Dissolved oxygen (DO) indicates the overall stream water quality and ecosystem health. We investigated emergent scaling of DO with the dominant environmental drivers in freshwater (non‐coastal) streams across the contiguous United States. Available data of monthly to quarterly sampling frequencies during 1998–2015 were obtained for 86 U.S. streams. Data analytics indicated water temperature (T_w) and pH (a proxy of carbon dioxide) dominating the key environmental process components of DO concentrations in the freshwater streams. The “climatic” process component (comprising T_w and net radiation) had, respectively, ∼3 and ∼9 times stronger control on DO than the “biogeochemical” (total nitrogen, total phosphorus, pH, and specific conductivity) and “hydro‐atmospheric” exchange (stream flow and atmospheric pressure) components. The predominant climatic control on stream DO was linked to the high extent of vegetated land (on average ∼53%) and steep slope (∼10%) in the draining watersheds, despite the notable presence of agricultural land (∼35%). An emergent power law scaling relationship was then developed to acceptably predict DO (mg/l) based on T_w (K) and pH, with the approximate exponents of −15/2 and 1/2, respectively (Nash‐Sutcliffe Efficiency = 0.72–0.73). The scaling law demonstrated the underlying organizing principles such as the depletion of stream DO due to reduced dissolution and increased metabolic respiration with the increasing temperature and nutrients. The scaling law was persistent across the various U.S. streams, representing gradients in climate, hydrology, biogeochemistry, and land use/cover. The findings would help understand and manage water quality and ecosystem health in freshwater streams across the United States and beyond.

Researchers have utilized Other Test Method (OTM) 33A to quantify methane emissions from natural gas infrastructure. Historically, errors have been reported based on a population of measurements compared to known controlled releases of methane. These errors have been reported as 2σ errors of ±70%. However, little research has been performed on the minimum attainable uncertainty of any one measurement. We present two methods of uncertainty estimation. The first was the measurement uncertainty of the state-of-the-art equipment, which was determined to be ±3.8% of the estimate. This was determined from bootstrapped measurements compared to controlled releases. The second approach of uncertainty estimation was a modified Hollinger and Richardson (H&R) method which was developed for quantifying the uncertainty of eddy covariance measurements. Using a modified version of this method applied to OTM 33A measurements, it was determined that uncertainty of any given measurement was ±17%. Combining measurement uncertainty with that of stochasticity produced a total minimum uncertainty of 17.4%. Due to the current nature of stationary single-sensor measurements and the stochasticity of atmospheric data, such uncertainties will always be present. This is critical in understanding the transport of methane emissions and indirect measurements obtained from the natural gas industry.

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.

Researchers are searching for ways to better quantify methane emissions from natural gas infrastructure. Current indirect quantification techniques (IQTs) allow for more frequent or continuous measurements with fewer personnel resources than direct methods but lack accuracy and repeatability. Two IQTs are Other Test Method (OTM) 33A and Eddy Covariance (EC). We examined a novel approach to improve the accuracy of single sensor IQT whereby the results from both OTM and EC were combined with two machine learning (ML) models, a random forest (RF) and a neural network (NN). Then, models were enhanced with feature reduction and hyper-parameter tuning and compared to traditional quantification methods. The NN and RF improved upon the default OTM by an average of 44% and 78%, respectively. When compared to traditional OTM estimates with low Data Quality Indicators (DQIs), RF and NN models reduced 1σ errors from ±66% to ±13% and ±34%, respectively. Models also reduced the standard deviation of estimates with 93% and 85% of estimates falling within ±50% of the known release rate. This approach can be deployed with single sensor systems at well sites to improve confidence in reported emissions, reducing the number of anomalous overestimates that would trigger unnecessary site evaluations. Additional improvements could be realized by expanding training datasets with more methane release rates. Further, deployment of such models in a variety of situations could enhance their ability help close the gap between bottom-up inventory and top-down studies by enabling continuous monitoring of temporal emissions that could identify with improved confidence, atypically higher emissions. Accurate remote single sensor systems are key in developing an improved understanding of methane emissions to enable industry to identify and reduce methane emissions.