our knowledge of the sources , exposures and toxicity of airborne 
 persistent organic pollutants is limited by our ability to accurately 
 and conveniently measure these compounds .
passive air sampling methods 
 are very attractive and have been widely adopted , yet commonly suffer 
 from a major limitation  determination of the effective sampling 
 volume to convert the mass accumulated on the sampler to an environmental 
 concentration .
this problem has now reached an urgent level as researchers and 
 governments around the world are expanding their monitoring programs 
 to measure and reduce air concentrations of toxic chemicals .
effective sampling volumes are determined from passive samplers 
 by one of three methods : use of depuration compounds , active sampling calibrations , and modeling approaches . spiking samples with depuration compounds ( dc ) prior to deployment 

is preferred and considered the most accurate way to incorporate site - specific 
 information .
unfortunately , the dc method 
 is expensive , labor intensive , and does not provide essential information 
 about sampler performance over the entire deployment time .
for example , 
 the dc method does not indicate if the accumulated compounds have 
 become equilibrated and does not describe the effective sampling volumes 
 for every compound that can be collected on the sampler .
determining 
 effective sampling volumes using active samplers to calibrate the 
 passive samplers is often considered the gold standard : active sampling 
 methods are promoted as official methods of the u.s . environmental 
 protection agency .
. nevertheless , 
 active sampler calibration are inaccurate when passive samplers are 
 deployed in a different environment than the original calibration . 

modeling methods are the most promising but have not yet become widely 
 accepted and adopted .
we provide a mathematical model to cheaply and effectively determine 
 the effective sampling volume of any gas - phase pollutant collected 
 by the most commonly used passive sampler method : the puf 
it accounts for 
 equilibration of compounds caused by high temperatures and long deployments . 

it works as well for compounds sampled during linear uptake as well 
 as accumulation in the curvilinear or equilibrium modes .
the specific 
 objectives of this study were to ( 1 ) improve the uptake model originally 
 published by petrich et al . through hourly 
 temperature correction of the kpuf partition 
 coefficient and
include the effective sampling volume calculation ; 
 ( 2 ) assess the reliability of puf  pas concentrations determined 
 by our model by comparing with active data ; ( 3 ) evaluate the performance 
 of the new method in matlab code ( available in the si ) , under varying meteorological scenarios .
this model is 
 based on an approach we have previously reported for a small set of 
 samples collected in chicago . here
we 
 show its effectiveness using independent reports from samples collected 
 in toronto , canada and lancaster , uk .
our model can be used anywhere in the world to determine effective 
 sampling volumes for puf  pas deployments . local hourly meteorological 
 data and the deployment start and stop times are the only required 
 metadata .
the samples were analyzed for all 209 pcb congeners 
 and integrated air concentrations are calculated .
the impact of our 
 model is most striking for lower molecular weight congeners and long 
 deployments .
we show that for these certain compounds and situations , 
 previous methods underestimate ambient concentrations , and therefore 
 underestimate the magnitude of current sources and exposures .
in passive air sampling , air passes through a passive air sampler 
 ( pas ) chamber where the chemical contaminants are deposited on a sampling 
 media , such as polyurethane foam ( puf ) . the uptake of pcbs on a puf 
pas 
 sampler can be modeled as a function of the air - side mass transfer 
 coefficient ( kv ) and the concentration 
 gradient between the surrounding air and puf sampler ( eq 1).1where mpuf is the mass ( ng ) of 
 the pcb congener on the sample ( puf ) , kv is the air - side mass transfer coefficient ( m d ) , as is the surface area of the puf 
 ( m ) , cair is the pcb concentration in the air 
 ( ng m ) , cpuf is the pcb concentration 
 on the puf ( ng g ) , and kpuf is the puf - air equilibrium partitioning coefficient ( m g).kpuf is the 
 congener specific puf - air partition coefficient .
this study is based on our previously reported method for estimating 
 deployment and congener specific hourly sampling rate ( rs ) from hourly meteorological data using first - principle 
 chemistry , physics , and fluid dynamics , calibrated from depuration 
 compounds .
the method used in this study 
 requires a uniform and widely available meteorological data and physical - chemical 
 parameters including hourly weather data parameters for temperature 
 ( k ) , pressure ( pa ) , wind speed ( m s ) , wind direction 
 ( degrees ) , water vapor mixing ration ( qv , kg / kg ) , molecular weight ( mw ) , octanol - air partitioning coefficient 
 ( koa ) at 25 c , and internal energies 
 of octanol  air transfer ( duoa ) . 
 for hourly weather data , the model uses the integrated surface database 
 ( isd ) data set obtained through noaa s national centers for 
 environmental information ( ncei ) web site .
we utilize the isd lite data set , which is a derived product from 
 the full isd data set , providing data for air temperature , dew point 
 temperature , sea level pressure , wind direction , wind speed , cloud 
 cover , and precipitation .
isd data sets are processed in matlab to 
 convert them to the necessary input format for the effective sampling 
 volume model and calculate the water vapor mixing ratio ( this script 
 is provided in the si ) . with these inputs
our matlab script calculates hourly measurements 
 of internal pas chamber air flow , molecular diffusivity , dynamic and 
 kinematic viscosities for both air and water , air - side mass transfer 
 coefficient ( kv ) , sampling rate ( rs ) , and finally the effective sampling volume 
 ( veff ) .
the only empirical parameter from 
 the petrich model is the advective mass transfer coefficient (  ) , 
 which was determined by calibration with results from depuration experiments . 

the range of ambient temperature used in the depuration calibration 
 was 6.4 to 23.3 c , and range of wind speed was 3.3 to 
 4.8 m s. if the compound accumulated on the puf  pas in the linear 
 uptake phase , the effective sampling volume is equivalent to the amount 
 of airflow through the sampler ( i.e. , sampling rate times time , rst ) .
if the sample has passed 
 the linear uptake phase , the effective sampling volume is corrected 
 for equilibrium .
the time to equilibrium ( i.e. , maximum effective 
 sampling volume ) is directly proportional to kpuf .
the congener specific effective 
 sampling volume was determined at each time step ( 1 h ) by rearranging eq 1 in terms of air volume 
 as follows,2where veff is the effective sampling volume ( m ) , vpuf is the volume of the puf ( m ) , 
 and dpuf is the density of the puf ( g m ) .
the final congener specific airborne concentrations were determined 
 by applying the final cumulative effective sampling volume to the 
 lab - determined analyte mass on the puf using eq 3.3 the model was also modified to adjust the puf - air partitioning 
 for hourly temperature , instead of assuming it to be constant for 
 the deployment time . the congener specific kpuf value was calculated from an empirical relationship with koa developed by shoeib and harner .
the kpuf was adjusted 
 for temperature at each time step ( 1 h ) by adjusting the koa used to calculate it using the following equation from 
 li et al .
( 2003),4where t is temperature ( k ) , 
 uoa is the internal energy of octanol  air 
 transfer ( j mol ) , r is the gas 
 constant ( j mol k ) . the reference 
 logkoa values at 25 c , were calculated 
 for all congeners based on their relative retention time using the 
 methods described by harner and bidleman .
the model and the script for processing the raw isd lite data sets 
 ( meteorological data ) were developed and run in matlab version r2015a . 

an accompanying file with the necessary physical - chemical 
 properties of all 209 pcb is provided as a csv ( comma separated values ) 
 file in the si .
this file is critical to 
 running the model . a step - by - step readme file to assist with identifying 
 an appropriate weather station , downloading the correct weather data , 
 processing the data with the provided script , and setting up a run 
 to obtain congener and deployment specific effective sampling volumes , 
 is also provided as a pdf in the si .
the 
 outputs of the model are a deployment specific effective sampling 
 volume ( m ) and sampling rate ( m / d ) for each 
 pcb congener .
the user is also alerted of high wind speed measurements 
 ( >5 m s ) , given as a percent of the total 
 number of measurements , for each sample .
pas samples collected 
 in the metropolitan chicago area to evaluate the model with active 
 sampling comparison and characterize the model s function under 
 varying meteorological scenarios . the  flying saucer  

pas sampler design ( with a 24 cm top bowl and 19.5 cm bottom bowl ) 
 is based on the  harner  puf  pas design .
dimensions , 14 cm diameter 
  1.3 cm thick ; surface area of 365 cm ; and density 
 of 0.0236 g cm .
samples were collected in approximately 
 6-week intervals ( average of 45 days ) from january 2012 to january 
 2014 .
all samples were collected in pairs with one sample remaining 
 at the university of iowa for analysis , and the other sample being 
 sent to indiana university for analysis for a different suite of environmental 
 contaminants .
prior to deployment of the samplers , the sampling media ( puf disk ) 
 was cleaned with multiple 24 h soxhlet extractions , dried by low - flow 
 nitrogen blow - down , wrapped in aluminum foil , and stored in a freezer 
 until shipment and deployment .
after collection , samples 
 were wrapped in combusted aluminum foil and shipped back to the university 
 of iowa , where they were kept refrigerated at 20 c until 
 extraction .
the puf samples were spiked with 50 ng of surrogate standards 
 ( pcb14 ( 3,5-dichlorobiphenyl ) , pcb65-d5 ( 2,3,5,6-tetrachlorabiphenyl - d5 , 
 deuterated ) , and pcb166 ( 2,3,4,4,5,6-hexachlorobiphenyl ) ) , 
 extracted with a 1:1 hexane : acetone mixture in an accelerated solvent 
 extractor , cleaned with an acidified silica column , and concentrated 
 with a caliper turbovap ii .
the samples were then spiked with 50 ng of internal standard ( pcb30-d5 
 ( 2,4,6-trichlorobiphenyl-2,3,4,5,6-d5 , 
 deuterated ) and pcb204 ( 2,2,3,4,4,5,6,6-octachlorobiphenyl ) ) 
 just prior to analysis by gas chromatography with tandem mass spectrometry 
 ( gc - ms / ms , agilent 6890n quattro micro gc , waters micromass ms technologies ) 
 using a modified epa method 1668a . all 
 209 pcb congeners were quantified as a collection of 156 individual 
 or coeluting chromatographic peaks .
quality assurance 
 and control ( qa / qc ) was evaluated with the use of surrogate pcb standards , 
 method and field blanks , and a comparison study with the indiana university 
 lab .
the average surrogate percent recoveries for pcb14 , pcb 65-d5 , 
 and pcb166 , were 75%  14% , 77%  16% , and 88%  15% 
 respectively .
method blanks and field blanks were analyzed in tandem 
 with samples . both method and field blanks experience the same soxhlet 
 cleanup as samples , but while the field blanks get shipped to deployment 
 site along with samples , method blanks remain in the laboratory freezer . 

total pcb masses ( geometric mean ( geometric standard error ) ) found 
 in method blanks ( n = 24 ) and field blanks ( n = 17 ) were 4.5 ( 1.0 ) and 4.9 ( 1.1 ) ng per puf , respectively . 
 a congener specific limit of quantification ( loq )
was applied to every 
 sample and was calculated as the upper limit of the 95% confidence 
 interval of method blanks . if congener masses in samples were below 
 than the loq , values were substituted with zero . on average
the congener 
 specific loq was 0.051 ng sample , and all loqs 
 were below 0.5 ng sample , with the exception of 
 a coelution of three congeners ( pcbs 85 + 116 + 117 ) which had an 
 loq of 0.53 ng sample .
the extracts of 10 
 samples from indiana university were analyzed at the university of 
 iowa with the parameters described previously .
the pcb mass found 
 from the two different data sets had no statistically significant 
 difference ( p = 0.28 , two - tailed paired t test ) .
pas 
 results obtained using the model determined effective sampling volumes 
 and hi - vol sampling at the same sampling site .
these comparisons were 
 done for chicago , il , lancaster , uk , and toronto , canada .
pas samples and thirty - one hi - vol 
 samples collected at the iadn site at the illinois institute of technology 
 ( iit ) .
the samples were all collected between january 
 first , 2012 , and february ninth , 2013 and analyzed for 42 pcb congeners 
 or coeluting congeners with varying physical - chemical properties . 
 the second evaluation used data reported from a field study in lancaster , 
 uk , where puf  pas and hi - vol samplers were deployed simultaneously 
 for the purposes of calibrating veff for 
 the puf  pas samples .
specifically , 
 one component of the lancaster study was to derive field based uptake 
 rates .
pas samples and collected 
 them at 1 , 2 , 3 , 4 , 6 , and 8 weeks .
pas 
 and low volume air samplers were deployed simultaneously in toronto , 
 also for calibration purposes .
the dimension of the puf disk and sampler housing for all three 
 experiments are given in table s2 of the si .
the puf disk parameters are specified in the first few lines of 
 the script and can be modified to accommodate a specific study .
the 
 sampler housing dimensions are assumed to be the same as the  flying - saucer  

however , this relationship can 
 be modified to accommodate another sampler if the relationship between 
 internal air velocity and external air velocity is known . in all three cases
, we calculated the concentrations measuring 
 using the puf  pas with the chemical mass that accumulated on 
 the puf media reported from each study , eq 3 , and the veff from our matlab 
 code .
the puf parameters were changed to accommodate the specific 
 puf disk parameters specified by the authors ( such as length , thickness , 
 density , and surface area ) .
the chemical masses were measured in our 
 laboratory for the chicago study , and the concentrations for the chicago 
 iadn hi - vol samples were provided by dr .
the chemical masses on the puf and concentrations 
 on the hi - vols were published by chaemfa et al . for the lancaster 
 study , and were provided by dr .
, the weather data was downloaded for the chicago ohare 
 airport ( ord ) . for the lancaster study
, we used weather data from 
 barrow / walney island airport ( bwf ) .
for the toronto study , we used 
 weather data from toronto pearson international airport ( yyz ) .
pas 
 to that of the active samplers in each case ( figure 1 ) .
pas vs hi - vol comparison for select pcb congeners for 
 chicago , il ( left ) , lancaster , uk ( middle ) , and toronto , ontario ( right ) . 

both hi - vol and puf  pas values were taken as geometric means . 

the red line represents the 1:1 line and the dashed lines represent 
 the 2:1 lines ( i.e. , factor of 2 ) .
a good agreement was found between our approach for estimating 
 concentrations from puf and hi - vol determined concentrations for the 
 three comparison conducted , yielding an average congener specific 
 method ratio ( active / passive ) of 1.1  1.2 .
pas sampling with an average method 
 ratio was 1.59  1.45 . while the lancaster and toronto comparisons 
 showed bias toward hi - vol sampling , with average method ratios of 
 0.91  0.20 and 0.62  0.58 , respectively .
some variability for these comparison could be due to pas concentrations 
 representing 48 weeks , while hi - vol measured concentrations 
 were calculated as the average value of 824 h measurements . 

this difference in collection methods might lead to variability in 
 the detection ability of the respective sampling media at low concentration . 
 for congener concentrations above 1 pg m , the 
 average method ratio was 0.99  0.92 , while congener concentrations 
 below 1 pg m , the average method ratio was 1.98 
  1.86 .
other sources of variability could be attributed to meteorological 
 variability during the deployment period and differences in sampler 
 design / installation .
previous studies have shown that sampler installation 
 ( fixed or freely swinging ) can affect the internal air velocity , and that slight changes in bowl dimensions can affect uptake performance .
the model was designed for a fixed sampler installation 
 with the dimension given ( table s2 ) , therefore 
 differences from the assumed installation can lead to uncertainty 
 in the model performance . puf 
pas samplers are 
 commonly run in accumulation mode and uptake is linear as a function 
 of time and the sampling rate ( rs ) .
as 
 accumulation approaches equilibrium , this assumption is no longer 
 valid and the use of rs will underestimate the airborne 
 concentrations , particularly for lower molecular weight compounds .
the effective sampling volume approach implemented 
 in our model corrects for equilibrium using hourly adjustments for kpuf .
we evaluate the severity of this problem 
 and illustrate the impact for a sample collected in chicago . for this 
 sample ( lemont site ,
deployed : 7/18/129/13/12 ) the impact 
 was especially severe because it was a warm period ( average deployment 
 temperature : 23.5 c ) and the deployment time ( 57 days ) was longer 
 than average ( figure 2 ) . using our model ,
the total concentration of pcbs yielded 504 pg 
 m , while assuming linear uptake the airborne concentration 
 would be 305 pg m. this is a 40% difference in 
 concentrations when assuming linear uptake .
for example , monochlorinated 
 pcb congeners show approximately an 85% difference and dichlorinated 
 pcb congeners show approximately a 65% difference .
therefore , samples 
 with a profile skewed to lower molecular weight congeners ( aroclor 
 1016/1242 ) , the total airborne pcb concentration could be significantly 
 under predicted using a linear approach .
pas sample 
 ( a ) using the veff calculated by this 
 study , ( b ) using an assumption of linear uptake , ( c ) and the difference 
 between the two results .
the percentages in parentheses are the percent 
 difference between equilibrium corrected and linear results .
large changes in temperature 
 during deployment , and temperature increases in particular , can lead 
 to errors in interpreting concentrations .
temperature changes affect 
 gas - particle partitioning of pcbs , sampling medium , and airborne concentration , which can lead to difficulty in interpreting results .
we accounted 
 for the effects of temperature changes on the sampling medium with 
 hourly temperature adjustments on kpuf ( a function of koa ) .
we examined the 
 impact of changing kpuf hourly compared 
 to a method of averaging the adjusted kpuf over the entire deployment time .
when the air temperature increases over the deployment period , the 
 capacity of the puf disk to uptake pcbs decreases .
this results in 
 a shorter time to equilibrium and could cause degassing of accumulated 
 chemical .
similarly , 
 temperature changes at the end of a deployment can dramatically impact 
 the interpretation of the integrated air concentrations .
using average 
 values will not adequately account for a consistent trend of temperature 
 change , and would display a bias for 
 temperatures at the beginning of the deployment .
figure 3 shows effective sampling volume 
 curves for a compound with koa of 10 with trends of decreasing and increasing temperature .
pcb1 is a clear example : the temperature trends at the end of the 
 deployment has a significant effect on the final effective sampling 
 volume computed with our approach because of the impact on the mass 
 transfer driven by kpuf .
however , pcb 
 congeners with koa greater than or equal 
 to 10 are still in the curvilinear or linear phase at 
 the end of the deployment , and the mass uptake is driven by kv and a temperature jump toward the end of the 
 deployment has little to no effect ( figure s2 ) .
example effective sampling volume curves for deployments with decreasing 
 temperature ( left ) and increasing temperature ( right ) at a koa of 10 ( pcb 1 ) .
the solid green line 
 represents the effective sampling volume calculated using the method 
 of this study , including hourly adjustments on kv and kpuf .
the dashed red line , 
 represent effective sampling volume calculated using average values 
 for kv and kpuf .
although the puf  pas 
 is designed to be deployed for 48 weeks , longer deployments 
 are convenient when sampling in remote places . our model for determining
we examine 
 the utility of our method through a set of samples collected at the 
 jardine water plant in chicago .
although most samples at this site 
 were deployed for 6 weeks , one sample was deployed for 344 days from 
 october 3th , 2011 to november 11th , 2012 ( figure 4 ) . by the end of the deployment ,
we predict 
 that 80 pcb congeners were no longer in the linear uptake and instead 
 were at equilibrium , degassing , or in the curvilinear portion of the 
 uptake curve . using our model to predict the final veff for every compound in the sample , we find the pcb 
 concentration for the long deployment sample was 780 pg m. this was not significantly different than the mean of all samples 
 collected at the same site ( 800  110 pg m ) .
this was true for most of the 159 congener sets as well : only 
 six congeners or coeluting congeners ( pcb3 , pcb4 , pcb16 , pcb35 , pcbs40 
 + 41 + 71 , pcb159 ) during the long deployment exhibited concentrations 
 outside the range of what was observed in all other samples ( n = 11 ) ( figure s4 ) .
trend in veff is plotted for a compound 
 with a koa of 10 that accumulated 
 in a puf 
pas sampler deployed in chicago over an unusually 
 long deployment period ( 344 days ) .
the solid green line represents the effective 
 sampling volume calculated using the method of this study , including 
 hourly adjustments on kv and kpuf .
the dashed red line represents the effective sampling 
 volume calculated without hourly adjustment of kv and kpuf .. wind speeds at higher velocities 
 can create sharply increased sampling rates , therefore decreasing 
 the time for the effective sampling volume to reach equilibrium levels . 

increased wind speeds decrease the thickness of the air - side boundary 
 layer and therefore increase the sampling rate .
demonstrated in laboratory experiments that 
 once the internal air velocity becomes greater than 1 m s ( 5 m s external air velocity ) , the sampling 
 rates increases drastically .
these results 
 have also been observed in the field using depuration compound determined 
 sampling rates at windy , coastal , and mountain sites .
our model does not provide drastically increased sampling 
 rates at values greater than 5 m s because 
 it is calibrated using depuration compound results with average wind 
 speeds over the deployment period ranged from 3.3 to 4.8 m s. this at least partly explains why we predict higher concentrations 
 using the puf  pas in toronto than measured with a hi - vol .
the 
 average wind speed in toronto during the study period was 5.1  
 3.0 m s and the average during the first 25 days 
 of the study was 6.0  3.4 m s. at wind speeds 
 greater than the calibration range , the effective sampling volume 
 will be under - predicted using our model .
the results of this study elucidate and 
 solve a major challenge in using puf 
pas samplers for determining 
 concentrations of semivolatile organic compounds ( svocs ) in air .
this 
 study provides an accessible method for determining the effective 
 sampling volume for any puf 
the model only 
 requires basic matlab knowledge , sampling metadata ( spatial location , 
 deployment data , collection date ) , the appropriate isd meteorological 
 data ( processing with the provided script ) , and laboratory determined 
 analyte mass .
the model results for the chicago comparison was 
 also compared with results obtained using the commonly used gaps template .
when using the gaps template it was assumed 
 that the sampling rate was 4 ( the default ) and particle phase sampling 
 rate as a fraction of gas - phase rate was 1 ( i.e. , equivalent ) .
from 
 only a subset of congeners compatible between the data sets , the method 
 ratio using the gaps template calculation was 0.75 , while the method 
 rate using the model was 1.09 .
a graph of this comparison can be found 
 in the si ( figure s5 ) .
for the lower molecular 
 weight congeners ( mono- to tetrachlorinated pcbs ) , the method ratio 
 using the gaps template calculation was 0.70 , while the method rate 
 using the model was 1.02 . while the gaps template provided a very 
 similar result the model is able to calculate a compound specific 
 sampling rate ( rs ) instead of assuming 
 the default from the gaps template .
we also explored the possibility of using a linear free energy 
 relationship ( lfer ) to predict kpuf for 
 pcbs partitioning to polyurethane foam disks .
the comparison 
 of the kpuf values for all 209 pcb congeners between the 
 lfer method and the koa method was an average of 0.8 log 
 units different at 0 c , and an average of 0.1 log units different 
 at 35 c .
a graph of this comparison can be found in the si ( figure s6 ) .
the model results for the chicago 
 comparison were recalculated using the temperature dependent lfer 
 for polyurethane foam given be sprunger et al . modified from kamprad and goss .
it was found the average method ratio for the chicago comparison 
 using the lfer determined kpuf was 1.63 , 
 compared to 1.59 for the comparison using the temperature corrected kpuf determined by the empirical relationship 
 proposed by shoeib and harner .
a graph 
 of this comparison can be found in the si ( figure s7 ) .
for pcbs with lower koa values ( < 10 ) , the lfer was 1.45 compared to 1.47 
 with the shoeib and harner relationship .
for pcbs with higher koa values ( > 10 ) , the lfer was 1.84 
 compared to 1.70 with the shoeib and harner relationship . from these 
 results we decided to utilize the empirical relationship proposed 
 by shoeib and harner for our study on pcbs .
however , the option to use the lfer to determine kpuf remains an option in the model , if the user chooses .
there are several uncertainties in passive sampling methods that 
 could improve the effectiveness of the model in certain scenarios . 

pas 
 is still a debated issue.at this time the model does not consider the effects of particle - phase 
 sampling rates on the effective sampling volume equation .
pcbs are 
 largely in the gas - phase and so this consideration is not important 
 for this study .
we have also assumed that the internal pas housing 
 temperature is equivalent to the ambient air temperature , but increased 
 internal sampler temperature could affect the capacity of the puf 
 disk .
some studies have also shown that svocs accumulate at greater 
 levels in the outer layers of the passive sampling media , indicating 
 a kinetic resistance to chemical transfer exists in the sampling media .
due to a lack of complete understanding of this process , the model 
 does not currently account for sampler side resistance .
adjustments 
 in the field operations or the model could potentially improve the 
 accuracy of the veff prediction due to 
 these issues . despite these uncertainties
, we assert that the model described 
 here can be utilized in any environment with weather parameters similar 
 to the temperature calibration range ( 6 to 23 c ) and 
 wind speed calibration range ( 35 m s ) . 

higher wind speeds will increase the uncertainty of predicting the 
 effective sampling volume , and therefore the prediction of airborne 
 svoc concentrations .
however , the effects of particle - phase sampling 
 rates should be considered for svocs with large koa values ( 10 ) .
given that the model 
 was calibrated with a limited number of samples , increasing the number 
 of samples , as well as increasing spatial and temporal variability , 
 this calibration could better describe a wider range of meteorological 
 conditions than what is observed in the city of chicago .
contrary to methods using depuration compounds and hi - vol calibrations , 
 our approach provides a platform for accounting for deployments with 
 significant temperature changes .
the equilibrium status of pcb congeners , 
 particularly the low molecular weight congeners ( mono- , di- , and tri- 
 pcb homologue groups ) can be significantly affected by temperature 
 changes toward the end of the deployment period causing a shift in 
 the equilibrium level .
changes in temperature and wind speed 
 can vary greatly over the course of a long deployment , thus using 
 average weather parameters to calculate effective sampling volume 
 can lead to an under prediction of airborne svoc concentrations .
 we have developed and evaluated a mathematical model to determine 
 the effective sampling volumes ( veff ) 
 of pcbs and similar compounds captured using polyurethane foam passive 
 air samplers ( puf  pas ) . 
 we account for the variability in wind 
 speed , air temperature , and equilibrium partitioning over the course 
 of the deployment of the samplers . 
 the model , provided as an annotated 
 matlab script , predicts the veff as a 
 function of physical - chemical properties of each compound and meteorology 
 from the closest integrated surface database ( isd ) data set obtained 
 through noaa s national centers for environmental information 
 ( ncei ) . 
 the model was developed to be user - friendly , only requiring 
 basic matlab knowledge . to illustrate the effectiveness of the model 
 , 
 we evaluated three independent data sets of airborne pcbs simultaneously 
 collected using passive and active samplers : at sites in chicago , 
 lancaster , uk , and toronto , canada . 
 the model provides veff values comparable to those using depuration compounds 
 and calibration against active samplers , yielding an average congener 
 specific concentration method ratio ( active / passive ) of 1.1  
 1.2 . 
 we applied the model to puf  
 pas samples collected in chicago 
 and show that previous methods can underestimate concentrations of 
 pcbs by up to 40% , especially for long deployments , deployments conducted 
 under warming conditions , and compounds with log koa values less than 
 8 . 
Introduction
Determining Effective Sampling Volumes
Model Implementation
Materials and Methods
Results and Discussion