Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO₂ and CO

Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at regional to city scale. Very few studies have focused on burning efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for deriving spatially-resolved ERs from asynchronous satellite measurements including 1) variations in meteorological conditions induced by different overpass times, 2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), and 3) interferences from the biosphere and biomass burning. In this study, we extended an established emission estimate approach to arrive at spatially-resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influence of the confounding factors listed above and further explain the intra-urban variations in ERs, we leveraged a Lagrangian atmospheric transport model and an urban land cover classification dataset and reported ERCO from the sounding level to the overpass- and city- levels. We found that the difference in the overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially-resolved ERCO. Specifically, a time difference of > 3 hours typically led to dramatic changes in the wind direction and shape of urban plumes and thereby making the calculation of accurate sounding-specific ERCO difficult. After removing those cases from consideration and applying a simple plume shift method when necessary, we discovered significant contrasts in combustion efficiencies between 1) two megacities versus two industry-oriented cities and 2) different regions within a city, based on six to seven nearly-coincident overpasses per city. Results suggest that the combustion efficiency for heavy industry in Los Angeles is slightly lower than its overall city-wide value (< 10 ppb-CO / ppm-CO2). In contrast, ERs related to the heavy industry in Shanghai are found to be much higher than Shanghai’s city-mean and more aligned with city-means of the two industry-oriented Chinese cities (approaching 20 ppb-CO / ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our first analysis provides guidance for estimating intra-city gradients in combustion efficiency from future missions, such as those that will map column CO2 and CO concentration simultaneously with high spatiotemporal resolutions.

A tracer release experiment to investigate uncertainties in drone-based emission quantification for methane point sources

Mapping trace gas emission plumes using in-situ measurements from unmanned aerial vehicles (UAV) is an emerging and attractive possibility to quantify emissions from localized sources. Here, we present the results of an extensive tracer-release experiment in Dübendorf, Switzerland, which was conducted to develop an optimal quantification method and to determine the related uncertainties under various environmental and sampling conditions. Atmospheric methane mole fractions were simultaneously measured using a miniaturized fast-response Quantum Cascade Laser Absorption Spectrometer (QCLAS) and an Active AirCore system mounted on a commercial drone. Emission fluxes were estimated using a mass-balance method by flying the drone-based system through a vertical cross-section downwind of the point-source perpendicular to the main wind direction at multiple altitudes. A refined kriging framework, called cluster-based kriging, was developed to spatially map individual methane measurement points into the whole measurement plane, while taking into account the different spatial scales between background and enhanced methane values in the plume. We found that the new kriging framework resulted in better quantification compared to ordinary kriging. The average bias of the estimated emissions was −1 % and the average residual of individual errors was 54 %. Direct comparison of QCLAS and AirCore measurements shows that AirCore measurements are smoothened by 20 s and temporally shifted and stretched by 7 s and 0.06 seconds for every second of QCLAS measurement, respectively. Applying these corrections to the AirCore measurements and successively calculating an emission estimate shows an enhancement of the accuracy by 3 % as compared to its uncorrected counterpart. Optimal plume sampling, including the downwind measurement distance, depends on wind- and turbulence conditions and it is furthermore limited by numerous parameters such as the maximum flight time, and the measurement accuracy. Under favorable measurement conditions, emissions could be quantified with an uncertainty of 30 %. Uncertainties increase when wind speeds are below 2.3 m s−1 and directional variability is above 33°, and when the downwind distance is above 75 m. In addition, the flux estimates were also compared to estimates from the well-established OTM-33A method involving stationary measurements. A good agreement was found, both approaches being close to the true-release and uncertainties of both methods usually capturing the true-release.

Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories

The Paris Agreement establishes a transparency framework for anthropogenic carbon dioxide (CO2) emissions. It's core component are inventory-based national greenhouse gas emission reports, which are complemented by independent estimates derived from atmospheric CO2 measurements combined with inverse modelling. It is, however, not known whether such a Monitoring and Verification Support (MVS) capacity is capable of constraining estimates of fossil-fuel emissions to an extent that is sufficient to provide valuable additional information. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of such an MVS capacity. Here we provide a novel assessment of the potential of a comprehensive data assimilation system using simulated XCO2 and other observations to constrain fossil fuel CO2 emission estimates for an exemplary 1-week period in 2008. We find that CO2M enables useful weekly estimates of country-scale fossil fuel emissions independent of national inventories. When extrapolated from the weekly to the annual scale, uncertainties in emissions are comparable to uncertainties in inventories, so that estimates from inventories and from the MVS capacity can be used for mutual verification. We further demonstrate an alternative, synergistic mode of operation, with the purpose of delivering a best fossil fuel emission estimate. In this mode, the assimilation system uses not only XCO2 and the other data streams of the previous (verification) mode, but also the inventory information. Finally, we identify further steps towards an operational MVS capacity.

A Road Map for Renewable Energy Integration with Subsea Processing Systems

This paper proposes a road map for the integration of renewable energy supply to power subsea processing systems. To replace the traditional power supply, like fossil fuel-based generators or grid power, a wind turbine generator (WTG) operating on a islanded mode has been introduced and discussed. A review of the state of the art of WTGs is performed, primarily focused on power and controls aspects, with identification of the main technological gaps left to achieve wind-powered subsea processing. To fully assess the renewable energy integration and current gaps, a study case is proposed which addresses a subsea compression train powered by offshore wind. A thorough analysis is conducted, with meteorological conditions based on the NCS (Norwegian Continental Shelf), where gas line packing is proposed as an innovative means of energy storage. Finally, an economic analysis as well as a CO2 emission estimate is presented to demonstrate the benefits of the proposed road map. Some further discussions and conclusions are presented as well as some propositions for future works.

Degradation of Polyvinyl Alcohol in US Wastewater Treatment Plants and Subsequent Nationwide Emission Estimate

Polyvinyl alcohol (PVA) is a water-soluble plastic commercially used in laundry and dish detergent pods (LDPs) for which a complete understanding of its fate in the environment and subsequent consequences is lacking. The objective of this study was to estimate the US nationwide emissions of PVA resulting from domestic use of LDPs, corroborated by a nationwide, online consumer survey and a literature review of its fate within conventional wastewater treatment plants (WWTPs). Peer-reviewed publications focusing on the degradation of PVA in critical processes of WWTPs were shortlisted as a part of the literature review, and subsequent degradation data was extracted and applied to a model with a set of assumptions. Survey and model results estimated that approximately 17,200 ± 5000 metric ton units per year (mtu/yr) of PVA are used from LDPs in the US, with 10,500 ± 3000 mtu/yr reaching WWTPs. Literature review data, when incorporated into our model, resulted in ~61% of PVA ending up in the environment via the sludge route and ~15.7% via the aqueous phase. PVA presence in the environment, regardless of its matrix, is a threat to the ecosystem due to the potential mobilization of heavy metals and other hydrophilic contaminants.

Monthly resolved modelled oceanic emissions of carbonyl sulphide and carbon disulphide for the period 2000–2019

Carbonyl sulphide (OCS) is the most abundant, long-lived sulphur gas in the atmosphere and a major supplier of sulphur to the stratospheric sulphate aerosol layer. The short-lived gas carbon disulphide (CS2) is oxidized to OCS and constitutes a major indirect source to the atmospheric OCS budget. The atmospheric budget of OCS is not well constrained due to a large missing source needed to compensate for substantial evidence that was provided for significantly higher sinks. Oceanic emissions are associated with major uncertainties. Here we provide a first, monthly resolved ocean emission inventory of both gases for the period 2000–2019 (available at https://doi.org/10.5281/zenodo.4297010) (Lennartz et al., 2020a). Emissions are calculated with a numerical box model (2.8∘×2.8∘ resolution at the Equator, T42 grid) for the oceanic surface mixed layer, driven by ERA5 data from ECMWF and chromophoric dissolved organic matter (CDOM) from Aqua MODIS. We find that interannual variability in OCS emissions is smaller than seasonal variability and is mainly driven by variations in CDOM, which influences both photochemical and light-independent production. A comparison with a global database of more than 2500 measurements reveals overall good agreement. Emissions of CS2 constitute a larger sulphur source to the atmosphere than OCS and equally show interannual variability connected to variability in CDOM. The emission estimate of CS2 is associated with higher uncertainties as process understanding of the marine cycling of CS2 is incomplete. We encourage the use of the data provided here as input for atmospheric modelling studies to further assess the atmospheric OCS budget and the role of OCS in climate.

Tropical forest CH4: from termite mounds to tower measurements

&lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Methane (CH&lt;sub&gt;4&lt;/sub&gt;) is one of the most important anthropogenic greenhouse gases.&amp;#160; Despite its importance, natural sources of methane, such as tropical wetlands and termites, are still not well understood and a large source of uncertainty in the tropical CH&lt;sub&gt;4&lt;/sub&gt; budget. The Amazon rainforest is a key region for the (global) CH&lt;sub&gt;4&lt;/sub&gt; budget but, due to its remote location, continous CH&lt;sub&gt;4&lt;/sub&gt; concentration and flux measurements are still rare.&lt;/p&gt; &lt;p&gt;The 50 m high K34 tower (field site ZF2) is located in a pristine &amp;#8216;Terra Firme&amp;#8217; tropical forest region 60 km northwest of Manaus (Brazil), and is located next to a waterlogged valley, a possible location for anaerobic CH&lt;sub&gt;4&lt;/sub&gt; production. In October 2018, in addition to the existing EC CO&lt;sub&gt;2&lt;/sub&gt; system, an in-situ FTIR-analyzer (measuring CO&lt;sub&gt;2&lt;/sub&gt;, CO, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O and &amp;#948;&lt;sup&gt;13&lt;/sup&gt;CO&lt;sub&gt;2&lt;/sub&gt;) was set up to measure tower profile concentrations, above and below the canopy, continuously. By analyses of vertical and temporal nighttime concentrations patterns, an emission estimate for all gases could be made, and an ecosystem emission of ~1 nmol CH&lt;sub&gt;4&lt;/sub&gt; m&lt;sup&gt;-2&lt;/sup&gt; s&lt;sup&gt;-1&lt;/sup&gt;&amp;#160; was estimated. In addition, by use of different types of flux chambers, possible&amp;#160; CH&lt;sub&gt;4&lt;/sub&gt; sinks and sources such as soils, trees, water and termite mounds were measured.&lt;/p&gt; &lt;p&gt;By combining tower and flux chamber measurements, the role and magnitude of different ecosystem sources could be assessed. In this presentation, an overview of the measured CH&lt;sub&gt;4&lt;/sub&gt; forest concentrations and fluxes will be given.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;

Urban methane emission estimate using measurements obtained by MUCCnet (Munich Urban Carbon Column network)

&lt;p&gt;In 2019, we established the Munich Urban Carbon Column network (MUCCnet) [1] that measures the column-averaged concentration gradients of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt; and CO using the differential column methodology (DCM, [2]). The network consists of five ground-based FTIR spectrometers (EM27/SUN from Bruker [3]), which are deployed both on the outskirts of Munich and in the city center. The distance between each outer spectrometer and the center station is approximately 10 km. Each spectrometer is protected by one of our fully automated enclosure systems [4], allowing us to run the network permanently. In addition, data are available from three one-month measurement campaigns in Munich between 2017 and 2019, each using five to six spectrometers.&lt;/p&gt;&lt;p&gt;To quantify urban methane emissions, we developed a Bayesian inverse modeling approach that was tested first in Indianapolis using campaign data from 2016 [5]. After adapting the modeling framework to the Munich case, we are able to use the large amount of data gathered by MUCCnet to quantify the methane emissions of the third largest city in Germany in detail. The framework takes the spatially resolved emission inventory TNO-GHGco (1 km x 1 km) as a prior estimate and refines it through the Bayesian inversion of the EM27/SUN observations. Our long-term dataset and continuous operation will provide new insights into Munich&amp;#8217;s urban carbon cycle and will allow us to evaluate climate protection measures in the future.&lt;/p&gt;&lt;p&gt;Thanks to the automation, we were also able to continue the measurements during the COVID-19 lockdowns in Germany, resulting in a unique dataset that allows us to verify and improve our model.&lt;/p&gt;&lt;p&gt;[1] Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-300, accepted, 2020.&lt;/p&gt;&lt;p&gt;[2] Chen, J., Viatte, C., Hedelius, J. K., Jones, T., Franklin, J. E., Parker, H., Gottlieb, E. W., Wennberg, P. O., Dubey, M. K., and Wofsy, S. C.: Differential column measurements using compact solar-tracking spectrometers, Atmos. Chem. Phys., 16, 8479&amp;#8211;8498, https://doi.org/10.5194/acp-16-8479-2016, 2016.&amp;#160;&lt;/p&gt;&lt;p&gt;[3] Gisi, M., Hase, F., Dohe, S., Blumenstock, T., Simon, A., and Keens, A.: XCO&lt;sub&gt;2&lt;/sub&gt;-measurements with a tabletop FTS using solar absorption spectroscopy, Atmos. Meas. Tech., 5, 2969&amp;#8211;2980, https://doi.org/10.5194/amt-5-2969-2012, 2012.&lt;/p&gt;&lt;p&gt;[4] Heinle, L. and Chen, J.: Automated enclosure and protection system for compact solar-tracking spectrometers, Atmos. Meas. Tech., 11, 2173&amp;#8211;2185, https://doi.org/10.5194/amt-11-2173-2018, 2018.&lt;/p&gt;&lt;p&gt;[5] Jones, T. S., Franklin, J. E., Chen, J., Dietrich, F., Hajny, K. D., Paetzold, J. C., Wenzel, A., Gately, C., Gottlieb, E., Parker, H., Dubey, M., Hase, F., Shepson, P. B., Mielke, L. H., and Wofsy, S. C.: Assessing Urban Methane Emissions using Column Observing Portable FTIR Spectrometers and a Novel Bayesian Inversion Framework, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-1262, in review, 2021.&lt;/p&gt;

Conflicting estimates of natural geologic methane emissions

Global bottom-up and top-down estimates of natural, geologic methane (CH4) emissions (average approximately 45 Tg yr–1) have recently been questioned by near-zero (approximately 1.6 Tg yr–1) estimates based on measurements of 14CH4 trapped in ice cores, which imply that current fossil fuel industries’ CH4 emissions are underestimated by 25%–40%. As we show here, such a global near-zero geologic CH4 emission estimate is incompatible with multiple independent, bottom-up emission estimates from individual natural geologic seepage areas, each of which is of the order of 0.1–3 Tg yr–1. Further research is urgently needed to resolve the conundrum before rejecting either method or associated emission estimates in global CH4 accounting.

Variability in Observation-Based Onroad Emission Constraints from a Near-Road Environment

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (∆CO:∆NOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA’s MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25 m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ∆CO:∆NOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115 m and 300 m) increases OLS and orthogonal regression estimates of ∆CO:∆NOx but not cross-road gradient ∆CO:∆NOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ∆CO:∆NOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.