Low-temperature gas from marine shales

Thermal cracking of kerogens and bitumens is widely accepted as the major source of natural gas (thermal gas). Decomposition is believed to occur at high temperatures, between 100 and 200°C in the subsurface and generally above 300°C in the laboratory. Although there are examples of gas deposits possibly generated at lower temperatures, and reports of gas generation over long periods of time at 100°C, robust gas generation below 100°C under ordinary laboratory conditions is unprecedented. Here we report gas generation under anoxic helium flow at temperatures 300° below thermal cracking temperatures. Gas is generated discontinuously, in distinct aperiodic episodes of near equal intensity. In one three-hour episode at 50°C, six percent of the hydrocarbons (kerogen & bitumen) in a Mississippian marine shale decomposed to gas (C1–C5). The same shale generated 72% less gas with helium flow containing 10 ppm O2 and the two gases were compositionally distinct. In sequential isothermal heating cycles (~1 hour), nearly five times more gas was generated at 50°C (57.4 μg C1–C5/g rock) than at 350°C by thermal cracking (12 μg C1–C5/g rock)

Low-temperature gas from marine shales: wet gas to dry gas over experimental time

Marine shales exhibit unusual behavior at low temperatures under anoxic gas flow. They generate catalytic gas 300° below thermal cracking temperatures, discontinuously in aperiodic episodes, and lose these properties on exposure to trace amounts of oxygen. Here we report a surprising reversal in hydrocarbon generation. Heavy hydrocarbons are formed before light hydrocarbons resulting in wet gas at the onset of generation grading to dryer gas over time. The effect is moderate under gas flow and substantial in closed reactions. In sequential closed reactions at 100°C, gas from a Cretaceous Mowry shale progresses from predominately heavy hydrocarbons (66% C5, 2% C1) to predominantly light hydrocarbons (56% C1, 8% C5), the opposite of that expected from desorption of preexisting hydrocarbons. Differences in catalyst substrate composition explain these dynamics. Gas flow should carry heavier hydrocarbons to catalytic sites, in contrast to static conditions where catalytic sites are limited to in-place hydrocarbons. In-place hydrocarbons and their products should become lighter with conversion thus generating lighter hydrocarbon over time, consistent with our experimental results

Gene prediction in metagenomic fragments: A large scale machine learning approach

<p>Abstract</p> <p>Background</p> <p>Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.</p> <p>Results</p> <p>We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability.</p> <p>Conclusion</p> <p>Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).</p

Weekly water quality monitoring data for the River Thames (UK) and its major tributaries (2009–2013): the Thames Initiative research platform

The River Thames and 15 of its major tributaries have been monitored at weekly intervals since March 2009. Monitored determinands include major nutrient fractions, anions, cations, metals, pH, alkalinity, and chlorophyll a and are linked to mean daily river flows at each site. This catchment-wide biogeochemical monitoring platform captures changes in the water quality of the Thames basin during a period of rapid change, related to increasing pressures (due to a rapidly growing human population, increasing water demand and climate change) and improvements in sewage treatment processes and agricultural practices. The platform provides the research community with a valuable data and modelling resource for furthering our understanding of pollution sources and dynamics, as well as interactions between water quality and aquatic ecology. Combining Thames Initiative data with previous (non-continuous) monitoring data sets from many common study sites, dating back to 1997, has shown that there have been major reductions in phosphorus concentrations at most sites, occurring at low river flow, and these are principally due to reduced loadings from sewage treatment works (STWs). This ongoing monitoring programme will provide the vital underpinning environmental data required to best manage this vital drinking water resource, which is key for the sustainability of the city of London and the wider UK economy. The Thames Initiative data set is freely available from the Centre for Ecology and Hydrology’s (CEH)Environmental Information Data Centre at https://doi.org/10.5285/e4c300b1-8bc3-4df2-b23a-e72e67eef2fd

Biogeochemical and climate drivers of wetland phosphorus and nitrogen release: implications for nutrient legacies and eutrophication risk

The dynamics and processes of nutrient cycling and release were examined for a lowland wetland‐pond system, draining woodland in southern England. Hydrochemical and meteorological data were analyzed from 1997 to 2017, along with high‐resolution in situ sensor measurements from 2016 to 2017. The results showed that even a relatively pristine wetland can become a source of highly bioavailable phosphorus (P), nitrogen (N), and silicon (Si) during low‐flow periods of high ecological sensitivity. The drivers of nutrient release were primary production and accumulation of biomass, which provided a carbon (C) source for microbial respiration and, via mineralization, a source of bioavailable nutrients for P and N co‐limited microorganisms. During high‐intensity nutrient release events, the dominant N‐cycling process switched from denitrification to nitrate ammonification, and a positive feedback cycle of P and N release was sustained over several months during summer and fall. Temperature controls on microbial activity were the primary drivers of short‐term (day‐to‐day) variability in P release, with subdaily (diurnal) fluctuations in P concentrations driven by water body metabolism. Interannual relationships between nutrient release and climate variables indicated “memory” effects of antecedent climate drivers through accumulated legacy organic matter from the previous year's biomass production. Natural flood management initiatives promote the use of wetlands as “nature‐based solutions” in climate change adaptation, flood management, and soil and water conservation. This study highlights potential water quality trade‐offs and shows how the convergence of climate and biogeochemical drivers of wetland nutrient release can amplify background nutrient signals by mobilizing legacy nutrients, causing water quality impairment and accelerating eutrophication risk

ENM2020 : A FREE ONLINE COURSE AND SET OF RESOURCES ON MODELING SPECIES NICHES AND DISTRIBUTIONS

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades-including a maturation of relevant theory and key concepts-but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an "Overview" talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.Peer reviewe

Dissolved inorganic carbon export from rivers of Great Britain: Spatial distribution and potential catchment-scale controls

Dissolved inorganic carbon (DIC) fluxes from the land to ocean have been quantified for many rivers globally. However, CO2 fluxes to the atmosphere from inland waters are quantitatively significant components of the global carbon cycle that are currently poorly constrained. Understanding, the relative contributions of natural and human-impacted processes on the DIC cycle within catchments may provide a basis for developing improved management strategies to mitigate free CO2 concentrations in rivers and subsequent evasion to the atmosphere. Here, a large, internally consistent dataset collected from 41 catchments across Great Britain (GB), accounting for ∼36% of land area (∼83,997 km2) and representative of national land cover, was used to investigate catchment controls on riverine dissolved inorganic carbon (DIC), bicarbonate (HCO3−) and free CO2 concentrations, fluxes to the coastal sea and annual yields per unit area of catchment. Estimated DIC flux to sea for the survey catchments was 647 kt DIC yr−1 which represented 69% of the total dissolved carbon flux from these catchments. Generally, those catchments with large proportions of carbonate and sedimentary sandstone were found to deliver greater DIC and HCO3− to the ocean. The calculated mean free CO2 yield for survey catchments (i.e. potential CO2 emission to the atmosphere) was 0.56 t C km−2 yr−1. Regression models demonstrated that whilst river DIC (R2 = 0.77) and HCO3− (R2 = 0.77) concentrations are largely explained by the geology of the landmass, along with a negative correlation to annual precipitation, free CO2 concentrations were strongly linked to catchment macronutrient status. Overall, DIC dominates dissolved C inputs to coastal waters, meaning that estuarine carbon dynamics are sensitive to underlying geology and therefore are likely to be reasonably constant. In contrast, potential losses of carbon to the atmosphere via dissolved CO2, which likely constitute a significant fraction of net terrestrial ecosystem production and hence the national carbon budget, may be amenable to greater direct management via altering patterns of land use

Morphology, genesis, and distribution of nanometer-scale pores in siliceous mudstones of the Mississippian Barnett Shale

ABSTRACT: Research on mudrock attributes has increased dramatically since shale-gas systems have become commercial hydrocarbon production targets. One of the most significant research questions now being asked focuses on the nature of the pore system in these mudrocks. Our work on siliceous mudstones from the Mississippian Barnett Shale of the Fort Worth Basin, Texas, shows that the pores in these rocks are dominantly nanometer in scale (nanopores). We used scanning electron microscopy to characterize Barnett pores from a number of cores and have imaged pores as small as 5 nm. Key to our success in imaging these nanopores is the use of Ar-ion-beam milling; this methodology provides flat surfaces that lack topography related to differential hardness and are fundamental for high-magnification imaging. Nanopores are observed in three main modes of occurrence. Most pores are found in grains of organic matter as intraparticle pores; many of these grains contain hundreds of pores. Intraparticle organic nanopores most commonly have irregular, bubblelike, elliptical cross sections and range between 5 and 750 nm with the median nanopore size for all grains being approximately 100 nm. Internal porosities of up to 20.2% have been measured for whole grains of organic matter based on point-count data from scanning electron microscopy analysis. These nanopores in the organic matter are the predominant pore type in the Barnett mudstones and they are related to thermal maturation. Nanopores are also found in bedding-parallel, wispy, organic-rich laminae as intraparticle pores in organic grains and as interparticle pores between organic matter, but this mode is not common. Although less abundant, nanopores are also locally present in fine-grained matrix areas unassociated with organic matter and as nano-to microintercrystalline pores in pyrite framboids. Intraparticle organic nanopores and pyrite-framboid intercrystalline pores contribute to gas storage in Barnett mudstones. We postulate that permeability pathways within the Barnett mudstones are along bedding-parallel layers of organic matter or a mesh network of organic matter flakes because this material contains the most pores