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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

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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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Related Experiment Video

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Watershed Planning within a Quantitative Scenario Analysis Framework
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A practitioner's guide for exploring water quality patterns using principal components analysis and Procrustes.

C J Sergeant1, E N Starkey2, K K Bartz3

  • 1National Park Service, Inventory and Monitoring Program, Southeast Alaska Network, 3100 National Park Road, Juneau, AK, USA. christopher_sergeant@nps.gov.

Environmental Monitoring and Assessment
|March 30, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a seven-step framework for principal components analysis (PCA) and Procrustes analysis to standardize water quality monitoring. Open-source R code is provided to help practitioners analyze complex water quality data efficiently.

Keywords:
GuidanceMonitoringOpen source codePrincipal components analysisProcrustesWater quality

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Area of Science:

  • Environmental Science
  • Ecology
  • Data Science

Background:

  • Sustainable water quality monitoring requires careful selection of variables and measurement scope.
  • Multivariate statistical methods like ordination are widely used but lack standardization.
  • Practitioners often face challenges with complex data analysis and lack accessible tools.

Purpose of the Study:

  • To present a standardized seven-step framework for principal components analysis (PCA).
  • To introduce and demonstrate the application of Procrustes analysis for comparing multivariate data matrices in water quality studies.
  • To provide open-source R code and case studies for efficient water quality data exploration.

Main Methods:

  • Development of a seven-step framework for principal components analysis (PCA).
  • Application of Procrustes analysis to assess concordance between multivariate water quality data matrices.
  • Utilizing three water quality case studies from US parklands to illustrate the methods.
  • Providing annotated R code and datasets for reproducibility and adaptation.

Main Results:

  • The framework facilitates efficient exploration of water quality patterns using PCA.
  • Procrustes analysis effectively quantifies the similarity between different water quality datasets.
  • Case studies demonstrate the ability to answer key monitoring questions regarding spatial and temporal variability.
  • The provided R code enables users to replicate analyses and apply them to new datasets.

Conclusions:

  • The proposed PCA and Procrustes analysis framework enhances the standardization and efficiency of water quality monitoring data analysis.
  • Open-source tools and case studies lower the barrier for practitioners to adopt advanced multivariate techniques.
  • This approach supports more robust interpretations of water quality regimes and variability across sites and time.