Project description:This is one of the validation datasets which goes with the manuscript published in Exposome titled an actionable annotation scoring framework for gas chromatography high resolution mass spectrometry. The dataset describes indoor and outdoor air samples collected using passive samplers from homes near natural gas compressor stations.
Project description:Passive air samplers (PAS) including polyurethane foam (PUF) are widely deployed as an inexpensive and practical way to sample semivolatile pollutants. However, concentration estimates from PAS rely on constant empirical mass transfer rates, which add unquantified uncertainties to concentrations. Here we present a method for modeling hourly sampling rates for semivolatile compounds from hourly meteorology using first-principle chemistry, physics, and fluid dynamics, calibrated from depuration experiments. This approach quantifies and explains observed effects of meteorology on variability in compound-specific sampling rates and analyte concentrations, simulates nonlinear PUF uptake, and recovers synthetic hourly concentrations at a reference temperature. Sampling rates are evaluated for polychlorinated biphenyl congeners at a network of Harner model samplers in Chicago, IL, during 2008, finding simulated average sampling rates within analytical uncertainty of those determined from loss of depuration compounds and confirming quasilinear uptake. Results indicate hourly, daily, and interannual variability in sampling rates, sensitivity to temporal resolution in meteorology, and predictable volatility-based relationships between congeners. We quantify the importance of each simulated process to sampling rates and mass transfer and assess uncertainty contributed by advection, molecular diffusion, volatilization, and flow regime within the PAS, finding that PAS chamber temperature contributes the greatest variability to total process uncertainty (7.3%).