Search USGS⌕ Search
Home/Archive/USGS

USGS · 70189905

Comparison of electrical conductivity calculation methods for natural waters

R. Blaine McCleskey·D. Kirk Nordstrom·Joseph N. Ryan
View original source ↗Export metadata ↓

Abstract

The capability of eleven methods to calculate the electrical conductivity of a wide range of natural waters from their chemical composition was investigated. A brief summary of each method is presented including equations to calculate the conductivities of individual ions, the ions incorporated, and the method's limitations. The ability of each method to reliably predict the conductivity depends on the ions included, effective accounting of ion pairing, and the accuracy of the equation used to estimate the ionic conductivities. The performances of the methods were evaluated by calculating the conductivity of 33 environmentally important electrolyte solutions, 41 U.S. Geological Survey standard reference water samples, and 1593 natural water samples. The natural waters tested include acid mine waters, geothermal waters, seawater, dilute mountain waters, and river water impacted by municipal waste water. The three most recent conductivity methods predict the conductivity of natural waters better than other methods. Two of the recent methods can be used to reliably calculate the conductivity for samples with pH values greater than about 3 and temperatures between 0 and 40°C. One method is applicable to a variety of natural water types with a range of pH from 1 to 10, temperature from 0 to 95°C, and ionic strength up to 1 m.

Explore related subjects

Limnology and Oceanography: MethodsArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R. Blaine McCleskey, D. Kirk Nordstrom, Joseph N. Ryan. 2012-11-30. Comparison of electrical conductivity calculation methods for natural waters. https://doi.org/10.4319/lom.2012.10.952

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

MusselFinder: Automated dreissenid mussel density estimation via robotic benthic imaging

Despite the widespread need to map the abundance of invasive quagga and zebr mussels ( Dreissena spp.) in lakes and reservoirs worldwide, current methods remain limited by data collection and analysis bottlenecks. Autonomous underwater vehicles (AUVs) combined with machine learning (ML) offer a scalable remote sensing approach for estimating mussel density (individuals m −2 ) across large spatial domains and a range of habitats that are difficult to sample with current techniques. We present MusselFinder: a deep neural network (DNN)-based pipeline that detects and enumerates live mussels in AUV-collected benthic images. MusselFinder estimates live mussel percent cover and density in nadir monocular images captured at known altitudes above the lakebed. In images of suitable clarity, MusselFinder achieved strong pixel-level localization (mussel-class Intersection over Union [IoU] > 0.76) and density prediction (log-density R 2 = 0.79–0.95) over a variety of bottom types. AUV-based image acquisition is more than three orders of magnitude faster than physical sampling by divers or ponar grabs, and DNN-based analysis can proceed at rates comparable to image acquisition. We used MusselFinder to analyze over 50,000 spatially referenced images at a spawning reef in Lake Michigan, representing a shift from small-scale, diver-collected datasets to large-scale monitoring in unconstrained environments. The method can be adapted to new imaging conditions using a modest training set and a user-friendly point-labeling technique, supporting broader-scale monitoring of one of the world's most invasive invertebrates.

Illinois, Michigan↗
USGS2023

An assessment of HgII to preserve carbonate system parameters in organic-rich estuarine waters

This work assesses the effectiveness of sample preservation techniques for measurements of pH T (total scale), total dissolved inorganic carbon (C T ), and total alkalinity (A T ) in organic-rich estuarine waters as well as the internal consistency of measurements and calculations (e.g., A T , pH T , and C T ) in these waters. Using mercuric chloride (HgCl 2 )-treated and untreated water samples, measurements of these carbonate system parameters were examined over a period of 3 months. Respiration of dissolved organic matter in untreated samples created large discrepancies in C T concentrations (~37 μ mol kg −1 increase, p < 0.0001), while C T was effectively constant in treated samples (3095.0 ± 1.14 μ mol kg −1 ). A T changes were observed for both treated and untreated samples, with HgCl 2 -treated samples showing the greatest variation (~ 26 μ mol kg −1 decrease, p < 0.001). In response to changing A T /C T ratios, pH T changes occurred in both treated and untreated samples but were relatively small in treated samples. Results in organic-rich estuarine waters that reflect the in situ carbonate system characteristics of the samples at the time of collection can be improved when samples obtained for C T and A T analysis are collected and stored separately. Accurate analyses of C T can be obtained by filtration and preservation with HgCl 2 . Accuracy of A T analyses can be improved by filtration and storage without adding HgCl 2 . The quality of pH T measurements can be improved by prompt analysis in the field and, if this cannot be accomplished, then samples can be preserved with HgCl 2 and measured in the laboratory within 1 week.

Limnology and Oceanography: Methods↗
USGS2023

Reconstructing missing data by comparing interpolation techniques: Applications for long-term water quality data

Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP) −1 , TN had a MAE of 0.39 mg (L-TN) −1 , and water velocity had a MAE of 0.10 m s −1 . The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗