This study uses multi-model ensemble results of 11 models from the second
phase of Task Force Hemispheric Transport of Air Pollution (HTAP II) to
calculate the global sulfur (S) and nitrogen (N) ...deposition in 2010. Modeled
wet deposition is evaluated with observation networks in North America,
Europe and East Asia. The modeled results agree well with observations, with
76–83 % of stations being predicted within ±50 % of
observations. The models underestimate SO42-, NO3- and
NH4+ wet depositions in some European and East Asian stations but
overestimate NO3- wet deposition in the eastern United States.
Intercomparison with previous projects (PhotoComp, ACCMIP and HTAP I) shows
that HTPA II has considerably improved the estimation of deposition at European
and East Asian stations. Modeled dry deposition is generally higher than the
“inferential” data calculated by observed concentration and modeled
velocity in North America, but the inferential data have high uncertainty,
too. The global S deposition is 84 Tg(S) in 2010, with 49 % in continental
regions and 51 % in the ocean (19 % of which coastal). The global N
deposition consists of 59 Tg(N) oxidized nitrogen (NOy) deposition and
64 Tg(N) reduced nitrogen (NHx) deposition in 2010. About 65 %
of N is deposited in continental regions, and 35 % in the ocean (15 %
of which coastal). The estimated outflow of pollution from land to ocean is
about 4 Tg(S) for S deposition and 18 Tg(N) for N deposition. Comparing our
results to the results in 2001 from HTAP I, we find that the global
distributions of S and N deposition have changed considerably during the
last 10 years. The global S deposition decreases 2 Tg(S) (3 %) from 2001
to 2010, with significant decreases in Europe (5 Tg(S) and 55 %), North
America (3 Tg(S) and 29 %) and Russia (2 Tg(S) and 26 %), and
increases in South Asia (2 Tg(S) and 42 %) and the Middle East (1 Tg(S)
and 44 %). The global N deposition increases by 7 Tg(N) (6 %),
mainly contributed by South Asia (5 Tg(N) and 39 %), East Asia (4 Tg(N)
and 21 %) and Southeast Asia (2 Tg(N) and 21 %). The NHx
deposition increases with no control policy on NH3 emission in North
America. On the other hand, NOy deposition has started to dominate in
East Asia (especially China) due to boosted NOx emission.
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 toNO2 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 anthropogenicNOx emissions; but the differences can be up to 100 % for SO2 and 40 % for NO2 in some grid cells. Improvements on SO2 andNO2 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 ofNH3 as well.
The recent update on the US National Ambient Air Quality Standards (NAAQS) of the ground-level ozone (O
/ can benefit from a better understanding of its source contributions in different US regions ...during recent years. In the Hemispheric Transport of Air Pollution experiment phase 1 (HTAP1), various global models were used to determine the O
source-receptor (SR) relationships among three continents in the Northern Hemisphere in 2001. In support of the HTAP phase 2 (HTAP2) experiment that studies more recent years and involves higher-resolution global models and regional models' participation, we conduct a number of regional-scale Sulfur Transport and dEposition Model (STEM) air quality base and sensitivity simulations over North America during May-June 2010. STEM's top and lateral chemical boundary conditions were downscaled from three global chemical transport models' (i.e., GEOS-Chem, RAQMS, and ECMWF C-IFS) base and sensitivity simulations in which the East Asian (EAS) anthropogenic emissions were reduced by 20 %. The mean differences between STEM surface O
sensitivities to the emission changes and its corresponding boundary condition model's are smaller than those among its boundary condition models, in terms of the regional/period-mean (<10 %) and the spatial distributions. An additional STEM simulation was performed in which the boundary conditions were downscaled from a RAQMS (Realtime Air Quality Modeling System) simulation without EAS anthropogenic emissions. The scalability of O
sensitivities to the size of the emission perturbation is spatially varying, and the full (i.e., based on a 100% emission reduction) source contribution obtained from linearly scaling the North American mean O
sensitivities to a 20% reduction in the EAS anthropogenic emissions may be underestimated by at least 10 %. The three boundary condition models' mean O
sensitivities to the 20% EAS emission perturbations are ~8% (May-June 2010)/~11% (2010 annual) lower than those estimated by eight global models, and the multi-model ensemble estimates are higher than the HTAP1 reported 2001 conditions. GEOS-Chem sensitivities indicate that the EAS anthropogenic NO
emissions matter more than the other EAS O
precursors to the North American O
, qualitatively consistent with previous adjoint sensitivity calculations. In addition to the analyses on large spatial-temporal scales relative to the HTAP1, we also show results on subcontinental and event scales that are more relevant to the US air quality management. The EAS pollution impacts are weaker during observed O
exceedances than on all days in most US regions except over some high-terrain western US rural/remote areas. Satellite O
(TES, JPL-IASI, and AIRS) and carbon monoxide (TES and AIRS) products, along with surface measurements and model calculations, show that during certain episodes stratospheric O
intrusions and the transported EAS pollution influenced O
in the western and the eastern US differently. Free-running (i.e., without chemical data assimilation) global models underpredicted the transported background O
during these episodes, posing difficulties for STEM to accurately simulate the surface O
and its source contribution. Although we effectively improved the modeled O
by incorporating satellite O
(OMI and MLS) and evaluated the quality of the HTAP2 emission inventory with the Royal Netherlands Meteorological Institute-Ozone Monitoring Instrument (KNMI-OMI) nitrogen dioxide, using observations to evaluate and improve O
source attribution still remains to be further explored.
We interpret space-borne observations from the TROPOspheric Monitoring Instrument (TROPOMI) in a multi-inversion framework to characterize the 2018–2019 global methane budget. Evaluation of the ...inverse solutions indicates that simultaneous source + sink optimization using methane observations alone remains an ill-posed problem – even with the dense TROPOMI sampling coverage. Employing remote carbon monoxide (CO) and hydroxyl radical (OH) observations with independent methane measurements to distinguish between candidate solutions, we infer from TROPOMI a global methane source of 587 (586–589) Tg yr−1 and sink of 571 Tg yr−1 for our analysis period. We apply a new downscaling method to map the derived monthly emissions to 0.1∘ × 0.1∘ resolution, using the results to uncover key gaps in the prior methane budget. The TROPOMI data point to an underestimate of tropical wetland emissions (a posteriori increase of +13 % 6 %–25 % or 20 7–25 Tg yr−1), with adjustments following regional hydrology. Some simple wetland parameterizations represent these patterns as accurately as more sophisticated process-based models. Emissions from fossil fuel activities are strongly underestimated over the Middle East (+5 2–6 Tg yr−1 a posteriori increase) and over Venezuela. The TROPOMI observations also reveal many fossil fuel emission hotspots missing from the prior inventory, including over Mexico, Oman, Yemen, Turkmenistan, Iran, Iraq, Libya, and Algeria. Agricultural methane sources are underestimated in India, Brazil, the California Central Valley, and Asia. Overall, anthropogenic sources worldwide are increased by +19 11–31 Tg yr−1 over the prior estimate. More than 45 % of this adjustment occurs over India and Southeast Asia during the summer monsoon (+8.5 3.1–10.7 Tg in July–October), likely due to rainfall-enhanced emissions from rice, manure, and landfills/sewers, which increase during this season along with the natural wetland source.
We apply airborne measurements across three seasons (summer, winter and spring 2017-2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key ...agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 16-23 Gg/d), while livestock (enteric/manure; 25 %, 15 14-17 Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature-hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with the Gridded EPA estimates during spring (to within 7 %) but are ∼25 % higher during summer and winter. Spatial analysis further shows good top-down and bottom-up agreement for beef facilities (with mainly enteric emissions) but larger (∼30 %) seasonal discrepancies for dairies and hog farms (with >40 % manure emissions). Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management.
We use a chemical transport model and its adjoint to examine the sensitivity of secondary inorganic aerosol formation to emissions of precursor trace gases from Asia. Sensitivity simulations indicate ...that secondary inorganic aerosol mass concentrations are most sensitive to ammonia (NH3) emissions in winter and to sulfur dioxide (SO2) emissions during the rest of the year. However, in the annual mean, the perturbations on Asian population‐weighted ground‐level secondary inorganic aerosol concentrations of 34% due to changing nitrogen oxide (NOx) emissions are comparable to those from changing either SO2 (41%) or NH3 (25%) emissions. The persistent sensitivity to NOx arises from the regional abundance of NH3 over Asia that promotes ammonium nitrate formation. IASI satellite observations corroborate the NH3 abundance. Projected emissions for 2020 indicate continued sensitivity to NOx emissions. We encourage more attention to NOx controls in addition to SO2 and NH3 controls to reduce ground‐level East Asian aerosol.
Key Point
Ground‐level Asian aerosols are sensitive to nitrogen oxide emissions.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Ammonia (NH3) emissions have large impacts on air quality and nitrogen
deposition, influencing human health and the well-being of sensitive
ecosystems. Large uncertainties exist in the “bottom-up” ...NH3 emission
inventories due to limited source information and a historical lack of
measurements, hindering the assessment of NH3-related environmental
impacts. The increasing capability of satellites to measure NH3
abundance and the development of modeling tools enable us to better
constrain NH3 emission estimates at high spatial resolution. In this
study, we constrain the NH3 emission estimates from the widely used
2011 National Emissions Inventory (2011 NEI) in the US using Infrared
Atmospheric Sounding Interferometer NH3 column density measurements
(IASI-NH3) gridded at a 36 km by 36 km horizontal resolution. With a
hybrid inverse modeling approach, we use the Community Multiscale Air Quality Modeling System (CMAQ) and its multiphase adjoint model to optimize NH3 emission estimates in April, July, and October.
Our optimized emission estimates suggest that the total NH3 emissions
are biased low by 26 % in 2011 NEI in April with overestimation in the Midwest
and underestimation in the Southern States. In July and October, the
estimates from NEI agree well with the optimized emission estimates, despite
a low bias in hotspot regions. Evaluation of the inversion performance using
independent observations shows reduced underestimation in simulated ambient
NH3 concentration in all 3 months and reduced underestimation in
NH4+ wet deposition in April. Implementing the optimized NH3
emission estimates improves the model performance in simulating PM2.5
concentration in the Midwest in April. The model results suggest that the
estimated contribution of ammonium nitrate would be biased high in a priori
NEI-based assessments. The higher emission estimates in this study also
imply a higher ecological impact of nitrogen deposition originating from
NH3 emissions.
This study investigates emission impacts of introducing inexpensive and efficient electric vehicles into the US light duty vehicle (LDV) sector. Scenarios are explored using the ANSWER-MARKAL model ...with a modified version of the Environmental Protection Agency’s (EPA) 9-region database. Modified cost and performance projections for LDV technologies are adapted from the National Research Council (2013) optimistic case. Under our optimistic scenario (OPT) we find 15% and 47% adoption of battery electric vehicles (BEVs) in 2030 and 2050, respectively. In contrast, gasoline vehicles (ICEVs) remain dominant through 2050 in the EPA reference case (BAU). Compared to BAU, OPT gives 16% and 36% reductions in LDV greenhouse gas (GHG) emissions for 2030 and 2050, respectively, corresponding to 5% and 9% reductions in economy-wide emissions. Total nitrogen oxides, volatile organic compounds, and SO2 emissions are similar in the two scenarios due to intersectoral shifts. Moderate, economy-wide GHG fees have little effect on GHG emissions from the LDV sector but are more effective in the electricity sector. In the OPT scenario, estimated well-to-wheels GHG emissions from full-size BEVs with 100-mile range are 62 gCO2-e mi–1 in 2050, while those from full-size ICEVs are 121 gCO2-e mi–1.
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IJS, KILJ, NUK, PNG, UL, UM
We perform observing system simulation experiments
(OSSEs) with the GEOS-Chem adjoint model to test how well methane emissions
over North America can be resolved using measurements from the ...TROPOspheric
Monitoring Instrument (TROPOMI) and similar high-resolution satellite
sensors. We focus analysis on the impacts of (i) spatial errors in the prior
emissions and (ii) model transport errors. Along with a standard
scale factor (SF) optimization we conduct a set of inversions using
alternative formalisms that aim to overcome limitations in the SF-based
approach that arise for missing sources. We show that 4D-Var analysis of the
TROPOMI data can improve monthly emission estimates at 25 km even with a
spatially biased prior or model transport errors (42 %–93 % domain-wide
bias reduction; R increases from 0.51 up to 0.73). However, when both errors
are present, no single inversion framework can successfully improve both the
overall bias and spatial distribution of fluxes relative to the prior on the
25 km model grid. In that case, the ensemble-mean optimized fluxes have a
domain-wide bias of 77 Gg d−1 (comparable to that in the prior), with
spurious source adjustments compensating for the transport errors.
Increasing observational coverage through longer-timeframe inversions does
not significantly change this picture. An inversion formalism that optimizes
emission enhancements rather than scale factors exhibits the best
performance for identifying missing sources, while an approach combining a
uniform background emission with the prior inventory yields the best
performance in terms of overall spatial fidelity – even in the presence of
model transport errors. However, the standard SF optimization outperforms
both of these for the magnitude of the domain-wide flux. For the common
scenario in which prior errors are non-random, approximate posterior error
reduction calculations (derived via gradient-based randomization) for the
inversions reflect the sensitivity to observations but have no spatial
correlation with the actual emission improvements. This demonstrates that
such information content analysis can be used for general observing system
characterization but does not describe the spatial accuracy of the posterior
emissions or of the actual emission improvements. Findings here highlight
the need for careful evaluation of potential missing sources in prior
emission datasets and for robust accounting of model transport errors in
inverse analyses of the methane budget.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Here we present results from an evaluation of model simulations from the
International Hemispheric Transport of Air Pollution Phase II (HTAPII) and
Chemistry Climate Model Initiative (CCMI) model ...inter-comparison projects
against a comprehensive series of ground-based, aircraft and satellite
observations of ozone mixing ratios made at various locations across India.
The study focuses on the recent past (observations from 2008 to 2013, models
from 2009–2010) as this is most pertinent to understanding the health
impacts of ozone. To our understanding this is the most comprehensive
evaluation of these models' simulations of ozone across the Indian
subcontinent to date. This study highlights some significant successes and
challenges that the models face in representing the oxidative chemistry of
the region. The multi-model range in area-weighted surface ozone over the Indian
subcontinent is 37.26–56.11 ppb, whilst the population-weighted range is
41.38–57.5 ppb. When compared against surface observations from the
Modelling Atmospheric Pollution and Networking (MAPAN) network of eight
semi-urban monitoring sites spread across India, we find that the models tend
to simulate higher ozone than that which is observed. However, observations
of NOx and CO tend to be much higher than modelled mixing
ratios, suggesting that the underlying emissions used in the models do not
characterise these regions accurately and/or that the resolution of the
models is not adequate to simulate the photo-chemical environment of these
surface observations. Empirical orthogonal function (EOF) analysis is used in
order to identify the extent to which the models agree with regards to the
spatio-temporal distribution of the tropospheric ozone column, derived using
OMI-MLS observations. We show that whilst the models agree with the spatial
pattern of the first EOF of observed tropospheric ozone column, most of the
models simulate a peak in the first EOF seasonal cycle represented by
principle component 1, which is later than the observed peak. This suggests a
widespread systematic bias in the timing of emissions or some other unknown
seasonal process. In addition to evaluating modelled ozone mixing ratios, we explore modelled
emissions of NOx, CO, volatile organic compounds (VOCs) and the ozone response to the
emissions. We find a high degree of variation in emissions from
non-anthropogenic sources (e.g. lightning NOx and biomass
burning CO) between models. Total emissions of NOx and CO
over India vary more between different models in the same model intercomparison project (MIP) than the same
model used in different MIPs, making it impossible to diagnose whether
differences in modelled ozone are due to emissions or model processes. We
therefore recommend targeted experiments to pinpoint the exact causes of
discrepancies between modelled and observed ozone and ozone precursors for
this region. To this end, a higher density of long-term monitoring sites
measuring not only ozone but also ozone precursors including speciated VOCs,
located in more rural regions of the Indian subcontinent, would enable
improvements in assessing the biases in models run at the resolution found in
HTAPII and CCMI.
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