Examining the Gas-Particle Partitioning of Organophosphate Esters

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Environmental Measurements Methods

Examining the Gas-Particle Partitioning of Organophosphate Esters (OPEs): How Reliable are Air Measurements? Joseph Ocheje Okeme, Timothy F. M Rodgers, Liisa M. Jantunen, and Miriam L. Diamond Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b04588 • Publication Date (Web): 26 Oct 2018 Downloaded from http://pubs.acs.org on October 29, 2018

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Examining the Gas-Particle Partitioning of Organophosphate Esters (OPEs):

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How Reliable are Air Measurements?

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Joseph O Okeme1, Timothy F. M. Rodgers2, Liisa M. Jantunen3,4, Miriam L. Diamond4,1,2 *

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Trail Toronto, ON, M1C 1A4, Canada

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M5S 3E5

University of Toronto Scarborough, Department of Physical and Environmental Science, 1265 Military Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada

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Egbert, ON, L0L 1N0, Canada

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4 Department

Air Quality Processes Research Section, Environment and Climate Change Canada, 6248 Eighth Line of Earth Sciences, 22 Russell Street, University of Toronto, Toronto, ON, M5S 3B1, Canada

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* Corresponding author: [email protected]

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Keywords: organophosphate esters, gas-particle partitioning, poly parameter linear free energy relationships, TCIPP, TCEP

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ABSTRACT

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Organophosphate esters (OPEs) in air have been found to be captured entirely on filters of typical

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active air samplers and thus designated as being in the particle phase. However, this particle fraction is

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unexpected, especially for more volatile tris(2-chloroethyl) phosphate (TCEP) and tris(chloroisopropyl)

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phosphate (TCIPP). We evaluated gas-particle partitioning in indoor and outdoor air for OPEs and

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polybrominated diphenyl ethers (PBDEs) using single parameter models (Junge-Pankow, Harner-

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Bidleman), and poly parameter linear free energy relationship (pp-LFER) models (Arp and co-workers).

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We also used the pp-LFER (Arp and co-workers) to estimate filter-air partitioning in active air samplers.

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We found that all gas-particle partitioning models predicted that TCEP and TCIPP should be in the

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gas phase, contrary to measurements. The pp-LFER better accounted for OPE measurements than the single

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parameter models, except for TCEP and TCIPP. Gas-particle partitioning of PBDEs was reasonably

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explained by all models. The pp-LFER for filter-air partitioning showed that gas-phase sorption to glass

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and especially quartz fiber filters used for active air samplers, could account for up to 100% of filter capture

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and explain the high particle fractions reported for TCIPP, tris(1,3-dichloro-2-propyl) phosphate (TDCIPP),

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and triphenyl phosphate (TPhP), but not TCEP.

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The misclassification of gas-particle partitioning can result in erroneous estimates of the fraction

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of chemical subject to gas-phase reactions and atmospheric scavenging, and hence atmospheric long-range

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transport.

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INTRODUCTION

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Semi-volatile organic compounds (SVOCs), with vapor pressures ranging between 10-9 to 10 Pa,

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can theoretically exist in both the gas and particle phases, but in terms of measurement, they are found in

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the gas or particle phase, or a mixture of both phases.1 The phase into which SVOCs partition is an important

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determinant of chemical hazard. Gas-particle partitioning influences SVOC bioavailability and hence

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toxicological significance.2,3 It also controls environmental fate, specifically the ability of the compounds

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to undergo atmospheric long-range transport, wet and dry deposition, and air-water exchange. Examples of

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this importance come from studies based on measurements conducted using active air samplers (AAS).4–7

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Modelling studies have confirmed the importance of faithfully describing gas-particle partitioning in order

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to capture chemical fate.8–10

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Gas- and particle-phase SVOCs are most commonly measured using AAS where low and high

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volume AAS (LV-ASS and HV-AAS) are used indoors and outdoors, respectively. Denuders are also used

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as they are more reliable for measuring gas-particle partitioning,11 but AAS are preferred because they are

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cheaper and much easier to use than denuders. Another disadvantage of denuders is that they have low

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sampling rate12 and thus have high detection limits and low temporal resolution when run for short and

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longer deployment times, respectively. The sampling train for AAS commonly consists of a filter, most

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commonly a glass or quartz fiber filter (GFF or QFF), followed by a sorbent, usually polyurethane foam

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(PUF) and/or styrene divinylbenzene copolymer (XAD) resin. The concentration of a compound retained

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on the filter relative to the sorbent is used to define the gas-to-particle phase ratio.1

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Active sampling measurements may be inaccurate due to sampling artifacts that result when: 1)

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particles are blown off the filter, 2) particle-sorbed compounds are stripped off the filter onto the sorbent,

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3) breakthrough occurs in the sorbent, and 4) gas-phase compounds sorb to the filter.13,14 Sampling

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parameters that effect these artifacts are air volume, flow rate, temperature, relative humidity and filter

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characteristics.1,14,15

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Gas-particle partitioning and sampling artifacts are reasonably well understood for long studied

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nonpolar SVOCs such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs) and

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polychlorinated dibenzodioxins and furans (PCDD/Fs).1,14,15 However, gas-particle partitioning is uncertain

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for compounds of emerging concern such as perfluoroalkyl carboxylic acids for which measurements and

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modelled environmental behavior present conflicting expectations.16,17 Gas-particle partitioning is also not

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well characterized for organophosphate esters (OPEs), which is the focus of this paper.

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OPEs have commonly been characterized as particle-phase compounds because most studies have

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detected them primarily on the filters of LV-AAS 18 and HV-ASS.7,19,20 The exception is Wolschke et al.21

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and Li et al.22,23 who reported high sorbent capture for some of the OPEs. Strict particle-phase distribution

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is a questionable expectation for all OPEs since they span a wide range of volatilities.24,25 Specifically, 3 ACS Paragon Plus Environment

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tris(2-chloroethyl) phosphate (TCEP), tris(2-chloropropyl)phosphate (TCIPP, also referred to as TCPP)26

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and tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) have vapor pressures higher than or similar to those

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of some penta-brominated diphenyl ethers (penta-BDEs) that have been measured above 50% in the gas

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phase.27–29 Single parameter models based on sub-cooled vapor pressure (P°L) and octanol air partition

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coefficient (KOA) predict that these OPEs should be in the gas phase.24,25

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A plausible explanation for this discrepancy between measured and modelled results for OPEs is

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that modelled estimates may be incorrect and/or that during measurement, relative humidity (RH) may

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facilitate sorption of the gas phase onto polar filters such as GFF and QFF.16,30–32 For example, gas-phase

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sorption to filters has been shown to be a significant source of sampling artifacts when measuring polar

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compounds such as perfluorooctanoic acids.17 Thus, we questioned whether some OPEs may be gas-phase

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compounds that are mis-characterized as those in the particle phase because they exhibit “pseudo-particle-

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phase” behavior when measured using AAS. The term pseudo-particle-phase is used here to mean false

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particle-phase compounds rather than a mixture of gas- and particle-phase product described elsewhere.33

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Here, we assessed how pseudo-particle phase behavior may affect gas-particle characterization of

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OPEs. To this end, we first compared measured and modelled gas-particle partitioning behavior of OPEs

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and polybrominated diphenyl eithers (PBDEs). Then, through modelling, we assessed the mechanism and

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importance of gas-phase sorption of OPEs and PBDEs to GFF and QFF. Target OPEs were: TCEP, TCIPP,

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TDCIPP, triphenyl phosphate (TPhP), isomers of tri-cresyl phosphate (TCPs, o, m and p), 2-

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ethylhexyldiphenyl phosphate (EHDPP), tris(2-butoxyethyl) phosphate (TBOEP), and tris(2-ethylhexyl)

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phosphate (TEHP) and PBDEs (BDE-28, -47, -100, -99, -154 and -153). Names and CAS numbers of target

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compounds are presented in the Table S1.

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METHODS FOR MEASURED AND THEORETICAL ESTIMATES OF GAS-PARTICLE AND

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FILTER-AIR PARTITIONING

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Measured gas-particle partitioning. Measured gas-particle partitioning, Kp

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fractions (fpart (M)) of OPEs and PBDEs were calculated as follows:34

(M)

(m3 g-1) and particle

109 Cpart/TSP

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𝐾𝑝(M) =

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𝑓𝑝𝑎𝑟𝑡(M) =

Eq. 1

Cgas Cpart

Eq. 2

Cgas

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Where Cpart and Cgas are the particle- and gas- phase concentrations (ng m-3) measured for the AAS filter

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and sorbent, respectively, and TSP (µgm-3) is the concentration of total suspended particles.

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Estimated gas-particle partitioning. To model the expected gas-particle partition (Kp) behavior of target

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compounds, we estimated their particle fractions (fpart) using single parameter relationships of Junge-

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Pankow34 and Harner-Bidleman35 and a poly parameter linear free energy relationship (pp-LFER) of Arp

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et al. 32

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The Junge-Pankow (J-P) model expresses sorption of a compound to the active sites of particles as a function of sub-cooled liquid vapor pressure:34

122 𝑐𝜃

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𝑓part (J ― P) = 𝑃°𝐿 + 𝑐𝜃

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𝐾𝑝(J ― P) = 𝑇𝑆𝑃(1 ― 𝑓part (J ― P))

Eq. 3

𝑓part (J ― P)

Eq. 4

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where c (Pa/cm) is a constant with an assumed value of 17.2. θ (cm2 aerosol cm-3 air) is the surface area

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absorbing aerosols per volume of air with values of 1.1 x 10-5, 1.0 x 10-6 and 1.0 x 10-7 cm2 aerosol cm-3 air

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used for outdoor urban, background and remote air. These θ values correspond to total suspended particles

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(TSP) of 0.055, 0.014 and 0.077 g m-3, respectively.15 P°L (Pa) is the sub-cooled liquid vapor pressure.

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The Harner-Bidleman (H-B) model35 correlates adsorption of a compound to particles, Kp (H-B) (m3 µg-1), to its octanol-air partition coefficient KOA:

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Eq. 5

Log 𝐾𝑃(H ― B) = log𝐾𝑂𝐴 + log𝑓OM ―11.91

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where fOM is the fraction of organic matter of particles in air with default values of 0.40, 0.19 and 0.08 (gom

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gTSP-1) for urban, background and remote air, respectively. 15 The particle fraction, fpart (H-B), was obtained by

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substituting Kp (H-B) into Eq 6:

139 𝐾𝑝(H ― B) × TSP

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𝑓𝑝𝑎𝑟𝑡 (H ― B) = 𝐾𝑝(H ― B) ×

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6

Eq.

TSP + 1

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The pp-LFER model32 relates the gas-particle partition coefficient, Kp (pp-LFERg/p) (m3 g-1), to the

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Gibb’s free energies that describe specific and non-specific equilibrium interactions of a compound between

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the gas and water insoluble organic matter (WIOM) component of particles:

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Log 𝐾𝑝 (pp ― LFER g/p) = 1.01𝑆 + 3.17𝐴 + 0.30𝐵 + 0.78𝐿 + 0.51𝑉 ― 7.42


Eq. 7

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𝐾𝑝 ( pp ― LFER g/p) × TSP

𝑓𝑝𝑎𝑟𝑡 (pp ― LFER g/p) = 𝐾

𝑝 (pp ― LFER g/p)

Eq. 8

× TSP + 1

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where the numbers of Eq. 7 are the system constants of the multilinear regression of log Kp (pp-LFERg/p) against

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Abraham solvation parameters denoted by the letters. S characterizes the dipolarity/polarizability of the

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compound, A is the electron acceptor property, B is the electron donor property, L is the partition ratio

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between gas-phase and hexadecane representing van de Waal’s property, and V is the McGowan’s

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volume.32 The solute descriptors were obtained from the UFZ-LSER database,36 except those of EHDPP,

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TBOEP and TEHP, which were estimated using Absolv.37 Eq. 8 was used to obtain particle fraction, fpart (pp-

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LFERg/p).

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Eq. 7 describes the absorption of nonpolar and polar SVOCs to the water insoluble organic matter

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(WIOM) in TSP. Specific conditions for this pp-LFER were 15 °C and 50% RH and particles representative

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of Berlin in winter which are recommended as proxy for generic aerosols because these aerosols displayed

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sorption behavior typical of common terrestrial TSP.38 However, Arp et al.32 postulated and tested a dual-

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phase sorption mechanism of SVOC sorption to WIOM and sorption of polar and ionizable SVOCs to a

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mixed aqueous phase constituting TSP. As such, we used the dual-phase model of Arp et al.32 to account

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for the WIOM as well as the mixed aqueous phase fraction of TSP that contains salt and water soluble

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organic matter (WSOM, Eq. S1). However, Eq. 7 at 50% RH gave the same estimates as the dual phase

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model (Eq. S1) at RH values of 28, 77 and 90%, which was expected for large and nonionizing compounds

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such as those tested here for which sorption is driven by interaction with WIOM, irrespective of RH.32

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Thus, reference is made to only estimates produced using Eq. 7 onwards to avoid duplication.

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We assessed the capacity of GFF and QFF to retain gas-phase compounds in the absence of particles

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using a pp-LFER model.16 The purpose here was to account for the component of the filter capture not

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explained by the tested gas-particle partitioning models. First, we used the Eq. 9 taken from Arp et al.16 to

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estimate specific retention volumes, VG, (m3/g) of GFF and QFF at 15 °C and 50% RH for the test OPEs

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and PBDEs:

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Log VG = 3.60𝐴 × 𝐸𝐷𝑠𝑢𝑟𝑓 + 5.11𝐵 × 𝐸𝐴𝑠𝑢𝑟𝑓 + 0.135 × 𝐿 𝛾𝑣𝑑𝑊 𝑠𝑢𝑟𝑓 +𝐶

Eq. 9

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Eq. 9 is currently the only model available for estimating gas-phase sorption to GFF and QFF. The numbers

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are the constants of a multilinear regression obtained from plotting log VG against the values of solute

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descriptors, A, B, and L (Abraham 1991). The terms EDsurf (0.56 ± 0.12 and 0.63 ± 0.05), EAsurf (0.56 ± 0.07

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and 0.69 ± 0.04) and 𝛾𝑣𝑑𝑊 𝑠𝑢𝑟𝑓 (4.16 ± 0.58 and 5.93 ± 0.24) are scaling factors (± standard errors) describing



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the ability of the GFF and QFF, respectively, to donate electrons, accept electrons, and participate in van

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der Waal’s interactions, respectively. C is -6.31 ± 0.41 for GFF and -7.53 ±0.41 for QFF.30

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Second, we estimated safe sampling volumes, VB (m3), of the filters for the target compounds at 15

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°C and 50% RH using Eq. 10 taken from the literature.39 VB is the volume of air from which the filter can

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sorb a given amount of a gas-phase compound without breakthrough:

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𝑉B = [(𝑉G × 𝑀filter) ― 𝑉Gtracer]/2

Eq. 10

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where Mfilter is the mass of the filter, and VGtracer (m3 g) is the specific retention volume of methane, used

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here as the un-retained compound.40,41 Estimates were made for the GFF commonly used in the LV-AAS

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(diameter 47 mm, thickness 330 µm, pore size 1µm, mass 1.1 g; Pall Corporation, Chester, PA), the HV-

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AAS, GFF (diameter 102 mm, thickness 330 μm, pore size 1 μm, mass 0.57 g; Pall Corporation, Chester,

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PA), and HV-AAS, QFF (diameter 110 mm, thickness 450 μm, pore size 2.2 μm, mass 0.80 g; Supelco,

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Oakville, ON, Canada).

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RESULTS

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Comparison of measured versus modelled particle fractions. Field measurements and estimates of fpart

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were compared for target compounds in relation to P°L and KOA to assess the performance of the three gas-

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particle partitioning models (Figure 1). All three models underestimated fpart for some or all OPEs, with the

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discrepancy being highest for the more volatile OPEs with log P°L > -2, namely TCEP and TCIPP. The

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models all agreed that high P°L compounds such as TCEP and TCIPP are expected to be mainly in the gas

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phase, which contradicts measurements that show them to be mostly, if not exclusively, in the particle-

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phase. pp-LFER estimates were the most comparable to measurements for the remaining OPEs. For PBDEs,

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estimates of all three methods were within the range of measured literature values of 3-23% (BDE-28), 4-

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63% (BDE-47), 6-90% (BDE-100 and -99) and 10-98% (BDE-153 and -154) (Table S2). The comparison

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between measured and estimated fpart outdoors is similar for indoors where measured fpart is ~ 99% for

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OPEs18,42,43 and < 20% for PBDEs.28,43

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fpart (J-P) and fpart (H-B) were comparable and have a negative and positive relationship with log P°L and

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log KOA, respectively, regardless of compound group. Compounds having log P°L ≤ -3.73 and log KOA ≥

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10.53 such as BDE-47, TCPs and TEHP are expected to have particle fractions greater than 50%. The

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relatively more volatile compounds such as TCEP, TCIPP and BDE-28 are expected to be mainly in the

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gas-phase.

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Particle fraction (fpart, %)

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Figure 1: Relationship between measured and predicted percent particle phase in urban air as a function of

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log P°L and log KOA. Measured OPEs where taken from Salamova et al.44 and minimum and maximum

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PBDE values were based on eight outdoor studies (Table S2). Modelled TCPs, TBEOP and TEHP are

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hidden behind measured values. Log P°L values were taken from Brommer et al.24 and Tittlemier et al.45



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and log KOA values were taken from Okeme et al.46 and Harner and Shoeib47 for OPEs and PBDEs,

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respectively.

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pp-LFER estimates of the particle-phase fraction depended on compound group, with OPEs having

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higher fpart (pp-LFERg/p) values compared to PBDEs of similar or higher log KOA or lower log P°L. Compared to

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fpart (J-P) and fpart (H-B), pp-LFER estimates (fpart (pp-LFERg/p)) were up to three times higher for OPEs and up to

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three times lower for PBDEs. The pp-LFER model was developed for 15 °C compared to ~20 to 25 °C for

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the Junge-Pankow and Harner-Bidleman models; we did not correct for this temperature difference between

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models. However, the temperature difference cannot account for the difference between pp-LFER and the

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single-parameter models. Arp et al.38 found that sorption to particles was similar for the temperature range

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of 15 to 55 °C.

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We evaluated the sensitivity to TSP concentrations of estimated fpart for urban, background and

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remote air (Figure S1). As expected14,15, fpart (H-B) and fpart (J-P) values for all compounds positively correlated

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with TSP and fOM in the order urban > background > remote air. Compounds with high P°L values remained

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in the gas phase and all other compounds (except BDE-153 and -154) partitioned to the gas phase in remote

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air. fpart (pp-LFERg/p) showed no dependence on TSP for the low and high volatility OPEs and BDE-28, whereas

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they varied with TSP (urban > background > remote) for TDCIPP, TPhP and the remaining PBDEs.

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To assess the mechanism driving the difference between the pp-LFER and the single-parameter

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model estimates, we regressed the sensitivity of fpart (pp-LFERg/p) to changes in the molecular interactions of

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Eq. 7 against the relative difference between estimates (Figure 2). The sensitivity was expressed as the

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percentage change in particle fraction when a solute descriptor was excluded in turn with replacement and

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the relative difference was calculated as fpart (pp-LFERg/p) and fpart (J-P or H-B). Correlation was insignificant for all

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descriptors except B (determination coefficient, r2, = 0.73, p < 0.001), suggesting that the discrepancy

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between fpart (pp-LFERg/p) and fpart (J-P or H-B) was positively correlated to electron donor-acceptor interactions. The

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correlation analysis is consistent with the conclusion that single linear parameter models based on P°L and

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KOA lack the mechanistic capability to account for all interactions occurring in partitioning systems,

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especially, for polar compounds.32,48–50

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252 253 Figure 2: Relationship between sensitivity of pp-LFER estimates and discrepancy between pp-LFER and 254 Harner-Bidleman estimates of particle fraction. Sensitivity was expressed as the percentage change in 255 particle fraction when a solute descriptor was excluded in turn with replacement. Relative difference was 256 calculated as absolute difference between fpart (pp-LFERg/p) and fpart (J-P or H-B) divided by mean of absolute sum of 257 estimates. 258 259

Comparison of the gas-particle partitioning models and the sensitivity analysis suggested that

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the pp-LFER estimates were more reliable than the single linear parameter models, considering the



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mechanistic basis of the pp-LFER model. Particle fraction for OPEs and PBDEs was driven by molecular

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interactions between compounds and particles rather than P°L or KOA. Based on the pp-LFER results, the

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expectations are that TCPs, TBEOP, TEHP and BDE-153 and -154 predominantly reside in the particle

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phase and TCEP, TCIPP and BDE-28 are in the gas phase, regardless of TSP, RH and temperature

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values. TDCIPP, TPhP and BDE-47, -99 and -100 have varying particle fractions depending on the TSP

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value. The pp-LFER model accounted reasonably well for the measured particle fraction of all the target

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compounds, except for TCEP and TCIPP for which the discrepancy was > 98%.

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pp-LFER estimated VG and VB of compounds on GFF. The difference between measurements and

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model estimates of sorption for TCEP and TCIPP is larger than can be justified by modelling

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uncertainties or variability in TSP concentrations. Therefore, we used the pp-LFER filter-air partitioning

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model of Arp et al. (Eq. 9)16 to assess the contribution to this difference that could result from gas-phase

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sorption of compounds to AAS filters. Specific retention volume (VG), and safe sampling volume (VB)

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values were used to describe the sorptive capacity of GFF and QFF. The modelled trend here was similar

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for VG and VB, as such, VB is used for simplicity and illustrative purposes.

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Within compound groups, log VB was inversely correlated to log P°L (Figure S2). This correlation

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was statistically significant for both PBDEs (r2= 0.97, p < 0.01) and OPEs (r2=0.57, p= 0.02). However,

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greater scatter was observed for OPEs probably because the OPEs are not homologs, unlike the PBDEs.

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Compared to log P°L, a stronger correlation was found between log VB and McGowan volume (r2 ≥ 0.94,

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p < 0.01) for both OPEs and PBDEs (Figure 3), suggesting that McGowan volume is a better predictor

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of filter sorption than vapor pressure. OPEs had VB values that were approximately three orders of

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magnitude higher than those of PBDEs of similar or lower P°L, indicating a much greater potential of the

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filters to retain gas-phase OPEs than PBDEs. Other studies also found polar compounds to sorb more

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than nonpolar compounds to polar surfaces.16,51

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According to the pp-LFER for filter sorption, the intensity of van der Waal’s and electron-

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donor/acceptor interactions, and the resultant retention volume, increased with molecular volume within

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a compound group (Figure 3, Table S12). VB values for GFF and QFF were higher for OPEs (e.g., TCEP

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and TCIPP) for which descriptor B values are ~ three times the values of PBDEs (e.g., BDEs-28 and

289

100) of comparable McGowan volume, suggesting that electron donor capability seemed to drive

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between-group variability.

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The GFF and QFF are bipolar sorbents that consist mainly of silicon dioxide (SiO2). At ambient

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humidity, the Si-O and -O of the filters undergo electron donor/acceptor interactions with the OH and -

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O groups of water. These interactions result in a film of water covering the surface of the filter.13 A range

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of 1 molecular layer to 5 - 10 molecular layers has been estimated to cover the surface of quartz sand at 11 ACS Paragon Plus Environment

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30% to 90% RH, respectively.13 Water molecules, in turn, interact with the compounds tested here by

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accepting electrons since the compounds can only donate electrons due to their monopolar nature. As

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discussed, modelling results for gas-particle partitioning showed that electron donor interaction is a key

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predictor for the higher sorption of OPEs compared to PBDEs. Thus, the two analyses of gas-particle

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and filter-air partitioning yield a consistent interpretation of the mechanism that could account for OPE

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retention on filters.

301 302

303 304

Figure 3. Relationships between predicted log specific retention volume VB on GFF and QFF and

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McGowan volume of selected OPEs and PBDEs at 15 °C and 50% RH. Log VB values were the same

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for TCPs isomers because the isomers had the same solvation parameters.

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Another factor contributing to between-group variability of VB may be that OPEs and PBDEs

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have different sorption mechanisms. Adsorption onto the surface of the water film is the common

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sorption mechanism for most organic compounds except for very polar compounds that can be absorbed

311

into the water film.30 OPEs, as polar compounds, may be adsorbed and/or absorbed to the water film

312

whereas PBDEs may only be adsorbed onto the surface of the water film. 12 ACS Paragon Plus Environment


313

Within and between group variability also depends on the properties of the filter type. Estimated

314

specific VG of QFF were two to three orders of magnitude higher than those of GFF, which is likely due

315

to differences in molecular interactions for filters. For example,

316

than of GFF, accounting for a difference of ~ 2-log VG units between the filters.

𝛾𝑣𝑑𝑊 𝑠𝑢𝑟𝑓 of QFF was ~ two times higher

317 318

IMPLICATIONS

319

During active air sampling, a filter is expected to retain a mass of a gas-phase compound until

320

the uptake capacity of the filter for that compound (VB) approaches the sampled air volume (V). As

321

sampling continues, the capacity of the filter is exceeded for the compounds, i.e., when V >VB, leading

322

to losses by blow off, or by “breakthrough” of the gas phase to the sorbent. At this point, AAS sampling

323

is assumed to reflect the “actual” gas-particle distribution of the compound in air.

324

To assess the gas-phase sorption artifact, we compared the VB values estimated here with air

325

volumes collected in field studies (Figure 4). For indoors and outdoors, 1 to 200 m3 and 300 to ~ 3000

326

m3 of air are commonly sampled using LV-AAS and HV-AAS, respectively. The comparison between

327

V and VB assumed that the ambient temperature was 15 °C, RH was 50% and that gas-particle

328

equilibration in air was achieved for target compounds. Filter capacity indoors are likely to be

329

overestimated here since indoor temperatures are 5 to 10 °C higher than the pp-LFER temperature. RH

330

could be higher than the modelled value of 50%. For example, Toronto’s long-term average RH is 70%

331

outdoors. However, an assumption of 50% is conservative as gas-phase sorption to surfaces is expected

332

to be lower at higher RH.30

333 334

Using GFF indoors. As a case study, Okeme and Yang et al.43 collected 140 m3 of air using an LV-AAS

335

in an indoor calibration study. The GFF captured < 2% of BDE-28 and -47, 17% of BDE-99 and ~

336

100% for the detected OPEs (Table 1). pp-LFER estimates of fpart were comparable with measurements

337

for the PBDEs whereas the discrepancy was high for TCEP, TCIPP and TPhP. Particle fractions were

338

calculated assuming a TSP value of 0.014 g m-3 for background air which is similar to average PM10

339

concentration of 12 µg m-3 measured indoors.43

340

Estimated average VB values for the measured PBDEs were ≤ 9% of 140 m3 of the volume of

341

air sampled indoors43 (Figure 4), suggesting that gas-phase sorption to the GFF used in the study should

342

be insignificant and that the measured fpart values should be reliable. However, for OPEs, gas-phase

343

sorption could account for up to 74 to 100% of GFF capture, considering the uncertainty margin of the

344

pp-LFER model. As such, the contribution from gas-phase sorption seemed to reconcile the discrepancy

345

between fpart (pp-LFERg/p) estimates and fpart (M) for TCIPP, TDCIPP and TPhP but not for TCEP (Table 1)


Page 14 of 25

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346

indoors.43 Measured fpart for TBEOP is not in doubt given the good agreement observed between the

347

measured and pp-LFER estimated value.

348 349

350 351

Figure 4. Comparison of sampled volumes from indoor and outdoor studies with safe sampling volumes

352

(VB) of GFF and QFF estimated here using pp-LFER at 15 °C and 50% RH. Error bars indicate standard

353

errors and asterisk indicate negative log values for GFF. The standard errors were calculated from the

354

standard errors of the scaling factors and constant c for Eq. 9.

355



356

Table 1. Measured (fpart (M)) and pp-LFER (fpart (pp-LFERg/p)) estimated particle fractions (%) and pp-LFER

357

estimated contribution of artifact to particle fraction (fpart artifact) of PBDEs and OPEs measured

358

indoors.43 fpart (M) 43(%) 100 100 100 100 100 100 90% measurement of TCEP for filters.

368 369

Using GFF and QFF outdoors. As mentioned, many field studies19,20,44,53,54 have reported unexpectedly

370

high particle fractions of > 80% for OPEs based on their concentrations captured on the filters of HV-

371

AAS (Table S4). As such, some other studies55–57 have assumed filter-captured OPEs to represent total

372

OPEs concentrations. Few exceptions are studies that reported < 20 to 50% GFF capture for some OPEs

373

including TCEP, TCIPP and TDCIPP21 and TCP22 and TEHP23. For the OPEs studies with >80% fpart,

374

VB estimates were up to 100% of V, considering uncertainties of VB estimates. This result implies that

375

gas-phase sorption could contribute substantially to the mass of OPEs captured on the GFF (excluding

376

Wolschke et al.

377

resulted from filter blow off or strip off into the sorbent caused by sampling ~ 2800 m3 of air, which was

378

about eight times the volume of air sampled by the other studies (e.g., Möller et al. 19). The lower particle

379

fractions reported by Li et al.22,23 for the less volatile OPEs such as TEHP and TCP compared to the more

380

volatile OPEs (e.g., TCPP) may be due to analytical uncertainties associated with measuring low

381

concentrations. For example, Li et al.22 reported median Cgas and Cpart values of non-detect and 0.02 pg

21,

Li et al.22,23). The higher sorbent capture reported by Woschke et al.21 probably


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Page 17 of 25


382

m-3, respectively, for TEHP compared to 10 and 35 pg m-3 for TCPP. Particle infiltration may also

383

increase sorbent capture,20,25 but this artifact would likely be minimal since the penetration efficiency of

384

fine particles through GFF is small, ranging from 0.003% to 0.15%.58 Potential gas-phase sorption

385

artifact could be > 100% for the OPEs with lower P°L such as TCPs, EHDPP, TBOEP and TEHP for

386

which VB > V. But as mentioned, this artifact is unlikely to affect these compounds because the pp-LFER

387

estimates here and field measurements (e.g., Salamova et al.44) agreed that these compounds should be

388

almost entirely in the particle phase, irrespective of TSP, RH and temperature.

389

Gas-phase sorption should be less of a concern when using GFF to sample PBDEs whether

390

indoors or outdoors because estimated VB values were ≤ 2% of V values and PBDE gas-particle

391

partitioning estimated using all models were comparable with measured values (Figure 1). For QFF, the

392

VB for most OPEs and PBDEs exceeded V values, suggesting the possibility of substantial gas-phase

393

sorption. Of course, the higher predisposition of QFF than of GFF to gas-phase sorption does not change

394

the true partitioning of the target compounds. It however increases the confusion in identifying particle

395

fractions when using QFF.

396 397

Long Range Transport of OPEs. As mentioned, gas-particle partitioning (Kp) is a key

398

parameter driving the long-range transport potential (LRTP) of SVOCs.8,59,60 LRTP is commonly

399

assessed using multimedia modelling tools such as the Organization for Economic Co-operation and

400

Development (OECD) LRTP Screening Tool61. The OECD tool is a mass balance model designed to

401

rapidly screen multiple compounds for the potential to undergo LRTP as judged against persistent

402

organic pollutants (POPs) such as PBDEs, using commonly available physical-chemical properties.

403

OPEs are enigmatic because several have been consistently found in air from remote regions56,62 although

404

the OECD model suggests that they do not have the physical-chemical properties consistent with POP-

405

like LRTP. For example, Sühring et al.7 reported that TCEP, TCIPP, TDCIPP and TPhP were measured

406

with greater than 75% detection frequency in air samples across the Canadian Arctic from 2007 – 2013.

407

We modelled LRTP of the target OPEs using the OECD LRTP Screening Tool61 based on Kp

408

values calculated from literature measurements using Eq. 1 and estimated using Junge-Pankow (Eq.4),

409

Harner-Bidleman (Eq. 5) and pp-FERg/p (Eq. 7) models. A brief description of the model is provided in

410

SI. The tool calculates the characteristic travel distance (CTD, km) for air and water and displays the

411

greater of air or water CTD values. Figure 5 shows the relationship between LRTP, expressed as CTD

412

and overall persistence (POV) of the target compounds in the environment with compounds falling in the

413

top right quadrant exhibiting POP-like behavior using octa-BDE63 as the reference. We varied Kp

414

estimates according to the models presented here and assumed ~100% in the particle fraction based on

415

previous measurements, as discussed above.44 16 ACS Paragon Plus Environment


416

Under the Junge-Pankow, Harner-Bidleman and pp-LFERg/p models, none of the target

417

compounds were in the upper-right POP-like quadrant, while the halogenated OPEs TCIPP, TCEP and

418

TDCIPP exhibited POP-like behavior under the measured Kp. Water transport determined the CTD for

419

the chlorinated OPEs (TCEP, TCIPP, TDCIPP and TCP) based on Junge-Pankow and Harner-Bidleman

420

models, and TCEP and TCIPP under the pp-LFERg/p model. In contrast, air transport determined the

421

CTD for all OPEs under the measured Kp scenario. Sühring et al.7 found that levels of chlorinated OPEs,

422

dominated by TCEP and TCIPP, were highest around river mouths, from which they hypothesized that

423

LRTP through rivers was the dominant transport pathway to the Arctic for these compounds and not

424

atmospheric transport. Water-borne LRTP is consistent with the OECD tool results obtained here using

425

the modelled and not measured Kp. The difference in LRTP between measured and estimated Kp

426

scenarios illustrate one impact, in this case estimating LRTP, of the uncertainties in measuring and

427

modelling OPE gas-particle partitioning, especially for the environmentally abundant compounds

428

TCIPP, TCEP and TDCIPP.

429

430 431

Figure 5: Comparison of the relationship between long-range transport potential (LRTP) and overall

432

persistence in the environment plotted using the OECD tool based on gas-particle partitioning values 17 ACS Paragon Plus Environment

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433

measured in the literature and estimated using Junge-Pankow, Harner-Bidleman, and pp-LFER models

434

with TSP value of 14 µg m-3. The vertical and horizontal lines represent the thresholds for LRTP

435

characterized by Characteristic Travel Distance (km) and overall persistence (POV).

436 437

Recommendations. Several studies have explored methods to reduce sampling artifacts. For example,

438

studies have used a double filter to partially correct gas-phase sorption of PAHs and PCDD/Fs to

439

QFF12,64. Based on the pp-LFER results presented here for gas-phase sorption, back filter corrections

440

may be unnecessary since the estimated sorption artifact was either negligible for some OPEs and most

441

PBDEs, or it was sufficiently high to hinder equilibration of compounds with both the front or back

442

filters. Another attempt at eliminating gas-phase sorption was the deactivation of filters by

443

siliconization.17 Siliconization reduced the sorption of gas-phase perfluorooctanol to GFF, but not

444

sufficiently to prevent mis-characterization of the particle fraction.17 Denuders may be used as

445

alternatives to conventional samplers due to their observed and anticipated lower artifact.1,11 But it is

446

debatable whether denuders are significantly more efficient than conventional AAS for measuring gas-

447

particle partitioning.65

448

Apart from measurement artifacts, field data and pp-LFER estimates can differ due to model

449

uncertainties. For example, Arp et al.16 found that pp-LFER for filter partitioning estimated VG values

450

that were 0.08 to 0.73 log-units lower than the values measured by Mader and Pankow31 for the sorption

451

of PAHs to QFF. Reasons for the discrepancy included variability of temperature and RH which are

452

challenging to capture in a model. Procedures for treating filters and conducting air sampling can also

453

introduce error. Arp et al.

454

changed the surface chemistry of a filter, thereby increasing gas-phase sorption. Whether filters would

455

maintain the conferred sorption is uncertain during field studies. Prediction errors can also result from

456

limitations of the pp-LFER training set.66,67 The filter model of Arp et al.16 has a calibration set that

457

consisted of compounds such as alcohols, alkylbenzenes, halogenated benzenes, fluorotelomer alcohols

458

and fluorotelomer olefins that were more volatile than the OPEs and PBDEs estimated here. Ideally,

459

calibration compounds should be analogous to target compounds to minimize prediction errors. Future

460

work should test the sorption of OPEs to filters in a chamber at variable values of RH, temperature and

461

TSP and should assess denuders as alternatives to filter/sorbent active samplers.

16

showed, using a chromatography experiment, that baking at ~ 600 °C

462

Based on the analysis presented here on the likelihood of there being a gas-phase sorption

463

artifact, we suggest that studies of OPEs in air have likely mischaracterized the gas-particle partitioning

464

of the more volatile OPEs, e.g., TCEP and TCIPP. As a precautionary note, measured particle fractions

465

of OPEs examined here that fall substantially outside the error margin of the pp-LFER model of Arp et



466

al.30 are most likely influenced, in part, by equilibrium sampling artifacts such as gas-phase sorption to

467

filter. Ways to minimize the error in reported gas-particle partitioning include the use of GFF instead of

468

QFF, reporting OPEs as bulk air concentrations and assuming that more volatile OPEs (e.g., TCEP and

469

TCIPP) to be gas-phase compounds when modelling their environmental behavior.

470 471

Supporting Information

472

Full names and CAS of target compounds, values of Abraham solvation parameters, literature values of

473

vapor pressure, octanol-air partition coefficients and particle fractions. Modelled particle fractions and

474

further details on the dual-phase model of gas-particle partitioning.

475

Acknowledgments

476

Research funding was provided by the Environment and Climate Change Canada (Contribution

477

Agreement GCXE17S017), Health Canada (Agreement No. 4500308341), and the Natural Sciences and

478

Engineering Research Council of Canada (NSERC, No. RGPAS 429679-12). The paper benefited from

479

discussions with Prof. Kai-Uwe Goss and Dr. Hans Peter Arp, comments from Dr. Tom Harner and data

480

from Dr. Lisa Melymuk. We thank Prof. J.Mark Parnis for his thoughts and efforts at modelling gas-

481

phase sorption COSMOtherm.

482 483

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Examining the Gas-Particle Partitioning of Organophosphate Esters

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