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At least 1,279 records · Page 71

TRACER-Sonde Ozonesonde and radiosonde data

The TRACER-Sonde campaign used electrochemical cell ozonesondes to measure vertical profiles of ozone, which can be a tracer for convective mixing, in conjunction with meteorological variables measured by a radiosonde. There were 32 days in which TRACER-Sonde had twice-daily ozonesondes from the TRACER M1 site at La Porte at ~11Z and ~15Z (6 am and 10 am LT). The ozonesonde profiles provide an additional method to validate boundary-layer height determinations and test assumptions about vertical mixing in pre-convective environments. There are six additional days with ~15Z radiosonde only (no ozone data) profiles.

54 ENVIRONMENTAL SCIENCES↗

Micro-Environmental Control System (Final Report)

This project developed an innovative micro-environmental control system (µX) that enables office buildings to reduce energy used for heating and cooling by 15% or more. The µX is a compact, quiet, and ergonomic device that is designed to be installed under an office workstation; it is designed to maintain occupant comfort when room thermostat setpoints are incremented by 4°F or more (warmer in the summer and cooler in the winter) to save energy. When ambient room temperatures are outside of the usual comfort range, the µX maintains occupant comfort by delivering personalized cooling or heating locally to each office worker. The µX provides personalized cooling using a micro vapor compression system that includes a new high-performance micro-scroll compressor and a novel thermal storage unit. The vapor compression system operates at night to freeze a phase-change material (PCM). During the workday, the cooling stored in the PCM is released as a cool breeze of air to make occupants more comfortable. The micro-scroll compressor was developed specifically for this application; it is smaller than any of its type, minimizing the amount of power needed. In heating mode, the µX maintains occupant comfort using a foot heating mat with an infrared reflective box. The µX R&D project was conducted by Syracuse University in collaboration with United Technologies Research Center, Air Innovations, Bush Technical, and Cornell University. Over the course of the initial three-year project, the team developed and evaluated four versions of the unit, advancing the concept to Technology Readiness Level 6. The capabilities of individual proof-of-concept prototypes were verified in tests that were conducted with: 1) units in psychrometric chambers, 2) an instrumented manikin in a laboratory, and 3) human subjects in laboratories that simulate office environments. The tests verified that the µX prototypes met or exceeded all performance targets required to enable office buildings to reduce energy used for heating and cooling by 15% or more by maintaining occupant comfort when thermostat setpoints are incremented by 4°F or more.

25 ENERGY STORAGE↗

Electrochemically-assisted removal of cadmium ions by redox active Cu-based metal-organic framework

An electrochemically-assisted wastewater treatment using Faradaic materials offers a promising technique for the selective removal of hazardous substances. Here, we demonstrate the reversible capture and release of cadmium ions in aqueous solutions, using a redox-active metal-organic framework (MOF) electrode. As-synthesized copper-based MOF (Cu-MOF-74; copper 2,5-dihydroxyterephthalate) is a highly attractive candidate for Faradaic electrosorption due to its large surface area, water stability, and redox-active metal nodes. Our work demonstrates the reversible capture and release of Cd 2+ ions assisted by the electrochemical redox reaction of Cu 2+ /Cu + within the MOF structure. Combined material characterization and electrosorption tests were carried out to determine the operational conditions for maximizing adsorption capacity, energy efficiency, and material stability, thus leading to excellent electrosorption (>100 mg g -1 ) and regeneration efficiency (>90%). This study demonstrates the feasibility of leveraging MOFs containing redox-active metal nodes for the selective separation of toxic cations, and paves the way for promising future applications of these 3-D porous structures for wastewater treatment and environmental remediation.

42 ENGINEERING↗

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.

59 BASIC BIOLOGICAL SCIENCES↗

Business Plan for a Domestic Supply of Superconducting Radiofrequency Cavities

Superconducting radiofrequency (SRF) cavities are critical components of modern particle accelerators. The industrialization of SRF technology in the U.S. has a long history, with several companies becoming interested in the technology in the past 30 years. However, there is currently no U.S. manufacturer with the capability to produce fully assembled, ready-to-test SRF cavities. A majority of these companies have either left the field or shut their doors. As a result, government-sponsored SRF accelerator projects in the U.S. largely rely on European vendors for the cavity supply. This presents not only a supply chain risk for scientific use cases, but also severely limits the commercialization of accelerator-based technologies. SRF accelerators have a range of strategic applications, including environmental remediation, semiconductor lithography, medical isotope production, and irradiation/sterilization. The nurturing of a domestic SRF industry is critical to maintaining U.S. economic competitiveness.

43 PARTICLE ACCELERATORS↗

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

54 ENVIRONMENTAL SCIENCES↗

Observations and Machine-Learned Models of Near-Surface Permafrost along the Koyukuk River, Alaska, USA

This dataset contains GeoTIFs (raster) and GeoPackages (vector) that map observations of near-surface permafrost and not-permafrost from a field campaign conducted near the village of Huslia, AK along the Koyukuk River and its floodplain in July 2018. These data were collected as part of a campaign to understand if and how permafrost impacts riverbank erosion. This problem cannot be assessed without knowing where permafrost exists. Permafrost was observed via frost probing (to a maximum depth of one meter), coring (to a maximum depth of two meters) and bank/bar excavations. An additional boat survey was performed wherein expert (Joel Rowland) judgment assessed the presence or absence of distinctive permafrost features (e.g., overhanging tundra mats, thermoerosional niching, ice wedges, active drainage of ice melt from soils). This dataset also contains the input features and results of two machine learning models (random forest and convolutional neural network) that extrapolate the observations to the full floodplain that may be useful for building, testing, or validating other machine-learned permafrost models. Permafrost data are provided as georasters of the same shape and geovectors (polylines/polygons) and are all projected into EPSG:32605. All data can be visualized with a GIS (QGIS, ArcGIS, etc.).

54 ENVIRONMENTAL SCIENCES↗

Cryogenic Carbon Capture™ (CCC) Status Report

The Cryogenic Carbon Capture™ (CCC) process separates CO2 from light gases in essentially any continuous process. CCC cools the gases to the frost or desublimation point of CO2 (-100 to -135 °C), separates and pressurizes the solids, and warms all streams to produce a CO2-depleted stream at ambient pressure and a pure (99+%) pressurized liquid CO2 stream typically to about 150 bar, both at ambient temperature. The process also recovers all gas moisture and most gas impurities less volatile than CO2 (NOx, SOx, Hg, PM, UHC, CCC, etc.) in separable streams. CCC nearly eliminates refrigeration energy for sensible temperature changes through heat integration. CCC does require energy to change the CO2 phase from a mixed vapor to a pressurized fluid, which represents the minimum energy required of any process for this separation. CCC uses additional energy for turbomachinery inefficiencies, heat losses, moisture removal and overall process pressure drop. Aside from these real-world energy demands, CCC operates near the minimum energy required to perform this gas separation by minimizing stream recycling. CCC compresses CO2 as a liquid, which is one of several reasons it costs about about half as much and consumes about half as much energy as an amine process when using flue gases with about 15% CO2. The process also has several major additional advantages, including (a) it is a bolt-on retrofit technology that does not need steam or any modification of existing equipment, (b) it recovers water and nearly all pollutants in addition to CO2 from the flue gas, (c) it enables highly efficient and cost effective energy storage at grid scale and on time scales of minutes, (d) it enables NG storage if the energy storage option is used, and (d) it has a small footprint and is minimally disruptive to existing plants, requiring only electrical power and a gas source to operate. Sustainable Energy Solutions (SES) has scaled this technology through several levels, the largest of which captures nominally 1 tonne of CO2/day and is called the skid system. Skid system field tests include utility-scale power plants, cement plants, heating plants, and other utility or industrial sites that burn natural gas, biomass, coal, shredded tires, municipal waste, and combinations of these fuels. These field tests produced 95-99% CO2 capture with CO2 purities of 99+% and initial CO2 contents that range from 4 to 28%. SES currently seeks to scale the system to merchant scale (10-80 tonnes of CO2 per day). In the process of doing so, SES has demonstrated the potential for CCC to contribute to energy storage and direct air capture in innovative and cost-effective ways. This presentation discusses the overall process and highlights results from field and in-house tests. These include (a) measured CO2 capture rates and operating conditions from in-house and field tests, and (b) predicted utility-scale costs and energy demands. This discussion also includes the application of the CCC technology to energy storage and direct air capture.

20 FOSSIL-FUELED POWER PLANTS↗

Wood Decomposition: Understanding Processes Regulating Carbon Transfer to Soil Carbon Pools Using FACE Wood at Multiple Scales

Dead wood comprises approximately 20% of forest biomass. While it is widely recognized for its role in ecosystem functions relative to habitat and nutrient cycling, there are considerable uncertainties associated with its role in the forest carbon (C) cycle. The principal uncertainty is whether dead wood contributes to the soil C pool. With little supporting evidence, the presumption is that C in wood is respired back to the atmosphere through decomposition processes. The inability to attribute forest soil C to its source components has been a barrier to addressing this important forest carbon cycle question. The U.S. Dept. of Energy’s Free Air CO 2 Enrichment (FACE) experiment produced wood that has a distinct δ 13 C signature that can be used to trace its fate through organic matter decomposition processes. Using two hardwood species (aspen, birch) from the Rhinelander FACE site and pine from the Duke FACE site, the FACE Wood Decomposition Experiment (FWDE) was established in 2011 to provide the foundation for addressing wood decomposition and its associated processes at the continental scale. Plots were installed on nine experimental forests representing a range in bioclimatic forest conditions within the continental U.S. Each identical plot has logs of the three species incubating on the soil surface and pine logs suspended in the air, representing standing dead wood. Abiotic conditions are monitored continuously, and soil and forest conditions characterized. Logs on the sites have been measured every two years to assess decay. This project was initiated in 2016 with specific objectives to: (a) determine whether dead wood C contributes to the soil C pool, (b) assess the fungal community composition mediating the decay, (c) assess the interactions of termites with microbial decay, and (d) develop a mechanistic model to simulate wood decomposition. The principal field sampling was conducted in 2017; correspondingly, the results reported here represent six years of decomposition. Wood decomposition varied from 13 to 75 % loss across the bioclimatic gradient. Sites in the southeastern U.S. coastal plain and piedmont had fastest decomposition rates for each of the species. In contrast sites in the Rocky Mountains had the slowest decay rates, which were associated with cold temperatures. Standing deadwood decomposes at a much slower rate than logs on the soil surface; a response due to differences in wood moisture content. Wood carbon was measured in the mineral soil across each of the sites; accordingly, this is the first study to affirm that a portion of the wood C does become part of the soil C pool. Averaged across the FWDE sites, approximately 1.4% of the soil C in the upper 10 cm of the mineral soil directly beneath the log was derived from the deadwood; within 50 cm of the log, wood C accounted for an average of 2.1%. These results demonstrate that the contribution of deadwood to the soil C pool is a soil volume surrounding the log; and it’s likely a larger volume than what we sampled. We also hypothesized that there would be more wood C in the mineral soil when termites were present. However, that hypothesis wasn’t supported by measurements on the Santee Experimental Forest termite exclusion (SEF-TX) plots under FACE logs that incubated for 6 years. Across all the sites, the majority of the wood C was transferred to the atmosphere. Approximately 7,000 fungal species were sequenced. The fungal communities were different among each of the sites suggesting local colonization; interestingly, within sites the three species also exhibited differences in fungal community composition. White rot was the dominant fungal form in pine and hardwood, which yields less lignin in the wood residue. Brown rot occurred in approximately 20% of the pine and aspen logs, and it did not occur in birch logs. The occurrence of brown rot was only weakly correlated to site factors. We conducted two additional experiments to assess the effects of termites on wood decomposition and the associated interactions with the fungal community. Pine and birch logs were incubated on the Santee Experimental Forest termite exclusion plots in upland and riparian settings. There was no difference in decay between areas where termites were able colonize and plots where they had been excluded. Highly variable foraging by termites was a primary contributor the result. Correspondingly, fungal communities did not vary on logs incubated among the two treatments, but they did reflect site and species differences. The second experiment was conducted on each of the nine FWDE sites where pine and birch wood blocks were incubated for two years with and without a wrapping designed to exclude termites and other arthropods. The effects of termites on wood decomposition could be confirmed on two of the four sites known to have subterranean termites. On the two southeastern sites, termites contributed approximately 8% to mass loss in pine blocks over two years. A major factor clouding the role of termites in wood decomposition is the inability to assess the time, duration, and intensity of the foraging with the logs. Another uncertainty is related to whether a piece of wood will be foraged. A process-based wood decomposition model was developed representing biological processes mediated by fungi, bacteria, termites, beetles, as well as physical and chemical processes. The Coarse Wood Decomposition Model (CWDM) was parameterized based on a thorough review of the literature and it was validated using data from the FWDE. In the initial setup, the model considers softwoods and hardwoods groups, as opposed to individual species. Sensitivity analyses incorporating climate data from 89 locations in North America demonstrated that CWDM was stable from the tropics to the boreal zone. Model calibration and validation showed it to perform very well. The CWDM is poised for additional testing with subsequent FWDE data as well as other data sets, and it is available for incorporation into forest biogeochemical models or DOEs Earth System Modeling framework.

54 ENVIRONMENTAL SCIENCES↗

High Depth‐of‐Discharge Zinc Rechargeability Enabled by a Self‐Assembled Polymeric Coating

Abstract Zinc has the potential for widespread use as an environmentally friendly and cost‐effective anode material pending the resolution of rechargeability issues caused by active material loss and shape change. Here, a self‐assembled Nafion‐coated Celgard 3501 (NC‐Celgard) separator is shown to enable unprecedented cycle life of a Zn anode in alkaline electrolyte at high depth‐of‐discharge (DOD Zn ). Using commercially relevant energy‐dense electrodes with high areal capacities of 60 mAh cm –2 , Zn–Ni cells tested at 20% DOD Zn cells achieve over 200 cycles while 50% DOD Zn cells achieve over 100 cycles before failure. The 20% and 50% DOD cells deliver an average of 132 and 180 Wh L –1 per cycle over their lifetime respectively. Rechargeability is attributed to the highly selective diffusion properties of the 300 nm thick negatively charged Nafion coating on the separator which prevents shorting by dendrites and inhibits redistribution of the active material. Crossover experiments show that the NC‐Celgard separator is practically impermeable to zincate ([Zn(OH) 4 ] 2– ), outperforming commercial Celgard, cellophane, Nafion 211 and 212 separators while still allowing hydroxide transport. This work demonstrates the efficacy of selective separators for increasing the cycle life of energy‐dense Zn electrodes without adding significant volume or complexity to the system.

Arnot, David J.↗

Decoupling size and surface effects of intermetallic CuPd nanocrystals for electrocatalytic nitrate reduction to ammonia

Nitrate pollution poses a major environmental challenge, but its electrochemical conversion to ammonia offers a sustainable waste-to-value solution. Here, in this study, we synthesized monodisperse, size-tunable B2-phase CuPd intermetallic nanocrystals (6–46 nm) and studied their performance in the electrochemical nitrate reduction reaction (eNO 3 RR). By using bromide ions to modulate Pd reduction and applying mild annealing, we achieved phase-pure B2 structures across all sizes. Catalytic testing revealed a volcano-like trend in ammonia yield, peaking at 33 nm nanocubes with a rate of 6.97 mol h −1 g −1 at −0.6 V vs. reversible hydrogen electrode (RHE). This optimum reflects a balance between the increased surface area of smaller particles and the enhanced exposure of active (100) facets in larger ones. Theoretical calculations indicated that the B2-CuPd (100) facet is favorable for nitrate adsorption, thereby supporting the high activity of nanocubes. Our results highlight the critical role of tuning both nanoparticle size and surface structure to maximize eNO 3 RR efficiency.

36 MATERIALS SCIENCE↗

Temporal variabilities of soil carbon dioxide fluxes from cornfield impacted by temperature and precipitation changes through high-frequent measurement and DAYCENT modelling

Soil carbon dioxide (CO 2 ) emissions from the field of corn (Zea mays L.) play an important role in global warming. This study investigated temporal variability of soil CO 2 fluxes (R s ) with soil temperature (T s ) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes on R s using the measured high-frequency data. R s trend was tested by Mann–Kendall and Sen Estimator. Predicted R s s under different climate scenarios were compared using Parallel-line Analysis. Herein, the findings indicated that daily R s exponentially increased with T s constrained by θ. During the θ of 27–31%, there was a strong exponential relationship between R s and T s , but the relationship was weaker for the θ of 38–41% and 22–26%. Soil environmental index (SEI, T s × θ) significantly impacted R s with linear regression R s 0.5 = 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends in R s s and relationships among R s and T s and θ in different years. Predicted yearly R s s, root R s s and corn yield in 2014–2049 increased with an increase in temperature scenarios, but the R s s significantly increased as temperature rose by 1°C or higher. Predicted yearly R s s, root R s s and yield reduced with precipitation scenario increase, and the root R s s and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearly R s from cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfield R s and evaluating sensitivities of different cropland R s s to temperature changes.

54 ENVIRONMENTAL SCIENCES↗

Piezoelectric property of PZT nanofibers characterized by resonant piezo-force microscopy

Nano-piezoelectric materials have drawn tremendous research interest. However, characterization of their piezoelectric properties, especially measuring the piezoelectric strain coefficients, remains a challenge. Normally, researchers use an AFM-based method to directly measure nano-materials’ piezoelectric strain coefficients. But, the extremely small piezoelectric deformation, the influence from the parasitic electrostatic force, and the environmental noise make the measurement results questionable. In this paper, a resonant piezo-force microscopy method was used to accurately measure the piezoelectric deformation from 1D piezoelectric nanofibers. During the experiment, the AFM tip was brought into contact with the piezoelectric sample and set to work at close to its first resonant frequency. A lock-in amplifier was used to pick up the sample’s deformation signal at the testing frequency. By using this technique, the piezoelectric strain constant d 33 of the Lead Zirconate Titanate (PZT) nanofiber with a diameter of 76 nm was measured. The result showed that d 33 of this PZT nanofiber was around 387 pm/V. Meanwhile, by tracking the piezoelectric deformation phase image, domain structures inside PZT nanofibers were identified.

36 MATERIALS SCIENCE↗

UKy-CAER Heat Integrated Transformative CO 2 Capture Process in Pulverized Coal Power Plants

The goal of this Phase II Topical Report is to summarize the work for public domain conducted on project DE-FE0031583. Building upon the Design Basis completed during Phase I and in accordance with the Statement of Project Objectives (SOPO), the University of Kentucky (UK) Center for Applied Energy Research (CAER) (Recipient) along with team members has completed the Front End Engineering Design (FEED) for its advanced, versatile post-combustion CO 2 capture system (CCS) at the 10 MWe large pilot scale to be installed at the Wyoming (WY) Integrated Test Center (ITC), using a heat integrated process with split-rich fed, two-stage stripping and any advanced solvent to enhance the CO 2 absorber performance and lower the cost of CO 2 capture. The proposed project (Phases I-III) involves the design, fabrication, installation, testing and evaluation of a large pilot scale facility that will demonstrate the UK CAER transformative CCS integrated with an operating power plant. The work performed in Phase I, detailed in the Phase I Topical Report that was submitted in March 2019, included formation of a project team, securing the host site, and completing pre-FEED design basis, a cost estimation, a preliminary techno-economic analysis (TEA) and an environmental information volume (EIV). The work performed in Phase II, detailed in this Phase II Topical Report, includes completion of FEEDs, completion of a process risk analysis (PRA), obtaining necessary permits, updating the EIV and TEA, updating the cost and schedule estimate for the Phase III scope of work (SOW) and securing the necessary cost share. Updated information is provided on the current status of the UK CAER CCS technology and the project is ready to begin immediately detailed design, construction and operation of the proposed pilot.

20 FOSSIL-FUELED POWER PLANTS↗

The toughest animals of the Earth versus global warming: Effects of long‐term experimental warming on tardigrade community structure of a temperate deciduous forest

Abstract Understanding how different taxa respond to global warming is essential for predicting future changes and elaborating strategies to buffer them. Tardigrades are well known for their ability to survive environmental stressors, such as drying and freezing, by undergoing cryptobiosis and rapidly recovering their metabolic function after stressors cease. Determining the extent to which animals that undergo cryptobiosis are affected by environmental warming will help to understand the real magnitude climate change will have on these organisms. Here, we report on the responses of tardigrades within a five‐year‐long, field‐based artificial warming experiment, which consisted of 12 open‐top chambers heated to simulate the projected effects of global warming (ranging from 0 to 5.5°C above ambient temperature) in a temperate deciduous forest of North Carolina (USA). To elucidate the effects of warming on the tardigrade community inhabiting the soil litter, three community diversity indices (abundance, species richness, and Shannon diversity) and the abundance of the three most abundant species ( Diphascon pingue , Adropion scoticum , and Mesobiotus sp.) were determined. Their relationships with air temperature, soil moisture, and the interaction between air temperature and soil moisture were tested using Bayesian generalized linear mixed models. Despite observed negative effects of warming on other ground invertebrates in previous studies at this site, long‐term warming did not affect the abundance, richness, or diversity of tardigrades in this experiment. These results are in line with previous experimental studies, indicating that tardigrades may not be directly affected by ongoing global warming, possibly due to their thermotolerance and cryptobiotic abilities to avoid negative effects of stressful temperatures, and the buffering effect on temperature of the soil litter substrate.

54 ENVIRONMENTAL SCIENCES↗

Quantifying Subsurface Biogeochemical Variability in a High Altitude Watershed During Winter Isolation

Shallow subsurface microbial and geochemical processes in watersheds are dynamic and responsive to seasonal and long-term environmental change. At high elevation or latitude, such changes may occur during winter months when normal sampling is impossible, impractical, unsafe, or may actually alter the processes being studied. Yet accurate modeling of biogeochemical parameters in these environments requires sampling that can capture events and persistent cold climate trends. Gaps in current models exist where data have not been collected during extended snow- or ice-covered periods. The goals of our exploratory study were to examine the biogeochemical processes occurring in a well-studied high altitude watershed during winter when the system is largely inaccessible. We tested the hypothesis that during snow cover in the East River (ER) watershed, episodic excursions of microbial community structure and biogeochemical processes and concentrations fluctuated from values extrapolated from pre- and post-snow time periods. Through our research we identified previously unresolved subsurface-surface transport phenomena and a heretofore under-appreciated methane cycle occurring during wintertime months, thus providing valuable insight into watershed processes. Our research showed that autonomous sampling techniques, first used in marine systems, is extremely versatile and can be adapted for use in harsh continental settings to examine temporal changes in freshwater chemistry and microbial community assemblage. Further, we found that by sampling during periods when systems are hard to access, will lend insight and better accountability for global budgets for methane, an important greenhouse gas. By strengthening our knowledge of wintertime biogeochemical relationships in high altitude watersheds our research leads to more complete descriptions of cryptic processes and ultimately will inform and improve the reactive transport models that simulate processes in these systems.

54 ENVIRONMENTAL SCIENCES↗

Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

09 BIOMASS FUELS↗