While this model revealed distance to active gas wells as exhibiting a negative control on methane concentrations, this does not indicate that gas wells are definitively causing higher methane concentrations; since these gas wells are inherently

producing from methane-rich strata this may indicate that methane concentrations are higher in close proximity to these particular formations, but it is not possible to discern the cause of the relationship without further investigation. PD0325901 Sulfate was also found to be negatively correlated to methane in this model, providing further evidence for some biologically driven methane production. This follows thermodynamic principles given that sulfate reduction yields more energy than methanogenesis; thus methane is produced when sulfate concentrations are reduced ( Schlesinger, 1997).

The three most significant variables in the model (p < 0.001) – hardness, sodium, and barium – together could explain 77% of the observed variation in dissolved methane. We acknowledge Epigenetics inhibitor that including both sodium and hardness could introduce some multicollinearity into the model since sodium and hardness (as the sum of magnesium and calcium) tend to be negatively correlated; however, we find that removing either sodium or hardness from the model strongly reduces its predictive power, indicating that they are both contributing to it. These results are informative for better understanding the drivers of observed methane patterns. Sodium was positively correlated with methane concentrations

and hardness was negatively correlated with methane. This is consistent with previously described geochemical patterns that indicated that methane likely resulted from bedrock-groundwater interactions and lengthy residence times. The positive correlation between barium and methane concentrations also indicates that there is a geologic relationship with methane patterns. While barium can be present Tau-protein kinase due to human activities, including use in gas well drilling mud, it also is naturally present in geologic formations. Barium has been found in western New York to be primarily sourced from the mineral barite (BaSO4) ( Moore and Staubitz, 1984), which may also be present in formations underlying this study region. Using measured environmental variables, regression models for methane were developed with high explanatory power. While these models were developed using data from Chenango County, New York, they could have similar predictive power in nearby areas of New York and Pennsylvania with similar shale-dominated bedrock geology. With other studies in New York observing some higher methane concentrations than here (Kappel and Nystrom, 2012 and Heisig and Scott, 2013), it will be important to refine this model to try to better capture these patterns. In the future, it would also be beneficial to work toward creating improved regression models based on more easily quantified parameters (e.g.