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Cui, Xinguang

Publications and source records attributed to Cui, Xinguang.

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

BioSiting Tool (BioSiting) v2

The BioSiting Tool provides a geospatial interface for analyzing bioeconomy resources and infrastructure across the continental U.S. The tool integrates empirical and modeled data from a broad range of sources. Bioeconomy resources mapped in the tool include agricultural residues, forest residues, municipal solid waste streams, food waste, manure, fats, oils and greases and potential yields of energy crops. Infrastructure mapped in the tool includes biorefineries, material recovery facilities, anaerobic digesters, wastewater treatment plants, combustion plants, district energy systems, crude oil pipelines, petroleum pipelines, natural gas pipelines, railways and freight terminals. Additional data layers include environmental justice indicators at the census tract level and carbon dioxide geologic storage potential. Users can select a location on the map, define a buffer radius in kilometers and generate an inventory of all bioecomony resources within the buffer zone. Data from the tool can be downloaded from individual buffer zones, or at the state or national level.

Huntington, Tyler↗
DOE OSTI2020

Machine learning to predict biomass sorghum yields under future climate scenarios

Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near-term and long-term pathways for scaling up bio-based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. In this work we used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

09 BIOMASS FUELS↗
DOE OSTI2019

Techno‐economic and greenhouse gas analyses of lignin valorization to eugenol and phenolic products in integrated ethanol biorefineries

Abstract This work presents techno‐economic and greenhouse gas (GHG) analyses of an ethanol biorefinery integrating lignin conversion into eugenol and other phenolics. Catalytic hydrogenolysis assisted by isopropanol (IPA) is used to convert the lignin recovered after ionic liquid (IL) pretreatment, saccharification, and fermentation. This process was compared to a biorefinery using lignin for energy generation and simulated in SuperPro Designer. Spatial analysis was performed to determine biorefinery locations and capacities in a Mexican state with potential for lignocellulosic biomass, including corn stover, sorghum stubble, and Jatropha fruit shells. Relative to the base case, diverting 50% of lignin to phenolics decreased the ethanol cost of production significantly due to the high market value of the co‐products. The minimum ethanol selling price (MESP) for this case was $2.02 gal −1 . The resulting cradle‐to‐gate GHG footprint of bioethanol was 21 g CO 2 ‐eq MJ −1 , a 78% reduction with respect to gasoline when system expansion is used for allocation. Using market value‐based allocation resulted in 82% GHG reduction. Analysis of scenarios showed that a biorefinery processing 3000 t day −1 biomass and diverting 80% of lignin to phenolics can potentially yield an MESP lower than $1.5 gal −1 . To achieve this, research should target a reduction in IL input by 30% and IPA input by 40%, together with more energy‐efficient separation processes. The reduction in IL and IPA can be achieved by decreasing their loading rates and increasing recycling. Sensitivity analysis showed that, for biomass prices higher than $45 t −1 , biorefinery capacities must exceed 5000 t d −1 biomass input. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd

Martinez‐Hernandez, Elias↗
DOE OSTI2018

Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

Nitrous oxide (N 2 O) is a potent long-lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N 2 O is the third largest contributor to the state's anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top-down inverse modeling. Here we present the first annual (2013–2014) statewide top-down estimates of anthropogenic N 2 O emissions. Utilizing continuous N 2 O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N 2 O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N 2 O could be an important component in meeting California's emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO 2 equivalent) by 2030 and 2050, respectively. Finally, our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations

Here, we report simulation experiments estimating the uncertainties in California regional fossil fuel and biosphere CO 2 exchanges that might be obtained by using an atmospheric inverse modeling system driven by the combination of ground-based observations of radiocarbon and total CO 2 , together with column-mean CO 2 observations from NASA's Orbiting Carbon Observatory (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for CO 2 exchange, in radiocarbon-based fossil fuel CO 2 measurements, in OCO-2 measurements, and in a regional atmospheric transport modeling system. Using these nominal assumptions for measurement and model uncertainties, we find that flask measurements of radiocarbon and total CO 2 at 10 towers can be used to distinguish between different fossil fuel emission data products for major urban regions of California. We then show that the combination of flask and OCO-2 observations yields posterior uncertainties in monthly-mean fossil fuel emissions of ~5–10%, levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. Similarly, we find that inversions yield uncertainties in monthly biosphere CO 2 exchange of ~6%–12%, depending on season, providing useful information on net carbon uptake in California's forests and agricultural lands. Finally, initial sensitivity analysis suggests that obtaining the above results requires control of systematic biases below approximately 0.5 ppm, placing requirements on accuracy of the atmospheric measurements, background subtraction, and atmospheric transport modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Abstract Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF‐STILT (Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< ~ 0.5 m/s), direction (< ~ 15°), and boundary layer height (< ~ 200 m) were small. However, random errors were large (~1.5–3.0 m/s for wind speed, ~ 40–60° for wind direction, and ~ 300–500 m for boundary layer height). Regression analysis of predicted and measured CO yielded near‐unity slopes (i.e., within 1.0 ± 0.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger (~30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF‐STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California.

Bagley, Justin E.↗
DOE OSTI2017

Estimating methane emissions from biological and fossil‐fuel sources in the San Francisco Bay Area

Abstract We present the first sector‐specific analysis of methane (CH 4 ) emissions from the San Francisco Bay Area (SFBA) using CH 4 and volatile organic compound (VOC) measurements from six sites during September – December 2015. We apply a hierarchical Bayesian inversion to separate the biological from fossil‐fuel (natural gas and petroleum) sources using the measurements of CH 4 and selected VOCs, a source‐specific 1 km CH 4 emission model, and an atmospheric transport model. We estimate that SFBA CH 4 emissions are 166–289 Gg CH 4 /yr (at 95% confidence), 1.3–2.3 times higher than a recent inventory with much of the underestimation from landfill. Including the VOCs, 82 ± 27% of total posterior median CH 4 emissions are biological and 17 ± 3% fossil fuel, where landfill and natural gas dominate the biological and fossil‐fuel CH 4 of prior emissions, respectively.

Jeong, Seongeun↗
DOE OSTI2016

Estimating methane emissions in California's urban and rural regions using multitower observations

Here, we present an analysis of methane (CH 4 ) emissions using atmospheric observations from 36 thirteen sites in California during June 2013 – May 2014. A hierarchical Bayesian inversion 37 method is used to estimate CH 4 emissions for spatial regions (0.3° pixels for major regions) by 38 comparing measured CH 4 mixing ratios with transport model (WRF-STILT) predictions based 39 on seasonally varying California-specific CH 4 prior emission models. The transport model is 40 assessed using a combination of meteorological and carbon monoxide (CO) measurements 41 coupled with the gridded California Air Resources Board (CARB) carbon monoxide (CO) 42 emission inventory. Hierarchical Bayesian inversion suggests that state annual anthropogenic 43 CH 4 emissions are 2.42 ± 0.49 Tg CH 4 /yr (at 95% confidence, including transport bias 44 uncertainty), higher (1.2 - 1.8 times) than the CARB current inventory (1.64 Tg CH 4 /yr in 2013). 45 We note that the estimated CH 4 emissions drop to 1.0 - 1.6 times the CARB inventory if we 46 correct for the 10% median CH 4 emissions assuming the bias in CO analysis is applicable to 47 CH 4 . The CH 4 emissions from the Central Valley and urban regions (San Francisco Bay and 48 South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, 49 respectively. This study suggests that the livestock sector is likely the major contributor to the 50 state total CH 4 emissions, in agreement with CARB’s inventory. Attribution to source sectors for 51 sub-regions of California using additional trace gas species would further improve the 52 quantification of California’s CH 4 emissions and mitigation efforts towards the California Global 53 Warming Solutions Act of 2006 (AB-32).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Fusion of mobile in situ and satellite remote sensing observations of chemical release emissions to improve disaster response

Chemical release disasters have serious consequences, disrupting ecosystems, society, and causing significant loss of life. Mitigating the destructive impacts relies on identification and mapping, monitoring, and trajectory forecasting. Improvements in sensor capabilities are enabling airborne and space-based remote sensing to support response activities. Key applications are improving transport models in complex terrain and improved disaster response. Understanding urban atmospheric transport in the Los Angeles Basin, where topographic influences on transport patterns are significant, was improved by leveraging the Aliso Canyon leak as an atmospheric tracer. Plume characterization data was collected by the AutoMObile trace Gas (AMOG) Surveyor, a commuter car modified for science. Mobile surface in situ CH 4 and winds were measured by AMOG Surveyor under Santa Ana conditions to estimate an emission rate of 365±30% Gg yr -1 . Vertical profiles were collected by AMOG Surveyor by leveraging local topography for vertical profiling to identify the planetary boundary layer at ~700 m. Topography significantly constrained plume dispersion by up to a factor of two. The observed plume trajectory was used to validate satellite aerosol optical depth-inferred atmospheric transport, which suggested the plume first was driven offshore, but then veered back towards land. Numerical long-range transport model predictions confirm this interpretation. Lastly, this study demonstrated a novel application of satellite aerosol remote sensing for disaster response.

54 ENVIRONMENTAL SCIENCES↗