Adsorption of Organic Compounds to Diesel Soot: Frontal Analysis

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Adsorption of organic compounds to diesel soot: Frontal analysis and polyparameter linear free energy relationship Zhijiang Lu, John K. MacFarlane, and Philip M. Gschwend Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03605 • Publication Date (Web): 20 Nov 2015 Downloaded from http://pubs.acs.org on November 22, 2015

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Adsorption of organic compounds to diesel soot: Frontal analysis and polyparameter linear free energy relationship

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Zhijiang Lu*, John MacFarlane and Philip Gschwend

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Ralph M. Parsons Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology

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77 Massachusetts Ave., Cambridge, MA 02139, USA

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*Corresponding author phone: (951)-318-5315; fax: (617)258-8850;

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E-mail: [email protected];[email protected]

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Word counts:

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Text: 5679 words

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Tables: 2

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Figures: 3

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Abstract

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Black carbons (BCs) dominate sorption of many hydrophobic organic compounds (HOCs) in soils and

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sediment, thereby reducing the HOCs' mobilities and bioavailabilities. However, we do not have data for

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diverse HOCs' sorption to BC as it is time-consuming and labor-intensive to obtain isotherms on soot and

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other BCs. In this study, we developed a frontal analysis chromatographic method to investigate

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adsorption of 21 organic compounds with diverse functional groups to NIST diesel soot. This method was

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precise and time efficient, typically taking only a few hours to obtain an isotherm. Based on 102 soot-

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carbon normalized sorption coefficients (KsootC) acquired at different sorbate concentrations, a sorbate-

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activity dependent polyparameter linear free energy relationship was established:

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log = 3.74 ± 0.11 +

−0.35 ± 0.02 log + −0.62 ± 0.10 + −3.35 ± 0.11 +

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−1.45 ± 0.09

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where V, E, A, and B are the sorbate's McGowan's characteristic volume, excess molar refraction, and

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hydrogen acidity and basicity, respectively; and ai is the sorbate's aqueous activity reflecting the system's

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approach to saturation. The difference in dispersive interactions with the soot versus with the water was

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the dominant factor encouraging adsorption, while H-bonding interactions discouraged this process.

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Using this relationship, soot-water and sediment/soil-water adsorption coefficients of HOCs of interest

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(PAHs and PCBs) were estimated and compared with results reported in literature.

(N=102, R2=0.96, SE=0.18)

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Introduction

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Sorption is one of the key processes controlling the transport, transformations, bioavailabilities,

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and toxicities of nonionic organic pollutants in soil and sediment. Traditionally, organic matter (OM) or

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organic carbon (OC) has been recognized as the critical component of natural solids driving this process.

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However, the importance of particular components of the organic matter, black carbons (BCs), as

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sorbents has been recognized in the last 20 years.1-6Although BCs only account for about 1% to 10% of

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OC in soils and sediments,4 they may dominate adsorption of some organic pollutants to soil and sediment,

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especially hydrophobic organic compounds (HOCs) like polycyclic aromatic hydrocarbons (PAHs) and

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polychlorinated biphenyls (PCBs).4, 5, 7 One particular form of BC, soot formed from condensation of gas

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phase hydrocarbon radicals, is known to be widespread in the environment, but our ability to estimate

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corresponding sorption coefficients is limited.

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Several difficulties complicate investigations of HOC adsorption to soot. First, the size of soot

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aggregates is about 0.1 to 1 µm.8 Due to its small size and low density, soot is difficult to handle in the

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laboratory and to separate from aqueous phases by filtration or centrifugation.7 Second, similar to

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adsorption to other BCs,9, 10 it may take weeks to months to reach equilibrium.7, 11 Moreover, soot can be

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expensive (e.g. about $500 g-1; https://www-s.nist.gov/srmors/view_detail.cfm?srm=2975). Therefore,

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faster, reliable, and material-efficient methods beyond the traditional batch-filtration/centrifugation

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methodologies would be very helpful.

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A few methods have been developed to overcome the separation issue. Nguyen et al. used

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polyaluminum chloride to facilitate the flocculation of soot particles and thereby measure the adsorption

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coefficients (Kd) of phenanthrene and 1,2,4-trichlorobenzene.7 Jonker and Koelmans12 applied

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polyoxymethylene (POM) solid phase extraction (SPE) to avoid soot-water separation; however, this

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method requires additional measurement of Kd on POM and controls to account for any collection of the

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soot on the POM. Bucheli and Gustafsson13 used an air-bridge system to establish soot-air-water

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equilibrium and measured Kd values for four PAHs (naphthalene, fluorene, phenanthrene, and pyrene). 3 ACS Paragon Plus Environment

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Sobek et al.11 used dialysis membranes to separate soot-bound sorbates from the remaining solutes.

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However, all these modified batch methods have not overcome the second difficulty: too long to reach

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equilibrium. Another issue of batch studies is uncertainty regarding reaching equilibration. Bucheli and

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Gustafsson13 used an HPLC method with methanol as a cosolvent to measure Kd values for PAHs;

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however, this method only obtained a single Kd value at a low sorbate concentration. Moreover, methanol

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may affect the extent of adsorption by swelling sorptive media and thus lead to an overestimation of Kd

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values.13

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In this study, we used frontal analysis (FA) to determine adsorption isotherms.14-16 In the "stair

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case" FA, each step represents a data point of an isotherm (Figure S1). The amount adsorbed on the

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sorbent can be calculated by an integral mass balance:

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= ∑ "#$%,' − $,' ()*⁄+,-./

Equation 1

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where qi is the sorbate concentration on sorbent of mass msorbent; Cj,w and Ci,w are the aqueous

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concentrations of the non-adsorbed tracer and the sorbate, respectively; and dv is the increment of eluting

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volume. Although FA has been frequently used in analytical chemistry,15,

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environmental chemistry has not been widely developed.18 In fact, its unique characteristics make FA

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extremely suitable for environmental applications: (1) it avoids solution-sorbent separation which

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facilitates investigating adsorption on tiny particles; (2) it decreases equilibrium time down to hours to

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days; (3) uncertainties are reduced regarding determination of the equilibrium endpoint and aqueous

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concentration since at equilibrium, the eluent concentration is the known concentration added; and (4) the

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investigator can reuse the sorbent for each sorbate and reduce sorbent consumption, especially when the

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sorbent is rare or precious.

17

its application in

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After using FA to determine isotherms of 21 sorbates with diverse functional groups on a well

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characterized model soot (NIST SRM 2975), polyparameter linear free energy relationship (ppLFER)

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modeling was applied in order to characterize the intermolecular interactions controlling chemical



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sorption between water-wet soot and aqueous solutions. This approach also facilitates predictions for

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other organic compounds of interest whose ppLFER parameters are known and fall within, or near, the

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tested parameter space. This ppLFER approach has been used extensively to evaluate sorption from

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aqueous solutions to dissolved organic carbon,19 carbon nanotubes,20 structural proteins,21 organisms and

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tissues.22 In this study, a sorbate activity-dependent ppLFER model was adopted to help us evaluate the

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evolving importance of intermolecular interactions as a function of surface coverage on the soot BC.9, 10

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Overall, the objectives of this study were to (1) develop a fast and reproducible method for measuring

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soot-water Kd values of organic compounds; (2) elucidate the underlying intermolecular interactions

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controlling this process; and (3) develop a prediction tool allowing estimation of Kd values of various

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compounds interacting with soot in soil or sediment.

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Materials and Methods

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Materials. Soot from diesel-powered forklifts (NIST SRM 2975) was purchased from the National

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Institute of Standards and Technology (NIST; Gaithersburg, MD, USA). Soot particles were mixed with

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450 ºC pre-combusted Ottawa sand similar to Plata et al.9 Briefly, two different amounts of soot particles

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were dispersed in ethyl acetate, spread over the sand, and stirred to dry. The soot-sand mixtures (roughly

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1% and 0.1% soot carbon by weight) were dried at 60 ºC for 3 days. The carbon contents of the two

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mixtures were determined using a CHN analyzer (Vario EL, Elementar America, Inc.; Mt Laurel, NJ,

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USA) and the values were 0.94 ± 0.02% and 0.11 ± 0.02% by weight, respectively.

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A total of 21 sorbates with diverse functional groups (e.g., ketone, ether, phenol, amine, halide)

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were selected (Table 1), although as discussed below, all but two (atrazine and dimethyl phthalate) are

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planar. Their logarithmic octanol-water partition coefficients (log KOW) and liquid or subcooled liquid

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solubilities ranged from 0.95 to 4.46 and from 3.05 mg L-1 to 105 g L-1(Table 1 and S1), respectively. The

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Abraham descriptors ranges of these sorbates are: V (McGowan's characteristic volume, representing

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molecular size, (cm3 mol-1)/100) from 0.715 to 1.62, E (the excess molar refraction, representing

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polarizability, (cm3 mol-1)/100) from 0.477 to 2.06, S (dipolarity with some contribution from 5 ACS Paragon Plus Environment

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polarizability, dimensionless) from 0.37 to 1.48, A (hydrogen acidity, dimensionless) from 0 to 0.67, and

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B (hydrogen basicity, dimensionless) from 0 to 1.01 (Table S1). These ranges of coverage for largely

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monofunctional organic compounds ensured that the selected sorbates represented a wide range

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intermolecular interactions with water and water-wet soot. Solute-saturated aqueous solutions were

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prepared by exposing excess amounts of each compound to 200 mL buffer solutions (5×10^-3 mol.L-1

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CaCl2 and 100×10^-6 mol L-1 NaHCO3 in 18×10^6 Ω UV-oxidized water, with pH near 6) in pre-

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combusted round bottom glass flasks by constantly shaking for at least for 2 weeks. Saturated stock

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solutions were diluted to desired concentrations (Table 1) using the same buffer solution.

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Frontal Chromatography. An Agilent 1100 HPLC system with a binary pump and a diode array

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detector (DAD) (Agilent Technologies, Santa Clara, CA, USA) was modified to accomplish this study.

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All unnecessary parts (online degasser, damper, solvent mixer) were removed and 1/16" OD stainless steel

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tubes instead of PTFE tubes were used to connect the mobile phase reservoir to the pump, the pump to the

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column, and the column to the diode array detector (DAD) in order to minimize system adsorption. A 69

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bars backpressure regulator (Chrom Tech; Apple Valley, MN, USA) was installed after the DAD to

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improve signal stability. About 2.3 g soot-sand mixtures or pure Ottawa sand were packed into stainless

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steel HPLC columns (8.1 cm length, 0.48 cm inside diameter) and were referred to as 1%, 0.1%, and

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sand-only columns in this study. The columns were flushed with the buffer solution (5×10^-3 mol L-1

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CaCl2 and 100×10-6 mol L-1 NaHCO3) at 0.2 mL min-1 for 2 weeks to wet the soot surfaces before running

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samples. Sorbate solutions were directly introduced into the 1% or 0.1% soot-sand column successively at

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0.2 ml min-1 from low to high concentrations at room temperature (22 ± 0.5 ºC, mean ± standard error).

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The same sorbate solutions were run through the sand-only column at the same flow conditions to assess

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any sorption to the sand and HPLC system. The flow rate was calibrated by measuring the volume of

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effluent collected in a graduated cylinder. The non-adsorptive tracer (10×10-6 mol L-1 sodium nitrate in

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the buffer solution) was analyzed daily before running any sorbate solutions. The flow rate was optimized

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by determining the isotherms of anisole (methoxybenzene) on the 1% soot column at 0.1, 0.2 and 0.4 mL



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min-1 (and duplicated at each flow rate). Also the adsorption coefficient of 0.9 mg L-1 bromobenzene was

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measured on the 0.1% soot column at 0.05, 0.1, 0.2, 0.3 and 0.4 mL min-1-. The reproducibility on the two

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columns was tested by carrying out quadruplicate measures of bromobenzene isotherms on both columns

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at 0.2 mL min-1-.

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Data analysis. Raw data recorded by the DAD detector at 2.5 Hz were exported into Microsoft Excel by

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the Agilent Chemstation and then resampled in Excel periodically into 1/10 of its original size. Preliminary

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tests showed that resampling did not reduce the accuracy of integration. The reduced elution curves were

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integrated according to Equation 1. The carbon-content normalized adsorption coefficient on soot (KsootC)

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was calculated as:

=

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12,3

12,4

=

#52,3667839:;

Equation 2

12,4

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where Ci, s and Ci, w are the sorbate concentrations on the soot and in the aqueous solution; qi, soot-sand and qi,

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sand

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fraction of carbon content of the soot-sand mixture. The logarithmic form of the Freundlich equation was

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used to fit Ci, s (mg kg-1) and Ci,w (mg L-1):

are the sorbate concentrations on soot-sand mixture and pure sand, respectively; and fc is the weight

@AB $, = @AB C + D@AB $,'

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Equation 3

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where, Kf [(mg kg-1)/(mg L-1)n] and n (dimensionless) are the Freundlich coefficient and exponent,

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respectively.

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Individually measured KsootC values for each sorbate were used to fit an activity-dependent ppLFER model:9, 10 @AB = *E + *F @AB + GE + GF @AB + HE + HF @AB I + E + F @AB +

JE + JF @AB + KE + KF @AB

Equation 4


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where ai is the sorbate activity calculated as the ratio of sorbate's aqueous concentration to its liquid or

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subcooled liquid solubility. Since sorption to the soot may be dependent on the degree of surface

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saturation, which we assume to be complete when the sorbent would be fully coated with a liquid sorbate,

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we assess the system's degree of saturation using the sorbates' liquid or subcooled liquid solubilities as the

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reference states. All these properties for each sorbate are listed in Table S1. The regression was carried

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out by forward linear regression in IBM SPSS Statistics 23 (IBM, Armonk, NY, USA). To ensure the

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robustness of the established ppLFER model, rigid statistic methods were applied.23 Outliers (unusual Y

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values), leverage points (unusually large X values), and highly influential points (points have great

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influence on regression parameters) were identified by studentized residual (SRE), leverage values, and

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Cook’s Distance and these extraordinary data point were further examined. Criteria were: SRE < 2,

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leverage value < 2 p/N (p is the number of parameters including constants and N is the sample size), and

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Cook's Distance < 1. Since the ppLFER parameters are somewhat correlated to each other (Table S2), it is

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necessary to check the multicollinearity of these fittings. Thus, the variance inflation factor (VIF) was

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calculated and the limit was 10. All other statistics in this study (e.g. t test, Pearson correlation) were

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carried out by SPSS. When applicable, mean ± 1 standard error is given unless otherwise specified.

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Results and Discussion

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Method Robustness. The robustness of FA was validated by examining (1) the repeatability of daily

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nitrate breakthrough curves; (2) the calculated anisole adsorption at different flow rates; and (3) the

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reproducibility of bromobenzene isotherms on both 1% and 0.1% soot columns. Nitrate breakthrough

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curves within a month on the 1% soot column showed no significant differences in the breakthrough

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volume and half maximum concentration (Figure S2). In early tests, we found nitrate and acetone, a

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highly water soluble nonionic compound, exhibited indistinguishable breakthrough curves (data not

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shown). This indicated that the system was stable and nitrate could be used as a non-sorptive tracer for

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this study. In order to determine the effect of flow rate on adsorption to soot, the anisole isotherm was

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measured at different flow rates on the 1% soot column (Figure S3). At all three flow rates, the isotherms 8 ACS Paragon Plus Environment


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fit the Freundlich equation very well (R2 > 0.998). More importantly, the Kf and n values were very close

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to one another (± 6% and ± 6%, respectively). The quadruplicate measurements of KsootC of 0.9 mg L-1

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bromobenzene at five different flow rates, 0.05, 0.1, 0.2, 0.3 and 0.4 mL min-1, were 2.29 ± 0.02, 2.28 ±

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0.01, 2.29 ± 0.02, 2.32 ± 0.05 and 2.34 ± 0.05 L kg-1, respectively. Although there were no significantly

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differences among them (p > 0.05), slightly larger variations (ca. 2%) existed at higher flow rates (0.3 and

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0.4 mL min-1). Therefore, 0.2 mL min-1 was chosen for sorption testing. In this study, 1,2,4-

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trichlorobenzene, naphthalene, 1-methylnaphthalene, 1-chloronaphthalene, and phenanthrene were

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analyzed using the 0.1% soot column due to their high adsorption affinities, while all other chemicals

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were analyzed using the 1% soot column. In order to merge the two datasets together, reproducibility of

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bromobenzene on two columns was tested (Figure 1). Overall, isotherms determined on both columns fit

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the Freundlich equation well, although fitting data from the 1% soot column was slightly better. This may

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be due to the retention of bromobenzene on the 0.1% column being too short such that the integration of

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the amount adsorbed would have larger error compared that on the 1% soot column. As clearly shown in

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Figure S1, larger error may arise when retention times of sorbate and non-adsorptive tracer are close to

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each other. This also indicated that suitable soot contents should be chosen to obtain reliable results.

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There were no significant differences among n values obtained on two columns (p > 0.05). For the log Kf

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values, there were slightly differences between some pairs, but the differences were within 10% (Figure

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1). Therefore, we concluded that FA was a robust method to determine adsorption coefficients of organic

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pollutants to soot.

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Sorption Isotherms. In this study, all data fit the Freundlich equation well with R2 > 0.994 (Table 1). All

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isotherms involved at least four solute concentrations, except phenanthrene, and most covered more than

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two orders of magnitudes of sorbate activity (exceptions were phenanthrene, 1,2,4- trichlorobenzene, and

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fluorobenzene). Generally, with increasing log KOW or decreasing water solubility, the Freundlich

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isotherm parameter log Kf increased, while the n value decreased. For example, the log Kf and n values for

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benzene, naphthalene and phenanthrene were 1.37, 2.80, 3.54 and 0.71, 0.55, 0.31, respectively. This


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trend was also observed for fluorobenzene, chlorobenzene, and 1,2,4-trichlorobenzene. This indicated that

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London dispersive forces had an important role on adsorption to soot from water. Additionally, this trend

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supported the conclusion that the FA method was reliable. Frontal analysis was much more time efficient compared to other methods.7,

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9, 10, 13, 24-27

For

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example, it took about 60 days to reach equilibrium in batch adsorption studies of 1,2,4-trichlorobenzene,7,

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25

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FA for all our test sorbates except phenanthrene. Moreover, the retention time of phenanthrene, or other

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high affinity chemicals, could be further reduced by decreasing the soot content in the column, for

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example to 0.01%. Therefore, the equilibrium time would be easily controlled within hours by adjusting

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the soot content.

while the FA method only took 6 hr (Table 2). In fact, it only took hours to obtain an isotherm by the

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The frontal analysis method was also accurate compared to other methods. For example, the log

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Kf and n values of naphthalene obtained by FA were only slight lower than values obtained by batch

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methods24 (Table 2). The values of 1,2,4-trichlorobenzene obtained by FA were slight lower than those

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obtained by batch and batch-flocculation methods.25 This may be caused by a narrow sorbate activity

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range in our work (from 6.83 ×10-3 to 1.66×10-1) (Table 1) that was due to the limitations of using a DAD

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detector, while in batch and batch-flocculation studies, a wider range (about 10-4 to 0.6) was used.7, 25 This

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may also explain the lower values of phenanthrene obtained by FA. Overall, FA is a fast, accurate method

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to determine the sorption isotherms of organic compounds to soot.

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The log Kf values of adsorption to NIST 2975 soot, NIST 1650b, and hexane soot were similar25

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(Table S3). This may due to similar surface areas and pore size distributions of these soots.25 However,

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the n values seen for NIST 1650b soot were consistently higher than those for the other soots. This may

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due to the higher amount of extractable hydrocarbon coatings on it. According to NIST, the percent

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extractable masses of 1650b and 2975 are 20.2 ± 0.4% and 2.7 ± 0.2%, respectively.28, 29 This extractable

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fraction may act as a linear absorbent and thus make the overall isotherm more linear.25



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The log Kf values of adsorption to soot, char, and granular activated carbon (GAC) followed the 9, 10

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trend: GAC > char > soot and the n values followed the trend: soot ≈ char > GAC

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adsorption to BC is strongly affected by surface area, surface area-normalized Kd of benzene was

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calculated (Figure S4). After normalization, adsorption to soot was much weaker than to char or GAC.

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This may be attributed to the different pore size distribution of soot: most of the pores are mesopores and

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macropores while only about 3~4% are micropores25 and the micropores do not extend inside the soot.30

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The different adsorption capacities of soot and char might be useful as a characterization method to

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distinguish different types of BC in environment. For example, phenanthrene and atrazine adsorption

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were higher in combusted Lake Ketelmeer sediment26 than by soot in this and previous studies25 (Table

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S3), and this may indicate the (co)presence of char in that sediment.

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Polyparameter Linear Free Energy Relationships. In order to understand the underlying

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intermolecular interactions controlling adsorption to soot, Equation 4 was used to obtain a chemical

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activity-dependent ppLFER model. A ppLFER model with five parameters (including the constant) was

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established:

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log = 3.74 ± 0.11 +

−0.35 ± 0.02 log + −0.62 ± 0.10 + −3.35 ± 0.11 +

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−1.45 ± 0.09

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Fitting the ppLFER with only four parameters (including a constant) is discussed in the Supporting

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Information (SI) (Text S1 and Figures S5-S8). This model gave excellent predictions of log KsootC values

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of sorbates (Figure 2). Slopes and intercepts were not significantly different than 1 and 0, respectively; the

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points were symmetrically distributed around a diagonal line. As mentioned in the method section, rigid

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statistic methods were used to diagnose unusual points in the regression. In this regression, no leverage

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(extreme X values) or highly influential data points were found. Also, VIF values of each parameters

256

ranged from 1.5 to 2.2, much lower than the limit, indicating multicollinearity of this fitting was tolerable.

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However, several suspicious outliers (extreme Y values) were identified: one with SRE 4.5 (the lowest

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concentration of atrazine, the red square in Figure 2) and four with SRE between 2 and 3 (the second

(N=102, R2=0.96, SE=0.18)


(Table S3). Since

Equation 5

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lowest concentration of atrazine and lowest tested concentrations of aniline, anisole and nitrobenzene,

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blue triangles in Figure 2). It should be noted that identifying outliers does not meaning these data points

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have to be automatically removed. In fact, closer investigation should be made to identify the reasons why

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the data points should (not) be removed and what would be the fitting improvement after removing the

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points. All these five data points fitted within the corresponding isotherms well (Table 1). Therefore, we

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do not believe these outliers resulted from inaccurate measurements of Kd values. All five data points

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were lowest concentrations tested except one for atrazine; also these chemicals have relatively larger B

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terms (Table S1). This might indicate that this ppLFER model has less predictive power for polar

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compounds at very low concentrations. Special attention should be paid to atrazine, whose two lowest

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concentration data points had the largest SRE among five outliers: one with SRE 4.5 and another with

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SRE 3.0. There might be several causes of this inaccuracy. First, most sorbates in the dataset are planar in

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structure, while atrazine is non-planar. Cornelissen et al. previously examined atrazine sorption to BC,

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noting this compound is not planar and only sorbed with an intensity comparable to absorption to

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amorphous organic matter (log Kd ≈ 3.1).31 Therefore, based on this one test sorbate, our ppLFER may

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overestimate the interactions between nonplanar sorbates and soot. Second, adsorption to soot might

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include a "pore-filling" process; due to the larger size of atrazine (e.g., molar volume of 185 cm3 mol-1 vs.

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phenanthrene and dimethyl phthalate both at 161 cm3 mol-1), there might be steric hindrance for atrazine

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to penetrate into micropores. Further studies with more non-planar and larger compounds would be

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necessary to test these hypotheses.

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The refit ppLFER models after removing these suspicious outliers showed no significant

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improvements. The ppLFER models after removing the data point SRE 4.5 and points SRE > 2 were:

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log = 3.76 ± 0.10 +

−0.37 ± 0.02 log + −0.64 ± 0.09 + −3.29 ± 0.10 +

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−1.51 ± 0.09

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and

(N=101, R2=0.97, SE=0.16)


Equation 6


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log = 3.78 ± 0.08 +

−0.38 ± 0.01 log + −0.63 ± 0.07 + −3.22 ± 0.08 +

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−1.57 ± 0.07

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Predictions of log KsootC values by these two models are shown in Figures S9 and S10, respectively.

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Compared to Equation 5, there were no significant differences in the coefficients of each parameter.

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Therefore, we still included these five data points in the regression, and Equation 5 was used in the

288

following discussions. Predictions of Freundlich coefficients and exponents by combing Equations 3 and

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5 are discussed in detail in SI (Text S2 and Figure S11).

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Interpreting the ppLFER and Comparison to Other "Black Carbons". The coefficients of the

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ppLFER reflect the differences in the sorbate's interactions with the solvent (water) versus the water-wet

292

sorbent (soot). Overall, V and E gave positive contributions to the adsorption, while B and A gave

293

negative contributions. Moreover, the overall contribution followed the order: V > B > (log ai)•E > A. The

294

strong positive contribution of the V terms indicated that differential London dispersion interactions are

295

most important for promoting adsorption. The activity dependency of E suggested that at lower degrees of

296

sorbate coverage on the soot, the importance of polarizability was larger. Viewing soot as stacks of

297

aromatic hydrocarbon sheets with large π electron clouds, this "E" dependency may be due to greater

298

polarizability of the faces of the soot surfaces in contrast to the edges. The negative contributions of the A

299

and B terms imply that both H-bonding accepting and donating interactions with water are not

300

compensated by interactions with the soot surface; in particular, the large negative B term showed that

301

soot had very limited electron accepting capacity compared to water. In contrast, the small negative A

302

term indicated that soot had strong electron donating capacity, almost as strong as the water did. Finally,

303

the negative intercept constants might result from two sources. First, there may be an unfavorable entropy

304

associated with moving from aqueous solution to the diesel soot surface, perhaps due to constrained

305

orientation; this kind of entropic contribution even exists in transferring zero size, ideal point solutes

306

between binary mixtures.32 Alternatively, the constant may reflect the combined contributions from

307

unrecognized intermolecular interactions (e.g. a (log a)V term) in our best-fit ppLFER model.

(N=97, R2=0.98, SE=0.13)


Equation 7

Page 15 of 31


308

Comparisons of the ppLFER model for soot to those for others BCs revealed the shared

309

properties of all BCs and also the unique characteristics of soot. Numerous investigators have utilized

310

ppLFER models to examine sorption to char,10 GAC,9,

311

(MWCNT)20, 36 (Table S5). In all of these studies, the V and B terms were always the most important ones

312

with V always encouraging adsorption to BC while B term always suppressed this process. This may due

313

to the similarity of these BC surfaces in that sorption to all of them chiefly reflects the net gain in London

314

dispersive interactions in moving from aqueous solution to predominantly aromatic surfaces. Moreover,

315

such surfaces have a significantly weaker ability to accept electron density from sorbates (B term) than

316

the solvent (water) does. Unlike adsorption to GAC, char, and MWCNT,9, 10, 36 in which a negative (log

317

a)•V term was observed, and was interpreted as indicating stronger sorption due to deeper sorbate

318

penetration into micropores at lower degrees of saturation (i.e., more negative values of log a), our soot

319

sorption ppLFER did not show such an effect. However, an activity-dependent E term was observed and

320

since V and E are strongly correlated (Pearson coefficient = 0.779, p = 0.00003) (Table S2), the (log a)•E

321

term may indicate the strongest soot sorption sites involve spaces near the contacts of the individual soot

322

spheres. In most studies of GAC, MWCNT, and char, the S or (log a)•S term encouraged the adsorption.

323

Only one study showed no influence.34 We saw no significant dependency of S term for sorption to our

324

diesel soot. This lack of S term may reflect a unique surface property of soot, that is limited polar

325

functionality as reflected by it very low O/C ratio compared to other BCs.37 A weak negative dependency

326

of the A term was also observed in MWCNT,36 but not in other previous studies of char and GAC. This

327

may suggest diesel soot and MWCNTs do not accept acidic protons as effectively as water, but more

328

studies are required to confirm this weak effect of the A term.

329

Prediction of log Kd of PAHs and PCBs in Soil and Sediment. To examine the applicability of the

330

ppLFER to real world soils and sediments, we assessed the accuracy of using it to estimate log Kd values

331

of PAHs and PCBs in sediments and soils where Kd values had been measured and whose black carbon

33-35

and multi-walled carbon nanotubes



Page 16 of 31

332

contents have been reported.2, 3, 6, 38, 39 For prediction of Kd values of PAHs and PCBs (Table S4) on soil

333

and sediment, both absorption into OC and adsorption onto soot BC were considered: M = NO O + N

334

Equation 8

335

where, fOC is the fraction of non-BC organic carbon and fsootC is the weight fraction of soot-carbon in the

336

soil or sediment; and Koc, and KsootC, are the OC- and soot-carbon-normalized solid-water adsorption

337

coefficients, respectively. Note that KsootC estimated using Eq. 5 already incorporates the concentration

338

dependency of soot sorption. Due to lack of information on the concentrations of non-soot BC, adsorption

339

onto other BC fractions was not covered in our estimation. Here, KOC was estimated according to KOW3

340

and KsootC was estimated using Equation 5. It should be noted that all the fsootC values were measured by

341

the thermal oxidation method (CTO-375) which mainly measured the soot content of the BC continuum.8,

342

40

343

this process,8 the fractional loss of NIST diesel soot (0.783 for NIST 2975)8 was used to correct for this

344

issue when estimating the Kd values on untreated soil and sediment.

Moreover, since CTO-375 might underestimate the soot fraction due to partial oxidation of soot during

345

Generally, estimated values for phenanthrene compared very well with reported results. For

346

example, Huang et al. reported isotherms for phenanthrene on 12 EPA soils and sediment for aqueous

347

concentrations ranging from about 1 to 1000 µg L-1.39 We recalculated the measured log Kd values at 100,

348

10, and 1 µg L-1 based on their reported isotherms and also predicted their values using the ppLFER

349

model and BC contents previously reported by our group.6 Since absorption into OC was linear while

350

adsorption to BC (soot) was non-linear, the relative importance of adsorption onto the BC fraction

351

increased as phenanthrene aqueous concentration decreased. At 100 µg L-1, absorption into OC was

352

dominant and good predictions were achieved, indicating that fOCKOC fractions were correctly estimated

353

for all the 12 EPA soils and sediments (Figure S12). At 10 µg L-1, the relative contribution of adsorption

354

onto BC compared to overall partition ranged from 16% to 62%, except EPA 22 (river sediment, IL)

355

whose contribution was 97% due to its extremely high BC/OC ratio (1.6±0.22% BC and 2.6±0.27% OC).

356

The predicted values fit the observed values well except for EPA 22 (Figure 3a), which may due to a 15 ACS Paragon Plus Environment

Page 17 of 31


357

mistakenly overestimated high BC fraction. At 1 µg L-1, the contribution of BC increased to 52-92%

358

except EPA 22. Although the data points were more scattered, the predicted and observed values were

359

close to each other except EPA 22 (Figure S13). The prediction of phenanthrene's adsorption on

360

combusted Boston Harbor sediment6 was also successful (Figure S14). While adsorption to other BC

361

fractions could also contribute to the overall sorption to soil and sediment, interestingly, the estimation

362

assuming only OC and soot predicted the overall Kd values of phenanthrene well. This might indicate soot

363

was the most important BC sorbent compared to other BCs in soil and sediment.

364

However, estimates for larger compounds deviated from the measured values substantially. For

365

example, laboratory measures of pyrene's adsorption to Boston Harbor sediments from South Dorchester

366

Bay and North Quincy Bay6 were generally much less than estimated using the ppLFER (Figure S15). A

367

similar phenomenon was found for predictions of Kd values of PAHs and PCBs measured using porewater

368

isolates and their associated sediment horizons by McGroddy (Figure 3b).38 For example, predictions of

369

fluorene and phenanthrene Kd values the Peddocks Island sediment were accurate. But Kd values of larger

370

PAHs were overestimated by about 1 to 2 logarithmic units (e.g., the predicted values of benzo[a]pyrene

371

were 0.8, 1.3 and 1.3 logarithmic units larger than the measured values in three different depths) and the

372

predicted value of the largest compounds dibenz[a,h]anthracene was about 5.2 logarithmic units off.

373

Moreover, predictions for the non-coplanar PCB congeners were generally less accurate than that of

374

PAHs (Figure 3b). It is possible that these field-observed Kd values were generally underestimated due to

375

the presence of colloids in the aqueous phase; larger PAHs and PCBs are more susceptible to this

376

methodological difficulty. Lohmann et al. calculated Kd values of several PAHs and PCBs by measuring

377

the porewater concentrations using polyethylene passive sampling techniques,3 thus the interferences

378

from colloids were eliminated. However, the ppLFER still overestimated Kd values for pyrene,

379

benzo[a]pyrene, PCB 44 and PCB 66 somewhat (Figure S16). The Kd values for benzo[a]pyrene was

380

overestimated by 0.8 logarithmic unit.



Page 18 of 31

381

It is also possible that the ppLFER model overestimated Kd values for large compounds. The

382

overestimation for larger PAHs might result from steric hindrance limiting access to the micropores

383

accessed by the smaller training sorbates. For example, the molar volumes for benzo[a]pyrene and

384

dibenz[a,h]anthracene are 218 and 244 cm3 mol-1, respectively, while the value for phenanthrene, the

385

largest PAH in the training set is only 161 cm3 mol-1. Also overestimates of Kd for non-planar PCB

386

congeners might have been caused by limited sorbate-sorbent surface juxtaposition as discussed above for

387

the prediction of atrazine adsorption.

388

Implications

389

Our study strongly indicated that FA was a robust method to investigate adsorption of organic

390

compounds to soot. This method was reproducible and time efficient. Also, it overcame several

391

disadvantages of the traditional batch method: Difficult soot-water separations, long equilibration times,

392

and high sorbent consumption. This method would be also applicable to other carbonaceous materials,

393

such as char, activate carbon, and carbon nanotubes. It may also be used to investigate the adsorption to

394

BC and OC in environmental samples, such as soil, sediment, and dust.

395

Secondly, distinct adsorption behavior was found for soot. This indicates that it is not simply the

396

total quantity of BC that matters, but also the BC type influences the extent of adsorption. Therefore,

397

detailed characterization of BC species is necessary to better understand the fate of organic pollutants in

398

BC-containing soil and sediment. However, few methods are available for this currently. Characterizing

399

the BC present at particular sites using several probe sorbates with distinct BC-specific sorption behaviors

400

might be possible.

401

Thirdly, successful fitting of the ppLFER model indicated that dispersive and H-bonding

402

accepting/donating interactions were dominant intermolecular forces controlling adsorption to soot.

403

Moreover, this ppLFER model is activity-dependent, thus it would be used as a tool to predict adsorption

404

coefficients of compounds of interest at various concentrations. Therefore, this approach should enable


Page 19 of 31


405

more accurate fate, bioavailability, and toxicity information to be obtained compared to KOW method for

406

compounds within the tested ppLFER parameter space.

407

Lastly, successful predictions of phenanthrene's Kd values on several soils and sediments by the

408

ppLFER model indicated that soot might be the most important BC sink, although the roles of other BCs

409

should be further investigated. Poor estimations of the adsorption coefficient of larger PAHs and PCBs

410

indicated that large molecular sizes and non-planar conformations might hinder their adsorption to soot

411

and other BCs. Improved estimation methods that capture the effects of larger sorbate sizes and non-

412

planarity in the model are needed.

413

Supporting Information

414

Supporting information contains 5 tables and 16 figures. This material are available free of charge

415

via the Internet at http://pubs.acs.org/.

416

Acknowledgments

417

This material is based upon work supported by the U.S. Army Corps of Engineers, Humphreys

418

Engineer Center Support Activity under Contract No. W912HQ-10-C-0005 awarded as part of the

419

SERDP program. Views, opinions, and/or findings contained in this report are those of the author(s) and

420

should not be construed as an official department of defense position or decision unless so designated by

421

other official documentation.



Page 20 of 31

422

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Page 24 of 31

Page 25 of 31


533

Tables

534

Table 1 Octanol-water partition coefficients, liquid or subcooled liquid solubilities, and Freundlich

535

isotherm parameters of the selected sorbates log Kf

n

R2

SE

N activity range

trichloroethene

Solubility (mg.L-1) 1280

1.55±0.02

0.75±0.02

0.999

0.028

5 1.63×10-4~3.67×10-2

benzene

1800

1.37±0.01

0.71±0.01

1.000

0.017

6 3.89×10-4~2.07×10-1

acetophenone

5500

2.00±0.01

0.70±0.01

0.999

0.027

5 6.60×10-6~2.84×10-3

aniline

36000

1.47±0.02

0.80±0.02

0.999

0.035

5 1.34×10-6~1.10×10-3

anisole

1600

2.10±0.01

0.58±0.01

1.000

0.011

5 1.04×10-4~4.77×10-2

toluene

470

1.72±0.02

0.79±0.01

0.999

0.033

5 1.62×10-4~2.13×10-1

fluorobenzene

1540

1.64±0.06

0.72±0.04

0.994

0.054

4 1.89×10-3~1.42×10-1

chlorobenzene

500

2.33±0.02

0.66±0.02

0.999

0.021

4 1.74×10-3~8.37×10-2

1,2,4trichlorobenzene bromobenzene

49

3.06±0.01

0.54±0.01

0.999

0.010

5 6.83×10-3~1.66×10-1

410

2.35±0.01

0.67±0.01

1.000

0.007

4 2.06×10-3~2.03×10-1

nitrobenzene

1900

2.22±0.01

0.51±0.01

0.999

0.014

5 8.10×10-5~1.18×10-2

2,4-dinitrotoluene

700

2.75±0.01

0.55±0.01

1.000

0.012

5 3.10×10-4~6.57×10-2

phenol

105262

1.47±0.03

0.77±0.03

0.996

0.058

5 2.09×10-6~6.58×10-4

4-chlorophenol

37116

2.35±0.02

0.62±0.02

0.995

0.046

7 5.98×10-6~2.64×10-3

4-cresol

22672

1.99±0.01

0.68±0.01

1.000

0.014

5 1.37×10-5~3.08×10-3

naphthalene

108

2.80±0.02

0.55±0.02

0.995

0.045

5 1.47×10-4~1.16×10-2

1methylnaphthalene 1chloronaphthalene dimethyl phthalate

25.8

3.03±0.01

0.53±0.01

1.000

0.010

5 5.82×10-4~1.80×10-1

17.4

3.36±0.01

0.42±0.01

0.997

0.020

5 1.35×10-3~1.48×10-1

4248

2.25±0.03

0.66±0.02

0.996

0.048

5 9.30×10-5~4.63×10-2

atrazine

996

2.37±0.02

0.81±0.02

0.998

0.030

4 8.13×10-5~6.34×10-3

phenanthrene

3.05

3.54±0.01

0.31±0.01

0.999

0.000

3 1.17×10-2~5.76×10-2

Compounds

536



Page 26 of 31

537

Table 2 Comparison of reported Freundlich isotherm parameters of selected sorbates on NIST soot

538

SRM 2975 by different methods. Sorbate naphthalene 1,2,4-trichlorobenzene

phenanthrene

Method frontal analysis, 8 hr batch, 28 d frontal analysis, 6 hr batch, flocculation, 59 d batch, 60 d frontal analysis, 33 hr batch, flocculation, 59 d batch, 60 d

log Kf 2.80 3.21 3.06 3.43 3.42 3.54 3.68 3.72

539


n 0.55 0.60 0.54 0.66 0.66 0.31 0.43 0.41

Ref. This study 24

This study 7 25

This study 7 25

Page 27 of 31


540

Figure captions

541

Figure 1. Reproducibility of bromobenzene isotherms on (a) 1% and (b) 0.1% soot columns.

542 543

Figure 2. Prediction of log10 KsootC by the ppLFER model. Error bars in the horizontal reflect propagated

544

errors using uncertainties in the fitted ppLFER coefficients (Equation 5).

545 546

Figure 3. Observed and ppLFER-predicted log Kd values on soil and sediment for (a) phenanthrene on 12

547

different EPA soils and sediments. Data from References 6 and 38. Regression does not include EPA 22

548

sediment. (b) PAHs and PCBs on the Peddocks Island sediment. Data from References 6 and 39Error bars

549

in the horizontal reflect propagated errors using uncertainties in the fitted ppLFER coefficients (Equation

550

5). Error bars in the vertical reflect 1 standard error of observed values.

551



Figure 1. Reproducibility of bromobenzene isotherms on (a) 1% and (b) 0.1% soot columns. 153x211mm (300 x 300 DPI)


Page 28 of 31

Page 29 of 31


Figure 2. Prediction of log10 KsootC by the ppLFER model. Error bars in the horizontal reflect propagated errors using uncertainties in the fitted ppLFER coefficients (Equation 5). 151x114mm (300 x 300 DPI)



Figure 3. Observed and ppLFER-predicted log Kd values on soil and sediment for (a) phenanthrene on 12 different EPA soils and sediments. Data from References 6 and 38. Regression does not include EPA 22 sediment. (b) PAHs and PCBs on the Peddocks Island sediment. Data from References 6 and 39Error bars in the horizontal reflect propagated errors using uncertainties in the fitted ppLFER coefficients (Equation 5). Error bars in the vertical reflect 1 standard error of observed values. 152x114mm (300 x 300 DPI)


Page 30 of 31

Page 31 of 31


Figure 3. Observed and ppLFER-predicted log Kd values on soil and sediment for (a) phenanthrene on 12 different EPA soils and sediments. Data from References 6 and 38. Regression does not include EPA 22 sediment. (b) PAHs and PCBs on the Peddocks Island sediment. Data from References 6 and 39Error bars in the horizontal reflect propagated errors using uncertainties in the fitted ppLFER coefficients (Equation 5). Error bars in the vertical reflect 1 standard error of observed values. 202x114mm (300 x 300 DPI)


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