Assessing aquifer vulnerability from lumped ...

Science of the Total Environment 624 (2018) 1550–1560

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Assessing aquifer vulnerability from lumped parameter modeling of modern water proportions in groundwater mixtures: Application to California's South Coast Range Benjamin Hagedorn ⁎, Natalie Clarke, Merik Ruane, Kirsten Faulkner Department of Geological Sciences, Long Beach State University, CA 90840, USA

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• An easy-to-apply method for assessing groundwater vulnerability is presented. • The method is valuable for remote settings with limited nitrogen application data. • The method relies on modern groundwater proportion (MWP) and dissolved oxygen (DO) data. • DO and MWP outperform land use variables in LR analysis.

a r t i c l e

i n f o

Article history: Received 3 October 2017 Received in revised form 9 December 2017 Accepted 11 December 2017 Available online xxxx Editor: G. Ashantha Goonetilleke Keywords: Groundwater vulnerability Nitrate contamination Lumped parameter modeling (LPM) Logistic regression (LR) Radiocarbon Tritium-helium

a b s t r a c t Groundwater in agriculture-dominated regions of California has historically experienced nitrate pollution due to the application of excess nitrogen fertilizers. This study examines the nitrate pollution vulnerability of groundwater in sedimentary aquifers of California's South Coast Range using stepwise logistic regression (LR) modeling. Our results indicate an overall excellent model fit, but an acceptable statistical significance, according to a Wald statistic (p-Wald) cutoff of 0.1, for only two explanatory variables: (1) the dissolved oxygen (DO) concentration, and (2) the modern (i.e., less than ~60 year old) water proportion (MWP) in the groundwater mixture. The latter parameter was estimated via Lumped Parameter Modeling (LPM) of groundwater tritium, helium and radiocarbon data that have been corrected for isotopic dilution and exchange using a modified Fontes and Garnier (F&G) approach. The observation that other explanatory variables on land cover (i.e., percentage of agricultural land use, abundance of septic tanks and leaking underground fuel tanks, etc.) were statistically insignificant points out the limitations of low-resolution land cover data in groundwater vulnerability assessments. Our results highlight the utility of quantitative groundwater age and mixing data to evaluate pollution probability in the saturated zone. The herein presented approach can thus provide valuable results in comparable settings where the availability of fertilizer application, crop nitrogen uptake, and soil texture data is limited. © 2017 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail addresses: [email protected] (B. Hagedorn), [email protected] (N. Clarke), [email protected] (M. Ruane) , [email protected] (K. Faulkner).

https://doi.org/10.1016/j.scitotenv.2017.12.115 0048-9697/© 2017 Elsevier B.V. All rights reserved.

1. Introduction Nitrate is one of the world's most widespread groundwater pollutants. It is derived primarily from overuse of synthetic fertilizers and

B. Hagedorn et al. / Science of the Total Environment 624 (2018) 1550–1560

manure in agriculture and, to a lesser degree, from leakage from urban wastewater and septic systems (WHO, 2011). The industrial production of nitrogen fertilizer due to the Haber-Bosch process has radically increased global agricultural productivity in the world (Erisman et al., 2008; Galloway et al., 2004), thereby stressing groundwater resources in agricultural watersheds worldwide (Ascott et al., 2017). Potential controls on nitrate leaching have been investigated in numerous case studies. For instance, field experiments have been performed controlling for alternative fertilizer and water management (Min et al., 2012), cropping systems (Ju et al., 2006), and soil types (Salo and Turtola, 2006). Even though these experiments provide valuable results, they have the disadvantage of being limited in scale and time such that routine applications may not be feasible, or the data is not suited for generalization (Buczko et al., 2010; Wick et al., 2012). A particular problem of larger-scale assessments of nitrate leaching controls is that nitrate concentrations in groundwater often respond slowly to changes in agricultural management practices making it difficult to determine causality (Wick et al., 2012). In California, an increasing number of public water supply wells exhibits nitrate concentrations that exceed the U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) for nitrate in drinking water of 10 mg per liter (as nitrogen) (Burow et al., 2012; Harter et al., 2012). Given that nitrogen fertilizer applications in California have steadily increased since the 1950s (Rosenstock et al., 2013) and given that soil percolation and recharge transit times in California can exceed timescales of decades (Visser et al., 2016), the nitrate impact on groundwater resources is likely a legacy for years and even decades to come. These facts highlight the need for a reliable groundwater vulnerability assessment method to delineate regions most susceptible to current and future nitrate pollution. Several methods exist for assessing nitrate pollution vulnerability. Overlay-Index methods, such as DRASTIC, are based on hydrogeological indices (i.e., Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and Conductivity). In DRASTIC, each index is assigned a rating and weighting number and all indices are applied in a linear combination to compute a vulnerability index (Aller et al., 1985). While DRASTIC can provide useful spatially distributed estimates, it is subject to uncertainty. This is due in particular to difficulties in reliably estimating the parameters Recharge and Impact of the vadose zone, even though these parameters have shown the greatest influence on the vulnerability index in sensitivity analyses (Hamza et al., 2017). Furthermore, DRASTIC is limited by the subjective parameter rating methods (Huan et al., 2012) and the fact that it does not account for any information on the geochemical (i.e., redox) condition in the saturated zone even though it exerts the strongest control on solute (and pollutant) persistence. A different approach utilizes a nitrogen balance to predict groundwater nitrate pollution vulnerability. Nitrogen balances can range in complexity from farm-gate balances, which account for nutrients in all kinds of products which enter and leave a farm, to more complex soil system balances that account for the nitrogen sources (fertilizer, manure, effluent, atmospheric deposition, and irrigation) and sinks (crop harvested nitrogen, atmospheric losses, and runoff) (Wick et al., 2012). The critical challenge of the nitrogen balance is that it requires extensive datasets on specific fertilizer applications and crop nitrogen uptake which, for many regions, are difficult to obtain (e.g., Viers et al., 2012). In the European Union, the Nitrates Directive (91/676/EEC) enforces strict reporting requirements on fertilizer and manure applications and storage by individual farmers inside nitrate vulnerable zones (NVZ). Likewise, in the U.S., the U.S. Department of Agriculture (USDA) Chemical Use Program periodically provides estimates of nitrogen fertilizer application for various crops from grower's reports. Still, for the state of California, several studies highlighted data limitations on nitrogen applications especially when focusing on the watershed scale (Harter et al., 2012; Viers et al., 2012). According to Rosenstock

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et al. (2013), the problem lies in the absence of a “comprehensive grower self-monitoring system for nitrogen applications” which would be needed to constrain applications over various crop types. Moreover, many previous studies relied on fertilizer sales data to constrain N applications even though these sales data are reported to the California Department of Food and Agriculture (CDFA) only at the state and county level (Rosenstock et al., 2013). As such, they likely portray a geographic distribution unlikely to match actual use as they neglect fertilizer stockpiling and fertilizer transported from one county or state to another. It is for those reasons that there is a remarkable discrepancy between growers' reports and experts' opinions on nitrogen applications in California (Rosenstock et al., 2013). Aside from this issue, the nitrate balance method is furthermore limited by (1) the assumption of longterm (i.e., decadal) steady-state dynamics of soil nitrogen (Viers et al., 2012), and (2) the fact that it estimates potential nitrate loading representative for below the root zone and not for the water table. Collectively, these factors can result in uncertainties in determining actual contaminant loading location and timing. This can be an issue particularly in arid environments where recharge rates are low (Hagedorn, 2015) and where unsaturated zones are thick and heterogeneous (Baram et al., 2016). Logistic regression (LR) is an alternate statistical method for aquifer vulnerability assessment that predicts pollution occurrence probability based on measured explanatory variables. LR studies on principal aquifers in the U.S. have indicated pollution from nitrate to be controlled by dissolved oxygen (DO) levels, the presence of irrigated farmlands, crop type, seasonal groundwater fluctuations, and soil categories (Gurdack and Qi, 2012). LR has the limitation that it is based on historical measurements of chemical, hydrogeological and geographical data that are rarely available at a concurrent time period. There is also uncertainty due to the difficulty of reliably estimating some of the data used as explanatory variables (e.g., land use, crop type, soil texture, unsaturated zone characteristics, etc.). This can be an issue particularly in remote study regions with only limited observational groundwater well, soil and land use/land cover data available. This uncertainty may contribute to the issue that aside from DO and presence of irrigated farmland, it has been difficult to identify independent explanatory variables of statistical significance in watershed-scale studies. For example, in a study of an agricultural region of Taiwan, Jang and Chen (2015) identified DO, soil texture and agricultural land use (i.e., ratio of fruit trees) as statistically significant indicators for nitrate pollution based on t- and chi square tests, but noted that only DO was of statistical significance in the actual LR analysis based on the Wald statistic. Similarly, Mair and ElKadi (2013) considered the typical significance level of the Wald statistic (p-Wald) of 0.05, too low for a LR assessment in Hawaii's Pearl Harbor and Honolulu aquifers since it excluded almost all explanatory variables. Another problem of LR-based vulnerability assessments is that they are often based on data observations from one discrete sampling campaign and often do not include any information on groundwater age, aside from measured tritium or radiocarbon concentrations. These can only provide some qualitative indication of groundwater age (i.e., young vs. mixed vs. old). As such, forecasting of future groundwater pollution vulnerability may not be appropriate. Calibration is an issue with all vulnerability assessment methods because it is typically based on measured contaminant (i.e., nitrate) concentrations and it has been shown to be difficult to establish a reliable background (i.e., natural) nitrate end-member in groundwater for any particular region. Furthermore, in the case of DRASTIC, the numerous input parameters and subjective ratings have shown to allow for various combinations of input parameters to match one target value (Huan et al., 2012). In this study, we introduce a new parameter for a LR-based groundwater vulnerability assessment that provides groundwater age and, more importantly, groundwater mixing information to

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illustrate actual pollution vulnerability at the water table. This parameter - the modern water proportion (MWP) in groundwater mixtures - is simulated via Lumped Parameter Modeling (LPM) of groundwater tritium, helium and corrected radiocarbon data. Combined with data on groundwater DO concentrations, the MWP quantitatively describes the degree of which modern (i.e., b60 year old), and potentially contaminated percolating water from the soil zone mixes with and influences uncontaminated pre-modern groundwater at the water table. The main advantage of the herein presented vulnerability assessment approach over other methods is that it is dependent only on groundwater age and composition data which, for the State of California, are available from online resources such as the Groundwater Ambient Monitoring and Assessment (GAMA) Program. This project specifically focuses on the South Coast Range–Coastal (SCRC) study unit (Fig. 1), one of the GAMA Priority Basins that are considered to be affected by agriculture-derived groundwater pollution and groundwater nitrate levels above the regulatory MCL. The methodology outlined here, however, may also be extended to the other watersheds with only limited information on potential pollution sources and pathways.

2. Materials and methods 2.1. Study area The SCRC study unit lies in southwestern California, in Santa Barbara and San Luis Obispo Counties, and covers an area of about 1980 km2. Altitudes range from sea level, where the study unit boundary reaches the Pacific Ocean, to about 1980 m above sea level in the San Rafael Mountains in the northeast. Land use is about 61% natural (i.e., grass, shrub and sand), 29% agricultural (i.e., pasture, hay and vineyards) and 10% urban; the latter concentrating on the towns of Santa Maria, San Luis Obispo, Lompoc, and Arroyo Grande (Burton et al., 2013). Based on lithological differences between the alluvial and terrace deposits found in the groundwater basins and upland areas, Mathany et al. (2010) classified the unit into two aquifer systems: Basins and Uplands. The basins system is defined by the presence of alluvium and dune sand formations of Holocene to Pliocene age. The uplands system is delineated by the location of terrace formations of Plio-Pleistocene non-marine and Pliocene marine origin. Groundwater flow basically follows topography towards the Pacific Ocean (Fig. 1), although three NW-SE

Fig. 1. SCRC study area showing distribution of Basins and Uplands wells with data record in the California Department of Public Health (CDPH) database. (Modified from Mathany et al. (2010) and Burton et al. (2013).)


trending fault zones (i.e., the Los Osos, Edna, and Santa Maria River faults) create local barriers to lateral groundwater movement (Mathany et al., 2010). Groundwater recharge occurs from a mixture of ambient recharge (percolation of precipitation, irrigation waters, and seepage from streams and rivers), subsurface inflow, and engineered recharge (urban and agricultural return water, treated wastewater, and lakes), while groundwater discharge occurs via groundwater pumping, used primarily for agricultural irrigation and secondarily for public water supply (Burton et al., 2013). 70 wells in the SCRC unit were sampled in 2008 as part of the GAMA Priority Basins Project for field water-quality indicators, organic and inorganic constituents, radioactive constituents, naturally occurring isotopes, and dissolved gases (Burton et al., 2013; Mathany et al., 2010). 6 of the 70 wells were designated trend sites and were thus sampled again in 2012 (Mathany, 2017). Although measured concentrations were less than health-based thresholds for most of the organic and inorganic constituents, detections of nitrate-N + nitrite-N (hereafter referred to as nitrate since the concentrations of nitrite were negligibly low) above the US MCl of 10 mg/L likely reflect the ongoing impact of agricultural activity in the area. 2.2. Theoretical approach – assessing aquifer vulnerability from groundwater age and mixing data The herein applied method computes an aquifer pollution probability based on the assumption that groundwater in sedimentary aquifers can be represented as a binary mixture of young and old water proportions (Bexfield et al., 2012; Corcho Alvarado et al., 2007; Jurgens et al., 2017; Solomon et al., 2010). This should be especially the case for groundwater from public supply wells in the southwestern U.S. which tend to be screened over long intervals to reach higher yields. Because the overuse of nitrogen fertilizers in California commenced in the early to mid-20th century, groundwater contamination vulnerability from nitrate leaching is considered to be especially increased when groundwater contains high proportions of modern water that was recharged less than ~60 years ago. This contamination should be especially persistent for supply wells screened in oxic groundwater zones where DO prevents the effect of denitrification. 2.2.1. Estimating modern water proportions from lumped parameter modeling Lumped parameter modeling (LPM) has shown to be a valuable approach for constraining groundwater age distributions and mixing processes in sedimentary aquifer systems (Turnadge and Smerdon, 2014). The LPM approach involves the convolution of a tracer input function with a tracer decay function and a weighting function to produce an output that is then matched to observed groundwater data. Different weighting functions have been applied in the past to sedimentary aquifers in temperate to semi-arid climate zones. The exponential mixing model (EMM), as typically used in past research (Cartwright et al., 2007; Dassi, 2010; Le Gal La Salle et al., 2001), applies to homogenous unconfined aquifers of constant thickness receiving uniform recharge. It requires wells to be screened over the entire saturated thickness of an aquifer; a condition that is rarely met in practice. To overcome this limitation, Jurgens et al. (2012) and (2017) applied the partial exponential mixing model (PEM), where the ratio of total vs. sampled aquifer thickness is used to refine groundwater ages for monitoring wells with relatively small well screens. The main limitation of the PEM is its dependency on well construction, aquifer thickness and water level data. A particular issue pertaining to the wells sampled in the SCRC study area is that they mainly comprise supply wells that were not gaged for groundwater levels during sampling. In fact, 51 of the 70 wells considered in this study did not have any groundwater level data available from USGS or California Department of Water Resources (DWR) databases. The wells that had available groundwater data exhibited significant water level fluctuations, which complicates the selection

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of values representative for hydrogeological conditions during sampling. There is also only limited information available on aquifer thicknesses given the complex tectonic setting of California's Coastal Ranges and the fact that local-scale groundwater models do not cover the whole extent of the study area (Bright et al., 1997; Bright et al., 1992). The Dispersion Model (DM) is a weighting function that represents an analytical solution to the one-dimensional advection-dispersion equation (Maloszewski and Zuber, 1982, 1996; Zuber and Maloszewski, 2001). It has the advantage that it does not rely on aquifer thickness, well construction or groundwater level data to compute groundwater ages. Rather, it requires input information on the apparent dispersion parameter (DP) which represents the reciprocal of the Peclet number and corresponds to the ratio of the dispersivity to the advective velocity and the characteristic flow length. Given these considerations and the lack of input data for a reliable application of the PEM, the DM was selected as the appropriate weighting function to model groundwater ages and MWPs in the SCRC study unit (Fig. 2). LPMs allow simulating the mean groundwater age of a sample (τmean) as the sum of transport times through the saturated (τsat) and the unsaturated zone (τuz) as: τmean ¼ τsat þ τuz

ð1Þ

Only τsat represents the actual transport from the aquifer inlet (i.e., recharge area) to its outlet (i.e., groundwater well) and is modeled by the DM. τuz represents the travel time delay between tracers in atmospheric precipitation and those assumed in recharge. Here, τuz is modeled by assuming piston-flow behaviour which means that a τuz of, e.g., 5 years causes the tracer (e.g., 3H) to enter the saturated zone 5 years after it reached the ground as precipitation (Jurgens et al., 2012). While a single DM can provide useful age constraints for samples exhibiting unimodal age distributions (i.e., modern vs. pre-modern), it is not appropriate for mixed samples with intermediate age tracer values. In this case, a binary mixing model (BMM) can provide more accurate results. In this study, we applied BMMs consisting of two separate DMs as: τmean ¼ f 1 τ1 þ ð1− f 1 Þτ2 þ τuz

ð2Þ

where τ1 and τ2 correspond to the saturated zone travel time of the modern groundwater component and the saturated zone travel time of the pre-modern groundwater component, respectively. f1 and f2 are the corresponding fractions of the modern and pre-modern components in the binary groundwater mixture. Because both the single DMs and the BMMs provide the mean age of the single or the two mixed groundwater components (τsat for the single DM; τ1 and τ2 for the BMM), they allow computing the modern water proportion (MWP) as: MWP ¼ f =τ

ð3Þ

In this equation, f is the relative fraction of the modern component (either 0% or 100% in the single DM; or 0% to 100% in the BMM and τ corresponds to the mean age of the modern groundwater in the single DM (τsat) or the mean age of the modern groundwater component (τ1) in the BMM. Using τ as the denominator in Eq. (3) allows to scale the MWP according to the changing groundwater vulnerability from changing contaminant applications. This assumes that N-fertilizers are the main source of nitrate pollution and that N-fertilizer use in the area has increased steadily between 1950 and 2010; as the statewide sales trend for N-fertilizer (Rosenstock et al., 2013) would imply.

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Fig. 2. Idealized aquifer configurations in which (a) a single dispersion model (DM) and (b) a binary mixing dispersion model (BMM) applies. In the singe DM, one extraction well removes modern groundwater from a shallow aquifer and another removes pre-modern water from a deep aquifer. Mixing occurs within both shallow and deep aquifer units because of variations in groundwater velocity related to aquifer heterogeneity. In the BMM, mixing occurs because of aquifer heterogeneities and because the extraction well is screened across both shallow and deep aquifers. The bottom graphs illustrate the LPM exit age frequency distribution, g(t), expected from the DMs (c) and the BMM (d). Note that the DMs in (c) represent independent response function examples at a mean groundwater age of 25 years for a low dispersion parameter (DP) of 0.01 (dashed red line) and a high DP of 0.1 (solid blue line). The BMM shown in (d) exemplifies a mixture comprised of 20% modern groundwater(τ1 = 10 years, DP = 0.01) and 80% pre-modern groundwater (τ2 = 50 years, DP = 0.1).

2.2.2. Logistic regression modeling of groundwater contamination probability Aquifer vulnerability can be expressed via LR as an occurrence probability of nitrate concentrations to exceed an anthropogenic pollution threshold. The typical form of LR can be expressed as (Mair and ElKadi, 2013). P ¼ eb0þbX = 1 þ eb0þbX

ð4Þ

where P is the pollution probability for a contaminant to exceed the pollution threshold, b0 is a scalar intercept parameter, X is a vector of n explanatory variables, and b is the vector of slope coefficient values so that bX = b1X1 + b2X2 … bnXn. In this study, the concentration of nitrate in groundwater from supply and monitoring wells was set as the dependent variable and the nine explanatory variables considered in the analysis were: MWP (1), total mean groundwater age (2), concentration of DO (3), percentage of natural land use within a 500 m radius around the well (4), percentage of agricultural land use within a 500 m radius around the well (5), percentage of urban land use within a 500 m radius around the well (6), septic tank density within a 500 m radius around the well (7), Leaking Underground Fuel Tank (LUFT) density within a Thiessen polygon (8) and distance to the nearest LUFT (9). The first two variables were modeled in this study, while the remaining variables were taken from published reports by Burton et al. (2013), Mathany et al. (2010) and Mathany (2017). Rather than applying all of the 9 potentially significant explanatory variables in one LR model, we first screened the data for multicollinearity and then applied a modified backward elimination analysis to determine variables of highest significance. Multicollinearity indicates a strong correlation between two or more explanatory variables and can translate into overinflated standard errors and a lower statistical significance of potentially significant variables (Midi et al., 2010). The dataset was screened for multicollinearity using the Variance

Inflation Factor (VIF) assuming a cutoff value of VIF = 2.5 (Allison, 1991). The modified backward elimination procedure applied herein started with all of the low VIF variables in one LR model and the variable that was least significant; i.e., the one with the largest p-Wald value, was removed and the model was then refitted. Each subsequent step removed the least significant variable in the model until all remaining variables exhibited individual p-Wald values that were below a threshold of 0.1. The resulting best-fit LR model was then manually re-tested with each of the discarded explanatory variables because they can become significant depending on the other variables in the model (Dallal, 2012). The chi-square and Hosmer and Lemeshow statistics (Hosmer and Lemeshow, 2000), as well as the McFadden, Cox and Snell and Nagelkerke R2 values (Helsel and Hirsch, 2002) were used to evaluate the best-fit LR model performance. All VIF and LR analyses were conducted using SAS v9.4 (SAS, 2010). Spatial autocorrelation (SAC) indicates a spatial pattern of features expressed as clustered, dispersed, or random. If objects are attracted (or repelled) by each other, it indicates they are not independent, which violates the basic assumption of the regression analysis. In this study, we followed the approach of others (e.g., Crase et al., 2012, etc.) and tested for SAC by computing the Moran's I statistic and associated z-score and p-value of residuals obtained from the best-fit LR model. Spatial autocorrelation analysis were conducted with ArcGIS's (ESRI, 2010) spatial analyst extension using two scenarios for the conceptualization of spatial relationships: Fixed Distance and Inverse Distance. The Fixed Distance option imposes a sphere of influence, or moving window of spatial interactions onto the data and each feature is analyzed within the context of those neighboring features located within a specified cutoff distance. In the Inverse Distance option, all features influence all other features, but the farther away a feature is within a range specified by the cutoff distance, the smaller the impact it has. In this study, we set the cutoff distance to the distance that ensures every feature has at least one neighbor.


2.3. Data The datasets collected in the GAMA Priority Basins Project in 2008 and 2012 were used as explanatory variables in the LR analysis. GAMA data were also used to develop single DMs and BMMs in TracerLPM (Jurgens et al., 2012) for the groundwater age and MWP calculation. Importantly, the application of measured 14C data in LPMs requires a correction procedure to account for various isotopic fractionation processes that can occur upon recharge. Similarly, He fractions derived from tritiogenic (3Hetrit) and radiogenic (4Herad) sources need to be estimated indirectly using a chemical inversion technique. The details on the estimation of 14Ccorrected, 3Hetrit and 4Herad in this study are provided in Appendix A of the supplementary materials. The geochemical data are listed in Appendix B of the supplementary materials. 2.3.1. Lumped parameter modeling datasets To select the appropriate LPM for age dating, groundwater samples were first classified as modern, when 3H N 0.2 TU and 14Ccorrected N 99 pmC, pre-modern when 3H b 0.2 TU and 14Ccorrected b 99 pmC and mixed when 3H N 0.2 TU and 14Ccorrected b 99 pmC. Samples with no 14 C data were not used for age dating since the lack of 14C data prohibits age estimation of the old groundwater fraction. Even though the ratio of 4 Herad over the measured 4He can be used as an independent indicator for old groundwater, the lack of reliable aquifer input information on 4 Herad limits the utility of this tracer for quantitative evaluations. As tracer input, time-series of atmospheric tracer data from Modesto, California, were used for 3H and 3Hetrit while Northern Hemisphere Zone 2 data were used as input for 14C. A reliable estimation of DPs for the DM requires the availability of time-series tracer data points for groundwater (Maloszewski and Zuber, 1982). Six wells of the study area were sampled twice in 2008 and in 2012. Of those, well SCRC\\B18, with 3H N 1.9 TU, 14Ccorrected = 94.1 pmC and a 4Herad to measured 4He ratio of 0%, is considered to align reasonable closely with the modern water end-member to be considered a valid proxy. SCRC-B18 data were therefore used to estimate the DP value of modern water. The best-fit DM for this well (Fig. 3) suggests near Piston Flow conditions for the modern component as indicated by the relatively long τuz (N20 years), short τsat (b1 year) and a DP value of ~ 0.01. The DP value of pre-modern groundwater cannot be constrained from time-series radiocarbon data given that the time frame that seperates the sampling events (i.e., 4 years from 2008 to

Fig. 3. TracerLPM output for best-fit time-series DM solution for SCRC-B18. The model yields τuz = 21 years, τsat = 0.87 years and DP = 0.012 at a total relative error of 0.1% (see text for explanation of model input parameters). Modeled 3H trend suggests conditions similar to Piston Flow for modern groundwater.

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2012) is too short to exert a detectable effect on measured radiocarbon contents. Because DP values exhibit a scale dependency and because pre-modern groundwater is expected to have flow path distances that exceed those of modern groundwater by at least one order of magnitude, a DP of pre-modern groundwater can still be assigned based on longitudinal dispersivities in the literature. Using the estimated modern DP value of 0.01 and Gelhar et al.'s (1992) dispersivity to scale relationship and assuming that pre-modern and modern groundwater advective flow velocities are equal yields a pre-modern DP value of 0.1. This value agrees with that reported for pre-modern groundwater elsewhere in the U.S. Southwest (Bexfield et al., 2012) and indicates a broader distribution of ages within that fraction. Single DMs and BMMs were applied for pre-modern, modern and mixed groundwater samples using DPs of 0.01 and 0.1 for modern and pre-modern groundwater components, respectively. Both DMs and BMMs were solved using TracerLPM's relative error optimization algorithm (Jurgens et al., 2012). For single DMs, the number of adjustable parameters was 2 (mean age and transport time in the unsaturated zone). For BMMs, the number of parameters was 4 (mean age of first component, mean age of second component, mixing fraction and transport in the unsaturated zone). Because the mean age of the pre-modern fraction can only be estimated with 14Ccorrected, all BMMs yield nonunique model solutions as larger fractions of modern water can theoretically offset lower 14Ccorrected values of older pre-modern groundwater. As a method to obtain unique solutions and to provide the most conservative estimate of MWPs, we followed the approach of Jurgens et al. (2017) and determined adjustable parameters for the youngest possible pre-modern water age that provided the best fit to the data. BMMs also provide non unique solutions for samples classified as mixed, but lacking 3Hetrit data needed to constrain the mean age of the modern component and transport time in the unsaturated zone. For those wells, modern water component age and unsaturated zone transport time could not be reliably estimated based on available data so these wells were excluded from further analysis. The final model dataset comprises 37 groundwater samples of which 11% are classified as modern, 43% as mixed and 46% as pre-modern. 2.3.2. Logistic regression modeling datasets Aside from the MWP and total mean groundwater age variables calculated in this study, additional variables for the LR analysis were taken from published work in the literature. Nitrate data used to establish the dependent variable, and DO data used as an explanatory variable stems from geochemical surveys described and summarized by Mathany et al. (2010) and Mathany (2017). The use of DO as an explanatory variable required the estimation of missing DO values for two wells (SCRC-B05, -B16) based on the logarithmic relationship between concurrent DO and Mn data (R2 = 0.53; relationship not shown). The approach for estimating land use/land cover data used as explanatory variables is described in detail in Appendix D of the report by Burton et al. (2013). The estimates are based on an enhanced version of the 30 m pixel resolution USGS National Land Cover Dataset (Nakagaki et al., 2007) that has been used in previous national and regional studies relating land use to water quality (Gilliom et al., 2006; Paul et al., 2007). The dataset characterizes land cover during the early 1990s and is classified into 25 landcover classifications (Nakagaki and Wolock, 2005) which were grouped into the 3 general land use classifications: urban, agricultural, and natural. Land use statistics were assigned for a circle within a 500 m radius around each individual well following the approach of Johnson and Belitz (2009). Septic tank density was determined from housing characteristics data from the 1990 U.S. Census (Burton et al., 2013). The density of septic tanks in each housing census block was calculated from the number of tanks and block area. The tank density around each well was calculated from the area-weighted mean of the block densities for blocks within a 500 m buffer around the well location. Estimates of the distance to the nearest LUFT and LUFT density were based on data obtained from the GeoTracker Geographic Information Management

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System (Burton et al., 2013). The LUFT density was estimated as the number of tanks surrounding a well in a Thiessen polygon (Burton et al., 2013). All explanatory variables were assigned continuous variables. Even though a categorical classification of variables such as DO into a binary function (i.e., oxic vs. anoxic) has yielded good LR model fits in other studies (e.g., Jang and Chen, 2015), it has shown to be not as sensitive an indicator as the actual DO concentration in terms of predicting contaminant concentrations at values close to the pollution threshold (Gurdack and Qi, 2012). 3. Results and discussion 3.1. Modeled groundwater ages LPM results of this study are shown in Appendix C. The total mean ages ranged from 52.9 to 59.1 years (median value: 56.3 years) for modern groundwater, 2748 to 54,958 years (median value: 13,876 years) for pre-modern groundwater and 799 to 16,959 years (median value: 2746 years) for mixed waters. Computed travel time delays between atmospheric concentration records and those assumed in recharge (τuz) in modern and mixed waters, ranged from 0 to 35 years (median value: 10.5 years), and most wells displayed a travel time delay of 18– 25 years. It is not clear what could have caused the observed variability in τuz. More information on water levels, which were not available for the sampling time frames, may shed more light on the processes that could cause a delay between recorded concentrations in the atmosphere or precipitation and concentrations in recharge. Relative modeling errors ranged from 0% to 6.44% (median value: 0.04%) for 35 out of 37 samples. The remaining two samples, SCRC-B10 and -H12, exhibited high errors of 23.8% and 37.3%, respectively, which likely reflect the low measured 3H values (b 0.4 TU) and translated errors in the estimates of 3 Hetrit. There is no significant linear correlation between total mean groundwater ages and the total well depth (Pearson R = 0.37; p N 0.01) or depth to the top of the screen interval (Pearson R = 0.41; p N 0.01; relationships not shown). However, the presence of pre-modern groundwater from wells as shallow as 300 ft. bgs (SCRC-H14) and the presence of mixed groundwater from wells N600 ft. deep (SCRC-B05, -H12) indicates that there are local variations in the groundwater agedepth trends. These variations could be due to the presence of high permeability conduits such as abandoned wells that may facilitate the transport of shallow (and potentially contaminated) groundwater into the deeper aquifer system (Harter et al., 2012). 3.2. Modeled aquifer vulnerability To develop the LR model, the concentration of nitrate was first transformed into a binary response based on observed ranges. One problem of LR is the selection of an appropriate threshold value for the binary dependent variable (in this case: the background value that distinguishes between natural and anthropogenic nitrate sources). Many previous studies in urban and/or agricultural regions (e.g., Liu et al., 2005; Mair and El-Kadi, 2013, etc.) assigned a value of 1 mg/L. Yet, Burkart and Stoner (2001) reported background NO3 concentrations in groundwater to vary significantly from as low as 0.2 mg/L in Ohio to as high as 100 mg/L in the Sahel Zone of Africa. From a regulatory standpoint, a practical background level could be the U.S. EPA MCL for drinking water of 10 mg/L. However, this level may be too high to capture the actual threshold values for anthropogenic contamination in agricultural settings (Koh et al., 2009). Given these considerations, we selected a value of 2 mg/L as a background because this value reflects the median nitrate concentration determined from observation wells in the California Coastal Basins principal aquifer (Gurdack and Qi, 2012). Consequently, the value of the dependent variable was set to 1 for concentrations N2 mg/L and 0 for concentrations ≤2 mg/L.

As the next step in the LR model development, we tested for multicollinearity using the VIF statistic. The VIF is the reciprocal of Tolerance and is computed as 1/(1 − r2), where r2 is the regression coefficient of one independent variable on all remaining independent variables. The VIF describes how inflated the variance of coefficient is and higher values (i.e., N2.5) indicate that the variable under consideration represents a linear combination of the independent variables already in the equation and that it should not be added to the regression equation. Calculated VIFs decreased from ≤ 1.5 × 106 to ≤2.63 when the highest VIF variable % natural land use was removed, and then further to ≤1.91 when the remaining highest VIF variable % urban land use was removed (Table 1). Only variables with VIFs b 2.5 (Allison, 1991) were considered in the LR model. The automated backward LR analysis sequentially eliminated the parameters LUFT density (p-Wald = 0.91), Septic tank density (p-Wald = 0.51), % agricultural land use (p-Wald = 0.44), Total groundwater age (p-Wald = 0.40) and Distance to nearest LUFT (p-Wald = 0.24) until only the parameters MWP (p-Wald = 0.072) and DO (p-Wald = 0.008) remained (Table 2). Combining each of the eliminated variables with the best-fit parameters DO and MWP did not result in any of the eliminated parameters becoming statistically significant (Table 2). This supports conclusions from previous assessments and highlights the issue of identifying reliable predictors for groundwater vulnerability assessments at the watershed scale (Mair and El-Kadi, 2013). SAC analysis indicates some clustering of best-fit model residuals as highlighted by slightly positive Moran's I values (Table 2). However, associated p levels were too high to reject the null hypothesis of randomly distributed data. Given this, the dataset was not considered to be significantly influenced by SAC and the application of an “autocovariate” correction variable (Augustin et al., 1996; Dormann, 2007) was not considered necessary. The LR model with only the statistically significant DO and MWP variables yielded the following equation for the occurrence probability P(u) of aquifer nitrate pollution: PðuÞ ¼ 1=ð1 þ expð−ð−4:42 þ 768 MWP þ 0:97 DOÞÞÞ

ð5Þ

The model results indicate an excellent fit with a chi-square of 31.7 (p b 0.001), Hosmer and Lemeshow test value of 19.5 (p N 0.5), and McFadden-, Cox and Snell- and Nagelkerke R2 values of 0.52, 0.58 and 0.77, respectively. The model predicts a total correct classification ratio of 91.9%, with 88.2% of predictions above the anthropogenic pollution threshold (sensitivity) and 95.0% for nitrate-N below the anthropogenic pollution threshold (specificity). It can thus be concluded that the simple 2 parameter LR model with DO and MWP as explanatory variables discriminates and calibrates well for groundwater nitrate contamination in the SCRC study area. 3.3. Trends of groundwater nitrate pollution vulnerability Analysis of geochemical and hydrogeological data from the SCRC unit in Southern California revealed highly variable groundwater solute chemistries and ages consistent with diverse solute (and pollutant) inputs and complex mixing histories along the groundwater flow paths. Nitrate concentrations were generally below the pollution threshold (b2 mg/L) at wells screened in the uplands aquifer system where there is no significant agricultural or urban development. High nitrate concentrations above the pollution threshold occur mainly at wells screened in the basins aquifer system and particularly in the alluvium of the Santa Maria River (Fig. 4a). This part of the study area is strongly affected by agricultural development (Fig. 4b) which indicates a positive control of this type of land use on groundwater nitrate pollution. However, this control is not evident for the wells screened in the alluvial deposits of the San Antonio Creek and Santa Ynez rivers as they exhibit significantly lower nitrate concentrations, despite the fact that these watersheds are dominated by agricultural land use as well. The

B. Hagedorn et al. / Science of the Total Environment 624 (2018) 1550–1560 Table 1 Multicollinearity statistics for potential explanatory variablesa. Parameter

Tolerance

VIF

Step 1 MWO DO Total groundwater age % agricultural land use % natural land use % urban land use Septic tank density LUFT density Distance to nearest LUFT

0.705 0.481 0.546 b0.001 b0.001 b0.001 0.749 0.599 0.653

1.42 2.08 1.83 1.4 × 106 1.5 × 106 6.8 × 105 1.33 1.67 1.53

Step 2 MWO DO Total groundwater age % agricultural land use % urban land use Septic tank density LUFT density Distance to nearest LUFT

0.705 0.481 0.546 0.461 0.380 0.749 0.599 0.653

1.42 2.08 1.83 2.17 2.63 1.33 1.67 1.53

Step 3 MWO DO Total groundwater age % agricultural land use Septic tank density LUFT density Distance to nearest LUFT

0.751 0.524 0.604 0.528 0.775 0.796 0.837

1.33 1.91 1.65 1.89 1.29 1.26 1.19

a

Highest VIF parameters removed in step 1 and 2 are shown in bold.

discrepancy in nitrate contamination between the watersheds reveals the limitation(s) of low resolution land cover data in explaining nitrate pollution. More finely-resolved land use data, in particular on crop type as well as on fertilizer inputs (ammonia and manure), is needed for a better understanding of agricultural development on nitrate pollution in the area. The map of modeled pollution probabilities for elevated nitrate (Eq. (5)) shows good agreement between high detection probability and

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observed detections (Fig. 4a, b). All of the modern groundwater samples (SCRC-B16, -U07, -U09 and -U10) exhibit a pollution probability of 100% because of high DO and MWP values and all of these wells are located in the basins aquifer system of the Santa Maria River alluvium. Similarly, the mixed-type groundwater wells for which a high pollution probability (N 50%) is predicted (i.e., wells SCRC-B05, -B18, -B22, -B28, -B30, -B36, -H06, -U03, and -U08) are, with only one exception (SCRC-H06), also all located in the basin aquifer system of the Santa Maria River. Collectively, the data from the modern and mixed samples indicate that permeable alluvium of the Santa Maria River watershed forms excellent transport pathways for excess nitrate-N fertilizer and oxygen in unsaturated zones. Mixed-type groundwater wells with a low (b50%) pollution probability (SCRC-B21, -B27, -B35, -B37, -H11, -H12, -U03 and -U15) are mainly found in the San Antonio Creek and Santa Ynez watershed. The reason for the low nitrate concentrations and low pollution vulnerability predictions in those watersheds is not clear. It is possible that the alluvial deposits in those watersheds differ from those of the Santa Maria River by being less permeable and by exhibiting more reducing soil water and recharge conditions. However, more data on soil and unconfined aquifer properties (i.e., textures, geochemical data such as ORP, etc.) are needed to better understand the specific impact(s) of the soil and vadose zones of the various basins. Of the 17 pre-modern-type groundwater wells, 4 (i.e., SCRC-B23, -B24, -H05 and -U06) exhibit high pollution probabilities of N50% due to high DO concentrations (4.6 mg/L–6.3 mg/L). This seems somewhat unrealistic given the old modeled groundwater ages (N2748 years) which are not consistent with any modern water influence. Interestingly, none of these wells is located in areas directly associated with intense agricultural activity (i.e., cultivated crops land cover in Fig. 4b). Well SCRC-H05, located in residential land along the foothills of the southern San Raphael Mountains, is actually located far upgradient from any agricultural activity in the riverine plains (Fig. 4b). Given this, it is possible that higher measured nitrate values (1.57 mg/L–4.03 mg/L) in those pre-modern-type groundwater wells actually correspond to nitrate input from natural sources (i.e., nitrification of soil-derived ammonium), rather than anthropogenic pollution from, e.g., N-fertilizers. This hypothesis is supported by the reported high nitrate concentration

Table 2 LR modeling results for nitrate pollution probability. Model

Best-fit backward elimination

Best-fit + variable





Variable

Intercept

Total groundwater age

% agricultural land use

Septic tank density

LUFT density

Distance to nearest LUFT

Variable status Coefficient b p-Walda p-Chi-Sqb p-HLc R2 McFaddend R2 Cox and Snelld R2 Nagelkerked Area under ROC curvee Moran's I (inverse distance band)f z-Scoref p-Valueg Moran's I (fixed distance band)f z-Scoref p-Valueg

Significant Significant Significant Eliminated −4.42 768 0.97 −0.0001 0.01 0.07 0.01 0.51 b0.001 0.97 0.62 0.58 0.77 0.96 0.22

Eliminated 0.0055 0.79


Eliminated −0.7019 0.82


a b c d e f g

MWP

DO

0.41 0.68 0.05 1.18 0.24

A p-Wald value ≤ 0.1 indicates statistical significance of parameters. A p-Chi-Sq value ≤ 0.1 indicates acceptable model discrimination. A p-HL value N 0.5 indicates good model calibration. A R2 value N 0.5 indicates good model prediction. An area under the receiver operating characteristic curve N 0.95 indicates excellent model fit. A Moran's I and z-score value N 0 indicates spatial clustering. A Moran's p-value N 0.01 indicates null hypothesis (random spatial distribution of LR residuals) cannot be rejected.

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Fig. 4. Maps of measured dissolved nitrate + nitrite concentrations (n = 37) (a), and nitrate pollution probability (b) with aerial proportion of developed land according to the 2011 National Land Cover Dataset (NLCD; data from USDA NRCS Geospatial Data Gateway; https://datagateway.nrcs.usda.gov/).

(3.49 mg/L) of paleogroundwater from the High Plains aquifer in the Midwest U.S. (McMahon et al., 2004). Isotopic data of dissolved NO3 can help in refining the baseline nitrate range in the pre-modern samples through the identification of dominant NO 3 sources affecting modern, mixed and pre-modern groundwater (Koh et al., 2010). The available δ15NNO3 and δ18ONO3 data (Fig. 5) is somewhat inconclusive because the dataset does not include specific data for local source candidates (i.e., precipitation, applied fertilizer, soil, manure and septic waste). These are needed to define more distinct and no overlying source end-member ranges. Of the wells for which isotopic data are available, only well SCRC-B27 exhibits a pollution probability of b 50%. Yet, this well has an isotopic fingerprint very similar to those of the high pollution probability wells SCRC-B23, -B28, -B30 and -B36. In fact, only the mixed-type groundwater well SCRC-B28 displays a δ 15N NO3 value that is too high to be explained by NH4 in fertilizer alone. This hints towards more manure-derived nitrogen applications in the recharge area of this particular well. It becomes clear, that isotopic data from more low and high pollution probability wells are needed to better distinguish between anthropogenic (manure and fertilizer) and natural (soil) nitrogen sources. Temporally, the LR model predicted a decreasing pollution probability (3.39% to 1.92%) for well SCRC-H08 and an increasing pollution probability (4.49% to 6.64%) for well SCRC-H11 between 2008 and 2012. Further time-series data may help in understanding how the vulnerability of the supply wells to contaminants varies throughout the year. Time-series data would also help refining our estimate of the dispersion parameter applied in the LP models (Fig. 3). Furthermore, time-series age tracer data in combination with information on water levels could allow constraining groundwater recharge rates that could be used to predict future nitrate groundwater trends for various nitrogen fertilizer application scenarios.

3.4. Model limitations and transferability of results The LR model, as implemented in this study, predicted one false high and two false low pollution probabilities above the pollution threshold of 2 mg/L. These false predictions highlight our model limitations for scenarios in which the two explanatory variables align on opposite sides of the measured spectrum. This is well exemplified for groundwater from wells SCRC-B24 and -U03. For SCRC-B24, our model predicted a false high pollution probability because the DO concentration (6.3 mg/L) was high enough to offset the effect of the low MWP (0 yr−1). For SCRC-U06, the opposite is the case and the model predicted a false low pollution probability because the MWP (4.02 × 10−3 yr−1) was not high enough to offset the low DO concentration (0.8 mg/L). In both instances, the addition of a third independent explanatory variable, such as soil texture and/or nitrogen application, could provide more clarification. Nevertheless, the false predictions expressed by our model are still useful for vulnerability assessments. The pre-modern groundwater composition at SCRC-B24, for instance, suggests no influence from any modern water, probably because of the well's upgradient location from major irrigated croplands (Fig. 4b). Its high DO content, however, still indicates pollution vulnerability so rigorous management of fertilizer application and irrigation should be implemented near that well to prevent future contamination. Another limitation of our LR model is that the pollution threshold nitrate value (2 mg/L) used to define the dependent variable may not be representative for conditions in some portions of the study area. For example, the false low pollution probability predicted for pre-modern (i.e., 4568 yr old) groundwater at SCRC-H13 may actually reflect a nitrate concertation (2.28 mg/L) that is derived from a natural source given that background nitrate values as high as 3.49 mg/L have been reported for paleogroundwater elsewhere in the in the U.S. (McMahon et al., 2004). It is the uncertainty inherent in the estimation of the nitrate


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Supplementary materials to this article can be found online at https://doi.org/10.1016/j.scitotenv.2017.12.115.

Acknowledgements The authors would like to thank the researchers of the California State Water Resources Control Board and the U.S. Geological Survey for compiling the datasets that made this study possible. The authors would also like to thank F. Peeters, B. Jurgens and J. Schmitt for their help and support with the geochemical data and models used in this study. Two anonymous reviewers are acknowledged for their valuable comments on an earlier manuscript draft. This work was supported through a research grant to B. H. by Long Beach State University (2017MGSS01). References Fig. 5. Isotopic data of SCRC groundwater nitrate (data from Mathany et al., 2010) and that of potential nitrate sources (end-member fields from Kendall and McDonnell, 1998). Wells SCRC-B23, -B27, -B30 and -B36 all fall in a small range of 3 potential nitrate sources: NH4 in fertilizer and acid rain, soil-N, and manure and septic waste. Sample SCRC-B28 plots within a 2 end-member field: soil-N and manure and septic waste. More data on local nitrate sources are needed for a more refined source evaluation.

background value that complicates vulnerability assessments for larger study areas where more than just one background value may be needed for various groundwater groups. This study relied on data obtained from production and monitoring wells screened in sedimentary aquifers. Given this, the method should not be applied to settings where the DMs and BMMs, as applied herein, are not applicable. This should especially be the case for dual porosity/ fractured rocks settings where matrix flow through intergranular pore spaces does not dominate. The presented approach should also not be applied to other contaminants with different source histories, environmental conditions and degradation characteristics. We furthermore recommend against the use of the herein presented data for mapping of groundwater protection zones since the well dataset is not considered large enough for a reasonable application of extrapolation geostatistics.

4. Conclusions and implications This study highlights the utility of two parameters, DO and MWP, for predicting nitrate contamination vulnerability in an agriculture-impacted setting like the SCRC unit in Southern California. Results from this study can thus be used to guide field investigations in areas with little data but similar hydrogeology and land use. A particular advantage of the method presented in this paper is that it is not dependent on nitrogen application and soil/crop nitrogen uptake information. However, the herein presented approach relies on assumptions and conceptual models that may not fully capture the groundwater system. As such, our results should be treated as cursory estimates of pollution vulnerability that should be refined and validated not only with future groundwater geochemical data for LR model cross-validation, but also with estimates from other, independent vulnerability assessment methods such as a soil nitrogen mass balance. While this study relied on data obtained from wells from within the SCRC study unit, future research should also consider the role of interbasin flow on the lateral transport of contaminants and how this lateral transport might affect LR assessments based on watershed specific explanatory and dependent variables. The role of abandoned wells as an explanatory variable also warrants further investigation as those can act as conduits for contaminated surface water to enter deeper zones.

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