Статті в журналах: "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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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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11

Kao, Jehng-Jung, Pei-Hao Li, Chin-Lien Lin, and Wen-Hsin Hu. "Siting analyses for water quality sampling in a catchment." Environmental Monitoring and Assessment 139, no. 1-3 (June 17, 2007): 205–15. http://dx.doi.org/10.1007/s10661-007-9828-6.

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12

Smith, Eric P., Alyaa Zahran, Mahmoud Mahmoud, and Keying Ye. "Evaluation of water quality using acceptance sampling by variables." Environmetrics 14, no. 4 (2003): 373–86. http://dx.doi.org/10.1002/env.592.

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13

Jansen, HM, GK Reid, RJ Bannister, V. Husa, SMC Robinson, JA Cooper, C. Quinton, and Ø. Strand. "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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14

Sadiq, Q., C. K. Ezeamaka, M. G. Daful, and I. A. Mustafa. "Evaluation of the Water Quality of River Kaduna, Nigeria Using Water Quality Index." Environmental Technology and Science Journal 13, no. 1 (September 6, 2022): 28–40. http://dx.doi.org/10.4314/etsj.v13i1.3.

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Water is a natural resource of fundamental importance and supports all life forms. The study evaluated the water quality of River Kaduna using the Canadian Council of Ministers of the Environment (CCME) Water Quality Index. The study covered both raining and dry seasons in 10 sampling points. Water parameters analysed were turbidity, Zn, Pb, Cd, Cr, Cu, Mn, Fe, dissolved oxygen, electrical conductivity, pH, TDS and Ni using standard laboratory techniques. The data obtained were used to develop Water Quality Index (WQI) across the 10 sampling points and results showed that the water quality at Barnawa, Kudenda, Tudun Wada, Makera and Angwan Muazu are poor as their index values ranged between 31.8 – 42 while Kawo, Angwan Dosa, Malali, Kigo and Angwan Rimi are marginal as their index ranged between 45 – 61.3. The study concluded that the variety and level of contaminants in River Kaduna was related to the anthropogenic activities in the various parts of Kaduna Metropolis from where run-off and contaminants were received, hence, the water quality of River Kaduna is deteriorating. There is therefore the need for proper environmental education and discouragement in the use of toxic chemicals for farming so that pollution can be controlled at the source.

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15

Koparan, Cengiz, A. Bulent Koc, Charles V. Privette, and Calvin B. Sawyer. "Adaptive Water Sampling Device for Aerial Robots." Drones 4, no. 1 (February 6, 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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16

Ehiorobo, S. I., and A. E. Ogbeibu. "Assessment of Water Quality of Okomu Wetland Using Water Quality Index." Nigerian Journal of Environmental Sciences and Technology 4, no. 2 (October 2020): 450–57. http://dx.doi.org/10.36263/nijest.2020.02.0224.

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The water quality of the Okomu Wetland was evaluated using the Water Quality Index (WQI) technique which provides a number that expresses overall water quality of a water body or water sample at a particular time. Sampling of physicochemical parameters spanned two years covering the wet and dry seasons and the water quality data were obtained from 10 sampling locations; Ponds 36, 52, 54, 61, 64, 90, 94, Arhakhuan Stream, Okomu River (Agekpukpu) and Okomu River (Iron bridge) all within the Okomu National Park. Parameters such as Total Dissolved Solids (TDS), Turbidity, pH, Electrical conductivity (EC), Chlorine (Cl), Nitrate (NO3), Sulphate (SO4), Sodium (Na), Magnesium (Mg), (Iron) Fe, Chromium (Cr), Zinc (Zn), Copper (Cu), Manganese (Mn), Lead (Pb), and Nikel (Ni) were used to compute WQI and the values obtained for the wetland ranged between 34.36 and 167.28. The Index shows that pond 36, 52 and 54 are unfit for drinking with values between 103.86 and 167.28; ponds 61 and 64 are of the very poor quality category with WQI values of 95.19 and 92.44 respectively, Pond 90, pond 94, Arhakhuan Stream and Okomu River (Agekpukpu) are of poor quality and WQI values between and 53.58 and 73.15. Whereas, the Okomu River (Iron bridge) is within the good water quality (34.36) category. The Okomu River by Iron bridge is of good quality rating while other sampled points were of poor, very poor or unfit for drinking though these water bodies are mostly free from anthropogenic activities because of the conservative status of the study area. A major source of pollution within the wetland is surface runoff. The water quality of the wetland may not be suitable for man’s consumption especially pond water which are majorly impacted by runoff, yet very important for the survival and sustenance of the forest animals and plants. The water quality index (WQI) interprets physicochemical characteristics of water by providing a value which expresses the overall water quality and thus, reveals possible pollution problems of a water body. It turns complex water quality data into information that is easily understandable and usable by scientists, researchers and the general public.

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17

Shah, Shailendra Kumar. "Water quality assessment of mardi river by using water quality index." BIBECHANA 10 (October 31, 2013): 100–107. http://dx.doi.org/10.3126/bibechana.v10i0.7106.

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The aimed study assesses the water quality of Mardi River applying National Sanitation Foundation (NSF) America developed index called Water Quality Index (WQI). This index is one of effective way to inform about water quality trends to the public and the policy makers for water quality management. As Mardi River is primary source of consumption to Pokhara city and Mardi Watershed entities, the water quality is important for public health and ecological aspects. The study starts with five different sampling stations having total fifteen samples along three (April, May, June) months of the year 2012 were analyzed in water laboratory. After the analysis the weight values and sub index were obtained from the NSFWQI method which results that Mardi River water has Medium degraded water quality ranges in class C and NSFWQI of Mardi river scores as 55.02 and there is high correlation between water quality parameters Nitrogen and Turbidity DOI: http://dx.doi.org/10.3126/bibechana.v10i0.7106 BIBECHANA 10 (2014) 100-107

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18

Kongprajug, Akechai, Namfon Booncharoen, Kanyaluck Jantakee, Natcha Chyerochana, Skorn Mongkolsuk, and Kwanrawee Sirikanchana. "Sewage-specific enterococcal bacteriophages and multiple water quality parameters for coastal water quality assessment." Water Science and Technology 79, no. 5 (October 31, 2018): 799–807. http://dx.doi.org/10.2166/wst.2018.460.

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Abstract Coastal water quality is deteriorating worldwide. Water quality monitoring is therefore essential for public health risk evaluation and the management of water bodies. This study investigated the feasibility of using bacteriophages of Enterococcus faecalis as sewage-specific faecal indicators, together with physicochemical (dissolved oxygen, pH, temperature and total suspended solids) and biological parameters, to assess coastal water quality using multivariate analysis incorporating non-detects. The principal component and cluster analyses demonstrated that coastal water quality was mostly influenced by biological parameters, including Escherichia coli and total coliforms, which were found in all 31 sampling sites, and enterococci, which was found in all but two sampling sites. The enterococcal bacteriophages AIM06 and SR14 were detected in 17 and 18 samples at concentrations up to 1,815 and 2,790 PFU/100 mL, respectively. Both bacteriophages co-presented in approximately 80% of phage-positive samples, and the concentrations at each site were not significantly different. Overall, either bacteriophage could be used to differentiate high- and low-level coastal water pollution, as grouped by cluster analysis. This study is the first to investigate the suitability of sewage-specific bacteriophages of E. faecalis for monitoring coastal water quality and emphasises the importance of a multivariate analysis with non-detects to facilitate coastal water quality monitoring and management.

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19

Ekhwan Toriman, Mohd, Hassan Mohammed Ali Alssgeer, Muhammad Barzani Gasim, Khairul Amri Kamarudin, Mabroka Mohamed Daw, and Laila Omaer Mohammed Alabyad. "Impacts of Land-Use Changes on Water Quality by an Application of GIS Analysis: a Case Study of Nerus River, Terengganu, Malaysia." International Journal of Engineering & Technology 7, no. 3.14 (July 25, 2018): 155. http://dx.doi.org/10.14419/ijet.v7i3.14.16877.

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The impact of land use change on water quality of Nerus River Kuala Terengganu is an event that needs to be taken seriously in this study. The objectives of the study area are to carried out 13 parameters water quality samplings and analysis of Nerus River as well as to classify water quality concentration based on NWQS and WQI classifications; to interpret 2000 and 2013 land use/land cover maps of Nerus River Basin and to evaluate water quality data by statistical technique such as similarities and dissimilarities between sampling stations to determine pollution sources. Methods that were used in study area GIS will use to classify land cover/land use changes in the catchment between 2000 and 2013 land use maps. Water quality analysis and monitoring were done based on three sampling stations during both dry and wet seasons, involving analysis 13 water quality parameters. Water quality classification is using the National Water Quality Standard (NWQS) and the Water Quality Index (WQI). Statistical analysis such as similarities and dissimilarities between sampling stations was applied. Results of the study show that the river was classified as class II (slightly polluted), III (moderately polluted) in accordance with previous studies.

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20

Parker, Louise V. "The Effects of Ground Water Sampling Devices on Water Quality: A Literature Review." Groundwater Monitoring & Remediation 14, no. 2 (May 1994): 130–41. http://dx.doi.org/10.1111/j.1745-6592.1994.tb00108.x.

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21

Rajagopal, R. "The Effect of Sampling Frequency on Ground Water Quality Characterization." Groundwater Monitoring & Remediation 6, no. 4 (December 1986): 65–73. http://dx.doi.org/10.1111/j.1745-6592.1986.tb01035.x.

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22

Tate, Kenneth W., Randy A. Dahlgren, Michael J. Singer, Barbara Allen-Diaz, and Edward R. Atwill. "Timing, frequency of sampling affect accuracy of water-quality monitoring." California Agriculture 53, no. 6 (November 1999): 44–48. http://dx.doi.org/10.3733/ca.v053n06p44.

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23

Khatib, Issam A. Al, and Reem S. Ghannam. "Microbiological water quality and sampling policy of public swimming pools." International Journal of Environmental Engineering 3, no. 2 (2011): 192. http://dx.doi.org/10.1504/ijee.2011.039454.

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24

Speight, Vanessa L., William D. Kalsbeek, and Francis A. DiGiano. "Randomized Stratified Sampling Methodology for Water Quality in Distribution Systems." Journal of Water Resources Planning and Management 130, no. 4 (July 2004): 330–38. http://dx.doi.org/10.1061/(asce)0733-9496(2004)130:4(330).

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25

Eliades, Demetrios G., Marios M. Polycarpou, and Bambos Charalambous. "A Security-Oriented Manual Quality Sampling Methodology for Water Systems." Water Resources Management 25, no. 4 (June 10, 2010): 1219–28. http://dx.doi.org/10.1007/s11269-010-9674-0.

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26

Liu, Yan, BingHui Zheng, Mei Wang, YanXue Xu, and YanWen Qin. "Optimization of sampling frequency for routine river water quality monitoring." Science China Chemistry 57, no. 5 (September 25, 2013): 772–78. http://dx.doi.org/10.1007/s11426-013-4968-8.

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27

Rode, M., and U. Suhr. "Uncertainties in selected surface water quality data." Hydrology and Earth System Sciences Discussions 3, no. 5 (September 21, 2006): 2991–3021. http://dx.doi.org/10.5194/hessd-3-2991-2006.

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

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28

Rode, M., and U. Suhr. "Uncertainties in selected river water quality data." Hydrology and Earth System Sciences 11, no. 2 (February 13, 2007): 863–74. http://dx.doi.org/10.5194/hess-11-863-2007.

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

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29

Bortoletto, E. C., H. A. Silva, C. M. Bonifácio, and C. R. G. Tavares. "Water quality monitoring of the Pirapó River watershed, Paraná, Brazil." Brazilian Journal of Biology 75, no. 4 suppl 2 (December 2015): 148–57. http://dx.doi.org/10.1590/1519-6984.00313suppl.

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This study aimed to evaluate the water quality of the Pirapó River watershed in Paraná, Brazil, and identify the critical pollution sites throughout the drainage basin. The water quality was monitored during the period from January 2011 to December 2012. Nine points distributed throughout the main channel of the Pirapó River were sampled for a total of 17 samplings. The water quality was evaluated based on the determination of 14 physical, chemical and microbiological parameters. Analysis of the variables monitored in the Pirapó River watershed using factor analysis/principal components analysis (FA/PCA) indicated the formation of three distinct groups of parameters: water temperature (Twater), dissolved oxygen (DO) and a group composed of total suspended solids (TSS), turbidity and nitrite (NO2–). The parameters Twater and DO exhibited a relationship with the seasonality, and the TSS, turbidity, and NO2– levels were correlated with surface runoff caused by rainfall events. Principal component analysis (PCA) of the sampling points enabled the selection of the 10 most important variables from among the 14 evaluated parameters. The results showed that the nitrate (NO3–), NO2–, TSS, turbidity and total phosphorous (TP) levels were related to the soil type, and the parameters DO, electrical conductivity (EC), ammoniacal nitrogen (N-NH3) and thermotolerant coliforms (TC) were related to organic matter pollution, with the P5 sampling site being the most critical site. The ordination diagram of the sampling points as a function of the PCA indicated a reduction from 9 to 5 sampling points, indicating the potential for decreasing the costs associated with monitoring.

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30

To, Thuy Chau. "WATER QUALITY ASSESSMENT OF SAIGON RIVER FOR PUBLIC WATER SUPPLY BASED ON WATER QUALITY INDEX." Vietnam Journal of Science and Technology 58, no. 5A (November 12, 2020): 85. http://dx.doi.org/10.15625/2525-2518/58/5a/15203.

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Water Quality Index (WQI) is a dimensional number that aggregates information from many water quality parameters according to a defined method. WQI is accepted as an efficient tool for water quality management. In this study, WQI of Saigon river for public water supply was calculated from nine water quality parameters including pH, suspended solids (SS), dissolved oxygen (DO), chemical oxygen demand (COD), nitrite, ammonia, phosphate, total dissolved iron and total coliform based on water quality data obtained monthly from January 2016 to December 2019 at three sampling sites. The results showed that most of WQI values belonged to class III (medium water quality with the WQIs of 35 – 64) and class IV (poor water quality with the WQIs of 11 – 34) and a deteriorating trend was observed from upstream to downstream of Saigon river. The river water quality could not be used for public water supply.

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31

Milz, Beatriz, Patricia Oliveira de Aquino, Jean Carlo Gonçalves Ortega, Ana Luisa Vietti Bitencourt, and Cristina Souza Freire Nordi. "Spatio-temporal variability of water quality in Billings Reservoir Central Body - São Paulo, Brazil." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 17, no. 3 (May 31, 2022): 1–16. http://dx.doi.org/10.4136/ambi-agua.2823.

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The Billings Reservoir is an important water body for public supply of the Metropolitan Region of São Paulo, Brazil, and water captation for public supply is located in the Rio Grande environmental compartment. This article evaluates the water quality of the environmental compartment Central Body I of the Billings Reservoir, which receives the reversed waters from the polluted Pinheiros River, at four sampling points with different contributions from the surroundings, seeking to verify the influence of seasonality on water quality and whether there was a difference in water quality between the sampling points. Water sampling was carried out on the surface at four points, in a longitudinal profile, covering two periods (dry and rainy) distributed in six samplings between 2016 and 2019. Analyzed variables included temperature, dissolved oxygen, pH, electrical conductivity, chlorophyll-a and nutrients (phosphorus and nitrogen). Space-Time Interaction tests revealed that physicochemical variables did not vary due to the interaction between sampling periods and points, but several variables varied significantly during the sampling period. The results of the Trophic State Index show that waters of Central Body I are classified as Hypereutrophic, highlighting the degradation of water quality in this compartment. This research will better inform public managers and assist their efforts to minimize and mitigate the effects of progressive water quality degradation in this reservoir. Keywords: eutrophication, Pinheiros river, urban reservoir.

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32

Mutlu, Ekrem, and Arzu Aydın Uncumusaoğlu. "Physicochemical Analysis of Water Quality of Brook Kuruçay." Turkish Journal of Agriculture - Food Science and Technology 4, no. 11 (November 16, 2016): 991. http://dx.doi.org/10.24925/turjaf.v4i11.991-998.946.

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In this study, through the analyses of water samples taken from 9 stations on the brook between July 2012 and June 2013, we aimed to determine the monthly and seasonal changes in water quality parameters of Brook Kuruçay, to determine the water quality properties, to reveal the pollution problems, to determine the suitability level in terms of aquatic life and to classify the quality of water in accordance with Surface Water Quality Management Regulation’s Inland Surface Water Classes criteria. The study area is located southeast of the Hafik District of Sivas city and the altitude is 2608 m. The water samples were collected from 9 stations established on the brook, and some physicochemical parameters and heavy metal concentrations were analyzed in water samples. The cleaning and maintenance of all of the equipment, land-type measurement tools, and glass sampling containers to be used in sampling were made 1 day before sampling. Sampling tubes were immersed into 15 cm below the water surface for taking water samples. Heavy metal concentrations were determined in the Sivas Provincial Control Laboratory in the same day with sampling (within 5 hours). The total alkalinity, total hardness, ammonium nitrogen, nitrite, nitrate, ammonium azote, phosphate, sulfite, sulfate, chloride, sodium, potassium, suspended solid matter (SSM), chemical oxygen demand (COD), biological oxygen demand (BOD), calcium, magnesium, ferrous, lead, copper, zinc, nickel, mercury and cadmium analyses of water samples were performed. As a result of the analyses, it was determined that, since Brook Kuruçay falls into the water resource class, which is the most sensitive to pollution, the water quality of the brook should be monitored regularly.

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33

Skeffington, R. A., S. J. Halliday, A. J. Wade, M. J. Bowes, and M. Loewenthal. "Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive." Hydrology and Earth System Sciences 19, no. 5 (May 26, 2015): 2491–504. http://dx.doi.org/10.5194/hess-19-2491-2015.

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Abstract. The EU Water Framework Directive (WFD) requires that the ecological and chemical status of water bodies in Europe should be assessed, and action taken where possible to ensure that at least "good" quality is attained in each case by 2015. This paper is concerned with the accuracy and precision with which chemical status in rivers can be measured given certain sampling strategies, and how this can be improved. High-frequency (hourly) chemical data from four rivers in southern England were subsampled to simulate different sampling strategies for four parameters used for WFD classification: dissolved phosphorus, dissolved oxygen, pH and water temperature. These data sub-sets were then used to calculate the WFD classification for each site. Monthly sampling was less precise than weekly sampling, but the effect on WFD classification depended on the closeness of the range of concentrations to the class boundaries. In some cases, monthly sampling for a year could result in the same water body being assigned to three or four of the WFD classes with 95% confidence, due to random sampling effects, whereas with weekly sampling this was one or two classes for the same cases. In the most extreme case, the same water body could have been assigned to any of the five WFD quality classes. Weekly sampling considerably reduces the uncertainties compared to monthly sampling. The width of the weekly sampled confidence intervals was about 33% that of the monthly for P species and pH, about 50% for dissolved oxygen, and about 67% for water temperature. For water temperature, which is assessed as the 98th percentile in the UK, monthly sampling biases the mean downwards by about 1 °C compared to the true value, due to problems of assessing high percentiles with limited data. Low-frequency measurements will generally be unsuitable for assessing standards expressed as high percentiles. Confining sampling to the working week compared to all 7 days made little difference, but a modest improvement in precision could be obtained by sampling at the same time of day within a 3 h time window, and this is recommended. For parameters with a strong diel variation, such as dissolved oxygen, the value obtained, and thus possibly the WFD classification, can depend markedly on when in the cycle the sample was taken. Specifying this in the sampling regime would be a straightforward way to improve precision, but there needs to be agreement about how best to characterise risk in different types of river. These results suggest that in some cases it will be difficult to assign accurate WFD chemical classes or to detect likely trends using current sampling regimes, even for these largely groundwater-fed rivers. A more critical approach to sampling is needed to ensure that management actions are appropriate and supported by data.

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34

Yustika, Rahmah Dewi, and Ratri Ariani. "Water quality in Cidurian watershed, Indonesia." E3S Web of Conferences 306 (2021): 04009. http://dx.doi.org/10.1051/e3sconf/202130604009.

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Studies about river water quality are essential since the decreasing of water quality could threaten hydrology watershed function. The objective of this study was to identify water quality in rainy and dry seasons of Cidurian watershed. Water quality data were obtained from Main River Basin Organization Territory Cidanau – Ciujung – Cidurian for 2018 and 2019. The parameters of water quality consist of total suspended solids (TSS), pH, dissolved oxygen (DO), chemical oxygen demand (COD), biochemical oxygen demand (BOD), phosphate (PO4), nitrite (NO2 – N), electrical conductivity (EC), temperature, Ca, and Mg. Water sampling location were in Jasinga (upstream), Neglasari (middle stream), Rancasumur (middle stream), and Tanara (downstream). The result showed that TSS concentration showed higher in rainy season than dry season in all sampling points with values higher than river water quality standard 50 mg/L. Therefore, need attention to adopt soil conservation practices in mixed tree crops, dry cultivation land, and crop plantation to decrease soil erosion. Downstream had values of pH, DO, COD, and BOD outside of water quality standards. Accordingly, government should issue some policies to protect from decreasing water quality. The information on river water quality in Cidurian watershed could support better watershed management for sustainable hydrology watershed function.

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35

Pekárová, Pavla, Ján Pekár, and Pavol Miklánek. "Impact of water sampling frequency on estimating water quality status in the Ondava River." Ecohydrology & Hydrobiology 6, no. 1-4 (January 2006): 105–13. http://dx.doi.org/10.1016/s1642-3593(06)70132-x.

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36

Hart, David, J. Santiago Rodriguez, Jonathan Burkhardt, Brian Borchers, Carl Laird, Regan Murray, Katherine Klise, and Terranna Haxton. "Quantifying Hydraulic and Water Quality Uncertainty to Inform Sampling of Drinking Water Distribution Systems." Journal of Water Resources Planning and Management 145, no. 1 (January 2019): 04018084. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001005.

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37

Fatah, Kaiwan. "EVALUATION GROUNDWATER QUALITY BY USING GIS AND WATER QUALITY INDEX TECHNIQUES FOR WELLS IN BARDARASH AREA, NORTHERN IRAQ." Iraqi Geological Journal 53, no. 2C (September 30, 2020): 87–104. http://dx.doi.org/10.46717/igj.53.2c.7rs-2020-09.07.

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Studying groundwater quality in arid and semi-arid regions is essential significant because it is used as a foremost alternative source for various purposes (human and animal consumption, economic, agriculture and irrigation). Geographic Information System and Water Quality Index techniques were utilized for visualizing and evaluating the variations of groundwater quality in the studied area. Total twelve wells were sampled and twelve groundwater quality (chemical) parameters; pH, Total Alkalinity, Total Hardness (TH), Total Dissolved Solid (TDS), Electrical Conductivity (Ec), Potassium (K), Nitrate (NO3), Sulfate (SO4), Chloride (Cl), Calcium (Ca), Magnesium (Mg) and Sodium (Na) were analyzed in the laboratory. Inverse Distance Weighted technique was used as a useful tool to create and anticipate spatial variation maps of the chemical parameters. Predicting or anticipating other areas not measured, identifying them and making use of them in the future without examining samples. The results of this research showed that 8.3% of the studied wells have excellent groundwater quality, and almost sampling wells about 75% found in good groundwater quality, while findings of groundwater quality of 16.7% studied wells belong to poor water quality due to standards of Water Quality Index. Moreover, spatial analysis in term of groundwater quality map showed that Excellent groundwater quality was detected in well 3, very good groundwater potential was noticed in six studied wells (wells 2, 6, 8, 10, 11 and 12), and other sampling wells (wells 4 and 7) were observed as good groundwater quality, while poor water quality was observed in wells (well 1 and 5). Hence, spatial distribution maps showed that the almost groundwater quality in the area about 1046.82 km² (99.04%) are suitable for drinking purpose, whereas proximate 10.18 km² (0.96%) are observed as poor water quality and inappropriate for consumptions especially in the southern part of the area.

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38

Ahmed, Mehreen, Rafia Mumtaz, and Zahid Anwar. "An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning." Applied Sciences 12, no. 24 (December 13, 2022): 12787. http://dx.doi.org/10.3390/app122412787.

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Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally.

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39

Skeffington, R. A., S. J. Halliday, A. J. Wade, M. J. Bowes, and M. Loewenthal. "Using high frequency water quality data to assess sampling strategies for the EU Water Framework Directive." Hydrology and Earth System Sciences Discussions 12, no. 1 (January 28, 2015): 1279–309. http://dx.doi.org/10.5194/hessd-12-1279-2015.

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Abstract. The EU Water Framework Directive (WFD) requires that the ecological and chemical status of water bodies in Europe should be assessed, and action taken where possible to ensure that at least "good" quality is attained in each case by 2015. This paper is concerned with the accuracy and precision with which chemical status in rivers can be measured given certain sampling strategies, and how this can be improved. High frequency (hourly) chemical data from four rivers in southern England were subsampled to simulate different sampling strategies for four parameters used for WFD classification: dissolved phosphorus, dissolved oxygen, pH and water temperature. These data sub-sets were then used to calculate the WFD classification for each site. Monthly sampling was less precise than weekly sampling, but the effect on WFD classification depended on the closeness of the range of concentrations to the class boundaries. In some cases, monthly sampling for a year could result in the same water body being assigned to one of 3 or 4 WFD classes with 95% confidence, whereas with weekly sampling this was 1 or 2 classes for the same cases. In the most extreme case, random sampling effects could result in the same water body being assigned to any of the 5 WFD quality classes. The width of the weekly sampled confidence intervals was about 33% that of the monthly for P species and pH, about 50% for dissolved oxygen, and about 67% for water temperature. For water temperature, which is assessed as the 98th percentile in the UK, monthly sampling biases the mean downwards by about 1 °C compared to the true value, due to problems of assessing high percentiles with limited data. Confining sampling to the working week compared to all seven days made little difference, but a modest improvement in precision could be obtained by sampling at the same time of day within a 3 h time window, and this is recommended. For parameters with a strong diel variation, such as dissolved oxygen, the value obtained, and thus possibly the WFD classification, can depend markedly on when in the cycle the sample was taken. Specifying this in the sampling regime would be a straightforward way to improve precision, but there needs to be agreement about how best to characterise risk in different types of river. These results suggest that in some cases it will be difficult to assign accurate WFD chemical classes or to detect likely trends using current sampling regimes, even for these largely groundwater-fed rivers. A more critical approach to sampling is needed to ensure that management actions are appropriate and supported by data.

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40

INTIMA, Danielle Polidorio. "SAMPLING PLAN FOR QUALITY MONITORING OF SUPPLIERS OF THE SANITATION SECTOR." Periódico Tchê Química 14, no. 27 (January 20, 2017): 39–43. http://dx.doi.org/10.52571/ptq.v14.n27.2017.39_periodico27_pgs_39_43.pdf.

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In order to produce water for human consumption, processes must be used that allow the removal of impurities present in the water to be treated. For this, alkalinizing chemicals, coagulants, disinfectants and others are added. Any product used in the treatment of water must promote its potability unconditionally, without the risk of transfer of any contaminant to the treated water. The quality control of these products is based on the performance of laboratory tests, which should not be restricted to the determination of the concentration of the active principle, ie, this monitoring should also include the determination of toxicity parameters present in the process inputs to ensure that the water produced meets the parameters required by the legislation. The problem is that not every sanitation company has dedicated laboratories to perform the amount of testing involved in this monitoring. Therefore, this work proposes a sampling plan for quality control of the chemicals used in water treatment. The main advantage of this statistical tool is the viability of monitoring the toxicity parameters of these products, without the need to increase the cost of this process.

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41

McCarty, Elizabeth, Rebecca Nichols, John McCreadie, and Jerome Grant. "Assessment of Aquatic Macroinvertebrate Sampling Methods for Nonregulatory Water Quality Programs." Journal of Environmental Quality 48, no. 6 (November 2019): 1749–57. http://dx.doi.org/10.2134/jeq2019.01.0024.

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42

Crane, Pamela E., and Stephen E. Silliman. "Sampling Strategies for Estimation of Parameters Related to Ground Water Quality." Ground Water 47, no. 5 (September 2009): 699–708. http://dx.doi.org/10.1111/j.1745-6584.2009.00578.x.

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43

Cetinkaya, Cem P., and Nilgun B. Harmancioglu. "Assessment of Water Quality Sampling Sites by a Dynamic Programming Approach." Journal of Hydrologic Engineering 17, no. 2 (February 2012): 305–17. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000420.

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44

Kvam, Paul H. "Ranked set sampling based on binary water quality data with covariates." Journal of Agricultural, Biological, and Environmental Statistics 8, no. 3 (September 2003): 271–79. http://dx.doi.org/10.1198/1085711032156.

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45

N. L. Klocke, R. W. Todd, G. W. Hergert, D. G. Watts, and A. M. Parkhurst. "Design, Installation, and Performance of Percolation Lysimeters for Water Quality Sampling." Transactions of the ASAE 36, no. 2 (1993): 429–35. http://dx.doi.org/10.13031/2013.28355.

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46

Ford, Ryan T., and Anthony Vodacek. "Determining improvements in Landsat spectral sampling for inland water quality monitoring." Science of Remote Sensing 1 (June 2020): 100005. http://dx.doi.org/10.1016/j.srs.2020.100005.

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47

Close, M. E. "EFFECT OF SERIAL CORRECTION ON GROUND WATER QUALITY SAMPLING FREQUENCY 1." JAWRA Journal of the American Water Resources Association 25, no. 3 (June 1989): 507–15. http://dx.doi.org/10.1111/j.1752-1688.1989.tb03086.x.

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48

McBride, Graham B., and David G. Smith. "SAMPLING AND ANALYTICAL TOLERANCE REQUIREMENTS FOR DETECTING TRENDS IN WATER QUALITY." Journal of the American Water Resources Association 33, no. 2 (April 1997): 367–73. http://dx.doi.org/10.1111/j.1752-1688.1997.tb03516.x.

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49

Abtew, Wossenu, and Barbara Powell. "WATER QUALITY SAMPLING SCHEMES FOR VARIABLE FLOW CANALS AT REMOTE SITES." Journal of the American Water Resources Association 40, no. 5 (October 2004): 1197–204. http://dx.doi.org/10.1111/j.1752-1688.2004.tb01579.x.

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50

Manache, Gemma, and Charles S. Melching. "Sensitivity Analysis of a Water-Quality Model Using Latin Hypercube Sampling." Journal of Water Resources Planning and Management 130, no. 3 (May 2004): 232–42. http://dx.doi.org/10.1061/(asce)0733-9496(2004)130:3(232).

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