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Testing Water Quality01:14

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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...
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Quality of Water01:19

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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...
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Quality Control01:05

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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ContDataQC: An R package and Shiny app for quality control of continuous water quality sensor data.

Michael J Pennino1, Jen Stamp2, Erik W Leppo2

  • 1U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, U.S. EPA, Ronald Reagan Building 71277, 1300 Pennsylvania Ave., NW, MC8623R, Washington, D.C. 20004, United States.

Softwarex
|June 23, 2025
PubMed
Summary

The ContDataQC R package offers a free, open-source solution for water quality monitoring programs to standardize and expedite continuous sensor data quality control (QC). This tool minimizes data errors and enhances data utilization for improved environmental monitoring.

Keywords:
Continuous monitoring dataOpen-source softwareQuality controlRSensor dataWater quality

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Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Continuous sensor data are crucial for water quality monitoring programs.
  • Existing quality control (QC) procedures can be time-consuming and inconsistent.
  • There is a need for standardized tools to manage and analyze sensor data effectively.

Purpose of the Study:

  • To introduce the ContDataQC R package, a free, open-source tool for automating water quality sensor data QC.
  • To describe the package's functionalities for data cleaning, merging, and visualization.
  • To demonstrate the application of ContDataQC in long-term regional monitoring networks.

Main Methods:

  • Development of the ContDataQC R package with functions for QC report generation, data merging, and time series plotting.
  • Configuration for nine common water quality parameters (e.g., temperature, dissolved oxygen, pH).
  • Implementation of a user-friendly R Shiny web application for accessibility without R software installation.

Main Results:

  • ContDataQC standardizes and accelerates the QC process for continuous sensor data.
  • The package helps minimize undetected data errors, improving data reliability.
  • Both R package and R Shiny web app versions are available, catering to users with and without R coding experience.

Conclusions:

  • ContDataQC provides a robust and accessible solution for water quality data management.
  • The tool supports long-term regional monitoring networks by ensuring data integrity and usability.
  • ContDataQC empowers users to maximize the value of their sensor data through efficient QC procedures.