Environ. Sci. Technol. 2008, 42, 8388–8394
Modeling Degradation and Metabolite Formation Kinetics of Estrone-3-sulfate in Agricultural Soils F R A N K F . S C H E R R , †,‡ A J I T K . S A R M A H , * ,† HONG J. DI,‡ AND KEITH C. CAMERON‡ Landcare Research New Zealand Limited, Private Bag 3127, Hamilton, New Zealand, and Soil and Physical Sciences Group, Agriculture and Life Sciences Division, P.O. Box 84, Lincoln University, Canterbury, New Zealand
Received July 4, 2008. Revised manuscript received September 3, 2008. Accepted September 8, 2008.
Estrone-3-sulfate (E1-3S), formed in the kidneys of pregnant cattle, can act as a precursor to the free hormone estrone (E1) known for its endocrine disrupting potential in wildlife. Laboratory microcosm studies were conducted to investigate the aerobic degradation of E1-3S in three contrasting pasture soils at 7.5, 15, and 25 °C. Deconjugation of E1-3S resulted in the formation of the metabolite E1. Two kinetic modelssa single first-order and a biexponential kinetic modelswere applied to fit the observed degradation dynamics and to derive degradation end-points (DT50 and DT90) for the parent compound and the metabolite for each condition. Model selection and evaluation of their performance were based on a suit of statistical measures (one-way ANOVA, AICc, R2adj, χ2 error-%, and SRMSE). The results showed rapid initial degradation of E1-3S, followed by a much slower decline with time, and rate of degradation was temperature dependent. The DT50 and DT90 values of E13S ranged from a few hours to several days, while the formation of the major metabolite (E1) was concomitant with E13S degradation in all nonsterile soils. The parent compound degradation and formation and subsequent dissipation of metabolite were successfully predicted by both models, however, the nonlinear biexponential model improved the goodness-offit parameters in most cases.
Introduction Estrone-3-sulfate (E1-3S) is a naturally occurring conjugate of the female steroid hormone estrone (E1) and plays a major role in the maternal circulation of pregnant cattle (1, 2). Estrogen-sulfates such as estradiol-3-sulfate (E2-3S) and E13S appear to be the major estrogenic compounds in cattle urine during pregnancy (1), and they are readily degradable in the environment in the presence of arylsulfatase enzymes (3-5). However, residues of E1-3S have been detected across the globe in various environmental media such as sewer systems (6), wastewater (7), wastewater treatment plant effluents (8, 9), river water (8, 10), Tokyo Bay sediments (11), and river sediments (12), indicating incomplete degradation of E1-3S in the environment. * Corresponding author phone: +64 7 859 3737; fax: +64 7 859 3701; e-mail:
[email protected]. † Landcare Research New Zealand Limited. ‡ Lincoln University. 8388
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Pasture soils receive animal wastes either through landapplication of effluents or by direct excretal input from grazing animals, which act as a major source for estrogen exposure in New Zealand’s dairying environment (13). The degradation of free estrogens such as 17β-estradiol (E2) and E1 has been studied by several authors (14, 15) with reported half-lives varying from a few hours to several weeks, and degradation rates were found to be dependent on soil moisture content and temperatures. From a laboratory study, Jacobsen et al. (16) observed E2 to convert to E1 more rapidly in manure-treated nonsterile soils, while Lucas and Jones (17) also observed rapid degradation of E2 and E1 in grassland soils amended with animal wastes. Although sewage bacteria and activated sludge have been found to degrade estrogen sulfates (6), no information exists about soils capability to degrade sulfate conjugated estrogen. In theory, hydrolysis of E1-3S results in the formation of E1, known for its endocrine disrupting potential in wildlife (18). Isobe and Shimada (2) recently pointed out the ability of E1-3S to induce apoptosis in the testicular cells of Japanese quails, with reduction of testicular weights. They suggested that E1-3S is one of the risk factors for endocrine disruption in wildlife. Conjugated estrone is dominant in the urine of dairy cows with the percentage of estrone sulfate varying between 87-98% during the late stages in pregnancy (1). Given that dairy cows are always pregnant apart from around 80 days a year (19) and given that nearly 3.5 million dairy cattle graze the New Zealand (NZ) pasture throughout the year, the total conjugate loading in NZ’s dairying environment is expected to be very high. The abundance and activity of arylsulfatase enzymes is expected to be much lower in the pastoral environment than in a sewer system. Therefore, if conjugated estrogens are not readily converted back to the free form, there is potential for them to leach into the deeper soil profile due to their ionic and less hydrophobic nature. Given the above scenarios, our objective was to investigate the degradation and metabolite formation dynamics of E13S in selected pasture soils under controlled laboratory conditions. Measured data were modeled using a simple firstorder kinetic and a first-order biexponential model to derive the degradation end-points for both parent compound and metabolite. Performance of the models was evaluated using an array of statistical measures.
Materials and Methods Chemicals. Estrone (>99% purity) and estrone-3-sulfate (g95% purity) were purchased from Sigma-Aldrich, Australia. Acetonitrile (Mallinckrodt ChromAR, g99.8% purity), dichloromethane (Mallinckrodt UltimAR, g99.9% purity), methanol (Mallinckrodt ChromAr, g99.9% purity), and ammonium sulfate (BDH Laboratory Supplier AnalaR, >99% purity) were obtained from Biolab Scientific, New Zealand. Dicyclohexylamine (Merck, >99% purity) was synthesized with concentrated hydrochloric acid (Ajax Finechem, 36%) to form solid dicyclohexylamine hydrochloride (DCH · HCl). Soils. Three topsoils (0-5 cm) with contrasting physicochemical properties (Supporting Information, Table S1) were collected from three geographic locations in NZ. Hamilton clay loam and Matawhero silt loam soils are from the Waikato and Hawke’s Bay regions in the North Island, while Gibsons fine sandy loam comes from the Marlborough Region in the South Island. Microbial biomass carbon (MBC) was determined by a fumigation-extraction method using a KC (fraction of biomass C mineralized to CO2) factor of 0.41 (20). Details describing the fumigation-extraction method are given in the Supporting Information, while descriptions 10.1021/es801850a CCC: $40.75
2008 American Chemical Society
Published on Web 10/21/2008
of soils and the methods used to determine the remaining properties can be found elsewhere (21, 22). Microcosms Study. For the microcosm study, soils were sieved (2 mm) and stored in the dark at 4 °C immediately after collection. After adjusting to 60% of its maximum water holding capacity (-33 kPa), 150 g of each soil was preincubated in 250-mL preserving jars at 7.5, 15, and 25 ( 1 °C in the dark for 5 days. The headspace in the jars was aerated regularly throughout the experiment to maintain aerobic conditions (CO2 < 2%), and a glass beaker containing 5 mL of water was placed in the jars to prevent the soil from drying. Before fortification, a subsample of 50 g was dried at 30 °C overnight, and the water content was determined gravimetrically. The lost water was reapplied with an aliquot of 1.875 mL of E1-3S stock solution (400 µg mL-1 in methanol), and the spiked soil was then mixed with the remaining 100 g of the preincubated soil to obtain a concentration of ∼5 mg kg-1. We chose this concentration since no data exist in the literature about the exposure concentration of E1-3S under pasture environment when intensive cattle grazing is practiced (see the Supporting Information for detail). One sterile control was prepared for each soil by autoclaving thrice (35 min at 122.5 °C and 1.13 bar) and incubated at 15 °C in the dark. Three subsamples were periodically (0, 2, 4, 8, 12, 24, 48, 72, 96, 144, 196, and 240 h) transferred from the jars into 35-mL glass centrifuge tubes (sealed with Teflon-lined screw caps) for extraction and analysis of E1-3S and formation of E1. For the sterile control samples, sampling was done at 0, 24, 72, 144, 196, and 240 h. Preliminary results showed the initial subsampling amount of 2 g resulted in insufficient detection limits at later stages. Subsampling was therefore increased over the first 2 days from 2 g (0 and 2 h) to 3 g (4 and 8 h), 4 g (12 and 24 h), and finally to 5 g (g48 h). Extraction and Analysis. In order to avoid any compound losses, extraction of the target compounds was performed instantaneously after subsampling by adding 0.25 mL of DCH · HCl (10 µg mL-1 in H2O) and 4.9 mL of DCM (respectively 5.9 mL for the 4- and 5-g samples) to the subsamples, sonication for 10 min at room temperature (22 ( 1 °C), and shaking for 24 h on an end-over-end shaker. An aliquot of 2 mL (4 mL for the 4- and 5-g samples) of the DCM phase was transferred to a HPLC amber glass vial, evaporated to dryness under a gentle stream of N2, reconstituted in 0.4 mL of 20% methanol in water, and analyzed by High Performance Liquid Chromatography (HPLC) equipped with an UV detector. Preliminary experiments yielded a recovery of 98.8 ( 5.89% for E1-3S and its metabolite E1 in five different soils (n ) 48) for this extraction method. However, the recovery of E1-3S in the sterile controls at t ) 0 showed some variation and accounted for 89.9 ( 7.49, 91.7 ( 5.51, and 86.3 ( 14.8% (n ) 3) in the Hamilton clay loam, Matawhero silt loam, and Gibsons fine sandy loam soils, respectively. Full details about HPLC conditions and retention times for E13S and E1 are given in the Supporting Information.
Data Analysis and Modeling Parent Compound Degradation. The parent compound degradation was fitted with a single first-order exponential decay model and a two-compartment first-order biexponential decay model, assuming no back conversion, no influence of sorption on the degradation, and no altering due to microbial growth. Both of these models can be described mathematically single first-order (SFO): Pt ) P0e-k1t
(1)
double first-order in parallel (DFOP): Pt ) P0[ge-k1t + (1 - g)e-k2t] (2) where t is time (h), k1 and k2 are the degradation rate constants (h-1), P0 is the initial amount of hormone, Pt is the total
amount of hormone at time t, and g is the fraction of P0 applied to compartment 1 of the DFOP model. According to eq 2, degradation takes place in two compartments: in the first compartment rapid degradation is expected to occur within the soil-water phase, where microorganisms have easy access to the compound. In the second compartment, degradation is slow, and the compound is expected to be adsorbed to soil particles or to be located in micropores within the soil matrix, with the degradation rate being governed by the slow desorption-diffusion processes (23). The speed at which the compound is transformed in the two compartments is expressed by their respective rate constants k1 (first compartment) and k2 (second compartment), and usually k1 > k2. Two compartment models may also fit to degradation of compounds that have two isomers. However, the presence and the nature of the thio-ester bond in E1-3S relates to one distinctive form of the molecule. Therefore isomer specific degradation patterns in our case can be excluded. Metabolite Formation and Dissipation. The metabolite formation and dissipation dynamics were modeled using the best fit from the respective parent compound as input (eqs 1 and 2), assuming metabolite degradation also follows a single first-order kinetic (SFO) or a two-compartment firstorder biexponential decay model (DFOP) SFO: Mt ) ffM(P0 - Pt)e-kM1t
(3)
DFOP: Mt ) ffM(P0 - Pt)[gMe-kM1t + (1 - gM)e-kM2t]
(4)
where ffMis the formation fraction of the metabolite, kM1 and kM2 are the metabolite degradation rate constants (h-1), and gM is the fraction of the metabolite applied to compartment 1. Data Handling and Fitting Procedure. The data-handling activities were conducted in accordance with the recommendations illustrated in the FOCUS (FOrum for Coordination of pesticide fate models and their USe) guidance document (24) i.e., any concentrations of the metabolite E1 detected at t ) 0 were added to the parent compound concentration. The arithmetic average of those corrected triplicate subsamples at t ) 0 was used as the initial parent compound concentration (P0), which was set to 100% and all subsequent parent compound (Pt) and metabolite (Mt) concentrations were expressed as percent remaining of P0. An initial screening showed negligible variation in parameter predictions when the fitting was performed with the normalized data sets compared with the absolute values. Therefore, fitting was performed on the normalized data sets. The statistical software R (version 2.6.1) with the nonlinear mixed effects (nlme) package employing nonlinear least-squares regression (nls) analysis with the Levenberg-Marquardt algorithm for parameter optimization was used to fit the measured data (25). For starting values and parameter constraints refer to the Supporting Information (Table S2). Times for 50% (DT50) and 90% (DT90) dissipation of the parent compound and its metabolite were calculated directly from the optimized first-order rate constants k in the case of the SFO model (DT50 ) ln 2/k and DT90 ) ln 10/k). For the DFOP model no analytical solution exists, and therefore an iterative procedure was employed using the solver tool in Excel (Microsoft Excel 2003 SP2) to derive dissipation times. Statistical Measures. A number of statistical indices were computed for model comparison and goodness-of-fit evaluation at a given soil and temperature, which included oneway analysis of variance (ANOVA), the adjusted coefficient of determination (R2adj), the Akaike Information Criterion for small sample size (AICc), a measurement error percentage to pass the chi-square (χ2) statistic at 5% significance level [err (5%)], and the scaled root mean squared error (SRMSE). These terms are defined in the Supporting Information. VOL. 42, NO. 22, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Degradation of E1-3S (A) and formation and dissipation of E1 (B) in Hamilton clay loam at 7.5 °C (red circles), 15 °C (blue squares), 25 °C (black triangles), and 15 °C sterile control (diamonds). Error bars represent one standard deviation of n ) 3 samples. *,#,$ indicate n ) 2 samples at 7.5, 15, and 25 °C, respectively. Insert denotes the first 75 h in detail. Lines display the best fit for 7.5 °C (solid red), 15 °C (dashed blue), and 25 °C (dashed black).
FIGURE 2. Degradation of E1-3S (A) and formation and dissipation of E1 (B) in Matawhero silt loam at 7.5 °C (red circles), 15 °C (blue squares), 25 °C (black triangles), and 15 °C sterile control (diamonds). Error bars represent one standard deviation of n ) 3 samples. *, # indicate n ) 2 samples at 7.5 °C and 15 °C, respectively. Lines display the best fit for 7.5 °C (solid black), 15 °C (dashed gray), and 25 °C (dashed black).
Results and Discussion Parent Compound Degradation and Modeling. The observed degradation of E1-3S with time is illustrated in Figures 1A (Hamilton clay loam), 2A (Matawhero silt loam), and 3A (Gibsons fine sandy loam), respectively. In general the degradation occurred without a lag phase, and E1-3S rapidly 8390
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FIGURE 3. Degradation of E1-3S (A) and formation and dissipation of E1 (B) in Gibsons fine sandy loam at 7.5 °C (red circles), 15 °C (blue squares), 25 °C (black triangles), and 15 °C sterile control (diamonds). Error bars represent one standard deviation of n ) 3 samples. Lines display the best fit for 7.5 °C (solid black), 15 °C (dashed gray), and 25 °C (dashed black). degraded in all soils, showing an effect of temperature on the degradation rate. With increasing temperature the degradation occurred faster in all the soils which was the most noticeable in the Gibsons fine sandy loam soil (Figure 3A). The persistence of E1-3S also showed temperature dependence and differed among the soils. In the Hamilton clay loam soil E1-3S dropped below MDL after 72 h (25 °C) and 96 h (15 °C) but remained detectable until 240 h (7.5 °C). Similarly, in the Matawhero silt loam soil E13S dropped below MDL before 240 h at 25 and 15 °C, but trace amounts could still be detected at 7.5 °C. In contrast, E1-3S remained detectable throughout the incubation period at all temperatures in the Gibsons fine sandy loam. The percent remaining after 240 h was temperature dependent with values of 1.1, 0.7, and 0.2% at 7.5, 15, and 25 °C, respectively. E1-3S did not significantly decrease in the sterile controls, though the recovery varied over the incubation period especially for the Matawhero and Gibsons soils (Figures 1A, 2A, and 3A, diamonds) which is possibly a result of autoclaving which has been shown to change sorptiondesorption patterns of soils (26). Gamma radiation which has been used to avoid soil structural changes was not available in our laboratory. To our knowledge this is the first study that reports degradation of E1-3S in agricultural soils, and thus a comparison is difficult between the present findings with respect to other soil microcosm studies involving the same compound. However, E1-3S degradation has been studied in a wastewater microcosm by D’Ascenzo et al. (6), who observed an acclimation period of 10 h. The absence of a lag phase in the nonsterile treatments and the lack of abiotic loss in the sterile controls in our study suggest the role of microorganisms in the degradation of E1-3S. This assumption can be supported by the values for the soils’ MBC (Supporting Information, Table 1). For instance, the Hamilton clay loam with the highest MBC value showed the fastest dissipation (Figure 1A), while the Gibsons fine sandy loam soil with the lowest MBC resulted in the lowest degradation rates. The necessity of microbial activity to degrade free E1 and other
TABLE 1. Optimised Parameters with Standard Error (SE), Statistical Measures, and Dissipation Times for the Two Models Fitted to the Measured Degradation Data of E1-3S in Three Soils under Three Temperaturesa optimized parameters
err (5%)
SRMSE
DT50 [h]
DT90 [h]
Hamilton Clay Loam 0.112 (0.008) 1.88 (5.85) 0.094 (0.009) 0.258 (0.022) 0.363 (0.054) 0.046 (0.030) 0.453 (0.021) 0.547 (0.022) 0.107 (0.028)
0.989 0.995 0.982 0.992 0.996 0.999
36.3 35.1 36.1 39.3 21.8 52.3
9.58 6.26 13.2 8.06 5.86 1.13
0.118 0.071 0.164 0.090 0.074 0.012
6.17 5.61 2.68 2.36 1.53 1.43
20.5 22.8 8.91 12.3 5.09 5.65
Matawhero Silt Loam 0.059 (0.009) 0.322 (0.098) 0.030 (0.005) 0.110 (0.008) 0.296 (0.189) 0.073 (0.024) 0.214 (0.017)
0.954 0.990 0.988 0.993 0.987
53.7 42.9 35.0 40.1 31.7
16.1 7.06 8.87 6.94 9.04
0.199 0.080 0.110 0.078 0.113
11.8 7.60 6.29 5.51 3.24
39.3 58.2 20.9 25.6 10.8
108 (2.31)
Gibsons Fine Sandy Loam 0.025 (0.002) 0.991
40.3
6.00
0.074
27.4
91.2
108 (4.37)
0.038 (0.005)
0.974
52.9
0.146
18.3
61.0
0.992 0.998
31.4 21.6
P0 (SE) [%]
7.5
SFO DFOPc SFO DFOPc SFO DFOPd
95.2 (2.83) 100 (2.30) 98.7 (3.93) 101 (2.60) 99.6 (2.05) 100 (0.04)
SFO DFOPd SFO DFOPb SFO DFOPe
90.2 (5.06) 102 (3.13) 97.5 (2.91) 101 (2.81) 103 (3.66)
SFO DFOPe SFO DFOPe SFO DFOPd
15 25
7.5 15 25
7.5 15 25
96.9 (2.50) 100 (1.30)
g (SE) [%]
15.5 (5.89) 84.0 (8.42) 89.1 (3.32)
41.6 (6.89) 35.9 (26.6)
32.4 (12.6)
0.171 (0.010) 0.533 (0.204)
k2 (SE) [h-1]
dissipation times
AICc
model
k1 (SE) [h-1]
statistical measures R2adj
T [°C]
0.118 (0.017)
11.8 9.53 4.34
0.118 0.049
4.05 3.46
13.5 16.2
a Bold letters show ′best fit’. b Statistical difference between SFO and DFOP at p < 0.05. c Statistical difference between SFO and DFOP at p < 0.01. d Statistical difference between SFO and DFOP at p < 0.001. e Did not converge.
hormones in soils has been reported (14, 27), however, without relating the rate of degradation to the soil’s microbial biomass. Elsewhere, Okayasu et al. (28) found a reduction of E1-3S from approximately 7 ng L-1 to 2 ng L-1 within the first 3 h of incubation in a batch degradation experiment involving activated sludge from a wastewater treatment plant. No further degradation was observed in the subsequent 6-h duration of their experiment. In wastewater and activated sludge, the degradation of E1-3S is probably governed by intracellular arylsulfatase enzymes in Escherichia coli (46, 28). It is conceivable that since soils also contain external arylsulfatase enzymes (29) to which the substrate E1-3S would readily be available without a diffusion barrier such as the cell membrane may explain the lack of an adoption phase and the continuous degradation in our study. Given the fact that E1-3S has been detected in a variety of environmental matrices with a broad range of pH (6-12) it can be assumed that the soils’ pH had no effect on the stability of E1-3S during the incubation. The optimized parameters from the two model fits, the corresponding degradation end-points (DT50, DT90), and the statistical indices to evaluate the goodness-of-fit are presented in Table 1. The SFO model described the observed degradation data well across the soils and temperatures. In general, R2adj were close to unity, the SRMSE scores were low, and the error-percents were < 15%, a threshold value in FOCUS (24). The exception was for the Matawhero silt loam at 7.5 °C, where the error-percent was 16.1%. The model predicted value for P0 deviated considerably from 100% for the Matawhero soil (7.5 °C) and Gibsons soil (7.5 and 15 °C); however, the predicted P0 was close to 100% for the remaining data sets (Table 1). The first-order rate constants of the SFO model were in the range of 0.025-0.453 h-1 and increased with the temperatures following an order: Hamilton clay loam > Matawhero silt loam > Gibsons fine sandy loam. The division of the degradation pattern in two parallel compartments, fast and slow, by the DFOP model did not yield a solution for three out of the nine data sets. However, applying the DFOP model improved the goodness-of-fit indices (R2adj, SRMSE and the minimum error-%) for the
remaining six data sets (Table 1). The estimates for P0 were close to 100% with low standard errors. The estimates for the split factor ′g’ (division between the fast and slow compartments) partly yielded high standard errors. For instance, the predicted value of 35.9% had a standard error of 26.6 for the Matawhero silt loam soil at 15 °C (Table 1), and this uncertainty was also reflected in the corresponding rate constant, k1. The general trend in the rate constants k1 and k2, therefore, did not show a consistent relationship with the increasing temperatures. The ANOVA significantly favored the DFOP model in all cases. However, on three occasions the AICc scores did not support the choice of the more complex DFOP model over SFO, likely due to the small sample size. The penalty term in AICc ( Supporting Information eq S.6) is sensitive to small sample size (25), and this effect was pronounced for the Hamilton clay loam at 25 °C where at a sample size of eight the addition of two more parameters within the DFOP model increased the AICc score by more than 2-fold due to the reduced degrees of freedom. Figure S2 (Supporting Information) lends further support to the decision for the DFOP model choice when the sum of statistical indices suggested its better performance over the SFO model. The composite box-whisker residual plots (Figure S2) indicate that dividing the degradation into a slow and a fast compartment resulted in less widespread residuals with medians very close to the zero line. In particular at later stages of degradation, the DFOP model was more accurate in predicting the observed dynamic than the SFO model (Figure S2). This is in agreement with a study by Herman and Scherer (30)who investigated the use of four different models including SFO and DFOP to predict the degradation for 61 data sets and concluded the DFOP model resulted in the overall most accurate predictions. Furthermore, their study (30)showed that a DFOP model is rather unlikely to converge when data are well described with the SFO model, which was observed in our case. The selection of the ′best model’ for each data set was based on an array of statistical indices. The model that had the majority of statistical measures in its favor (bold letters VOL. 42, NO. 22, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Optimized Parameters with Their Standard Error (SE), Statistical Measures, and Dissipation Times for the Two Models Fitted to the Measured Formation and Degradation of E1 in Three Soils under Three Temperaturesa optimized parameters T [°C]
model
ffM (SE) [%]
7.5
SFO DFOPe SFO DFOPe SFO DFOPb
27.2 (4.79) 16.9 (3.52) 14.0 (0.72) 14.8 (0.62)
SFO DFOP SFO DFOP SFO DFOPd
12.4 16.1 16.4 24.0 27.7 32.7
(1.22) (12.1) (1.94) (9.35) (2.60) (2.70)
SFO DFOPb SFO DFOPc SFO DFOPe
17.2 62.9 27.7 41.0 8.84
(1.95) (31.2) (4.37) (8.09) (1.01)
15 25
7.5 15 25
7.5 15 25
gM (SE) [%]
93.4 (3.57)
26.5 (52.7) 46.7 (21.7) 80.6 (8.80)
75.7 (11.6) 48.4 (9.77)
kM1 (SE) [h-1]
statistical measures kM2 (SE) [h-1]
R2adj
dissipation times
AICc
err (5%)
SRMSE
DT50 [h]
Hamilton Clay Loam 0.102 (0.022) 0.903
13.2
60.6
0.470
6.80
22.6
0.086 (0.027)
0.868
17.6
73.2
0.510
8.06
26.8
0.991 0.996
-22.3 -24.3
15.8 7.23
0.141 0.082
5.24 nsf
17.4 18.2
0.132 (0.009) 0.159 (0.015)
0.015 (0.010)
DT90 [h]
0.006 0.215 0.014 0.135 0.068 0.122
Matawhero Silt Loam (0.001) 0.954 (0.690) 0.006 (0.002) 0.943 (0.003) 0.930 (0.171) 0.011 (0.004) 0.931 (0.010) 0.971 (0.028) 0.017 (0.008) 0.990
12.3 22.8 20.3 28.1 11.3 6.54
41.2 19.3 56.0 23.0 40.2 10.2
0.225 0.220 0.298 0.262 0.224 0.116
108 nsf 48.7 nsf 10.2 nsf
361 332 162 154 33.7 41.1
0.015 0.324 0.023 0.138 0.023
Gibsons Fine Sandy Loam (0.002) 0.956 (0.165) 0.013 (0.002) 0.975 (0.002) 0.980 (0.072) 0.019 (0.002) 0.989 (0.005) 0.933
1.11 2.57 0.39 0.87 3.6
32.7 13.4 27.4 9.28 37.3
0.227 0.153 0.164 0.106 0.338
47.5 nsf 30.0 nsf 30.0
158 nsf 100 84.5 100
a Bold letters show ′best fit’. b Statistical difference between SFO and DFOP at p < 0.05. c Statistical difference between SFO and DFOP at p < 0.01. d Statistical difference between SFO and DFOP at p < 0.001. e Did not converge. f No solution.
in Table 2) was selected to plot the data (Figures 1A, 2A, and 3A) and to serve as the parent input function for the subsequent metabolite modeling. Overall the DFOP model was found to be superior to the SFO model for describing the parent compound degradation data in our study. The investigation of more complex kinetic models than SFO has been recommended to describe laboratory degradation data in order to avoid an underestimation of degradation rates at later sampling times and to obtain appropriate degradation end-points (24, 30). The model-derived DT50 and DT90 in our study suggest a correlation with the soil types, with soils having greater MBC resulting in lower values for DT50 and DT90 (Table 1). D’Ascenzo et al. (6) reported a half-life of approximately 2.5 d at 20 °C with an initial concentration of 25 µg L-1. The DT50 values in our study are much lower and fell in the range of a few hours to 1 d indicating that the soil biota was capable of utilizing E1-3S as a substrate. However, in the Gibsons soil containing low MBC, E1-3S persisted > 10 d, particularly at low temperatures (Figure 1A and Table 1). DFOP degradation patterns may also be a result of reduced microbial activity toward the end of long dated incubation studies (24) which could explain the longest persistence of E1-3S in the Gibsons soil. However, as arylsulfatases are mainly exoenzymes, their abundance is not restricted to microbial activity (29). Thus, association with clay and organic matter particles may allow the enzymes to be distributed in both the solid and liquid phases of soils permitting degradation of E1-3S in both compartments. Metabolite Formation, Dissipation, and Modeling. The formation of E1 is plotted in Figures 1B (Hamilton clay loam), 2B (Matawhero silt loam), and 3B (Gibsons fine sandy loam). E1 was detected in all but the sterile-soil microcosms, and the formation was concomitant with the dissipation of E13S and rapid in each case. The cleavage of the thio-ester bond at position C3 of the E1-3S molecule leads to the formation of E1, which is mediated by naturally occurring arylsulfatases (4, 17, 28). The absence of E1 in the sterile controls highlights the role of biological activity to deconjugate E1-3S forming E1. The deconjugation of E1-3S to E1 in media other than soil has been reported only once before 8392
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(28). Studies conducted in wastewater systems involving steroid conjugates also support this deconjugation pathway (6, 32, 33), and reconjugation ex-vivo and ex-vitro has not been reported in the literature. Therefore, E1 can be regarded as a metabolite of E1-3S. Although the formation and degradation pattern of E1 seemed to be temperature dependent, the observations were inconsistent across the three soils (Figures 1A-3A). In the Hamilton clay loam soil, E1 formed rapidly with a peak reaching 10% (15 °C) and 11% (7.5 °C) of the initial parent compound after 8 h and dissipated rapidly thereafter (Figure 1B). No initial build-up of E1 was detected at 25 °C. The maximum percentage of E1 at 25 °C (6.8% of parent compound) occurred at 2 h after incubation. In the Matawhero silt loam soil E1 peaked after 72 (7.5 °C), 12 (15 °C), and 4 h (25 °C), respectively, and on average maximum percentage formed increased slightly with increases in the temperatures (Figure 2B). The percent of E1 remaining at 240 h was comparatively higher than in the Hamilton clay loam, with values ranging from 0.5-2% at the three temperatures (Figure 2B). In the Gibsons fine sandy loam soil it took 2 d for E1 to attain 7% at 7.5 °C; however, between 12 and 24 h, E1 had already reached a peak of around 9% (15 °C) and 7% (25 °C) (Figure 3B). The rate of decline in E1 dissipation after reaching the peak was comparably slower at 7.5 and 15 °C, and < 1% remained on the last sampling event (Figure 3B). However, at 25 °C, E1 dissipated to a level of 1% of the parent compound within 96 h. The results of the two model fits to describe the formation and degradation of E1, metabolite degradation end-points, and corresponding statistical measures are summarized in Table 2. Similar techniques to describe metabolite formation and degradation have been successfully employed for androgens (27) and pesticides (31, 34) in soils, applying the SFO model. In our study the SFO model gave a solution for all data sets (Table 2). Although the majority of the SFO fits resulted in R2adj > 0.9 suggesting a good fit, the associated error-percents were > 15% threshold. The SFO model predicted values for the formation fraction (ffM) decreased with increasing temperatures for the Hamilton clay loam (27.2 to 14.0%) but showed an increase for the Matawhero
silt loam (12.4 to 27.4%), while no clear trend was observed for the Gibsons fine sandy loam. The matching predicted values for the rate constant (kM1) and corresponding endpoints (DT50 and DT90) did not seem to be influenced by the increases in temperatures in the Hamilton clay loam and Gibsons fine sandy loam soils, however, increased with temperature for the Matawhero silt loam (Table 2). Applying the DFOP model improved some of the fits but failed to provide a solution under the given parameter constraints on three occasions. For instance, at 7.5 and 15 °C in the Hamilton clay loam no solution was found; however, the DFOP model significantly (p < 0.05) improved the fit at 25 °C, and this was confirmed by the low values of statistical measures (Table 2). Similarly, the DFOP model resulted in a better fit for the Matawhero silt loam at 25 °C (p < 0.001). In the Gibsons fine sandy loam soil the DFOP model improved the fits for 7.5 and 15 °C but failed to converge at 25 °C. Overall, the DFOP model was able to predict a higher formation fraction (ffM) of the metabolite, most noticeable for the Gibsons fine sandy loam soil. Neither the SFO nor the DFOP model were able to accurately predict the initial peak formation of E1 after 8 h in the Hamilton clay loam at 7.5 and 15 °C under the given parameter constraints (Figure 1B insert). It is noteworthy that the iterative procedure to determine the values for DT50 and DT90 from the DFOP model requires a hypothetical value for the maximum amount of metabolite formed, which in theory is determined by the predicted formation fraction in eq 4 (calculated as Mmax ) ffMP0). However, a solution cannot be found unless the percentage of the metabolite remaining at the given dissipation time (i.e., Mt)DT50/90 ) nMmax with, n ) 0.5 for DT50 and n ) 0.1 for DT90) is covered by the fitted curve. This occurred in a few instances for the DFOP model, which are indicated by ′ns’(no solution) in Table 2. Degradation of E1 was investigated by Colucci et al. (14) in a loam, a sandy loam, and a silt loam soil at 30 °C and a moisture content of 13%. The authors concluded that E1 would dissipate in agricultural soils with DT50 values in the order of hours or a few days, which is in agreement with the DT50 values in our study. However, no DT90 values were given by Colucci et al. (14). The dissipation values in our study suggest that E1 as a metabolite of E1-3S degradation can persist longer than a few hours with DT90 values of >10 d, especially at low temperatures (Table 2). Degradation halflives for E1 in agricultural soils were reported in the range of 1.5-46 d by Lucas and Jones (17). The authors investigated persistence of E1 as influenced by the exposure matrix and pointed out that estrogens applied to the soils with manure degrade 2-7-fold faster than estrogens applied with sheep urine. Moreover, a lag-phase of 2-10 d was also observed in urine-amended soil microcosms (17). In the present study E1-3S was applied in aqueous matrix, and it can be assumed that livestock urine would contain a plethora of organic compounds and nutrients that would compete for the necessary arylsulfatase enzyme activity. In addition, urine may also influence the local microbial population by altering the carbon and nitrogen ratio and pH of soils which may hinder dissipation of E1-3S under field conditions. Furthermore, livestock also excrete antibiotics that are administered into the body of the animals (35), which can inhibit the biodegradation of estrogens (36) and thus possibly limit the biodegradation for the estrogen-sulfates, allowing the compounds to persist longer under field conditions. Certainly more work is required to investigate this hypothesis under realistic field conditions, and this can be a focus of future research.
Acknowledgments Frank Scherr thanks Landcare Research for the Ph.D. scholarship. We thank Linda Lee (Purdue University, West
Lafayette, U.S.A.) for providing helpful comments on an earlier version of the manuscript. This study was funded by the Foundation for Research, Science and Technology of New Zealand (Contract No. C09X0217).
Supporting Information Available Additional information on soils, HPLC conditions, rationale of choosing initial concentration, descriptions of the parameter starting values, constraints for the nonlinear regression optimization, the definition of statistical terms, and residual analysis of the model fits via composite box-whisker plots. This material is available free of charge via the Internet at http://pubs.acs.org.
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