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We tested the hypothesis that carbon dioxide (CO2) uptake fluxes in coastal salt marshes follow ecological similitudes (parameter reductions) and distinct environmental regimes. The hypothesis was evaluated utilizing data from four salt marshes in Waquoit Bay, MA, USA collected during May-October 2013. Using dimensional analysis method from fluid mechanics and engineering, we reduced five flux and ecological variables (CO2 uptake, light, soil temperature, salinity, and atmospheric pressure) into two mechanistically meaningful dimensionless groups: (a) light use efficiency number (LUE = CO2 uptake normalized by daylight) and (b) biogeochemical number (BGC = interactions among soil temperature, salinity, and atmospheric pressure). Graphical exploration of the dimensionless numbers with the observed data revealed an emergent pattern that was distinctly characterized by high, transitional, and low LUE regimes. Transitions among the identified regimes were dictated by thresholds of soil temperature and salinity. Low LUE regime corresponded to unfavorable environmental conditions (soil temperature 17C and salinity > 30ppt), whereas high LUE regime was governed by favorable conditions (soil temperature > 17C and salinity 30ppt). The identified emergent pattern and environmental thresholds would provide key insights into the underlying organizing principles of CO2 uptake and the major environmental drivers in coastal salt marshes. 

We tested the hypothesis that carbon dioxide (CO2) uptake fluxes in coastal salt marshes follow ecological similitudes (parameter reductions) and distinct environmental regimes. The hypothesis was evaluated utilizing data from four salt marshes in Waquoit Bay, MA, USA collected during May-October 2013. Using dimensional analysis method from fluid mechanics and engineering, we reduced five flux and ecological variables (CO2 uptake, light, soil temperature, salinity, and atmospheric pressure) into two mechanistically meaningful dimensionless groups: (a) light use efficiency number (LUE = CO2 uptake normalized by daylight) and (b) biogeochemical number (BGC = interactions among soil temperature, salinity, and atmospheric pressure). Graphical exploration of the dimensionless numbers with the observed data revealed an emergent pattern that was distinctly characterized by high, transitional, and low LUE regimes. Transitions among the identified regimes were dictated by thresholds of soil temperature and salinity. Low LUE regime corresponded to unfavorable environmental conditions (soil temperature 17C and salinity > 30ppt), whereas high LUE regime was governed by favorable conditions (soil temperature > 17C and salinity 30ppt). The identified emergent pattern and environmental thresholds would provide key insights into the underlying organizing principles of CO2 uptake and the major environmental drivers in coastal salt marshes. 

Other test method (OTM) 33A has been used to quantify emissions from natural gas sites since it was introduced by the Environmental Protection Agency (EPA). The method relies on point source Gaussian (PSG) assumptions to estimate emissions rates from a targeted site or source. However, the method often results in low accuracy (typically ±70%, even under conducive conditions). These accuracies were verified with controlled-release experiments. Typically, controlled releases were performed for short periods (15–20 min) under atmospheric conditions that were ideal for effective plume transport. We examined three methane release rates from three distances over various periods of time ranging from seven hours to seven days. Data were recorded continuously from a stationary tower. Atmospheric conditions were highly variable and not always conducive to conventional OTM 33A calculations. OTM 33A estimates were made for 20-min periods when the mean wind direction corresponded to ±90° of the direction from the controlled release to the tower. Further analyses were performed by varying the frequency of the data, the length of the individual OTM 33A periods and the size of the wind angle used to filter data. The results suggested that different (than conventionally used) period lengths, wind filters, data acquisition frequencies and data quality filters impacted the accuracy of OTM 33A when applied to long term measurements.