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On the Impact of Granularity of Space-Based Urban CO2 Emissions in Urban Atmospheric Inversions: A Case Study for Indianapolis, IN
Quantifying greenhouse gas (GHG) emissions from cities is a key challenge towards effective emissions management. An inversion analysis from the INdianapolis FLUX experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city using CO2 data collected by the dense tower network deployed across the city. However, city-level emission data, used as a priori emissions, are also a key component in the atmospheric inversion framework. Currently, fine-grained emission inventories (EIs) able to resolve GHG city emissions at high spatial resolution, are only available for few major cities across the globe. Following the INFLUX inversion case with a global 1x1 km ODIAC fossil fuel CO2 emission dataset, we further improved the ODIAC emission field and examined its utility as a prior for the city scale inversion. We disaggregated the 1x1 km ODIAC non-point source emissions using geospatial datasets such as the global road network data and satellite-data driven surface imperviousness data to a 3030 m resolution. We assessed the impact of the improved emission field on the inversion result, relative to priors in previous studies (Hestia and ODIAC). The posterior total emission estimate (5.1 MtC/yr) remains statistically similar to the previous estimate with ODIAC (5.3 MtC/yr). However, the distribution of the flux corrections was very close to those of Hestia inversion and the model-observation mismatches were significantly reduced both in forward and inverse runs, even without hourly temporal changes in emissions. EIs reported by cities often do not have estimates of spatial extents. Thus, emission disaggregation is a required step when verifying those reported emissions using atmospheric models. Our approach offers gridded emission estimates for global cities that could serves as a prior for inversion, even without locally reported EIs in a systematic way to support city-level Measuring, Reporting and Verification (MRV) practice implementation.
Challenges in Evaluating Relationships Between Quantitative Data (Carbon Dioxide) and Qualitative Data (Self-Reported Visual Changes)
Understanding the nuances in clinical data is critical in developing a successful data analysis plan. Carbon dioxide (CO2) data are collected on board the International Space Station (ISS) in a continuous stream. Clinical data on ISS are primarily collected via conversations between individual crewmembers and NASA Flight Surgeons during weekly Private Medical Conferences (PMC). Law, et.al, 20141 demonstrated a statistically significant association between weekly average CO2 levels on ISS and self-reported headaches over the reporting period from March 14, 2001 to May 31, 2012. The purpose of this analysis is to describe the evaluation of a possible association between visual changes and CO2 levels on ISS and to discuss challenges in developing an appropriate analysis plan. METHODS & PRELIMINARY RESULTS: A first analysis was conducted following the same study design as the published work on CO2 and self-reported headaches1; substituting self-reported changes in visual acuity in place of self-reported headaches. The analysis demonstrated no statistically significant association between visual impairment characterized by vision symptoms self-reported during PMCs and ISS average CO2 levels over ISS missions. Closer review of the PMC records showed that vision outcomes are not well-documented in terms of clinical severity, timing of onset, or timing of resolution, perhaps due to the incipient nature of vision changes. Vision has been monitored in ISS crewmembers, pre- and post-flight, using standard optometry evaluations. In-flight visual assessments were limited early in the ISS program, primarily consisting of self-perceived changes reported by crewmembers. Recently, on-orbit capabilities have greatly improved. Vision data ranges from self-reported post-flight changes in visual acuity, pre- to postflight changes identified during fundoscopic examination, and in-flight progression measured by advanced on-orbit clinical imaging capabilities at predetermined testing intervals. In contrast, CO2 data are recorded in a continuous stream over time; however, for the initial analysis this data was categorized into weekly averages.
BOREAS TE-5 Diurnal CO2 Canopy Profile Data
The BOREAS TE-5 team collected several data sets to investigate the vegetation-atmosphere CO2 and H2O exchange processes. These data were collected to provide detailed information within the canopy during times when TE-05 sampled canopy CO2 for carbon and oxygen isotope analysis. These measurements were made in both the NSA and SSA at the OJP, OBS, UBS, and OA sites from 25-May1994 to 08-Sep1994. CO2 profile data were not collected at SSA-OA during the first IFC. The data are available in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).
BOREAS TF-3 Automated Chamber CO2 Flux Data from the NSA-OBS
The BOReal Ecosystem Atmosphere Study Tower Flux (BOREAS TF-3) and Trace Gas Biogeochemistry (TGB-1) teams collected automated CO2 chamber flux data in their efforts to fully describe the CO2 flux at the Northern Study Area-Old Black Spruce (NSA-OBS) site. This data set contains fluxes of CO2 at the NSA-OBS site measured using automated chambers. In addition to reporting the CO2 flux, it reports chamber air temperature, moss temperature, and light levels during each measurement. The data set covers the period from 23-Sep-1995 through 26-Oct-1995 and from 28-May-1996 through 21-Oct-1996. The data are stored in tabular ASCII files.
BOREAS TF-8 NSA-OJP Tower Flux, Meteorological, and Soil Temperature Data
The BOREAS TF-8 team collected energy, CO2, and water vapor flux data at the BOREAS NSA-OJP site during the growing season of 1994 and most of the year for 1996. The data are available in tabular ASCII files.
Effects of inspired CO2, hyperventilation, and time on VA/Q inequality in the dog
In a recent study by Tsukimoto et al. (J. Appl. Physiol. 68: 2488-2493, 1990), CO2 inhalation appeared to reduce the size of the high ventilation-perfusion ratio (VA/Q) mode commonly observed in anesthetized mechanically air-ventilated dogs. In that study, large tidal volumes (VT) were used during CO2 inhalation to preserve normocapnia. To separate the influences of CO2 and high VT on the VA/Q distribution in the present study, we examined the effect of inspired CO2 on the high VA/Q mode using eight mechanically ventilated dogs (4 given CO2, 4 controls). The VA/Q distribution was measured first with normal VT and then with increased VT. In the CO2 group at high VT, data were collected before, during, and after CO2 inhalation. With normal VT, there was no difference in the size of the high VA/Q mode between groups [10.5 +/- 3.5% (SE) of ventilation in the CO2 group, 11.8 +/- 5.2% in the control group]. Unexpectedly, the size of the high VA/Q mode decreased similarly in both groups over time, independently of the inspired PCO2, at a rate similar to the fall in cardiac output over time. The reduction in the high VA/Q mode together with a simultaneous increase in alveolar dead space (estimated by the difference between inert gas dead space and Fowler dead space) suggests that poorly perfused high VA/Q areas became unperfused over time. A possible mechanism is that elevated alveolar pressure and decreased cardiac output eliminate blood flow from corner vessels in nondependent high VA/Q regions.
A Suite of High-Resolution Atmospheric Carbon Dioxide Simulations in Support of the OCO-3 Snapshot Area Mapping (SAM) Mode Observation: PSU-WRF, CSU-OLAM and NASA GEOS
Cities and power plants are responsible more than 70% of GHG emissions. The emissions from the subnational localized sources need to be accurately quantified and properly managed in order to achieve the Paris Climate Agreement goals. The accurate estimation of these emission is also crucial for assessing the capacity of natural sinks to uptake the carbon released into the atmosphere that ultimately defines our emission allowance for the 1.5 or 2.0 degree temperature goals. New data collected by the Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Mapping (SAM) observations should provide a tremendous new opportunity for us to study CO2 emissions from targeted large localized sources, such as cities, power plants and beyond. Since 2018 (prior to the OCO-3 launch), we have studied the observational strategies for the SAM mode observation in order to collect the useful data for estimating CO2 emissions from target sources. To maximize the benefit of the SAM mode observation data for quantifying CO2 emission, it is important to define how to observe the localized sources depending on the local environmental and emission specificities. We employ a suite of state-of-the-art CO2 modeling systems, such as PSU's WRF-CO2, CSU's OLAM and NASA's GEOS models. All of these CO2 modeling systems are prescribedwith the high-resolution fuel CO2 emission estimates from the ODIAC data product to achieve realistic urban CO2 variations. We focus on cities with established ground-based observation networks, such as Los Angeles, Indianapolis, and Paris. We have examined the urban emission signal detectability in response to the influence of local background conditions that can observed by the SAM and biospheric contributions that will be a new challenge for urban emission inverse estimation. Based on the results of our simulation experiments, we plan to propose city-specific observation strategies. Upon the availability of the OCO-3 data, we will attempt to estimate city emissions using inverse models. We also developed synthetic OCO-3 data using NASA's GEOS5 model, which should be useful to assess the utility of the OCO-3 data in combination with data collected by carbon satellites in other orbits, such as NASA's OCO-2 and Japanese GOSAT-1/2. The synthetic data also provide an opportunity to study the errors due to clouds and aerosols, which have been not fully studied in the past.
Airborne CW lidar measurements of atmospheric backscatter at 9.1 and 10.6 microns
Atmospheric backscatter data collected during the Global Backscatter Experiment (GLOBE) 1 and GLOBE 2 Survey Missions were partially analyzed. These data were collected using the MSFC's 9.1 and 10.6 micron focused continuous wave CO2 coherent detection lidars. In general, both data sets have shown reasonably good agreement with data collected by the JPL's pulsed nadir and zenith viewing CO2 system and with the ground-based, pulsed CO2 system operated by NOAA's Wave Propagation Laboratory. Generally good agreement was also noted with IR backscatter estimates produced by Mie calculations using aerosol size and composition data obtained during the GLOBE 1 and GLOBE 2 missions. The focus of current research and plans for future research are presented.
Understanding Pilot Breathing – A Case Study in Systems Engineering
This position paper assesses the Pilot Breathing Assessment’s driving requirement to collect missing data on pilot breathing pressure, flow, rate, oxygen and CO2, simultaneously with aircraft data. Repeat measures data were collected over a 2-year period from 5 NASA test-pilots flying 8 flight profiles on NASA F-15 and F/A-18 (A, B) aircraft. This Pilot Breathing Assessment Systems Engineering (PiBASE) position paper serves as a case-study of systems engineering that accommodates the human as a distinct part of the whole. It acknowledges that a successful machine design intended for human use must include human systems biology/physiology as an integral part of planning, design, and implementation.
A Quantum Annealing Computer Team Addresses Climate Change Predictability
The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be converted to a BM algorithm implementation on the QAC. The first integer adder has been implemented on the D-Wave 2X by A. Shehab that will perform HAAR wavelets for image compression of MODIS scenes. Finally, based on the next generations of QACs, we are preparing a 5-year performance road map on the scalability of the current QAC algorithms.
Non-invasive optical detection of glucose in cell culture nutrient medium
The objective of the proposed research was to begin the development of a non-invasive optical sensor for measuring glucose concentration in the output medium of cell cultures grown in a unique NASA bioreactor referred to as an integrated rotating-wall vessel (IRWV). The input, a bovine serum based nutrient media, has a known glucose concentration. The cells within the bioreactor digest a portion of the glucose. Thus, the non-invasive optical sensor is needed to monitor the decrease in glucose due to cellular consumption since the critical parameters for sustained cellular productivity are glucose and pH. Previous glucose sensing techniques have used chemical reactions to quantify the glucose concentration. Chemical reactions, however, cannot provide for continuous, real time, non-invasive measurement as is required in this application. Our effort while in the fellowship program was focused on the design, optical setup, and testing of one bench top prototype non-invasive optical sensor using a mid-infrared absorption spectroscopy technique. Glucose has a fundamental vibrational absorption peak in the mid-infrared wavelength range at 9.6 micron. Preliminary absorption data using a CO2 laser were collected at this wavelength for water based glucose solutions at different concentrations and one bovine serum based nutrient medium (GTSF) with added glucose. The results showed near linear absorption responses for the glucose-in-water data with resolutions as high at 108 mg/dl and as low as 10 mg/dl. The nutrient medium had a resolution of 291 mg/dl. The variability of the results was due mainly to thermal and polarization drifts of the laser while the decrease in sensitivity to glucose in the nutrient medium was expected due to the increase in the number of confounders present in the nutrient medium. A multispectral approach needs to be used to compensate for these confounders. The CO2 laser used for these studies was wavelength tunable (9.2 to 10.8 micrometers), however, it was to unstable across wavelengths to test the multispectral approach. From this research, further NASA support was obtained to continue the work throughout the year in which a more stable light source will be used at smaller, near-infrared, wavelengths. It is anticipated that a more compact, non-invasive, optical glucose sensor will be realized which can be used with a bioreactor on future space shuttle missions. It is also anticipated that a multispectral optical sensor may be used to determine the concentration of other molecules needed within the NASA bioreactor, such as fructose and galactose.
BOREAS TGB-1/TGB-3 NEE Data over the NSA Fen
The BOReal Ecosystem-Atmosphere Study Trace Gas Biogeochemistry (BOREAS TGB-1) and TGB-3 teams collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the Northern Study Area (NSA). This data set contains Net Ecosystem Exchange of CO2 (NEE) measurements collected with chambers at the NSA fen in 1994 and 1996. Gas samples were extracted approximately every 7 days from chambers and analyzed at the NSA lab facility. The data are provided in tabular ASCII files.
BOREAS TE-5 Tree Ring and Carbon Isotope Ratio Data
The BOREAS TE-5 team collected several data sets to investigate the vegetation-atmosphere CO2 and H2O exchange processes. These data include tree ring widths and cellulose carbon isotope data from coniferous trees collected at the BOREAS NSA and SSA in 1993 and 1994 by the BOREAS TE-5 team. Ring width data are provided for both Picea mariana and Pinus banksiana. The carbon isotope data are provided only for Pinus banksiana. The data are provided in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
BOREAS TGB-12 Rn-222 Activity Data over the NSA
The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. Sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of carbon in regrowing mosses could be determined. All the data are used to: (1) calculate the inventory of carbon and nitrogen in moss and mineral soil layers at NSA sites, (2) determine the rates of input and turnover (using both accumulation since the last stand-killing fire and radiocarbon data), and (3) link changes in soil respiration rate to shifts in the C-14 content of soil CO2 to determine the average "age" respired CO2 . These Rn-222 activity data were collected from 15-Nov-1993 to 16-Aug-1994 over the NSA sites. They are useful for determining the rate of gas exchange between soil and the overlying atmosphere. The data in this data set are stored in tabular ASCII files.
BOREAS TGB-12 Rn-222 Flux Data over the NSA
The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. Sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of carbon in new moss growth could be determined. All the data are used to 1) calculate the inventory of carbon and nitrogen in moss and mineral soil layers at NSA sites, 2) determine the rates of input and turnover (using both accumulation since the last stand-killing fire and radiocarbon data), and 3) link changes in soil respiration rate to shifts in the C-14 content of soil CO2 to determine the average "age" respired CO2. These Rn-222 flux data were collected from 15-Nov-1993 to 16-Aug-1994 over the NSA sites. The data in this data set are stored in tabular ASCII files.
Evaluation of Convective Transport in the GEOS-5 Chemistry and Climate Model
The NASA Goddard Earth Observing System (GEOS-5) Chemistry and Climate Model (CCM) consists of a global atmospheric general circulation model and the combined stratospheric and tropospheric chemistry package from the NASA Global Modeling Initiative (GMI) chemical transport model. The subgrid process of convective tracer transport is represented through the Relaxed Arakawa-Schubert parameterization in the GEOS-5 CCM. However, substantial uncertainty for tracer transport is associated with this parameterization, as is the case with all global and regional models. We have designed a project to comprehensively evaluate this parameterization from the point of view of tracer transport, and determine the most appropriate improvements that can be made to the GEOS-5 convection algorithm, allowing improvement in our understanding of the role of convective processes in determining atmospheric composition. We first simulate tracer transport in individual observed convective events with a cloud-resolving model (WRF). Initial condition tracer profiles (CO, CO2, O3) are constructed from aircraft data collected in undisturbed air, and the simulations are evaluated using aircraft data taken in the convective anvils. A single-column (SCM) version of the GEOS-5 GCM with online tracers is then run for the same convective events. SCM output is evaluated based on averaged tracer fields from the cloud-resolving model. Sensitivity simulations with adjusted parameters will be run in the SCM to determine improvements in the representation of convective transport. The focus of the work to date is on tropical continental convective events from the African Monsoon Multidisciplinary Analyses (AMMA) field mission in August 2006 that were extensively sampled by multiple research aircraft.
Potential Improvements in Global Carbon Flux Estimates from a Network of Laser Heterodyne Radiometer Measurements of Column Carbon Dioxide
We present observing system simulation experiments (OSSEs) to evaluate the impact of a proposed network of ground-based miniaturized laser heterodyne radiometer (mini-LHR) instruments that measure atmospheric column-averaged carbon dioxide (XCO2) with a 1 ppm precision. A particular strength of this passive measurement approach is its insensitivity to clouds and aerosols due to its direct sun pointing and narrow field of view (0.2°). Developed at the NASA Goddard Space Flight Center (GSFC), these portable, low-cost mini-LHR instruments were designed to operate in tandem with the sun photometers used by the AErosol RObotic NETwork (AERONET). This partnership allows us to leverage the existing framework of AERONET's global ground network of more than 500 sites as well as providing simultaneous measurements of aerosols that are known to be a major source of error in retrievals of XCO2 from passive nadir-viewing satellite observations. We show, using the global 3-D GEOS-Chem chemistry transport model, that a deployment of 50 mini-LHRs at strategic (but not optimized) AERONET sites significantly improves our knowledge of global and regional land-based CO2 fluxes. This improvement varies seasonally and ranges 58%–81% over southern lands, 47%–76% over tropical lands, 71%–92% over northern lands, and 64%–91% globally. We also show significant added value from combining mini-LHR instruments with the existing ground-based NOAA flask network. Collectively, these data result in improved a posteriori CO2 flux estimates on spatial scales of ∼10 km2, especially over North America and Europe, where the ground-based networks are densest. Our studies suggest that the mini-LHR network could also play a substantive role in reducing carbon flux uncertainty in Arctic and tropical systems by filling in geographical gaps in measurements left by ground-based networks and space-based observations. A realized network would also provide necessary data for the quinquennial global stock takes that form part of the Paris Agreement.