Quantifying tropospheric OH concentrations and coal methane emissions using remote sensing

Abstract

This thesis is divided into two parts. In Chapter 1, I use an analytical Bayesian inversion framework to show what we can (and cannot) learn from Thermal Infrared (TIR) and Shortwave Infrared (SWIR) satellite observations of methane. In Chapter 2, I demonstrate how to develop national coal-mine methane inventories with aircraft campaigns using high- resolution hyperspectral instruments, with a view towards recently-launched hyperspectral satellite constellations. An abstract for each chapter follows. Chapter 1. The hydroxyl radical (OH) is the main oxidant in the troposphere and controls the lifetime of many atmospheric pollutants including methane. Global annual mean tropospheric OH concentrations ([OH]) have been inferred since the late 1970s using the methyl chloroform (MCF) proxy. However, concentrations of MCF are now approaching the detection limit, and a replacement proxy is urgently needed. Previous inversions of GOSAT satellite measurements of methane in the SWIR have shown success in quantifying [OH] independently of methane emissions, and observing system simulations have suggested that TIR measurements may provide additional constraints on OH. Here we combine TIR satellite observations of methane from AIRS with SWIR observations from GOSAT in a three-year (2013-2015) analytical Bayesian inversion optimizing both methane emissions and OH concentrations. We examine how much information can be achieved on the interannual, seasonal, and latitudinal features of the OH distribution using information from MCF data as well as the ACCMIP ensemble of global atmospheric chemistry models to construct a full prior error covariance matrix for OH concentrations for use in the inversion. This is iii essential to avoid overfit to observations. Our results show that GOSAT alone is sufficient to quantify [OH] and its interannual variability independently of methane emissions, and that AIRS adds little information. The ability to constrain the latitudinal variability of OH is limited by strong error correlations. There is no information on OH at mid-latitudes, but there is some information on the NH/SH interhemispheric ratio, showing this ratio to be lower than currently simulated in models. There is also some information on the seasonal variation of OH concentrations, though it mainly confirms that simulated by models. Future satellite observations of methane will continue to improve our understanding of methane emissions and consequently [OH] and its interannual variability. Chapter 2. Underground coal mines are important global sources of methane but emis- sion estimates are uncertain. Emission estimates for individual mines from Carbon Mapper aircraft remote sensing surveys in the U.S. agree within 20% with direct measurements used for national emission reporting (IPCC Tier 3 estimate). Such direct measurements are unavailable in most countries, which rely on estimated emission factors (EFs) applied to coal production rates. We find that EFs from IPCC Tier 1 and Model for Calculating Coal Mine Methane (MC2M) methods would overestimate U.S. emissions threefold due to incorrect dependence on mine depth. An IPCC Tier 2 method using measured basin-specific mine gas content agrees with direct emission measurements but does not account for gob well emissions and requires gas content data that are generally unavailable. We show that limited Carbon Mapper surveys successfully estimate basin-specific EFs for ventilation shafts and gob wells, enabling estimates of basin- and national-scale emissions.