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Soil and Sediment Contamination: An InternationalJournal

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A Biosurfactant/Polystyrene Polymer PartitionSystem for Remediating Coal Tar-ContaminatedSediment

Nicholas M. Wilton, Christian D. Zeigler, Riccardo Leardi & Albert Robbat Jr.

To cite this article: Nicholas M. Wilton, Christian D. Zeigler, Riccardo Leardi & Albert RobbatJr. (2016): A Biosurfactant/Polystyrene Polymer Partition System for Remediating Coal Tar-Contaminated Sediment, Soil and Sediment Contamination: An International Journal, DOI:10.1080/15320383.2016.1190955

To link to this article: http://dx.doi.org/10.1080/15320383.2016.1190955

Accepted author version posted online: 01Jun 2016.Published online: 01 Jun 2016.

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A Biosurfactant/Polystyrene Polymer Partition System forRemediating Coal Tar-Contaminated Sediment

Nicholas M. Wiltona, Christian D. Zeiglera, Riccardo Leardib, and Albert Robbat, Jr.a

aDepartment of Chemistry, Tufts University, Medford, MA, USA; bDepartment of Pharmacy, University of Genoa,Genoa, Italy

ABSTRACTA sustainable, green chemistry process is proposed for the cleanup ofcoal tar impacted sediment in under 2 hr. A mixture of proteins andpolypeptides, extracted from corn gluten meal and hemp, when mixedwith sediment and polystyrene foam pellets (PFPs), serves to mobilizetar, which sorbs onto PFP. Since the sorbent floats, coal tar is easilyextracted from the agitation vessel. An empirically derived 4-dimensional surface response model accurately predicts removal ratesof the tar and operational costs of the system under variousexperimental conditions. At optimum relative to cost, 81% of the twoto six ring polycyclic aromatic hydrocarbons (PAHs) and 73% of thetotal tar mass are removed despite high sediment organic carboncontent (16.4%) and silty fines (»85%). Multiple PFP extractions (n D 2)of the same sediment/biosurfactant mixture yielded 94% extraction ofPAH. Scanning electron microscope images illustrate free-phase tar(globule) sorption onto the foam. A field pilot was conducted in which25 kg of sediment was processed. Results were in excellent agreementwith both lab (10 g) experiments and model predictions. The process isconsidered sustainable and green because the active ingredients arederived from renewable crop materials, recycled polystyrene is used,and the biosurfactant is recyclable which reduces water demand andtreatment costs, with the recovered tar used as fuel and sediment asbeneficial reuse material.

KEYWORDSBiosurfactant; coal tar;environmental; polycyclicaromatic hydrocarbons;polymer partitioning;polystyrene; remediation

Introduction

From the early 1800’s to the mid-1950’s, manufactured gas plants (MGP) supplied light andheat for residential homes and industries. MGP sites are a persistent source of pollutionfrom coal tar (Abrams and Loague, 2000), which was released into soil and water bodies dur-ing operations. Some estimate that the number of MGP sites in need of remediation approx-imates 50,000 (Hatheway, 2011); thus, the cost of cleanup will be staggering. One example isa report from New York State, which estimates it will cost $3 billion to clean 250 coal tarsites. Since remediation costs of former utility sites are ultimately borne by consumers (Stein,2011), a more efficient, cost-effective process is needed.

CONTACT Albert Robbat, Jr. albert.robbat@tufts.edu Department of Chemistry, Tufts University, 62 Talbot Avenue,Medford, MA 02155, USA.

Color versions of one or more of the figures in this article can be found online at www.tandfonline.com/bssc.© 2016 Taylor & Francis Group, LLC

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The two most often employed strategies for MGP site remediation are dig-haul-landfilland dig-haul-thermal treatment. The first moves the contamination from one location toanother and does not eliminate the possibility of pollutants leaching into the subsurface(Anderson et al., 1987; LeBlanc et al., 2000). The second increases the carbon footprint(Cormier et al., 2006; McGowan et al., 1996). For sediments, both strategies generally relyon excavation and either mechanical or gravity dewatering of the mud. Typically, every cubicmeter of sediment results in 1.2 cubic meters of material shipped off-site due to stabilizingagent additions. Moreover, review of U.S. Environmental Protection Agency (USEPA)records indicates that the number of remediation projects stopped due to community com-plaints from odor and traffic is significant. For these reasons, remediation technologies thatallow on-site beneficial reuse of treated MGP sediment are desired.

Bacteria (Shin et al., 2006; Zhang et al., 1997), fungi (Bhardwaj et al., 2013) and plants(Dai et al., 2013) produce biosurfactant compounds that significantly reduce the interfacialtension between hydrocarbon-based nonaqueous-phase liquids (NAPL) and water, therebyincreasing the bioavailable material (Cameotra and Bollag, 2003; Coppotelli et al., 2010;Peng et al., 2015) compared to synthetic surfactants (Pei et al., 2010). Although the vastmajority of these compounds are produced biosynthetically (Cameotra et al., 2010), othersare produced or extracted from renewable feedstocks, e.g., red ash trees (Blythe, 2015; Guoet al., 2007). These chemicals are amphiphilic due to their fatty acid, protein or peptide con-tent (Dwyer et al., 2014; Schaller et al., 2015; Silva et al., 2014).

Biosurfactants have been investigated and found to be relatively “green” compared to syn-thetic surfactants because they are biodegradable and less toxic (Mulligan, 2005). Whilepetroleum remediation has been extensively investigated (Silva et al., 2014), few studies havefocused on using biosurfactants to remediate coal tar sites. For example, Garcia-Junco et al.(2003) found biosurfactants greatly enhanced the solubilization of PAH into the aqueousphase from a model NAPL. �Sliwka et al. (2009) evaluated the effectiveness of rhamnolipidsfrom Pseudomonas aeruginosa to degrade residual coal tar, achieving a 28% reduction over14 days. Khanna et al. (2011) studied the effects of Bacillus spp. found in coal tar-impactedsoil. Using pyrene as an indicator of PAH reduction, a 56% decrease in concentration wasobtained over a 4-day period. Bezza and Chirwa (2016) reported an 86% reduction in PAHin a creosote-contaminated soil in a bio-slurry reactor over 45 days using a biosurfactantisolated from P. aeruginosa. Despite their promise, biosurfactants are expensive compared tosynthetic ones are not usually cost-competitive with dig-and-haul remediation of NAPL-contaminated sites (Marchant and Banat, 2012).

Two-phase partition reactors using solid polymer sorbents and mobilizing agents havebeen found to increase ex situ separation efficiencies of contaminants from solids (Parentet al., 2012; Rehmann and Dagulis, 2007; Tomei et al., 2013, 2014). Critical to the success oftwo-phase polymer partition systems are the properties of the polymer, the concentration,volume, and ability of the surfactant to liberate organics from sediment/soil, and the contacttime between pollutant and polymer. While researchers have studied the biodegradation ofpolymer-sorbed contaminants, none have utilized a biosurfactant as the mobilizing phase inthe soil treatment step. Yeom et al. (1996) treated coal tar-impacted soil with Brij 30 surfac-tant and Tenax [poly(2,6-diphenyl-p-phenylene oxide) polymer]. After 12 days, phenan-threne concentrations decreased by 25%. In contrast, Rehmann et al. (2008) reported 80%removal of phenanthrene, fluoranthene and pyrene from fortified sediments in 2 days using30% isopropanol and polyurethane beads. The contaminant-sorbed beads were then

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biodegraded, regenerating them for another batch. Peyda et al. (2013) constructed a responsesurface model to study the removal of petroleum from a fortified soil using a 2-propanol/polyurethane bead system. Eighty percent extraction efficiency was achieved in 3 days.

This study reports the results of a new, high-throughput, crop-based biosurfactant/polystyrene foam reactor system. The biosurfactant, CT1, is a complex mixture of plantpolypeptides and fat. According to test protocols established by the USEPA (2002), themixture is nontoxic for fresh (Daphnia magna and Pimephales promelas) and salt(Mysidopsis bahia and Menidia beryllina) water organisms. When CT1 is used withpolystyrene, coal tar rapidly emulsifies and then adsorbs onto polystyrene due to strongp–p interactions between aromatics in the tar and the polymer. Mass balance experi-ments indicate removal rates for bulk tar were the same as that of aromatics. Polysty-rene foam is an appealing engineering solution for heavy oils and tars because it floatsin water and is easily recovered from the agitation vessel along with sorbed hydrocar-bons. It is also an attractive alternative to other solid polymer adsorbents since recycledmaterial can be used. Toward this end, a response surface model was developed to opti-mize the biosurfactant/polymer system. The reactor yielded > 80% coal tar recoveryfrom highly aged river sediment in 2 hr.

Experiment

Materials

Coal tar contaminated sediment was obtained from the Grand Calumet River in June 2013.The manufactured gas plant operated on the banks of the river from 1901 to 1950. Highconcentrations of tar persist in the sediment to this day. The river bottom was collected byback hoe and placed into a 10-m3 rolloff for testing. Approximately 200 L of sediment wasshipped to Tufts University for further study. The biosurfactant, CT1 was obtained fromGreenStract, LLP (New York, NY). CT1 is a mixture of proteins and polypeptides extractedfrom corn gluten meal and hemp. Corn gluten meal is a protein-rich feed, containing about65% crude protein, used as a source of protein and energy for livestock and fish. Hemp pro-teins serve a variety of functions in the human body, including the supply of amino acids forthe growth and maintenance of body tissue. CT1 is biodegradable, highly digestible, andwhen tested by an independent lab was found nontoxic for fresh and saltwater organismsused by the USEPA to determine suitability for remediation projects (report supplied uponrequest). Polystyrene home insulation panels (density D 20 kg/m3) were purchased from alocal hardware store. The panels, containing 30% recycled material, were ground to makepolystyrene foam pellets (PFPs, 3–10 mm in diameter).

Analytical grade dichloromethane and toluene were purchased from VWRTM (Radnor,PA). Calibration mix #5 (the 16 USEPA priority pollutant PAH), internal standard mix(acenaphthene-d10, chrysene-d12, 1,4-dichlorobenzene-d4, naphthalene-d8, perylene-d12, andphenanthrene-d10), surrogate mix SOM01.1 (2-methylnaphthalene-d12, and fluoranthene-d12), and copper granules were obtained from Restek (Bellafonte, PA). Polypropylene syrin-ges, 12 mL, and fiber glass filter tips, 1 mM, were obtained from MicroLiter Analytical Sup-plies, Inc. (Suwanee, GA) and Tisch (Cleves, OH), respectively. Whatman #1 filter paper90 mm was purchased from GE Healthcare (Pittsburgh, PA). Hydromatrix drying agent waspurchased from Agilent Technologies (Santa Clara, CA).

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Sample preparation

To model the biosurfactant-enhanced polymer partition process, 10 g of sediment was sealedin 4-oz glass jars with known amounts of CT1 and PFP, for details see Table 1. Duct tape wasapplied to the outer surface of each jar to increase friction. The sample was agitated at90 rpm using a fixed speed dual drum rotary rock tumbler from Harbor Freight Tools(Calabasas, CA). After mixing, the supernatant was skimmed to collect PFP using a 16 meshscreen. After collection, PFP was gently sprayed with water to wash soil particles from thesurface. After the solids settled, CT1 and wash water were decanted from the “cake” thatformed at the bottom of the jar. Before gas chromatography/mass spectrometry (GC/MS)and total organic carbon (TOC) analysis, »1 g of cake was dried in an oven overnight at90�C to determine the dry weight of the sediment.

Table 1. Experimental conditions and results of PAH extracted from coal tar impacted sediment.

Mvolume:Smass (mL/g) CT1 Conc (%) PFPmass:Smass (g/g) Mixing time (hr) % Removal

1 1 (¡1) 2 (1) 0.065 (1) 0.5 (¡1) 37.72 1 (¡1) 2 (1) 0.065 (1) 1 (¡0.33) 46.23a 1 (¡1) 2 (1) 0.065 (1) 2 (1) 50.73b 1 (¡1) 2 (1) 0.065 (1) 2 (1) 50.84 2 (0) 1 (0) 0.065 (1) 2 (1) 67.15 1 (¡1) 0.1 (¡0.9) 0.065 (1) 2 (1) 39.76 1 (¡1) 0.5 (¡0.5) 0.065 (1) 2 (1) 50.57 1 (¡1) 0 (¡1) 0.065 (1) 2 (1) 14.98 1 (¡1) 2 (1) 2.2 £ 10¡3 (¡0.93) 2 (1) 41.99 1 (¡1) 2 (1) 4.3 £ 10¡3 (¡0.87) 2 (1) 44.110 1 (¡1) 2 (1) 0.022 (¡0.33) 2 (1) 48.211 1 (¡1) 2 (1) 0 (¡1) 2 (1) 16.412 1 (¡1) 1 (0) 0.043 (0.33) 1 (¡0.33) 27.413 1 (¡1) 0.5 (¡0.5) 0.043 (0.33) 0.5 (¡1) 40.014 2 (0) 0.5 (¡0.5) 0.065 (1) 2 (1) 70.315a 2 (0) 2 (1) 0.065 (1) 2 (1) 83.215b 2 (0) 2 (1) 0.065 (1) 2 (1) 83.615c 2 (0) 2 (1) 0.065 (1) 2 (1) 80.416 2 (0) 2 (1) 0.022 (¡0.33) 1 (¡0.33) 51.417 1 (¡1) 0 (¡1) 0 (¡1) 0.5 (¡1) 20.318 1 (¡1) 2 (1) 0 (¡1) 0.5 (¡1) 21.919 2 (0) 0 (¡1) 0.065 (1) 0.5 (¡1) 54.320 2 (0) 0 (¡1) 0 (¡1) 2 (1) 5.621 3 (1) 0 (¡1) 0.033 (0) 2 (1) 64.622 2 (0) 2 (1) 0 (¡1) 2 (1) 34.523 3 (1) 0 (¡1) 0 (¡1) 0.5 (¡1) 14.124a 3 (1) 2 (1) 0.065 (1) 0.5 (¡1) 61.524b 3 (1) 2 (1) 0.065 (1) 0.5 (¡1) 60.924c 3 (1) 2 (1) 0.065 (1) 0.5 (¡1) 62.625 3 (1) 0 (¡1) 0.065 (1) 1.5 (0.33) 75.826a 3 (1) 2 (1) 0.065 (1) 2 (1) 79.626b 3 (1) 2 (1) 0.065 (1) 2 (1) 81.026c 3 (1) 2 (1) 0.065 (1) 2 (1) 81.327 2 (0) 2 (1) 0.130 2 (1) 91.228 1 (¡1) 2 (1) 0.065 (1) 10 54.029 2.5 (0.5) 2 (1) 0.065 (1) 2 (1) 94.2

Notes:1) % RecoveryD ([initial] – [final])/[initial]£ 100.2) ¡1 (minimum) to 1 (maximum) codified model variables.3) Experiments 27–29 were not used to determine the model.4) In Experiment 29, CT1 was recycled and fresh PFP was used to treat the same sediment twice at optimum model conditions.

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To determine extraction efficiency by GC/MS, an automated pressurized liquid extractionand solvent evaporation system from Fluid Management Systems (Watertown, MA) wasused to extract the samples (Robbat and Wilton, 2014). Twenty microliters of 2000 mg/mLsurrogate solution in dichloromethane was injected onto 2 g sediment. The sediment wasmixed with 2 g Hydromatrix and added to a 40-mL extraction cell; the remaining deadvolume of the cell was filled with Hydromatrix. The system was programmed to deliver sol-vent to the extraction cell at 20 mL/min for 2.4 min and then pressurize to 1500 psi over2.5 min. The pressurized cell was heated to 120�C in 5 min. The temperature and pressurewere held constant for 20 min before the cell was allowed to cool to room temperature over20 min, and then depressurized. Solvent was flushed through the cell at 20 mL/min for1.3 min before N2 gas purged the residual solvent. Extracts were delivered to the evaporationunit and concentrated to »2 mL in the presence of 2 g copper granules to remove elementalsulfur. The evaporation unit was programmed to heat the extract to 65�C under 12 PSI ofN2. Sample extracts were passed through a polypropylene syringe fitted with 1 mM fiber glassfilters along with solvent washes to remove any remaining fines. The final extract volumewas approximately 3–4 mL.

Mass balance experiments were performed to assess total solvent extractable materials(TSEM). Three samples, each of untreated and treated sediment, were dried and then groundto a fine powder in a mortar and pestle. For the TSEM experiments, the sediment was treatedunder optimum model conditions. All samples, 4 g each, were extracted 5 times for 10 minwith 10 mL of a 1:1 toluene/dichloromethane mixture using a Branson 5200 UltrasonicCleaner (Danbury, CT). The extracts were filtered, concentrated under a gentle stream ofnitrogen, and then baked overnight at 90�C to evaporate residual solvent, with the remainingtar mass weighed.

To evaluate extraction efficiency in the field, a 0.25-m3 cement mixer, operating at90 rpm, was used to agitate CT1, sediment, and PFP. A total of 25 kg of river sediment wasused in each experiment. Field experiment 16 (Table 1) was replicated to assess operationalscalability. A 2% CT1 solution was added to the cement mixer at a mobile phase volume(Mvolume) to sediment mass (Smass) ratio of 2:1. PFP was added at a ratio of 0.022:1 g/g(PFPmass:Smass) to the sediment and mixed for 1 hr. After agitation, PFP floated to the top ofthe mixer and were removed via slotted shovel. The suspended fines were collected in 16-ozjars, sealed, and shipped to Tufts for analysis. The settled particles formed a “cake” over-night. After decanting the supernatant, PAH analysis was performed on the remaining solidsas previously described.

Equipment

A Vario MICRO cube analyzer from ElementarTM (Hanau, Germany) was used to measureTOC in the sediment. A Shimadzu (Columbia, MD) model QP2010C GC/MS was used toanalyze the samples. Helium gas served as the carrier gas at 100 kPa head pressure. About1-mL sample injections were made. The high-temperature fused silica Rxi-5MS column(30 m £ 0.25 mm £ 0.25 mm) was obtained from Restek�. The GC was temperature pro-grammed as follows: 60�C for 1 min, 6.5�C/min to 320�C, and hold for 5 min. The inlet,interface, and ion source were maintained at 320�C, 280�C, and 230�C, respectively. The MSwas operated in full-scan mode from m/z 50 to 350. Ion Analytics© (Andover, MA) spectraldeconvolution software was used to analyze the data.

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Before and after extraction treatment, images of PFP were taken using a Phenom Prodesktop scanning electron microscope (SEM). Carbon tape was used to affix the sample tothe holder. Dust was removed with Dust-Off� cleaner before loading samples into theinstrument. All samples were imaged without sputter-coating in the charge-up reductionmode.

Experimental design

Matlab was used to model the extraction efficiency of the system. The four variables testedwere Mvolume:Smass (X1), CT1 concentration (X2), PFPmass:Smass (X3), and mixing time (X4).The model below provides information on both individual variables and their interactions

Y D b0C b1X1 C b2X2C b3X3 C b4X4C b12X1X2C b13X1X3 C b14X1X4 C b24X2X4

C b34X3X4C b11X12C b22X2

2C b33X32 C b44X4

2

Experiments 1–16, 27 and 28 were used to determine the min (¡1) and max (1) values of themodel under field-practical conditions: X1 D 1–3 mL/g; X2 D 0–2% active ingredient; X3 D 0–0.065 g/g; and X4 D 0.5–2.0 hr. Given these initial experiments, we used the D-Optimal experi-mental design approach to find the subset of experiments that leads to the highest determinantof the information matrix, which corresponds to the smallest variance of the coefficients in themodel. The D-Optimal approach, combined with the 16 original experiments, reduced the num-ber of remaining experiments required in the model. In order to compare subsets with differentnumbers of experiments, the normalized determinant MD det/np was taken into account, wheren is the number of experiments and p is the number of parameters to be estimated. When thenumber of experiments increases, both the numerator (quality of information) and the denomi-nator (experimental effort) increase. The normalized determinant weights the quality of theinformation, expressed as the variance of the coefficients in the model, by the experimental effort.From this, nine additional experiments were needed to improve model accuracy. These experi-ments are listed in Table 1 as 17–25. Experiments 15, 24 and 26 were repeated three-times eachand experiment 3 was repeated twice to determine model variability. Experiment 26 was per-formed at the maximum condition for each of the four variables, while experiment 3 wasrepeated to investigate systematic effects due to differences in time between when experiments1–16 and 17–26 were performed. As a result, the model consists of 33 experiments under condi-tions 1–26 (16 initial, 9 D-Optimal, one at maximum condition, and replicates).

Results and discussion

Coal tar, the primary pollutant at MGP sites, is a dense nonaqueous-phase liquid (DNAPL).It consists of thousands of chemicals that can migrate long distances from their release point.Polycyclic aromatic hydrocarbons (PAHs) are critically important regulatory benchmarksfor classifying and cleaning hazardous waste sites because of their carcinogenicity (Irigarayand Belpomme, 2010; Jacob, 2008), mutagenicity (Mahadevan et al., 2007; Toyooka andIbuki, 2007), teratogenicity (Billiard et al., 2008), and toxicity (Patri, 2009; USEPA, 2003).Although PAH attract most of the attention from environmentalists so too should tar mass.Since coal tar is denser than water, it sinks once liberated from solids. In this project, we

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determined the amount of biosurfactant needed to liberate tar from sediment and theamount of solid polymer needed to sorb and remove the tar from solution. To optimize thesystem, we developed a 4-dimensional surface response model based on 33 experiments andreplicates. The river from which the sediment was collected is shown in Figure 1A. Despite70 years of weathering, coal tar is visible along the river banks and in the sediment at depthsof up to 3 m, as shown in Figure 1B. Also visible is the tar film on the water surface. The sed-iment, a thick, black silt, was 30–35% solids, 60% water, and 5–10% tar by mass. Eighty-fivepercent of the solids were<250 mm; the remainder<50 mm. TOC was 16.4§ 0.3%, indicat-ing high organic matter content in the sample. Table 2 shows both total PAH content in theinitial sediment as well as TSEM, an indicator of bulk tar content. TSEM was found to be6.6 § 1.3% by mass, while total PAH concentration in the river bottom was 6900 mg/g, com-prising 10.5% of the extractable organics. Table 3 lists initial PAH concentrations, whichwere between 1600 mg/g for naphthalene and 30 mg/g for dibenz[a,h]anthracene.

Surface response model

Although modeling has been used to predict the outcome of remediation processes(Bravo-Linares et al., 2013; Peyda et al., 2013; Qin et al., 2009) and chemical reactions(Leardi, 2009), it has not been used to study PAH extraction of coal tar-impacted sediment.Predictive tools are useful for determining optimum process conditions and for obtainingestimates of time and material cost under desired conditions. For instance, if the goal is toprocess 300 m3 of sediment per day to remediate a 10,000-m3 site, the statistical experimen-tal design should find the optimum mobilizing phase-to-sediment (Mvolume:Smass) ratio,biosurfactant (CT1) concentration, polymer-to-sediment (PFPmass:Smass) ratio, and mixingtime to process the material, trading off cost versus recovery. Since the amount of water

Figure 1. (A) Remediation site and (B) impact of coal tar in the river after 70 years of weathering.

Table 2. Comparison of total polycyclic aromatic hydrocarbon (PAH) and total mass (TSEM) concentra-tions, mg/g, before and after treatment at optimum model predicted conditions.

Compound Initial (n D 3) Optimized treatment (n D 3) % Removal

Total PAH (GC/MS) 6900 § 164 1284 § 58 81.4Total coal tar (TSEM) 65,800 § 12,500 17,800 § 2200 73.0

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needed to liberate tar from sediment is both a materials and water treatment cost, it is essen-tial to know if water in the river bottom can be used to make a pumpable fluid and if theCT1 solution itself is reusable.

Table 1 lists the initial experiments used to define the minimum (¡1) and maximum(C1) values for each variable and for the model itself, where X1 D Mvolume:Smass, X2 D CT1concentration, X3 D PFPmass:Smass, X4 Dmixing time, and Y the PAH removal rate

Y D 45:5 C 6:2 X1�ð Þ C 5:4 X2

�ð Þ C 18:9 X3���ð Þ C 4:9 X4

�ð Þ¡ 6:5 X1X2

�ð Þ C 12:2 X1X3��ð Þ C 4:9 X3X4¡ 11:3 X1

2 �ð Þ

The following statistics were obtained: R2A D 0.79 (adjusted coefficient of determination),

ZZ D 10.3 (standard deviation of the residuals), ZZ D 68.4% (explained variance in cross-validation), ZZ D 12.8 (cross-validation root mean square error), �P < 0.05, ��P < 0.01,���P < 0.001.

The PFPmass:Smass ratio is the most impactful variable, since tar sorption is dependent onthe amount of PFP. The fact that Mvolume:Smass ratio is a quadratic function implies too lowor too high a ratio will result in inefficient recoveries. Figure 2A shows the plot of CT1 con-centration versus Mvolume:Smass ratio, where PFPmass:Smass and mixing time are optimized.When Mvolume:Smass ratio is greater than 2:1, PAH extraction efficiency will be > 75%. Fromthe equation, biosurfactant volume influences extraction efficiency more than concentration.For example, at high volume extraction efficiency is largely independent of concentrationbut at low volume higher concentrations of the biosurfactant provides some benefit.

The X1X3 term describes how PFPmass and Mvolume influence extraction efficiency as afunction of sediment mass treated. Figure 2B shows the Mvolume:Smass versus PFPmass:Smass

plot, where the concentration of CT1 and mixing time are constant. Improvement in PAHremoval is linked to PFPmass. Optimum extraction occurs when Mvolume and PFPmass increasewith one another, with the local maximum achieved at 2.5:1 mL/g and 0.065:1 g/g,

Table 3. PAH concentrations before and after treatment at optimum model predicted conditions, mg/g.

Compound Initial Optimized treatment % Removal % Removal by ring number

Naphthalene 1575§ 25 365 § 24 76.8 —Acenaphthylene 78 § 4 11 § 1 85.7 3-ring:

82.3§ 2.0Acenaphthene 1017§ 52 194 § 12 80.9Fluorene 407§ 20 78 § 1 80.9Phenanthrene 1342§ 52 236 § 6 82.4Anthracene 413§ 9 75 § 3 81.8Fluoranthene 385§ 15 66 § 5 82.9 4-ring:

83.8§ 1.3Pyrene 650§ 11 106 § 7 83.7Benz[a]anthracene 266§ 4 38 § 3 85.6Chrysene 229§ 2 39 § 3 83.0Benzo[b]fluoranthene 81 § 7 11 § 1 86.2 5- and 6-ring

85.3§ 4.4Benzo[k]fluoranthene 108§ 2 11 § 3 89.6Benzo[a]pyrene 177§ 10 26 § 3 85.1Indeno[1,2,3-cd]pyrene 75 § 6 9 § 6 88.0Dibenz[ah]anthracene 30 § 3 7 § 2 78.2Benzo[ghi]perylene 66 § 6 10 § 2 84.8

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respectively. The balance struck between slurry volume and sediment mass is intuitive: toosmall a volume for a given mass results in a non-pumpable fluid with poor mixing efficien-cies. Alternately, too large a volume reduces extraction efficiency by increasing interparticledistances. Compared to experiment 15, the doubling of PFP mass increases the volume ofboth the sorbent and CT1 (at optimum conditions), with little increase in PAH recovery, seeexperiment 27.

The plot of PFPmass:Smass versus mixing time, where biosurfactant volume and concentra-tion are constant is shown in Figure 2C. For a given Smass, the extraction efficiency is a func-tion of the amount of foam, agitation time, and mixing rate. In all of these experiments, themixing rate was held constant. At high PFPmass more time is needed to reach optimal extrac-tion efficiency than at low PFPmass. However, increasing the agitation time in experiment3 beyond 2–10 hr (see experiment 28) yielded marginal gain in recovery, i.e., 51% versus54%. At low PFPmass, 0.5 hr is needed to saturate the available surface area to reach maxi-mum recovery under the conditions imposed.

The surface response model yielded an optimum extraction condition of Mvolume:Smass

2.5:1 mL/g (0.5), CT1 2% (1), PFPmass:Smass 0.065:1 g/g (1), and mixing time 2 hr (1). Basedon these parameters, an 83% reduction in PAH concentration is expected. Excellent

Figure 2. Surface response maps of the most significant interaction variables from the model including (A)X1 vs. X2, (B) X1 vs. X3, and (C) X3 vs. X4. Values in boxes represent percent reduction in total sediment PAHconcentration.

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agreement was obtained between predicted and actual (81.4%) amounts; see Table 2. Whenthe same CT1/sediment mixture was extracted a second time with fresh PFP, 94% removalwas obtained (see experiment 29 in Table 1). Extraction efficiency must be balanced againstthe volume of polystyrene and mixing time to achieve a given recovery.

Table 3 lists individual PAH concentrations before and after a single treatment under theoptimum extraction conditions. Interestingly, 2-ring, 3-ring, 4-ring, 5-ring and 6-ring PAHwere extracted by the polymer partition system without preference. In contrast, Yeom et al.(1996) studied the efficiency of Brij 30, a synthetic, nonionic surfactant, and Tenax polymerto dissolve PAH from coal tar impacted soil over a 12-day period. They found that PAH rap-idly adsorbed onto Tenax pellets from the aqueous phase but the removal from soil/tarmatrix was size dependent, e.g., naphthalene > phenanthrene > pyrene > benzo[a]pyrene.

PAH also served as a good estimate of total hydrocarbon removal as exhibited by a73% decrease in coal tar mass by TSEM; see Table 2, which was only 8.4% different fromPAH reduction by GC/MS. Additionally, tar mass removal can be qualitatively observed bythe change in sediment color before (black) and after treatment (brown), Figure 3A and B.Mass transfer of tar to PFP is shown in Figure 3C and D, where the dark color change in thePFP beads can be contrasted with the lighter color of the soil after treatment. It is hypothe-sized that the observed discoloration of PFP, as well as the equivalent removal of TSEM andindividual and total PAH is due to tar mobilization followed by sorption of tar particles toPFP. In addition to solubilizing NAPL, surfactants can mobilize NAPL free product, greatlyenhancing remediation efficiency (Harwell et al., 1999; Knox et al., 2009; Sabatini et al., 1998).Since the coal tar is mobilized rather than solubilized, particles of tar should be observable onthe foam. SEM images were taken of the PFP surface pre- (Figure 3E) and post-treatment(Figure 3F). Note the smoothness of the untreated polystyrene beads whereas post-treatmentbeads are far rougher due to collisions with soil particles in the reactor. The dark, patchy accu-mulation of tar coated the on the surface supports the hypothesis of tar mobilization and freeproduct release from the sediment. Release of free product helps to explain the speed at whichthe biosurfactant-enhanced polymer partition system operates (hr) as opposed to other poly-mer partition processes that rely upon diffusional mass transfer (days).

Because the river sediment is a suspension of coal tar, water and solids, the followingremoval mechanism is proposed: the surfactant reduces interfacial tension between the tarand water, mobilizing it from solids and creating an emulsion. Once released, tar preferen-tially sorbs onto polystyrene foam since it contains 96% aromatic carbon (Kershaw andBlack, 1993). Pi stacking (p–p interactions) between polystyrene phenyl groups and tar pro-duce dispersion forces resulting in efficient sorption of tar from the emulsion; see Figure 4.Because tar is adsorbed as a thin liquid layer, a relatively large volume of adsorbent is neededto reduce tar content by 81%. In contrast, a tenfold decrease in polystyrene mass is needed toextract similar amounts of heavy oil-impacted soil, work which will be presented in a forthcoming publication. The difference in the amounts of polystyrene needed is due to the differ-ences in aromatic and aliphatic content in the heavy hydrocarbon matrices.

Field test and cost estimate

Experiments were conducted in the field to evaluate system performance and assess practicalaspects of deployment. A portable cement mixer was used to treat 25 kg of sediment. Wechose this mode of agitation because it was consistent with lab-scale experiments, simple to

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Figure 3. River sediment (A) before and (B) after treatment at optimum conditions. Polystyrene foam pel-lets (C) before and (D) after treatment. SEM images of the foam (E) before and (F) after treatment, reflec-tion due to thin film of surface tar.

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employ in the field, and minimized on-site impacts. Experiment 16 was chosen because ituses the minimum volume of PFP and mixing time to obtain a 50% removal rate. Very closeagreement was obtained among field (48 § 10%, n D 3) and lab (51%) experiments, andmodel predictions (46%). These results demonstrate the accuracy of the model to predictoutcomes and treatment tradeoffs.

The high cost and inefficacy of biosurfactants has been a major drawback toward wide-spread industry adoption (Marchant and Banat, 2012). In our case, the sorbent increases effi-ciency while reducing the amount of biosurfactant needed to achieve a stated hydrocarbonreduction, which makes the process cost-competitive. Table 4 lists the cost estimate forremediation of the river shown in Figure 1. The estimate of »$3.7 million is based on anactual coal tar remediation project based on discussions with contractors for the site ownerand the USEPA. These include the operational cost of dredging, stabilizing, and landfilling10,551 m3 of sediment at the processing rate of 115 m3/day. Since the surfactant/polymersystem is a high-throughput process, the cost to treat the same material at 229 m3/day is»$2.5 million for a total cost savings of »$1 million, or 28%. The model was used to predicta system operating condition that would lead to an 80% removal rate. Recall the optimumremoval of 81% was achieved using 2% CT1 (model predicted 83%). The cost estimate in thetable is based on model predictions of 80% extraction efficiency under the following condi-tions Mvolume:Smass 2.5:1 mL/g (0.5), CT1 1% (0), PFPmass:Smass 0.065:1 g/g (1), and mixingtime 2 hr (1), which we achieved in the lab. Since CT1 volume is a significant cost driver, themodel was used to trade extraction efficiency versus cost/benefit.

Additional drivers include the cost of equipment, sorbent, process water and cleanup. Fordig and haul, equipment and labor are variable costs and included in the disposal and back-fill charges. For the surfactant/polymer process, labor and equipment are shown separately,which include scalper/desander, oil/water separator, mixing tank, centrifuge, and foam ther-mal densifier. Fixed costs are time dependent. For example, the longer it takes to completethe project the higher the engineering oversight and air monitoring costs will be.

The surfactant/polymer process concentrates tar onto polystyrene, which can be heated(Rashid and Sarker, 2013) or blended with oil (Zhang et al., 2009) to produce fuel oil.

Figure 4. Illustration of p–p attractive forces between coal tar components (e.g., PAH) and polystyrene.

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Table4.

Costcomparison

betweendig-and-haulland

fillandthebiosurfactant-enhanced

polymer

partition

system

.

Processun

itsStandard

process

GSprocess

Sitesedimentvolum

em

310,551

10,551

Anticipated

rate

m3 /day

115

229

Constructio

ntim

eweeks

189

Costsaving

sPercentsavings

(%)

Testcase

constructio

ncosts

$3,689,575

$2,651,499

$1,038,076.21

28Fixedcost(timedepend

ent)

$1,383,974

$691,987

$691,987.00

50Mobilizatio

n/demobilizatio

n,constructio

n$1,005,974

$502,987

$502,987.00

50Engineeringoversigh

tand

airm

onito

ring

$378,000

$189,000

$189,000.00

50Variablecost(treatmentd

ependent)

$2,309,262

$1,959,512

$349,750.03

15

Standard

process

GSprocess

Standard

process

GSprocess

Descriptio

nUnit

Unitp

rice

units

units

cost

cost

Costsaving

sPercentsavings

Watertreatm

ent

L$0.03

2,110,213

5,638,401

$66,902

$178,760

¡$111,858.01

¡167

Backfill

tonne

$5.51

16,272

0$89,702

$0$89,702.32

100

Disposal:Non-hazardous

sediment

tonn

e$66.15

11,390

0$753,473

$0$2,058,456.45

96Disposal:Hazardous

sediment

tonne

$286.66

4,881

0$1,399,184

$0Disposal:Tarand

polystyrenefoam

tonn

e$66.15

01,424

$0$94,201

CT1biosurfactant

L$5.28

0116,419

$0$615,160

¡$615,159.84

n/a

Recycled

polystyrenefoam

tonne

$468.58

0814

$0$381,422

¡$381,422.27

n/a

Equipm

entand

labor

m3

$65.39

010,551

$0$689,969

¡$689,968.61

n/a

Note:Costestim

ates

arebasedon

pilotp

rojectdiscussionswith

siteow

ners,EPA

andcontractors.

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Moreover, since tar is separated from sediment, the non-leachable solids can be used as abeneficial reuse material or transported off-site as non-hazardous waste. By heating the tar-sorbed polystyrene on-site to its glass transition temperature (»100�C), sorbent volume isreduced by 85%. Further savings are based on recycling water reclaimed from the sedimentafter centrifugation, which was shown to remove, on average, 75% of water from the soil.Noteworthy is that none of these estimates include potential cost recovery from re-sellingreclaimed tar/polystyrene for fuel.

Model predictions can be used to estimate remediation costs. If for example, capping thesediment was acceptable at a 50% tar reduction, 50% cost savings would accrue compared todig-haul and landfill, including capping costs since the remediation project approved for theriver included the addition of sand, clay and cap separately. In this case, operating conditionswould be Mvolume:Smass 2.2:1 mL/g (0.2), CT1 0% (¡1), PFPmass:Smass 0.040:1 g/g (0.1), andmixing time 2 hr (1). On the other hand, should > 90% extraction be desired, not onlywould costs savings fall to 5%, time is increased to process the tar/surfactant emulsionthrough two polystyrene batches.

Conclusion

The development of cost-effective green remediation technologies is a critically importantnext step in improving hazardous waste site cleanup projects, especially for coal tar andcrude oil sites. The CT1/polystyrene foam partitioning system is promising in this regard forits efficiency and because it utilizes only recycled and renewable materials. The model pro-vides site owners and regulators with an accurate estimate of heavy hydrocarbon removalrates under different cost/benefit conditions, while providing a safety net compared to cap-ping only by removing the bulk of the hydrocarbon mass before returning the sediment tothe location it came from.

CT1 is a grain extract, produced from agricultural crops including hemp and corn. Workis in progress to compare this biosurfactant mixture and other surfactants for both coal tarand crude oil extraction efficiencies in sediment and soil. Polystyrene is an appealing mate-rial as it has strong sorption characteristics and floats in water, aiding removal of contami-nants from the reactor. It is also an abundant waste product; although more than 2.5 milliontons was classified as solid waste in 2009, <1% was recycled, in part due to low demand(CIWMB, 2004; USEPA, 2009). Importantly, the use of sustainable extractants and recycledpolystyrene foam meets greener cleanup objectives outlined by the U.S. Environmental Pro-tection Agency (USEPA, 1999) and American Society for Testing and Materials (ASTM,2013).

Acknowledgments

The authors thank Shimadzu Corporation for gifting GC/MS, Ni Source, and Haley & Aldrich, whoprovided the means to collect volumes of river sediment and conduct on-site experiments on theprocess.

Funding

The authors appreciate the financial support provided by Tufts University and GreenStract, LLP.

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Disclosure

Albert Robbat and Christian Zeigler are minority shareholders (<5% each) in GreenStract, LLP.

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