Assessing Impacts of Atmospheric Conditions on Efficiency and Siting of Large-Scale Direct Air Capture Facilities.

The cost and efficiency of direct air capture (DAC) of carbon dioxide (CO2) will be decisive in determining whether this technology can play a large role in decarbonization. To probe the role of meteorological conditions on DAC we examine, at 1 × 1° resolution for the continental United States (U.S.), the impacts of temperature, humidity, atmospheric pressure, and CO2 concentration for a representative amine-based adsorption process. Spatial and temporal variations in atmospheric pressure and CO2 concentration lead to strong variations in the CO2 available in ambient air across the U.S. The specific DAC process that we examine is described by a process model that accounts for both temperature and humidity. A process that assumes the same operating choices at all locations in the continental U.S. shows strong variations in performance, with the most influential variables being the H2O gas phase volume fraction and temperature, both of which are negatively correlated with DAC productivity for the specific process that we consider. The process also shows a moderate positive correlation of ambient CO2 with productivity and recovery. We show that optimizing the DAC process at seven representative locations to reflect temporal and spatial variations in ambient conditions significantly improves the process performance and, more importantly, would lead to different choices in the sites for the best performance than models based on a single set of process conditions. Our work provides a framework for assessing spatial variations in DAC performance that could be applied to any DAC process and indicates that these variations will have important implications in optimizing and siting DAC facilities.

A dataset of recorded electricity outages by United States county 2014-2022.

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018-2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

Predicted structural proteome of Sphagnum divinum and proteome-scale annotation.

MOTIVATION: Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank and fewer than 200 Sphagnum-related genes have structural models available in the AlphaFold Protein Structure Database. Tools and resources are needed to help bridge these gaps, and to enable the analysis of other structural proteomes now made possible by accurate structure prediction. RESULTS: We present the predicted structural proteome (25 134 primary transcripts) of Sphagnum divinum computed using AlphaFold, structural alignment results of all high-confidence models against an annotated nonredundant crystallographic database of over 90,000 structures, a structure-based classification of putative Enzyme Commission (EC) numbers across this proteome, and the computational method to perform this proteome-scale structure-based annotation. AVAILABILITY AND IMPLEMENTATION: All data and code are available in public repositories, detailed at https://github.com/BSDExabio/SAFA. The structural models of the S. divinum proteome have been deposited in the ModelArchive repository at https://modelarchive.org/doi/10.5452/ma-ornl-sphdiv.

Validating Safecast data by comparisons to a U. S. Department of Energy Fukushima Prefecture aerial survey.

Safecast is a volunteered geographic information (VGI) project where the lay public uses hand-held sensors to collect radiation measurements that are then made freely available under the Creative Commons CC0 license. However, Safecast data fidelity is uncertain given the sensor kits are hand assembled with various levels of technical proficiency, and the sensors may not be properly deployed. Our objective was to validate Safecast data by comparing Safecast data with authoritative data collected by the U. S. Department of Energy (DOE) and the U. S. National Nuclear Security Administration (NNSA) gathered in the Fukushima Prefecture shortly after the Daiichi nuclear power plant catastrophe. We found that the two data sets were highly correlated, though the DOE/NNSA observations were generally higher than the Safecast measurements. We concluded that this high correlation alone makes Safecast a viable data source for detecting and monitoring radiation.