Methane leak detection and sizing over long distances using dual frequency comb laser spectroscopy and a bootstrap inversion technique

Advances in natural gas extraction technology have led to increased activity in the production and transport sectors in the United States, and, as a consequence, an increased need for reliable monitoring of methane leaks to the atmosphere. We present a statistical methodology in combination with an observing system for the detection and attribution of fugitive emissions of methane from distributed potential source location landscapes such as natural gas production sites. We measure long (&gt;500 m), integrated open path concentrations of atmospheric methane using a dual frequency comb spectrometer and combine measurements with an atmospheric transport model to infer leak locations and strengths using a novel statistical method, the non-zero minimum bootstrap (NZMB). The new statistical method allows us to determine whether the empirical distribution of possible source strengths for a given location excludes zero. Using this information, we identify leaking source locations (i.e., natural gas wells) through rejection of the null hypothesis that the source is not leaking. The method is tested with a series of synthetic data inversions with varying measurement density and varying levels of model-data mismatch. It is also tested with field observations of 1) a non-leaking source location and 2) a source location where a controlled emission of 2.1 E-5 kg s-1 of methane gas is released over a period of several hours. This series of synthetic data tests and outdoor field observations using a controlled methane release demonstrate the viability of the approach for the detection and sizing of very small (&lt;2 g m-1 ) leaks of methane across large distances (4+ km2 in synthetic tests). The field tests demonstrate the ability to attribute small atmospheric enhancements of 18 ppb to the emitting source location against a background of combined atmospheric (e.g., background methane variability) and measurement uncertainty of 6 ppb (1-sigma), when measurements are averaged over 2 minutes. The results of the synthetic and field data testing show that the new observing system and statistical approach greatly decreases the incidence of false alarms (that is, wrongly identifying a well site to be leaking) compared with the same tests that don&rsquo;t use the NZMB approach, and therefore offers increased leak detection and sizing capabilities.</p

Topics in stochastic growth models

Stochastic growth models are very common in real life owing to their ability to capture the underlying mechanisms. This thesis considers three of such models. Each model can be seen as describing the evolution in time of a complex population of interacting ``particles": competing types of individuals in the first model, nodes in a dynamic network in the second, and species in an ecosystem in the third. A common feature of these models is that the population size grows in time and is represented by a transient (generalized) birth and death Markov process. This dissertation studies asymptotic structure of the ``particles landscape" which is represented in these three models by, respectively, type structure of the population, graph of interconnections, and the empirical distribution of species fitness.</p

A Modified vegetation photosynthesis and respiration model (VPRM) for the Eastern USA and Canada, evaluated with comparison to atmospheric observations and other biospheric models

Atmospheric CO2 measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO2 flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO2 fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non-linear temperature, and water stress. Flux estimates from VPRM, the Carnegie-Ames-Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO2 observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine-scale atmospheric CO2 variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain-specific parameters, and high-quality input data sets

The Impact of COVID‐19 on CO 2

Responses to COVID‐19 have resulted in unintended reductions of city‐scale carbon dioxide (CO(2)) emissions. Here, we detect and estimate decreases in CO(2) emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO(2) observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course‐correct CO(2) reduction activities efficiently