Journal articles: 'Water sampling'

1

Sharpley, Andrew. "Water Sampling." Journal of Environmental Quality 19, no. 2 (1990): 352. http://dx.doi.org/10.2134/jeq1990.00472425001900020031x.

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2

Edmond, John M. "Water sampling." Geochimica et Cosmochimica Acta 54, no. 6 (1990): 1862. http://dx.doi.org/10.1016/0016-7037(90)90425-k.

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3

El-Shaarawi, A. H. "Statistical aspects of setting a sampling strategy." Journal français d’hydrologie 17, no. 1 (1986): 37–45. http://dx.doi.org/10.1051/water/19861701037.

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4

Madrid, Yolanda, and Zoyne Pedrero Zayas. "Water sampling: Traditional methods and new approaches in water sampling strategy." TrAC Trends in Analytical Chemistry 26, no. 4 (2007): 293–99. http://dx.doi.org/10.1016/j.trac.2007.01.002.

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5

Ore, John-Paul, Sebastian Elbaum, Amy Burgin, and Carrick Detweiler. "Autonomous Aerial Water Sampling." Journal of Field Robotics 32, no. 8 (2015): 1095–113. http://dx.doi.org/10.1002/rob.21591.

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6

Malard, Florian, Jean-Louis Reygrobellet, Roger Laurent, and Jacques Mathieu. "Developments in sampling the fauna of deep water-table aquifers." Archiv für Hydrobiologie 138, no. 3 (1997): 401–32. http://dx.doi.org/10.1127/archiv-hydrobiol/138/1997/401.

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7

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

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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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Salman, Shaaban Ali, Muzoun Al Dhaheri, Peter Dawson, and Sreenatha Anavatti. "Autonomous Water Sampling Payload Design." International Review of Aerospace Engineering (IREASE) 13, no. 3 (2020): 120. http://dx.doi.org/10.15866/irease.v13i3.18374.

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Griffiths, J., A. Egorov, L. Montuori, L. Ascolillo, and E. N. F. Naumova. "WATER SAMPLING AND CRYPTOSPORIDIUM SEROPREVALENCE." Epidemiology 14, Supplement (2003): S138. http://dx.doi.org/10.1097/00001648-200309001-00343.

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10

Thomas, O., and F. Theraulaz. "Analytical assistance for water sampling." TrAC Trends in Analytical Chemistry 13, no. 9 (1994): 344–48. http://dx.doi.org/10.1016/0165-9936(94)85004-6.

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11

Mines, Barry S., John L. Davidson, David Bloomquist, and Thomas B. Stauffer. "Sampling of VOCs with the BAT Ground Water Sampling System." Groundwater Monitoring & Remediation 13, no. 1 (1993): 115–20. http://dx.doi.org/10.1111/j.1745-6592.1993.tb00428.x.

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12

Kramer, K. J. M. "Inorganic Contaminants in the Water Column: Sampling and Sampling Strategy." International Journal of Environmental Analytical Chemistry 57, no. 3 (1994): 179–88. http://dx.doi.org/10.1080/03067319408027424.

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13

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

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14

Pohlmann, K. F., and J. W. Hess. "Generalized Ground Water Sampling Device Matrix." Groundwater Monitoring & Remediation 8, no. 4 (1988): 82–84. http://dx.doi.org/10.1111/j.1745-6592.1988.tb01106.x.

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15

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

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16

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

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17

Basurko, O. C., and E. Mesbahi. "Statistical representativeness of ballast water sampling." Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 225, no. 3 (2011): 183–90. http://dx.doi.org/10.1177/1475090211412682.

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18

Gollasch, Stephan, and Matej David. "Recommendations for representative ballast water sampling." Journal of Sea Research 123 (May 2017): 1–15. http://dx.doi.org/10.1016/j.seares.2017.02.010.

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19

Teasdale, P. "Pore water sampling with sediment peepers." TrAC Trends in Analytical Chemistry 14, no. 6 (1995): 250–56. http://dx.doi.org/10.1016/0165-9936(95)91617-2.

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20

Stamer, John K. "Water supply implications of herbicide sampling." Journal - American Water Works Association 88, no. 2 (1996): 76–85. http://dx.doi.org/10.1002/j.1551-8833.1996.tb06504.x.

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21

Crane, Ken, and Nick Vertelman. "KENNICK Deicer Thaws Water Sampling Stations." Opflow 36, no. 9 (2010): 26. http://dx.doi.org/10.1002/j.1551-8701.2010.tb02354.x.

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22

Neale, J. "Sampling sediment under warm deep water." Quaternary Science Reviews 15, no. 5-6 (1996): 581–90. http://dx.doi.org/10.1016/0277-3791(96)00010-8.

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23

Lariosa, Irish Mae G., Jeanette C. Pao, Charles Alver G. Banglos, et al. "Drone-Based Automatic Water Sampling System." IEEE Access 12 (2024): 35109–24. http://dx.doi.org/10.1109/access.2024.3372655.

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24

Sunjka, Dragana, and Sanja Lazic. "Water sampling techniques for continous monitoring of pesticides in water." Pesticidi i fitomedicina 32, no. 2 (2017): 85–93. http://dx.doi.org/10.2298/pif1702085s.

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Good ecological and chemical status of water represents the most important aim of the Water Framework Directive 2000/60/EC, which implies respect of water quality standards at the level of entire river basin (2008/105/EC and 2013/39/EC). This especially refers to the control of pesticide residues in surface waters. In order to achieve the set goals, a continuous monitoring program that should provide a comprehensive and interrelated overview of water status should be implemented. However, it demands the use of appropriate analysis techniques. Until now, the procedure for sampling and quantification of residual pesticide quantities in aquatic environment was based on the use of traditional sampling techniques that imply periodical collecting of individual samples. However, this type of sampling provides only a snapshot of the situation in regard to the presence of pollutants in water. As an alternative, the technique of passive sampling of pollutants in water, including pesticides has been introduced. Different samplers are available for pesticide sampling in surface water, depending on compounds. The technique itself is based on keeping a device in water over a longer period of time which varies from several days to several weeks, depending on the kind of compound. In this manner, the average concentrations of pollutants dissolved in water during a time period (time-weighted average concentrations, TWA) are obtained, which enables monitoring of trends in areal and seasonal variations. The use of these techniques also leads to an increase in sensitivity of analytical methods, considering that pre-concentration of analytes takes place within the sorption medium. However, the use of these techniques for determination of pesticide concentrations in real water environments requires calibration studies for the estimation of sampling rates (Rs). Rs is a volume of water per time, calculated as the product of overall mass transfer coefficient and area of the receiving phase exposed to the external environment, and it is substance specific.

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25

Gollasch, Stephan. "A new ballast water sampling device for sampling organisms above 50 micron." Aquatic Invasions 1, no. 1 (2006): 46–50. http://dx.doi.org/10.3391/ai.2006.1.1.12.

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26

Koparan, Cengiz, A. Bulent Koc, Charles V. Privette, and Calvin B. Sawyer. "Adaptive Water Sampling Device for Aerial Robots." Drones 4, no. 1 (2020): 5. http://dx.doi.org/10.3390/drones4010005.

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Water quality monitoring and predicting the changes in water characteristics require the collection of water samples in a timely manner. Water sample collection based on in situ measurable water quality indicators can increase the efficiency and precision of data collection while reducing the cost of laboratory analyses. The objective of this research was to develop an adaptive water sampling device for an aerial robot and demonstrate the accuracy of its functions in laboratory and field conditions. The prototype device consisted of a sensor node with dissolved oxygen, pH, electrical conductivity, temperature, turbidity, and depth sensors, a microcontroller, and a sampler with three cartridges. Activation of water capturing cartridges was based on in situ measurements from the sensor node. The activation mechanism of the prototype device was tested with standard solutions in the laboratory and with autonomous water sampling flights over the 11-ha section of a lake. A total of seven sampling locations were selected based on a grid system. Each cartridge collected 130 mL of water samples at a 3.5 m depth. Mean water quality parameters were measured as 8.47 mg/L of dissolved oxygen, pH of 5.34, 7 µS/cm of electrical conductivity, temperature of 18 °C, and 37 Formazin Nephelometric Unit (FNU) of turbidity. The dissolved oxygen was within allowable limits that were pre-set in the self-activation computer program while the pH, electrical conductivity, and temperature were outside of allowable limits that were specified by Environmental Protection Agency (EPA). Therefore, the activation mechanism of the device was triggered and water samples were collected from all the sampling locations successfully. The adaptive water sampling with Unmanned Aerial Vehicle-assisted water sampling device was proved to be a successful method for water quality evaluation.

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27

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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28

Steele, Heather L., and James K. Hardy. "Permeation sampling of phthalate esters in water." Journal of Environmental Science and Health, Part A 44, no. 4 (2009): 340–45. http://dx.doi.org/10.1080/10934520802659661.

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29

Bryden, Gregg W., William R. Mabey, and Keith M. Robine. "Sampling for Toxic Contaminants in Ground Water." Groundwater Monitoring & Remediation 6, no. 2 (1986): 67–72. http://dx.doi.org/10.1111/j.1745-6592.1986.tb01241.x.

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30

Smethie, William, Dale Chayes, Richard Perry, Peter Schlosser, and Ronny Friedrich. "A Rosette for Sampling Ice-Covered Water." Oceanography 24, no. 3 (2011): 160–61. http://dx.doi.org/10.5670/oceanog.2011.67.

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31

Varilly, Patrick, and David Chandler. "Water Evaporation: A Transition Path Sampling Study." Journal of Physical Chemistry B 117, no. 5 (2013): 1419–28. http://dx.doi.org/10.1021/jp310070y.

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32

R. D. Harmel, K. W. King, and R. M. Slade. "AUTOMATED STORM WATER SAMPLING ON SMALL WATERSHEDS." Applied Engineering in Agriculture 19, no. 6 (2003): 667–74. http://dx.doi.org/10.13031/2013.15662.

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33

Barceló, D., and M. C. Hennion. "Sampling of polar pesticides from water matrices." Analytica Chimica Acta 338, no. 1-2 (1997): 3–18. http://dx.doi.org/10.1016/s0003-2670(96)00440-0.

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34

Wells, David E. "Sampling persistent organic contaminants in sea water." TrAC Trends in Analytical Chemistry 13, no. 9 (1994): 339–43. http://dx.doi.org/10.1016/0165-9936(94)85003-8.

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35

Falter, J. L., and F. J. Sansone. "Shallow pore water sampling in reef sediments." Coral Reefs 19, no. 1 (2000): 93–97. http://dx.doi.org/10.1007/s003380050233.

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36

Murphy, Kathleen Lancaster. "Water Sampling for Legionella: Managing Positive Results." Biology of Blood and Marrow Transplantation 20, no. 2 (2014): S123. http://dx.doi.org/10.1016/j.bbmt.2013.12.184.

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37

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

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38

Brown, Michael S., Gregory J. Fiechtner, J. V. Rudd, David A. Zimdars, Matthew Warmuth, and James R. Gord. "Water-Vapor Detection Using Asynchronous THz Sampling." Applied Spectroscopy 60, no. 3 (2006): 261–65. http://dx.doi.org/10.1366/000370206776342670.

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39

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

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40

Sansone, Francis J., Christine C. Andrews, Robert W. Buddemeier, and Gordon W. Tribble. "Well-point sampling of reef interstitial water." Coral Reefs 7, no. 1 (1988): 19–22. http://dx.doi.org/10.1007/bf00301977.

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41

Wang, Weifang, Mingxia Kang, and Ruming Kang. "Autonomous Microcontroller-Based Aerial Water Sampling Device." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 255–58. http://dx.doi.org/10.5954/icarob.2023.os10-2.

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42

Vivian, B. J., and J. N. Quinton. "Automated water sampling in ephemeral hydrological systems." Earth Surface Processes and Landforms 18, no. 9 (1993): 863–68. http://dx.doi.org/10.1002/esp.3290180911.

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43

Mauclaire, L., P. Marmonier, and J. Gibert. "Sampling water and sediment in interstitial habitats: a comparison of coring and pumping techniques." Fundamental and Applied Limnology 142, no. 1 (1998): 111–23. http://dx.doi.org/10.1127/archiv-hydrobiol/142/1998/111.

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44

Blodau, Christian, and Tim R. Moore. "MACROPOROSITY AFFECTS WATER MOVEMENT AND PORE WATER SAMPLING IN PEAT SOILS." Soil Science 167, no. 2 (2002): 98–109. http://dx.doi.org/10.1097/00010694-200202000-00002.

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45

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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46

de Verneil, Alain, Louise Rousselet, Andrea M. Doglioli, et al. "OUTPACE long duration stations: physical variability, context of biogeochemical sampling, and evaluation of sampling strategy." Biogeosciences 15, no. 7 (2018): 2125–47. http://dx.doi.org/10.5194/bg-15-2125-2018.

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Abstract. Research cruises to quantify biogeochemical fluxes in the ocean require taking measurements at stations lasting at least several days. A popular experimental design is the quasi-Lagrangian drifter, often mounted with in situ incubations or sediment traps that follow the flow of water over time. After initial drifter deployment, the ship tracks the drifter for continuing measurements that are supposed to represent the same water environment. An outstanding question is how to best determine whether this is true. During the Oligotrophy to UlTra-oligotrophy PACific Experiment (OUTPACE) cruise, from 18 February to 3 April 2015 in the western tropical South Pacific, three separate stations of long duration (five days) over the upper 500 m were conducted in this quasi-Lagrangian sampling scheme. Here we present physical data to provide context for these three stations and to assess whether the sampling strategy worked, i.e., that a single body of water was sampled. After analyzing tracer variability and local water circulation at each station, we identify water layers and times where the drifter risks encountering another body of water. While almost no realization of this sampling scheme will be truly Lagrangian, due to the presence of vertical shear, the depth-resolved observations during the three stations show most layers sampled sufficiently homogeneous physical environments during OUTPACE. By directly addressing the concerns raised by these quasi-Lagrangian sampling platforms, a protocol of best practices can begin to be formulated so that future research campaigns include the complementary datasets and analyses presented here to verify the appropriate use of the drifter platform.

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47

Chen, J., and Y. Deng. "Identifiability analysis of the CSTR river water quality model." Water Science and Technology 53, no. 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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48

Ascott, Matthew J., Marianne E. Stuart, Daren C. Gooddy, et al. "Provenance of drinking water revealed through compliance sampling." Environmental Science: Processes & Impacts 21, no. 6 (2019): 1052–64. http://dx.doi.org/10.1039/c8em00437d.

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49

Jansen, HM, GK Reid, RJ Bannister, et al. "Discrete water quality sampling at open-water aquaculture sites: limitations and strategies." Aquaculture Environment Interactions 8 (August 23, 2016): 463–80. http://dx.doi.org/10.3354/aei00192.

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50

Zelenchuk, A. V., V. A. Zelenchuk, and V. A. Krylenkov. "The Development of Mobile Deep-Water Sampling Technique from Polar Water Areas." Океанология 53, no. 6 (2013): 838–42. http://dx.doi.org/10.7868/s0030157413060099.

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Journal articles on the topic 'Water sampling'

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