Search NASA⌕ Search

SEARCH · Search NASA

Results for “Temporal Analysis of Products”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

An Integrative Model for Soil Biogeochemistry and Methane Processes: I. Model Structure and Sensitivity Analysis

Abstract Environmental changes are anticipated to generate substantial impacts on carbon cycling in peatlands, affecting terrestrial‐climate feedbacks. Understanding how peatland methane (CH 4 ) fluxes respond to these changing environments is critical for predicting the magnitude of feedbacks from peatlands to global climate change. To improve predictions of CH 4 fluxes in response to changes such as elevated atmospheric CO 2 concentrations and warming, it is essential for Earth system models to include increased realism to simulate CH 4 processes in a more mechanistic way. To address this need, we incorporated a new microbial‐functional group‐based CH 4 module into the Energy Exascale Earth System land model (ELM) and tested it with multiple observational data sets at an ombrotrophic peatland bog in northern Minnesota. The model is able to simulate observed land surface CH 4 fluxes and fundamental mechanisms contributing to these throughout the soil profile. The model reproduced the observed vertical distributions of dissolved organic carbon and acetate concentrations. The seasonality of acetoclastic and hydrogenotrophic methanogenesis—two key processes for CH 4 production—and CH 4 concentration along the soil profile were accurately simulated. Meanwhile, the model estimated that plant‐mediated transport, diffusion, and ebullition contributed to ∼23.5%, 15.0%, and 61.5% of CH 4 transport, respectively. A parameter sensitivity analysis showed that CH 4 substrate and CH 4 production were the most critical mechanisms regulating temporal patterns of surface CH 4 fluxes both under ambient conditions and warming treatments. This knowledge will be used to improve Earth system model predictions of these high‐carbon ecosystems from plot to regional scales.

58 GEOSCIENCES↗

Source apportionment of airborne volatile organic compounds near unconventional oil and gas development

Oil and natural gas (ONG) extraction emits volatile organic compounds (VOCs). Certain VOCs are identified as hazardous air pollutants (HAPS) while others contribute to ozone formation. This study examines the impact of ONG operations on VOC levels during the development of multi-well ONG pads in suburban Broomfield, Colorado. From October 2018 to December 2020, weekly VOC measurements were taken at 18 sites across the area. These included spots near well pads, in adjacent neighborhoods, and at a background site, covering various stages of well pad development including drilling, hydraulic fracturing, flowback, and production. Analysis using Positive Matrix Factorization (PMF) identified six factors, including combustion, background/biogenic sources, light and complex alkanes, drilling activities, and ONG acetylene. Factors linked to local ONG activities exhibited clear temporal and spatial correlations with Broomfield well development. Benzene source analysis revealed distinct contribution gradients, with ONG-related sources notably influencing areas near the well pads, particularly in pre-production. ONG-related weekly benzene contributions varied from 9% to 63% at a community background site and 18% to 89% in a neighborhood close to a well pad.

54 ENVIRONMENTAL SCIENCES↗

$K^{*}(892)^0$ production and the time between freeze-outs in $^{40}$Ar+$^{45}$Sc collisions by NA61/SHINE at the CERN SPS

The analysis of the production of strange $K^{*}(892)^0$ resonances allows us to better understand the temporal evolution of high-energy nucleus--nucleus collisions. In particular, the ratio of $K^{*}(892)^0$ to charged kaon yields is used to determine the time interval between chemical and kinetic freeze-outs. In this paper, the first measurements of $K^{*}(892)^0$ production in central $^{40}$Ar+$^{45}$Sc collisions at the CERN Super Proton Synchrotron are reported. They were performed by NA61/SHINE at collision center-of-mass energies per nucleon pair $\sqrt{s_\mathrm{NN}}$ = 8.8, 11.9, 16.8 GeV. The obtained $\langle K^{*}(892)^0 \rangle/\langle K^{+} \rangle $ and $\langle K^{*}(892)^0 \rangle/\langle K^{-} \rangle$ mean multiplicity ratios are compared with corresponding results in $p$+$p$ collisions, allowing for an estimate of the time interval between chemical and thermal freeze-outs in the $^{40}$Ar+$^{45}$Sc system. These are the first such results reported for $^{40}$Ar+$^{45}$Sc collisions.

Adrich, P. [NCBJ, Warsaw] (ORCID:0000000270195451)↗

Meta-analysis of North American Arctic and boreal aboveground biomass datasets: assessing accuracy, dynamics, and similarities

The North American arctic and boreal regions (ABRs) are rapidly warming and experiencing intensifying disturbances. Accurately quantifying aboveground biomass (AGB) is critical for understanding the impacts of these changes on the carbon cycle and for designing climate change mitigation strategies. Several AGB maps have been developed for the North American ABRs, including recent contributions from National Aeronautics and Space Administration’s Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. However, these maps differ widely in training data, methodology, and resulting AGB density estimates. Presently, a comprehensive comparative evaluation is lacking, making it difficult for users to select datasets suited to their research or management needs. Here, in this study, we conducted a comparative analysis of nine AGB density datasets across North American ABRs, specifically for Alaska and Canada. We (1) summarized AGB by ecoregion and Canadian provinces, (2) evaluated their accuracy against field-based measurements, (3) analyzed spatial and temporal similarities among datasets, and (4) assessed their ability to capture disturbance (fire and harvest) impacts on AGB. We found substantial variation in regional and local AGB estimates across datasets, with overall accuracy ranging from R 2 = 0.25–0.62 and Bias% from −47.8% to 69.9% when validated against field plots. Despite these differences, most datasets have comparatively consistent spatial patterns in AGB (r > 0.8 for most cases). In contrast, agreement on the temporal patterns of AGB change is generally low. We found datasets with spatial resolutions ⩽300 m are capable of capturing disturbance impacts on AGB dynamics, though sensitivity varies across products. Our findings and dataset summary provide guidance for selecting appropriate AGB datasets for different applications within our study area. Our analysis also highlights the need to decrease map bias and increase capability to detect temporal change to decrease uncertainty of AGB datasets potentially by using training data which is representative of major plant functional types within the mapped area.

ABoVE↗

Daily, 30 m Resolution NDSI Data for the East River Watershed, CO for 2000-2020

This dataset contains daily Normalized Difference Snow Index (NDSI) values at 30 m spatial resolution for the East River watershed in Colorado, USA. The temporal range of these data includes water years 2001-2020. These data were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). This model fuses low spatial and high temporal resolution data from MODIS (500 m, daily) with high spatial and low temporal resolution data from Landsat (30 m, 16 days) to create a 30m synthetic daily snow product. This product allows for the analysis of historical snow covered area trends in the East River Watershed at fine spatiotemporal resolutions where it was not available previously. This research was performed as a part of the Department of Energy’s Subsurface Biogeochemical Research Program with the primary intent of better understanding the timing and spatial patterns of water delivery to the Critical Zone in mountain watersheds. Each .zip file contains one "water year" of data (October 1 - September 30; i.e., water year 2010 starts October 1, 2010 and ends September 30, 2011). Each zip file contains the following: STARFM daily Normalized Difference Snow Index (NDSI) fusion data files in GeoTiff format with one layer for each day between Landsat data acquisition dates (i.e., for dates of Landsat acquisition, the Landsat image is included for that date). The study area is located in an area of Landsat path overlap, so Landsat dates acquisitions are every 7-9 days. Landsat NDSI files containing the high spatial (30m), low temporal (7-9 days due to Landsat path overlap) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Dates for which no Landsat data were obtained are included as NoData layers. MODIS NDSI files containing the high temporal (daily), low spatial (500m) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Please note the MODIS data were resampled to 30m pixels for input into the STARFM model. The data have a scale factor of 10,000 and a no data value of -32767. The projection of all datasets is WGS 84 (EPSG: 4326), which has a latitude/longitude based degree resolution of 0.0002694946 X 0.0002694946, and approximates to the 30 m spatial resolution mentioned above. The Layer Index files in .csv format. They contain information for each layer in the above GeoTiff files regarding the corresponding date for each layer, the fraction of pixels in the image that contain valid data (missing data is due to either cloud cover or poor data quality; these values are not percent snow cover). Dates of Landsat overpass are indicated in these files. If no Landsat data were able to be obtained due to cloud cover or lack of Landsat Tier 1 data available on Google Earth Engine, this is also noted.

EARTH SCIENCE > CRYOSPHERE > SNOW/ICE↗

Soil Moisture Buffers the Impact of Precipitation Variability on Ecosystem Productivity

Water availability governs ecosystem productivity, yet estimates of vegetation sensitivity to water can differ greatly depending on whether the sensitivity is examined spatially or temporally. In particular, the spatial sensitivity is often reported to be much stronger than temporal sensitivities, leading to highly uncertain projections of ecosystem responses to future climate change when using space-for-time substitution. The large difference between spatial and temporal sensitivities remains unexplained. Prior research, however, primarily relied on precipitation as the water availability proxy, whereas vegetation responds to soil moisture. Here, we combined satellite estimates of vegetation productivity with soil moisture data across water-limited ecosystems of the continental United States (CONUS) to identify a convergent sensitivity of productivity to water availability. Using precipitation, we show that temporal sensitivity is 66% lower than spatial sensitivity overall. Our analysis identified the cause of the difference to be primarily driven by the seasonal variability of water availability, rooting depth, and soil properties. When using soil moisture instead of precipitation, we observed widespread convergence in the spatial and temporal sensitivities—that is, the two sensitivities became much more similar in magnitude across all water-limited ecosystems within CONUS. These results show that overlooking soil hydrology can inflate perceived discrepancies between spatial and temporal vegetation sensitivities, leading to biased projections of ecosystem dynamics under future hydro-climatic change.

Wang, Huiqi [University of California, Berkeley, C↗

Proteome profiling of enriched membrane-associated proteins unraveled a novel sophorose and cello-oligosaccharide transporter in Trichoderma reesei

Trichoderma reesei is an organism extensively used in the bioethanol industry, owing to its capability to produce enzymes capable of breaking down holocellulose into simple sugars. The uptake of carbohydrates generated from cellulose breakdown is crucial to induce the signaling cascade that triggers cellulase production. However, the sugar transporters involved in this process in T. reesei remain poorly identified and characterized. To address this gap, this study used temporal membrane proteomics analysis to identify five known and nine putative sugar transporters that may be involved in cellulose degradation by T. reesei. Docking analysis pointed out potential ligands for the putative sugar transporter Tr44175. Further functional validation of this transporter was carried out in Saccharomyces cerevisiae. The results showed that Tr44175 transports a variety of sugar molecules, including cellobiose, cellotriose, cellotetraose, and sophorose. This study has unveiled a transporter Tr44175 capable of transporting cellobiose, cellotriose, cellotetraose, and sophorose. Our study represents the first inventory of T. reesei sugar transportome once exposed to cellulose, offering promising potential targets for strain engineering in the context of bioethanol production.

59 BASIC BIOLOGICAL SCIENCES↗

Capturing O2 Desorption Through Isoconversional Kinetics for CFD Application

The present work examines desorption mechanisms and kinetics of the calcium-doped strontium perovskite at 25% calcium content (i.e., Sr_0.75 Ca_0.25 FeO_(3-δ)). Laboratory-scale, low inventory, fixed bed redox cycle experiments were developed and conducted in both dry and steam-based environments for three isothermal temperatures, 450, 500 and 550C. The temporal evolution of the conversion extent of the material sample over the redox cycles were obtained through the analysis of the gaseous products of the redox reactions. Using subsequently isoconversional differential methods, it was found that the activation energy exhibits a strong dependence on the extent of conversion during the desorption of this perovskite. In addition, the reconstruction of the reaction model showed that the desorption kinetics are controlled by a three-step mechanism. A successful a priori verification of the isoconversional desorption kinetics against the dry and steam-based environment desorption experimental data are shown.

Konan, Ndri A.↗

Viability of Reclaiming Municipal Wastewater for Potential Microalgae-Based Biofuel Production in the U.S.

Reclaimed municipal wastewater is a crucial component in biofuel production, especially in regions experiencing increasing freshwater scarcity. However, accurately estimating the potential for fuel production is challenging because of the uneven distribution of biofuel feedstock regions and wastewater treatment plants (WWTPs). This study assesses the viability of using reclaimed municipal water for algal biomass production in pond systems co-located with WWTPs under scenarios driven by biomass production and based on water transport logistics. We performed state- and county-level analysis of reclaimed water resources throughout the United States based on WWTP facility data. We overlaid these data onto estimated algae facility sites and examined the temporal resource availability to address seasonal variations in cultivation demand. Our findings reveal that 2694 billion liters per year of reclaimed water could potentially be used to produce 42.2 million metric tons (ash-free dry weight) of algal biomass, equivalent to 29.2 billion liters of renewable diesel equivalent (RDe). The use of reclaimed water would double current national water reuse and expand such reuse significantly in 455 counties across the United States. However, when we limit the construction of algae facilities to counties that can fully meet their water demand in order to minimize water transport burdens, the available supply decreases by 80%, to 512 billion liters, resulting in annual production of 12.2 billion liters of RDe, which still doubles current biodiesel production. Our analysis highlights the degree to which the location and flow of WWTPs and water transport affect the deployment of algae biofuel facilities and tradeoffs. These findings underscore the importance of improving the current WWTP infrastructure for reclaimed water reuse, especially in southern states.

09 BIOMASS FUELS↗

A phenology- and trend-based approach for accurate mapping of sea-level driven coastal forest retreat

The rapid replacement of upland forest by encroaching marshland is a striking manifestation of global sea-level rise (SLR). Timely and high-resolution information on the location and extent of transition forest (the ecotone between upland forest and marsh where tree mortality due to seawater intrusion begins) is fundamental to understanding the processes and patterns of SLR-driven landscape reorganization. Despite its significance, accurate characterization of salt-impacted transition forest remains challenging due to the complexity of coastal environments, scarcity of ground-truth data, and the lack of effective mapping algorithms. Here we use the full archive of Landsat images between 1984 and 2021 to investigate the spectral, temporal, and phenological characteristics of transition forest, and develop a robust framework for monitoring coastal vegetation shifts in the mid-Atlantic U.S., a global SLR hotspot. Here, we found that transition forest exhibits strong negative NDVI trends and a deviation of land surface phenology from marsh and upland forest that distinguishes itself from surrounding vegetation. By integrating temporal trends and land surface phenology, our results demonstrate superior discrimination between marsh and coastal forests to existing map products (e.g. NOAA Coastal Change Analysis Program, National Land Cover Database) that allows a reliable identification of the coastal treeline. We applied the approach to map regional land cover in 1985, 2000 and 2020 (overall classification accuracy >92%) and found that the area of coastal forest decreased by 22.0% from 1985 to 2020, the majority of which transitioned to marshland (92.3%, 5.3 × 10 3 ha). Based upon fine-scale patterns of coastal transgression, we created a practical workflow for spatially explicit quantification of forest retreat rates. Concurrent with rising sea level, coastal forests migrated upslope from 0.63 (± 0.27) m above sea level in 1985 to 0.78 (± 0.32) m above sea level in 2020, and horizontal forest retreat rates accelerated from 3.1 (range of 0–36) m yr -1 during 1985–2000 to 4.7 (0–55) m yr -1 during 2001–2020. As SLR continues to accelerate, our study may serve as a scalable solution for consistent tracking of coastal landscape evolution that is urgently needed for sustainable forest and wetland management.

54 ENVIRONMENTAL SCIENCES↗

Critical needs to close monitoring gaps in pan-tropical wetland CH 4 emissions

Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH 4 ). The FLUXNET-CH 4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH 4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH 4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH 4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH 4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH 4 emissions and reduce bias by 79% (from 76 to 16 TgCH 4 y -1 ), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

54 ENVIRONMENTAL SCIENCES↗

Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes

Aerosol pH is a fundamental property of aerosols in terms of atmospheric chemistry and its impact on air quality, climate, and health. Precise estimation of aerosol pH in chemical transport models (CTMs) is critical for aerosol modeling and thus influences policy development that partially relies on results from model simulations. We report the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) simulated PM 2.5 pH over China during a period with heavy haze episodes in Beijing, and explore the sensitivity of the modeled aerosol pH to factors including emissions of nonvolatile cations (NVCs) and NH 3 , aerosol phase state assumption, and heterogeneous production of sulfate. We find that default WRF-Chem could predict spatial patterns of PM 2.5 pH over China similar to other CTMs, but with generally lower pH values, largely due to the underestimation of alkaline species (NVCs and NH 3 ) and the difference in thermodynamic treatments between different models. Increasing NH 3 emissions in the model would improve the modeled pH in comparison with offline thermodynamic model calculations of pH constrained by observations. In addition, we find that the aerosol phase state assumption and heterogeneous sulfate production are important in aerosol pH predictions for regions with low relative humidity (RH) and high anthropogenic SO 2 emissions, respectively. These factors should be better constrained in model simulations of aerosol pH in the future. Analysis of the modeled temporal trend of PM 2.5 pH in Beijing over a haze episode reveals a clear decrease in pH from 5.2 ± 0.9 in a clean period to 3.6 ± 0.5 in a heavily polluted period. The increased acidity under more polluted conditions is largely due to the formation and accumulation of secondary species including sulfuric acid and nitric acid, even though being modified by alkaline species (NVCs, NH 3 ). Our result suggests that NO 2 oxidation is unlikely to be important for heterogeneous sulfate production during the Beijing haze as the effective pH for NO 2 oxidation of S(IV) is at a higher pH of ~6.

54 ENVIRONMENTAL SCIENCES↗

Spatial and temporal baseline of perfluorooctanesulfonic acid retained in sediment core samples from Puget Sound, Washington, USA

Per- and poly-fluorinated alkyl substances (PFAS) are a group of highly persistent synthetic chemicals utilized in many industrial and consumer products, and — most significantly toward introduction to the marine environment — in fire-fighting foams. Recently, PFAS have been linked to adverse health effects, prompting the need to understand transport and fate. Puget Sound’s main basin, hosting densely populated urban and industrial areas, is known to be contaminated with various persistent organic pollutants and provides a central focus for ecosystem monitoring. Perfluorooctanesulfonic acid, a manufactured PFAS and biodegradation product, partitions to marine sediments and thus can be used as a primary indicator toward regulatory efforts. The current study offers a spatial and temporal analysis of the Puget Sound from cores collected adjacent, Tacoma and Seattle, WA, as well as cores from central Hood Canal and Carr Inlet.

Per- and poly-fluorinated alkyl substances (PFAS),↗

Dimensionally Reduced Model for Rapid and Accurate Prediction of Gas Saturation, Pressure, and Brine Production in a CO 2 Storage Application: Case Study Using the SACROC Field as Part of SMART Task 5

This technical report presents work conducted by the sub surface analysis team of the Strategic Systems Analysis & Engineering group at NETL for Task 5 of SMART Phase 1. This study involved the development of deep learning models for CO 2 geologic storage that are capable of accurate prediction of spatio-temporal outputs of CO 2 saturation, pressure, and brine production in three dimensional space over a storage operation's injection and post-injection timeframes. The model framework involves ensembling multi-layer encoder networks that provide dimesionality reduction of geologic inputs with fully connected long short-term memory (LSTM) neural networks that generate time-series prediction This approach offers a means to maximize training time efficiency, reduce computational memory burden, and minimize prediction turnaround.

54 ENVIRONMENTAL SCIENCES↗

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

A unified ensemble soil moisture dataset across the continental United States

Abstract A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions and making recommendations for drought response planning.

54 ENVIRONMENTAL SCIENCES↗

Dynamic self-assembly of compartmentalized DNA nanotubes

Abstract Bottom-up synthetic biology aims to engineer artificial cells capable of responsive behaviors by using a minimal set of molecular components. An important challenge toward this goal is the development of programmable biomaterials that can provide active spatial organization in cell-sized compartments. Here, we demonstrate the dynamic self-assembly of nucleic acid (NA) nanotubes inside water-in-oil droplets. We develop methods to encapsulate and assemble different types of DNA nanotubes from programmable DNA monomers, and demonstrate temporal control of assembly via designed pathways of RNA production and degradation. We examine the dynamic response of encapsulated nanotube assembly and disassembly with the support of statistical analysis of droplet images. Our study provides a toolkit of methods and components to build increasingly complex and functional NA materials to mimic life-like functions in synthetic cells.

59 BASIC BIOLOGICAL SCIENCES↗

The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies

This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the global and regional atmospheric modelling community's need of a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of high scientific quality while also ensuring that the results remain relevant to policymakers. HTAP_v3, an ad hoc global mosaic of anthropogenic inventories, has been developed by integrating official inventories over specific areas (North America, Europe, Asia including Japan and South Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO 2 , NO $x$ , CO, non-methane volatile organic compounds (NMVOCs), NH 3 , PM 10 , PM 2.5 , black carbon (BC), and organic carbon (OC), with a spatial resolution of 0.1º × 0.1º and time intervals of months and years, covering the period 2000–2018. The emissions are further disaggregated into 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past 2 decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policymaking, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

54 ENVIRONMENTAL SCIENCES↗