, 2010). For the latter possibility,
Na-Cl water could have been present in shallow groundwater as a result of natural hydraulic connections to underlying strata and the idea of such connections is supported by the documentation of natural fractures (Jacobi, 2002), particularly J1 and J2 joint sets, in the Geneseo Shale (of the Genesee Group) which underlies the western portion of the county (Fig. 1) (Engelder et al., 2009). The lack of differences in methane concentrations across Pifithrin-�� mw different bedrock formations in which water wells were finished also supports the possibility that methane-rich Na-Cl water is migrating from deeper formations. In either case, this water chemistry is indicative of increased interaction with bedrock and less contribution of meteoric (precipitation-derived) water that would have infiltrated through overlying calcareous sediments (Fleisher, 1993). This extended residence time and potential interaction
with methane-rich strata (e.g. black shale) could have led to relatively higher methane concentrations (Molofsky et al., 2013). The Na-HCO3 groundwater and its associated dissolved methane likely resulted from groundwater residence time and rock-water interaction as well as redox processes. Longer residence times typically lead to increased concentrations of Na and HCO3 due to cation exchange between calcium and sodium Decitabine mw clonidine and oxidation of organic matter, and can also promote biological methane production as oxygen is used up and methanogenesis is thermodynamically favored (Kresse et al., 2012 and Thorstenson and Fisher, 1979). The methane isotopic signatures also support the presence of some microbial methane, with the majority of δ13C-CH4 values falling between −40 and −60‰, indicating likely mixing of biogenic and thermogenic methane (Whiticar, 1999). To better predict patterns in dissolved methane, it is useful to model the relationship between methane and readily
measurable environmental parameters. Such parameters could be GIS-derived characteristics described in previous sections or water quality and geochemical characteristics like specific conductance or sodium concentration. It is also important that such parameters be continuous, rather than classifications like ‘valley’ vs. ‘upslope’. Table 2 displays the results of the best multivariate regression models using selected variables from the full suite of landscape and chemical parameters. An initial model was developed using nine variables that were selected based on their Pearson correlation with methane. Using the six variables found to be significant (p < 0.05) – hardness, barium, chloride, sodium, sulfate and distance from active gas wells – a regression model was created that could explain 82% of variation in observed methane patterns (Fig. S3).