Biochar and Activated Carbon for Enhanced Trace Organic

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Biochar and Activated Carbon for Enhanced Trace Organic Contaminant Retention in Stormwater Infiltration Systems Bridget Anne Ulrich, Eugenia Im, David Werner, and Christopher P. Higgins Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b00376 • Publication Date (Web): 24 Apr 2015 Downloaded from http://pubs.acs.org on May 3, 2015

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Environmental Science & Technology

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Manuscript

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BIOCHAR AND ACTIVATED CARBON FOR ENHANCED TRACE ORGANIC

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CONTAMINANT RETENTION IN STORMWATER INFILTRATION SYSTEMS

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Bridget A. Ulrich,† Eugenia A. Im,† David Werner,§ and Christopher P. Higgins†,* †

Department of Civil and Environmental Engineering Colorado School of Mines, Golden, CO §

School of Civil Engineering and Geosciences Newcastle University, Newcastle upon Tyne, United Kingdom Submitted to Environmental Science & Technology *To whom correspondence should be addressed. E-mail: [email protected] Phone: (720)-984-2116 Fax: (303)-273-3413 Number of pages: Number of figures: Number of tables: Number of words:

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ACS Paragon Plus Environment

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ABSTRACT

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To assess the effectiveness of biochar and activated carbon (AC) for enhanced trace

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organic contaminant (TOrC) retention in stormwater infiltration systems, an approach

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combining forward-prediction modelling and laboratory verification experiments was

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employed. Batch and column tests were conducted using representative TOrCs and

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synthetic stormwater. Based on batch screening tests, two commercially available

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biochars (BN-biochar and MCG-biochar) and an AC were investigated. The AC

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exhibited the strongest sorption, followed by MCG-biochar and BN-biochar. Langmuir

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isotherms provided better fits to equilibrium data than Freundlich isotherms. Due to

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superior sorption kinetics, 0.2 wt% MCG-biochar in saturated sand columns retained

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TOrCs more effectively than 1.0 wt% BN-biochar. A forward-prediction intraparticle

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diffusion model based on the Langmuir isotherm adequately predicted column results

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when calibrated using only batch parameters, as indicated by a Monte Carlo uncertainty

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analysis. Case study simulations estimated that an infiltration basin amended with F300-

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AC or MCG-biochar could obtain sorption-retarded breakthrough times for atrazine of 54

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years or 5.8 years, respectively, at a 1 in/hr infiltration rate. These results indicate that

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biochars or ACs with superior sorption capacity and kinetics can enhance TOrC retention

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in infiltration systems, and performance under various conditions can be predicted using

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results from batch tests.

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INTRODUCTION

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Urbanization has degraded the quality and quantity of urban water resources, and

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ineffective stormwater management has played a major role.1 Flooding and erosion have

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worsened as urban landscapes have become less permeable.2 Further, urban stormwater


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runoff has contaminated receiving waters.3 While occurrence and treatability of metals

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and nutrients has been widely assessed,4,5 less information is available for trace organic

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contaminants (TOrCs). It is clear that TOrCs are widely present in urban runoff at

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harmful levels.6,7 For example, multiple studies have detected the herbicides diuron and

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atrazine at μg/L levels in roadside and roof runoff, respectively.7–9

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Distributed stormwater treatment presents an opportunity to mitigate

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contamination to receiving waters while potentially augmenting water supplies.1,10 For

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example, stormwater infiltration basins, where runoff is infiltrated rapidly through sand-

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based media, have become a popular urban stormwater best management practice.11

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However, some contaminants, polar TOrCs in particular,12 may pass through these

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systems. For example, less than 50% removal of atrazine was observed during in situ

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challenge tests for a field-scale vegetated bioinfiltration basin.13 This suggests that

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sorption by conventional organic amendments (i.e. mulch and compost) and naturally

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developed biological material does not achieve adequate retention of polar, recalcitrant

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TOrCs.

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Engineered sorbents are a promising means of enhancing TOrC retention. Black

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carbons (BCs), such as activated carbon (AC) and biochar, are especially attractive

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because their high sorptive affinities enable even small amounts to achieve significant

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retention. The BC particle size will be an important performance parameter, as larger

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particles allow faster infiltration while smaller particles allow faster sorption kinetics.

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However, BC particle size will be difficult to control during construction. The amount of

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BC added to base infiltration media (i.e., the BC dose in sand) presents a more plausible

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design variable, as well as a performance trade-off: an increased dose of BC will increase


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TOrC retention but also may decrease hydraulic conductivity.14 Potential advantages of

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both biochar and AC make it unclear which is best-suited for this application. Biochar

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more effectively retains heavy metals,15 acts as a sink for atmospheric carbon dioxide,16

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and may be less expensive than AC if produced from waste material.17 However,

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adequate TOrC retention may be maintained at lower AC doses due to its higher sorption

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capacity, potentially allowing faster infiltration.

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BC sorption of commonly detected stormwater TOrCs, such as diuron,18

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benzotriazole,19 and atrazine,18,20 has been widely studied in batch experiments. Sorption

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capacity and isotherm nonlinearity increase with production temperature, pore volume,

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and surface area (SA),21–23 and dissolved organic carbon (DOC) suppresses sorption by

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pore blockage.24–27 In contrast, the effects of BC on TOrC transport in porous media have

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been less widely studied, and previous studies have focused on hydrophobic TOrCs.28–30

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Strong kinetic sorption effects have been observed in sediments containing BC, which

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have been attributed to intraparticle diffusion.31,32 Considering that kinetic effects

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increase for shorter distances and faster flows,28 kinetic limitations may be especially

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relevant for infiltration systems.

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The objective of this study was to verify a forward-prediction model for the

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sorption-controlled retention of TOrCs in BC-amended sand, such that TOrC

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breakthrough times for BC-amended infiltration basins could be predicted. It was

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hypothesized that significant retention of polar TOrCs could be achieved at high

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infiltration rates and low BC doses, despite fouling by DOC, and further, that kinetic

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effects could be described by sorption-retarded intraparticle diffusion. The following

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three questions were addressed: (1) Can the TOrC sorption performance of commercially


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available BCs be assessed using simple batch experiments? (2) Which forward-prediction

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transport models, when calibrated using parameters obtained only from batch

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experiments, best predict TOrC retention in BC-amended sand? (3) Can BC dose be

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adjusted in infiltration basins to simultaneously achieve effective TOrC retention and fast

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infiltration? To address these questions, a series of batch and column tests were

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conducted using commercially available BCs, synthetic stormwater containing DOC, and

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representative polar stormwater TOrCs.

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EXPERIMENTAL SECTION

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Materials. Eighteen BCs (characterization and production details in Tables S1 and S2)

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were initially screened (described below), and two commercially available biochars and

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one AC were selected for further evaluation in model verification experiments. The two

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wood-based biochars were a pyrolysis biochar produced by Biochar Now in Berthoud,

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CO (BN-biochar; 108 m2/g SA) and a gasification biochar produced by Mountain Crest

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Gardens in Etna, CA (MCG-biochar; 318 m2/g SA). The AC was Calgon Filtersorb® 300

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(F300-AC; 883 m2/g SA). BC SA and pore volume were determined by N2 adsorption,

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and solid densities (dBC) were estimated by helium adsorption, both at 77 K using an

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automated surface area analyzer. Micropore volume was determined from N2 adsorption

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data using the t method.33 BC intraparticle porosity (pBC) was estimated gravimetrically

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by water uptake. Small BC particle diameters (dpart) were used to achieve homogeneous

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surface properties (less than 246 μm for screening experiments, between 53 and 246 μm

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for model verification experiments), and BCs were rinsed with deionized water and air

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dried to remove aggregated small particles. The suite of representative stormwater TOrCs

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included 2,4-diphenoxyacetic acid (2,4-D), benzotriazole, atrazine, diuron, oryzalin,


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tris(3-chloro-2-propyl)phosphate (TCPP), prometon, and fipronil (properties and standard

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sources in Table S3), and was selected according to occurrence in urban stormwater,

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recalcitrance, and mobility.6

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TOrC Analysis. Aqueous TOrC concentrations were quantified by liquid

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chromatography – tandem mass spectrometry (LC-MS/MS), using a 1200 Series binary

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pump (Agilent), a CTC Analytics HTS PAL autosampler (LEAP Technologies) with a 1

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mL sample loop, a 150 x 46 mm Luna C18 column with a 5 µm particle size

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(Phenomenex), and a 3200 QTrap MS/MS (ABSciex). TOrCs were analyzed by

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electrospray ionization (ESI) in positive or negative mode (details in Table S4). Eluents

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prepared with HPLC-grade water and OptimaTM methanol (Fisher Scientific) were

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pumped at an overall flow rate of 0.8 mL/min throughout the gradient method. The ESI

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positive eluent contained 2 mM ammonium formate (Sigma Aldrich) and 0.1% formic

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acid (Fluka), and the ESI negative eluent contained 4 mM ammonium acetate

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(Mallinckrodt). Isotope dilution was performed as described elsewhere.34 Briefly,

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aqueous aliquots were diluted by 10% with a solution of methanol containing 4 μg/L

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isotope surrogate, such that the final surrogate concentration was 0.4 μg/L.

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General Methodology for Batch Experiments. Batch experiments were carried out in

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triplicate in amber bottles containing BC and synthetic stormwater. Synthetic stormwater

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was prepared from deionized water and salts to obtain total carbonate, nitrogen, and

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phosphorus levels representative of event mean concentrations in urban runoff (Table

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S5).3 Its pH was initially adjusted to 7, and moderate buffering capacity from 1 mM

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bicarbonate allowed observation of effects from BC-induced pH changes. The synthetic

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stormwater was either “clean” or contained added DOC (source varied, described below).


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The volume of synthetic stormwater (200 mL or 400 mL) and mass of BC (1 mg to 30

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mg) were adjusted to achieve a fractional TOrC uptake between 0.2 and 0.8 and limit

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uncertainty in calculated partition coefficients (Kd, Calculation S1).35 Sodium azide was

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also added to the synthetic stormwater to prevent biodegradation and biological fouling.

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BCs were pre-equilibrated in synthetic stormwater overnight to simulate abiotic fouling,

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then TOrCs were added to batches with a methanol carrier, such that methanol

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concentrations were less than 0.1 vol%. During sample collection, 1530 μL aliquots were

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diluted with isotope surrogate solution, centrifuged, and transferred to autosampler vials.

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Additional methodology details (i.e., pH effects, QA/QC) are provided in the supporting

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information (Method S1).

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Batch Screening. Batch screening experiments were conducted to identify commercially

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available biochars to represent a realistic range of performance in model verification

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experiments, using a reproducible and broadly applicable method for BC selection. Only

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wood-based biochars were evaluated due to their commercial availability and

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demonstrated sorption capacity,17 and F300-AC was evaluated in tandem to set a

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performance benchmark. A short equilibration time of 5 days was used to simultaneously

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assess effects of kinetics and capacity, and the initial aqueous TOrC concentration (Cw,0)

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was 10 μg/L. Initial tests were conducted in clean synthetic stormwater for 17 biochars

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and F300-AC. Eight biochars that spanned the observed performance range were

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subsequently tested using a reproducible ‘standard’ synthetic stormwater, which

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contained 20 mg/L Suwanee River Natural Organic Matter (SRNOM; International

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Humic Substances Society, St. Paul, MN) and 500 mg/L sodium azide. BN-biochar and


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MCG-biochar were selected for further evaluation because they spanned the full range of

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observed performance.

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Model Calibration. Batch kinetic and isotherm experiments were conducted in a

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representative matrix to obtain calibration parameters. Experiments were carried out over

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67 days, and bottles contained 3 mg MCG-biochar, 25 mg BN-biochar, or 1 mg F300-AC

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in 200 mL ‘representative’ synthetic stormwater (elemental composition in Table S6)

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containing 200 mg/L sodium azide. This matrix was prepared by adding DOC to clean

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synthetic stormwater with a concentrated stock to obtain DOC levels representative of a

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first flush event (10 – 30 mg/L).36 Five gallons of runoff were collected during an August

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storm from a residential curb in Golden, CO (DOC 2.5 mg/L; Sievers TOC Analyzer),

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equilibrated for one month with 53 g aspen leaves and 100 g grass from the bank of Clear

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Creek (Golden, CO), and 450 g plant-based EcoGro compost (A1 Organics; Eaton, CO),

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then filtered to 0.45 μm (118 mg/L DOC). For kinetic experiments, sampling occurred

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every 1 – 14 days, and the Cw,0 and DOC were 10 µg/L and 10 mg/L, respectively. For

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isotherm experiments, Cw,0 ranged from 3 – 100 µg/L, and the DOC was 30 mg/L. To

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further evaluate DOC effects, sorption kinetics were also assessed for MCG-biochar at 30

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mg/L DOC, and 67-day Kd values were also determined at 10 mg/L DOC and 10 µg/L

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Cw,0 for the three BCs. Only ESI positive TOrCs were analyzed in model calibration and

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verification experiments due to constraints with respect to sample quantity, and models

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were not calibrated for diuron because its sorbed fraction was greater than 0.8 in all

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batches.

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Parameters describing equilibrium sorption as either linear or nonlinear were obtained from the equilibrium data. Linear Kd values (Calculation S1) were averaged


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from the linear isotherm regions, and best-fits to the Langmuir (Equation S1) and

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Freundlich isotherms (Equation S2) were obtained using Igor Pro.37 As the residual mean

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square error (RMSE) demonstrated better fits for the Langmuir isotherm (Table S7), the

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nonlinear sorption parameters were the Langmuir coefficient (Kl) and maximum TOrC

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concentration on the BC (Cs,max). Further, two kinetic parameters were obtained from the

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kinetic data. Tortuosity was used to describe intraparticle diffusion kinetics, as obtained

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from the best-fit of a sorption-retarded diffusive uptake model (Equation S3).38 A first-

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order rate for TOrC mass transfer to the BC was also estimated by Equation S4. In

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addition to these sorption parameters, transport models were also calibrated for BC

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particle properties (pBC, dBC, dpart) and column properties (porosity, n; dispersion

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coefficient, Edisp; described below).

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Transport Models. Four forward-prediction models describing one-dimensional,

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sorption-retarded solute transport in porous media were evaluated: (I) a model assuming

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local sorption equilibrium (Equation S5); (II) a model assuming first-order sorption

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kinetics (Equation S6); and two models describing sorption-retarded intraparticle

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diffusion kinetics, assuming (III) linear sorption, and (IV) Langmuir sorption (Equation

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S7). Models I – III have been previously verified for sediments containing BC.28 Model

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IV uses the same partial differential equations as Model III, with modifications to account

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for Langmuir sorption (Equation S8).

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Model Verification. Column experiments were conducted with 15 cm length x 2.5 cm ID

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Kimble-Chase Kontes Chromaflex columns (Fischer Scientific) and a standard sand

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(Sigma Aldrich; 2.54 cm3/g solid density) with 210 – 297 μm diameter particles, such

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that the ratio of column diameter to particle size exceeded the minimum value of 50


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recommended to limit boundary effects.39 A 12 cm portion of each column was dry-

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packed with a sand-BC mixture, which was fixed between 2 cm sand plugs at the inlet

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and outlet. There was no visible BC particle migration in the outlet sand plugs. Different

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doses were used for each BC (0.4 wt% F300-AC, 0.2 wt% MCG-biochar, 1.0 wt% BN-

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biochar) due to wide variability in TOrC retention and constraints with respect to

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experimental duration. A homogeneous F300-AC distribution could not be achieved at a

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lower dose, thus effluent concentrations (Cw,effluent) were much lower and results are

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reported as breakthrough pore volumes (Cw,effluent above quantification limit, 0.025 µg/L).

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The porosity of the BC-amended column portion (nBC 0.35 ± 0.1, Table 1) was estimated

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gravimetrically after saturation, and corrected to account for the porosity in the sand

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plugs (nsand 0.32).

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Columns were flushed with CO2 and then deionized water in upflow to ensure

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saturation. Tracer tests were carried out to confirm that early breakthrough did not occur

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using KBr as a conservative tracer and a Hach® conductivity meter. Edisp values were

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obtained from best-fits to tracer data using Hydrus 1D (Table 1). Control columns

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without BC confirmed less than 5% loss of TOrCs to column components. A

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multichannel peristaltic pump delivered influent at 0.42 mL/min (2.1 ± 0.2 in/hr or 5.3 ±

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0.5 cm/hr linear velocity) to represent infiltration in a basin sized to drain in 24 hr

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(recommended rates for sizing calculations are 4.9 and 1.3 in/hr for sand and loamy sand,

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respectively).40 A pulsed influent profile was carried out to observe kinetic effects (i.e.,

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fronting and tailing during breakthrough and desorption, respectively). Columns were

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equilibrated overnight with representative synthetic stormwater (10 mg/L DOC, 200

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mg/L sodium azide) to allow pre-equilibration with DOC, pulsed with TOrCs (Cw,influent


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20 µg/L) for 4 days, then flushed with TOrC-free representative synthetic stormwater for

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8 days. Effluent samples were collected at 1 – 30 pore volume intervals throughout the

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pulse and desorption phases, and isotope dilution was carried out directly in autosampler

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vials. The effluent pH ranged from 7.2 – 8.5, and varied by less than 0.1 log units

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throughout the experiments.

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Transport models were verified by comparison of column data and model

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predictions, based on a Monte Carlo analysis evaluating the uncertainty of outputs from

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Models III and IV, as described elsewhere.28 Briefly, the standard deviation of the model

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output was determined after 50 simulations, using parameter inputs which were normally

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distributed around their means with estimated (30% of the mean parameter) or

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experimental standard deviations (Table 1). Uncertainty bounds were generated by

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adding and subtracting the model-output standard deviation from the mean-parameter

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output.

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Case Study. Case study simulations were carried out to predict sorption-controlled

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atrazine breakthrough times for an infiltration basin amended with F300-AC or MCG-

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biochar, and to assess the potential performance trade-off associated with increased BC

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dose (increased TOrC retention and decreased hydraulic conductivity). Ranges of

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possible BC doses were identified by setting minimum specifications for two cases: (Case

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1) a maximum breakthrough time (when 95% removal can no longer be maintained) at a

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minimum infiltration rate of 1 in/hr, and (Case 2) a maximum infiltration rate for a

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minimum breakthrough time of 1 year. Simulations with Model IV were carried out for

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an infiltration basin with an underdrain, such that the infiltration rate was the unsaturated

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hydraulic conductivity. The dependence of saturated hydraulic conductivity on BC dose


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was estimated (Calculation S2) according to results from falling head tests (Method S2)

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and previously observed trends,14 using sand dosed with F300-AC and MCG-biochar.

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The unsaturated hydraulic conductivity was estimated assuming 90% saturation

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(Calculation S3).41,42 The simulated basin was sized to 103 m2 to allow 12 hr drainage of

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50% runoff volume from a 3 acre catchment during an 80th percentile storm (Calculation

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S4).40 Annual treatment rates for a catchment receiving 16 inches of precipitation per

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year were estimated as 65 pore volumes and 78 pore volumes for basins with depths of 3

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feet and 1 foot, respectively (Calculation S5). To account for long-term fouling,

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Langmuir parameters were approximated as half of experimental values, and tortuosity

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was doubled (Table S12).24,43 An average BC dpart of 1 mm was simulated to represent

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increased kinetic effects for larger particles. To account for channeling, the estimated

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pore volumes to breakthrough was halved to approximate a 50% bed contact volume

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(30% reduction previously observed for a conservative tracer).44

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RESULTS AND DISCUSSION

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Batch Screening. Batch screening tests revealed trends in the 5-day LogKd dependent on

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TOrC LogKow and BC surface properties, and the presence of DOC weakened trends.

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Representative results are shown in Figure 1 (additional results in Table S8). LogKd

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values increased with TOrC LogKow, and LogKd was suppressed in the presence of 20

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mg/L SRNOM (Figure 1A). Diuron had the highest LogKd, though its LogKow is similar

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to the other TOrCs, while 2,4-D had the lowest LogKd. Benzotriazole also had a

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relatively high LogKd despite a low LogKow, particularly for BN-biochar. Among the

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different BCs, significant correlations (Pearson p-values < 0.05, Table S9) with LogKd

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values were observed for N2 pore volume (Figure 1B) for most TOrCs, and for SA in


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comparatively fewer cases. Further, peak LogKd values were observed for mesopore

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volumes between 0.1 and 0.2 cm3/g (Figure 1C), potentially due to counteracting effects

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of faster diffusion and fewer sorption sites in mesopores. MCG-biochar performed

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especially well, achieving LogKd values similar to F300-AC.

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These results show that previously identified TOrC sorption trends can be

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extended to commercially available biochars in the presence of DOC.22 In light of the

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difficulties predicting performance from measureable BC properties, BC selection will

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likely be most successful if based on batch tests, and the method described herein may

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provide a reproducible and broadly applicable BC selection method. Nevertheless, if

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batch tests are not possible, N2 pore volume and mesopore volume may indicate sorption

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performance.

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Model Calibration. Best-fits of the sorption-retarded kinetic model to the apparent

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LogKd (LogKd,app) for benzotriazole are shown in Figure 2A (additional results in Figure

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S1). The kinetic model fit the batch data well; the SSR for the fit to LogKd,app was below

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0.1 for all cases (Table 1). TOrC sorption to MCG-biochar appeared to reach equilibrium

300

at 30 days (no detectable change Kd,app), while sorption of some TOrCs to BN-biochar

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and F300-AC did not reach equilibrium within 67 days (atrazine and benzotriazole for

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BN-biochar; atrazine, prometon, and TCPP for F300-AC). For these cases, both

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equilibrium LogKd (LogKd,eq) and tortuosity were adjusted, resulting in LogKd,eq values

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that were 3% - 12% greater than the 67-day LogKd,app values (Figure S1). Kinetics were

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fastest (i.e. lowest tortuosity) for MCG-biochar, followed by F300AC and BN-biochar

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(Table 1). Overall, higher DOC and lower mesopore volume resulted in slower kinetics


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(Table S10), suggesting that pre-equilibration with DOC might increase intraparticle

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mass transfer resistances by pore blockage.45

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Best-fits of the Langmuir isotherm to batch data for benzotriazole are shown in

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Figure 2B (additional results in Figure S2) and Langmuir parameters are shown in Table

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1. Though the Freundlich isotherm has been widely applied for TOrC sorption to BC,46

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previous studies have primarily used clean matrices. It is likely that pre-equilibration with

313

DOC affected the sorption mechanism; pre-equilibration with humic substances has

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previously been observed to increase isotherm linearity for sorption of non-polar aromatic

315

compounds to biochar.27 The highest sorption capacity was achieved by F300-AC,

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followed by MCG-biochar and BN-biochar. LogKd values were lower in the presence of

317

30 mg/L DOC than 10 mg/L DOC, indicating that higher DOC levels suppressed sorption

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(Table S11). LogKd suppression increased with molecular size (TCPP >prometon

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>atrazine >benzotriazole), as previously observed.27 Benzotriazole and atrazine exhibited

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the greatest nonlinearity in the relevant Cw range, as indicated by high Kl and low Cs,max.

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While the isotherm parameters may be affected by the isotherm Cw range (only points

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with fsorbed >0.2 are included), all isotherms included the maximum Cw in column tests

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(20 μg/L), thus sufficiently accounting for the expected nonlinearity.

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Model Verification. Figure 3 shows representative pulsed breakthrough curves for

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columns containing (A) 1.0 wt% BN-biochar and (B) 0.2 wt% MCG-biochar. MCG-

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biochar retained TOrCs more effectively than BN-biochar. Atrazine, prometon, and

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TCPP, with LogKow values between 2.5 and 3.0, behaved similarly. Though its LogKow is

328

also in this range, diuron showed especially enhanced retention. Further, BN-biochar

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showed relatively strong retention of benzotriazole, despite its lower LogKow of 1.4. For


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columns with 0.4 wt% AC, the breakthrough order was TCPP, prometon (both at 363

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pore volumes), atrazine (370 pore volumes), and benzotriazole (428 pore volumes);

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diuron breakthrough was not observed within 428 pore volumes. These results indicate

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that BCs with high sorption capacities and fast kinetics effectively retain TOrCs, and that

334

LogKow does not indicate TOrC breakthrough order.

335

Strong kinetic effects were observed for all column experiments, indicated by

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fronting and tailing in breakthrough curves. Figure 4 shows predictions from all four

337

models for prometon using the mean parameter values shown in Table 1 (additional

338

results in Figure S3). Models I (first-order kinetic) and II (local equilibrium)

339

overestimated and underestimated retention, respectively, while Models III (diffusion-

340

limited, linear) and IV (diffusion-limited, Langmuir) were in better agreement with

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column results. Predictions were generally improved when models were calibrated using

342

tortuosity values from batches containing 30 mg/L DOC (example for atrazine in Figures

343

S4A and S4B), possibly because elevated DOC levels better represented increased

344

fouling in column systems. TCPP was the exception to this trend (Figures S4C and S4D).

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As TCPP sorption was most suppressed by DOC (Table S11), a threefold higher DOC

346

level may have overestimated fouling effects. Because kinetic parameters were not

347

additionally determined at 30 mg/L DOC for BN-biochar, the models could not calibrated

348

to account for increased fouling, and TOrC retention is over-predicted for BN-biochar.

349

To characterize the significance of discrepancies between model predictions and

350

observations, a Monte Carlo uncertainty analysis was carried out for Models III and IV.

351

Figure 5 shows that for MCG-biochar columns, most deviations from mean model

352

predictions fell within the uncertainty bounds (additional results in Figures S5 – S9). This


15


Page 16 of 29

353

agreement is notable in light of inherent differences between batch and column sorption

354

processes, and the complexity of the representative synthetic stormwater. The upper

355

uncertainty bounds were in better agreement with BN-biochar breakthrough curves

356

(Figures S6 – S9), but still over-predicted retention, suggesting that calibration at higher

357

DOC levels is necessary.

358

Trends in nonlinearity observed during isotherm experiments were reflected in

359

model predictions. Models III and IV gave similar predictions for TCPP (Figures 5A and

360

5B) and prometon (Figure S5), as expected due to their observed sorption linearity for Cw

361

up to 20 μg/L. There were more pronounced differences between predictions from

362

Models III and IV for atrazine (Figures 5C and 5D) and benzotriazole (Figures 5E and

363

5F), which exhibited stronger nonlinearity in the relevant Cw range (i.e. lower Cs,max and

364

higher Kl for sorption to MCG-biochar). Model IV predicted sharper peaks than Model

365

III, potentially indicative of BC being at or near sorption capacity. For atrazine, the

366

uncertainty bounds accounted for most deviations from the predictions for both models.

367

For benzotriazole, which showed the greatest nonlinearity in the relevant Cw range, only

368

Model IV predicted the observed sharp peak, though a comparatively larger portion of the

369

observed curve fell outside of the uncertainty bounds. These deviations may be due to

370

differences in the ionized fraction of benzotriazole between batch and column

371

experiments, since the pH was near its pKa of 8.2.

372

Case Study Simulation. Figure 6 shows the results of the case study predicting atrazine

373

breakthrough times for an infiltration basin with varying doses of F300-AC and MCG-

374

biochar, assuming that no biodegradation occurs and infiltration rate is determined by BC

375

dose. When BC dose is increased, infiltration rate decreases and atrazine breakthrough


16

Page 17 of 29


376

time increases, representing the performance trade-off associated with increased dose.

377

Infiltration basins amended with both MCG-biochar and F300-AC were predicted to meet

378

the minimum design criteria set for Case 1 (1 in/hr minimum infiltration rate) and Case 2

379

(1 year minimum breakthrough time). For F300-AC, a maximum breakthrough time of 54

380

years at a dose of 12.3 wt% AC was estimated for Case 1, and a maximum infiltration

381

rate of 4.3 in/hr at a dose of 4.9 wt% was estimated for Case 2. For MCG-biochar, a

382

maximum breakthrough time of 5.8 years at a dose of 4.5 wt% was estimated for Case 1,

383

and a maximum infiltration rate of 3.0 in/hr at a dose of 2.9 wt% was estimated for Case

384

2.

385

These results suggest that both biochar and AC could obtain atrazine

386

breakthrough times greater than five years by sorption only, and that AC may prevent

387

breakthrough for multiple decades. However, to dose a 100 m2 infiltration basin at 12.3

388

wt% AC would require approximately 11.8 tons, which may be cost prohibitive with AC

389

prices near $1500/ton.47 A dose of 5 wt% (4.8 tons) may be more reasonable. Further,

390

application of biochar may have economic benefits, as break-even costs below $250/ton

391

have been estimated for production from waste material.17 Additionally, in light of the

392

superior metals retention and carbon sequestration benefits of biochar,15,16 it is unclear if

393

this additional predicted retention makes AC more advantageous. If sufficient space were

394

available to construct a basin to drain at 1 in/hr, the longer hydraulic retention time of the

395

biochar-amended media may allow increased biodegradation and reduced kinetic effects.

396

Environmental Implications. A significant outcome of this study was the verification of

397

a forward-prediction model to describe sorption-retarded transport of TOrCs in saturated

398

BC-amended sand. This model may broadly enable the design of treatment systems for


17


Page 18 of 29

399

targeted removal of a wide variety of TOrCs under diverse operating conditions, as

400

calibration requires only batch data. Initial case study simulations predicted that either

401

biochar or AC could effectively enhance polar TOrC retention in stormwater infiltration

402

basins, providing a potentially cost-effective means of mitigating contamination of urban

403

receiving waters. Further, the described methodology for batch screening experiments

404

may provide a reproducible means of screening BCs for diverse applications. This

405

outcome may also be especially significant in light of the wide performance variability of

406

commercially available biochars evidenced in this study.

407

Additional work is necessary to address several potential opportunities and

408

challenges. Frequent media replacement and disposal may be avoided if retained TOrCs

409

can be degraded.48 Recent work has suggested that amendment of soils with biochar

410

enhances the biodegradation of some TOrCs,49 motivating further studies with

411

biologically active media. Further, difficulties predicting the effects of long-term fouling

412

by DOC, clogging, and channeling may present engineering challenges, and field

413

validation experiments will be necessary to ensure that adequate TOrC removal can be

414

achieved under actual environmental conditions.

415

Supporting information. This material is available free of charge via the Internet at

416

http://pubs.acs.org, and contains a comprehensive summary of batch and column data,

417

model outputs, calculations, and methodology details, as is referenced throughout the

418

text.

419

Author information

420

Corresponding author. Christopher P. Higgins


18

Page 19 of 29


421

Acknowledgements.

422

Science Foundation Graduate Research Fellowship under Grant No. DGE-1057607 and

423

the National Science Foundation Engineering Research Center Program under

424

Cooperative Agreement EEC-1028968 (ReNUWIt). Collaboration with Newcastle

425

University was facilitated by EPSRC grant EP/K021737/1. The authors also thank Dr.

426

David Rutherford for SA analyses and helpful discussions.

427

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566

FIGURES

567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584

Figure 1. Representative results from batch screening tests in clean and standard synthetic stormwater (SSW). (A) Five-day LogKd versus LogKow for sorption of eight TOrCs to MCG-biochar. (B) Five-day LogKd versus N2 total pore volume for sorption of fipronil to eight BCs. (C) Five-day LogKd versus mesopore volume for sorption of TCPP to eight BCs.


23


585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609

Page 24 of 29

Figure 2. Representative model calibration results for benzotriazole (complete results in Figures S1 and S2). (A) Sorption-retarded diffusion model fit to batch kinetic data. (B) Langmuir isotherm fit to batch equilibrium data. Dotted lines indicate a 95% confidence interval for the isotherm fit.

610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630


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Page 25 of 29

631 632 633 634 635 636 637 638 639


Table 1. Parameters used for model predictions, as determined from batch calibration experiments (ATZ = atrazine, BTA = benzotriazole, PRN = Prometon). The standard deviation values used for the Monte Carlo uncertainty analysis are shown in parentheses. The SSR is for the fit of the sorption-retarded diffusion model to experimental LogKd,app, and the RMSE is for the fit of the Langmuir model to experimental Cs. KINETIC

τ [-]

SSR [L/kg]

LINEAR LANGMUIR ISOTHERM LogKd

KL

Cs,max

RMSE

[L/kg]

[L/µg]

[g/kg]

[g/kg]

3.8 (0.2) 4.5 (0.2) 3.5 (0.2) 3.6 (0.1)

0.059 (0.018) 0.290 (0.087) 0.049 (0.019) 0.013 (0.010)

0.15 (0.05) 0.23 (0.07) 0.12 (0.04) 0.37 (0.11)

4.9 (0.3) 5.0 (0.2) 4.6 (0.2) 4.6 (0.1)

0.145 (0.043) 0.160 (0.036) 0.031 (0.009) 0.0027 (0.001)

1.13 (0.34) 1.14 (0.34) 1.83 (0.54) 14.8 (4.4)

5.7 (0.3) 5.5 (0.2) 5.4 (0.2) 5.4 (0.2)

0.099 (0.030) 0.113 (0.034) 0.065 (0.020) 0.068 (0.020)

6.5 (2.0) 5.1 (1.5) 5.7 (1.7) 14.0 (4.2)

COLUMN -4

Edisp x10

PARTICLE

[cm2/s]

n

pBC

[-]

[-]

[g/cm3]

dBC

1.4 (0.1)

0.36 (0.01)

0.6 (0.1)

1.5 (0.1)

3.1 (0.2)

0.34 (0.01)

0.56 (0.1)

1.7 (0.1)

3.4 (0.2)

0.35 (0.01)

0.77 (0.1)

2.0 (0.1)

BN-BIOCHAR ATZ BTA PRN TCPP

636 (191) 1546 (464) 382 (115) 442 (133)

0.0055 0.0334 0.0048 0.0500

0.010 0.010 0.012 0.144

MCG-BIOCHAR 17.1 (5.1) 28.7 (8.6) 16.4 (4.9) 8.2 (2.4)

ATZ BTA PRN TCPP

0.0080 0.0067 0.0020 0.0538

0.087 0.083 0.073 0.117

F300-AC ATZ BTA PRN TCPP

96.4 (28.9) 11.7 (3.5) 94.2 (28.3) 57.2 (17.2)

0.0848 0.0195 0.0122 0.0296

0.525 0.371 0.414 0.619

640


25


Page 26 of 29

641 642 643 644 645 646

Figure 3. Representative pulsed breakthrough curves for columns packed with (A) 1.0 wt% BN-biochar and (B) 0.2 wt% MCG-biochar, for a 100 pore volume pulse of 20 μg/L TOrCs, followed by a 328 pore volume flush. Column experiments were conducted in duplicate or triplicate, results are shown for a single experiment to improve clarity.

647 648 649 650 651

Figure 4. Observed prometon effluent profiles for columns packed with (A) 0.2 wt% MCG-biochar and (B) 1.0 wt% BN-biochar, shown with predictions using mean parameters from Models I (equilibrium), II (first-order kinetic), III (linear, diffusionlimited), and IV (Langmuir, diffusion-limited).


26

Page 27 of 29

652 653 654 655 656


Figure 5. MCG-biochar column breakthrough curves and uncertainty analysis results for Models III (linear, diffusion-limited) and IV (Langmuir, diffusion-limited), as determined for TCPP (A,B), atrazine (C,D), and benzotriazole (E,F). Solid lines and dotted lines indicate the mean-parameter output and uncertainty bounds, respectively.


27


657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675

Page 28 of 29

Figure 6. Results from a case study on atrazine breakthrough in BC-sand beds with depths between 1 foot and 3 feet for (A) MCG-biochar and (B) F300-AC. Imax and Imin are the maximum and minimum infiltration rates, and BTmax and BTmin are the maximum and minimum breakthrough times, respectively. Infiltration rates are set by BC dose, assuming that hydraulic conductivity decreases with increased BC dose. For Case 1, the solid lines represent the design constraint of a 1 in/hr Imin, and the dotted lines indicate the corresponding predicted BTmax. For Case 2, the solid lines represent the design constraint of a 1 year BTmin, and the dotted lines indicate the corresponding BC dose predicted for Imax. The shaded area indicates the feasible design region.


28

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TOC Image.


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Biochar and Activated Carbon for Enhanced Trace Organic

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