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Published on: September 13, 2019
Sampling in freshwater environments: suspended particle traps and variability in the final data
Sabrina Barbizzi1, Alessandra Pati
1Agenzia per la Protezione dell'Ambiente e per i Servizi Tecnici (APAT), Servizio Metrologia Ambientale, Via di Castel Romano, 100-00128 Roma, Italy. sabrina.barbizzi@apat.it
This study presents a practical method to estimate measurement uncertainty in environmental monitoring. The approach successfully quantified uncertainties for cesium-137 ((137)Cs) and total carbon in freshwater suspended particles.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Ecosystem Monitoring
Background:
- Accurate measurement uncertainty is crucial for environmental monitoring and risk assessment.
- Previous methods for uncertainty estimation in environmental sampling have limitations.
- Cesium-137 ((137)Cs) and total carbon are key environmental indicators in aquatic ecosystems.
Purpose of the Study:
- To apply a practical method for estimating measurement uncertainty, including sampling and analysis components.
- To evaluate the uncertainty associated with (137)Cs activity concentration and total carbon content in freshwater suspended particles.
- To compare the contributions of spatial, sampling, and analysis variances to the total uncertainty.
Main Methods:
- Adapted Ramsey's method for soil investigations to estimate measurement uncertainty.
- Applied the methodology to suspended particle sampling in a freshwater ecosystem.
- Quantified uncertainty components for (137)Cs activity concentration and total carbon content.
Main Results:
- Relative expanded measurement uncertainties were 12.3% for (137)Cs and 4.5% for total carbon.
- For (137)Cs, measurement (sampling+analysis) variance was the major contributor to total variance.
- For total carbon, spatial variance was the dominant contributor to total variance.
Conclusions:
- The adapted method provides a practical approach for estimating measurement uncertainty in freshwater suspended particle analysis.
- Understanding the dominant sources of uncertainty is vital for optimizing sampling and analytical strategies.
- The method offers insights into the reliability of environmental data for (137)Cs and total carbon.
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