@article{liang_zhang_chen_zhang_liu_chen_mao_shen_qu_chen_et al._2023, title={East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis}, volume={23}, ISSN={["1680-7324"]}, DOI={10.5194/acp-23-8039-2023}, abstractNote={Abstract. We apply atmospheric methane column retrievals from two
different satellite instruments (Greenhouse gases Observing SATellite – GOSAT; TROPOspheric Monitoring Instrument – TROPOMI) to a regional inversion
framework to quantify East Asian methane emissions for 2019 at
0.5∘ × 0.625∘ horizontal resolution. The goal
is to assess if GOSAT (relatively mature but sparse) and TROPOMI (new and
dense) observations inform consistent methane emissions from East Asia with
identically configured inversions. Comparison of the results from the two
inversions shows similar correction patterns to the prior inventory in
central northern China, central southern China, northeastern China, and Bangladesh,
with less than 2.6 Tg a−1 differences in regional posterior emissions.
The two inversions, however, disagree over some important regions,
particularly in northern India and eastern China. The methane emissions
inferred from GOSAT observations are 7.7 Tg a−1 higher than those from
TROPOMI observations over northern India but 6.4 Tg a−1 lower over eastern
China. The discrepancies between the two inversions are robust against
varied inversion configurations (i.e., assimilation window and error
specifications). We find that the lower methane emissions from eastern China
inferred by the GOSAT inversion are more consistent with independent
ground-based in situ and total column (TCCON) observations, indicating that the
TROPOMI retrievals may have high XCH4 biases in this region. We also
evaluate inversion results against tropospheric aircraft observations over
India during 2012–2014 by using a consistent GOSAT inversion of earlier
years as an intercomparison platform. This indirect evaluation favors lower
methane emissions from northern India inferred by the TROPOMI inversion. We
find that in this case the discrepancy in emission inference is contributed
by differences in data coverage (almost no observations by GOSAT vs. good
spatial coverage by TROPOMI) over the Indo-Gangetic Plain. The two
inversions also differ substantially in their posterior estimates for
northwestern China and neighboring Kazakhstan, which is mainly due to
seasonally varying biases between GOSAT and TROPOMI XCH4 data that
correlate with changes in surface albedo.
                    }, number={14}, journal={ATMOSPHERIC CHEMISTRY AND PHYSICS}, author={Liang, Ruosi and Zhang, Yuzhong and Chen, Wei and Zhang, Peixuan and Liu, Jingran and Chen, Cuihong and Mao, Huiqin and Shen, Guofeng and Qu, Zhen and Chen, Zichong and et al.}, year={2023}, month={Jul}, pages={8039–8057} }
 @article{lu_jacob_zhang_shen_sulprizio_maasakkers_varon_qu_chen_hmiel_et al._2023, title={Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and fields tied to metrics}, volume={120}, ISSN={["1091-6490"]}, DOI={10.1073/pnas.2217900120}, abstractNote={
            The United States is the world’s largest oil/gas methane emitter according to current national reports. Reducing these emissions is a top priority in the US government’s climate action plan. Here, we use a 2010 to 2019 high-resolution inversion of surface and satellite observations of atmospheric methane to quantify emission trends for individual oil/gas production regions in North America and relate them to production and infrastructure. We estimate a mean US oil/gas methane emission of 14.8 (12.4 to 16.5) Tg a
            −1
            for 2010 to 2019, 70% higher than reported by the US Environmental Protection Agency. While emissions in Canada and Mexico decreased over the period, US emissions increased from 2010 to 2014, decreased until 2017, and rose again afterward. Increases were driven by the largest production regions (Permian, Anadarko, Marcellus), while emissions in the smaller production regions generally decreased. Much of the year-to-year emission variability can be explained by oil/gas production rates, active well counts, and new wells drilled, with the 2014 to 2017 decrease driven by reduction in new wells and the 2017 to 2019 surge driven by upswing of production. We find a steady decrease in the oil/gas methane intensity (emission per unit methane gas production) for almost all major US production regions. The mean US methane intensity decreased from 3.7% in 2010 to 2.5% in 2019. If the methane intensity for the oil/gas supply chain continues to decrease at this pace, we may expect a 32% decrease in US oil/gas emissions by 2030 despite projected increases in production.
          }, number={17}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, author={Lu, Xiao and Jacob, Danile J. and Zhang, Yuzhong and Shen, Lu and Sulprizio, Melissa P. and Maasakkers, Joannes D. and Varon, Daniel J. and Qu, Zhen and Chen, Zichong and Hmiel, Benjamin and et al.}, year={2023}, month={Apr} }
 @article{qu_jacob_zhang_shen_varon_lu_scarpelli_bloom_worden_parker_2022, title={Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations}, volume={17}, url={http://dx.doi.org/10.1088/1748-9326/ac8754}, DOI={10.1088/1748-9326/ac8754}, abstractNote={Abstract}, number={9}, journal={Environmental Research Letters}, publisher={IOP Publishing}, author={Qu, Zhen and Jacob, Daniel J and Zhang, Yuzhong and Shen, Lu and Varon, Daniel J and Lu, Xiao and Scarpelli, Tia and Bloom, Anthony and Worden, John and Parker, Robert}, year={2022}, month={Sep}, pages={094003} }
 @article{varon_jacob_sulprizio_estrada_downs_shen_hancock_nesser_qu_penn_et al._2022, title={Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations}, volume={15}, url={http://dx.doi.org/10.5194/gmd-15-5787-2022}, DOI={10.5194/gmd-15-5787-2022}, abstractNote={Abstract. We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25∘ × 0.3125∘ (≈ 25 km × 25 km) resolution by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of period-average emissions on the 0.25∘ × 0.3125∘ grid including error statistics, information content, and visualization code for inspection of results. The inversion uses an advanced research-grade algorithm fully documented in the literature. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI's capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.}, number={14}, journal={Geoscientific Model Development}, publisher={Copernicus GmbH}, author={Varon, Daniel and Jacob, Daniel J. and Sulprizio, Melissa and Estrada, Lucas A. and Downs, William B. and Shen, Lu and Hancock, Sarah E. and Nesser, Hannah and Qu, Zhen and Penn, Elise and et al.}, year={2022}, month={Jul}, pages={5787–5805} }
 @article{chen_jacob_nesser_sulprizio_lorente_varon_lu_shen_qu_penn_et al._2022, title={Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations}, volume={22}, url={http://dx.doi.org/10.5194/acp-22-10809-2022}, DOI={10.5194/acp-22-10809-2022}, abstractNote={Abstract. We quantify methane emissions in China and the contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. The inversion uses as a prior estimate the latest 2014 national sector-resolved anthropogenic emission inventory reported by the Chinese government to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as a direct evaluation of that inventory. Emissions are optimized with a Gaussian mixture model (GMM) at up to 0.25∘×0.3125∘ resolution. The optimization is done analytically assuming log-normally distributed errors on prior emissions. Errors and information content on the optimized estimates are obtained directly from the analytical solution and also through a 36-member inversion ensemble. Our best estimate for total anthropogenic emissions in China is 65.0 (57.7–68.4) Tg a−1, where parentheses indicate the uncertainty range determined by the inversion ensemble. Contributions from individual sectors include 16.6 (15.6–17.6) Tg a−1 for coal, 2.3 (1.8–2.5) for oil, 0.29 (0.23–0.32) for gas, 17.8 (15.1–21.0) for livestock, 9.3 (8.2–9.9) for waste, 11.9 (10.7–12.7) for rice paddies, and 6.7 (5.8–7.1) for other sources. Our estimate is 21% higher than the Chinese inventory reported to the UNFCCC (53.6 Tg a−1), reflecting upward corrections to emissions from oil (+147 %), gas (+61 %), livestock (+37 %), waste (+41 %), and rice paddies (+34 %), but downward correction for coal (−15 %). It is also higher than previous inverse studies (43–62 Tg a−1) that used the much sparser GOSAT satellite observations and were conducted at coarser resolution. We are in particular better able to separate coal and rice emissions. Our higher livestock emissions are attributed largely to northern China where GOSAT has little sensitivity. Our higher waste emissions reflect at least in part a rapid growth in wastewater treatment in China. Underestimate of oil emissions in the UNFCCC report appears to reflect unaccounted-for super-emitting facilities. Gas emissions in China are mostly from distribution, in part because of low emission factors from production and in part because 42 % of the gas is imported. Our estimate of emissions per unit of domestic gas production indicates a low life-cycle loss rate of 1.7 % (1.3 %–1.9 %), which would imply net climate benefits from the current “coal-to-gas” energy transition in China. However, this small loss rate is somewhat misleading considering China's high gas imports, including from Turkmenistan where emission per unit of gas production is very high.}, number={16}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Chen, Zichong and Jacob, Daniel J. and Nesser, Hannah and Sulprizio, Melissa P. and Lorente, Alba and Varon, Daniel and Lu, Xiao and Shen, Lu and Qu, Zhen and Penn, Elise and et al.}, year={2022}, month={Aug}, pages={10809–10826} }
 @article{lu_jacob_wang_maasakkers_zhang_scarpelli_shen_qu_sulprizio_nesser_et al._2022, title={Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) atmospheric observations}, volume={22}, url={http://dx.doi.org/10.5194/acp-22-395-2022}, DOI={10.5194/acp-22-395-2022}, abstractNote={Abstract. We quantify methane emissions and their 2010–2017 trends by sector in the
contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in
situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric
methane observations. The inversion uses as a prior estimate the national
anthropogenic emission inventories for the three countries reported by the US
Environmental Protection Agency (EPA), Environment and Climate Change Canada
(ECCC), and the Instituto Nacional de Ecología y Cambio Climático
(INECC) in Mexico to the United Nations Framework Convention on Climate Change
(UNFCCC) and thus serves as an evaluation of these inventories in terms of
their magnitudes and trends. Emissions are optimized with a Gaussian mixture
model (GMM) at 0.5∘×0.625∘ resolution and for
individual years. Optimization is done analytically using lognormal error
forms. This yields closed-form statistics of error covariances and information
content on the posterior (optimized) estimates, allows better representation
of the high tail of the emission distribution, and enables construction of a
large ensemble of inverse solutions using different observations and
assumptions. We find that GOSAT and in situ observations are largely
consistent and complementary in the optimization of methane emissions for
North America. Mean 2010–2017 anthropogenic emissions from our base GOSAT + in situ inversion, with ranges from the inversion ensemble, are 36.9
(32.5–37.8) Tg a−1 for CONUS, 5.3 (3.6–5.7) Tg a−1
for Canada, and 6.0 (4.7–6.1) Tg a−1 for Mexico. These are higher
than the most recent reported national inventories of 26.0 Tg a−1
for the US (EPA), 4.0 Tg a−1 for Canada (ECCC), and
5.0 Tg a−1 for Mexico (INECC). The correction in all three
countries is largely driven by a factor of 2 underestimate in emissions from
the oil sector with major contributions from the south-central US, western
Canada, and southeastern Mexico. Total CONUS anthropogenic emissions in our
inversion peak in 2014, in contrast to the EPA report of a steady decreasing
trend over 2010–2017. This reflects offsetting effects of increasing
emissions from the oil and landfill sectors, decreasing emissions from the gas
sector, and flat emissions from the livestock and coal sectors. We find
decreasing trends in Canadian and Mexican anthropogenic methane emissions over
the 2010–2017 period, mainly driven by oil and gas emissions. Our best
estimates of mean 2010–2017 wetland emissions are 8.4
(6.4–10.6) Tg a−1 for CONUS, 9.9 (7.8–12.0) Tg a−1
for Canada, and 0.6 (0.4–0.6) Tg a−1 for Mexico. Wetland
emissions in CONUS show an increasing trend of +2.6 (+1.7 to
+3.8)% a−1 over 2010–2017
correlated with precipitation.
                    }, number={1}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Lu, Xiao and Jacob, Daniel J. and Wang, Haolin and Maasakkers, Joannes and Zhang, Yuzhong and Scarpelli, Tia R. and Shen, Lu and Qu, Zhen and Sulprizio, Melissa P. and Nesser, Hannah and et al.}, year={2022}, month={Jan}, pages={395–418} }
 @article{jacob_varon_cusworth_dennison_frankenberg_gautam_guanter_kelley_mckeever_ott_et al._2022, title={Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane}, volume={22}, url={http://dx.doi.org/10.5194/acp-22-9617-2022}, DOI={10.5194/acp-22-9617-2022}, abstractNote={Abstract. We review the capability of current and scheduled satellite
observations of atmospheric methane in the shortwave infrared (SWIR) to
quantify methane emissions from the global scale down to point sources. We
cover retrieval methods, precision and accuracy requirements, inverse and
mass balance methods for inferring emissions, source detection thresholds,
and observing system completeness. We classify satellite instruments as area
flux mappers and point source imagers, with complementary attributes. Area
flux mappers are high-precision (<1 %) instruments with 0.1–10 km
pixel size designed to quantify total methane emissions on regional to
global scales. Point source imagers are fine-pixel (<60 m)
instruments designed to quantify individual point sources by imaging of the
plumes. Current area flux mappers include GOSAT (2009–present), which
provides a high-quality record for interpretation of long-term methane
trends, and TROPOMI (2018–present), which provides global continuous daily
mapping to quantify emissions on regional scales. These instruments already
provide a powerful resource to quantify national methane emissions in
support of the Paris Agreement. Current point source imagers include the
GHGSat constellation and several hyperspectral and multispectral land
imaging sensors (PRISMA, Sentinel-2, Landsat-8/9, WorldView-3), with
detection thresholds in the 100–10 000 kg h−1 range that enable
monitoring of large point sources. Future area flux mappers, including
MethaneSAT, GOSAT-GW, Sentinel-5, GeoCarb, and CO2M, will increase the
capability to quantify emissions at high resolution, and the MERLIN lidar
will improve observation of the Arctic. The averaging times required by area
flux mappers to quantify regional emissions depend on pixel size, retrieval
precision, observation density, fraction of successful retrievals, and
return times in a way that varies with the spatial resolution desired. A
similar interplay applies to point source imagers between detection
threshold, spatial coverage, and return time, defining an observing system
completeness. Expanding constellations of point source imagers including
GHGSat and Carbon Mapper over the coming years will greatly improve
observing system completeness for point sources through dense spatial
coverage and frequent return times.
                    }, number={14}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Jacob, Daniel J. and Varon, Daniel and Cusworth, Daniel H. and Dennison, Philip E. and Frankenberg, Christian and Gautam, Ritesh and Guanter, Luis and Kelley, John and McKeever, Jason and Ott, Lesley E. and et al.}, year={2022}, month={Jul}, pages={9617–9646} }
 @article{shen_gautam_omara_zavala-araiza_maasakkers_scarpelli_lorente_lyon_sheng_varon_et al._2022, title={Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins}, volume={22}, url={http://dx.doi.org/10.5194/acp-22-11203-2022}, DOI={10.5194/acp-22-11203-2022}, abstractNote={Abstract. We use satellite methane observations from the Tropospheric Monitoring Instrument (TROPOMI), for May 2018 to February 2020, to quantify methane emissions from individual oil and natural gas (O/G) basins in the US and Canada using a high-resolution (∼25 km) atmospheric inverse analysis. Our satellite-derived emission estimates show good consistency with in situ field measurements (R=0.96) in 14 O/G basins distributed across the US and Canada. Aggregating our results to the national scale, we obtain O/G-related methane emission estimates of 12.6±2.1 Tg a−1 for the US and 2.2±0.6 Tg a−1 for Canada, 80 % and 40 %, respectively, higher than the national inventories reported to the United Nations. About 70 % of the discrepancy in the US Environmental Protection Agency (EPA) inventory can be attributed to five O/G basins, the Permian, Haynesville, Anadarko, Eagle Ford, and Barnett basins, which in total account for 40 % of US emissions. We show more generally that our TROPOMI inversion framework can quantify methane emissions exceeding 0.2–0.5 Tg a−1 from individual O/G basins, thus providing an effective tool for monitoring methane emissions from large O/G basins globally.}, number={17}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Shen, Lu and Gautam, Ritesh and Omara, Mark and Zavala-Araiza, Daniel and Maasakkers, Joannes and Scarpelli, Tia and Lorente, Alba and Lyon, David and Sheng, Jianxiong and Varon, Daniel and et al.}, year={2022}, month={Sep}, pages={11203–11215} }
 @article{balamurugan_chen_qu_bi_keutsch_2022, title={Secondary PM2.5 decreases significantly less than NO2 emission reductions during COVID lockdown in Germany}, volume={22}, url={http://dx.doi.org/10.5194/acp-22-7105-2022}, DOI={10.5194/acp-22-7105-2022}, abstractNote={Abstract. This study estimates the influence of anthropogenic emission reductions on the concentration of particulate matter with a diameter smaller than 2.5 µm (PM2.5) during the 2020 lockdown period in German metropolitan areas. After accounting for meteorological effects, PM2.5 concentrations during the spring 2020 lockdown period were 5 % lower compared to the same time period in 2019. However, during the 2020 pre-lockdown period (winter), PM2.5 concentrations with meteorology accounted for were 19 % lower than in 2019. Meanwhile, NO2 concentrations with meteorology accounted for dropped by 23 % during the 2020 lockdown period compared to an only 9 % drop for the 2020 pre-lockdown period, both compared to 2019. SO2 and CO concentrations with meteorology accounted for show no significant changes during the 2020 lockdown period compared to 2019. GEOS-Chem (GC) simulations with a COVID-19 emission reduction scenario based on the observations (23 % reduction in anthropogenic NOx emission with unchanged anthropogenic volatile organic compounds (VOCs) and SO2) are consistent with the small reductions of PM2.5 during the lockdown and are used to identify the underlying drivers for this. Due to being in a NOx-saturated ozone production regime, GC OH radical and O3 concentrations increased (15 % and 9 %, respectively) during the lockdown compared to a business-as-usual (BAU, no lockdown) scenario. Ox (equal to NO2+O3) analysis implies that the increase in ozone at nighttime is solely due to reduced NO titration. The increased O3 results in increased NO3 radical concentrations, primarily during the night, despite the large reductions in NO2. Thus, the oxidative capacity of the atmosphere is increased in all three important oxidants, OH, O3, and NO3. PM nitrate formation from gas-phase nitric acid (HNO3) is decreased during the lockdown as the increased OH concentration cannot compensate for the strong reductions in NO2, resulting in decreased daytime HNO3 formation from the OH + NO2 reaction. However, nighttime formation of PM nitrate from N2O5 hydrolysis is relatively unchanged. This results from the fact that increased nighttime O3 results in significantly increased NO3, which roughly balances the effect of the strong NO2 reductions on N2O5 formation. Ultimately, the only small observed decrease in lockdown PM2.5 concentrations can be explained by the large contribution of nighttime PM nitrate formation, generally enhanced sulfate formation, and slightly decreased ammonium. This study also suggests that high PM2.5 episodes in early spring are linked to high atmospheric ammonia concentrations combined with favorable meteorological conditions of low temperature and low boundary layer height. Northwest Germany is a hot-spot of NH3 emissions, primarily emitted from livestock farming and intensive agricultural activities (fertilizer application), with high NH3 concentrations in the early spring and summer months. Based on our findings, we suggest that appropriate NOx and VOC emission controls are required to limit ozone, and that should also help reduce PM2.5. Regulation of NH3 emissions, primarily from agricultural sectors, could result in significant reductions in PM2.5 pollution.
                    }, number={11}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Balamurugan, Vigneshkumar and Chen, Jia and Qu, Zhen and Bi, Xiao and Keutsch, Frank N.}, year={2022}, month={Jun}, pages={7105–7129} }
 @article{qu_henze_worden_jiang_gaubert_theys_wei_2022, title={Sector‐Based Top‐Down Estimates of NOx, SO2, and CO Emissions in East Asia}, volume={49}, url={http://dx.doi.org/10.1029/2021gl096009}, DOI={10.1029/2021gl096009}, abstractNote={Abstract}, number={2}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Qu, Zhen and HENZE, DAVEN and Worden, Helen and Jiang, Zhe and Gaubert, Benjamin and Theys, Nicolas and wei}, year={2022}, month={Jan} }
 @article{worden_cusworth_qu_yin_zhang_bloom_ma_byrne_scarpelli_maasakkers_et al._2022, title={The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data  and a priori inventory estimates}, volume={22}, url={http://dx.doi.org/10.5194/acp-22-6811-2022}, DOI={10.5194/acp-22-6811-2022}, abstractNote={Abstract. We use optimal estimation (OE) to quantify methane fluxes
based on total column CH4 data from the Greenhouse Gases Observing
Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We
then project these fluxes to emissions by sector at 1∘ resolution and
then to each country using a new Bayesian algorithm that accounts for prior
and posterior uncertainties in the methane emissions. These estimates are
intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here
and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are
∼ 263 ± 24 Tg CH4 yr−1, anthropogenic fossil emissions
are 82 ± 12 Tg CH4 yr−1, and natural wetland/aquatic emissions are
180 ± 10 Tg CH4 yr−1. These estimates are consistent with previous
inversions based on GOSAT data and the GEOS-Chem model. In addition,
anthropogenic fossil estimates are consistent with those reported to the
United Nations Framework Convention on Climate Change (80.4 Tg CH4 yr−1 for 2019). Alternative priors can be easily tested with our new Bayesian
approach (also known as prior swapping) to determine their impact on
posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on
isotopic evidence) and find little impact on our posterior fluxes. This
indicates that these alternative inventories are inconsistent with our
remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the
prior inventories. We find that total emissions for approximately 57
countries can be resolved with this observing system based on the
degrees-of-freedom for signal metric (DOFS > 1.0) that can be
calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of
prior inventories. The top five emitting countries (Brazil, China, India,
Russia, USA) emit about half of the global anthropogenic budget, similar to
our choice of prior emissions but with the posterior emissions shifted
towards the agricultural sector and less towards fossil emissions,
consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different
(although within uncertainty) than bottom-up inventories, isotopic evidence,
or estimates based on sparse in situ data, indicating a need for further
studies reconciling these different approaches for quantifying the methane
budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and
those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon
Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and
sectoral attribution of subsequent methane emissions estimates.
                    }, number={10}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Worden, John R. and Cusworth, Daniel H. and Qu, Zhen and Yin, Yi and Zhang, Yuzhong and Bloom, A. Anthony and Ma, Shuang and Byrne, Brendan and Scarpelli, Tia and Maasakkers, Joannes and et al.}, year={2022}, month={May}, pages={6811–6841} }
 @article{scarpelli_jacob_grossman_lu_qu_sulprizio_zhang_reuland_gordon_worden_2022, title={Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations}, volume={22}, url={http://dx.doi.org/10.5194/acp-22-3235-2022}, DOI={10.5194/acp-22-3235-2022}, abstractNote={Abstract. We present an updated version of the Global Fuel Exploitation
Inventory (GFEI) for methane emissions and evaluate it with results from
global inversions of atmospheric methane observations from satellite (GOSAT)
and in situ platforms (GLOBALVIEWplus). GFEI allocates methane emissions
from oil, gas, and coal sectors and subsectors to a 0.1∘ × 0.1∘ grid by using the national emissions reported by individual
countries to the United Nations Framework Convention on Climate Change
(UNFCCC) and mapping them to infrastructure locations. Our updated GFEI v2
gives annual emissions for 2010–2019 that incorporate the most recent UNFCCC
national reports, new oil–gas well locations, and improved spatial
distribution of emissions for Canada, Mexico, and China. Russia's oil–gas
emissions in its latest UNFCCC report (4.1 Tg a−1 for 2019) decrease by
83 % compared to its previous report while Nigeria's latest reported
oil–gas emissions (3.1 Tg a−1 for 2016) increase 7-fold compared to
its previous report, reflecting changes in assumed emission factors. Global
gas emissions in GFEI v2 show little net change from 2010 to 2019 while oil
emissions decrease and coal emissions slightly increase. Global emissions
from the oil, gas, and coal sectors in GFEI v2 (26, 22, and 33 Tg a−1,
respectively in 2019) are lower than the EDGAR v6 inventory (32, 44, and 37 Tg a−1 in 2018) and lower than the IEA inventory for oil and gas (38
and 43 Tg a−1 in 2019), though there is considerable variability between
inventories for individual countries. GFEI v2 estimates higher emissions by
country than the Climate TRACE inventory, with notable exceptions in Russia,
the US, and the Middle East where TRACE is up to an order of magnitude
higher than GFEI v2. Inversion results using GFEI as a prior estimate
confirm the lower Russian emissions in the latest UNFCCC report but find
that Nigeria's reported UNFCCC emissions are too high. Oil–gas emissions are
generally underestimated by the national inventories for the highest
emitting countries including the US, Venezuela, Uzbekistan, Canada, and
Turkmenistan. Offshore emissions tend to be overestimated. Our updated GFEI
v2 provides a platform for future evaluation of national emission
inventories reported to the UNFCCC using the newer generation of satellite
instruments such as TROPOMI with improved coverage and spatial resolution.
This increased observational data density will be especially beneficial in
regions where current inversion systems have limited sensitivity including
Russia. Our work responds to recent aspirations of the Intergovernmental
Panel on Climate Change (IPCC) to integrate top-down and bottom-up
information into the construction of national emission inventories.
                    }, number={5}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Scarpelli, Tia and Jacob, Daniel J. and Grossman, Shayna and Lu, Xiao and Qu, Zhen and Sulprizio, Melissa P. and Zhang, Yuzhong and Reuland, Frances and Gordon, Deborah and Worden, John R.}, year={2022}, month={Mar}, pages={3235–3249} }
 @article{cusworth_bloom_ma_miller_bowman_yin_maasakkers_zhang_scarpelli_qu_et al._2021, title={A Bayesian framework for deriving sector-based methane emissions from top-down fluxes}, volume={2}, url={http://dx.doi.org/10.1038/s43247-021-00312-6}, DOI={10.1038/s43247-021-00312-6}, abstractNote={Abstract}, number={1}, journal={Communications Earth & Environment}, publisher={Springer Science and Business Media LLC}, author={Cusworth, Daniel H. and Bloom, A. Anthony and Ma, Shuang and Miller, Charles and Bowman, Kevin and Yin, Yi and Maasakkers, Joannes D. and Zhang, Yuzhong and Scarpelli, Tia R. and Qu, Zhen and et al.}, year={2021}, month={Nov} }
 @article{qu_jacob_shen_lu_zhang_scarpelli_nesser_sulprizio_maasakkers_bloom_et al._2021, title={Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments}, volume={21}, url={http://dx.doi.org/10.5194/acp-21-14159-2021}, DOI={10.5194/acp-21-14159-2021}, abstractNote={Abstract. We evaluate the global atmospheric methane column retrievals from the new TROPOMI satellite instrument and apply them to a global inversion of
methane sources for 2019 at 2∘ × 2.5∘ horizontal resolution. We compare the results to an inversion using the sparser but
more mature GOSAT satellite retrievals and to a joint inversion using both TROPOMI and GOSAT. Validation of TROPOMI and GOSAT with TCCON
ground-based measurements of methane columns, after correcting for retrieval differences in prior vertical profiles and averaging kernels using the
GEOS-Chem chemical transport model, shows global biases of −2.7 ppbv for TROPOMI and −1.0 ppbv for GOSAT and regional biases of
6.7 ppbv for TROPOMI and 2.9 ppbv for GOSAT. Intercomparison of TROPOMI and GOSAT shows larger regional discrepancies exceeding
20 ppbv, mostly over regions with low surface albedo in the shortwave infrared where the TROPOMI retrieval may be biased. Our inversion uses
an analytical solution to the Bayesian inference of methane sources, thus providing an explicit characterization of error statistics and information
content together with the solution. TROPOMI has ∼ 100 times more observations than GOSAT, but error correlation on the
2∘ × 2.5∘ scale of the inversion and large spatial inhomogeneity in the number of observations make it less useful than
GOSAT for quantifying emissions at that scale. Finer-scale regional inversions would take better advantage of the TROPOMI data density. The TROPOMI
and GOSAT inversions show consistent downward adjustments of global oil–gas emissions relative to a prior estimate based on national inventory
reports to the United Nations Framework Convention on Climate Change but consistent increases in the south-central US and in Venezuela. Global
emissions from livestock (the largest anthropogenic source) are adjusted upward by TROPOMI and GOSAT relative to the EDGAR v4.3.2 prior estimate. We
find large artifacts in the TROPOMI inversion over southeast China, where seasonal rice emissions are particularly high but in phase with extensive
cloudiness and where coal emissions may be misallocated. Future advances in the TROPOMI retrieval together with finer-scale inversions and improved
accounting of error correlations should enable improved exploitation of TROPOMI observations to quantify and attribute methane emissions on the
global scale.
                    }, number={18}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Qu, Zhen and Jacob, Daniel J. and Shen, Lu and Lu, Xiao and Zhang, Yuzhong and Scarpelli, Tia R. and Nesser, Hannah and Sulprizio, Melissa P. and Maasakkers, Joannes and Bloom, A. Anthony and et al.}, year={2021}, month={Sep}, pages={14159–14175} }
 @article{nawaz_henze_harkins_cao_nault_jo_jimenez_anenberg_goldberg_qu_2021, title={Impacts of sectoral, regional, species, and day-specific emissions on air pollution and public health in Washington, DC}, volume={9}, url={http://dx.doi.org/10.1525/elementa.2021.00043}, DOI={10.1525/elementa.2021.00043}, abstractNote={We present a novel source attribution approach that incorporates satellite data into GEOS-Chem adjoint simulations to characterize the species-specific, regional, and sectoral contributions of daily emissions for 3 air pollutants: fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2). This approach is implemented for Washington, DC, first for 2011, to identify urban pollution sources, and again for 2016, to examine the pollution response to changes in anthropogenic emissions. In 2011, anthropogenic emissions contributed an estimated 263 (uncertainty: 130–444) PM2.5- and O3-attributable premature deaths and 1,120 (391–1795) NO2 attributable new pediatric asthma cases in DC. PM2.5 exposure was responsible for 90% of these premature deaths. On-road vehicle emissions contributed 51% of NO2-attributable new asthma cases and 23% of pollution-attributable premature deaths, making it the largest contributing individual sector to DC’s air pollution–related health burden. Regional emissions, originating from Maryland, Virginia, and Pennsylvania, were the most responsible for pollution-related health impacts in DC, contributing 57% of premature deaths impacts and 89% of asthma cases. Emissions from distant states contributed 34% more to PM2.5 exposure in the wintertime than in the summertime, occurring in parallel with strong wintertime westerlies and a reduced photochemical sink. Emission reductions between 2011 and 2016 resulted in health benefits of 76 (28–149) fewer pollution-attributable premature deaths and 227 (2–617) fewer NO2-attributable pediatric asthma cases. The largest sectors contributing to decreases in pollution-related premature deaths were energy generation units (26%) and on-road vehicles (20%). Decreases in NO2-attributable pediatric asthma cases were mostly due to emission reductions from on-road vehicles (63%). Emission reductions from energy generation units were found to impact PM2.5 more than O3, while on-road vehicle emission reductions impacted O3 proportionally more than PM2.5. This novel method is capable of capturing the sources of urban pollution at fine spatial and temporal scales and is applicable to many urban environments, globally.}, number={1}, journal={Elementa: Science of the Anthropocene}, publisher={University of California Press}, author={Nawaz, M. O. and Henze, D. K. and Harkins, C. and Cao, H. and Nault, B. and Jo, D. and Jimenez, J. and Anenberg, S. C. and Goldberg, D. L. and Qu, Z.}, year={2021}, month={Dec} }
 @article{campbell_tong_tang_baker_lee_saylor_stein_ma_lamsal_qu_2021, title={Impacts of the COVID-19 economic slowdown on ozone pollution in the U.S.}, volume={264}, url={http://dx.doi.org/10.1016/j.atmosenv.2021.118713}, DOI={10.1016/j.atmosenv.2021.118713}, abstractNote={In this work, we use observations and experimental emissions in a version of NOAA's National Air Quality Forecasting Capability to show that the COVID-19 economic slowdown led to disproportionate impacts on near-surface ozone concentrations across the contiguous U.S. (CONUS). The data-fusion methodology used here includes both U.S. EPA Air Quality System ground and the NASA Aura satellite Ozone Monitoring Instrument (OMI) NO2 observations to infer the representative emissions changes due to the COVID-19 economic slowdown in the U.S. Results show that there were widespread decreases in anthropogenic (e.g., NOx) emissions in the U.S. during March-June 2020, which led to widespread decreases in ozone concentrations in the rural regions that are NOx-limited, but also some localized increases near urban centers that are VOC-limited. Later in June-September, there were smaller decreases, and potentially some relative increases in NOx emissions for many areas of the U.S. (e.g., south-southeast) that led to more extensive increases in ozone concentrations that are partly in agreement with observations. The widespread NOx emissions changes also alters the O3 photochemical formation regimes, most notably the NOx emissions decreases in March-April, which can enhance (mitigate) the NOx-limited (VOC-limited) regimes in different regions of CONUS. The average of all AirNow hourly O3 changes for 2020-2019 range from about +1 to -4 ppb during March-September, and are associated with predominantly urban monitoring sites that demonstrate considerable spatiotemporal variability for the 2020 ozone changes compared to the previous five years individually (2015-2019). The simulated maximum values of the average O3 changes for March-September range from about +8 to -4 ppb (or +40 to -10%). Results of this work have implications for the use of widespread controls of anthropogenic emissions, particularly those from mobile sources, used to curb ozone pollution under the current meteorological and climate conditions in the U.S.}, journal={Atmospheric Environment}, publisher={Elsevier BV}, author={Campbell, Patrick C. and Tong, Daniel and Tang, Youhua and Baker, Barry and Lee, Pius and Saylor, Rick and Stein, Ariel and Ma, Siqi and Lamsal, Lok and Qu, Zhen}, year={2021}, month={Nov}, pages={118713} }
 @article{qu_wu_henze_li_sonenberg_mao_2021, title={Transboundary transport of ozone pollution to a US border region: A case study of Yuma}, volume={273}, url={http://dx.doi.org/10.1016/j.envpol.2020.116421}, DOI={10.1016/j.envpol.2020.116421}, abstractNote={High concentrations of ground-level ozone affect human health, plants, and animals. Reducing ozone pollution in rural regions, where local emissions are already low, poses challenge. We use meteorological back-trajectories, air quality model sensitivity analysis, and satellite remote sensing data to investigate the ozone sources in Yuma, Arizona and find strong international influences from Northern Mexico on 12 out of 16 ozone exceedance days. We find that such exceedances could not be mitigated by reducing emissions in Arizona; complete removal of state emissions would reduce the maximum daily 8-h average (MDA8) ozone in Yuma by only 0.7% on exceeding days. In contrast, emissions in Mexico are estimated to contribute to 11% of the ozone during these exceedances, and their reduction would reduce MDA8 ozone in Yuma to below the standard. Using satellite-based remote sensing measurements, we find that emissions of nitrogen oxides (NOx, a key photochemical precursor of ozone) increase slightly in Mexico from 2005 to 2016, opposite to decreases shown in the bottom-up inventory. In comparison, a decrease of NOx emissions in the US and meteorological factors lead to an overall of summer mean and annual MDA8 ozone in Yuma (by ∼1–4% and ∼3%, respectively). Analysis of meteorological back-trajectories also shows similar transboundary transport of ozone at the US-Mexico border in California and New Mexico, where strong influences from Northern Mexico coincide with 11 out of 17 and 6 out of 8 ozone exceedances. 2020 is the final year of the U.S.-Mexico Border 2020 Program, which aimed to reduce pollution at border regions of the US and Mexico. Our results indicate the importance of sustaining a substantial cooperative program to improve air quality at the border area.}, journal={Environmental Pollution}, publisher={Elsevier BV}, author={Qu, Zhen and Wu, Dien and Henze, Daven K. and Li, Yi and Sonenberg, Mike and Mao, Feng}, year={2021}, month={Mar}, pages={116421} }
 @article{balamurugan_chen_qu_bi_gensheimer_shekhar_bhattacharjee_keutsch_2021, title={Tropospheric NO2 and O3 Response to COVID‐19 Lockdown Restrictions at the National and Urban Scales in Germany}, volume={126}, url={http://dx.doi.org/10.1029/2021jd035440}, DOI={10.1029/2021jd035440}, abstractNote={Abstract}, number={19}, journal={Journal of Geophysical Research: Atmospheres}, publisher={American Geophysical Union (AGU)}, author={Balamurugan, Vigneshkumar and Chen, Jia and Qu, Zhen and Bi, Xiao and Gensheimer, Johannes and Shekhar, Ankit and Bhattacharjee, Shrutilipi and keutsch}, year={2021}, month={Oct} }
 @article{qu_jacob_silvern_shah_campbell_valin_murray_2021, title={US COVID‐19 Shutdown Demonstrates Importance of Background NO2 in Inferring NOx Emissions From Satellite NO2 Observations}, volume={48}, url={http://dx.doi.org/10.1029/2021gl092783}, DOI={10.1029/2021gl092783}, abstractNote={Abstract}, number={10}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Qu, Zhen and Jacob, Daniel J. and Silvern, Rachel F. and Shah, Viral and Campbell, Patrick C. and Valin, Lukas and Murray, Lee Thomas}, year={2021}, month={May} }
 @article{shen_zavala-araiza_gautam_omara_scarpelli_sheng_sulprizio_zhuang_zhang_qu_et al._2021, title={Unravelling a large methane emission discrepancy in Mexico using satellite observations}, volume={260}, url={http://dx.doi.org/10.1016/j.rse.2021.112461}, DOI={10.1016/j.rse.2021.112461}, abstractNote={We use satellite observations from the Tropospheric Monitoring Instrument (TROPOMI) to map and quantify methane emissions from eastern Mexico using an atmospheric inverse analysis. Eastern Mexico covers the vast majority of the national oil and gas production. Using TROPOMI measurements from May 2018 to December 2019, our methane emission estimates for eastern Mexico are 5.0 ± 0.2 Tg a−1 for anthropogenic sources and 1.5 ± 0.1 Tg a−1 for natural sources, representing 45% and 34% higher annual methane fluxes respectively compared to the most recent estimates based on the Mexican national greenhouse gas inventory. Our results show that Mexico's oil and gas sector has the largest discrepancy, with oil and gas emissions (1.3 ± 0.2 Tg a−1) higher by a factor of two relative to bottom-up estimates—accounting for a quarter of total anthropogenic emissions. Our satellite-based inverse modeling estimates show that more than half of the oil/gas emissions in eastern Mexico are from the southern onshore basin (0.79 ± 0.13 Tg a−1), pointing at high emission sources which are not represented in current bottom-up inventories (e.g., venting of associated gas, high-emitting gathering/processing facilities related to the transport of associated gas from offshore). These findings suggest that stronger mitigation measures are critical to curbing the anthropogenic footprint of methane emissions in Mexico, especially the large contribution from the oil and gas sector.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Shen, Lu and Zavala-Araiza, Daniel and Gautam, Ritesh and Omara, Mark and Scarpelli, Tia and Sheng, Jianxiong and Sulprizio, Melissa P. and Zhuang, Jiawei and Zhang, Yuzhong and Qu, Zhen and et al.}, year={2021}, month={Jul}, pages={112461} }
 @article{zhang_jacob_lu_maasakkers_scarpelli_sheng_shen_qu_sulprizio_chang_et al._2020, title={Attribution  of  the accelerating  increase  in  atmospheric  methane  during 2010–2018 by inverse analysis of GOSAT observations}, volume={9}, url={http://dx.doi.org/10.5194/acp-2020-964}, DOI={10.5194/acp-2020-964}, abstractNote={Abstract. We conduct a global inverse analysis of 2010–2018 GOSAT satellite observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes 2010–2018 anthropogenic methane emissions and their trends on a 4º × 5º grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane) also for individual years. We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. Our inversion successfully reduces the errors against the independent methane observations from the TCCON network and reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil/gas), and too low for Venezuela (oil/gas) and the U.S. (oil/gas). We show that the 2010–2018 increase in global methane emissions is mainly driven by tropical wetlands (Amazon and tropical Africa), boreal wetlands (Eurasia), and tropical livestock (South Asia, Africa, Brazil), with no significant trend in oil/gas emissions. While the rise in tropical livestock emissions is consistent with bottom-up estimates of rapidly growing cattle populations, the rise in wetland emissions needs to be better understood. The sustained acceleration of growth rates in 2016–2018 relative to 2010–2013 is mostly from wetlands, while the peak methane growth rates in 2014–2015 are also contributed by low OH concentrations (2014) and high fire emissions (2015). Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.
                        }, publisher={Copernicus GmbH}, author={Zhang, Yuzhong and Jacob, Daniel J. and Lu, Xiao and Maasakkers, Joannes and Scarpelli, Tia R. and Sheng, Jianxiong and Shen, Lu and Qu, Zhen and Sulprizio, Melissa P. and CHANG, Jinfeng and et al.}, year={2020}, month={Sep} }
 @article{lu_jacob_zhang_maasakkers_sulprizio_shen_qu_scarpelli_nesser_yantosca_et al._2020, title={Global methane budget and trend, 2010–2017: complementarity of inverse analyses  using in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) observations}, volume={9}, url={http://dx.doi.org/10.5194/acp-2020-775}, DOI={10.5194/acp-2020-775}, abstractNote={Abstract. We use satellite (GOSAT) and in situ (GLOBALVIEWplus CH4 ObsPack) observations of atmospheric methane in a joint global inversion of methane sources, sinks, and trends for the 2010–2017 period. The inversion is done by analytical solution to the Bayesian optimization problem, yielding closed-form estimates of information content to assess the consistency and complementarity (or redundancy) of the satellite and in situ datasets. We find that GOSAT and in situ observations are to a large extent complementary, with GOSAT providing a stronger overall constraint on the global methane distributions, but in situ observations being more important for northern mid-latitudes and for relaxing global error correlations between methane emissions and the main methane sink (oxidation by OH radicals). The GOSAT observations achieve 212 independent pieces of information (DOFS) for quantifying mean 2010–2017 anthropogenic emissions on 1009 global model grid elements, and a DOFS of 122 for 2010–2017 emission trends. Adding the in situ data increases the DOFS by about 20–30 %, to 262 and 161 respectively for mean emissions and trends. Our joint inversion finds that oil/gas emissions in the US and Canada are underestimated relative to the values reported by these countries to the United Nations Framework Convention on Climate Change (UNFCCC) and used here as prior estimates, while coal emissions in China are overestimated. Wetland emissions in North America are much lower than in the mean WetCHARTs inventory used as prior estimate. Oil/gas emissions in the US increase over the 2010–2017 period but decrease in Canada and Europe. Our joint GOSAT+in situ inversion yields a global methane emission of 551 Tg a−1 averaged over 2010–2017 and a methane lifetime of 11.2 years against oxidation by tropospheric OH (86 % of the methane sink).
                        }, publisher={Copernicus GmbH}, author={Lu, Xiao and Jacob, Daniel J. and Zhang, Yuzhong and Maasakkers, Joannes and Sulprizio, Melissa P. and Shen, Lu and Qu, Zhen and Scarpelli, Tia R. and Nesser, Hannah and Yantosca, Robert and et al.}, year={2020}, month={Sep} }
 @article{qu_henze_cooper_neu_2020, title={Impacts of global NOx inversions on NO2 and ozone simulations}, volume={20}, url={http://dx.doi.org/10.5194/acp-20-13109-2020}, DOI={10.5194/acp-20-13109-2020}, abstractNote={Abstract. Tropospheric NO2 and ozone simulations have large uncertainties,
but their biases, seasonality, and trends can be improved with NO2
assimilations. We perform global top-down estimates of monthly NOx
emissions using two Ozone Monitoring Instrument (OMI) NO2 retrievals (NASAv3 and DOMINOv2) from 2005
to 2016 through a hybrid 4D-Var/mass balance inversion. Discrepancy in
NO2 retrieval products is a major source of uncertainties in the
top-down NOx emission estimates. The different vertical sensitivities
in the two NO2 retrievals affect both magnitude and seasonal variations
of top-down NOx emissions. The 12-year averages of regional NOx
budgets from the NASA posterior emissions are 37 % to 53 % smaller than
the DOMINO posterior emissions. Consequently, the DOMINO posterior surface NO2
simulations greatly reduced the negative biases in China (by 15 %) and the
US (by 22 %) compared to surface NO2 measurements. Posterior NOx
emissions show consistent trends over China, the US, India, and Mexico
constrained by the two retrievals. Emission trends are less robust over
South America, Australia, western Europe, and Africa, where the two
retrievals show less consistency. NO2 trends have more consistent
decreases (by 26 %) with the measurements (by 32 %) in the US from 2006
to 2016 when using the NASA posterior emissions. The performance of posterior ozone
simulations has spatial heterogeneities from region to region. On a global
scale, ozone simulations using NASA-based emissions alleviate the double
peak in the prior simulation of global ozone seasonality. The higher
abundances of NO2 from the DOMINO posterior simulations increase the global
background ozone concentrations and therefore reduce the negative biases
more than the NASA posterior simulations using GEOS-Chem v12 at remote
sites. Compared to surface ozone measurements, posterior simulations have
more consistent magnitude and interannual variations than the prior
estimates, but the performance from the NASA-based and DOMINO-based
emissions varies across ozone metrics. The limited availability of remote-sensing data and the use of prior NOx diurnal variations hinder
improvement of ozone diurnal variations from the assimilation, and therefore
have mixed performance on improving different ozone metrics. Additional
improvements in posterior NO2 and ozone simulations require more
precise and consistent NO2 retrieval products, more accurate diurnal
variations of NOx and VOC emissions, and improved simulations of ozone
chemistry and depositions.
                    }, number={21}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Qu, Zhen and Henze, Daven K. and Cooper, Owen R. and Neu, Jessica L.}, year={2020}, month={Nov}, pages={13109–13130} }
 @article{elguindi_granier_stavrakou_darras_bauwens_cao_chen_gon_dubovik_fu_et al._2020, title={Intercomparison of Magnitudes and Trends in Anthropogenic Surface Emissions From Bottom‐Up Inventories, Top‐Down Estimates, and Emission Scenarios}, volume={8}, url={http://dx.doi.org/10.1029/2020ef001520}, DOI={10.1029/2020ef001520}, abstractNote={Abstract}, number={8}, journal={Earth's Future}, publisher={American Geophysical Union (AGU)}, author={Elguindi, Nellie and Granier, C. and Stavrakou, Trisevgeni and Darras, S. and Bauwens, M. and Cao, Hansen and CHEN, CHENG and Gon, Hugo Denier and Dubovik, Oleg and Fu, Tzung-May and et al.}, year={2020}, month={Aug} }
 @article{wang_wang_xu_henze_qu_yang_2020, title={Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 1: Formulation and sensitivity analysis}, volume={20}, url={http://dx.doi.org/10.5194/acp-20-6631-2020}, DOI={10.5194/acp-20-6631-2020}, abstractNote={Abstract. SO2 and NO2 observations from the Ozone Mapping and Profiler Suite (OMPS) sensor are used for the first time in conjunction with the GEOS-Chem adjoint model to optimize both SO2 and NOx emission estimates over China for October 2013. Separate and joint (simultaneous) optimizations of SO2 and NO2 emissions are both conducted and compared. Posterior emissions, compared to the prior, yield improvements in simulating columnar SO2 and NO2, in comparison to measurements from the Ozone Monitoring Instrument (OMI) and OMPS. The posterior SO2 and NOx emissions from separate inversions are 748 Gg S and 672 Gg N, which are 36 % and 6 % smaller than prior MIX emissions (valid for 2010), respectively. In spite of the large reduction of SO2 emissions over the North China Plain, the simulated sulfate–nitrate–ammonium aerosol optical depth (AOD) only decrease slightly, which can be attributed to (a) nitrate rather than sulfate as the dominant contributor to AOD and (b) replacement of ammonium sulfate with ammonium nitrate as SO2 emissions are reduced. For joint inversions, both data quality control and the weight given to SO2 relative to NO2 observations can affect the spatial distributions of the posterior emissions. When the latter is properly balanced, the posterior emissions from assimilating OMPS SO2 and NO2 jointly yield a difference of −3 % to 15 % with respect to the separate assimilations for total anthropogenic SO2 emissions and ±2 % for total anthropogenic NOx emissions; but the differences can be up to 100 % for SO2 and 40 % for NO2 in some grid cells. Improvements on SO2 and NO2 simulations from the joint inversions are overall consistent with those from separate inversions. Moreover, the joint assimilations save ∼ 50 % of the computational time compared to assimilating SO2 and NO2 separately in a sequential manner of computation. The sensitivity analysis shows that a perturbation of NH3 to 50 % (20 %) of the prior emission inventory can (a) have a negligible impact on the separate SO2 inversion but can lead to a decrease in posterior SO2 emissions over China by −2.4 % (−7.0 %) in total and up to −9.0 % (−27.7 %) in some grid cells in the joint inversion with NO2 and (b) yield posterior NOx emission decreases over China by −0.7 % (−2.8 %) for the separate NO2 inversion and by −2.7 % (−5.3 %) in total and up to −15.2 % (−29.4 %) in some grid cells for the joint inversion. The large reduction of SO2 between 2010 and 2013, however, only leads to ∼ 10 % decrease in AOD regionally; reducing surface aerosol concentration requires the reduction of emissions of NH3 as well.}, number={11}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Wang, Yi and Wang, Jun and Xu, Xiaoguang and Henze, Daven K. and Qu, Zhen and Yang, Kai}, year={2020}, month={Jun}, pages={6631–6650} }
 @article{qu_henze_theys_wang_wang_2019, title={Hybrid Mass Balance/4D‐Var Joint Inversion of NOx and SO2 Emissions in East Asia}, volume={124}, url={http://dx.doi.org/10.1029/2018jd030240}, DOI={10.1029/2018jd030240}, abstractNote={Abstract}, number={14}, journal={Journal of Geophysical Research: Atmospheres}, publisher={American Geophysical Union (AGU)}, author={Qu, Zhen and HENZE, DAVEN and Theys, Nicolas and Wang, Jun and Wang, Wei}, year={2019}, month={Jul}, pages={8203–8224} }
 @article{qu_henze_li_theys_wang_wang_wang_han_shim_dickerson_et al._2019, title={SO2 Emission Estimates Using OMI SO2 Retrievals for 2005–2017}, volume={124}, url={http://dx.doi.org/10.1029/2019jd030243}, DOI={10.1029/2019jd030243}, abstractNote={Abstract}, number={14}, journal={Journal of Geophysical Research: Atmospheres}, publisher={American Geophysical Union (AGU)}, author={Qu, Zhen and HENZE, DAVEN and Li, Can and Theys, Nicolas and Wang, Yi and Wang, Jun and Wang, Wei and Han, Jihyun and Shim, Changsub and Dickerson, Russell and et al.}, year={2019}, month={Jul}, pages={8336–8359} }
 @article{jiang_mcdonald_worden_worden_miyazaki_qu_henze_jones_arellano_fischer_et al._2018, title={Unexpected slowdown of US pollutant emission reduction in the past decade}, volume={115}, url={http://dx.doi.org/10.1073/pnas.1801191115}, DOI={10.1073/pnas.1801191115}, abstractNote={Significance}, number={20}, journal={Proceedings of the National Academy of Sciences}, publisher={Proceedings of the National Academy of Sciences}, author={Jiang, Zhe and McDonald, Brian C. and Worden, Helen and Worden, John R. and Miyazaki, Kazuyuki and Qu, Zhen and Henze, Daven K. and Jones, Dylan B. A. and Arellano, Avelino and Fischer, Emily V. and et al.}, year={2018}, month={May}, pages={5099–5104} }
 @article{qu_henze_capps_wang_xu_wang_keller_2017, title={Monthly top‐down NOx emissions for China (2005–2012): A hybrid inversion method and trend analysis}, volume={122}, url={http://dx.doi.org/10.1002/2016jd025852}, DOI={10.1002/2016jd025852}, abstractNote={Abstract}, number={8}, journal={Journal of Geophysical Research: Atmospheres}, publisher={American Geophysical Union (AGU)}, author={Qu, Zhen and HENZE, DAVEN and Capps, Shannon and Wang, Yi and Xu, Xiaoguang and Wang, Jun and Keller, Martin}, year={2017}, month={Apr}, pages={4600–4625} }
 @article{wang_wang_xu_henze_wang_qu_2016, title={A new approach for monthly updates of anthropogenic sulfur dioxide emissions from space: Application to China and implications for air quality forecasts}, volume={43}, url={http://dx.doi.org/10.1002/2016gl070204}, DOI={10.1002/2016gl070204}, abstractNote={Abstract}, number={18}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Wang, Yi and Wang, Jun and Xu, Xiaoguang and Henze, Daven K. and Wang, Yuxuan and Qu, Zhen}, year={2016}, month={Sep}, pages={9931–9938} }