Statistical analysis of water-quality data affected by limits of detection
Abstract
Many water-quality problems are related to substances present at concentrations too low to be measured precisely. Obtaining information from a monitoring system that produces many results near the fringes of analytic capabilities is not straightforward. This thesis discusses concerns one should have when statistically analyzing water-quality data from such a system. Two general approaches are discussed. The traditional approach is to regard all measurements as precise or imprecise. Precise results are simply numerical responses, for which statistical analysis may lead to valid and sound monitoring information. Imprecise results are reported as ND, or not detected, with criteria for reporting based on categories of measurement precision. Measurement error that leads to censoring is described. The impact of this error on the statistical characteristics of water-quality data is illustrated, using a model appropriate for analyte concentrations near the limit of detection. Loss of information due to censoring is demonstrated, and it is proposed that a numerical results be reported for all measurements. It is also suggested that an estimate of data precision accompany all results. This would permit the data user to censor at levels of uncertainty chosen by the user, rather than having information censored by the measurement process.
- Authors:
- Publication Date:
- Research Org.:
- Colorado State Univ., Fort Collins (USA)
- OSTI Identifier:
- 6503206
- Resource Type:
- Thesis/Dissertation
- Resource Relation:
- Other Information: Thesis (Ph. D)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; 29 ENERGY PLANNING, POLICY AND ECONOMY; WATER POLLUTION MONITORS; ACCURACY; WATER QUALITY; DATA ANALYSIS; INFORMATION VALIDATION; ERRORS; RECOMMENDATIONS; ENVIRONMENTAL QUALITY; MEASURING INSTRUMENTS; MONITORS; TESTING; VALIDATION; 500200* - Environment, Atmospheric- Chemicals Monitoring & Transport- (-1989); 290300 - Energy Planning & Policy- Environment, Health, & Safety
Citation Formats
Porter, P S. Statistical analysis of water-quality data affected by limits of detection. United States: N. p., 1986.
Web.
Porter, P S. Statistical analysis of water-quality data affected by limits of detection. United States.
Porter, P S. 1986.
"Statistical analysis of water-quality data affected by limits of detection". United States.
@article{osti_6503206,
title = {Statistical analysis of water-quality data affected by limits of detection},
author = {Porter, P S},
abstractNote = {Many water-quality problems are related to substances present at concentrations too low to be measured precisely. Obtaining information from a monitoring system that produces many results near the fringes of analytic capabilities is not straightforward. This thesis discusses concerns one should have when statistically analyzing water-quality data from such a system. Two general approaches are discussed. The traditional approach is to regard all measurements as precise or imprecise. Precise results are simply numerical responses, for which statistical analysis may lead to valid and sound monitoring information. Imprecise results are reported as ND, or not detected, with criteria for reporting based on categories of measurement precision. Measurement error that leads to censoring is described. The impact of this error on the statistical characteristics of water-quality data is illustrated, using a model appropriate for analyte concentrations near the limit of detection. Loss of information due to censoring is demonstrated, and it is proposed that a numerical results be reported for all measurements. It is also suggested that an estimate of data precision accompany all results. This would permit the data user to censor at levels of uncertainty chosen by the user, rather than having information censored by the measurement process.},
doi = {},
url = {https://www.osti.gov/biblio/6503206},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jan 01 00:00:00 EST 1986},
month = {Wed Jan 01 00:00:00 EST 1986}
}