improving the soilplusveg model to ... -...
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Accepted Manuscript
Improving the SoilPlusVeg model to evaluate rhizoremediation and PCB fate incontaminated soils
Elisa Terzaghi, Melissa Morselli, Elisabetta Zanardini, Cristiana Morosini, GiuseppeRaspa, Antonio Di Guardo
PII: S0269-7491(18)31602-6
DOI: 10.1016/j.envpol.2018.06.039
Reference: ENPO 11234
To appear in: Environmental Pollution
Received Date: 10 April 2018
Revised Date: 25 May 2018
Accepted Date: 12 June 2018
Please cite this article as: Terzaghi, E., Morselli, M., Zanardini, E., Morosini, C., Raspa, G., Di Guardo,A., Improving the SoilPlusVeg model to evaluate rhizoremediation and PCB fate in contaminated soils,Environmental Pollution (2018), doi: 10.1016/j.envpol.2018.06.039.
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Improving the SoilPlusVeg model to evaluate rhizoremediation and 1
PCB fate in contaminated soils 2 3 Elisa Terzaghia, Melissa Morsellia, Elisabetta Zanardinia, Cristiana Morosinia, Giuseppe Raspab, and 4
Antonio Di Guardoa∗ 5 6 a Department of Science and High Technology (DiSAT), University of Insubria, Via Valleggio 11, Como, Italy 7 b Department of Chemical Materials Environmental Engineering (DICMA), Sapienza University of Rome, Via 8 Eudossiana 18, Rome, Italy 9 10 Abstract 11
Tools to predict environmental fate processes during remediation of persistent organic pollutants 12
(POPs) in soil are desperately needed since they can elucidate the overall behavior of the chemical 13
and help to improve the remediation process. A dynamic multimedia fate model (SoilPlusVeg) was 14
further developed and improved to account for rhizoremediation processes. The resulting model 15
was used to predict Polychlorinated Biphenyl (PCB) fate in a highly contaminated agricultural field 16
(1089 ng/g d.w.) treated with tall fescue (Festuca arundinacea), a promising plant species for the 17
remediation of contaminated soils. The model simulations allowed to calculate the rhizoremediation 18
time (about 90 years), given the available rhizoremediation half-lives and the levels and fingerprints 19
of the PCB congeners, to reach the legal threshold, to show the relevance of the loss processes from 20
soil (in order of importance: degradation, infiltration, volatilization, etc.) and their dependence on 21
meteorological and environmental dynamics (temperature, rainfall, DOC concentrations). The 22
simulations showed that the effective persistence of PCBs in soil is deeply influenced by the 23
seasonal variability. The model also allowed to evaluate the role of DOC as a possible enhancer of 24
PCB degradation as a microorganism “spoon feeder” of PCBs in the soil solution. Additionally, we 25
preliminary predicted how the contribution of PCB metabolites could modify the PCB fingerprint 26
and their final total concentrations. This shows that the SoilPlusVeg model could be used in 27
selecting the best choices for a sustainable rhizoremediation of a POP contaminated site. 28
∗ Corresponding author. E-mail addresses: [email protected] (E. Terzaghi), [email protected] (M. Morselli), [email protected] (E. Zanardini), [email protected] (C. Morosini), [email protected] (G. Raspa), [email protected] (A. Di Guardo).
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was developed and tested 30
Keywords: PCBs, soil, DOC, half-lives, bioavailability 31
1. Introduction 32
Polychlorinated Biphenyls (PCBs) are persistent organic pollutants produced for more than 50 years 33
in many countries for different uses such as electrical equipment (capacitors and transformers), 34
hydraulic and heat transfer systems, building materials (joint sealants, paint), additives (of 35
pesticides, inks, waxes, adhesives), etc. (de Voogt and Brinkman, 1989; Erickson, 1997). Their 36
production stopped in most countries by early 1980s, when several governments imposed the ban of 37
these chemicals because of an increasing concern about their environmental and human health risks. 38
PCBs, due to their physico-chemical properties, persist in the environment and bioaccumulate in the 39
food chain posing adverse effects to the environment and human health, including cancer (IARC, 40
2015). Even though PCB production ceased more than 20 years ago, these chemicals are still 41
measured in environmental samples and can be released from secondary sources such as 42
contaminated sites (Morselli et al., 2018). Several techniques (excavation, incineration, and 43
transport to landfills) are nowadays available to remediate contaminated sites; however, although 44
they could provide satisfying results in a short period of time, they are not economically affordable 45
for large areas, which require in situ treatments (Gomes et al., 2013). Therefore, in the last two 46
decades bioremediation technologies (i.e. bioaugmentation, phytoremediation, rhizoremediation) 47
have become ever more important as alternative techniques to remediate contaminated sites, being 48
less expensive, not disruptive and more suitable for large contaminated areas (Passatore et al., 49
2014). Rhizoremediation based on the plant enhancement of the microbial degrading activity in the 50
rhizosphere i.e., the soil firmly adhering to the roots, is among the most important bioremediation 51
processes for contaminations by hydrophobic organic chemicals such as PCBs (Vergani et al., 52
2017). Many studies have been conducted to investigate the potential of plant-microbe interactions 53
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Mackova et al., 2009; Teng et al., 2010; Li et al., 2013; Meggo and Schnoor, 2013; Ancona et al., 55
2017) with respect to natural attenuation i.e., natural detoxification of organic contaminants resulted 56
from biotic and abiotic processes (Lee and Davis, 2000), providing useful data such as 57
rhizoremediation half-lives (Terzaghi et al., 2018) which can be employed to predict soil 58
concentration temporal trends, as well as the time needed to achieve regulatory thresholds. 59
PCB fate in soil is also influenced by other processes including volatilization, infiltration, diffusion, 60
runoff and root uptake (Thibodeaux et al., 2011); however, their importance during a 61
rhizoremediation treatment has never been evaluated. Only a dynamic multimedia fate model could 62
help to integrate all these processes and investigate their relative importance, taking also into 63
account the variability of meteorological and environmental parameters, prior to the setup of the 64
remediation plan for a contaminated site. Different “phytoremediation models” are currently 65
available (Ouyang, 2002; Sung et al., 2004; Ouyang, 2008; Manzoni et al., 2011; Canales-Pastrana 66
and Paredes, 2013); however, they are mainly applied to more polar compounds and metals, rather 67
than to hydrophobic chemicals such as PCBs, and they were developed to simulate chemical plant 68
uptake, phytovolatilization and transport in the vadose zone and aquifers rather than the enhanced 69
biodegradation process due to the plant-microbe interactions; moreover, they generally include a 70
simplified soil compartment parameterization but require a large amount of inputs (i.e. plant 71
physiological parameters) that are often difficult to obtain, increasing model complexity. Recently, a 72
full dynamic model (SoilPlusVeg model) was developed to predict the environmental fate of 73
organic chemicals in an air-vegetation-litter-soil system (Terzaghi et al., 2017). In this work, 74
SoilPlusVeg was improved in a number of ways (see 2.1) and illustrative simulations were run to 75
show the potential of this tool in driving the setup of the remediation plan of an agricultural field 76
contaminated by PCBs. We aimed to 1) compare the remediation time of the field under natural 77
attenuation and rhizoremediation, 2) investigate the loss processes from soil and their dependence 78
on meteorological and environmental dynamics (temperature, rainfall, DOC concentrations), 3) 79
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uptake from soil and air. Additionally, we preliminary tried to predict how the contribution of PCB 81
metabolites could modify PCB fingerprint and final total PCB concentrations. 82
83
2. Materials and Methods 84
2.1 SoilPlusVeg model description and improvements 85
SoilPlusVeg is a dynamic multimedia fate model, based on the fugacity approach, developed to 86
investigate the fate of hydrophobic organic chemicals in the air/vegetation/litter/soil system 87
(Terzaghi et al., 2017). It includes 1) a two-layered dynamic air compartment, namely lower air 88
(LA) and upper air (UA), representing the planet boundary layer (PBL) and the residual layer 89
respectively, which vary in height on an hourly basis; 2) a multi-layered soil, bare or covered by up 90
to three litter horizons; 3) a vegetation compartment with roots, stem and leaves. The vegetation 91
compartment can be used to simulate mono- or multi-specific forests/woods as well as shrubs and 92
herbaceous plants. In this model, the compartment capacities are expressed in terms of Z values 93
(describing the capability of the different environmental compartments to retain the chemical), 94
while transport and transformation processes are computed by means of D values (expressing 95
advective, diffusive and degradation processes) (Mackay, 2001). In the soil compartment, organic 96
chemicals can undergo different transfer and loss processes including 1) volatilization, 2) diffusion 97
up and down the soil layers, 3) infiltration (in a truly dissolved form and associated to the dissolved 98
organic carbon (DOC), 4) biodegradation and 5) root uptake. For the present work SoilPlusVeg 99
model was modified: 1) adopting the enhanced biodegradation half-lives due to plant-microbe 100
interactions as refined rhizoremediation loss process from the soil compartment (see 2.1.1), 2) 101
subdividing the root compartment into several thinner layers to accurately reconstruct the chemical 102
fate in the root-soil system (Figure A.1); root biomass was distributed in each soil layer according 103
to the Gale and Grigal approach (Gale and Grigal, 1987); and 3) including an alternative equation 104
to estimate dissolved organic carbon (DOC)-water partition coefficient (KDOC) developed for 105
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chemical in all compartments, water and organic matter in soil. The chemical mass-balance in each 107
compartment is expressed by a 1st-order ordinary differential equation (ODE) and the resulting 108
ODE system is solved using a 5th-order accurate, diagonally implicit Runge–Kutta method with 109
adaptive time stepping (ESDIRK) (Semplice et al., 2012). More details concerning model 110
parameterization can be found in the original papers (Terzaghi et al., 2017). 111
The model is written in Visual Basic 6 for Windows and available as standard Windows 112
application. 113
2.1.1 Enhanced biodegradation process: rhizoremediation half-lives 114
Enhanced biodegradation due to plant-microbe interactions was modelled considering PCB half-115
lives derived from rhizoremediation experiments (HLrhizo) reported in the literature (Terzaghi et al., 116
2018) (Table A.1). The Walker approach (Walker, 1974) was used to update rhizoremediation half-117
lives considering the actual temperature and moisture conditions of the simulated soil. Since 118
SoilPlusVeg model does not currently include a proper mass balance for PCB metabolites it was 119
assumed that degraded congeners completely disappeared from the system instead of transforming 120
to less chlorinated congeners or smaller compounds. However, a very preliminary attempt to 121
estimate PCB fingerprint temporal changes considering also the contribution of metabolites was 122
made using first order decay (see 3.2.5). 123
2.1.2 DOC-mediated infiltration process: a more realistic approach 124
In the original version of SoilPlusVeg the DOC-mediated infiltration process was modelled 125
considering the KDOC equation reported in Durjava et al., 2007; being this relationship more suitable 126
to simulate Aldrich humic acid (which is not a typical DOC), the equation reported in Burkard, 127
2000 for naturally occurring DOC was added to the current version of the model. KDOC predicted 128
with the two equations could differ up to more than two orders of magnitude. A preliminary 129
sensitivity analysis (Terzaghi et al., 2017) highlighted that both KDOC and DOC concentrations were 130
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compared focusing on the difference of the amount of PCB transported towards the deeper soil 132
layers and on PCB soil concentrations at the end of the simulation period. Although DOC turnover 133
has not been implemented yet in the soil organic carbon mass balance of the SoilPlusVeg model, 134
the seasonal variability of DOC was accounted for to rebuild a seasonal DOC production profile as 135
reported in Terzaghi et al., 2017. 136
2.2 Simulation scenario 137
Several long-term simulations (100 years) were run with the SoilPlusVeg model for the ten PCB 138
families (see Table A.2 for physico-chemical properties) to evaluate: 1) the influence of plant-139
microbe interaction in reducing remediation time for a PCB contaminated soil, comparing the 140
results obtained considering generic half-lives (HLgen) (“natural attenuation” simulations) 141
(Paasivirta and Sinkkonen, 2009) and rhizoremediation half-lives (“rhizoremediation” simulations) 142
(Terzaghi et al. 2018), 2) the effect of modelling KDOC with a more realistic equation (“Durjava” 143
simulations vs. “Burkhard” simulations) and 3) the role of DOC as possible enhancer of PCB 144
biodegradation (“DOC” vs. “NO DOC” simulations). 145
A 40 cm (5 layers of 8 cm each) loamy sand soil characterized by 0.6% organic carbon (OC) and an 146
average DOC concentration in soil water of ~15 mg/L was simulated. The model domain was set to 147
1 ha to reproduce an agricultural field of tall fescue (Festuca arundinacea), a promising plant 148
species in PCB contaminated soil bioremediation (Dzantor et al., 2000; Checkol et al., 2004; Shen 149
et al., 2009; Li et al., 2013). A below ground biomass of 1.932 kg/m2 and an above ground biomass 150
of 0.9 kg/m2 were assumed (Bolinder et al., 2002), while Specific Leaf Area (SLA) and Leaf Area 151
Index (LAI) were set to of 10 m2/kg (Woodward et al., 1983) and a of 9 m2/m2 respectively. 152
Vegetation biomass was assumed to be constant (no growth) during the simulation period to 153
evaluate the rhizoremediation processes in full growth conditions. Moreover, transpiration did not 154
cease at night. A well-mixed and homogeneous soil was simulated considering a fixed initial 155
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reproduce a real situation in term of soil concentrations was made, a PCB fingerprint similar to that 157
reported in Di Guardo et al., 2017 for an agricultural field located in a National Priority Site for 158
remediation (SIN Brescia-Caffaro, Italy) was considered (Table A.3). This field is characterized by 159
high PCB concentrations that exceed the Italian regulatory threshold for residential soil (60 ng/g 160
d.w. of total PCBs) and a fingerprint (see Table A.3 for chlorination classes) dominated (within 161
each class) by PCB 209, 153, 180, 118, 199, 66, 206, 15, 28 and 1. Simulations were run with an 162
initial total PCB concentration of 1089 ng/g dw, with PCB 209 representing the 44% of the 163
fingerprint. The air compartment structure and surface meteorological parameters (temperature, 164
rainfall and solar radiation) were parameterized as reported in Ghirardello et al., 2010. A clean air 165
compartment was considered at the beginning of the simulations (no background concentration and 166
emission to air), while PM10 background concentration was set to 30 µg/m3. The same air 167
compartment structure and meteorological scenario were used for the whole simulation period. 168
3. Results and discussion 169
3.1 Time needed to achieve a regulatory threshold: natural attenuation vs. rhizoremediation 170
The following results are presented comparing model predictions to the Italian regulatory threshold 171
for residential soil (60 ng/g d.w. of total PCBs), although different values may be available for 172
different countries/legislations and therefore time to reach such value may change. Total PCB 173
concentration temporal profiles (average of the 5 soil layers) over the 100-year simulation period 174
for “natural attenuation” and “rhizoremediation” simulations are shown in Figure 1. At the end of 175
“natural attenuation” simulations 181 ng/g dw of total PCB were predicted in soil, indicating that 176
100 years of simulation are not enough to reach the regulatory threshold. A different picture 177
appeared for “rhizoremediation” simulations: the regulatory threshold was satisfied just before the 178
end of the simulation period when soil concentrations reached a value of 59 ng/g d.w. at the 99th 179
year, highlighting the importance of plant-microbe interaction in reducing remediation time. 180
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62nd for the “rhizoremediation” and “natural attenuation” simulations respectively) (Figure A.2) 182
since this layer is mainly characterized by chemical losses rather than gains (it does not receive 183
chemicals through infiltration and the amount deposited from air and leaves is very small). 184
Focusing on the temporal evolution of PCB fingerprint in the “rhizoremediation” simulations, low 185
chlorinated PCBs (mono to penta) reached values lower than the regulatory threshold after just 7 186
years, while about 40 years were necessary for more chlorinated congeners excluding deca-PCB. 187
The latter represented ~90% of the fingerprint at the end of the simulations due to its high initial 188
concentrations (486 ng/g d.w.), high hydrophobicity (log Kow:8.26) and low bioavailability which 189
increases its rhizoremediation half-life (about 12 years) as supported by the negative relationship 190
between PCB concentration reductions with time (log Ct0/Ct100, where Ct0 is the initial 191
concentration, while Ct100 is the final concentration) and the log Kow of each PCB families (R2 :0.79) 192
(Figure A.3). A longer simulation (200 years) run only for PCB 209 showed that 1) about 50 193
additional years (146th year) were necessary to meet the regulatory threshold in “natural 194
attenuation” simulations considering just this congener and 2) its concentration reached values 195
comparable to those measured in a remote site located in Northern Italy at about 200 km away from 196
the SIN Brescia-Caffaro (0.031-0.364 ng/g dw) (Tremolada et al., 2015) in more than 200 years 197
considering both “natural attenuation” and “rhizoremediation” simulations. 198
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Figure 1. PCB soil concentration trend over a 100-year simulation period considering natural 201 attenuation (top) and rhizoremediation (bottom) compared with the Italian legal threshold 202
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3.2 PCB fate in the soil compartment 204
3.2.1 Enhanced biodegradation vs. other processes 205
Figure 2 shows the contribution of each loss process (enhanced biodegradation, infiltration, 206
volatilization, diffusion down and root uptake) with respect to the initial PCB amount for the 1st 207
layer of soil over the 1st year of the simulated period (“Durjava” simulations). Run-off and diffusion 208
up do not appear because they did not occur in the simulated scenario and in the 1st layer 209
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infiltration, representing the 44-87% and 4-56% of total losses from soil, respectively. Infiltration 211
encompasses both truly dissolved chemical and DOC-associated chemical: the less chlorinated 212
congeners (mono) moved through deeper soil layers mainly in a truly dissolved form, while DOC 213
mediated transport predominated for more chlorinated ones (di to deca) as expected from previous 214
field data (Moeckel et al., 2008) and modelling approaches (Terzaghi et al., 2017). 215
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Figure 2. PCB losses from the soil compartment (mol) and residue moles for the 1st soil layer 217 during the 1st year of simulation (EnhBiodeg, enhanced biodegradation; InfiltDiss, truly dissolved 218 infiltration; InfiltDOC, DOC mediated infiltration; Volat, volatilization; DiffDown, diffusion down; 219 RootUp, root uptake; Residue, residue moles) 220
For mono-PCB, volatilization and diffusion down were also important fluxes, while root uptake was 221
the least important process for all PCB families (0.02-2.3% of total losses). Additionally, the upper 222
biomass translocation accounted for 0.00001% (deca-PCB) to 7% (mono-PCB) of the initial PCB 223
amount in soil, showing that phytoextraction for PCBs (especially the most chlorinated ones) is not 224
relevant for this species. However, it was shown (Huelster et al., 1994) that other plant species 225
(such as some Cucurbita spp.) are capable to translocate to a higher extent chemicals of comparable 226
physical-chemical properties (dioxins and furans) to the upper part of the plant. 227
0%
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EnhBiodeg InfiltDiss InfiltDOC Volat DiffDown RootUp Residue
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simulation years considering the sum of total moles lost during a certain year and layer (Table A.4 229
for penta-PCBs); however, focusing on short-term variability (hourly) the contribution of each loss 230
process could vary up to two orders of magnitude, mainly depending on the variability of 231
meteorological and environmental parameters (see 3.2.2). 232
3.2.2 Temporal variability of loss processes 233
SoilPlusVeg being a dynamic model, allows to investigate the influence of meteorological and 234
environmental variability on the predicted soil concentrations as well as on the chemical losses from 235
soil and towards the adjacent compartments (air, crop and deeper soil layers). Figure 3 shows the 236
temporal trend of loss processes for penta-PCBs, meteorological parameters (temperature and 237
rainfall) and DOC concentration in soil pore-water (year 1, soil layer 1). Biodegradation, 238
infiltration, diffusion and volatilization showed a seasonal variability strictly dependent on 239
meteorological parameters and DOC concentration, while root uptake was only dependent on 240
decreasing chemical amount in soil. Volatilization was high during the hottest hours of the year (in 241
July), while infiltration was favored during rainy hours and periods of higher DOC concentrations 242
(July-September). Biodegradation was influenced by both soil temperature and water content 243
showing the highest values during hours characterized by high temperature and rainfall. Such 244
results could be relevant for the evaluation of short-term exposure of humans and ecosystems to 245
organic chemicals during a contaminated site remediation procedure. This shows that in realistic 246
conditions biodegradation could vary of up to a factor of 15 for along the year due to temperature 247
and soil humidity variations. The seasonal variability of meteorological parameters and DOC 248
concentrations justified the change in loss process relative importance mentioned in 3.2.1. For 249
example, enhanced biodegradation contribution could range from 8 to 99% while infiltration from 250
0.3% to 92% of loss processes (Table A.5) and depending on rainfall amount these two processes 251
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vs. 8% on Jan 25 with 1.8 mm/d of rain). 253
3.2.3 DOC mediated PCB infiltration: comparison of different approaches 254
Infiltration fluxes (1st layer, 1st year) estimated considering Burkard approach (Burkhard, 2000) 255
(“Burkard” simulations) were a factor of about 2 (mono-PCB) to about 430 (deca-PCB) lower than 256
those predicted considering Durjava equation (Durjava et al., 2007). This had the effect of lowering 257
PCB transport towards deeper soil layers and reducing the potential risk of groundwater 258
contamination, but of increasing soil remediation time since total PCB concentrations (average of 259
five layers) predicted at the end of the simulation period were a factor of about 2 higher (86 ng/g 260
dw) (Figure A.4) than those calculated with “Durjava” simulations. 261
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Figure 3. Penta-PCB loss fluxes (on an hourly basis) temporal trend (1st layer, 1st year) and 266 temperature, rainfall and DOC concentrations variability ((EnhBiodeg, enhanced biodegradation; 267 Infilt, infiltration; Volat, volatilization; DiffDown, diffusion down; RootUp, root uptake) 268
269 270
3.2.4 Role of DOC in degradation 271
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extractable residue (NER) derived from chemical or physical interactions of parent compounds and 273
metabolites to the soil matrix, 2) freely dissolved in soil pore-water, 3) associated to DOC and/or 274
POC in soil (Terzaghi et al., 2018). NER influences the high persistence of PCBs in soil reducing 275
their bioavailability until soil organic carbon degradation favors their remobilization (Gevao et al., 276
2000). DOC has instead a dual role, i.e., it can reduce PCB biodegradation by lowering free organic 277
chemical concentration, but it also acts as a PCB carrier moving the chemical from soil solids 278
enriching their concentrations in soil pore-water (Tejeda-Agredano et al., 2014). DOC pool, being 279
one of the main source of carbon for soil microorganisms, shows a high turnover with degradation 280
half-lives lower than 1 h in field for low molecular weight DOC (Bengtson and Bengtsson, 2007; 281
Boddy et al., 2007). Although the fate of DOC associated chemicals during DOC degradation has 282
not been investigated yet, it could be hypothesized that DOC associated PCBs would be more 283
available to PCB degrading microorganisms. For example, Fava and Piccolo, 2002 demonstrated 284
that the hydrophobic domain of humic substances could solubilize PCBs, while the hydrophilic one 285
could favor PCB-microorganism interactions; if PCB-microorganism interactions are stronger than 286
PCB-DOC interactions an increase of bioavailability and therefore biodegradability could appear. 287
When comparing simulations “DOC” vs. “NO DOC” (i.e. DOC concentration was set to 0 mg/L) it 288
was evident that the presence of DOC could increase PCB bulk concentration in soil pore-water up 289
to a factor of 1800 (compared to the simulation without DOC) and that a strong relationship 290
between this increase (ratio of bulk water concentration considering DOC and without considering 291
DOC) and chemical hydrophobicity (log KOW) existed (Figure A.5). 292
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other hand it could enormously enhance PCB disappearance from the system reducing their half-297
lives because of the potential enhanced bioavailability. In other terms DOC could act as a “spoon-298
feeder” for PCB degrading microorganisms when they are associated to the common DOC degrader 299
microorganisms, enhancing PCB degradation as well as other processes that are limited by the low 300
bioavailability of these chemicals, e.g. root uptake and /or volatilization. 301
3.2.5 PCB metabolites 302
SoilPlusVeg does not currently account for PCB metabolite evolution since the mass balance is 303
performed one chemical at a time. PCB metabolites could instead be generated under aerobic and 304
anaerobic conditions. PCB congeners with a number of chlorine ≥ 4 can undergo anaerobic 305
reductive dechlorination, resulting in PCBs with a lower number of chlorines; on the contrary PCB 306
congeners with a number of chlorine < 4 can undergo aerobic (Kim and Picardal, 2001) and/or 307
cometabolic aerobic oxidation resulting in different intermediates (dihydroxy-dihydro-308
chlorobiphenyl, dihydroxy-chlorobiphenyl, chlorobenzoates and chlorinated aliphatic acids) before 309
their complete mineralization to carbone dioxide, chlorine and water (van Aken et al., 2010). In the 310
last few decades many PCB degradation pathways were identified and some models were 311
developed to analyze changes in a soil/sediment contamination profile (fingerprint) due to microbial 312
activities (mainly reductive dichlorination) (Karcher et al., 2007; Hughes et al., 2010; Johnson and 313
Book, 2014; Demirtepe et al., 2015). However, these models are based on the susceptibility of 314
individual PCB to undergo bacterial transformation which is related to the number and position of 315
chlorine atoms on biphenyl and ignore the influence of the environmental scenario on chemical 316
degradation as well as the other loss processes. A very preliminary attempt to estimate PCB 317
fingerprint change with time (after 10th and 100th years) according to a first order kinetic model and 318
considering the contribution of metabolites is presented in Figure 4. For the calculation, these 319
worst-case assumptions were considered, i.e., the degraded moles of “n” chlorinated PCBs (where 320
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transformed to tri-PCBs and added to the total amount of tri-PCB) neglecting that 1) mono to tetra 322
PCB could undergo aerobic degradation generating other type of metabolites and that 2) the “new” 323
PCBs would volatilize, infiltrate and diffuse (e.g. considering only first order kinetics degradation). 324
The purpose of this simulation was to estimate the maximum contribution of the dechlorination 325
processes in influencing persistence of each class of congeners. Additionally, these simulations 326
were performed assuming constant rhizoremediation half-lives (not updated with soil temperature 327
and water content). When considering the contribution of metabolites, total PCB concentrations 328
were a factor of 2 and 37 higher after 10 and 100 years respectively. Fingerprints were highly 329
different: when neglecting metabolites, deca-PCB predominated after 100 years and concentrations 330
of the other congeners were negligible (Figure 4, right side); when including the metabolites in the 331
mass balance hepta- and octa-PCBs showed the highest concentrations, followed by nona- and 332
hexa-PCBs and the less chlorinated ones (penta- to mono-PCBs (Figure 4 left side). 333
334
335
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336
337
Figure 4. PCB (white) and their metabolites (grey) soil concentration (ng/g d.w.) at different simulation times including (left) and neglecting (right) 338 PCB metabolites in the calculation (0 stands for <0.001) 339
0.8 7 12 2794
204 17277
11
483
0
200
400
600
MONO DI TRI TETRA PENTA HEXA HEPTA OCTA NONA DECA
PC
B c
once
ntra
tion
(ng/
g dw
)
PCBs
22 24 38 34 35 5078 76
155
263
0
100
200
300
MONO DI TRI TETRA PENTA HEXA HEPTA OCTA NONA DECA
PC
B c
once
ntra
tion
(ng/
g dw
)
PCBs PCB metabolites
0.6 0.71.2 1.3 1.6
2.7
5.1 5.0
2.8
0.60.00
2.00
4.00
6.00
MONO DI TRI TETRA PENTA HEXA HEPTA OCTA NONA DECA
PC
B c
once
ntra
tion
(ng/
g dw
)
PCBs PCB metabolites
0.8 7 12 2794
204 17277
11
483
0
200
400
600
MONO DI TRI TETRA PENTA HEXA HEPTA OCTA NONA DECA
PC
B c
once
ntra
tion
(ng/
g dw
)
PCBs
0 0 0 0 0.1 740 29 5
263
0
100
200
300
MONO DI TRI TETRA PENTA HEXA HEPTA OCTA NONA DECA
PC
B c
once
ntra
tion
(ng/
g dw
)
PCBs
0 0 0 0 0 0 0 0 00.6
0.00
2.00
4.00
6.00
MONO DI TRI TETRA PENTA HEXA HEPTA OCTA NONA DECA
PC
B c
once
ntra
tion
(ng/
g dw
)
PCBs
YR1
YR10
YR100
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340 341 3.3 PCB concentration in air and vegetation (leaves, stem, roots) 342
343 The SoilPlusVeg model allows to understand the influence of the contaminated soil on the 344
surrounding compartments (air and vegetation) during the whole remediation period, investigating 345
for example the 1) air concentration temporal trend due to the potential soil degassing and the 2) 346
bioaccumulation in vegetation biomass and therefore the amount that can potentially enter the food 347
chain. The first 10 years of the simulation were characterized by the highest PCB levels in all 348
compartments with maximum concentration of 10 pg/m3, 6 ng/g dw, 0.3 ng/g dw and 185 ng/g dw 349
in air, leaves, stem and roots respectively; these concentrations significantly decreased in the 350
following years due to the increasingly PCB loss from the soil compartment (the only PCB source 351
in the system) with time (see also 3.2). Due to their direct contact with the highly contaminated soil, 352
roots showed the highest concentrations; on the contrary, leaves (which mainly accumulated these 353
organic contaminants from air, as confirmed by the similarity in their fingerprints, Figure 5), 354
showed lower concentrations than roots, but higher than stem which was not easily reached by these 355
poorly translocated hydrophobic compounds. In Figure 5 PCB fingerprint in air, leaves, stem, 356
roots and soil after 1, 50 and 100 years are reported for comparison. PCB fingerprints varied, not 357
only among compartments, but also with time enriching with high chlorinated and most persistent 358
congeners at the end of the simulation period. 359
360
361 362 363 364 365 366
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367 368
Figure 5 - PCB concentrations in air (ng/m3), leaves (ng/g dw), stem (ng/g dw), roots (ng/g dw, 369 average of 5 layers) and soil (ng/g dw, average of 5 layers) after 1, 50 and years 370
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371
4. Conclusions 372
The simulations ran with the improved version of SoilPlusVeg highlighted the potential role of this 373
model in evaluating PCB soil concentration temporal trend in a field due to natural attenuation 374
and/or rhizoremediation. In particular, it was shown that the remediation time of the simulated soil 375
was significantly reduced in the “rhizoremediation” simulation, showing the importance of plant-376
microbe interactions in lowering PCB half-lives in soil and therefore the time required to satisfy 377
regulatory threshold. It was also shown that enhanced biodegradation and infiltration (especially 378
DOC-associated) were the most important loss processes, while root uptake was negligible. 379
However, the relative importance of each loss process could vary up to two orders of magnitude 380
considering the short-term variability, being loss processes influenced by the dynamics of 381
meteorological parameters (temperature and rain) and DOC concentrations. Therefore, SoilPlusVeg 382
model could help in evaluating the performance of phytoremediation under different scenarios 383
(changing climate, water availability, etc.), considering both spatial and temporal variability of 384
those parameters that may affect chemical fate in soil (organic carbon, DOC, temperature, water, 385
root biomass, etc.). It also allows to predict the fluctuations of the degradative ability of the soil 386
microbial community over the whole year and its variability with soil depth. Among the potential 387
improvement, SoilPlusVeg would benefit from an additional calibration of specific fluxes (such as 388
updated rhizoremediation half-lives, the role of DOC, its composition and concentration on 389
enhancing mobility and modulating bioavailability of POPs); additionally, a better description of 390
DOC turnover would allow to accurately predict the fluxes dependent on its concentrations such as 391
infiltration but also biodegradation, root uptake and volatilization. 392
393
394
5 Supporting Information 395
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Supplementary data to this article can be found in the Appendix. 396
Please contact the corresponding author to obtain a copy of the SoilPlusVeg model or visit the 397
EMG (Environmental Modelling Group) site at the University of Insubria. 398
http://disat.uninsubria.it/~antonio.diguardo/data/index.html 399
6. Acknowledgements 400
The authors would like to acknowledge the collaborators of the “Caffaro Working Group”: Sara 401
Borin, Francesca Mapelli, Stefano Armiraglio, Simone Anelli, Vanna M. Sale and Paolo Nastasio 402
and the funding agency Ente Regionale per i Servizi all'Agricoltura e alle Foreste (ERSAF), 403
Decreto ERSAF n. III/5426 del 09.12.2013. Professor Bruno Cerabolini and Luca Bechini are also 404
kindly acknowledged for their help in the vegetation compartment parameterization. The 405
Department of Science and High Technology of the University of Insubria is kindly acknowledged 406
for funding part of the salary of Elisa Terzaghi. We also thank six anonymous reviewers for their 407
contribution in focusing the article and increase its readability. 408
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Highlights
1) Rhizoremediation reduced PCB remediation in soil time by a factor of about 2
2) Enhanced biodegradation and DOC mediated infiltration were the most important losses
3) The more realistic KDOC equation decreased PCB infiltration of a factor of 2 to 430
4) DOC mediated infiltration could be relevant in enhancing PCB degradation.
5) DOC increases PCB bulk water concentration acting as a “spoon feeder” for bacteria
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