratory illness, and eye, ear, and skin ailments. RESULTS: Fecal streptococci exposure was predictive of acute febrile respiratory illness, while fecal coliform exposure was predictive of ear ailments. Estimated thresholds of effect occurred at bather exposures above 60 fecal streptococci and 100 fecal coliform per 100 ml of water, respectively. Although no relationship was found between eye ailments and indicator organism exposure, compared with nonbathers, bathers were at higher risk for eye ailments. CONCLUSIONS: Nonenteric illness can be transmitted via recreational contact with marine waters contaminated with sewage. These results argue against the use of a single indicator to establish water quality standards. PMID:8806373

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

EPA Science Inventory

Methods are needed improve the timeliness and accuracy of recreational water‐quality assessments. Traditional culture methods require 18–24 h to obtain results and may not reflect current conditions. Predictive models, based on environmental and water quality variables, have been...

Early warning of limit-exceeding concentrations of cyanobacteria and cyanotoxins in drinking water reservoirs by inferential modelling.

PubMed

Recknagel, Friedrich; Orr, Philip T; Bartkow, Michael; Swanepoel, Annelie; Cao, Hongqing

2017-11-01

An early warning scheme is proposed that runs ensembles of inferential models for predicting the cyanobacterial population dynamics and cyanotoxin concentrations in drinking water reservoirs on a diel basis driven by in situ sonde water quality data. When the 10- to 30-day-ahead predicted concentrations of cyanobacteria cells or cyanotoxins exceed pre-defined limit values, an early warning automatically activates an action plan considering in-lake control, e.g. intermittent mixing and ad hoc water treatment in water works, respectively. Case studies of the sub-tropical Lake Wivenhoe (Australia) and the Mediterranean Vaal Reservoir (South Africa) demonstrate that ensembles of inferential models developed by the hybrid evolutionary algorithm HEA are capable of up to 30days forecasts of cyanobacteria and cyanotoxins using data collected in situ. The resulting models for Dolicospermum circinale displayed validity for up to 10days ahead, whilst concentrations of Cylindrospermopsis raciborskii and microcystins were successfully predicted up to 30days ahead. Implementing the proposed scheme for drinking water reservoirs enhances current water quality monitoring practices by solely utilising in situ monitoring data, in addition to cyanobacteria and cyanotoxin measurements. Access to routinely measured cyanotoxin data allows for development of models that predict explicitly cyanotoxin concentrations that avoid to inadvertently model and predict non-toxic cyanobacterial strains. Copyright © 2017 Elsevier B.V. All rights reserved.

Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development?

NASA Astrophysics Data System (ADS)

Honti, Mark; Schuwirth, Nele; Rieckermann, Jörg; Stamm, Christian

2017-03-01

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual iWaQa model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

NIR spectroscopy for the quality control of Moringa oleifera (Lam.) leaf powders: Prediction of minerals, protein and moisture contents.

PubMed

Rébufa, Catherine; Pany, Inès; Bombarda, Isabelle

2018-09-30

A rapid methodology was developed to simultaneously predict water content and activity values (a w ) of Moringa oleifera leaf powders (MOLP) using near infrared (NIR) signatures and experimental sorption isotherms. NIR spectra of MOLP samples (n = 181) were recorded. A Partial Least Square Regression model (PLS2) was obtained with low standard errors of prediction (SEP of 1.8% and 0.07 for water content and a w respectively). Experimental sorption isotherms obtained at 20, 30 and 40 °C showed similar profiles. This result is particularly important to use MOLP in food industry. In fact, a temperature variation of the drying process will not affect their available water content (self-life). Nutrient contents based on protein and selected minerals (Ca, Fe, K) were also predicted from PLS1 models. Protein contents were well predicted (SEP of 2.3%). This methodology allowed for an improvement in MOLP safety, quality control and traceability. Published by Elsevier Ltd.

Prediction of ground water quality index to assess suitability for drinking purposes using fuzzy rule-based approach

NASA Astrophysics Data System (ADS)

Gorai, A. K.; Hasni, S. A.; Iqbal, Jawed

2016-11-01

Groundwater is the most important natural resource for drinking water to many people around the world, especially in rural areas where the supply of treated water is not available. Drinking water resources cannot be optimally used and sustained unless the quality of water is properly assessed. To this end, an attempt has been made to develop a suitable methodology for the assessment of drinking water quality on the basis of 11 physico-chemical parameters. The present study aims to select the fuzzy aggregation approach for estimation of the water quality index of a sample to check the suitability for drinking purposes. Based on expert's opinion and author's judgement, 11 water quality (pollutant) variables (Alkalinity, Dissolved Solids (DS), Hardness, pH, Ca, Mg, Fe, Fluoride, As, Sulphate, Nitrates) are selected for the quality assessment. The output results of proposed methodology are compared with the output obtained from widely used deterministic method (weighted arithmetic mean aggregation) for the suitability of the developed methodology.

Extreme weather events: Should drinking water quality management systems adapt to changing risk profiles?

PubMed

Khan, Stuart J; Deere, Daniel; Leusch, Frederic D L; Humpage, Andrew; Jenkins, Madeleine; Cunliffe, David

2015-11-15

Among the most widely predicted and accepted consequences of global climate change are increases in both the frequency and severity of a variety of extreme weather events. Such weather events include heavy rainfall and floods, cyclones, droughts, heatwaves, extreme cold, and wildfires, each of which can potentially impact drinking water quality by affecting water catchments, storage reservoirs, the performance of water treatment processes or the integrity of distribution systems. Drinking water guidelines, such as the Australian Drinking Water Guidelines and the World Health Organization Guidelines for Drinking-water Quality, provide guidance for the safe management of drinking water. These documents present principles and strategies for managing risks that may be posed to drinking water quality. While these principles and strategies are applicable to all types of water quality risks, very little specific attention has been paid to the management of extreme weather events. We present a review of recent literature on water quality impacts of extreme weather events and consider practical opportunities for improved guidance for water managers. We conclude that there is a case for an enhanced focus on the management of water quality impacts from extreme weather events in future revisions of water quality guidance documents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Virginia Water Resources: Utilizing NASA Earth Observations to Monitor the Extent of Harmful Algal Blooms in Virginia Rivers

NASA Astrophysics Data System (ADS)

Lubkin, S. H.; Morgan, C.

2015-12-01

Harmful algal bloom species have had an increasing ecological impact on the Chesapeake Bay Watershed where they disrupt water chemistry, kill fish and cause human illness. In Virginia, scientists from Virginia Institute of Marine Science and Old Dominion University monitor HABs and their effect on water quality; however, these groups lack a method to monitor HABs in real time. This limits the ability to document associated water quality conditions and predict future blooms. Band reflectance values from Landsat 8 Surface Reflectance data (USGS Earth Explorer) and MODIS Chlorophyll imagery (NOAA CoastWatch) were cross calibrated to create a regression model that calculated concentrations of chlorophyll. Calculations were verified with in situ measurements from the Virginia Estuarine and Coastal Observing System. Imagery produced with the Chlorophyll-A calculation model will allow VIMS and ODU scientists to assess the timing, magnitude, duration and frequency of HABs in Virginia's Chesapeake watershed and to predict the environmental and water quality conditions that favor bloom development.

RAPIDLY-MEASURED INDICATORS OF RECREATIONAL WATER QUALITY ARE PREDICTIVE OF SWIMMING-ASSOCIATED GASTROINTESTINAL ILLNESS

EPA Science Inventory

Fecal indicator bacteria (FIB) are used to monitor recreational water quality worldwide. Current methods of measuring FIB require at least 24-hours for growth of bacterial colonies. We conducted studies at four Great Lake beaches to examine the relationship between novel and fas...

Evaluating the Accuracy of Common Runoff Estimation Methods for New Impervious Hot-Mix Asphalt

EPA Science Inventory

Accurately predicting runoff volume from impervious surfaces for water quality design events (e.g., 25.4 mm) is important for sizing green infrastructure stormwater control measures to meet water quality and infiltration design targets. The objective of this research was to quan...

OVERBURDEN MINERALOGY AS RELATED TO GROUND-WATER CHEMICAL CHANGES IN COAL STRIP MINING

EPA Science Inventory

A research program was initiated to define and develop an inclusive, effective, and economical method for predicting potential ground-water quality changes resulting from the strip mining of coal in the Western United States. To utilize the predictive method, it is necessary to s...

Applying online WEPP to assess forest watershed hydrology

USDA-ARS?s Scientific Manuscript database

The U.S. Army Corps of Engineers (USACE) and the Great Lakes Commission are developing technologies and predictive tools to aid in watershed management with an ultimate goal of improving and preserving the water quality in the Great Lakes Basin. A new version of the online Water Erosion Prediction P...

Can Sanitary Inspection Surveys Predict Risk of Microbiological Contamination of Groundwater Sources? Evidence from Shallow Tubewells in Rural Bangladesh

PubMed Central

Ercumen, Ayse; Naser, Abu Mohd; Arnold, Benjamin F.; Unicomb, Leanne; Colford, John M.; Luby, Stephen P.

2017-01-01

Accurately assessing the microbiological safety of water sources is essential to reduce waterborne fecal exposures and track progress toward global targets of safe water access. Sanitary inspections are a recommended tool to assess water safety. We collected 1,684 water samples from 902 shallow tubewells in rural Bangladesh and conducted sanitary surveys to assess whether sanitary risk scores could predict water quality, as measured by Escherichia coli. We detected E. coli in 41% of tubewells, mostly at low concentrations. Based on sanitary scores, 31% of wells were low risk, 45% medium risk, and 25% high or very high risk. Older wells had higher risk scores. Escherichia coli levels were higher in wells where the platform was cracked or broken (Δlog10 = 0.09, 0.00–0.18) or undercut by erosion (Δlog10 = 0.13, 0.01–0.24). However, the positive predictive value of these risk factors for E. coli presence was low (< 50%). Latrine presence within 10 m was not associated with water quality during the wet season but was associated with less frequent E. coli detection during the dry season (relative risk = 0.72, 0.59–0.88). Sanitary scores were not associated with E. coli presence or concentration. These findings indicate that observed characteristics of a tubewell, as measured by sanitary inspections in their current form, do not sufficiently characterize microbiological water quality, as measured by E. coli. Assessments of local groundwater and geological conditions and improved water quality indicators may reveal more clear relationships. Our findings also suggest that the dominant contamination route for shallow groundwater sources is short-circuiting at the wellhead rather than subsurface transport. PMID:28115666

Can Sanitary Inspection Surveys Predict Risk of Microbiological Contamination of Groundwater Sources? Evidence from Shallow Tubewells in Rural Bangladesh.

PubMed

Ercumen, Ayse; Naser, Abu Mohd; Arnold, Benjamin F; Unicomb, Leanne; Colford, John M; Luby, Stephen P

2017-03-01

AbstractAccurately assessing the microbiological safety of water sources is essential to reduce waterborne fecal exposures and track progress toward global targets of safe water access. Sanitary inspections are a recommended tool to assess water safety. We collected 1,684 water samples from 902 shallow tubewells in rural Bangladesh and conducted sanitary surveys to assess whether sanitary risk scores could predict water quality, as measured by Escherichia coli . We detected E. coli in 41% of tubewells, mostly at low concentrations. Based on sanitary scores, 31% of wells were low risk, 45% medium risk, and 25% high or very high risk. Older wells had higher risk scores. Escherichia coli levels were higher in wells where the platform was cracked or broken (Δlog 10 = 0.09, 0.00-0.18) or undercut by erosion (Δlog 10 = 0.13, 0.01-0.24). However, the positive predictive value of these risk factors for E. coli presence was low (< 50%). Latrine presence within 10 m was not associated with water quality during the wet season but was associated with less frequent E. coli detection during the dry season (relative risk = 0.72, 0.59-0.88). Sanitary scores were not associated with E. coli presence or concentration. These findings indicate that observed characteristics of a tubewell, as measured by sanitary inspections in their current form, do not sufficiently characterize microbiological water quality, as measured by E. coli . Assessments of local groundwater and geological conditions and improved water quality indicators may reveal more clear relationships. Our findings also suggest that the dominant contamination route for shallow groundwater sources is short-circuiting at the wellhead rather than subsurface transport.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

PubMed

Heddam, Salim; Kisi, Ozgur

2017-07-01

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Modeling Benthic Sediment Processes to Predict Water ...

EPA Pesticide Factsheets

The benthic sediment acts as a huge reservoir of particulate and dissolved material (within interstitial water) which can contribute to loading of contaminants and nutrients to the water column. A benthic sediment model is presented in this report to predict spatial and temporal benthic fluxes of nutrients and chemicals in Narragansett Bay. A benthic sediment model is presented in this report to identify benthic flux into the water column in Narragansett Bay. Benthic flux is essential to properly model water quality and ecology in estuarine and coastal systems.

Early warning of changing drinking water quality by trend analysis.

PubMed

Tomperi, Jani; Juuso, Esko; Leiviskä, Kauko

2016-06-01

Monitoring and control of water treatment plants play an essential role in ensuring high quality drinking water and avoiding health-related problems or economic losses. The most common quality variables, which can be used also for assessing the efficiency of the water treatment process, are turbidity and residual levels of coagulation and disinfection chemicals. In the present study, the trend indices are developed from scaled measurements to detect warning signs of changes in the quality variables of drinking water and some operating condition variables that strongly affect water quality. The scaling is based on monotonically increasing nonlinear functions, which are generated with generalized norms and moments. Triangular episodes are classified with the trend index and its derivative. Deviation indices are used to assess the severity of situations. The study shows the potential of the described trend analysis as a predictive monitoring tool, as it provides an advantage over the traditional manual inspection of variables by detecting changes in water quality and giving early warnings.

A Review of Surface Water Quality Models

PubMed Central

Li, Shibei; Jia, Peng; Qi, Changjun; Ding, Feng

2013-01-01

Surface water quality models can be useful tools to simulate and predict the levels, distributions, and risks of chemical pollutants in a given water body. The modeling results from these models under different pollution scenarios are very important components of environmental impact assessment and can provide a basis and technique support for environmental management agencies to make right decisions. Whether the model results are right or not can impact the reasonability and scientificity of the authorized construct projects and the availability of pollution control measures. We reviewed the development of surface water quality models at three stages and analyzed the suitability, precisions, and methods among different models. Standardization of water quality models can help environmental management agencies guarantee the consistency in application of water quality models for regulatory purposes. We concluded the status of standardization of these models in developed countries and put forward available measures for the standardization of these surface water quality models, especially in developing countries. PMID:23853533

Relating road salt to exceedances of the water quality standard for chloride in New Hampshire streams.

PubMed

Trowbridge, Philip R; Kahl, J Steve; Sassan, Dari A; Heath, Douglas L; Walsh, Edward M

2010-07-01

Six watersheds in New Hampshire were studied to determine the effects of road salt on stream water quality. Specific conductance in streams was monitored every 15 min for one year using dataloggers. Chloride concentrations were calculated from specific conductance using empirical relationships. Stream chloride concentrations were directly correlated with development in the watersheds and were inversely related to streamflow. Exceedances of the EPA water quality standard for chloride were detected in the four watersheds with the most development. The number of exceedances during a year was linearly related to the annual average concentration of chloride. Exceedances of the water quality standard were not predicted for streams with annual average concentrations less than 102 mg L(-1). Chloride was imported into three of the watersheds at rates ranging from 45 to 98 Mg Cl km(-2) yr(-1). Ninety-one percent of the chloride imported was road salt for deicing roadways and parking lots. A simple, mass balance equation was shown to predict annual average chloride concentrations from streamflow and chloride import rates to the watershed. This equation, combined with the apparent threshold for exceedances of the water quality standard, can be used for screening-level TMDLs for road salt in impaired watersheds.

Simulating the Response of Urban Water Quality to Climate and Land Use Change in Partially Urbanized Basins

NASA Astrophysics Data System (ADS)

Sun, N.; Yearsley, J. R.; Nijssen, B.; Lettenmaier, D. P.

2014-12-01

Urban stream quality is particularly susceptible to extreme precipitation events and land use change. Although the projected effects of extreme events and land use change on hydrology have been resonably well studied, the impacts on urban water quality have not been widely examined due in part to the scale mismatch between global climate models and the spatial scales required to represent urban hydrology and water quality signals. Here we describe a grid-based modeling system that integrates the Distributed Hydrology Soil Vegetation Model (DHSVM) and urban water quality module adpated from EPA's Storm Water Management Model (SWMM) and Soil and water assessment tool (SWAT). Using the model system, we evaluate, for four partially urbanized catchments within the Puget Sound basin, urban water quality under current climate conditions, and projected potential changes in urban water quality associated with future changes in climate and land use. We examine in particular total suspended solids, toal nitrogen, total phosphorous, and coliform bacteria, with catchment representations at the 150-meter spatial resolution and the sub-daily timestep. We report long-term streamflow and water quality predictions in response to extreme precipitation events of varying magnitudes in the four partially urbanized catchments. Our simulations show that urban water quality is highly sensitive to both climatic and land use change.

Suitability of different Escherichia coli enumeration techniques to assess the microbial quality of different irrigation water sources.

PubMed

Truchado, P; Lopez-Galvez, F; Gil, M I; Pedrero-Salcedo, F; Alarcón, J J; Allende, A

2016-09-01

The use of fecal indicators such as Escherichia coli has been proposed as a potential tool to characterize microbial contamination of irrigation water. Recently, not only the type of microbial indicator but also the methodologies used for enumeration have been called into question. The goal of this study was to assess the microbial quality of different water sources for irrigation of zucchini plants by using E. coli as an indicator of fecal contamination and the occurrence of foodborne pathogens. Three water sources were evaluated including reclaimed secondary treated water (RW-2), reclaimed tertiary UV-C treated water (RW-3) and surface water (SW). The suitability of two E. coli quantification techniques (plate count and qPCR) was examined for irrigation water and fresh produce. E. coli levels using qPCR assay were significantly higher than that obtained by plate count in all samples of irrigation water and fresh produce. The microbial quality of water samples from RW-2 was well predicted by qPCR, as the presence of foodborne pathogens were positively correlated with high E. coli levels. However, differences in the water characteristics influenced the suitability of qPCR as a tool to predict potential contamination in irrigation water. No significant differences were obtained between the number of cells of E. coli from RW-2 and RW-3, probably due to the fact that qPCR assay cannot distinguish between viable and dead cells. These results indicated that the selection of the most suitable technique for enumeration of indicator microorganisms able to predict potential presence of fecal contamination might be influenced by the water characteristics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Surface water-quality assessment of the lower Kansas River basin, Kansas and Nebraska: analysis of available water-quality data through 1986

USGS Publications Warehouse

Jordan, P.R.; Stamer, J.K.

1991-01-01

Beginning in 1986, the U.S. Congress appropriated funds for the U.S. Geological Survey to test and refine concepts for a National Water-Quality Assessment (NAWQA) Program. The long-term goals of the full-scale program are to: (1) provide a nationally consistent description of current water-quality conditions for a large part of the Nation's surface- and ground-water resources; (2) define long-term trends (or lack of trends) in water quality; and (3) identify, describe, and explain, insofar as possible, the major factors that affect current conditions and trends in water quality. This information, obtained on a continuing basis, will be made available to water managers, policy makers, and the public to provide an improved scientific basis for evaluating the effectiveness of water-quality-management programs and for predicting the likely effects of contemplated changes in land-and water-management practices. At present (1990), the assessment program is in a pilot phase in seven areas that represent diverse hydrologic environments and water-quality conditions.This report completes one of the first activities undertaken as part of the lower Kansas River basin pilot study, which was to compile, screen, and interpret available water-quality data for the study unit through 1986. The report includes information on the sources and types of water-quality data available, the utility of available water-quality data for assessment purposes, and a description of current water-quality conditions and trends and their relation to natural and human factors.

Nowcasting recreational water quality

USGS Publications Warehouse

Boehm, Alexandria B.; Whitman, Richard L.; Nevers, Meredith; Hou, Deyi; Weisberg, Stephen B.

2007-01-01

Advances in molecular techniques may soon provide new opportunities to provide more timely information on whether recreational beaches are free from fecal contamination. However, an alternative approach is the use of predictive models. This chapter presents a summary of these developing efforts. First, we describe documented physical, chemical, and biological factors that have been demonstrated by researchers to affect bacterial concentrations at beaches and thus represent logical parameters for inclusion in a model. Then, we illustrate how various types of models can be applied to predict water quality at freshwater and marine beaches.

Predicting Recreational Water Quality Using Turbidity in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2004-7

USGS Publications Warehouse

Brady, Amie M.G.; Bushon, Rebecca N.; Plona, Meg B.

2009-01-01

The Cuyahoga River within Cuyahoga Valley National Park (CVNP) in Ohio is often impaired for recreational use because of elevated concentrations of bacteria, which are indicators of fecal contamination. During the recreational seasons (May through August) of 2004 through 2007, samples were collected at two river sites, one upstream of and one centrally-located within CVNP. Bacterial concentrations and turbidity were determined, and streamflow at time of sampling and rainfall amounts over the previous 24 hours prior to sampling were ascertained. Statistical models to predict Escherichia coli (E. coli) concentrations were developed for each site (with data from 2004 through 2006) and tested during an independent year (2007). At Jaite, a sampling site near the center of CVNP, the predictive model performed better than the traditional method of determining the current day's water quality using the previous day's E. coli concentration. During 2007, the Jaite model, based on turbidity, produced more correct responses (81 percent) and fewer false negatives (3.2 percent) than the traditional method (68 and 26 percent, respectively). At Old Portage, a sampling site just upstream from CVNP, a predictive model with turbidity and rainfall as explanatory variables did not perform as well as the traditional method. The Jaite model was used to estimate water quality at three other sites in the park; although it did not perform as well as the traditional method, it performed well - yielding between 68 and 91 percent correct responses. Further research would be necessary to determine whether using the Jaite model to predict recreational water quality elsewhere on the river would provide accurate results.

Effect of water quality sampling time and frequency on storm load predictions of a prominent regression-based model

USDA-ARS?s Scientific Manuscript database

High frequency in situ measurements of nitrate can greatly reduce the uncertainty in nitrate flux estimates. Water quality databases maintained by various federal and state agencies often consist of pollutant concentration data obtained from periodic grab samples collected from gauged reaches of a s...

Water quality modeling in the systems impact assessment model for the Klamath River basin - Keno, Oregon to Seiad Valley, California

USGS Publications Warehouse

Hanna, R. Blair; Campbell, Sharon G.

2000-01-01

This report describes the water quality model developed for the Klamath River System Impact Assessment Model (SIAM). The Klamath River SIAM is a decision support system developed by the authors and other US Geological Survey (USGS), Midcontinent Ecological Science Center staff to study the effects of basin-wide water management decisions on anadromous fish in the Klamath River. The Army Corps of Engineersa?? HEC5Q water quality modeling software was used to simulate water temperature, dissolved oxygen and conductivity in 100 miles of the Klamath River Basin in Oregon and California. The water quality model simulated three reservoirs and the mainstem Klamath River influenced by the Shasta and Scott River tributaries. Model development, calibration and two validation exercises are described as well as the integration of the water quality model into the SIAM decision support system software. Within SIAM, data are exchanged between the water quantity model (MODSIM), the water quality model (HEC5Q), the salmon population model (SALMOD) and methods for evaluating ecosystem health. The overall predictive ability of the water quality model is described in the context of calibration and validation error statistics. Applications of SIAM and the water quality model are described.

Application of Hyperspectral Remote Sensing Techniques to Evaluate Water Quality in Turbid Coastal Waters of South Carolina.

NASA Astrophysics Data System (ADS)

Ali, K. A.; Ryan, K.

2014-12-01

Coastal and inland waters represent a diverse set of resources that support natural habitat and provide valuable ecosystem services to the human population. Conventional techniques to monitor water quality using in situ sensors and laboratory analysis of water samples can be very time- and cost-intensive. Alternatively, remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic and unique water quality parameters. Existing remote sensing ocean color products, such as the water quality proxy chlorophyll-a, are based on ocean derived bio-optical models that are primarily calibrated in Case 1 type waters. These traditional models fail to work when applied in turbid (Case 2 type), coastal waters due to spectral interference from other associated color producing agents such as colored dissolved organic matter and suspended sediments. In this work, we introduce a novel technique for the predictive modeling of chlorophyll-a using a multivariate-based approach applied to in situ hyperspectral radiometric data collected from the coastal waters of Long Bay, South Carolina. This method uses a partial least-squares regression model to identify prominent wavelengths that are more sensitive to chlorophyll-a relative to other associated color-producing agents. The new model was able to explain 80% of the observed chlorophyll-a variability in Long Bay with RMSE = 2.03 μg/L. This approach capitalizes on the spectral advantage gained from current and future hyperspectral sensors, thus providing a more robust predicting model. This enhanced mode of water quality monitoring in marine environments will provide insight to point-sources and problem areas that may contribute to a decline in water quality. The utility of this tool is in its versatility to a diverse set of coastal waters and its use by coastal and fisheries managers with regard to recreation, regulation, economic and public health purposes.

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

NASA Astrophysics Data System (ADS)

Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global search and Bayesian inference schemes.

An Integrated Modeling Framework Forecasting Ecosystem Exposure-- A Systems Approach to the Cumulative Impacts of Multiple Stressors

NASA Astrophysics Data System (ADS)

Johnston, J. M.

2013-12-01

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework 'iemWatersheds' has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water Assessment Tool (SWAT) predicts surface water and sediment runoff and associated contaminants; the Watershed Mercury Model (WMM) predicts mercury runoff and loading to streams; the Water quality Analysis and Simulation Program (WASP) predicts water quality within the stream channel; the Habitat Suitability Index (HSI) model scores physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator (BASS) predicts fish growth, population dynamics and bioaccumulation of toxic substances. The capability of the Framework to address cumulative impacts will be demonstrated for freshwater ecosystem services and mountaintop mining.

Predicting Plausible Impacts of Sets of Climate and Land Use Change Scenarios on Water Resources

EPA Science Inventory

Global changes in climate and land use can alTect the quantity and quality of water resources. Hence, we need a methodology to predict these ramifications. Using the Little Miami River (LMR) watershed as a case study, this paper describes a spatial analytical approach integrating...

Modeling erosion from forest roads with WEPP

Treesearch

J. McFero Grace

2007-01-01

Forest roads can be major sources of soil erosion from forest watersheds. Sediments from forest roads are a concern due to their potential delivery to stream systems resulting in degradation of water quality. The Water Erosion Prediction Project (WEPP) was used to predict erosion from forest road components under different management practices. WEPP estimates are...

Relations between hydrology, water quality, and taste-and-odor causing organisms and compounds in Lake Houston, Texas, April 2006-September 2008

USGS Publications Warehouse

Beussink, Amy M.; Graham, Jennifer L.

2011-01-01

Lake Houston is a surface-water-supply reservoir and an important recreational resource for the city of Houston, Texas. Growing concerns over water quality in Lake Houston prompted a detailed assessment of water quality in the reservoir. The assessment focused on water-quality constituents that affect the aesthetic quality of drinking water. The hydrologic and water-quality conditions influencing the occurrence of taste-and-odor causing organisms and compounds in Lake Houston were assessed using discrete and continuously monitored water-quality data collected during April 2006– September 2008. The hydrology of Lake Houston is characterized by rapidly changing conditions. During inflow events, water residence time can change by orders of magnitude within a matter of hours. Likewise, the reservoir can stratify and destratify over a period of several hours, even during non-summer and at relatively short water residence times, given extended periods with warm temperatures and little wind. The rapidly changing hydrology likely influences all other aspects of water quality in Lake Houston, including the occurrence of taste-and-odor causing organisms and compounds. Water quality in Lake Houston varied with respect to season and water residence time but typically was indicative of turbid, eutrophic to hypereutrophic conditions. In general, turbidity and nutrient concentrations were largest during non-summer (October–May) and when water residence times were relatively short (less than 100 days), which reflects the influence of inflow events on water-quality conditions. Large inflow events can cause substantial changes in water-quality conditions over relatively short periods of time (hours). The taste-and-odor causing organisms cyanobacteria and actinomycetes bacteria were always present in Lake Houston. Cyanobacterial biovolume was largest during summer (June– September) and when water residence time was greater than 100 days. Annual maxima in cyanobacterial biovolume occurred during July-September of each year, when temperatures were larger than 27 degrees Celsius and water residence times were longer than 400 days. In contrast, actinomycetes bacteria were most abundant during non-summer and when water residence times were less than 100 days, reflecting the close association between these organisms and transport of suspended sediments. Geosmin and 2-methylisoborneol are the taste-and-odor causing compounds most commonly produced by cyanobacteria and actinomycetes bacteria. Geosmin was detected more frequently (62 percent of samples) than 2-methylisoborneol (29 percent of samples) in Lake Houston. Geosmin exceeded the human detection threshold (10 nanograms per liter) only once during the study period and 2-methylisoborneol exceeded the human detection threshold twice. Manganese is a naturally occurring trace element that can occasionally cause taste-andodor problems in drinking water. Manganese concentrations exceeded the human detection threshold (about 50 micrograms per liter) in about 50 percent of samples collected near the surface and 84 percent of samples collected near the bottom. The cyanotoxin microcystin was detected relatively infrequently (16 percent of samples) and at small concentrations (less than or equal to 0.2 micrograms per liter). The abundance of the taste-and-odor causing organisms cyanobacteria and actinomycetes bacteria in Lake Houston was coupled with inflow events and subsequent changes in water-quality conditions. Cyanobacterial biovolume (biomass) in Lake Houston was largest during warm periods with little inflow and relatively small turbidity values. In contrast, actinomycetes bacteria were most abundant following inflow events when turbidity was relatively large. Severe taste-and-odor problems were not observed during the study period, precluding quantification of the hydrologic and water-quality conditions associated with large concentrations of taste-and-odor causing compounds and development of predictive models. Reservoir inflow (water residence time) and turbidity, variables related to the abundance of potential taste-andodor causing organisms, are currently (2011) continuously measured in Lake Houston, and predictive models could be developed in the future when the hydrologic and water-quality conditions associated with taste-and-odor problems have been better quantified. Seasonal and water residence time influences on water-quality conditions altered relations between hydrologic and water-quality conditions and taste-and-odor causing organisms and compounds. Future data collection and development of predictive models need to account for the variability associated with season and water residence time. 

Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA.

PubMed

Heddam, Salim

2016-09-01

This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.

Social Perception of Public Water Supply Network and Groundwater Quality in an Urban Setting Facing Saltwater Intrusion and Water Shortages

NASA Astrophysics Data System (ADS)

Alameddine, Ibrahim; Jawhari, Gheeda; El-Fadel, Mutasem

2017-04-01

Perceptions developed by consumers regarding the quality of water reaching their household can affect the ultimate use of the water. This study identified key factors influencing consumers' perception of water quality in a highly urbanized coastal city, experiencing chronic water shortages, overexploitation of groundwater, and accelerated saltwater intrusion. Household surveys were administered to residents to capture views and perceptions of consumed water. Concomitantly, groundwater and tap water samples were collected and analyzed at each residence for comparison with perceptions. People's rating of groundwater quality was found to correlate to the measured water quality both in the dry and wet seasons. In contrast, perceptions regarding the water quality of the public water supply network did not show any correlation with the measured tap water quality indicators. Logistic regression models developed to predict perception based on salient variables indicated that age, apartment ownership, and levels of total dissolved solids play a significant role in shaping perceptions regarding groundwater quality. Perceptions concerning the water quality of the public water supply network appeared to be independent of the measured total dissolved solids levels at the tap but correlated to those measured in the wells. The study highlights misconceptions that can arise as a result of uncontrolled cross-connections of groundwater to the public supply network water and the development of misaligned perceptions based on prior consumption patterns, water shortages, and a rapidly salinizing groundwater aquifer.

Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs

PubMed Central

Gu, Qing; Deng, Jinsong; Wang, Ke; Lin, Yi; Li, Jun; Gan, Muye; Ma, Ligang; Hong, Yang

2014-01-01

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources. PMID:24919129

Social Perception of Public Water Supply Network and Groundwater Quality in an Urban Setting Facing Saltwater Intrusion and Water Shortages.

PubMed

Alameddine, Ibrahim; Jawhari, Gheeda; El-Fadel, Mutasem

2017-04-01

Perceptions developed by consumers regarding the quality of water reaching their household can affect the ultimate use of the water. This study identified key factors influencing consumers' perception of water quality in a highly urbanized coastal city, experiencing chronic water shortages, overexploitation of groundwater, and accelerated saltwater intrusion. Household surveys were administered to residents to capture views and perceptions of consumed water. Concomitantly, groundwater and tap water samples were collected and analyzed at each residence for comparison with perceptions. People's rating of groundwater quality was found to correlate to the measured water quality both in the dry and wet seasons. In contrast, perceptions regarding the water quality of the public water supply network did not show any correlation with the measured tap water quality indicators. Logistic regression models developed to predict perception based on salient variables indicated that age, apartment ownership, and levels of total dissolved solids play a significant role in shaping perceptions regarding groundwater quality. Perceptions concerning the water quality of the public water supply network appeared to be independent of the measured total dissolved solids levels at the tap but correlated to those measured in the wells. The study highlights misconceptions that can arise as a result of uncontrolled cross-connections of groundwater to the public supply network water and the development of misaligned perceptions based on prior consumption patterns, water shortages, and a rapidly salinizing groundwater aquifer.

Identification and assessment of potential water quality impact factors for drinking-water reservoirs.

PubMed

Gu, Qing; Deng, Jinsong; Wang, Ke; Lin, Yi; Li, Jun; Gan, Muye; Ma, Ligang; Hong, Yang

2014-06-10

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources.

Conjunctively optimizing flash flood control and water quality in urban water reservoirs by model predictive control and dynamic emulation

NASA Astrophysics Data System (ADS)

Galelli, Stefano; Goedbloed, Albert; Schmitter, Petra; Castelletti, Andrea

2014-05-01

Urban water reservoirs are a viable adaptation option to account for increasing drinking water demand of urbanized areas as they allow storage and re-use of water that is normally lost. In addition, the direct availability of freshwater reduces pumping costs and diversifies the portfolios of drinking water supply. Yet, these benefits have an associated twofold cost. Firstly, the presence of large, impervious areas increases the hydraulic efficiency of urban catchments, with short time of concentration, increased runoff rates, losses of infiltration and baseflow, and higher risk of flash floods. Secondly, the high concentration of nutrients and sediments characterizing urban discharges is likely to cause water quality problems. In this study we propose a new control scheme combining Model Predictive Control (MPC), hydro-meteorological forecasts and dynamic model emulation to design real-time operating policies that conjunctively optimize water quantity and quality targets. The main advantage of this scheme stands in its capability of exploiting real-time hydro-meteorological forecasts, which are crucial in such fast-varying systems. In addition, the reduced computational requests of the MPC scheme allows coupling it with dynamic emulators of water quality processes. The approach is demonstrated on Marina Reservoir, a multi-purpose reservoir located in the heart of Singapore and characterized by a large, highly urbanized catchment with a short (i.e. approximately one hour) time of concentration. Results show that the MPC scheme, coupled with a water quality emulator, provides a good compromise between different operating objectives, namely flood risk reduction, drinking water supply and salinity control. Finally, the scheme is used to assess the effect of source control measures (e.g. green roofs) aimed at restoring the natural hydrological regime of Marina Reservoir catchment.

Modelling sewer sediment deposition, erosion, and transport processes to predict acute influent and reduce combined sewer overflows and CO(2) emissions.

PubMed

Mouri, Goro; Oki, Taikan

2010-01-01

Understanding of solids deposition, erosion, and transport processes in sewer systems has improved considerably in the past decade. This has provided guidance for controlling sewer solids and associated acute pollutants to protect the environment and improve the operation of wastewater systems. Although measures to decrease combined sewer overflow (CSO) events have reduced the amount of discharged pollution, overflows continue to occur during rainy weather in combined sewer systems. The solution lies in the amount of water allotted to various processes in an effluent treatment system, in impact evaluation of water quality and prediction technology, and in stressing the importance of developing a control technology. Extremely contaminated inflow has been a serious research subject, especially in connection with the influence of rainy weather on nitrogen and organic matter removal efficiency in wastewater treatment plants (WWTP). An intensive investigation of an extremely polluted inflow load to WWTP during rainy weather was conducted in the city of Matsuyama, the region used for the present research on total suspended solid (TSS) concentration. Since the inflow during rainy weather can be as much as 400 times that in dry weather, almost all sewers are unsettled and overflowing when a rain event is more than moderate. Another concern is the energy consumed by wastewater treatment; this problem has become important from the viewpoint of reducing CO(2) emissions and overall costs. Therefore, while establishing a prediction technology for the inflow water quality characteristics of a sewage disposal plant is an important priority, the development of a management/control method for an effluent treatment system that minimises energy consumption and CO(2) emissions due to water disposal is also a pressing research topic with regards to the quality of treated water. The procedure to improve water quality must make use of not only water quality and biotic criteria, but also modelling systems to enable the user to link the effect of changes in urban sewage systems with specific quality, energy consumption, CO(2) emission, and ecological improvements of the receiving water.

A Learning Progression for Water in Socio-Ecological Systems

ERIC Educational Resources Information Center

Gunckel, Kristin L.; Covitt, Beth A.; Salinas, Ivan; Anderson, Charles W.

2012-01-01

Providing model-based accounts (explanations and predictions) of water and substances in water moving through environmental systems is an important practice for environmental science literacy and necessary for citizens confronting global and local water quantity and quality issues. In this article we present a learning progression for water in…

A summary of the U.S. Geological Survey National Water-Quality Assessment program

USGS Publications Warehouse

Hirsch, R.M.; Alley, W.M.; Wilber, W.G.

1988-01-01

Beginning in 1986, the Congress appropriated funds for the U.S. Geological Survey to test and refine concepts for a National Water Quality Assessment Program. At present, the program is in a pilot phase with field studies occurring in seven areas around the Nation. In 1990, a committee of the National Academy of Sciences will complete an evaluation of the design and potential utility of the program. A decision about moving to full-scale implementation will be made upon completion of this evaluation. The program is intended to address a wide range of national water quality issues that include chemical contamination, acidification, eutrophication, salinity, sedimentation, and sanitary quality. The goals of the program are to: (1) provide nationally consistent descriptions of current water quality conditions for a large part of the Nation 's water resources; (2) define long-term trends (or lack of trends) in water quality; and (3) identify and describe the relations of both current conditions and trends in water quality to natural and human factors. This information will be provided to water managers, policy makers, and the public to provide an improved scientific basis for evaluating the effectiveness of water quality management programs and for predicting the likely effects of contemplated changes in land- and water-management practices. (USGS)

Cross-Sectional And Longitudinal Uncertainty Propagation In Drinking Water Risk Assessment

NASA Astrophysics Data System (ADS)

Tesfamichael, A. A.; Jagath, K. J.

2004-12-01

Pesticide residues in drinking water can vary significantly from day to day. However, drinking water quality monitoring performed under the Safe Drinking Water Act (SDWA) at most community water systems (CWSs) is typically limited to four data points per year over a few years. Due to limited sampling, likely maximum residues may be underestimated in risk assessment. In this work, a statistical methodology is proposed to study the cross-sectional and longitudinal uncertainties in observed samples and their propagated effect in risk estimates. The methodology will be demonstrated using data from 16 CWSs across the US that have three independent databases of atrazine residue to estimate the uncertainty of risk in infants and children. The results showed that in 85% of the CWSs, chronic risks predicted with the proposed approach may be two- to four-folds higher than that predicted with the current approach, while intermediate risks may be two- to three-folds higher in 50% of the CWSs. In 12% of the CWSs, however, the proposed methodology showed a lower intermediate risk. A closed-form solution of propagated uncertainty will be developed to calculate the number of years (seasons) of water quality data and sampling frequency needed to reduce the uncertainty in risk estimates. In general, this methodology provided good insight into the importance of addressing uncertainty of observed water quality data and the need to predict likely maximum residues in risk assessment by considering propagation of uncertainties.

Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014

USGS Publications Warehouse

Smith, Erik A.; Kiesling, Richard L.; Ziegeweid, Jeffrey R.

2017-07-20

Fish habitat can degrade in many lakes due to summer blue-green algal blooms. Predictive models are needed to better manage and mitigate loss of fish habitat due to these changes. The U.S. Geological Survey (USGS), in cooperation with the Minnesota Department of Natural Resources, developed predictive water-quality models for two agricultural land-use dominated lakes in Minnesota—Madison Lake and Pearl Lake, which are part of Minnesota’s sentinel lakes monitoring program—to assess algal community dynamics, water quality, and fish habitat suitability of these two lakes under recent (2014) meteorological conditions. The interaction of basin processes to these two lakes, through the delivery of nutrient loads, were simulated using CE-QUAL-W2, a carbon-based, laterally averaged, two-dimensional water-quality model that predicts distribution of temperature and oxygen from interactions between nutrient cycling, primary production, and trophic dynamics.The CE-QUAL-W2 models successfully predicted water temperature and dissolved oxygen on the basis of the two metrics of mean absolute error and root mean square error. For Madison Lake, the mean absolute error and root mean square error were 0.53 and 0.68 degree Celsius, respectively, for the vertical temperature profile comparisons; for Pearl Lake, the mean absolute error and root mean square error were 0.71 and 0.95 degree Celsius, respectively, for the vertical temperature profile comparisons. Temperature and dissolved oxygen were key metrics for calibration targets. These calibrated lake models also simulated algal community dynamics and water quality. The model simulations presented potential explanations for persistently large total phosphorus concentrations in Madison Lake, key differences in nutrient concentrations between these lakes, and summer blue-green algal bloom persistence.Fish habitat suitability simulations for cool-water and warm-water fish indicated that, in general, both lakes contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer, without any periods of lethal oxythermal habitat. For Madison and Pearl Lakes, examples of important cool-water fish, particularly game fish, include northern pike (Esox lucius), walleye (Sander vitreus), and black crappie (Pomoxis nigromaculatus); examples of important warm-water fish include bluegill (Lepomis macrochirus), largemouth bass (Micropterus salmoides), and smallmouth bass (Micropterus dolomieu). Sensitivity analyses were completed to understand lake response effects through the use of controlled departures on certain calibrated model parameters and input nutrient loads. These sensitivity analyses also operated as land-use change scenarios because alterations in agricultural practices, for example, could potentially increase or decrease nutrient loads.

Quantitative water quality with ERTS-1. [Kansas water resources

NASA Technical Reports Server (NTRS)

Yarger, H. L.; Mccauley, J. R.; James, G. W.; Magnuson, L. M.; Marzolf, G. R.

1974-01-01

Analyses of ERTS-1 MSS computer compatible tapes of reservoir scenes in Kansas along with ground truth show that MSS bands and band ratios can be used for reliable prediction of suspended loads up to at least 900 ppm. The major reservoirs in Kansas, as well as in other Great Plains states, are playing increasingly important roles in flood control, recreation, agriculture, and urban water supply. Satellite imagery is proving useful for acquiring timely low cost water quality data required for optimum management of these fresh water resources.

Post-fire Water Quality in the Western United States: Understanding and Predicting Short and Long-term Response

NASA Astrophysics Data System (ADS)

Hogue, T. S.; Rust, A.

2016-12-01

Fire frequency is increasing across mid-elevation forests, especially in the Northern Rockies, Sierra Nevada, southern Cascades, as well as the coastal ranges in California and southern Oregon. Numerous studies have noted increased discharge, floods and debris flows after wildfire. More recent work also shows increased water yield during dry seasons for up to ten years post-fire. However, few studies have evaluated long-term water quality response in fire-impacted watersheds. The current presentation will overview recent development of an extensive database on post-fire water quality response across the western U.S. A range of water quality parameters were gathered from 271 burned watersheds through local, state and federal agencies. Short and long-term response was evaluated for watersheds with at least 5 years of pre-fire data. Over 30 watersheds showed significant increases in NO3-, NO2-, NH3, and total nitrogen loading in the initial five years after fire and remained elevated ten years after fire. The burn severity influenced the degree of nitrogen response, where more severely burned watersheds showed higher nitrogen loading than less severely burned watersheds. Dissolved and total phosphorous showed significant increases in 32 watersheds for the first five years after fire. Dissolved ions such as calcium, magnesium, and chloride were also exported from over 32 watersheds, primarily during the first five years after fire, with the majority of impacted watersheds returning to pre-fire water quality conditions after ten years. Ongoing work includes evaluating key determinants that drive short and long-term response and developing predictive models for post-fire water quality. Watersheds impacted by wildfire are known to pose significant risks for downstream communities. Understanding short and long-term water quality change that can impact regional water supplies is critical for establishing potential treatment priorities and alternative source planning.

Finite Difference Formulation for Prediction of Water Pollution

NASA Astrophysics Data System (ADS)

Johari, Hanani; Rusli, Nursalasawati; Yahya, Zainab

2018-03-01

Water is an important component of the earth. Human being and living organisms are demand for the quality of water. Human activity is one of the causes of the water pollution. The pollution happened give bad effect to the physical and characteristic of water contents. It is not practical to monitor all aspects of water flow and transport distribution. So, in order to help people to access to the polluted area, a prediction of water pollution concentration must be modelled. This study proposed a one-dimensional advection diffusion equation for predicting the water pollution concentration transport. The numerical modelling will be produced in order to predict the transportation of water pollution concentration. In order to approximate the advection diffusion equation, the implicit Crank Nicolson is used. For the purpose of the simulation, the boundary condition and initial condition, the spatial steps and time steps as well as the approximations of the advection diffusion equation have been encoded. The results of one dimensional advection diffusion equation have successfully been used to predict the transportation of water pollution concentration by manipulating the velocity and diffusion parameters.

Statistical analysis of surface-water-quality data in and near the coal-mining region of southwestern Indiana, 1957-80

USGS Publications Warehouse

Martin, Jeffrey D.; Crawford, Charles G.

1987-01-01

The Surface Mining Control and Reclamation Act of 1977 requires that applications for coal-mining permits contain information about the water quality of streams at and near a proposed mine. To meet this need for information, streamflow, specific conductance, pH, and concentrations of total alkalinity, sulfate, dissolved solids, suspended solids, total iron, and total manganese at 37 stations were analyzed to determine the spatial and seasonal variations in water quality and to develop equations for predicting water quality. The season of lowest median streamflow was related to the size of the drainage area. Median streamflow was least during fall at 15 of 16 stations having drainage areas greater than 1,000 square miles but was least during summer at 17 of 21 stations having drainage areas less than 1,000 square miles. In general, the season of lowest median specific conductance occurred during the season of highest streamflow except at stations on the Wabash River. Median specific conductance was least during summer at 9 of 9 stations on the Wabash River, but was least during winter or spring (the seasons of highest streamflow) at 27 of the remaining 28 stations. Linear, inverse, semilog, log-log, and hyperbolic regression models were used to investigate the functional relations between water-quality characteristics and streamflow. Of 186 relations investigated, 143 were statistically significant. Specific conductance and concentrations of total alkalinity and sulfate were negatively related to streamflow at all stations except for a positive relation between total alkalinity concentration and streamflow at Patoka River near Princeton. Concentrations of total alkalinity and sulfate were positively related to specific conductance at all stations except for a negative relation at Patoka River near Princeton and for a positive and negative relation at Patoka River at Jasper. Most of these relations are good, have small confidence intervals, and will give reliable predictions of the water-quality variables listed above. The poorest relations are typically at stations in the Patoka River watershed. Suspended-solids concentration was positively related to streamflow at all but two stations on the Patoka River. These relations are poor, have large confidence intervals, and will give less reliable predictions of suspended-solids concentration. Predictive equations for the regional relations between dissolved-solids concentration and specific conductance and between sulfate concentration and specific conductance, and the seasonal patterns of water quality, are probably valid for the coal-mining regions of Illinois and western Kentucky.

Seasonal Phosphorus Sources and Loads to Upper Klamath Lake, Oregon, as Determined by a Dynamic SPARROW Model

NASA Astrophysics Data System (ADS)

Saleh, D.; Domagalski, J. L.; Smith, R. A.

2016-12-01

The SPARROW (SPAtially-Referenced Regression On Watershed Attributes) model, developed by the U.S. Geological Survey, has been used to identify and quantify the sources of nitrogen and phosphorus in watersheds and to predict their fluxes and concentration at specified locations downstream. Existing SPARROW models use a hybrid statistical approach to describe an annual average ("steady-state") relationship between sources and stream conditions based on long-term water quality monitoring data and spatially-referenced explanatory information. Although these annual models are useful for some management purposes, many water quality issues stem from intra- and inter-annual changes in constituent sources, hydrologic forcing, or other environmental conditions, which cause a lag between watershed inputs and stream water quality. We are developing a seasonal dynamic SPARROW model of sources, fluxes, and yields of phosphorus for the watershed (approximately 9,700 square kilometers) draining to Upper Klamath Lake, Oregon. The lake is hyper-eutrophic and various options are being considered for water quality improvement. The model was calibrated with 11 years of water quality data (2000 to 2010) and simulates seasonal loads and yields for a total of 44 seasons. Phosphorus sources to the watershed include animal manure, farm fertilizer, discharges of treated wastewater, and natural sources (soil and streambed sediment). The model predicts that phosphorus delivery to the lake is strongly affected by intra- and inter-annual changes in precipitation and by temporary seasonal storage of phosphorus in the watershed. The model can be used to predict how different management actions for mitigating phosphorus sources might affect phosphorus loading to the lake as well as the time required for any changes in loading to occur following implementation of the action.

Predicting Fecal Indicator Bacteria Fate and Removal in Urban Stormwater at the Watershed Scale

NASA Astrophysics Data System (ADS)

Wolfand, J.; Hogue, T. S.; Luthy, R. G.

2016-12-01

Urban stormwater is a major cause of water quality impairment, resulting in surface waters that fail to meet water quality standards and support their designated uses. Of the many stormwater pollutants, fecal indicator bacteria are particularly important to track because they are directly linked to pathogens which jeopardize public health; yet, their fate and transport in urban stormwater is poorly understood. Monitoring fecal bacteria in stormwater is possible, but due to the high variability of fecal indicators both spatially and temporally, single grab or composite samples do not fully capture fecal indicator loading. Models have been developed to predict fecal indicator bacteria at the watershed scale, but they are often limited to agricultural areas, or areas that receive frequent rainfall. Further, it is unclear whether best management practices (BMPs), such as bioretention or engineered wetlands, are able to reduce bacteria to meet water quality standards at watershed outlets. This research seeks to develop a model to predict fecal indicator bacteria in urban stormwater in a semi-arid climate at the watershed scale. Using the highly developed Ballona Creek watershed (89 mi2) located in Los Angeles County as a case study, several existing mechanistic models are coupled with a hydrologic model to predict fecal indicator concentrations (E. coli, enterococci, fecal coliform, and total coliform) at the outfall of Ballona Creek watershed, Santa Monica Bay. The hydrologic model was developed using InfoSWMM Sustain, calibrated for flow from WY 1998-2006 (NSE = 0.94; R2 = 0.95), and validated from WY 2007-2015 (NSE = 0.93; R2 = 0.95). The developed coupled model is being used to predict fecal indicator fate and transport and evaluate how BMPs can be optimized to reduce fecal indicator loading to surface waters and recreational beaches.

Progress and lessons learned from water-quality monitoring networks

USGS Publications Warehouse

Myers, Donna N.; Ludtke, Amy S.

2017-01-01

Stream-quality monitoring networks in the United States were initiated and expanded after passage of successive federal water-pollution control laws from 1948 to 1972. The first networks addressed information gaps on the extent and severity of stream pollution and served as early warning systems for spills. From 1965 to 1972, monitoring networks expanded to evaluate compliance with stream standards, track emerging issues, and assess water-quality status and trends. After 1972, concerns arose regarding the ability of monitoring networks to determine if water quality was getting better or worse and why. As a result, monitoring networks adopted a hydrologic systems approach targeted to key water-quality issues, accounted for human and natural factors affecting water quality, innovated new statistical methods, and introduced geographic information systems and models that predict water quality at unmeasured locations. Despite improvements, national-scale monitoring networks have declined over time. Only about 1%, or 217, of more than 36,000 US Geological Survey monitoring sites sampled from 1975 to 2014 have been operated throughout the four decades since passage of the 1972 Clean Water Act. Efforts to sustain monitoring networks are important because these networks have collected information crucial to the description of water-quality trends over time and are providing information against which to evaluate future trends.

Hydrogeology, ground-water quality, and source of ground water causing water-quality changes in the Davis well field at Memphis, Tennessee

USGS Publications Warehouse

Parks, William S.; Mirecki, June E.; Kingsbury, James A.

1995-01-01

NETPATH geochemical model code was used to mix waters from the alluvial aquifer with water from the Memphis aquifer using chloride as a conservative tracer. The resulting models indicated that a mixture containing 3 percent alluvial aquifer water mixed with 97 percent unaffected Memphis aquifer water would produce the chloride concentration measured in water from the Memphis aquifer well most affected by water-quality changes. NETPATH also was used to calculate mixing percentages of alluvial and Memphis aquifer Abstract waters based on changes in the concentrations of selected dissolved major inorganic and trace element constituents that define the dominant reactions that occur during mixing. These models indicated that a mixture containing 18 percent alluvial aquifer water and 82 percent unaffected Memphis aquifer water would produce the major constituent and trace element concentrations measured in water from the Memphis aquifer well most affected by water-quality changes. However, these model simulations predicted higher dissolved methane concentrations than were measured in water samples from the Memphis aquifer wells.

Use of tolerance values to diagnose water-quality stressors to aquatic biota in New England streams

USGS Publications Warehouse

Meador, M.R.; Carlisle, D.M.; Coles, J.F.

2008-01-01

Identification of stressors related to biological impairment is critical to biological assessments. We applied nationally derived tolerance indicator values for four water-quality variables to fish and benthic macroinvertebrate assemblages at 29 sites along an urban gradient in New England. Tolerance indicator values (TIVs), as biologically based predictors of water-quality variables, were determined for dissolved oxygen, nitrite plus nitrate (nitrate), total phosphorus, and water temperature for each site based on observed biological assemblages (TIVO), and for expected assemblages (TIVE). The quotient method, based on a ratio of the TIVs for observed and expected assemblages (tolerance units), was used to diagnose potential water-quality stressors. In addition, the ratio of measured water-quality values to water-quality criteria (water-quality units) was calculated for each water-quality variable to assess measured water-quality stressors. Results from a RIVPACS predictive model for benthic macroinvertebrates and Bray-Curtis dissimilarity for fish were used to classify sites into categories of good or impaired ecological condition. Significant differences were detected between good and impaired sites for all biological tolerance units (fish and benthic macroinvertebrate assemblages averaged) except for nitrate (P = 0.480), and for all water-quality units except for nitrate (P = 0.183). Diagnosis of water-quality stressors at selected sites was, in general, consistent with State-reported causes of impairment. Tolerance units for benthic macroinvertebrate and fish assemblages were significantly correlated for water temperature (P = 0.001, r = 0.63), dissolved oxygen (P = 0.001, r = 0.61), and total phosphorus (P = 0.001, r = 0.61), but not for nitrate (P = 0.059, r = -0.35). Differences between the two assemblages in site-specific diagnosis of water-quality stressors may be the result of differences in nitrate tolerance.

High-Performance Integrated Control of water quality and quantity in urban water reservoirs

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.; Goedbloed, A.

2015-11-01

This paper contributes a novel High-Performance Integrated Control framework to support the real-time operation of urban water supply storages affected by water quality problems. We use a 3-D, high-fidelity simulation model to predict the main water quality dynamics and inform a real-time controller based on Model Predictive Control. The integration of the simulation model into the control scheme is performed by a model reduction process that identifies a low-order, dynamic emulator running 4 orders of magnitude faster. The model reduction, which relies on a semiautomatic procedural approach integrating time series clustering and variable selection algorithms, generates a compact and physically meaningful emulator that can be coupled with the controller. The framework is used to design the hourly operation of Marina Reservoir, a 3.2 Mm3 storm-water-fed reservoir located in the center of Singapore, operated for drinking water supply and flood control. Because of its recent formation from a former estuary, the reservoir suffers from high salinity levels, whose behavior is modeled with Delft3D-FLOW. Results show that our control framework reduces the minimum salinity levels by nearly 40% and cuts the average annual deficit of drinking water supply by about 2 times the active storage of the reservoir (about 4% of the total annual demand).

Prediction of Water Quality Parameters Using Statistical Methods: A Case Study in a Specially Protected Area, Ankara, Turkey

NASA Astrophysics Data System (ADS)

Alp, E.; Yücel, Ö.; Özcan, Z.

2014-12-01

Turkey has been making many legal arrangements for sustainable water management during the harmonization process with the European Union. In order to make cost effective and efficient decisions, monitoring network in Turkey has been expanding. However, due to time and budget constraints, desired number of monitoring campaigns can not be carried. Hence, in this study, independent parameters that can be measured easily and quickly are used to estimate water quality parameters in Lake Mogan and Eymir using linear regression. Nonpoint sources are one of the major pollutant components in Eymir and Mogan lakes. In this paper, a correlation between easily measurable parameters, DO, temperature, electrical conductivity, pH, precipitation and dependent variables, TN, TP, COD, Chl-a, TSS, Total Coliform is investigated. Simple regression analysis is performed for each season in Eymir and Mogan lakes by using SPSS Statistical program using the water quality data collected between 2006-2012. Regression analysis demonstrated significant linear relationship between measured and simulated concentrations for TN (R2=0.86), TP (R2=0.85), TSS (R2=0.91), Chl-a (R2=0.94), COD (R2=0.99), T. Coliform (R2=0.97) which are the best results in each season for Eymir and Mogan Lakes. The overall results of this study shows that by using easily measurable parameters even in ungauged situation the water quality of lakes can be predicted. Moreover, the outputs obtained from the regression equations can be used as an input for water quality models such as phosphorus budget model which is used to calculate the required reduction in the external phosphorus load to Lake Mogan to meet the water quality standards.

Evaluation of SWAT for estimating ET in irrigated and dryland cropping systems in the Texas High Plains

USDA-ARS?s Scientific Manuscript database

Hydrologic models such as SWAT are used extensively for predicting water availability and water quality responses to alternative management practices. Modeling results have been used by regulatory agencies for developing remedial measures for impaired water bodies and for water planning purposes. Ho...

Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

USGS Publications Warehouse

Gonzalez, Raul; Conn, Kathleen E.; Crosswell, Joey; Noble, Rachel

2012-01-01

Coastal and estuarine waters are the site of intense anthropogenic influence with concomitant use for recreation and seafood harvesting. Therefore, coastal and estuarine water quality has a direct impact on human health. In eastern North Carolina (NC) there are over 240 recreational and 1025 shellfish harvesting water quality monitoring sites that are regularly assessed. Because of the large number of sites, sampling frequency is often only on a weekly basis. This frequency, along with an 18–24 h incubation time for fecal indicator bacteria (FIB) enumeration via culture-based methods, reduces the efficiency of the public notification process. In states like NC where beach monitoring resources are limited but historical data are plentiful, predictive models may offer an improvement for monitoring and notification by providing real-time FIB estimates. In this study, water samples were collected during 12 dry (n = 88) and 13 wet (n = 66) weather events at up to 10 sites. Statistical predictive models for Escherichiacoli (EC), enterococci (ENT), and members of the Bacteroidales group were created and subsequently validated. Our results showed that models for EC and ENT (adjusted R2 were 0.61 and 0.64, respectively) incorporated a range of antecedent rainfall, climate, and environmental variables. The most important variables for EC and ENT models were 5-day antecedent rainfall, dissolved oxygen, and salinity. These models successfully predicted FIB levels over a wide range of conditions with a 3% (EC model) and 9% (ENT model) overall error rate for recreational threshold values and a 0% (EC model) overall error rate for shellfish threshold values. Though modeling of members of the Bacteroidales group had less predictive ability (adjusted R2 were 0.56 and 0.53 for fecal Bacteroides spp. and human Bacteroides spp., respectively), the modeling approach and testing provided information on Bacteroidales ecology. This is the first example of a set of successful statistical predictive models appropriate for assessment of both recreational and shellfish harvesting water quality in estuarine waters.

Persistent Urban Impacts on Surface Water Quality Mediated by Stormwater Recharge

NASA Astrophysics Data System (ADS)

Gabor, R. S.; Brooks, P. D.; Neilson, B. T.; Bowen, G. J.; Jameel, M. Y.; Hall, S. J.; Eiriksson, D.; Millington, M. R.; Gelderloos, A.

2016-12-01

Growing population centers along mountain watersheds put added stress on sensitive hydrologic systems and create water quality impacts downstream. We examined the mountain-to-urban transition in watersheds on Utah's Wasatch Front to identify mechanisms by which urbanization impacts water resources. Rivers in the Wasatch flow from the mountains directly into an urban landscape, where they are subject to channelization, stormwater runoff systems, and urban inputs to water quality from sources such as road salt and fertilizer. As part of an interdisciplinary effort within the iUTAH project, multiple synoptic surveys were performed and a variety of measurements were made, including basic water chemistry along with discharge, water isotopes, and nutrients. Red Butte Creek, a stream in Salt Lake City, does not show significant urban impact to water quality until several kilometers after it enters the city where concentrations of solutes such as chloride and nitrate more than triple in a gaining reach. Groundwater springs discharging to this gaining section demonstrate urban-impacted water chemistry, suggesting that during baseflow a contaminated alluvial aquifer significantly controls stream chemistry. By combining hydrometric and hydrochemical observations we were able to estimate that these groundwater springs were 17-20% urban runoff. We were then able to predict the chemistry of urban runoff that feeds into the alluvial aquifer. Samples collected from storm culverts, roofs, and asphalt during storms had chemistry values within the range of those predicted by the mixing model. This evidence that urbanization affects the water quality of baseflow through impacted groundwater suggests that stormwater mitigation may not be sufficient for protecting urban watersheds, and quantifying these persistent groundwater mediated impacts is necessary to evaluate the success of restoration efforts. By comparing these results from Red Butte Creek with similar studies from other rivers in the Wasatch Front and other alluvial systems, we can quantify how characteristics such as discharge patterns and land-use determine alluvial recharge controls on surface water quality.

Comparative assessment for future prediction of urban water environment using WEAP model: A case study of Kathmandu, Manila and Jakarta

NASA Astrophysics Data System (ADS)

Kumar, Pankaj; Yoshifumi, Masago; Ammar, Rafieiemam; Mishra, Binaya; Fukushi, Ken

2017-04-01

Uncontrolled release of pollutants, increasing extreme weather condition, rapid urbanization and poor governance posing a serious threat to sustainable water resource management in developing urban spaces. Considering half of the world's mega-cities are in the Asia and the Pacific with 1.7 billion people do not access to improved water and sanitation, water security through its proper management is both an increasing concern and an imperative critical need. This research work strives to give a brief glimpse about predicted future water environment in Bagmati, Pasig and Ciliwung rivers from three different cities viz. Manila, Kathmandu and Jakarta respectively. Hydrological model used here to foresee the collective impacts of rapid population growth because of urbanization as well as climate change on unmet demand and water quality in near future time by 2030. All three rivers are major source of water for different usage viz. domestic, industrial, agriculture and recreation but uncontrolled withdrawal and sewerage disposal causing deterioration of water environment in recent past. Water Evaluation and Planning (WEAP) model was used to model river water quality pollution future scenarios using four indicator species i.e. Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and Nitrate (NO3). Result for simulated water quality as well as unmet demand for year 2030 when compared with that of reference year clearly indicates that not only water quality deteriorates but also unmet demands is increasing in future course of time. This also suggests that current initiatives and policies for water resource management are not sufficient enough and hence immediate and inclusive action through transdisciplinary research.

Statistical procedures for determination and verification of minimum reporting levels for drinking water methods.

PubMed

Winslow, Stephen D; Pepich, Barry V; Martin, John J; Hallberg, George R; Munch, David J; Frebis, Christopher P; Hedrick, Elizabeth J; Krop, Richard A

2006-01-01

The United States Environmental Protection Agency's Office of Ground Water and Drinking Water has developed a single-laboratory quantitation procedure: the lowest concentration minimum reporting level (LCMRL). The LCMRL is the lowest true concentration for which future recovery is predicted to fall, with high confidence (99%), between 50% and 150%. The procedure takes into account precision and accuracy. Multiple concentration replicates are processed through the entire analytical method and the data are plotted as measured sample concentration (y-axis) versus true concentration (x-axis). If the data support an assumption of constant variance over the concentration range, an ordinary least-squares regression line is drawn; otherwise, a variance-weighted least-squares regression is used. Prediction interval lines of 99% confidence are drawn about the regression. At the points where the prediction interval lines intersect with data quality objective lines of 50% and 150% recovery, lines are dropped to the x-axis. The higher of the two values is the LCMRL. The LCMRL procedure is flexible because the data quality objectives (50-150%) and the prediction interval confidence (99%) can be varied to suit program needs. The LCMRL determination is performed during method development only. A simpler procedure for verification of data quality objectives at a given minimum reporting level (MRL) is also presented. The verification procedure requires a single set of seven samples taken through the entire method procedure. If the calculated prediction interval is contained within data quality recovery limits (50-150%), the laboratory performance at the MRL is verified.

Predicted pH at the domestic and public supply drinking water depths, Central Valley, California

USGS Publications Warehouse

Rosecrans, Celia Z.; Nolan, Bernard T.; Gronberg, Jo Ann M.

2017-03-08

This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer.Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/.Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7.

Assessment and management of the performance risk of a pilot reclaimed water disinfection process.

PubMed

Zhou, Guangyu; Zhao, Xinhua; Zhang, Lei; Wu, Qing

2013-10-01

Chlorination disinfection has been widely used in reclaimed water treatment plants to ensure water quality. In order to assess the downstream quality risk of a running reclaimed water disinfection process, a set of dynamic equations was developed to simulate reactions in the disinfection process concerning variables of bacteria, chemical oxygen demand (COD), ammonia and monochloramine. The model was calibrated by the observations obtained from a pilot disinfection process which was designed to simulate the actual process in a reclaimed water treatment plant. A Monte Carlo algorithm was applied to calculate the predictive effluent quality distributions that were used in the established hierarchical assessment system for the downstream quality risk, and the key factors affecting the downstream quality risk were defined using the Regional Sensitivity Analysis method. The results showed that the seasonal upstream quality variation caused considerable downstream quality risk; the effluent ammonia was significantly influenced by its upstream concentration; the upstream COD was a key factor determining the process effluent risk of bacterial, COD and residual disinfectant indexes; and lower COD and ammonia concentrations in the influent would mean better downstream quality.

Diffusive gradients in thin films technique provide robust prediction of metal bioavailability and toxicity in estuarine sediments.

PubMed

Amato, Elvio D; Simpson, Stuart L; Jarolimek, Chad V; Jolley, Dianne F

2014-04-15

Many sediment quality assessment frameworks incorporate contaminant bioavailability as a critical factor regulating toxicity in aquatic ecosystems. However, current approaches do not always adequately predict metal bioavailability to organisms living in the oxidized sediment surface layers. The deployment of the diffusive gradients in thin films (DGT) probes in sediments allows labile metals present in pore waters and weakly bound to the particulate phase to be assessed in a time-integrated manner in situ. In this study, relationships between DGT-labile metal fluxes within 5 mm of the sediment-water interface and lethal and sublethal effects to the amphipod Melita plumulosa were assessed in a range of contaminated estuarine sediments during 10-day laboratory-based bioassays. To account for differing toxicities of metals, DGT fluxes were normalized to water (WQG) or sediment quality guidelines or toxicity thresholds specific for the amphipod. The better dose-response relationship appeared to be the one based on WQG-normalized DGT fluxes, which successfully predicted toxicity despite the wide range of metals and large variations in sediment properties. The study indicated that the labile fraction of metals measured by DGT is useful for predicting metal toxicity to benthic invertebrates, supporting the applicability of this technique as a rapid monitoring tool for sediments quality assessments.

DRAINMOD-GIS: a lumped parameter watershed scale drainage and water quality model

Treesearch

G.P. Fernandez; G.M. Chescheir; R.W. Skaggs; D.M. Amatya

2006-01-01

A watershed scale lumped parameter hydrology and water quality model that includes an uncertainty analysis component was developed and tested on a lower coastal plain watershed in North Carolina. Uncertainty analysis was used to determine the impacts of uncertainty in field and network parameters of the model on the predicted outflows and nitrate-nitrogen loads at the...

Atrazine transport within a coastal zone in Southeastern Puerto Rico: a sensitivity analysis of an agricultural field model and riparian zone management model

USDA-ARS?s Scientific Manuscript database

Water quality models are used to predict effects of conservation practices to mitigate the transport of herbicides to water bodies. We used two models - the Agricultural Policy/Environmental eXtender (APEX) and the Riparian Ecosystem Management Model (REMM) to predict the movement of atrazine from ...

Applying online WEPP to assess forest watershed hydrology

Treesearch

S. Dun; J. Q. Wu; W. J. Elliot; J. R. Frankenberger; D. C. Flanagan; D. K. McCool

2011-01-01

The U.S. Army Corps of Engineers (USACE) and the Great Lakes Commission are developing technologies and predictive tools to aid in watershed management with an ultimate goal of improving and preserving the water quality in the Great Lakes Basin. A new version of the online Water Erosion Prediction Project (WEPP) GIS interface has been developed to assist in evaluating...

Land use change detection with LANDSAT-2 data for monitoring and predicting regional water quality degradation. [Arkansas

NASA Technical Reports Server (NTRS)

Macdonald, H.; Steele, K. (Principal Investigator); Waite, W.; Rice, R.; Shinn, M.; Dillard, T.; Petersen, C.

1977-01-01

The author has identified the following significant results. Comparison between LANDSAT 1 and 2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing LANDSAT change detection analyses.

Sensitivity of stream water age to climatic variability and land use change: implications for water quality

NASA Astrophysics Data System (ADS)

Soulsby, Chris; Birkel, Christian; Geris, Josie; Tetzlaff, Doerthe

2016-04-01

Advances in the use of hydrological tracers and their integration into rainfall runoff models is facilitating improved quantification of stream water age distributions. This is of fundamental importance to understanding water quality dynamics over both short- and long-time scales, particularly as water quality parameters are often associated with water sources of markedly different ages. For example, legacy nitrate pollution may reflect deeper waters that have resided in catchments for decades, whilst more dynamics parameters from anthropogenic sources (e.g. P, pathogens etc) are mobilised by very young (<1 day) near-surface water sources. It is increasingly recognised that water age distributions of stream water is non-stationary in both the short (i.e. event dynamics) and longer-term (i.e. in relation to hydroclimatic variability). This provides a crucial context for interpreting water quality time series. Here, we will use longer-term (>5 year), high resolution (daily) isotope time series in modelling studies for different catchments to show how variable stream water age distributions can be a result of hydroclimatic variability and the implications for understanding water quality. We will also use examples from catchments undergoing rapid urbanisation, how the resulting age distributions of stream water change in a predictable way as a result of modified flow paths. The implication for the management of water quality in urban catchments will be discussed.

Factors affecting Escherichia coli concentrations at Lake Erie public bathing beaches

USGS Publications Warehouse

Francy, Donna S.; Darner, Robert A.

1998-01-01

The environmental and water-quality factors that affect concentrations of Escherichia coli (E. coli) in water and sediment were investigated at three public bathing beachesEdgewater Park, Villa Angela, and Sims Parkin the Cleveland, Ohio metropolitan area. This study was done to aid in the determination of safe recreational use and to help water- resource managers assess more quickly and accurately the degradation of recreational water quality. Water and lake-bottom sediments were collected and ancillary environmental data were compiled for 41 days from May through September 1997. Water samples were analyzed for E. coli concentrations, suspended sediment concentrations, and turbidity. Lake- bottom sediment samples from the beach area were analyzed for E. coli concentrations and percent dry weight. Concentrations of E. coli were higher and more variable at Sims Park than at Villa Angela or Edgewater Park; concentrations were lowest at Edgewater Park. Time-series plots showed that short-term storage (less than one week) of E. coli in lake-bottom sediments may have occurred, although no evidence for long-term storage was found during the sampling period. E. coli concentrations in water were found to increase with increasing wave height, but the resuspension of E. coli from lake-bottom sediments by wave action could not be adequately assessed; higherwave heights were often associated with the discharge of sewage containing E. coli during or after a rainfall and wastewater-treatment plant overflow. Multiple linear regression (MLR) was used to develop models to predict recreational water quality at the in water. The related variables included turbidity, antecedent rainfall, antecedent weighted rainfall, volumes of wastewater-treatment plant overflows and metered outfalls (composed of storm-water runoff and combined-sewer overflows), a resuspension index, and wave heights. For the beaches in this study, wind speed, wind direction, water temperature, and the prswimmers were not included in the model because they were shown to be statistically unrelated to E. coli concentrations. From the several models developed, one model was chosen that accounted for 58 percent of the variability in E. coli concentrations. The chosen MLR model contained weighted categorical rainfall, beach-specific turbidity, wave height, and terms to correct for the different magnitudes of E. coli concentrations among the three beaches. For 1997, the MLR model predicted the recreational water quality as well as, and in some cases better than, antecedent E. coli concentrations (the current method). The MLR model improved the sensitivity of the prediction and the percentage of correct predictions over the current method; however, the MLR model predictions still erred to a similar degree as the current method with regard to false negatives. A false negative would allow swimming when, in fact, the bathing water standard was exceeded. More work needs to be done to validate the MLR model with data collected during other recreational seasons, especially during a season with a greater frequency and intensity of summer rains. Studies could focus on adding to the MLR model other environmental and water-quality variables that improve the predictive ability of the model. These variables might include concentrations of E. coli in deeper sediments outside the bathing area, the direction of lake currents, site-specific-rainfall amounts, time-of-day information on overflows and metered outfalls, concentrations of E. coli in treated wastewater-treatment plant effluents, and occurrences of sewage-line breaks. Rapid biological or chemical methods for determination of recreational water quality could also be used as variables in model refinements. Possible methods include the use of experimental rapid assay methods for determination of E. coli concentrations or other fecal indicators and the use of chemical tracers for fecal contamination, such as coprostanol (a degradation

Questions and Answers About the Effects of Septic Systems on Water Quality in the La Pine Area, Oregon

USGS Publications Warehouse

Williams, John S.; Morgan, David S.; Hinkle, Stephen R.

2007-01-01

Nitrate levels in the ground-water aquifer underlying the central Oregon city of La Pine and the surrounding area are increasing due to contamination from residential septic systems. This contamination has public health implications because ground water is the sole source of drinking water for area residents. The U.S. Geological Survey, in cooperation with Deschutes County and the Oregon Department of Environmental Quality, studied the movement and chemistry of nitrate in the aquifer and developed computer models that can be used to predict future nitrate levels and to evaluate alternatives for protecting water quality. This fact sheet summarizes the results of that study in the form of questions and answers.

DECISION-SUPPORT TOOLS FOR PREDICTING THE PERFORMANCE OF WATER DISTRIBUTION AND WASTEWATER COLLECTION SYSTEMS

EPA Science Inventory

Water and wastewater infrastructure systems represent a major capital investment; utilities must ensure they are getting the highest yield possible on their investment, both in terms of dollars and water quality. Accurate information related to equipment, pipe characteristics, l...

DECISION-SUPPORT TOOLS FOR PREDICTING THE PERFORMANCE OF WATER DISTRIBUTION AND WASTEWATER COLLECTION SYSTEMS

EPA Science Inventory

Water and wastewater infrastructure systems represent a major capital investment; utilities must ensure they are getting the highest yield possible on their investment, both in terms of dollars and water quality. Accurate information related to equipment, pipe characteristics, lo...

Improving Water Management Decision Support Tools Using NASA Satellite and Modeling Data

NASA Astrophysics Data System (ADS)

Toll, D. L.; Arsenault, K.; Nigro, J.; Pinheiro, A.; Engman, E. T.; Triggs, J.; Cosgrove, B.; Alonge, C.; Boyle, D.; Allen, R.; Townsend, P.; Ni-Meister, W.

2006-05-01

One of twelve Applications of National priority within NASA's Applied Science Program, the Water Management Program Element addresses concerns and decision making related to water availability, water forecast and water quality. The goal of the Water Management Program Element is to encourage water management organizations to use NASA Earth science data, models products, technology and other capabilities in their decision support tools for problem solving. The Water Management Program Element partners with Federal agencies, academia, private firms, and may include international organizations. This paper further describes the Water Management Program with the objective of informing the applications community of the potential opportunities for using NASA science products for problem solving. We will illustrate some ongoing and application Water Management projects evaluating and benchmarking NASA data with partnering federal agencies and their decision support tools: 1) Environmental Protection Agency for water quality; 2) Bureau of Reclamation for water supply, demand and forecast; and 3) NOAA National Weather Service for improved weather prediction. Examples of the types of NASA contributions to the these agency decision support tools include: 1) satellite observations within models assist to estimate water storage, i.e., snow water equivalent, soil moisture, aquifer volumes, or reservoir storages; 2) model derived products, i.e., evapotranspiration, precipitation, runoff, ground water recharge, and other 4-dimensional data assimilation products; 3) improve water quality, assessments by using improved inputs from NASA models (precipitation, evaporation) and satellite observations (e.g., temperature, turbidity, land cover) to nonpoint source models; and 4) water (i.e., precipitation) and temperature predictions from days to decades over local, regional and global scales.

Water Quality Projects Summary for the Mid-Columbia and Cumberland River Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Kevin M.; Witt, Adam M.; Hadjerioua, Boualem

Scheduling and operational control of hydropower systems is accompanied with a keen awareness of the management of water use, environmental effects, and policy, especially within the context of strict water rights policy and generation maximization. This is a multi-objective problem for many hydropower systems, including the Cumberland and Mid-Columbia river systems. Though each of these two systems have distinct operational philosophies, hydrologic characteristics, and system dynamics, they both share a responsibility to effectively manage hydropower and the environment, which requires state-of-the art improvements in the approaches and applications for water quality modeling. The Department of Energy and Oak Ridge Nationalmore » Laboratory have developed tools for total dissolved gas (TDG) prediction on the Mid-Columbia River and a decision-support system used for hydropower generation and environmental optimization on the Cumberland River. In conjunction with IIHR - Hydroscience & Engineering, The University of Iowa and University of Colorado s Center for Advanced Decision Support for Water and Environmental Systems (CADSWES), ORNL has managed the development of a TDG predictive methodology at seven dams along the Mid-Columbia River and has enabled the ability to utilize this methodology for optimization of operations at these projects with the commercially available software package Riverware. ORNL has also managed the collaboration with Vanderbilt University and Lipscomb University to develop a state-of-the art method for reducing high-fidelity water quality modeling results into surrogate models which can be used effectively within the context of optimization efforts to maximize generation for a reservoir system based on environmental and policy constraints. The novel contribution of these efforts is the ability to predict water quality conditions with simplified methodologies at the same level of accuracy as more complex and resource intensive computing methods. These efforts were designed to incorporate well into existing hydropower and reservoir system scheduling models, with runtimes that are comparable to existing software tools. In addition, the transferability of these tools to assess other systems is enhanced due the use of simplistic and easily attainable values for inputs, straight-forward calibration of predictive equation coefficients, and standardized comparison of traditionally familiar outputs.« less

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.

PubMed

Kulinkina, Alexandra V; Walz, Yvonne; Koch, Magaly; Biritwum, Nana-Kwadwo; Utzinger, Jürg; Naumova, Elena N

2018-06-04

Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance of surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.

Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea.

PubMed

Hasani Sangani, Mohammad; Jabbarian Amiri, Bahman; Alizadeh Shabani, Afshin; Sakieh, Yousef; Ashrafi, Sohrab

2015-04-01

Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.

Investigation of the relationship between drinking water quality based on content of inorganic components and landform classes using fuzzy AHP (case study: south of Firozabad, west of Fars province, Iran)

NASA Astrophysics Data System (ADS)

Mokarram, Marzieh; Sathyamoorthy, Dinesh

2016-10-01

In this study, the fuzzy analytic hierarchy process (AHP) is used to study the relationship between drinking water quality based on content of inorganic components and landform classes in the south of Firozabad, west of Fars province, Iran. For determination of drinking water quality based on content of inorganic components, parameters of calcium (Ca), chlorine (Cl), magnesium (Mg), thorium (TH), sodium (Na), electrical conductivity (EC), sulfate (SO4), and total dissolved solids (TDS) were used. It was found that 8.29 % of the study area has low water quality; 64.01 %, moderate; 23.33 %, high; and 4.38 %, very high. Areas with suitable drinking water quality based on content of inorganic components are located in parts of the south-eastern and south-western parts of the study area. The relationship between landform class and drinking water quality based on content of inorganic components shows that drinking water quality based on content of inorganic components is high in the stream, valleys, upland drainages, and local ridge classes, and low in the plain small and midslope classes. In fact we can predict water quality using extraction of landform classes from a digital elevation model (DEM) by the Topographic Position Index (TPI) method, so that streams, valleys, upland drainages, and local ridge classes have more water quality than the other classes. In the study we determined that without measurement of water sample characteristics, we can determine water quality by landform classes.

Predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwestern United States

USGS Publications Warehouse

Anning, David W.; Paul, Angela P.; McKinney, Tim S.; Huntington, Jena M.; Bexfield, Laura M.; Thiros, Susan A.

2012-01-01

The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is conducting a regional analysis of water quality in the principal aquifer systems across the United States. The Southwest Principal Aquifers (SWPA) study is building a better understanding of the susceptibility and vulnerability of basin-fill aquifers in the region to groundwater contamination by synthesizing baseline knowledge of groundwater-quality conditions in 16 basins previously studied by the NAWQA Program. The improved understanding of aquifer susceptibility and vulnerability to contamination is assisting in the development of tools that water managers can use to assess and protect the quality of groundwater resources.Human-health concerns and economic considerations associated with meeting drinking-water standards motivated a study of the vulnerability of basin-fill aquifers to nitrate con­tamination and arsenic enrichment in the southwestern United States. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents about 190,600 square miles of basin-fill aquifers in parts of Arizona, California, Colorado, Nevada, New Mexico, and Utah. The statistical models, referred to as classifiers, reflect natural and human-related factors that affect aquifer vulnerability to contamina­tion and relate nitrate and arsenic concentrations to explana­tory variables representing local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions. The classifiers were unbiased and fit the observed data well, and misclassifications were primarily due to statistical sampling error in the training datasets.The classifiers were designed to predict concentrations to be in one of six classes for nitrate, and one of seven classes for arsenic. Each classification scheme allowed for identification of areas with concentrations that were equal to or exceeding the U.S. Environmental Protection Agency drinking-water standard. Whereas 2.4 percent of the area underlain by basin-fill aquifers in the study area was predicted to equal or exceed this standard for nitrate (10 milligrams per liter as N; mg/L), 42.7 percent was predicted to equal or exceed the standard for arsenic (10 micrograms per liter; μg/L). Areas predicted to equal or exceed the drinking-water standard for nitrate include basins in central Arizona near Phoenix; the San Joaquin, Inland, and San Jacinto basins of California; and the San Luis Valley of Colorado. Much of the area predicted to equal or exceed the drinking-water standard for arsenic is within a belt of basins along the western portion of the Basin and Range Physiographic Province in Nevada, California, and Arizona. Predicted nitrate and arsenic concentrations are substantially lower than the drinking-water standards in much of the study area—about 93.0 percent of the area underlain by basin-fill aquifers was less than one-half the standard for nitrate (5.0 mg/L), and 50.2 percent was less than one-half the standard for arsenic (5.0 μg/L).

An approach to predict water quality in data-sparse catchments using hydrological catchment similarity

NASA Astrophysics Data System (ADS)

Pohle, Ina; Glendell, Miriam; Stutter, Marc I.; Helliwell, Rachel C.

2017-04-01

An understanding of catchment response to climate and land use change at a regional scale is necessary for the assessment of mitigation and adaptation options addressing diffuse nutrient pollution. It is well documented that the physicochemical properties of a river ecosystem respond to change in a non-linear fashion. This is particularly important when threshold water concentrations, relevant to national and EU legislation, are exceeded. Large scale (regional) model assessments required for regulatory purposes must represent the key processes and mechanisms that are more readily understood in catchments with water quantity and water quality data monitored at high spatial and temporal resolution. While daily discharge data are available for most catchments in Scotland, nitrate and phosphorus are mostly available on a monthly basis only, as typified by regulatory monitoring. However, high resolution (hourly to daily) water quantity and water quality data exist for a limited number of research catchments. To successfully implement adaptation measures across Scotland, an upscaling from data-rich to data-sparse catchments is required. In addition, the widespread availability of spatial datasets affecting hydrological and biogeochemical responses (e.g. soils, topography/geomorphology, land use, vegetation etc.) provide an opportunity to transfer predictions between data-rich and data-sparse areas by linking processes and responses to catchment attributes. Here, we develop a framework of catchment typologies as a prerequisite for transferring information from data-rich to data-sparse catchments by focusing on how hydrological catchment similarity can be used as an indicator of grouped behaviours in water quality response. As indicators of hydrological catchment similarity we use flow indices derived from observed discharge data across Scotland as well as hydrological model parameters. For the latter, we calibrated the lumped rainfall-runoff model TUWModel using multiple objective functions. The relationships between indicators of hydrological catchment similarity, physical catchment characteristics and nitrate and phosphorus concentrations in rivers are then investigated using multivariate statistics. This understanding of the relationship between catchment characteristics, hydrological processes and water quality will allow us to implement more efficient regulatory water quality monitoring strategies, to improve existing water quality models and to model mitigation and adaptation scenarios to global change in data-sparse catchments.

Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems?

PubMed

Brooks, Bryan W; Lazorchak, James M; Howard, Meredith D A; Johnson, Mari-Vaughn V; Morton, Steve L; Perkins, Dawn A K; Reavie, Euan D; Scott, Geoffrey I; Smith, Stephanie A; Steevens, Jeffery A

2016-01-01

In this Focus article, the authors ask a seemingly simple question: Are harmful algal blooms (HABs) becoming the greatest inland water quality threat to public health and aquatic ecosystems? When HAB events require restrictions on fisheries, recreation, and drinking water uses of inland water bodies significant economic consequences result. Unfortunately, the magnitude, frequency, and duration of HABs in inland waters are poorly understood across spatiotemporal scales and differentially engaged among states, tribes, and territories. Harmful algal bloom impacts are not as predictable as those from conventional chemical contaminants, for which water quality assessment and management programs were primarily developed, because interactions among multiple natural and anthropogenic factors determine the likelihood and severity to which a HAB will occur in a specific water body. These forcing factors can also affect toxin production. Beyond site-specific water quality degradation caused directly by HABs, the presence of HAB toxins can negatively influence routine surface water quality monitoring, assessment, and management practices. Harmful algal blooms present significant challenges for achieving water quality protection and restoration goals when these toxins confound interpretation of monitoring results and environmental quality standards implementation efforts for other chemicals and stressors. Whether HABs presently represent the greatest threat to inland water quality is debatable, though in inland waters of developed countries they typically cause more severe acute impacts to environmental quality than conventional chemical contamination events. The authors identify several timely research needs. Environmental toxicology, environmental chemistry, and risk-assessment expertise must interface with ecologists, engineers, and public health practitioners to engage the complexities of HAB assessment and management, to address the forcing factors for HAB formation, and to reduce the threats posed to inland surface water quality. © 2015 SETAC.

Using a Data-Driven Approach to Understand the Interaction between Catchment Characteristics and Water Quality Responses

NASA Astrophysics Data System (ADS)

Western, A. W.; Lintern, A.; Liu, S.; Ryu, D.; Webb, J. A.; Leahy, P.; Wilson, P.; Waters, D.; Bende-Michl, U.; Watson, M.

2016-12-01

Many streams, lakes and estuaries are experiencing increasing concentrations and loads of nutrient and sediments. Models that can predict the spatial and temporal variability in water quality of aquatic systems are required to help guide the management and restoration of polluted aquatic systems. We propose that a Bayesian hierarchical modelling framework could be used to predict water quality responses over varying spatial and temporal scales. Stream water quality data and spatial data of catchment characteristics collected throughout Victoria and Queensland (in Australia) over two decades will be used to develop this Bayesian hierarchical model. In this paper, we present the preliminary exploratory data analysis required for the development of the Bayesian hierarchical model. Specifically, we present the results of exploratory data analysis of Total Nitrogen (TN) concentrations in rivers in Victoria (in South-East Australia) to illustrate the catchment characteristics that appear to be influencing spatial variability in (1) mean concentrations of TN; and (2) the relationship between discharge and TN throughout the state. These important catchment characteristics were identified using: (1) monthly TN concentrations measured at 28 water quality gauging stations and (2) climate, land use, topographic and geologic characteristics of the catchments of these 28 sites. Spatial variability in TN concentrations had a positive correlation to fertiliser use in the catchment and average temperature. There were negative correlations between TN concentrations and catchment forest cover, annual runoff, runoff perenniality, soil erosivity and catchment slope. The relationship between discharge and TN concentrations showed spatial variability, possibly resulting from climatic and topographic differences between the sites. The results of this study will feed into the hierarchical Bayesian model of river water quality.

Modelling the effect of wildfire on forested catchment water quality using the SWAT model

NASA Astrophysics Data System (ADS)

Yu, M.; Bishop, T.; van Ogtrop, F. F.; Bell, T.

2016-12-01

Wildfire removes the surface vegetation, releases ash, increase erosion and runoff, and therefore effects the hydrological cycle of a forested water catchment. It is important to understand chnage and how the catchment recovers. These processes are spatially sensitive and effected by interactions between fire severity and hillslope, soil type and surface vegetation conditions. Thus, a distributed hydrological modelling approach is required. In this study, the Soil and Water Analysis Tool (SWAT) is used to predict the effect of 2001/02 Sydney wild fire on catchment water quality. 10 years pre-fire data is used to create and calibrate the SWAT model. The calibrated model was then used to simulate the water quality for the 10 years post-fire period without fire effect. The simulated water quality data are compared with recorded water quality data provided by Sydney catchment authority. The mean change of flow, total suspended solid, total nitrate and total phosphate are compare on monthly, three month, six month and annual basis. Two control catchment and three burn catchment were analysed.

JPL's Role in Advancing Earth System Science to Meet the Challenges of Climate and Environmental Change

NASA Technical Reports Server (NTRS)

Evans, Diane

2012-01-01

Objective 2.1.1: Improve understanding of and improve the predictive capability for changes in the ozone layer, climate forcing, and air quality associated with changes in atmospheric composition. Objective 2.1.2: Enable improved predictive capability for weather and extreme weather events. Objective 2.1.3: Quantify, understand, and predict changes in Earth s ecosystems and biogeochemical cycles, including the global carbon cycle, land cover, and biodiversity. Objective 2.1.4: Quantify the key reservoirs and fluxes in the global water cycle and assess water cycle change and water quality. Objective 2.1.5: Improve understanding of the roles of the ocean, atmosphere, land and ice in the climate system and improve predictive capability for its future evolution. Objective 2.1.6: Characterize the dynamics of Earth s surface and interior and form the scientific basis for the assessment and mitigation of natural hazards and response to rare and extreme events. Objective 2.1.7: Enable the broad use of Earth system science observations and results in decision-making activities for societal benefits.

An innovative approach for Predicting Farmers' Adaptive Behavior at the Large Watershed Scale: Implications for Water Quality and Crop Yields

NASA Astrophysics Data System (ADS)

Valcu-Lisman, A. M.; Gassman, P. W.; Arritt, R. W.; Kling, C.; Arbuckle, J. G.; Roesch-McNally, G. E.; Panagopoulos, Y.

2017-12-01

Projected changes in the climatic patterns (higher temperatures, changes in extreme precipitation events, and higher levels of humidity) will affect agricultural cropping and management systems in major agricultural production areas. The concept of adaption to new climatic or economic conditions is an important aspect of the agricultural decision-making process. Adopting cover crops, reduced tillage, extending the drainage systems and adjusting crop management are only a few examples of adaptive actions. These actions can be easily implemented as long as they have private benefits (increased profits, reduced risk). However, each adaptive action has a different impact on water quality. Cover crops and no till usually have a positive impact on water quality, but increased tile drainage typically results in more degraded water quality due primarily to increased export of soluble nitrogen and phosphorus. The goal of this research is to determine the changes in water quality as well in crop yields as farmers undertake these adaptive measures. To answer this research question, we need to estimate the likelihood that these actions will occur, identify the agricultural areas where these actions are most likely to be implemented, and simulate the water quality impacts associated with each of these scenarios. We apply our modeling efforts to the whole Upper-Mississippi River Basin Basin (UMRB) and the Ohio-Tennessee River Basin (OTRB). These two areas are critical source regions for the re-occurring hypoxic zone in the gulf of Mexico. The likelihood of each adaptive agricultural action is estimated using data from a survey conducted in 2012. A large, representative sample of farmers in the Corn Belt was used in the survey to elicit behavioral intentions regarding three of the most important agricultural adaptation strategies (no-till, cover crops and tile drainage). We use these data to study the relationship between intent to adapt, farmer characteristics, farm characteristics, and weather characteristics, and to predict the probability of adoption for each action. Next, we use these estimated probabilities to create different scenarios for the two large scale-watersheds. Finally, we simulate the impact of these scenarios on water quality using calibrated UMRB and OTRB SWAT water quality models.

Development and assessment of an integrated ecological modelling framework to assess the effect of investments in wastewater treatment on water quality.

PubMed

Holguin-Gonzalez, Javier E; Boets, Pieter; Everaert, Gert; Pauwels, Ine S; Lock, Koen; Gobeyn, Sacha; Benedetti, Lorenzo; Amerlinck, Youri; Nopens, Ingmar; Goethals, Peter L M

2014-01-01

Worldwide, large investments in wastewater treatment are made to improve water quality. However, the impacts of these investments on river water quality are often not quantified. To assess water quality, the European Water Framework Directive (WFD) requires an integrated approach. The aim of this study was to develop an integrated ecological modelling framework for the River Drava (Croatia) that includes physical-chemical and hydromorphological characteristics as well as the ecological river water quality status. The developed submodels and the integrated model showed accurate predictions when comparing the modelled results to the observations. Dissolved oxygen and nitrogen concentrations (ammonium and organic nitrogen) were the most important variables in determining the ecological water quality (EWQ). The result of three potential investment scenarios of the wastewater treatment infrastructure in the city of Varaždin on the EWQ of the River Drava was assessed. From this scenario-based analysis, it was concluded that upgrading the existing wastewater treatment plant with nitrogen and phosphorus removal will be insufficient to reach a good EWQ. Therefore, other point and diffuse pollution sources in the area should also be monitored and remediated to meet the European WFD standards.

Identification of Important Parameter from Leachate Solid Waste Landfill on Water Quality, Case Study of Pesanggrahan River

NASA Astrophysics Data System (ADS)

Yanidar, R.; Hartono, D. M.; Moersidik, S. S.

2018-03-01

Cipayung Landfill takes waste generation from Depok City approximately ± 750 tons/day of solid waste. The south and west boundaries of the landfill is Pesanggarahan River which 200m faraway. The objectives of this study are to indicate an important parameter which greatly affects the water quality of Pesanggrahan River and purpose the dynamic model for improving our understanding of the dynamic behavior that captures the interactions and feedbacks important parameter in river in order to identify and assess the effects of the treated leachate from final solid waste disposal activity as it responds to changes over time in the river. The high concentrations of BOD and COD are not the only cause significantly affect the quality of the pesanggrahan water, it also because the river has been contaminated in the upstream area. It need the water quality model to support the effectiveness calculation of activities for preventing a selected the pollutant sources the model should be developed for simulating and predicting the trend of water quality performance in Pesanggrahan River which can potentially be used by policy makers in strategic management to sustain river water quality as raw drinking water.

Regression equations for estimating concentrations of selected water-quality constituents for selected gaging stations in the Red River of the North Basin, North Dakota, Minnesota, and South Dakota

USGS Publications Warehouse

Williams-Sether, Tara

2004-01-01

The Dakota Water Resources Act, passed by the U.S. Congress on December 15, 2000, authorized the Secretary of the Interior to conduct a comprehensive study of future water-quantity and quality needs of the Red River of the North Basin in North Dakota and possible options to meet those water needs. Previous Red River of the North Basin studies conducted by the Bureau of Reclamation used streamflow and water-quality data bases developed by the U.S. Geological Survey that included data for 1931-84. As a result of the recent congressional authorization and results of previous studies by the Bureau of Reclamation, redevelopment of the streamflow and water-quality data bases with current data through 1999 are needed in order to evaluate and predict the water-quantity and quality effects within the Red River of the North Basin. This report provides updated statistical summaries of selected water-quality constituents and streamflow and the regression relations between them.  Available data for 1931-99 were used to develop regression equations between 5 selected water-quality constituents and streamflow for 38 gaging stations in the Red River of the North Basin. The water-quality constituents that were regressed against streamflow were hardness (as CaCO3), sodium, chloride, sulfate, and dissolved solids. Statistical summaries of the selected water-quality constituents and streamflow for the gaging stations used in the regression equations development and the applications and limitations of the regression equations are presented in this report.

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

USDA-ARS?s Scientific Manuscript database

Precision irrigation management in wine grape production is hindered by the lack of a reliable method to easily quantify and monitor vine water status. Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality. A crop water stress ind...

The contribution of space observations to water resources management; Proceedings of the Symposium, Bangalore, India, May 29-June 9, 1979

NASA Technical Reports Server (NTRS)

Salomonson, V. V. (Editor); Bhavsar, P. D.

1980-01-01

The symposium focused on hydrology, soil moisture estimation and ground water exploration, wetlands monitoring and water quality estimation, hydrometeorology, snow and ice monitoring, and evapotranspiration estimation. Other problems discussed include surface water and flood mapping, watershed runoff estimation and prediction, and new space systems contributing to water resources management.

Derivation of freshwater quality criteria for zinc using interspecies correlation estimation models to protect aquatic life in China.

PubMed

Feng, C L; Wu, F C; Dyer, S D; Chang, H; Zhao, X L

2013-01-01

Species sensitivity distributions (SSDs) are usually used in the development of water quality criteria and require a large number of toxicity values to define a hazard level to protect the majority of species. However, some toxicity data for certain chemicals are limited, especially for endangered and threatened species. Thus, it is important to predict the unknown species toxicity data using available toxicity data. To address this need, interspecies correlation estimation (ICE) models were developed by US EPA to predict acute toxicity of chemicals to diverse species based on a more limited data set of surrogate species toxicity data. Use of SSDs generated from ICE models allows for the prediction of protective water quality criteria, such as the HC5 (hazard concentration, 5th percentile). In the present study, we tested this concept using toxicity data collected for zinc. ICE-based-SSDs were generated using three surrogate species (common carp (Cyprinus carpio), rainbow trout (Oncorhynchus mykiss), and Daphnia magna) and compared with the measured-based SSD and corresponding HC5. The results showed that no significant differences were observed between the ICE- and the measured-based SSDs and HC5s. Furthermore, the examination of species placements within the SSDs indicated that the most sensitive species to zinc were invertebrates, especially crustaceans. Given the similarity of SSD and HC5s for zinc, the use of ICE to derive potential water quality criteria for diverse chemicals in China is proposed. Further, a combination of measured and ICE-derived data will prove useful for assessing water quality and chemical risks in the near future. Copyright © 2012 Elsevier Ltd. All rights reserved.

Assessing the Hydrologic Performance of the EPA's Nonpoint Source Water Quality Assessment Decision Support Tool Using North American Land Data Assimilation System (Products)

NASA Technical Reports Server (NTRS)

Lee, S.; Ni-Meister, W.; Toll, D.; Nigro, J.; Guiterrez-Magness, A.; Engman, T.

2010-01-01

The accuracy of streamflow predictions in the EPA's BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) decision support tool is affected by the sparse meteorological data contained in BASINS. The North American Land Data Assimilation System (NLDAS) data with high spatial and temporal resolutions provide an alternative to the NOAA National Climatic Data Center (NCDC)'s station data. This study assessed the improvement of streamflow prediction of the Hydrological Simulation Program-FORTRAN (HSPF) model contained within BASINS using the NLDAS 118 degree hourly precipitation and evapotranspiration estimates in seven watersheds of the Chesapeake Bay region. Our results demonstrated consistent improvements of daily streamflow predictions in five of the seven watersheds when NLDAS precipitation and evapotranspiration data was incorporated into BASINS. The improvement of using the NLDAS data is significant when watershed's meteorological station is either far away or not in a similar climatic region. When the station is nearby, using the NLDAS data produces similar results. The correlation coefficients of the analyses using the NLDAS data were greater than 0.8, the Nash-Sutcliffe (NS) model fit efficiency greater than 0.6, and the error in the water balance was less than 5%. Our analyses also showed that the streamflow improvements were mainly contributed by the NLDAS's precipitation data and that the improvement from using NLDAS's evapotranspiration data was not significant; partially due to the constraints of current BASINS-HSPF settings. However, NLDAS's evapotranspiration data did improve the baseflow prediction. This study demonstrates the NLDAS data has the potential to improve stream flow predictions, thus aid the water quality assessment in the EPA nonpoint water quality assessment decision tool.

Towards automating measurements and predictions of Escherichia coli concentrations in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2012–14

USGS Publications Warehouse

Brady, Amie M. G.; Meg B. Plona,

2015-07-30

A computer program was developed to manage the nowcasts by running the predictive models and posting the results to a publicly accessible Web site daily by 9 a.m. The nowcasts were able to correctly predict E. coli concentrations above or below the water-quality standard at Jaite for 79 percent of the samples compared with the measured concentrations. In comparison, the persistence model (using the previous day’s sample concentration) correctly predicted concentrations above or below the water-quality standard in only 68 percent of the samples. To determine if the Jaite nowcast could be used for the stretch of the river between Lock 29 and Jaite, the model predictions for Jaite were compared with the measured concentrations at Lock 29. The Jaite nowcast provided correct responses for 77 percent of the Lock 29 samples, which was a greater percentage than the percentage of correct responses (58 percent) from the persistence model at Lock 29.

Local and Long-Distance Effects of Land Use Change on Nutrient Levels in Streams and Rivers of the Conterminous United States

NASA Astrophysics Data System (ADS)

Smith, R. A.; Alexander, R. B.; Schwarz, G. E.

2003-12-01

Determining the effects of land use change (e.g. urbanization, deforestation) on water quality at large spatial scales has been difficult because water quality measurements in large rivers with heterogeneous basins show the integrated effects of multiple factors. Moreover, the observed effects of land use changes on water quality in small homogeneous stream basins may not be indicative of downstream effects (including effects on such ecologically relevant characteristics as nutrient levels and elemental ratios) because of loss processes occurring during downstream transport in river channels. In this study we used the USGS SPARROW (Spatially-Referenced Regression on Watersheds) models of total nitrogen (TN) and total phosphorus (TP) in streams and rivers of the conterminous US to examine the effects of various aspects of land use change on nutrient concentrations and flux from the pre-development era to the present. The models were calibrated with data from 370 long-term monitoring stations representing a wide range of basin sizes, land use/cover classes, climates, and physiographies. The non-linear formulation for each model includes 20+ statistically estimated parameters relating to land use/cover characteristics and other environmental variables such as temperature, soil conditions, hill slope, and the hydraulic characteristics of 2200 large lakes and reservoirs. Model predictions are available for 62,000 river/stream channel nodes. Model predictions of pre-development water quality compare favorably with nutrient data from 63 undeveloped (reference) sites. Error statistics are available for predictions at all nodes. Model simulations were chosen to compare the effects of selected aspects of land use change on nutrient levels at large and small basin scales, lacustrine and coastal receiving waters, and among the major US geographic regions.

Improvement of water quality at Dongbin Harbor with construction of an inland canal, Korea.

PubMed

Cho, Yong-Sik

2014-01-01

The behaviors of the water body of Dongbin Harbor located at Pohang City, Gyongpook Province, in Korea were numerically simulated in this study. A canal was planned to connect the harbor and the Hyeongsan River to improve water quality inside the harbor. The current system was first simulated by using a commercial program RMA2, with respect to both tidal currents and river flow. The progress inside the harbor from a supply of fresh water from the Hyeongsan River was then predicted by using RMA4. Both the present and future conditions (before and after construction of an inland canal) were taken into consideration in numerical simulations. It is concluded that the water quality inside the harbor can be improved considerably after construction of the canal.

A Web-Based Decision Support System for Assessing Regional Water-Quality Conditions and Management Actions

USGS Publications Warehouse

Booth, N.L.; Everman, E.J.; Kuo, I.-L.; Sprague, L.; Murphy, L.

2011-01-01

The U.S. Geological Survey National Water Quality Assessment Program has completed a number of water-quality prediction models for nitrogen and phosphorus for the conterminous United States as well as for regional areas of the nation. In addition to estimating water-quality conditions at unmonitored streams, the calibrated SPAtially Referenced Regressions On Watershed attributes (SPARROW) models can be used to produce estimates of yield, flow-weighted concentration, or load of constituents in water under various land-use condition, change, or resource management scenarios. A web-based decision support infrastructure has been developed to provide access to SPARROW simulation results on stream water-quality conditions and to offer sophisticated scenario testing capabilities for research and water-quality planning via a graphical user interface with familiar controls. The SPARROW decision support system (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water-quality conditions and to describe, test, and share modeled scenarios of future conditions. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000-scale River Reach File (RF1) and 1:100,000-scale National Hydrography Dataset (medium-resolution, NHDPlus) stream networks. ?? 2011 American Water Resources Association. This article is a U.S. Government work and is in the public domain in the USA.

Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study)

NASA Astrophysics Data System (ADS)

Mehrdad Mirsanjari, Mir; Mohammadyari, Fatemeh

2018-03-01

Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.

Comparison of Measured to Predicted Estimations of Nonpoint Source Contaminants Using Conservation Practices in an Agriculturally-Dominated Watershed in Northeast Arkansas, USA.

PubMed

Frasher, Sarah K; Woodruff, Tracy M; Bouldin, Jennifer L

2016-06-01

In efforts to reduce nonpoint source runoff and improve water quality, Best Management Practices (BMPs) were implemented in the Outlet Larkin Creek Watershed. Farmers need to make scientifically informed decisions concerning BMPs addressing contaminants from agricultural fields. The BMP Tool was developed from previous studies to estimate BMP effectiveness at reducing nonpoint source contaminants. The purpose of this study was to compare the measured percent reduction of dissolved phosphorus (DP) and total suspended solids to the reported percent reductions from the BMP Tool for validation. Similarities were measured between the BMP Tool and the measured water quality parameters. Construction of a sedimentation pond resulted in 74 %-76 % reduction in DP as compared to 80 % as predicted with the BMP Tool. However, further research is needed to validate the tool for additional water quality parameters. The BMP Tool is recommended for future BMP implementation as a useful predictor for farmers.

PREDICTION OF TOTAL DISSOLVED GAS EXCHANGE AT HYDROPOWER DAMS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadjerioua, Boualem; Pasha, MD Fayzul K; Stewart, Kevin M

2012-07-01

Total dissolved gas (TDG) supersaturation in waters released at hydropower dams can cause gas bubble trauma in fisheries resulting in physical injuries and eyeball protrusion that can lead to mortality. Elevated TDG pressures in hydropower releases are generally caused by the entrainment of air in spillway releases and the subsequent exchange of atmospheric gasses into solution during passage through the stilling basin. The network of dams throughout the Columbia River Basin (CRB) are managed for irrigation, hydropower production, flood control, navigation, and fish passage that frequently result in both voluntary and involuntary spillway releases. These dam operations are constrained bymore » state and federal water quality standards for TDG saturation which balance the benefits of spillway operations designed for Endangered Species Act (ESA)-listed fisheries versus the degradation to water quality as defined by TDG saturation. In the 1970s, the United States Environmental Protection Agency (USEPA), under the federal Clean Water Act (Section 303(d)), established a criterion not to exceed the TDG saturation level of 110% in order to protect freshwater and marine aquatic life. The states of Washington and Oregon have adopted special water quality standards for TDG saturation in the tailrace and forebays of hydropower facilities on the Columbia and Snake Rivers where spillway operations support fish passage objectives. The physical processes that affect TDG exchange at hydropower facilities have been studied throughout the CRB in site-specific studies and routine water quality monitoring programs. These data have been used to quantify the relationship between project operations, structural properties, and TDG exchange. These data have also been used to develop predictive models of TDG exchange to support real-time TDG management decisions. These empirically based predictive models have been developed for specific projects and account for both the fate of spillway and powerhouse flows in the tailrace channel and resultant exchange in route to the next downstream dam. Currently, there exists a need to summarize the general finding from operational and structural TDG abatement programs conducted throughout the CRB and for the development of a generalized prediction model that pools data collected at multiple projects with similar structural attributes. A generalized TDG exchange model can be tuned to specific projects and coupled with water regulation models to allow the formulation of optimal daily water regulation schedules subject to water quality constraints for TDG supersaturation. A generalized TDG exchange model can also be applied to other hydropower dams that affect TDG pressures in tailraces and can be used to develop alternative operational and structural measures to minimize TDG generation. It is proposed to develop a methodology for predicting TDG levels downstream of hydropower facilities with similar structural properties as a function of a set of variables that affect TDG exchange; such as tailwater depth, spill discharge and pattern, project head, and entrainment of powerhouse releases. TDG data from hydropower facilities located throughout the northwest region of the United States will be used to identify relationships between TDG exchange and relevant dependent variables. Data analysis and regression techniques will be used to develop predictive TDG exchange expressions for various structural categories.« less

Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods.

PubMed

Mustonen, Satu M; Tissari, Soile; Huikko, Laura; Kolehmainen, Mikko; Lehtola, Markku J; Hirvonen, Arja

2008-05-01

The distribution of drinking water generates soft deposits and biofilms in the pipelines of distribution systems. Disturbances in water distribution can detach these deposits and biofilms and thus deteriorate the water quality. We studied the effects of simulated pressure shocks on the water quality with online analysers. The study was conducted with copper and composite plastic pipelines in a pilot distribution system. The online data gathered during the study was evaluated with Self-Organising Map (SOM) and Sammon's mapping, which are useful methods in exploring large amounts of multivariate data. The objective was to test the usefulness of these methods in pinpointing the abnormal water quality changes in the online data. The pressure shocks increased temporarily the number of particles, turbidity and electrical conductivity. SOM and Sammon's mapping were able to separate these situations from the normal data and thus make those visible. Therefore these methods make it possible to detect abrupt changes in water quality and thus to react rapidly to any disturbances in the system. These methods are useful in developing alert systems and predictive applications connected to online monitoring.

Application of SWAT-HS, a lumped hillslope model to simulate hydrology in the Cannonsville Reservoir watershed, New York

NASA Astrophysics Data System (ADS)

Hoang, Linh; Schneiderman, Elliot; Mukundan, Rajith; Moore, Karen; Owens, Emmet; Steenhuis, Tammo

2017-04-01

Surface runoff is the primary mechanism transporting substances such as sediments, agricultural chemicals, and pathogens to receiving waters. In order to predict runoff and pollutant fluxes, and to evaluate management practices, it is essential to accurately predict the areas generating surface runoff, which depend on the type of runoff: infiltration-excess runoff and saturation-excess runoff. The watershed of Cannonsville reservoir is part of the New York City water supply system that provides high quality drinking water to nine million people in New York City (NYC) and nearby communities. Previous research identified saturation-excess runoff as the dominant runoff mechanism in this region. The Soil and Water Assessment Tool (SWAT) is a promising tool to simulate the NYC watershed given its broad application and good performance in many watersheds with different scales worldwide, for its ability to model water quality responses, and to evaluate the effect of management practices on water quality at the watershed scale. However, SWAT predicts runoff based mainly on soil and land use characteristics, and implicitly considers only infiltration-excess runoff. Therefore, we developed a modified version of SWAT, referred to as SWAT-Hillslope (SWAT-HS), which explicitly simulates saturation-excess runoff by redefining Hydrological Response Units (HRUs) based on wetness classes with varying soil water storage capacities, and by introducing a surface aquifer with the ability to route interflow from "drier" to "wetter" wetness classes. SWAT-HS was first tested at Town Brook, a 37 km2 headwater watershed draining to the Cannonsville reservoir using a single sub-basin for the whole watershed. SWAT-HS performed well, and predicted streamflow yielded Nash-Sutcliffe Efficiencies of 0.68 and 0.87 at the daily and monthly time steps, respectively. More importantly, it predicted the spatial distribution of saturated areas accurately. Based on the good performance in the Town Brook watershed, we scale-up the application of SWAT-HS to the 1160 km2 Cannonsville watershed utilizing a setup of multiple sub-basins, and evaluate the model performance on flow simulation at different gauged locations in the watershed. Results from flow predictions will be used as a basis for evaluating the ability of SWAT-HS to make sediment and nutrient loading estimates.

Effects of land use patterns on stream water quality: a case study of a small-scale watershed in the Three Gorges Reservoir Area, China.

PubMed

Huang, Zhilin; Han, Liyang; Zeng, Lixiong; Xiao, Wenfa; Tian, Yaowu

2016-02-01

In this study, we have considered the relationship between the spatial configuration of land use and water quality in the Three Gorges Reservoir Area. Using land use types, landscape metrics, and long-term water quality data, as well as statistical and spatial analysis, we determined that most water quality parameters were negatively correlated with non-wood forest and urban areas but were strongly positively correlated with the proportion of forest area. Landscape indices such as patch density, contagion, and the Shannon diversity index were able to predict some water quality indicators, but the mean shape index was not significantly related to the proportions of farmland and water in the study area. Regression relationships were stronger in spring and fall than in summer, and relationships with nitrogen were stronger than those of the other water quality parameters (R(2) > 0.80) in all three seasons. Redundancy analysis showed that declining stream water quality was closely associated with configurations of urban, agricultural, and forest areas and with landscape fragmentation (PD) caused by urbanization and agricultural activities. Thus, a rational land use plan of adjusting the land use type, controlling landscape fragmentation, and increasing the proportion of forest area would help to achieve a healthier river ecosystem in the Three Gorges Reservoir Area (TGRA).

Spatial prediction of water quality variables along a main river channel, in presence of pollution hotspots.

PubMed

Rizo-Decelis, L D; Pardo-Igúzquiza, E; Andreo, B

2017-12-15

In order to treat and evaluate the available data of water quality and fully exploit monitoring results (e.g. characterize regional patterns, optimize monitoring networks, infer conditions at unmonitored locations, etc.), it is crucial to develop improved and efficient methodologies. Accordingly, estimation of water quality along fluvial ecosystems is a frequent task in environment studies. In this work, a particular case of this problem is examined, namely, the estimation of water quality along a main stem of a large basin (where most anthropic activity takes place), from observational data measured along this river channel. We adapted topological kriging to this case, where each watershed contains all the watersheds of the upstream observed data ("nested support effect"). Data analysis was additionally extended by taking into account the upstream distance to the closest contamination hotspot as an external drift. We propose choosing the best estimation method by cross-validation. The methodological approach in spatial variability modeling may be used for optimizing the water quality monitoring of a given watercourse. The methodology presented is applied to 28 water quality variables measured along the Santiago River in Western Mexico. Copyright © 2017 Elsevier B.V. All rights reserved.

Absorption and fluorescence properties of chromophoric dissolved organic matter: implications for the monitoring of water quality in a large subtropical reservoir.

PubMed

Liu, Xiaohan; Zhang, Yunlin; Shi, Kun; Zhu, Guangwei; Xu, Hai; Zhu, Mengyuan

2014-12-01

The development of techniques for real-time monitoring of water quality is of great importance for effectively managing inland water resources. In this study, we first analyzed the absorption and fluorescence properties in a large subtropical reservoir and then used a chromophoric dissolved organic matter (CDOM) fluorescence monitoring sensor to predict several water quality parameters including the total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), dissolved organic carbon (DOC), and CDOM fluorescence parallel factor analysis (PARAFAC) components in the reservoir. The CDOM absorption coefficient at 254 nm (a(254)), the humic-like component (C1), and the tryptophan-like component (C3) decreased significantly along a gradient from the northwest to the lake center, northeast, southwest, and southeast region in the reservoir. However, no significant spatial difference was found for the tyrosine-like component (C2), which contributed only four marked peaks. A highly significant linear correlation was found between the a(254) and CDOM concentration measured using the CDOM fluorescence sensor (r(2) = 0.865, n = 76, p < 0.001), indicating that CDOM concentrations could act as a proxy for the CDOM absorption coefficient measured in the laboratory. Significant correlations were also found between the CDOM concentration and TN, TP, COD, DOC, and the maximum fluorescence intensity of C1, suggesting that the real-time monitoring of CDOM concentrations could be used to predict these water quality parameters and trace the humic-like fluorescence substance in clear aquatic ecosystems with DOC <2 mg/L and total suspended matter (TSM) concentrations <15 mg/L. These results demonstrate that the CDOM fluorescence sensor is a useful tool for on-line water quality monitoring if the empirical relationship between the CDOM concentration measured using the CDOM fluorescence sensor and the water quality parameters is calibrated and validated.

Runoff delay exerts a strong control on the field-scale removal of manure-borne fecal bacteria with runoff

USDA-ARS?s Scientific Manuscript database

The microbial safety of surface waters is an ongoing issue which is threatened by the transport of manure-borne bacteria to water sources used for irrigation or recreation. Predictive modeling has become an effective tool to forecast the microbial quality of water duringprecipitation events, however...

40 CFR 230.61 - Chemical, biological, and physical evaluation and testing.

Code of Federal Regulations, 2011 CFR

2011-07-01

... potential effects on the water column and on communities of aquatic organisms. (1) Evaluation of chemical... be obtained from bioassays in lieu of chemical tests. (2) Water column effects. (i) Sediments... locations within the sediment. An elutriate test may be used to predict the effect on water quality due to...

40 CFR 230.61 - Chemical, biological, and physical evaluation and testing.

Code of Federal Regulations, 2010 CFR

2010-07-01

... potential effects on the water column and on communities of aquatic organisms. (1) Evaluation of chemical... be obtained from bioassays in lieu of chemical tests. (2) Water column effects. (i) Sediments... locations within the sediment. An elutriate test may be used to predict the effect on water quality due to...

Prediction of E. coli release from streambed to water column during base flow periods using SWAT model

USDA-ARS?s Scientific Manuscript database

Microbial water quality in streams is of importance for recreation, irrigation, and other uses. The streambed sediment has been shown to harbor large fecal indicator bacteria (FIB) population that can be released to water column during high-flow events when sediments are resuspended. There have been...

Lumped Parameter Models for Predicting Nitrogen Transport in Lower Coastal Plain Watersheds

Treesearch

Devendra M. Amatya; George M. Chescheir; Glen P. Fernandez; R. Wayne Skaggs; F. Birgand; J.W. Gilliam

2003-01-01

hl recent years physically based comprehensive disfributed watershed scale hydrologic/water quality models have been developed and applied 10 evaluate cumulative effects of land arld water management practices on receiving waters, Although fhesc complex physically based models are capable of simulating the impacts ofthese changes in large watersheds, they are often...

Effect of Climate Change on Water Temperature and Attainment of Water Temperature Criteria in the Yaquina Estuary, Oregon (USA)

EPA Science Inventory

There is increasing evidence that our planet is warming and this warming is also resulting in rising sea levels. Estuaries which are located at the interface between land and ocean are impacted by these changes. We used CE-QUAL-W2 water quality model to predict changes in water...

Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

PubMed

Liu, Mei; Lu, Jun

2014-09-01

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.

Use of Principal Components Analysis and Kriging to Predict Groundwater-Sourced Rural Drinking Water Quality in Saskatchewan

PubMed Central

McLeod, Lianne; Bharadwaj, Lalita; Epp, Tasha; Waldner, Cheryl L.

2017-01-01

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between water quality and type 2 diabetes or cardiovascular disease. Arsenic in drinking water has been linked to a variety of chronic diseases and previous studies have identified multiple wells with arsenic above the drinking water standard of 0.01 mg/L; therefore, arsenic concentrations were of specific interest. Principal components analysis was applied to obtain principal component (PC) scores to summarize mixtures of correlated parameters identified as health standards and those identified as aesthetic objectives in the Saskatchewan Drinking Water Quality Standards and Objective. Ordinary, universal, and empirical Bayesian kriging were used to interpolate arsenic concentrations and PC scores in southern Saskatchewan, and the results were compared. Empirical Bayesian kriging performed best across all analyses, based on having the greatest number of variables for which the root mean square error was lowest. While all of the kriging methods appeared to underestimate high values of arsenic and PC scores, empirical Bayesian kriging was chosen to summarize large scale geographic trends in groundwater-sourced drinking water quality and assess exposure to mixtures of trace metals and ions. PMID:28914824

Use of Principal Components Analysis and Kriging to Predict Groundwater-Sourced Rural Drinking Water Quality in Saskatchewan.

PubMed

McLeod, Lianne; Bharadwaj, Lalita; Epp, Tasha; Waldner, Cheryl L

2017-09-15

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between water quality and type 2 diabetes or cardiovascular disease. Arsenic in drinking water has been linked to a variety of chronic diseases and previous studies have identified multiple wells with arsenic above the drinking water standard of 0.01 mg/L; therefore, arsenic concentrations were of specific interest. Principal components analysis was applied to obtain principal component (PC) scores to summarize mixtures of correlated parameters identified as health standards and those identified as aesthetic objectives in the Saskatchewan Drinking Water Quality Standards and Objective. Ordinary, universal, and empirical Bayesian kriging were used to interpolate arsenic concentrations and PC scores in southern Saskatchewan, and the results were compared. Empirical Bayesian kriging performed best across all analyses, based on having the greatest number of variables for which the root mean square error was lowest. While all of the kriging methods appeared to underestimate high values of arsenic and PC scores, empirical Bayesian kriging was chosen to summarize large scale geographic trends in groundwater-sourced drinking water quality and assess exposure to mixtures of trace metals and ions.

The nematode Caenorhabditis elegans as an integrated toxicological tool to assess water quality and pollution.

PubMed

Clavijo, Araceli; Kronberg, María Florencia; Rossen, Ariana; Moya, Aldana; Calvo, Daniel; Salatino, Santa Esmeralda; Pagano, Eduardo Antonio; Morábito, José Antonio; Munarriz, Eliana Rosa

2016-11-01

Determination of water quality status in rivers is critical to establish a sustainable water management policy. For this reason, over the last decades it has been recommended to perform integrated water assessments that include water quantities and physicochemical, ecological and toxicological tests. However, sometimes resources are limited and it is not possible to perform large-scale chemical determinations of pollutants or conduct numerous ecotoxicological tests. To overcome this problem we use and measure the growth, as a response parameter, of the soil nematode Caenorhabditis elegans to assess water quality in rivers. The C. elegans is a ubiquitous organism that has emerged as an important model organism in aquatic and soil toxicology research. The Tunuyán River Basin (Province of Mendoza, Argentina) has been selected as a representative traditional water monitoring system to test the applicability of the C. elegans toxicological bioassay to generate an integrated water quality evaluation. Jointly with the C. elegans toxic assays, physicochemical and bacteriological parameters were determined for each monitoring site. C. elegans bioassays help to identify different water qualities in the river basin. Multivariate statistical analysis (PCA and linear regression models) has allowed us to confirm that traditional water quality studies do not predict potential toxic effects on living organisms. On the contrary, physicochemical and bacteriological analyzes explain <62% of the C. elegans growth response variability, showing that ecotoxicological bioassays are important to obtain a realistic scenario of water quality threats. Our results confirm that the C. elegans bioassay is a sensible and suitable tool to assess toxicity and should be implemented in routine water quality monitoring. Copyright © 2016 Elsevier B.V. All rights reserved.

Influence of land use on the quantity and quality of runoff along Israel's coastal strip

NASA Astrophysics Data System (ADS)

Goldshleger, Naftaly; Asaf, Lior; Maor, Alon; Garzuzi, Jamil Jamil

2013-04-01

This study presents an analysis of the quantity and quality of urban runoff from various land uses by remote-sensing and GIS technology coupled with hydrological and chemical monitoring. The study areas were located in the cities of Herzliya and Ra'anana, in Israel's coastal plain, where extensive urbanization has taken place over the last 30 years. Land uses in urban basins were analyzed; rain and runoff were measured and sampled at measurement stations representing different land uses (residential, industrial, commercial, roads, gas station). The aim was to analyze land uses by different remote-sensing and GIS techniques, to evaluate the quality and quantity of urban storm water from various land uses, and to verify a method for predicting the impact of urban land uses on quantity and quality of urban storm water. The quality of urban storm water from residential areas was generally very high, and the water is suitable for reuse or direct recharge into the local aquifer. In light of the serious state of the Israeli water sector and the large amounts of unused runoff produced by Israel's cities, together with the high quality of urban storm water drained from the residential areas, it is important to exploit this water source

A model for predicting daily peak visitation and implications for recreation management and water quality: evidence from two rivers in Puerto Rico.

PubMed

Santiago, Luis E; Gonzalez-Caban, Armando; Loomis, John

2008-06-01

Visitor use surveys and water quality data indicates that high visitor use levels of two rivers in Puerto Rico does not appear to adversely affect several water quality parameters. Optimum visitor use to maximize visitor defined satisfaction is a more constraining limit on visitor use than water quality. Our multiple regression analysis suggests that visitor use of about 150 visitors per day yields the highest level of visitor reported satisfaction, a level that does not appear to affect turbidity of the river. This high level of visitor use may be related to the gregarious nature of Puerto Ricans and their tolerance for crowding on this densely populated island. The daily peak visitation model indicates that regulating the number of parking spaces may be the most effective way to keep visitor use within the social carrying capacity.

LANDSAT-1 data as it has been applied for land use and water quality data by the Virginia State Water Control Board. 1: The state project. 2: Monitoring water quality from LANDSAT

NASA Technical Reports Server (NTRS)

Trexler, P. L.; Barker, J. L.

1975-01-01

LANDSAT-1 imagery has been used for water quality and land use monitoring in and around the Swift Creek and Lake Chesdin Reservoirs in Virginia. This has proved useful by (1) helping determine valid reservoir sampling stations, (2) monitoring areas not accessible by land or water, (3) giving the State a viable means of measuring Secchi depth readings in these inaccessible areas, (4) giving an overview of trends in changing sedimentation loadings over a given time period and classifying these waters into various categories, (5) enabling the State to inventory all major lakes and reservoirs and computing their acreage, (6) monitoring land use changes in any specific area, (7) evaluating possible long-term environmental effects of nearby developments, and (8) monitoring and predicting population shifts with possible impact on water quality problems. The main problems in the long-term use of such imagery appear to be cost and lack of consistency due to cloud cover limitations.

Multiphase Modelling of Bacteria Removal in a CSO Stream

EPA Science Inventory

Indicator bacteria are an important determinant of water quality in many water resources management situations. They are also one of the more complex phenomena to model and predict. Sources abound, the populations are dynamic and influenced by many factors, and mobility through...

77 FR 71191 - 2012 Recreational Water Quality Criteria

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-29

... Criteria AGENCY: Environmental Protection Agency (EPA). ACTION: Notice of availability of the 2012... for beach monitoring, quantitative polymerase chain reaction (qPCR), for the detection of enterococci... managing recreational waters, such as predictive modeling; the EPA is providing a beach action value for...

Effect of land cover, stream discharge, and precipitation on water quality in Puerto Rico

NASA Astrophysics Data System (ADS)

Hall, J. S.; Uriarte, M.

2017-12-01

In 2015, Puerto Rico experienced one of the worst droughts in its history, causing widespread water rationing and sparking concerns for future resources. The drought represents precipitation extremes that provide valuable insight into the effects of land cover (LC), on modulating discharge and water quality indices at varying spatial scales. We used data collected from 38 water quality and 55 precipitation monitoring stations in Puerto Rico from 2005 to 2016, paired with a 2010 land cover map to (1) determine whether temporal variability in discharge, precipitation, or antecedent precipitation was a better predictor of water quality, (2) find the spatial scale where LC has the greatest impact on water quality, and (3) quantify impacts of LC on water quality indices, including dissolved oxygen (mg/L), total nitrogen (mg/L), phosphorous (mg/L), turbidity (NTRU), fecal coliforms (colony units/100mL) and instantaneous discharge (ft3/s). The resulting linear mixed effects models account for between 36-68% of the variance in water quality. Preliminary results indicate that phosphorous and nitrogen were best predicted from instantaneous stream discharge, the log of discharge was the better predictor for turbidity and fecal coliforms, and summed 2 and 14-day antecedent precipitation indices were better predictors for dissolved oxygen and discharge, respectively. Increased urban and pasture area reliably decreased water quality in relation to forest cover, while agriculture and wetlands had little or mixed effects. Turbidity and nitrogen responded to a watershed level LC, while phosphorous, fecal coliforms, and discharge responded to LC in 60 m riparian buffers at the watershed scale. Our results indicate that LC modulates changing precipitation regimes and the ensuing impacts on water quality at a range of spatial scales.

Associations between Perceptions of Drinking Water Service Delivery and Measured Drinking Water Quality in Rural Alabama

PubMed Central

Wedgworth, Jessica C.; Brown, Joe; Johnson, Pauline; Olson, Julie B.; Elliott, Mark; Forehand, Rick; Stauber, Christine E.

2014-01-01

Although small, rural water supplies may present elevated microbial risks to consumers in some settings, characterizing exposures through representative point-of-consumption sampling is logistically challenging. In order to evaluate the usefulness of consumer self-reported data in predicting measured water quality and risk factors for contamination, we compared matched consumer interview data with point-of-survey, household water quality and pressure data for 910 households served by 14 small water systems in rural Alabama. Participating households completed one survey that included detailed feedback on two key areas of water service conditions: delivery conditions (intermittent service and low water pressure) and general aesthetic characteristics (taste, odor and color), providing five condition values. Microbial water samples were taken at the point-of-use (from kitchen faucets) and as-delivered from the distribution network (from outside flame-sterilized taps, if available), where pressure was also measured. Water samples were analyzed for free and total chlorine, pH, turbidity, and presence of total coliforms and Escherichia coli. Of the 910 households surveyed, 35% of participants reported experiencing low water pressure, 15% reported intermittent service, and almost 20% reported aesthetic problems (taste, odor or color). Consumer-reported low pressure was associated with lower gauge-measured pressure at taps. While total coliforms (TC) were detected in 17% of outside tap samples and 12% of samples from kitchen faucets, no reported water service conditions or aesthetic characteristics were associated with presence of TC. We conclude that consumer-reported data were of limited utility in predicting potential microbial risks associated with small water supplies in this setting, although consumer feedback on low pressure—a risk factor for contamination—may be relatively reliable and therefore useful in future monitoring efforts. PMID:25046635

Associations between perceptions of drinking water service delivery and measured drinking water quality in rural Alabama.

PubMed

Wedgworth, Jessica C; Brown, Joe; Johnson, Pauline; Olson, Julie B; Elliott, Mark; Forehand, Rick; Stauber, Christine E

2014-07-18

Although small, rural water supplies may present elevated microbial risks to consumers in some settings, characterizing exposures through representative point-of-consumption sampling is logistically challenging. In order to evaluate the usefulness of consumer self-reported data in predicting measured water quality and risk factors for contamination, we compared matched consumer interview data with point-of-survey, household water quality and pressure data for 910 households served by 14 small water systems in rural Alabama. Participating households completed one survey that included detailed feedback on two key areas of water service conditions: delivery conditions (intermittent service and low water pressure) and general aesthetic characteristics (taste, odor and color), providing five condition values. Microbial water samples were taken at the point-of-use (from kitchen faucets) and as-delivered from the distribution network (from outside flame-sterilized taps, if available), where pressure was also measured. Water samples were analyzed for free and total chlorine, pH, turbidity, and presence of total coliforms and Escherichia coli. Of the 910 households surveyed, 35% of participants reported experiencing low water pressure, 15% reported intermittent service, and almost 20% reported aesthetic problems (taste, odor or color). Consumer-reported low pressure was associated with lower gauge-measured pressure at taps. While total coliforms (TC) were detected in 17% of outside tap samples and 12% of samples from kitchen faucets, no reported water service conditions or aesthetic characteristics were associated with presence of TC. We conclude that consumer-reported data were of limited utility in predicting potential microbial risks associated with small water supplies in this setting, although consumer feedback on low pressure-a risk factor for contamination-may be relatively reliable and therefore useful in future monitoring efforts.

Interaction and influence of two creeks on Escherichia coli concentrations of nearby beaches: Exploration of predictability and mechanisms

USGS Publications Warehouse

Nevers, M.B.; Whitman, R.L.; Frick, W.E.; Ge, Z.

2007-01-01

The impact of river outfalls on beach water quality depends on numerous interacting factors. The delivery of contaminants by multiple creeks greatly complicates understanding of the source contributions, especially when pollution might originate up- or down-coast of beaches. We studied two beaches along Lake Michigan that are located between two creek outfalls to determine the hydrometeorologic factors influencing near-shore microbiologic water quality and the relative impact of the creeks. The creeks continuously delivered water with high concentrations of Escherichia coli to Lake Michigan, and the direction of transport of these bacteria was affected by current direction. Current direction reversals were associated with elevated E. coli concentrations at Central Avenue beach. Rainfall, barometric pressure, wave height, wave period, and creek specific conductance were significantly related to E. coli concentration at the beaches and were the parameters used in predictive models that best described E. coli variation at the two beaches. Multiple inputs to numerous beaches complicates the analysis and understanding of the relative relationship of sources but affords opportunities for showing how these complex creek inputs might interact to yield collective or individual effects on beach water quality.

Does the scientific underpinning of regulatory tools to estimate bioavailability of nickel in freshwaters matter? The European-wide environmental quality standard for nickel.

PubMed

Peters, Adam; Schlekat, Christian E; Merrington, Graham

2016-10-01

A bioavailability-based environmental quality standard (EQS) was established for nickel in freshwaters under the European Union's Water Framework Directive. Bioavailability correction based on pH, water hardness, and dissolved organic carbon is a demonstrable improvement on existing hardness-based quality standards, which may be underprotective in high-hardness waters. The present study compares several simplified bioavailability tools developed to implement the Ni EQS (biomet, M-BAT, and PNECPro) against the full bioavailability normalization procedure on which the EQS was based. Generally, all tools correctly distinguished sensitive waters from insensitive waters, although with varying degrees of accuracy compared with full normalization. Biomet and M-BAT predictions were consistent with, but less accurate than, full bioavailability normalization results, whereas PNECpro results were generally more conservative. The comparisons revealed important differences in tools in development, which results in differences in the predictions. Importantly, the models used for the development of PNECpro use a different ecotoxicity dataset, and a different bioavailability normalization approach using fewer biotic ligand models (BLMs) than that used for the derivation of the Ni EQS. The failure to include all of the available toxicity data, and all of the appropriate NiBLMs, has led to some significant differences between the predictions provided by PNECpro and those calculated using the process agreed to in Europe under the Water Framework Directive and other chemicals management programs (such as REACH). These considerable differences mean that PNECpro does not reflect the behavior, fate, and ecotoxicity of nickel, and raises concerns about its applicability for checking compliance against the Ni EQS. Environ Toxicol Chem 2016;35:2397-2404. © 2016 SETAC. © 2016 SETAC.

An evaluation of the relative quality of dike pools for benthic macroinvertebrates in the Lower Missouri River, USA

USGS Publications Warehouse

Poulton, B.C.; Allert, A.L.

2012-01-01

A habitat-based aquatic macroinvertebrate study was initiated in the Lower Missouri River to evaluate relative quality and biological condition of dike pool habitats. Water-quality and sediment-quality parameters and macroinvertebrate assemblage structure were measured from depositional substrates at 18 sites. Sediment porewater was analysed for ammonia, sulphide, pH and oxidation-reduction potential. Whole sediments were analysed for particle-size distribution, organic carbon and contaminants. Field water-quality parameters were measured at subsurface and at the sediment-water interface. Pool area adjacent and downstream from each dike was estimated from aerial photography. Macroinvertebrate biotic condition scores were determined by integrating the following indicator response metrics: % of Ephemeroptera (mayflies), % of Oligochaeta worms, Shannon Diversity Index and total taxa richness. Regression models were developed for predicting macroinvertebrate scores based on individual water-quality and sediment-quality variables and a water/sediment-quality score that integrated all variables. Macroinvertebrate scores generated significant determination coefficients with dike pool area (R2=0.56), oxidation–reduction potential (R2=0.81) and water/sediment-quality score (R2=0.71). Dissolved oxygen saturation, oxidation-reduction potential and total ammonia in sediment porewater were most important in explaining variation in macroinvertebrate scores. The best two-variable regression models included dike pool size + the water/sediment-quality score (R2=0.84) and dike pool size + oxidation-reduction potential (R2=0.93). Results indicate that dike pool size and chemistry of sediments and overlying water can be used to evaluate dike pool quality and identify environmental conditions necessary for optimizing diversity and productivity of important aquatic macroinvertebrates. A combination of these variables could be utilized for measuring the success of habitat enhancement activities currently being implemented in this system.

Are extreme hydro-meteorological events a prerequisite for extreme water quality impacts? Exploring climate impacts on inland and coastal waters

NASA Astrophysics Data System (ADS)

Michalak, A. M.; Balaji, V.; Del Giudice, D.; Sinha, E.; Zhou, Y.; Ho, J. C.

2017-12-01

Questions surrounding water sustainability, climate change, and extreme events are often framed around water quantity - whether too much or too little. The massive impacts of extreme water quality impairments are equally compelling, however. Recent years have provided a host of compelling examples, with unprecedented harmful algal blooms developing along the West coast, in Utah Lake, in Lake Erie, and off the Florida coast, and huge hypoxic dead zones continuing to form in regions such as Lake Erie, the Chesapeake Bay, and the Gulf of Mexico. Linkages between climate change, extreme events, and water quality impacts are not well understood, however. Several factors explain this lack of understanding, including the relative complexity of underlying processes, the spatial and temporal scale mismatch between hydrologists and climatologists, and observational uncertainty leading to ambiguities in the historical record. Here, we draw on a number of recent studies that aim to quantitatively link meteorological variability and water quality impacts to test the hypothesis that extreme water quality impairments are the result of extreme hydro-meteorological events. We find that extreme hydro-meteorological events are neither always a necessary nor a sufficient condition for the occurrence of extreme water quality impacts. Rather, extreme water quality impairments often occur in situations where multiple contributing factors compound, which complicates both attribution of historical events and the ability to predict the future incidence of such events. Given the critical societal importance of water quality projections, a concerted program of uncertainty reduction encompassing observational and modeling components will be needed to examine situations where extreme weather plays an important, but not solitary, role in the chain of cause and effect.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

USGS Publications Warehouse

Francy, Donna S.; Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M.G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches

USGS Publications Warehouse

Nevers, Meredith B.; Whitman, Richard L.

2011-01-01

Efforts to improve public health protection in recreational swimming waters have focused on obtaining real-time estimates of water quality. Current monitoring techniques rely on the time-intensive culturing of fecal indicator bacteria (FIB) from water samples, but rapidly changing FIB concentrations result in management errors that lead to the public being exposed to high FIB concentrations (type II error) or beaches being closed despite acceptable water quality (type I error). Empirical predictive models may provide a rapid solution, but their effectiveness at improving health protection has not been adequately assessed. We sought to determine if emerging monitoring approaches could effectively reduce risk of illness exposure by minimizing management errors. We examined four monitoring approaches (inactive, current protocol, a single predictive model for all beaches, and individual models for each beach) with increasing refinement at 14 Chicago beaches using historical monitoring and hydrometeorological data and compared management outcomes using different standards for decision-making. Predictability (R2) of FIB concentration improved with model refinement at all beaches but one. Predictive models did not always reduce the number of management errors and therefore the overall illness burden. Use of a Chicago-specific single-sample standard-rather than the default 235 E. coli CFU/100 ml widely used-together with predictive modeling resulted in the greatest number of open beach days without any increase in public health risk. These results emphasize that emerging monitoring approaches such as empirical models are not equally applicable at all beaches, and combining monitoring approaches may expand beach access.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection.

PubMed

Francy, Donna S; Stelzer, Erin A; Duris, Joseph W; Brady, Amie M G; Harrison, John H; Johnson, Heather E; Ware, Michael W

2013-03-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Monitoring bacterial indicators of water quality in a tidally influenced delta: A Sisyphean pursuit.

PubMed

Partyka, Melissa L; Bond, Ronald F; Chase, Jennifer A; Atwill, Edward R

2017-02-01

The Sacramento-San Joaquin Delta Estuary (Delta) is the confluence of two major watersheds draining the Western Sierra Nevada mountains into the Central Valley of California, ultimately terminating into San Francisco Bay. We sampled 88 sites once a month for two years (2006-2008) over 87 separate sampling events for a total of 1740 samples. Water samples were analyzed for fecal indicator bacteria (Escherichia coli, enterococci and fecal coliforms), and 53 other physiochemical, land use, and environmental characteristics. The purpose of the study was to create a baseline of microbial water quality in the Delta and to identify various factors (climatic, land use, tidal, etc.) that were associated with elevated concentrations of indicator bacteria. Fecal indicator bacteria generally had weak to modest relationships to environmental conditions; the strength and direction of which varied for each microbial indicator, drainage region, and across seasons. Measured and unmeasured, site-specific effects accounted for large portions of variance in model predictions (ρ=0.086 to 0.255), indicating that spatial autocorrelation was a major component of water quality outcomes. The effects of tidal cycling and lack of connectivity between waterways and surrounding landscapes likely contributed to the lack of association between local land uses and microbial outcomes, though weak associations may also be indicative of mismatched spatiotemporal scales. The complex nature of this system necessitates continued monitoring and regular updates to statistical models designed to predict microbial water quality. Copyright © 2016 Elsevier B.V. All rights reserved.

Water quality and algal community dynamics of three deepwater lakes in Minnesota utilizing CE-QUAL-W2 models

USGS Publications Warehouse

Smith, Erik A.; Kiesling, Richard L.; Galloway, Joel M.; Ziegeweid, Jeffrey R.

2014-01-01

Water quality, habitat, and fish in Minnesota lakes will potentially be facing substantial levels of stress in the coming decades primarily because of two stressors: (1) land-use change (urban and agricultural) and (2) climate change. Several regional and statewide lake modeling studies have identified the potential linkages between land-use and climate change on reductions in the volume of suitable lake habitat for coldwater fish populations. In recent years, water-resource scientists have been making the case for focused assessments and monitoring of sentinel systems to address how these stress agents change lakes over the long term. Currently in Minnesota, a large-scale effort called “Sustaining Lakes in a Changing Environment” is underway that includes a focus on monitoring basic watershed, water quality, habitat, and fish indicators of 24 Minnesota sentinel lakes across a gradient of ecoregions, depths, and nutrient levels. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Department of Natural Resources, developed predictive water quality models to assess water quality and habitat dynamics of three select deepwater lakes in Minnesota. The three lakes (Lake Carlos in Douglas County, Elk Lake in Clearwater County, and Trout Lake in Cook County) were assessed under recent (2010–11) meteorological conditions. The three selected lakes contain deep, coldwater habitats that remain viable during the summer months for coldwater fish species. Hydrodynamics and water-quality characteristics for each of the three lakes were simulated using the CE-QUAL-W2 model, which is a carbon-based, laterally averaged, two-dimensional water-quality model. The CE-QUAL-W2 models address the interaction between nutrient cycling, primary production, and trophic dynamics to predict responses in the distribution of temperature and oxygen in lakes. The CE-QUAL-W2 models for all three lakes successfully predicted water temperature, on the basis of the two metrics of absolute mean error and root mean square error, using measured inputs of water temperature and nutrients. One of the main calibration tools for CE-QUAL-W2 model development was the vertical profile temperature data, available for all three lakes. For all three lakes, the absolute mean error and root mean square error were less than 1.0 degree Celsius and 1.2 degrees Celsius, respectively, for the different depth ranges used for vertical profile comparisons. In Lake Carlos, simulated water temperatures compared better to measured water temperatures in the epilimnion than in the hypolimnion. The reverse was true for the other two lakes, Elk Lake and Trout Lake, where the simulated results were slightly better for the hypolimnion than the epilimnion. The model also was used to approximate the location of the thermocline throughout the simulation periods, approximately April to November, in all three lake models. Deviations between the simulated and measured water temperatures in the vertical lake profile commonly were because of an offset in the timing of thermocline shifts rather than the simulated results missing thermocline shifts altogether.

Cytotoxic drugs in drinking water: a prediction and risk assessment exercise for the thames catchment in the United kingdom.

PubMed

Rowney, Nicole C; Johnson, Andrew C; Williams, Richard J

2009-12-01

Cytotoxic, also known as antineoplastic, drugs remain an important weapon in the fight against cancer. This study considers the water quality implications for the Thames catchment (United Kingdom) arising from the routine discharge of these drugs after use, down the drain and into the river. The review focuses on 13 different cytotoxic drugs from the alkylating agent, antimetabolite, and anthracycline antibiotic families. A geographic-information-system-based water quality model was used in the present study. The model was informed by literature values on consumption, excretion, and fate data to predict raw drinking water concentrations at the River Thames abstraction points at Farmoor, near Oxford, and Walton, in West London. To discover the highest plausible values, upper boundary values for consumption and excretion together with lower removal values for sewage treatment were used. The raw drinking water cytotoxic drug maximum concentrations at Walton (the higher of the two) representative of mean and low flow conditions were predicted to be 11 and 20 ng/L for the five combined alkylating agents, 2 and 4 ng/L for the three combined antimetabolites, and 0.05 and 0.10 ng/L the for two combined anthracycline antibiotics, respectively. If they were to escape into tap water, then the highest predicted concentrations would still be a factor of between 25 and 40 below the current recommended daily doses of concern. Although the risks may be negligible for healthy adults, more concern may be associated with special subgroup populations, such as pregnant women, their fetuses, and breast-feeding infants, due to their developmental vulnerability.

Predicting Impacts of Increased CO2 and Climate Change on the Water Cycle and Water Quality in the Semiarid James River Basin of the Midwestern USA

USGS Publications Warehouse

Wu, Yiping; Liu, Shu-Guang; Gallant, Alisa L.

2012-01-01

Emissions of greenhouse gases and aerosols from human activities continue to alter the climate and likely will have significant impacts on the terrestrial hydrological cycle and water quality, especially in arid and semiarid regions. We applied an improved Soil and Water Assessment Tool (SWAT) to evaluate impacts of increased atmospheric CO2 concentration and potential climate change on the water cycle and nitrogen loads in the semiarid James River Basin (JRB) in the Midwestern United States. We assessed responses of water yield, soil water content, groundwater recharge, and nitrate nitrogen (NO3–N) load under hypothetical climate-sensitivity scenarios in terms of CO2, precipitation, and air temperature. We extended our predictions of the dynamics of these hydrological variables into the mid-21st century with downscaled climate projections integrated across output from six General Circulation Models. Our simulation results compared against the baseline period 1980 to 2009 suggest the JRB hydrological system is highly responsive to rising levels of CO2 concentration and potential climate change. Under our scenarios, substantial decrease in precipitation and increase in air temperature by the mid-21st century could result in significant reduction in water yield, soil water content, and groundwater recharge. Our model also estimated decreased NO3–N load to streams, which could be beneficial, but a concomitant increase in NO3–N concentration due to a decrease in streamflow likely would degrade stream water and threaten aquatic ecosystems. These results highlight possible risks of drought, water supply shortage, and water quality degradation in this basin.

Occurrence of waterborne pathogens and Escherichia coli at offshore drinking water intakes in lake Ontario.

PubMed

Edge, T A; Khan, I U H; Bouchard, R; Guo, J; Hill, S; Locas, A; Moore, L; Neumann, N; Nowak, E; Payment, P; Yang, R; Yerubandi, R; Watson, S

2013-10-01

The occurrence of waterborne pathogens was investigated at three drinking water intakes located about 2 km offshore in Lake Ontario. Water sampling was conducted over 3 years for Campylobacter spp., Cryptosporidium spp., Giardia spp., cultivable enteric viruses, and water quality parameters. All pathogens were detected in the offshore source water for each water treatment plant (WTP1 to WTP3), although at relatively low frequencies and concentrations. Giardia was the most common pathogen, occurring in 36% of water samples from the influent of WTP1 (n = 46), and with a maximum concentration of 0.70 cysts/liter in this influent. Cryptosporidium occurred as frequently as 15% in the WTP2 influent (n = 35), with a maximum concentration of 0.40 oocysts/liter in the WTP1 influent. The human Bacteroidales HF183 DNA marker was most common in the WTP1 influent (19%), and this was the only WTP where the Cryptosporidium hominis genotype was detected. No water quality parameter was predictive of pathogen occurrence across all three WTP influents. Escherichia coli was often below detection when pathogens were detected, and spikes in E. coli concentrations often did not coincide with pathogen occurrence. After summer rain events, river plumes had E. coli concentrations as high as 222 CFU/100 ml in surface waters 2 km offshore, without impacting drinking water intakes below the thermocline on the lake bottom. At times, prechlorination to control mussels at offshore intake cribs compromised the use of E. coli for "raw" water quality assessment, particularly for chlorine-resistant Cryptosporidium. E. coli measured by standard methods did not reliably predict pathogen occurrence at drinking water intakes in offshore ecosystems.

Occurrence of Waterborne Pathogens and Escherichia coli at Offshore Drinking Water Intakes in Lake Ontario

PubMed Central

Khan, I. U. H.; Bouchard, R.; Guo, J.; Hill, S.; Locas, A.; Moore, L.; Neumann, N.; Nowak, E.; Payment, P.; Yang, R.; Yerubandi, R.; Watson, S.

2013-01-01

The occurrence of waterborne pathogens was investigated at three drinking water intakes located about 2 km offshore in Lake Ontario. Water sampling was conducted over 3 years for Campylobacter spp., Cryptosporidium spp., Giardia spp., cultivable enteric viruses, and water quality parameters. All pathogens were detected in the offshore source water for each water treatment plant (WTP1 to WTP3), although at relatively low frequencies and concentrations. Giardia was the most common pathogen, occurring in 36% of water samples from the influent of WTP1 (n = 46), and with a maximum concentration of 0.70 cysts/liter in this influent. Cryptosporidium occurred as frequently as 15% in the WTP2 influent (n = 35), with a maximum concentration of 0.40 oocysts/liter in the WTP1 influent. The human Bacteroidales HF183 DNA marker was most common in the WTP1 influent (19%), and this was the only WTP where the Cryptosporidium hominis genotype was detected. No water quality parameter was predictive of pathogen occurrence across all three WTP influents. Escherichia coli was often below detection when pathogens were detected, and spikes in E. coli concentrations often did not coincide with pathogen occurrence. After summer rain events, river plumes had E. coli concentrations as high as 222 CFU/100 ml in surface waters 2 km offshore, without impacting drinking water intakes below the thermocline on the lake bottom. At times, prechlorination to control mussels at offshore intake cribs compromised the use of E. coli for “raw” water quality assessment, particularly for chlorine-resistant Cryptosporidium. E. coli measured by standard methods did not reliably predict pathogen occurrence at drinking water intakes in offshore ecosystems. PMID:23835181

Predicting impacts of increased CO₂ and climate change on the water cycle and water quality in the semiarid James River Basin of the Midwestern USA.

PubMed

Wu, Yiping; Liu, Shuguang; Gallant, Alisa L

2012-07-15

Emissions of greenhouse gases and aerosols from human activities continue to alter the climate and likely will have significant impacts on the terrestrial hydrological cycle and water quality, especially in arid and semiarid regions. We applied an improved Soil and Water Assessment Tool (SWAT) to evaluate impacts of increased atmospheric CO(2) concentration and potential climate change on the water cycle and nitrogen loads in the semiarid James River Basin (JRB) in the Midwestern United States. We assessed responses of water yield, soil water content, groundwater recharge, and nitrate nitrogen (NO(3)-N) load under hypothetical climate-sensitivity scenarios in terms of CO(2), precipitation, and air temperature. We extended our predictions of the dynamics of these hydrological variables into the mid-21st century with downscaled climate projections integrated across output from six General Circulation Models. Our simulation results compared against the baseline period 1980 to 2009 suggest the JRB hydrological system is highly responsive to rising levels of CO(2) concentration and potential climate change. Under our scenarios, substantial decrease in precipitation and increase in air temperature by the mid-21st century could result in significant reduction in water yield, soil water content, and groundwater recharge. Our model also estimated decreased NO(3)-N load to streams, which could be beneficial, but a concomitant increase in NO(3)-N concentration due to a decrease in streamflow likely would degrade stream water and threaten aquatic ecosystems. These results highlight possible risks of drought, water supply shortage, and water quality degradation in this basin. Published by Elsevier B.V.

DEVELOPMENT OF MICROBIAL METAGENOMIC MARKERS FOR ENVIRONMENTAL MONITORING AND RISK ASSESSMENT

EPA Science Inventory

The microbiological water quality standards established by EPA depend on culturing fecal indicator bacteria to predict the risks associated with water usage. For decades this has been the favored approach to microbiological monitoring in spite of the fact that culture-based meth...

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Runoff delay exerts a strong control on the field-scale removal of manure-borne fecal bacteria with runoff

USDA-ARS?s Scientific Manuscript database

The microbial safety of surface waters is an ongoing issue which is threatened by the transport of manure-borne bacteria to water sources used for irrigation or recreation. Predictive modeling has become an effective tool to forecast the microbial quality of water during precipitation events, howeve...

Coastal Water Quality Modeling in Tidal Lake: Revisited with Groundwater Intrusion

NASA Astrophysics Data System (ADS)

Kim, C.

2016-12-01

A new method for predicting the temporal and spatial variation of water quality, with accounting for a groundwater effect, has been proposed and applied to a water body partially connected to macro-tidal coastal waters in Korea. The method consists of direct measurement of environmental parameters, and it indirectly incorporates a nutrients budget analysis to estimate the submarine groundwater fluxes. Three-dimensional numerical modeling of water quality has been used with the directly collected data and the indirectly estimated groundwater fluxes. The applied area is Saemangeum tidal lake that is enclosed by 33km-long sea dyke with tidal openings at two water gates. Many investigations of groundwater impact reveal that 10 50% of nutrient loading in coastal waters comes from submarine groundwater, particularly in the macro-tidal flat, as in the west coast of Korea. Long-term monitoring of coastal water quality signals the possibility of groundwater influence on salinity reversal and on the excess mass outbalancing the normal budget in Saemangeum tidal lake. In the present study, we analyze the observed data to examine the influence of submarine groundwater, and then a box model is demonstrated for quantifying the influx and efflux. A three-dimensional numerical model has been applied to reproduce the process of groundwater dispersal and its effect on the water quality of Saemangeum tidal lake. The results show that groundwater influx during the summer monsoon then contributes significantly, 20% more than during dry season, to water quality in the tidal lake.

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

USGS Publications Warehouse

Yang, X.; Jin, W.

2010-01-01

Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO3NO2-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO3NO2-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO3NO2-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution. ?? 2010 Elsevier Ltd.

Adequacy of Nasqan data to describe areal and temporal variability of water quality of the San Juan River Drainage basin upstream from Shiprock New Mexico

USGS Publications Warehouse

Goetz, C.L.; Abeyta, Cynthia G.

1987-01-01

Analyses indicate that water quality in the San Juan River drainage basin upstream from Shiprock, New Mexico, is quite variable from station to station. Analyses are based on water quality data from the U.S. Geological Survey WATSTORE files and the New Mexico Environmental Improvement Division 's files. In the northeastern part of the basin, most streams are calcium-bicarbonate waters. In the northwestern and southern part of the basin, the streams are calcium-sulfate and sodium-sulfate waters. Geology, climate, and land use and water use affect the water quality. Statistical analysis shows that streamflow, suspended-sediment, dissolved-iron, dissolved-orthophosphate-phosphorus, dissolved-sodium, dissolved-sulfate, and dissolved-manganese concentrations, specific conductance, and pH are highly variable among most stations. Dissolved-radium-226 concentration is the least variable among stations. A trend in one or more water quality constituents for the time period, October 1, 1973, through September 30, 1981, was detected at 15 out of 36 stations tested. The NASQAN stations Animas River at Farmington and San Juan River at Shiprock, New Mexico, record large volumes of flow that represent an integration of the flow from many upstream tributaries. The data collected do not represent what is occurring at specific points upstream in the basin, but do provide accurate information on how water quality is changing over time at the station location. A water quality, streamflow model would be necessary to predict accurately what is occurring simultaneously in the entire basin. (USGS)

Water quality, sediment, and soil characteristics near Fargo-Moorhead urban areas as affected by major flooding of the Red River of the north

Treesearch

A.C. Guy; T.M. DeSutter; F.X.M. Casey; R. Kolka; H. Hakk

2012-01-01

Spring flooding of the Red River of the North (RR) is common, but little information exits on how these flood events affect water and overbank sediment quality within an urban area. With the threat of the spring 2009 flood in the RR predicted to be the largest in recorded history and the concerns about the flooding of farmsteads, outbuildings, garages, and basements,...

AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya

NASA Astrophysics Data System (ADS)

Banerjee, Polash; Ghose, Mrinal Kanti; Pradhan, Ratika

2018-05-01

Spatial analysis of water quality impact assessment of highway projects in mountainous areas remains largely unexplored. A methodology is presented here for Spatial Water Quality Impact Assessment (SWQIA) due to highway-broadening-induced vehicular traffic change in the East district of Sikkim. Pollution load of the highway runoff was estimated using an Average Annual Daily Traffic-Based Empirical model in combination with mass balance model to predict pollution in the rivers within the study area. Spatial interpolation and overlay analysis were used for impact mapping. Analytic Hierarchy Process-Based Water Quality Status Index was used to prepare a composite impact map. Model validation criteria, cross-validation criteria, and spatial explicit sensitivity analysis show that the SWQIA model is robust. The study shows that vehicular traffic is a significant contributor to water pollution in the study area. The model is catering specifically to impact analysis of the concerned project. It can be an aid for decision support system for the project stakeholders. The applicability of SWQIA model needs to be explored and validated in the context of a larger set of water quality parameters and project scenarios at a greater spatial scale.

Estimating irrigation water use in the humid eastern United States

USGS Publications Warehouse

Levin, Sara B.; Zarriello, Phillip J.

2013-01-01

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to develop the models as well as two independent validation datasets from Georgia and Virginia that were not used in model development. Irrigation water-use estimates from the logistic regression method more closely matched mean reported irrigation rates than estimates from the crop-water-demand model when compared to the irrigation data used to develop the equations. The root mean squared errors (RMSEs) for the logistic regression estimates of mean annual irrigation ranged from 0.3 to 2.0 inches (in.) for the five crop types; RMSEs for the crop-water-demand models ranged from 1.4 to 3.9 in. However, when the models were applied and compared to the independent validation datasets from southwest Georgia from 2010, and from Virginia from 1999 to 2007, the crop-water-demand model estimates were as good as or better at predicting the mean irrigation volume than the logistic regression models for most crop types. RMSEs for logistic regression estimates of mean annual irrigation ranged from 1.0 to 7.0 in. for validation data from Georgia and from 1.8 to 4.9 in. for validation data from Virginia; RMSEs for crop-water-demand model estimates ranged from 2.1 to 5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia data. In general, regression-based models performed better in areas that had quality daily or weekly irrigation data from which the regression equations were developed; however, the regression models were less reliable than the crop-water-demand models when applied outside the area for which they were developed. In most eastern coastal states that do not have quality irrigation data, the crop-water-demand model can be used more reliably. The development of predictive models of irrigation water use in this study was hindered by a lack of quality irrigation data. Many mid-Atlantic and New England states do not require irrigation water use to be reported. A survey of irrigation data from 14 eastern coastal states from Maine to Georgia indicated that, with the exception of the data in Georgia, irrigation data in the states that do require reporting commonly did not contain requisite ancillary information such as irrigated area or crop type, lacked precision, or were at an aggregated temporal scale making them unsuitable for use in the development of predictive models. Confidence in the reliability of either modeling method is affected by uncertainty in the reported data from which the models were developed or validated. Only through additional collection of quality data and further study can the accuracy and uncertainty of irrigation water-use estimates be improved in the humid eastern United States.

Predicting aged pork quality using a portable raman device

USDA-ARS?s Scientific Manuscript database

Objectives: A need exists for a better on-line evaluation method for pork quality. Raman spectroscopy evaluates structure and composition of food samples, with advantage of being portable, non-invasive and insensitive to water. The objectives of this study were to evaluate the correlation between Ra...

Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

PubMed

Stoica, C; Camejo, J; Banciu, A; Nita-Lazar, M; Paun, I; Cristofor, S; Pacheco, O R; Guevara, M

2016-01-01

Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

Cladophora in the Great Lakes: impacts on beach water quality and human health.

PubMed

Verhougstraete, M P; Byappanahalli, M N; Rose, J B; Whitman, R L

2010-01-01

Cladophora in the Great Lakes grows rapidly during the warm summer months, detaches, and becomes free-floating mats as a result of environmental conditions, eventually becoming stranded on recreational beaches. Cladophora provides protection and nutrients, which allow enteric bacteria such as Escherichia coli, enterococci, Shigella, Campylobacter, and Salmonella to persist and potentially regrow in the presence of the algae. As a result of wind and wave action, these microorganisms can detach and be released to surrounding waters and can influence water quality. Enteric bacterial pathogens have been detected in Cladophora mats; E. coli and enterococci may populate to become part of the naturalized microbiota in Cladophora; the high densities of these bacteria may affect water quality, resulting in unnecessary beach closures. The continued use of traditional fecal indicators at beaches with Cladophora presence is inadequate at accurately predicting the presence of fecal contamination. This paper offers a substantial review of available literature to improve the knowledge of Cladophora impacts on water quality, recreational water monitoring, fecal indicator bacteria and microorganisms, and public health and policy.

Cladophora in the Great Lakes: Impacts on beach water quality and human health

USGS Publications Warehouse

Verhougstraete, M.P.; Byappanahalli, Muruleedhara N.; Rose, J.B.; Whitman, Richard L.

2010-01-01

Cladophora in the Great Lakes grows rapidly during the warm summer months, detaches, and becomes free-floating mats as a result of environmental conditions, eventually becoming stranded on recreational beaches. Cladophora provides protection and nutrients, which allow enteric bacteria such as Escherichia coli, enterococci, Shigella, Campylobacter, and Salmonella to persist and potentially regrow in the presence of the algae. As a result of wind and wave action, these microorganisms can detach and be released to surrounding waters and can influence water quality. Enteric bacterial pathogens have been detected in Cladophora mats; E. coli and enterococci may populate to become part of the naturalized microbiota in Cladophora; the high densities of these bacteria may affect water quality, resulting in unnecessary beach closures. The continued use of traditional fecal indicators at beaches with Cladophora presence is inadequate at accurately predicting the presence of fecal contamination. This paper offers a substantial review of available literature to improve the knowledge of Cladophora impacts on water quality, recreational water monitoring, fecal indicator bacteria and microorganisms, and public health and policy.

Uncertainties in selected river water quality data

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.

2007-02-01

Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise from natural or anthropogenic causes. Empirical quality of river water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected river water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2005). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties, measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerably to the overall uncertainty of river water quality data. Temporal autocorrelation of river water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments (500-3000 km2) reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended sediments none of the methods investigated produced very reliable load estimates when weekly concentrations data were used. Uncertainties associated with loads estimates based on infrequent samples will decrease with increasing size of rivers.

Uncertainties in selected surface water quality data

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.

2006-09-01

Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise form natural or anthropogenic causes. Empirical quality of surface water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected surface water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2006). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability's within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerable to the overall uncertainty of surface water quality data. Temporal autocorrelation of surface water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended sediments none of the methods investigated produced very reliable load estimates when weekly concentrations data were used. Uncertainties associated with loads estimates based on infrequent samples will decrease with increasing size of rivers.

Dietary composition of black drum Pogonias cromis in a hypersaline estuary reflects water quality and prey availability.

PubMed

Rubio, K S; Ajemian, M; Stunz, G W; Palmer, T A; Lebreton, B; Beseres Pollack, J

2018-06-22

The Baffin Bay estuary is a hypersaline system in the Gulf of Mexico that supports an important recreational and commercial fishery for black drum Pogonias cromis, a benthic predator. Seasonal measurements of water quality variables, benthic macrofauna densities and biomass, and determination of P. cromis food sources using stomach-content and stable-isotope analyses were carried out to determine how P. cromis food sources change with water quality and how this may affect P. cromis diet. Gut-content analysis indicated P. cromis selectively consumed bivalves Mulinia lateralis and Anomalocardia auberiana. Isotope compositions demonstrated that P. cromis relied on these benthic food resources produced in the Baffin Bay estuary year-round. Biomass and densities of these bivalves were influenced by changes in water quality variables, particularly salinity and dissolved oxygen. Thus, this paper demonstrates the relationship between water quality variables, benthic macrofauna, and their use as food resources by a carnivorous fish species, and emphasizes the need for integrated assessments when studying the effects of water quality on ecosystem function. Holistic approaches such as these can provide important information for management and conservation of fishery resources and can improve predictions of ecosystem response to climate variability. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Total suspended solids concentrations and yields for water-quality monitoring stations in Gwinnett County, Georgia, 1996-2009

USGS Publications Warehouse

Landers, Mark N.

2013-01-01

The U.S. Geological Survey, in cooperation with the Gwinnett County Department of Water Resources, established a water-quality monitoring program during late 1996 to collect comprehensive, consistent, high-quality data for use by watershed managers. As of 2009, continuous streamflow and water-quality data as well as discrete water-quality samples were being collected for 14 watershed monitoring stations in Gwinnett County. This report provides statistical summaries of total suspended solids (TSS) concentrations for 730 stormflow and 710 base-flow water-quality samples collected between 1996 and 2009 for 14 watershed monitoring stations in Gwinnett County. Annual yields of TSS were estimated for each of the 14 watersheds using methods described in previous studies. TSS yield was estimated using linear, ordinary least-squares regression of TSS and explanatory variables of discharge, turbidity, season, date, and flow condition. The error of prediction for estimated yields ranged from 1 to 42 percent for the stations in this report; however, the actual overall uncertainty of the estimated yields cannot be less than that of the observed yields (± 15 to 20 percent). These watershed yields provide a basis for evaluation of how watershed characteristics, climate, and watershed management practices affect suspended sediment yield.

Assessment of domestic water quality: case study, Beirut, Lebanon.

PubMed

Korfali, Samira Ibrahim; Jurdi, Mey

2007-12-01

In urban cities, the environmental services are the responsibility of the public sector, where piped water supply is the norm for urban household. Likewise, in Beirut City (capital of Lebanon) official water authorities are the main supplier of domestic water through a network of piping system that leaks in many areas. Beirut City and its suburbs are overpopulated since it is the residence of 1/3 of the Lebanese citizens. Thus, Beirut suffers deficiency in meeting its water demand. Water rationing, as a remedial action, is firmly established since four decades by the Lebanese Water Authorities. Consumers resorted then to private wells to supplement their domestic water needs. Consequently, household water quality is influenced by external factors relating to well water characteristics and internal factors depending on the types of the pipes of the distribution network and cross connections to sewer pipes. These factors could result in chemical and microbial contamination of drinking water. The objective of this study is to investigate domestic water quality variation in Beirut City emerging form the aforementioned factors. The presented work encircles a typical case study of Beirut City (Ras Beirut). Results showed deterioration pattern in domestic water quality. The predicted metal species and scales within the water pipes of distribution network depended on water pH, hardness, sulfate, chloride, and iron. The corrosion of iron pipes mainly depended on Mg hardness.

Factors controlling stream water nitrate and phosphor loads during precipitation events

NASA Astrophysics Data System (ADS)

Rozemeijer, J.; van der Velde, Y.; van Geer, F.; de Rooij, G. H.; Broers, H.; Bierkens, M. F.

2009-12-01

Pollution of surface waters in densely populated areas with intensive land use is a serious threat to their ecological, industrial and recreational utilization. European and national manure policies and several regional and local pilot projects aim at reducing pollution loads to surface waters. For the evaluation of measures, water authorities and environmental research institutes are putting a lot of effort into monitoring surface water quality. Within regional surface water quality monitoring networks, the measurement locations are usually situated in the downstream part of the catchment to represent a larger area. The monitoring frequency is usually low (e.g. monthly), due to the high costs for sampling and analysis. As a consequence, human induced trends in nutrient loads and concentrations in these monitoring data are often concealed by the large variability of surface water quality caused by meteorological variations. Because this natural variability in surface water quality is poorly understood, large uncertainties occur in the estimates of (trends in) nutrient loads or average concentrations. This study aims at uncertainty reduction in the estimates of mean concentrations and loads of N and P from regional monitoring data. For this purpose, we related continuous records of stream water N and P concentrations to easier and cheaper to collect quantitative data on precipitation, discharge, groundwater level and tube drain discharge. A specially designed multi scale experimental setup was installed in an agricultural lowland catchment in The Netherlands. At the catchment outlet, continuous measurements of water quality and discharge were performed from July 2007-January 2009. At an experimental field within the catchment we collected continuous measurements of precipitation, groundwater levels and tube drain discharges. 20 significant rainfall events with a variety of antecedent conditions, durations and intensities were selected for analysis. Singular and multiple regression analysis were used to identify relations between the N and P response to the rainfall events and the quantitative event characteristics. We successfully used these relations to predict the N and P responses to events and to improve the interpolation between low frequency grab sample measurements. Incorporating the predicted concentration changes during high discharge events dramatically improved the precision of our load estimations.

Model design for predicting extreme precipitation event impacts on water quality in a water supply reservoir

NASA Astrophysics Data System (ADS)

Hagemann, M.; Jeznach, L. C.; Park, M. H.; Tobiason, J. E.

2016-12-01

Extreme precipitation events such as tropical storms and hurricanes are by their nature rare, yet have disproportionate and adverse effects on surface water quality. In the context of drinking water reservoirs, common concerns of such events include increased erosion and sediment transport and influx of natural organic matter and nutrients. As part of an effort to model the effects of an extreme precipitation event on water quality at the reservoir intake of a major municipal water system, this study sought to estimate extreme-event watershed responses including streamflow and exports of nutrients and organic matter for use as inputs to a 2-D hydrodynamic and water quality reservoir model. Since extreme-event watershed exports are highly uncertain, we characterized and propagated predictive uncertainty using a quasi-Monte Carlo approach to generate reservoir model inputs. Three storm precipitation depths—corresponding to recurrence intervals of 5, 50, and 100 years—were converted to streamflow in each of 9 tributaries by volumetrically scaling 2 storm hydrographs from the historical record. Rating-curve models for concentratoin, calibrated using 10 years of data for each of 5 constituents, were then used to estimate the parameters of a multivariate lognormal probability model of constituent concentrations, conditional on each scenario's storm date and streamflow. A quasi-random Halton sequence (n = 100) was drawn from the conditional distribution for each event scenario, and used to generate input files to a calibrated CE-QUAL-W2 reservoir model. The resulting simulated concentrations at the reservoir's drinking water intake constitute a low-discrepancy sample from the estimated uncertainty space of extreme-event source water-quality. Limiting factors to the suitability of this approach include poorly constrained relationships between hydrology and constituent concentrations, a high-dimensional space from which to generate inputs, and relatively long run-time for the reservoir model. This approach proved useful in probing a water supply's resilience to extreme events, and to inform management responses, particularly in a region such as the American Northeast where climate change is expected to bring such events with higher frequency and intensity than have occurred in the past.

Effect of aquifer storage and recovery (ASR) on recovered stormwater quality variability.

PubMed

Page, D W; Peeters, L; Vanderzalm, J; Barry, K; Gonzalez, D

2017-06-15

Aquifer Storage and Recovery (ASR) is increasingly being considered as a means of reusing urban stormwater to supplement available urban water resources. Storage of stormwater in an aquifer has been shown to affect water quality but it has also been claimed that storage will also decrease the stormwater quality variability making for improved predictability and management. This study is the first to document the changes in stormwater quality variability as a result of subsurface storage at four full scale ASR sites using advanced statistical techniques. New methods to examine water quality are required as data is often highly left censored and so traditional measures of variability such as the coefficient of variation are inappropriate. It was observed that for some water quality parameters (most notably E. coli) there was a marked improvement of water quality and a significant decrease in variability at all sites. This means that aquifer storage prior to engineered treatment systems may be advantageous in terms of system design to avoid over engineering. For other parameters such as metal(loids)s and nutrients the trend was less clear due to the numerous processes occurring during storage leading to an increase in variability, especially for geogenic metals and metalloids such as iron and arsenic. Depending upon the specific water quality parameters and end use, use of ASR may not have a dampening effect on stormwater quality variability. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Watershed Models for Predicting Nitrogen Loads from Artificially Drained Lands

Treesearch

R. Wayne Skaggs; George M. Chescheir; Glenn Fernandez; Devendra M. Amatya

2003-01-01

Non-point sources of pollutants originate at the field scale but water quality problems usually occur at the watershed or basin scale. This paper describes a series of models developed for poorly drained watersheds. The models use DRAINMOD to predict hydrology at the field scale and a range of methods to predict channel hydraulics and nitrogen transport. In-stream...

Influences of water quality and climate on the water-energy nexus: A spatial comparison of two water systems.

PubMed

Stang, Shannon; Wang, Haiying; Gardner, Kevin H; Mo, Weiwei

2018-07-15

As drinking water supply systems plan for sustainable management practices, impacts from future water quality and climate changes are a major concern. This study aims to understand the intraannual changes of energy consumption for water treatment, investigate the relative importance of water quality and climate indicators on energy consumption for water treatment, and predict the effects of climate change on the embodied energy of treated, potable water at two municipal drinking water systems located in the northeast and southeast US. To achieve this goal, a life cycle assessment was first performed to quantify the monthly energy consumption in the two drinking water systems. Regression and relative importance analyses were then performed between climate indicators, raw water quality indicators, and chemical and energy usages in the treatment processes to determine their correlations. These relationships were then used to project changes in embodied energy associated with the plants' processes, and the results were compared between the two regions. The projections of the southeastern US water plant were for an increase in energy demand resulted from an increase of treatment chemical usages. The northeastern US plant was projected to decrease its energy demand due to a reduced demand for heating the plant's infrastructure. The findings indicate that geographic location and treatment process may determine the way climate change affects drinking water systems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Water resources during drought conditions and postfire water quality in the upper Rio Hondo Basin, Lincoln County, New Mexico, 2010-13

USGS Publications Warehouse

Sherson, Lauren R.; Rice, Steven E.

2015-07-16

Changes in climate and increased groundwater and surface-water use are likely to affect the availability of water in the upper Rio Hondo Basin. Increased drought probably will increase the potential for wildfires, which can affect downstream water quality and increase flood potential. Climate-research predicted decreases in winter precipitation may have an adverse effect on the amount of groundwater recharge that occurs in the upper Rio Hondo Basin, given the predominance of winter precipitation recharge as indicated by the stable isotope results. Decreases in surface-water supplies because of persistent drought conditions and reductions in the quality of water because of the effects of wildfire may lead to a larger reliance on groundwater reserves in the upper Rio Hondo Basin. Decreasing water levels because of increasing groundwater withdrawal could reduce base flows in the Rio Bonito and Rio Ruidoso. Well organized and scientifically supported regional water-resources management will be necessary for dealing with the likely scenario of increases in demand coupled with decreases in supply in the upper Rio Hondo Basin.

Dynamic modeling of the Ganga river system: impacts of future climate and socio-economic change on flows and nitrogen fluxes in India and Bangladesh.

PubMed

Whitehead, P G; Sarkar, S; Jin, L; Futter, M N; Caesar, J; Barbour, E; Butterfield, D; Sinha, R; Nicholls, R; Hutton, C; Leckie, H D

2015-06-01

This study investigates the potential impacts of future climate and socio-economic change on the flow and nitrogen fluxes of the Ganga river system. This is the first basin scale water quality study for the Ganga considering climate change at 25 km resolution together with socio-economic scenarios. The revised dynamic, process-based INCA model was used to simulate hydrology and water quality within the complex multi-branched river basins. All climate realizations utilized in the study predict increases in temperature and rainfall by the 2050s with significant increase by the 2090s. These changes generate associated increases in monsoon flows and increased availability of water for groundwater recharge and irrigation, but also more frequent flooding. Decreased concentrations of nitrate and ammonia are expected due to increased dilution. Different future socio-economic scenarios were found to have a significant impact on water quality at the downstream end of the Ganga. A less sustainable future resulted in a deterioration of water quality due to the pressures from higher population growth, land use change, increased sewage treatment discharges, enhanced atmospheric nitrogen deposition, and water abstraction. However, water quality was found to improve under a more sustainable strategy as envisaged in the Ganga clean-up plan.

Application of LANDSAT to the Surveillance and Control of Eutrophication in Saginaw Bay

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. LANDSAT digital data and ground truth measurements for Saginaw Bay (Lake Huron), Michigan, for 3 June 1974 can be correlated by stepwise linear regression technique and the resulting equations used to estimate invisible water quality parameters in nonsampled areas. Correlation of these parameters with each other indicates that the transport of Saginaw River water can now be traced by a number of water quality features, one or more of which are directly detected by LANDSAT. Five of the 12 water quality parameters are best correlated with LANDSAT band 6 measurements alone. One parameter (temperature) relates to band 5 alone and the remaining six may be predicted with varying degrees of accuracy from a combination of two bands (first band 6 and generally band 4 second).

Effects of urbanization on the water quality of lakes in Eagan, Minnesota

USGS Publications Warehouse

Ayers, M.A.; Payne, G.A.; Have, Mark A.

1980-01-01

Three phosphorus-prediction models developed during the study are applicable to shallow (less than about 12 feet), nonstratifying lakes and ponds. The data base was not sufficient to select an appropriate model to predict the effects of future loading from continuing urbanization on the deeper lakes.

Disentangling the Effects of Water Stress on Carbon Acquisition, Vegetative Growth, and Fruit Quality of Peach Trees by Means of the QualiTree Model.

PubMed

Rahmati, Mitra; Mirás-Avalos, José M; Valsesia, Pierre; Lescourret, Françoise; Génard, Michel; Davarynejad, Gholam H; Bannayan, Mohammad; Azizi, Majid; Vercambre, Gilles

2018-01-01

Climate change projections predict warmer and drier conditions. In general, moderate to severe water stress reduce plant vegetative growth and leaf photosynthesis. However, vegetative and reproductive growths show different sensitivities to water deficit. In fruit trees, water restrictions may have serious implications not only on tree growth and yield, but also on fruit quality, which might be improved. Therefore, it is of paramount importance to understand the complex interrelations among the physiological processes involved in within-tree carbon acquisition and allocation, water uptake and transpiration, organ growth, and fruit composition when affected by water stress. This can be studied using process-based models of plant functioning, which allow assessing the sensitivity of various physiological processes to water deficit and their relative impact on vegetative growth and fruit quality. In the current study, an existing fruit-tree model (QualiTree) was adapted for describing the water stress effects on peach ( Prunus persica L. Batsch) vegetative growth, fruit size and composition. First, an energy balance calculation at the fruit-bearing shoot level and a water transfer formalization within the plant were integrated into the model. Next, a reduction function of vegetative growth according to tree water status was added to QualiTree. Then, the model was parameterized and calibrated for a late-maturing peach cultivar ("Elberta") under semi-arid conditions, and for three different irrigation practices. Simulated vegetative and fruit growth variability over time was consistent with observed data. Sugar concentrations in fruit flesh were well simulated. Finally, QualiTree allowed for determining the relative importance of photosynthesis and vegetative growth reduction on carbon acquisition, plant growth and fruit quality under water constrains. According to simulations, water deficit impacted vegetative growth first through a direct effect on its sink strength, and; secondly, through an indirect reducing effect on photosynthesis. Fruit composition was moderately affected by water stress. The enhancements performed in the model broadened its predictive capabilities and proved that QualiTree allows for a better understanding of the water stress effects on fruit-tree functioning and might be useful for designing innovative horticultural practices in a changing climate scenario.

Optimizing fish and stream-water mercury metrics for calculation of fish bioaccumulation factors

Treesearch

Paul Bradley; Karen Riva Murray; Barbara C. Scudder Elkenberry; Christopher D. Knightes; Celeste A. Journey; Mark A. Brigham

2016-01-01

Mercury (Hg) bioaccumulation factors (BAFs; ratios of Hg in fish [Hgfish] and water[Hgwater]) are used to develop Total Maximum Daily Load and water quality criteria for Hg-impaired waters. Protection of wildlife and human health depends directly on the accuracy of site-specific estimates of Hgfish and Hgwater and the predictability of the relation between these...

Predicting toxic effects of copper on aquatic biota in mineralized areas by using the Biotic Ligand Model

USGS Publications Warehouse

Smith, Kathleen S.; Ranville, James F.; Adams, M.; Choate, LaDonna M.; Church, Stan E.; Fey, David L.; Wanty, Richard B.; Crock, James G.

2006-01-01

The chemical speciation of metals influences their biological effects. The Biotic Ligand Model (BLM) is a computational approach to predict chemical speciation and acute toxicological effects of metals on aquatic biota. Recently, the U.S. Environmental Protection Agency incorporated the BLM into their regulatory water-quality criteria for copper. Results from three different laboratory copper toxicity tests were compared with BLM predictions for simulated test-waters. This was done to evaluate the ability of the BLM to accurately predict the effects of hardness and concentrations of dissolved organic carbon (DOC) and iron on aquatic toxicity. In addition, we evaluated whether the BLM and the three toxicity tests provide consistent results. Comparison of BLM predictions with two types of Ceriodaphnia dubia toxicity tests shows that there is fairly good agreement between predicted LC50 values computed by the BLM and LC50 values determined from the two toxicity tests. Specifically, the effect of increasing calcium concentration (and hardness) on copper toxicity appears to be minimal. Also, there is fairly good agreement between the BLM and the two toxicity tests for test solutions containing elevated DOC, for which the LC50 is 3-to-5 times greater (less toxic) than the LC50 for the lower-DOC test water. This illustrates the protective effects of DOC on copper toxicity and demonstrates the ability of the BLM to predict these protective effects. In contrast, for test solutions with added iron there is a decrease in LC50 values (increase in toxicity) in results from the two C. dubia toxicity tests, and the agreement between BLM LC50 predictions and results from these toxicity tests is poor. The inability of the BLM to account for competitive iron binding to DOC or DOC fractionation may be a significant shortcoming of the BLM for predicting site- specific water-quality criteria in streams affected by iron-rich acidic drainage in mined and mineralized areas.

Water-resources data index for Osceola National Forest, Florida

USGS Publications Warehouse

Seaber, Paul R.; Hull, Robert W.

1979-01-01

The U.S. Geological Survey conducted an intensive investigation from December 1975 to December 1977 of the geohydrology of Osceola National Forest, Fla. The primary purpose was to provide the geohydrological understanding needed to predict the impact of potential phosphate industry operations in the forest on the natural hydrologic system. The investigation involved test drilling, implementation of a hydrologic monitoring network, water-quality sampling, comprehensive aquifer tests, and literature study. This report is an index to the type, source, location, and availability of the data used in the interpretive investigation. The indexes include: geological, geophysical, ground water, surface water, quality of water, meteorological, climatological, aquifer tests, maps, photographs, elevations, and reference publications. The manner of storage and retrieval of the data is decribed also. (Woodard-USGS).

Hourly Water Quality Dynamics in Rivers Downstream of Urban Areas: Quantifying Seasonal Variation and Modelling Impacts of Urban Growth

NASA Astrophysics Data System (ADS)

Hutchins, M.; McGrane, S. J.; Miller, J. D.; Hitt, O.; Bowes, M.

2016-12-01

Continuous monitoring of water flows and quality is invaluable in improving understanding of the influence of urban areas on river health. When used to inform predictive modelling, insights can be gained as to how urban growth may affect the chemical and biological quality of rivers as they flow downstream into larger waterbodies. Water flow and quality monitoring in two urbanising sub-catchments (<100 km2) of the River Thames (southern UK) is described. Temperature, conductivity, turbidity, dissolved oxygen (DO) and ammonium (NH4) were measured at downstream locations where long term flow records are available, but particular focus is given to monitoring of an extended set of sites during prolonged winter rainfall. In the Ray sub-catchment streams were monitored in which urban cover varied across a range of 7-78%. A rural-urban gradient in DO was apparent in the low flow period prior to the storms. Transient low DO (< 8 mg L-1) as a response to pollutant first flushes was particularly apparent in urban streams but this was followed by a rapid recovery. Chronic effects lasting for three to four weeks were only seen downstream of a sewage treatment works (STW). In this respect temperature- and respiration-driven DO sags in summer were at least if not more severe than those driven by the winter storms. Likewise, although winter storm NH4 concentrations violated EU legislation downstream of the STW, they were lower than summer concentrations in pollutant flushes following dry spells. In contrast the predominant phenomenon affecting water quality in the Cut during the storms was dilution. Here, a river water quality model was calibrated and applied over the course of a year to capture the importance of periphyton photosynthesis and respiration cycles in determining water quality and to predict the influence of hypothetical urban growth on downstream river health. The periods monitored intensively, dry spells followed by prolonged rainfall, represent: (i) marked changes in conditions likely to become more prevalent in future, (ii) situations under which water quality in urban areas is likely to be particularly vulnerable, being influenced for example by first flush effects followed by capacity exceedance at STW. Despite this, whilst being somewhat long lasting in places, impacts on DO were not severe.

Relationship and Variation of qPCR and Culturable Enterococci Estimates in Ambient Surface Waters Are Predictable

EPA Science Inventory

The quantitative polymerase chain reaction (qPCR) method provides rapid estimates of fecal indicator bacteria densities that have been indicated to be useful in the assessment of water quality. Primarily because this method provides faster results than standard culture-based meth...

Future riverine nitrogen export to US coastal regions: Prospects for improving water quality amid population growth.

EPA Science Inventory

Excess nitrogen (N) in the environment degrades ecosystems and adversely affects human health. Here we examine predictions of contemporary (2000) and future (2030) coastal N loading in the continental US by the Nutrient Export from WaterSheds (NEWS) model. Future scenarios were b...

Multivariate analysis of water quality and environmental variables in the Great Barrier Reef catchments

NASA Astrophysics Data System (ADS)

Ryu, D.; Liu, S.; Western, A. W.; Webb, J. A.; Lintern, A.; Leahy, P.; Wilson, P.; Watson, M.; Waters, D.; Bende-Michl, U.

2016-12-01

The Great Barrier Reef (GBR) lagoon has been experiencing significant water quality deterioration due in part to agricultural intensification and urban settlement in adjacent catchments. The degradation of water quality in rivers is caused by land-derived pollutants (i.e. sediment, nutrient and pesticide). A better understanding of dynamics of water quality is essential for land management to improve the GBR ecosystem. However, water quality is also greatly influenced by natural hydrological processes. To assess influencing factors and predict the water quality accurately, selection of the most important predictors of water quality is necessary. In this work, multivariate statistical techniques - cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) - are used to reduce the complexity derived from the multidimensional water quality monitoring data. Seventeen stations are selected across the GBR catchments, and the event-based measurements of 12 variables monitored during 9 years (2006 - 2014) were analysed by means of CA and PCA/FA. The key findings are: (1) 17 stations can be grouped into two clusters according to the hierarchical CA, and the spatial dissimilarity between these sites is characterised by the different climatic and land use in the GBR catchments. (2) PCA results indicate that the first 3 PCs explain 85% of the total variance, and FA on the entire data set shows that the varifactor (VF) loadings can be used to interpret the sources of spatial variation in water quality on the GBR catchments level. The impact of soil erosion and non-point source of pollutants from agriculture contribution to VF1 and the variability in hydrological conditions and biogeochemical processes can explain the loadings in VF2. (3) FA is also performed on two groups of sites identified in CA individually, to evaluate the underlying sources that are responsible for spatial variability in water quality in the two groups. For the Cluster 1 sites, spatial variations in water quality are likely from the agricultural inputs (fertilises) and for the Cluster 2 sites, the differences in hydrological transport is responsible for large spatial variations in water quality. These findings can be applied to water quality assessment along with establish effective water and land management in the future.

Prototypic automated continuous recreational water quality monitoring of nine Chicago beaches

USGS Publications Warehouse

Dawn Shively,; Nevers, Meredith; Cathy Breitenbach,; Phanikumar, Mantha S.; Kasia Przybyla-Kelly,; Ashley M. Spoljaric,; Richard L. Whitman,

2015-01-01

Predictive empirical modeling is used in many locations worldwide as a rapid, alternative recreational water quality management tool to eliminate delayed notifications associated with traditional fecal indicator bacteria (FIB) culturing (referred to as the persistence model, PM) and to prevent errors in releasing swimming advisories. The goal of this study was to develop a fully automated water quality management system for multiple beaches using predictive empirical models (EM) and state-of-the-art technology. Many recent EMs rely on samples or data collected manually, which adds to analysis time and increases the burden to the beach manager. In this study, data from water quality buoys and weather stations were transmitted through cellular telemetry to a web hosting service. An executable program simultaneously retrieved and aggregated data for regression equations and calculated EM results each morning at 9:30 AM; results were transferred through RSS feed to a website, mapped to each beach, and received by the lifeguards to be posted at the beach. Models were initially developed for five beaches, but by the third year, 21 beaches were managed using refined and validated modeling systems. The adjusted R2 of the regressions relating Escherichia coli to hydrometeorological variables for the EMs were greater than those for the PMs, and ranged from 0.220 to 0.390 (2011) and 0.103 to 0.381 (2012). Validation results in 2013 revealed reduced predictive capabilities; however, three of the originally modeled beaches showed improvement in 2013 compared to 2012. The EMs generally showed higher accuracy and specificity than those of the PMs, and sensitivity was low for both approaches. In 2012 EM accuracy was 70–97%; specificity, 71–100%; and sensitivity, 0–64% and in 2013 accuracy was 68–97%; specificity, 73–100%; and sensitivity 0–36%. Factors that may have affected model capabilities include instrument malfunction, non-point source inputs, and sparse calibration data. The modeling system developed is the most extensive, fully-automated system for recreational water quality developed to date. Key insights for refining and improving large-scale empirical models for beach management have been developed through this multi-year effort.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-01-01

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R 2 =0.82), but it was not robust in extrapolation (R 2 =0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a regional ocean color algorithm using field- and satellite-derived datasets: Long Bay, South Carolina

NASA Astrophysics Data System (ADS)

Ryan, Kimberly Susan

Coastal and inland waters represent a diverse set of resources that support natural habitat and provide numerous ecosystem services to the human population. Conventional techniques to monitor water quality using in situ sensors and laboratory analysis of water samples can be very time- and cost-intensive. Alternatively, remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic and unique water quality parameters. However, bio and geo-optical models are required that relate the remotely sensed spectral data with color producing agents (CPAs) that define the water quality. These CPAs include chlorophyll-a, suspended sediments, and colored-dissolved organic matter. Developing these models may be challenging for coastal environments such as Long Bay, South Carolina, due to the presence of multiple optically interfering CPAs. In this work, a regionally tiered ocean color model was developed using band ratio techniques to specifically predict the variability of chlorophyll-a concentrations in the turbid Long Bay waters. This model produced higher accuracy results (r-squared = 0.62; RMSE = 0.87 micrograms per liter) compared to the existing models, which gave a highest r-squared value of 0.58 and RMSE = 0.99 micrograms per liter. To further enhance the retrievals of chlorophyll-a in these optically complex waters, a novel multivariate-based approach was developed using current generation hyperspectral data. This approach uses a partial least-squares regression model to identify wavelengths that are more sensitive to chlorophyll-a relative to other associated CPAs. This model was able to explain 80% of the observed chlorophyll-a variability in Long Bay with RMSE = 2.03 micrograms per liter. This approach capitalizes on the spectral advantage gained from hyperspectral sensors, thus providing a more robust predicting model. This enhanced mode of water quality monitoring in marine environments will provide insight to point-sources and problem areas that may contribute to a decline in water quality. Moreover, remote sensing applications such as this can be used as a tool for coastal and fisheries managers with regard to recreation, regulation, economic and public health purposes.

Geospatial distribution modeling and determining suitability of groundwater quality for irrigation purpose using geospatial methods and water quality index (WQI) in Northern Ethiopia

NASA Astrophysics Data System (ADS)

Gidey, Amanuel

2018-06-01

Determining suitability and vulnerability of groundwater quality for irrigation use is a key alarm and first aid for careful management of groundwater resources to diminish the impacts on irrigation. This study was conducted to determine the overall suitability of groundwater quality for irrigation use and to generate their spatial distribution maps in Elala catchment, Northern Ethiopia. Thirty-nine groundwater samples were collected to analyze and map the water quality variables. Atomic absorption spectrophotometer, ultraviolet spectrophotometer, titration and calculation methods were used for laboratory groundwater quality analysis. Arc GIS, geospatial analysis tools, semivariogram model types and interpolation methods were used to generate geospatial distribution maps. Twelve and eight water quality variables were used to produce weighted overlay and irrigation water quality index models, respectively. Root-mean-square error, mean square error, absolute square error, mean error, root-mean-square standardized error, measured values versus predicted values were used for cross-validation. The overall weighted overlay model result showed that 146 km2 areas are highly suitable, 135 km2 moderately suitable and 60 km2 area unsuitable for irrigation use. The result of irrigation water quality index confirms 10.26% with no restriction, 23.08% with low restriction, 20.51% with moderate restriction, 15.38% with high restriction and 30.76% with the severe restriction for irrigation use. GIS and irrigation water quality index are better methods for irrigation water resources management to achieve a full yield irrigation production to improve food security and to sustain it for a long period, to avoid the possibility of increasing environmental problems for the future generation.

Continuous Water Quality Monitoring in the Sacramento-San Joaquin Delta to support Ecosystem Science

NASA Astrophysics Data System (ADS)

Downing, B. D.; Bergamaschi, B. A.; Pellerin, B. A.; Saraceno, J.; Sauer, M.; Kraus, T. E.; Burau, J. R.; Fujii, R.

2013-12-01

Characterizing habitat quality and nutrient availability to food webs is an essential step for understanding and predicting the success of pelagic organisms in the Sacramento-San Joaquin Delta (Delta). The difficulty is that water quality and nutrient supply changes continuously as tidal and wind-driven currents move new water parcels to and from comparatively static geomorphic settings. Understanding interactions between nutrient cycling, suspended sediment, and plankton dynamics with flow and tidal range relative to position in the estuary is critical to predicting and managing bottom up effects on aquatic habitat in the Delta. Historically, quantifying concentrations and loads in the Delta has relied on water quality data collected at monthly intervals. Current in situ optical sensors for nutrients, dissolved organic matter (DOM) and algal pigments (chlorophyll-A, phycocyanin) allow for real-time, high-frequency measurements on time scales of seconds, and extending up to years. Such data is essential for characterizing changes in water quality over short and long term temporal scales as well as over broader spatial scales. High frequency water quality data have been collected at key stations in the Delta since 2012. Sensors that continuously measure nitrate, DOM, algal pigments and turbidity have been co-located at pre-existing Delta flow monitoring stations. Data from the stations are telemetered to USGS data servers and are designed to run autonomously with a monthly service interval, where sensors are cleaned and checked against calibration standards. The autonomous system is verified against discrete samples taken monthly and intensively over periodic ebb to flood tidal cycles. Here we present examples of how coupled optical and acoustic data from the sensor network to improve our understanding of nutrient and DOM dynamics and fluxes. The data offer robust quantitative estimates of concentrations and constituent fluxes needed to investigate biogeochemical processes in tidal reaches of the Delta. The data is available in real time on the web and has proven invaluable for anticipating interactions between nutrient supply and the Delta landscape, and is useful for continued research in aspects of pelagic habitat quality, algal productivity, and food web dynamics.

Wildfire and the future of water supply.

PubMed

Bladon, Kevin D; Emelko, Monica B; Silins, Uldis; Stone, Micheal

2014-08-19

In many parts of the world, forests provide high quality water for domestic, agricultural, industrial, and ecological needs, with water supplies in those regions inextricably linked to forest health. Wildfires have the potential to have devastating effects on aquatic ecosystems and community drinking water supply through impacts on water quantity and quality. In recent decades, a combination of fuel load accumulation, climate change, extensive droughts, and increased human presence in forests have resulted in increases in area burned and wildfire severity-a trend predicted to continue. Thus, the implications of wildfire for many downstream water uses are increasingly concerning, particularly the provision of safe drinking water, which may require additional treatment infrastructure and increased operations and maintenance costs in communities downstream of impacted landscapes. A better understanding of the effects of wildfire on water is needed to develop effective adaptation and mitigation strategies to protect globally critical water supplies originating in forested environments.

Study on Coagulant Dosing Control System of Micro Vortex Water Treatment

NASA Astrophysics Data System (ADS)

Fengping, Hu; Qi, Fan; Wenjie, Hu; Xizhen, He; Hongling, Dai

2018-03-01

In view of the characteristics of nonlinearity, large time delay and multi disturbance in the process of coagulant dosing in water treatment, it is difficult to control the dosage of coagulant. According to the four indexes of raw water quality parameters (raw water flow, turbidity, pH value) and turbidity of sedimentation tank, the micro vortex coagulation dosing control model is constructed based on BP neural network and GA. The forecast results of BP neural network model are ideal, and after the optimization of GA, the prediction accuracy of the model is partly improved. The prediction error of the optimized network is ±0.5 mg/L, and has a better performance than non-optimized network.

Predicting Mountainous Watershed Biogeochemical Dynamics, Including Response to Droughts and Early Snowmelt

NASA Astrophysics Data System (ADS)

Hubbard, S. S.; Williams, K. H.; Long, P.; Agarwal, D.; Banfield, J. F.; Beller, H. R.; Bouskill, N.; Brodie, E.; Maxwell, R. M.; Nico, P. S.; Steefel, C. I.; Steltzer, H.; Tokunaga, T. K.; Wainwright, H. M.

2016-12-01

Climate change, extreme weather, land-use change, and other perturbations are significantly reshaping interactions with in watersheds throughout the world. While mountainous watersheds are recognized as the water towers for the world, hydrological processes in watersheds also mediate biogeochemical processes that support all terrestrial life. Developing predictive understanding of watershed hydrological and biogeochemical functioning is challenging, as complex interactions occurring within a heterogeneous watershed can lead to a cascade of effects on downstream water availability and quality. Although these interactions can have significant implications for energy production, agriculture, water quality, and other benefits valued by society, uncertainty associated with predicting watershed function is high. The Watershed Function project aims to substantially reduce this uncertainty through developing a predictive understanding of how mountainous watersheds retain and release downgradient water, nutrients, carbon, and metals. In particular, the project is exploring how early snowmelt, drought, and other disturbances will influence mountainous watershed dynamics at seasonal to decadal timescales. The Watershed Function project is being carried out in a headwater mountainous catchment of the Upper Colorado River Basin, within a watershed characterized by significant gradients in elevation, vegetation and hydrogeology. A system-within system project perspective posits that the integrated watershed response to disturbances can be adequately predicted through consideration of interactions and feedbacks occurring within a limited number of subsystems, each having distinct vegetation-subsurface biogeochemical-hydrological characteristics. A key technological goal is the development of scale-adaptive simulation capabilities that can incorporate genomic information where and when it is useful for predicting the overall watershed response to disturbance. Through developing and integrating new microbial ecology, geochemical, hydrological, ecohydrological, computational and geophysical approaches, the project is developing new insights about biogeochemical dynamics from genome to watershed scales.

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

PubMed Central

Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M. G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public. PMID:23291550

Tools for beach health data management, data processing, and predictive model implementation

USGS Publications Warehouse

,

2013-01-01

This fact sheet describes utilities created for management of recreational waters to provide efficient data management, data aggregation, and predictive modeling as well as a prototype geographic information system (GIS)-based tool for data visualization and summary. All of these utilities were developed to assist beach managers in making decisions to protect public health. The Environmental Data Discovery and Transformation (EnDDaT) Web service identifies, compiles, and sorts environmental data from a variety of sources that help to define climatic, hydrologic, and hydrodynamic characteristics including multiple data sources within the U.S. Geological Survey and the National Oceanic and Atmospheric Administration. The Great Lakes Beach Health Database (GLBH-DB) and Web application was designed to provide a flexible input, export, and storage platform for beach water quality and sanitary survey monitoring data to compliment beach monitoring programs within the Great Lakes. A real-time predictive modeling strategy was implemented by combining the capabilities of EnDDaT and the GLBH-DB for timely, automated prediction of beach water quality. The GIS-based tool was developed to map beaches based on their physical and biological characteristics, which was shared with multiple partners to provide concepts and information for future Web-accessible beach data outlets.

An assessment of two-step linear regression and a multifactor probit analysis as alternatives to acute to chronic ratios in the estimation of chronic response from acute toxicity data to derive water quality guidelines.

PubMed

Slaughter, Andrew R; Palmer, Carolyn G; Muller, Wilhelmine J

2007-04-01

In aquatic ecotoxicology, acute to chronic ratios (ACRs) are often used to predict chronic responses from available acute data to derive water quality guidelines, despite many problems associated with this method. This paper explores the comparative protectiveness and accuracy of predicted guideline values derived from the ACR, linear regression analysis (LRA), and multifactor probit analysis (MPA) extrapolation methods applied to acute toxicity data for aquatic macroinvertebrates. Although the authors of the LRA and MPA methods advocate the use of extrapolated lethal effects in the 0.01% to 10% lethal concentration (LC0.01-LC10) range to predict safe chronic exposure levels to toxicants, the use of an extrapolated LC50 value divided by a safety factor of 5 was in addition explored here because of higher statistical confidence surrounding the LC50 value. The LRA LC50/5 method was found to compare most favorably with available experimental chronic toxicity data and was therefore most likely to be sufficiently protective, although further validation with the use of additional species is needed. Values derived by the ACR method were the least protective. It is suggested that there is an argument for the replacement of ACRs in developing water quality guidelines by the LRA LC50/5 method.

[Scientific substantiation of perfection of sanitary bacteriological monitoring in drinking water use].

PubMed

Rakhmanin, Iu A; Zhuravlev, P V; Aleshnia, V V; Panasovets, O P; Artemova, T Z; Zagaĭnova, A V; Gipp, E K

2014-01-01

Criterion of the epidemic safety of drinking water is the absence of pathogenic and potentially pathogenic microorganisms. Currently, water quality control is performed in terms of the index of total coliform bacteria (TCB). TCB index oriented to the labile lactose sign has not sufficient relevance in the determination of the degree of the epidemic danger in the water use in relation to Salmonella and potentially pathogenic microorganisms. The frequency of detection of GCB in standard quality of drinking water as well as the application of the methodology for the assessment of the microbial risk of the occurrence of bacterial intestinal infections with the use of integral index--GCB, provide the most reliable prediction of risk in the occurrence of water-caused intestinal infections and more objectively reflect the epidemiological importance of drinking water in their distribution among the population. Proceeding from the data obtained, it is advisable to carry out the quality control of drinking water with the use of the broader indicator index GCB- detected from basic signs of the Enterobacteriaceae family--glucose fermentation and oxidase test and oxidase test.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis.

PubMed

Ebrahimi, Milad; Gerber, Erin L; Rockaway, Thomas D

2017-05-15

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessment of the climate change impacts on fecal coliform contamination in a tidal estuarine system.

PubMed

Liu, Wen-Cheng; Chan, Wen-Ting

2015-12-01

Climate change is one of the key factors affecting the future microbiological water quality in rivers and tidal estuaries. A coupled 3D hydrodynamic and fecal coliform transport model was developed and applied to the Danshuei River estuarine system for predicting the influences of climate change on microbiological water quality. The hydrodynamic and fecal coliform model was validated using observational salinity and fecal coliform distributions. According to the analyses of the statistical error, predictions of the salinity and the fecal coliform concentration from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict the fecal coliform contamination as a result of climate change, including the change of freshwater discharge and the sea level rise. We found that the reduction of freshwater discharge under climate change scenarios resulted in an increase in the fecal coliform concentration. The sea level rise would decrease fecal coliform distributions because both the water level and the water volume increased. A reduction in freshwater discharge has a negative impact on the fecal coliform concentration, whereas a rising sea level has a positive influence on the fecal coliform contamination. An appropriate strategy for the effective microbiological management in tidal estuaries is required to reveal the persistent trends of climate in the future.

Coast Salish and U.S. Geological Survey 2009 Tribal Journey water quality project

USGS Publications Warehouse

Akin, Sarah K.; Grossman, Eric E.

2010-01-01

The Salish Sea, contained within the United States and British Columbia, Canada, is the homeland of the Coast Salish Peoples and contains a diverse array of marine resources unique to this area that have sustained Coast Salish cultures and traditions for millennia. In July 2009, the Coast Salish People and U.S. Geological Survey conducted a second water quality study of the Salish Sea to examine spatial and temporal variability of environmental conditions of these surface waters as part of the annual Tribal Journey. Six canoes of approximately 100 towed multi parameter water-quality sondes as the Salish People traveled their ancestral waters during the middle of summer. Sea surface temperature, salinity, pH, dissolved oxygen, and turbidity were measured simultaneously at ten-second intervals, and more than 54,000 data points spanning 1,300 kilometers of the Salish Sea were collected. The project also synthesized Coast Salish ecological knowledge and culture with scientific monitoring to better understand and predict the response of coastal habitats and marine resources. Comparisons with data collected in 2008 reveal significantly higher mean surface-water temperatures in most subbasins in 2009 linked to record air temperatures that affected the Pacific Northwest in July 2009. Through large-scale spatial measurements collected each summer, the project helps to identify patterns in summer water quality, areas of water-quality impairment, and trends occurring through time.

Mapping human health risks from exposure to trace metal contamination of drinking water sources in Pakistan.

PubMed

Bhowmik, Avit Kumar; Alamdar, Ambreen; Katsoyiannis, Ioannis; Shen, Heqing; Ali, Nadeem; Ali, Syeda Maria; Bokhari, Habib; Schäfer, Ralf B; Eqani, Syed Ali Musstjab Akber Shah

2015-12-15

The consumption of contaminated drinking water is one of the major causes of mortality and many severe diseases in developing countries. The principal drinking water sources in Pakistan, i.e. ground and surface water, are subject to geogenic and anthropogenic trace metal contamination. However, water quality monitoring activities have been limited to a few administrative areas and a nationwide human health risk assessment from trace metal exposure is lacking. Using geographically weighted regression (GWR) and eight relevant spatial predictors, we calculated nationwide human health risk maps by predicting the concentration of 10 trace metals in the drinking water sources of Pakistan and comparing them to guideline values. GWR incorporated local variations of trace metal concentrations into prediction models and hence mitigated effects of large distances between sampled districts due to data scarcity. Predicted concentrations mostly exhibited high accuracy and low uncertainty, and were in good agreement with observed concentrations. Concentrations for Central Pakistan were predicted with higher accuracy than for the North and South. A maximum 150-200 fold exceedance of guideline values was observed for predicted cadmium concentrations in ground water and arsenic concentrations in surface water. In more than 53% (4 and 100% for the lower and upper boundaries of 95% confidence interval (CI)) of the total area of Pakistan, the drinking water was predicted to be at risk of contamination from arsenic, chromium, iron, nickel and lead. The area with elevated risks is inhabited by more than 74 million (8 and 172 million for the lower and upper boundaries of 95% CI) people. Although these predictions require further validation by field monitoring, the results can inform disease mitigation and water resources management regarding potential hot spots. Copyright © 2015 Elsevier B.V. All rights reserved.

Use the predictive models to explore the key factors affecting phytoplankton succession in Lake Erhai, China.

PubMed

Zhu, Rong; Wang, Huan; Chen, Jun; Shen, Hong; Deng, Xuwei

2018-01-01

Increasing algae in Lake Erhai has resulted in frequent blooms that have not only led to water ecosystem degeneration but also seriously influenced the quality of the water supply and caused extensive damage to the local people, as the lake is a water resource for Dali City. Exploring the key factors affecting phytoplankton succession and developing predictive models with easily detectable parameters for phytoplankton have been proven to be practical ways to improve water quality. To this end, a systematic survey focused on phytoplankton succession was conducted over 2 years in Lake Erhai. The data from the first study year were used to develop predictive models, and the data from the second year were used for model verification. The seasonal succession of phytoplankton in Lake Erhai was obvious. The dominant groups were Cyanobacteria in the summer, Chlorophyta in the autumn and Bacillariophyta in the winter. The developments and verification of predictive models indicated that compared to phytoplankton biomass, phytoplankton density is more effective for estimating phytoplankton variation in Lake Erhai. CCA (canonical correlation analysis) indicated that TN (total nitrogen), TP (total phosphorus), DO (dissolved oxygen), SD (Secchi depth), Cond (conductivity), T (water temperature), and ORP (oxidation reduction potential) had significant influences (p < 0.05) on the phytoplankton community. The CCA of the dominant species found that Microcystis was significantly influenced by T. The dominant Chlorophyta, Psephonema aenigmaticum and Mougeotia, were significantly influenced by TN. All results indicated that TN and T were the two key factors driving phytoplankton succession in Lake Erhai.

Application of biomonitoring and support vector machine in water quality assessment*

PubMed Central

Liao, Yue; Xu, Jian-yu; Wang, Zhu-wei

2012-01-01

The behavior of schools of zebrafish (Danio rerio) was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine (SVM) was developed. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration (LC50) of a pollutant. The data were used to develop a method to evaluate water quality, so as to give an early indication of toxicity. Four kinds of metal ions (Cu2+, Hg2+, Cr6+, and Cd2+) were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm (GA) was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality. PMID:22467374

Optical spectral signatures of liquids by means of fiber optic technology for product and quality parameter identification

NASA Astrophysics Data System (ADS)

Mignani, A. G.; Ciaccheri, L.; Mencaglia, A. A.; Diaz-Herrera, N.; Garcia-Allende, P. B.; Ottevaere, H.; Thienpont, H.; Attilio, C.; Cimato, A.; Francalanci, S.; Paccagnini, A.; Pavone, F. S.

2009-01-01

Absorption spectroscopy in the wide 200-1700 nm spectral range is carried out by means of optical fiber instrumentation to achieve a digital mapping of liquids for the prediction of important quality parameters. Extra virgin olive oils from Italy and lubricant oils from turbines with different degrees of degradation were considered as "case studies". The spectral data were processed by means of multivariate analysis so as to obtain a correlation to quality parameters. In practice, the wide range absorption spectra were considered as an optical signature of the liquids from which to extract product quality information. The optical signatures of extra virgin olive oils were used to predict the content of the most important fatty acids. The optical signatures of lubricant oils were used to predict the concentration of the most important parameters for indicating the oil's degree of degradation, such as TAN, JOAP anti-wear index, and water content.

[Modern problems of maintenance of hygienic safety of drinking water consumption at the regional level].

PubMed

Tulakin, A V; Tsyplakova, G V; Ampleeva, G P; Kozyreva, O N; Pivneva, O S; Trukhina, G M

Problems of hygienic reliability of the drinking water use in regions of the Russian Federation are observed in the article. The optimization of the water use was shown must be based on the bearing in mind of regional peculiarities of the shaping of water quality of groundwater and surface sources of the water use, taking into account of the effectiveness of regional water protection programs, programs for water treatment, coordination of the activity of economic entities and oversight bodies in the management of water quality on the basis of socio-hygienic monitoring. Regional problems requiring hygienic justification and accounting, include such issues as complex hydrological, hydrogeological, climatic and geographical conditions, pronouncement of the severity of anthropogenic pollution of sources of water supply, natural conditions of the shaping of water quality, efficiency of the water treatment. There is need in the improvement of the problems of the water quality monitoring, including with the use of computer technology, which allows to realize regional hygienic monitoring and spatial-temporal analysis of the water quality, to model the water quality management, to predict conditions of the water use by population in regions taking into account peculiarities of the current health situation. In the article there is shown the practicability of the so-called complex concept of multiple barriers suggesting the combined use of chemical oxidation and physical methods of the preparation of drinking water. It is required the further development of legislation for the protection of water bodies from pollution with the bigging up the status of sanitary protection zones; timely revision of the regulatory framework, establishing sanitary-epidemiological requirements to potable water and drinking water supply. The problem of the provision of the population with safe drinking water requires complex solution within the framework of the implementation of target programs adopted at the Federal and regional levels.

Water Quality, Hydrology, and Response to Changes in Phosphorus Loading of Nagawicka Lake, a Calcareous Lake in Waukesha County, Wisconsin

USGS Publications Warehouse

Garn, Herbert S.; Robertson, Dale M.; Rose, William J.; Goddard, Gerald L.; Horwatich, Judy A.

2006-01-01

Nagawicka Lake is a 986-acre, usually mesotrophic, calcareous lake in southeastern Wisconsin. Because of concern over potential water-quality degradation of the lake associated with further development in its watershed, a study was conducted by the U.S. Geological Survey from 2002 to 2006 to describe the water quality and hydrology of the lake; quantify sources of phosphorus, including those associated with urban development; and determine the effects of past and future changes in phosphorus loading on the water quality of the lake. All major water and phosphorus sources were measured directly, and minor sources were estimated to construct detailed water and phosphorus budgets for the lake. The Bark River, near-lake surface inflow, precipitation, and ground water contributed 74, 8, 12, and 6 percent of the inflow, respectively. Water leaves the lake primarily through the Bark River outlet (88 percent) or by evaporation (11 percent). The water quality of Nagawicka Lake has improved dramatically since 1980 as a result of decreasing the historical loading of phosphorus to the lake. Total input of phosphorus to the lake was about 3,000 pounds in monitoring year (MY) 2003 and 6,700 pounds in MY 2004. The largest source of phosphorus entering the lake was the Bark River, which delivered about 56 percent of the total phosphorus input, compared with about 74 percent of the total water input. The next largest contributions were from the urbanized near-lake drainage area, which disproportionately accounted for 37 percent of the total phosphorus input but only about 5 percent of the total water input. Simulations with water-quality models within the Wisconsin Lakes Modeling Suite (WiLMS) indicated the response of Nagawicka Lake to 10 phosphorus-loading scenarios. These scenarios included historical (1970s) and current (base) years (MY 2003-04) for which lake water quality and loading were known, six scenarios with percentage increases or decreases in phosphorus loading from controllable sources relative to the base years 2003-04, and two scenarios corresponding to specific management actions. Because of the lake's calcareous character, the average simulated summer concentration of total phosphorus for Nagawicka Lake was about 2 times that measured in the lake. The models likely over-predict because they do not account for coprecipitation of phosphorus and dissolved organic matter with calcite, negligible release of phosphorus from the deep sediments, and external phosphorus loading with abnormally high amounts of nonavailable phosphorus. After adjusting the simulated results for the overestimation of the models, a 50-percent reduction in phosphorus loading resulted in an average predicted phosphorus concentration of 0.008 milligrams per liter (mg/L) (a decrease of 46 percent). With a 50-percent increase in phosphorus loading, the average predicted concentration was 0.020 mg/L (an increase of 45 percent). With the changes in land use under the assumed future full development conditions, the average summer total phosphorus concentration should remain similar to that measured in MY 2003-04 (approximately 0.014 mg/L). However, if stormwater and nonpoint controls are added to achieve a 50-percent reduction in loading from the urbanized near-lake drainage area, the average summer total phosphorus concentration should decrease from the present conditions (MY 2003-04) to 0.011 mg/L. Slightly more than a 25-percent reduction in phosphorus loading from that measured in MY 2003-04 would be required for the lake to be classified as oligotrophic.

Effects of Land Use Land Cover (LULC) and Climate on Simulation of Phosphorus loading in the Southeast United States Region

NASA Astrophysics Data System (ADS)

Jima, T. G.; Roberts, A.

2013-12-01

Quality of coastal and freshwater resources in the Southeastern United States is threatened due to Eutrophication as a result of excessive nutrients, and phosphorus is acknowledged as one of the major limiting nutrients. In areas with much non-point source (NPS) pollution, land use land cover and climate have been found to have significant impact on water quality. Landscape metrics applied in catchment and riparian stream based nutrient export models are known to significantly improve nutrient prediction. The regional SPARROW (Spatially Referenced Regression On Watershed attributes), which predicts Total Phosphorus has been developed by the Southeastern United States regions USGS, as part of the National Water Quality Assessment (NAWQA) program and the model accuracy was found to be 67%. However, landscape composition and configuration metrics which play a significant role in the source, transport and delivery of the nutrient have not been incorporated in the model. Including these matrices in the models parameterization will improve the models accuracy and improve decision making process for mitigating and managing NPS phosphorus in the region. The National Land Cover Data 2001 raster data will be used (since the base line is 2002) for the region (with 8321 watersheds ) with fragstats 4.1 and ArcGIS Desktop 10.1 for the analysis of landscape matrices, buffers and creating map layers. The result will be imported to the Southeast SPARROW model and will be analyzed. Resulting statistical significance and model accuracy will be assessed and predictions for those areas with no water quality monitoring station will be made.

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes.

PubMed

Soranno, Patricia A; Bacon, Linda C; Beauchene, Michael; Bednar, Karen E; Bissell, Edward G; Boudreau, Claire K; Boyer, Marvin G; Bremigan, Mary T; Carpenter, Stephen R; Carr, Jamie W; Cheruvelil, Kendra S; Christel, Samuel T; Claucherty, Matt; Collins, Sarah M; Conroy, Joseph D; Downing, John A; Dukett, Jed; Fergus, C Emi; Filstrup, Christopher T; Funk, Clara; Gonzalez, Maria J; Green, Linda T; Gries, Corinna; Halfman, John D; Hamilton, Stephen K; Hanson, Paul C; Henry, Emily N; Herron, Elizabeth M; Hockings, Celeste; Jackson, James R; Jacobson-Hedin, Kari; Janus, Lorraine L; Jones, William W; Jones, John R; Keson, Caroline M; King, Katelyn B S; Kishbaugh, Scott A; Lapierre, Jean-Francois; Lathrop, Barbara; Latimore, Jo A; Lee, Yuehlin; Lottig, Noah R; Lynch, Jason A; Matthews, Leslie J; McDowell, William H; Moore, Karen E B; Neff, Brian P; Nelson, Sarah J; Oliver, Samantha K; Pace, Michael L; Pierson, Donald C; Poisson, Autumn C; Pollard, Amina I; Post, David M; Reyes, Paul O; Rosenberry, Donald O; Roy, Karen M; Rudstam, Lars G; Sarnelle, Orlando; Schuldt, Nancy J; Scott, Caren E; Skaff, Nicholas K; Smith, Nicole J; Spinelli, Nick R; Stachelek, Joseph J; Stanley, Emily H; Stoddard, John L; Stopyak, Scott B; Stow, Craig A; Tallant, Jason M; Tan, Pang-Ning; Thorpe, Anthony P; Vanni, Michael J; Wagner, Tyler; Watkins, Gretchen; Weathers, Kathleen C; Webster, Katherine E; White, Jeffrey D; Wilmes, Marcy K; Yuan, Shuai

2017-12-01

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600-12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales. © The Author 2017. Published by Oxford University Press.

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

PubMed Central

Bacon, Linda C; Beauchene, Michael; Bednar, Karen E; Bissell, Edward G; Boudreau, Claire K; Boyer, Marvin G; Bremigan, Mary T; Carpenter, Stephen R; Carr, Jamie W; Christel, Samuel T; Claucherty, Matt; Conroy, Joseph D; Downing, John A; Dukett, Jed; Filstrup, Christopher T; Funk, Clara; Gonzalez, Maria J; Green, Linda T; Gries, Corinna; Halfman, John D; Hamilton, Stephen K; Hanson, Paul C; Henry, Emily N; Herron, Elizabeth M; Hockings, Celeste; Jackson, James R; Jacobson-Hedin, Kari; Janus, Lorraine L; Jones, William W; Jones, John R; Keson, Caroline M; King, Katelyn B S; Kishbaugh, Scott A; Lathrop, Barbara; Latimore, Jo A; Lee, Yuehlin; Lottig, Noah R; Lynch, Jason A; Matthews, Leslie J; McDowell, William H; Moore, Karen E B; Neff, Brian P; Nelson, Sarah J; Oliver, Samantha K; Pace, Michael L; Pierson, Donald C; Poisson, Autumn C; Pollard, Amina I; Post, David M; Reyes, Paul O; Rosenberry, Donald O; Roy, Karen M; Rudstam, Lars G; Sarnelle, Orlando; Schuldt, Nancy J; Scott, Caren E; Smith, Nicole J; Spinelli, Nick R; Stachelek, Joseph J; Stanley, Emily H; Stoddard, John L; Stopyak, Scott B; Stow, Craig A; Tallant, Jason M; Thorpe, Anthony P; Vanni, Michael J; Wagner, Tyler; Watkins, Gretchen; Weathers, Kathleen C; Webster, Katherine E; White, Jeffrey D; Wilmes, Marcy K; Yuan, Shuai

2017-01-01

Abstract Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states. LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600–12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales. PMID:29053868

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

USGS Publications Warehouse

Soranno, Patricia A.; Bacon, Linda C.; Beauchene, Michael; Bednar, Karen E.; Bissell, Edward G.; Boudreau, Claire K.; Boyer, Marvin G.; Bremigan, Mary T.; Carpenter, Stephen R.; Carr, Jamie W.; Cheruvelil, Kendra S.; Christel, Samuel T.; Claucherty, Matt; Collins, Sarah M.; Conroy, Joseph D.; Downing, John A.; Dukett, Jed; Fergus, C. Emi; Filstrup, Christopher T.; Funk, Clara; Gonzalez, Maria J.; Green, Linda T.; Gries, Corinna; Halfman, John D.; Hamilton, Stephen K.; Hanson, Paul C.; Henry, Emily N.; Herron, Elizabeth M.; Hockings, Celeste; Jackson, James R.; Jacobson-Hedin, Kari; Janus, Lorraine L.; Jones, William W.; Jones, John R.; Keson, Caroline M.; King, Katelyn B.S.; Kishbaugh, Scott A.; Lapierre, Jean-Francois; Lathrop, Barbara; Latimore, Jo A.; Lee, Yuehlin; Lottig, Noah R.; Lynch, Jason A.; Matthews, Leslie J.; McDowell, William H.; Moore, Karen E.B.; Neff, Brian; Nelson, Sarah J.; Oliver, Samantha K.; Pace, Michael L.; Pierson, Donald C.; Poisson, Autumn C.; Pollard, Amina I.; Post, David M.; Reyes, Paul O.; Rosenberry, Donald; Roy, Karen M.; Rudstam, Lars G.; Sarnelle, Orlando; Schuldt, Nancy J.; Scott, Caren E.; Skaff, Nicholas K.; Smith, Nicole J.; Spinelli, Nick R.; Stachelek, Joseph J.; Stanley, Emily H.; Stoddard, John L.; Stopyak, Scott B.; Stow, Craig A.; Tallant, Jason M.; Tan, Pang-Ning; Thorpe, Anthony P.; Vanni, Michael J.; Wagner, Tyler; Watkins, Gretchen; Weathers, Kathleen C.; Webster, Katherine E.; White, Jeffrey D.; Wilmes, Marcy K.; Yuan, Shuai

2017-01-01

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600–12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.

Environmental factors limiting fertilisation and larval success in corals

NASA Astrophysics Data System (ADS)

Woods, Rachael M.; Baird, Andrew H.; Mizerek, Toni L.; Madin, Joshua S.

2016-12-01

Events in the early life history of reef-building corals, including fertilisation and larval survival, are susceptible to changes in the chemical and physical properties of sea water. Quantifying how changes in water quality affect these events is therefore important for understanding and predicting population establishment in novel and changing environments. A review of the literature identified that levels of salinity, temperature, pH, suspended sediment, nutrients and heavy metals affect coral early life-history stages to various degrees. In this study, we combined published experimental data to determine the relative importance of sea water properties for coral fertilisation success and larval survivorship. Of the water properties manipulated in experiments, fertilisation success was most sensitive to suspended sediment, copper, salinity, phosphate and ammonium. Larval survivorship was sensitive to copper, lead and salinity. A combined model was developed that estimated the joint probability of both fertilisation and larval survivorship in sea water with different chemical and physical properties. We demonstrated the combined model using water samples from Sydney and Lizard Island in Australia to estimate the likelihood of larvae surviving through both stages of development to settlement competency. Our combined model could be used to recommend targets for water quality in coastal waterways as well as to predict the potential for species to expand their geographical ranges in response to climate change.

Drinking Water Microbiome as a Screening Tool for ...

EPA Pesticide Factsheets

Many water utilities in the US using chloramine as disinfectant treatment in their distribution systems have experienced nitrification episodes, which detrimentally impact the water quality. Here, we used 16S rRNA sequencing data to generate high-resolution taxonomic profiles of the bulk water (BW) microbiome from a chloraminated drinking water distribution system (DWDS) simulator. The DWDS was operated through four successive operational schemes, including two stable events (SS) and an episode of nitrification (SF), followed by a ‘chlorine burn’ (SR) by switching disinfectant from chloramine to free chlorine. Specifically, this study focuses on biomarker discovery and their potential use to classify SF episodes. Principal coordinate analysis identified two major clusters (SS and SF; PERMANOVA, p 0.976, p < 0.01). Furthermore, models were able to correctly predict 95% (AUC = 0.983, n = 104) and 96% (AUC = 0.973, n = 72) of samples of the DWDS (community structure of two published studies) and water quality datasets, respectively. The results from this study demonstrate the feasibility of selected BW microbiome signatures as predictive biomarkers of nitrification in DWDS. This new information can be used to optimize current nitrification monitoring plans. The purpose of this research is to add to our knowledge of chloramine and chlorine disinfectants, with regards to effects on the microbial communities in drinking water distribution systems. We used a

76 FR 34799 - Permanent Dam Safety Modification at Cherokee, Fort Loudoun, Tellico, and Watts Bar Dams, TN

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-14

... notice is provided in accordance with the Council on Environmental Quality's regulations (40 CFR parts... interconnected, fabric-lined, sand-filled HESCO containers in order to safely pass predicted worst-case..., but will not necessarily be limited to, the potential impacts on water quality, aquatic and...

Effect of Climate Change on Water Temperature and ...

EPA Pesticide Factsheets

There is increasing evidence that our planet is warming and this warming is also resulting in rising sea levels. Estuaries which are located at the interface between land and ocean are impacted by these changes. We used CE-QUAL-W2 water quality model to predict changes in water temperature as a function of increasing air temperatures and rising sea level for the Yaquina Estuary, Oregon (USA). Annual average air temperature in the Yaquina watershed is expected to increase about 0.3 deg C per decade by 2040-2069. An air temperature increase of 3 deg C in the Yaquina watershed is likely to result in estuarine water temperature increasing by 0.7 to 1.6 deg C. Largest water temperature increases are expected in the upper portion of the estuary, while sea level rise may ameliorate some of the warming in the lower portion of the estuary. Smallest changes in water temperature are predicted to occur in the summer, and maximum changes during the winter and spring. Increases in air temperature may result in an increase in the number of days per year that the 7-day maximum average temperature exceeds 18 deg C (criterion for protection of rearing and migration of salmonids and trout) as well as other water quality concerns. In the upstream portion of the estuary, a 4 deg C increase in air temperature is predicted to cause an increase of 40 days not meeting the temperature criterion, while in the lower estuary the increase will depend upon rate of sea level rise (rang

Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin.

PubMed

Kang, Joo-Hyon; Lee, Seung Won; Cho, Kyung Hwa; Ki, Seo Jin; Cha, Sung Min; Kim, Joon Ha

2010-07-01

This study reveals land-use factors that explain stream water quality during wet and dry weather conditions in a large river basin using two different linear models-multiple linear regression (MLR) models and constrained least squares (CLS) models. Six land-use types and three topographical parameters (size, slope, and permeability) of the watershed were incorporated into the models as explanatory variables. The suggested models were then demonstrated using a digitized elevation map in conjunction with the land-use and the measured concentration data for Escherichia coli (EC), Enterococci bacteria (ENT), and six heavy metal species collected monthly during 2007-2008 at 50 monitoring sites in the Yeongsan Watershed, Korea. The results showed that the MLR models can be a powerful tool for predicting the average concentrations of pollutants in stream water (the Nash-Sutcliffe (NS) model efficiency coefficients ranged from 0.67 to 0.95). On the other hand, the CLS models, with moderately good prediction performance (the NS coefficients ranged 0.28-0.85), were more suitable for quantifying contributions of respective land-uses to the stream water quality. The CLS models suggested that industrial and urban land-uses are major contributors to the stream concentrations of EC and ENT, whereas agricultural, industrial, and mining areas were significant sources of many heavy metal species. In addition, the slope, size, and permeability of the watershed were found to be important factors determining the extent of the contribution from each land-use type to the stream water quality. The models proposed in this paper can be considered useful tools for developing land cover guidelines and for prioritizing locations for implementing management practices to maintain stream water quality standard in a large river basin. Copyright 2010 Elsevier Ltd. All rights reserved.

Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

USDA-ARS?s Scientific Manuscript database

Few studies have attempted to quantify mass balances of both pesticides and degradates in multiple agricultural settings of the United States. We used inverse modeling to calibrate the Root Zone Water Quality Model (RZWQM) for predicting the unsaturated-zone transport and fate of metolachlor, metola...

ASSESSING THE PREDICTIVE CAPABILITY OF LANDSCAPE SAMPLING UNITS OF VARYING SCALE IN THE ANALYSIS OF ESTUARINE CONDITION

EPA Science Inventory

Landscape structure metrics are often used to predict water and sediment quality of lakes, streams, and estuaries; however, the sampling units used to generate the landscape metrics are often at an irrelevant spatial scale. They are either too large (i.e., an entire watershed) or...

Study of the geothermal production potential in the Williston Basin, North Dakota

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chu, Min H.

1991-09-10

Preliminary studies of geothermal production potential for the North Dakota portion of the Williston Basin have been carried out. Reservoir data such as formation depth, subsurface temperatures, and water quality were reviewed for geothermal brine production predictions. This study, in addition, provides important information about net pay thickness, porosity, volume of geothermal water available, and productivity index for future geothermal direct-use development. Preliminary results show that the Inyan Kara Formation of the Dakota Group is the most favorable geothermal resource in terms of water quality and productivity. The Madison, Duperow, and Red River Formations are deeper formations but because ofmore » their low permeability and great depth, the potential flow rates from these three formations are considerably less than those of the Inyan Kara Formation. Also, poor water quality and low porosity will make those formations less favorable for geothermal direct-use development.« less

Future riverine nitrogen export to US coastal regions: Prospects for improving water quality considering population growth

EPA Science Inventory

Excess nitrogen (N) in the environment degrades ecosystems and adversely affects human health. Here we examine predictions of contemporary (2000) and future (2030) coastal N loading in the continental US by the Nutrient Export from WaterSheds (NEWS) model. Future output is from s...

Development and evaluation of the microbial fate and transport module for the Agricultural Policy/Environmental eXtender (APEX) model

USDA-ARS?s Scientific Manuscript database

Microbial contamination of waters in agricultural watershed is the critical public health issue. The watershed-scale model has been proven to be one of the candidate tools for predicting microbial water quality and evaluating management practices. The Agricultural Policy/Environmental eXtender (APEX...

Watershed Regressions for Pesticides (WARP) for Predicting Annual Maximum and Annual Maximum Moving-Average Concentrations of Atrazine in Streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.; Crawford, Charles G.

2008-01-01

Regression models were developed for predicting annual maximum and selected annual maximum moving-average concentrations of atrazine in streams using the Watershed Regressions for Pesticides (WARP) methodology developed by the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS). The current effort builds on the original WARP models, which were based on the annual mean and selected percentiles of the annual frequency distribution of atrazine concentrations. Estimates of annual maximum and annual maximum moving-average concentrations for selected durations are needed to characterize the levels of atrazine and other pesticides for comparison to specific water-quality benchmarks for evaluation of potential concerns regarding human health or aquatic life. Separate regression models were derived for the annual maximum and annual maximum 21-day, 60-day, and 90-day moving-average concentrations. Development of the regression models used the same explanatory variables, transformations, model development data, model validation data, and regression methods as those used in the original development of WARP. The models accounted for 72 to 75 percent of the variability in the concentration statistics among the 112 sampling sites used for model development. Predicted concentration statistics from the four models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites. Overall, performance of the models for the development and validation sites supports the application of the WARP models for predicting annual maximum and selected annual maximum moving-average atrazine concentration in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams with inadequate direct measurements of atrazine concentrations, the WARP model predictions for the annual maximum and the annual maximum moving-average atrazine concentrations can be used to characterize the probable levels of atrazine for comparison to specific water-quality benchmarks. Sites with a high probability of exceeding a benchmark for human health or aquatic life can be prioritized for monitoring.

Estimation of water quality by UV/Vis spectrometry in the framework of treated wastewater reuse.

PubMed

Carré, Erwan; Pérot, Jean; Jauzein, Vincent; Lin, Liming; Lopez-Ferber, Miguel

2017-07-01

The aim of this study is to investigate the potential of ultraviolet/visible (UV/Vis) spectrometry as a complementary method for routine monitoring of reclaimed water production. Robustness of the models and compliance of their sensitivity with current quality limits are investigated. The following indicators are studied: total suspended solids (TSS), turbidity, chemical oxygen demand (COD) and nitrate. Partial least squares regression (PLSR) is used to find linear correlations between absorbances and indicators of interest. Artificial samples are made by simulating a sludge leak on the wastewater treatment plant and added to the original dataset, then divided into calibration and prediction datasets. The models are built on the calibration set, and then tested on the prediction set. The best models are developed with: PLSR for COD (R pred 2 = 0.80), TSS (R pred 2 = 0.86) and turbidity (R pred 2 = 0.96), and with a simple linear regression from absorbance at 208 nm (R pred 2 = 0.95) for nitrate concentration. The input of artificial data significantly enhances the robustness of the models. The sensitivity of the UV/Vis spectrometry monitoring system developed is compatible with quality requirements of reclaimed water production processes.

Predicting the vulnerability of streams to episodic acidification and potential effects on aquatic biota in Shenandoah National Park, Virginia

USGS Publications Warehouse

Rice, Karen C.; Deviney, Frank A.; Hornberger, George M.; Webb, James R.

2006-01-01

Acidic deposition is one of the most serious environmental problems affecting Shenandoah National Park in north-central Virginia. The park is the third most contaminated park in the National Park System because of the deposition of acid rain. Acid rain affects headwater streams in the park by temporarily reducing the acid-neutralizing capacity (ANC) of the water, a process termed episodic acidification. In turn, the increase in acidic components in streamwater can have deleterious effects on the aquatic biota.Although acidic deposition to the park is relatively uniform across its land area, the water-quality response of streamwater during rain events varies substantially. This response is a function of the underlying geology and topographic attributes of watersheds.Geologic and topographic data for the park's 231 watersheds are readily available; however, long-term (years and tens of years) measurements of streamwater ANC and accompanying discharge are not and would be prohibitively expensive to collect. Modeled predictions of the vulnerability of the park's streams to episodic acidification are an alternative to long-term water-quality monitoring. These predictions can aid park officials in making management decisions.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessment and prediction of land ecological environment quality change based on remote sensing-a case study of the Dongting lake area in China

NASA Astrophysics Data System (ADS)

Hu, Wenmin; Wang, Zhongcheng; Li, Chunhua; Zhao, Jin; Li, Yi

2018-02-01

Multi-source remote sensing data is rarely used for the comprehensive assessment of land ecologic environment quality. In this study, a digital environmental model was proposed with the inversion algorithm of land and environmental factors based on the multi-source remote sensing data, and a comprehensive index (Ecoindex) was applied to reconstruct and predict the land environment quality of the Dongting Lake Area to assess the effect of human activities on the environment. The main finding was that with the decrease of Grade I and Grade II quality had a decreasing tendency in the lake area, mostly in suburbs and wetlands. Atmospheric water vapour, land use intensity, surface temperature, vegetation coverage, and soil water content were the main driving factors. The cause of degradation was the interference of multi-factor combinations, which led to positive and negative environmental agglomeration effects. Positive agglomeration, such as increased rainfall and vegetation coverage and reduced land use intensity, could increase environmental quality, while negative agglomeration resulted in the opposite. Therefore, reasonable ecological restoration measures should be beneficial to limit the negative effects and decreasing tendency, improve the land ecological environment quality and provide references for macroscopic planning by the government.

Climate change and the impact of increased rainfall variability on sediment transport and catchment scale water quality

NASA Astrophysics Data System (ADS)

Hancock, G. R.; Willgoose, G. R.; Cohen, S.

2009-12-01

Recently there has been recognition that changing climate will affect rainfall and storm patterns with research directed to examine how the global hydrological cycle will respond to climate change. This study investigates the effect of different rainfall patterns on erosion and resultant water quality for a well studied tropical monsoonal catchment that is undisturbed by Europeans in the Northern Territory, Australia. Water quality has a large affect on a range of aquatic flora and fauna and a significant change in sediment could have impacts on the aquatic ecosystems. There have been several studies of the effect of climate change on rainfall patterns in the study area with projections indicating a significant increase in storm activity. Therefore it is important that the impact of this variability be assessed in terms of catchment hydrology, sediment transport and water quality. Here a numerical model of erosion and hydrology (CAESAR) is used to assess several different rainfall scenarios over a 1000 year modelled period. The results show that that increased rainfall amount and intensity increases sediment transport rates but predicted water quality was variable and non-linear but within the range of measured field data for the catchment and region. Therefore an assessment of sediment transport and water quality is a significant and complex issue that requires further understandings of the role of biophysical feedbacks such as vegetation as well as the role of humans in managing landscapes (i.e. controlled and uncontrolled fire). The study provides a robust methodology for assessing the impact of enhanced climate variability on sediment transport and water quality.

Tracking acid mine-drainage in Southeast Arizona using GIS and sediment delivery models

USGS Publications Warehouse

Norman, L.M.; Gray, F.; Guertin, D.P.; Wissler, C.; Bliss, J.D.

2008-01-01

This study investigates the application of models traditionally used to estimate erosion and sediment deposition to assess the potential risk of water quality impairment resulting from metal-bearing materials related to mining and mineralization. An integrated watershed analysis using Geographic Information Systems (GIS) based tools was undertaken to examine erosion and sediment transport characteristics within the watersheds. Estimates of stream deposits of sediment from mine tailings were related to the chemistry of surface water to assess the effectiveness of the methodology to assess the risk of acid mine-drainage being dispersed downstream of abandoned tailings and waste rock piles. A watershed analysis was preformed in the Patagonia Mountains in southeastern Arizona which has seen substantial mining and where recent water quality samples have reported acidic surface waters. This research demonstrates an improvement of the ability to predict streams that are likely to have severely degraded water quality as a result of past mining activities. ?? Springer Science+Business Media B.V. 2007.

The Interaction of Spacecraft Cabin Atmospheric Quality and Water Processing System Performance

NASA Technical Reports Server (NTRS)

Perry, Jay L.; Croomes, Scott D. (Technical Monitor)

2002-01-01

Although designed to remove organic contaminants from a variety of waste water streams, the planned U.S.- and present Russian-provided water processing systems onboard the International Space Station (ISS) have capacity limits for some of the more common volatile cleaning solvents used for housekeeping purposes. Using large quantities of volatile cleaning solvents during the ground processing and in-flight operational phases of a crewed spacecraft such as the ISS can lead to significant challenges to the water processing systems. To understand the challenges facing the management of water processing capacity, the relationship between cabin atmospheric quality and humidity condensate loading is presented. This relationship is developed as a tool to determine the cabin atmospheric loading that may compromise water processing system performance. A comparison of cabin atmospheric loading with volatile cleaning solvents from ISS, Mir, and Shuttle are presented to predict acceptable limits to maintain optimal water processing system performance.

Modeling Hydrologic Transport through the Critical Zone: Lessons from Catchment-Scale and Lysimeter Studies

NASA Astrophysics Data System (ADS)

Benettin, P.; Queloz, P.; Bailey, S. W.; McGuire, K. J.; Rinaldo, A.; Botter, G.

2015-12-01

Water age distributions can be used to address a number of environmental challenges, such as modeling the dynamics of river water quality, quantifying the interactions between shallow and deep flow systems and understanding nutrient loading persistence. Moreover, as the travel time of a water particle is the time available for biogeochemical reactions, it can be explicitly used to predict the concentration of non-conservative solutes, as e.g. those derived by mineral weathering. In recent years, many studies acknowledged the dynamic nature of streamflow age and linked it to observed variations in stream water quality. In this new framework, water stored within a catchment can be seen as a pool that is selectively "sampled" by streams and vegetation, determining the chemical composition of discharge and evapotranspiration. We present results from a controlled lysimeter experiment and real-world catchments, where the theoretical framework has been used to reproduce water quality datasets including conservative tracers (e.g. chloride and water stable isotopes) and weathering-derived solutes (like silicon and sodium). The approach proves useful to estimate the catchment water storage involved in solute mixing and sheds light on how solutes and water of different ages are selectively removed by vegetation and soil drainage.

Quality of surface-water runoff in selected streams in the San Antonio segment of the Edwards aquifer recharge zone, Bexar County, Texas, 1997-2012

USGS Publications Warehouse

Opsahl, Stephen P.

2012-01-01

During 1997–2012, the U.S. Geological Survey, in cooperation with the San Antonio Water System, collected and analyzed water-quality constituents in surface-water runoff from five ephemeral stream sites near San Antonio in northern Bexar County, Texas. The data were collected to assess the quality of surface water that recharges the Edwards aquifer. Samples were collected from four stream basins that had small amounts of developed land at the onset of the study but were predicted to undergo substantial development over a period of several decades. Water-quality samples also were collected from a fifth stream basin located on land protected from development to provide reference data by representing undeveloped land cover. Water-quality data included pH, specific conductance, chemical oxygen demand, dissolved solids (filtered residue on evaporation in milligrams per liter, dried at 180 degrees Celsius), suspended solids, major ions, nutrients, trace metals, and pesticides. Trace metal concentration data were compared to the Texas Commission on Environmental Quality established surface water quality standards for human health protection (water and fish). Among all constituents in all samples for which criteria were available for comparison, only one sample had one constituent which exceeded the surface water criteria on one occasion. A single lead concentration (2.76 micrograms per liter) measured in a filtered water sample exceeded the surface water criteria of 1.15 micrograms per liter. The average number of pesticide detections per sample in stream basins undergoing development ranged from 1.8 to 6.0. In contrast, the average number of pesticide detections per sample in the reference stream basin was 0.6. Among all constituents examined in this study, pesticides, dissolved orthophosphate phosphorus, and dissolved total phosphorus demonstrated the largest differences between the four stream basins undergoing development and the reference stream basin with undeveloped land cover.

Using LiDAR datasets to improve HSPF water quality modeling in the Red River of the North Basin

NASA Astrophysics Data System (ADS)

Burke, M. P.; Foreman, C. S.

2013-12-01

The Red River of the North Basin (RRB), located in the lakebed of ancient glacial Lake Agassiz, comprises one of the flattest landscapes in North America. The topography of the basin, coupled with the Red River's direction of flow from south to north results in a system that is highly susceptible to flooding. The magnitude and frequency of flood events in the RRB has prompted several multijurisdictional projects and mitigation efforts. In response to the devastating 1997 flood, an International Joint Commission sponsored task force established the need for accurate elevation data to help improve flood forecasting and better understand risks. This led to the International Water Institute's Red River Basin Mapping Initiative, and the acquisition LiDAR Data for the entire US portion of the RRB. The resulting 1 meter bare earth digital elevation models have been used to improve hydraulic and hydrologic modeling within the RRB, with focus on flood prediction and mitigation. More recently, these LiDAR datasets have been incorporated into Hydrological Simulation Program-FORTRAN (HSPF) model applications to improve water quality predictions in the MN portion of the RRB. RESPEC is currently building HSPF model applications for five of MN's 8-digit HUC watersheds draining to the Red River, including: the Red Lake River, Clearwater River, Sandhill River, Two Rivers, and Tamarac River watersheds. This work is being conducted for the Minnesota Pollution Control Agency (MPCA) as part of MN's statewide watershed approach to restoring and protecting water. The HSPF model applications simulate hydrology (discharge, stage), as well as a number of water quality constituents (sediment, temperature, organic and inorganic nitrogen, total ammonia, organic and inorganic phosphorus, dissolved oxygen and biochemical oxygen demand, and algae) continuously for the period 1995-2009 and are formulated to provide predictions at points of interest within the watersheds, such as observation gages, management boundaries, compliance points, and impaired water body endpoints. Incorporation of the LiDAR datasets has been critical to representing the topographic characteristics that impact hydrologic and water quality processes in the extremely flat, heavily drained sub-basins of the RRB. Beyond providing more detailed elevation and slope measurements, the high resolution LiDAR datasets have helped to identify drainage alterations due to agricultural practices, as well as improve representation of channel geometry. Additionally, when available, LiDAR based hydraulic models completed as part of the RRB flood mitigation efforts, are incorporated to further improve flow routing. The MPCA will ultimately use these HSPF models to aid in Total Maximum Daily Load (TMDL) development, permit development/compliance, analysis of Best Management Practice (BMP) implementation scenarios, and other watershed planning and management objectives. LiDAR datasets are an essential component of the water quality models build for the watersheds within the RRB and would greatly benefit water quality modeling efforts in similarly characterized areas.

Using "big data" to optimally model hydrology and water quality across expansive regions

USGS Publications Warehouse

Roehl, E.A.; Cook, J.B.; Conrads, P.A.

2009-01-01

This paper describes a new divide and conquer approach that leverages big environmental data, utilizing all available categorical and time-series data without subjectivity, to empirically model hydrologic and water-quality behaviors across expansive regions. The approach decomposes large, intractable problems into smaller ones that are optimally solved; decomposes complex signals into behavioral components that are easier to model with "sub- models"; and employs a sequence of numerically optimizing algorithms that include time-series clustering, nonlinear, multivariate sensitivity analysis and predictive modeling using multi-layer perceptron artificial neural networks, and classification for selecting the best sub-models to make predictions at new sites. This approach has many advantages over traditional modeling approaches, including being faster and less expensive, more comprehensive in its use of available data, and more accurate in representing a system's physical processes. This paper describes the application of the approach to model groundwater levels in Florida, stream temperatures across Western Oregon and Wisconsin, and water depths in the Florida Everglades. ?? 2009 ASCE.

Evaluation and Prediction of Water Resources Based on AHP

NASA Astrophysics Data System (ADS)

Li, Shuai; Sun, Anqi

2017-01-01

Nowadays, the shortage of water resources is a threat to us. In order to solve the problem of water resources restricted by varieties of factors, this paper establishes a water resources evaluation index model (WREI), which adopts the fuzzy comprehensive evaluation (FCE) based on analytic hierarchy process (AHP) algorithm. After considering influencing factors of water resources, we ignore secondary factors and then hierarchical approach the main factors according to the class, set up a three-layer structure. The top floor is for WREI. Using analytic hierarchy process (AHP) to determine weight first, and then use fuzzy judgment to judge target, so the comprehensive use of the two algorithms reduce the subjective influence of AHP and overcome the disadvantages of multi-level evaluation. To prove the model, we choose India as a target region. On the basis of water resources evaluation index model, we use Matlab and combine grey prediction with linear prediction to discuss the ability to provide clean water in India and the trend of India’s water resources changing in the next 15 years. The model with theoretical support and practical significance will be of great help to provide reliable data support and reference for us to get plans to improve water quality.

Water quality prediction of marine recreational beaches receiving watershed baseflow and stormwater runoff in southern California, USA.

PubMed

He, Li-Ming Lee; He, Zhen-Li

2008-05-01

Beach advisories are issued to the public in California when the concentration of fecal indicator bacteria (FIB), including total coliform, fecal coliform (or Escherichia coli), and Enterococcus, exceed their recreational water health standards, or when the amount of a rainfall event is above the pre-determined threshold. However, it is not fully understood about how and to what degree stormwater runoff or baseflow exerts impacts on beach water quality. Furthermore, current laboratory methods used to determine the FIB levels take 18-96 h, which is too slow to keep pace with changes in FIB levels in water. Thus, a beach may not be posted when it is contaminated, and may be posted under advisory when bacterial levels have already decreased to within water quality standards. The study was designed to address the above critical issues. There were large temporal and spatial variations in FIB concentrations along two popular State Beaches in San Diego, CA, USA. The rainstorm-induced runoff from the watersheds exerts significant impacts on the marine recreational water quality of the beaches adjacent to lagoons during the first 24-48 h after a rain event. The large volume of stormwater runoff discharging to beaches caused high FIB concentrations in beach water not only at the lagoon outlet channel and the mixing zone, but also at the locations 90 m away from the channel northward or southward along the shoreline. The geomorphology of beach shoreline, distance from the outlet channel, wind strength, wind direction, tide height, wave height, rainfall, time lapse after a rainstorm, or channel flow rate played a role in affecting the distribution of FIB concentrations in beach water. Despite the great temporal and spatial variability of FIB concentrations along a shoreline, the artificial neural network-based models developed in this study are capable of successfully predicting FIB concentrations at different beaches, different locations, and different times under baseflow or rainstorm conditions. The models are based on readily measurable variables including temperature, conductivity, pH, turbidity, channel water flow, rainfall, and/or time lapse after a rainstorm. The established models will help fill the current gap between beach posting and actual water quality and make more meaningful and effective decisions on beach closures and advisories.

Predicting the thermal effects of dam removal on the Klamath River

USGS Publications Warehouse

Bartholow, J.M.; Campbell, S.G.; Flug, M.

2004-01-01

The Klamath River once supported large runs of anadromous salmonids. Water temperature associated with multiple mainstem hydropower facilities might be one of many factors responsible for depressing Klamath salmon stocks. We combined a water quantity model and a water quality model to predict how removing the series of dams below Upper Klamath Lake might affect water temperatures, and ultimately fish survival, in the spawning and rearing portions of the mainstem Klamath. We calibrated the water quantity and quality models and applied them for the hydrometeorological conditions during a 40-year postdam period. Then, we hypothetically removed the dams and their impoundments from the models and reestimated the river’s water temperatures. The principal thermal effect of dam and reservoir removal would be to restore the timing (phase) of the river’s seasonal thermal signature by shifting it approximately 18 days earlier in the year, resulting in river temperatures that more rapidly track ambient air temperatures. Such a shift would likely cool thermal habitat conditions for adult fall chinook (Oncorhynchus tshawytscha) during upstream migration and benefit mainstem spawning. By contrast, spring and early summer temperatures could be warmer without dams, potentially harming chinook rearing and outmigration in the mainstem. Dam removal might affect the river’s thermal regime during certain conditions for over 200 km of the mainstem.

Associations of aquatic invertebrates and water quality in the ecology of an emerging tropical disease

NASA Astrophysics Data System (ADS)

Benbow, M.; Merritt, R. W.; Kimbirauskas, R.; Kolar, R.

2005-05-01

Mycobacterium ulcerans Infection is commonly called Buruli ulcer, a rapidly emerging skin disease that is often disfiguring and causes severe and lasting morbidity in developing nations of the tropics and sub-tropics. Outbreaks of BU are nearly always associated with slow-flowing aquatic habitats affected by human-mediated landscape changes, and biting aquatic insects are thought to play a role in transmission. As a part of a World Health Organization initiative, we are determining landscape factors that determine water quality conditions conducive for enhanced M. ulcerans growth and abundance in the aquatic environment. In June 2004 we collected water quality and invertebrate data from 12 water bodies near Accra, Ghana, Africa. Preliminary analyses found predator-dominated communities (from 47% - 64%) with Hemiptera (e.g., Belostomatidae and Naucoridae) most often collected. Using exploratory canonical correspondence analysis, sites separated out by functional feeding groups and water quality variables. Higher water hardness and total suspended solids was most associated with scrapers (i.e., snails) and shrimp, respectively. PCR evidence suggests that M. ulcerans is found among snails, fish and invertebrates. Future studies are proposed that take a multi-scale, multidisciplinary approach for identifying disturbance metrics that can be used to predict human Buruli ulcer incidence near monitored water bodies.

Assessing Receiving Water Quality Impacts due to Flow Path Alteration in Residential Catchments, using the Stormwater and Wastewater Management Model

NASA Astrophysics Data System (ADS)

Wolosoff, S. E.; Duncan, J.; Endreny, T.

2001-05-01

The Croton water supply system, responsible for supplying approximately 10% of New York City's water, provides an opportunity for exploration into the impacts of significant terrestrial flow path alteration upon receiving water quality. Natural flow paths are altered during residential development in order to allow for construction at a given location, reductions in water table elevation in low lying areas and to provide drainage of increased overland flow volumes. Runoff conducted through an artificial drainage system, is prevented from being attenuated by the natural environment, thus the pollutant removal capacity inherent in most natural catchments is often limited to areas where flow paths are not altered by development. By contrasting the impacts of flow path alterations in two small catchments in the Croton system, with different densities of residential development, we can begin to identify appropriate limits to the re-routing of runoff in catchments draining into surface water supplies. The Stormwater and Wastewater Management Model (SWMM) will be used as a tool to predict the runoff quantity and quality generated from two small residential catchments and to simulate the potential benefits of changes to the existing drainage system design, which may improve water quality due to longer residence times.

Simulation of water environmental capacity and pollution load reduction using QUAL2K for water environmental management.

PubMed

Zhang, Ruibin; Qian, Xin; Yuan, Xingcheng; Ye, Rui; Xia, Bisheng; Wang, Yulei

2012-12-07

In recent years, water quality degradation associated with rapid socio-economic development in the Taihu Lake Basin, China, has attracted increasing attention from both the public and the Chinese government. The primary sources of pollution in Taihu Lake are its inflow rivers and their tributaries. Effective water environmental management strategies need to be implemented in these rivers to improve the water quality of Taihu Lake, and to ensure sustainable development in the region. The aim of this study was to provide a basis for water environmental management decision-making. In this study, the QUAL2K model for river and stream water quality was applied to predict the water quality and environmental capacity of the Hongqi River, which is a polluted tributary in the Taihu Lake Basin. The model parameters were calibrated by trial and error until the simulated results agreed well with the observed data. The calibrated QUAL2K model was used to calculate the water environmental capacity of the Hongqi River, and the water environmental capacities of COD(Cr) NH(3)-N, TN, and TP were 17.51 t, 1.52 t, 2.74 t and 0.37 t, respectively. The results showed that the NH(3)-N, TN, and TP pollution loads of the studied river need to be reduced by 50.96%, 44.11%, and 22.92%, respectively to satisfy the water quality objectives. Thus, additional water pollution control measures are needed to control and reduce the pollution loads in the Hongqi River watershed. The method applied in this study should provide a basis for water environmental management decision-making.

Simulation of Water Environmental Capacity and Pollution Load Reduction Using QUAL2K for Water Environmental Management

PubMed Central

Zhang, Ruibin; Qian, Xin; Yuan, Xingcheng; Ye, Rui; Xia, Bisheng; Wang, Yulei

2012-01-01

In recent years, water quality degradation associated with rapid socio-economic development in the Taihu Lake Basin, China, has attracted increasing attention from both the public and the Chinese government. The primary sources of pollution in Taihu Lake are its inflow rivers and their tributaries. Effective water environmental management strategies need to be implemented in these rivers to improve the water quality of Taihu Lake, and to ensure sustainable development in the region. The aim of this study was to provide a basis for water environmental management decision-making. In this study, the QUAL2K model for river and stream water quality was applied to predict the water quality and environmental capacity of the Hongqi River, which is a polluted tributary in the Taihu Lake Basin. The model parameters were calibrated by trial and error until the simulated results agreed well with the observed data. The calibrated QUAL2K model was used to calculate the water environmental capacity of the Hongqi River, and the water environmental capacities of CODCr NH3-N, TN, and TP were 17.51 t, 1.52 t, 2.74 t and 0.37 t, respectively. The results showed that the NH3-N, TN, and TP pollution loads of the studied river need to be reduced by 50.96%, 44.11%, and 22.92%, respectively to satisfy the water quality objectives. Thus, additional water pollution control measures are needed to control and reduce the pollution loads in the Hongqi River watershed. The method applied in this study should provide a basis for water environmental management decision-making. PMID:23222206

A real-time control framework for urban water reservoirs operation

NASA Astrophysics Data System (ADS)

Galelli, S.; Goedbloed, A.; Schwanenberg, D.

2012-04-01

Drinking water demand in urban areas is growing parallel to the worldwide urban population, and it is acquiring an increasing part of the total water consumption. Since the delivery of sufficient water volumes in urban areas represents a difficult logistic and economical problem, different metropolitan areas are evaluating the opportunity of constructing relatively small reservoirs within urban areas. Singapore, for example, is developing the so-called 'Four National Taps Strategies', which detects the maximization of water yields from local, urban catchments as one of the most important water sources. However, the peculiar location of these reservoirs can provide a certain advantage from the logistical point of view, but it can pose serious difficulties in their daily management. Urban catchments are indeed characterized by large impervious areas: this results in a change of the hydrological cycle, with decreased infiltration and groundwater recharge, and increased patterns of surface and river discharges, with higher peak flows, volumes and concentration time. Moreover, the high concentrations of nutrients and sediments characterizing urban discharges can cause further water quality problems. In this critical hydrological context, the effective operation of urban water reservoirs must rely on real-time control techniques, which can exploit hydro-meteorological information available in real-time from hydrological and nowcasting models. This work proposes a novel framework for the real-time control of combined water quality and quantity objectives in urban reservoirs. The core of this framework is a non-linear Model Predictive Control (MPC) scheme, which employs the current state of the system, the future discharges furnished by a predictive model and a further model describing the internal dynamics of the controlled sub-system to determine an optimal control sequence over a finite prediction horizon. The main advantage of this scheme stands in its reduced computational requests and the capability of exploiting real-time hydro-meteorological information, which are crucial for an effective operation of these fast-varying hydrological systems. The framework is here demonstrated on the operation of Marina Reservoir (Singapore), whose recent construction in late 2008 increased the effective catchment area to about 50% of the total available. Its operation, which accounts for drinking water supply, flash floods control and water quality standards, is here designed by combining the MPC scheme with the process-based hydrological model SOBEK. Extensive simulation experiments show the validity of the proposed framework.

The Use of Traditional Hydrologic Field Techniques as a Means of Quantifying Mine-Related Contamination Impacts on a Surface Water Body, Norrbotten County, Sweden

NASA Astrophysics Data System (ADS)

Little, S. F. B.; Walder, I. F.; Cadol, D. D.

2016-12-01

The Malmberget/Vitåfors mining facility, located in Norrbotten County, Sweden, is the world's second largest underground iron ore mine, comprised of roughly 20 steeply dipping magnetite-hematite ore lenses, with an underground area of approximately 5 x 2.5km. Since its' opening in 1892, over 350Mt of ore have been removed from Malmberget, and another 350Mt of iron reserves have been declared proven and probable. The state-owned mining company, LKAB, operates the facility. They have increased production in the past years, effectively doubling the amount of ore processed annually, between 1998 and 2013. Despite these changes, the volume of water used within the system has not grown proportionally, and is not predicted to do so in the future. This is due to increases in process-water recycling, adding to the demands placed on this water. As it is reused, the conservative and trace element concentrations grow, affecting overall water quality. Some portion of the spent process water is released on a daily basis into the nearby Lina River. This discharge is generated in two ways: (1) By means of monitored release via outlet pipes, and (2) through diffuse leakage and subsurface flow originating at the facility's tailings and settling ponds. This study aims to describe both the quality and quantity of the second form of discharge- with the ultimate goal of predicting these attributes given projected ore processing and water-recycling increases. With limited data- consisting primarily of routine water sampling- an understanding of the nature of this leakage must be gained through combined geochemical modeling and site characterization. With this objective in mind, fieldwork was conducted to quantify the volume of flow between groundwater and surface water bodies in the portion of the river adjacent to the mine. This utilized two basic hydrologic techniques: stream gaging, and the deployment of simple seepage meters. The data collected from this investigation was then used to construct a hydrologic model illustrating the proposed movement of water from the tailings and settling ponds- chronicling the path to its eventual release into the gaining river. Further coupling of the hydrologic and geochemical information will improve the accuracy of this prediction, in addition to addressing the question of water quality.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Taiping; Yang, Zhaoqing

Increased eutrophication and degraded water quality in estuarine and coastal waters have been a worldwide environmental concern. While it is commonly accepted that anthropogenic impact plays a major role in many emerging water quality issues, natural conditions such as restricted water circulations controlled by geometry may also substantially contribute to unfavorable water quality in certain ecosystems. To elucidate the contributions from different factors, a hydrodynamic-water quality model that integrates both physical transport and pollutant loadings is particularly warranted. A preliminary modeling study using the Environmental Fluid Dynamic Code (EFDC) is conducted to investigate hydrodynamic circulation and low dissolved oxygen (DO)more » in Hood Canal, a representative fjord in the U.S. Pacific Northwest. Because the water quality modeling work is still ongoing, this paper focuses on the progress in hydrodynamic modeling component. The hydrodynamic model has been set up using the publicly available forcing data and was calibrated against field observations or NOAA predictions for tidal elevation, current, salinity and temperature. The calibrated model was also used to estimate physical transport timescales such as residence time in the estuary. The preliminary model results demonstrate that the EFDC Hood Canal model is capable of capturing the general circulation patterns in Hood Canal, including weak tidal current and strong vertical stratification. The long residence time (i.e., on the order of 100 days for the entire estuary) also indicates that restricted water circulation could contribute to low DO in the estuary and also makes the system especially susceptible to anthropogenic disturbance, such as excess nutrient input.« less

Development and testing of watershed-scale models for poorly drained soils

Treesearch

Glenn P. Fernandez; George M. Chescheir; R. Wayne Skaggs; Devendra M. Amatya

2005-01-01

Watershed-scale hydrology and water quality models were used to evaluate the crrmulative impacts of land use and management practices on dowrzstream hydrology and nitrogen loading of poorly drained watersheds. Field-scale hydrology and nutrient dyyrutmics are predicted by DRAINMOD in both models. In the first model (DRAINMOD-DUFLOW), field-scale predictions are coupled...

Modeling and Management of Increased Urban Stormwater Runoff Using InfoSWMM Sustain in the Berkeley Neighborhood of Denver, Colorado

NASA Astrophysics Data System (ADS)

Panos, C.; Hogue, T. S.; McCray, J. E.

2016-12-01

Few urban studies have evaluated the hydrologic impacts of redevelopment - for example, a rapid conversion from single to multi-family homes - known as infill, or re-urbanization. Redevelopment provides unique stormwater challenges as private property owners in many cities are not mandated to undertake stormwater retrofits leading to an overall increase in stormwater quantity and decrease in quality. This research utilizes a version of the EPA's Storm Water Management Model (SWMM), InfoSWMM Sustain, to model and analyze the impacts of impervious cover change due to redevelopment on stormwater quantity and quality in Denver, Colorado, with a focus on the Berkeley Neighborhood, where the percent imperviousness is expected to increase significantly from a current value of 53% by 2025. We utilize flow data from multiple pressure transducers installed directly within the storm sewer network as well as water quality data from storm and low flow sampling to initially calibrate InfoSWMM Sustain using September 2015 through September 2016 storm data. Model scenarios include current land cover conditions as well as future imperviousness predictions from redevelopment. The Urban Drainage and Flood Control District's Colorado Urban Hydrograph Procedure (CUHP) model is also implemented and used for calibration and comparison to the InfoSWMM stormwater model. Model simulations predicting an average annual stormwater runoff for the basin will be used to inform stormwater capture for the Berkeley Neighborhood on the downstream Willis Case Golf Course, where treatment trains are being designed to provide irrigation water (a 250 ac-ft per year demand) and improved water quality for discharge to the nearby receiving waters of Clear Creek. Ultimately, study results will better inform regional stormwater capture requirements when transitioning from single to multi-family units by providing a quantitative basis for treatment and regulation priorities.

Spatiotemporal dynamics of landscape pattern and hydrologic process in watershed systems

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Tsvetkova, Olga

2011-06-01

SummaryLand use change is influenced by spatial and temporal factors that interact with watershed resources. Modeling these changes is critical to evaluate emerging land use patterns and to predict variation in water quantity and quality. The objective of this study is to model the nature and emergence of spatial patterns in land use and water resource impacts using a spatially explicit and dynamic landscape simulation. Temporal changes are predicted using a probabilistic Markovian process and spatial interaction through cellular automation. The MCMC (Monte Carlo Markov Chain) analysis with cellular automation is linked to hydrologic equations to simulate landscape patterns and processes. The spatiotemporal watershed dynamics (SWD) model is applied to a subwatershed in the Blackstone River watershed of Massachusetts to predict potential land use changes and expected runoff and sediment loading. Changes in watershed land use and water resources are evaluated over 100 years at a yearly time step. Results show high potential for rapid urbanization that could result in lowering of groundwater recharge and increased storm water peaks. The watershed faces potential decreases in agricultural and forest area that affect open space and pervious cover of the watershed system. Water quality deteriorated due to increased runoff which can also impact stream morphology. While overland erosion decreased, instream erosion increased from increased runoff from urban areas. Use of urban best management practices (BMPs) in sensitive locations, preventive strategies, and long-term conservation planning will be useful in sustaining the watershed system.

Using biotic ligand models to predict metal toxicity in mineralized systems

USGS Publications Warehouse

Smith, Kathleen S.; Balistrieri, Laurie S.; Todd, Andrew S.

2015-01-01

The biotic ligand model (BLM) is a numerical approach that couples chemical speciation calculations with toxicological information to predict the toxicity of aquatic metals. This approach was proposed as an alternative to expensive toxicological testing, and the U.S. Environmental Protection Agency incorporated the BLM into the 2007 revised aquatic life ambient freshwater quality criteria for Cu. Research BLMs for Ag, Ni, Pb, and Zn are also available, and many other BLMs are under development. Current BLMs are limited to ‘one metal, one organism’ considerations. Although the BLM generally is an improvement over previous approaches to determining water quality criteria, there are several challenges in implementing the BLM, particularly at mined and mineralized sites. These challenges include: (1) historically incomplete datasets for BLM input parameters, especially dissolved organic carbon (DOC), (2) several concerns about DOC, such as DOC fractionation in Fe- and Al-rich systems and differences in DOC quality that result in variations in metal-binding affinities, (3) water-quality parameters and resulting metal-toxicity predictions that are temporally and spatially dependent, (4) additional influences on metal bioavailability, such as multiple metal toxicity, dietary metal toxicity, and competition among organisms or metals, (5) potential importance of metal interactions with solid or gas phases and/or kinetically controlled reactions, and (6) tolerance to metal toxicity observed for aquatic organisms living in areas with elevated metal concentrations.

A coupled model approach to reduce nonpoint-source pollution resulting from predicted urban growth: A case study in the Ambos Nogales watershed

USGS Publications Warehouse

Norman, L.M.; Guertin, D.P.; Feller, M.

2008-01-01

The development of new approaches for understanding processes of urban development and their environmental effects, as well as strategies for sustainable management, is essential in expanding metropolitan areas. This study illustrates the potential of linking urban growth and watershed models to identify problem areas and support long-term watershed planning. Sediment is a primary source of nonpoint-source pollution in surface waters. In urban areas, sediment is intermingled with other surface debris in transport. In an effort to forecast the effects of development on surface-water quality, changes predicted in urban areas by the SLEUTH urban growth model were applied in the context of erosion-sedimentation models (Universal Soil Loss Equation and Spatially Explicit Delivery Models). The models are used to simulate the effect of excluding hot-spot areas of erosion and sedimentation from future urban growth and to predict the impacts of alternative erosion-control scenarios. Ambos Nogales, meaning 'both Nogaleses,' is a name commonly used for the twin border cities of Nogales, Arizona and Nogales, Sonora, Mexico. The Ambos Nogales watershed has experienced a decrease in water quality as a result of urban development in the twin-city area. Population growth rates in Ambos Nogales are high and the resources set in place to accommodate the rapid population influx will soon become overburdened. Because of its remote location and binational governance, monitoring and planning across the border is compromised. One scenario described in this research portrays an improvement in water quality through the identification of high-risk areas using models that simulate their protection from development and replanting with native grasses, while permitting the predicted and inevitable growth elsewhere. This is meant to add to the body of knowledge about forecasting the impact potential of urbanization on sediment delivery to streams for sustainable development, which can be accomplished in a virtual environment. Copyright ?? 2008 by Bellwether Publishing, Ltd. All rights reserved.

Modeling the cadmium balance in Australian agricultural systems in view of potential impacts on food and water quality.

PubMed

de Vries, W; McLaughlin, M J

2013-09-01

The historical build up and future cadmium (Cd) concentrations in top soils and in crops of four Australian agricultural systems are predicted with a mass balance model, focusing on the period 1900-2100. The systems include a rotation of dryland cereals, a rotation of sugarcane and peanuts/soybean, intensive dairy production and intensive horticulture. The input of Cd to soil is calculated from fertilizer application and atmospheric deposition and also examines options including biosolid and animal manure application in the sugarcane rotation and dryland cereal production systems. Cadmium output from the soil is calculated from leaching to deeper horizons and removal with the harvested crop or with livestock products. Parameter values for all Cd fluxes were based on a number of measurements on Australian soil-plant systems. In the period 1900-2000, soil Cd concentrations were predicted to increase on average between 0.21 mg kg(-1) in dryland cereals, 0.42 mg kg(-1) in intensive agriculture and 0.68 mg kg(-1) in dairy production, which are within the range of measured increases in soils in these systems. Predicted soil concentrations exceed critical soil Cd concentrations, based on food quality criteria for Cd in crops during the simulation period in clay-rich soils under dairy production and intensive horticulture. Predicted dissolved Cd concentrations in soil pore water exceed a ground water quality criterion of 2 μg l(-1) in light textured soils, except for the sugarcane rotation due to large water leaching fluxes. Results suggest that the present fertilizer Cd inputs in Australia are in excess of the long-term critical loads in heavy-textured soils for dryland cereals and that all other systems are at low risk. Calculated critical Cd/P ratios in P fertilizers vary from <50 to >1000 mg Cd kg P(-1) for the different soil, crop and environmental conditions applied. Copyright © 2013 Elsevier B.V. All rights reserved.

Development of a regional bio-optical model for water quality assessment in the US Virgin Islands

NASA Astrophysics Data System (ADS)

Kerrigan, Kristi Lisa

Previous research in the US Virgin Islands (USVI) has demonstrated that land-based sources of pollution associated with watershed development and climate change are local and global factors causing coral reef degradation. A good indicator that can be used to assess stress on these environments is the water quality. Conventional assessment methods based on in situ measurements are timely and costly. Satellite remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic nature of water quality parameters by applying bio-optical models. Chlorophyll-a, suspended sediments (TSM), and colored-dissolved organic matter are color-producing agents (CPAs) that define the water quality and can be measured remotely. However, the interference of multiple optically active constituents that characterize the water column as well as reflectance from the bottom poses a challenge in shallow coastal environments in USVI. In this study, field and laboratory based data were collected from sites on St. Thomas and St. John to characterize the CPAs and bottom reflectance of substrates. Results indicate that the optical properties of these waters are a function of multiple CPAs with chlorophyll-a values ranging from 0.10 to 2.35 microg/L and TSM values from 8.97 to 15.7 mg/L. These data were combined with in situ hyperspectral radiometric and Landsat OLI satellite data to develop a regionally tiered model that can predict CPA concentrations using traditional band ratio and multivariate approaches. Band ratio models for the hyperspectral dataset (R2 = 0.35; RMSE = 0.10 microg/L) and Landsat OLI dataset (R2 = 0.35; RMSE = 0.12 microg/L) indicated promising accuracy. However, a stronger model was developed using a multivariate, partial least squares regression to identify wavelengths that are more sensitive to chlorophyll-a (R2 = 0.62, RMSE = 0.08 microg/L) and TSM (R2 = 0.55). This approach takes advantage of the full spectrum of hyperspectral data, thus providing a more robust predictive model. Models developed in this study will significantly improve near-real time and long-term water quality monitoring in USVI and will provide insight to factors contributing to coral reef decline.

Water Quality and Herbivory Interactively Drive Coral-Reef Recovery Patterns in American Samoa

PubMed Central

Houk, Peter; Musburger, Craig; Wiles, Phil

2010-01-01

Background Compared with a wealth of information regarding coral-reef recovery patterns following major disturbances, less insight exists to explain the cause(s) of spatial variation in the recovery process. Methodology/Principal Findings This study quantifies the influence of herbivory and water quality upon coral reef assemblages through space and time in Tutuila, American Samoa, a Pacific high island. Widespread declines in dominant corals (Acropora and Montipora) resulted from cyclone Heta at the end of 2003, shortly after the study began. Four sites that initially had similar coral reef assemblages but differential temporal dynamics four years following the disturbance event were classified by standardized measures of ‘recovery status’, defined by rates of change in ecological measures that are known to be sensitive to localized stressors. Status was best predicted, interactively, by water quality and herbivory. Expanding upon temporal trends, this study examined if similar dependencies existed through space; building multiple regression models to identify linkages between similar status measures and local stressors for 17 localities around Tutuila. The results highlighted consistent, interactive interdependencies for coral reef assemblages residing upon two unique geological reef types. Finally, the predictive regression models produced at the island scale were graphically interpreted with respect to hypothesized site-specific recovery thresholds. Conclusions/Significance Cumulatively, our study purports that moving away from describing relatively well-known patterns behind recovery, and focusing upon understanding causes, improves our foundation to predict future ecological dynamics, and thus improves coral reef management. PMID:21085715

Water quality and herbivory interactively drive coral-reef recovery patterns in American Samoa.

PubMed

Houk, Peter; Musburger, Craig; Wiles, Phil

2010-11-10

Compared with a wealth of information regarding coral-reef recovery patterns following major disturbances, less insight exists to explain the cause(s) of spatial variation in the recovery process. This study quantifies the influence of herbivory and water quality upon coral reef assemblages through space and time in Tutuila, American Samoa, a Pacific high island. Widespread declines in dominant corals (Acropora and Montipora) resulted from cyclone Heta at the end of 2003, shortly after the study began. Four sites that initially had similar coral reef assemblages but differential temporal dynamics four years following the disturbance event were classified by standardized measures of 'recovery status', defined by rates of change in ecological measures that are known to be sensitive to localized stressors. Status was best predicted, interactively, by water quality and herbivory. Expanding upon temporal trends, this study examined if similar dependencies existed through space; building multiple regression models to identify linkages between similar status measures and local stressors for 17 localities around Tutuila. The results highlighted consistent, interactive interdependencies for coral reef assemblages residing upon two unique geological reef types. Finally, the predictive regression models produced at the island scale were graphically interpreted with respect to hypothesized site-specific recovery thresholds. Cumulatively, our study purports that moving away from describing relatively well-known patterns behind recovery, and focusing upon understanding causes, improves our foundation to predict future ecological dynamics, and thus improves coral reef management.

New submodel for watershed-scale simulations of fecal bacteria fate and transport at agricultural and pasture lands

USDA-ARS?s Scientific Manuscript database

Microbial contamination of waters is the critical public health issue. The watershed-scale process-based modeling of bacteria fate and transport (F&T) has been proven to serve as the useful tool for predicting microbial water quality and evaluating management practices. The objective of this work is...

AN EXPERT SYSTEM FOR HYDRODYNAMIC MIXING ZONE ANAYLSIS OF CONVENTIONAL AND TOXIC SUBMERGED SINGLE PORT DISCHARGES (CORMIX1)

EPA Science Inventory

U.S. water quality policy includes the concept of a mixing zone, a limited area or volume of water where the initial dilution of a discharge occurs. he Cornell Mixing Zone Expert System (CORMIX1) was developed to predict the dilution and trajectory of a submerged single port disc...

Evaluating the role of ion composition on the toxicity of copper to Ceriodaphnia dubia in very hard waters.

PubMed

Gensemer, Robert W; Naddy, Rami B; Stubblefield, William A; Hockett, J Russell; Santore, Robert; Paquin, Paul

2002-09-01

The mitigating effect of increasing hardness on metal toxicity is reflected in water quality criteria in the United States over the range of 25-400 mgl(-1) (as CaCO(3)). However, waters in the arid west of the US frequently exceed 400 mgl(-1) hardness, and the applicability of hardness-toxicity relationships in these waters is unknown. Acute toxicity tests with Ceriodaphnia dubia were conducted at hardness levels ranging from approximately 300 to 1,200 mgl(-1) using reconstituted waters that mimic two natural waters with elevated hardness: (1) alkaline desert southwest streams (Las Vegas Wash, NV), and (2) low alkalinity waters from a CaSO(4)-treated mining effluent in Colorado. The moderately-alkaline EPA synthetic hard water was also included for comparison. Copper toxicity did not consistently vary as a function of hardness, but likely as a function of other water quality characteristics (e.g., alkalinity or other correlated factors). The hardness equations used in regulatory criteria, therefore, may not provide an accurate level of protection against copper toxicity in all types of very hard waters. However, the mechanistic Biotic ligand model generally predicted copper toxicity within +/-2X of observed EC(50) values, and thus may be more useful than hardness for modifying water quality criteria.

Enterococci in the environment

USGS Publications Warehouse

Byappanahalli, Muruleedhara N.; Nevers, Meredith B.; Korajkic, Asja; Staley, Zachery R.; Harwood, Valerie J.

2012-01-01

Enterococci are common, commensal members of gut communities in mammals and birds, yet they are also opportunistic pathogens that cause millions of human and animal infections annually. Because they are shed in human and animal feces, are readily culturable, and predict human health risks from exposure to polluted recreational waters, they are used as surrogates for waterborne pathogens and as fecal indicator bacteria (FIB) in research and in water quality testing throughout the world. Evidence from several decades of research demonstrates, however, that enterococci may be present in high densities in the absence of obvious fecal sources and that environmental reservoirs of these FIB are important sources and sinks, with the potential to impact water quality. This review focuses on the distribution and microbial ecology of enterococci in environmental (secondary) habitats, including the effect of environmental stressors; an outline of their known and apparent sources, sinks, and fluxes; and an overview of the use of enterococci as FIB. Finally, the significance of emerging methodologies, such as microbial source tracking (MST) and empirical predictive models, as tools in water quality monitoring is addressed. The mounting evidence for widespread extraenteric sources and reservoirs of enterococci demonstrates the versatility of the genus Enterococcus and argues for the necessity of a better understanding of their ecology in natural environments, as well as their roles as opportunistic pathogens and indicators of human pathogens.

Enterococci in the Environment

PubMed Central

Byappanahalli, Muruleedhara N.; Nevers, Meredith B.; Korajkic, Asja; Staley, Zachery R.

2012-01-01

Summary: Enterococci are common, commensal members of gut communities in mammals and birds, yet they are also opportunistic pathogens that cause millions of human and animal infections annually. Because they are shed in human and animal feces, are readily culturable, and predict human health risks from exposure to polluted recreational waters, they are used as surrogates for waterborne pathogens and as fecal indicator bacteria (FIB) in research and in water quality testing throughout the world. Evidence from several decades of research demonstrates, however, that enterococci may be present in high densities in the absence of obvious fecal sources and that environmental reservoirs of these FIB are important sources and sinks, with the potential to impact water quality. This review focuses on the distribution and microbial ecology of enterococci in environmental (secondary) habitats, including the effect of environmental stressors; an outline of their known and apparent sources, sinks, and fluxes; and an overview of the use of enterococci as FIB. Finally, the significance of emerging methodologies, such as microbial source tracking (MST) and empirical predictive models, as tools in water quality monitoring is addressed. The mounting evidence for widespread extraenteric sources and reservoirs of enterococci demonstrates the versatility of the genus Enterococcus and argues for the necessity of a better understanding of their ecology in natural environments, as well as their roles as opportunistic pathogens and indicators of human pathogens. PMID:23204362

Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams

USGS Publications Warehouse

Balistrieri, Laurie S.; Nimick, David A.; Mebane, Christopher A.

2012-01-01

Evaluating water quality and the health of aquatic organisms is challenging in systems with systematic diel (24 hour) or less predictable runoff-induced changes in water composition. To advance our understanding of how to evaluate environmental health in these dynamic systems, field studies of diel cycling were conducted in two streams (Silver Bow Creek and High Ore Creek) affected by historical mining activities in southwestern Montana. A combination of sampling and modeling tools were used to assess the toxicity of metals in these systems. Diffusive Gradients in Thin Films (DGT) samplers were deployed at multiple time intervals during diel sampling to confirm that DGT integrates time-varying concentrations of dissolved metals. Thermodynamic speciation calculations using site specific water compositions, including time-integrated dissolved metal concentrations determined from DGT, and a competitive, multiple-metal biotic ligand model incorporated into the Windemere Humic Aqueous Model Version 6.0 (WHAM VI) were used to determine the chemical speciation of dissolved metals and biotic ligands. The model results were combined with previously collected toxicity data on cutthroat trout to derive a relationship that predicts the relative survivability of these fish at a given site. This integrative approach may prove useful for assessing water quality and toxicity of metals to aquatic organisms in dynamic systems and evaluating whether potential changes in environmental health of aquatic systems are due to anthropogenic activities or natural variability.

A network extension of species occupancy models in a patchy environment applied to the Yosemite toad (Anaxyrus canorus)

USGS Publications Warehouse

Berlow, Eric L.; Knapp, Roland A.; Ostoja, Steven M.; Williams, Richard J.; McKenny, Heather; Matchett, John R.; Guo, Qinghau; Fellers, Gary M.; Kleeman, Patrick; Brooks, Matthew L.; Joppa, Lucas

2013-01-01

A central challenge of conservation biology is using limited data to predict rare species occurrence and identify conservation areas that play a disproportionate role in regional persistence. Where species occupy discrete patches in a landscape, such predictions require data about environmental quality of individual patches and the connectivity among high quality patches. We present a novel extension to species occupancy modeling that blends traditionalpredictions of individual patch environmental quality with network analysis to estimate connectivity characteristics using limited survey data. We demonstrate this approach using environmental and geospatial attributes to predict observed occupancy patterns of the Yosemite toad (Anaxyrus (= Bufo) canorus) across >2,500 meadows in Yosemite National Park (USA). A. canorus, a Federal Proposed Species, breeds in shallow water associated with meadows. Our generalized linear model (GLM) accurately predicted ~84% of true presence-absence data on a subset of data withheld for testing. The predicted environmental quality of each meadow was iteratively ‘boosted’ by the quality of neighbors within dispersal distance. We used this park-wide meadow connectivity network to estimate the relative influence of an individual Meadow’s ‘environmental quality’ versus its ‘network quality’ to predict: a) clusters of high quality breeding meadows potentially linked by dispersal, b) breeding meadows with high environmental quality that are isolated from other such meadows, c) breeding meadows with lower environmental quality where long-term persistence may critically depend on the network neighborhood, and d) breeding meadows with the biggest impact on park-wide breeding patterns. Combined with targeted data on dispersal, genetics, disease, and other potential stressors, these results can guide designation of core conservation areas for A. canorus in Yosemite National Park.

Increasing in-stream nitrogen concentrations under different bioenergy crop management practices in central Germany

NASA Astrophysics Data System (ADS)

Jomaa, Seifeddine; Thraen, Daniela; Rode, Michael

2015-04-01

Understanding how nitrogen fluxes respond to changes in land use and agriculture practices is crucial for improving instream water quality prediction. In central Germany, expansion of bioenergy crops such as maize and rape for ethanol production during the last decade led to increasing of fertilizer application rates. To examine the effect of these changes, surface water quality of a drinking water reservoir catchment was investigated for more than 30 years. The Weida catchment (99.5 km2) is part of the Elbe river basin and has a share of 67% agricultural land use with significant changes in agricultural practices within the investigation period. For the period 2004-2012, the share of maize and rape has been increased by 52% and 20%, respectively, for enhancing bioenergy production. To achieve our gaols, the semi-distributed hydrological water quality HYPE (Hydrological Predictions for the Environment) model was calibrated for discharge and inorganic nitrogen concentrations (IN) during the period 1997-2000.The model was validated successfully (with lowest performance of NSE = 0.78 and PBIAS = 3.74% for discharge) for three different periods 1983-1987, 1989-1996 and 2000-2003, which are charaterized by different fertilizer application rates. Results showed that the HYPE model reproduced reasonably well discharge and IN daily loads (with lowest NSE = 0.64 for IN-load). In addition, the HYPE model was evaluated successfully to predict the discharge and IN concentrations for the period 2004-2012, where detailed input data in terms of crops management (field-specific survey) have been considered. Land use and crop rotations scenarios, with high hypothetical percentage of acceptance by the farmers, revealed that continuous conversion of agricultural land into bioenergy crops, will most likely, lead to an enrichment of in-stream nitrogen, especially after spring storms.

Modeling riverine nitrate export from an East-Central Illinois watershed using SWAT.

PubMed

Hu, X; McIsaac, G F; David, M B; Louwers, C A L

2007-01-01

Reliable water quality models are needed to forecast the water quality consequences of different agricultural nutrient management scenarios. In this study, the Soil and Water Assessment Tool (SWAT), version 2000, was applied to simulate streamflow, riverine nitrate (NO(3)) export, crop yield, and watershed nitrogen (N) budgets in the upper Embarras River (UER) watershed in east-central Illinois, which has extensive maize-soybean cultivation, large N fertilizer input, and extensive tile drainage. During the calibration (1994-2002) and validation (1985-1993) periods, SWAT simulated monthly and annual stream flows with Nash-Sutcliffe coefficients (E) ranging from 0.67 to 0.94 and R(2) from 0.75 to 0.95. For monthly and annual NO(3) loads, E ranged from -0.16 to 0.45 and R(2) from 0.36 to 0.74. Annual maize and soybean yields were simulated with relative errors ranging from -10 to 6%. The model was then used to predict the changes in NO(3) output with N fertilizer application rates 10 to 50% lower than original application rates in UER. The calibrated SWAT predicted a 10 to 43% decrease in NO(3) export from UER and a 6 to 38% reduction in maize yield in response to the reduction in N fertilizer. The SWAT model markedly overestimated NO(3) export during major wet periods. Moreover, SWAT estimated soybean N fixation rates considerably greater than literature values, and some simulated changes in the N cycle in response to fertilizer reduction seemed to be unrealistic. Improving these aspects of SWAT could lead to more reliable predictions in the water quality outcomes of nutrient management practices in tile-drained watersheds.

Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy

PubMed Central

Diago, Maria P.; Fernández-Novales, Juan; Gutiérrez, Salvador; Marañón, Miguel; Tardaguila, Javier

2018-01-01

Assessing water status and optimizing irrigation is of utmost importance in most winegrowing countries, as the grapevine vegetative growth, yield, and grape quality can be impaired under certain water stress situations. Conventional plant-based methods for water status monitoring are either destructive or time and labor demanding, therefore unsuited to detect the spatial variation of moisten content within a vineyard plot. In this context, this work aims at the development and comprehensive validation of a novel, non-destructive methodology to assess the vineyard water status distribution using on-the-go, contactless, near infrared (NIR) spectroscopy. Likewise, plant water status prediction models were built and intensely validated using the stem water potential (ψs) as gold standard. Predictive models were developed making use of a vast number of measurements, acquired on 15 dates with diverse environmental conditions, at two different spatial scales, on both sides of vertical shoot positioned canopies, over two consecutive seasons. Different cross-validation strategies were also tested and compared. Predictive models built from east-acquired spectra yielded the best performance indicators in both seasons, with determination coefficient of prediction (RP2) ranging from 0.68 to 0.85, and sensitivity (expressed as prediction root mean square error) between 0.131 and 0.190 MPa, regardless the spatial scale. These predictive models were implemented to map the spatial variability of the vineyard water status at two different dates, and provided useful, practical information to help delineating specific irrigation schedules. The performance and the large amount of data that this on-the-go spectral solution provides, facilitates the exploitation of this non-destructive technology to monitor and map the vineyard water status variability with high spatial and temporal resolution, in the context of precision and sustainable viticulture. PMID:29441086

Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy.

PubMed

Diago, Maria P; Fernández-Novales, Juan; Gutiérrez, Salvador; Marañón, Miguel; Tardaguila, Javier

2018-01-01

Assessing water status and optimizing irrigation is of utmost importance in most winegrowing countries, as the grapevine vegetative growth, yield, and grape quality can be impaired under certain water stress situations. Conventional plant-based methods for water status monitoring are either destructive or time and labor demanding, therefore unsuited to detect the spatial variation of moisten content within a vineyard plot. In this context, this work aims at the development and comprehensive validation of a novel, non-destructive methodology to assess the vineyard water status distribution using on-the-go, contactless, near infrared (NIR) spectroscopy. Likewise, plant water status prediction models were built and intensely validated using the stem water potential (ψ s ) as gold standard. Predictive models were developed making use of a vast number of measurements, acquired on 15 dates with diverse environmental conditions, at two different spatial scales, on both sides of vertical shoot positioned canopies, over two consecutive seasons. Different cross-validation strategies were also tested and compared. Predictive models built from east-acquired spectra yielded the best performance indicators in both seasons, with determination coefficient of prediction ([Formula: see text]) ranging from 0.68 to 0.85, and sensitivity (expressed as prediction root mean square error) between 0.131 and 0.190 MPa, regardless the spatial scale. These predictive models were implemented to map the spatial variability of the vineyard water status at two different dates, and provided useful, practical information to help delineating specific irrigation schedules. The performance and the large amount of data that this on-the-go spectral solution provides, facilitates the exploitation of this non-destructive technology to monitor and map the vineyard water status variability with high spatial and temporal resolution, in the context of precision and sustainable viticulture.

Regionalisation of parameters of a large-scale water quality model in Lithuania using PAIC-SWAT

NASA Astrophysics Data System (ADS)

Zarrineh, Nina; van Griensven, Ann; Sennikovs, Juris; Bekere, Liga; Plunge, Svajunas

2015-04-01

To comply with the EU Water Framework Directive, all water bodies need to achieve good ecological status. To reach these goals, the Environmental Protection Agency (AAA) has to elaborate river basin districts management plans and programmes of measures for all catchments in Lithuania. For this purpose, a Soil and Water Assessment Tool (SWAT) model was set up for all Lithuanian catchments using the most recent version of SWAT2012 rev627 implemented and imbedded in a Python workflow by the Center of Processes Analysis and Research (PAIC). The model was calibrated and evaluated using all monitoring data of river discharge, nitrogen and phosphorous concentrations and load. A regionalisation strategy has been set up by identifying 13 hydrological regions according to the runoff formation and hydrological conditions. In each region, a representative catchment was selected and calibrated using a combination of manual and automated calibration techniques. After final parameterization and fulfilling of calibrating and validating evaluation criteria, the same parameters sets have been extrapolated to other catchments within the same hydrological region. Multi variable cal/val strategy was implemented for the following variables: river flow and in-stream NO3, Total Nitrogen, PO4 and Total Phosphorous concentrations. The criteria used for calibration, validation and extrapolation are: Nash-Sutcliffe Efficiency (NSE) for flow and R-squared for water quality variables and PBIAS (percentage bias) for all variables. For the hydrological calibration, NSE values greater than 0.5 should be achieved, while for validation and extrapolation the threshold is respectively 0.4 and 0.3. PBIAS errors have to be less than 20% for calibration and for validation and extrapolation less than 25% and 30%, respectively. In water quality calibration, R-squared should be achieved to 0.5 for calibration and for validation and extrapolation to 0.4 and 0.3 respectively for nitrogen variables. Besides PBIAS error should be less than 40% for calibration, and less than 70% for validation and extrapolation for all mentioned water quality variables. For the flow calibration, daily discharge data for 62 stations were provided for the period 1997-2012. For more than 500 stations, water quality data was provided and 135 data-rich stations was pre-processed in a database containing all observations from 1997-2012. Finally by implementing this regionalisation strategy, the model could satisfactorily predict the selected variables so that in the hydrological part more than 90% of stations fulfilled the criteria and in the water quality part more than 95% of stations fulfilled the criteria. Keywords: Water Quality Modelling, Regionalisation, Parameterization, Nitrogen and Phosphorus Prediction, Calibration, PAIC-SWAT.

At the nexus of fire, water and society

USGS Publications Warehouse

Martin, Deborah

2016-01-01

The societal risks of water scarcity and water-quality impairment have received considerable attention, evidenced by recent analyses of these topics by the 2030 Water Resources Group, the United Nations and the World Economic Forum. What are the effects of fire on the predicted water scarcity and declines in water quality? Drinking water supplies for humans, the emphasis of this exploration, are derived from several land cover types, including forests, grasslands and peatlands, which are vulnerable to fire. In the last two decades, fires have affected the water supply catchments of Denver (CO) and other southwestern US cities, and four major Australian cities including Sydney, Canberra, Adelaide and Melbourne. In the same time period, several, though not all, national, regional and global water assessments have included fire in evaluations of the risks that affect water supplies. The objective of this discussion is to explore the nexus of fire, water and society with the hope that a more explicit understanding of fire effects on water supplies will encourage the incorporation of fire into future assessments of water supplies, into the pyrogeography conceptual framework and into planning efforts directed at water resiliency.

Predicting nonpoint stormwater runoff quality from land use

PubMed Central

2018-01-01

Evaluating the impact of urban development on natural ecosystem processes has become an increasingly complex task for planners, environmental scientists, and engineers. As the built environment continues to grow, unregulated nonpoint pollutants from increased human activity and large-scale development severely stress urban streams and lakes resulting in their currently impaired or degraded state. In response, integrated water quality management programs have been adopted to address these unregulated nonpoint pollutants by utilizing best management practices (BMPs) that treat runoff as close to the source as possible. Knowing where to install effective BMPs is no trivial task, considering budget constraints and the spatially extensive nature of nonpoint stormwater runoff. Accordingly, this paper presents an initial, straightforward and cost-effective methodology to identify critical nonpoint pollutant source watersheds through correlation of water quality with land use. Through an illustrative application to metropolitan Denver, Colorado, it is shown how this method can be used to aid stormwater professionals to evaluate and specify retrofit locations in need of water quality treatment features reduce, capture and treat stormwater runoff prior to entering receiving waters. PMID:29742172

Predicting nonpoint stormwater runoff quality from land use.

PubMed

Zivkovich, Brik R; Mays, David C

2018-01-01

Evaluating the impact of urban development on natural ecosystem processes has become an increasingly complex task for planners, environmental scientists, and engineers. As the built environment continues to grow, unregulated nonpoint pollutants from increased human activity and large-scale development severely stress urban streams and lakes resulting in their currently impaired or degraded state. In response, integrated water quality management programs have been adopted to address these unregulated nonpoint pollutants by utilizing best management practices (BMPs) that treat runoff as close to the source as possible. Knowing where to install effective BMPs is no trivial task, considering budget constraints and the spatially extensive nature of nonpoint stormwater runoff. Accordingly, this paper presents an initial, straightforward and cost-effective methodology to identify critical nonpoint pollutant source watersheds through correlation of water quality with land use. Through an illustrative application to metropolitan Denver, Colorado, it is shown how this method can be used to aid stormwater professionals to evaluate and specify retrofit locations in need of water quality treatment features reduce, capture and treat stormwater runoff prior to entering receiving waters.

Environmental and economic analysis of switchgrass production for water quality improvement in northeast Kansas.

PubMed

Nelson, Richard G; Ascough, James C; Langemeier, Michael R

2006-06-01

The primary objectives of this research were to determine SWAT model predicted reductions in four water quality indicators (sediment yield, surface runoff, nitrate nitrogen (NO(3)-N) in surface runoff, and edge-of-field erosion) associated with producing switchgrass (Panicum virgatum) on cropland in the Delaware basin in northeast Kansas, and evaluate switchgrass break-even prices. The magnitude of potential switchgrass water quality payments based on using switchgrass as an alternative energy source was also estimated. SWAT model simulations showed that between 527,000 and 1.27 million metric tons (Mg) of switchgrass could be produced annually across the basin depending upon nitrogen (N) fertilizer application levels (0-224 kg N ha(-1)). The predicted reductions in sediment yield, surface runoff, NO(3)-N in surface runoff, and edge-of-field erosion as a result of switchgrass plantings were 99, 55, 34, and 98%, respectively. The average annual cost per hectare for switchgrass ranged from about 190 US dollars with no N applied to around 345 US dollars at 224 kg N ha(-1) applied. Edge-of-field break-even price per Mg ranged from around 41 US dollars with no N applied to slightly less than 25 US dollars at 224 kg N ha(-1) applied. A majority of the switchgrass produced had an edge-of-field break-even price of 30 Mg(-1) US dollars or less. Savings of at least 50% in each of the four water quality indicators could be attained for an edge-of-field break-even price of 22-27.49 US dollars Mg(-1).

Nutrient and sediment concentrations, yields, and loads in impaired streams and rivers in the Taunton River Basin, Massachusetts, 1997-2008

USGS Publications Warehouse

Barbaro, Jeffrey R.; Sorenson, Jason R.

2013-01-01

Rapid development, population growth, and the changes in land and water use accompanying development are placing increasing stress on water resources in the Taunton River Basin. An assessment by the Massachusetts Department of Environmental Protection determined that a number of tributary streams to the Taunton River are impaired for a variety of beneficial uses because of nutrient enrichment. Most of the impaired reaches are in the Matfield River drainage area in the vicinity of the City of Brockton. In addition to impairments of stream reaches in the basin, discharge of nutrient-rich water from the Taunton River contributes to eutrophication of Mount Hope and Narragansett Bays. To assess water quality and loading in the impaired tributary stream reaches in the basin, the U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection compiled existing water-quality data from previous studies for the period 1997-2006, developed and calibrated a Hydrological Simulation Program-FORTRAN (HSPF) precipitation-runoff model to simulate streamflow in areas of the basin that contain the impaired reaches for the same time period, and collected additional streamflow and water-quality data from sites on the Matfield and Taunton Rivers in 2008. A majority of the waterquality samples used in the study were collected between 1999 and 2006. Overall, the concentration, yield, and load data presented in this report represent water-quality conditions in the basin for the period 1997-2008. Water-quality data from 52 unique sites were used in the study. Most of the samples from previous studies were collected between June and September under dry weather conditions. Simulated or measured daily mean streamflow and water-quality data were used to estimate constituent yields and loads in the impaired tributary stream reaches and the main stem of the Taunton River and to develop yield-duration plots for reaches with sufficient water-quality data. Total phosphorus concentrations in the impaired-reach areas ranged from 0.0046 to 0.91 milligrams per liter (mg/L) in individual samples (number of samples (n)=331), with a median of 0.090 mg/L; total nitrogen concentrations ranged from 0.34 to 14 mg/L in individual samples (n=139), with a median of 1.35 mg/L; and total suspended solids concentrations ranged from 2/d) for total phosphorus and 100 lb/mi2/d for total nitrogen in these reaches. In most of the impaired reaches not affected by the Brockton Advanced Water Reclamation Facility outfall, yields were lower than in reaches downstream from the outfall, and the difference between measured and threshold yields was fairly uniform over a wide range of flows, suggesting that multiple processes contribute to nonpoint loading in these reaches. The Northeast and Mid-Atlantic SPAtially-Referenced Regression On Watershed (SPARROW) models for total phosphorus and total nitrogen also were used to estimate annual nutrient loads in the impaired tributary stream reaches and main stem of the Taunton River and predict the distribution of these loads among point and diffuse sources in reach drainage areas. SPARROW is a regional, statistical model that relates nutrient loads in streams to upstream sources and land-use characteristics and can be used to make predictions for streams that do not have nutrient-load data. The model predicts mean annual loads based on longterm streamflow and water-quality data and nutrient source conditions for the year 2002. Predicted mean annual nutrient loads from the SPARROW models were consistent with the measured yield and load data from sampling sites in the basin. For conditions in 2002, the Brockton Advanced Water Reclamation Facility outfall accounted for over 75 percent of the total nitrogen load and over 93 percent of the total phosphorus load in the Salisbury Plain and Matfield Rivers downstream from the outfall. Municipal point sources also accounted for most of the load in the main stem of the Taunton River. Multiple municipal wastewater discharges in the basin accounted for about 76 and 46 percent of the delivered loads of total phosphorus and total nitrogen, respectively, to Mount Hope Bay. For similarly sized watersheds, total delivered loads were lower in watersheds without point sources compared to those with point sources, and sources associated with developed land accounted for most of the delivered phosphorus and nitrogen loads to the impaired reaches. The concentration, yield, and load data evaluated in this study may not be representative of current (2012) point-source loading in the basin; in particular, most of the water-quality data used in the study (1999-2006) were collected prior to completion of upgrades to the Brockton Advanced Water Reclamation Facility that reduced total phosphorus and nitrogen concentrations in treated effluent. Effluent concentration data indicate that, for a given flow rate, effluent loads of total phosphorus and total nitrogen declined by about 80 and 30 percent, respectively, between the late 1990s and 2008 in response to plant upgrades. Consequently, current (2012) water-quality conditions in the impaired reaches downstream from the facility likely have improved compared to conditions described in the report.

Community Perception of Water Quality in a Mining-Affected Area: A Case Study for the Certej Catchment in the Apuseni Mountains in Romania

NASA Astrophysics Data System (ADS)

Dogaru, Diana; Zobrist, Jürg; Balteanu, Dan; Popescu, Claudia; Sima, Mihaela; Amini, Manouchehr; Yang, Hong

2009-06-01

Mining-contaminated sites and the affected communities at risk are important issues on the agenda of both researchers and policy makers, particularly in the former communist block countries in Eastern Europe. Integrated analyses and expert based assessments concerning mining affected areas are important in providing solid policy guidelines for environmental and social risk management and mitigation. Based on a survey for 103 households conducted in a former mining site in the Certej Catchment of the Apuseni Mountains, western Romania, this study assesses local communities’ perceptions on the quality of water in their living area. Logistic regression was used to examine peoples’ perception on the quality of the main river water and of the drinking water based on several predictors relating to social and economic conditions. The results from the perception analysis were then compared with the measurements of heavy metal contamination of the main river and drinking water undertaken in the same study area. The findings indicate that perception and measurement results for the water quality in the Certej Catchment are convergent, suggesting an obvious risk that mining activities pose on the surface water. However, the perception on drinking water quality was little predicted by the regression model and does not seem to be so much related to mining as to other explanatory factors, such as special mineralogy of rock and soils or improper water treatment infrastructure, facts suggested by the measurements of the contaminants. Discussion about the implications of these joint findings for risk mitigation policies completes this article.

Community perception of water quality in a mining-affected area: a case study for the Certej catchment in the Apuseni Mountains in Romania.

PubMed

Dogaru, Diana; Zobrist, Jürg; Balteanu, Dan; Popescu, Claudia; Sima, Mihaela; Amini, Manouchehr; Yang, Hong

2009-06-01

Mining-contaminated sites and the affected communities at risk are important issues on the agenda of both researchers and policy makers, particularly in the former communist block countries in Eastern Europe. Integrated analyses and expert based assessments concerning mining affected areas are important in providing solid policy guidelines for environmental and social risk management and mitigation. Based on a survey for 103 households conducted in a former mining site in the Certej Catchment of the Apuseni Mountains, western Romania, this study assesses local communities' perceptions on the quality of water in their living area. Logistic regression was used to examine peoples' perception on the quality of the main river water and of the drinking water based on several predictors relating to social and economic conditions. The results from the perception analysis were then compared with the measurements of heavy metal contamination of the main river and drinking water undertaken in the same study area. The findings indicate that perception and measurement results for the water quality in the Certej Catchment are convergent, suggesting an obvious risk that mining activities pose on the surface water. However, the perception on drinking water quality was little predicted by the regression model and does not seem to be so much related to mining as to other explanatory factors, such as special mineralogy of rock and soils or improper water treatment infrastructure, facts suggested by the measurements of the contaminants. Discussion about the implications of these joint findings for risk mitigation policies completes this article.

Prediction of biological integrity based on environmental similarity--revealing the scale-dependent link between study area and top environmental predictors.

PubMed

Bedoya, David; Manolakos, Elias S; Novotny, Vladimir

2011-03-01

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio. Instream (chemical and physical quality) and offstream parameters (regional and local upstream land uses, stream fragmentation, and point source density and intensity) are used for this purpose. The IBI predictions are obtained using the environmental site-similarity concept and following a simple to implement leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram--a hierarchical clustering tree of environmental observations--built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model's over-predictions. Local land use conditions explained some of the model's IBI under-predictions at the state-scale since none of the variables within this group were included in the best final predictive model. Under-predicted sites also had higher levels of downstream fragmentation. The proposed variables ranking and predictive modeling methodology is very well suited for the analysis of hierarchical environments, such as natural fresh water systems, with many cross-correlated environmental variables. It is computationally efficient, can be fully automated, does not make any pre-conceived assumptions on the variables interdependency structure (such as linearity), and it is able to rank variables in a database and generate IBI predictions using only non-parametric easy to implement hierarchical clustering. Copyright © 2011 Elsevier Ltd. All rights reserved.

Tools for studying dry-cured ham processing by using computed tomography.

PubMed

Santos-Garcés, Eva; Muñoz, Israel; Gou, Pere; Sala, Xavier; Fulladosa, Elena

2012-01-11

An accurate knowledge and optimization of dry-cured ham elaboration processes could help to reduce operating costs and maximize product quality. The development of nondestructive tools to characterize chemical parameters such as salt and water contents and a(w) during processing is of special interest. In this paper, predictive models for salt content (R(2) = 0.960 and RMSECV = 0.393), water content (R(2) = 0.912 and RMSECV = 1.751), and a(w) (R(2) = 0.906 and RMSECV = 0.008), which comprise the whole elaboration process, were developed. These predictive models were used to develop analytical tools such as distribution diagrams, line profiles, and regions of interest (ROIs) from the acquired computed tomography (CT) scans. These CT analytical tools provided quantitative information on salt, water, and a(w) in terms of content but also distribution throughout the process. The information obtained was applied to two industrial case studies. The main drawback of the predictive models and CT analytical tools is the disturbance that fat produces in water content and a(w) predictions.

Statistical Approaches to Interpretation of Local, Regional, and National Highway-Runoff and Urban-Stormwater Data

USGS Publications Warehouse

Tasker, Gary D.; Granato, Gregory E.

2000-01-01

Decision makers need viable methods for the interpretation of local, regional, and national-highway runoff and urban-stormwater data including flows, concentrations and loads of chemical constituents and sediment, potential effects on receiving waters, and the potential effectiveness of various best management practices (BMPs). Valid (useful for intended purposes), current, and technically defensible stormwater-runoff models are needed to interpret data collected in field studies, to support existing highway and urban-runoffplanning processes, to meet National Pollutant Discharge Elimination System (NPDES) requirements, and to provide methods for computation of Total Maximum Daily Loads (TMDLs) systematically and economically. Historically, conceptual, simulation, empirical, and statistical models of varying levels of detail, complexity, and uncertainty have been used to meet various data-quality objectives in the decision-making processes necessary for the planning, design, construction, and maintenance of highways and for other land-use applications. Water-quality simulation models attempt a detailed representation of the physical processes and mechanisms at a given site. Empirical and statistical regional water-quality assessment models provide a more general picture of water quality or changes in water quality over a region. All these modeling techniques share one common aspect-their predictive ability is poor without suitable site-specific data for calibration. To properly apply the correct model, one must understand the classification of variables, the unique characteristics of water-resources data, and the concept of population structure and analysis. Classifying variables being used to analyze data may determine which statistical methods are appropriate for data analysis. An understanding of the characteristics of water-resources data is necessary to evaluate the applicability of different statistical methods, to interpret the results of these techniques, and to use tools and techniques that account for the unique nature of water-resources data sets. Populations of data on stormwater-runoff quantity and quality are often best modeled as logarithmic transformations. Therefore, these factors need to be considered to form valid, current, and technically defensible stormwater-runoff models. Regression analysis is an accepted method for interpretation of water-resources data and for prediction of current or future conditions at sites that fit the input data model. Regression analysis is designed to provide an estimate of the average response of a system as it relates to variation in one or more known variables. To produce valid models, however, regression analysis should include visual analysis of scatterplots, an examination of the regression equation, evaluation of the method design assumptions, and regression diagnostics. A number of statistical techniques are described in the text and in the appendixes to provide information necessary to interpret data by use of appropriate methods. Uncertainty is an important part of any decisionmaking process. In order to deal with uncertainty problems, the analyst needs to know the severity of the statistical uncertainty of the methods used to predict water quality. Statistical models need to be based on information that is meaningful, representative, complete, precise, accurate, and comparable to be deemed valid, up to date, and technically supportable. To assess uncertainty in the analytical tools, the modeling methods, and the underlying data set, all of these components need be documented and communicated in an accessible format within project publications.

Subtask 1.18 - A Decision Tool for Watershed-Based Effluent Trading

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xixi Wang; Bethany A. Kurz; Marc D. Kurz

2006-11-30

Handling produced water in an economical and environmentally sound manner is vital to coalbed methane (CBM) development, which is expected to increase up to 60% in the next 10-15 years as the demand for natural gas increases. Current produced water-handling methods (e.g., shallow reinjection and infiltration impoundments) are too costly when implemented on a well-by-well basis. A watershed-based effluent credit trading approach may be a means of managing produced water at reduced cost while meeting or surpassing water quality regulations. This market-based approach allows for improved water quality management by enabling industrial, agricultural, and municipal discharge facilities to meet watermore » quality permit requirements by purchasing pollutant reduction credits from other entities within the same watershed. An evaluation of this concept was conducted for the Powder River Basin (PRB) of Montana and Wyoming by the Energy & Environmental Research Center (EERC). To conduct this assessment, the EERC collected and evaluated existing water quality information and developed the appropriate tools needed to assess the environmental and economic feasibility of specific trading scenarios. The accomplishments of this study include (1) an exploration of the available PRB water quantity and quality data using advanced statistical techniques, (2) development of an integrated water quality model that predicts the impacts of CBM produced water on stream salinity and sodicity, (3) development of an economic model that estimates costs and benefits from implementing potential trading options, (4) evaluation of hypothetical trading scenarios between select watersheds of the PRB, and (5) communication of the project concept and results to key state and federal agencies, industry representatives, and stakeholders of the PRB. The preliminary results of a basinwide assessment indicate that up to $684 million could be saved basinwide without compromising water quality as a result of implementing a watershed-based credit-trading approach.« less

Global access to safe water: accounting for water quality and the resulting impact on MDG progress.

PubMed

Onda, Kyle; LoBuglio, Joe; Bartram, Jamie

2012-03-01

Monitoring of progress towards the Millennium Development Goal (MDG) drinking water target relies on classification of water sources as "improved" or "unimproved" as an indicator for water safety. We adjust the current Joint Monitoring Programme (JMP) estimate by accounting for microbial water quality and sanitary risk using the only-nationally representative water quality data currently available, that from the WHO and UNICEF "Rapid Assessment of Drinking Water Quality". A principal components analysis (PCA) of national environmental and development indicators was used to create models that predicted, for most countries, the proportions of piped and of other-improved water supplies that are faecally contaminated; and of these sources, the proportions that lack basic sanitary protection against contamination. We estimate that 1.8 billion people (28% of the global population) used unsafe water in 2010. The 2010 JMP estimate is that 783 million people (11%) use unimproved sources. Our estimates revise the 1990 baseline from 23% to 37%, and the target from 12% to 18%, resulting in a shortfall of 10% of the global population towards the MDG target in 2010. In contrast, using the indicator "use of an improved source" suggests that the MDG target for drinking-water has already been achieved. We estimate that an additional 1.2 billion (18%) use water from sources or systems with significant sanitary risks. While our estimate is imprecise, the magnitude of the estimate and the health and development implications suggest that greater attention is needed to better understand and manage drinking water safety.

Climate Adaptation Capacity for Conventional Drinking Water Treatment Facilities

NASA Astrophysics Data System (ADS)

Levine, A.; Goodrich, J.; Yang, J.

2013-12-01

Water supplies are vulnerable to a host of climate- and weather-related stressors such as droughts, intense storms/flooding, snowpack depletion, sea level changes, and consequences from fires, landslides, and excessive heat or cold. Surface water resources (lakes, reservoirs, rivers, and streams) are especially susceptible to weather-induced changes in water availability and quality. The risks to groundwater systems may also be significant. Typically, water treatment facilities are designed with an underlying assumption that water quality from a given source is relatively predictable based on historical data. However, increasing evidence of the lack of stationarity is raising questions about the validity of traditional design assumptions, particularly since the service life of many facilities can exceed fifty years. Given that there are over 150,000 public water systems in the US that deliver drinking water to over 300 million people every day, it is important to evaluate the capacity for adapting to the impacts of a changing climate. Climate and weather can induce or amplify changes in physical, chemical, and biological water quality, reaction rates, the extent of water-sediment-air interactions, and also impact the performance of treatment technologies. The specific impacts depend on the watershed characteristics and local hydrological and land-use factors. Water quality responses can be transient, such as erosion-induced increases in sediment and runoff. Longer-term impacts include changes in the frequency and intensity of algal blooms, gradual changes in the nature and concentration of dissolved organic matter, dissolved solids, and modulation of the microbiological community structure, sources and survival of pathogens. In addition, waterborne contaminants associated with municipal, industrial, and agricultural activities can also impact water quality. This presentation evaluates relationships between climate and weather induced water quality variability and the capacity of treatment facilities and supporting water infrastructure to deliver safe drinking water consistently and reliably. Simulation models of water treatment facilities are used to evaluate the outcome of specific source water quality scenarios on treatment system performance and reliability. Modeling results are used to evaluate the process and operational capacity to respond to transient water quality changes and adapt to longer-term variability in water quality and availability. In some cases, changes in temperature and mineral content serve to improve the overall treatment performance. In addition, the integration of microbially enhanced treatment systems such as biological filtration can provide additional capacity. Conversely, changes in the nutrient and temperature dynamics can trigger algal and cyanobacterial blooms that can impair performance. Research needs are identified and the importance of developing more integrated modeling systems is highlighted.

Artificial neural networks applied to flow prediction scenarios in Tomebamba River - Paute watershed, for flood and water quality control and management at City of Cuenca Ecuador

NASA Astrophysics Data System (ADS)

Cisneros, Felipe; Veintimilla, Jaime

2013-04-01

The main aim of this research is to create a model of Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba River both, at real time and in a certain day of year. As inputs we are using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance This research includes two ANN models: Back propagation and a hybrid model between back propagation and OWO-HWO. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as: MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error are minimal. These predictions are useful for flood and water quality control and management at City of Cuenca Ecuador

Characterization of the glass transition of water predicted by molecular dynamics simulations using nonpolarizable intermolecular potentials.

PubMed

Kreck, Cara A; Mancera, Ricardo L

2014-02-20

Molecular dynamics simulations allow detailed study of the experimentally inaccessible liquid state of supercooled water below its homogeneous nucleation temperature and the characterization of the glass transition. Simple, nonpolarizable intermolecular potentials are commonly used in classical molecular dynamics simulations of water and aqueous systems due to their lower computational cost and their ability to reproduce a wide range of properties. Because the quality of these predictions varies between the potentials, the predicted glass transition of water is likely to be influenced by the choice of potential. We have thus conducted an extensive comparative investigation of various three-, four-, five-, and six-point water potentials in both the NPT and NVT ensembles. The T(g) predicted from NPT simulations is strongly correlated with the temperature of minimum density, whereas the maximum in the heat capacity plot corresponds to the minimum in the thermal expansion coefficient. In the NVT ensemble, these points are instead related to the maximum in the internal pressure and the minimum of its derivative, respectively. A detailed analysis of the hydrogen-bonding properties at the glass transition reveals that the extent of hydrogen-bonds lost upon the melting of the glassy state is related to the height of the heat capacity peak and varies between water potentials.

Water quality parameters controlling the photodegradation of two herbicides in surface waters of the Columbia Basin, Washington.

PubMed

Furman, Olha S; Yu, Miao; Teel, Amy L; Watts, Richard J

2013-11-01

The water quality parameters nitrate-nitrogen, dissolved organic carbon, and suspended solids were correlated with photodegradation rates of the herbicides atrazine and 2,4-D in samples collected from four sites in the Columbia River Basin, Washington, USA. Surface water samples were collected in May, July, and October 2010 and analyzed for the water quality parameters. Photolysis rates for the two herbicides in the surface water samples were then evaluated under a xenon arc lamp. Photolysis rates of atrazine and 2,4-D were similar with rate constants averaging 0.025 h(-1) for atrazine and 0.039 h(-1) for 2,4-D. Based on multiple regression analysis, nitrate-nitrogen was the primary predictor of photolysis for both atrazine and 2,4-D, with dissolved organic carbon also a predictor for some sites. However, at sites where suspended solids concentrations were elevated, photolysis rates of the two herbicides were controlled by the suspended solids concentration. The results of this research provide a basis for evaluating and predicting herbicide photolysis rates in shallow surface waters. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multiple stressor effects on biological quality elements in the Ebro River: Present diagnosis and predicted responses.

PubMed

Herrero, Albert; Gutiérrez-Cánovas, Cayetano; Vigiak, Olga; Lutz, Stefanie; Kumar, Rohini; Gampe, David; Huber-García, Verena; Ludwig, Ralf; Batalla, Ramon; Sabater, Sergi

2018-07-15

Multiple abiotic stressors affect the ecological status of water bodies. The status of waterbodies in the Ebro catchment (NE Spain) is evaluated using the biological quality elements (BQEs) of diatoms, invertebrates and macrophytes. The multi-stressor influence on the three BQEs was evaluated using the monitoring dataset available from the catchment water authority. Nutrient concentrations, especially total phosphorus (TP), affected most of the analyzed BQEs, while changes in mean discharge, water temperature, or river morphology did not show significant influences. Linear statistical models were used to evaluate the change of water bodies' ecological status under different combinations of future socioeconomic and climate scenarios. Changes in land use, rainfall, water temperature, mean discharge, TP and nitrate concentrations were modeled according to the future scenarios. These revealed an evolution of the abiotic stressors that could lead to a general decrease in the ecosystem quality of water bodies within the Ebro catchment. This deterioration was especially evidenced on the diatoms and invertebrate biological indices, mainly because of the foreseen increase in TP concentrations. Water bodies located in the headwaters were seen as the most sensitive to future changes. Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.

Applying linear discriminant analysis to predict groundwater redox conditions conducive to denitrification

NASA Astrophysics Data System (ADS)

Wilson, S. R.; Close, M. E.; Abraham, P.

2018-01-01

Diffuse nitrate losses from agricultural land pollute groundwater resources worldwide, but can be attenuated under reducing subsurface conditions. In New Zealand, the ability to predict where groundwater denitrification occurs is important for understanding the linkage between land use and discharges of nitrate-bearing groundwater to streams. This study assesses the application of linear discriminant analysis (LDA) for predicting groundwater redox status for Southland, a major dairy farming region in New Zealand. Data cases were developed by assigning a redox status to samples derived from a regional groundwater quality database. Pre-existing regional-scale geospatial databases were used as training variables for the discriminant functions. The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes. The models predict 23% of the region as being reducing at shallow depths (<15 m), and 37% at medium depths (15-75 m). Predictions were made at a sub-regional level to determine whether improvements could be made with discriminant functions trained by local data. The results indicated that any gains in predictive success were offset by loss of confidence in the predictions due to the reduction in the number of samples used. The regional scale model predictions indicate that subsurface reducing conditions predominate at low elevations on the coastal plains where poorly drained soils are widespread. Additional indicators for subsurface denitrification are a high carbon content of the soil, a shallow water table, and low-permeability clastic sediments. The coastal plains are an area of widespread groundwater discharge, and the soil and hydrology characteristics require the land to be artificially drained to render the land suitable for farming. For the improvement of water quality in coastal areas, it is therefore important that land and water management efforts focus on understanding hydrological bypassing that may occur via artificial drainage systems.

Assessing the impacts of land use on downstream water quality using a hydrologically sensitive area concept.

PubMed

Giri, Subhasis; Qiu, Zeyuan; Zhang, Zhen

2018-05-01

Understanding the relationship between land use and water quality is essential to improve water quality through carefully managing landscape change. This study applies a linear mixed model at both watershed and hydrologically sensitive areas (HSAs) scales to assess such a relationship in 28 northcentral New Jersey watersheds located in a rapidly urbanizing region in the United States. Two models differ in terms of the geographic scope used to derive land use matrices that quantify land use conditions. The land use matrices at the watershed and HSAs scales represent the land use conditions in these watersheds and their HSAs, respectively. HSAs are the hydrological "hotspots" in a watershed that are prone to runoff generation during storm events. HSAs are derived using a soil topographic index (STI) that predicts hydrological sensitivity of a landscape based on a variable source area hydrology concept. The water quality indicators in these models are total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) concentrations in streams observed at the watershed outlets. The modeling results suggest that presence of low density urban land, agricultural land and wetlands elevate while forest decreases TN, TP and/or TSS concentrations in streams. The watershed scale model tends to emphasize the role of agricultural lands in water quality degradation while the HSA scale model highlights the role of forest in water quality improvement. This study supports the hypothesis that even though HSAs are relatively smaller area compared to watershed, still the land uses within HSAs have similar impacts on downstream water quality as the land uses in entire watersheds, since both models have negligible differences in model evaluation parameters. Inclusion of HSAs brings an interesting perspective to understand the dynamic relationships between land use and water quality. Copyright © 2018 Elsevier Ltd. All rights reserved.

Measured Copper Toxicity to Cnesterodon decemmaculatus (Pisces: Poeciliidae) and Predicted by Biotic Ligand Model in Pilcomayo River Water: A Step for a Cross-Fish-Species Extrapolation

PubMed Central

Casares, María Victoria; de Cabo, Laura I.; Seoane, Rafael S.; Natale, Oscar E.; Castro Ríos, Milagros; Weigandt, Cristian; de Iorio, Alicia F.

2012-01-01

In order to determine copper toxicity (LC50) to a local species (Cnesterodon decemmaculatus) in the South American Pilcomayo River water and evaluate a cross-fish-species extrapolation of Biotic Ligand Model, a 96 h acute copper toxicity test was performed. The dissolved copper concentrations tested were 0.05, 0.19, 0.39, 0.61, 0.73, 1.01, and 1.42 mg Cu L−1. The 96 h Cu LC50 calculated was 0.655 mg L−1 (0.823 − 0.488). 96-h Cu LC50 predicted by BLM for Pimephales promelas was 0.722 mg L−1. Analysis of the inter-seasonal variation of the main water quality parameters indicates that a higher protective effect of calcium, magnesium, sodium, sulphate, and chloride is expected during the dry season. The very high load of total suspended solids in this river might be a key factor in determining copper distribution between solid and solution phases. A cross-fish-species extrapolation of copper BLM is valid within the water quality parameters and experimental conditions of this toxicity test. PMID:22523491

Anthropogenic water bodies as drought refuge for aquatic macroinvertebrates and macrophytes.

PubMed

Dodemaide, David T; Matthews, Ty G; Iervasi, Dion; Lester, Rebecca E

2018-03-01

Ecological research associated with the importance of refuges has tended to focus on natural rather than anthropogenic water bodies. The frequency of disturbances, including drought events, is predicted to increase in many regions worldwide due to human-induced climate change. More frequent disturbance will affect freshwater ecosystems by altering hydrologic regimes, water chemistry, available habitat and assemblage structure. Under this scenario, many aquatic biota are likely to rely on permanent water bodies as refuge, including anthropogenic water bodies. Here, macroinvertebrate and macrophyte assemblages from waste-water treatment and raw-water storages (i.e. untreated potable water) were compared with nearby natural water bodies during autumn and winter 2013. We expected macroinvertebrate and macrophyte assemblages in raw-water storages to be representative of natural water bodies, while waste-water treatment storages would not, due to degraded water quality. However, water quality in natural water bodies differed from raw-water storages but was similar to waste-water treatment storages. Macroinvertebrate patterns matched those of water quality, with no differences occurring between natural water bodies and waste-water treatment storages, but assemblages in raw-water storages differed from the other two water bodies. Unexpectedly, differences associated with raw-water storages were attributable to low abundances of several taxa. Macrophyte assemblages in raw-water storages were representative of natural water bodies, but were less diverse and abundant in, or absent from, waste-water treatment storages. No clear correlations existed between any habitat variables and macroinvertebrate assemblages but a significant correlation between macrophyte assemblages and habitat characteristics existed. Thus, there were similarities in both water quality and macroinvertebrate assemblages between natural water bodies and waste-water treatment storages, and similarities in macrophyte assemblages between raw-water storages and natural water bodies. These similarities illustrate that anthropogenic water storages support representative populations of some aquatic biota across the landscape, and thus, may provide important refuge following disturbance where dispersal capabilities allow. Copyright © 2017 Elsevier B.V. All rights reserved.

Testing and design life analysis of polyurea liner materials

NASA Astrophysics Data System (ADS)

Ghasemi Motlagh, Siavash

Certainly, water pipes, as part of an underground infrastructure system, play a key role in maintaining quality of life, health, and wellbeing of human kind. As these potable water pipes reach the end of their useful life, they create high maintenance costs, loss of flow capacity, decreased water quality, and increased dissatisfaction. There are several different pipeline renewal techniques available for different applications, among which linings are most commonly used for the renewal of water pipes. Polyurea is a lining material applied to the interior surface of the deteriorated host pipe using spray-on technique. It is applied to structurally enhance the host pipe and provide a barrier coating against further corrosion or deterioration. The purpose of this study was to establish a relationship between stress, strain and time. The results obtained from these tests were used in predicting the strength of the polyurea material during its planned 50-year design life. In addition to this, based on the 10,000 hours experimental data, curve fitting and Findley power law models were employed to predict long-term behavior of the material. Experimental results indicated that the tested polyurea material offers a good balance of strength and stiffness and can be utilized in structural enhancement applications of potable water pipes.

Water quality modeling in the dead end sections of drinking water (Supplement)

EPA Pesticide Factsheets

Dead-end sections of drinking water distribution networks are known to be problematic zones in terms of water quality degradation. Extended residence time due to water stagnation leads to rapid reduction of disinfectant residuals allowing the regrowth of microbial pathogens. Water quality models developed so far apply spatial aggregation and temporal averaging techniques for hydraulic parameters by assigning hourly averaged water demands to the main nodes of the network. Although this practice has generally resulted in minimal loss of accuracy for the predicted disinfectant concentrations in main water transmission lines, this is not the case for the peripheries of the distribution network. This study proposes a new approach for simulating disinfectant residuals in dead end pipes while accounting for both spatial and temporal variability in hydraulic and transport parameters. A stochastic demand generator was developed to represent residential water pulses based on a non-homogenous Poisson process. Dispersive solute transport was considered using highly dynamic dispersion rates. A genetic algorithm was used tocalibrate the axial hydraulic profile of the dead-end pipe based on the different demand shares of the withdrawal nodes. A parametric sensitivity analysis was done to assess the model performance under variation of different simulation parameters. A group of Monte-Carlo ensembles was carried out to investigate the influence of spatial and temporal variation

Optimization of microwave assisted extraction of essential oils from Iranian Rosmarinus officinalis L. using RSM.

PubMed

Akhbari, Maryam; Masoum, Saeed; Aghababaei, Fahimeh; Hamedi, Sepideh

2018-06-01

In this study, the efficiencies of conventional hydro-distillation and novel microwave hydro-distillation methods in extraction of essential oil from Rosemary officinalis leaves have been compared. In order to attain the best yield and also highest quality of the essential oil in the microwave assisted method, the optimal values of operating parameters such as extraction time, microwave irradiation power and water volume to plant mass ratio were investigated using central composite design under response surface methodology. Optimal conditions for obtaining the maximum extraction yield in the microwave assisted method were predicted as follows: extraction time of 85 min, microwave power of 888 W, and water volume to plant mass ratio of 0.5 ml/g. The extraction yield at these predicted conditions was computed as 0.7756%. The qualities of the obtained essential oils under designed experiments were optimized based on total contents of four major compounds (α-pinene, 1,8-cineole, camphor and verbenone) which determined by gas chromatography equipped with mass spectroscopy (GC-MS). The highest essential oil quality (55.87%) was obtained at extraction time of 68 min; microwave irradiation power of 700 W; and water volume to plant mass ratio of zero.

Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Li, Dan; Yang, Haizhen; Liang, XiaoFeng

2010-11-01

Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

Bioavailability of cyanide and metal-cyanide mixtures to aquatic life.

PubMed

Redman, Aaron; Santore, Robert

2012-08-01

Cyanide can be toxic to aquatic organisms, and the U.S. Environmental Protection Agency has developed ambient water-quality criteria to protect aquatic life. Recent work suggests that considering free, rather than total, cyanide provides a more accurate measure of the biological effects of cyanides and provides a basis for water-quality criteria. Aquatic organisms are sensitive to free cyanide, although certain metals can form stable complexes and reduce the amount of free cyanide. As a result, total cyanide is less toxic when complexing metals are present. Cyanide is often present in complex effluents, which requires understanding how other components within these complex effluents can affect cyanide speciation and bioavailability. The authors have developed a model to predict the aqueous speciation of cyanide and have shown that this model can predict the toxicity of metal-cyanide complexes in terms of free cyanide in solutions with varying water chemistry. Toxicity endpoints based on total cyanide ranged over several orders of magnitude for various metal-cyanide mixtures. However, predicted free cyanide concentrations among these same tests described the observed toxicity data to within a factor of 2. Aquatic toxicity can be well-described using free cyanide, and under certain conditions the toxicity was jointly described by free cyanide and elevated levels of bioavailable metals. Copyright © 2012 SETAC.

HABs Monitoring and Prediction

EPA Science Inventory

Monitoring techniques for harmful algal blooms (HABs) vary across temporal and spatial domains. Remote satellite imagery provides information on water quality at relatively broad spatial and lengthy temporal scales. At the other end of the spectrum, local in-situ monitoring tec...

Predicting water filter and bottled water use in Appalachia: a community-scale case study.

PubMed

Levêque, Jonas G; Burns, Robert C

2017-06-01

A questionnaire survey was conducted in order to assess residents' perceptions of water quality for drinking and recreational purposes in a mid-sized city in northcentral West Virginia. Two logistic regression analyses were conducted in order to investigate the factors that influence bottle use and filter use. Results show that 37% of respondents primarily use bottled water and that 58% use a household filter when drinking from the tap. Respondents with lower levels of environmental concern, education levels, and lower organoleptic perceptions were most likely to perceive health risks from tap water consumption, and were most likely to use bottled water. Income, age, and organoleptic perceptions were predictors of water filter use among respondents. Clean water for recreational purposes was not found to be significant with either of these models. Our results demonstrate that bottle use and filter use are explained differently. We argue that more education and better communication about local tap water quality would decrease the use of bottled water. We demonstrate that household filters could be used as an alternative to bottled water.

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China

PubMed Central

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density. PMID:27028017

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China.

PubMed

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R(2) = 0.86-0.93 for 72 data sets collected in the industrial river and R(2) = 0.60-0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals' concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density.

Development of a three dimensional numerical water quality model for continental shelf applications

NASA Technical Reports Server (NTRS)

Spaulding, M.; Hunter, D.

1975-01-01

A model to predict the distribution of water quality parameters in three dimensions was developed. The mass transport equation was solved using a non-dimensional vertical axis and an alternating-direction-implicit finite difference technique. The reaction kinetics of the constituents were incorporated into a matrix method which permits computation of the interactions of multiple constituents. Methods for the computation of dispersion coefficients and coliform bacteria decay rates were determined. Numerical investigations of dispersive and dissipative effects showed that the three-dimensional model performs as predicted by analysis of simpler cases. The model was then applied to a two dimensional vertically averaged tidal dynamics model for the Providence River. It was also extended to a steady state application by replacing the time step with an iteration sequence. This modification was verified by comparison to analytical solutions and applied to a river confluence situation.

Assessing metaldehyde concentrations in surface water catchments and implications for drinking water abstraction

NASA Astrophysics Data System (ADS)

Asfaw, Alemayehu; Shucksmith, James; Smith, Andrea; Cherry, Katherine

2015-04-01

Metaldehyde is an active ingredient in agricultural pesticides such as slug pellets, which are heavily applied to UK farmland during the autumn application season. There is current concern that existing drinking water treatment processes may be inadequate in reducing potentially high levels of metaldehyde in surface waters to below the UK drinking water quality regulation limit of 0.1 µg/l. In addition, current water quality monitoring methods can miss short term fluctuations in metaldehyde concentration caused by rainfall driven runoff, hampering prediction of the potential risk of exposure. Datasets describing levels, fate and transport of metaldehyde in river catchments are currently very scarce. This work presents results from an ongoing study to quantify the presence of metaldehyde in surface waters within a UK catchment used for drinking water abstraction. High resolution water quality data from auto-samplers installed in rivers are coupled with radar rainfall, catchment characteristics and land use data to i) understand which hydro-meteorological characteristics of the catchment trigger the peak migration of metaldehyde to surface waters; ii) assess the relationship between measured metaldehyde levels and catchment characteristics such as land use, topographic index, proximity to water bodies and runoff generation area; iii) describe the current risks to drinking water supply and discuss mitigation options based on modelling and real-time control of water abstraction. Identifying the correlation between catchment attributes and metaldehyde generation will help in the development of effective catchment management strategies, which can help to significantly reduce the amount of metaldehyde finding its way into river water. Furthermore, the effectiveness of current water quality monitoring strategy in accurately quantifying the generation of metaldehyde from the catchment and its ability to benefit the development of effective catchment management practices has also been investigated.

Application of receptor models on water quality data in source apportionment in Kuantan River Basin

PubMed Central

2012-01-01

Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management. PMID:23369363

Hydraulic modeling of clay ceramic water filters for point-of-use water treatment.

PubMed

Schweitzer, Ryan W; Cunningham, Jeffrey A; Mihelcic, James R

2013-01-02

The acceptability of ceramic filters for point-of-use water treatment depends not only on the quality of the filtered water, but also on the quantity of water the filters can produce. This paper presents two mathematical models for the hydraulic performance of ceramic water filters under typical usage. A model is developed for two common filter geometries: paraboloid- and frustum-shaped. Both models are calibrated and evaluated by comparison to experimental data. The hydraulic models are able to predict the following parameters as functions of time: water level in the filter (h), instantaneous volumetric flow rate of filtrate (Q), and cumulative volume of water produced (V). The models' utility is demonstrated by applying them to estimate how the volume of water produced depends on factors such as the filter shape and the frequency of filling. Both models predict that the volume of water produced can be increased by about 45% if users refill the filter three times per day versus only once per day. Also, the models predict that filter geometry affects the volume of water produced: for two filters with equal volume, equal wall thickness, and equal hydraulic conductivity, a filter that is tall and thin will produce as much as 25% more water than one which is shallow and wide. We suggest that the models can be used as tools to help optimize filter performance.

Relationship between landscape characteristics and surface water quality.

PubMed

Chang, C L; Kuan, W H; Lui, P S; Hu, C Y

2008-12-01

The effects of landscape characteristics on surface water quality were evaluated in terms of land-use condition, soil type and slope. The case area, the Chichiawan stream in the Wulin catchment in Taiwan, is Formosan landlocked salmon's natural habitat. Due to the agriculture behavior and mankind's activities, the water and environmental quality has gradually worsened. This study applied WinVAST model to predict hydrological responses and non-point source pollution (NPSP) exports in the Wulin catchment. The land-use condition and the slope of land surface in a catchment are major effect factors for watershed responses, including flows and pollutant exports. This work discussed the possible variation of watershed responses induced by the change of land-use condition, soil type and slope, etc. The results show that hydrological responses are highly relative to the value of Curve Number (CN); Pollutant exports have large relation to the average slope of the land surface in the Wulin catchment.

Impact of potential phosphate mining on the hydrology of Osceola National Forest, Florida

USGS Publications Warehouse

Miller, James A.; Hughes, G.H.; Hull, R.W.; Vecchioli, John; Seaber, P.R.

1978-01-01

Potentially exploitable phosphate deposits underlie part of Osceola National Forest, Fla. Hydrologic conditions in the forest are comparable with those in nearby Hamilton County, where phosphate mining and processing have been ongoing since 1965. Given similarity of operations, hydroloigc effects of mining in the forest are predicted. Flow of stream receiving phosphate industry effluent would increase somewhat during mining, but stream quality would not be greatly affected. Local changes in the configuration of the water table and the quality of water in the surficial aquifer will occur. Lowering of the potentiometric surface of the Floridan aquifer because of proposed pumpage would be less than five feet at nearby communities. Flordian aquifer water quality would be appreciably changed only if industrial effluent were discharged into streams which recharge the Flordian through sinkholes. The most significant hydrologic effects would occur at the time of active mining: long-term effects would be less significant. (Woodard-USGS)

Interrelationships between fish tissue mercury concentrations and water quality for South Dakota natural lakes and impoundments

USGS Publications Warehouse

Chipps, Steven R.; Stetler, Larry; Stone, James J.; McCutcheon, Cindy M.

2011-01-01

The purpose of this study was to determine whether water quality parameters commonly associated with primary productivity may be used to predict the susceptibility of a specific water body to exceed proposed fish consumption advisory limitation of 0.3 mg kg−1. South Dakota currently has nine lakes and impoundments that exceed fish tissue mercury advisory limits of 1.0 mg kg−1 total mercury, far exceeding US Environmental Protection Agency and Food and Drug Administration 0.3 mg kg−1 consumption criteria. Previous studies suggest that increased aquatic productivity may mitigate the effects of biological production and subsequent uptake of methyl mercury through bio-dilution; however, it is uncertain whether these trends may exist within highly alkaline and highly productive aquatic conditions common to South Dakota lakes and impoundments. Water quality parameters and fish tissue mercury data for northern pike and walleye were collected and assessed using existing South Dakota Department of Environment and Natural Resources and Game Fish and Parks data. The data was initially screened using both parametric linear regression and non-parametric Mann–Whitney rank sum comparisons and further assessed using binary logistic regression and stepwise logistic regression methodology. Three separate phosphorus measurements (total, total dissolved, and Trophic State Index) and pH were determined to significantly correlate with increased mercury concentrations for the northern pike-in-impoundments model. However, phosphorus surprisingly was not a strong predictor for the remaining scenarios modeled. For the northern pike-in-natural lakes models, alkalinity was the most significant water quality parameter predicting increased mercury concentrations. Mercury concentrations for the walleye-in-natural lakes models were further influenced by pH and alkalinity. The water quality and fish tissue mercury interrelationships determined within this study suggest aquatic productivity, and consequential eutrophication processes appear to be reasonable indicators of fish tissue mercury susceptibility for aquatic conditions common to South Dakota and highlight the continuing need to minimize eutrophication through effective watershed management strategies.

An algal model for predicting attainment of tiered biological criteria of Maine's streams and rivers

USGS Publications Warehouse

Danielson, Thomas J.; Loftin, Cyndy; Tsomides, Leonidas; DiFranco, Jeanne L.; Connors, Beth; Courtemanch, David L.; Drummond, Francis; Davies, Susan

2012-01-01

State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1–6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant model will broaden detection of biological impairment and further diagnose sources of impairment. The algal discriminant model is specific to Maine, but our approach of explicitly tying an assessment tool to tiered aquatic-life goals is widely transferrable to other regions, taxonomic groups, and waterbody types.

Validation of a biotic ligand model on site-specific copper toxicity to Daphnia magna in the Yeongsan River, Korea.

PubMed

Park, Jinhee; Ra, Jin-Sung; Rho, Hojung; Cho, Jaeweon; Kim, Sang Don

2018-03-01

The objective of this study was to determine whether the water effect ratio (WER) or biotic ligand model (BLM) could be applied to efficiently develop water quality criteria (WQC) in Korea. Samples were collected from 12 specific sites along the Yeongsan River (YSR), Korea, including two sewage treatment plants and one estuary lake. A copper toxicity test using Daphnia magna was performed to determine the WER and to compare to the BLM prediction. The results of the WER from YSR samples also indicated significantly different copper toxicities in all sites. The model-based predictions showed that effluent and estuary waters had significantly different properties in regard to their ability to be used to investigate water characteristics and copper toxicity. It was supposed that the slight water characteristics changes, such as pH, DOC, hardness, conductivity, among others, influence copper toxicity, and these variable effects on copper toxicity interacted with the water composition. The 38% prediction was outside of the validation range by a factor of two in all sites, showing a poor predictive ability, especially in STPs and streams adjacent to the estuary, while the measured toxicity was more stable. The samples that ranged from pH 7.3-7.7 generated stable predictions, while other samples, including those with lower and the higher pH values, led to more unstable predictions. The results also showed that the toxicity of Cu in sample waters to D. magna was closely proportional to the amounts of acidity, including the carboxylic and phenolic groups, as well as the DOC concentrations. Consequently, the acceptable prediction of metal toxicity in various water samples needs the site-specific results considering the water characteristics such as pH and DOC properties particularly in STPs and estuary regions. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting the melting temperature of ice-Ih with only electronic structure information as input.

PubMed

Pinnick, Eric R; Erramilli, Shyamsunder; Wang, Feng

2012-07-07

The melting temperature of ice-Ih was calculated with only electronic structure information as input by creating a problem-specific force field. The force field, Water model by AFM for Ice and Liquid (WAIL), was developed with the adaptive force matching (AFM) method by fitting to post-Hartree-Fock quality forces obtained in quantum mechanics∕molecular mechanics calculations. WAIL predicts the ice-Ih melting temperature to be 270 K. The model also predicts the densities of ice and water, the temperature of maximum density of water, the heat of vaporizations, and the radial distribution functions for both ice and water in good agreement with experimental measurements. The non-dissociative WAIL model is very similar to a flexible version of the popular TIP4P potential and has comparable computational cost. By customizing to problem-specific configurations with the AFM approach, the resulting model is remarkably more accurate than any variants of TIP4P for simulating ice-Ih and water in the temperature range from 253 K and 293 K under ambient pressure.

Incorporating Climate Change Predictions into Watershed Restoration and Protection Strategies (WRAPS) in the Upper Mississippi River Basin

NASA Astrophysics Data System (ADS)

Burke, M. P.; Foreman, C. S.

2014-12-01

Development of the Watershed Restoration and Protection Strategies (WRAPS) for the Pine and Leech Lake River Watersheds is underway in Minnesota. Project partners participating in this effort include the Minnesota Pollution Control Agency (MPCA), Crow Wing Soil and Water Conservation District (SWCD), Cass County, and other local partners. These watersheds are located in the Northern Lakes and Forest ecoregion of Minnesota and drain to the Upper Mississippi River. To support the Pine and Leech Lake River WRAPS, watershed-scale hydrologic and water-quality models were developed with Hydrological Simulation Program-FORTRAN (HSPF). The HSPF model applications simulate hydrology (discharge, stage), as well as a number of water quality constituents (sediment, temperature, organic and inorganic nitrogen, total ammonia, organic and inorganic phosphorus, dissolved oxygen and biochemical oxygen demand, and algae) continuously for the period 1995-2009 and provide predictions at points of interest within the watersheds, such as observation gages, management boundaries, compliance points, and impaired water body endpoints. The model applications were used to evaluate phosphorus loads to surface waters under resource management scenarios, which were based on water quality threats that were identified at stakeholder meetings. Simulations of land use changes including conversion of forests to agriculture, shoreline development, and full build-out of cities show a watershed-wide phosphorus increases of up to 80%. The retention of 1.1 inches of runoff from impervious surfaces was not enough to mitigate the projected phosphorus load increases. Changes in precipitation projected by climate change models led to a 20% increase in annual watershed phosphorus loads. The scenario results will inform the implementation strategies selected for the WRAPS.

The effects of sewage discharge on water quality and phytoplankton of Hawai'ian coastal waters.

PubMed

Parnell, P Ed

2003-05-01

The effects of sewage discharge on algal populations and the quality of Hawai'ian coastal waters were investigated. Two outfalls were studied. One discharges primary treated sewage and the other discharges secondary treated sewage but are otherwise similar. This enabled comparisons of the effects of these different levels of treatment on the water quality and algal productivity of receiving waters. Plumes were followed and repeatedly sampled in a time-series manner. Rhodamine dye was used as a conservative tracer to compare the dilution behavior of the plume constituents MRP, NO(3)+NO(2), NH(4), Silicate, TDP, TDN, total bacteria, PC, and PN. Rates of initial dilution ranged from two to almost three orders of magnitude, and were in reasonable agreement with engineering model predictions. Dilution of plume constituents approximated that of Rhodamine until background concentrations were reached, typically within 10 min of discharge. Chl a concentrations did not increase through time in the primary sewage plume but did increase up to 30% in the secondary sewage plume. However, rates of far-field dilution were so rapid that the increase could not have been due to algal growth. The increase was attributed to the plume mixing with a water mass whose relative chl a concentrations were greater. Rates of secondary dilution ranged from 2 to 3 orders of magnitude resulting in total dilutions of 10(5)-10(6) within 3 h of discharge. These rates of secondary dilution were much greater than model predictions. From a nutrient standpoint, secondary treatment exhibited no advantages over primary treatment because dilutions were so rapid. Copyright 2002 Elsevier Science B.V.

Utilization of municipal wastewater for cooling in thermoelectric power plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Safari, Iman; Walker, Michael E.; Hsieh, Ming-Kai

2013-09-01

A process simulation model has been developed using Aspen Plus® with the OLI (OLI System, Inc.) water chemistry model to predict water quality in the recirculating cooling loop utilizing secondary- and tertiary-treated municipal wastewater as the source of makeup water. Simulation results were compared with pilot-scale experimental data on makeup water alkalinity, loop pH, and ammonia evaporation. The effects of various parameters including makeup water quality, salt formation, NH 3 and CO 2 evaporation mass transfer coefficients, heat load, and operating temperatures were investigated. The results indicate that, although the simulation model can capture the general trends in the loopmore » pH, experimental data on the rates of salt precipitation in the system are needed for more accurate prediction of the loop pH. It was also found that stripping of ammonia and carbon dioxide in the cooling tower can influence the cooling loop pH significantly. The effects of the NH 3 mass transfer coefficient on cooling loop pH appear to be more significant at lower values (e.g., k NH3 < 4×10 -3 m/s) when the makeup water alkalinity is low (e.g., <90 mg/L as CaCO 3). The effect of the CO2 mass transfer coefficient was found to be significant only at lower alkalinity values (e.g., k CO2<4×10 -6 m/s).« less

Evaluating the impact of ambient benzene vapor concentrations on product water from Condensation Water From Air technology.

PubMed

Kinder, Katherine M; Gellasch, Christopher A; Dusenbury, James S; Timmes, Thomas C; Hughes, Thomas M

2017-07-15

Globally, drinking water resources are diminishing in both quantity and quality. This situation has renewed interest in Condensation Water From Air (CWFA) technology, which utilizes water vapor in the air to produce water for both potable and non-potable purposes. However, there are currently insufficient data available to determine the relationship between air contaminants and the rate at which they are transferred from the air into CWFA untreated product water. This study implemented a novel experimental method utilizing an environmental test chamber to evaluate how air quality and temperature affects CWFA untreated product water quality in order to collect data that will inform the type of water treatment required to protect human health. This study found that temperature and benzene air concentration affected the untreated product water from a CWFA system. Benzene vapor concentrations representing a polluted outdoor environment resulted in benzene product water concentrations between 15% and 23% of the USEPA drinking water limit of 5μg/l. In contrast, product water benzene concentrations representing an indoor industrial environment were between 1.4 and 2.4 times higher than the drinking water limit. Lower condenser coil temperatures were correlated with an increased concentration of benzene in the product water. Environmental health professionals and engineers can integrate the results of this assessment to predict benzene concentrations in the product water and take appropriate health protective measures. Published by Elsevier B.V.

Regression modeling of particle size distributions in urban storm water: advancements through improved sample collection methods

USGS Publications Warehouse

Fienen, Michael N.; Selbig, William R.

2012-01-01

A new sample collection system was developed to improve the representation of sediment entrained in urban storm water by integrating water quality samples from the entire water column. The depth-integrated sampler arm (DISA) was able to mitigate sediment stratification bias in storm water, thereby improving the characterization of suspended-sediment concentration and particle size distribution at three independent study locations. Use of the DISA decreased variability, which improved statistical regression to predict particle size distribution using surrogate environmental parameters, such as precipitation depth and intensity. The performance of this statistical modeling technique was compared to results using traditional fixed-point sampling methods and was found to perform better. When environmental parameters can be used to predict particle size distributions, environmental managers have more options when characterizing concentrations, loads, and particle size distributions in urban runoff.

Ground-water vulnerability to nitrate contamination in the mid-atlantic region

USGS Publications Warehouse

Greene, Earl A.; LaMotte, Andrew E.; Cullinan, Kerri-Ann; Smith, Elizabeth R.

2005-01-01

The U.S. Environmental Protection Agency?s (USEPA) Regional Vulnerability Assessment (ReVA) Program has developed a set of statistical tools to support regional-scale, integrated ecological risk-assessment studies. One of these tools, developed by the U.S. Geological Survey (USGS), is used with available water-quality data obtained from USGS National Water-Quality Assessment (NAWQA) and other studies in association with land cover, geology, soils, and other geographic data to develop logistic-regression equations that predict the vulnerability of ground water to nitrate concentrations exceeding specified thresholds in the Mid-Atlantic Region. The models were developed and applied to produce spatial probability maps showing the likelihood of elevated concentrations of nitrate in the region. These maps can be used to identify areas that currently are at risk and help identify areas where ground water has been affected by human activities. This information can be used by regional and local water managers to protect water supplies and identify land-use planning solutions and monitoring programs in these vulnerable areas.

Applications of fluorescence spectroscopy for predicting percent wastewater in an urban stream

USGS Publications Warehouse

Goldman, Jami H.; Rounds, Stewart A.; Needoba, Joseph A.

2012-01-01

Dissolved organic carbon (DOC) is a significant organic carbon reservoir in many ecosystems, and its characteristics and sources determine many aspects of ecosystem health and water quality. Fluorescence spectroscopy methods can quantify and characterize the subset of the DOC pool that can absorb and re-emit electromagnetic energy as fluorescence and thus provide a rapid technique for environmental monitoring of DOC in lakes and rivers. Using high resolution fluorescence techniques, we characterized DOC in the Tualatin River watershed near Portland, Oregon, and identified fluorescence parameters associated with effluent from two wastewater treatment plants and samples from sites within and outside the urban region. Using a variety of statistical approaches, we developed and validated a multivariate linear regression model to predict the amount of wastewater in the river as a function of the relative abundance of specific fluorescence excitation/emission pairs. The model was tested with independent data and predicts the percentage of wastewater in a sample within 80% confidence. Model results can be used to develop in situ instrumentation, inform monitoring programs, and develop additional water quality indicators for aquatic systems.

Predicting rheological behavior and baking quality of wheat flour using a GlutoPeak test.

PubMed

Rakita, Slađana; Dokić, Ljubica; Dapčević Hadnađev, Tamara; Hadnađev, Miroslav; Torbica, Aleksandra

2018-06-01

The purpose of this research was to gain an insight into the ability of the GlutoPeak instrument to predict flour functionality for bread making, as well as to determine which of the GlutoPeak parameters show the best potential in predicting dough rheological behavior and baking performance. Obtained results showed that GlutoPeak parameters correlated better with the indices of extensional rheological tests which consider constant dough hydration than with those which were performed at constant dough consistency. The GlutoPeak test showed that it is suitable for discriminating wheat varieties of good quality from those of poor quality, while the most discriminating index was maximum torque (MT). Moreover, MT value of 50 BU and aggregation energy value of 1,300 GPU were set as limits of wheat flour quality. The backward stepwise regression analysis revealed that a high-level prediction of indices which are highly affected by protein content (gluten content, flour water absorption, and dough tenacity) was achieved by using the GlutoPeak indices. Concerning bread quality, a moderate prediction of specific loaf volume and an intense level prediction of breadcrumb textural properties were accomplished by using the GlutoPeak parameters. The presented results indicated that the application of this quick test in wheat transformation chain for the assessment of baking quality would be useful. Baking test is considered as the most reliable method for assessing wheat-baking quality. However, baking test requires trained stuff, time, and large sample amount. These disadvantages have led to a growing demand to develop new rapid tests which would enable prediction of baked product quality with a limited flour size. Therefore, we tested the possibility of using a GlutoPeak tester to predict loaf volume and breadcrumb textural properties. Discrimination of wheat varieties according to quality with a restricted flour amount was also examined. Furthermore, we proposed the limit values of GlutoPeak parameters which would be highly beneficial for millers and bakers when determine suitability of flour for end-use. © 2017 Wiley Periodicals, Inc.

EPA Office of Water (OW): 2002 SPARROW Total NP (Catchments)

EPA Pesticide Factsheets

SPARROW (SPAtially Referenced Regressions On Watershed attributes) is a watershed modeling tool with output that allows the user to interpret water quality monitoring data at the regional and sub-regional scale. The model relates in-stream water-quality measurements to spatially referenced characteristics of watersheds, including pollutant sources and environmental factors that affect rates of pollutant delivery to streams from the land and aquatic, in-stream processing . The core of the model consists of a nonlinear regression equation describing the non-conservative transport of contaminants from point and non-point (or ??diffuse??) sources on land to rivers and through the stream and river network. SPARROW estimates contaminant concentrations, loads (or ??mass,?? which is the product of concentration and streamflow), and yields in streams (mass of nitrogen and of phosphorus entering a stream per acre of land). It empirically estimates the origin and fate of contaminants in streams and receiving bodies, and quantifies uncertainties in model predictions. The model predictions are illustrated through detailed maps that provide information about contaminant loadings and source contributions at multiple scales for specific stream reaches, basins, or other geographic areas.

Simulation of dissolved oxygen and biochemical oxygen demand, Plantation Canal, Broward County, Florida with an evaluation of the QUAL-I model for use in south Florida

USGS Publications Warehouse

Russo, Thomas N.; McQuivey, Raul S.

1975-01-01

A mathematical model; QUAL-I, developed by the Texas Water Development Board, was evaluated as a management tool in predicting the spatial and temporal distribution of dissolved oxygen and biochemical oxygen demand in Plantation Canal. Predictions based on the QUAL-I model, which was verified only against midday summer-flow conditions, showed that improvement of quality of inflows from sewage treatment plants and use of at least 130 cubic feet per second of dilution water would improve water quality in the canal significantly. The model was not fully amenable to use on Plantation Canal because: (1) it did not consider photosynthetic production, nitrification, and benthic oxygen demand as sources and sinks of oxygen; (2) the model assumptions of complete mixing, transport, and steady state were not met; and (3) the data base was inadequate because it consisted of only one set of data for each case. However, it was felt that meaningful results could be obtained for some sets of conditions. (Woodard-USGS)

Quantifying Urban Watershed Stressor Gradients and Evaluating How Different Land Cover Datasets Affect Stream Management.

PubMed

Smucker, Nathan J; Kuhn, Anne; Charpentier, Michael A; Cruz-Quinones, Carlos J; Elonen, Colleen M; Whorley, Sarah B; Jicha, Terri M; Serbst, Jonathan R; Hill, Brian H; Wehr, John D

2016-03-01

Watershed management and policies affecting downstream ecosystems benefit from identifying relationships between land cover and water quality. However, different data sources can create dissimilarities in land cover estimates and models that characterize ecosystem responses. We used a spatially balanced stream study (1) to effectively sample development and urban stressor gradients while representing the extent of a large coastal watershed (>4400 km(2)), (2) to document differences between estimates of watershed land cover using 30-m resolution national land cover database (NLCD) and <1-m resolution land cover data, and (3) to determine if predictive models and relationships between water quality and land cover differed when using these two land cover datasets. Increased concentrations of nutrients, anions, and cations had similarly significant correlations with increased watershed percent impervious cover (IC), regardless of data resolution. The NLCD underestimated percent forest for 71/76 sites by a mean of 11 % and overestimated percent wetlands for 71/76 sites by a mean of 8 %. The NLCD almost always underestimated IC at low development intensities and overestimated IC at high development intensities. As a result of underestimated IC, regression models using NLCD data predicted mean background concentrations of NO3 (-) and Cl(-) that were 475 and 177 %, respectively, of those predicted when using finer resolution land cover data. Our sampling design could help states and other agencies seeking to create monitoring programs and indicators responsive to anthropogenic impacts. Differences between land cover datasets could affect resource protection due to misguided management targets, watershed development and conservation practices, or water quality criteria.

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

NASA Astrophysics Data System (ADS)

Shaw, Amelia R.; Smith Sawyer, Heather; LeBoeuf, Eugene J.; McDonald, Mark P.; Hadjerioua, Boualem

2017-11-01

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. The reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

DOE PAGES

Shaw, Amelia R.; Sawyer, Heather Smith; LeBoeuf, Eugene J.; ...

2017-10-24

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2more » is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.« less

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shaw, Amelia R.; Sawyer, Heather Smith; LeBoeuf, Eugene J.

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2more » is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.« less

Regional Assessment of the Relationship Between Landscape Attributes and Water Quality in Five National Parks of the Rocky Mountains

NASA Astrophysics Data System (ADS)

Nanus, L.; Williams, M. W.; Campbell, D. H.

2005-12-01

Atmospheric deposition of pollutants threatens pristine environments around the world. However, scientifically-based decisions regarding management of these environments has been confounded by spatial variability of atmospheric deposition, particularly across regional scales at which resource management is typically considered. A statistically based methodology coupled within GIS is presented that builds on small alpine lake and sub-alpine catchments scale to identify deposition-sensitive lakes across larger watershed and regional scales. The sensitivity of 874 alpine and subalpine lakes to acidification from atmospheric deposition of nitrogen and sulfur was estimated using statistical models relating water quality and landscape attributes in Glacier National Park, Yellowstone National Park, Grand Teton National Park, Rocky Mountain National Park and Great Sand Dunes National Park and Preserve. Water-quality data measured during synoptic lake surveys were used to calibrate statistical models of lake sensitivity. In the case of nitrogen deposition, water quality data were supplemented with dual isotopic measurements of d15N and d18O of nitrate. Landscape attributes for the lake basins were derived from GIS including the following explanatory variables; topography (basin slope, basin aspect, basin elevation), bedrock type, vegetation type, and soil type. Using multivariate logistic regression analysis, probability estimates were developed for acid-neutralizing capacity, nitrate, sulfate and DOC concentrations, and lakes with a high probability of being sensitive to atmospheric deposition were identified. Water-quality data collected at 60 lakes during fall 2004 were used to validate statistical models. Relationships between landscape attributes and water quality vary by constituent, due to spatial variability in landscape attributes and spatial variation in the atmospheric deposition of pollutants within and among the five National Parks. Predictive ability, model fit and sensitivity were first assessed for each of the five National Parks individually, to evaluate the utility of this methodology for prediction of alpine and sub-alpine lake sensitivity across the catchment scale. A similar assessment was then performed, treating the five parks as a group. Validation results showed that 85 percent of lakes sampled were accurately identified by the model as having a greater than 60 percent probability of acid-neutralizing capacity concentrations less than 200 microequivalents per liter. Preliminary findings indicate good predictive ability and reasonable model fit and sensitivity, suggesting that logistic regression modeling coupled within a GIS framework is an appropriate approach for remote identification of deposition-sensitive lakes across the Rocky Mountain region. To assist resource management decisions regarding alpine and sub-alpine lakes across this region, screening procedures were developed based on terrain and landscape attribute information available to all participating parks. Since the screening procedure is based on publicly available data, our methodology and similar screening procedures may be applicable to other National Parks with deposition-sensitive surface waters.

Impact of geostationary satellite water vapor channel data on weather analysis and forecasting

NASA Technical Reports Server (NTRS)

Velden, Christopher S.

1995-01-01

Preliminary results from NWP impact studies are indicating that upper-tropospheric wind information provided by tracking motions in sequences of geostationary satellite water vapor imagery can positively influence forecasts on regional scales, and possibly on global scales as well. The data are complimentary to cloud-tracked winds by providing data in cloud-free regions, as well as comparable in quality. First results from GOES-8 winds are encouraging, and further efforts and model impacts will be directed towards optimizing these data in numerical weather prediction (NWP). Assuming successful launches of GOES-J and GMS-5 satellites in 1995, high quality and resolution water vapor imagers will be available to provide nearly complete global upper-tropospheric wind coverage.

Seasonal Dynamics of River Corridor Exchange Across the Continental United States

NASA Astrophysics Data System (ADS)

Gomez-Velez, J. D.; Harvey, J. W.; Scott, D.; Boyer, E. W.; Schmadel, N. M.

2017-12-01

River corridors store and convey mass and energy from landscapes to the ocean, altering water quality and ecosystem functioning at the local, reach, and watershed scales. As water moves through river corridors from headwaters streams to coastal estuaries, dynamic exchange between the river channel and its adjacent riparian, floodplain, and hyporheic zones, combined with ponded waters such as lakes and reservoirs, results in the emergence of hot spots and moments for biogeochemical transformations. In this work, we used the model Networks with EXchange and Subsurface Storage (NEXSS) to estimate seasonal variations in river corridor exchange fluxes and residence times along the continental United States. Using a simple routing scheme, we translate these estimates into a cumulative measure of river corridor connectivity at the watershed scale, differentiating the contributions of hyporheic zones, floodplains, and ponded waters. We find that the relative role of these exchange subsystems changes seasonally, driven by the intra-seasonal variability of discharge. In addition, we find that seasonal variations in discharge and the biogeochemical potential of hyporheic zones are out of phase. This behavior results in a significant reduction in hyporheic water quality functions during high flows and emphasizes the potential importance of reconnecting floodplains for managing water quality during seasonal high flows. Physical parameterizations of river corridor processes are critical to model and predict water quality and to sustainably manage water resources under present and future socio-economic and climatic conditions. Parsimonious models like NEXSS can play a key role in the design, implementation, and evaluation of sustainable management practices that target both water quantity and quality at the scale of the nation. This research is a product of the John Wesley Powell Center River Corridor Working Group.

Unravel biophysical factors on river water quality response in Chilean Central-Southern watersheds.

PubMed

Yevenes, Mariela A; Arumí, José L; Farías, Laura

2016-05-01

Identifying the key anthropogenic (land uses) and natural (topography and climate) biophysical drivers affecting river water quality is essential for efficient management of water resources. We tested the hypothesis that water quality can be predicted by different biophysical factors. Multivariate statistics based on a geographical information system (GIS) were used to explore the influence of factors (i.e., precipitation, topography, and land uses) on water quality (i.e., nitrate (NO 3 (-)), phosphate (PO 4 (3-)), silicate (Si(OH)4), dissolved oxygen (DO), suspended solids (TSS), biological oxygen demand (DO), temperature (T), conductivity (EC), and pH) for two consecutive years in the Itata and Biobío river watersheds, Central Chile (36° 00' and 38° 30'). The results showed that (NO 3 (-)), (PO 4 (3-)), Si(OH)4, TSS, EC, and DO were higher during rainy season (austral fall, winter, and spring), whereas BOD and temperature were higher during dry season. The spatial variation of these parameters in both watersheds was related to land use, topography (e.g., soil moisture, soil hydrological group, and erodability), and precipitation. Soil hydrological group and soil moisture were the strongest explanatory predictors for PO 4 (3-) , Si(OH)4 and EC in the river, followed by land use such as agriculture for NO 3 (-) and DO and silviculture for TSS and Si(OH)4. High-resolution water leaching and runoff maps allowed us to identify agriculture areas with major probability of water leaching and higher probability of runoff in silviculture areas. Moreover, redundancy analysis (RDA) revealed that land uses (agriculture and silviculture) explained in 60 % the river water quality variation. Our finding highlights the vulnerability of Chilean river waters to different biophysical drivers, rather than climate conditions alone, which is amplified by human-induced degradation.

A one-dimensional, steady-state, dissolved-oxygen model and waste-load assimilation study for South Fork, Wildcat Creek, Clinton County, Indiana

USGS Publications Warehouse

Crawford, Charles G.; Wilber, William G.; Peters, James G.

1979-01-01

The Indiana State Board of Health is developing a State water-quality management plan that includes establishing limits for wastewater effluents discharged into Indiana streams. A digital model calibrated to conditions in South Fork Wildcat Creek was used to predict alternatives for future waste loadings that would be compatible with Indiana stream water-quality standards defined for two critical hydrologic conditions, summer and winter low flows. Natural streamflow during the 7-day, 10-year low flow is zero, so no benefit from dilution is provided. The Indiana State Board of Health 's projected ammonia-nitrogen concentration for the Frankfort wastewater-treatment facility will violate the instream total ammonia-nitrogen standard of 2.5 mg/l and 4.0 mg/l during summer and winter low flows, respectively. The model indicates that nitrification and algal respiration were significant factors affecting the dissolved-oxygen dynamics of South Fork Wildcat Creek during two water-quality surveys. Stream water quality during the two water-quality surveys was degraded by the discharge of wastewater receiving only primary treatment. Benthic deposits resulting from this wastewater discharge seem to exert a considerable oxygen demand. The discharge of partially treated wastewater should be eliminated when a new wastewater-treatment facility becomes operational in mid-1979. Therefore, benthic-oxygen demand due to benthic deposits should become negligible at that time.

Water Quality Modeling in the Dead End Sections of Drinking ...

EPA Pesticide Factsheets

Dead-end sections of drinking water distribution networks are known to be problematic zones in terms of water quality degradation. Extended residence time due to water stagnation leads to rapid reduction of disinfectant residuals allowing the regrowth of microbial pathogens. Water quality models developed so far apply spatial aggregation and temporal averaging techniques for hydraulic parameters by assigning hourly averaged water demands to the main nodes of the network. Although this practice has generally resulted in minimal loss of accuracy for the predicted disinfectant concentrations in main water transmission lines, this is not the case for the peripheries of a distribution network. This study proposes a new approach for simulating disinfectant residuals in dead end pipes while accounting for both spatial and temporal variability in hydraulic and transport parameters. A stochastic demand generator was developed to represent residential water pulses based on a non-homogenous Poisson process. Dispersive solute transport was considered using highly dynamic dispersion rates. A genetic algorithm was used to calibrate the axial hydraulic profile of the dead-end pipe based on the different demand shares of the withdrawal nodes. A parametric sensitivity analysis was done to assess the model performance under variation of different simulation parameters. A group of Monte-Carlo ensembles was carried out to investigate the influence of spatial and temporal variations

Extending WEPP technology to predict fine sediment and phosphorus delivery from forested hillslopes

Treesearch

William Elliot; Erin Brooks; Drea Em Traeumer; Mariana Dobre

2015-01-01

In many watersheds, including the Great Lakes and Lake Tahoe Basins, two basins where the land cover is dominated by forests, the pollutants of concern are fine sediments and phosphorus. Forest runoff is generally low in nitrogen, and coarse sediment does not adversely impact the quality of lake waters. Predictive tools are needed to estimate not simply sediment, but...

Vulnerability of shallow groundwater and drinking-water wells to nitrate in the United States

USGS Publications Warehouse

Nolan, Bernard T.; Hitt, Kerie J.

2006-01-01

Two nonlinear models were developed at the national scale to (1) predict contamination of shallow ground water (typically < 5 m deep) by nitrate from nonpoint sources and (2) to predict ambient nitrate concentration in deeper supplies used for drinking. The new models have several advantages over previous national-scale approaches. First, they predict nitrate concentration (rather than probability of occurrence), which can be directly compared with water-quality criteria. Second, the models share a mechanistic structure that segregates nitrogen (N) sources and physical factors that enhance or restrict nitrate transport and accumulation in ground water. Finally, data were spatially averaged to minimize small-scale variability so that the large-scale influences of N loading, climate, and aquifer characteristics could more readily be identified. Results indicate that areas with high N application, high water input, well-drained soils, fractured rocks or those with high effective porosity, and lack of attenuation processes have the highest predicted nitrate concentration. The shallow groundwater model (mean square error or MSE = 2.96) yielded a coefficient of determination (R2) of 0.801, indicating that much of the variation in nitrate concentration is explained by the model. Moderate to severe nitrate contamination is predicted to occur in the High Plains, northern Midwest, and selected other areas. The drinking-water model performed comparably (MSE = 2.00, R2 = 0.767) and predicts that the number of users on private wells and residing in moderately contaminated areas (>5 to ≤10 mg/L nitrate) decreases by 12% when simulation depth increases from 10 to 50 m.

Vulnerability of shallow groundwater and drinking-water wells to nitrate in the United States.

PubMed

Nolan, Bernard T; Hitt, Kerie J

2006-12-15

Two nonlinear models were developed at the national scale to (1) predict contamination of shallow ground water (typically < 5 m deep) by nitrate from nonpoint sources and (2) to predict ambient nitrate concentration in deeper supplies used for drinking. The new models have several advantages over previous national-scale approaches. First, they predict nitrate concentration (rather than probability of occurrence), which can be directly compared with water-quality criteria. Second, the models share a mechanistic structure that segregates nitrogen (N) sources and physical factors that enhance or restrict nitrate transport and accumulation in ground water. Finally, data were spatially averaged to minimize small-scale variability so that the large-scale influences of N loading, climate, and aquifer characteristics could more readily be identified. Results indicate that areas with high N application, high water input, well-drained soils, fractured rocks or those with high effective porosity, and lack of attenuation processes have the highest predicted nitrate concentration. The shallow groundwater model (mean square error or MSE = 2.96) yielded a coefficient of determination (R(2)) of 0.801, indicating that much of the variation in nitrate concentration is explained by the model. Moderate to severe nitrate contamination is predicted to occur in the High Plains, northern Midwest, and selected other areas. The drinking-water model performed comparably (MSE = 2.00, R(2) = 0.767) and predicts that the number of users on private wells and residing in moderately contaminated areas (>5 to < or =10 mg/L nitrate) decreases by 12% when simulation depth increases from 10 to 50 m.

Enhancing VELMA's Watershed Delineation and Performance with Ancillary Stream Data

EPA Science Inventory

VELMA (Visualizing Ecosystems for Land Management Assessment) is a hydro-ecological landscape disturbance model developed to predict the effectiveness of alternative green infrastructure scenarios for protecting water quality, and also to estimate potential ecosystem service co-b...

Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay.

PubMed

Jacobs, J M; Rhodes, M; Brown, C W; Hood, R R; Leight, A; Long, W; Wood, R

2014-11-01

To construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters of Chesapeake Bay for implementation in ecological forecasting systems. We evaluated and applied previously published qPCR assays to water samples (n = 1636) collected from Chesapeake Bay from 2007-2010 in conjunction with State water quality monitoring programmes. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Water quality at points-of-use in the Galapagos Islands.

PubMed

Gerhard, William A; Choi, Wan Suk; Houck, Kelly M; Stewart, Jill R

2017-04-01

Piped drinking water is often considered a gold standard for protecting public health but research is needed to explicitly evaluate the effect of centralized treatment systems on water quality in developing world settings. This study examined the effect of a new drinking water treatment plant (DWTP) on microbial drinking water quality at the point-of-use on San Cristobal Island, Galapagos using fecal indicator bacteria total coliforms and Escherichia coli. Samples were collected during six collection periods before and after operation of the DWTP began from the freshwater sources (n=4), the finished water (n=6), and 50 sites throughout the distribution system (n=287). This study found that there was a significant decrease in contamination by total coliforms (two orders of magnitude) and E. coli (one order of magnitude) after DWTP operation began (p<0.001). However, during at least one post-construction collection cycle, total coliforms and E. coli were still found at 66% and 28% of points-of-use (n=50), respectively. During the final collection period, conventional methods were augmented with human-specific Bacteroides assays - validated herein - with the goal of elucidating possible microbial contamination sources. Results show that E. coli contamination was not predictive of contamination by human wastes and suggests that observed indicator bacteria contamination may have environmental origins. Together these findings highlight the necessity of a holistic approach to drinking water infrastructure improvements in order to deliver high quality water through to the point-of-use. Copyright © 2017 Elsevier GmbH. All rights reserved.

Water contamination risks associated with a combination of planned and unplanned fire in south eastern Australia

NASA Astrophysics Data System (ADS)

Sheridan, G. J.; Nyman, P.; Langhans, C.; Noske, P. J.; Lane, P. N. J.

2014-12-01

Planned burning reduces fuel loads in forests, potentially reducing the severity of subsequent wildfires. However planned burning also increases the risk of a significant water quality impact by maintaining a proportion of the catchment in a burnt condition conducive to generating high magnitude erosion events (eg. debris flows). Differences in the frequency and magnitude of planned and unplanned fire, combined with poorly understood relationships between fire severity and hydrologic impacts, means that predictions of the net water contamination risks associated with any particular fire regime are difficult to predict. This presentation synthesises results from 10 years of point, plot and catchment-scale post-fire hydrology and erosion studies in SE Australia to estimate the likely benifits and risks of planned burning scenarios from a drinking water supply perspective

Modeling sediment transport from an off-road vehicle trail stream crossing using WEPP model

Treesearch

Renee' D. Ayala; Puneet Srivastava; Christian J. Brodbeck; Emily A. Carter; Timothy P. McDonald

2005-01-01

There is a limited information available pertaining to the adverse effects of Off-Road-Vehicle (ORV) use and trail impacts. As a result, this study was initiated in 2003 to (a) quantify water quality impacts of an ORV trail stream crossing through monitoring of total suspended solids, and (b) conduct WEPP (Water Erosion Prediction Project) simulations to determine long...

Challenges and opportunities to improve understanding on wetland ecosystem and function at the local catchment scale: data fusion, data-model integration, and prediction uncertainty.

NASA Astrophysics Data System (ADS)

Yeo, I. Y.

2016-12-01

Wetlands are valuable landscape features that provide important ecosystem functions and services. The ecosystem processes in wetlands are highly dependent on the hydrology. However, hydroperiod (i.e., change dynamics in inundation extent) is highly variable spatially and temporarily, and extremely difficult to predict owing to the complexity in hydrological processes within wetlands and its interaction with surrounding areas. This study reports the challenges and progress in assessing the catchment scale benefits of wetlands to regulate hydrological regime and water quality improvement in agricultural watershed. A process-based watershed model, Soil and Water Assessment Tool (SWAT) was improved to simulate the cumulative impacts of wetlands on downstream. Newly developed remote sensing products from LiDAR intensity and time series Landsat records, which show the inter-annual changes in fraction inundation, were utilized to describe the change status of inundated areas within forested wetlands, develop spatially varying wetland parameters, and evaluate the predicted inundated areas at the landscape level. We outline the challenges on developing the time series inundation mapping products at a high spatial and temporal resolution and reconciling the catchment scale model with the moderate remote sensing products. We then highlight the importance of integrating spatialized information to model calibration and evaluation to address the issues of equi-finality and prediction uncertainty. This integrated approach was applied to the upper region of Choptank River Watershed, the agricultural watershed in the Coastal Plain of Chesapeake Bay Watershed (in US). In the Mid- Atlantic US, the provision of pollution regulation services provided by wetlands has been emphasized due to declining water quality within the Chesapeake Bay and watersheds, and the preservation and restoration of wetlands has become the top priority to manage nonpoint source water pollution.

Analyzing the Relative Linkages of Land Use and Hydrologic Variables with Urban Surface Water Quality using Multivariate Techniques

NASA Astrophysics Data System (ADS)

Ahmed, S.; Abdul-Aziz, O. I.

2015-12-01

We used a systematic data-analytics approach to analyze and quantify relative linkages of four stream water quality indicators (total nitrogen, TN; total phosphorus, TP; chlorophyll-a, Chla; and dissolved oxygen, DO) with six land use and four hydrologic variables, along with the potential external (upstream in-land and downstream coastal) controls in highly complex coastal urban watersheds of southeast Florida, U.S.A. Multivariate pattern recognition techniques of principle component and factor analyses, in concert with Pearson correlation analysis, were applied to map interrelations and identify latent patterns of the participatory variables. Relative linkages of the in-stream water quality variables with their associated drivers were then quantified by developing dimensionless partial least squares (PLS) regression model based on standardized data. Model fitting efficiency (R2=0.71-0.87) and accuracy (ratio of root-mean-square error to the standard deviation of the observations, RSR=0.35-0.53) suggested good predictions of the water quality variables in both wet and dry seasons. Agricultural land and groundwater exhibited substantial controls on surface water quality. In-stream TN concentration appeared to be mostly contributed by the upstream water entering from Everglades in both wet and dry seasons. In contrast, watershed land uses had stronger linkages with TP and Chla than that of the watershed hydrologic and upstream (Everglades) components for both seasons. Both land use and hydrologic components showed strong linkages with DO in wet season; however, the land use linkage appeared to be less in dry season. The data-analytics method provided a comprehensive empirical framework to achieve crucial mechanistic insights into the urban stream water quality processes. Our study quantitatively identified dominant drivers of water quality, indicating key management targets to maintain healthy stream ecosystems in complex urban-natural environments near the coast.

Water pollution risk simulation and prediction in the main canal of the South-to-North Water Transfer Project

NASA Astrophysics Data System (ADS)

Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Cheng, Xi

2014-11-01

The middle route of the South-to-North Water Transfer Project (MRP) will divert water to Beijing Tuancheng Lake from Taocha in the Danjiangkou reservoir located in the Hubei province of China. The MRP is composed of a long canal and complex hydraulic structures and will transfer water in open channel areas to provide drinking water for Beijing, Shijiazhuang and other cities under extremely strict water quality requirements. A large number of vehicular accidents, occurred on the many highway bridges across the main canal would cause significant water pollution in the main canal. To ensure that water quality is maintained during the diversion process, the effects of pollutants on water quality due to sudden pollution accidents were simulated and analyzed in this paper. The MIKE11 HD module was used to calculate the hydraulic characteristics of the 42-km Xishi-to-Beijuma River channel of the MRP. Six types of hydraulic structures, including inverted siphons, gates, highway bridges, culverts and tunnels, were included in this model. Based on the hydrodynamic model, the MIKE11 AD module, which is one-dimensional advection dispersion model, was built for TP, NH3-N, CODMn and F. The validated results showed that the computed values agreed well with the measured values. In accordance with transportation data across the Dianbei Highway Bridge, the effects of traffic accidents on the bridge on water quality were analyzed. Based on simulated scenarios with three discharge rates (ranged from 12 m3/s to 17 m3/s, 40 m3/s, and 60 m3/s) and three pollution loading concentration levels (5 t, 10 t and 20 t) when trucks spill their contents (i.e., phosphate fertilizer, cyanide, oil and chromium solution) into the channel, emergency measures were proposed. Reasonable solutions to ensure the water quality with regard to the various types of pollutants were proposed, including treating polluted water, maintaining materials, and personnel reserves.

Prediction of Recreational Safety of the Passaic and Pompton Rivers (NJ) Using Escherichia coli as an Indicator

NASA Astrophysics Data System (ADS)

Rossi, A.; Wu, M. S.; Lee, L.

2017-12-01

Pathogens are considered the most common cause of impairment for water quality in New Jersey. Contact with waterbodies containing high concentrations of pathogens, accounts for waterborne disease outbreaks not only in the United States but worldwide. This implies a financial burden on the health system both in terms of wellbeing and of treatment costs, for instance. For these reasons, the detection and enumeration of pathogen indicators in recreational waters are part of the water quality and public health monitoring performed by U.S. Environmental Protection Agency (EPA) and other government agencies. A high concentration of a pathogen indicator, like Escherichia coli, is commonly used method for assessing water quality in freshwater ecosystems. In the present research, we used the EPA-approved Membrane Filtration Method m-ColiBlue24 to detect E. coli Colony Forming Unit (CFU) in water samples collected at the Pompton and the Passaic rivers during the summer 2016. These are some of the most polluted rivers in New Jersey, located at a highly urbanized area with a high population density. In addition to looking at the magnitude and statistical significance of the variables, a multivariate regression model was applied to estimate threshold values where the value-response relationship changes. Among all the parameters tested (specific conductance, dissolved oxygen, pH, dissolved organic carbon, chlorophyll a, nitrite + nitrate, turbidity, water temperature, precipitation) preliminary results indicate specific conductance and rain as significant. In particular, we found that when the cumulated rainfall level in the preceding 48 hours exceeds 1.27 mm, the predicted probability that the Escherichia coli count will exceed the EPA safety threshold value for primary contact recreation water (126 CFU/100 mL) increases by 0.44. Our approach, not only complements the usual screening approach, but it also provides cheap and quick way of generating information that allows managers of recreational waters to estimate recreational risk levels and close out beaches when necessary.

At the nexus of fire, water and society

PubMed Central

2016-01-01

The societal risks of water scarcity and water-quality impairment have received considerable attention, evidenced by recent analyses of these topics by the 2030 Water Resources Group, the United Nations and the World Economic Forum. What are the effects of fire on the predicted water scarcity and declines in water quality? Drinking water supplies for humans, the emphasis of this exploration, are derived from several land cover types, including forests, grasslands and peatlands, which are vulnerable to fire. In the last two decades, fires have affected the water supply catchments of Denver (CO) and other southwestern US cities, and four major Australian cities including Sydney, Canberra, Adelaide and Melbourne. In the same time period, several, though not all, national, regional and global water assessments have included fire in evaluations of the risks that affect water supplies. The objective of this discussion is to explore the nexus of fire, water and society with the hope that a more explicit understanding of fire effects on water supplies will encourage the incorporation of fire into future assessments of water supplies, into the pyrogeography conceptual framework and into planning efforts directed at water resiliency. This article is part of the themed issue ‘The interaction of fire and mankind’. PMID:27216505

Characterization of bottom-sediment, water, and elutriate chemistry at selected stations at Reelfoot Lake, Tennessee

USGS Publications Warehouse

Broshears, R.E.

1991-01-01

To better-understand and predict the potential effect of dredging on water quality at Reelfoot Lake, chemical analyses were conducted on samples of lake water, bottom sediment, and elutriate water. Chemical analyses were conducted on samples of lake water, bottom sediment, and elutriate water collected at five stations in the lake during November 1988. Lake water was of the calcium magnesium bicarbonate type with an average dissolved-solids concentration of 120 milligrams per liter. Trace constituents were present in bottom sediments at concentrations representative of their average relative abundance in the earth?s crust. Elutriate waters prepared by mixing bottom sediment and lake water had suspended-solids concentrations as high as 2,000 milligrams per liter which exerted significant oxygen demand Trace constituents in the unfiltered elutriate waters were elevated with respect to lake water; elevated concentrations were attributable to the increased suspended-solids concentrations. Concentrations of total-recoverable copper, lead., and zinc in many elutriate waters exceeded U.S. Environmental Protection Agency?s water-quality criteria for the protection of freshwater aquatic life. The toxicity of elutriate waters, as measured by a 48-hour bioassay with Ceriodaphnia dubia, was low.

Linking water quality guidelines to the natural characteristics of catchments in order to support distinct aquatic ecosystems: Water quality guidelines for suspended particulate matter

NASA Astrophysics Data System (ADS)

Bilotta, G. S.; Grove, M. K.; Harrison, C.; Joyce, C. B.; Peacock, C.

2012-12-01

The natural characteristics of a catchment provide a template that controls the background rates of geomorphological processes operating within that catchment, which in-turn determines the background physico-chemical and hydro-morphological characteristics of the catchment's surface waters. Large differences in the natural characteristics of catchments (e.g. geology, topography, climate), lead to unique physico-chemical and hydro-morphological conditions that support unique freshwater communities. However, this uniqueness is not always recognised in international water quality guidelines, which often attempt to apply blanket water-quality guidelines to 'protect' a wide range of ecosystems. In this paper we investigate the natural characteristics that control background concentrations of suspended particulate matter (SPM - including nano-scale particles to sand-sized sediments), which is a well-known cause of ecological degradation. At present, the management of SPM is hampered by a lack of understanding of the SPM conditions that water quality managers should aim to achieve in contrasting environments in order to support good ecological status. To address this, in this paper we examine the SPM preferences of contrasting biological communities that are in reference condition (minimal anthropogenic disturbance and high ecological status). We analyse historical SPM data collected on a monthly basis from a wide range of reference-condition temperate environments (638 stream/river sites comprising 42 different biological community-types). This analysis reveals that there are statistically significant differences (One-way ANOVA p < 0.001) between the background SPM concentrations observed in contrasting communities that are in reference condition. Mean background SPM concentrations for contrasting communities ranged from 1.7 to 26.2 mg L-1 (i.e. more than a 15-fold difference). We propose a model for predicting environment-specific water quality guidelines for SPM. In order to develop this model, the 638 reference-condition sites were first classified into one of five mean background SPM ranges (0.00-5.99, 6.00-11.99, 12.00-17.99, 18.00-23.99 and >24.00 mg L-1). Stepwise Multiple Discriminant Analysis (MDA) of these ranges showed that a site's SPM range can be predicted as a function of: mean annual air temperature, mean annual precipitation, mean altitude of upstream catchment, distance from source, slope to source, channel width and depth, the percentage of catchment area comprised of clay, chalk, and hard rock solid geology, and the percentage of the catchment area comprised of blown sand/landslide material as the surface (drift) material. Although the model is still being improved and developed, this research highlights the need to link water quality guidelines to the natural characteristics of catchments and the physico-chemical preferences of the biological communities that would naturally inhabit them.

Predicting Fish Growth Potential and Identifying Water Quality Constraints: A Spatially-Explicit Bioenergetics Approach

NASA Astrophysics Data System (ADS)

Budy, Phaedra; Baker, Matthew; Dahle, Samuel K.

2011-10-01

Anthropogenic impairment of water bodies represents a global environmental concern, yet few attempts have successfully linked fish performance to thermal habitat suitability and fewer have distinguished co-varying water quality constraints. We interfaced fish bioenergetics, field measurements, and Thermal Remote Imaging to generate a spatially-explicit, high-resolution surface of fish growth potential, and next employed a structured hypothesis to detect relationships among measures of fish performance and co-varying water quality constraints. Our thermal surface of fish performance captured the amount and spatial-temporal arrangement of thermally-suitable habitat for three focal species in an extremely heterogeneous reservoir, but interpretation of this pattern was initially confounded by seasonal covariation of water residence time and water quality. Subsequent path analysis revealed that in terms of seasonal patterns in growth potential, catfish and walleye responded to temperature, positively and negatively, respectively; crappie and walleye responded to eutrophy (negatively). At the high eutrophy levels observed in this system, some desired fishes appear to suffer from excessive cultural eutrophication within the context of elevated temperatures whereas others appear to be largely unaffected or even enhanced. Our overall findings do not lead to the conclusion that this system is degraded by pollution; however, they do highlight the need to use a sensitive focal species in the process of determining allowable nutrient loading and as integrators of habitat suitability across multiple spatial and temporal scales. We provide an integrated approach useful for quantifying fish growth potential and identifying water quality constraints on fish performance at spatial scales appropriate for whole-system management.

Modelling the catchment-scale environmental impacts of wastewater treatment in an urban sewage system for CO₂ emission assessment.

PubMed

Mouri, Goro; Oki, Taikan

2010-01-01

Water shortages and water pollution are a global problem. Increases in population can have further acute effects on water cycles and on the availability of water resources. Thus, wastewater management plays an important role in mitigating negative impacts on natural ecosystems and human environments and is an important area of research. In this study, we modelled catchment-scale hydrology, including water balances, rainfall, contamination, and urban wastewater treatment. The entire water resource system of a basin, including a forest catchment and an urban city area, was evaluated synthetically from a spatial distribution perspective with respect to water quantity and quality; the Life Cycle Assessment (LCA) technique was applied to optimize wastewater treatment management with the aim of improving water quality and reducing CO₂ emissions. A numerical model was developed to predict the water cycle and contamination in the catchment and city; the effect of a wastewater treatment system on the urban region was evaluated; pollution loads were evaluated quantitatively; and the effects of excluding rainwater from the treatment system during flooding and of urban rainwater control on water quality were examined. Analysis indicated that controlling the amount of rainwater inflow to a wastewater treatment plant (WWTP) in an urban area with a combined sewer system has a large impact on reducing CO₂ emissions because of the load reduction on the urban sewage system.

The science, information, and engineering needed to manage water availability and quality in 2050: Chapter 23

USGS Publications Warehouse

Hirsch, Robert M.

2012-01-01

This chapter explores four water resources issues: 1) hydrologic variability, hazards, water supply and ecosystem preservation; 2) urban landscape design; 3) non-point source water quality, and 4) climate change, resiliency, and nonstationarity. It also considers what science, technology, and engineering practice may be needed in the coming decades to sustain water supplies and ecosystems in the face of increasing stresses from a growing demand for water. Dealing with these four water resource issues in the highly uncertain future would will demand predictive models that are rooted in real-world data. In a non-stationary world, continuity of observations is crucial. All watersheds are influenced by human actions through changes in land use, water use, and climate. The focus of water planning and management between today and 2050 will depend more than ever on collection and analysis of long-term data to learn about the evolving state of the system, understanding ecosystem processes in the water and on the landscape, and finding innovative ways to manage water as a shared resource. This includes sharing water with our neighbors on the landscape, sharing with the other species that depend on water, and sharing with future generations.

Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07

USGS Publications Warehouse

Oden, Timothy D.; Asquith, William H.; Milburn, Matthew S.

2009-01-01

In December 2005, the U.S. Geological Survey in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (total coliform and Escherichia coli), atrazine, and suspended sediment at two U.S. Geological Survey streamflow-gaging stations upstream from Lake Houston near Houston (08068500 Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Texas). The data from the discrete water-quality samples collected during 2005-07, in conjunction with monitored real-time data already being collected - physical properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), streamflow, and rainfall - were used to develop regression models for predicting water-quality constituent concentrations for inflows to Lake Houston. Rainfall data were obtained from a rain gage monitored by Harris County Homeland Security and Emergency Management and colocated with the Spring Creek station. The leaps and bounds algorithm was used to find the best subsets of possible regression models (minimum residual sum of squares for a given number of variables). The potential explanatory or predictive variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, rainfall, and time (to account for seasonal variations inherent in some water-quality data). The response variables at each site were nitrite plus nitrate nitrogen, total phosphorus, organic carbon, Escherichia coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities as a means to estimate concentrations of the various constituents under investigation, with accompanying estimates of measurement uncertainty. Each regression equation can be used to estimate concentrations of a given constituent in real time. In conjunction with estimated concentrations, constituent loads were estimated by multiplying the estimated concentration by the corresponding streamflow and applying the appropriate conversion factor. By computing loads from estimated constituent concentrations, a continuous record of estimated loads can be available for comparison to total maximum daily loads. The regression equations presented in this report are site specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the methods that were developed and documented could be applied to other tributaries to Lake Houston for estimating real-time water-quality data for streams entering Lake Houston.

Elements of a predictive model for determining beach closures on a real time basis: the case of 63rd Street Beach Chicago

USGS Publications Warehouse

Olyphant, Greg A.; Whitman, Richard L.

2004-01-01

Data on hydrometeorological conditions and E. coli concentration were simultaneously collected on 57 occasions during the summer of 2000 at 63rd Street Beach, Chicago, Illinois. The data were used to identify and calibrate a statistical regression model aimed at predicting when the bacterial concentration of the beach water was above or below the level considered safe for full body contact. A wide range of hydrological, meteorological, and water quality variables were evaluated as possible predictive variables. These included wind speed and direction, incoming solar radiation (insolation), various time frames of rainfall, air temperature, lake stage and wave height, and water temperature, specific conductance, dissolved oxygen, pH, and turbidity. The best-fit model combined real-time measurements of wind direction and speed (onshore component of resultant wind vector), rainfall, insolation, lake stage, water temperature and turbidity to predict the geometric mean E.coliconcentration in the swimming zone of the beach. The model, which contained both additive and multiplicative (interaction) terms, accounted for 71% of the observed variability in the log E. coliconcentrations. A comparison between model predictions of when the beach should be closed and when the actualbacterial concentrations were above or below the 235 cfu 100 ml-1 threshold value, indicated that the model accurately predicted openingsversus closures 88% of the time.

Comparison of ground-water quality in samples from selected shallow and deep wells in the central Oklahoma aquifer, 2003-2005

USGS Publications Warehouse

Becker, Carol J.

2006-01-01

The aquifer units of the Central Oklahoma aquifer underlie about 2,890 square miles of central Oklahoma and are used extensively to supply water for municipal, domestic, industrial, and agricultural needs. The Central Oklahoma aquifer also is commonly referred to as the Garber-Wellington aquifer because the Garber Sandstone and Wellington Formation yield the greatest quantities of usable water for domestic and high-capacity wells. The major water-quality concerns for the Central Oklahoma aquifer described by the U.S. Geological Survey National Water Quality Assessment Program (1987 to 1992) were elevated concentrations of nitrate nitrogen in shallow water and the occurrence of arsenic, chromium, and selenium in parts of the aquifer. The quality of water from deep public-water supply wells in the Central Oklahoma aquifer is monitored by the State of Oklahoma. The chemical quality of water from shallow domestic wells is not monitored, and, therefore, there is a concern that well owners may be unknowingly ingesting water with nitrate nitrogen, arsenic, chromium, selenium, and other chemical constituents at concentrations that are considered harmful. As a result of this concern, the Oklahoma Department of Environmental Quality and the U.S. Geological Survey collaborated on a study to sample water during June 2003 through August 2005 from 23 shallow wells (less than 200 feet in depth) and 28 deep wells (200 feet or greater in depth) completed in the bedrock aquifer units of the Central Oklahoma aquifer. The objectives of the study were to describe the chemical quality of water from shallow and deep wells and to determine if the differences in constituent concentrations are statistically significant. Water from shallow wells had significantly higher concentrations of calcium, magnesium, bicarbonate, sulfate, chloride, and nitrate nitrogen than water from deep wells. There were no significant differences between concentrations of dissolved solids, sodium, and fluoride in water from shallow and deep wells. Water from 9 shallow wells had nitrate nitrogen concentrations greater than 2 milligrams per liter, suggesting nitrogen sources at land surface have had an effect on water from these wells. Water from three shallow wells (13 percent) exceeded the nitrate nitrogen maximum contaminant level of 10 milligrams per liter in drinking water. Water from shallow wells had significantly lower concentrations of arsenic, chromium, iron, and selenium than water from deep wells, whereas, concentrations of barium, copper, manganese, and zinc were similar. Water-quality data indicate that arsenic frequently occurs in shallow ground water from the Central Oklahoma aquifer, but at low concentrations (<10 micrograms per liter). The occurrence of chromium and selenium in water from shallow wells was infrequent and at low concentrations in this study. It does not appear that the quality of water from a shallow well can be predicted based on the quality of water from a nearby deep well. The results show that in general terms, shallow ground water has significantly higher concentrations of most major ions and significantly lower concentrations of arsenic, chromium, and selenium than water from deep wells.

Precipitation Effects on Microbial Pollution in a River: Lag Structures and Seasonal Effect Modification

PubMed Central

Tornevi, Andreas; Bergstedt, Olof; Forsberg, Bertil

2014-01-01

Background The river Göta Älv is a source of freshwater for 0.7 million swedes. The river is subject to contamination from sewer systems discharge and runoff from agricultural lands. Climate models projects an increase in precipitation and heavy rainfall in this region. This study aimed to determine how daily rainfall causes variation in indicators of pathogen loads, to increase knowledge of variations in river water quality and discuss implications for risk management. Methods Data covering 7 years of daily monitoring of river water turbidity and concentrations of E. coli, Clostridium and coliforms were obtained, and their short-term variations in relation with precipitation were analyzed with time series regression and non-linear distributed lag models. We studied how precipitation effects varied with season and compared different weather stations for predictive ability. Results Generally, the lowest raw water quality occurs 2 days after rainfall, with poor raw water quality continuing for several more days. A rainfall event of >15 mm/24-h (local 95 percentile) was associated with a three-fold higher concentration of E. coli and 30% higher turbidity levels (lag 2). Rainfall was associated with exponential increases in concentrations of indicator bacteria while the effect on turbidity attenuated with very heavy rainfall. Clear associations were also observed between consecutive days of wet weather and decreased water quality. The precipitation effect on increased levels of indicator bacteria was significant in all seasons. Conclusions Rainfall elevates microbial risks year-round in this river and freshwater source and acts as the main driver of varying water quality. Heavy rainfall appears to be a better predictor of fecal pollution than water turbidity. An increase of wet weather and extreme events with climate change will lower river water quality even more, indicating greater challenges for drinking water producers, and suggesting better control of sources of pollution. PMID:24874010

[GIS and scenario analysis aid to water pollution control planning of river basin].

PubMed

Wang, Shao-ping; Cheng, Sheng-tong; Jia, Hai-feng; Ou, Zhi-dan; Tan, Bin

2004-07-01

The forward and backward algorithms for watershed water pollution control planning were summarized in this paper as well as their advantages and shortages. The spatial databases of water environmental function region, pollution sources, monitoring sections and sewer outlets were built with ARCGIS8.1 as the platform in the case study of Ganjiang valley, Jiangxi province. Based on the principles of the forward algorithm, four scenarios were designed for the watershed pollution control. Under these scenarios, ten sets of planning schemes were generated to implement cascade pollution source control. The investment costs of sewage treatment for these schemes were estimated by means of a series of cost-effective functions; with pollution source prediction, the water quality was modeled with CSTR model for each planning scheme. The modeled results of different planning schemes were visualized through GIS to aid decision-making. With the results of investment cost and water quality attainment as decision-making accords and based on the analysis of the economic endurable capacity for water pollution control in Ganjiang river basin, two optimized schemes were proposed. The research shows that GIS technology and scenario analysis can provide a good guidance to the synthesis, integrity and sustainability aspects for river basin water quality planning.

Water and health in Europe. A joint report from the European Environment Agency and the WHO Regional Office for Europe.

PubMed

2002-01-01

Shortage of water may be most urgent health problem currently facing some European countries. Climate change is predicted to influence water availability, especially in coastal areas. The extend of provision of piped drinking-water supplies to households varies across Europe and between urban and rural populations. The utilization of water for irrigation and for industry exerts pressure on water resources. Changes in populaton distribution and density are key factors influencing the quality of water resources. Outbreaks of waterborne diseases continue to occur across Europe, and minor supply problems are encountered in all countries. Inadequate sewerage systems are a significant threat to public health. Numerous chemicals are found throughout the aquatic environment. Eutrophication is a major threat to European surface waters. Considerate evidence has accrued linking the quality of bathing water with minor illnesses. Additional efforts are required to sustain the European Region's water resources and to provide safe water. Partnerships and cooperation are needed between the environment and health sectors at al levels of government to disseminate technology, to improve management and to provide financial and institutional support to ensure access to safe water and sanitation for all.

Management scenarios for the Jordan River salinity crisis

USGS Publications Warehouse

Farber, E.; Vengosh, A.; Gavrieli, I.; Marie, Amarisa; Bullen, T.D.; Mayer, B.; Holtzman, R.; Segal, M.; Shavit, U.

2005-01-01

Recent geochemical and hydrological findings show that the water quality of the base flow of the Lower Jordan River, between the Sea of Galilee and the Dead Sea, is dependent upon the ratio between surface water flow and groundwater discharge. Using water quality data, mass-balance calculations, and actual flow-rate measurements, possible management scenarios for the Lower Jordan River and their potential affects on its salinity are investigated. The predicted scenarios reveal that implementation of some elements of the Israel-Jordan peace treaty will have negative effects on the Jordan River water salinity. It is predicted that removal of sewage effluents dumped into the river (???13 MCM/a) will significantly reduce the river water's flow and increase the relative proportion of the saline groundwater flux into the river. Under this scenario, the Cl content of the river at its southern point (Abdalla Bridge) will rise to almost 7000 mg/L during the summer. In contrast, removal of all the saline water (16.5 MCM/a) that is artificially discharged into the Lower Jordan River will significantly reduce its Cl concentration, to levels of 650-2600 and 3000-3500 mg/L in the northern and southern areas of the Lower Jordan River, respectively. However, because the removal of either the sewage effluents or the saline water will decrease the river's discharge to a level that could potentially cause river desiccation during the summer months, other water sources must be allocated to preserve in-stream flow needs and hence the river's ecosystem. ?? 2005 Elsevier Ltd. All rights reserved.

Systems Approach to Climate, Water, and Diarrhea in Hubli-Dharwad, India.

PubMed

Mellor, Jonathan; Kumpel, Emily; Ercumen, Ayse; Zimmerman, Julie

2016-12-06

Anthropogenic climate change will likely increase diarrhea rates for communities with inadequate water, sanitation, or hygiene facilities including those with intermittent water supplies. Current approaches to study these impacts typically focus on the effect of temperature on all-cause diarrhea while excluding precipitation and diarrhea etiology while not providing actionable adaptation strategies. We develop a partially mechanistic, systems approach to estimate future diarrhea prevalence and design adaptation strategies. The model incorporates downscaled global climate models, water quality data, quantitative microbial risk assessment, and pathogen prevalence in an agent-based modeling framework incorporating precipitation and diarrhea etiology. It is informed using water quality and diarrhea data from Hubli-Dharwad, India-a city with an intermittent piped water supply exhibiting seasonal water quality variability vulnerable to climate change. We predict all-cause diarrhea prevalence to increase by 4.9% (Range: 1.5-9.0%) by 2011-2030, 11.9% (Range: 7.1-18.2%) by 2046-2065, and 18.2% (Range: 9.1-26.2%) by 2080-2099. Rainfall is an important modifying factor. Rotavirus prevalence is estimated to decline by 10.5% with Cryptosporidium and E. coli prevalence increasing by 9.9% and 6.3%, respectively, by 2080-2099 in this setting. These results suggest that ceramic water filters would be recommended as a climate adaptation strategy over chlorination. This work highlights the vulnerability of intermittent water supplies to climate change and the urgent need for improvements.

A study of Minnesota forests and lakes using data from Earth Resources Technology Satellites

NASA Technical Reports Server (NTRS)

1974-01-01

Highlights of research and practical benefits are discussed for the following projects which utilized ERTS 1 data to provide municipal, state, federal, and industrial users with environmental resource information for the state of Minnesota: (1) forest disease detection and control; (2) evaluation of water quality by remote sensing techniques; (3) forest vegetation classification and management; (4) detection of saline soils in the Red River Valley; (5) snowmelt flood prediction; (6) remote sensing applications to hydrology; (7) Rice Creek watershed project; (8) water quality in Lake Superior and the Duluth Superior Harbor; and (9) determination of Lake Superior currents from turbidity patterns.

Modelling of in-stream nitrogen and phosphorus concentrations using different sampling strategies for calibration data

NASA Astrophysics Data System (ADS)

Jomaa, Seifeddine; Jiang, Sanyuan; Yang, Xiaoqiang; Rode, Michael

2016-04-01

It is known that a good evaluation and prediction of surface water pollution is mainly limited by the monitoring strategy and the capability of the hydrological water quality model to reproduce the internal processes. To this end, a compromise sampling frequency, which can reflect the dynamical behaviour of leached nutrient fluxes responding to changes in land use, agriculture practices and point sources, and appropriate process-based water quality model are required. The objective of this study was to test the identification of hydrological water quality model parameters (nitrogen and phosphorus) under two different monitoring strategies: (1) regular grab-sampling approach and (2) regular grab-sampling with additional monitoring during the hydrological events using automatic samplers. First, the semi-distributed hydrological water quality HYPE (Hydrological Predictions for the Environment) model was successfully calibrated (1994-1998) for discharge (NSE = 0.86), nitrate-N (lowest NSE for nitrate-N load = 0.69), particulate phosphorus and soluble phosphorus in the Selke catchment (463 km2, central Germany) for the period 1994-1998 using regular grab-sampling approach (biweekly to monthly for nitrogen and phosphorus concentrations). Second, the model was successfully validated during the period 1999-2010 for discharge, nitrate-N, particulate-phosphorus and soluble-phosphorus (lowest NSE for soluble phosphorus load = 0.54). Results, showed that when additional sampling during the events with random grab-sampling approach was used (period 2011-2013), the hydrological model could reproduce only the nitrate-N and soluble phosphorus concentrations reasonably well. However, when additional sampling during the hydrological events was considered, the HYPE model could not represent the measured particulate phosphorus. This reflects the importance of suspended sediment during the hydrological events increasing the concentrations of particulate phosphorus. The HYPE model could reproduce the total phosphorus during the period 2011-2013 only when the sediment transport-related model parameters was re-identified again considering the automatic sampling during the high-flow conditions.

Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality

NASA Astrophysics Data System (ADS)

Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira

2013-05-01

Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

Biological indicators for monitoring water quality of MTF canals system

NASA Technical Reports Server (NTRS)

Sethi, S. L.

1975-01-01

Biological models, diversity indexes, were developed to predict environmental effects of NASA's Mississippi test facility (MTF) chemical operations on canal systems in the area. To predict the effects on local streams, a physical model of unpolluted streams was established. The model is fed by artesian well water free of background levels of pollutants. The species diversity and biota composition of unpolluted MTF stream was determined; resulting information will be used to form baseline data for future comparisons. Biological modeling was accomplished by adding controlled quantities or kinds of chemical pollutants and evaluating the effects of these chemicals on the biological life of the stream.

Coastal retreat and improved water quality mitigate losses of seagrass from sea level rise.

PubMed

Saunders, Megan I; Leon, Javier; Phinn, Stuart R; Callaghan, David P; O'Brien, Katherine R; Roelfsema, Chris M; Lovelock, Catherine E; Lyons, Mitchell B; Mumby, Peter J

2013-08-01

The distribution and abundance of seagrass ecosystems could change significantly over the coming century due to sea level rise (SLR). Coastal managers require mechanistic understanding of the processes affecting seagrass response to SLR to maximize their conservation and associated provision of ecosystem services. In Moreton Bay, Queensland, Australia, vast seagrass meadows supporting populations of sea turtles and dugongs are juxtaposed with the multiple stressors associated with a large and rapidly expanding human population. Here, the interactive effects of predicted SLR, changes in water clarity, and land use on future distributions of seagrass in Moreton Bay were quantified. A habitat distribution model of present day seagrass in relation to benthic irradiance and wave height was developed which correctly classified habitats in 83% of cases. Spatial predictions of seagrass and presence derived from the model and bathymetric data were used to initiate a SLR inundation model. Bathymetry was iteratively modified based on SLR and sedimentary accretion in seagrass to simulate potential seagrass habitat at 10 year time steps until 2100. The area of seagrass habitat was predicted to decline by 17% by 2100 under a scenario of SLR of 1.1 m. A scenario including the removal of impervious surfaces, such as roads and houses, from newly inundated regions, demonstrated that managed retreat of the shoreline could potentially reduce the overall decline in seagrass habitat to just 5%. The predicted reduction in area of seagrass habitat could be offset by an improvement in water clarity of 30%. Greater improvements in water clarity would be necessary for larger magnitudes of SLR. Management to improve water quality will provide present and future benefits to seagrasses under climate change and should be a priority for managers seeking to compensate for the effects of global change on these valuable habitats. © 2013 John Wiley & Sons Ltd.

Modelling the impacts of global change on concentrations of Escherichia coli in an urban river

NASA Astrophysics Data System (ADS)

Jalliffier-Verne, Isabelle; Leconte, Robert; Huaringa-Alvarez, Uriel; Heniche, Mourad; Madoux-Humery, Anne-Sophie; Autixier, Laurène; Galarneau, Martine; Servais, Pierre; Prévost, Michèle; Dorner, Sarah

2017-10-01

Discharges of combined sewer system overflows (CSOs) affect water quality in drinking water sources despite increasing regulation and discharge restrictions. A hydrodynamic model was applied to simulate the transport and dispersion of fecal contaminants from CSO discharges and to quantify the impacts of climate and population changes on the water quality of the river used as a drinking water source in Québec, Canada. The dispersion model was used to quantify Escherichia coli (E. coli) concentrations at drinking water intakes. Extreme flows during high and low water events were based on a frequency analysis in current and future climate scenarios. The increase of the number of discharges was quantified in current and future climate scenarios with regards to the frequency of overflows observed between 2009 and 2012. For future climate scenarios, effects of an increase of population were estimated according to current population growth statistics, independently of local changes in precipitation that are more difficult to predict than changes to regional scale hydrology. Under ;business-as-usual; scenarios restricting increases in CSO discharge frequency, mean E. coli concentrations at downstream drinking water intakes are expected to increase by up to 87% depending on the future climate scenario and could lead to changes in drinking water treatment requirements for the worst case scenarios. The greatest uncertainties are related to future local discharge loads. Climate change adaptation with regards to drinking water quality must focus on characterizing the impacts of global change at a local scale. Source water protection planning must consider the impacts of climate and population change to avoid further degradation of water quality.

Potential health implications of water resources depletion and sewage discharges in the Republic of Macedonia.

PubMed

Hristovski, Kiril D; Pacemska-Atanasova, Tatjana; Olson, Larry W; Markovski, Jasmina; Mitev, Trajce

2016-08-01

Potential health implications of deficient sanitation infrastructure and reduced surface water flows due to climate change are examined in the case study of the Republic of Macedonia. Changes in surface water flows and wastewater discharges over the period 1955-2013 were analyzed to assess potential future surface water contamination trends. Simple model predictions indicated a decline in surface water hydrology over the last half century, which caused the surface waters in Macedonia to be frequently dominated by >50% of untreated sewage discharges. The surface water quality deterioration is further supported by an increasing trend in modeled biochemical oxygen demand trends, which correspond well with the scarce and intermittent water quality data that are available. Facilitated by the climate change trends, the increasing number of severe weather events is already triggering flooding of the sewage-dominated rivers into urban and non-urban areas. If efforts to develop a comprehensive sewage collection and treatment infrastructure are not implemented, such events have the potential to increase public health risks and cause epidemics, as in the 2015 case of a tularemia outbreak.

iSPUW: integrated sensing and prediction of urban water for sustainable cities

NASA Astrophysics Data System (ADS)

Noh, S. J.; Nazari, B.; Habibi, H.; Norouzi, A.; Nabatian, M.; Seo, D. J.; Bartos, M. D.; Kerkez, B.; Lakshman, L.; Zink, M.; Lee, J.

2016-12-01

Many cities face tremendous water-related challenges in this Century of the City. Urban areas are particularly susceptible not only to excesses and shortages of water but also to impaired water quality. To addresses these challenges, we synergistically integrate advances in computing and cyber-infrastructure, environmental modeling, geoscience, and information science to develop integrative solutions for urban water challenges. In this presentation, we describe the various efforts that are currently ongoing in the Dallas-Fort Worth Metroplex (DFW) area for iSPUW: real-time high-resolution flash flood forecasting, inundation mapping for large urban areas, crowdsourcing of water observations in urban areas, real-time assimilation of crowdsourced observations for street and river flooding, integrated control of lawn irrigation and rainwater harvesting for water conservation and stormwater management, feature mining with causal discovery for flood prediction, and development of the Arlington Urban Hydroinformatics Testbed. Analyzed is the initial data of sensor network for water level and lawn monitoring, and cellphone applications for crowdsourcing flood reports. New data assimilation approaches to deal with categorical and continuous observations are also evaluated via synthetic experiments.

Use of predictive models and rapid methods to nowcast bacteria levels at coastal beaches

USGS Publications Warehouse

Francy, Donna S.

2009-01-01

The need for rapid assessments of recreational water quality to better protect public health is well accepted throughout the research and regulatory communities. Rapid analytical methods, such as quantitative polymerase chain reaction (qPCR) and immunomagnetic separation/adenosine triphosphate (ATP) analysis, are being tested but are not yet ready for widespread use.Another solution is the use of predictive models, wherein variable(s) that are easily and quickly measured are surrogates for concentrations of fecal-indicator bacteria. Rainfall-based alerts, the simplest type of model, have been used by several communities for a number of years. Deterministic models use mathematical representations of the processes that affect bacteria concentrations; this type of model is being used for beach-closure decisions at one location in the USA. Multivariable statistical models are being developed and tested in many areas of the USA; however, they are only used in three areas of the Great Lakes to aid in notifications of beach advisories or closings. These “operational” statistical models can result in more accurate assessments of recreational water quality than use of the previous day's Escherichia coli (E. coli)concentration as determined by traditional culture methods. The Ohio Nowcast, at Huntington Beach, Bay Village, Ohio, is described in this paper as an example of an operational statistical model. Because predictive modeling is a dynamic process, water-resource managers continue to collect additional data to improve the predictive ability of the nowcast and expand the nowcast to other Ohio beaches and a recreational river. Although predictive models have been shown to work well at some beaches and are becoming more widely accepted, implementation in many areas is limited by funding, lack of coordinated technical leadership, and lack of supporting epidemiological data.

Application of the target lipid model and passive samplers to characterize the toxicity of bioavailable organics in oil sands process-affected water.

PubMed

Redman, Aaron D; Parkerton, Thomas F; Butler, Josh David; Letinski, Daniel J; Frank, Richard A; Hewitt, L Mark; Bartlett, Adrienne J; Gillis, Patricia Leigh; Marentette, Julie R; Parrott, Joanne L; Hughes, Sarah A; Guest, Rodney; Bekele, Asfaw; Zhang, Kun; Morandi, Garrett; Wiseman, Steve B; Giesy, John P

2018-06-14

Oil sand operations in Alberta, Canada will eventually include returning treated process-affected waters to the environment. Organic constituents in oil sand process-affected water (OSPW) represent complex mixtures of nonionic and ionic (e.g. naphthenic acids) compounds, and compositions can vary spatially and temporally, which has impeded development of water quality benchmarks. To address this challenge, it was hypothesized that solid phase microextraction fibers coated with polydimethylsiloxane (PDMS) could be used as a biomimetic extraction (BE) to measure bioavailable organics in OSPW. Organic constituents of OSPW were assumed to contribute additively to toxicity, and partitioning to PDMS was assumed to be predictive of accumulation in target lipids, which were the presumed site of action. This method was tested using toxicity data for individual model compounds, defined mixtures, and organic mixtures extracted from OSPW. Toxicity was correlated with BE data, which supports the use of this method in hazard assessments of acute lethality to aquatic organisms. A species sensitivity distribution (SSD), based on target lipid model and BE values, was similar to SSDs based on residues in tissues for both nonionic and ionic organics. BE was shown to be an analytical tool that accounts for bioaccumulation of organic compound mixtures from which toxicity can be predicted, with the potential to aid in the development of water quality guidelines.

A formal approach for the prediction of the critical heat flux in subcooled water

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lombardi, C.

1995-09-01

The critical heat flux (CHF) in subcooled water at high mass fluxes are not yet satisfactory correlated. For this scope a formal approach is here followed, which is based on an extension of the parameters and the correlation used for the dryout prediction for medium high quality mixtures. The obtained correlation, in spite of its simplicity and its explicit form, yields satisfactory predictions, also when applied to more conventional CHF data at low-medium mass fluxes and high pressures. Further improvements are possible, if a more complete data bank will be available. The main and general open item is the definitionmore » of a criterion, depending only on independent parameters, such as mass flux, pressure, inlet subcooling and geometry, to predict whether the heat transfer crisis will result as a DNB or a dryout phenomenon.« less

Artificial neural network modeling of dissolved oxygen in reservoir.

PubMed

Chen, Wei-Bo; Liu, Wen-Cheng

2014-02-01

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

NASA Astrophysics Data System (ADS)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with ARMA(1,2) errors were fit to the observations. Preliminary model validation exercises at a 30-day forecast horizon show that the ARMA error models generally improve the predictive skill of the linear regression rating curves. Skill seems to vary based on the ambient hydrologic conditions at the onset of the forecast. For example, ARMA error model forecasts issued before a high flow/turbidity event do not show significant improvements over the rating curve approach. However, ARMA error model forecasts issued during the "falling limb" of the hydrograph are significantly more accurate than rating curves for both single day and accumulated event predictions. In order to assist in reservoir operations decisions associated with turbidity events and general water supply reliability, DEP has initiated design of an Operations Support Tool (OST). OST integrates a reservoir operations model with 2D hydrodynamic water quality models and a database compiling near-real-time data sources and hydrologic forecasts. Currently, OST uses conventional flow-turbidity rating curves and hydrologic forecasts for predictive turbidity inputs. Given the improvements in predictive skill over traditional rating curves, the ARMA error models are currently being evaluated as an addition to DEP's Operations Support Tool.

Use of the Water, Energy, and Biogeochemical Model (WEBMOD) to Simulate Water Quality at Five U.S. Geological Survey Research Watersheds

NASA Astrophysics Data System (ADS)

Webb, R. M.; Leavesley, G. H.; Shanley, J. B.; Peters, N. E.; Aulenbach, B. T.; Blum, A. E.; Campbell, D. H.; Clow, D. W.; Mast, M. A.; Stallard, R. F.; Larsen, M. C.; Troester, J. W.; Walker, J. F.; White, A. F.

2003-12-01

The Water, Energy, and Biogeochemical Model (WEBMOD) was developed as an aid to compare and contrast basic hydrologic and biogeochemical processes active in the diverse hydroclimatic regions represented by the five U.S. Geological Survey (USGS) Water, Energy, and Biogeochemical Budget (WEBB) sites: Loch Vale, Colorado; Trout Lake, Wisconsin; Sleepers River, Vermont; Panola Mountain, Georgia; and Luquillo Experimental Forest, Puerto Rico. WEBMOD simulates solute concentrations for vegetation canopy, snow pack, impermeable ground, leaf litter, unsaturated and saturated soil zones, riparian zones and streams using selected process modules coupled within the USGS Modular Modeling System (MMS). Source codes for the MMS hydrologic modules include the USGS Precipitation Runoff Modeling System, the National Weather Service Hydro-17 snow model, and TOPMODEL. The hydrologic modules distribute precipitation and temperature to predict evapotranspiration, snow accumulation, snow melt, and streamflow. Streamflow generation mechanisms include infiltration excess, saturated overland flow, preferential lateral flow, and base flow. Input precipitation chemistry, and fluxes and residence times predicted by the hydrologic modules are input into the geochemical module where solute concentrations are computed for a series of discrete well-mixed reservoirs using calls to the geochemical engine PHREEQC. WEBMOD was used to better understand variations in water quality observed at the WEBB sites from October 1991 through September 1997. Initial calibrations were completed by fitting the simulated hydrographs with those measured at the watershed outlets. Model performance was then refined by comparing the predicted export of conservative chemical tracers such as chloride, with those measured at the watershed outlets. The model succeeded in duplicating the temporal variability of net exports of major ions from the watersheds.

Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.

PubMed

Black, L E; Brion, G M; Freitas, S J

2007-06-01

Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.

Effects of future climate and land use scenarios on riverine source water quality.

PubMed

Delpla, Ianis; Rodriguez, Manuel J

2014-09-15

Surface water quality is particularly sensitive to land use practices and climatic events that affect its catchment. The relative influence of a set of watershed characteristics (climate, land use, morphology and pedology) and climatic variables on two key water quality parameters (turbidity and fecal coliforms (FC)) was examined in 24 eastern Canadian catchments at various spatial scales (1 km, 5 km, 10 km and the entire catchment). A regression analysis revealed that the entire catchment was a better predictor of water quality. Based on this information, linear mixed effect models for predicting turbidity and FC levels were developed. A set of land use and climate scenarios was considered and applied within the water quality models. Four land use scenarios (no change, same rate of variation, optimistic and pessimistic) and three climate change scenarios (B1, A1B and A2) were tested and variations for the near future (2025) were assessed and compared to the reference period (2000). Climate change impacts on water quality remained low annually for this time horizon (turbidity: +1.5%, FC: +1.6%, A2 scenario). On the other hand, the influence of land use changes appeared to predominate. Significant benefits for both parameters could be expected following the optimistic scenario (turbidity: -16.4%, FC: -6.3%; p < 0.05). However, pessimistic land use scenario led to significant increases on an annual basis (turbidity: +11.6%, FC: +15.2%; p < 0.05). Additional simulations conducted for the late 21st century (2090) revealed that climate change impacts could become equivalent to those modeled for land use for this horizon. Copyright © 2014 Elsevier B.V. All rights reserved.

Inverse modeling with RZWQM2 to predict water quality

USDA-ARS?s Scientific Manuscript database

Agricultural systems models such as RZWQM2 are complex and have numerous parameters that are unknown and difficult to estimate. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals...

EVALUATION OF TWO METHODS FOR PREDICTION OF BIOACCUMULATION FACTORS

EPA Science Inventory

Two methods for deriving bioaccumulation factors (BAFs) used by the U.S. Environmental Protection Agency (EPA) in development of water quality criteria were evaluated using polychlorinated biphenyls (PCB) data from the Hudson River and Green Bay ecosystems. Greater than 90% of th...

Numerical modeling of overland flow due to rainfall-runoff

USDA-ARS?s Scientific Manuscript database

Runoff is a basic hydrologic process that can be influenced by management activities in agricultural watersheds. Better description of runoff patterns through modeling will help to understand and predict watershed sediment transport and water quality. Normally, runoff is studied with kinematic wave ...

QUANTIFYING SPATIAL POSITION OF WETLANDS FOR STREAM HABITAT QUALITY PREDICTION

EPA Science Inventory

A watershed's capacity to store and filter water, and the resulting effects on the hydrologic regine, is a key forcing function for insteam processes and community structure. However, methods for describing wetland position have traditionally been qualitative. A Geographic Info...

ASSESSING THE IMPACT OF ENVIRONMENTAL STRESSORS ON MACROINVERTEBRATE INDICATORS IN OHIO

EPA Science Inventory

Macroinvertebrate indicators are used as assessment endpoints for surface water quality monitoring in Ohio. The purpose of this study is to explain and predict the impact of environmental stressors on macroinvertebrate communities as measured by the Ohio Environmental Protection...

A METHODOLOGY FOR ESTIMATING UNCERTAINTY OF A DISTRIBUTED HYDROLOGIC MODEL: APPLICATION TO POCONO CREEK WATERSHED

EPA Science Inventory

Utility of distributed hydrologic and water quality models for watershed management and sustainability studies should be accompanied by rigorous model uncertainty analysis. However, the use of complex watershed models primarily follows the traditional {calibrate/validate/predict}...

Application of Deep Learning and Supervised Learning Methods to Recognize Nonlinear Hidden Pattern in Water Stress Levels from Spatiotemporal Datasets across Rural and Urban US Counties

NASA Astrophysics Data System (ADS)

Eisenhart, T.; Josset, L.; Rising, J. A.; Devineni, N.; Lall, U.

2017-12-01

In the wake of recent water crises, the need to understand and predict the risk of water stress in urban and rural areas has grown. This understanding has the potential to improve decision making in public resource management, policy making, risk management and investment decisions. Assuming an underlying relationship between urban and rural water stress and observable features, we apply Deep Learning and Supervised Learning models to uncover hidden nonlinear patterns from spatiotemporal datasets. Results of interest includes prediction accuracy on extreme categories (i.e. urban areas highly prone to water stress) and not solely the average risk for urban or rural area, which adds complexity to the tuning of model parameters. We first label urban water stressed counties using annual water quality violations and compile a comprehensive spatiotemporal dataset that captures the yearly evolution of climatic, demographic and economic factors of more than 3,000 US counties over the 1980-2010 period. As county-level data reporting is not done on a yearly basis, we test multiple imputation methods to get around the issue of missing data. Using Python libraries, TensorFlow and scikit-learn, we apply and compare the ability of, amongst other methods, Recurrent Neural Networks (testing both LSTM and GRU cells), Convolutional Neural Networks and Support Vector Machines to predict urban water stress. We evaluate the performance of those models over multiple time spans and combine methods to diminish the risk of overfitting and increase prediction power on test sets. This methodology seeks to identify hidden nonlinear patterns to assess the predominant data features that influence urban and rural water stress. Results from this application at the national scale will assess the performance of deep learning models to predict water stress risk areas across all US counties and will highlight a predominant Machine Learning method for modeling water stress risk using spatiotemporal data.

WetDATA Hub: Democratizing Access to Water Data to Accelerate Innovation through Data Visualization, Predictive Analytics and Artificial Intelligence Applications

NASA Astrophysics Data System (ADS)

Sarni, W.

2017-12-01

Water scarcity and poor quality impacts economic development, business growth, and social well-being. Water has become, in our generation, the foremost critical local, regional, and global issue of our time. Despite these needs, there is no water hub or water technology accelerator solely dedicated to water data and tools. There is a need by the public and private sectors for vastly improved data management and visualization tools. This is the WetDATA opportunity - to develop a water data tech hub dedicated to water data acquisition, analytics, and visualization tools for informed policy and business decisions. WetDATA's tools will help incubate disruptive water data technologies and accelerate adoption of current water data solutions. WetDATA is a Colorado-based (501c3), global hub for water data analytics and technology innovation. WetDATA's vision is to be a global leader in water information, data technology innovation and collaborate with other US and global water technology hubs. ROADMAP * Portal (www.wetdata.org) to provide stakeholders with tools/resources to understand related water risks. * The initial activities will provide education, awareness and tools to stakeholders to support the implementation of the Colorado State Water Plan. * Leverage the Western States Water Council Water Data Exchange database. * Development of visualization, predictive analytics and AI tools to engage with stakeholders and provide actionable data and information. TOOLS Education: Provide information on water issues and risks at the local, state, national and global scale. Visualizations: Development of data analytics and visualization tools based upon the 2030 Water Resources Group methodology to support the implementation of the Colorado State Water Plan. Predictive Analytics: Accessing publically available water databases and using machine learning to develop water availability forecasting tools, and time lapse images to support city / urban planning.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Application of Water Quality Model of Jordan River to Evaluate Climate Change Effects on Eutrophication

NASA Astrophysics Data System (ADS)

Van Grouw, B.

2016-12-01

The Jordan River is a 51 mile long freshwater stream in Utah that provides drinking water to more than 50% of Utah's population. The various point and nonpoint sources introduce an excess of nutrients into the river. This excess induces eutrophication that results in an inhabitable environment for aquatic life is expected to be exacerbated due to climate change. Adaptive measures must be evaluated based on predictions of climate variation impacts on eutrophication and ecosystem processes in the Jordan River. A Water Quality Assessment Simulation Program (WASP) model was created to analyze the data results acquired from a Total Maximum Daily Load (TMDL) study conducted on the Jordan River. Eutrophication is modeled based on levels of phosphates and nitrates from point and nonpoint sources, temperature, and solar radiation. It will simulate the growth of phytoplankton and periphyton in the river. This model will be applied to assess how water quality in the Jordan River is affected by variations in timing and intensity of spring snowmelt and runoff during drought in the valley and the resulting effects on eutrophication in the river.

Water Quality Improvement through Reductions of Pollutant Loads on Small Scale of Bioretention System

NASA Astrophysics Data System (ADS)

Elyza Muha, Norshafa; Mohd Sidek, Lariyah; Jajarmizadeh, Milad

2016-03-01

Bioretention system is introduced as an important topic namely Urban Storm Water Management Manual for Malaysia (MSMA) by the Department of Irrigation and Drainage Malaysia (DID) in May 2012. The main objective of this paper is to evaluate the performance of water quality for small scale bioretention system under tropical climate via MUSIC model. Two bioretention systems 1 and 2 are observed based on the difference media depth. The result of bioretention system is compared with a reference model which has infrastructure with Urban Stormwater Improvement Conceptualisation (MUSIC) for pollutants load reduction and water quality results. Assessment of results via MUSIC software indicates a significant percentage of reduction for Total Suspended Solid (TSS), Total Phosphorus (TP) and Total Nitrogen (TN). The prediction of pollutant reduction via using MUSIC has the harmony for requirement in MSMA. TSS pollutant reduction is more than 80%, while for TP and TN more than 50%. The outcome of this study can be helpful for improvement of the existing MSMA guidelines for application of bioretention systems in Malaysia.

Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6

USGS Publications Warehouse

Nevers, Meredith; Byappanahalli, Muruleedhara; Phanikumar, Mantha S.; Whitman, Richard L.

2016-01-01

Mathematical models have been widely applied to surface waters to estimate rates of settling, resuspension, flow, dispersion, and advection in order to calculate movement of particles that influence water quality. Of particular interest are the movement, survival, and persistence of microbial pathogens or their surrogates, which may contaminate recreational water, drinking water, or shellfish. Most models devoted to microbial water quality have been focused on fecal indicator organisms (FIO), which act as a surrogate for pathogens and viruses. Process-based modeling and statistical modeling have been used to track contamination events to source and to predict future events. The use of these two types of models require different levels of expertise and input; process-based models rely on theoretical physical constructs to explain present conditions and biological distribution while data-based, statistical models use extant paired data to do the same. The selection of the appropriate model and interpretation of results is critical to proper use of these tools in microbial source tracking. Integration of the modeling approaches could provide insight for tracking and predicting contamination events in real time. A review of modeling efforts reveals that process-based modeling has great promise for microbial source tracking efforts; further, combining the understanding of physical processes influencing FIO contamination developed with process-based models and molecular characterization of the population by gene-based (i.e., biological) or chemical markers may be an effective approach for locating sources and remediating contamination in order to protect human health better.

Copper bioavailability and toxicity to Mytilus galloprovincialis in Shelter Island Yacht Basin, San Diego, CA.

PubMed

Bosse, Casey; Rosen, Gunther; Colvin, Marienne; Earley, Patrick; Santore, Robert; Rivera-Duarte, Ignacio

2014-08-15

The bioavailability and toxicity of copper (Cu) in Shelter Island Yacht Basin (SIYB), San Diego, CA, USA, was assessed with simultaneous toxicological, chemical, and modeling approaches. Toxicological measurements included laboratory toxicity testing with Mytilus galloprovincialis (Mediterranean mussel) embryos added to both site water (ambient) and site water spiked with multiple Cu concentrations. Chemical assessment of ambient samples included total and dissolved Cu concentrations, and Cu complexation capacity measurements. Modeling was based on chemical speciation and predictions of bioavailability and toxicity using a marine Biotic Ligand Model (BLM). Cumulatively, these methods assessed the natural buffering capacity of Cu in SIYB during singular wet and dry season sampling events. Overall, the three approaches suggested negligible bioavailability, and isolated observed or predicted toxicity, despite an observed gradient of increasing Cu concentration, both horizontally and vertically within the water body, exceeding current water quality criteria for saltwater. Published by Elsevier Ltd.

Appraisal of long term groundwater quality of peninsular India using water quality index and fractal dimension

NASA Astrophysics Data System (ADS)

Rawat, Kishan Singh; Singh, Sudhir Kumar; Jacintha, T. German Amali; Nemčić-Jurec, Jasna; Tripathi, Vinod Kumar

2017-12-01

A review has been made to understand the hydrogeochemical behaviour of groundwater through statistical analysis of long term water quality data (year 2005-2013). Water Quality Index ( WQI), descriptive statistics, Hurst exponent, fractal dimension and predictability index were estimated for each water parameter. WQI results showed that majority of samples fall in moderate category during 2005-2013, but monitoring site four falls under severe category (water unfit for domestic use). Brownian time series behaviour (a true random walk nature) exists between calcium (Ca^{2+}) and electric conductivity (EC); magnesium (Mg^{2+}) with EC; sodium (Na+) with EC; sulphate (SO4^{2-}) with EC; total dissolved solids (TDS) with chloride (Cl-) during pre- (2005-2013) and post- (2006-2013) monsoon season. These parameters have a closer value of Hurst exponent ( H) with Brownian time series behaviour condition (H=0.5). The result of times series analysis of water quality data shows a persistent behaviour (a positive autocorrelation) that has played a role between Cl- and Mg^{2+}, Cl- and Ca^{2+}, TDS and Na+, TDS and SO4^{2-}, TDS and Ca^{2+} in pre- and post-monsoon time series because of the higher value of H (>1). Whereas an anti-persistent behaviour (or negative autocorrelation) was found between Cl- and EC, TDS and EC during pre- and post-monsoon due to low value of H. The work outline shows that the groundwater of few areas needs treatment before direct consumption, and it also needs to be protected from contamination.

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Integrating Predictive Modeling with Control System Design for Managed Aquifer Recharge and Recovery Applications

NASA Astrophysics Data System (ADS)

Drumheller, Z. W.; Regnery, J.; Lee, J. H.; Illangasekare, T. H.; Kitanidis, P. K.; Smits, K. M.

2014-12-01

Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization led to reduced natural recharge rates and overuse. Scientists and engineers have begun to re-investigate the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. MAR systems offer the possibility of naturally increasing groundwater storage while improving the quality of impaired water used for recharge. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. Our project seeks to ease the operational challenges of MAR facilities through the implementation of active sensor networks, adaptively calibrated flow and transport models, and simulation-based meta-heuristic control optimization methods. The developed system works by continually collecting hydraulic and water quality data from a sensor network embedded within the aquifer. The data is fed into an inversion algorithm, which calibrates the parameters and initial conditions of a predictive flow and transport model. The calibrated model is passed to a meta-heuristic control optimization algorithm (e.g. genetic algorithm) to execute the simulations and determine the best course of action, i.e., the optimal pumping policy for current aquifer conditions. The optimal pumping policy is manually or autonomously applied. During operation, sensor data are used to assess the accuracy of the optimal prediction and augment the pumping strategy as needed. At laboratory-scale, a small (18"H x 46"L) and an intermediate (6'H x 16'L) two-dimensional synthetic aquifer were constructed and outfitted with sensor networks. Data collection and model inversion components were developed and sensor data were validated by analytical measurements.

Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

NASA Astrophysics Data System (ADS)

Theologou, I.; Patelaki, M.; Karantzalos, K.

2015-04-01

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

Methods for estimating monthly mean concentrations of selected water-quality constituents for stream sites in the Red River of the North basin, North Dakota and Minnesota

USGS Publications Warehouse

Guenthner, R.S.

1991-01-01

Future development of the Garrison Diversion Unit may divert water from the Missouri River into the Sheyenne River and the Red River of the North for municipal and industrial use. The U.S. Bureau of Reclamation's Canals, Rivers, and Reservoirs Salinity Accounting Procedures model can be used to predict the effect various operating plans could have on water quality in the Sheyenne River and the Red River of the North. The model uses, as Input, monthly means of streamflow and selected water-quality constituents for a 54-year period at 28 nodes on the Sheyenne River and the Red River of the North. This report provides methods for estimating monthly mean concentrations of selected water-quality constituents that can be used for input to and calibration of the salinity model.Mater-quality data for 32 gaging stations can be used to define selected water-quality characteristics at the 28 model nodes. Materquality data were retrieved from the U.S. Geological Survey's National Mater Data Storage and Retrieval System data base and statistical summaries were prepared. The frequency of water-quality data collection at the gaging stations is inadequate to define monthly mean concentrations of the individual water-quality constituents for all months for the 54-year period; therefore, methods for estimating monthly mean concentrations were developed. Relations between selected water-quality constituents [dissolved solids, hardness (as CaCO3), sodium, sulfate, and chloride] and streamflow were developed as the primary method to estimate monthly mean concentrations. Relations between specific conductance and streamflow and relations between selected water-quality constituents [dissolved solids, hardness (as CaCO3), sodium, sulfate, and chloride] and specific conductance were developed so that a cascaded-regression relation could be developed as a second method of estimating monthly mean concentrations and, thus, utilize a large specific-conductance data base. Information about the quantity and the quality of ground water discharging to the Sheyenne River is needed for model input for reaches of the river where ground water accounts for a substantial part of streamflow during periods of low flow. Ground-water discharge was identified for two reaches of the Sheyenne River. Ground-water discharge to the Sheyenne River in the vicinity of Warwick, N.Dak., was about 14.8 cubic feet per second and the estimated dissolved-solids concentration was about 441 milligrams per liter during October 15 and 16, 1986. Ground-water discharge to the Sheyenne River in a reach between Lisbon and Kindred, N.Dak., ranged from an average of 25.3 cubic feet per second during September 13 to November 19, 1963, to about 45.0 cubic feet per second during October 21 and 22, 1986. Dissolved-solids concentration was estimated at about 442 milligrams per liter during October 21 and 22, 1986.

Prediction of Groundwater Quality Trends Resulting from Anthropogenic Changes in Southeast Florida.

PubMed

Yi, Quanghee; Stewart, Mark

2018-01-01

The effects of surface water flow system changes caused by constructing water-conservation areas and canals in southeast Florida on groundwater quality under the Atlantic Coastal Ridge was investigated with numerical modeling. Water quality data were used to delineate a zone of groundwater with low total dissolved solids (TDS) within the Biscayne aquifer under the ridge. The delineated zone has the following characteristics. Its location generally coincides with an area where the Biscayne aquifer has high transmissivities, corresponds to a high recharge area of the ridge, and underlies a part of the groundwater mound formed under the ridge prior to completion of the canals. This low TDS groundwater appears to be the result of pre-development conditions rather than seepage from the canals constructed after the 1950s. Numerical simulation results indicate that the time for low TDS groundwater under the ridge to reach equilibrium with high TDS surface water in the water-conservation areas and Everglades National Park are approximately 70 and 60 years, respectively. The high TDS groundwater would be restricted to the water-conservation areas and the park due to its slow eastward movement caused by small hydraulic gradients in Rocky Glades and its mixing with the low TDS groundwater under the high-recharge area of the ridge. The flow or physical boundary conditions such as high recharge rates or low hydraulic conductivity layers may affect how the spatial distribution of groundwater quality in an aquifer will change when a groundwater flow system reaches equilibrium with an associated surface water flow system. © 2017, National Ground Water Association.

Wastewater quality monitoring system using sensor fusion and machine learning techniques.

PubMed

Qin, Xusong; Gao, Furong; Chen, Guohua

2012-03-15

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Linking catchment characteristics and water chemistry with the ecological status of Irish rivers.

PubMed

Donohue, Ian; McGarrigle, Martin L; Mills, Paul

2006-01-01

Requirements of the EU Water Framework Directive for the introduction of ecological quality objectives for surface waters and the stipulation that all surface waters in the EU must be of 'good' ecological status by 2015 necessitate a quantitative understanding of the linkages among catchment attributes, water chemistry and the ecological status of aquatic ecosystems. Analysis of lotic ecological status, as indicated by an established biotic index based primarily on benthic macroinvertebrate community structure, of 797 hydrologically independent river sites located throughout Ireland showed highly significant inverse associations between the ecological status of rivers and measures of catchment urbanisation and agricultural intensity, densities of humans and cattle and chemical indicators of water quality. Stepwise logistic regression suggested that urbanisation, arable farming and extent of pasturelands are the principal factors impacting on the ecological status of streams and rivers in Ireland and that the likelihood of a river site complying with the demands of the EU Water Framework Directive, and be of 'good' ecological status, can be predicted with reasonable accuracy using simple models that utilise either widely available landcover data or chemical monitoring data. Non-linear landcover and chemical 'thresholds' derived from these models provide a useful tool in the management of risk in catchments, and suggest strongly that more careful planning of land use in Ireland is essential in order to restore and maintain water quality as required by the Directive.

Simulating Streamflow and Dissolved Organic Matter Export from small Forested Watersheds

NASA Astrophysics Data System (ADS)

Xu, N.; Wilson, H.; Saiers, J. E.

2010-12-01

Coupling the rainfall-runoff process and solute transport in catchment models is important for understanding the dynamics of water-quality-relevant constituents in a watershed. To simulate the hydrologic and biogeochemical processes in a parametrically parsimonious way remains challenging. The purpose of this study is to quantify the export of water and dissolved organic matter (DOM) from a forested catchment by developing and testing a coupled model for rainfall-runoff and soil-water flushing of DOM. Natural DOM plays an important role in terrestrial and aquatic systems by affecting nutrient cycling, contaminant mobility and toxicity, and drinking water quality. Stream-water discharge and DOM concentrations were measured in a first-order stream in Harvard Forest, Massachusetts. These measurements show that stream water DOM concentrations are greatest during hydrologic events induced by rainfall or snowmelt and decline to low, steady levels during periods of baseflow. Comparison of the stream-discharge data to calculations of a simple rainfall-runoff model reveals a hysteretic relationship between stream-flow rates and the storage of water within the catchment. A modified version of the rainfall-runoff model that accounts for hysteresis in the storage-discharge relationship in a parametrically simple way is capable of describing much, but not all, of the variation in the time-series data on stream discharge. Our ongoing research is aimed at linking the new rainfall-runoff formulation with coupled equations that predict soil-flushing and stream-water concentrations of DOM as functions of the temporal change in catchment water storage. This model will provide a predictive tool for examining how changes in climatic variables would affect the runoff generation and DOM fluxes from terrestrial landscape.

[Expected epidemic risk from the minimum drinking water content of protozoan agents of enteric parasitic diseases].

PubMed

Novosil'tsev, G I; Chernyshenko, A I; Rusanova, N A; Gracheva, M N; Mel'nikova, L I

2010-01-01

The paper presents information on lambliasis and cryptosporidiosis outbreaks associated with drinking water contamination-associated. It discusses a risk for the emergence of mass outbreaks of lambliasis and cryptosporidiosis among the population of the municipalities of administrative district centers and other human settlements, which are to exercise sanitary and parasitological control over the quality of water of its centralized drinking supply. The significance of this water contamination by lamblia cysts and cryptosporidium oocysts is considered. Calculations are given to predict an epidemic risk and possible ways of its prevention.

Using Hyperspectral Remote Sensing Models to Determine Phytoplankton Density in the Coastal Waters of Long Bay, South Carolina

NASA Astrophysics Data System (ADS)

Harrington, J. E.; Ali, K.

2013-12-01

The southeast coastal region is one of the fastest growing regions in the United States and the increasing utilization of open water bodies has led to the deterioration of water quality and aquatic ecology, placing the future of these resources at risk. In coastal zones, a key index that can be used to assess the stress on the environment is the water quality. The shallow nearshore waters of Long Bay, South Carolina (SC) are heavily influenced by multiple biogeochemical constituents or color producing agents (CPAs) such as, phytoplankton, suspend matter, and dissolved organic carbon. The interaction of the various chemical, biological and physical components gives rise to the optical complexity observed in the coastal waters producing turbid waters. Ecological stress on these environments is reflected by the increase in the frequency and severity of Harmful Algal Blooms (HABs), a prime agent of water quality deterioration, including foul odors and tastes, deoxygenation of bottom waters (hypoxia), toxicity, fish kills, and food web alterations. These are of great concern for human health and are detrimental to the marine life. Therefore, efficient monitoring tools are required for early detection and forecasting purposes as well as to understand the state of the conditions and better protect, manage and address the question of how various natural and anthropogenic factors affect the health of these environments. This study assesses the efficiency remote sensing as a potential tool for accurate and timely detection of HABs, as well as for providing high spatial and temporal resolution information regarding the biogeodynamics in coastal water bodies. Existing blue-green and NIR-red based remote sensing algorithms are applied to the reflectance data obtained using ASD spectroradiometer to predict the amount of chlorophyll, an independent of other associated CPAs in the Long Bay waters. The pigment is the primary light harvesting pigment in all phytoplankton and is used as an index for the estimation of phytoplankton density. Efficiency of the algorithms were evaluated through a least squares regression and residual analysis. Results show that for prediction models of chlorophyll a concentrations, the Oc4v4 by Reilly et al (2000), two -band blue-green empirical algorithm yielded coefficients of determination as high as 0.64 with RMSE=0.29μg/l for an aggregated dataset (n=62, P<0.05). The NIR-red -based two-band algorithm by Dekker et al. (1993) and Gitelson et al. (2000) gave the best chlorophyll a prediction model, with R2 =0.79, RMSE=0.19μg/l. The results illustrate the potential of remote sensing in accounting for the chlorophyll a variability in the turbid waters of Long Bay, SC.

Predicting the impact of logging activities on soil erosion and water quality in steep, forested tropical islands

NASA Astrophysics Data System (ADS)

Wenger, Amelia S.; Atkinson, Scott; Santini, Talitha; Falinski, Kim; Hutley, Nicholas; Albert, Simon; Horning, Ned; Watson, James E. M.; Mumby, Peter J.; Jupiter, Stacy D.

2018-04-01

Increasing development in tropical regions provides new economic opportunities that can improve livelihoods, but it threatens the functional integrity and ecosystem services provided by terrestrial and aquatic ecosystems when conducted unsustainably. Given the small size of many islands, communities may have limited opportunities to replace loss and damage to the natural resources upon which they depend for ecosystem service provisioning, thus heightening the need for proactive, integrated management. This study quantifies the effectiveness of management strategies, stipulated in logging codes-of-practice, at minimizing soil erosion and sediment runoff as clearing extent increases, using Kolombangara Island, Solomon Islands as a case study. Further, we examine the ability of erosion reduction strategies to maintain sustainable soil erosion rates and reduce potential downstream impacts to drinking water and environmental water quality. We found that increasing land clearing—even with best management strategies in place—led to unsustainable levels of soil erosion and significant impacts to downstream water quality, compromising the integrity of the land for future agricultural uses, consistent access to clean drinking water, and important downstream ecosystems. Our results demonstrate that in order to facilitate sustainable development, logging codes of practice must explicitly link their soil erosion reduction strategies to soil erosion and downstream water quality thresholds, otherwise they will be ineffective at minimizing the impacts of logging activities. The approach taken here to explicitly examine soil erosion rates and downstream water quality in relation to best management practices and increasing land clearing should be applied more broadly across a range of ecosystems to inform decision-making about the socioeconomic and environmental trade-offs associated with logging, and other types of land use change.

Hydrodynamic modelling of recreational water quality using Escherichia coli as an indicator of microbial contamination

NASA Astrophysics Data System (ADS)

Eregno, Fasil Ejigu; Tryland, Ingun; Tjomsland, Torulv; Kempa, Magdalena; Heistad, Arve

2018-06-01

Microbial contamination of recreational beaches is often at its worst after heavy rainfall events due to storm floods that carry fecal matter and other pollutants from the watershed. Similarly, overflows of untreated sewage from combined sewerage systems may discharge directly into coastal water or via rivers and streams. In order to understand the effect of rainfall events, wind-directions and tides on the recreational water quality, GEMSS, an integrated 3D hydrodynamic model was applied to assess the spreading of Escherichia coli (E. coli) at the Sandvika beaches, located in the Oslo fjord. The model was also used to theoretically investigate the effect of discharges from septic tanks from boats on the water quality at local beaches. The model make use of microbial decay rate as the main input representing the survival of microbial pathogens in the ocean, which vary widely depending on the type of pathogen and environmental stress. The predicted beach water quality was validated against observed data after a heavy rainfall event using Nash-Sutcliffe coefficient (E) and the overall result indicated that the model performed quite well and the simulation was in - good agreement with the observed E. coli concentrations for all beaches. The result of this study indicated that: 1) the bathing water quality was poor according to the EU bathing water directive up to two days after the heavy rainfall event depending on the location of the beach site. 2) The discharge from a boat at 300-meter distance to the beaches slightly increased the E. coli levels at the beaches. 3) The spreading of microbial pathogens from its source to the different beaches depended on the wind speed and the wind direction.

Predicting river travel time from hydraulic characteristics

USGS Publications Warehouse

Jobson, H.E.

2001-01-01

Predicting the effect of a pollutant spill on downstream water quality is primarily dependent on the water velocity, longitudinal mixing, and chemical/physical reactions. Of these, velocity is the most important and difficult to predict. This paper provides guidance on extrapolating travel-time information from one within bank discharge to another. In many cases, a time series of discharge (such as provided by a U.S. Geological Survey stream gauge) will provide an excellent basis for this extrapolation. Otherwise, the accuracy of a travel time extrapolation based on a resistance equation can be greatly improved by assuming the total flow area is composed of two parts, an active and an inactive area. For 60 reaches of 12 rivers with slopes greater than about 0.0002, travel times could be predicted to within about 10% by computing the active flow area using the Manning equation with n = 0.035 and assuming a constant inactive area for each reach. The predicted travel times were not very sensitive to the assumed values of bed slope or channel width.

Impacts of global change on the concentrations and dilution of combined sewer overflows in a drinking water source.

PubMed

Jalliffier-Verne, Isabelle; Leconte, Robert; Huaringa-Alvarez, Uriel; Madoux-Humery, Anne-Sophie; Galarneau, Martine; Servais, Pierre; Prévost, Michèle; Dorner, Sarah

2015-03-01

This study presents an analysis of climate change impacts on a large river located in Québec (Canada) used as a drinking water source. Combined sewer overflow (CSO) effluents are the primary source of fecal contamination of the river. An analysis of river flowrates was conducted using historical data and predicted flows from a future climate scenario. A spatio-temporal analysis of water quality trends with regard to fecal contamination was performed and the effects of changing flowrates on the dilution of fecal contaminants were analyzed. Along the river, there was a significant spatial trend for increasing fecal pollution downstream of CSO outfalls. Escherichia coli concentrations (upper 95th percentile) increased linearly from 2002 to 2012 at one drinking water treatment plant intake. Two critical periods in the current climate were identified for the drinking water intakes considering both potential contaminant loads and flowrates: local spring snowmelt that precedes river peak flow and extra-tropical storm events that occur during low flows. Regionally, climate change is expected to increase the intensity of the impacts of hydrological conditions on water quality in the studied basin. Based on climate projections, it is expected that spring snowmelt will occur earlier and extreme spring flowrates will increase and low flows will generally decrease. High and low flows are major factors related to the potential degradation of water quality of the river. However, the observed degradation of water quality over the past 10 years suggests that urban development and population growth may have played a greater role than climate. However, climate change impacts will likely be observed over a longer period. Source water protection plans should consider climate change impacts on the dilution of contaminants in addition to local land uses changes in order to maintain or improve water quality. Copyright © 2014 Elsevier B.V. All rights reserved.

Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA.

PubMed

Bhat, Shirish; Motz, Louis H; Pathak, Chandra; Kuebler, Laura

2015-01-01

A geostatistical method was applied to optimize an existing groundwater-level monitoring network in the Upper Floridan aquifer for the South Florida Water Management District in the southeastern United States. Analyses were performed to determine suitable numbers and locations of monitoring wells that will provide equivalent or better quality groundwater-level data compared to an existing monitoring network. Ambient, unadjusted groundwater heads were expressed as salinity-adjusted heads based on the density of freshwater, well screen elevations, and temperature-dependent saline groundwater density. The optimization of the numbers and locations of monitoring wells is based on a pre-defined groundwater-level prediction error. The newly developed network combines an existing network with the addition of new wells that will result in a spatial distribution of groundwater monitoring wells that better defines the regional potentiometric surface of the Upper Floridan aquifer in the study area. The network yields groundwater-level predictions that differ significantly from those produced using the existing network. The newly designed network will reduce the mean prediction standard error by 43% compared to the existing network. The adoption of a hexagonal grid network for the South Florida Water Management District is recommended to achieve both a uniform level of information about groundwater levels and the minimum required accuracy. It is customary to install more monitoring wells for observing groundwater levels and groundwater quality as groundwater development progresses. However, budget constraints often force water managers to implement cost-effective monitoring networks. In this regard, this study provides guidelines to water managers concerned with groundwater planning and monitoring.

Innovative Tools for Water Quality/Quantity Management: New York City's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Schaake, J. C.; Day, G. N.; Porter, J.; Sheer, D. P.; Pyke, G.

2011-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies more than 1 billion gallons of water per day to over 9 million customers. Recently, DEP has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. This presentation describes the technical structure of OST, including the underlying water supply and water quality models, data sources and database management, reservoir inflow forecasts, and the functionalities required to meet the needs of a diverse group of end users. OST is a major upgrade of DEP's current water supply - water quality model, developed to evaluate alternatives for controlling turbidity in NYC's Catskill reservoirs. While the current model relies on historical hydrologic and meteorological data, OST can be driven by forecasted future conditions. It will receive a variety of near-real-time data from a number of sources. OST will support two major types of simulations: long-term, for evaluating policy or infrastructure changes over an extended period of time; and short-term "position analysis" (PA) simulations, consisting of multiple short simulations, all starting from the same initial conditions. Typically, the starting conditions for a PA run will represent those for the current day and traces of forecasted hydrology will drive the model for the duration of the simulation period. The result of these simulations will be a distribution of future system states based on system operating rules and the range of input ensemble streamflow predictions. DEP managers will analyze the output distributions and make operation decisions using risk-based metrics such as probability of refill. Currently, in the developmental stages of OST, forecasts are based on antecedent hydrologic conditions and are statistical in nature. The statistical algorithm is a relatively simple and versatile, but lacks short-term skill critical for water quality and spill management. To improve short-term skill, OST will ultimately operate with meteorologically driven hydrologic forecasts provided by the National Weather Service (NWS). OST functionalities will support a wide range of DEP uses, including short term operational projections, outage planning and emergency management, operating rule development, and water supply planning. A core use of OST will be to inform reservoir management strategies to control and mitigate turbidity events while ensuring water supply reliability. OST will also allow DEP to manage its complex reservoir system to meet multiple objectives, including ecological flows, tailwater fisheries and recreational releases, and peak flow mitigation for downstream communities.

Integrated Water and Sanitation Risk Assessment and Modeling in the Upper Sonora River basin (Northwest, Mexico)

NASA Astrophysics Data System (ADS)

Mayer, A. S.; Robles-Morua, A.; Halvorsen, K. E.; Vivoni, E. R.; Auer, M. T.

2011-12-01

Studies that integrate human dimensions and the biophysical characteristics of watersheds are necessary to meet the challenge of sustainable water resources development. In this project, we integrated perspectives from sociology, hydrology, and environmental engineering to examine and suggest solutions for managing waterborne disease risks associated with wastewater contamination in the Sonora River basin (SRB), a semiarid rural basin in northwest Mexico. This research consisted of four sub-projects. First, we assessed the perceptions of risks associated with wastewater contamination of water resources in rural communities in the SRB through a series of semi-structured interviews Results from this study indicate that there are major differences in risk perceptions among health professionals, government officials, and lay citizens. Government officials and lay citizens tend to underestimate the severity of the problems related to water related risks. Second, a fully distributed hydrologic model was used to make streamflow predictions in the un-gauged SRB. Synthetic flows generated from the hydrologic model were used to evaluate pollutant transport processes associated with wastewater loadings to the Sonora River. The hydrologic model revealed that the high degree of spatio-temporal variability of runoff in the SRB is associated with links between runoff generation mechanisms and land-atmosphere interactions. Third, a surface water quality model was used to assess the impact of wastewater discharges and develop pathogen contamination indicators in two sites along the Sonora River. To parameterize the water quality model, pathogenic indicator loadings and removal rates were estimated, along with their uncertainty. Results from the water quality modeling show regions in the watershed that may be exceeding pathogenic standards, but also that uncertainty in model parameters requires a probabilistic approach for estimating risks. Finally, a workshop was conducted to explore the use of participatory modeling frameworks in less developed regions. Results indicate that respondents agreed strongly with the hydrologic and water quality modeling methodologies presented and considered the modeling results useful. Our results also show that participatory modeling approaches can have short term impacts as seen in the changes in water-related risk perceptions. In total, these projects revealed that water resources management solutions need to take into account variations across the human landscape (i.e. risk perceptions) and variations in the biophysical response of watersheds to natural phenomena (i.e. streamflow generation) and to anthropogenic activities (i.e. contaminant fate and transport). In addition, this work underscores the notion that sustainable water resources solutions need to contend with uncertainty in our understanding and predictions of human perceptions and biophysical systems.

Simple estimate of entrainment rate of pollutants from a coastal discharge into the surf zone.

PubMed

Wong, Simon H C; Monismith, Stephen G; Boehm, Alexandria B

2013-10-15

Microbial pollutants from coastal discharges can increase illness risks for swimmers and cause beach advisories. There is presently no predictive model for estimating the entrainment of pollution from coastal discharges into the surf zone. We present a novel, quantitative framework for estimating surf zone entrainment of pollution at a wave-dominant open beach. Using physical arguments, we identify a dimensionless parameter equal to the quotient of the surf zone width l(sz) and the cross-flow length scale of the discharge la = M(j) (1/2)/U(sz), where M(j) is the discharge's momentum flux and U(sz) is a representative alongshore velocity in the surf zone. We conducted numerical modeling of a nonbuoyant discharge at an alongshore uniform beach with constant slope using a wave-resolving hydrodynamic model. Using results from 144 numerical experiments we develop an empirical relationship between the surf zone entrainment rate α and l(sz)/(la). The empirical relationship can reasonably explain seven measurements of surf zone entrainment at three diverse coastal discharges. This predictive relationship can be a useful tool in coastal water quality management and can be used to develop predictive beach water quality models.

What are the most crucial soil factors for predicting the distribution of alpine plant species?

NASA Astrophysics Data System (ADS)

Buri, A.; Pinto-Figueroa, E.; Yashiro, E.; Guisan, A.

2017-12-01

Nowadays the use of species distribution models (SDM) is common to predict in space and time the distribution of organisms living in the critical zone. The realized environmental niche concept behind the development of SDM imply that many environmental factors must be accounted for simultaneously to predict species distributions. Climatic and topographic factors are often primary included, whereas soil factors are frequently neglected, mainly due to the paucity of soil information available spatially and temporally. Furthermore, among existing studies, most included soil pH only, or few other soil parameters. In this study we aimed at identifying what are the most crucial soil factors for explaining alpine plant distributions and, among those identified, which ones further improve the predictive power of plant SDMs. To test the relative importance of the soil factors, we performed plant SDMs using as predictors 52 measured soil properties of various types such as organic/inorganic compounds, chemical/physical properties, water related variables, mineral composition or grain size distribution. We added them separately to a standard set of topo-climatic predictors (temperature, slope, solar radiation and topographic position). We used ensemble forecasting techniques combining together several predictive algorithms to model the distribution of 116 plant species over 250 sites in the Swiss Alps. We recorded the variable importance for each model and compared the quality of the models including different soil proprieties (one at a time) as predictors to models having only topo-climatic variables as predictors. Results show that 46% of the soil proprieties tested become the second most important variable, after air temperature, to explain spatial distribution of alpine plants species. Moreover, we also assessed that addition of certain soil factors, such as bulk soil water density, could improve over 80% the quality of some plant species models. We confirm that soil pH remains one of the most important soil factor for predicting plant species distributions, closely followed by water, organic and inorganic carbon related properties. Finally, we were able to extract three main categories of important soil properties for plant species distributions: grain size distribution, acidity and water in the soil.

Analysis of Groundwater Reserved in Dusun Ngantru Sekaran Village East Java

NASA Astrophysics Data System (ADS)

Pandjaitan, N. H.; Waspodo, R. S. B.; Karunia, T. U.; Mustikasari, N.

2018-05-01

Limited capacity of fresh water in some areas in Indonesia made some regions had drought problem or lack of surface water. One of the solutions was increasing ground water used. This research aimed to identify aquifer and the pattern of ground water flow and also to determine potential of groundwater reserved in Dusun Ngantru. The result would be use to find the right location to be used as groundwater wells. The method used in this research was geoelectric method. This method was used to determine the condition of aquifer and rocks under the soil and to define hydrogeological condition of Dusun Ngantru.The analysis results can be used as a reference of where and what kind of groundwater runs underneath, in order to be optimally utilized. The results of hydrogeological studies and the distribution of aquifer showed that there were unconfined and semi aquifers. The direction of the groundwater flow in the study site varied greatly as the lithologic arrangement varied just as much. In the study locations there were Ledok formation, Mundu formation, and Lidah formation. Groundwater potential ware predicted of 55.33 m3/day or 0.64 lt/s. Based on water quality standard in Indonesia, the water quality of wells were classified as first class quality.

Most oxidative stress response in water samples comes from unknown chemicals: the need for effect-based water quality trigger values.

PubMed

Escher, Beate I; van Daele, Charlotte; Dutt, Mriga; Tang, Janet Y M; Altenburger, Rolf

2013-07-02

The induction of adaptive stress response pathways is an early and sensitive indicator of the presence of chemical and non-chemical stressors in cells. An important stress response is the Nrf-2 mediated oxidative stress response pathway where electrophilic chemicals or chemicals that cause the formation of reactive oxygen species initiate the production of antioxidants and metabolic detoxification enzymes. The AREc32 cell line is sensitive to chemicals inducing oxidative stress and has been previously applied for water quality monitoring of organic micropollutants and disinfection byproducts. Here we propose an algorithm for the derivation of effect-based water quality trigger values for this end point that is based on the combined effects of mixtures of regulated chemicals. Mixture experiments agreed with predictions by the mixture toxicity concept of concentration addition. The responses in the AREc32 and the concentrations of 269 individual chemicals were quantified in nine environmental samples, ranging from treated effluent, recycled water, stormwater to drinking water. The effects of the detected chemicals could explain less than 0.1% of the observed induction of the oxidative stress response in the sample, affirming the need to use effect-based trigger values that account for all chemicals present.

Parameterizing water quality analysis and simulation program (WASP) for carbon-based nanomaterials

EPA Science Inventory

Carbon nanotubes (CNT) and graphenes are among the most popular carbon-based nanomaterials due to their unique electronic, mechanic and structural properties. Exposure modeling of these nanomaterials in the aquatic environment is necessary to predict the fate of these materials. ...