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Characterization and Application of Passive Samplers for Monitoring of Pesticides in Water
Published on: August 3, 2016
Simulating and explaining passive air sampling rates for semivolatile compounds on polyurethane foam passive
Nicholas T Petrich1, Scott N Spak, Gregory R Carmichael
1Department of Civil & Environmental Engineering, The University of Iowa , Iowa City, Iowa 52242, United States.
Environmental Science & Technology
|July 11, 2013
Summary
This study introduces a new method to model hourly sampling rates for passive air samplers (PAS) using meteorology, reducing uncertainties in pollutant concentration estimates. The approach accurately simulates pollutant uptake and variability, improving the reliability of semivolatile pollutant monitoring.
Area of Science:
- Environmental Chemistry
- Atmospheric Science
- Analytical Chemistry
Background:
- Passive air samplers (PAS), like polyurethane foam (PUF) samplers, are cost-effective for monitoring semivolatile pollutants.
- Current concentration estimates from PAS are limited by unquantified uncertainties due to assumed constant mass transfer rates.
Purpose of the Study:
- To develop and validate a novel method for modeling hourly sampling rates of semivolatile compounds using first-principle physics and chemistry.
- To quantify the impact of meteorological variables on sampling rates and analyte concentrations in PAS.
- To improve the accuracy and reduce uncertainties in pollutant concentration measurements from PAS.
Main Methods:
- Developed a model integrating chemistry, physics, and fluid dynamics to calculate hourly sampling rates based on meteorology.
- Calibrated the model using depuration experiments to simulate nonlinear PUF uptake.
- Evaluated the model for polychlorinated biphenyl congeners using data from Harner model samplers in Chicago, IL (2008).
Main Results:
- The model successfully simulated hourly sampling rates and recovered synthetic hourly concentrations.
- Simulated average sampling rates aligned with those determined from depuration experiments, confirming quasilinear uptake.
- Identified significant hourly, daily, and interannual variability in sampling rates, influenced by meteorological data resolution.
- PAS chamber temperature was found to be the largest contributor to total process uncertainty (7.3%).
Conclusions:
- The developed first-principle modeling approach enhances the accuracy of passive air sampling for semivolatile pollutants.
- Meteorological conditions significantly influence sampling rates and analyte concentrations, necessitating hourly resolution for accurate assessments.
- This method provides a robust framework for understanding and quantifying uncertainties in PAS data, improving environmental monitoring.
