s u m m a r yNitrate is a major source of contamination of groundwater in the United States and around the world. We tested the applicability of multiple groundwater age tracers ( 3 H, 3 He, 4 He, 14 C, 13 C, and 85 Kr) in projecting future trends of nitrate concentration in 9 long-screened, public drinking water wells in Turlock, California, where nitrate concentrations are increasing toward the regulatory limit. Very low 85 Kr concentrations and apparent 3 H/ 3 He ages point to a relatively old modern fraction (40-50 years), diluted with pre-modern groundwater, corroborated by the onset and slope of increasing nitrate concentrations. An inverse Gaussian-Dirac model was chosen to represent the age distribution of the sampled groundwater at each well. Model parameters were estimated using a Bayesian inference, resulting in the posterior probability distribution -including the associated uncertainty -of the parameters and projected nitrate concentrations. Three scenarios were considered, including combined historic nitrate and age tracer data, the sole use of nitrate and the sole use of age tracer data. Each scenario was evaluated based on the ability of the model to reproduce the data and the level of reliability of the nitrate projections. The tracer-only scenario closely reproduced tracer concentrations, but not observed trends in the nitrate concentration. Both cases that included nitrate data resulted in good agreement with historical nitrate trends. Use of combined tracers and nitrate data resulted in a narrower range of projections of future nitrate levels. However, use of combined tracer and nitrate resulted in a larger discrepancy between modeled and measured tracers for some of the tracers. Despite nitrate trend slopes between 0.56 and 1.73 mg/L/year in 7 of the 9 wells, the probability that concentrations will increase to levels above the MCL by 2040 are over 95% for only two of the wells, and below 15% in the other wells, due to a leveling off of reconstructed historical nitrate loadings to groundwater since about 1990.

Phytate (IP 6 ) is often the most common organic P compound particularly in agricultural soils. understanding the fate of inositol phosphate (IP x ) in the environment in terms of isomeric composition and concentration and assessing relative resistance to (or preference for) degradation is essential to estimate the potential role of IP x in generating inorganic P (P i ) as well as overall P cycling in the environment. In this study, we analyzed IP 6 degradation by four common phosphohydrolase enzymes (phytase from wheat [Triticum aestivum l.] and Aspergillus niger and acid phosphatase from wheat germ and potato [Solanum tuberosum l.]), with particular focus on degradation pathways, isomer kinetic decay rate, and isotope effects using a combination of high-performance ion chromatography, nuclear magnetic resonance, stable isotopes, and process-based modeling techniques. our results show that the degradation pathways are often distinct among enzymes. The process-based Bayesian inverse modeling was used to capture the trend and magnitude of the measured concentrations for each IP x isomer and to determine the decay constants. Furthermore, o isotope ratios (d 18 o P ) of released P i enabled the identification of isotopically identical phosphate moieties in phytate derived from natural sources. Distinctly different fractionation factors, degradation pathways, and kinetic decay rate coefficients among the enzymes studied could lead to potential discrimination and tracking of phytate sources and products as well as active enzymes present in the environment.Abbreviations: HPIC, high-performance ion chromatography; IP, inositol phosphate; NMR, nuclear magnetic resonance; P i , inorganic phosphorus; pNPP, para-nitrophenyl phosphate.I nositol phosphates are a group of organic P compounds widely present in the natural environment (Turner et al., 2002). Phytate (the salt of myo-inositol 1,2,3,4,5,6-hexakisphosphate or IP 6 ) is a P storage molecule in cereals and grains and represents between 60 and 80% of P in mature seeds (Raboy, 1997). Since it is reported that ?51 million tonnes of phytate is formed in commercially produced fruits and crop seeds every year (Lott et al., 2000), a good fraction of phytate in seeds and grains is released to the soil environment as plant residues and animal manures (Dao, 2007;Gerke, 2015). Phytate readily sorbs onto minerals or precipitates with soil cations and organic matter, and then accumulates to constitute an often dominant class of organic P (Celi and Barberis, 2007;Giles and Cade-Menun, 2014). Although sorption and precipitation immobilizes a large fraction of phytate in soil, there is a potential for its transfer to water bodies with soil particulates and colloids (Turner et al., 2002;Turner and Newman, 2005 Core Ideas• Phytate is degraded through distinct pathways for a particular enzyme.• oxygen isotope ratios of phosphate moieties in phytate are isotopically identical.• These findings bring new insights into tracking phytate sources in the environment.

The evolution of the joint distribution of groundwater age, velocity, and arrival times based on a Markov model for the velocities of fluid particles in heterogeneous porous media has been quantified. An explicit evolution equation for the joint distribution of age, arrival time, and particle velocity is derived, which is equivalent to a continuous time random walk for age, velocity, and arrival time. The approach is fully parameterized by the correlation model and the distribution of groundwater flow velocities. The transition probability for subsequent particle velocities along streamlines is implemented by a Copula, which is an efficient method to generate a correlated velocity series with prescribed marginal distribution. We discuss different solution methods based on finite‐differences and random walk particle tracking. The latter is based on continuous time random walks, whose transition times are obtained kinematically from the flow velocities. Specifically, we discuss a renormalization scheme to accelerate the particle‐tracking simulations based on the definition of aggregate particle transitions while at the same time renormalizing velocity correlation. The impact of velocity correlation and velocity distribution on the evolution of age at different distances from the inlet plane is also studied. At distances of the order of the correlation length, persistent particle velocities give the same behavior as stochastic streamtube models. For velocity distributions which give rise to transition times with finite variance, the age distributions evolve toward an inverse Gaussian. For heavy‐tailed weighting times, they evolve toward stable distribution as the distance from the inlet increases.