Agency: Admissibility against the Principal of Statements Made against His Interest by the Agent

Agency: Admissibility against the Principal of Statements Made against His Interest by the Agen

Agency: Admissibility against the Principal of Statements Made against His Interest by the Agent

Agency: Admissibility against the Principal of Statements Made against His Interest by the Agen

Locating and quantifying gas emission sources using remotely obtained concentration data

We describe a method for detecting, locating and quantifying sources of gas emissions to the atmosphere using remotely obtained gas concentration data; the method is applicable to gases of environmental concern. We demonstrate its performance using methane data collected from aircraft. Atmospheric point concentration measurements are modelled as the sum of a spatially and temporally smooth atmospheric background concentration, augmented by concentrations due to local sources. We model source emission rates with a Gaussian mixture model and use a Markov random field to represent the atmospheric background concentration component of the measurements. A Gaussian plume atmospheric eddy dispersion model represents gas dispersion between sources and measurement locations. Initial point estimates of background concentrations and source emission rates are obtained using mixed L2-L1 optimisation over a discretised grid of potential source locations. Subsequent reversible jump Markov chain Monte Carlo inference provides estimated values and uncertainties for the number, emission rates and locations of sources unconstrained by a grid. Source area, atmospheric background concentrations and other model parameters are also estimated. We investigate the performance of the approach first using a synthetic problem, then apply the method to real data collected from an aircraft flying over: a 1600 km^2 area containing two landfills, then a 225 km^2 area containing a gas flare stack

Avoiding methane emission rate underestimates when using the divergence method

Methane is a powerful greenhouse gas, and a primary target for mitigating climate change in the short-term future due to its relatively short atmospheric lifetime and greater ability to trap heat in Earth's atmosphere compared to carbon dioxide. Top-down observations of atmospheric methane are possible via drone and aircraft surveys as well as satellites such as the TROPOspheric Monitoring Instrument (TROPOMI). Recent work has begun to apply the divergence method to produce regional methane emission rate estimates. Here we show that when the divergence method is applied to spatially incomplete observations of methane, it can result in negatively biased time-averaged regional emission rates. We show that this effect can be counteracted by adopting a procedure in which daily advective fluxes of methane are time-averaged before the divergence method is applied. Using such a procedure with TROPOMI methane observations, we calculate yearly Permian emission rates of 3.1, 2.4 and 2.7 million tonnes per year for the years 2019 through 2021. We also show that highly-resolved plumes of methane can have negatively biased estimated emission rates by the divergence method due to the presence of turbulent diffusion in the plume, but this is unlikely to affect regional methane emission budgets constructed from TROPOMI observations of methane. The results from this work are expected to provide useful guidance for future implementations of the divergence method for emission rate estimation from satellite data -- be it for methane or other gaseous species in the atmosphere.Comment: 19 pages, 10 figures, submitted to Environmental Research Letter

Health, education, and social care provision after diagnosis of childhood visual disability

Aim: To investigate the health, education, and social care provision for children newly diagnosed with visual disability.Method: This was a national prospective study, the British Childhood Visual Impairment and Blindness Study 2 (BCVIS2), ascertaining new diagnoses of visual impairment or severe visual impairment and blindness (SVIBL), or equivalent vi-sion. Data collection was performed by managing clinicians up to 1-year follow-up, and included health and developmental needs, and health, education, and social care provision.Results: BCVIS2 identified 784 children newly diagnosed with visual impairment/SVIBL (313 with visual impairment, 471 with SVIBL). Most children had associated systemic disorders (559 [71%], 167 [54%] with visual impairment, and 392 [84%] with SVIBL). Care from multidisciplinary teams was provided for 549 children (70%). Two-thirds (515) had not received an Education, Health, and Care Plan (EHCP). Fewer children with visual impairment had seen a specialist teacher (SVIBL 35%, visual impairment 28%, χ2p < 0.001), or had an EHCP (11% vs 7%, χ2p < 0 . 01).Interpretation: Families need additional support from managing clinicians to access recommended complex interventions such as the use of multidisciplinary teams and educational support. This need is pressing, as the population of children with visual impairment/SVIBL is expected to grow in size and complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

A New Technique for Monitoring the Atmosphere above Onshore Carbon Storage Projects that can Estimate the Locations and Mass Emission Rates of Detected Sources

Carbon Capture and Storage (CCS) projects could grow to become a major new industrial activity over the next few decades; but securing the associated climate benefit is critically dependent on ensuring high integrity containment of the injected CO2. Our technique, called LightSource, is based on commercially available optical gas sensors that measure path-averaged CO2 gas concentrations along beams scanned over part of an onshore CCS site. Inter-beam correlations are used to infer the current local ambient background concentration. Statistically significant discrepancies between the multiple beams' path-averaged concentration measurements can be used to infer the existence of a source by applying the methods of statistical process control. This allows the estimation of the anomalous concentration on each beam that is associated with the inferred source(s). Using these anomalous concentration data in conjunction with a gas dispersion model, high frequency wind velocity and turbulence intensity data, we can solve the inverse gas dispersion problem to estimate the location and mass emission rates of the source(s) that best explain the data. The system does not require sources to be situated within the beam pattern unlike tomographic approaches which in addition require more intensive instrumentation. We have evaluated the LightSource system's performance under field conditions at the Quest CCS project site in Alberta Canada. All calibrated releases of tempered CO2 at emission rates of up to 300 kg/hr were successfully detected. © 2017 The Authors