Chemosphere 144 (2016) 65e74
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Review
A synthesis of parameters related to the binding of neutral organic compounds to charcoal Sarah E. Hale a, *, Hans Peter H. Arp a, Darya Kupryianchyk a, Gerard Cornelissen a, b, c a
Department of Environmental Engineering, Norwegian Geotechnical Institute (NGI), P.O. Box 3930, Ullevål Stadion, N-0806 Oslo, Norway Department of Plant and Environmental Sciences (NMBU), Norwegian University of Life Sciences, 5003 Ås, Norway c Department of Applied Environmental Sciences (ITM), Stockholm University, 10691 Stockholm, Sweden b
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
Charcoal-water partitioning coefficients (KD values) were compiled from the literature. KD values were correlated with charcoal and organic compound properties. Production temperature and surface area were most strongly correlated with KD.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 May 2015 Received in revised form 28 July 2015 Accepted 18 August 2015 Available online xxx
The sorption strength of neutral organic compounds to charcoal, also called biochar was reviewed and related to charcoal and compound properties. From 29 studies, 507 individual Freundlich sorption coefficients were compiled that covered the sorption strength of 107 organic contaminants. These sorption coefficients were converted into charcoal-water distribution coefficients (KD) at aqueous concentrations of 1 ng/L, 1 mg/L and 1 mg/L. Reported log KD values at 1 mg/L varied from 0.38 to 8.25 across all data. Variation was also observed within the compound classes; pesticides, herbicides and insecticides, PAHs, phthalates, halogenated organics, small organics, alcohols and PCBs. Five commonly reported variables; charcoal production temperature T, surface area SA, H/C and O/C ratios and organic compound octanol ewater partitioning coefficient, were correlated with KD values using single and multiple-parameter linear regressions. The sorption strength of organic compounds to charcoals increased with increasing charcoal production temperature T, charcoal SA and organic pollutant octanolewater partitioning coefficient and decreased with increasing charcoal O/C ratio and charcoal H/C ratio. T was found to be correlated with SA (r2 ¼ 0.66) and O/C (r2 ¼ 0.50), particularly for charcoals produced from wood feedstocks (r2 ¼ 0.73 and 0.80, respectively). The resulting regression: log KD ¼ (0.18 ± 0.06) log Kow þ (5.74 ± 1.40) log T þ (0.85 ± 0.15) log SA þ (1.60 ± 0.29) log OC þ (0.89 ± 0.20) log HC þ (13.20 ± 3.69), r2 ¼ 0.60, root mean squared error ¼ 0.95, n ¼ 151 was obtained for all variables. This information can be used as an initial screening to identify charcoals for contaminated soil and sediment remediation. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Charcoal Biochar Sorption Surface area Temperature Hydrophobicity Elemental composition
* Corresponding author. E-mail address:
[email protected] (S.E. Hale). http://dx.doi.org/10.1016/j.chemosphere.2015.08.047 0045-6535/© 2015 Elsevier Ltd. All rights reserved.
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S.E. Hale et al. / Chemosphere 144 (2016) 65e74
Contents 1. 2.
3.
4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.1. Scope of the review article . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.2. Charcoal-water distribution coefficients (KD values) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.3. Single-parameter and multiple-parameter linear regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1. Summary of previously reported charcoal properties and their inter-correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.2. Summary of previously reported charcoal-water distribution coefficients (KD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3. Single-parameter regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1. Relationship between KD and charcoal physicochemical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.2. Relationship between KD and organic pollutant KOW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.4. Multiple-parameter linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.5. Biases in the single and multiple-parameter linear regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Future work and implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Supplementary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
1. Introduction Charcoal is the solid product formed during the incomplete combustion (pyrolysis) of biomass and is also referred to as biochar. Interest in biochar has increased dramatically in recent years owing in principle to the many touted environmental benefits of its use as a soil amendment (Lehmann, 2007). Charcoal can contribute to the mitigation of climate change (by sequestering carbon and suppressing nitrous oxide emissions) (Rondon et al., 2007; Yanai et al., 2007) and specifically when added to acidic soils with low pH and often low cation exchange capacity (CEC), it can improve the agricultural properties of these soil, as charcoal is able to increase pH and cation exchange capacity (CEC) (Liang et al., 2006). In addition, charcaol is able to improve plant available water (Glaser et al., 2002), improve mycorrhizal interactions (Warnock et al., 2007), and provide nutrients that are essential to plant growth (Yamato et al., 2006) to soils with variable physical and biological properties. Charcoal production can be an effective part of agricultural waste management as the process provides a utility for agricultural waste that would otherwise be disposed of and can also generate energy. In addition to these benefits, many charcoals can strongly sorb organic pollutants (Chen and Chen, 2009; Sun et al., 2012a,b) as well as some metals (Zheng et al., 2012; Khan et al., 2013) and thus for certain types of pollutants and soils or sediments, charcoal amendments could be used for contaminant remediation (Ahmad et al., 2014). Similar to activated carbon (a manmade material and produced from biomass or anthracite coal and exposed to an activation process), charcoal could thus be added to polluted soils and sediments as a way to sequester pollutants, thereby hindering them from leaching or being taken up by organisms (Zimmerman et al., 2005; Ghosh et al., 2011). Charcoal is a form of black carbon (as is activated carbon), composed of rigid and planar stacks of highly disordered polyaromatic hydrocarbon sheets (graphene) with relatively few polar functionalities (ketone, ether, hydroxyl, quinoid, carboxyl and other functional groups) on its surface compared to other forms of soil organic matter (e.g. humus) (Cornelissen et al., 2005). Black carbon materials (including charcoal) have high carbon contents, large microporous networks and high surface areas (Allen-King et al., 2002; Zhu and Pignatello, 2005). These properties not only render charcoal resistant to
chemical and microbial degradation (Spokas, 2010), where halflives of between 8 and 4000 years have been reported for charcoals (Gurwick et al., 2013), but are also responsible for the high sorption strength of organic compounds to charcoal. Sorption to soils and sediments is one of the most important environmental processes related to organic pollutants as it controls their environmental mobility and bioavailability and thus their risk. Sorption to charcoal is expected to be similar to other condensed organic matters such as soot (Jonker and Koelmans, 2002), coal (Cornelissen and Gustafsson, 2005; Yang et al., 2008), kerogen (Cornelissen and Gustafsson, 2005) and lampblack (Hong and Luthy, 2007) based on their similar properties. The sorption of organic pollutants to carbonaceous geosorbents occurs via physisorption (i.e. reversible sorption that does not involve the formation of covalent bonds) on the exterior surface and in meso-, macroand micropores (Jonker and Koelmans, 2002; Koelmans et al., 2006) and also via pore filling (Kleineidam et al., 2002). During these physisorption processes, intermolecular forces occur between charcoal functional groups and organic pollutants, such as ionic interactions, electron-donor-acceptor (EDA) interactions (e.g. Lewis acid-base (H-bonding) and pep electron donor-acceptor (EDA) interactions) and van der Waal interactions (i.e. between dipoles, induced dipoles and instantly induced dipoles (i.e. London dispersion)) (Israelachvili, 1992). Previous studies have indicated pore filling, H-bonding interactions and a specific type of EDA interaction known as the pep EDA interaction are the prominent mechanisms of sorption of organic compounds (Zhu and Pignatello, 2005). Although physisorption processes dominate, chemisorption process may take place and sorb organic pollutants (in their charged state) to carbonaceous geosrbents. Chemisorption involves surface reactions where organic compounds participate in bondmaking and bond-breaking processes (Schwarzenbach et al., 2003). Pore filling occurs when organic pollutants enter micropores within the charcoal structure and can be a descriptive factor in the sorption strength of such materials (Kleineidam et al., 2002). The extent of pore filling is highly dependent on the overlap in size of the organic pollutant and the pore itself. The pep EDA interaction has been the subject of scrutiny (Grimme, 2008) and involves interactions between a p electron rich donor and a p electron poor acceptor which lead to a pep EDA stacking interaction (Zhu and Pignatello, 2005). In a charcoal-organic pollutant system both the
S.E. Hale et al. / Chemosphere 144 (2016) 65e74
charcoal and the organic pollutant can act as either electron donors or acceptors. For example, graphene units of charcoal can act as donors or other surface functional groups such as polycarboxylated aromatics, charged heterocyclic amines and quinones can act as acceptors. The strength of pep EDA interactions are a function of i) degree of condensation, ii) polycondensate sheet size and iii) extent to which sheet edges are substituted with electron withdrawing oxygen containing functional groups (Zhu and Pignatello, 2005; Sun et al., 2012b). H-bond interactions can also occur in charcoalorganic pollutant system in cases where polar organic pollutants like phenols, anilines, nitrocompounds and monoflourocompounds, which contain highly electronegative N, O, and F atoms and are able to form bonds with H donor functional groups on the surface of charcoal (Endo et al., 2009; Sun et al., 2012b). Isolating and quantifying sorption mechanisms for all types of charcoals in use today is very challenging and involves many different mechanisms that are affected by charcoal production methods and biomass feedstocks. The first step needed in this area is to gain an empirical overview about the variation in sorption behaviour amongst different types of charcoals, as well as how this variability may be accounted for by key parameters describing the charcoal. Thus, the state of current knowledge related to the sorption of neutral organic compounds to charcoals is the focus of this data synthesis. The influence of charcoal physical characteristics and organic pollutant properties on the charcoal-water distribution coefficient were explored empirically through single and multiple-parameter linear regressions. As such this synthesis builds upon that reported by Ahmad et al. (2014) in which discussion around this began, but charcoal-water distribution coefficients were not reported. Such knowledge is important for the practical use of charcoal in applications such as the remediation of contaminated soils and sediments. This review is not intended to provide an in depth mechanistic review of sorption mechanisms, but a synthesis and compilation of data and relationships between parameters. 2. Materials and methods 2.1. Scope of the review article Peer-reviewed literature about the sorption of neutral organic compounds to charcoal was reviewed and a comprehensive list of charcoal-water distribution coefficients (KD values) was compiled. Commonly reported variables related to charcoal production process, charcoal physical properties and organic compound physicochemical properties were also taken from these literature. Single and multiple-parameter linear regressions were constructed to identify whether these commonly measured parameters could account for the variability of sorption behaviour. Note that this review does not focus on the sorption of ionic/ionizable compounds or metals to charcoal, (Beesley et al., 2011), charcoals formed from hydrothermal carbonization (i.e., hydrochars (Berge et al., 2011)) or the effects of other competing processes such as bi/multi-solute systems and the presence of soil (and thus pore blocking and sorption attenuation (Kwon and Pignatello, 2005)).
67
2011; Sun et al., 2011; Ahmad et al., 2012; Chen et al., 2012; Graber et al., 2012; Sun et al., 2012b; Gomez-Eyles et al., 2013; Li et al., 2013; Qiu et al., 2013; Sun et al., 2013; Wang et al., 2013b; Xie et al., 2013; Zhang et al., 2013; Zhao et al., 2013; Xiao et al., 2014) were used in this review. In the majority of cases the reported data was in the form of Freundlich sorption isotherms in which Freundlich sorption coefficients (KFr (mg/kg) (mg/L)n) and isotherm nonlinearity were reported: n Ccharcoal ¼ KFr CW
where Ccharcoal (mg/kg) is the concentration of pollutants on the charcoal at equilibrium, n () is the Freundlich exponent and CW (mg/L) is the concentration of pollutants in the water phase at equilibrium. In order to allow a comparison between reported data and to remove the dependence of sorption on the nonlinearity of the isotherms, average Freundlich sorption coefficients were converted to charcoal-water distribution coefficients (KD) by calculating them from the reported sorption isotherms at three particular values of CW (where X ¼ 1 ng/L, 1 mg/L and 1 mg/L):
KD ðat CW ¼ XÞ ¼
Ccharcoal at CW ¼ X ; CW ¼ X
In the majority of cases experiments were carried out at the
mg/L concentration range and therefore we chose one concentration that was higher and one that was lower to investigate whether relationships were applicable over this wide concentration range. A complete list of all KD values at 1 mg/L, 1 mg/L and 1 ng/L is presented in Table S1 of the Supplementary material (SM). Data is presented by referring to calculated KD values at these concentrations. Four charcoal parameters (charcoal production temperature, SA, O/C, H/C) and one organic compound parameter (the octanolewater partitioning coefficient, KOW) were chosen for correlation analysis with KD values. These parameters were selected as i) there are mechanistic links, albeit indirect, between these parameters and KD, ii) these parameters are among those most often reported in the literature, iii) they are also most commonly used in subsequent data interpretation of charcoal KD values and, iv) are those that can be controlled in charcoal production in order to produce a desirable product. As sorption of neutral organic compounds is controlled by the surface functionality of the charcoal, H, C and O content is important, and also by the pores available for sorption, SA is also a relevant parameter. Temperature of charcoal production is an underlying factor able to control these variables. In order to avoid data bias and ensure full coverage and environmental relevance all data was used in regressions (thus wide temperature ranges, SA and elemental ratios were assured). For surface area measurements, studies using N2 as the probe gas were considered as there was only one study that reported CO2 surface area measurements (Sun et al., 2013). This is noteworthy, as limited access of N2 to pores at the experimental temperature of 77 K means that the CO2 surface area measured at room temperature is likely more representative of the charcoal surface area that organic pollutants can sorb to (Pignatello and Xing, 1996).
2.2. Charcoal-water distribution coefficients (KD values) Twenty eight literature studies containing data for the sorption of any neutral organic compound to any charcoal were identified and in total 507 individual KD values (James et al., 2005; Bornemann et al., 2007; Wang and Xing, 2007; Chen et al., 2008; Xu et al., 2008; Chen and Chen, 2009; Loganathan et al., 2009; Wang et al., 2009; Kasozi et al., 2010; Zheng et al., 2010; Chen and Huang, 2011; Hale et al., 2011; Kong et al., 2011; Lian et al.,
2.3. Single-parameter and multiple-parameter linear regression analysis The KD values were grouped into the feedstocks; wood, grass and crop waste (including grasses, cellulose, nutshells and other crop refuse) and other waste (including animal waste, sewage sludge and car tyres). Then for each feedstock, as well as for “all feedstocks”, a single-parameter regression analysis was carried out
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S.E. Hale et al. / Chemosphere 144 (2016) 65e74
in order to identify the degree to which individual property parameters correlated with KD values. Correlations were carried out on log transformed data as explained in the SM on page S2. In addition, the data was checked to see if it was better described as normally distributed or log-normally distributed data using Pearson's moment coefficient of skewness, g (see SM). Multiple-parameter regression analysis was also carried out to identify the degree to which all of the parameters could collectively be used to predict KD. In addition, the inter-correlation between parameters was investigated, the data was checked for skewness and the multiple-parameter linear regressions were tested using a validation set comprising 75% of the data set that was randomly selected. Regression analysis was conducted in Microsoft Excel 2013 using the Analysis ToolPak Add-on. Further details related to all of the statistical methods and regressions are given in the supplementary material. The discussion below is primarily focused on log KD values normalised to a water concentration of 1 mg/L as the majority of studies were carried out in this concentration range. All results for log KD values normalised to 1 ng/L and 1 mg/L are given in the SM. 3. Results and discussion 3.1. Summary of previously reported charcoal properties and their inter-correlation An overview of compiled charcoal properties and compound properties specific to the KD values is presented in Table 1. The reported H/C data ranged over three orders of magnitude (from 0.001 to 1.26), O/C data over two orders of magnitude (from 0.02 to 1.0), SA over three orders of magnitude (from 0.25 to 669 m2/g), charcoal production temperature T from 373 to 1123 K and organic compound KOW values from 1.0 to 4.1. The distribution of these variables was investigated in order to ascertain if the reported data distributed normally or log-normally (see SM). H/C and O/C ratios were distributed more log-normally than normally, though SA and T were distributed more normally. Log values were subsequently used for all parameters based on fundamental thermodynamic theory (Goss and Schwarzenbach, 2001), as explained in the SM. With the exception of H/C ratio, all parameters exhibited substantial skewing (see the supplementary material). This skewing is due to more data being available for some of the charcoals than others. As is evident from Table S2, the data for the variables cluster around a median value for each compound, and the inter quartile ranges are much narrower for particular compounds than the total ranges within a compound class. A regression analysis was also carried out to investigate if any of the charcoal parameters were correlated with each other, the results of which are presented in Table S3aed in the SM. The parameters exhibiting the most correlation were charcoal production temperature and SA (coefficient of determination, r2 ¼ 0.66) and charcoal production temperature and O/C ratio (r2 ¼ 0.50), particularly for charcoals from wood feedstocks (r2 ¼ 0.73 and 0.80,
respectively). The parameters SA and O/C were correlated with a coefficient of determination of 0.31. The coefficient of determination for other combinations (each of the five parameters were checked for correlation with all other parameters) of charcoal parameters were much lower (r2 ¼ 0.23 or less). 3.2. Summary of previously reported charcoal-water distribution coefficients (KD) Tables S1 and S2 in the supplementary material provide a summary of the average KD values (thus considering the standard deviation across studies), normalised to a water phase concentration of 1 mg/L for each organic compound where literature existed. Reported KD values varied over eight orders of magnitude, from 0.38 (MTBE on maize charcoal) to 8.25 (PCB 149 þ 123 on acai pit charcoal), depending on the compound and the charcoal. The range of average log KD values for different compound classes was from 3.40 to 5.69 for pesticides, herbicides and insecticides, from 5.26 to 7.16 for PAHs, from 4.47 to 5.41 for phthalates, from 4.49 to 6.38 for halogenated organics, from 3.14 to 4.18 for small organics, from 3.03 to 5.63 for alcohols and from 4.43 to 7.27 for PCBs. The compounds for which the greatest amount of data points have been measured were phenanthrene (range of reported values 2.02e7.71, n ¼ 42), and naphthalene (range of reported values 2.01e7.87, n ¼ 29). The range in this PAH data indicates that sorption to charcoal can vary by up to 5 orders of magnitude, though tends to cluster around a median as in the case of phenanthrene (median: 5.82, interquartile range: 5.23e6.21). 3.3. Single-parameter regression analysis Eqs. (1)e(5) in Table 2 show the single-parameter regressions for log KD values normalised to 1 mg/L for the complete data set versus; log charcoal production temperature (Eq. (1)), log charcoal SA (Eq. (2)), log charcoal H/C (Eq. (3)), log charcoal O/C (Eq. (4)) and log organic compound KOW (Eq. (5)). Table S4a and b show the resulting single-parameter regressions for KD values calculated at 1 ng/L and 1 mg/L. Coefficients of determination for Eqs. (1)e(5) ranged from 0.05 for log O/C to 0.32e0.34 for log SA and log T. Fig. 1 shows the correlations of charcoal log KD with production temperature T (n ¼ 349), charcoal SA (n ¼ 477), charcoal H/C ratio (n ¼ 187), charcoal O/C ratio (n ¼ 184) and organic pollutant octanolewater partitioning coefficient (n ¼ 507), plotted according to feedstock materials (wood, grass and crop waste and other waste). Fig. S1 shows the corresponding data without such a classification. 3.3.1. Relationship between KD and charcoal physicochemical properties Charcoal production temperature and surface area were the two variables that showed the strongest correlation with log KD, although r2 values were low (log KD ¼ 7.93 ± 0.59 log T þ 17.37 ± 1.70, r2 ¼ 0.34, Eq. (1), Table 2 and
Table 1 Summary of previously reported charcoal and organic compound properties. Statistic
HC ratio ()
OC ratio ()
SA (m2/g)
T (K)
log Kow
Average Median 25th percentile 75th percentile Maximum Minimum Number of data points
0.054 0.048 0.033 0.097 1.26 0.001 187
0.23 0.26 0.14 0.51 1.00 0.02 184
111 164 52 464 669 0.250 472
762 873 673 873 1123 373 350
4.1 3.9 3.4 4.6 8.6 1.0 507
S.E. Hale et al. / Chemosphere 144 (2016) 65e74
69
Table 2 Summary of statistical analysis for the single-parameter linear regression (Eqs. (1)e(5)) and the multiple-parameter linear regression (Eqs. (6)e(9)) for Log KD normalized to a water concentration of 1 mg/L. Feedstock
Equation
All
log KD ¼ (7.93 ± 0.59) log T þ (17.37 ± 1.70)
(1)
All
log KD ¼ (0.91 ± 0.06) log SA þ (3.67 ± 0.13)
(2)
All
log KD ¼ (1.85 ± 0.22) log HC þ (2.62 ± 0.29)
(3)
All
log KD ¼ (0.78 ± 0.26) log OC þ (4.47 ± 0.20)
(4)
All
log KD ¼ (0.23 ± 0.03) log Kow þ (4.34 ± 0.14)
(5)
All
log log log log log log log log log log log log
Grass and crop waste
Wood
Other feedstocks
KD ¼ (0.18 ± 0.06) log Kow þ (5.74 ± 1.40) T þ (0.85 ± 0.15) log SA þ (1.60 ± 0.29) OC þ (0.89 ± 0.20) log HC þ (13.20 ± 3.69)(6) KD ¼ (0.06 ± 0.08) log Kow þ (5.91 ± 1.76) T þ (0.09 ± 0.19) log SA þ (0.49 ± 0.44) OC þ (1.37 ± 0.26) log HC þ (13.44 ± 4.67)(7) KD ¼ (0.04 ± 0.10) log Kow þ (11.70 ± 2.79) T þ (1.22 ± 0.25) log SA þ (3.14 ± 0.67) OC þ (0.01 ± 0.45) log HC þ (28.10 ± 7.05)(8) KD ¼ (0.70 ± 0.21) log Kow þ (5.83 ± 4.05) T þ (1.07 ± 0.77) log SA þ (1.00 ± 0.61) OC þ (3.54 ± 1.12) log HC þ (13.29 ± 10.07) (9)
r2
rmse
n
0.34 0.32 0.28 0.05 0.13 0.60
1.10
350
0.97
472
1.29
187
1.46
184
1.11
507
0.95
151
0.67
0.81
65
0.81
0.78
55
0.64
0.93
25
rmse ¼ root mean squared error.
log KD ¼ 0.91 ± 0.06 log SA þ 3.67 ± 0.13, r2 ¼ 0.32, Eq. (2), Table 2). On a statistical level, this is partly accounted for by the good degree of correlation between log SA and log T as discussed below. Charcoal production temperature has been previously identified as a key variable affecting the resulting charcoal's physicochemical properties and therefore in turn the sorption of organic compounds (Chen et al., 2012; Rutherford et al., 2012; Xiao et al., 2014). During pyrolysis, a series of complex reactions take place that convert aliphatic components contained in the charcoal precursor biomass to aromatic components in the resultant charcoal. The reactions proceed via the: i) degradation of hemicellulose and partial destruction of alkyl moieties (220e315 C), ii) degradation of cellulose (315e400 C) and iii) chain fragmentation of lignin to release monomeric phenol units (>400 C) (Chen et al., 2012; Uchimiya et al., 2013; Wang et al., 2013a; Mimmo et al., 2014). As the amorphous aromatic components within charcoal begin to arrange into turboclastic crystallites, the SA of the charcoal increases dramatically (Chun et al., 2004; Chen et al., 2012; Wang et al., 2013a). Several authors have noted a peak in SA at around a production temperature of 600 C due to a structural ordering and micropore coalescence around this temperature (Mimmo et al., 2014) or due to a restructuring and onset of an ash melting process (Brown et al., 2006). Corresponding to this, a pore selective sorption mechanism (based upon the size of the charcoal pores and the organic compounds themselves) often occurs for charcoals made at higher temperatures (Chen et al., 2008; Chen and Chen, 2009) and a polarity selective mechanism (based upon the polarity of charcoal surface functional groups and organic compounds that are sorbing to the charcoal) at lower temperatures. The previously reported literature contained data for charcoals produced between 100 and 850 C, thus providing a diverse data set and including materials dominated by both aliphatic and aromatic carbon (Sun et al., 2012b). Whilst the inclusion of charcoals produced at temperatures as low as 100 C (5 data points) in the complete data set can be questioned, they were included for completeness and to avoid bias. Fig. 2a shows the correlation of log KD with charcoal production temperature for seven selected studies in which the charcoals were produced at minimally four different temperatures from 100 to 800 C (Chen et al., 2008; Chen and Chen, 2009; Chen and Yuan, 2011; Sun et al., 2011, 2012b; Chen et al., 2012; Xiao et al., 2014). Fig. 2b shows the correlation of log KD and SA for six of these same studies where SA data was reported (Chen et al., 2008; Chen and Chen, 2009; Chen and Yuan, 2011;
Chen et al., 2012; Sun et al., 2012b; Xiao et al., 2014). The resulting single-parameter linear regression for this selected data set was Log KD ¼ 9.46 log T- 21.68 and the r2 value was 0.53, indicating a stronger correlation for these selected studies than within the whole data set. For two of these studies (Chen et al., 2008, 2012) a continuous increase in KD with production temperature over the complete temperature range (up to 700 C) was observed. For the remaining four studies (Chen and Chen, 2009; Sun et al., 2011, 2012b; Xiao et al., 2014) an increase in KD values was seen up to temperatures of around 400e600 C, where they decreased, before increasing again. These trends are mirrored by the correlation of KD with SA (Chen and Chen, 2009; Sun et al., 2012b; Xiao et al., 2014). Several reasons were given for the increase, decrease and further increase in KD values by the authors of the studies, related both to the charcoal SA itself and to the properties of the organic compound. For example, the adsorption of phthalates to charcoals produced from grass and wood (Sun et al., 2012b) was dependant on the size of the phthalates which showed variable increases in adsorption relative to SA. The sorption strength of naphthalene and 1-naphthol was dependent on the amount of available sorption sites which was not only affected by the absolute SA, but also by the overlap between the molecular geometry of the organic pollutants and the sorption sites (Chen and Chen, 2009). Charcoal elemental composition is also directly influenced by charcoal feedstock and production temperature as it is related to the reactions that take place as the biomass is converted to charcoal. As pyrolysis temperature and time of heating increase, the amount of C increases, the amounts of H and O decrease and this leads to lower H/C and O/C ratios (Lehmann and Joseph, 2009; Xiao et al., 2014). This inter-relationship was observed in our study, with logelog correlation r2 values between T and H/C and O/C being 0.23 and 0.31, respectively. Within the data set compiled here, the elemental H/C ratio was able to describe log KD to a greater extent than the elemental O/C ratio (r2 ¼ 0.28 and r2 ¼ 0.05, respectively); indicating that the loss of H from the charcoal can be ascribed to a greater change in KD and is thus a better (though still weak), indicator of increasing sorption capacity than the loss of O. 3.3.2. Relationship between KD and organic pollutant KOW As well as those of the charcoal, the properties of the organic pollutants (e.g. molecular weight and size, hydrophobicity,
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Fig. 1. The correlation of log KD vs. a) Log SA, b) Log T, c) Log H/C, d) Log O/C and e) log organic compound KOW for the data set split according to biomass feedstock (wood ( ), grass and crop waste ( ) and other waste ( )).
polarity) can also affect the strength of sorption. The relationship between Log Kow and Log KD is given in Eq. (5) in Table 2 (log KD ¼ 0.23 ± 0.03 log Kow þ 4.34 ± 0.14, r2 ¼ 0.13). The correlation with log Kow was not as good as with log SA and log T, which is indicative that the SA and T influence the charcoal KD more than the hydrophobicity of the contaminant itself. While it can be questioned how applicable a relationship with KOW is for some of these compounds, KOW is an easily accessible parameter and provides a commonly used basis for correlation with other partitioning coefficients. The molecular volume of a compound would also provide useful information with regards to binding to charcoal, however this parameter is rarely reported in sorption studies and also suffers shortcomings. Whilst molecular volume provides
information about size it does not provide information about spatial arrangement which is critical when pore filling takes place. Of the literature surveyed, only three studies contained data for the sorption of three or more compounds with varying hydrophobicity to the same charcoal (Chen et al., 2008; Lian et al., 2011; Sun et al., 2012b) and these are shown in Fig. 2c. The sorption of diethyl phthalate, dibutyl phthalate and butyl benzyl phthalate to grass and wood charcoals was correlated with compound hydrophobicity, but was also influenced by charcoal physicochemical properties (Sun et al., 2012b). According to these authors, the strongest sorption combination was observed for dibutyl phthalate (log Kow ¼ 4.8) to charcoals produced at 500 C that had amorphous carbon properties (and thus a possible additional absorptive
S.E. Hale et al. / Chemosphere 144 (2016) 65e74
71
Fig. 2. The correlation of log KD with a) log T for selected studies which contain sorption data for a charcoal produced at at least four temperatures, b) log SA for selected studies that contain sorption data for a charcoal produced from the same feedstock, but at different temperatures (and therefore having variable SA) and c) log organic compound KOW values for selected studies that compare the sorption of at least three compounds to the same charcoal.
Fig. 3. The correlation between log KD measured and log KD predicted using the correlation coefficients in Eq. (6) in Table 2 for KD values normalised to water concentrations of 1 mg/L, 1 mg/L and 1 ng/L. MLR ¼ Multiple Linear relationship (Table 2 Eqs. (6)e(9)).
mechanism), rather than for charcoals produced at the highest production temperatures. In order to achieve maximum sorption, the molecular dimensions of the organic pollutant must match those of the pores in the charcoal. The pollutants themselves may have restrictive shapes and therefore contact with the charcoal surface may be limited due to steric hindrance (Endo et al., 2009) and the distance the pollutant can penetrate into a given pore be reduced (Pignatello and Xing, 1996). Size exclusion becomes more severe as sorption volume becomes dominated by small micropores (Lattao et al., 2014) and thus larger compounds are not able to access sorption sites, as many pollutants have sizes in the same order of magnitude as charcoal nanopores (5e10 nm). As compound hydrophobicity generally increases with increasing compound molecular diameter, for a charcoal dominated by small micropores the sorption of larger compounds would theoretically be impaired (Amstaetter et al., 2012; Lattao et al., 2014). However, for the literature surveyed here, pore size distribution of the charcoals was rarely reported. 3.4. Multiple-parameter linear regression Multiple-parameter linear regressions based on all five parameters were determined for the complete data set and for the data
set divided by feedstock (Eqs. (6)e(9), Table 2 for log KD at 1 mg/L and in Table S5aed for KD at 1 ng/L and 1 mg/L). Note that this type of multiple-parameter linear regression can only be carried out for isotherms in which all parameter data are available. The resulting overall regression equation for the complete data set was; log KD ¼ 0.18 ± 0.06 log Kow þ 5.74 ± 1.40 log T þ 0.85 ± 0.15 log SA þ 1.60 ± 0.29 log OC þ 0.89 ± 0.20 log HC þ 13.20 ± 3.69 (Eq. (6), Table 2), with substantially improved coefficients of determination compared to the single-parameter linear free energy relationships (r2 ¼ 0.60). It should be emphasized that this equation is not meant to describe all of the mechanistic interactions occurring during sorption, but rather the best possible way of estimating this charcoal KD from commonly available data of relevance to sorption, and therefore provides a tool that can be used for screening purposes. Fig. 3 shows the correlation between measured log KD values and modelled log KD values using the coefficients in Eq. (6) and plotted according to feedstock. The multiple linear regression fits most of the data within a factor 30 (1.5 orders of magnitude). For KD at 1 mg/L, 9% of data lie outside this factor (3% for KD at 1 mg/L and 30% for KD at 1 ng/L). The present data were within a similar variability range as that previously reported for contaminated sediments (Arp et al., 2009). The data points that lie furthest from the 1:1 line are mainly charcoals produced at temperatures
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S.E. Hale et al. / Chemosphere 144 (2016) 65e74
below 350 C, as marked in Fig. 3, which is around the temperature where amorphous aromatic components within charcoal begin to arrange into turboclastic crystallites. Below 350 C charcoals may contain complex amorphous subdomains that exhibit decreased or increased sorption compared to carbonaceous fractions in the charcoal, depending on their structure (e.g. humic materials sorb less than charcoals but tars can sorb more strongly than charcoal) (Arp et al., 2009). It is also interesting to note that within the data there is a greater difference in KD values for the same compound produced at different temperatures than for different compounds produced at the same temperature. For example the log KD for naphthalene for orange peel biochar produced at 150 C was 2.1 compared to 7.3 for the same biochar and compound but with a production temperature of 700 C. However the log KD value for 1naphthol for the same biochar produced at 150 C was 2.1 and was 6.7 for the same biochar produced at 700 C. The correlation statistics, in terms of r2 and rmse, differ from one another depending on the concentration the KD values are normalized to, be it 1 mg/L (r2 ¼ 0.46, rmse ¼ 0.71), 1 mg/L (r2 ¼ 0.60, rmse 0.95) or 1 ng/L (r2 ¼ 0.56, rmse 1.55). The best correlation, as quantified by the r2, is for 1 mg/L normalized data, and the best model performance for predicting KD values, as quantified by rmse, is for 1 mg/L normalized data. A likely explanation for the best r2 at 1 mg/L is that the majority of experiments were carried out in the mg/L water concentration range, and thus any errors in the reported Freundlich “n” values will lead to positive or negative biases in log KD values. As evident from Fig. 3, the overall scatter of KD values is the smallest at 1 mg/L, which accounts for having the best rmse despite potential extrapolation biases. Splitting the dataset up into different feedstocks (Eqs. (7)e(9), Table 2) improved the correlations compared to considering all feedstocks together (Eq. (6); r2 ¼ 0.60). The correlation for wood
was the strongest (r2 ¼ 0.81), followed by grasses and crop waste (r2 ¼ 0.67), and then other feedstocks (r2 ¼ 0.64). It is likely that the relative homogeneity of the wood charcoals (when compared to the other freedstocks) lead to the improved correlations. The other feedstock category contained a broad mixture of feedstocks suggesting the need to further separate charcoal feedstock in order to improve the prediction of sorption strength. 3.5. Biases in the single and multiple-parameter linear regressions As mentioned above, the shortcoming of these multiple-parameter linear regressions are that some of the assumed independent variables are not completely independent of each other, such as SA and T. In addition, the calibration data set showed non-normality of all parameters except H/C, likely because some charcoals and compounds were represented within the data set more than others. Thus, despite the large data set, these biases will have some impact on the regressions themselves, regardless of the mechanistic basis. As discussed in the SM, the inter-correlation between SA, T and OC, indicates that not all of these parameters may be needed to make screening level estimations of charcoal log KD values. In Table 3, multiple linear regressions excluding either just log SA or both log SA and log O/C are presented. As can be seen, the quality of the fits (as indicated by the coefficients of determination) are not as good as when considering all input parameters in the majority of cases, however the differences are quite small. For example for wood charcoals removing O/C ratio from the statistical analysis does not change the quality of fit, thus suggesting in this case a knowledge of T and H/C allows a prediction about sorption strength to be made. Multiple-parameter linear regressions for the complete data set were compared with those calculated based on 75% of the data set (selected randomly) in order to tests the agreement between the
Table 3 Multiple-parameter linear regressions after considering multicollinearity (Eqs. (10)e(17)) as well as the multiple-parameter linear regressions using all variables (Eqs. (6)e(9) from Table 2). Feedstock
Equation: log KD at water concentration of 1 mg/L
All
log log log log log log log log log log log log log log log log log log log log log log log log log log log log log log log log
All
All Grass and crop waste
Grass and crop waste
Grass and crop waste Wood
Wood
Wood Other feedstocks
Other feedstocks
Other feedstocks
KD ¼ (0.18 ± 0.06) log Kow þ (5.74 ± 1.40) T þ (0.85 ± 0.15) log SA þ (1.60 ± 0.29) OC þ (0.89 ± 0.20) log HC þ (13.20 ± 3.69) (6) KD ¼ (0.17 ± 0.06) log Kow þ (9.66 ± 1.06) T þ (1.44 ± 0.26) log OC þ (1.05 ± 0.20) HC þ (23.35 ± 2.81) (10) KD ¼ (0.19 ± 0.06) log Kow þ (5.87 ± 0.83) T þ (1.11 ± 0.21) log HC þ (13.66 ± 2.24)(11a) KD ¼ (0.06 ± 0.08) log Kow þ (5.91 ± 1.76) T þ (0.09 ± 0.19) log SA þ (0.49 ± 0.44) OC þ (1.37 ± 0.26) log HC þ (13.44 ± 4.67) (7) KD ¼ (0.03 ± 0.08) log Kow þ (6.26 ± 1.57) T þ (0.41 ± 0.41) log OC þ (1.44 ± 0.24) HC þ (14.30 ± 4.17) (12) KD ¼ (0.02 ± 0.08) log Kow þ (4.99 ± 0.94) T þ (1.54 ± 0.23) log HC þ (11.03 ± 2.63) (13) KD ¼ (0.04 ± 0.10) log Kow þ (11.70 ± 2.79) T þ (1.22 ± 0.25) log SA þ (3.14 ± 0.67) OC þ (0.01 ± 0.45) log HC þ (28.10 ± 7.05) (8) KD ¼ (0.09 ± 0.11) log Kow þ (18.35 ± 2.50) T þ (2.46 ± 0.75) log OC þ (0.07 ± 0.42) HC þ (44.95 ± 6.47) (14) KD ¼ (0.07 ± 0.11) log Kow þ (11.50 ± 1.44) T þ (0.04 ± 0.44) log HC þ (27.06 ± 3.55) (15) KD ¼ (0.70 ± 0.21) log Kow þ (5.83 ± 4.05) T þ (1.07 ± 0.77) log SA þ (1.00 ± 0.61) OC þ (3.54 ± 1.12) log HC þ (13.29 ± 10.07) (9) KD ¼ (0.61 ± 0.18) log Kow þ (1.04 ± 2.61) T þ (1.44 ± 0.43) log OC þ (3.15 ± 1.00) HC þ (2.48 ± 6.37) (16) KD ¼ (0.80 ± 0.17) log Kow þ (0.17 ± 1.89) T þ (1.14 ± 0.92) log HC þ (0.06 ± 4.81) (17)
r2
rmse
n
0.60
0.95
151
0.53
1.01
177
0.47
1.09
182
0.67
0.81
65
0.65
0.80
75
0.65
0.80
76
0.81
0.78
55
0.69
0.95
66
0.66
1.00
71
0.64
0.93
25
0.63
0.95
30
0.42
1.13
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regressions. Table S6 in the supplementary material shows the regressions and indicates that there was no significant difference in any of the slope or intercept values. Further, there was no substantial change to the predictive performance of the models, though a slight improvement of performance was observed for the validation set (i.e. slightly smaller rmse and fewer percentages of outliers more than a 1.5 log units).
4. Future work and implications While the most often reported charcoal and compound variables were taken into consideration in this work, additional factors possibly affecting the sorption of organic compounds to charcoals include charring duration, pore size distribution, sorption attenuation and the fraction of ash contained in the charcoal. While charring duration is often reported in the literature, uncertainty exists surrounding whether the desired charring temperature has been maintained throughout the reactor during the complete pyrolysis period. Charcoal pore size distribution is important in determining the overlap in the molecular dimensions of the organic pollutant and the charcoal pores and possibly quantifying pore size exclusion effects, but unfortunately this parameter is rarely reported and only one paper quantitatively investigated this theme (Lattao et al., 2014). Sorption attenuation is a well-known phenomenon in activated carbon research, where pores can be blocked by soil organic matter following environmental exposure to soils or sediments and has been shown that a similar phenomenon occurs for charcoals, from a very strong effect (Teixido et al., 2013), to a limited effect (Hale et al., 2011). Thus far, the ash fraction of charcoal has not been considered with regard to its effect on adsorption since the materials used in these literature studies were mostly relatively pure plant residues. The inorganic ash fraction probably provides only a small contribution to the total amount of sorption sites for the studied organic compounds. When selecting a charcoal with the purpose of sequestering pollutants as part of a soil remediation strategy there are essentially two parameters that can be manipulated: the type of feedstock biomass and the charcoal pyrolysis temperature. Here we investigated the effect of pyrolysis temperature more thoroughly than the effect of feedstock, indicating an area for further research. It was shown that a first indication of the sorption affinity of the charcoal, often correct within a factor of 30, can be obtain based on the charcoal production temperature, resulting charcoal H/C ratio and octanolewater partitioning coefficient of the organic compound. When better assessment is needed and data are available, SA and O/C ratio can be added for a slight further improvement in predictive power.
Acknowledgements This work was primarily funded by a FriPro stipend from the Research Council of Norway (grant number 217918). H.P.H. Arp was supported by the NGI stipend fund (12116) and the Research Council of Norway Leiv Eriksson fund (225077). Darya Kupryianchyk was mainly funded by the internal NGI postdoc scheme.
Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2015.08.047.
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