Methane Mitigation:Methods to Reduce Emissions, on the Path to the Paris Agreement

The atmospheric methane burden is increasing rapidly, contrary to pathways compatible with the goals of the 2015 United Nations Framework Convention on Climate Change Paris Agreement. Urgent action is required to bring methane back to a pathway more in line with the Paris goals. Emission reduction from “tractable” (easier to mitigate) anthropogenic sources such as the fossil fuel industries and landfills is being much facilitated by technical advances in the past decade, which have radically improved our ability to locate, identify, quantify, and reduce emissions. Measures to reduce emissions from “intractable” (harder to mitigate) anthropogenic sources such as agriculture and biomass burning have received less attention and are also becoming more feasible, including removal from elevated-methane ambient air near to sources. The wider effort to use microbiological and dietary intervention to reduce emissions from cattle (and humans) is not addressed in detail in this essentially geophysical review. Though they cannot replace the need to reach “net-zero” emissions of CO2, significant reductions in the methane burden will ease the timescales needed to reach required CO2 reduction targets for any particular future temperature limit. There is no single magic bullet, but implementation of a wide array of mitigation and emission reduction strategies could substantially cut the global methane burden, at a cost that is relatively low compared to the parallel and necessary measures to reduce CO2, and thereby reduce the atmospheric methane burden back toward pathways consistent with the goals of the Paris Agreement

Carbon isotopic signature of coal-derived methane emissions to the atmosphere: from coalification to alteration

Currently, the atmospheric methane burden is rising rapidly, but the extent to which shifts in coal production contribute to this rise is not known. Coalbed methane emissions into the atmosphere are poorly characterised, and this study provides representative δ13CCH4 signatures of methane emissions from specific coalfields. Integrated methane emissions from both underground and opencast coal mines in the UK, Australia and Poland were sampled and isotopically characterised. Progression in coal rank and secondary biogenic production of methane due to incursion of water are suggested as the processes affecting the isotopic composition of coal-derived methane. An averaged value of −65 ‰ has been assigned to bituminous coal exploited in open cast mines and of −55 ‰ in deep mines, whereas values of −40 and −30 ‰ can be allocated to anthracite opencast and deep mines respectively. However, the isotopic signatures that are included in global atmospheric modelling of coal emissions should be region- or nation-specific, as greater detail is needed, given the wide global variation in coal type

Social anxiety symptoms in young children:Investigating the interplay of theory of mind and expressions of shyness

Children’s early onset of social anxiety may be associated with their social understanding, and their ability to express emotions adaptively. We examined whether social anxiety in 48-month-old children (N = 110; 54 boys) was related to: a) a lower level of theory of mind (ToM); b) a lower proclivity to express shyness in a positive way (adaptive); and c) a higher tendency to express shyness in a negative way (non-adaptive). In addition, we investigated to what extent children’s level of social anxiety was predicted by the interaction between ToM and expressions of shyness. Children’s positive and negative expressions of shyness were observed during a performance task. ToM was measured with a validated battery, and social anxiety was assessed using both parents’ reports on questionnaires. Socially anxious children had a lower level of ToM, and displayed more negative and less positive shy expressions. However, children with a lower level of ToM who expressed more positive shyness were less socially anxious. Additional results show that children who displayed shyness only in a negative manner were more socially anxious than children who expressed shyness only in a positive way and children who did not display any shyness. Moreover, children who displayed both positive and negative expressions of shyness were more socially anxious than children who displayed shyness only in a positive way. These findings highlight the importance of ToM development and socio-emotional strategies, and their interaction, on the early development of social anxiety

A multi-tracer approach to constraining artesian groundwater discharge into an alluvial aquifer

Understanding pathways of recharge to alluvial aquifers is important for maintaining sustainable access to groundwater resources. Water balance modelling is often used to proportion recharge components and guide sustainable groundwater allocations. However, it is not common practice to use hydrochemical evidence to inform and constrain these models. Here we compare geochemical versus water balance model estimates of artesian discharge into an alluvial aquifer, and demonstrate why multi-tracer geochemical analyses should be used as a critical component of water budget assessments. We selected a site in Australia where the Great Artesian Basin (GAB), the largest artesian basin in the world, discharges into the Lower Namoi Alluvium (LNA), an extensively modelled aquifer, to convey the utility of our approach. Water stable isotopes (δ18O and δ2H) and the concentrations of Na+ and HCO3− suggest a continuum of mixing in the alluvial aquifer between the GAB (artesian component) and surface recharge, whilst isotopic tracers (3H, 14C, and 36Cl) indicate that the alluvial groundwater is a mixture of groundwaters with residence times of < 70 years and groundwater that is potentially hundreds of thousands of years old, which is consistent with that of the GAB. In addition, Cl− concentrations provide a means to calculate a percentage estimate of the artesian contribution to the alluvial groundwater. In some locations, an artesian contribution of up to 70 % is evident from the geochemical analyses, a finding that contrasts with previous regional-scale water balance modelling estimates that attributed 22 % of all inflow for the corresponding zone within the LNA to GAB discharge. Our results show that hydrochemical investigations need to be undertaken as part of developing the conceptual framework of a catchment water balance model, as they can improve our understanding of recharge pathways and better constrain artesian discharge to an alluvial aquifer

Hydrochemical apportioning of irrigation groundwater sources in an alluvial aquifer

River floodplains sustain irrigated agriculture worldwide. Despite generalised groundwater level falls, limited hard data are available to apportion groundwater sources in many irrigated regions. In this paper, we propose a workflow based on: hydrochemical analysis, water stable isotopes, radiocarbon contents and multivariate statistical analysis to facilitate the quantification of groundwater source attribution at regional scales. Irrigation water supply wells and groundwater monitoring wells sampled in the alluvial aquifer of the Condamine River (Queensland, Australia) are used to test this approach that can easily be implemented in catchments worldwide. The methodology identified four groundwater sources: 1) river/flood water; 2) modified river/flood water; 3) groundwater recharged through regional volcanic materials and 4) groundwater recharged predominantly through sands and/or sandstone materials. The first two sources are characterised by fresh water, dominant sodium bicarbonate chemistry, short residence time and depleted water stable isotope signatures. Groundwater sources 3 and 4 are characterised by saline groundwater, sodium chloride chemistries, enriched water stable isotopes and very low radiocarbon contents, inferred to correspond to long residence times. The majority of wells assessed are dominated by flood water recharge, linked to decadal >300 mm rainfall events and associated flooding in the region. The approach presented here provides a groundwater source fingerprint, reinforcing the importance of floodwater recharge in the regional water budgets. This apportioning of groundwater sources will allow irrigators, modelers and managers to assess the long-term sustainability of groundwater use in alluvial catchments.This research was funded by the Cotton Research and Development Corporation, Australia, Grant Number UNSW1401. The authors would like to thank Lucienne Martel for her assistance with sample collection. We would also like to thank all cotton growers who provided access to their irrigation water supply wells and the staff at the Queensland Department of Natural Resources and Mines (Toowoomba, Office) who facilitated at short notice access to the government groundwater monitoring wells. Dr. Matthias Raiber (CSIRO) is also acknowledged for conversations and assistance in the field during sampling. Chris Dimovski, Barbora Gallagher, Robert Chisari, Henri Wong, Brett Rowling and Vlad Levchenko from ANSTO are also acknowledged for their constant logistic and analytical support. Laura Scheiber and Enric Vázquez-Suñé would like to thanks Spanish Ministry of Science and Innovation (Project CEX2018-000794-S). The authors also thank Lisa Williams for editing and proofreading the manuscript.Peer reviewe

Learning and Detecting Stuttering Disorders

Part 7: Deep Learning - Convolutional ANNInternational audienceStuttering is a widespread speech disorder involving about the $$5\%$$ of the population and the $$2.5\%$$ of children under the age of 5. Much work in literature studies causes, mechanisms and epidemiology and much work is devoted to illustrate treatments, prognosis and how to diagnose stutter. Relevantly, a stuttering evaluation requires the skills of a multi-dimensional team. An expert speech-language therapist conduct a precise evaluation with a series of tests, observations, and interviews. During an evaluation, a speech language therapist perceive, record and transcribe the number and types of speech disfluencies that a person produces in different situations. Stuttering is very variable in the number of repeated syllables/words and in the secondary aspects that alter the clinical picture. This work wants to help in the difficult task of evaluating the stuttering and recognize the occurrencies of disfluency episodes like repetitions and prolongations of sounds, syllables, words or phrases silent pauses, hesitations or blocks before speech. In particular, we propose a deep-learning based approach able at automatically detecting difluent production point in the speech helping in early classification of the problems providing the number of disfluencies and time intervals where the disfluencies occur. A deep learner is built to preliminarily valuate audio fragments. However, the scenario at hand contains some peculiarities making the detection challenging. Indeed, (i) fragments too short lead to uneffective classification since a too short audio fragment is not able to capture the stuttering episode; and (ii) fragments too long lead to uneffective classification since stuttering episode can have a very small duration and, then, the much fluent speaking contained in the fragment masks the disfluence. So, we design an ad-hoc segment classifier that, exploiting the output of a deep learner working with non too short fragments, classifies each small segment composing an audio fragment by estimating the probability of containing a disfluence