Bayesian atmospheric tomography for detection and quantification of methane emissions : application to data from the 2015 Ginninderra release experiment

Detection and quantification of greenhouse-gas emissions is important for both compliance and environment conservation. However, despite several decades of active research, it remains predominantly an open problem, largely due to model errors and assumptions that appear at each stage of the inversion processing chain. In 2015, a controlled-release experiment headed by Geoscience Australia was carried out at the Ginninderra Controlled Release Facility, and a variety of instruments and methods were employed for quantifying the release rates of methane and carbon dioxide from a point source. This paper proposes a fully Bayesian approach to atmospheric tomography for inferring the methane emission rate of this point source using data collected during the experiment from both point-and path-sampling instruments. The Bayesian framework is designed to account for uncertainty in the parameterisations of measurements, the meteorological data, and the atmospheric model itself when performing inversion using Markov chain Monte Carlo (MCMC). We apply our framework to all instrument groups using measurements from two release-rate periods. We show that the inversion framework is robust to instrument type and meteorological conditions. From all the inversions we conducted across the different instrument groups and release-rate periods, our worst-case median emission rate estimate was within 36% of the true emission rate. Further, in the worst case, the closest limit of the 95% credible interval to the true emission rate was within 11% of this true value

The Ginninderra CH4 and CO2 release experiment: An evaluation of gas detection and quantification techniques

A methane (CH4) and carbon dioxide (CO2) release experiment was held from April to June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. Eight quantification techniques were assessed: three tracer ratio techniques (two mobile); backwards Lagrangian stochastic modelling; forwards Lagrangian stochastic modelling; Lagrangian stochastic (LS) footprint modelling; atmospheric tomography using point and using integrated line sensors. The majority of CH4 estimates were within 20% of the actual CH4 release rate (5.8 g/min), with the tracer ratio technique providing the closest estimate to both the CH4 and CO2 release rates (100 g/min). Once the release rate was known, the majority of revised estimates were within 10% of the actual release rate. The study illustrates the power of measuring the emission rate using multiple simultaneous methods and obtaining an ensemble median or mean. An ensemble approach to estimating the CH4 emission rate proved successful with the ensemble median estimate within 16% for the actual release rate for the blind release experiment and within 2% once the release rate was known. The release also provided an opportunity to assess the effectiveness of stationary and mobile ground and aerial CH4 detection technologies. Sensor detection limits and sampling rates were found to be significant limitations for CH4 and CO2 detection. A hyperspectral imager\u27s capacity to image the CH4 release from 100 m, and a Boreal CH4 laser sensor\u27s ability to track moving targets suggest the future possibility to map gas plumes using a single laser and mobile aerial reflector

Introducing the longitudinal MADIP and its role in understanding income dynamics in Australia

Understanding the determinants, dynamics and distribution of income within a country is an area of ongoing research and policy interest. There is a lot we do know about income dynamics in Australia. However, we have limited information on several key aspects, including detailed information on the spatial dimensions of income inequality, income mobility in and out of the top of the distribution, sociodemographic characteristics at the top of the distribution, the top of the household income distribution, the impact of local geographic inequality and other characteristics on income change, and the complete income distribution of small subpopulations of particular policy interest. Our knowledge has been limited by a lack of data germane to these issues. The opening up of access to data from the Multi-Agency Data Integration Project (MADIP), including the Basic Longitudinal Extract 2011 (BLE2011), provides an opportunity to fill some of these research gaps. The aim of this paper is to begin the external validation of these data, with a particular focus on what the data can tell us about the distribution, dynamics and determinants of income in Australia. Our view is that the BLE2011 has the potential to shed new light on these aspects. However, analysis of the dataset should be done with caution, taking into account some key limitations, including incomplete linkage (which is likely to be nonrandom) and more limited income information for those who do not complete a tax return. These limitations notwithstanding, the BLE2011 and other datasets from MADIP should form an important part of the social science infrastructure in Australia.This report was commisioned by ANU Centre for Social Research & Method

Introducing the Longitudinal Multi-Agency Data Integration Project and Its Role in Understanding Income Dynamics in Australia

The opening up of access to data from the Multi-Agency Data Integration Project provides an opportunity to fill a number of research gaps on inequality in Australia. Our paper begins the external validation of these data, focusing on what the data can tell us about the distribution, dynamics and determinants of income in Australia. It has the potential to shed new light on these aspects, but the dataset should be used with caution, taking into account some key limitations, including incomplete linkage and more limited income information for those who do not complete a tax return

The Ginninderra CH4 and CO2 release experiment : an evaluation of gas detection and quantification techniques

A methane (CH4) and carbon dioxide (CO2) release experiment was held from April to June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. Eight quantification techniques were assessed: three tracer ratio techniques (two mobile); backwards Lagrangian stochastic modelling; forwards Lagrangian stochastic modelling; Lagrangian stochastic (LS) footprint modelling; atmospheric tomography using point and using integrated line sensors. The majority of CH4 estimates were within 20% of the actual CH4 release rate (5.8 g/min), with the tracer ratio technique providing the closest estimate to both the CH4 and CO2 release rates (100 g/min). Once the release rate was known, the majority of revised estimates were within 10% of the actual release rate. The study illustrates the power of measuring the emission rate using multiple simultaneous methods and obtaining an ensemble median or mean. An ensemble approach to estimating the CH4 emission rate proved successful with the ensemble median estimate within 16% for the actual release rate for the blind release experiment and within 2% once the release rate was known. The release also provided an opportunity to assess the effectiveness of stationary and mobile ground and aerial CH4 detection technologies. Sensor detection limits and sampling rates were found to be significant limitations for CH4 and CO2 detection. A hyperspectral imager's capacity to image the CH4 release from 100 m, and a Boreal CH4 laser sensor's ability to track moving targets suggest the future possibility to map gas plumes using a single laser and mobile aerial reflector