Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

11.8K
With the advancement of technology and the rise in end-user expectations, the need and use of higher temporal resolution data for pollutant load estimation has increased. This protocol describes a method for continuous in situ water quality monitoring to obtain higher temporal resolution data for informed water resource management...
11.8K
Quality of Water01:19

Quality of Water

506
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
506
Water Quality Analysis via Indicator Organisms08:17

Water Quality Analysis via Indicator Organisms

31.2K
Source: Laboratories of Dr. Ian Pepper and Dr. Charles Gerba -The University of Arizona
Demonstrating Author: Luisa Ikner
Water quality analysis monitors anthropogenic influences such as pollutants, nutrients, pathogens, and any other constituent that can impact the water’s integrity as a resource. Fecal contamination contributes microbial pathogens that threaten plant, animal, and human health with disease or illness. Increasing water demands and strict quality standards require that...
31.2K
Testing Water Quality01:14

Testing Water Quality

374
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
374
Sediment Core Sectioning and Extraction of Pore Waters under Anoxic Conditions09:21

Sediment Core Sectioning and Extraction of Pore Waters under Anoxic Conditions

9.4K
A protocol for sectioning sediment cores and extracting pore waters under anoxic conditions in order to permit analysis of redox sensitive species in both solids and fluids is...
9.4K
Determination of Microbial Extracellular Enzyme Activity in Waters, Soils, and Sediments using High Throughput Microplate Assays15:23

Determination of Microbial Extracellular Enzyme Activity in Waters, Soils, and Sediments using High Throughput Microplate Assays

40.3K
Microplate based procedures are described for the colorimetric or fluorometric analysis of extracellular enzyme activity. These procedures allow for the rapid assay of such activity in large numbers of environmental samples within a manageable time...
40.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Stage-stratified prognostic impact of comorbidities on breast cancer-specific survival: A population-based flexible parametric modelling study.

Cancer epidemiology·2026
Same author

A data-informed multidimensional composite score for stress assessment.

Acta psychologica·2026
Same author

Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours.

PloS one·2026
Same author

Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.

PLoS computational biology·2026
Same author

A localised risk model for liver fluke infection.

Veterinary parasitology, regional studies and reports·2026
Same author

Correction: SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks.

PloS one·2026

Related Experiment Video

Updated: Jan 20, 2026

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.8K

Predicting sediment and nutrient concentrations from high-frequency water-quality data.

Catherine Leigh1,2,3, Sevvandi Kandanaarachchi1,4, James M McGree1,3

  • 1ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), Australia.

Plos One
|August 31, 2019
PubMed
Summary
This summary is machine-generated.

High-frequency sensors can predict riverine total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations, overcoming manual sampling limitations for improved water-quality monitoring in the Great Barrier Reef lagoon catchment.

More Related Videos

Microbial Indicators and Most Probable Number Analysis for Water Quality
08:17

Microbial Indicators and Most Probable Number Analysis for Water Quality

Published on: April 30, 2023

31.2K
Quality of Water
01:19

Quality of Water

506

Related Experiment Videos

Last Updated: Jan 20, 2026

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.8K
Microbial Indicators and Most Probable Number Analysis for Water Quality
08:17

Microbial Indicators and Most Probable Number Analysis for Water Quality

Published on: April 30, 2023

31.2K
Quality of Water
01:19

Quality of Water

506

Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Ecological Modeling

Background:

  • Traditional river water-quality monitoring relies on manual sampling, which is insufficient for capturing temporal variations in sediment and nutrient concentrations.
  • High-frequency data are crucial for understanding and managing water quality, especially concerning constituents like total suspended solids (TSS) and oxidized nitrogen (NOx) that impact aquatic ecosystems.
  • Limitations in manual sampling frequency hinder effective management of riverine inputs into sensitive environments like the Great Barrier Reef lagoon.

Purpose of the Study:

  • To develop predictive models for total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations using high-frequency in situ sensor data.
  • To assess the efficacy of turbidity, conductivity, and river level as surrogate variables for predicting TSS and NOx.
  • To improve the temporal resolution and accuracy of water-quality monitoring in rivers flowing into the Great Barrier Reef.

Main Methods:

  • Developed generalized-linear mixed-effects models with autoregressive correlation structures.
  • Utilized high-frequency time series data from in situ turbidity, conductivity, and river level sensors.
  • Calibrated models using manually collected water-quality data from freshwater and estuarine river sites.

Main Results:

  • Turbidity effectively predicted total suspended solids (TSS) across both freshwater and estuarine sites.
  • A combination of turbidity, conductivity, and river level showed predictive capability for oxidized nitrogen (NOx), though with greater complexity and lower generalizability.
  • Prediction intervals increased during high-flow events, emphasizing the need to quantify uncertainty in surrogate-based models.

Conclusions:

  • High-frequency sensor data and surrogate modeling offer a viable solution to overcome limitations of manual water-quality sampling.
  • Turbidity is a reliable surrogate for TSS, while NOx prediction requires more complex multi-variable models.
  • Incorporating temporal dynamics and uncertainty measures is essential for robust water-quality monitoring and transferable predictive models.