Готові списки джерел за темами / Water quality sampling

Добірка наукової літератури з теми "Water quality sampling"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Water quality sampling"

1

Colverson, Peter. "Wildland Water Quality Sampling and Analysis." Journal of Environmental Quality 22, no. 3 (July 1993): 634. http://dx.doi.org/10.2134/jeq1993.00472425002200030033x.

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2

Hsueh, Ya-Wen, and R. Rajagopal. "Modeling Ground Water Quality Sampling Decisions." Groundwater Monitoring & Remediation 8, no. 4 (September 1988): 121–34. http://dx.doi.org/10.1111/j.1745-6592.1988.tb01112.x.

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3

Moore, Barry C., and John D. Stednick. "Wildland Water Quality Sampling and Analysis." Journal of Range Management 45, no. 2 (March 1992): 222. http://dx.doi.org/10.2307/4002790.

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4

Bolt, M. D. "Visualizing Water Quality Sampling-Events in Florida." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 73–79. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-73-2015.

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Water quality sampling in Florida is acknowledged to be spatially and temporally variable. The rotational monitoring program that was created to capture data within the state’s thousands of miles of coastline and streams, and millions of acres of lakes, reservoirs, and ponds may be partly responsible for inducing the variability as an artifact. Florida’s new dissolved-oxygen-standard methodology will require more data to calculate a percent saturation. This additional data requirement’s impact can be seen when the new methodology is applied retrospectively to the historical collection. To understand how, where, and when the methodological change could alter the environmental quality narrative of state waters requires addressing induced bias from prior sampling events and behaviors. Here stream and coastal water quality data is explored through several modalities to maximize understanding and communication of the spatiotemporal relationships. Previous methodology and expected-retrospective calculations outside the regulatory framework are found to be significantly different, but dependent on the spatiotemporal perspective. Data visualization is leveraged to demonstrate these differences, their potential impacts on environmental narratives, and to direct further review and analysis.
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5

Kwon, Se-Hyug, and Yo-Sang Lee. "Similarity of Sampling Sites by Water Quality." Communications for Statistical Applications and Methods 17, no. 1 (January 31, 2010): 39–45. http://dx.doi.org/10.5351/ckss.2010.17.1.039.

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6

Martin, Barb, and Traci Lichtenberg. "Accurate Water Quality Sampling Improves Process Controls." Opflow 42, no. 5 (May 2016): 8–9. http://dx.doi.org/10.5991/opf.2016.42.0030.

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7

Reicherts, Jeffrey D., and Charles William Emerson. "Monitoring bathing beach water quality using composite sampling." Environmental Monitoring and Assessment 168, no. 1-4 (July 16, 2009): 33–43. http://dx.doi.org/10.1007/s10661-009-1089-0.

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8

Chen, J., and Y. Deng. "Identifiability analysis of the CSTR river water quality model." Water Science and Technology 53, no. 1 (January 1, 2006): 93–99. http://dx.doi.org/10.2166/wst.2006.011.

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Conceptual river water quality models are widely known to lack identifiability. The causes for that can be due to model structure errors, observational errors and less frequent samplings. Although significant efforts have been directed towards better identification of river water quality models, it is not clear whether a given model is structurally identifiable. Information is also limited regarding the contribution of different unidentifiability sources. Taking the widely applied CSTR river water quality model as an example, this paper presents a theoretical proof that the CSTR model is indeed structurally identifiable. Its uncertainty is thus dominantly from observational errors and less frequent samplings. Given the current monitoring accuracy and sampling frequency, the unidentifiability from sampling frequency is found to be more significant than that from observational errors. It is also noted that there is a crucial sampling frequency between 0.1 and 1 day, over which the simulated river system could be represented by different illusions and the model application could be far less reliable.
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9

Ramsey, Charles A. "Considerations in Sampling of Water." Journal of AOAC INTERNATIONAL 98, no. 2 (March 1, 2015): 316–20. http://dx.doi.org/10.5740/jaoacint.14-251.

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Анотація:
Abstract Sampling water is no different than sampling any other media. It starts with the development of Sample Quality Criteria, understanding of material properties, then application of the Theory of Sampling. The main difference with sampling water as opposed to solids is the material properties. This paper addresses some of the material properties and consequences of those properties for the development of the sampling protocols. Two properties that must be addressed for water are the temporal nature and the inclusion of suspended solids. Examples are provided for three specific water sampling scenarios which may have application to other water sampling scenarios.
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10

Hilton, J., T. Carrick, E. Rigg, and J. P. Lishman. "Sampling strategies for water quality monitoring in lakes: The effect of sampling method." Environmental Pollution 57, no. 3 (1989): 223–34. http://dx.doi.org/10.1016/0269-7491(89)90014-6.

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Дисертації з теми "Water quality sampling"

1

Haggarty, Ruth Alison. "Evaluation of sampling and monitoring designs for water quality." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3789/.

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Анотація:
Assessing water quality is of crucial importance to both society and the environment. Deterioration in water quality through issues such as eutrophication presents substantial risk to human health, plant and animal life, and can have detrimental effects on the local economy. Long-term data records across multiple sites can be used to investigate water quality and risk factors statistically, however, identification of underlying changes can only be successful if there is a sufficient quantity of data available. As vast amounts of resources are required for the implementation and maintenance of a monitoring network, logistically and financially it is not possible to employ continuous monitoring of all water environments. This raises the question as to the optimal design for long-term monitoring networks which are capable of capturing underlying changes. Two of the main design considerations are clearly where to sample, and how frequently to sample. The principal aim of this thesis is to use statistical analysis to investigate frequently used environmental monitoring networks, developing new methodology where appropriate, so that the design and implementation of future networks can be made as effective and cost efficient as possible. Using data which have been provided by the Scottish Environment Protection Agency, several data from Scottish lakes and rivers and a range of determinands are considered in order to explore water quality monitoring in Scotland. Chapter 1 provides an introduction to environmental monitoring and both existing statistical techniques, and potential challenges which are commonly encountered in the analysis of environmental data are discussed. Following this, Chapter 2 presents a simulation study which has been designed and implemented in order to evaluate the nature and statistical power for commonly used environmental sampling and monitoring designs for surface waters. The aim is to answer questions regarding how many samples to base the chemical classification of standing waters, and how appropriate the currently available data in Scotland are for detecting trends and seasonality. The simulation study was constructed to investigate the ability to detect the different underlying features of the data under several different sampling conditions. After the assessment of how often sampling is required to detect change, the remainder of the thesis will attempt to address some of the questions associated with where the optimal sampling locations are. The European Union Water Framework Directive (WFD) was introduced in 2003 to set compliance standards for all water bodies across Europe, with an aim to prevent deterioration, and ensure all sites reach `good' status by 2015. One of the features of the WFD is that water bodies can be grouped together and the classification of all members of the group is then based on the classification of a single representative site. The potential misclassification of sites means one of the key areas of interest is how well the existing groups used by SEPA for classification capture differences between the sites in terms of several chemical determinands. This will be explored in Chapter 3 where a functional data analysis approach will be taken in order to investigate some of the features of the existing groupings. An investigation of the effect of temporal autocorrelation on our ability to distinguish groups of sites from one another will also be presented here. It is also of interest to explore whether fewer, or indeed more groups would be optimal in order to accurately represent the trends and variability in the water quality parameters. Different statistical approaches for grouping standing waters will be presented in Chapter 4, where the question of how many groups is statistically optimal is also addressed. As in Chapter 3, these approaches for grouping sites will be based on functional data in order to include the temporal dynamics of the variable of interest within any analysis of group structure obtained. Both hierarchical and model based functional clustering are considered here. The idea of functional clustering is also extended to the multivariate setting, thus enabling information from several determinands of interest to be used within formation of groups. This is something which is of particular importance in view of the fact that the WFD classification encompasses a range of different determinands. In addition to the investigation of standing waters, an entirely different type of water quality monitoring network is considered in Chapter 5. While standing waters are assumed to be spatially independent of one another there are several situations where this assumption is not appropriate and where spatial correlation between locations needs to be accounted for. Further developments of the functional clustering methods explored in Chapter 4 are presented here in order to obtain groups of stations that are not only similar in terms of mean levels and temporal patterns of the determinand of interest, but which are also spatially homogenous. The river network data explored in Chapter 5 introduces a set of new challenges when considering functional clustering that go beyond the inclusion of Euclidean distance based spatial correlation. Existing methodology for estimating spatial correlation are combined with functional clustering approaches and developed to be suitable for application on sites which lie along a river network. The final chapter of this thesis provides a summary of the work presented and discussion of limitations and suggestions for future directions.
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2

Xie, Xiongfei. "Operation Optimization and Water Quality Simulation of Potable Water Distribution System." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5406.

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A potable water distribution system (WDS) consists of pipes, pumps, valves, storage tanks, control and supporting components. Traditionally, it has two basic functions. First, provides end users with potable water at sufficient pressures and good water quality. Second, provides sufficient pressure and flow for fire fighting. Currently, potable water is still the least expensive material for fire fighting. To accomplish these two goals, water utilities have to consider the integrity and security of the water network. As a result, this research selected three research topics that are closely related to the daily operation of water utilities and water quality simulation. The first study is on optimal sampling design for chlorine decay model calibration. Three questions are investigated: (1) What is the minimum number of chlorine sample locations a water network needs? (2) How many combinations of sampling locations are available? (3) What is the optimal location combination? To answer the first two questions, the mathematical expressions of the chlorine concentrations between any two sampling locations are developed and sampling point relationship matrices are generated, then a mixed integer programming (MIP) algorithm is developed. Once obtained, the solutions to the first two questions are used to calculate the chlorine decay wall reaction coefficients and sensitivity matrix of chlorine concentration wall reaction coefficients; then, sampling location combinations achieved in the second question are sorted using a D-optimality algorithm. The model frame is demonstrated in a case study. The advantage of this method, compared to the traditional iterative sensitivity matrix method, is that a prior knowledge or estimation of wall reaction coefficients is not necessary. The second study is on optimizing the operation scheduling of automatic flushing device (AFD) in water distribution system. Discharging stagnant water from the pipeline through AFD is a feasible method to maintain water quality. This study presents a simulation-based optimization method to minimize total AFD discharge volume during a 24-hour horizon. EPANET 2.0 is used as hydraulics and water quality simulator. This is formulated as a single objective optimization problem. The decision variables are the AFD operation patterns. The methodology has three phases. In the first phase, AFD discharge capacities are calculated, whether existing AFDs are able to maintain chlorine residuals in the water network is also evaluated. In the second phase, the decision variables are converted to AFD discharge rates. A reduced gradient algorithm is used to quickly explore and narrow down the solution space. At the end of this phase, decision variables are switched back to the AFD operation patterns. In the third phase, simulated annealing is used to search intensively to exploit the global minimum. The method is demonstrated on the water system located at the south end of Pinellas County, Florida where AFD optimal operation patterns are achieved. The third study is on simulating contaminant intrusion in water distribution system. When contaminant matrix is introduced into water distribution system, it reacts with chlorine in bulk water rapidly and causes fast disinfectant depletion. Due to the difficulties in identifying contaminant types and chemical and biological properties, it is a challenging task to use EPANET-MSX to simulate chlorine decay under contaminant attack. EPANET 2.0 is used in the study to accomplish this goal. However, EPANET 2.0 cannot directly simulate chlorine depletion in the event of contamination attack because it assigns one time-independent bulk reaction coefficient to one specific pipe during the simulation. While under contaminant intrusion, chlorine decay bulk coefficient is not a constant. Instead, it is a temporal and spatial variable. This study presents an innovative approach for simulating contaminant intrusion in water distribution systems using EPANET multiple times. The methodology has six general steps. First, test bulk reaction coefficients of contaminant matrix in chemical lab. The uniqueness of this study is that the contaminant matrix is studied as a whole. The investigations of chemical, biological properties of individual aqueous constituents are not needed. Second, assume the contaminants as nonreactive, using EPANET 2.0 to identify where, when and at what concentrations of the inert contaminants will pass by in the water network. Third, determine the number of chlorine residual simulations based on the results in step two. Fourth, use EPANET to simulate the chlorine residual in the water network without the occurrence of contamination. Fifth, assign contaminated bulk coefficients to contaminated pipes; use EPANET to simulate the chlorine residual in the pipe network. Lastly, the chlorine concentrations of the impacted moments of impacted junctions are replaced with the results calculated in step five. This methodology is demonstrated in the south Pinellas County water distribution system.
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3

Lin, Daorui. "Global Sensitivity of Water Quality Modeling in the Gulf of Finland." Thesis, KTH, Mark- och vattenteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180285.

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The Gulf of Finland is the most eutrophied water body in the Baltic Sea, which is mainly caused by nutrient loads produced by human activities in its surrounding cities. In order to solve this environmental problem, a computational model based on the understanding the relations between eutrophication, water quality and sediments is needed to forecast the water quality variance in response to the natural and anthropogenic influences. A precise water quality model can be useful to assist the policy making in the Gulf of Finland, and even for the whole Baltic Sea. Kiirikki model, as one of these models describing the water quality of Baltic Sea in response of water quality variance, is a sediment and ecosystem based model, treating different sub-basins and layers as boxes. This study aims to assess the parameters’ sensitivity level on the scale of the Gulf of Finland. Firstly, the Morris sampling strategy is applied to generate economic OAT (One factor At a Time) samples before screening 50 out of 100 trajectories with distance as large as possible. In order to assess their sensitivity, index and indicator are needed. EE (elementary effect) is adopted to be the assessment index and four core eutrophication indicators from HELCOM 2009a are to be analyzed. By comparing the (σ,μ) and (σ,μ*) plots of each parameters’ EE values (σ is standard deviation, μ is mean value and μ* is the absolute mean value), some parameters are identified as potential sensitive parameter, such as the minimum biomass of cyanobacteria (Cmin), critical point of CO2 flux (CCr), the optimal temperature for detritus phosphorous mineralization (Toptgamma), maximum loss rate of algae (RAmax), optimal temperature for the growth of other algae (ToptmuA), Coefficient for temperature limiting factor for the growth of cyanobacteria (aTmuC), half-saturation coefficient of radiation for cyanobacteria (KIC) and so on. In contrast, the other parameters are ruled out as having very low values in terms of σ, μ and μ*. This is because the feature of Morris sampling strategy makes it easier to achieve high variance of the outputs, resulting into generally higher σ. Therefore, further investigation with different strategies is needed after the initial screening of the non-sensitive parameters in this study.
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4

Leitch, Katherine McArthur. "Estimating Tributary Phosphorus Loads Using Flow-Weighted Composite Storm Sampling." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/10078.

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Quantification of total phosphorus (TP) loads entering a lake or reservoir is important because phosphorus is most often the limiting nutrient in terms of algae growth, thus phosphorus can control the extent of eutrophication. Four methods for assessing the annual tributary phosphorus loads to two different Virginia reservoirs were analyzed, three methods that use tributary monitoring program data and one that uses land-use and rainfall data. In this project, one tributary has been extensively monitored for many years and served as a control on which the other methods were tested. The key difference between this research and previous studies is the inclusion of flow-weighted composite storm sampling instead of simple grab sample analyses of storm flow. Three of the methods employed flow stratification, and the impact of the base flow separation point was examined. It was found that the Regression Method developed in this research was the least sensitive to the base flow separation point, which is a valuable attribute because a wrong choice will not significantly affect the estimate. The Monte Carlo Method was found to underestimate the TP loads. The amount of rainfall impacted the accuracy of the methods, with more error occurring in a year with lower precipitation.
Master of Science
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5

Longsmith, Rebecca Johnson. "A BIOLOGICAL MONITORING INTERNSHIP WITH THE OHIO ENVIRONMENTAL PROTECTION AGENCY, DIVISION OF SURFACE WATER." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1451932666.

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Speakman, Anne Kathryn. "A WATER QUALITY INTERNSHIP WITH THE OHIO ENVIRONMENTAL PROTECTION AGENCY’S DIVISION OF SURFACE WATER." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1417012918.

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Defenbaugh, Angela Lynn. "Evaluating Ohio River Basin Waters: A Water Quality and Water Resources Internship with the Ohio River Valley Water Sanitation Commission." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1389295851.

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Del, Valle Lemuel Alejandro. "Water Quality Internship with the Ohio Environmental Protection Agency." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1470088630.

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9

Lindsey, Melanie. "Sampling Frequency for Semi-Arid Streams and Rivers: Implications for National Parks in the Sonoran Desert Network." Thesis, The University of Arizona, 2010. http://hdl.handle.net/10150/193445.

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In developing a water quality monitoring program, the sampling frequency chosen should be able to reliably detect changes in water quality trends. Three datasets are evaluated for Minimal Detectable Change in surface water quality to examine the loss of trend detectability as sampling frequency decreases for sites within the National Park Service's Sonoran Desert Network by re-sampling the records as quarterly and annual datasets and by superimposing step and linear trends over the natural data to estimate the time it takes the Seasonal Kendall Test to detect trends of a specific threshold. Wilcoxon Rank Sum analyses found that monthly and quarterly sampling consistently draw from the same distribution of trend detection times; however, annual sampling can take significantly longer. Therefore, even with a loss in power from reduced sampling, quarterly sampling of Park waters adequately detects trends (70%) compared to monthly whereas annual sampling is insufficient in trend detection (30%).
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10

Pence, Rachel Anabel. "Comparison of Quantitative and Semi-Quantitative Assessments of Benthic Macroinvertebrate Community Response to Elevated Salinity in central Appalachian Coalfield Streams." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/86787.

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Anthropogenic salinization of freshwater is a global concern. In freshwater environments, elevated levels of major ions, measured as total dissolved solids (TDS) or specific conductance (SC), can cause adverse effects on aquatic ecosystem structure and function. In central Appalachia, eastern USA, studies largely rely on Rapid Bioassessment Protocols with semi-quantitative sampling to characterize benthic macroinvertebrate community response to increased salinity caused by surface coal mining. These protocols require subsampling procedures and identification of fixed numbers of individuals regardless of organism density, limiting measures of community structure. Quantitative sampling involves enumeration of all individuals collected within a defined area and typically includes larger sample sizes relative to semi-quantitative sampling, allowing expanded characterization of the benthic community. Working in central Appalachia, I evaluated quantitative and semi-quantitative methods for bioassessments in headwater streams salinized by coal mining during two time periods. I compared the two sampling methods for capability to detect SC-induced changes in the macroinvertebrate community. Quantitative sampling consistently produced higher estimates of taxonomic richness than corresponding semi-quantitative samples, and differences between sampling methods were found for community composition, functional feeding group, dominance, tolerance, and habit metrics. Quantitative methods were generally stronger predictors of benthic community-metric responses to SC and were more sensitive for detecting SC-induced changes in the macroinvertebrate community. Quantitative methods are advantageous compared to semi-quantitative sampling methods when characterizing benthic macroinvertebrate community structure because they provide more complete estimates of taxonomic richness and diversity and produce metrics that are stronger predictors of community response to elevated SC.
Master of Science
Surface coal mining in central Appalachia, eastern USA, contributes to increased salinity of surface waters, causing adverse effects on water quality and aquatic life. Stream condition is often evaluated through sampling of benthic macroinvertebrates because they are ubiquitous in aquatic environments and differ in sensitivity to various types of pollution and environmental stressors. In central Appalachia, studies have largely relied on semi-quantitative sampling methods to characterize effects of elevated salinity on benthic macroinvertebrate communities in headwater streams. These methods are ‘semiquantitative’ because processing of samples requires subsampling procedures and identification of a fixed number of individuals, regardless of the number of organisms that were originally collected. In contrast, quantitative sampling involves identification and counting of all collected individuals, often resulting in organism counts that are much higher than those of semi-quantitative samples. Quantitative samples are typically more time-consuming and expensive to process but allow for expanded description of the benthic macroinvertebrate community and characterization of community-wide response to an environmental stressor such as elevated salinity. Working in central Appalachian streams, I compared 1) depictions of benthic macroinvertebrate community structure; 2) benthic community response to elevated salinity; and 3) the minimum levels of salinity associated with community change between quantitative and semi-quantitative methods. Quantitative sampling methods provide many advantages over semi-quantitative methods by providing more complete enumerations of the taxa present, thus enhancing the ability to evaluate aquatic-life condition and to characterize overall benthic macroinvertebrate community response to elevated salinity caused by surface coal mining.
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Книги з теми "Water quality sampling"

1

Stednick, John D. Wildland water quality sampling and analysis. San Diego: Academic Press, 1991.

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2

Li, Yuncong, and Kati W. Migliaccio. Water quality concepts, sampling, and analyses. Boca Raton, FL: CRC Press, 2011.

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3

Huntzinger, Thomas L. An experiment in representative ground-water sampling for water-quality analysis. Lawrence, Kan: The Survey, 1989.

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4

Huntzinger, Thomas L. An experiment in representative ground-water sampling for water-quality analysis. Lawrence, Kan: The Survey, 1989.

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5

Huntzinger, Thomas L. An experiment in representative ground-water sampling for water-quality analysis. Lawrence, Kan: The Survey, 1989.

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6

Désilets, L. Criteria for basin selection and sampling station macrolocation. Ottawa, Canada: Inland Waters Directorate, Water Quality Branch, 1988.

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7

Harsham, Keith D. Water sampling for pollution regulation. Luxembourg: Gordon and Breach, 1995.

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8

Ryberg, Karen R. Water-quality trend analysis and sampling design for the Devils Lake Basin, North Dakota, January 1965 through September 2003. Reston, VA: U.S. Dept. of the Interior, U.S. Geological Survey, 2006.

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9

Wang, Bronwen. Water-quality data for selected sites on Reversed, Rush, and Alger Creeks and Gull and Silver Lakes, Mono County, California, April 1994 to March 1995. Sacramento, Calif: U.S. Geological Survey, 1995.

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10

H, Nash, McCall, G. J. H. 1920-, and Association of Geoscientists for International Development., eds. Groundwater quality. London: Chapman & Hall, 1995.

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Частини книг з теми "Water quality sampling"

1

El-Shaarawi, A. H., and S. R. Esterby. "Sampling Recreational Waters." In Statistical Framework for Recreational Water Quality Criteria and Monitoring, 69–89. Chichester, UK: John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470518328.ch5.

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2

Burn, D. H. "Water Quality Sampling for Nutrient Loading Estimation." In Water Pollution: Modelling, Measuring and Prediction, 505–17. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3694-5_36.

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3

Bogialli, Sara, Stefano Polesello, and Sara Valsecchi. "Quality Issues in Water Sampling, Sample Pre-Treatment and Monitoring." In Transformation Products of Emerging Contaminants in the Environment, 281–302. Chichester, United Kingdom: John Wiley and Sons Ltd, 2014. http://dx.doi.org/10.1002/9781118339558.ch09.

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4

Wymer, Larry J. "The EMPACT Beaches: A Case Study in Recreational Water Sampling." In Statistical Framework for Recreational Water Quality Criteria and Monitoring, 113–34. Chichester, UK: John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470518328.ch7.

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5

Ryan, Barbara J., and Denis F. Healy. "Water Quality Sampling Program at Low-Level Radioactive Groundwater Contamination Site." In ACS Symposium Series, 242–54. Washington, DC: American Chemical Society, 1991. http://dx.doi.org/10.1021/bk-1991-0465.ch014.

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Amirah, A. S. N., W. H. Tan, W. Faridah, A. M. Andrew, N. A. N. Zainab, S. Ragunathan, and M. S. N. Shahniza. "Assessment of a Self-sustaining Drainage Ditch: Water Quality Monitoring and Sampling." In Lecture Notes in Mechanical Engineering, 1037–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0866-7_91.

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Beggel, Sebastian, Joachim Pander, and Jürgen Geist. "Ecological Indicators for Surface Water Quality - Methodological Approaches to Fish Community Assessments in China and Germany." In Terrestrial Environmental Sciences, 47–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80234-9_2.

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AbstractAssessments of aquatic ecosystem health rely increasingly on biological indicators such as fish community structure, but national approaches differ. To use bioindicators efficiently and to allow cross-country comparisons, standardized tools and methods are required. Within this study, currently applied procedures for stream ecosystem assessment in China and Germany are summarized and active and passive fish sampling methodologies used in both countries are investigated. The methodological comparison was based on the results of a joint Chinese German workshops within the SINOWATER project in 2016. A joint sampling campaign was then conducted in 2017 at 6 representative sites within 70 km of the Fan River, a tributary to the Liao River System in Liaoning province, China. Active methods comprised single-pass electrofishing methods as typically applied in Germany and China as well as seining. As passive methods, common minnow traps, gill-netting and longline-fishing were used. To allow the comparability between methods, a standardized sampling design comprising several replicates at each site was chosen, covering a range of different ecological stream conditions. By comparison of the different fishing methodologies, electrofishing yielded the best overall results to assess fish biodiversity in terms of species abundance, richness and catch per unit effort. Differences in the effectiveness of the different electrofishing approaches mostly depended on the power source used. To cover the full spectrum of the fish community and to detect very rare species, a combination of different active and passive methods was most useful. If electrofishing is the method of choice, it is very important to adjust the gear power to river specific conditions such as flow, size and depth. The results of this joint Chinese-German study may aid in the selection of suitable sampling methods for fish community assessments in the future.
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Mills, Graham A., Branislav Vrana, and Richard Greenwood. "The Potential of Passive Sampling to Support Regulatory Monitoring of the Chemical Quality of Environmental Waters." In Rapid Chemical and Biological Techniques for Water Monitoring, 53–69. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745427.ch2a.

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Denver, Judith M. "Groundwater-Sampling Network To Study Agrochemical Effects on Water Quality in the Unconfined Aquifer." In ACS Symposium Series, 139–49. Washington, DC: American Chemical Society, 1991. http://dx.doi.org/10.1021/bk-1991-0465.ch007.

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Goulter, I. C., A. Kusmulyono, and D. P. Irwin. "Improved Predictions of Water Quality Values and Design of Sampling Strategies Based on Entropy Theory." In Integrated Approach to Environmental Data Management Systems, 89–106. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5616-5_9.

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Тези доповідей конференцій з теми "Water quality sampling"

1

Tryby, Michael E., and James G. Uber. "Representative Water Quality Sampling in Water Distribution Systems." In World Water and Environmental Resources Congress 2001. Reston, VA: American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40569(2001)404.

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Chong, Meng Nan, Rupak Aryal, Jatinder Sidhu, Janet Tang, Simon Toze, and Ted Gardner. "Urban stormwater quality monitoring: From sampling to water quality analysis." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146598.

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K.W. King and R.D. Harmel. "Considerations in Selecting a Water Quality Sampling Strategy." In 2001 Sacramento, CA July 29-August 1,2001. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2001. http://dx.doi.org/10.13031/2013.7391.

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Grayman, Walter M., Vanessa L. Speight, and James G. Uber. "Using Monte-Carlo Simulation to Evaluate Alternative Water Quality Sampling Plans." In Eighth Annual Water Distribution Systems Analysis Symposium (WDSA). Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40941(247)17.

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5

Shuo, Jia, Zhang Yonghui, Ran Wen, and Tong Kebin. "The Unmanned Autonomous Cruise Ship for Water Quality Monitoring and Sampling." In 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC). IEEE, 2017. http://dx.doi.org/10.1109/iccsec.2017.8447040.

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Ford, W. I., and J. F. Fox. "Use of Water Quality Model Uncertainty Analysis to Develop Sampling Design Criteria for In-stream Carbon." In World Environmental and Water Resources Congress 2013. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412947.326.

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Newman, Connor P., Devin Castendyk, Brian Straight, Pierre Filiatreault, and Americo Pino. "REMOTE WATER-QUALITY SAMPLING OF NEVADA PIT LAKES USING UNMANNED AIRCRAFT SYSTEMS." In Joint 70th Annual Rocky Mountain GSA Section / 114th Annual Cordilleran GSA Section Meeting - 2018. Geological Society of America, 2018. http://dx.doi.org/10.1130/abs/2018rm-313820.

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Wesley C. Wright, Ronald E. Yoder, and John R. Buchanan. "A Comparison of Grab versus Flow Dependent Sampling for Monitoring Water Quality." In 2004, Ottawa, Canada August 1 - 4, 2004. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2004. http://dx.doi.org/10.13031/2013.17079.

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Wang, Xinjun, Gaoliang Liao, Ding Zhu, Jinling Yao, and Xiaowei Bai. "Study on the Sampling Quality of Wetness Measurement Probe for Thermodynamic Methods." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68262.

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Software FLUENT was applied to conduct the numerical calculations of the sampling velocity at the sampling nozzle inlet of the wetness measurement probe and the trajectories of water droplets in the steam flow. The steam wetness of samples and the percentage of the droplets with different diameters entering the sampling nozzle were ascertained. The results showed that wetness measurement probe affected the flow of vapor phase at some degrees. Especially, there was a deflection of stream line nearby the sampling nozzle. It was showed that the isokinetic sampling could not be accomplished because of the viscosity of vapor. The larger the angle between the steam flow direction and the center line of the sampling nozzle was, the lower the average sampling velocity at sampling nozzle inlet section was. The percentage of water droplets captured by sampling nozzle increased with the augmentation of water droplet diameters. When the water droplet diameter was 5μm, the sampling nozzle would capture all water droplets in the corresponding area of the sampling nozzle inlet. When the sampling nozzle was dead against the upper stream, the wetness of sample extracted by sampling nozzle was lower than that of the measured steam. In contrast, the wetness of sample was larger than that of the measured steam when the angle between the sampling nozzle and upper stream was ± 5° or ± 10° respectively. The results have showed that the wetness error increased with the augmentation of sampling nozzle diameters, the vapor velocity and the angle between upper steam and center line of the sampling nozzle.
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Yu, Kwisun Park, and Laura J. Harrell. "Efficient Strategies for Sampling Uncertain Parameters in a Genetic Algorithm-Based Chance-Constrained Watershed Water Quality Management Problem." In World Environmental and Water Resources Congress 2006. Reston, VA: American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40856(200)119.

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Звіти організацій з теми "Water quality sampling"

1

Koch, J. W. II, F. D. Martin, and H. M. Westbury. Par Pond refill water quality sampling. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/574526.

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Bergeron, B. P. Scoping Sampling at the Site 300 Water Line ProjectSOIL SAMPLING AND ANALYSIS PLAN /QUALITY ASSURANCE PLAN. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1577238.

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Hall, S. H., and S. P. Juracich. External quality control in ground-water sampling and analysis at the Hanford Site. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/10109598.

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Hall, S. H., and S. P. Juracich. External quality control in ground-water sampling and analysis at the Hanford Site. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/6012396.

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Peterson, Robert E., and Gregory W. Patton. Water Quality Sampling Locations Along the Shoreline of the Columbia River, Hanford Site, Washington. Office of Scientific and Technical Information (OSTI), December 2009. http://dx.doi.org/10.2172/972341.

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Hamilton, S. High-density, high quality regional sampling of water supply wells: Ontario's ambient groundwater geochemical program. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/297730.

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Gareau, Keith. Investigation and Evaluation of Current and Emerging Whole-Water Sampling Technologies for U.S. Geological Survey National Water Quality Assessment Program. Portland State University, June 2011. http://dx.doi.org/10.15760/mem.20.

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Ray, S. R., and Jim Vohden. Preliminary water quality sampling for the Fairbanks International Airport bioremediation project: November 1990 through October 1991. Alaska Division of Geological & Geophysical Surveys, 1991. http://dx.doi.org/10.14509/1526.

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Vohden, Jim. Summary of inorganic water quality sampling in the Fairbanks railroad industrial area, Fairbanks, Alaska: 1994-1995. Alaska Division of Geological & Geophysical Surveys, 1995. http://dx.doi.org/10.14509/1700.

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Desiderati, Christopher. Carli Creek Regional Water Quality Project: Assessing Water Quality Improvement at an Urban Stormwater Constructed Wetland. Portland State University, 2022. http://dx.doi.org/10.15760/mem.78.

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Stormwater management is an ongoing challenge in the United States and the world at-large. As state and municipal agencies grapple with conflicting interests like encouraging land development, complying with permits to control stormwater discharges, “urban stream syndrome” effects, and charges to steward natural resources for the long-term, some agencies may turn to constructed wetlands (CWs) as aesthetically pleasing and functional natural analogs for attenuating pollution delivered by stormwater runoff to rivers and streams. Constructed wetlands retain pollutants via common physical, physicochemical, and biological principles such as settling, adsorption, or plant and algae uptake. The efficacy of constructed wetlands for pollutant attenuation varies depending on many factors such as flow rate, pollutant loading, maintenance practices, and design features. In 2018, the culmination of efforts by Clackamas Water Environment Services and others led to the opening of the Carli Creek Water Quality Project, a 15-acre constructed wetland adjacent to Carli Creek, a small, 3500-ft tributary of the Clackamas River in Clackamas County, OR. The combined creek and constructed wetland drain an industrialized, 438-acre, impervious catchment. The wetland consists of a linear series of a detention pond and three bioretention treatment cells, contributing a combined 1.8 acres of treatment area (a 1:243 ratio with the catchment) and 3.3 acre-feet of total runoff storage. In this study, raw pollutant concentrations in runoff were evaluated against International Stormwater BMP database benchmarks and Oregon Water Quality Criteria. Concentration and mass-based reductions were calculated for 10 specific pollutants and compared to daily precipitation totals from a nearby precipitation station. Mass-based reductions were generally higher for all pollutants, largely due to runoff volume reduction on the treatment terrace. Concentration-based reductions were highly variable, and suggested export of certain pollutants (e.g., ammonia), even when reporting on a mass-basis. Mass load reductions on the terrace for total dissolved solids, nitrate+nitrite, dissolved lead, and dissolved copper were 43.3 ± 10%, 41.9 ± 10%, 36.6 ± 13%, and 43.2 ± 16%, respectively. E. coli saw log-reductions ranging from -1.3 — 3.0 on the terrace, and -1.0 — 1.8 in the creek. Oregon Water Quality Criteria were consistently met at the two in-stream sites on Carli Creek for E. coli with one exception, and for dissolved cadmium, lead, zinc, and copper (with one exception for copper). However, dissolved total solids at the downstream Carli Creek site was above the Willamette River guidance value 100 mg/L roughly 71% of the time. The precipitation record during the study was useful for explaining certain pollutant reductions, as several mechanisms are driven by physical processes, however it was not definitive. The historic rain/snow/ice event in mid-February 2021 appeared to impact mass-based reductions for all metals. Qualitatively, precipitation seemed to have the largest effect on nutrient dynamics, specifically ammonia-nitrogen. Determining exact mechanisms of pollutant removals was outside the scope of this study. An improved flow record, more targeted storm sampling, or more comprehensive nutrient profiles could aid in answering important questions on dominant mechanisms of this new constructed wetland. This study is useful in establishing a framework and baseline for understanding this one-of-a-kind regional stormwater treatment project and pursuing further questions in the future.
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