Bulk hydrogen abundances in the lunar highlands: Measurements from orbital neutron data Academic research paper on "Physical sciences"

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Bulk hydrogen abundances in the lunar highlands: Measurements from orbital neutron data

David J. Lawrence a'*, Patrick N. Peplowskia, Jeffrey B. Plescia a, Benjamin T. Greenhagen a, Sylvestre Maurice b, Thomas H. Prettymanc

a The Johns Hopkins University, Applied Physics Laboratory, Laurel, MD, 20723, USA bInstitut de Recherche en Astrophysique et Planétologie, Toulouse, France cPlanetary Science Institute, Tucson, AZ, 85721, USA

ARTICLE INFO ABSTRACT

The first map of bulk hydrogen concentrations in the lunar highlands region is reported. This map is derived using data from the Lunar Prospector Neutron Spectrometer (LP-NS). We resolve prior ambiguities in the interpretation of LP-NS data with respect to non-polar hydrogen concentrations by comparing the LP-NS data with maps of the 750 nm albedo reflectance, optical maturity, and the wavelength position of the thermal infrared Christiansen Feature. The best explanation for the variations of LP-NS epi-thermal neutron data in the lunar highlands is variable amounts of solar-wind-implanted hydrogen. The average hydrogen concentration across the lunar highlands and away from the lunar poles is 65 ppm. The highest hydrogen values range from 120 ppm to just over 150 ppm. These values are consistent with the range of hydrogen concentrations from soils and regolith breccias at the Apollo 16 highlands landing site. Based on a moderate-to-strong correlation of epithermal neutrons and orbit-based measures of surface maturity, the map of highlands hydrogen concentration represents a new global maturity index that can be used for studies of the lunar soil maturation process. We interpret these hydrogen concentrations to represent a bulk soil property related to the long-term impact of the space environment on the lunar surface. Consequently, the derived hydrogen concentrations are not likely related to the surficial enhancements (top tens to hundreds of microns) or local time variations of OH/ H2O measured with spectral reflectance data.

© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND

license (http://creativecommons.org/licenses/by-nc-nd/4XI/).

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Article history: Received 15 August 2014 Revised 5 November 2014 Accepted 10 January 2015 Available online 28 January 2015

Keyword: Moon, surface Spectroscopy

1. Introduction

In recent years, our understanding of the nature of volatile materials on the Moon, and in particular hydrogen, has changed dramatically (see review by Lawrence (2011) and references therein). This new understanding has resulted from multiple new types of measurements from orbital remote sensing and lunar sample analysis. Recent spacecraft observations and data analyses of the lunar poles have revealed new information about both the species, and spatial and depth-dependent distribution of volatiles within permanently shaded regions (Colaprete et al., 2010; Spudis et al., 2010; Miller et al., 2014; Lucey et al., 2014). Away from the poles, orbital spectral reflectance measurements have identified surficial enhancements of H2O/OH (Clark, 2009; Pieters et al., 2009; Sunshine et al., 2009; Klima et al., 2013). Such enhancements may be due to exogenic and endogenic processes. New analyses of Apollo soil samples (e.g., Saal et al., 2008) have

* Corresponding author.

revealed unexpectedly high amounts of water in lunar materials that are derived from the deep lunar interior. Despite these advances in our understanding of lunar volatiles, there does not exist a map of bulk lunar hydrogen abundances for non-polar regions. Such a map would be useful to link orbital remote-sensing data to sample data (e.g., Lawrence et al., 2002), as well as to help understand processes that relate to lunar volatiles.

Global count rates of epithermal neutrons from the Lunar Prospector Neutron Spectrometer (LP-NS) (Maurice et al., 2004) show the promise of constraining non-polar lunar hydrogen concentrations. However, uncertainties in the interpretation of these data have prevented a definitive understanding of how non-polar epi-thermal neutron measurements relate to hydrogen concentrations. In particular, spatial variations of epithermal neutrons in the lunar highlands can be caused by variations both in hydrogen (Johnson et al., 2002) and iron concentrations (Lawrence et al., 2006). Within the nearside Procellarum KREEP Terrane (PKT) (Jolliff et al., 2000), epithermal neutrons are clearly affected by the thermal neutron absorption from high concentrations of the rare-earth elements

http://dx.doi.org/10.1016/jicarus.2015.01.005 0019-1035/© 2015 The Authors. Published by Elsevier Inc.

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

(REE) gadolinium and samarium (Lawrence et al., 2006). While the spatial association between epithermal neutrons and REEs is clear, a quantitative understanding of how the epithermal neutron data relate to hydrogen within the PKT requires a better understanding of the neutron transport systematics (Lawrence et al., 2006; Prettyman et al., 2014) along with the integration of new information about how hydrogen may be enriched in KREEP-enhanced materials (McCubbin et al., 2011; Prettyman et al., 2014).

In this study, we report the first non-polar map of bulk hydrogen concentrations for highlands regions using the LP data that have a spatial resolution of approximately 45 km. New interpretative insight for the LP epithermal neutron data is provided by optical albedo and optical maturity data (Section 3) from the Clementine spacecraft (Lucey et al., 2000a), as well as Christiansen Feature (CF) data from the Lunar Reconnaissance Orbiter (LRO) Diviner Lunar Radiometer instrument (Greenhagen et al., 2010). Specifically, these data provide important information that help resolve ambiguities of LP-NS data in the lunar highlands. In this paper, we provide background information about the LP-NS global epithermal neutron data (Section 2), compare these data to the Clementine and LRO Diviner data in the lunar highlands regions (Section 3), derive a map of bulk highlands hydrogen concentrations (Section 4), and provide some discussion and conclusions (Section 5).

2. Global epithermal neutron data

Planetary epithermal neutrons, which have kinetic energies greater than ~0.5 eV and less than ~500 keV, are downscattered from neutrons created by nuclear spallation reactions when galactic cosmic rays impinge on airless or nearly airless planetary bodies (Prettyman, 2014). Epithermal neutrons provide a robust measure of planetary hydrogen concentrations due to their efficient momentum transfer with hydrogen atoms (Feldman et al., 1998). Because the typical path-length of epithermal neutrons through lunar-type soils is tens of cm, these measurements reflect the bulk hydrogen concentration for the top tens of cm of the lunar surface. A global lunar map (Fig. 1) of epithermal neutrons derived from LP-NS data (Maurice et al., 2004) is well delineated by the polar regions plus the three compositional terranes identified by Jolliff et al. (2000): (1) Procellarum KREEP Terrane (PKT); (2) Feldspathic Highlands Terrane (FHT); and (3) South Pole Aitken (SPA) basin terrane. This map, which has an effective spatial resolution of

approximately 45 km, uses 0.5° x 0.5° pixels, where the epithermal neutron count rates have been smoothed as described by Maurice et al. (2004). The map was derived using Lunar Prospector Reduced Spectrometer data that are available at the NASA Planetary Data System. Regions poleward of ~70° show decreases in epi-thermal neutron count rates that are interpreted to be the result of enhanced hydrogen concentrations. These data have been extensively studied (e.g., Feldman et al., 1998; Maurice et al., 2004; Lawrence et al., 2006; Teodoro et al., 2010) and polar maps of footprint-averaged hydrogen concentrations (Lawrence et al., 2006) and inferred hydrogen concentrations within permanently shaded regions (e.g., Feldman et al., 1998,2000, 2001; Teodoro et al., 2010) have been derived.

The PKT and SPA terrane show epithermal neutron count rate decreases that are spatially correlated with regions having high concentrations of the neutron absorbers iron, titanium, thorium, and the REE elements gadolinium and samarium (Lawrence et al., 2006). While the count-rate decreases due to iron and titanium are quantitatively understood, the quantitative magnitude of the decreases that are spatially correlated with gadolinium and samarium are larger than can be currently accounted for by neutron transport simulations (Lawrence et al., 2006). It is possible that in some locations the observed count-rate decrease is due to both REE neutron absorption and KREEP-correlated enhanced hydrogen (Prettyman et al., 2014).

The behavior of epithermal neutrons in the FHT, where REE and thorium concentrations are low (Elphic et al., 2000; Lawrence et al., 2003), has also not been fully understood until now. There is an expectation that epithermal neutrons in this region should be related to the concentrations of solar wind-implanted hydrogen. If this is the case, then young craters, which have relatively little exposure to solar wind, should be depleted in hydrogen and enhanced in epithermal neutrons. Using an early version of LP-NS data, Johnson et al. (2002) showed that surface maturity (Section 3 and Lucey et al., 2000a), which is a proxy for surface age via surface weathering and hence implanted hydrogen, had a poor correlation with global epithermal neutrons. Johnson et al. (2002) suggested that factors in addition to solar wind hydrogen, such as nearside enhancements of REEs, might be causing the poor correlation. However, they did not carry out an analysis of epithermal neutron data in REE-rich and REE-poor regions. In this study, we focus on the epithermal neutron data in the FHT, which mostly has uniformly low REE and iron concentrations. As a consequence,

17.8 18.1 18.4 18.8 19.1 19.4 19.7 20.1 20.4 20.7 21.0

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East Longitude

Fig. 1. Global map of epithermal-neutron count rate in units of counts per second (cps) as given by Maurice et al. (2004). The PKT, FHT, and SPA terranes, as defined by Jolliff et al. (2000) on the basis of thorium and iron concentrations are indicated. The specific lines separating the terranes are delineated here based on thermal neutron data (Maurice et al., 2004), which are sensitive to both thorium and iron concentrations (Elphic et al., 2000).

these data have the fewest ambiguities with respect to count-rate decreases from neutron absorbers.

3. Comparison of Clementine reflectance and Diviner data with epithermal neutrons

Three datasets can provide new understanding for epithermal neutrons in the FHT: maps of the 750 nm albedo, optical maturity, and a thermal infrared emissivity maximum known as the Christiansen Feature (CF). Each of these datasets will be compared with LP-NS epithermal neutron data in the FHT. The goal in carrying out this comparison is to assess the level of correlation and try to isolate the dominant factor that causes the count-rate variation of epithermal neutrons across the FHT. This is accomplished by examining the scatter about the correlation when the epithermal neutrons are compared with the other datasets. A strong correlation with little scatter indicates that the two measurements are likely varying due to a single factor. In contrast, a weak correlation with a large and/or asymmetric scatter indicates that there are likely multiple factors causing variations in one or both of the datasets.

Variations in the 750 nm albedo have been attributed to be dominantly due to variations in the concentrations of iron and titanium as well as variable degree of surface maturity (Lucey et al., 2000a,b). Within the FHT, it is expected that most of the variations in 750 nm albedo (aside from small exposures of iron-rich mare regions) are due to surface maturity. Maturity effects are caused by long-term modifications of lunar soils by micrometeorite bombardment. These effects change the optical properties of the soil by reducing its spectral contrast and causing it to darken and redden (Fischer and Pieters, 1994; Lucey et al., 2000a). Based, in part, on the observation that the effects of composition and maturity can be separated, Lucey et al. (2000a) used an empirical relationship of the 750-950 nm reflectance ratios to derive a parameter known as optical maturity (OMAT). Ideally, OMAT is independent of compositional variations (mostly iron and titanium) of the material, and isolates variations due to surface maturity.

The wavelength position of the CF, which occurs between 7 im and 9 im for common lunar silicates, is mapped using data from Diviner (Greenhagen et al., 2010). The CF-wavelength provides diagnostic information about bulk silicate polymerization such that the frequency shifts to longer wavelengths with increasing mafic content (Conel, 1969; Logan et al., 1973; Salisbury and Walter, 1989). Sample analyses have shown that the CF-wave-length position is well correlated with the iron content in lunar soils (Allen et al., 2012). In addition, based on an empirical correlation of the CF wavelength with the 750 nm albedo (Lucey et al., 2010), it has been reported that in the lunar highlands the CF-wavelength shows an unexpected, systematic variation with soil maturity whereby mature soils have CF-wavelengths shifted to longer wavelengths by ~0.1 im compared to immature soils (Greenhagen et al., 2010; Allen et al., 2012). The reason for this correlation with the 750 nm albedo, and hence maturity, is not well understood but is likely related to variable thermal structure in the very-near surface caused by differences in optical penetration depth with albedo (Lucey et al., 2013).

Factors that are related to variations in the albedo, OMAT, and CF wavelength values - iron content and soil maturity for albedo and CF, and soil maturity for OMAT - are also two of the identified factors that can cause variations in epithermal neutrons. Changes in iron content can cause variations in epithermal neutrons due to its relatively large neutron-absorption cross section. The magnitude of the epithermal neutron variation with iron content is understood and quantitatively verified, such that an iron concentration change of 15 wt.% will reduce the epithermal neutron count rate by 1 cps (Fig. 8 of Lawrence et al., 2006). Soil maturity is

correlated with solar wind implantation, such that in general more mature soils have increased hydrogen content than less mature soils due to longer exposure to the solar wind (McKay et al., 1991). The measured bulk hydrogen content in lunar samples ranges from 1 to ~100 ppm hydrogen (Haskin and Warren, 1991), which is a concentration range that is easily quantified with orbital epithermal neutron data (Lawrence et al., 2006).

The direct comparison between albedo, OMAT, and CF wavelength with epithermal neutrons is shown in Figs. 2-4. Because the spatial footprint of the LP-NS data (~45 km2) (Maurice et al., 2004) is much larger than the spatial footprint of the reflectance and thermal infrared data (<1 km2 per pixel), all data need to be resampled to have pixels with the same size. To achieve good statistical precision for global trends, yet preserve the highest pixel resolution, the optimum resampled pixel size was found to be approximately 150 km x 150 km (or 5° x 5° at the equator). Fig. 5 shows maps of each of the four datasets rebinned to approximately equal-area pixels that have a size of 5° x 5° at the equator. The CF data have the most restrictive latitude coverage (±60° latitude), so a latitude cutoff of ±58° is used to reduce edge effects near the latitude boundary at 60°. Within this latitude band (±58°), unrestricted (whole Moon) data are shown as black points in Fig. 2 through Fig. 4. FHT-restricted data (gray points) are selected by identifying locations with thorium concentrations [Th] less than 1 ppm (Lawrence et al., 2003). Variations in the epithermal neutron count rate due to variable surface temperatures (Feldman et al., 2001) are small (<1%) in the latitude range being considered.

A comparison of each of the three datasets with epithermal neutron count rate shows clear trends in almost all cases. For unrestricted data, both the 750 nm albedo and CF wavelength data are clearly correlated with epithermal neutron count rate. However, the comparison of unrestricted OMAT versus epithermal neutron data shows little-to-no correlation. This last comparison is qualitatively consistent with the poor global correlation between OMAT and epithermal neutron count rate reported by Johnson et al. (2002).

FHT-restricted albedo and CF data appear to show a better correlation than the unrestricted data based on the observation that the FHT-restricted data have less scatter than the unrestricted data. This qualitative observation can be quantitatively assessed in two ways. First, the degree of correlation can be quantified with two standard methods: the Pearson linear-correlation coefficient (or

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100 150 200 250

Albedo (arbitrary units)

Fig. 2. Scatter plot of 750 nm albedo (Lucey et al., 2000a) versus epithermal neutrons, where both datasets have been resampled to approximately equal area pixel sizes of 5° x 5° at the equator (or 150 km by 150 km), and limited to latitudes equatorward of ±58°. Within this latitude range, the black data points show unrestricted data, and the gray data points show values from the FHT that have thorium concentrations less than 1 ppm.

Fig. 3. Scatter plot of optical maturity (OMAT) (Lucey et al., 2000a) versus epithermal neutrons, where both datasets have been resampled to approximately equal area pixel sizes of 5° x 5° at the equator (or 150 km by 150 km), and limited to latitudes equatorward of ±58°. Within this latitude range, the black data points show unrestricted data, and the gray data points show values from the FHT that have thorium concentrations less than 1 ppm.

Fig. 4. Scatter plot of CF wavelength (Greenhagen et al., 2010) versus epithermal neutrons, where both datasets have been resampled to approximately equal area pixel sizes of 5° x 5° at the equator (or 150 km by 150 km), and limited to latitudes equatorward of ±58°. Within this latitude range, the black data points show unrestricted data, and the gray data points show values from the FHT that have thorium concentrations less than 1 ppm.

Pearson-r), and the Spearman rank-order correlation coefficient (Press et al., 2007). The Pearson-r coefficient is a common way to assess correlation, but it is only strictly valid for data that are distributed as a binormal, or two-dimensional Gaussian distribution (Press et al., 2007). Thus, caution should be used when drawing conclusions by comparing Pearson-r coefficients from different sets of measurements, especially when the underlying joint data distributions are significantly different from a binormal distribution. The Spearman rank-order coefficient is not as dependent on the underlying distribution as it determines the correlation based on the rank values after each dataset has been independently sorted.

Table 1 shows both the Pearson-r and Spearman rank-order coefficients for all three dataset comparisons as well as for the unrestricted and FHT-restricted data. Except for the unrestricted OMAT dataset, all other datasets, both unrestricted and FHT-restricted, show a moderate-to-good correlation. Surprisingly, for the albedo and CF datasets, the unrestricted data have correlation coefficients that are slightly closer to 1 (0.75-0.85) than the

FHT-restricted data (0.63-0.69). These larger correlation coefficients may be due, in part, to the larger dynamic range covered by the respective datasets. However, as noted above, the unrestricted data have more scatter and clearly do not have a binormal distribution. Thus, making strong conclusions based on these observed differences between the unrestricted and FHT-restricted correlation coefficients is not warranted.

Second, we assess the underlying distributions by deriving linear correlation lines from the FHT-restricted data, and characterize the scatter about the correlation lines. Except for the OMAT-to-epi-thermal-neutron count-rate comparison, use of the unrestricted data provide similar correlation lines as when using the FHT-restricted data. A corrected epithermal neutron count rate, Cepi,corr, for each comparison is determined using

= Cepi -(aCepi + b) + Ce,

where a and b are fitted constants that are obtained using a minimum chi-squared fit procedure (Press et al., 2007), Cepi is the original epithermal neutron count rate, and Cepi,const is an arbitrary offset of 18 cps, which was chosen to be roughly the mean value of the count rate distribution. Histograms of the corrected epithermal neutron count rates are shown in Figs. 6-8, for the albedo, OMAT, and CF datasets, respectively. In each case, the unrestricted data are shown in black and the FHT-restricted data are shown in gray. Dashed lines show Gaussian fits to each distribution. As expected, in all cases the FHT-restricted data show a narrower distribution than the unrestricted data. In particular, the FHT-restricted data are well fit by a Gaussian distribution, which is in contrast to the unrestricted data that show significant tails for low count rates. This result indicates that for the unrestricted data, more than one factor is likely causing the epithermal neutron count-rate variations. Based on prior discussion, it is known that the non-FHT variation in epithermal neutron count rate is caused by a combination of REE, iron, and possibly hydrogen variations, while the non-FHT albedo and CF variations are related to iron (and to a lesser extent titanium for albedo).

The fitted widths, r, for FHT-restricted data are shown in Fig. 6 through Fig. 8. A standard error, rr, of these widths can be estimated using r/V2N (Press et al., 2007), where N is the number of data points used in the fit. In this case, N = 830, which is the number of 150 km x 150 km approximately equal-area pixels with [Th] < 1 ppm. The 3 - rr values are listed on Fig. 6 through Fig. 8. The width of all three FHT-restricted distributions is consistent to within three standard error values. Therefore, we conclude that the albedo, OMAT, and CF datasets all correct the FHT-restricted epi-thermal neutron count rates by reducing the neutron count rate variation by a similar amount.

These results point to the conclusion that all four measurements - albedo, OMAT, CF, and epithermal neutron count rates -are linked by a single, common factor in the FHT. In the case of albedo and CF, both are sensitive to variations in both maturity and iron. However, for the FHT, except for small-area mare regions, OMAT was derived to be sensitive only to maturity. Because maturity is observationally related to surface exposure and solar wind hydrogen implantation, we conclude that hydrogen is the dominant factor that controls epithermal neutron count rate variations within the FHT.

We can test the consistency of this conclusion with lunar sample data by calculating the implied dynamic range of hydrogen concentrations if the FHT-restricted epithermal neutron count rate variations are due only to hydrogen. Fig. 2 through Fig. 4 show that the change in epithermal neutron count rates within the FHT, where epithermal neutrons correlate with albedo, OMAT, and CF data, is 18.8 - 17.7 = 1.1 cps. Based on Eq. (2) of Lawrence et al. (2006), this change in count rate (1.1 cps) corresponds to a

a 16.7 16.9 17.1 17.2 17.4 17.6 17.8 1B.0 18.1 18.3 18.5

Ep¡thermal Neutrons (cps)

110 124 136 152 166 180 194 208 222 236 250

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East Longitude

0.10 0.12 0.13 0.15 0.16 0.18 0.20 0.21 0,23 0.24 O.i

CF Wavelength (um)

7.90 7.95 7.99 6.03 8.06 6.12 6.17 8.22 8.26 8.31 8.35

Fig. 5. Global maps of the four datasets used in Fig. 2 through Fig. 4 where the datasets are rebinned to approximately equal area pixels with a size of 5° x 5° at the equator. (a) Epithermal neutrons; (b) albedo; (c) OMAT; (d) CF wavelength.

Table 1

Correlation coefficients for different dataset comparisons with epithermal neutron count rates.

Dataset Unrestricted FHT-restricted

Pearson-r Spearman rank order Pearson-r Spearman rank order

750 nm albedo 0.818 0.850 0.676 0.688

OMAT 0.232 0.250 0.606 0.594

CF wavelength -0.754 -0.637 -0.798 -0.631

Albedo-Corrected Epithermal Neutrons (cps) OMAT-Corrected Epithermal Neutrons (cps)

Fig. 6. Histograms of epithermal neutron data from regions equatorward of ±58° that have been corrected for FHT-restricted variations of 750 nm albedo using Eq. (1). The fit parameters from Eq. (1) are a = -3.21 ± 0.14 and b = 44.3 ± 1.1, where the uncertainties are one standard deviation. Unrestricted, whole Moon data are shown gray; FHT-restricted data for regions with thorium concentrations less than 1 ppm are shown in black. Gaussian fits to both distributions are shown with the dashed lines. The Gaussian width of the FHT-restricted data is shown along with the three-times standard error of this width.

Fig. 7. Histograms of epithermal neutron data from regions equatorward of ±58° that have been corrected for FHT-restricted variations of OMAT using Eq. (1). The fit parameters from Eq. (1) are a = 5.71 ± 0.26 and b = 17.2 ± 0.04, where the uncertainties are one standard deviation. Unrestricted, whole Moon data are shown gray; FHT-restricted data for regions with thorium concentrations less than 1 ppm are shown in black. Gaussian fits to both distributions are shown with the dashed lines. The Gaussian width of the FHT-restricted data is shown along with the three-times standard error of this width.

16.5 17.0 17.5 18.0 18.5 19.0

CF-Corrected Epithermal Neutrons (cps)

Fig. 8. Histograms of epithermal neutron data from regions equatorward of ±58° that have been corrected for FHT-restricted variations of CF wavelength using Eq. (1). The fit parameters from Eq. (1) are a = 8.4 x 10-3±3.2 x 10—4 and b = 16.5 ± 0.065, where the uncertainties are one standard deviation. Unrestricted, whole Moon data are shown gray; FHT-restricted data for regions with thorium concentrations less than 1 ppm are shown in black. Gaussian fits to both distributions are shown with the dashed lines. The Gaussian width of the FHT-restricted data is shown along with the three-times standard error of this width.

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0 5 10 15 20

Fe (wt. %)

Fig. 9. Scatter plot of iron concentration (Lawrence et al., 2002) versus epithermal neutrons, where both datasets have been resampled to approximately equal area pixel sizes of 5° x 5° at the equator (or 150 km by 150 km), and restricted for latitudes equatorward of ±58°. Within this latitude range, the black data points show unrestricted data, and the gray data points show values from the FHT that have thorium concentrations less than 1 ppm. The black/gray line shows the line with a slope of 1/15 cps/wt.% iron and offset of 18.2 cps.

hydrogen concentration variation of 113 ppm, which is consistent with the range measured in lunar samples (Haskin and Warren, 1991).

4. FHT hydrogen concentration map

Before deriving an FHT-restricted map of hydrogen concentrations, remaining epithermal neutron count rate variations from FHT iron variations need to be considered. Specifically, there are some locations within the FHT, such as Mare Australe (39°S, 93°E), with iron concentrations (~7-8 wt.%) that are greater than the nominal highlands values of 3-5 wt.%. Based on the scaling that 15 wt.% iron will cause a 1 cps change in the epithermal neutron count rate (Lawrence et al., 2006), the largest iron concentration variation of 5 wt.% corresponds to a 0.33 cps change in the epither-mal neutron count rate. This count-rate change in turn corresponds to an apparent 40-ppm hydrogen variation, which is a substantial fraction of the full hydrogen dynamic range in the FHT. To

minimize this systematic bias, a correction needs to be made for iron variations within the FHT.

Fig. 9 shows iron concentrations measured with the LP gamma-ray spectrometer (Lawrence et al., 2002) versus epithermal neutrons for the same restrictions used in Fig. 2 through Fig. 4. The gray/black line shows the 1 cps per 15 wt.% iron variation with an 18.2 cps offset (18.2 cps is the mean epithermal neutron count rate within the FHT). This variation was derived using neutron transport calculations (Lawrence et al., 2006). Fig. 9 shows that for the whole-Moon, the slope of the measured data corresponds well with the calculated variation, thus validating the calculated variation. Based on this validation, the epithermal neutron count rates, Cepi, are corrected using the following relation, where [Fe] is the weight percent iron:

Cepi,Fe corr — Cepi — [Fe]' (2)

With the corrected epithermal neutron count rate map, we can now derive a map of bulk hydrogen concentrations ([H]) in the FHT using the relation that links hydrogen concentration to epithermal

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Fig. 10. Map of bulk hydrogen concentrations in the FHT. The black regions indicate locations where thorium concentrations are greater than 1 ppm and therefore excluded from this analysis.

neutron count rate [Eq. (2) of Lawrence et al. (2006)]. Following the reasoning of Feldman et al. (2001) (pg. 23,240) and Lawrence et al. (2006), we adopt the 99th percentile level of the measured epither-mal neutron count rate as the value where [H] is zero. This is done to reduce the sensitivity of the hydrogen concentrations to statistical outliers that might be present when using the maximum epi-thermal neutron count rate value. By fixing the 99th-percentile epithermal neutron count rate to zero ppm hydrogen, we are introducing a possible systematic offset to the calibration of hydrogen concentrations. Thus, in a strict sense, the mapped values represent a lower limit on the hydrogen concentrations. However, because lunar sample studies show that the most immature lunar soils have hydrogen concentrations of 5 ppm or less (Haskin and Warren, 1991), we expect that the offset introduced by this procedure is small.

Fig. 10 shows the hydrogen concentration map derived using the epithermal neutron count rate map of Fig. 1. Regions having thorium concentrations greater than 1 ppm are shown in black. The mean hydrogen concentration for non-polar locations (equa-torward of ±80°) is 65 ppm and the maximum value is 159 ppm. However, this maximum value is on the high-end tail of the distribution, and a more representative maximum value is likely the 99th percentile, which is 121 ppm H. If one compares the mapped FHT hydrogen concentrations with lunar sample concentrations, the most representative are those from the Apollo 16 soils and reg-olith breccias, which range from 3.9 to 146 ppm (Haskin and Warren, 1991). The global highlands values are therefore consistent with the most highlands-like Apollo samples. In regards to the mapped hydrogen distribution, there are clear hydrogen depletions around many young craters (e.g., Tycho, 11 °W, 43°S), some large geologic features such as Orientale basin (93°W, 19°S), as well as much of the northwest highlands north of the equator and west of 90°W. In addition, there are indications that a number of smaller craters have less hydrogen than their immediate surroundings. Finally, there are regions (e.g., southern hemisphere from 0° to 135°E) are characterized by higher than average hydrogen concentrations.

5. Discussion, conclusions, and future work

The spatial correlation of FHT-restricted epithermal neutrons and measurements of optical maturity has provided the confidence to conclude that there is only one primary factor that causes epi-thermal neutron count-rate variations in the lunar highlands. Of the four primary causes of epithermal neutron count-rate variations - REE, iron, titanium, and hydrogen concentrations - REE and titanium are ruled out as a factor because of the FHT selection. Iron is also ruled out as a dominant contributor because of the almost uniformly low values in the FHT. Further, explicit corrections have been made to minimize any remaining iron-related epi-thermal neutron variations. Hydrogen is the only remaining factor that can cause FHT-restricted epithermal neutron variations. We note that because of the relatively large pixels used to derive the correlations, these correlations do not necessarily hold at smaller (<150 km) spatial scales. Nevertheless, the derived hydrogen map does not depend on a robust correlation between it and the other datasets at spatial scales less than 150 km. Therefore, it has an effective spatial resolution of 45 km (Maurice et al., 2004).

Because the final hydrogen map is ultimately derived independently of the orbit-measured maturity parameters, it should be emphasized that these reported FHT hydrogen concentrations represent a new, bulk (depth < 1 m) soil measurement that is independent of any surface (depth <100 im) soil optical property. A number of implications follow from this point. The hydrogen concentration map provides a new and independent index to characterize the maturity of bulk lunar soils, and can, in principle,

provide additional information about the lunar soil maturation process. As discussed by Lucey et al. (2000a), various types of maturity indices (amount of nanophase iron, amount of agglutinate glass, soil grain size, OMAT) are affected by many factors (composition, density, depth, solar exposure, etc.) that should not be considered a priori correlated. The observation that these parameters do show moderate-to-good correlations demonstrates that, on average, any parcel of lunar soil records an effective maturity that is consistent across variable time and depth scales. While sample studies have shown that hydrogen content correlates reasonably well with the nanophase measure of maturity (Fig. 7.14 of McKay et al., 1991), the results shown here demonstrate that this sample-based relationship holds across the entire FHT.

Nevertheless, despite the moderate, mutual correlations among the maturity parameters, none of the correlations are perfect, and the degree of noncorrelation can reveal key information about the factors that affect the maturation process. Of the known maturity parameters, only a few (OMAT, CF-wavelength, and now mapped hydrogen concentration) are measureable from orbit. Therefore, a detailed study of the neutron-derived hydrogen concentrations may provide new constraints or reinforce old constraints. For example, these data show that apart from the lunar poles, the FHT hydrogen saturation concentration ([H] = 120-150 ppm) is consistent with what is known from samples. In addition, based on the zeroth order correlation of FHT-epithermal neutrons and the orbit-measured surface maturity parameters, it can be concluded that the general time scale needed to reach hydrogen saturation is comparable to the same time scale (~1 Gyr) needed to express the full range of optical maturity (Lucey et al., 2000a).

At a finer correlation level, the correspondence between optical maturity and hydrogen concentration might not necessarily hold. For example, certain compositions might show biases to retain hydrogen more effectively than other compositions. While the bulk iron and titanium abundances across the FHT are mostly uniformly low (Lucey et al., 2000b; Lawrence et al., 2002), other bulk composition parameters, such as magnesium concentrations, do appear to show significant variations across the FHT (Prettyman et al., 2006; Peplowski and Lawrence, 2013) and in principle could be linked to compositional effects related to hydrogen retention. More recent measures of optical maturity (Nettles et al., 2011) show different compositional variations that can be compared to the derived hydrogen concentrations. All these dependencies could be investigated using multivariate analyses of the type that have been successfully carried out for compositional datasets at Mars (e.g., Karunatillake et al., 2012).

In addition to compositional studies, the mapped hydrogen concentrations can be characterized as a function of relative surface age. Lucey et al. (2000a) investigated the relationship of optical maturity and crater-age frequency and found a moderate correlation between these two parameters for surface ages younger than the boundary between the Copernican and Eratosthenian eras (~1.3 Ga) (Stoffler and Ryder, 2001). However, this comparison depended on the type of material being investigated (crater interior or crater ejecta), which indicates that surfaces formed at similar times can exhibit different optical maturity values depending on the starting material. Since the hydrogen concentration represents a fundamentally different soil parameter than optical maturity, new insight into the soil maturation process can be gained by knowing how the hydrogen concentration varies with relative surface age. Is there a global relationship between surface age and hydrogen concentration, and if so, does it vary across the Moon in a monotonic fashion or is the hydrogen saturation value reached at a well-defined time boundary as is seen for the optical maturity? If there turns out not to be a strong relationship between hydrogen concentrations and relative age, what would such a result imply for our understanding of solar wind implantation and the maturation process? These questions can be

addressed with a detailed study of hydrogen concentration and relative surface ages based on crater count statistics.

A few final conclusions can be made about surficial hydrogen based on the results given here. First, since the neutron-measured hydrogen concentrations represent a bulk soil property that is related to the long-term exposure of the top tens of cm to the space environment, the neutron data are not measuring a surficial (top 10-100 s of im) property. Therefore, these bulk hydrogen measurements and spectral-reflectance-based surficial OH/H2O measurements are not measuring the same soil quantity. Nevertheless, because the interpretation that FHT epithermal neutrons are driven by hydrogen variations is based, in part, on the large-area-pixel (150 km by 150 km) correlation of orbital maturity metrics versus neutron count rates, the presence of isolated, small-scale (<50-100 km) enhancements thicker than 0.5-1 cm (Lawrence et al., 2011) is not ruled out by this study. In addition, because the neutron data correlate well with orbital-based optical maturity parameters, and such optical maturity parameters persist with no reported time variability, it is unlikely that epithermal neutrons are sensitive to broad-area surficial and time-variable OH/H2O concentrations (Sunshine et al., 2009; Hendrix et al., 2012). Thus, over geologic time scales, space-weathering processes likely control the distribution of hydrogen at decimeter depths.

Acknowledgments

The authors are grateful for detailed and constructive reviews of this paper by Drs. Noah Petro and Suniti Karunatillake. This work was supported by multiple grants including the NASA Lunar Science Institute, the NASA Solar System Exploration Research Virtual Institute, and the Lunar Advanced Science and Exploration Research program (NNX13AJ61G).

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