Figure 8. Comparison of observed and modelled surface
concentrations of NH3. Data are EMEP and UKEAP site
measurements (points) and the model (background) for March-September
2016. Inset values are the Pearson’s spatio-temporal correlation
coefficient (R ) and the model NMB for coincident monthly means.
6 Error analysis of the top-down emissions
The reported relative error for NAEI NH3 emissions is
31% (Ricardo, 2018b). Quantifiable random errors that contribute to
total March-September satellite-derived emissions include uncertainties
in retrieval of NH3, and in the modelled relationship
between NH3 emissions and column densities (Eq. (1)).
For the latter we test sensitivity to modelled sulfate aerosol and
nitric acid abundances and prior assumptions of the spatial and temporal
variability of NH3 emissions. IASI NH3retrieval errors for columns ≥ 2 × 1015 molecules
cm-2 range from 0.7-34%. Retrieval errors larger than
34% do occur, but are in locations with very low emissions. The CrIS
NH3 column errors across all grids range from 0.2-25%.
Errors due to uncertainties in the magnitude and variability in
SO2 and NOx emissions that affect
abundance of sulfate and nitrate aerosols and hence the abundance and
vertical distribution of NH3 are small compared to
column density retrieval errors. We estimate the error contribution of
these as the change in top-down emissions due to a perturbation in
SO2 emissions for sulfate and NOxemissions for nitric acid. The percent change in top-down emissions from
a 50% decrease in SO2 emissions is 4-5%. A 50%
increase in NOx emissions increases nitric acid by 14%,
aerosol nitrate by 11%, and satellite-derived NH3emissions by 8-9%. The limited sensitivity to sulfate and nitrate in
the UK is because NH3 is in excess due to the success of
emission controls targeting SO2 and NOxsources and absence of these for NH3 sources. This would
not occur in regions and times with large unregulated
SO2 and NOx sources. We find that
(E /Ω)model used to convert satellite observations
of column densities to emissions (Eq. (1)) is relatively insensitive to
pertubations in NH3 emissions, so is relatively
unaffected by errors in the spatial and temporal variability of
NH3 emissions in GEOS-Chem. A 50% increase in
NH3 emissions only causes a small (3-4%) decrease in
satellite-derived NH3 emissions. The total relative
error from adding these individual errors in quadrature is 11-36% for
IASI and 9-27% for CrIS and is dominated by errors in retrieval of the
columns. Total emissions for March-September are 198.7 ± 61.6 Gg for the
bottom-up emissions and up to 271.5 ± 97.7 Gg for IASI and 389.4 ± 105.1
Gg for CrIS.
There are also known systematic biases in the satellite observations.
Some studies reported that IASI NH3 column densities are
biased low by 25-50% compared to ground-based measurements (Dammers et
al., 2017; Whitburn et al., 2016a). However, these comparisons were for
earlier versions of the IASI NH3 product. The version
used here is consistent with columns derived with aircraft observations
(Guo et al., 2021), though Guo et al. (2021) caution that their
comparison is limited in time (summer) and location (Colorado, US) and
sensitive to errors in column estimates from integrating aircraft
measurements. There are no observations of the vertical distribution of
NH3 over the UK. The CrIS column amounts display a
gradual increase with time (Figure S1) that we correct for in this work,
though further work is required to determine the cause. Biases in the
satellite-derived emissions due to differences in overpass times of the
two instruments is mitigated by sampling modelled columns
(Ωmodel in Eq. (1)) during the satellite overpass.
Both satellite products preferentially sample clear-sky conditions. The
bias that this imparts on the top-down emissions estimates is
challenging to quantify. The modelled emissions and columns used to
derive top-down emissions ((E /Ω)model in Eq. (1))
are sampled under all-sky conditions, though there would likely be
compensating effects of sampling clear-sky conditions on
(E /Ω)model. Warmer temperatures and absence of
clouds increase Ω by suppressing the amount of NH3 that
partitions to the aqueous phase (Stelson & Seinfeld, 1982; Walters et
al., 2018), but E l also increases in response to
warmer temperatures (Sutton et al., 2013). Preferentially sampling
clear-sky conditions likely has the largest impact on
Ωsat. We find that the effect is greatest in July when
boundary-layer clear-sky air temperatures, according to GEOS-Chem, are
warmer than all-sky scenes by 5.6ºC during the morning overpass and
5.3ºC during the afternoon overpass. According to Sutton et al. (2013),
5°C warmer temperatures increase NH3 emissions by 42%.
Clear-sky temperatures are only 1.6-1.7 ºC warmer in the preceding month
(June), so the greater clear-sky temperature in July may in part account
for the discrepancies between observed and modelled NH3emissions in that month (Figure 6) and the steep increase in July
columns and emissions relative to June (Figures 7 and S5). A challenge
though of using GEOS-Chem to diagnose sensitivity of air temperature to
cloud cover is that the model is inferior to the satellite observations
at resolving clouds, due to its coarser spatial resolution (25-31 km),
and only 3-12% of daily overpass model data are retained in each month
after filtering for cloudy scenes (GEOS-FP cloud fractions
> 0.1). NH3 emissions in GEOS-Chem also do
not include changes in farming practices in response to shifts in
meteorology.
7 Conclusions
Emissions of ammonia (NH3) in the UK are mostly
(>80%) from agriculture and are challenging to estimate
with bottom-up approaches and validate exclusively with current
ground-based networks. Here we used satellite observations of
NH3 in March-September for multiple years from the
Infrared Atmospheric Sounding Interferometer (IASI) (2008-2018) and the
Cross-track Infrared Sounder (CrIS) (2013-2018) with the GEOS-Chem
chemical transport model to derive top-down monthly emissions across the
UK at high spatial resolution (~10 km).
Total top-down March-September emissions are 272 Gg from IASI and 389 Gg
from CrIS. Bottom-up emissions estimated with the UK National
Atmospheric Emission Inventory (NAEI) annual emissions and GEOS-Chem
monthly scaling factors are 27% less than IASI-derived emissions and
49% less than CrIS-derived emissions. This is supported by a 38-42%
underestimate in surface NH3 concentrations from
GEOS-Chem driven with the NAEI. We infer UK top-down annual
anthropogenic NH3 emissions of 383-431 Gg from IASI and
559-642 Gg from CrIS compared to 276 Gg from the NAEI. Seasonality in
the top-down emissions confirms the well-known spring April peak from
fertilizer and manure use, but there is also a summer July peak
coincident with intensive dairy farming that is absent in the bottom-up
emissions.
The relative error in the top-down emissions, mostly due to
NH3 column retrieval errors, is 11-36% for IASI and
9-27% for CrIS and is similar to the error reported for the NAEI
(31%). The top-down emissions estimates are relatively insensitive to
model uncertainties in SO2, NOx and
NH3 emissions, as NH3 is in excess and
the relationship between modelled NH3 columns and
emissions is near-linear.
Our study demonstrates the tremendous potential to use satellite
observations to derive NH3 emissions and assess
bottom-up emissions under particularly challenging observing conditions
(cloudy, cool) in the UK. This is critical for assessing reliability of
inventories used to inform policies and mitigation strategies. The
discrepancy between bottom-up and top-down emissions identified here
warrants further investigation of both approaches.
Acknowledgments, Samples, and Data
The authors are grateful for helpful discussions with Daven Henze and
Hansen Cao. EAM and AKP acknowledge funding from DEFRA (contract
reference ecm_55415) and EAM acknowledges additional funding from
NERC/EPSRC (grant number EP/R513465/1). The research in Belgium was
funded by the F.R.S.-FNRS and the Belgian State Federal Office for
Scientific, Technical and Cultural Affairs (Prodex 645 arrangement
IASI.FLOW). Both MVD and LC are supported by the Belgian F.R.S.-FNRS.
The top-down and bottom-up emissions estimated in this work are publicly
available from the UCL Data Repository
(https://doi.org/10.5522/04/14566635). The CrIS CFPR
NH3 data are created by Environment and Climate Change
Canada and hosted by the Meteorological Service of Canada (MSC)
Datamart. Access to the CrIS NH3 data can be requested
from MWS (mark.shephard@canada.ca). The IASI NH3 data
are publicly available from the IASI data catalogue
(https://iasi.aeris-data.fr/nh3/).
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