Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery

Sentinel-2 satellite imagery has been shown by studies to be capable of detecting and quantifying methane emissions from oil and gas production. However, current methods lack performance calibration with ground-truth testing. This study developed a multi-band–multi-pass–multi-comparison-date methane retrieval algorithm that enhances Sentinel-2 sensitivity to methane plumes. The method was calibrated using data from a large-scale controlled-release test in Ehrenberg, Arizona, in fall 2021, with three algorithm parameters tuned based on the true emission rates. Tuned parameters are the pixel-level concentration upper-bound threshold during extreme value removal, the number of comparison dates, and the pixel-level methane concentration percentage threshold when determining the spatial extent of a plume. We found that a low value of the upper-bound threshold during extreme value removal can result in false negatives. A high number of comparison dates helps enhance the algorithm sensitivity to the plumes in the target date, but values in excess of 12 d are neither necessary nor computationally efficient. A high percentage threshold when determining the spatial extent of a plume helps enhance the quantification accuracy, but it may harm the yes/no detection accuracy. We found that there is a trade-off between quantification accuracy and detection accuracy. In a scenario with the highest quantification accuracy, we achieved the lowest quantification error and had zero false-positive detections; however, the algorithm missed three true plumes, which reduced the yes/no detection accuracy. In contrast, all of the true plumes were detected in the highest detection accuracy scenario, but the emission rate quantification had higher errors. We illustrated a two-step method that updates the emission rate estimates in an interim step, which improves quantification accuracy while keeping high yes/no detection accuracy. We also validated the algorithm's ability to detect true positives and true negatives in two application studies.</p

Splice Site Mutations in the ATP7A Gene

Menkes disease (MD) is caused by mutations in the ATP7A gene. We describe 33 novel splice site mutations detected in patients with MD or the milder phenotypic form, Occipital Horn Syndrome. We review these 33 mutations together with 28 previously published splice site mutations. We investigate 12 mutations for their effect on the mRNA transcript in vivo. Transcriptional data from another 16 mutations were collected from the literature. The theoretical consequences of splice site mutations, predicted with the bioinformatics tool Human Splice Finder, were investigated and evaluated in relation to in vivo results. Ninety-six percent of the mutations identified in 45 patients with classical MD were predicted to have a significant effect on splicing, which concurs with the absence of any detectable wild-type transcript in all 19 patients investigated in vivo. Sixty-seven percent of the mutations identified in 12 patients with milder phenotypes were predicted to have no significant effect on splicing, which concurs with the presence of wild-type transcript in 7 out of 9 patients investigated in vivo. Both the in silico predictions and the in vivo results support the hypothesis previously suggested by us and others, that the presence of some wild-type transcript is correlated to a milder phenotype

Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations

We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25∘ × 0.3125∘ (≈ 25 km × 25 km) resolution by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of period-average emissions on the 0.25∘ × 0.3125∘ grid including error statistics, information content, and visualization code for inspection of results. The inversion uses an advanced research-grade algorithm fully documented in the literature. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI's capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.</p

Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action

We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to ∼25 km × 25 km resolution, using spatially allocated national United Nations Framework Convention on Climate Change (UNFCCC) reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35 % higher than the prior bottom-up inventories (+103 % for gas, +53 % for waste, +49 % for livestock, −14 % for oil) with large variability across countries. Oil and gas account for 38 % of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0–5.5) Tg a−1; best estimate and uncertainty range), Turkmenistan (4.4 (2.8–5.1) Tg a−1), Saudi Arabia (4.3 (2.4–6.0) Tg a−1), Algeria (3.5 (2.4–4.4) Tg a−1), Egypt (3.4 (2.5–4.0) Tg a−1), and Turkey (3.0 (2.0–4.1) Tg a−1). Most oil–gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil–gas emission hotspots in Turkmenistan, Algeria, and Oman and offshore in the Persian Gulf. We show that oil–gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil–gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil–gas emission inventories. We examined the methane intensity, defined as the upstream oil–gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil–gas sector and using as reference the 0.2 % target set by the industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through the capture of associated gas, modern infrastructure, and the concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2 % would achieve a 90 % decrease in oil–gas upstream emissions and a 26 % decrease in total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge.</p

Cellular Radiosensitivity: How much better do we understand it?

Purpose: Ionizing radiation exposure gives rise to a variety of lesions in DNA that result in genetic instability and potentially tumorigenesis or cell death. Radiation extends its effects on DNA by direct interaction or by radiolysis of H2O that generates free radicals or aqueous electrons capable of interacting with and causing indirect damage to DNA. While the various lesions arising in DNA after radiation exposure can contribute to the mutagenising effects of this agent, the potentially most damaging lesion is the DNA double strand break (DSB) that contributes to genome instability and/or cell death. Thus in many cases failure to recognise and/or repair this lesion determines the radiosensitivity status of the cell. DNA repair mechanisms including homologous recombination (HR) and non-homologous end-joining (NHEJ) have evolved to protect cells against DNA DSB. Mutations in proteins that constitute these repair pathways are characterised by radiosensitivity and genome instability. Defects in a number of these proteins also give rise to genetic disorders that feature not only genetic instability but also immunodeficiency, cancer predisposition, neurodegeneration and other pathologies. Conclusions: In the past fifty years our understanding of the cellular response to radiation damage has advanced enormously with insight being gained from a wide range of approaches extending from more basic early studies to the sophisticated approaches used today. In this review we discuss our current understanding of the impact of radiation on the cell and the organism gained from the array of past and present studies and attempt to provide an explanation for what it is that determines the response to radiation

Variations in the NBN/NBS1 gene and the risk of breast cancer in non-BRCA1/2 French Canadian families with high risk of breast cancer

<p>Abstract</p> <p>Background</p> <p>The Nijmegen Breakage Syndrome is a chromosomal instability disorder characterized by microcephaly, growth retardation, immunodeficiency, and increased frequency of cancers. Familial studies on relatives of these patients indicated that they also appear to be at increased risk of cancer.</p> <p>Methods</p> <p>In a candidate gene study aiming at identifying genetic determinants of breast cancer susceptibility, we undertook the full sequencing of the <it>NBN </it>gene in our cohort of 97 high-risk non-<it>BRCA1 </it>and -<it>BRCA2 </it>breast cancer families, along with 74 healthy unrelated controls, also from the French Canadian population. <it>In silico </it>programs (ESEfinder, NNSplice, Splice Site Finder and MatInspector) were used to assess the putative impact of the variants identified. The effect of the promoter variant was further studied by luciferase gene reporter assay in MCF-7, HEK293, HeLa and LNCaP cell lines.</p> <p>Results</p> <p>Twenty-four variants were identified in our case series and their frequency was further evaluated in healthy controls. The potentially deleterious p.Ile171Val variant was observed in one case only. The p.Arg215Trp variant, suggested to impair NBN binding to histone γ-H2AX, was observed in one breast cancer case and one healthy control. A promoter variant c.-242-110delAGTA displayed a significant variation in frequency between both sample sets. Luciferase reporter gene assay of the promoter construct bearing this variant did not suggest a variation of expression in the MCF-7 breast cancer cell line, but indicated a reduction of luciferase expression in both the HEK293 and LNCaP cell lines.</p> <p>Conclusion</p> <p>Our analysis of <it>NBN </it>sequence variations indicated that potential <it>NBN </it>alterations are present, albeit at a low frequency, in our cohort of high-risk breast cancer cases. Further analyses will be needed to fully ascertain the exact impact of those variants on breast cancer susceptibility, in particular for variants located in <it>NBN </it>promoter region.</p