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Intervention analysis in environmental engineering.

K W Hipel1, A I McLeod

  • 1Department of Systems Design Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada.

Environmental Monitoring and Assessment
|November 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces intervention analysis for water quality time series, enabling trend detection and modeling from external factors. These methods aid in environmental impact assessments by statistically evaluating changes in water quality data.

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Area of Science:

  • Environmental Science
  • Statistical Modeling
  • Water Resource Management

Background:

  • Water quality time series often exhibit trends influenced by external interventions.
  • Accurate trend identification is crucial for environmental impact assessment and water resource management.

Purpose of the Study:

  • To describe intervention analysis techniques for identifying and statistically modeling trends in water quality time series.
  • To demonstrate the application of these methods in environmental impact assessment.

Main Methods:

  • Exploratory data analysis using graphical methods (e.g., locally weighted regression smooth) and statistical tests (e.g., Mann-Kendall test).
  • Confirmatory data analysis employing parametric methods, including time series modeling and regression models, to estimate trend magnitudes and address missing data.

Main Results:

  • Intervention analysis effectively detects and models trends caused by external factors in water quality data.
  • Graphical and statistical methods aid in visual and quantitative trend examination.
  • Parametric models rigorously estimate the impact of interventions on water quality series.

Conclusions:

  • Intervention analysis is a valuable tool for understanding and quantifying changes in water quality over time.
  • The described techniques support robust environmental impact assessments by providing statistical evidence of trend changes.
  • Effective application of these methods enhances the scientific basis for water quality management decisions.