Scholarly article on topic 'Multivariate soil fertility relationships for predicting the environmental persistence of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils'

Multivariate soil fertility relationships for predicting the environmental persistence of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils Academic research paper on "Environmental engineering"

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{"2 / 4 / 6-Trinitrotoluene" / "1 / 3 / 5-Trinitro-1 / 3 / 5-tricyclohexane" / "Soil fertility" / "Partial least squares regression" / "Contaminant environmental persistence"}

Abstract of research paper on Environmental engineering, author of scientific article — Chelsea K. Katseanes, Mark A. Chappell, Bryan G. Hopkins, Brian D. Durham, Cynthia L. Price, et al.

Abstract After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed overcoming this problem by considering the environmental persistence of these munition constituents (MC) as multivariate mathematical functions over a variety of taxonomically distinct soil types, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments where the disappearance kinetics of TNT and RDX were measured over a >300 h period in taxonomically distinct soils. Classical fertility-based soil measurements were log-transformed, statistically decomposed, and correlated to TNT and RDX disappearance rates (k -TNT and k -RDX ) using multivariate dimension-reduction and correlation techniques. From these efforts, we generated multivariate linear functions for k parameters across different soil types based on a statistically reduced set of their chemical and physical properties: Calculations showed that the soil properties exhibited strong covariance, with a prominent latent structure emerging as the basis for relative comparisons of the samples in reduced space. Loadings describing TNT degradation were largely driven by properties associated with alkaline/calcareous soil characteristics, while the degradation of RDX was attributed to the soil organic matter content – reflective of an important soil fertility characteristic. In spite of the differing responses to the munitions, batch data suggested that the overall nutrient dynamics were consistent for each soil type, as well as readily distinguishable from the other soil types used in this study. Thus, we hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished “soil types” may provide the means for potentially predicting complex phenomena in soils.

Academic research paper on topic "Multivariate soil fertility relationships for predicting the environmental persistence of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils"

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Journal of Environmental Management

journal homepage: www.elsevier.com/locate/jenvman

Environmental

'Management

Research article

Multivariate soil fertility relationships for predicting the environmental persistence of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils

Chelsea K. Katseanes a'1, Mark A. Chappell b' *■1, Bryan G. Hopkins a, Brian D. Durham b, Cynthia L. Price b, Beth E. Porter b, Lesley F. Miller b

a Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT, USA b Environmental Laboratory, US Army Engineer Research & Development Center, Vicksburg, MS, USA

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ARTICLE INFO

Article history: Received 8 February 2017 Received in revised form 2 August 2017 Accepted 4 August 2017

Keywords:

2,4,6-Trinitrotoluene 1,3,5-Trinitro-1,3,5-tricyclohexane Soil fertility

Partial least squares regression Contaminant environmental persistence

ABSTRACT

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed overcoming this problem by considering the environmental persistence of these munition constituents (MC) as multivariate mathematical functions over a variety of taxonomically distinct soil types, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments where the disappearance kinetics of TNT and RDX were measured over a >300 h period in taxonomically distinct soils. Classical fertility-based soil measurements were log-transformed, statistically decomposed, and correlated to TNT and RDX disappearance rates (k-TNT and k-RDX) using multivariate dimension-reduction and correlation techniques. From these efforts, we generated multivariate linear functions for k parameters across different soil types based on a statistically reduced set of their chemical and physical properties: Calculations showed that the soil properties exhibited strong covariance, with a prominent latent structure emerging as the basis for relative comparisons of the samples in reduced space. Loadings describing TNT degradation were largely driven by properties associated with alkaline/ calcareous soil characteristics, while the degradation of RDX was attributed to the soil organic matter content — reflective of an important soil fertility characteristic. In spite of the differing responses to the munitions, batch data suggested that the overall nutrient dynamics were consistent for each soil type, as well as readily distinguishable from the other soil types used in this study. Thus, we hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished "soil types" may provide the means for potentially predicting complex phenomena in soils.

Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://

creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Developing TNT (2,4,6-trinitrotoluene) and RDX (1,3,5-trinitro-1,3,5-tricyclohexane) explosive compounds was an important 20th century innovation to military and industrial technology, providing marked improvements in explosive yield and stability against

* Corresponding author.

E-mail address: mark.a.chappell@usace.army.mil (M.A. Chappell).

1 Co-first authors.

thermal and physical shock. Yet, the widespread deployment of these munition constituents (MC) resulted in substantial soil and groundwater legacy contamination associated with manufacture and military training, storage, and demolition activities. These legacy contamination issues were previously documented in detail over the last three decades (Binks et al., 1995; Jenkins et al., 2001; Pennington et al., 2001; Hewitt et al., 2007; Clausen, 2011) across a variety of military training and demolition ranges as well as ammunition plants, with extensive efforts undertaken to control, predict, and mitigate on-site contamination.

http://dx.doi.org/10.1016/j.jenvman.2017.08.005

0301-4797/Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Discovering and parameterizing the biogeochemical processes controlling the fate and persistence of MC are viewed as key to predicting their behavior in the soil environment. Typically, the major processes controlling MC environmental fate are partitioned in terms of the sorption, biodegradation rate, and transport parameters in soils. Previously, we demonstrated the limitations of predicting TNT and RDX sorption purely from the perspective of humified organic matter phases in soils and sediments (Chappell et al., 2011a, and references therein) citing uncertainties of three to four orders of magnitude for the distribution coefficient (KD) describing TNT and RDX in soils (a similar range existing for KOC values representing the KD values normalized by the soil total organic carbon, TOC concentration). We argued that estimating MC sorption through KOC values represented a univariate oversimplification of soil surface-promoted "partitioning" behavior. Multilinear regression modeling of sorption data emphasized the importance of soil Ca and Fe content, the cation exchange capacity (CEC), and percent clay composition and, for TNT, the insignificance of TOC (Chappell, 2011; Chappell et al., 2011a; Bridges et al., 2017). Katseanes et al. (2016) expanded these functions using partial least squares regression (PLS) modeling on a sample set containing a more extensive soil characterization matrix, finding that soil K, % CaCO3, and the soil buffering capacity for exchangeable NHj (PBCNH4) - a soil Quantity-Intensity based parameter as statistically significant.

While there exists a good knowledge of the fundamental mechanisms driving the biodegradation of TNT and RDX, theoretically rigorous representations of these mechanisms in the complex multiphasic soil system remain challenging. TNT and RDX can degrade via a number of different abiotic reductive mechanisms (Price et al., 1995,1997; Devlin et al., 1998; Emmrich, 1999, 2001; Hofstetter et al., 1999; Hansen et al., 2003; Nefso et al., 2005; O'Sullivan et al., 2011), yet the MC environmental persistence is largely mediated by soil/sediment biodegradation via microbiological activity (McCormick et al., 1976, 1981; Kaplan and Kaplan, 1982; Bradley and Chapelle, 1995; Comfort et al., 1995; Price et al., 1995; Lewis et al., 1996, 1997; Riefler and Smets, 2000; Spain et al., 2000; Esteve-Nunez et al., 2001; Walker et al., 2006; Yost et al., 2007; Rosen and Lotufo, 2010; Chappell et al., 2011b; Montgomery et al., 2011). All of these compounds degrade in contact with the soil solid phase, but the degradation rate is enhanced at higher pH and lower Eh values, characteristic of basic, electron-rich environments. Accordingly, the commonly identified degradation products represent basic amino or aminohydroxy derivatives (Price et al., 2001; Thorn and Kennedy, 2002; Yost et al., 2007) consistent with reduction reactions detailed in the scientific literature (Thorn et al., 2002; Qasim et al., 2007). Yet, with all this mechanistic information, it remains difficult to confidently predict MC persistence in soils. For example, the distribution of published MC degradation parameters in soils demonstrated a high uncertainty associated with the calculated degradation coefficients (k, h-1) in soil (Bridges et al., 2017). The values for kTNTand kRDX were 0.226 ± 0.924 (median = 0.0087, min = 0.0003, max = 4.36) and 0.030 ± 0.064 (median = 0.008, min = 9 x 10~5, max = 0.240) for TNT and RDX, respectively. By comparison, RDX generally exhibits a greater resistance against degradation in soil suspensions than TNT, showing usually minimal degradation potential under aerobic soil conditions, but requiring prolonged periods of anaerobic soil conditions (Shen et al., 2000; Price et al., 2001; Larson et al., 2008) system Eh.

The main purpose of this study was to investigate our hypothesis that soil fertility data served as a predictor of the degradation kinetics of TNT and RDX across varying range of taxonomically distinct soils. In agriculture, Liebig's Law of the Minimum provides a useful conceptual theory for connecting nutrient deficiencies to

limits in optimal crop yield, stating that maximum crop yield is not determined necessarily by the concentration of all resources (such as nutrients, water, etc.), but by those that are the most limiting. Applying Lieberg's Law to the incidental MC biodegradation in soil, one would expect the existence of nutrient threshold concentrations that limit microbial activity in soils to also influence MC persistence. Currently, we are not aware of any empirical functions for k-TNT and k-RDX related to soil properties, although the influence of major soil components such as water, C, N, and P on TNT and RDX biodegradation rates have been reported previously. For example, elevated MC degradation rates were reported with additions of various C substrates (McCormick et al., 1984; Boopathy, 2002; Fahrenfeld et al., 2013) that stimulated microbial activity. On the other hand, studies also showed that addition of N sources alone such as nitrate and ammonium reduced MC degradation rates (Sherburne et al., 2005; Beller, 2002; Singh et al., 2008; Bernstein et al., 2011). In these studies, easily degradable N sources precluded MC degradation, as there was less of a need for microorganisms to break down the more recalcitrant MC molecules.

Combinations of multiple nutrient deficiencies are also possible. For example, both C and N could be limiting factors that affect microbial activity in the same soil. In this case, the N limitation could promote microbial-mediated MC degradation due to a lack of more easily degradable sources. Limiting C concentrations will overall inhibit the growth of microbial populations and biotically mediated transformations in soils. There also could also be further complex interactions/limitations due to deficient micronutrient concentrations. It is possible for deficiencies of Fe or Mn, for example, to be the main limiting factor for biologic growth — a focus of soil fertility evaluations of agricultural soils but rarely considered when evaluating microbial driven degradation of xenobiotics.

Here, we show ordination-based methods to be particularly useful for comprehending different soil and environmental samples characterized with extensive physical and chemical attribute matrices, viewing soils as a formulated material composed of a variety of constituents. In this work, we focus on rigorous and explicit applications to draw out the latent structure among soil fertility variables and develop multivariate models predicting TNT and RDX degradation in different soil types. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to interpret the environmental risks of training activities on sites.

2. Materials and methods

2.1. Soil selection and characterization

Non-contaminated soils were collected from various locations in the United States representing different soil types and inherent fertility characteristics: Catlin (Fine-silty, mixed, superactive, mesic, Oxyaquic Argiudolls), Playas-Saltair (Fine-silty, mixed, mesic Typic Salorthids), Ruston (Fine-loamy, siliceous, semiactive, thermic Typic Paleudults), Skumpah (Mixed, mesic Typic, Torripsamments), Smithdale (Fine-loamy, siliceous, subactive, thermic Typic Haplu-dults), and Sunev soil (Fine-loamy, carbonatic, thermic Udic Cal-ciustolls). All soils were collected and used in experiments within one calendar year. Details of the processing, preparation, and characterization of these soils are reported in Katseanes et al. (2016).

2.2. Munition degradation experiments

Batch incubations were conducted using soil suspensions in

airtight reactors created from 2.8-L Fernbach flasks following the procedure of Chappell et al. (2011b). Each flask was equipped with a rubber stopper modified to include a septum, Eh, pH, temperature probes, and a sampling tube (See Fig. S1). Soil was added in a 1:10 soil-to-water ratio using double-deionized water. The background electrolyte was set by the equilibrium concentration of salts released from the suspended soils. The Fernbach flasks were placed in a water bath with an automatic cooling system to maintain temperature. Sediments were kept in suspension with a magnetic stirrer. Background levels of Eh, pH, temperature, and CO2 were measured before spiking the systems with TNT or RDX. Reactors were covered with aluminum foil prior to adding dissolved munition constituents to prevent incidental photodegradation of compounds. Solutions were equilibrated for three days before adding a spike solution of 1.3 mL of 40 mg TNT or RDX mL-1 in acetone to reach an overall initial concentration of approx. 20—25 mg TNT or RDX L_1 in the reactor. Suspension and gas samples were taken with time after the addition of munition. The reactors were operated as atmospherically closed systems but were opened daily for 10 min to limit the development of anaerobic conditions. Collected suspension samples were filtered through 0.45 mm filters to separate solids from liquids. Dissolved TNT and its degradation products 2-amino-4,6-dinitrotoluene (2ADNT) and 4-amino-2,6-dinitrotoluene (4ADNT) and RDX concentrations were measured using by EPA Method 8330B on a HP1100 Series LC/MSD system high-pressure liquid chromatography (HPLC) system (Hewlett Packard Company, Palo Alto, CA, USA). Additional carbon dioxide, dissolved organic carbon, and nutrient measurements made in the batch systems are described in the Supporting Information section. Collected suspensions were also centrifuged to collect solids, and extracted using a 50:50 acetonitrile/water solution as described by EPA Method 8330B.

2.3. Data modeling

Kinetic data for MC disappearance were modeled by least-squares fit using a first-order rate law. The rate of disappearance (kMC) was calculated from the following:

MC(t) = MCasym + MCbe-k"ct (1)

where, MC (t) = concentration MC at each time point, MCasym = asymptote of the modeled line, MCo = modeled original MC concentration (at t = 0), and k-MC = the first order rate constant describing MC (either TNT or RDX) disappearance. Additionally, the slope (k-1) of the initial linear portion of the kinetic curve was calculated by:

k_1 = MC0 x kMC (2)

For this work, the degradation rate data were not normalized by MC0 (as is commonly done during the kinetic modeling) in order to preserve the quantitative differences in the absolute values of the rate constants among the different soils. Thus, kMC and k-1 were defined in units of mg L_1 h_1. Thus, the persistence half lives (t1/2) were calculated by:

ti/2 = (0.697/kMC) x MCorginal (3)

where, MCoriginal is the actual initial concentration of MC at the beginning of the experiments (t = 0), thus distinguishing it from the fitted parameter MC0 in Eq. (1).

Correlations between latent structure contained within soil fertility characterization data and kMC were explored using multi-variate Partial Least Squares (PLS) regression. Data transformations

(log10), PLS modeling calculations, and variable selection were described previously (Katseanes et al., 2016), except we limited transformations to those variables with high ratios between their minimum and maximum values and were highly skewed (based on test of normality) (Eriksson et al., 2013) The data were also examined using an orthogonal partial least squares (OPLS) algorithm (Trygg and Wold, 2002), which verified that none of the Y-explained variance in the PLS prediction models was attributed to non-correlated (and orthogonal) structure in X.

3. Results

3.1. Soil fertility interpretation

A detailed chemical and physical assessment of the soils was discussed previously (Katseanes et al., 2016). To summarize, the Catlin and Sunev Mollisols were considered the most fertile soils used in this study, (likely to support the greatest microbiological activity) while the Ruston and Smithdale Ultisols were the most acid and possessed the lowest macronutrient levels, and lowest CEC values — properties typical of soils from the Southeastern U.S., and were expected to support much lower microbiological activity relative to the Mollisol soils. The Playas-Saltair and Skumpah soils were categorized as sodic and saline-sodic soils, respectively, and considered to be the least fertile, greatly limiting microbiological activity due to their alkaline pH, high %CaCO3, and soluble salts, as well as likelihood to promote abiotic alkaline hydrolysis of TNT (Hansen et al., 2003, and references therein).

3.2. Kinetics of MC disappearance

Kinetic data for the disappearance of TNT and RDX in the different soil suspensions (Fig. 1) were, for the most part, well-described by the first-order kinetic model (Tables 1 and 2). The initial fate of MC disappearance was rapid (note that MC concentration at t = 0 represents the calculated initial concentration) as represented by k-TNT-1 and k-RDX-1. For TNT, k-TNT-1 values were highest in the Bonneville Salt Flats soils (37—38 mg L^1 h^1) followed by the fertile Mollisols (1—2 mg L_1 h_1) and then the poorly fertile Ultisols (0.4—0.6 mg L^1 h^1). For RDX, k-RDX-1 values were highest for the fertile Mollisols soils (6—20 mg L_1 h_1), following Bonneville Salt Flats soils (0.3—0.5 mg L_1 h_1), and again, the poorly fertile Ultisols (0.007—0.012 mg L^1 h^1). In general, the kTNT and kRDX values were less than ki-1 values (assumed related to the previously reported sorption values), but generally followed the same order in terms of soil types. For TNT, the highest k values were attributed to the Bonneville Flats soils (2.3 mg L_1 h_1), followed by the fertile Mollisol soils (0.102—0.114 mg L^1 h^1) and then the poorly fertile Ultisols (0.037—0.044 mg L^1 h^1). Note that this trend is generally reflected in the calculated initial disappearance rate, k-TNT-1. For RDX, the highest k values were observed for the fertile Mollisol soils (1.4—2.0 mg L_1 h_1) followed by the Bonneville soils (0.087—0.174 mg L^1 h^1) and the poorly fertile Ultisol soils (0.012—0.014 mg L^1 h^1).

The implication of alkaline hydrolysis mechanisms in the degradation of TNT seemed obvious in these experiments. We attempted to clarify the degree in which persistence was driven by abiotic vs. biotic conditions by simultaneously measuring different soluble solutes and CO2 (assuming soil microbiological respiration) generated by the experiments. For TNT, we observed clear increases in the concentration of 4ADNT with time for the Catlin, Smithdale, and Sunev soils (Fig. S-1). Since alkaline hydrolysis results in the same degradation products as biodegradation, 2ADNT was measured in both Bonneville Flat soils.

Fig. 1. (A) Measured TNT and (B) RDX concentrations in solution with time.

Table 1

Fitted kinetic parameters for TNT degradation based on Eq. (1).

Soil TNTasym, (mg L-1) TNT0 (mg L-1) k-TNT (mg L 1 h 1) Adjusted r2 kTNT-1 (mg L 1 h 1) t-1/2 a(h)

Catlin <0.01 20.00 ± 0.14 0.114 ± 0.030 0.99965 2.290 121

Playas-Saltair 3.67 ± 0.12 16.32 ± 0.51 2.273 ± 0.880 0.99331 37.103 6

Ruston 9.81 ± 0.96 10.04 ± 1.02 0.044 ± 0.018 0.94583 0.443 315

Skumpah 3.27 ± 0.31 16.73 ± 0.33 2.314 ± 0.506 0.99793 38.721 6

Smithdale 4.40 ± 2.31 15.38 ± 2.34 0.037 ± 0.015 0.93384 0.567 375

Sunev 7.56 ± 2.47 11.75 ± 2.60 0.102 ± 0.054 0.84205 1.21 136

a Calculated by t1/2 = [Ln(2)/k-TNTl x original TNT.

Table 2

Fitted kinetic parameters for RDX degradation based on Eq. (1).

Soil RDXasym, (mg L-1) RDX0 (mg L-1) k-RDX (mg L-1 h-1) Adjusted r2 kRDX-1 (mg L-1 h-1) t-1/2 a (h)

Catlin 12.43 ± 0.36 10.59 ± 0.67 1.954 ± 0.469 0.965 20.692 8

Playas-Saltair 19.15 ± 0.23 6.31 ± 2.88 0.087 ± 0.067 0.704 0.549 202

Ruston 19.19 ± 0.09 0.87 ± 0.09 0.014 ± 0.005 0.940 0.012 996

Skumpah 19.73 ± 0.21 1.46 ± 0.81 0.174 ± 0.46 0.466 0.254 83

Smithdale 13.96 ± 0.11 0.57 ± 0.13 0.012 ± 0.008 0.694 0.007 830

Sunev 18.03 ± 0.21 4.97 ± 0.50 1.367 ± 1.696 0.914 6.794 12

Calculated by tj/2 = [Ln(2)/k-RDXl x original RDX.

3.3. Nutrient and CO2 fluctuations during the kinetic experiments

Headspace measurements showed clear increases in CO2 (Fig. S.3) for the fertile Catlin and Sunev Mollisols during the incubation experiments for both TNT and RDX. We attributed these CO2 increases as an indicator of biomass activity occurring within the soils — a potential basis for distinguishing between (incidental) biotic and abiotic degradation. Furthermore, we observed fluctuations in certain dissolved solutes as indicators of bioactivity, such as decreases in NH4-N, NO3-N, NO2-N, TOC, PO4, and SO4 with time in some of the soils (Figs. S.4—7), especially the more fertile Mollisols

— the exception being the very alkaline Playas-Saltair, where NH4-N losses most likely resulted from the high pH driving ammonia volatilization. Increasing concentrations of exchangeable cations, such as Na, Ca, K, and Mg (Figs. S.8—9) suggested that the continual mechanical stirring promoted mineral dispersion and dissolution. Fluctuations in the dissolved Fe and Mn suggested that the system redox (Eh) were generally poised by these heavy metal oxides (Figs. S.10—11). Follow-up measurements to closely monitor pH, Eh, and temperature (Fig. S.12) showed that the soil systems remained generally aerobic over the duration of the incubations.

3.4. Multivariate modeling: MC disappearance kinetics and soil fertility characteristics

PLS modeling was used to correlate the most statistically important soil fertility variables for predicting TNT and RDX degradation rates in the batch experiments. Based on the shape of the validation curve, we chose a two-factor PLS model (Fig. S.13), which explained 89% of the variance in the y-data. Fig. 2 shows that PLS generated linear calibration functions (statistical diagnostics presented in Table 3). The distribution of data points along the linear trend line remained skewed due to the three-order of magnitude range in kMC values in spite of mathematical preprocessing (e.g., log transformations). For TNT, the Bonneville Salt Flats Playas-Saltair and Skumpah soils represented the upper portion of the regression curve (presumably due to abiotic hydrolysis), while the upper portion of regression curve for RDX was driven by the fertile Mollisols. Thus, RMSE values (Table 3) were sufficiently high so that only the kMC values could be predicted with confidence on the Salt Flats soils for TNT and the Mollisol soils for RDX. kMC values for soils at the bottom of the calibration curve could not be statistically distinguished among one another, reflecting an inherent limitation in the small dataset used for this study.

In spite of the relatively high RMSE values (limiting the resolution of the predictions), substantial latent structure was apparent in the score and loading plots for the PLS model. For example, the Salt Flats soils were negatively loaded in Factor 1 (Fig. 3A) while the

Table 3

Fitting statistics for the PLS model predicting the persistence (k.TNTand k.jwx) for TNT and RDX for the taxonomically distinct soils used in this study.

Parameter k-TNT k-RDX

Slope 0.9996 1.001

Offset (mgL-1 h-1) 0.010 6.034 x 10-5

RMSEC [RMSEV] 0.3576 [0.5976] 0.3812 [0.4668]

R2 0.9417 0.8774

Fig. 2. Correlation curves for the 2-Factor PLS-based pedo-informatic functions predicting the measured and predicted modeled persistence rate constants, k.mr and k.RDX for (A) TNT and (B) RDX to the chemical and physical properties of the taxonomically distinct soils selected for this study.

Fig. 3. Scores (A) and X-Y loading (B) plots showing the distribution of samples in reduced space in Factors 1—2 (representing 89% of the explained variance in the data) from PLS-based pedo-informatic models correlating the modeled persistence rate constants, k-TNT and k-RDX for TNT and RDX to the chemical and physical properties of the taxonomically distinct soils selected for this study.

non-saline/sodic soils were positively loaded in Factor-1. Note the clustering of loadings for the alkaline soil characteristics (pH, high % CaCO3, soluble salts, CaP, Cl, Na, S, K) and kTNr in the negative reduced space of Factor 1 (Fig. 3B). On the other hand, the explained variance in Factor 2 may be explained in terms of the soil nutrient and other constituent concentrations. Thus, the soils with higher concentrations of constituents were positively loaded in Factor 2, and the soils low in constituents negatively loaded in Factor 2. In particular, kRDX mapped with the constituents that correlated with the Catlin and Sunev Mollisols. Here, the PLS modeling pointed to the importance of the soil fertility and this connection to soil biological activity (implied in the incidental biodegradation of the TNT and RDX compounds). These relationships were not be identified based on constituent concentrations alone, as demonstrated previously using principal components analysis (PCA) where the

alkaline soil properties clustered with the other fertility-based constituent concentrations (Katseanes et al., 2016).

Using stability tests (Martens and N«s, 1989), we calculated functions for MC degradation rates for the different soil types based on the statistically significant regression coefficients for the two-factor validated model. For TNT, kTNT = -0.071 NH4-N - 0.035 NO3-N + 0.0763 (log Na) + 0.0700 B + 0.0617 (log Cl) + 0.0796 % CaCO3, predicting TNT degradation kinetics in the different soils as directly proportional to particular saline/sodic soil properties and inversely related to inorganic nitrogen species as described previously. Thus, both abiotic and biotic degradation processes were apparently captured in the prediction function. Interestingly, the regression equation emphasized the importance of soil B for predicting kTNT, yet follow-up studies conducted by us did not articulate this relationship (data not shown). Our previous PCA of the soil properties (Katseanes et al., 2016) showed that soil B was highly correlated with soil CaCO3, but OPLS analysis (described in the Materials and Methods section) did not detect orthogonal structure in X contributing to the functions describing k-MC.

For RDX, kRDX = 0.102 %OM. Here, organic matter represented the only statistically significant variable for predicting k^^, most likely given the low number of samples (and thus low degrees of freedom) and the bias introduced by the two saline/sodic soils on

the overall model. Yet, even with this limitation, the regression function suggested an inherent connection of soil microbiological activity to the potential decomposition of soil organic carbon.

3.5. Soil functionality profiles

Whenever testing different soils, we commonly assume that each soil exhibits behavior bound within certain external limits. Thus, each soil type is expected to exhibit its own characteristic behavior, regardless of the actual MC transformation rates. To demonstrate this, we conducted PCA on the previously discussed solution-phase data to determine if there was sufficient latent structure to distinguish the soils. We calculated an overall 5-PC model explaining 85% of the variance in the data. The score plot (Fig. 4A) showed separation of the samples based on which MC was tested: soils from the RDX incubation experiments loaded positively in PC 1 (37 %EV) while soils used in the TNT experiments negatively loaded in PC 1. The loading plot (Fig. 4B) showed that these differences were largely driven by the higher CO2 levels evolved during the TNT incubation experiments relative to the experiments with RDX. In PC 2 (27 %EV) we observed the similar separation of the soils with the saline/sodic soils positively loaded in PC 2, the acid Ultisols negatively loaded in PC 2, and the fertile

Fig. 4. PCA results from a 5-PC model explaining 84.5% of the data variance in the solution-phase constituents measured during the TNT and RDX soil incubations experiments. (A) Score plot comparing PC 1—2, (B) corresponding loading plot for the variables for PC 1—2; (C) score plot after rotating the matrix by 90° to give PC 2—3 (C) corresponding loading plot for PC 2—3.

Mollisols clustered in the middle of the plot, as reported above. But, it can be seen that the soil types readily clustered regardless whether the incubation involved TNT or RDX when the matrix was rotated to show PC 2—3 (Fig. 4C). Here, the more fertile Mollisols were positively loaded in PC 3 (6.9 %EV) and the less fertile soils negatively loaded in PC 3. Thus, the latent structure in the solution phase data suggests that the inherent behavior of the different soil types was preserved even if there were notable differences in the overall soil biological activity driving MC degradation.

This study showed how the latent structure contained in classical soil fertility data was used to generate prediction functions for the degradation of MC in soils. Without application of a design of experiments (DOE) approach, the PLS functions cannot be claimed as statistically "causal". Yet, the fact that the latent structure in the soil characterization data correlated with the complex processes driving the chemical transformation rates of TNT and RDX is notable, particularly given the low degrees of freedom of this small dataset. Similar to our previous work, this supports our theory that the latent structure contained in soil characterization data may be key to overcoming the large uncertainty in environmental models for predicting the complex biogeochemical processes in soils — this uncertainty largely attributed to soil's nearly universal spatial heterogeneity. Thus, exploring this latent structure in soil characterization data opens up opportunities to impose PLS models onto hierarchical frameworks, such as pedomorphological soil classification systems — an approach we referred previously to as Pedo-informatics (Chappell, 2016). Current research is underway to explore multivariate models using larger soil datasets in order to enhance the resolution (decrease the RMSE) in the PLS prediction models. These datasets are currently being generated using soils collected from different suborders (with the NRCS soil classification system).

Acknowledgements

The use of trade, product, or firm names in this report is for descriptive purposes only and does not imply endorsement by the U.S. Government. The tests described and the resulting data presented herein, unless otherwise noted, were obtained from research conducted under the Environmental Quality Technology Program of the US Army Corps of Engineers by the US Army Engineer Research and Development Center (ERDC). Permission was granted by the Chief of Engineers to publish this information. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. The authors express gratitude to Dr. Elizabeth Ferguson, Technical Director of the US Army ERDC Environmental Quality Technology Program for support of this research.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jenvman.2017.08.005.

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