Simulating and Explaining Passive Air Sampling Rates for

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Simulating and explaining passive air sampling rates for semivolatile compounds on polyurethane foam passive samplers Nicholas T Petrich, Scott Spak, Greg R Carmichael, Dingfei Hu, Andres Martinez, and Keri C. Hornbuckle Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es401532q • Publication Date (Web): 09 Jul 2013 Downloaded from http://pubs.acs.org on July 14, 2013

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Environmental Science & Technology

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Simulating and explaining passive air sampling rates for semi-volatile

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compounds on polyurethane foam passive samplers

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Nicholas T. Petrich1,2, Scott N. Spak1,2,3*, Gregory R. Carmichael1,2,4, Dingfei Hu1,5, Andres

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Martinez1,5, Keri C. Hornbuckle1,2,5*

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1. Department of Civil & Environmental Engineering, The University of Iowa, Iowa City, Iowa, United States, 52242 2. Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, Iowa, United States, 52242 3. Public Policy Center and School of Urban & Regional Planning, The University of Iowa, Iowa City, Iowa, United States, 52242 4. Department of Chemical & Biochemical Engineering, The University of Iowa, Iowa City, Iowa, United States, 52242 5. IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa, United States, 52242

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*Phone: (319) 335-9993 (S.N.S); (319) 384-0789 (K.C.H.). Fax: (319) 335-6801 (S.N.S.); (319)

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335-5660 (K.C.H.). E-mail: [email protected] (S.N.S.); [email protected]

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(K.C.H.).

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Keywords: Great Lakes, PCB concentrations, Chicago, persistent organic pollutants

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1

ACS Paragon Plus Environment


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Abstract

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Passive air samplers (PAS) including polyurethane foam (PUF) are widely deployed as an

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inexpensive and practical way to sample semi-volatile pollutants. However, concentration

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estimates from PAS rely on constant empirical mass transfer rates, which add unquantified

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uncertainties to concentrations. Here we present a method for modeling hourly sampling rates

29

for semi-volatile compounds from hourly meteorology using first-principle chemistry, physics,

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and fluid dynamics, calibrated from depuration experiments. This approach quantifies and

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explains observed effects of meteorology on variability in compound-specific sampling rates and

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analyte concentrations; simulates nonlinear PUF uptake; and recovers synthetic hourly

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concentrations at a reference temperature. Sampling rates are evaluated for polychlorinated

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biphenyl congeners at a network of Harner model samplers in Chicago, Illinois during 2008,

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finding simulated average sampling rates within analytical uncertainty of those determined from

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loss of depuration compounds, and confirming quasi-linear uptake. Results indicate hourly, daily

37

and interannual variability in sampling rates, sensitivity to temporal resolution in meteorology,

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and predictable volatility-based relationships between congeners. We quantify importance of

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each simulated process to sampling rates and mass transfer and assess uncertainty contributed

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by advection, molecular diffusion, volatilization, and flow regime within the PAS, finding PAS

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chamber temperature contributes the greatest variability to total process uncertainty (7.3%).

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TOC/Abstract Art

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Introduction Passive air samplers (PAS) collect atmospheric trace compounds under ambient

47

conditions onto polyurethane foam (PUF), semi-permeable membranes, and polymer resin

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sampling media.1-8 This versatile, low-cost technique proves useful for establishing spatial

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gradients in air toxics and emerging contaminants of concern, and in sampling semi-volatile

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organic compounds (SVOCs) for which real-time observations remain challenging. Many

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SVOCs, including polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons, are

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known or potential human carcinogens, toxic, bio-accumulative, and subject to long-range

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chemical transport.2 Passive air sampling is also routinely used in atmospheric monitoring for

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mercury, ammonia, aerosols, and nitrogen dioxide.9, 10

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In passive air sampling, ambient air flows through the PAS chamber and the

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compound’s mass accumulates onto sampling media throughout the sampler’s deployment

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period. Without the controlled flow rates of active methods, concentrations must be calculated

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from estimated passive sampling rates. Traditionally, PAS has been described as the uptake of

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a chemical onto sampling media (e.g. polyurethane foam7) as a function of sampling rate (Rs)

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and the fugacity gradient between the ambient air and the PUF disk, with uptake onto the disk

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defined as 1, 4, 5, 7:

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= ∙ ∙(

)

(1)

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where represents the mass of a gaseous or semi-volatile compound on the PUF (ng),

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the empirical constant mass transfer coefficient (m s-1), the surface area of the PUF (m2),

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the analyte concentration in the air (ng m-3), the compound’s concentration on the PUF (ng

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m-3), and the PUF:air equilibrium partition coefficient (m3 g-1). The product of x is RS,

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which may be determined experimentally from loss of depuration compounds or uptake of native

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compounds.4 For RS estimation by loss of depuration compounds, the PUF disk is spiked with a

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known amount of chemical prior to exposure and then the mass transfer coefficient is calculated 3



70

based on its first order elimination rate during the entire deployment.5, 11 Alternatively, the

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uptake of native compounds entails deploying a series of clean PUF disks in a setting with a

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known ambient concentration and deriving a linear uptake curve to calculate RS.5, 12 These two approaches in determining RS lead to a range of issues regarding

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concentration observations gathered using PAS. As with the analyzed mass from a PAS, these

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techniques result in only one RS value for the entire sampling period, with unquantified

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uncertainties and unknown variability. Beyond the large quantified and unquantified errors in the

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analytical approach to calculating RS for individual samples, empirical RS values are often used

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for different periods and different locations from those in which they were originally determined,

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leading to even greater uncertainties in resultant analyte air concentrations.

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Empirical and theoretical studies suggest that much of the uncertainty stems from the

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effects of meteorology on RS, primarily with a focus on isolating how individual meteorological

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parameters such as wind speed11 and temperature13 affect observed variability. Klánová et al.

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found that RS for gas-phase compounds, including PCBs, exhibits a linear correlation to wind

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speed and inverse correlation to temperature.14 The angle at which the ambient air enters the

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PAS chamber also affects flow within the chamber, with positive angles of 15° and 30°

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increasing RS by 30% and 50%, respectively, and a -10° angle leading to a 40% reduction.15

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While studies have attempted to address these issues through computational fluid dynamic

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modeling,15, 16 and theoretical models of the passive sampling uptake process have been

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developed,1 no comprehensive process model to date has directly simulated and quantified the

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comprehensive effects of atmospheric dynamics on observed passive sampling flow rates. The PCB concentration in air can be experimentally determined from Equation 2:

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∗

92

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where MPCB is the total accumulated mass on the PUF (ng) and t is the total deployment time

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(d). This approach assumes that is constant over the entire sampling period. However, daily

(2)

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observations in urban areas consistently invalidate this assumption.17, 18 Hsu et al. showed that

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variability in ambient air concentration for ∑PCBs in 22 daily samples taken in a 50-day period

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near Chicago (0.22 to 1.80 ng m-3) using active sampling was larger than the average

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concentration (1.0 ± 0.5 ng m-3). 17 The high short-term variability in PCB analyte air

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concentration can be explained by the influences of meteorology on volatilization (temperature)

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and source location (wind); in that study, concentration was highly correlated with daily time

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series of temperature (r2 = 0.46) and slightly correlated with wind speed (0.19) and wind

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direction (0.17). The assumption of negligible loss from the PUF (i.e., ≈ 0) throughout the

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deployment period, another necessary precondition for this reduced-form expression of the

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fundamental passive sampling relationships, may also be invalid for SVOCs depending on the

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length of the deployment.

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In this study, a method to estimate hourly sampling rates, analyte air concentrations

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(CAir), and PCB uptake on PAS-PUF samplers (CPUF) from first principle chemistry, physics,

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mass transfer, and computational fluid dynamics is presented. Using data from seven

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deployments of samplers from our PCB sampling network in Chicago6, 19 flow rates are

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calibrated with empirical depuration approaches to refine the model, and process contributions

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to uncertainties are estimated.

112 113

Experimental Methods & Data

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Modeling Approach This model reconsiders the theoretical passive sampling relationship in Equation 1 to

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incorporate temporal variability in mass transfer, sampling rate, and PUF concentration based

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on discrete temporal information on ambient air concentrations and meteorology (Equation 3).

118

, !", !#, $, %& ∗ ∗ , %&

& &

(3)

5



119

where CAIR is a function of both ambient temperature (T) and time (t), CPUF varies in time

120

due to resolved mass transfer, and kv varies in time as a result of meteorological conditions

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known from prior empirical and theoretical studies to impact passive sampling rates, including

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wind speed (WS), wind direction (WD), temperature and pressure (P). The dependency of the

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mass transfer coefficient on these ambient parameters is derived from boundary layer mass

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transfer theory, where the Sherwood number representing the ratio of convective to diffusive

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mass transfer is dependent on the flow conditions represented by the Reynolds Number and the

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ratio of the diffusive momentum to the diffusion of mass.20 From this theory, the sampling rate

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equation becomes

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' ()* ∗ +, ∗ # 1-. ∗ / .-0 ∗ 1 0 ∗ 20-1 3 ∗

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where fPBL is the fraction of ambient analyte air concentration inside the PUF boundary layer,

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and the first bracket represents the mass transfer coefficient, where γ is an advective mass

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transfer coefficient specific to the PAS housing and media (dimensionless), D is the molecular

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diffusivity for each compound (m s-1), ν is the kinematic viscosity (m2 s-1), VA is the air velocity

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(m s-1), L is the diameter of the PUF disk (m), β represents the differences between momentum

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transport and mass transport (dimensionless), and α represents the flow regime (laminar or

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turbulent) as a function of air velocity. We considered α and β values consistent with flow over a

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flat plate. Expressions and values for each component in Equation 4 are presented in the SI.

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(4)

Uptake of an SVOC on the PAS is a function of accumulation of mass on the PUF over

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time and the mass of the PUF, as indicated in Equation 5:

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4-1

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where dm/dt is the accumulation of SVOC mass onto the PUF for that time period (ng), and

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mPUF is mass of the PUF (g). To calculate the CPUF to KPUF ratio, KPUF was computed using a

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logarithmic relationship between the octanol-air partition coefficient KOA and KPUF from Shoeib &

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Harner7 (Equation 6):

5⁄ 5

(5)

6


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100.6366∗log89

&-3.1774

(6)

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Using this dynamic model, we can predict the meteorology-driven variation in the

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ambient concentrations of the SVOC during the deployment period. The hourly concentration of

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SVOCs in the atmosphere at ambient temperature T can be computed by first correcting the

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measured integrated air concentration to a standard temperature, then modeling the hourly

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concentrations as a function of hourly meteorology during deployment period. Using Equation 7:

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:;< ∗ =

151

where C298 is the PCB concentration at 298K (ng m-3) calculated from the integrated CPUF and

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analyzed mass, ∆UOA is octanol-air enthalpy for PCBs (J/mol), R is the gas constant (J mol-1-K-

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1

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CAir varies predictably with temperature for SVOCs, the Van’t Hoff relationship explains

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variability between T and a reference temperature (here, 298 K). The ratio of T to TPAS considers

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volatility of PCBs in the conditions within PAS chamber, as temperature within the chamber

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differs from ambient air temperature due to solar heating during the day and radiative cooling at

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night,13 and is consistent with the temperature of the local built environment.13 Thus, the ratio of

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T/TPAS ensures that the computed CAir reflects ambient air concentrations rather than the

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concentration in the PAS to which the PUF disk was directly exposed. Further details are

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presented in the Supporting Information (SI).

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>89 1 1 @ - B A A298 ?

∗ C

C

9D

(7)

), T298 is the temperature 298 K, and TPAS is the PAS internal chamber temperature (K). Since

The fraction of ambient analyte air concentration present in the air flowing immediately

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over the PUF disk, fPBL, determines the SVOC mass capable of transfer to the PUF.

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Computational fluid dynamics modeling found that simulated PCB concentration in the turbulent

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boundary layer around the PUF disk in a Harner model sampler is lower than that in free flowing

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air in the PAS chamber, with fPBL during turbulent flow (internal air velocities ≥ 0.5 m s-1) of

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approximately 0.5. At lower air velocities, laminar flow prevails, with fPBL ≈ 1.16

168 7



169 170

PAS PUF Observations & Loss of Depuration Compound Experiments Hourly gas phase RS were calculated in MATLAB21 for 156 PCB congeners and co-

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eluting groups of congeners22 in Chicago during 2008, including two isotope labeled depuration

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compounds with negligible ambient concentrations, 2,4,4’-trichlorobiphenyl (13C12) (C13-PCB

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28) and 2,3,3’,5,5’-pentachlorobiphenyl (13C12) (C13-PCB 111), representing low and high

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molecular mass PCB compounds. A total of 83 PAS PUFs were deployed during the study

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period in Harner model samplers at 32 locations in Chicago, and sampling rates determined

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from loss of depuration compounds from seven PUF samples at seven sites.19 The PUF

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deployments with depuration compounds covered most of 2008, except for an eight week period

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from mid-September to mid-November, and 11 days in late December and early January.

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Deployments ranged from 24 to 76 d, with an average of 43 ± 8 d. All samples were extracted

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and analyzed following methods described by Hu et al. (2008).23 Using methods described by

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Persoon et al. (2010),6 the depuration compound experiment consisted of spiking clean PUF

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disks with 40 ng of PCB congeners 28 (13C-PCB 28) and 111 (13C-PCB 111) from a mixture of

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EPA Method 1668A labeled clean-up standard solution (Cambridge Isotope Laboratories). The

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PUFs were spiked by placing them in amber jars and then adding the depuration compounds in

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20 mL acetone. The PUFs were dried in the jars, capped with Teflon lids, sealed in Ziploc bags

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and stored in a -20°C freezer until deployment. Each PUF was deployed at one of the seven

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locations. After deployment, PUF disks were collected and analyzed to determine the remaining

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PCB mass of each congener. The remaining fraction of the spiked depuration compounds was

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29% and 96%, for C13-PCB28 and C13-PCB11, respectively. We determined kv from

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difference between the initial spiked tracer mass and final PUF analyzed mass, and then

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determined RS from the product of kv x As (Table 1).

192 193

Meteorology 8


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Temporal variability in mass transfer was calculated using hourly modeled and observed

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meteorology. The Weather Research and Forecasting Model (WRF) version 3.324, 25 was used

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to simulate meteorology and land surface temperatures at 1.33 km horizontal resolution over

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Chicago and 12 km over the Upper Midwest and Northeast United States. WRF operates by

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integrating the atmospheric primitive equations with physics parameterizations, including sub-

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models simulating land surface processes, boundary layer dynamics, convection, microphysics

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and radiation.26 A detailed description of the WRF configurations is presented in the SI in the

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Meteorology Simulation section. Results from WRF for this application met or exceeded

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community standards for atmospheric chemical transport modeling, as presented in SI under

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Meteorological Evaluation. We also employed hourly observations from Automated Weather

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Observing System (AWOS) units at 16 local area airports, obtained from the Research Data

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Archive at the National Center for Atmospheric Research, Computational and Information

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Systems Laboratory.27 From both WRF and AWOS observations, we used temperature, three-

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dimensional wind vectors, surface pressure and relative humidity as inputs to the sampling rate

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model. We employed 2 m ambient air temperature from WRF and AWOS, and used WRF

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surface temperature (TSKIN) as a proxy for TPAS, the sampler’s internal temperature.

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As meteorology simulated by numerical weather models includes errors in accuracy that

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may influence sampling rate, hourly de-biased meteorology was calculated by subtracting the

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hourly domain-average bias (the average difference between model and observation at the 16

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airports) from the WRF simulated meteorology to yield bias-corrected meteorological fields.

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These bias-corrected fields thus combine the accuracy of hourly observations with the fine

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spatial features resolved by urban and regional scale modeled meteorology.

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The impacts of PAS chamber air velocity on kv were calculated from the empirical linear

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relationship determined from wind tunnel experiments relating ambient wind speed Va to internal

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air velocity,11 as the Harner design reduces internal wind speed, advection, and in turn RS. We 9



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also employed a polynomial regression on empirical results reported by May et al. from wind

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tunnel studies, where RS depends on the angle between the 3D wind vector and the PAS

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apparatus.15 We considered wind vectors for swinging tethered samplers as deployed,

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accounting for sampler mass in an hourly equilibrium angle to the wind. Results differed by less

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than 1% using this second approach (SI).

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Results and Discussion

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We first evaluate the model using depuration experiments and quantify hourly variability and

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process contributions and estimate uncertainty at a representative site. We identify variability

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between compounds, months, and years at the Chicago sampling network, and extend analysis

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to simulated spatial patterns across Chicago and the Great Lakes. We then evaluate linearity in

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hourly PUF-air mass transfer and contextualize RS uncertainty.

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Model Evaluation

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We evaluated modeled RS for the two C13-labeled depuration compounds, C13-PCB 28 and

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C13-PCB 111. First, values for the advective mass transfer coefficient γ were calibrated to yield

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an empirical constant relating the observed sampling rates specific to the Harner samplers’

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design to first principles mass transfer processes. We fitted γ using Equation 4 at seven

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sampling sites in Chicago by Euler’s method from C-13 PCB 28 depuration compound RS

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(Table 1) and the numerical model. The median γ C13-PCB 28 value from the seven

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deployments was 0.076. First principles only account for flow over a foam disk floating in space,

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with no enclosure, so this reduction is expected, especially since the Harner sampler is

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designed to modulate flow rate. We then estimated modeled RS for each PUF. Normalized

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mean difference between modeled and depuration C13-PCB 28 RS using the median γ value

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was -5% and normalized mean absolute difference was 22%. These differences are consistently 10


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less than or comparable to the estimated analytical uncertainty of 15-25% for congener-specific

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PCB depuration compounds for our laboratory.22 Thus, model results fall within the

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contemporary range of analytical uncertainty even when using a single representative median

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value for γ. Results were similar for C13-PCB 111 (SI), and indicated that γ scales between

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compounds by the ratio of log(KOA), with normalized mean difference of -4% and normalized

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mean absolute difference of 36% using γ values (median = 0.087) scaled from C13-PCB 28.

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Sample-specific anomalies in fitted γ (Table 1) were investigated further and found to be

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correlated with both wind speed (R2 = 0.76) and temperature (R2 = 0.46), suggesting additional

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process contributions to mass transfer not yet resolved by the model. Multiple linear regression

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on wind speed and temperature (SI) provide γ estimates tailored to each deployment (MLR γ),

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which further improve estimates of modeled RS, (Table 1).

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Table 1. Modeled and depuration PCB 28 RS for each depuration compound sample. RS and γ reported for sample-specific fit, multiple linear regression, and mean values. A median value for γ of 0.076 is suggested from all samples.

Site

Sampling Period

Sample Specific γ

MLR γ

Depuration RS (m3 d-1)

Dewey Dawes Corkery Gresham Chase St. Elizabeth Webster

1/17 – 2/26 2/22 – 4/11 4/18 – 5/16 5/16 – 7/31 6/30 – 8/11 8/11 – 9/19 11/13 – 12/19

0.06 0.08 0.09 0.08 0.19 0.10 0.06

0.07 0.07 0.06 0.11 0.18 0.09 0.06

5.00 5.53 5.94 3.84 6.79 5.67 5.06

Mean

0.09

0.09

5.40

259 260 261 11


Average Model RS (m3 d-1) Using Using Median MLR γ γ (0.076) 6.43 6.06 5.55 5.31 5.13 4.28 3.87 5.63 2.66 6.28 4.55 5.52 6.51 4.81 4.96

5.41


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Hourly Compound-Specific Sampling Rates

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Using this modeling approach, hourly RS can be determined anywhere and at any time for all

264

SVOCs from meteorology, KOA, and molecular weight alone, using empirical mass transfer

265

coefficient γ from prior depuration experiments. The sampling rate RS is not constant during a

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deployment period, and is highly dependent on local hourly weather. Modeled hourly RS for PCB

267

congener 28 vary by a factor of 5 (Figure 1) over a PAS deployment period due to meteorology.

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Modeled RS resolves diurnal and synoptic weather cycles in sampling, seen as the small and

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large amplitude peaks, respectively. Sampling rate was also computed using averaged

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simulated meteorology during each PUF deployment to determine if average values could be

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simply scaled from reference conditions based on representative average deployment

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meteorology. As shown in Figure 1, average meteorology greatly underestimated RS. Average

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VA and TSkin were approximately 40% and 21% of the hourly variability seen in VA and TSkin

274

during the deployment period. Since sampling rate is an exponential function of both VA and T,

275

the use of average values produces consistently lower advective and diffusive sampling rates.

276

The PCB 28 RS time series at the Dawes site from 2/22/2008 to 4/11/2008 indicated

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minimal impact of weather model biases on modeled RS. We found a 1.8% average difference

278

between debiased and raw model meteorological data. In contrast, temperature plays a vital

279

role in variability in RS, where using TSkin rather than ambient T as a proxy for TPAS leads to a

280

7.3% average reduction in total RS. A complete sensitivity analysis is presented in SI.

281

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Figure 1. RS at the Dawes sampling site from 2/22/2008 to 4/11/2008 for PCB congener 28, including the hourly Rs predicted from our model (black); average loss of depuration Rs (red); modeled RS using average meteorology (green); and RS due to molecular diffusion (blue).

286 287 288

Diffusive and Advective Contributions to Sampling Rate Sampling rate was disaggregated into advective and diffusive components to quantify

289

their roles in the net sampling rate. Figure 1 shows hourly total and diffusive sampling, with

290

advective contribution as the difference. The relative contributions vary with wind speed, with

291

higher PAS chamber air velocities leading to a larger contribution by advective mass transfer.

292

The average RS from diffusion for PCB 28 at the Dawes sampling site during February-April

293

2008 was 2.73 ± 1.07 m3 d-1, consistent with average reported indoor RS values of 2.45 for PCB

294

28 determined by loss of depuration compounds.5 Thus, this model might also be used to

13



295

estimate indoor RS, due primarily to diffusion, using indoor temperatures and air circulation

296

rates.

297 298 299

Sampling Rate Variability among SVOCs We found simulated RS to be congener dependent, although the effect is smaller than

300

variability in meteorology. Rs increases with KOA. There is a consistent 27% range in RS

301

between low and high molecular weight congeners in each deployment (Figure 2) and a 20%

302

range among homolog groups (Table 2). Sampling rate decreases with increasing molecular

303

weight along KOA isopleths, indicating that differences in RS between SVOCs under ambient

304

conditions are due more to variability in KOA (γ) than to differences in molecular diffusion (D).

305

Therefore, accurate estimation of SVOC concentrations from PAS PUF samples requires

306

compound-specific sampling rates rather than the use or averaging of an arbitrary set of

307

depuration compounds, and highlights this practice as a source of uncertainty in contemporary

308

concentration estimates from passive air sampling. By removing the analytical uncertainty

309

inherent to depuration experiments, these findings explain the variability in RS values derived for

310

a range of compounds in prior depuration studies,28-31 and show that the theoretical variability in

311

RS is of similar magnitude to contemporary analytical uncertainties of ~20-25% for PCBs in

312

ambient air 32, 33.

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Figure 2. RS dependency on Log(KOA) and homolog group (colors, from pink = 1 to black = 10)

314

for each PCB congener during the PUF deployment at the Dawes sampling site from 2/22/2008

315

to 4/11/2008.

316 317

Not only is there congener-specific variability in RS between using regional versus local

318

scale meteorology, there is also seasonal and annual variability, with maxima in winter and

319

summer (~40% greater than boreal spring). There is less than 5% difference in average monthly

320

RS in the warmest months (July and August) compared to the coldest (January and February),

321

with advection and diffusion each contributing 50 ±6% to the total. Differences in monthly

322

average RS between 2008 and 2011 using observed meteorology at Chicago O’Hare Airport

323

varied from 0.97) over 60+ day deployment periods in winter and spring. In the

18


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cooler months, hourly uptake follows a staircase pattern, with spikes corresponding to large

385

episodic increases in ambient wind speed and passive sampling rate during synoptic frontal

386

passages, and higher linear uptake rates during sustained periods of high winds. While PUF

387

uptake follows this quasi-linear trend for 12-13 weeks throughout the winter, this is not the case

388

during the warmer months. More volatile and low molecular weight (LMW) compounds begin to

389

saturate after 6 weeks of deployment in the summer, when linear uptake gradually slows,

390

followed by loss in concentration from the PUF into the atmosphere. Sampling rates respond to

391

saturation, and are also sensitive to the dynamic impacts of temperature on saturation levels.

392

After the PUF becomes saturated at high temperatures, LMW compounds volatize off the PUF

393

disk until a new PUF concentration equilibrium point is reached. That equilibrium concentration

394

may reach higher values during cold spells (e.g. near 8/15 in Figure 4), and then lower again as

395

the temperature rises. This trend continues if the PUFs are left out longer than 12 weeks.

396

Saturation can lead to a >20% underprediction of quantified mass for LMW compounds.

397

However, this is only the case for volatile LMW compounds, as linear uptake occurs for less

398

volatile congeners throughout long summer deployments (Figure 4).

399

Modeled mass uptake onto the PUF shows that it is likely that previous methods used

400

for determining CAir have underpredicted concentrations for LMW compounds during long

401

deployments in warm conditions if one assumes constant linear uptake throughout as PAS PUF

402

deployment. In turn, the quantified analyzed mass may not be representative of the true mass

403

uptake during PAS PUF deployment period. Saturation can be estimated from KPUF and can be

404

corrected for using temperature, deployment length and kV from depuration compounds, 35, 36

405

but only if a representative range of such compounds is included on each PUF. In the Great

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Lakes and other areas with high variability in temperature, cloud cover and wind speed during a

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PUF deployment, even correcting using daily average temperature may not resolve the effects

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of PAS internal temperatures within a metal housing under full sunlight. However, nonlinear 19



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uptake is not a requirement when using the numerical model described here, as loss can be

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quantified and CAir calculated from the linear uptake period.

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Figure 4. Simulated PUF concentration (ng/g) at the Dawes sampling site from June 1 to

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August 31 2008: PCB 11 (blue, left axis) and PCB 153 (green, right axis). For low molecular

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weight congeners, concentration on the PUF during the first 6 weeks (red) increases linearly

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(black trendline, R2 = 0.9).

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Remaining Uncertainties in Passive Sampling

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This study identifies and quantifies uncertainties in the knowledge of passive air

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sampling processes, and provides a modular platform for evaluating their impacts on sampling

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rate in the field. We identify temperature in the PAS chamber as the single most important

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unresolved condition for predicting seasonality in RS. The simulated net process uncertainty of

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Simulating and Explaining Passive Air Sampling Rates for

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