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Aircraft and satellite observations reveal historical gap between top-down and bottom-up CO2 emissions from Canadian oil sands
Abstract
Measurement-based estimates of greenhouse gas (GHG) emissions from complex industrial operations are challenging to obtain, but serve as an important, independent check on inventory-reported emissions. Such top-down estimates, while important for oil and gas (O&G) emissions globally, are particularly relevant for Canadian oil sands (OS) operations, which represent the largest O&G contributor to national GHG emissions. We present a multifaceted top-down approach for estimating CO2 emissions that combines aircraft-measured CO2/NOx emission ratios (ERs) with inventory and satellite-derived NOx emissions from OMI and TROPOMI and apply it to the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. Historical CO2 emissions were reconstructed for the surface mining region and average top-down estimates were found to be >65% higher than facility-reported, bottom-up estimates from 2005–2020. Higher top-down vs. bottom-up emissions estimates were also consistently obtained for individual surface mining and in situ extraction facilities, which represent a growing category of energy-intensive OS operations. Although the magnitudes of the measured discrepancies vary between facilities, they combine such that the observed reporting gap for total AOSR emissions is ≥ (31 ± 8) Mt for each of the last three years (2018–2020). This potential underestimation is large and broadly highlights the importance of continued review and refinement of bottom-up estimation methodologies and inventories. The ER method herein offers a powerful approach for upscaling measured facility-level or regional fossil-fuel CO2 emissions by taking advantage of satellite remote sensing observations.
To combat global warming, Canada has committed to
reducing greenhouse gases to be (GHGs) 40 %–45 % below 2005 emission levels by 2025. Monitoring emissions and deriving accurate inventories are essential to reaching these goals. Airborne methods can provide regional and area source measurements with small error if ideal conditions for sampling are met. In this study, two airborne mass-balance box-flight algorithms were compared to assess the extent of their agreement and their performance under various conditions. The Scientific Aviation's (SciAv) Gaussian algorithm and the Environment and Climate Change Canada's top-down emission rate retrieval algorithm (TERRA) were applied to data from five samples. Estimates were compared using standard procedures, by systematically testing other method fits, and by investigating the effects on the estimates when method assumptions were not met. Results indicate that in standard scenarios the SciAv and TERRA mass-balance box-flight methods produce similar estimates that agree (3 %–25 %) within algorithm uncertainties (4 %–34 %). Implementing a sample-specific surface extrapolation procedure for the SciAv
algorithm may improve emission estimation. Algorithms disagreed when
non-ideal conditions occurred (i.e., under non-stationary atmospheric
conditions). Overall, the results provide confidence in the box-flight
methods and indicate that emissions estimates are not overly sensitive to
the choice of algorithm but demonstrate that fundamental algorithm
assumptions should be assessed for each flight. Using a different method,
the Airborne Visible InfraRed Imaging Spectrometer – Next Generation
(AVIRIS-NG) independently mapped individual plumes with emissions 5 times
larger than the source SciAv sampled three days later. The range in
estimates highlights the utility of increased sampling to get a more
complete understanding of the temporal variability of emissions and to
identify emission sources within facilities. In addition, hourly on-site
activity data would provide insight to the observed temporal variability in
emissions and make a comparison to reported emissions more straightforward.
Wildfire impacts on air quality and climate are expected to be exacerbated
by climate change with the most pronounced impacts in the boreal biome.
Despite the large geographic coverage, there is limited information on
boreal forest wildfire emissions, particularly for organic compounds, which
are critical inputs for air quality model predictions of downwind impacts.
In this study, airborne measurements of 193 compounds from 15 instruments,
including 173 non-methane organics compounds (NMOG), were used to provide
the most detailed characterization, to date, of boreal forest wildfire
emissions. Highly speciated measurements showed a large diversity of
chemical classes highlighting the complexity of emissions. Using
measurements of the total NMOG carbon (NMOGT), the ΣNMOG was
found to be 50 % ± 3 % to 53 % ± 3 % of NMOGT, of which, the
intermediate- and semi-volatile organic compounds (I/SVOCs) were estimated
to account for 7 % to 10 %. These estimates of I/SVOC emission factors
expand the volatility range of NMOG typically reported. Despite extensive
speciation, a substantial portion of NMOGT remained unidentified
(47 % ± 15 % to 50 % ± 15 %), with expected contributions from more
highly-functionalized VOCs and I/SVOCs. The emission factors derived in this
study improve wildfire chemical speciation profiles and are especially
relevant for air quality modelling of boreal forest wildfires. These
aircraft-derived emission estimates were further linked with those derived
from satellite observations demonstrating their combined value in assessing
variability in modelled emissions. These results contribute to the
verification and improvement of models that are essential for reliable
predictions of near-source and downwind pollution resulting from boreal
forest wildfires.
Tailings ponds in the oil sands (OS) region in Alberta, Canada, have been associated with fugitive emissions of volatile organic compounds (VOCs) and other pollutants to the atmosphere. However, the contribution of tailings ponds to the total fugitive emissions of VOCs from OS operations remains uncertain. To address this knowledge gap, a field study was conducted in the summer of 2017 at Suncor’s Pond 2/3 to estimate emissions of a suite of pollutants including 68 VOCs using a combination of micrometeorological methods and measurements from a flux tower. The results indicate that in 2017, Pond 2/3 was an emission source of 3322 ± 727 tons of VOCs including alkanes, aromatics, and oxygenated and sulfur-containing organics. While the total VOC emissions were approximately a factor of 2 higher than those reported by Suncor, the individual VOC species emissions varied by up to a factor of 12. A chemical mass balance (CMB) receptor model was used to estimate the contribution of the tailings pond to VOC pollution events in a nearby First Nations and Metis community in Fort McKay. CMB results indicate that Suncor Pond 2/3 contributed up to 57% to the total mass of VOCs measured at Fort McKay, reinforcing the importance of accurate VOC emission estimation methods for tailings ponds.
Tailings ponds in the Alberta oil sands region are significant sources of fugitive emissions of methane to the atmosphere, but detailed knowledge on
spatial and temporal variabilities is lacking due to limitations of the methods deployed under current regulatory compliance monitoring programs. To
develop more robust and representative methods for quantifying fugitive emissions, three micrometeorological flux methods (eddy covariance,
gradient, and inverse dispersion) were applied along with traditional flux chambers to determine fluxes over a 5-week period. Eddy covariance flux
measurements provided the benchmark. A method is presented to directly calculate stability-corrected eddy diffusivities that can be applied to
vertical gas profiles for gradient flux estimation. Gradient fluxes were shown to agree with eddy covariance within 18 %, while inverse
dispersion model flux estimates were 30 % lower. Fluxes were shown to have only a minor diurnal cycle (15 % variability) and were weakly
dependent on wind speed, air, and water surface temperatures. Flux chambers underestimated the fluxes by 64 % in this particular campaign. The
results show that the larger footprint together with high temporal resolution of micrometeorological flux measurement methods may result in more
robust estimates of the pond greenhouse gas emissions.
Satellite-derived and reported sulfur dioxide (SO2) emissions from the Canadian oil sands are shown to have been consistent up to 2013. Post-2013, these sources of emissions data diverged, with reported emissions dropping by a factor of two, while satellite-derived emissions for the region remained relatively constant, with the discrepancy (satellite-derived emissions minus reported emissions) peaking at 50 kt(SO2) yr⁻¹ around 2016. The 2013–2014 period corresponds to when new flue-gas desulfurization units came on-line. Previous work has established a high level of consistency between at-stack SO2 emissions observations and satellite estimates, and surface monitoring network SO2 concentrations over the same multi-year period show similar trends as the satellite data, with a slight increase in concentrations post-2013. No clear explanation for this discrepancy currently exists. The implications of the discrepancy towards estimated total sulfur deposition to downwind ecosystems were estimated relative to 2013 emissions levels, with the satellite-derived values leaving the area of regional critical load exceedances of aquatic ecosystems largely unchanged from 2013 values, 335 000 km², and reported values potentially decreasing this area to 185 000 km².
Changes in CO2 emissions during the COVID-19 pandemic have been estimated from indicators on activities like transportation and electricity generation. Here, we instead use satellite observations together with bottom-up information to track the daily dynamics of CO2 emissions during the pandemic. Unlike activity data, our observation-based analysis deploys independent measurement of pollutant concentrations in the atmosphere to correct misrepresentation in the bottom-up data and can provide more detailed insights into spatially explicit changes. Specifically, we use TROPOMI observations of NO2 to deduce 10-day moving averages of NOx and CO2 emissions over China, differentiating emissions by sector and province. Between January and April 2020, China’s CO2 emissions
fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels before the coronavirus outbreak at the beginning of January 2020 owing to the fast economic recovery in provinces where industrial activity is concentrated.
In order to track progress towards the global climate
targets, the parties that signed the Paris Climate Agreement will regularly
report their anthropogenic carbon dioxide (CO2) emissions based on
energy statistics and CO2 emission factors. Independent evaluation of
this self-reporting system is a fast-growing research topic. Here, we study
the value of satellite observations of the column CO2 concentrations to
estimate CO2 anthropogenic emissions with 5 years of the Orbiting
Carbon Observatory-2 (OCO-2) retrievals over and around China. With the
detailed information of emission source locations and the local wind, we
successfully observe CO2 plumes from 46 cities and industrial regions
over China and quantify their CO2 emissions from the OCO-2
observations, which add up to a total of 1.3 Gt CO2 yr−1 that
accounts for approximately 13 % of mainland China's annual emissions. The
number of cities whose emissions are constrained by OCO-2 here is 3 to
10 times larger than in previous studies that only focused on large cities and
power plants in different locations around the world. Our satellite-based
emission estimates are broadly consistent with the independent values from
China's detailed emission inventory MEIC but are more different from those
of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC),
especially for the emission estimates for the individual cities. These
results demonstrate some skill in the satellite-based emission
quantification for isolated source clusters with the OCO-2, despite the
sparse sampling of this instrument not designed for this purpose. This skill
can be improved by future satellite missions that will have a denser spatial
sampling of surface emitting areas, which will come soon in the early 2020s.
Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis, which embodies a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards once completed. This new reanalysis replaces the ERA‐Interim reanalysis that was started in 2006 (spanning 1979 onwards). ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA‐Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3‐hourly at half the horizontal resolution). This paper describes the general setup of ERA5, as well as a basic evaluation of characteristics and performance. Focus is on the dataset from 1979 onwards that is currently publicly available. Re‐forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA‐Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global‐mean correlation with monthly‐mean GPCP data is increased from 67% to 77%. In general low‐frequency variability is found to be well‐represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA‐Interim, MERRA‐2 and JRA‐55 reanalyses. This article is protected by copyright. All rights reserved.
We present a method to infer CO2 emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO2), which could serve as complementary verification of bottom-up inventories or be used to supplement these inventories. We demonstrate its utility on eight large and isolated US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NOx) emissions from US power plants using Ozone Monitoring Instrument (OMI) NO2 tropospheric vertical column densities (VCDs) averaged over the ozone season (May–September) and a “top-down” approach that we previously developed. Second, we determine the relationship between NOx and CO2 emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal quality, boiler firing technology, NOx emission control device type, and any change in operating conditions. Third, we estimate CO2 emissions for power plants using the OMI-estimated NOx emissions and the CEMS NOx∕CO2 emission ratio. We find that the CO2 emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the US CEMS measurements, with a relative difference of 8 %±41 % (mean ± standard deviation). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO2 emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by comparing the derived NOx∕CO2 emission ratios for the US with those from a bottom-up emission inventory for other countries and applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other independent estimates. Though our analysis is limited to a few power plants, we expect to be able to apply our method to more US (and world) power plants when multi-year data records become available from new OMI-like sensors with improved capabilities, such as the TROPOspheric Monitoring Instrument (TROPOMI), and upcoming geostationary satellites, such as the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument.
Despite its key role in climate change, large uncertainties persist in our knowledge of the anthropogenic emissions of carbon dioxide (CO2) and no global observing system exists that allows us to monitor emissions from localized CO2 sources with sufficient accuracy. The Orbiting Carbon Observatory-2 (OCO-2) satellite allows retrievals of the column-average dry-air mole fractions of CO2 (XCO2). However, regional column-average enhancements of individual point sources are usually small, compared to the background concentration and its natural variability, and often not much larger than the satellite's measurement noise. This makes the unambiguous identification and quantification of anthropogenic emission plume signals challenging. NO2 is co-emitted with CO2 when fossil fuels are combusted at high temperatures. It has a short lifetime on the order of hours so that NO2 columns often greatly exceed background and noise levels of modern satellite sensors near sources, which makes it a suitable tracer of recently emitted CO2. Based on six case studies (Moscow, Russia; Lipetsk, Russia; Baghdad, Iraq; Medupi and Matimba power plants, South Africa; Australian wildfires; and Nanjing, China), we demonstrate the usefulness of simultaneous satellite observations of NO2 and XCO2. For this purpose, we analyze co-located regional enhancements of XCO2 observed by OCO-2 and NO2 from the Sentinel-5 Precursor (S5P) satellite and estimate the CO2 plume's cross-sectional fluxes. We take advantage of the nearly simultaneous NO2 measurements with S5P's wide swath and small measurement noise by identifying the source of the observed XCO2 enhancements, excluding interference with remote upwind sources, allowing us to adjust the wind direction, and by constraining the shape of the CO2 plumes. We compare the inferred cross-sectional fluxes with the Emissions Database for Global Atmospheric Research (EDGAR), the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC), and, in the case of the Australian wildfires, with the Global Fire Emissions Database (GFED). The inferred cross-sectional fluxes range from 31 MtCO2 a⁻¹ to 153 MtCO2 a⁻¹ with uncertainties (1σ) between 23 % and 72 %. For the majority of analyzed emission sources, the estimated cross-sectional fluxes agree, within their uncertainty, with either EDGAR or ODIAC or lie somewhere between them. We assess the contribution of multiple sources of uncertainty and find that the dominating contributions are related to the computation of the effective wind speed normal to the plume's cross section. The flux uncertainties are expected to be reduced by the planned European Copernicus anthropogenic CO2 monitoring mission (CO2M), which will provide not only precise measurements with high spatial resolution but also imaging capabilities with a wider swath of simultaneous XCO2 and NO2 observations. Such a mission, particularly if performed by a constellation of satellites, will deliver CO2 emission estimates from localized sources at an unprecedented frequency and level of accuracy.
The oil and gas (O&G) sector represents a large source of greenhouse gas (GHG) emissions globally. However, estimates of O&G emissions rely upon bottom-up approaches, and are rarely evaluated through atmospheric measurements. Here, we use aircraft measurements over the Canadian oil sands (OS) to derive the first top-down, measurement-based determination of the their annual CO2 emissions and intensities. The results indicate that CO2 emission intensities for OS facilities are 13–123% larger than those estimated using publically available data. This leads to 64% higher annual GHG emissions from surface mining operations, and 30% higher overall OS GHG emissions (17 Mt) compared to that reported by industry, despite emissions reporting which uses the most up to date and recommended bottom-up approaches. Given the similarity in bottom-up reporting methods across the entire O&G sector, these results suggest that O&G CO2 emissions inventory data may be more uncertain than previously considered.
TROPOMI, on-board the Sentinel-5 Precursor satellite is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral range. From these spectra several important air quality and climate-related atmospheric constituents are retrieved at an unprecedented high spatial resolution, including nitrogen dioxide (NO2). We present the first retrievals of TROPOMI NO2 over the Canadian Oil Sands, contrasting them with observations from the OMI satellite instrument, and demonstrate its ability to resolve individual plumes and highlight its potential for deriving emissions from individual mining facilities. Further, the first TROPOMI NO2 validation is presented, consisting of aircraft and surface in-situ NO2 observations, as well as ground-based remote-sensing measurements between March and May 2018. Our comparisons show that the TROPOMI NO2 vertical column densities are highly correlated with the aircraft and surface in-situ NO2 observations, and the ground-based remote-sensing measurements with a low bias (15-30 %) over the Canadian Oil Sands.
Plain language summary:
Nitrogen dioxide (NO2) is a pollutant that is linked to respiratory health issues and has negative environmental impacts such as soil and water acidification. Near the surface the most significant sources of NO2 are fossil fuel combustion and biomass burning. With a recently launched satellite instrument (TROPOspheric Monitoring Instrument; TROPOMI) NO2 can be measured with an unprecedented combination of accuracy, spatial coverage, and resolution. This work presents the first TROPOMI NO2 measurements near the Canadian Oil Sands and shows that these measurements have an outstanding ability to detect NO2 on a very high horizontal resolution that is unprecedented for satellite NO2 observations. Further, these satellite measurements are in excellent agreement with aircraft and ground-based measurements.
Methane emissions from the U.S. oil and natural gas supply chain were estimated using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 Tg/y, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. EPA inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO2 from natural gas combustion. Significant emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.
Aircraft-based measurements of methane (CH4) and other air pollutants in the Athabasca Oil Sands Region (AOSR) were made during a summer intensive field campaign between 13 August and 7 September 2013 in support of the Joint Canada–Alberta Implementation Plan for Oil Sands Monitoring. Chemical signatures were used to identify CH4 sources from tailings ponds (BTEX VOCs), open pit surface mines (NOy and rBC) and elevated plumes from bitumen upgrading facilities (SO2 and NOy). Emission rates of CH4 were determined for the five primary surface mining facilities in the region using two mass-balance methods. Emission rates from source categories within each facility were estimated when plumes from the sources were spatially separable. Tailings ponds accounted for 45 % of total CH4 emissions measured from the major surface mining facilities in the region, while emissions from operations in the open pit mines accounted for ∼ 50 %. The average open pit surface mining emission rates ranged from 1.2 to 2.8 t of CH4 h⁻¹ for different facilities in the AOSR. Amongst the 19 tailings ponds, Mildred Lake Settling Basin, the oldest pond in the region, was found to be responsible for the majority of tailings ponds emissions of CH4 ( > 70 %). The sum of measured emission rates of CH4 from the five major facilities, 19.2 ± 1.1 t CH4 h⁻¹, was similar to a single mass-balance determination of CH4 from all major sources in the AOSR determined from a single flight downwind of the facilities, 23.7 ± 3.7 t CH4 h⁻¹. The measured hourly CH4 emission rate from all facilities in the AOSR is 48 ± 8 % higher than that extracted for 2013 from the Canadian Greenhouse Gas Reporting Program, a legislated facility-reported emissions inventory, converted to hourly units. The measured emissions correspond to an emissions rate of 0.17 ± 0.01 Tg CH4 yr⁻¹ if the emissions are assumed as temporally constant, which is an uncertain assumption. The emission rates reported here are relevant for the summer season. In the future, effort should be devoted to measurements in different seasons to further our understanding of the seasonal parameters impacting fugitive emissions of CH4 and to allow for better estimates of annual emissions and year-to-year variability.
This overview paper highlights the successes of the Ozone Monitoring
Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and
research resulting in many new findings. Due to its unprecedented spatial
resolution, in combination with daily global coverage, OMI plays a unique
role in measuring trace gases important for the ozone layer, air quality, and
climate change. With the operational very fast delivery (VFD; direct
readout) and near real-time (NRT) availability of the data,
OMI also plays an
important role in the development of operational services in the atmospheric
chemistry domain.
Airborne measurements of methane emissions from oil and gas infrastructure were completed over two regions of Alberta, Canada. These top-down measurements were directly compared with region-specific bottom-up inventories that utilized current industry-reported flaring and venting volumes (reported data) and quantitative estimates of unreported venting and fugitive sources. For the 50 × 50 km measurement region near Red Deer, characterized by natural gas and light oil production, measured methane fluxes were more than 17 times greater than that derived from directly reported data but consistent with our region-specific bottom-up inventory-based estimate. For the 60 × 60 km measurement region near Lloydminster, characterized by significant cold heavy oil production with sand (CHOPS), airborne measured methane fluxes were five times greater than directly reported emissions from venting and flaring and four times greater than our region-specific bottom up inventory-based estimate. Extended across Alberta, our results suggest that reported venting emissions in Alberta should be 2.5 ± 0.5 times higher, and total methane emissions from the upstream oil and gas sector (excluding mined oil sands) are likely at least 25–50% greater than current government estimates. Successful mitigation efforts in the Red Deer region will need to focus on the >90% of methane emissions currently unmeasured or unreported.
Reported sulfur dioxide (SO2) emissions from US and Canadian sources
have declined dramatically since the 1990s as a result of emission control
measures. Observations from the Ozone Monitoring Instrument (OMI) on NASA's
Aura satellite and ground-based in situ measurements are examined to verify
whether the observed changes from SO2 abundance measurements
are quantitatively consistent with the reported changes in emissions. To make
this connection, a new method to link SO2 emissions and satellite
SO2 measurements was developed. The method is based on fitting satellite
SO2 vertical column densities (VCDs) to a set of functions of OMI pixel
coordinates and wind speeds, where each function represents a statistical
model of a plume from a single point source. The concept is first
demonstrated using sources in North America and then applied to Europe. The
correlation coefficient between OMI-measured VCDs (with a local bias removed)
and SO2 VCDs derived here using reported emissions for 1° by
1° gridded data is 0.91 and the best-fit line has a slope near
unity, confirming a very good agreement between observed SO2 VCDs and
reported emissions. Having demonstrated their consistency, seasonal and
annual mean SO2 VCD distributions are calculated, based on reported
point-source emissions for the period 1980–2015, as would have been seen by
OMI. This consistency is further substantiated as the emission-derived VCDs
also show a high correlation with annual mean SO2 surface concentrations
at 50 regional monitoring stations.
Reported sulfur dioxide (SO 2) emissions from U.S. and Canadian sources have declined dramatically since the 1990s as a result of emissions control measures. Observations from the Ozone Monitoring Instrument (OMI) on NASA's 15 Aura satellite and ground-based in-situ measurements are examined to verify whether the observed changes from SO 2 abundance measurements are quantitatively consistent with the reported changes in emissions. To make this connection, a new method to link SO 2 emissions and satellite SO 2 measurements was developed. The method is based on fitting satellite SO 2 vertical column densities (VCDs) to a set of functions of OMI pixel coordinates and wind speeds, where each function represents a statistical model of a plume from a single point source. The concept is first demonstrated using sources in North 20 America, and then applied to Europe. The correlation coefficient between OMI-measured VCDs (with a local bias removed) and SO 2 VCDs derived here using reported emissions for 1° by 1° gridded data is 0.91 and the best-fit line has a slope near unity, confirming a very good agreement between observed SO 2 VCDs and reported emissions. Having demonstrated their consistency, seasonal and annual mean SO 2 VCD distributions are calculated, based on reported point-source emissions for the period 1980-2015, as would have been seen by OMI. This consistency is further substantiated as the emissions-derived 25 VCDs also show a high correlation with annual mean SO 2 surface concentrations at 50 regional monitoring stations.
Significance
Validation of volatile organic compound (VOC) emission reports, especially from large industrial facilities, is rarely attempted. Given uncertainties in emission reports, their evaluation and validation will build confidence in emission inventories. It is shown that a top-down approach can provide measurement-based emission rates for such emission validation. Comparisons with emission reports from Alberta oil sands surface mining facilities revealed significant differences in VOC emissions between top-down emissions rates and reports. Comparison with VOC species emission reports using currently accepted estimation methods indicates that emissions were underestimated in the reports for most species. This exercise shows that improvements in the accuracy and completeness of emissions estimates from complex facilities would enhance their application to assessing the impacts of such emissions.
Effectively mitigating methane emissions from the natural gas supply chain requires addressing the disproportionate influence of high-emitting sources. Here we use a Monte Carlo simulation to aggregate methane emissions from all components on natural gas production sites in the Barnett Shale production region (Texas). Our total emission estimates are two-thirds of those derived from independent site-based measurements. Although some high-emitting operations occur by design (condensate flashing and liquid unloadings), they occur more than an order of magnitude less frequently than required to explain the reported frequency at which high site-based emissions are observed. We conclude that the occurrence of abnormal process conditions (for example, malfunctions upstream of the point of emissions; equipment issues) cause additional emissions that explain the gap between component-based and site-based emissions. Such abnormal conditions can cause a substantial proportion of a site's gas production to be emitted to the atmosphere and are the defining attribute of super-emitting sites.
Fossil-fuel (FF) burning releases carbon dioxide (CO2) together with
many other chemical species, some of which, such as nitrogen dioxide
(NO2) and carbon monoxide (CO), are routinely monitored from space. This
study examines the feasibility of estimation of FF CO2 emissions from
large industrial regions by using NO2 and CO column retrievals from
satellite measurements in combination with simulations by a mesoscale
chemistry transport model (CTM). To this end, an inverse modeling method is
developed that allows estimating FF CO2 emissions from different sectors
of the economy, as well as the total CO2 emissions, in a given region.
The key steps of the method are (1) inferring "top-down" estimates of the
regional budget of anthropogenic NOx and CO emissions from satellite
measurements of proxy species (NO2 and CO in the case considered)
without using formal a priori constraints on these budgets, (2) the application
of emission factors (the NOx-to-CO2 and CO-to-CO2 emission
ratios in each sector) that relate FF CO2 emissions to the proxy species
emissions and are evaluated by using data of "bottom-up" emission
inventories, and (3) cross-validation and optimal combination of the estimates of
CO2 emission budgets derived from measurements of the different proxy
species. Uncertainties in the top-down estimates of the NOx and CO
emissions are evaluated and systematic differences between the measured and
simulated data are taken into account by using original robust techniques
validated with synthetic data. To examine the potential of the method, it was
applied to the budget of emissions for a western European region including 12
countries by using NO2 and CO column amounts retrieved from,
respectively, the OMI and IASI satellite measurements and simulated by the
CHIMERE mesoscale CTM, along with the emission conversion factors based on
the EDGAR v4.2 emission inventory. The analysis was focused on evaluation of
the uncertainty levels for the top-down NOx and CO emission estimates
and "hybrid" estimates (that is, those based on both atmospheric measurements
of a given proxy species and respective bottom-up emission inventory data)
of FF CO2 emissions, as well as on examining consistency between the FF
NO2 emission estimates derived from measurements of the different proxy
species. It is found that NO2 measurements can provide much stronger
constraints to the total annual FF CO2 emissions in the study region
than CO measurements, the accuracy of the NO2-measurement-based CO2
emission estimate being mostly limited by the uncertainty in the top-down
NOx emission estimate. Nonetheless, CO measurements are also found to be
useful as they provide additional constraints to CO2 emissions and
enable evaluation of the hybrid FF CO2 emission estimates obtained from
NO2 measurements. Our most reliable estimate for the total annual FF
CO2 emissions in the study region in 2008 (2.71 ± 0.30 Pg CO2)
is found to be about 11 and 5 % lower than the respective estimates
based on the EDGAR v.4.2 (3.03 Pg CO2) and CDIAC (2.86 Pg CO2)
emission inventories, with the difference between our estimate and the CDIAC
inventory data not being statistically significant. In general, the results
of this study indicate that the proposed method has the potential to become a
useful tool for identification of possible biases and/or inconsistencies in
the bottom-up emission inventory data regarding CO2, NOx, and CO
emissions from fossil-fuel burning in different regions of the world.
Top-down approaches to measure total integrated emissions provide
verification of bottom-up, temporally-resolved, inventory-based
estimations. Aircraft-based measurements of air pollutants from
sources in the Canadian oil sands were made in support of the Joint
Canada–Alberta Implementation Plan on Oil Sands Monitoring during
a summer intensive field campaign between 13 August and
7 September 2013. The measurements contribute to knowledge needed in
support of the Joint Canada–Alberta Implementation Plan on Oil Sands
Monitoring. This paper describes a Top-down Emission Rate Retrieval
Algorithm (TERRA) to determine facility emissions of pollutants, using
SO2 and CH4 as examples, based on the aircraft
measurements. In this algorithm, the flight path around a facility at
multiple heights is mapped to a two-dimensional vertical screen
surrounding the facility. The total transport of SO2 and
CH4 through this screen is calculated using aircraft wind
measurements, and facility emissions are then calculated based on the
divergence theorem with estimations of box-top losses, horizontal and
vertical turbulent fluxes, surface deposition, and apparent losses due
to air densification and chemical reaction. Example calculations for
two separate flights are presented. During an upset condition of
SO2 emissions on one day, these calculations are within
5% of the industry-reported, bottom-up measurements. During
a return to normal operating conditions, the SO2 emissions are
within 11% of industry-reported, bottom-up
measurements. CH4 emissions calculated with the algorithm are
relatively constant within the range of uncertainties. Uncertainty of
the emission rates is estimated as 20%, which is primarily due to
the unknown SO2 and CH4 mixing ratios near the surface
below the lowest flight level.
Power plants constitute roughly 40% of carbon dioxide (CO2) emissions in the United States. Climate change science, air pollution regulation, and potential carbon trading policies rely on accurate, unbiased quantification of these large point sources. Two US federal agencies—the Department of Energy and the Environmental Protection Agency—tabulate the emissions from US power plants using two different methodological approaches. We have analyzed those two data sets and have found that when averaged over all US facilities, the median percentage difference is less than 3%. However, this small difference masks large, non-Gaussian, positive and negative differences at individual facilities. For example, over the 2001–2009 time period, nearly one-half of the facilities have monthly emission differences that exceed roughly ±6% and one-fifth exceed roughly ±13%. It is currently not possible to assess whether one, or both, of the datasets examined here are responsible for the emissions difference. Differences this large at the individual facility level raise concerns regarding the operationalization of policy within the United States such as the recently announced Clean Power Plan. This policy relies on the achievement of state-level CO2 emission rate targets. When examined at the state-level we find that one-third of the states have differences that exceed 10% of their assigned reduction amount. Such levels of uncertainty raise concerns about the ability of individual states to accurately quantify emission rates in order to meet the regulatory targets.
Significance
Past studies reporting divergent estimates of methane emissions from the natural gas supply chain have generated conflicting claims about the full greenhouse gas footprint of natural gas. Top-down estimates based on large-scale atmospheric sampling often exceed bottom-up estimates based on source-based emission inventories. In this work, we reconcile top-down and bottom-up methane emissions estimates in one of the country’s major natural gas production basins using easily replicable measurement and data integration techniques. These convergent emissions estimates provide greater confidence that we can accurately characterize the sources of emissions, including the large impact that a small proportion of high-emitters have on total emissions and determine the implications for mitigation.
Top-down approaches to measure total integrated emissions provide
verification of bottom-up, temporally resolved, inventory-based estimations.
Aircraft-based measurements of air pollutants from sources in the Canadian
oil sands were made in support of the Joint Canada–Alberta Implementation
Plan for Oil Sands Monitoring during a summer intensive field campaign
between 13 August and 7 September 2013. The measurements contribute to
knowledge needed in support of the Joint Canada–Alberta Implementation Plan
for Oil Sands Monitoring. This paper describes the top-down emission rate
retrieval algorithm (TERRA) to determine facility emissions of pollutants,
using SO2 and CH4 as examples, based on the aircraft measurements.
In this algorithm, the flight path around a facility at multiple heights is
mapped to a two-dimensional vertical screen surrounding the facility. The
total transport of SO2 and CH4 through this screen is calculated
using aircraft wind measurements, and facility emissions are then calculated
based on the divergence theorem with estimations of box-top losses,
horizontal and vertical turbulent fluxes, surface deposition, and apparent
losses due to air densification and chemical reaction. Example calculations
for two separate flights are presented. During an upset condition of
SO2 emissions on one day, these calculations are within 5 % of the
industry-reported, bottom-up measurements. During a return to normal
operating conditions, the SO2 emissions are within 11 % of
industry-reported, bottom-up measurements. CH4 emissions calculated
with the algorithm are relatively constant within the range of
uncertainties. Uncertainty of the emission rates is estimated as less than
30 %, which is primarily due to the unknown SO2 and CH4 mixing
ratios near the surface below the lowest flight level.
A new method to estimate sulfur dioxide (SO2) lifetimes and emissions from point sources using satellite measurements is described. The method is based on fitting satellite SO2 vertical column density to a three-dimensional parameterization as a function of the coordinates and wind speed An effective lifetime (or, more accurately, decay time) and emission rate are then determined from the parameters of the fit. The method was applied to measurements from the Ozone Monitoring Instrument (OMI) processed with the new Principal Component Analysis (PCA) algorithm in the vicinity of approximately 50 large US near-point sources. The obtained results were then compared with available emissions inventories. The correlation between estimated and reported emissions was about 0.91 with the estimated lifetimes between 4 and 12 hours. It is demonstrated that individual sources with annual SO2 emissions as low as 30 kt y-1
can produce a statistically significant signal in OMI data.
Satellite remote sensing is increasingly being used to monitor air quality
over localized sources such as the Canadian oil sands. Following an initial
study, significantly low biases have been identified in current NO2 and
SO2 retrieval products from the Ozone Monitoring Instrument (OMI)
satellite sensor over this location resulting from a combination of its rapid
development and small spatial scale. Air mass factors (AMFs) used to convert
line-of-sight "slant" columns to vertical columns were re-calculated for
this region based on updated and higher resolution input information
including absorber profiles from a regional-scale
(15 km × 15 km resolution) air quality model, higher spatial and
temporal resolution surface reflectivity, and an improved treatment of snow.
The overall impact of these new Environment Canada (EC) AMFs led to
substantial increases in the peak NO2 and SO2 average vertical
column density (VCD), occurring over an area of intensive surface mining, by
factors of 2 and 1.4, respectively, relative to estimates made with previous
AMFs. Comparisons are made with long-term averages of NO2 and SO2
(2005–2011) from in situ surface monitors by using the air quality model to
map the OMI VCDs to surface concentrations. This new OMI-EC product is able
to capture the spatial distribution of the in situ instruments (slopes of
0.65 to 1.0, correlation coefficients of >0.9). The
concentration absolute values from surface network observations were in
reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly
30%. Several complications were addressed including correction for the
interference effect in the surface NO2 instruments and smoothing and
clear-sky biases in the OMI measurements. Overall these results highlight the
importance of using input information that accounts for the spatial and
temporal variability of the location of interest when performing retrievals.
There has been increased scrutiny of the Alberta oil sands due to their
high carbon intensity (CI) relative to conventional crude oil. Relying
entirely on public and peer-reviewed data sources, we examine historical
trends in the CI of oil sands extraction, upgrading, and refining.
Monthly data were collected and interpolated from 1970 to 2010
(inclusive) for each oil sands project. Results show a reduction in oil
sands CI over time, with industry-average full-fuel cycle
(well-to-wheels, WTW) CI declining from 165 gCO2e
MJ-1 higher heating value (HHV) of reformulated
gasoline (RFG) to 105 (-12, +9) gCO2e
MJ-1 HHV RFG. 2010 averages by production pathways are
102 gCO2e MJ-1 for Mining and 111
gCO2e MJ-1 for in situ. The CI of
mining-based projects has declined due to upgrader efficiency
improvements and a shift away from coke to natural gas as a process
fuel. In situ projects have benefitted from substantial reductions in
fugitive emissions from bitumen batteries. Both mining and in situ
projects have benefitted from improved refining efficiencies. However,
despite these improvements, the CI of oil sands production (on a
pathway-average basis) ranges from 12 to 24% higher than CI values from
conventional oil production. Due to growing output, total emissions from
the oil sands continue to increase despite improved efficiency: total
upstream emissions were roughly 65 MtCO2e in 2010, or 9% of
Canada’s emissions.
Methane emissions from U.S. and Canadian natural gas systems appear larger than official estimates.
Results from the first assessment of air quality over the Canadian oil
sands-one of the largest industrial undertakings in human
history-using satellite remote sensing observations of two
pollutants, nitrogen dioxide (NO2) and sulfur dioxide
(SO2), are presented. High-resolution maps were created that
revealed distinct enhancements in both species over an area (roughly
30 km × 50 km) of intensive surface mining at scales of
a few kilometers. The magnitude of these enhancements, quantified in
terms of total mass, are comparable to the largest seen in Canada from
individual sources. The rate of increase in NO2 between 2005
and 2010 was assessed at 10.4 ± 3.5%/year and resulted from
increases both in local values as well as the spatial extent of the
enhancement. This is broadly consistent with both surface-measurement
trends and increases in annual bitumen production. An increase in
SO2 was also found, but given larger uncertainties, it is not
statistically significant.
Multi-annual satellite measurements of tropospheric NO2
columns are used for evaluation of CO2 emission changes in
China in the period from 1996 to 2008. Indirect annual top-down
estimates of CO2 emissions are derived from the satellite
NO2 columns measurements by means of a simple inverse
modeling procedure involving simulations performed with the CHIMERE
mesoscale chemistry transport model and the CO2 to
NOx emission ratios from the Emission Database for Global
Atmospheric Research version 4.2 (EDGAR v4.2) global anthropogenic
emission inventory. Exponential trends in the normalized time series of
annual emission are evaluated separately for the periods from 1996 to
2001 and from 2001 to 2008. The results indicate that the both periods
manifest strong positive trends in the CO2 emissions, and
that the trend in the second period was significantly larger than the
trend in the first period. Specifically, the trends in the first and
second periods are estimated to be in the range from 3.7 to 8.0 and from
9.5 to 13.0 percent per year, respectively, taking into account both
statistical and probable systematic uncertainties. Comparison of our
top-down estimates of the CO2 emission changes with the
corresponding bottom-up estimates provided by EDGAR v4.2 and Global
Carbon Project (GCP) emission inventories reveals that while
acceleration of the CO2 emission growth in the considered
period is a common feature of the both kinds of estimates, nonlinearity
in the CO2 emission changes may be strongly exaggerated in
the emission inventories. Specifically, the atmospheric NO2
observations do not confirm the existence of a sharp bend in the
emission inventory data time series in the period from 2000 to 2002. A
significant quantitative difference is revealed between the bottom-up
and top-down estimates of the CO2 emission trend in the
period from 1996 to 2001 (specifically, the trend was not positive
according to the emission inventories, but is strongly positive in our
estimates). These results confirm the findings of earlier studies which
indicated probable large uncertainties in the energy production and
other activity data from international energy statistics used as the
input information in the emission inventories for China. For the period
from 2001 to 2008, the different kinds of estimates agree within the
uncertainty range. In general, satellite measurements of tropospheric
NO2 are shown to be a useful source of information on
CO2 sources colocated with sources of nitrogen oxides; the
corresponding potential of these measurements should be exploited
further in future studies.
The magnitude of Canada's oil sands reserves, their rapidly expanding and energy intensive production, combined with existing and upcoming greenhouse gas (GHG) emissions regulations motivate an evaluation of oil sands-derived fuel production from a life cycle perspective. Thirteen studies of GHG emissions associated with oil sands operations are reviewed. The production of synthetic crude oil (SCO) through surface mining and upgrading (SM&Up) or in situ and upgrading (IS&Up) processes is reported to result in emissions ranging from 62 to 164 and 99 to 176 kgCO2eq/bbl SCO, respectively (or 9.2–26.5 and 16.2–28.7 gCO2eq MJ−1 SCO, respectively), compared to 27–58 kgCO2eq/bbl (4.5–9.6 gCO2eq MJ−1) of crude for conventional oil production. The difference in emissions intensity between SCO and conventional crude production is primarily due to higher energy requirements for extracting bitumen and upgrading it into SCO. On a 'well-to-wheel' basis, GHG emissions associated with producing reformulated gasoline from oil sands with current SM&Up, IS&Up, and in situ (without upgrading) technologies are 260–320, 320–350, and 270–340 gCO2eq km−1, respectively, compared to 250–280 gCO2eq km−1 for production from conventional oil. Some variation between studies is expected due to differences in methods, technologies studied, and operating choices. However, the magnitude of the differences presented suggests that a consensus on the characterization of life cycle emissions of the oil sands industry has yet to be reached in the public literature. Recommendations are given for future studies for informing industry and government decision making.
ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF.
An "event-based" approach to characterize complex air pollutant mixtures was applied in the Oil Sands region of northern Alberta, Canada. This approach was developed to better-inform source characterization and attribution of the air pollution in the Indigenous community of Fort McKay, within the context of the lived experience of residents. Principal component analysis was used to identify the characteristics of primary pollutant mixtures, which were related to hydrocarbon emissions, fossil fuel combustion, dust, and oxidized and reduced sulfur compounds. Concentration distributions of indicator compounds were used to isolate sustained air pollution "events". Diesel-powered vehicles operating in the mines were found to be an important source during NOx events. Industry-specific volatile organic compound (VOC) profiles were used in a chemical mass balance model for source apportionment, which revealed that nearby oil sands operations contribute to 86% of the total mass of nine VOC species (2-methylpentane, hexane, heptane, octane, benzene, toluene, m,p-xylene, o-xylene, and ethylbenzene) during VOC events. Analyses of the frequency distribution of air pollution events indicate that Fort McKay is regularly impacted by multiple mixtures simultaneously, underscoring the limitations of an exceedance-based approach relying on a small number of air quality standards as the only tool to assess risk.
Heavy-oil is and will be an essential part of energy supply. The most common and practical way of producing any type of heavy-oil is steam injection; although this technique is both effectively and efficiently applied worldwide, there are many environmental and ecological concerns limiting the application. One of the most critical drawbacks of this proven method is the generation of greenhouse gases (GHG), which happens when the process obtains the remarkable amounts of steam needed. The focus of this paper is to discuss the eco-friendly methodologies to minimize GHG emissions (oil or natural gas consumption to generate steam) while sustaining the recovery at a comparable level of the currently available conventional in-situ heavy-oil and bitumen recovery techniques. New generation in-situ techniques for heavy oil recovery including chemically assisted waterflooding or gas injection (non-thermal), steam-based applications with additives (nano-based smart materials, surface-active agents, solvents), along with non-steam (solvent and electromagnetic heating) are outlined and a comparative analysis in terms of efficiency improvement while mitigating GHG emission is provided in this review paper. Comprehensive chemical lists for their potential use in each type of application are also provided. It is shown that a reduction of 20%–100% energy (oil or natural gas consumption) can be expected using the new generation in-situ recovery techniques for heavy-oil.
Black carbon (BC) emissions from the Canadian oil sands (OS) surface mining facilities in Alberta were investigated using aircraft measurements. BC emission rates were derived with a top-down mass balance approach, and were found to be linearly related to the volume of oil sands ore mined at each facility. Two emission factors were determined from the measurements. Production-based BC emission factors were in the range of 0.6 to 1.7 g/tonne mined OS ore, whereas fuel based BC emission factors were between 95 and 190 mg/kg-fuel, depending upon the facility. The annual BC emission, at 707 ± 117 tonnes/year for the facilities, was determined using the production based emission factors and annual production data. Although this annual emission is in reasonable agreement with the BC annual emissions reported in the latest version of the Canadian national BC inventory (within 16%), the relative split between off road diesel and stack sources is significantly different between the measurements and the inventory. This measurement evidence highlights the fact that the stack sources of BC may be overestimated and the off road diesel sources may be underestimated in the inventory, and points to the need for improved BC emission data from diesel sources within facilities.
Fossil-fuel CO2 emissions and their trends in eight U.S. megacities during 2006-2017 are inferred by combining satellite-derived NOX emissions with bottom-up city-specific NOX-to-CO2 emission ratios. A statistical model is fit to a collection NO2 plumes observed from the Ozone Monitoring Instrument (OMI), and is used to calculate top-down NOX emissions. Decreases in OMI-derived NOX emissions are observed across the eight cities from 2006 to 2017 (-17% in Miami to -58% in Los Angeles), and are generally consistent with long-term trends of bottom-up inventories (-25% in Miami to -49% in Los Angeles), but there are some interannual discrepancies. City-specific NOX-to-CO2 emission ratios, used to calculate inferred CO2, are estimated through annual bottom-up inventories of NOX and CO2 emissions disaggregated to 1 × 1 km2 resolution. Over the study period, NOX-to-CO2 emission ratios have decreased by ~40% nationwide (-24% to -51% for our studied cities), which is attributed to a faster reduction in NOX when compared to CO2 due to policy regulations and fuel type shifts. Combining top-down NOX emissions and bottom-up NOX-to-CO2 emission ratios, annual fossil-fuel CO2 emissions are derived. Inferred OMI-based top-down CO2 emissions trends vary between +7% in Dallas to -31% in Phoenix. For 2017, we report annual fossil-fuel CO2 emissions to be: Los Angeles 113 ± 49 Tg/yr; New York City 144 ± 62 Tg/yr; and Chicago 55 ± 24 Tg/yr. A study in the Los Angeles area, using independent methods, reported a 2013-2016 average CO2 emissions rate of 104 Tg/yr and 120 Tg/yr, which suggests that the CO2 emissions from our method are in good agreement with other studies' top-down estimates. We anticipate future remote sensing instruments - with better spatial and temporal resolution - will better constrain the NOX-to-CO2 ratio and reduce the uncertainty in our method.
We present a statistically-enhanced version of the GreenHouse gas emissions of current Oil Sands Technologies model that facilitates characterization of variability of greenhouse gas (GHG) emissions associated with mining and upgrading of bitumen from Canadian oil sands. Over 30 years of publicly-available project-specific operating data are employed as inputs, enabling Monte Carlo simulation of individual projects and the entire industry, for individual years and project life cycles. We estimate that median lifetime GHG intensities range from 89 to 137 kg CO2eq/bbl synthetic crude oil (SCO) for projects that employ upgrading. The only project producing dilbit that goes directly to a refinery has a median lifetime GHG intensity of 51 kg CO2eq/bbl dilbit. As SCO and dilbit are distinct products with different downstream processing energy requirements, a life cycle assessment (“well to wheel”) is needed to properly compare them. Projects do not reach steady-state in terms of median GHG intensity. Projects with broader distributions of annual GHG intensities and higher median values are linked to specific events (e.g., project expansions). An implication for policymakers is that no specific technology or operating factor can be directly linked to GHG intensity and no particular project or year of operation can be seen as representative of the industry or production technology.
Greenhouse gas (GHG) emissions associated with extraction of bitumen from oil sands can vary from project to project and over time. However, the nature and magnitude of this variability have yet to be incorporated into environmental life cycle studies. We present a statistically enhanced life cycle based model (GHOST-SE) for assessing variability of GHG emissions associated with the extraction of bitumen using in situ techniques in Alberta, Canada. It employs publicly-available, company-reported operating data, facilitating assessment of inter- and intra-project variability as well as the time evolution of GHG emissions from commercial in situ oil sands projects. We estimate the median GHG emissions associated with bitumen production via cyclic steam stimulation (CSS) to be 77 kg CO2eq/bbl bitumen (80% CI: 61 – 109 kg CO2eq/bbl), whereas median GHG emissions for individual CSS projects may range from 75 to 118 kg CO2eq/bbl. By contrast, median GHG emissions associated with and via steam assisted gravity drainage (SAGD) are to be 68 kg CO2eq/bbl bitumen (80% CI: 49 – 102 kg CO2eq/bbl); median emissions for individual SAGD projects range from 52 to 172 kg CO2eq/bbl. We also show that the median emissions intensity of Alberta’s CSS and SAGD projects have been relatively stable from 2000 to 2013, despite greater than six-fold growth in production. Variability between projects is the single largest source of variability (driven in part by reservoir characteristics) but intra-project variability (e.g., startups, interruptions), is also important and must be considered in order to We conclude that there is a need to consider the distributions of GHG emissions associated with in situ oil sands extraction due to the variability among and within projects particularly over time.inform research or policy priorities.
Greenhouse gas (GHG) regulations affecting U.S. transportation fuels require holistic examination of the life-cycle emissions of U.S. petroleum feedstocks. With an expanded system boundary that included land disturbance-induced GHG emissions, we estimated well-to-wheels (WTW) GHG emissions of U.S. production of gasoline and diesel sourced from Canadian oil sands. Our analysis was based on detailed characterization of the energy intensities of 27 oil sands projects, representing industrial practices and technological advances since 2008. Four major oil sands production pathways were examined, including bitumen and synthetic crude oil (SCO) from both surface mining and in situ projects. Pathway-average GHG emissions from oil sands extraction, separation, and upgrading ranged from ~6.1 to ~27.3 g CO2 equivalents per megajoule (in lower heating value, CO2e/MJ). This range can be compared to ~4.4 g CO2e/MJ for U.S. conventional crude oil recovery. Depending on the extraction technology and product type output of oil sands projects, the WTW GHG emissions for gasoline and diesel produced from bitumen and SCO in U.S. refineries were in the range of 100-115 and 99-117 g CO2e/MJ, respectively, representing, on average, about 18% and 21% higher emissions than those derived from U.S. conventional crudes. WTW GHG emissions of gasoline and diesel derived from diluted bitumen ranged from 97 to 103 and 96 to 104 g CO2e/MJ, respectively, showing the effect of diluent use on fuel emissions.
The ESA (European Space Agency) Sentinel-5 Precursor (S-5 P) is a low Earth orbit polar satellite to provide information and services on air quality, climate and the ozone layer in the timeframe 2015–2022. The S-5 P mission is part of the Global Monitoring of the Environment and Security (GMES) Space Component Programme. The payload of the mission is the TROPOspheric Monitoring Instrument (TROPOMI) that will measure key atmospheric constituents including ozone, NO2, SO2, CO, CH4, CH2O and aerosol properties. TROPOMI has heritage to both the Ozone Monitoring Instrument (OMI) as well as to the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY). The S-5 P will extend the data records of these missions as well as be a preparatory mission for the Sentinel-5 mission planned for 2020 onward. The mission is pre-operational and is the link between the current scientific and the operational Sentinel-4/-5 missions. This contribution describes the science and mission objectives, the mission and the instrument, and the data products. While building on a solid foundation of the heritage instruments, the S-5P/TROPOMI mission is an exciting step forward with a strong focus on the troposphere. This is achieved by a combination of a high spatial resolution and improved signal-to-noise, as well as dedicated data product development. It is anticipated that the S-5 P mission will make a large contribution to the monitoring of the global atmospheric composition, as well as to the scientific knowledge of relevant atmospheric processes.
Estimates of fossil-fuel CO2 emissions are needed to address a variety of climate-change mitigation concerns over a broad range of spatial and temporal scales. We compared two data sets that report power-plant CO2 emissions in the conterminous U.S. for 2004, the most recent year reported in both data sets. The data sets were obtained from the Department of Energy's Energy Information Administration (EIA) and the Environmental Protection Agency's eGRID database. Conterminous U.S. total emissions computed from the data sets differed by 3.5% for total plant emissions (electricity plus useful thermal output) and 2.3% for electricity generation only. These differences are well within previous estimates of uncertainty in annual U.S. fossil-fuel emissions. However, the corresponding average absolute differences between estimates of emissions from individual power plants were much larger, 16.9% and 25.3%, respectively. By statistical analysis, we identified several potential sources of differences between EIA and eGRID estimates for individual plants. Estimates that are based partly or entirely on monitoring of stack gases (reported by eGRID only) differed significantly from estimates based on fuel consumption (as reported by EIA). Differences in accounting methods appear to explain differences in estimates for emissions from electricity generation from combined heat and power plants, and for total and electricity generation emissions from plants that burn nonconventional fuels (e.g., biomass). Our analysis suggests the need for care in utilizing emissions data from individual power plants, and the need for transparency in documenting the accounting and monitoring methods used to estimate emissions.
Canada Gazette, part I, volume 155, number 51: notice with respect to reporting of greenhouse gases (GHGs) for 2021
- Government of Canada
Multiannual changes of CO2 emissions in China: indirect estimates derived from satellite measurements of tropospheric NO2 columns
- Berezin
The ERA5 global reanalysis
- H Hersbach
number 51: notice with respect to reporting of greenhouse gases (GHGs) for 2021
- Canada Government Of
Government of Canada. 2021. Canada Gazette, part I, volume
155, number 51: notice with respect to reporting of greenhouse
gases (GHGs) for 2021. Canada Gazette 155:227-281.