Experimental data and modeling for sulfachloropyridazine and sulfamethazine adsorption/desorption on agricultural acid soils

1 Experimental data and modeling for sulfachloropyridazine and sulfamethazine 1 adsorption/desorption on agricultural acid soils 2 3 Conde-Cid, M.1*, Fernández-Calviño, D.1, Nóvoa-Muñoz, J.C.1, Núñez-Delgado, A.2, Fernández-4 Sanjurjo, M.J.2, Arias-Estévez, M.1, Álvarez-Rodríguez, E.2 5 6 1 Soil Science and Agricultural Chemistry, Fac. Sciences, Univ. Vigo, 32004 Ourense, Spain 7 2 Dept. Soil Science and Agricultural Chemistry, Engineering Polytechnic School, Univ. Santiago de 8 Compostela, 27002 Lugo, Spain 9 10 * Corresponding author. E-mail address: [email protected] (Conde-Cid, M.) 11 12 Declarations of interest: none 13 14 Abstract 15 Taking into account that certain soil components could retain sulfonamides, in this work 50 crop soils (as 16 porous materials) were studied as regards their adsorption/desorption characteristics for two sulfonamide 17 antibiotics, specifically sulfamethazine (SMT) and sulfachloropyridazine (SCP), using batch-type 18 experiments. Both adsorption and desorption curves fitted well to the Linear and Freundlich equations. 19 Adsorption parameters showed low values for both antibiotics, indicating that a high mobility can be 20 expected for these compounds in soils. In addition, adsorption parameters were lower for SMT than for 21 SCP, due to their different degree of hydrophobicity. Soil organic carbon (OC) content was the soil 22 characteristic showing the highest influence on adsorption and desorption processes for both sulfonamides. 23 In fact, those soils having the highest OC content were the ones presenting the highest adsorption and the 24 lowest desorption results. As next step, multiple linear regression analyses were carried out, which were 25 used to develop robust model functions to predict the values of adsorption and desorption parameters for 26 SCP and SMT. Input variables used to feed models were easily determinable soil characteristics, such as 27 OC, cation exchange capacity, and pH. These models could facilitate a rapid and low-cost screening to find 28 vulnerable soils, which will be those with the lowest values for the adsorption parameters. Having that 29 information derived from simulations, an appropriate management could be programmed for most 30 vulnerable soils, to minimize the entry of pollutants in different environmental compartments and, finally, 31 into the food chain, thus reducing risks and impacts on human and environmental health. 32 2 33 Keywords: adsorption/desorption; antibiotics; crop soils; regression models; sulfonamides 34 35 1. Introduction 36 The use of veterinary antibiotics has greatly increased last years, mainly due to a higher livestock demand 37 (Van-Boeckel et al., 2015). In parallel, residues of these antibiotics are considered a growing environmental 38 concern (Guo et al., 2017), as they may favor resistant bacteria, toxic effects in non-target animals, 39 ecological impacts in agro-systems, and effects on human health after ingestion through the food chain 40 (Bártíková et al., 2016). In fact, this kind of pollution can result in degradation of soil and water 41 compartments (Bastos et al., 2018). 42 Sulfonamides are one of the groups of antibiotics most widely used in veterinary medicine, favored by their 43 wide spectrum, low cost, and relatively high efficacy against bacterial diseases (Sarmah et al., 2006). In 44 fact, sulfonamides consumption for veterinary medicine in Europe was just behind that of tetracyclines and 45 penicillins during 2016 (EMA, 2018). Sulfachloropyridazine (SCP) and sulfamethazine (SMT) are two of 46 the main sulfonamides administered to livestock (Sarmah et al., 2006). Both are poorly absorbed, causing 47 that up to 90% are excreted through feces and urine (Kuppusamy et al., 2018; Wang et al., 2015), then 48 reaching soil when spread as organic amendments. Due to that, SCP and SMT are detected in amended 49 soils at concentrations in the order of μg kg-1 (Aust et al., 2008; Conde-Cid et al., 2018a; García-Galán et 50 al., 2013; Karcı and Balcıoğlu, 2009; Ok et al., 2011;Yang et al., 2016). Once on soil, the environmental 51 fate of these sulfonamides depends on physicochemical and biological processes taking place in the soil-52 water system, including degradation, adsorption/desorption, leaching, and runoff, affected by 53 characteristics of the antibiotics and also by soil characteristics (Carvalho and Santos, 2016). Previous 54 studies demonstrated that sulfonamides are very stable in the dark, also experiencing very low degree of 55 hydrolysis in environmental conditions (Białk-Bielinska et al., 2012; Biošić et al., 2017; Conde-Cid et al., 56 2018b). They are susceptible to photo-degradation (Conde-Cid et al., 2018b), although it is only relevant 57 in the most superficial layer of soils. As a result, adsorption/desorption processes are of main relevance 58 regarding bioavailability and mobility of sulfonamides in the environment. Due to their low adsorption 59 coefficients, sulfonamides are considered as the most mobile antibiotics in the soil, implying high risk of 60 entry into the food chain, and being a hazard for human and environmental health (Pikkemaat et al., 2016). 61 3 In fact, SCP and SMT have been detected in surface waters at concentrations of the order of ng L-1 (Huang 62 et al., 2019; Jia et al., 2018; Liu et al., 2019) to μg L-1 (Kim et al., 2019), as well as in groundwater (Cabeza 63 et al., 2012; García-Galán et al., 2010; Huntscha et al., 2012; Liu et al., 2019), and in drinking water (Hu et 64 al., 2018; Leung et al., 2013). Sulfonamides have also been detected in a wide variety of crops, showing 65 bioaccumulation effects in plant tissues (Conde-Cid et al., 2018a; Du and Liu, 2012; Zhao et al., 2019). 66 Previous studied have focused on adsorption of SMT and SCP in whole soils (Boxall et al., 2002; Chu et 67 al., 2013; Figueroa-Diva et al., 2010; Leal et al., 2013; Lertpaitoonpan et al., 2009; Rath et al., 2019; 68 Srinivasan et al., 2014; Thiele-Bruhn et al., 2004; Vieira et al., 2017; Wang et al., 2015), as well as in 69 isolated soil components (Essington et al., 2010; Guo et al., 2017; Tang et al., 2018; Thiele-Bruhn et al., 70 2004), and on adsorption of other sulfonamide antibiotics on other porous sorbent materials (Hussain et al., 71 2018; Martucci et al., 2013), which is considered a promising alternative for various kinds of environmental 72 pollutants (Núñez-Delgado et al., 2015). In addition, other studies focused on the effect of pH on adsorption 73 (Essington et al., 2010; Liu et al., 2017; Wang et al., 2015), or on the effect of the presence of different 74 cations (Essington et al., 2010; Liu et al., 2017; Tang et al., 2018). However, these studies were carried out 75 using a small number of soils, and desorption was not evaluated in most of them, even if it is a key aspect 76 to determine the reversibility of the adsorption process. 77 Taking all that into account, the main objective of this study is to determine adsorption and desorption 78 parameters for SCP and SMT using a large number of crop soils (n = 50), allowing to develop robust models 79 to estimate retention of SCP and SMT in soils with different characteristics. This would facilitate the 80 identification of vulnerable soils from rapid and low-cost laboratory determinations. After that, appropriate 81 measures could be implemented to correctly management these soils, thus minimizing mobilization and 82 bioavailability of these pollutants, decreasing risks of entry into the food chain, and of subsequent adverse 83 effects on human and environmental health. 84 85 2. Materials and methods 86 2.1. Chemicals 87 The antibiotics SMT (99.6% purity), and SCP (99.7% purity) were from Sigma-Aldrich (Barcelona, Spain), 88 with main chemical characteristics shown in Table S1 (Supplementary Material). Acetonitrile was of HPLC 89 4 grade, from Fisher Scientific (Madrid, Spain), while all other chemicals were of high purity analytical 90 grade, from Panreac (Barcelona, Spain). All solutions were prepared with milliQ water, from Millipore 91 equipment (Millipore, Madrid, Spain). 92 93 2.2. Soil samples 94 Soil samples were taken in 50 agricultural plots, and in 18 pastureland plots, all of them situated in Galicia 95 (NW Spain). An Edelman probe was used to take 10-20 soil subsamples (0-20 cm depth) in each plot. These 96 subsamples were then combined to perform a composite sample (≈2 kg) per plot. Once at the laboratory, 97 all soil samples were air dried, sieved by 2 mm mesh, and stored in polyethylene bottles until being 98 analyzed. 99 Soil characteristics were determined following standard methods (Tan, 1996). Regarding soil texture, sand 100 (2-0.05 mm) was quantified using wet sieving, while silt (0.05-0.002 mm), and clay (<0.002 mm) was 101 determined by means of the international method of pipetting. Regarding pH value, it was measured in 102 water (pHw) and in 0.1M KCl (pHKCl) (soil:liquid ratio of 1:2.5), using a combined glass electrode (model 103 2001, Crison, Barcelona, Spain). Total organic carbon (OC) and total nitrogen (N) were quantified by 104 elemental analysis (LECO CHN-1000, LECO Corporation, St. Joseph, MI, USA). Exchangeable basic 105 cations (Nae, Ke, Cae and Mge), were determined after displacement with 0.2 M NH4Cl (Sumner and Miller, 106 1996), while 1M KCl was used to displace exchangeable aluminum (Ale) (Bertsch and Bloom, 1996). All 107 these cations were quantified by flame atomic absorption and emission spectrometry (AAnalyst 200, Perkin 108 Elmer, Boston, MA, USA), and finally the effective cation exchange capacity (eCEC) was estimated as the 109 sum of the exchangeable Ca, Mg, Na, K, and Al. 110 111 2.3. Experiments on adsorption and desorption 112 Adsorption and desorption at equilibrium were studied for SCP and for SMT by means of batch-type 113 experiments. The procedure consisted in performing suspensions including individually each of both 114 sulfonamides and 2 grams of each of the soil samples, into 15 mL capacity polypropylene centrifuge tubes 115 (Deltalab, Barcelona, Spain). Seven different concentrations were used for each antibiotic (2.5, 5, 10, 20, 116 30, 40 and 50 μM), in all cases containing 0.005 M CaCl2 as background electrolyte. Then, the resulting 117 5 suspensions were shaken for 24 hours in the dark, using a rotary shaker set at 50 rpm, with temperature 118 regulated at 25 ± 2 °C. Previously performed kinetics experiments indicated that 24 h was enough to reach 119 equilibrium. After that, all suspensions were centrifuged at 4000 rpm for 15 minutes using a Rotina 35R 120 (Hettich Zentrifugen, Tuttingen, Germany), giving supernatants that were filtered through 0.45 μm pore 121 size nylon syringe filters (Fisherbrand, Madrid, Spain), and then stored at 4 ºC until analyses. All these 122 filtered supernatants were later analyzed for both sulfonamides by means of HPLC (details shown below), 123 as well as for pH. In each case, the amount of each of both antibiotics adsorbed to each soil sample was 124 calculated by difference between the amount initially added and that present in the equilibrium solution 125 after 24 h of agitation. All determinations were carried out in triplicate. 126 To study desorption, soil precipitates derived from the adsorption trials were weighed, allowing the 127 determination of the amount of occluded solution. Then they were re-suspended in 5 mL of 0.005 M CaCl2, 128 shaken, centrifuged, filtered and the resulting liquids were analyzed in the same way as in the adsorption 129 phase. As in the case of adsorption, all determinations were carried out in triplicate. 130 In addition, control samples (containing just the different concentrations of each of both antibiotics, but no 131 soil) were performed to test eventual losses of antibiotics due to sorption to tubes or to abiotic degradation, 132 with no relevant losses detected. Previously (Conde-Cid et al., 2018b), biotic degradation had been 133 discarded for both sulfonamides. 134 135 2.4. Quantification of sulfonamides 136 The quantification of SCP and SMT concentrations present in the equilibrium solutions was carried out 137 using a HPLC liquid chromatograph (Dionex Corporation, Sunnyvale, USA) equipped with a P680 138 quaternary pump, an ASI-100 auto-sampler, a TCC-100 thermostatized column compartment, and a 139 UVD170U detector. Chromatographic separations were performed in a Luna C18 column (150 mm long; 140 4.6 mm internal diameter; 5 µm particle size) from Phenomenex (Madrid, Spain), and a guard column (4 141 mm long; 2 mm internal diameter; 5 µm particle size), packed with the same material as the column. The 142 injection volume was 50 µL, and the flow rate 1.5 mL min-1. The mobile phase consisted of acetonitrile 143 (phase A) and 0.01 mol L-1 phosphoric acid (phase B). A linear gradient elution program was run from 5 to 144 32% of phase A (and 95 to 68% of phase B) within 10.5 min. The initial conditions were re-established in 145 6 2 min and held for 2.5 min. The total analysis time was 15 min, with a retention time of 7.0 min for SMT, 146 and 9.6 min for SCP, and the wavelength used for detection was 270 nm. 147 148 2.5. Fitting to adsorption models and statistical treatment 149 Experimental data corresponding to adsorption and desorption trials were adjusted to the Linear (Eq. 1) and 150 Freundlich models (Eq. 2). 151 𝑞𝑞𝑎𝑎=𝐾𝐾𝑑𝑑𝐶𝐶𝑒𝑒𝑒𝑒 (Eq. 1) 152 𝑞𝑞𝑎𝑎=𝐾𝐾𝐹𝐹𝐶𝐶𝑒𝑒𝑒𝑒 1/𝑛𝑛 (Eq. 2) 153 where qa (μmol kg-1) is the amount of antibiotic adsorbed on the soil at equilibrium; Ceq (μmol L-1) is the 154 concentration of antibiotic present in solution at equilibrium; Kd (L kg-1) is the coefficient of distribution in 155 the Linear model; KF (L1/n µmol1-1/n kg-1) and n are parameters in the Freundlich model. 156 The statistical package R version 3.1.3 (R core Team, 2015) and the package nlstools for R (Baty et al., 157 2015) were used to carry out adjustment of experimental data to the Linear and Freundlich models. In 158 addition, the statistical package SPSS 15.0 was used to perform Pearson´s correlations and multiple 159 regression analyses. 160 161 3. Results and discussion 162 3.1. Characteristics of soil samples 163 Basic descriptive statistics, including average, median, maximum and minimum values for the main 164 physicochemical characteristics of the studied crop soils (n = 50) are shown in Table 1. Table S2 165 (Supplementary material) shows individual data for each of these 50 soils. 166 All the studied soils were acidic, with average value 5.0 for pHW, and range 4.1 to 6.3, while pHKCl was 167 always lower, indicating that negative charges dominated in these soils. The eCEC values were very 168 variable, ranging from 3.88 to 16.47 cmolc kg-1 (average 6.07), as were OC contents (ranging from 1.1 to 169 10.9%, average 2.7%), and N (ranging 0.1-0.8%, average 0.2%). Calcium (as Ca2+) was the exchangeable 170 cation dominant in most soils. As regards texture related results, sand ranged from 41 to 70% (average 171 59%), while silt ranged from 12 to 31% (average 20%), and clay ranged from 16 to 34% (average 21%). 172 7 173 3.2. Adsorption of sulfonamides 174 Figure 1 shows SCP and SMT adsorption curves for 12 selected soils, as example of the 50 different crop 175 soils here studied, whereas Fig. S1 (Supplementary material) shows adsorption curves for all 50 crop soils. 176 The amount of adsorbed SCP and SMT increased in all cases in parallel to the increase in the initial 177 concentration of antibiotic added. Furthermore, the maximum soil adsorption capacity was not reached in 178 the experimental conditions used (maximum C0 = 50 μmol L-1, soil:solution ratio 1:2.5). Following the 179 classification of Giles et al. (1973), SCP and SMT adsorption curves are C-type. This kind of curve is 180 indicative of a constant distribution of the antibiotics between the soil and the solution, which would mean 181 that the adsorption of both sulfonamides is independent of the concentration for the range here tested. Also, 182 this curves are characteristic for interactions between hydrophobic compounds and soil organic matter 183 (Calvet, 1989), which indicates that organic matter plays an important role in the adsorption of 184 sulfonamides. In addition, SCP adsorption was always higher than that of SMT, as shown by the slope of 185 the adsorption curves (Figures 1 and S1). Both the Linear and Freundlich models satisfactorily described 186 adsorption curves for the 50 crop soils here studied, with R2 (correlation coefficient) values >0.956 in all 187 cases (Tables 2 and S3). Previously, other authors had used both models to describe adsorption curves for 188 SCP (Laak et al., 2006b; Srinivasan et al., 2013; Vieira et al., 2017), and for STM (Lertpaitoonpan et al., 189 2009; Pavlović et al., 2014), with R2 values close to 1. 190 Regarding values of the n Freundlich’s parameter, they were close to 1 for both sulfonamides, ranging for 191 SCP between 1 and 1.4 (average 1.2), and between 0.8 and 1.3 for SMT (average 1.2) (Table 2). Taking 192 into account that when n equals 1, the Freundlich equation becomes linear, this aids to explain why both 193 models satisfactorily describe all the adsorption curves. 194 In addition, the values of the coefficient of distribution (Kd(ads)) were between 0.3 and 23.1 L kg-1 (average 195 of 3.7 L kg-1) for SCP, while they were generally lower for SMT, ranging between 0.5 and 14.2 L kg-1 196 (average 1.7 L kg-1) (Table 2). Similarly, the values of the KF(ads) Freundlich parameter were also higher for 197 SCP, ranging between 0.6 and 21.8 L1/n µmol1-1/n kg-1 (average 5.3 L1/n µmol1-1/n kg-1), while ranging 198 between 0.9 and 16 L1/n µmol1-1/n kg-1 (average 2.7 L1/n µmol1-1/n kg-1) for SMT, confirming a greater affinity 199 of SCP for soils. Similar behavior was previously found by Srinivasan et al. (2014), who obtained for SCP 200 Kd(ads) values between 1.87 and 10.59 L kg-1 (average of 7.40 L kg-1), while they were between 0.96 and 201 5.08 L kg-1 (average of 2.45 L kg-1) for SMT. Rath et al. (2019) obtained KF(ads) values between 1.9 and 5.6 202 8 L1/n µmol1-1/n kg-1 (average 4.0) for SCP, and between 0.9 and 3.7 L1/n µmol1-1/n kg-1 (average 2.6) for SMT, 203 also indicative of the higher affinity of SCP for soils. In addition, Leal et al. (2013) also found similar 204 behavior in their experiments. 205 This different behavior for adsorption of SCP and SMT suggests that the R substituent on the base of 206 sulfonamide structure is of main relevance in the adsorption process. 207 It should be noted that SCP and SMT are amphoteric molecules with different values for the acid 208 dissociation constant (pKa, Table S1). In the present work, the adsorption process took place at a pH of 5.1 209 ± 0.7 for SCP, and 5.0 ± 0.7 for SMT (taking into account the whole set of 50 crop soils, and the 7 initial 210 concentrations used, n = 1,050). At pH 5.0, SMT is almost completely neutral (99.6%) while, for SCP, 26% 211 of the species are in anionic form (74% of zwitterionic species). In view of that, less adsorption could be 212 expected for SCP than for SMT, due to the repulsion taking place between SCP anionic species and the 213 surfaces of the different soil components, which present a predominance of negative charges at pH 5.0. 214 However, as previously comment, the results of the present study clearly indicate that SCP has higher 215 affinity than SMT for soils, which would mean that the predominance of one or other species (cationic, 216 zwitterionic and/or anionic) does not explain the different adsorption behavior for both sulfonamides. In 217 fact, it is in agreement with that observed by Leal et al. (2013), who did not find relations between 218 adsorption of different sulfonamides to soils and the values of their acid dissociation constants (pKa). 219 Another characteristic of the molecules of sulfonamides, which is also dependent on the R substituent on 220 the base of sulfonamide structure, is the hydrophobicity. In this regard, the value of the partition coefficient 221 octanol-water (KOW) must be taken into account. This coefficient describes the propensity of the molecules 222 to dissolve in a non-polar medium. At pH 5.0, SMT has a log KOW value of 0.27, while it is 0.69 for SCP 223 (Table S1), suggesting that adsorption of sulfonamides can be highly influenced by hydrophobicity, and 224 specifically that adsorption is higher for more hydrophobic (with higher log KOW values) molecules. In a 225 similar way Figueroa-Diva et al. (2010) found the following adsorption sequence: Sulfadimethoxine> 226 Sulfamethoxazole ~ SMT> Sulfamerazine, coincident with the trend of the log KOW values (1.63, 0.89, 0.28 227 and 0.14, respectively). Also, Leal et al. (2013) studied the adsorption of 5 sulfonamides in 13 soils, finding 228 that sulfadiazine (which had the lowest log KOW value) showed the least degree of adsorption to soils. 229 In general, the values of Kd(ads) and KF(ads) obtained in this work for SCP and SMT (Tables 2 and S3) are of 230 the same order as those reported by other authors for these antibiotics in other soils (Leal et al., 2013; Rath 231 9 et al., 2019; Srinivasan et al., 2014), as well as for other sulfonamides (Hu et al., 2019; Leal et al., 2013; 232 Rath et al., 2019). However, they are clearly lower than those found for other classes of antibiotics, such as 233 tetracyclines, fluoroquinolones or macrolides (Wang and Wang, 2015). Specifically, Laak et al. (2006a) 234 reported Kd(ads) values of up to 7199 L kg-1 for oxytetracycline, and up to 387 L kg-1 for tylosine (a 235 macrolide), while Leal et al. (2013) found Kd(ads) values of up to 1,277,874 L kg-1 for ciprofloxacin (a 236 fluoroquinolone), which are several orders of magnitude higher than those observed in our work for SMT 237 (0.5-14.2 L kg-1, Table 2) and for SCP (0.3-23.1 L kg-1, Table 2). This is a further evidence of the high 238 mobility of sulfonamides in soils, with subsequent high bioavailability and risks of transport to surface and 239 ground-waters. 240 241 Relation between adsorption of sulfonamides and soil characteristics 242 Bearing in mind that SCP and SMT adsorption were very variable for the different soils studied (SCP 243 adsorption ranged between 12 and 92%, average of 60%, while SMT adsorption ranged between 18 and 244 87%, average of 41%), a bivariate Pearson’s correlation was performed to determine which soil 245 characteristics have a greater influence on the adsorption process. 246 On the other hand, multiple linear regression analyses were also carried out, considering adsorption 247 parameters Kd(ads) and KF(ads) as dependent variables, and the different soil characteristics as independent 248 variables. As a result, it was shown (Table 3) that, for both SCP and for SMT, Kd(ads) and KF(ads) correlated 249 positively and significantly with soil parameters related to organic matter (OC and N), and with clay 250 content, as well as with variables related to the exchange complex, such as eCEC, Mge, Ale, Nae and Ke; 251 and in addition, they correlated negatively with sand content, and also with pH in the case of SCP. Leal et 252 al. (2013) also found for adsorption of sulfonamides in acid soils that Kd(ads) correlated positively with 253 eCEC, OC content, and with clay content, while correlated negatively with sand content. Similarly, Vieira 254 et al. 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Mean, maximum, minimum and median for the main physicochemical characteristics of the 50 589 crop soils studied 590 pH W pH KCl Ca e Mg e Na e K e Al e eCEC N OC Sand Silt Clay cmol c kg-1 % Mean 5.0 4.3 2.93 0.67 0.28 1.15 1.04 6.07 0.23 2.7 59 20 21 Minimum 4.1 3.7 1.02 0.31 0.12 0.40 0.01 3.88 0.09 1.1 41 12 16 Maximum 6.3 6.2 13.29 1.68 0.57 2.96 2.66 16.47 0.84 10.9 70 31 34 Median 4.9 4.2 2.20 0.57 0.27 1.12 0.99 5.30 0.21 2.4 60 20 20 pHw: pH in water; pHKCl: pH in 0.1 M potassium chloride; Cae: exchangeable calcium; Mge: exchangeable 591 magnesium; Nae: exchangeable sodium; Ke: exchangeable potassium; Ale: exchangeable aluminum; eCEC: 592 effective cation exchange capacity; N: nitrogen; OC: organic carbon 593 594 595 24 Table 2. Mean, maximum, minimum and median for the parameters corresponding to the adjustment of 596 adsorption and desorption data to the Linear and Freundlich models, using the 50 crop soils studied 597 SCP SMT Linear Freundlich Linear Freundlich Adsorption K d(ads) R2 K F(ads) n (ads) R2 K d(ads) R2 K F(ads) n (ads) R2 Mean 3.7 0.992 5.3 1.2 0.995 1.7 0.992 2.7 1.2 0.993 Minimum 0.3 0.956 0.6 1.0 0.960 0.5 0.974 0.9 1.0 0.972 Maximum 23.1 0.999 21.8 1.4 1.000 14.2 0.999 16.0 1.3 0.999 Median 3.0 0.995 4.7 1.2 0.997 1.3 0.992 2.3 1.2 0.994 Desorption K d(des) R2 K F(des) n (des) R2 K d(des) R2 K F(des) n (des) R2 Mean 10.9 0.985 12.1 1.1 0.988 13.5 0.96 15.7 1.3 0.953 Minimum 5.3 0.866 5.2 0.6 0.866 3.2 0.85 4.3 0.8 0.843 Maximum 65.4 0.999 73.7 1.3 1.000 133.9 0.99 117.6 3.7 0.996 Median 8.7 0.994 9.6 1.1 0.995 6.2 0.97 8.8 1.2 0.965 Kd (L kg-1): distribution constant in Linear model; KF (L1/n µmol1-1/n kg-1) and n (dimensionless): Freundlich 598 constants; R2: correlation coefficient between experimental and model data 599 600 601 25 Table 3. Pearson's r values for correlations between Kd(ads) and KF(ads) parameters and different soil 602 characteristics (number of soil samples = 50) 603 SCP SMT K d(ads) K F(ads) K d(ads) K F(ads) pH W -0.317* -0.283* -0.260 -0.213 pH KCl -0.227 -0.232 -0.192 -0.172 Ca e 0.150 0.112 0.177 0.192 Mg e 0.472** 0.405** 0.472** 0.455** Na e 0.372** 0.300* 0.313* 0.257 K e 0.309* 0.259 0.184 0.127 Al e 0.446** 0.484** 0.454** 0.459** eCEC 0.370** 0.327* 0.373** 0.374** N 0.891** 0.877** 0.875** 0.857** OC 0.891** 0.880** 0.797** 0.879** Sand -0.405** -0.436** -0.369** -0.439** Silt 0.101 0.180 0.086 0.185 Clay 0.571** 0.542** 0.528** 0.543** pHw: pH in water; pHKCl: pH in 0.1 M potassium chloride; Cae: exchangeable calcium; Mge: exchangeable 604 magnesium; Nae: exchangeable sodium; Ke: exchangeable potassium; Ale: exchangeable aluminum; eCEC: 605 effective cation exchange capacity; N: nitrogen; OC: organic carbon. Kd: distribution constant in Linear 606 model; KF: Freundlich constant. * Significant at p≤0.05. ** Significant at p≤0.01607