Collected Water Quality Data Research Articles

Ubiquitous Mobile Sensing for Water Quality Monitoring and Reporting within Lake Victoria Basin

As the human population growth and industry pressure in most developing countries continue to increase, effective water quality monitoring and evaluation has become critical for water resources management programs. This paper presents the ubiquitous mobile sensing system for water quality data collection and monitoring applications in developing countries. The system was designed based on the analysis of the existing solution. Open source hardware and software was used to develop the prototype of the system. Field testing of the system conducted in Nkokonjero, Uganda and Mwanza, Tanzania verified the functionalities of the system and its practical application in actual environment. Results show that proposed solution is able to collect and present data in a mobile environment.

Blue Earth River quality monitoring using ARCHER hyperspectral data and field measurements

In past decades, remote sensing studies on water quality mapping mainly focused on lakes using medium resolution imagery. Little research utilizes hyperspectral images to assess river water quality. This study aims to assess the capability of using Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) for river water quality mapping and to characterize spatial patterns of turbidity and chlorophyll in the Blue Earth River (BER) system of Minnesota. The BER was characterized by both hyperspectral imagery (HSI) and laboratory analysis of synchronously collected water quality data. The optimal bands for water quality mapping were determined using a method based on hyperspectral profiles and derivative analysis. Finally, based on the regression analysis and modelling, we mapped continuous surface water turbidity. The results revealed that the ratio of HSI band 17 to band 9 effectively determined turbidity and chlorophyll concentrations. The study also found that turbidity and chlorophyll in the river generally increases downstream.

System Design of Water Quality Monitoring Robot with Automatic Navigation and Self-test Capability

This paper proposes a water quality monitoring system of underwater robot with self-test and diagnosis capability. The system consists of underwater robot, water quality data collection module, self-test and diagnosis module, wireless communication module, and shore-based facility. The measurement results from each sensor of water quality data collection module are transmitted to the built-in micro-processor of the underwater robot. And the self-test and diagnosis module based on DSP connects with the built-in microprocessor through RS232. The self-test and diagnosis module conducts wavelet transform for the sensor information, generating extreme points of the wavelet transform, which are used to detect the abnormal state of the current system. The threshold method is introduced to reduce the influence of the noise. The disturbance of the noise can be alleviated by setting threshold for high frequency parameters of the wavelet transform. The exceptional information was detected by the self-test module and the water quality data information was transmitted to the shore-based facility through the wireless communication module. The shore-based facility can process, analyze and determine the collected information, and then give feedback to the underwater robot. The feasibility of the system design and the rationality of the water quality parameter measurement results were verified by experiments and on-site tests.

Identifying critical source areas of nonpoint source pollution with SWAT and GWLF

Identification of critical source areas (CSAs) (areas contributing most of the pollutants in a watershed) is important for cost-effective implementation of best management practices. Identification of such areas is often done through watershed modeling. Various watershed models are available for this purpose, however it is not clear if the choice (and complexity) of a model would lead to differences in locations of CSAs. The objective of this study was to use two models of different complexity for identifying CSAs. The relatively complex Soil and Water Assessment Tool (SWAT) and the simpler Generalized Watershed Loading Function (GWLF) were used to identify CSAs of sediment and nutrients in the Saugahatchee Creek watershed in east central Alabama. Models were calibrated and validated for streamflow, sediment, total nitrogen (TN) and total phosphorus (TP) at a monthly time scale. While both models performed well for streamflow, SWAT performed slightly better than GWLF for sediment, TN and TP. Sub-watersheds dominated by urban land use were among those producing the highest amount of sediment, TN and TP loads, and thus identified as CSAs. Sub-watersheds with some amount of agricultural crops were also identified as CSAs of TP and TN. A few hay/pasture dominated sub-watersheds were identified as CSAs of TN. The identified land use source areas were also supported by field collected water quality data. A combined index was used to identify the sub-watersheds (CSAs) that need to be targeted for overall reduction of sediment, TN and TP. While many CSAs identified by SWAT and GWLF were the same, some CSAs were different. Therefore, this study concludes that model choice will affect the location of some CSAs.

C-HMI 기반의 원격지 중수도 설비 실시간 제어와 모니터링 구현

The wastewater reclamation and reusing system has been rising as an alternative of water resource exhaustion that the whole world is experiencing. In order to be able to bring about improvement of the existing wastewater reclamation and reusing system, this research has developed of Conversion-Human Machine Interaction (C-HMI) based real-time control and monitoring system such as a sensor module and gate module, web monitoring system. This system was communication almost-error-free in various environment and situation. As a result, we have achieved our goal that has to doing work correctly as a sensor and gateway module that communication error is less than 0.2% throughout the embodied system and add that it can be easily controled and configured as an interface equipment to a complex sensor of water quality. According to this, the construction of a database capable of analyzing and assessing collection, storage and various elements of reliable water quality and flow rate data can be possible.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian weighted least squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality datasets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares regression and Bayesian weighted least squares regression for the estimation of uncertainty associated with pollutant build-up prediction using limited datasets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

A band-ratio algorithm for retrieving open-lake chlorophyll values from satellite observations of the Great Lakes

The U.S. Environmental Protection Agency's Great Lakes National Program Office (GLNPO) has collected water quality data from the five Great Lakes annually since 1993. We used the GLNPO observations made since 2002 along with coincident measurements made by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate-resolution Imaging Spectroradiometer (MODIS) to develop a new band-ratio algorithm for estimating chlorophyll concentrations in the Great Lakes from satellite observations. The new algorithm is based on a third-order polynomial model using the same maximum band ratios employed in the standard NASA algorithms (OC4 for SeaWiFS and OC3M for MODIS). The sensor-specific coefficients for the new algorithm were obtained by fitting the relationship to several hundred matched field and satellite observations. Although there are some seasonal variations in some lakes, the relationship between the observed chlorophyll values and those modeled using the new coefficients is fairly stable from lake to lake and across years. The accuracy of the satellite chlorophyll estimates derived from the new algorithm was improved substantially relative both to the standard NASA retrievals and to previously published algorithms tuned to individual lakes. Monte-Carlo fits to randomly selected subsets of the observations allowed us to estimate the uncertainty associated with the retrievals purely as a function of the satellite data. Our results provide, for the first time, a single simple band ratio method for retrieving chlorophyll concentrations in the offshore “open” waters of the Great Lakes from satellite observations.

Foundations of the Operational Data Base on Surface Waters Quality Formation

Approaches to formation of a new kind of informational/analytical system, namely “Operational Data Base “Hydrochemistry”, alongside with the currently functioning “Regime Reference Data Bank “Surface Waters Quality” of the state monitoring of the Russian Federation water bodies, have been considered. The principal idea was securing of improved organization of the systems of collection, processing, structuring, storage updating, converting and output of initial and processed hydro/chemical data for information support of decision making process in the sphere of water/economic complex management both at regional and Federal levels. The operational data base concept takes into account both emergencies’ registration by regular monitoring and additional collection of surface water quality data by different expeditions and agencies.

A comparative assessment of institutional frameworks for managing drinking water quality

The global burden of disease attributable to contaminated drinking water calls for effective strategies for ensuring drinking water quality. To characterize institutional and policy approaches towards water quality management, we compared national and sub-national institutional frameworks for drinking water provision and management in nine developing countries, focusing on roles, responsibilities and capacity for water quality monitoring. Responsibilities for operational and surveillance (compliance) water quality monitoring of formal urban water supplies are typically well defined, with attention placed on both activities. Legal requirements for surveillance monitoring of community and smaller supplies are generally also in place, however, standards for operational monitoring vary considerably between countries. In practice, resources and capacity for consistent operational and surveillance monitoring of rural and informal urban supplies are limited. To improve oversight and management in these settings, we hypothesize that surveillance agencies could increase the use of audit-based water quality data collection from formal urban water suppliers and target the resulting efficiency gains towards increased direct surveillance of rural and informal water supplies. In addition there is a need for capacity building and technology development that supports increased operational monitoring and data reporting from resource-poor settings.

Modeling water, carbon, and nitrogen dynamics for two drained pine plantations under intensive management practices

This paper reports results of a study to test the reliability of the DRAINMOD-FOREST model for predicting water, soil carbon (C) and nitrogen (N) dynamics in intensively managed forests. The study site, two adjacent loblolly pine ( Pinus taeda L.) plantations (referred as D2 and D3), are located in the coastal plain of North Carolina, USA. Controlled drainage (with weir and orifice) and various silvicultural practices, including nitrogen (N) fertilizer application, thinning, harvesting, bedding, and replanting, were conducted on the study site. Continuous collection of hydrological and water quality data (1988–2008) were used for model evaluation. Comparison between predicted and measured hydrologic variables showed that the model accurately predicted long-term subsurface drainage dynamics and water table fluctuations in both loblolly pine plantations. Predicted mean and standard deviation of annual drainage matched measured values very well: 431 ± 217 vs. 436 ± 231 mm for D2 site and 384 ± 152 vs. 386 ± 160 mm for D3 site. Nash–Sutcliffe coefficients (NSE) were above 0.9 for drainage predictions on annual and monthly basis and above 0.86 for predictions of daily water table fluctuations. Compared to measurements in other similar studies, the model also reasonably estimated long-term dynamics of organic matter pools on forest floor and in forest soil. Predicted mean and standard deviation of annual nitrate exports were comparable to measured values: 1.6 ± 1.3 vs. 1.5 ± 1.5 kg ha −1 for D2 site, and 1.4 ± 1.3 vs. 1.3 ± 1.1 kg ha −1 for D3 site, respectively. Predicted nitrate export dynamics were also in excellent agreement with field measurements as indicated by NSE above 0.90 and 0.84 on annual and monthly bases, respectively. The model, thus successfully tested, was applied to predicted hydrological and biogeochemical responses to drainage water management and silvicultural practices. Specifically, the model predicted reduced rainfall interception and ET after clear cutting, both of which led to increased water yield and elevated water table, as expected. The model also captured temporary changes in nitrogen transformations following forest harvesting, including increased mineralization, nitrification, denitrification, and decreased plant uptake. Overall, this study demonstrated that DRAINMOD-FOREST can predict water, C and N dynamics in drained pine forests under intensive management practices.

Application of Build-up and Wash-off Models for an East-Australian Catchment

Estimation of stormwater pollutants is a pre-requisite for the protection and improvement of the aquatic environment and for appropriate management options. The usual practice for the stormwater quality prediction is performed through water quality modeling. However, the accuracy of the prediction by the models depends on the proper estimation of model parameters. This paper presents the estimation of model parameters for a catchment water quality model developed for the continuous simulation of stormwater pollutants from a catchment to the catchment outlet. The model is capable of simulating the accumulation and transportation of the stormwater pollutants; suspended solids (SS), total nitrogen (TN) and total phosphorus (TP) from a particular catchment. Rainfall and water quality data were collected for the Hotham Creek Catchment (HTCC), Gold Coast, Australia. Runoff calculations from the developed model were compared with the calculated discharges from the widely used hydrological models, WBNM and DRAINS. Based on the measured water quality data, model water quality parameters were calibrated for the above-mentioned catchment. The calibrated parameters are expected to be helpful for the best management practices (BMPs) of the region. Sensitivity analyses of the estimated parameters were performed to assess the impacts of the model parameters on overall model estimations of runoff water quality. This paper demonstrates the calibration of a catchment water quality model to obtain a set of parameters for which the predicted pollutant concentrations are close to the measured concentrations. Two major pollutants were analysed for the calibration of the model: TN and TP. These pollutants were chosen for the calibration of the model because of the data availability. Nonetheless, these parameters are generally used as indicators of water quality in receiving water bodies (13). For the evaluation of the hydrologic outputs of the model, the peak discharges were compared with the calculated discharges from the WBNM and DRAINS models, which are widely used in Australia. Water quality data (TN and TP) was collected from several locations within the HTCC on the Gold Coast, Australia. The calibration of the model was demonstrated using the collected water quality data from the Gold Coast, Australia. The calibrated parameters are expected to be useful for the future use of the model to develop BMPs for pollution

Experiences and recommendations in deploying a real-time, water quality monitoring system

Monitoring of water quality at a river basin level to meet the requirements of the Water Framework Directive (WFD) using conventional sampling and laboratory-based techniques poses a significant financial burden. Wireless sensing systems offer the potential to reduce these costs considerably, as well as provide more useful, continuous monitoring capabilities by giving an accurate idea of the changing environmental and water quality in real time. It is unlikely that the traditional spot/grab sampling will provide a reasonable estimate of the true maximum and/or mean concentration for a particular physicochemical variable in a water body with marked temporal variability. When persistent fluctuations occur, it is likely only to be detected through continuous measurements, which have the capability of detecting sporadic peaks of concentration. Thus, in situ sensors capable of continuous sampling of parameters required under the WFD would therefore provide more up-to-date information, cut monitoring costs and provide better coverage representing long-term trends in fluctuations of pollutant concentrations. DEPLOY is a technology demonstration project, which began planning and station selection and design in August 2008 aiming to show how state-of-the-art technology could be implemented for cost-effective, continuous and real-time monitoring of a river catchment. The DEPLOY project is seen as an important building block in the realization of a wide area autonomous network of sensors capable of monitoring the spatial and temporal distribution of important water quality and environmental target parameters. The demonstration sites chosen are based in the River Lee, which flows through Ireland's second largest city, Cork, and were designed to include monitoring stations in five zones considered typical of significant river systems-–these monitor water quality parameters such as pH, temperature, depth, conductivity, turbidity and dissolved oxygen. Over one million data points have been collected since the multi-sensor system was deployed in May 2009. Extreme meteorological events have occurred during the period of deployment and the collection of real-time water quality data as well as the knowledge, experience and recommendations for future deployments are discussed.

Hourly Analyses of Hydrological and Water Quality Simulations Using the ESWAT Model

Detailed analyses of hydrological and water quality variables are very important to study the dynamic processes in a river basin. In this study, we have further modified the Enhanced Soil and Water Assessment Tool (ESWAT) model by incorporating hourly evapotranspiration and overland flow routing modules. Results from comparison of the performances by two ESWAT versions indicate that the modified version performed better than the original model. The modified ESWAT model has reasonably reproduced observed time series runoff and most commonly collected water quality data. In addition, input data availability at required spatial and temporal resolutions is the major bottleneck in implementing many detailed hydrological models. In this paper, we have also developed a robust methodology to successfully disaggregate daily rainfall data into hourly datasets. Furthermore, we have assessed the implications of such daily rainfall disaggregation schemes on subsequent simulation of hydrological and water quality variables at river basin level. The outcomes suggest that the multivariate rainfall disaggregation scheme better reproduced observed rainfall and runoff data.

Quantification and relative comparison of different types of uncertainties in sewer water quality modeling

Quantifiable sources of uncertainty have been identified for a case study of integrated modeling of a sewer system with a more downstream wastewater treatment plant and storage sedimentation tank. The different sources were classified in model input and model-structure-related uncertainties. They were quantified and propagated towards the uncertainty in the event-based prediction of sewer emissions (flow, and physico-chemical water quality concentrations and loads). Based on the concept of variance decomposition, the total prediction uncertainty was split into the contributions of the various uncertainty sources and the different submodels. Although the results strongly depend on the water quality variable considered, it is in most general terms concluded that the uncertainty contribution by the water quality submodels is an order of magnitude higher than that for the flow submodels. Future model improvement should therefore mainly focus on water quality data collection, which would reduce current problems of spurious model calibration and verification, but also of knowledge gaps in in-sewer processes.

UNEP‐GEMS/Water Programme—water quality data, GEMStat and open web services—and Japanese cooperation

AbstractThe purpose of this paper is threefold. First, it demonstrates how monitoring stations that collect water quality data can be situated globally via satellite data from Google Earth. Important technical issues such as interoperability and Open Web Services are discussed in this context. Second, it illustrates how researchers at local levels can benefit from this global technology. The discussion draws from the online water quality database, GEMStat, which contains water quality data and sediment load calculations from around the world. These types of data, collected locally, can be shown to bear global implications through Internet technology. GEMStat has been expanded to include Open Web Services to enable interoperability with other online databases. Third, it illustrate an international framework of cooperation through GEMS/Water Japan, introducing on‐site monitoring activities as well as management of international river basin (Mekong/La Plata). Considerations for future application framework are presented in conclusion. Copyright © 2007 John Wiley & Sons, Ltd.

How hurricane attributes determine the extent of environmental effects: Multiple hurricanes and different coastal systems

The most recent spate of hurricanes to strike the United States and Caribbean (1989 to the present) has occurred when many of the affected areas had long-term water quality and biological data collection efforts ongoing, as well as special follow-up studies. These data have allowed researchers to obtain a much clearer picture of how individual characteristics of hurricanes interact with human land use to lead to various types and degrees of environmental effects. Common deleterious water quality effects associated with hurricanes include excessive nutrient loading, algal blooms, elevated biochemical oxygen demand and subsequent hypoxia and anoxia, fish and invertebrate kills, aquatic animal displacements, large scale releases of chemical pollutants and debris from damaged human structures, exacerbated spread of exotic species and pathogens, and pollution of water with fecal microbial pathogens. These and other effects may or may not occur, or occur to varying degrees, depending upon individual hurricane characteristics including category, point of landfall, wind speed, amount of rainfall, and path after landfall. Landfall in a populous area, a post-landfall trajectory upriver toward a headwater region, passage along a floodplain containing pollution sources (such as wastewater treatment plants, concentrated animal feeding operations, and septic systems), and intensity sufficient to damage power generation will all lead to increased environmental damage. We suggest a number of recommendations for post-hurricane water sampling parameters and techniques, and provide several management-oriented recommendations for better coastal and floodplain land use aimed at lessening the water quality effects of hurricanes.

English

The Tar Creek Superfund Site is a portion of the abandoned lead and zinc mining area known as the Tri-State Mining District (OK, KS and MO) and includes approximately 104 km 2 of disturbed land surface and contaminated water resources in northeastern Oklahoma. Underground mining from the 1890s through the 1960s degraded over 1000 surface ha, and left nearly 500 km of tunnels, 165 million tons of processed mine waste materials (chat), 300 ha of tailings impoundments and over 2600 shafts and boreholes. The site was listed on the National Priorities List (NPL) in 1983 and received a Hazard Ranking System score of 58.15. Initial remediation efforts in the 1980s focused on addressing surface and ground water quality. In 1993, an Indian Health Service study demonstrated that 35% of children had blood lead levels above thresholds dangerous to human health. Since 1995, the focus has been excavation and replacement of contaminated residential soils. In 2004, the University of Oklahoma (OU) and Oklahoma Department of Environmental Quality (ODEQ) began a series of related projects to demonstrate applicable technologies and establish a longer-term remediation and restoration process. The site was divided into five perimeter areas where initial projects would be focused and a single core area, based loosely on watershed and community boundaries. OU is leading projects on i) construction and evaluation of a passive treatment system to address contaminated mine drainage, ii) establishment and assessment of a test road section incorporating chat into asphalt pavement and iii) remediation and restoration monitoring in support of all projects, including collection of water quality, hydrology, air quality, soil ,chat, fine tailings and meteorological data. ODEQ projects include i) mine hazard attenuation (i.e., closing open shafts and boreholes), ii) chat utilization as fill and in pavement, iii) land reclamation to productive use and iv) stream restoration. Initial projects are anticipated to be completed in 3-5 years.

INFLUENCE OF DIFFERENT TEMPORAL SAMPLING STRATEGIES ON ESTIMATING TOTAL PHOSPHORUS AND SUSPENDED SEDIMENT CONCENTRATION AND TRANSPORT IN SMALL STREAMS1

ABSTRACT: Various temporal sampling strategies are used to monitor water quality in small streams. To determine how various strategies influence the estimated water quality, frequently collected water quality data from eight small streams (14 to 110 km2) in Wisconsin were systematically subsampled to simulate typically used strategies. These subsets of data were then used to estimate mean, median, and maximum concentrations, and with continuous daily flows used to estimate annual loads (using the regression method) and volumetrically weighted mean concentrations. For each strategy, accuracy and precision in each summary statistic were evaluated by comparison with concentrations and loads of total phosphorus and suspended sediment estimated from all available data. The most effective sampling strategy depends on the statistic of interest and study duration. For mean and median concentrations, the most frequent fixed period sampling economically feasible is best. For maximum concentrations, any strategy with samples at or prior to peak flow is best. The best sampling strategy to estimate loads depends on the study duration. For one‐year studies, fixed period monthly sampling supplemented with storm chasing was best, even though loads were overestimated by 25 to 50 percent. For two to three‐year load studies and estimating volumetrically weighted mean concentrations, fixed period semimonthly sampling was best.

Potential influence of sugarcane cultivation on estuarine water quality of Louisiana's gulf coast.

Sugarcane is cultivated on some 170000 ha of land in south central and southwestern Louisiana. This acreage drains into bayous and rivers that empty into Louisiana's coastal bays and estuaries. For a number of years the state's Department of Agriculture and Forestry and Department of Environmental Quality have collected water quality data from this sugarcane area. Study of these data shows that approximately one in five detections of atrazine is above the maximum contaminant level (MCL) for drinking water. Currently there is no U.S. atrazine standard for protection of aquatic life. February and October detections of this herbicide are probably due to sugarcane cultivation. Nitrate levels have remained below the MCL for drinking water, but nitrate and phosphorus concentrations may pose a potential for eutrophication problems. The contribution of sugarcane production to the nutrient status of Louisiana's coastal water bodies is difficult to assess because there are other sources of nutrients in the area and native soil phosphorus levels are high. Cultural practices such as subsurface drains, open drainage ditches, and postharvest residue management have potential through enhancement of soil infiltration for decreasing sugarcane's contribution to water quality problems in southern and coastal Louisiana. A new field project is being installed at the Louisiana State University Agricultural Experiment Station's Sugarcane Research Station at St. Gabriel to assess the water quality benefits of these practices with respect to sugarcane cultivation.

Community data - where does the value lie? Assessing confidence limits of community collected water quality data

Waterwatch is a national community-based monitoring network that aims to involve community groups and individuals in the protection and management of waterways. Waterwatch Victoria has the dual objectives of catchment education and water quality monitoring. The educational outcomes are evident, with the Waterwatch programme facilitating over 9,000 students to monitor more than 2,000 sites in waterways in 2000. This paper aims to assess the scientific value of community-collected data, through examining differences between Waterwatch data and professionally collected data. The study looked at all aspects of volunteer data collection, including data confidence protocols, equipment, and data analysis. All professional data was collated by the Victorian Water Quality Monitoring Network (VWQMN). The parameters examined in this study were turbidity, electrical conductivity (EC), pH and total phosphorus. The level of agreement between community-collected data and professional data varied temporally and spatially. Waterwatch data for EC and pH appeared to be very similar to professionally collected data. Equipment used by Waterwatch volunteers for turbidity and total phosphorus appeared to be limited in accuracy to moderate ranges. Overall the VWQMN professionally collected data showed less variance, suggesting greater variability, potentially due to inaccuracies, in volunteer collected data