The determination of pH in hypersaline lakes with a conventional combination glass electrode

Golan, Rotem; Gavrieli, Ittai; Lazar, Boáz; Ganor, Jiwchar

doi:10.4319/lom.2014.12.810

Cited by 16 publications

(6 citation statements)

References 16 publications

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“…pH fluctuations in the water may depend on freshwater inflows supplied continuously in the lakes in inflow from precipitation, tributaries, rain, snow and groundwater, and from losses through evaporation which change the pH and salinity also. During dilution with freshwater the pH of the brine in saline water bodies rises [16] and it decreases during evaporation [17]. Similar decrease in pH with increasing ionic strength was observed at the case of Dead Sea [15,16] and Great Salt Lake [18].…”

Section: Discussionsupporting

confidence: 59%

“…During dilution with freshwater the pH of the brine in saline water bodies rises [16] and it decreases during evaporation [17]. Similar decrease in pH with increasing ionic strength was observed at the case of Dead Sea [15,16] and Great Salt Lake [18]. Salinity in the Galgas Lake ranged from 60‰ in February, 2017 to 350‰ in June, 2017 and July, 2018.…”

Section: Discussionmentioning

confidence: 56%

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Annual and Seasonal Variation in Artemia Population in Relation to Water Quality Parameters in Lake Galgas in Crimea, Russia

Ivanovna¹,

Grigor’evich²,

Viktorovich³

et al. 2019

AJEE

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Aim: To study seasonal changes of hydrological parameters of the water and Artemia population status in the hypersaline Galgas Lake in the period of 2017-2019. Study Area and Duration: The study was carried out in the Crimea, a peninsula on the northern coast of the Black Sea in Eastern Europe in 2017-2019. Methodology: Water (brine) and Artemia samples were collected monthly from the lake located at the coastal line of the Black Sea. The parameters namely temperature, pH, salinity, Eh, and oxygen concentration of were determined in the water. Artemia population including the developmental stages and the cysts hatching rate of the cysts were measured in different seasons in the study period. The differences in hydrochemical characteristics of the study area in different years and seasons were observed. Results: The results showed that the hydrochemical characteristics of the study area varied with the climate peculiarities. In summer months 2017, the lake was dried out. In the hot period, the temperature of the air and water was above +30°C, the pH ranged insignificantly, and the oxygen concentration was low. Artemia cysts were found throughout the year, however the nauplii were mostly available during spring and autumn seasons, while the adults were found in spring and summer months. Conclusion: The study concluded that the changes of the water parameters depended on season and especially on air temperature. The changes of Artemia population were different in examined years and depended on the water characteristics and especially on temperature and oxygen concentration. The obtained results could be helpful for the local climate changes consequences studies and their influence on aquatic biota.

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Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 56%

Annual and Seasonal Variation in Artemia Population in Relation to Water Quality Parameters in Lake Galgas in Crimea, Russia

Ivanovna¹,

Grigor’evich²,

Viktorovich³

et al. 2019

AJEE

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“…An AC voltage of 10 kHz is adopted as the voltage source for the measuring circuit so as to ignore the parasitic impedance where, α = V out =V G is the constant gain of the source follower circuit. Generally, the reference voltage is applied to the glass reference electrode to measure the pH of the solution [19]. However, it would be difficult to measure the pH of sweat on human skin using the glass reference electrode because it needs to be immersed in the target solution.…”

Section: Methodsmentioning

confidence: 99%

Monitoring system with multimodal sensor for electrical conductivity and hydrogen-ion concentration of causative agents of underarm odor

Hirano¹,

Isomura²,

Futagawa³

2018

J Transl Sci

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This study proposes a novel system to monitor the pH characteristics and electrical conductivity of 3-Hydroxy-3-methylhexanoic acid and 3-mercapto-3-methyl-1hexanol, which are causative agents of underarm odor, to estimate underarm odor. A multimodal sensor, which can measure electrical conductivity, temperature, and pH, and an AgCl external electrode were adopted in the system to measure the pH of the agents on the skin. Experiments were conducted to measure the accuracy of the electrical conductivity and the pH for 3-hydroxy-3-methylhexanoic acid and 3-mercapto-3-methyl-1-hexanol to verify the applicability of the proposed system. The results showed that the proposed system may be able to accurately measure the pH and electrical conductivity of the causative agents.

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“…Traditionally, the determination of water pH mainly adopts the glass electrode method [ 2 ]. The measurements of this method are accurate, but the process is cumbersome and needs the pH buffers to calibrate.…”

Section: Introductionmentioning

confidence: 99%

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network

2022

Sensors

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pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction () was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.

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The determination of pH in hypersaline lakes with a conventional combination glass electrode

Cited by 16 publications

References 16 publications

Annual and Seasonal Variation in Artemia Population in Relation to Water Quality Parameters in Lake Galgas in Crimea, Russia

Annual and Seasonal Variation in Artemia Population in Relation to Water Quality Parameters in Lake Galgas in Crimea, Russia

Monitoring system with multimodal sensor for electrical conductivity and hydrogen-ion concentration of causative agents of underarm odor

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network

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