Modeling the Uptake of Neutral Organic Chemicals on XAD Passive

Oct 31, 2013 - James M. Armitage,* Stephen J. Hayward, and Frank Wania ... University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario,. ...
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Modeling the Uptake of Neutral Organic Chemicals on XAD Passive Air Samplers under Variable Temperatures, External Wind Speeds and Ambient Air Concentrations (PAS-SIM) James M. Armitage,* Stephen J. Hayward, and Frank Wania Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada, M1C 1A4 S Supporting Information *

ABSTRACT: The main objective of this study was to evaluate the performance and demonstrate the utility of a fugacitybased model of XAD passive air samplers (XAD-PAS) designed to simulate the uptake of neutral organic chemicals under variable temperatures, external wind speeds and ambient air concentrations. The model (PAS-SIM) simulates the transport of the chemical across the air-side boundary layer and within the sampler medium, which is segmented into a user-defined number of thin layers. Model performance was evaluated using data for polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) from a field calibration study (i.e., active and XAD-PAS data) conducted in Egbert, Ontario, Canada. With some exceptions, modeled PAS uptake curves are in good agreement with the empirical PAS data. The results are highly encouraging, given the uncertainty in the active air sampler data used as input and other uncertainties related to model parametrization (e.g., sampler−air partition coefficients, the influence of wind speed on sampling rates). The study supports the further development and evaluation of the PAS-SIM model as a diagnostic (e.g., to aid interpretation of calibration studies and monitoring data) and prognostic (e.g., to inform design of future passive air sampling campaigns) tool.

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INTRODUCTION The use of passive air samplers (PAS) such as polyurethane foam (PUF) disks and styrene-divinylbenzene-copolymeric resin (XAD) cylinders for monitoring atmospheric concentrations of neutral organic chemicals has greatly expanded over the past decade.1−7 The main advantages of passive air samplers are low cost and minimal infrastructure requirements. These features translate into the potential for deployment in more remote regions compared to active air samplers (AAS). A prominent example of PAS deployment is the Global Atmospheric Passive Sampling (GAPS) Network,1,2 which now includes more than fifty sites. Ambient air concentrations can be inferred from the mass of chemical measured on PAS material using two approaches.8−11 If equilibrium between sampler and air has been achieved, air concentrations can be estimated using the sampler-air partition coefficient (KSA). Alternatively, air concentrations can be estimated from a (compound-specific) passive sampling rate (PSR, m3·d−1). The values obtained using the PSR represent time-integrated air concentrations for the deployment period. The latter approach is typically applied for the majority of outdoor field studies on semivolatile organic compounds (SVOCs), given the variation in conditions (e.g., temperature, ambient air concentrations) likely to be experienced by the sampler and the potentially large differences in time to equilibrium for the chemicals being analyzed. © 2013 American Chemical Society

As PSRs are typically required to translate the mass of chemical sequestered on a PAS into an ambient air concentration, research to better understand sampler uptake kinetics under various conditions has been a priority. The most important empirical approach is the calibration study, where AAS are used concurrently with PAS over the deployment period (e.g., refs 5, 9, and 12−14). PSRs can be estimated directly using the measured ambient air concentrations obtained through the AAS and the measured mass of chemical on the PAS material. The potential influence of external wind speed (W) on PSR (i.e., as W↑, PSR↑) has also emerged as an important research topic, leading to both empirical and computational studies to better characterize this effect.9,15−21 Because of its influence on uptake kinetics (e.g., length of deployment period where it is valid to assume linear uptake), the sorption capacity of different PAS materials have also been studied more extensively. For example, polyparameter linear free energy relationships have been determined for both PUF and XAD samplers, allowing the estimation of KSA and its temperature-dependence (ΔUSA, kJ mol−1) from solute descriptors.22,23 Received: Revised: Accepted: Published: 13546

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Although the quantitative understanding of the behavior of neutral organic chemicals in PAS systems has improved over the past decade, there are still substantial uncertainties related to the estimation of PSRs under field conditions, as evidenced by the variability/discrepancies in reported values (e.g., ref 24). One practical approach to reduce this uncertainty is the use of depuration compounds (DC) spiked onto the PAS material prior to deployment (e.g., refs 1, 12, and 19), since the loss of the DCs can be used to infer site-specific PSRs. Resistance to transport within porous sampler material (i.e., PUF and XAD) may complicate the use of DCs to some extent however.25,26 Overall, some important inconsistencies remain when considering the body of PAS literature (e.g., in the observed relationship between molecular weight/size and PSR between different studies), 24,25 suggesting the need for further investigation and clarification. The main objective of this study is to develop a practical modeling tool to simulate the uptake of neutral organic chemicals on XAD−PAS under variable temperatures, external wind speeds and ambient air concentrations. Model development is based on the assimilation of available knowledge (i.e., theoretical and practical considerations) as detailed in the following section and the Supporting Information (SI). The performance of the model is evaluated using a 365 day field calibration study of polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs). A key issue for simulating PUF-PAS is the treatment of particle-bound chemicals, given the uncertainties in quantifying sampling rates for the fraction of chemical sorbed to aerosols and lack of knowledge regarding the factors driving this phenomenon in the field.20,24 However, the evidence currently available for XAD-PAS indicates that uptake of particle-bound chemicals is minimal. Accordingly, only the fraction of the chemical in the gas phase is assumed to be available for uptake in the model simulations. Empirical data and other considerations supporting this assumption for XAD-PAS are presented in the SI (Section S1).

Figure 1. Conceptual representation of the PAS−SIM model for XAD−PAS. Diffusivities in air (BA) and within the sampler (BS−P) are used to estimate mass transfer coefficients (MTCs) characterizing transport in the air-side boundary layer (kA) and within the sampler (kS−P). Exchange between ambient air and the sampler is described using transport D values, which are a function of the overall exchange MTC, area of exchange and sorption capacity of the phase (ZA for air, ZS for the sampler). The overall sorption capacity of the sampler is characterized by the sampler-air partition coefficient (KSA). The fugacity gradient between air ( fA) and the sampler ( f S(1)) and within the sampler (e.g., f S(1) vs f S(2)) determines the net driving force for exchange. The outer layer of the sampler can accumulate chemical from the air (DU,A−S) and also from the adjacent layer of the sampler (DU,S−S), whereas losses occur to the air (DE,S−A) and the adjacent layer of the sampler (DE,S−S). As the top and bottom of the XAD-PAS are typically capped, these surface areas are excluded when calculating exchange between air and the outer layer of the sampler. Transport within the sampler is a function of kS−P and the area of exchange between layers. The model also allows for degradation of the chemical within the sampler (DR,S). Note that while the sampler is represented as two layers in Figure 1, simulations were conducted here with the sampler divided into 25 layers. See the SI for complete details.

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MATERIALS AND METHODS Basic Model Description. The basic theoretical description of the behavior of neutral organic chemicals in PAS systems has been reviewed previously.8−11 Chemical exchange between the air inside the sampler housing and the PAS can be modeled using rate constants derived from two-film theory (i.e., transport across an air-side and sampler-side boundary layer) with the sampler treated as a single well-mixed compartment.11 More recently, Zhang and Wania26 developed a more complex approach which explicitly models the transport of chemicals within porous sampler medium from the outer boundary layer toward the interior. Petrich et al. 27 also developed a sophisticated mathematical model of PUF-PAS and applied it to simulate the uptake of polychlorinated biphenyls (PCBs) under field conditions. The model developed for the current study (Passive Air Sampler Simulator, PAS−SIM) is fugacitybased and is represented conceptually in Figure 1. Complete details are provided in the SI (Section S2−S6). PAS−SIM is designed to simulate the behavior of neutral organic chemicals in XAD but could potentially be modified to simulate other passive sampling media.10,13,28,29 As shown in Figure 1, exchange between ambient air and the sampler medium is modeled based on diffusivities (Bi), which are used to derive mass transfer coefficients (ki) and transport D values (as per the fugacity approach).30 The overall capacity

of the sampler to accumulate chemical is characterized by the sampler-air partition coefficient (KSA). However, the sampler is segmented into a user-defined number of thin layers allowing the model to explicitly consider exchange between the air inside the sampler housing and the outer layer of the passive sampler (i.e., sampler−side boundary layer) and subsequent transport within the sampler itself. As described in the SI, the model accounts for the influence of temperature and external wind speed on model input parameters (e.g., KSA, diffusivity in air) and hence can be applied to simulate the behavior of contaminants under varying ambient conditions. The distribution of the chemical between the gaseous and aerosol phase in the ambient air is also considered, allowing the model to distinguish between chemicals that are predominantly in the gas phase versus predominantly sorbed to aerosol. As noted above, the fraction of chemical sorbed to aerosol in the atmosphere is not sampled by the simulated XAD-PAS (i.e., only the fraction of the chemical in the gas phase is assumed to be available to accumulate on the sampler). The model allows the calculation of two types of sampling rates, termed the inherent sampling rate (PSRW, m3·d−1) and the apparent sampling rate (PSRA, m3· d−1). PSRW represents the gross uptake rate and is a function of sampler dimensions (surface area), temperature (diffusivity) and external wind speed. It is calculated as shown below. 13547

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Figure 2. Influence of temperature (top panels) on modeled sampler uptake curves assuming a constant external wind speed (W = 2.5 m s−1) and influence of external wind speed (bottom panels) on modeled sampler uptake curves assuming a constant temperature (T = 15 °C) for hypothetical chemicals with log KSA values of 5, 7, and 9 (at 25 °C). MS is the modeled amount of chemical on the sampler over time (arbitrary units). Ratio in bottom panels is the amount of chemical on the sampler assuming the highest wind speed divided by the mass of chemical on the sampler assuming the lowest wind speed at the end of the deployment period (365 days). The influence of temperature on the sampler uptake curve for a hypothetical chemical with a log KSA value of 8 (at 25 °C) is shown as an insert (top-right panel).

PSR W = ωk SAAS

chemical properties, sampler dimensions) and define the ambient environmental conditions (e.g., average daily temperature and diurnal variation, external wind speed) and air concentrations. Model output includes the total mass of chemical in the sampler, as well as the fugacity and mass of chemical in each layer on an hourly and daily basis. Model Application and Evaluation. The PAS−SIM model was parametrized to represent an XAD−PAS (10 cm height, 1 cm radius) and first applied in a generic context using hypothetical chemicals with log KSA values ranging from 5 to 9 at 25 °C. Note that this range covers the estimated values for most PCBs and PAHs. Other properties of the hypothetical chemicals (e.g., temperature-dependence ΔUSA, aerosol-air partition coefficient KQA) are presented in the SI (Section S5). Simulations were conducted assuming a constant (unit) air concentration but i) at different air temperatures (−5 to 35 °C) assuming an external wind speed of 2.5 m s−1 and ii) with different external wind speeds (0.5 to 8 m s−1) assuming the same temperature (15 °C). The purpose of these simulations is to demonstrate basic features of the model’s behavior and the potential influence of ambient environmental conditions on the modeled uptake curves. The model was then parametrized and applied to simulate the uptake of ten PCBs and seven PAHs on XAD-PAS under field conditions. Key details of this outdoor calibration study are provided in the SI (Section S7). In brief, PCBs and PAHs were sampled continuously using a low volume active air sampler (LV−AAS) at the Egbert CARE facility (Egbert, Ontario, Canada) over a 365 day period (March, 2006− February, 2007). Air was sampled at a rate of 2.9 ± 0.2 m3·d−1

(1)

−1

where kSA (m·d ) is the overall mass transfer coefficient for exchange between ambient air and the outer layer of the PAS, AS (m2) is the surface area of exchange, and ω is a scaling factor accounting for the influence of external wind speed (SI, Sections S2 and S4). PSRA represents the net uptake rate (i.e., accounting for loss of chemical from the sampler via revolatilization) and is calculated as follows. PSRA =

MS CA − AVE·t

(2)

where MS is the (modeled) amount of chemical on the sampler (mol) at the end of the deployment period, CA−AVE is the average total air concentration over the deployment period (mol·m−3), and t is the length of deployment (d). Note that the model-derived PSRA is calculated following the same approach often used to calculate sampling rates from empirical data (i.e., calibration studies). The model-derived PSRA is a function of PSRW and the proximity to sampler-air equilibrium achieved during the deployment period. Additionally, because it is based on total air concentrations, the extent to which the chemical is particle-bound (i.e., assumed unavailable for uptake on XADPAS) also influences PSRA. Model Implementation. The PAS−SIM model is implemented in Microsoft Excel 2010 using the Visual Basic for Applications (VBA) programming language. All calculations are solved numerically given a user-defined time step. The user is expected to compile and enter key input parameters (e.g., 13548

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Figure 3. Input air concentrations (pg m−3, red lines) and simulated uptake curves (ng per sampler) for six PCB congeners assuming stagnant air boundary layer thickness of 0.015 m (black line), 0.01 m (dashed line), and 0.0075 m (dotted line). Empirical data (i.e., reported mass per sampler) are shown as the open circles (average) with the error bars indicating the range. Note that day 0 = March 1 (2006), day 180 = August 27, and day 365 = February 28 (2007).

the Egbert CARE facility were not available and so were estimated using data from Toronto Pearson International Airport (70 km south). These inputs along with the chemical properties (10 PCBs, 7 PAHs) and sampler characteristics (e.g., dimensions, porosity) used for these simulations are compiled in the SI (Section S2, S7). All calculations were conducted using the default property values assuming an air-side boundary layer thickness (δA) of 0.015 m, which results in baseline sampling rates (i.e., under stagnant air conditions, see SI, Section S2) of approximately 0.3 m3·dm−2·d−1, in agreement with surface-area normalized values reported for low molecular weight PCBs in a recent indoor calibration study.25 All PCB and PAH simulations were then repeated assuming δA equals 0.01 and 0.0075 m. Model performance was evaluated by comparing measured and modeled PAS uptake curves over

through a PUF−XAD−PUF plug (5 g of XAD, between 2 cm × 3 cm PUF) with no glass fiber filter (GFF) in place to filter out and sample particulate matter. PUF−XAD−PUF plugs were collected approximately every two weeks over the 365 day deployment period. Ten XAD samplers were deployed and retrieved two at a time (i.e., field duplicates) after 56, 122, 185, 241, and 365 days. All samples were analyzed following established protocols.9,13 Model simulations were conducted using the measured air concentration profiles based on the LV− AAS data and the mean daily temperatures recorded at the sampling site, which ranged from −18.8 to +31.5 °C. The temperature within the sampler housing is assumed to be equal to the ambient air temperature in the model code as there are no data specific to this field calibration that could be used to make such adjustments.31 External wind speed data specific to 13549

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Table 1. Empirically Derived Apparent Sampling Rates (PSRA), Model-Derived Apparent Sampling Rates (PSRA), and Inherent Sampling Rates (PSRW) for All Simulated PCBsa empirically derived PSRA μ ± 1 SD congener PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB

15 18 31/28 52 49 44 101 99 87 137

deployment day 0−56 0.82 0.62 0.77 0.94 1.06

± ± ± ± ±

0.22 0.17 0.52 0.24 0.28

0.38 0.70 0.51 0.52

± ± ± ±

0.14 0.19 0.29 0.26

day 0−365 0.62 0.52 0.47 0.64 0.62 0.65 0.53 0.50 0.48 0.42

± ± ± ± ± ± ± ± ± ±

model derivedb PSRA deployment day 0−56

0.09 0.05 0.06 0.04 0.17 0.07 0.05 0.07 0.04 0.05

0.45 0.46 0.46 0.45 0.46 0.45 0.42 0.42 0.41 0.32

0.63 0.67 0.66 0.65 0.66 0.65 0.62 0.61 0.60 0.47

model derivedc PSRW

day 0−365 0.38 0.40 0.41 0.42 0.42 0.43 0.41 0.43 0.43 0.38

0.50 0.54 0.55 0.58 0.58 0.60 0.59 0.61 0.62 0.55

deployment day 0−365 0.73 0.70 0.70 0.68 0.68 0.68 0.66 0.66 0.66 0.64

Note that PCBs are ordered by estimated log KSA value at 25 °C (lowest to highest). Empirically derived PSRAs are based on the amounts of chemical on each of the two samplers (arithmetic mean ±1 SD of the field duplicates) retrieved for the indicated deployment periods. bModelderived PSRAs are for simulations assuming δA = 0.015 m (italicized) and 0.01 m (regular font) and reflect net uptake rate of chemical over time (i.e., account for evaporation of chemical back to air) cModel-derived PSRW (averaged over deployment period) are for simulations assuming δA = 0.010 m and reflect gross uptake of chemical over time (i.e., do not account for loss of chemical from the sampler because of revolatilization back to air). a

time. PAS field data and model output were also used to compare empirical and model-derived PSRAs at different deployment lengths. Insight into the behavior of neutral organic chemicals in PAS can also be gained by comparing model-derived PSRAs to PSRWs as these two outputs characterize net and gross uptake rates respectively. Accordingly, the extent to which modelderived PSRA values at the end of the deployment period deviate from PSRW values (averaged over the simulation period) gives some indication of the extent to which accumulation of the chemical on the sampler deviates from the “linear” uptake phase (i.e., when net chemical uptake ≪ gross uptake due to substantial revolatization to the ambient air).

revolatilization from the outer layer are also higher as T increases, resulting in a lower MS at Day 365 and hence lower apparent sampling rate (PSRA) over the deployment period (i.e., net uptake↓). Essentially, as T increases and log KSA↓, the hypothetical chemical deviates further from the linear uptake phase (i.e., enters the curvilinear phase) and therefore exhibits a lower PSRA. In contrast, the hypothetical chemical with log KSA = 9 (at 25 °C) exhibits a positive relationship between temperature and MS over the deployment period. This pattern is observed because (i) PSRW↑ as T↑, (ii) uptake effectively remains in the linear phase at all temperatures (although losses via revolatilization do occur), and (iii) the fraction of this hypothetical chemical in the gas phase (i.e., the fraction available for uptake on XAD-PAS) is sensitive to temperature over the range considered (unlike the other hypothetical chemicals). Accordingly, both the inherent and apparent sampling rates increase with temperature. Note that this pattern is still observed if the role of partitioning to aerosols is omitted (results not shown). However, the sensitivity to temperature is reduced as it is driven solely by the influence of T on PSRW (as T↑, diffusivity in air↑). As a result, the simulated uptake curves diverge to a lesser extent. As shown for the hypothetical chemical with log KSA = 8 (Figure 1, inset), uptake curves showing very little sensitivity to temperature can also occur, which reflects a balancing of the counteracting influences of these different factors. External wind speed exerts a consistent influence on sampler uptake kinetics (as W↑, PSRW↑). However, the extent to which apparent sampling rates increase as a function of external wind speed depends on the length of deployment and log KSA. For example, the hypothetical chemical with log KSA = 5 approaches equilibrium by Day 60 regardless of wind speed and hence exhibits the same MS and PSRA when retrieved at Day 365. On the other hand, twice as much hypothetical chemical with log KSA = 9 accumulates on the sampler by Day 365 assuming W = 8 m·s−1 compared to W = 0.5 m·s−1. This pattern emerges because this chemical remains in the linear uptake phase throughout the deployment period at all wind speeds (i.e., relatively low losses) and therefore exhibits a positive relationship between W and MS (and PSRA). The hypothetical chemical with log KSA = 7 also exhibits a positive relationship between W and MS (and PSRA) but the differences in MS at

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RESULTS AND DISCUSSION General Model Behavior. Uptake curves for hypothetical chemicals at different temperatures but same external wind speed and different external wind speeds but same temperature are shown in Figure 2. As can be seen in Figure 2, some uptake curves exhibit all three expected uptake phases (i.e., linear uptake, curvilinear uptake and approach to equilibrium), demonstrating that the model formulation produces output consistent with expectations based on current theory. Temperature (T) can influence the sampler uptake curve by i) changing diffusivity (as T↑, diffusivity↑, PSRW↑) and ii) changing the sorption capacity of the sampler (as T↑, log KSA↓). For the hypothetical chemical with log KSA = 5 (at 25 °C), there is a negative relationship between temperature and amount of chemical accumulated on the sampler (MS) by the end of the 365 day deployment period. This pattern is observed because the sampler approaches equilibrium with the ambient air early in the deployment period for all scenarios, meaning that the amount accumulated is essentially a function of log KSA alone (i.e., equilibrium partitioning). The hypothetical chemical with log KSA = 7 (at 25 °C) also exhibits a negative relationship between temperature and MS over the majority of the deployment period even though the chemical remains in the linear/curvilinear uptake phase at all air temperatures. In this case, while the inherent sampling rate (PSRW) increases as T increases (i.e., gross uptake is highest at T = 35 °C), losses of chemical because of 13550

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empirical data on aerosol-air partitioning is not available for this field calibration study, this issue is not addressed further. However, air monitoring data for a range of PCBs collected in 2001/2002 using an active sampler at a different site near Egbert, Ontario reported negligible concentrations (MDLs = 0.063−0.58 pg m−3) on the GFF for all congeners at all times.32 As the PCBs collected in that study include higher-chlorinated congeners than considered here, it is plausible that the extent of sorption to aerosols for the higher-chlorinated congeners (e.g., PCB 137) is overestimated in the current model calculations. A noteworthy feature of the results presented in Table 1 and SI Table S11 is the scattered relationship between the estimated PSRAs and key chemical properties expected to influence accumulation on the sampler (e.g., molecular weight, molar volume, log KSA). For example, there is no consistent relationship between physical-chemical properties (e.g., molecular weight or volume or KSA) and PSRA. In most cases, the empirical PSRA values calculated for the first 56 days of deployment are higher than the PSRAs calculated for all other deployment periods. However, due to the smaller amount of analytes being quantified, the uncertainty in the empirical PSRAs is also higher for the 0−56 day deployment period compared to others (Table 1 and SI Table S11). Regardless, the empirical results could be interpreted to suggest that the samplers retrieved toward the end of the deployment period have begun to deviate substantially from the linear uptake phase for all PCBs. The trend in model-derived PSRAs over the deployment period for some of the lower-chlorinated PCBs (i.e., the PCBs with lower KSA) is broadly consistent with that interpretation (Table 1 and SI Table S13). However, the patterns in the model output are influenced by colder temperatures (↑KSA) toward the end of the deployment period. Furthermore, sorption capacity is not the only consideration. Another key factor is the trend in ambient air concentrations (Figure 3 and SI Figure S3), which generally (but not uniformly) show higher levels during the midportion of the deployment period (day 90−240, Spring−Summer) compared to the beginning and end (Fall−Winter). As the thermodynamic gradient governing chemical exchange at any given time is determined by the fugacity in air (determined by ambient air temperature and concentration) and the fugacity in the outer layer (determined by concentration and KSA), it is not necessarily obvious when periods of peak net uptake rate (i.e., the linear uptake phase) are occurring throughout the deployment period and when periods of reduced net uptake (i.e., curvilinear uptake phase) may be experienced. For example, peak ambient air concentrations of PCBs coincide with the highest air temperatures and hence lowest KSA values during the deployment period. Ambient air concentrations then tend to decline (i.e., reducing net uptake) but this change occurs as temperature falls and KSA values correspondingly increase (i.e., supporting net uptake). Based on the comparison of model-derived PSRW and PSRA values for the 365 day deployment period, the model indicates that PCB 15, 18, and 31/28 are most sensitive to the changes in temperature and air concentration (i.e., deviate more substantially from linear uptake). While these results are consistent with expectations based on the generic simulations presented in Figure 2 and log KSA values of the selected PCBs, the ability to simultaneously account for the influence of changing air concentration and ambient temperatures on the thermodynamic gradient experienced by the chemical between air and the outer sampler layer is clearly a valuable feature of the PAS−SIM model.

Day 365 are smaller (because the sampler is closer to achieving equilibrium with the air). Taken together, the generic simulations illustrate how temperature and external wind speed can substantially influence sampler uptake curves as well as the importance of considering sorption capacity (KSA) and length of deployment when interpreting PAS monitoring data. Field Calibration Study: PCBs. Measured air concentration profiles used as model input, the measured PAS data and the simulated uptake curves for selected PCBs are presented in Figure 3. Empirically derived apparent sampling rates for all PCBs at two deployment lengths are presented in Table 1 along with model-derived PSRAs and PSRWs. Empirically derived and model-derived PSRAs for the other deployment lengths are presented in the SI (Tables S11−S13). Model output for the PCBs omitted from Figure 3 is also presented in the SI (Figure S3). As shown in Figure 3 and SI Figure S3, good agreement between model output and monitoring data is observed for most PCBs with respect to the shape of the uptake curves. Closer agreement in absolute terms is typically seen when assuming a stagnant air-side boundary layer thickness (δA) of 0.01 or 0.015 m, depending on the congener. While it is not possible to isolate the main input parameter underlying discrepancies between observed and modeled PAS uptake curves, the model inputs used to account for the influence of external wind speed on sampling rate (ω and the W profile itself) are plausible candidates. Erroneous LV-AAS data used as model input is another potential source of discrepancy. Regardless, considering the lack of model calibration (fitting to field PAS data) and many uncertainties inherent to this exercise, the performance of the model for PCBs is very encouraging with respect to reproducing the observed XADPAS uptake curves. Model-derived PSRA values (Table 1 and SI Table S13) are broadly consistent with the empirically derived values in terms of absolute magnitude (i.e., generally within a factor of 2) but exhibit some discrepancies in relative terms for different deployment lengths (depending on the congener). Lower molecular weight PCBs exhibit a higher modeled PSRA over day 0−56 compared to day 0−365 (consistent with the empirical PSRAs) whereas higher molecular weight PCBs exhibit little change or the opposite pattern. The opposite relationship between deployment length and model-derived PSRA can be explained by the role of aerosol-air partitioning (KQA) in the model calculations. The estimated KQA values for the higher molecular weight PCBs (e.g., PCB 137) along with the temperature-dependencies (SI Table S7) lead to a substantial fraction of the chemical in the atmosphere shifting to the aerosol phase in colder months and hence becoming unavailable for uptake on XAD−PAS. For example, the modelderived PSRAs at the beginning of the deployment period (day 0−56, TAVE = 2.6 °C) for such PCBs are substantially suppressed because of this phenomenon. The effect is less influential at the end of the deployment period because the sampler has experienced higher ambient total air concentrations and fraction of chemical in the gas phase over the Spring− Summer period (i.e., conditions favorable for uptake). When the simulations for PCB 137 are repeated assuming no aerosols in the atmosphere (i.e., aerosol volume fraction set to zero), the model-derived PSRA values are approximately 35% and 15% higher over the 0−56 and 0−365 deployment period respectively (and so are approximately equal). Given that 13551

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Figure 4. Input air concentrations (ng m−3, red lines) and simulated uptake curves (ng per sampler) for naphthalene (NAP), phenanthrene (PHE), fluoranthrene (FLA) and pyrene (PYR) assuming stagnant air boundary layer thicknesses of 0.015 m (black line), 0.01 m (dashed line) and 0.0075 m (dotted line). Empirical data (i.e., reported mass per sampler) are shown as the open circles (average) with the error bars indicating the range. Note that day 0 = March 1 (2006), day 180 = August 27, and day 365 = February 28 (2007).

Field Calibration Study: PAHs. Measured air concentration profiles used as model input, the measured PAS data and the simulated uptake curves for selected PAHs are presented in Figure 4. Empirically derived PSRAs for all deployment lengths are presented in the SI (Table S11). Model output for PAHs omitted from Figure 4 are also presented in the SI (Figure S4). Model performance for fluoranthrene (FLA) and pyrene (PYR) (Figure 4) is reasonable both in absolute terms and with respect to the shape of the modeled uptake curve, particularly when assuming δA = 0.01 m. Model output for phenanthrene (PHE) (Figure 4), fluorene (FLU), and chrysene (CHR) (SI Figure S4) systematically underestimates the empirical PAS data but is more consistent in terms of the uptake curve shape. Interestingly, the model output does not reproduce the shape of the empirical PAS uptake curves for naphthalene (NAP) (Figure 4) and acenaphthene (ACE) (SI Figure S4), the most volatile PAHs. The empirical PAS uptake curves for NAP and ACE appear to follow the three theoretical phases one would expect if ambient conditions and air concentrations were relatively stable over the deployment period. However, the LV−AAS data for NAP exhibit a four-fold increase in ambient air concentration between day 180 and day 365 of the deployment at the same time as mean daily air temperature decreases from +15−20 °C to below −10 °C (i.e., log KSA increases substantially). In contrast, the measured amounts of NAP on the PAS are approximately equal over the same period (i.e., no apparent net uptake despite higher air concentrations and increased sampler capacity). There are also similar discrepancies between empirical PAS and LV-AAS data for

ACE. Given the trends in the LV-AAS data and ambient air temperature between Day 180−365, the PAS-SIM model output cannot replicate the shape of the empirical PAS uptake curve for NAP and ACE under the current model assumptions (e.g., linear sorption isotherms/no saturation of sorption sites). As one or both sets of measurements may be erroneous for these two compounds, additional data are required to resolve the apparent discrepancies. Other aspects of the model performance for PAHs are discussed further in the SI (Section S8). Future Considerations. The PAS−SIM model is intended to be a practical tool which can potentially be used for different purposes, such as (i) probing the potential influence of seasonal changes in environmental conditions and ambient air concentrations on sampler uptake curves (e.g., simulating polar, temperate and tropical sampling sites), (ii) aiding the interpretation of field calibration data (i.e., matched AAS and PAS data), and (iii) comparing the performance of different sampler materials (e.g., varying in porosity) or sampler geometries (varying in surface area) for a given deployment scenario. The behavior of depuration compounds (DC) under different environmental conditions and over varying deployment lengths could also be simulated in order to develop guidance for DC selection. One of the main advantages of the PAS-SIM model is the relative ease with which it can be parametrized by the user to represent field conditions (i.e., variable air temperatures, ambient air concentrations, external wind speeds). The model is based on established PAS theory but also incorporates more recent considerations related to transport within the porous medium itself.25,26 For example, 13552

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because the model is segmented into thin layers, it is capable of replicating the nonuniform distribution of chemicals observed for PCBs within the PAS. Uptake and elimination on the PAS is determined by the concentration (fugacity) in the outer layer, which is not artificially diluted by immediately distributing the accumulated amount of chemical into the full volume of the sampler. Sampling rates based on loss of DC over the deployment period could also be calculated under different assumptions regarding the initial distribution of these compounds within the PAS. In these contexts, the assumption of instantaneous equilibrium between sampler solids and pore air may need to be revisited in the future. Treating the pore air and sampler solids as separate compartments within each thin layer would allow sorption/desorption kinetics to be incorporated into the model formulation and exchange to be calculated explicitly.26 Insufficient data on sorption/desorption kinetics are available at this time to guide model parametrization however. In general though, this modification is expected to increase transport efficiency within the PAS (i.e., enhance the effective diffusivity) since the fraction of chemical in pore air will become a function not only of KSA but also the sorption/desorption kinetics. A key outstanding issue is the extent to which different PAS accumulate particle-bound chemicals. Aerosol entrapment has been observed for PUF-PAS 20 and chemicals expected to be predominantly particle-bound (e.g., high molecular weight PAHs and brominated flame retardants) have been quantified on PUF in numerous studies (e.g., refs 18, 24, and 33). While some studies with PUF-PAS have indicated that sampling rates for particle-bound chemicals are lower than chemicals predominantly in the gas phase, others have reported similar or even higher PSRs for particle-bound chemicals.24,33 As discussed in the SI (Section S1), particle-bound chemicals do not appear to be sampled efficiently by XAD-PAS. This is consistent with the PUF-PAS studies reporting substantially reduced PSRs for such compounds.18,24 Additional studies with both XAD- and PUF-PAS are required to develop a more quantitative understanding of sampling rates for particle-bound chemicals and how different factors (e.g., wind speed/ turbulence, particle size, and density, porosity, and pore size of sampler material) influence the uptake kinetics. Revisions to the PAS-SIM model could then be implemented to allow the tool to be applicable to a broader range of potential target analytes and sampler materials. For the time being, it is recommended that applications of the PAS-SIM model for simulating PUF-PAS be limited to chemicals predominantly in the gas phase throughout the year. Besides this important research question, improving the accuracy and reliability of the expressions characterizing the wind speed effect on PSR is also desirable for future realistic/site-specific applications of the model.

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Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +1 416 287 7277. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The Natural Sciences and Engineering Research Council of Canada and the Canadian Foundation for Climate and Atmospheric Sciences are acknowledged for funding support.

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ASSOCIATED CONTENT

S Supporting Information *

Additional details on model development and parametrization. This material is available free of charge via the Internet at http://pubs.acs.org. The PAS−SIM model is freely available upon request to the Wania Group (www.utsc.utoronto.ca/ ∼wania/). 13553

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