Concentrations Of Water Quality Constituents Research Articles

Comparative Use of Hydrologic Indicators to Determine the Effects of Flow Regimes on Water Quality in Three Channels across Southern Florida, USA

This study determines the relationships between water flow and water quality in three types of channels in southern Florida, USA: Shark River Slough, Peace River, and Hillsboro Canal. Peace River most resembles a natural channel with floodplain connectivity, sinuosity, and uninhibited flow. Shark River Slough has a natural, shallow channel with sheet flow, while the Hillsboro Canal is the most modified channel due to dredging, straightening, and regulated flow. Hydrologic indices for each channel were estimated to characterize flow regimes and flow variability, while concentration–discharge (C–Q) relationships were determined to quantify the impact of flow regime on water quality. The greatest variability in flow occurred at the Hillsboro Canal, followed by Peace River and Shark River Slough. Connectivity to floodplains and long durations of low and high flow pulses at Peace River and Shark River Slough contributed to the dilution of water quality constituent concentrations at higher flows. Conversely, the channelized characteristics of the Hillsboro Canal resulted in an enrichment of constituents, especially during high flows. This study suggests that C–Q relationships can be used in canal discharge management to prevent water quality degradation of sensitive downstream wetland and aquatic ecosystems.

Statistical analysis and physicochemical characteristics of groundwater ‎quality parameters: a case study

ABSTRACT The assessment of groundwater quality was carried out by taking the shallow wells samples near the bank of River Indus at the downstream Kotri barrage; the study area covers the arid region of Hyderabad, Sindh. The space occupied by the wells was about 1-2 km used for the quantification of geochemical characteristics of the shallow aquifer that lies on the left side of the river. A total of 13 groundwater samples were collected during pre-monsoon and post-monsoon seasons. All the groundwater samples were examined for the characterisation of physicochemical parameters such as Total Dissolved Solids ranged from 320 mg/l to 4011 mg/l, Total Hardness ranged from 120 mg/l to 1320 mg/l, Chloride ranged from 160 mg/l to 2260 mg/l, Sulphate ranged from 61 mg/l to 777 mg/l, pH ranged from 6.9 to 8.1. The findings of the study demonstrate the presence of high quantity physicochemical parameters at a shallow aquifer in contrast to guidelines values by the World Health Organization (WHO) for drinking water. Besides, the T-value was applied to check the variation in pre- and post-monsoon results between parameters, which found significant for chloride 2.701 with p-value 0.031, for fluoride 3.039 with p-value 0.019 and for iron 2.957 with p-value 0.021. Moreover, Correlation coefficients among different parameters were determined which showed that as the correlations between parameters become weaker it also becomes insignificant. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents. A significantly positive correlation at 1 and 5% found between many parameters. The total hardness with 0.969, chloride with 0.988, and sulphate with 0.994 values were found significant using the conventional level of significance (0.05). However, no apparent changes were visualised in the quality of water during the onset of pre- and post-monsoon seasons over the year.

Electrical Conductivity as a General Predictor of Multiple Parameters in Tigris River Based on Statistical Regression Model

Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very good startup to establish a rule of thumb in the laboratories to compare between observations. The importance of linear regression equations in predicting surface water quality parameters is a method that can be applied to any other location.

Evaluation of Two Management Strategies for Harvested Emergent Vegetation on Immature Mosquito Abundance and Water Quality.

Emergent macrophytes play critical roles in water treatment processes of free-water surface constructed treatment wetlands. Management strategies for plant biomass affect wetland function and mosquito populations. Sinking of harvested macrophyte biomass is thought to provide organic carbon that enhances denitrifying bacteria important for nutrient removal while concomitantly reducing harborage for mosquitoes. The effects of sinking versus floating dried plant biomass (California bulrush [Schoenoplectus californicus]) on immature mosquito abundance and water quality (nutrient levels, oxygen demand, and physicochemical variables) were examined in mesocosms (28-m2 ponds or 1.4-m2 wading pools) under different flow regimes in 4 studies. The numbers of mosquito larvae in earthen ponds with floating vegetation were greater than in ponds with sunken vegetation on most dates but did not differ significantly between the 2 vegetation treatments in experiments using wading pools. Differences of the abundance of Anopheles larvae between the 2 vegetation management treatments were larger than for Culex larvae when naturally occurring larval mosquito predators were present. At high turnover rates (>2 pond volumes/day), water quality did not differ significantly between the vegetation management treatments and the water supply. At low turnover rates (approximately 2-6% of water volume/day), water quality differed significantly between the 2 vegetation management treatments and the water supply. Sinking vegetation can enhance the effectiveness of mosquito control but, depending on water management practices, may raise the concentrations of water quality constituents in discharges that are regulated under the Clean Water Act.

Estimation Bias in Water-Quality Constituent Concentrations and Fluxes: A Synthesis for Chesapeake Bay Rivers and Streams

Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of less than 18% for flux and less than 7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.

On Abandoning Hypothesis Testing in Environmental Standard Compliance Assessment.

We use basic characteristics of statistical significance test to argue the abandonment of hypothesis testing in environmental standard (or criterion) compliance assessment. The typical sample size used for environmental assessment is small, and the natural variation of many water quality constituent concentrations is high. These conditions lead to low statistical power of the hypothesis tests used in the assessment process. As a result, using hypothesis testing is often inefficient in detecting noncompliance. When a noncompliance is detected, it is frequently due to sampling or other types of error. We illustrate the problems using two examples, through which we argue that these problems cannot be resolved under the current practice of assessing compliance one water at a time. We recommend that the hypothesis testing framework be replaced by a statistical estimation approach, which can more effectively leverage information from assessments on similar waters using a probabilistic assessment approach.

Development of urban runoff model FFC-QUAL for first-flush water-quality analysis in urban drainage basins

An urban runoff model that is able to compute the runoff, the pollutant loadings, and the concentrations of water-quality constituents in urban drainages during the first flush was developed. This model, which is referred to as FFC-QUAL, was modified from the existing ILLUDAS model and added for use during the water-quality analysis process for dry and rainy periods. For the dry period, the specifications of the coefficients for the discharge and water quality were used. During rainfall, we used the Clark and time–area methods for the runoff analyses of pervious and impervious areas to consider the effects of the subbasin shape; moreover, four pollutant accumulation methods and the washoff equation for computing the water quality each time were used. According to the verification results, FFC-QUAL provides generally similar output as the measured data for the peak flow, total runoff volume, total loadings, peak concentration, and time of peak concentration for three rainfall events in the Gunja subbasin. In comparison with the ILLUDAS, SWMM, and MOUSE models, there is little difference between these models and the model developed in this study. The proposed model should be useful in urban watersheds because of its simplicity and its capacity to model common pollutants (e.g., biological oxygen demand, chemical oxygen demand, Escherichia coli, suspended solids, and total nitrogen and phosphorous) in runoff. The proposed model can also be used in design studies to determine how changes in infrastructure will affect the runoff and pollution loads.

A Mathematical Model of a Shallow and Eutrophic Lake (Stiucii – Romania)

The existence of lakes with eutrophication problems(54% in Romania) demands a predictive tool for the quality of these ecosystems. This paper proposes an eutrophication model that describes the ecological behavior of a eutrophic closed ecosystem. This model was used to study the shallow lake - Stiucii located in Cluj area - Romania. The conceptual model analyzes a simply food web (nutrients, primary producers and secondary consumers), and reproduces the spatial and temporal concentration of water quality constituents. The results provided by the model show a good correlation with the recorded values of the studied parameters. The RMSE values for the total nitrogen, zooplankton biomass and ammonia obtained are: 0.2 for zooplankton, 0.21 for ammonia and 0.35 for total nitrogen.

Concentration Characteristics of Runoff Pollutants using Statistical Techniques during a Storm: the Case Research of Driveway Site in Korea

This research was conducted to investigate the magnitude and nature of the emission of runoff pollutants with the goal of quantifying stormwater pollutant concentrations from driveways site. A statistical summary for concentration characteristics of driveway runoff is provided based on the various analysis techniques. Runoff water qualities in temporal variation were generally higher in wet season than in dry season with a high degree of variability. The 95 % confidence intervals of washed-off pollutant concentration are ranged to 82.4-141.4 mgL -1 for TSS, 53.3-87.6 mgL -1 for Turbidity, 19.0-38.7 mgL -1 for BOD, 34.0-59.2 mgL -1 for COD, 16.4-27.4 mgL -1 for DOC, 4.3-5.6 mgL -1 for TN and 0.25-0.34 mgL -1 for TP, showing the high values based on the surface or lake quality standards in Korea. The first flush effect is mostly occurred within initial 30 min of storm duration. Concentrations of water quality constituents in urban stormwater are often expressed in probabilistic terms using statistics such as the mean and standard deviation and selected quintiles. Three probability distributions, weibull in TSS and Turbidity, lognormal in BOD and COD and gamma in DOC, TN and TP were fitted and the goodness-of-fit was assessed using the Anderson-Darling test. The correlation matrix of all water quality variables shows significant relationships between all parameters (P<0.001) and the correlation of between TSS and Turbidity showed relatively strong positive relationship (r=0.825). This research presents the concentration changes and characteristics during storm occurrence. The new concept explaining the relationship among the emission effects of runoff pollutants are suggested using statistical techniques.

Relevance Vector Machine Models of Suspended Fine Sediment Transport in a Shallow Lake—I: Data Collection

Mud Lake is part of a wildlife refuge located in southeastern Idaho and is operated by a power company to maximize power consumption, while providing for delivery of irrigation flows. Mud Lake is used as a sediment trap for the water flowing from the Bear River into the adjacent Bear Lake. This study explores the use of multivariable relevance vector machine (MVRVM) modeling to predict suspended fine sediment and other water quality constituent concentrations, and their spatial and temporal distribution. In this article, the first of two, we describe an experimental design and data collection program for the observations of water quality constituent data and hydraulic parameters to support creation of the MVRVM model. We briefly describe the MVRVM modeling approach, describe the development of the experimental program and the resulting observations, and finally discuss the suitability of the data to modeling with the MVRVM. Details of the MVRVM model and its application are provided in a second article. Success of the MVRVM will confirm the ability of statistical learning tools to predict sediment concentrations, and will lead the way for scientists to expand the use of the MVRVM for modeling of suspended fine sediment and water quality in other complex natural systems.

Innovative monitoring of combined sewer overflow (CSO) quality in the Liguori catchnment (Cosenza, Italy)

Estimates of urban wet-weather flow pollutant loads are required to assess their impact on receiving waters and optimize wastewater treatment plant (WWTP) and combined sewer overflow (CSO) operations. In particular, a quantitative characterization of flow quality by means of fairly accurate monitoring is required for developing a rational planning approach and preferential treatment strategies. The main aim of this paper is to examine the qualitative–quantitative characteristics of wet-weather flow in an urbanized test catchment in Cosenza, Italy, and to compare three studied water quality constituent concentrations (total suspended solids (TSS), total chemical oxygen demand (CODt) and filtered chemical oxygen demand (CODf)) obtained by laboratory analyses to those measured by a UV/Vis spectrometer (‘spectrolyzer scan’), which represents a most modern and efficient spectrometric instrument requiring little maintenance. The results of such comparisons indicated the feasibility of using in situ spectroscopy to provide fairly accurate concentrations of the constituents studied, particularly in the case of CODt and CODf.

Cattle Grazing and Yosemite Toad (Bufo canorus Camp) Breeding Habitat in Sierra Nevada Meadows

Abstract Exclusion of cattle by fencing has been proposed to alleviate possible negative grazing impacts on hydrologic, water quality, and cover habitat conditions within Sierra Nevada meadows used by Yosemite toads ( Bufo canorus Camp) for breeding. Our objectives were to: 1) determine associations between breeding pool habitat conditions and use of potential breeding pools by toads; and 2) determine how habitat conditions respond to cattle exclusion treatments on the Sierra National Forest, California. We randomly selected two toad occupied and two unoccupied breeding pools in each of nine meadows for this study ( n = 36 breeding pools). After baseline data collection in 2006, three meadow fencing treatments were implemented over the course of 3 yr. Treatments were fencing to exclude cattle from the entire meadow; fencing to exclude cattle from toad breeding and rearing areas, with grazing allowed in the remaining unfenced portion of the meadow; and cattle grazing allowed across entire meadow. We monitored hydrologic, water quality, and cover habitat variables as well as toad occupancy during the breeding seasons of 2006 through 2008. Concentrations of water quality constituents were uniformly low all years regardless of treatment. Occupied pools were shallower, warmer, and more nitrogen enriched than unoccupied breeding pools. We found no evidence of improved toad breeding pool habitat conditions following fencing compared to standard US Forest Service grazing management.

Bayesian Estimation of Optical Properties of Nearshore Estuarine Waters: A Gibbs Sampling Approach

A novel approach is developed for the retrieval of inherent optical properties of coastal water, from which water-quality constituent concentrations can be obtained. The technique combines an analytical bio-optical model with statistical modeling for the formulation of posterior probability distributions of phytoplankton absorption, backscattering, and colored dissolved organic matter absorption; a Gibbs Sampler is employed for optimization. In contrast to other methods that typically provide point estimates of the unknown parameters, the proposed method estimates posterior distributions of the parameters, quantifying the uncertainty present in the problem and revealing correlation patterns. The method is tested successfully on synthetic reflectance data and real data measured <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> in the Hudson/Raritan Estuary of New York-New Jersey.

An assessment of the accuracy and precision of water quality parameters retrieved with the Matrix Inversion Method.

There is an increasing demand for quantitative inland water quality observations from satellites. One approach to estimate water quality constituent (chlorophyll and other pigments, total suspended material, and colored dissolved organic matter) concentrations from remote sensing radiance measured over inland waters is to apply a Matrix Inversion Method (MIM). Using the Hydrolight® radiative transfer model and typical water quality constituent concentrations from subtropical Wivenhoe Dam, Australia, this article demonstrates that significant improvements in the accuracy and precision of retrieved water quality constituent values can be obtained by using semianalytically estimated values for the proportionality factor that are calculated separately for each spectral band of the MERIS sensor. Furthermore, it shows that overdetermined systems of equations can be used to mitigate the effect of unknown and endemic sources of error in the remote sensing system. The residuals left after the inversion of the reflectance spectrum can also be used to assign a reliability measure to the retrievals of total suspended material (TSM) and colored dissolved organic matter (CDOM), but not for chlorophyll a. The results of this study may be used to improve algorithms for the remote sensing of water quality for freshwater impoundments.

A sensor network for high frequency estimation of water quality constituent fluxes using surrogates

Characterizing spatial and temporal variability in the fluxes and stores of water and water borne constituents is important in understanding the mechanisms and flow paths that carry constituents to a stream and through a watershed. High frequency data collected at multiple sites can be used to more effectively quantify spatial and temporal variability in water quality constituent fluxes than through the use of low frequency water quality grab sampling. However, for many constituents (e.g., sediment and phosphorus) in-situ sensor technology does not currently exist for making high frequency measurements of constituent concentrations. In this paper we describe how water quality measures such as turbidity or specific conductance, which can be measured in-situ with high frequency, can be used as surrogates for other water quality constituents that cannot economically be measured with high frequency to provide continuous time series of water quality constituent concentrations and fluxes. We describe the observing infrastructure required to make high frequency estimates of water quality constituent fluxes based on surrogate data at multiple sites within a sensor network supporting an environmental observatory. This includes the supporting sensor, communication, data management, and data storage and processing infrastructure. We then provide a case study implementation in the Little Bear River watershed of northern Utah, USA, where a wireless sensor network has been developed for estimating total phosphorus and total suspended solids fluxes using turbidity as a surrogate.

도시유역의 유출&amp;#x00B7;수질해석을 위한 ILLUDAS-NPS 모형

ILLUDAS-NPS 모형은 초기강우에 의한 수질항목별 오염부하량 및 농도계산을 위하여 개발하였다. 이 모형은 국내의 도시지역 유출해석에 주로 사용되는 ILLUDAS 모형에 건기 및 우기시의 수질해석 과정들을 추가한 것으로써 건기시의 경우 유량 및 수질 계산은 계수지정법을 사용하였으며, 우기시의 경우 유량 계산은 기존 ILLUDAS 모형의 알고리즘, 수질 계산은 일일 오염물 축적법과 쓸림방정식을 적용하여 계산시간별 오염물질 부하량 및 농도 등을 계산하였다. 모형의 검정을 위하여 홍제천 유역의 3개 강우사상을 대상으로 검토한 결과 총부하량, 첨두농도, 첨두농도 발생시간 등에서 전반적으로 실측치와 유사한 결과를 얻을 수 있었다. 또한, 이 모형에 의한 계산결과를 SWMM, STORM 등의 기존 도시유출<TEX>${\cdot}$</TEX>수질 모형들의 결과와 비교 검토하였는바, SWMM 모형과 비교적 잘 일치함을 확인할 수 있었다. An ILLUDAS-NPS model was developed which is able to compute pollutant loadings and the concentrations of water quality constituents. This model is based on the existing ILLUDAS model, and added for use in the water quality analysis process during dry and rainy periods. For dry period, the specifications of coefficients for discharge and water quality were used. During rainfall, we used the daily pollutant accumulation method and the washoff equation for computing water quality each time. According to the results of verification, the ILLUDAS-NPS model provides generally similar outputs with the measured data on total loadings, peak concentration and time of peak concentration for three rainfall events in the Hong-je Basin. In comparison with the SWMM and STORM models, it was shown that there is little difference between ILLUDAS-NPS and SWMM.

DETECTING REDUCTIONS IN SEDIMENT LOADS ASSOCIATED WITH OHIO'S CONSERVATION RESERVE ENHANCEMENT PROGRAM1

ABSTRACT: Small systematic changes in loads or concentrations of water quality constituents are difficult to detect against the background of short term fluctuations (“noise”) that result from weather and climate effects. Minimum Detectable Change Analysis (MDCA) uses prior knowledge of a water quality constituent to determine how much change must occur (e.g., from implementation of conservation practices) for the change to be statistically significant. In this paper we use MDCA to determine whether the goal of the Ohio Lake Erie Conservation Reserve Enhancement Program (CREP), to reduce sediment loads by an average of 6 percent over 10 years, represents a large enough change to be detected. We conclude that this amount of change is unlikely to be detected as statistically significant, even with the high frequency sampling program planned for evaluating it. The minimum detectable change ranges from about 7 to 9 percent for three different rivers.

Application of the log-normal and normal distributions to stormwater quality parameters

Concentrations of water quality constituents in urban stormwater are often expressed in probabilistic terms—using statistics such as the mean and standard deviation and selected quantiles. In many studies, the log-normal distribution has been assumed to apply. In this 3-year study, the distributions of concentrations of 14 constituents in five sources of run-off were studied—parking-lot run-off discharging into an on-stream pond, baseflow and event flow in a small suburban creek feeding the same on-stream pond, and the pond outflow under both baseflow and event flow conditions. Two probability distributions, log-normal and normal, were fitted and the goodness-of-fit was assessed using probability plots and the Cramer-von Mises test. Of the two, the log-normal was the better distribution in most of the cases tested. It was more suitable for parking-lot run-off and creek baseflow, and somewhat less suitable for creek event flow and pond baseflow. With a few exceptions, the log-normal distribution did not apply for soluble constituents (total dissolved solids, chlorides, sulphate, COD) and/or event outflow from the pond. In these cases the normal distribution was preferred. The composition of outflow from the pond was controlled by intense mixing of the incoming event run-off with the water stored in the pond. The assumption of an inappropriate probability distribution can result in substantial errors when estimating the mean concentration for censored data. This in turn can affect calculation of pollutant loads and extrapolation to estimate quantiles.

Automatically modeling, simulating and explaining physical systems

This paper presents an interactive framework for constructing models from high-level specifications of real physical systems, deriving low-level simulations from the constructed models and then generating explanations of the simulated behavior to help engineers interpret and control the real physical systems. To demonstrate our method, we show an example of modeling river networks, which are eco-environmental engineering systems, to provide a computer-based solution to problems of simulating and controlling concentrations of water quality constituents in river networks[1].

Formalizing computational river networks

Abstract We present a simple framework, called computational network, for formalizing river networks to provide a computer-based solution to problems of simulating concentrations of water quality constituents in river networks. The method was applied on a river network in Beijing-Tianjin area. The framework is developed by an object-oriented approach integrating the modelling of the static structure of river networks with that of the dynamic behavior of water quality constituents in them. Similar to artificial neural networks, computational networks can be used for learning, problem solving and controlling.