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V T Farewell1

  • 1Department of Health Studies and Statistics and Actuarial Science, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada.

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This study applies non-parametric methods, typically used for time-to-event data, to analyze left-censored water quality data. This approach helps in comparing trace substance levels when concentrations are below detection limits.

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Area of Science:

  • Environmental Science
  • Statistics
  • Water Quality Analysis

Background:

  • Trace substance analysis in ambient water often yields concentrations below detection limits, posing challenges for statistical analysis.
  • Traditional time-to-event analysis methods handle right-censored data, but water quality data frequently exhibits left-censorship.

Purpose of the Study:

  • To adapt non-parametric procedures for analyzing left-censored water quality data.
  • To develop a method for comparing trace substance levels, incorporating data below detection limits.
  • To illustrate the application of these methods using real-world water quality examples.

Main Methods:

  • Utilizing non-parametric estimation for the cumulative distribution function of left-censored data.
  • Combining unconditional and partial likelihoods for regression analysis of data with detection limits.
  • Developing modifications for matched pair data and time-dependent observations.

Main Results:

  • A non-parametric estimate of the cumulative distribution function for left-censored water quality data is achievable.
  • A combined likelihood approach enables regression analysis for trace substance comparisons.
  • The methodology was successfully applied to compare toxic substance levels in the Niagara River.

Conclusions:

  • Non-parametric methods offer a viable approach for analyzing left-censored water quality data.
  • The proposed regression methodology effectively handles data below detection limits for comparative studies.
  • This framework provides a foundation for advanced water quality data analysis, including time-dependent and matched pair data.