Documenting measurement sensitivity and bias of field-measured parameters in water quality monitoring programs
| Autor: | Roy J. Irwin, Pete E. Penoyer, David P. Thoma |
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| Rok vydání: | 2011 |
| Předmět: |
Quality Control
Engineering media_common.quotation_subject Statistics as Topic Management Monitoring Policy and Law computer.software_genre Sensitivity and Specificity Field (computer science) Water Quality Water Pollution Chemical Calibration Quality (business) Sensitivity (control systems) General Environmental Science media_common Warning system business.industry Uncertainty General Medicine Pollution Reliability engineering Data point Data quality Data mining Water quality business computer Water Pollutants Chemical Environmental Monitoring |
| Zdroj: | Environmental Monitoring and Assessment. 184:5387-5398 |
| ISSN: | 1573-2959 0167-6369 |
| DOI: | 10.1007/s10661-011-2347-5 |
| Popis: | Measurement sensitivity and bias quality control metrics are commonly reported for water-quality parameters measured in the laboratory. Less commonly recognized is that they should also be reported for field-measured parameters. Periodic evaluation helps document data quality and can help serve as early warning if there are problems with methods or techniques that could negatively affect ability to interpret threshold values and trends over time. This study focuses on traditional assessment of bias and introduces a new method for estimating measurement sensitivity of water-quality parameters measured monthly in the field. Alternative measurement sensitivity is a new data quality indicator used to demonstrate how quantifying sensitivity at the measurement level can improve understanding the uncertainty affecting each reported data value. That, in turn, can help interpret the meaning of results from many separate data points measured in the field. In this 30-month study, pH and specific conductance consistently met, and dissolved oxygen did not always meet NPS and USGS quality control standards for bias. Evaluation of dissolved oxygen bias and sensitivity during the study provided impetus to improve calibration techniques that resulted in data that later met quality goals. |
| Databáze: | OpenAIRE |
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