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\begin{document}

\title{UK ammonia emissions estimated with satellite observations and GEOS-Chem}



\author[1,1]{Eloise Ann Marais}%
\author[2,2]{Alok K Pandey}%
\author[3,3]{Martin Van Damme}%
\author[4,4]{Lieven Clarisse}%
\author[5,5]{Pierre-Francois Coheur}%
\author[6,6]{Mark W. Shephard}%
\author[7,7]{Karen Cady-Pereira}%
\author[8,8]{Tom Misselbrook}%
\author[9,9]{Lei Zhu}%
\author[10,10]{Gan Luo}%
\author[11,11]{Fangqun Yu}%
\affil[1]{University College London}%
\affil[2]{University of Leicester}%
\affil[3]{Universite Libre de Bruxelles}%
\affil[4]{Universite libre de Bruxelles (ULB)}%
\affil[5]{Universite Libre de Bruxelles CP160/09}%
\affil[6]{Environment and Climate Change Canada}%
\affil[7]{Atmospheric and Environmental Research, Inc.}%
\affil[8]{Rothamsted Research Ltd}%
\affil[9]{School of Environmental Science and Engineering, Southern University of Science and Technology}%
\affil[10]{ASRC, SUNY-Albany}%
\affil[11]{University at Albany}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Agricultural emissions of ammonia (NH3) impact air quality, human
health, and the vitality of aquatic and terrestrial ecosystems. In the
UK, there are few direct policies regulating anthropogenic NH3 emissions
and development of sustainable mitigation measures necessitates reliable
emissions estimates. Here we use observations of column densities of NH3
from two space-based sensors (IASI and CrIS) with the GEOS-Chem model to
derive top-down NH3 emissions for the UK at fine spatial
(\textasciitilde{}10 km) and time (monthly) scales. We focus on
March-September when there is adequate spectral signal to reliably
retrieve NH3. We estimate total emissions of 272 Gg from IASI and 389 Gg
from CrIS. Bottom-up emissions are 27\% less than IASI and 49\% less
than CrIS. There are also differences in seasonality. Top-down and
bottom-up emissions agree on a spring April peak due to fertilizer and
manure application, but there is also a comparable summer July peak in
the top-down emissions that is not in the bottom-up emissions and
appears to be associated with dairy cattle farming. We estimate relative
errors in the top-down emissions of 11-36\% for IASI and 9-27\% for
CrIS, dominated by column density retrieval errors. The bottom-up versus
top-down emissions discrepancies estimated in this work impact model
predictions of the environmental damage caused by NH3 emissions and
warrant further investigation.%
\end{abstract}%



\sloppy


Eloise A. Marais\textsuperscript{1}, Alok K. Pandey\textsuperscript{2},
Martin Van Damme\textsuperscript{3}, Lieven Clarisse\textsuperscript{3},
Pierre-Fran\selectlanguage{ngerman}çois Coheur\textsuperscript{3}, Mark W.
Shephard\textsuperscript{4}, Karen E. Cady-Pereira\textsuperscript{5},
Tom Misselbrook\textsuperscript{6}, Lei Zhu\textsuperscript{7}, Gan
Luo\textsuperscript{8}, Fangqun Yu\textsuperscript{8}

\textsuperscript{1}Department of Geography, University College London,
London, UK.\textsuperscript{2} School of Physics and Astronomy,
University of Leicester, Leicester, UK.\textsuperscript{3} Université
libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and
Atmospheric Remote Sensing (SQUARES), Brussels,
Belgium.\textsuperscript{4} Environment and Climate Change Canada,
Toronto, Ontario, Canada.\textsuperscript{5} Atmospheric and
Environmental Research (AER), Lexington, MA, USA.\textsuperscript{6}
Department of Sustainable Agriculture Sciences, Rothamsted Research,
Okehampton, UK.\textsuperscript{7} School of Environmental Science and
Engineering, Southern University of Science and Technology, Shenzhen,
China.\textsuperscript{8} Atmospheric Sciences Research Center,
University at Albany, Albany, New York, USA.

Corresponding author: Eloise A. Marais (e.marais@ucl.ac.uk)

Key Points:

\begin{itemize}
\tightlist
\item
  Satellite observations of NH\textsubscript{3} from 2 sensors (IASI,
  CrIS) are used to estimate UK NH\textsubscript{3} emissions in Mar-Sep
  at fine scales (10 km, monthly)
\item
  Satellite-derived NH\textsubscript{3} emissions total 272 Gg from IASI
  and 389 Gg from CrIS and exhibit a spring (April) and summer (July)
  peak
\item
  Bottom-up emissions used for research and policy are 27-49\% less than
  the satellite-derived estimates and miss the summer emissions peak
\end{itemize}

Abstract

Agricultural emissions of ammonia (NH\textsubscript{3}) impact air
quality, human health, and the vitality of aquatic and terrestrial
ecosystems. In the UK, there are few direct policies regulating
anthropogenic NH\textsubscript{3} emissions and development of
sustainable mitigation measures necessitates reliable emissions
estimates. Here we use observations of column densities of
NH\textsubscript{3} from two space-based sensors (IASI and CrIS) with
the GEOS-Chem model to derive top-down NH\textsubscript{3} emissions for
the UK at fine spatial (\textasciitilde{}10 km) and time (monthly)
scales. We focus on March-September when there is adequate spectral
signal to reliably retrieve NH\textsubscript{3}. We estimate total
emissions of 272 Gg from IASI and 389 Gg from CrIS. Bottom-up emissions
are 27\% less than IASI and 49\% less than CrIS. There are also
differences in seasonality. Top-down and bottom-up emissions agree on a
spring April peak due to fertilizer and manure application, but there is
also a comparable summer July peak in the top-down emissions that is not
in the bottom-up emissions and appears to be associated with dairy
cattle farming. We estimate relative errors in the top-down emissions of
11-36\% for IASI and 9-27\% for CrIS, dominated by column density
retrieval errors. The bottom-up versus top-down emissions discrepancies
estimated in this work impact model predictions of the environmental
damage caused by NH\textsubscript{3} emissions and warrant further
investigation.

\textbf{Plain Language Summary}

Emissions of ammonia, mostly from agriculture, are often a dominant
contributor to fine particles in countries with well-established
policies that have led to large reductions in other precursors of such
pollutants detrimental to our health. Here we use a model and
observations of ammonia from two space-based sensors to estimate
emissions in the UK where there are no direct policies regulating
agricultural sources of ammonia. The satellite-derived emissions,
limited to March-September when conditions are ideal for viewing ammonia
from space, total 272 kilotonnes from an instrument that passes overhead
in the morning and 389 kilotonnes from an instrument with a midday
overpass. Though the emissions estimates differ for the two instruments,
both exhibit a spring (April) peak due to fertilizer and manure use and
summer (July) peak likely associated with dairy cattle farming. The
summer peak is missing in bottom-up emissions and total March-September
emissions from these inventories are also 27-49\% less than those
derived with satellites. Further research is needed to address these
discrepancies, as such inventories are widely used for developing
policies and assessing environmental damage caused by ammonia.

1 Introduction

Agricultural practices such as synthetic fertilizer and manure use and
livestock farming release large quantities of ammonia
(NH\textsubscript{3}) to the atmosphere. Once emitted,
NH\textsubscript{3} partitions to acidic aerosols to form ammonium that
contributes to mass concentrations of fine particles
(PM\textsubscript{2.5}) hazardous to health (Cohen et al., 2017; Dockery
et al., 1993; Vohra et al., 2021b). NH\textsubscript{3} and ammonium
also deposit to the Earth's surface and drastically alter the natural
nitrogen balance of terrestrial and aquatic ecosystems (Galloway, 1998;
Johnson \& Carpenter, 2010; Vitousek et al., 1997).

In the UK, agriculture is the dominant (\textgreater{}80\%) source of
NH\textsubscript{3} emissions (Ricardo, 2018b), mostly from nitrogen
fertilizer use, manure management, and farming of dairy and beef cattle
(DEFRA, 2019). Modelling studies suggest that the largest and most
extensive decline in PM\textsubscript{2.5} in the UK would be achieved
by targeting NH\textsubscript{3} sources (Vieno et al., 2016), but only
large pig and poultry farms are required to adopt best practices and
technologies that reduce NH\textsubscript{3} emissions (DEFRA, 2019).
There are additional policy options under consideration, such as
limiting the use of solid urea fertilizer, a large source of
NH\textsubscript{3} in the UK (DEFRA, 2020a). The UK is a signatory of
the United Nations Economic Commission for Europe (UNECE) Gothenburg
protocol, lesgislated through the UK National Emission Ceilings
Regulations adopted in 2018 (UK, 2018). This commits the UK to an
anthropogenic NH\textsubscript{3} emission ceiling of 297 Gg, informed
by annual emissions estimates from the UK National Atmospheric Emissions
Inventory (NAEI). The UK is also required as part of the protocol to
reduce NH\textsubscript{3} emissions by 8\% in 2020 and beyond relative
to emissions in 2005 (UNECE, 2019). The estimated decline in
NH\textsubscript{3} emissions from 1980 to 2017 is 0.2\%
a\textsuperscript{-1} due to a steep decline in vehicular emissions of
NH\textsubscript{3} in 1998-2007 and a recent increase in agricultural
emissions since 2013 mostly due to increased use of urea-based
fertilizers (Ricardo, 2020). Any future policies targeting
NH\textsubscript{3} emissions would also need to consider increases in
emissions as the atmosphere warms (Sutton et al., 2013).

Estimates of the contribution of NH\textsubscript{3} emissions to
PM\textsubscript{2.5} and mobilization of nitrogen in aquatic and
terrestrial ecosystems, assessment of attainment of emissions ceilings
commitments and targets, and decisions on effective mitigation measures
demand accurate estimates of NH\textsubscript{3} emissions. The NAEI of
annual total and mapped UK NH\textsubscript{3} emissions is published
each year. These are obtained at high spatial resolution (1 km) with a
model that uses climatological environmental factors and incorporates
detailed information about farming activities that contribute to
NH\textsubscript{3} emissions. The ability to validate the inventory is
challenging, as there are no long-term measurements of
NH\textsubscript{3} fluxes. There is a network of very reliable
measurements of rural 24-hour mean surface concentrations of
NH\textsubscript{3} that cover the full latitudinal extent of the UK
from Cornwall in the south to Shetland in the north (Tang et al., 2018),
but there are large monitoring gaps in-between. Individual sites are
also unlikely to be representative of inventory grid cells for an
emission source with large spatial variability. Satellite observations
of NH\textsubscript{3} retrieved from infrared spectral measurements
offer complete coverage of the UK and routine daily measurements in the
absence of clouds and under good retrieval conditions. Satellites
observe NH\textsubscript{3} molecules throughout the atmospheric column,
but the majority are within the planetary boundary layer and most of the
variability in the column is typically due to NH\textsubscript{3} at or
near the surface (Clarisse et al., 2010; Nowak et al., 2010; Schiferl et
al., 2016; Vohra et al., 2021a).

Retrieval of NH\textsubscript{3} from space-based instruments was first
described by Beer et al. (2008) for the Tropospheric Emission
Spectrometer (TES) instrument. Satellite NH\textsubscript{3} retrieval
products have since undergone substantial retrieval development
(Clarisse et al., 2009; Shephard et al., 2011; 2020; Shephard \&
Cady-Pereira, 2015; Van Damme et al., 2014a; 2017; 2021; Whitburn et
al., 2016a), intercomparisons (Dammers et al., 2019), and validation
against ground-based observations of total atmospheric column densities
and surface concentrations of NH\textsubscript{3} (Dammers et al., 2016;
2017; Van Damme et al., 2015a; Vohra et al., 2021a). These products have
also seen extensive use in characterizing NH\textsubscript{3} emissions.
This includes detecting global and regional NH\textsubscript{3} emission
hotspots (Cady-Pereira et al., 2017; Clarisse et al., 2019; Dammers et
al., 2019; Shephard et al., 2020; Van Damme et al., 2018), constraining
NH\textsubscript{3} emissions from biomass burning (Adams et al., 2019;
Whitburn et al., 2016b), evaluating regional emission inventories (Chen
et al., 2021; Fortems-Cheiney et al., 2020), identifying underestimated
or missing NH\textsubscript{3} sources in widely used global and
regional emission inventories and models (Heald et al., 2012; Hickman et
al., 2018; Van Damme et al., 2014b), and determining long-term local and
regional trends and variability in NH\textsubscript{3} (Hickman et al.,
2020; Van Damme et al., 2015b; 2021; Vohra et al., 2021a).

Here we use satellite observations of NH\textsubscript{3} and the
GEOS-Chem chemical transport model (CTM) to derive top-down
NH\textsubscript{3} emissions for the UK and evaluate the NAEI inventory
and current understanding of seasonality in emissions as represented in
GEOS-Chem. This includes the use of surface observations from the UK
monitoring network to evaluate the model driven with the NAEI to
corroborate findings from the satellite observations.

2 Space-based observations of column densities of NH\textsubscript{3}

Satellite observations of NH\textsubscript{3} retrieved in the infrared
portion of the light spectrum rely on the spectral signal that depends
on the atmospheric state, such as abundance and vertical distribution of
NH\textsubscript{3} and thermal contrast between the surface of the
Earth and the overlying atmosphere (Clarisse et al., 2010; Shephard et
al., 2011). Two prominent products are available from contemporary
space-based instruments that pass overhead in the morning (the Infrared
Atmospheric Sounding Interferometer or IASI) and midday (the Cross-track
Infrared Sounder or CrIS). These products use distinct retrieval
approaches, offering two independent datasets to assess the potential to
use satellite observations to constrain the magnitude and seasonality of
UK NH\textsubscript{3} emissions.

\textbf{2.1 Infrared Atmospheric Sounding Interferometer
NH\textsubscript{3}}

The IASI instrument onboard the Metop-A satellite was launched into
low-Earth polar sun synchronous orbit in October 2006. The instrument
has two overpass times in the morning (09h30 local solar time or LST)
and at night (21h30 LST), providing global coverage twice a day. The
elliptical IASI pixels range in ground pixel resolution from 12 km × 12
km at nadir (directly below the instrument) to about 20 km × 39 km at
the edges of the 2200-km-wide swath (Clarisse et al., 2011). The data
product we use is the Level 2 cloud-free reanalysis product of total
column NH\textsubscript{3} (version 3R-ERA5) (Van Damme et al., 2021).
The retrieval uses machine learning, specifically a neural network
trained relationship between column NH\textsubscript{3} and a so-called
hyperspectral range index or HRI, where the HRI is a measure of the
relative enhancement in the spectral signature due to
NH\textsubscript{3} (Van Damme et al., 2014a; 2017; Whitburn et al.,
2016a). The data product includes reported retrieval errors estimated by
perturbing individual input parameters in the neural network framework
(Whitburn et al., 2016a). Products resulting from the neural network
retrieval approach have been validated against global and regional
networks of ground-based NH\textsubscript{3} observations of surface
concentrations and column densities (Dammers et al., 2016; Guo et al.,
2021; Vohra et al., 2021a; Whitburn et al., 2016a). In general, IASI
NH\textsubscript{3} reproduces the temporal variability in surface
concentrations of NH\textsubscript{3}, but exhibits a low bias (Dammers
et al., 2017; Whitburn et al., 2016a).

We use daytime (09h30 LST) IASI NH\textsubscript{3} for 2008-2018 to
obtain multiyear monthly means. This dampens influence of interannual
variability and ensures consistency with NAEI
NH\textsubscript{3}emissions that are estimated with 30-year mean
meteorology (Ricardo, 2019a). We grid the data to finer spatial
resolution (0.1° × 0.1°; \textasciitilde{} 10 km) than the native
resolution of the instrument using the tessellation oversampling
technique described in Zhu et al. (2017) and Sun et al. (2018). This
takes advantage of the spatial variability in coverage of individual
orbits and the long data record from IASI to reduce noise and smooth out
spatial gradients in the gridded product (Sun et al., 2018). Briefly,
tessellation involves weighting individual IASI pixels by the area of
overlap with the target grid and also includes error-weighting using the
reported retrieval error. In our application of the tessellation
gridding technique, we approximate the area of IASI pixels as a
quadrilateral polygon, where the corners of each polygon are estimated
as the distance midway between the centres of neighbouring IASI pixels.

Retrieval of NH\textsubscript{3} over the UK is challenging, due to
persistent clouds and relatively cool conditions. Extreme retrievals,
identified as absolute columns \textgreater{} 5 ×
10\textsuperscript{17}molecules cm\textsuperscript{-2}, are removed. We
also exclude IASI NH\textsubscript{3} columns retrieved on 26-27 July
2018, coincident with the summer 2018 heat wave (McCarthy et al., 2019).
Record high temperatures (\textgreater{} 30°C) lead to UK IASI
NH\textsubscript{3}column densities 4-times greater (\textasciitilde{}4
× 10\textsuperscript{16} molecules cm\textsuperscript{-2}) than the UK
July multiyear mean (\textasciitilde{}1 ×
10\textsuperscript{16}molecules cm\textsuperscript{-2}). Including these
days increases the July multiyear mean by 11\% and reduces its
representativeness as a climatological mean for comparison to the NAEI.
A similarly large influence of heat waves on IASI NH\textsubscript{3}
columns was reported for the summer 2010 heat wave over mainland Europe
(Van Damme et al., 2014b). After using oversampling to grid the data to
0.1° × 0.1°, gridded multiyear means with large relative error
(\textgreater{}50\%) are removed. This leads to loss of the majority of
IASI NH\textsubscript{3} columns in October-February, so only
March-September multiyear means are considered. Additional filtering is
applied to the gridded multiyear monthly means to remove extreme values
identified as columns \textless{} -1 × 10\textsuperscript{16} molecules
cm\textsuperscript{-2} and \textgreater{} 1 ×
10\textsuperscript{17}molecules cm\textsuperscript{-2}. These only
account for \textless{}0.1\% of the March-September data, but affect
spatial consistency between IASI and CrIS.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Monthly multiyear (2008-2018) mean IASI
NH\textsubscript{3}. Data are gridded to 0.1\selectlanguage{ngerman}° × 0.1° using oversampling
(see text for details). Grey grids, limited to Scotland, have
\textless{} 10 observations.

Figure 1 shows the gridded March-September multiyear monthly mean IASI
NH\textsubscript{3} columns. The number of observations in each grid
ranges from 11 to 128. Values over Scotland are very low (typically
\textless{} 2 × 10\textsuperscript{15} molecules cm\textsuperscript{-2})
due to weak signal, lower agricultural activity than the rest of the UK,
and greater distance from sources in mainland Europe. The range in IASI
NH\textsubscript{3} over the rest of the country of 4-8 ×
10\textsuperscript{15} molecules cm\textsuperscript{-2} is much less
than the NH\textsubscript{3} hotspots in other parts of the world.
Columns over global hotspots such as North China, West Africa, the Po
Valley (Italy), and the Indo-Gangetic Plain (India) exceed 2 ×
10\textsuperscript{16} molecules cm\textsuperscript{-2} (Cady-Pereira et
al., 2017; Dammers et al., 2019; Van Damme et al., 2014b; 2018). These
are associated with industrial and agricultural activity in India and
China, and intense seasonal open burning of biomass and relatively low
abundance of acidic aerosols in West Africa and northern India. Warm
temperatures in these regions also increase
NH\textsubscript{3}emissions, suppress partitioning of
NH\textsubscript{3} to aerosols, and enhance the spectral signal.

\textbf{2.2 Cross-track Infrared Sounder NH\textsubscript{3}}

The first CrIS sensor launched into low-Earth polar sun synchronous
orbit in October 2011 is onboard the NOAA Suomi-NPP satellite. Like
IASI, CrIS observes the Earth twice daily, though in the early afternoon
(13h30 LST) and after midnight (01h30 LST) (Goldberg et al., 2013). It
has the same swath width as IASI and similar ground pixel resolution (14
km circular pixels at nadir). The fast physical retrieval (CFPR)
approach used to retrieve NH\textsubscript{3} columns is described in
detail in Shephard \& Cady-Pereira (2015) and Shephard et al. (2020).
Briefly, it is based on conventional optimal estimation that involves
minimizing the difference between observed and calculated outgoing
spectral radiances with a priori vertical profiles of
NH\textsubscript{3} (Rodgers, 2000). CFPR uses three prior
NH\textsubscript{3} profiles representing polluted, moderately polluted,
and remote conditions (Shephard et al., 2020) that are selected based on
the ammonia spectral signal. This is different to standard optimal
estimation that uses prior information that is independent of the
observations and imposes spatial and temporal information. The CFPR
retrieval generates averaging kernels that quantify the vertical
sensitivity of the retrieval. These typically peak between 900 and 750
hPa (\textasciitilde{}1-2.5 km altitude) (Dammers et al., 2017; Shephard
\& Cady-Pereira, 2015).

We use the Level 2 CrIS NH\textsubscript{3} CFPR version 1.6 product for
2013-2018. The predecessor product (version 1.5) exhibited a positive
bias for NH\textsubscript{3} \textless{} 1 ×
10\textsuperscript{16}molecules cm\textsuperscript{-2}, as values were
only retrieved over scenes exceeding the instrument detection limit of
\textasciitilde{}2 × 10\textsuperscript{15} molecules
cm\textsuperscript{-2} (Dammers et al., 2017; Shephard \& Cady-Pereira,
2015). This approach filtered out cloud-free scenes below the instrument
detection limit and indirectly removed cloudy scenes when the
NH\textsubscript{3} signal below clouds could not be detected. In
version 1.6 clouds are explicitly identified with information from the
space-based Visible Infrared Imaging Radiometer Suite (VIIRS) (White et
al., 2021). We use daytime cloud-free CrIS observations with quality
flag \selectlanguage{english}[?] 4 (Shephard et al., 2020) and thermal contrast \textgreater{} 0
K, where thermal contrast is the difference between the reported
temperatures at the surface and the lowest atmospheric layer. We
identify and correct for a positive trend in the CrIS baseline that
appears to be erroneous, as it is not apparent in the IASI data. We do
this by estimating a statistically significant (p-value = 0.03) increase
in monthly mean background NH\textsubscript{3}columns over Scotland
(Figure S1) of 2.21 x 10\textsuperscript{13}molecules
cm\textsuperscript{-2} per month (amounting to 1.6 x
10\textsuperscript{15} molecules cm\textsuperscript{-2} over the whole
record) and subtract this from individual CrIS NH\textsubscript{3}column
retrievals. We grid the corrected data to 0.1deg x 0.1deg using the same
tessellation code used for IASI, but without error weighting. The
individual total column errors include measurement and representative
errors and cover a much narrower range (5-55\% (Shephard et al., 2020))
than those for IASI (5\% to \textgreater{}100\%). As a result, higher
relative weighting would be applied to low column densities, leading to
anomalously low gridded values in the CrIS multiyear means. For
consistency with IASI, and because of weak spectral signal in autumn and
winter, we only consider CrIS retrievals in March to September.

Figure 2 shows the gridded March-September CrIS
NH\textsubscript{3}multiyear monthly mean columns. As with IASI, we
filter for extreme values in the multiyear means (column densities
\textless{} -1 x 10\textsuperscript{16} molecules cm\textsuperscript{-2}
and \textgreater{} 1 x 10\textsuperscript{17} molecules
cm\textsuperscript{-2}), removing \textless{}0.1\% of the gridded data.
Observations during the July 2018 heatwave only increase the July
multiyear mean by 1.6\%, but for consistency with IASI these days are
also removed. The number of CrIS retrievals in each grid ranges from 11
to 96. The CrIS multiyear means are roughly double those for IASI
(Figure 1; Figure S2), in part because CrIS passes overhead at midday
when higher ambient temperatures lead to greater volatilization of
NH\textsubscript{3}. Differences in vertical sensitivity and distinct
retrieval approaches likely also contribute. Difference are particularly
large in September when background NH\textsubscript{3} is 5.3 x
10\textsuperscript{15} molecules cm\textsuperscript{-2} more in CrIS
than IASI, obtained as the intercept from regressing CrIS against IASI.
The spatial correlation between CrIS and IASI multiyear means is\emph{R}
\textless{} 0.5 in most months (March, June-September),\emph{R} = 0.53
in May, and \emph{R} = 0.55 in April. If extreme values in the gridded
products are retained, the spatial correlation degrades to \emph{R} =
0.42 in April and \emph{R} = 0.29 in May.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Monthly multiyear (2013-2018) mean CrIS
NH\textsubscript{3}. Data are gridded to 0.1\selectlanguage{ngerman}° × 0.1° with oversampling
(see text for details). Grey grids, limited to Scotland, have
\textless{} 10 observations.

3 The GEOS-Chem chemical transport model

We use the GEOS-Chem CTM version 12.1.0
(https://doi.org/10.5281/zenodo.1553349) to derive UK
NH\textsubscript{3} emissions from IASI and CrIS. The model is driven
with NASA GEOS-FP assimilated meteorology from the Global Modeling and
Assimilation Office (GMAO). Model simulations are conducted on a
horizontal grid at 0.25° × 0.3125° (\textasciitilde{}25 km latitude ×
\textasciitilde{}31 km longitude) nested over western Europe
(32.75-61.25°N, 15°W-40°E). The model extends over 47 vertical layers
from the Earth's surface to 0.01 hPa. Dynamic (3-hourly) boundary
conditions are from a global GEOS-Chem simulation at 4° × 5°.

Anthropogenic emissions over the UK, including from agriculture, are
updated in GEOS-Chem to include gridded emissions from the NAEI for 2016
(Ricardo, 2018a). These are annual totals on a 1 km × 1 km grid
available at https://naei.beis.gov.uk/data/map-uk-das (last accessed
August 2019). The agricultural NH\textsubscript{3} emissions
incorporated in the NAEI are calculated at coarser resolution (5 km)
than the NAEI with the nitrogen balance models of Webb \& Misselbrook
(2004) for livestock sources and Misselbrook et al. (2006) for
fertilizer sources. These models are driven with 30-year mean
meteorology for 1981-2010, so the NH\textsubscript{3} emissions
represent a climatological mean (Ricardo, 2019a). Other anthropogenic
NH\textsubscript{3} emissions in the NAEI are typically calculated as
the product of emission and activity factors representative of the year
of interest and mapped to the 1 km NAEI emissions grid (Ricardo, 2018b).
Mainlaind Europe anthropogenic emissions for 2016 are updated with the
gridded (0.1° × 0.1°) product provided by the European Monitoring and
Evaluation Programme (EMEP)
(http://www.ceip.at/new\_emep-grid/01\_grid\_data; last accessed
September 2019. Now at
https://www.ceip.at/the-emep-grid/gridded-emissions).

Temporal variability of annual NAEI and EMEP
NH\textsubscript{3}emissions is represented in GEOS-Chem with gridded
monthly scaling factors and spatially uniform diurnal scaling factors.
Monthly scaling factors are from the Generation of European Emission
Data for Episodes (GENEMIS) project detailed in Friedrich (2000). These
lead to peak NH\textsubscript{3} emissions in April. Hourly scaling
factors are from Zhu et al. (2015) calculated using information about
the dependence of NH\textsubscript{3} on aerodynamic resistance, surface
temperature and Henry's law. As a result of these, 30\% of
NH\textsubscript{3} is emitted at midday (noon-2pm LST) coincident with
the CrIS overpass and 20\% in the morning (9am-noon LST) coincident with
the IASI overpass. Natural NH\textsubscript{3} sources are from
inventories already in GEOS-Chem. These include natural emissions from
soils and the ocean from the Global Emissions InitiAtive (GEIA)
inventory (Bouwman et al., 1997) and inland and coastal seabird
emissions from the Riddick et al. (2012) inventory. We halve the GEIA
inventory emissions, as in Paulot et al. (2014), informed by a 50\%
overestimate identified by Simpson et al. (1999).

NH\textsubscript{3} is a semi-volatile acid buffer that neutralizes
acidic sulfate and nitrate aerosols, so its abundance depends on the
abundance of these acidic aerosols. Sulfate forms from oxidation of
SO\textsubscript{2} and nitrates from aerosol uptake of nitric acid
formed from oxidation of NO\textsubscript{x}. The version of the NAEI we
use includes outdated mapping of the location of ships and no vertical
or temporal information for aircraft emissions. To address these issues,
we separate ship and aircraft emissions from other sources in the lumped
``Other Transport and Mobile Machinery'' category of the NAEI emissions
inventory and replace ship emissions with updated estimates that use
geospatial information from the automatic identification system
(Ricardo, 2017). We convert the NAEI aircraft emissions to monthly
estimates and distribute these vertically up to 1 km (the altitude limit
of the NAEI emissions) by deriving vertical and temporal scaling factors
from the global Aviation Emissions Inventory version 2.0 (AEIv2) used in
GEOS-Chem (Stettler et al., 2011). Above 1 km, the AEIv2 emissions are
used. The existing temporal scaling factors in GEOS-Chem that are
applied to NAEI SO\textsubscript{2} and NO\textsubscript{x} emissions
lead to peak emissions in winter, due to an increase in energy demand.
SO\textsubscript{2} is emitted in the model as 95\%
SO\textsubscript{2}and 5\% sulfate, using sulfate-to-SO\textsubscript{2}
emission ratios for Europe reported by Chin et al. (2000). NAEI
emissions are gridded to a uniform 0.1° × 0.1° grid for input to the
Harmonized Emissions Component (HEMCO) processing package version
2.1.010 (Keller et al., 2014) that maps all emissions to the model grid
and applies relevant scaling factors.

The model includes detailed coupled gas- and aerosol-phase chemistry.
Sulfate aerosols are formed in the model from oxidation of
SO\textsubscript{2} in the gas phase by OH and in the aqueous phase in
clouds by ozone and hydrogen peroxide (Park et al., 2004). Partitioning
of NH\textsubscript{3} between the gas and acidic aerosol phase is
determined dynamically with the thermodynamic equilibrium model
ISORROPIA-II (Fountoukis \& Nenes, 2007). Wet and dry deposition,
terminal sinks of NH\textsubscript{3}, are represented with a standard
resistances-in-series scheme for dry deposition (Wesely, 1989) and, for
wet deposition, includes scavenging in and below clouds (Amos et al.,
2012).

We use network site measurements of trace gases and aerosols to evaluate
model accuracy at reproducing surface concentrations of
NH\textsubscript{3}, SO\textsubscript{2}, and sulfate. These include 2
rural sites (Auchencorth Moss in Scotland, Chilbolton Observatory in
southern England) that form part of the EMEP network and the mostly
rural UK Eutrophying and Acidifying Atmospheric Pollutants (UKEAP)
network. The 2 EMEP sites include hourly measurements from Monitor for
AeRosols and Gases in Air (MARGA) instruments (Stieger et al., 2017; ten
Brink et al., 2007; Twigg et al., 2015; Walker et al., 2019). The UKEAP
network includes monthly measurements from low-cost denuder filter
sampling packs (Tang et al., 2018). In 2016, there were 30 sites for
SO\textsubscript{2} and sulfate and 51 for NH\textsubscript{3}. The
MARGA data are from the EMEP Chemical Coordinating Centre EBAS database
(http://ebas.nilu.no/; last accessed February 2020) (Tørseth et al.,
2012) and the UKEAP data are from the UK-AIR data archive
(https://uk-air.defra.gov.uk/data/data-availability; last accessed
November 2020).

To ensure consistency between the model and observations, the model is
sampled from the lowest to the top model layer during the satellite
overpass times of 08-11 LST for use with IASI and 12-15 LST for use with
CrIS, and as monthly 24-hour means in the lowest model layer for
comparison to the surface observations. The model is sampled in
March-September 2016 following a 2-month spin-up for chemical
initialization.

4 UK bottom-up emissions of NH\textsubscript{3}

Figure 3 shows the spatial distribution of annual UK
NH\textsubscript{3}emissions for 2016 from the NAEI. Table 1 gives the
breakdown by sector. Annual emissions for 2016 total 298 Gg, mostly
(84\%) from agriculture. Natural emissions of 21.6 Gg (7\% of the total)
are consistent with annual total natural emissions in GEOS-Chem of 21.8
Gg. According to GEOS-Chem, these include soils, vegetation and the
ocean (together 18.7 Gg) and seabirds (3.10 Gg). NAEI anthropogenic
NH\textsubscript{3}emissions total 276 Gg, 21 Gg less than the UNECE
Gothenburg protocol emissions ceiling of 297 Gg (UNECE, 2019). The NAEI
version we implement in GEOS-Chem and evaluate against top-down
estimates was released in 2018. Two NAEI versions have been released
since. Reported differences in NH\textsubscript{3} emissions across
these versions for consistent years is minor, just 1-3\% (Ricardo,
2019b; 2020).

The spatial patterns in Figure 3 coincide with farming activities that
dominate NH\textsubscript{3} emissions according to the modelling study
by Hellsten et al. (2008). They used the same Webb \& Misselbrook (2004)
nitrogen balance model as the NAEI to identify regionally dominant
farming activities. The agricultural sources that dominate
NH\textsubscript{3} emissions include sheep farming along the Welsh
border where emissions are low, and large sources like pig and poultry
farming and fertilizer use in east England and dairy and beef cattle
farming in west England and Northern Ireland. Hellsten et al. (2008)
used agricultural activity data for 2000. Detailed geospatial farming
activity data is confidential and publicly available data are limited to
decadal maps of farming activities in England for 2000 and 2010 and
annual regional and national statistics. The decadal maps suggest that
locations of intensive crop and livestock farming in England are
relatively unchanged (DEFRA, 2016b; a). The regional statistics document
large changes in the number of livestock and the amount of nitrogen
fertilizer used from 2000 to 2016 that would affect trends in emissions.
In general, livestock numbers in the UK have declined by 20\% for sheep,
11\% for dairy and beef cattle, and 25\% for pigs (DEFRA, 2020b).
Poultry, specifically table chickens, have increased by 10\% in the UK,
with the largest increase of 42\% in Northern Ireland (DEFRA, 2020b).
Nitrogen-based fertilizer usage, a dominant NH\textsubscript{3} source
in east England (Hellsten et al., 2008), declined by 19\% in the UK,
though the relative proportion of urea-based fertilizer has increased
(Ricardo, 2020). Regional changes in nitrogen-based fertilizers range
from a 3\% increase in Scotland to a 37\% decrease in Northern Ireland
(AIC, 2020).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Annual UK NH\textsubscript{3} emissions for 2016.
Data are in tonnes per year per 0.1\selectlanguage{ngerman}° × 0.1° grid from the NAEI. Inset
value is the UK annual total. Boxes demarcate regions with broadly
similar NH\textsubscript{3} source types: Northern Ireland (N. Ireland),
Northern England and a portion of southern Scotland (N. England),
southwest UK (SW UK), and southeast UK (SE UK).

\textbf{Table 1.} UK sector emissions of NH\textsubscript{3} according
to the NAEI \textsuperscript{a}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Sources} & \textbf{NH\textsubscript{3} {[}Gg
a\textsuperscript{-1}{]}}\tabularnewline
\midrule
\endhead
Agriculture & 248.9\tabularnewline
Natural \textsuperscript{b} & 21.6\tabularnewline
Waste & 14.2\tabularnewline
Point sources & 4.4\tabularnewline
Road transport & 4.4\tabularnewline
Other \textsuperscript{c} & 4.2\tabularnewline
\textbf{Total} & \textbf{297.7}\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{a} Spatial distribution of UK NAEI
NH\textsubscript{3}emissions are in Figure 3. \textsuperscript{b}
Contributors to natural emissions, according to GEOS-Chem, are soils,
vegetation and the ocean (together 18.7 Gg) and seabirds (3.1 Gg).
\textsuperscript{c} Other is industrial and domestic combustion (2.9 Gg)
and solvent use (1.3 Gg).

Inversion of column densities of NH\textsubscript{3} to estimate
top-down surface emissions can be complicated by dependence of
NH\textsubscript{3} abundance on acidic sulfate aerosols formed from
oxidation of SO\textsubscript{2} and acidic nitrate formed from uptake
of nitric acid from NO\textsubscript{x} sources. UK
SO\textsubscript{2}emissions are dominated by large industrial and
energy sector point sources, ships, domestic and industrial combustion,
and traffic (Ricardo, 2018b). UK NO\textsubscript{x} emissions are
dominated by transport, energy generation and manufacturing (Ricardo,
2018b). We find particularly large discprepancies between monthly mean
March-September 2016 observed (EMEP and UKEAP) SO\textsubscript{2}
concentrations and those from the model driven with the NAEI (Figure
S3). The model normalized mean bias (NMB) is \textgreater{}600\% for
modelled SO\textsubscript{2} \textgreater{} 2 \selectlanguage{greek}µ\selectlanguage{english}g m\textsuperscript{-3}
at sites influenced by point sources in Yorkshire and 205\% for modelled
SO\textsubscript{2} \textless{} 2 \selectlanguage{greek}µ\selectlanguage{english}g m\textsuperscript{-3}. Modelled
sulfate is also greater than the observations (NMB of 17\%) (Figure S3).
This would enhance partitioning of NH\textsubscript{3} to acidic
aerosols to form ammonium, leading to a positive bias in the relative
amount of NH\textsubscript{x} (NH\textsubscript{3} + ammonium) present
as ammonium.

Positive model biases in both SO\textsubscript{2} and sulfate (Figure
S3) suggest an overestimate in NAEI SO\textsubscript{2} emissions that
have implications for UK compliance with commitments to emissions
ceilings and reductions. There are many factors other than emissions
that could contribute to model biases. These include, but are not
limited to, misrepresentation of the height at which
SO\textsubscript{2}is emitted from tall stacks, a reported positive bias
in mainland Europe SO\textsubscript{2} emissions (Luo et al., 2020), and
uncertainties in dry (Fowler et al., 2001; 2007) and wet (Luo et al.,
2019) deposition. We conducted sensitivity simulations to assess the
contribution of these uncertainties to modelled SO\textsubscript{2} and
sulfate. Details of these simulations and the effect on
SO\textsubscript{2} and sulfate concentrations are in the accompanying
Supplementary. The factor we find to have the largest influence relative
to the model bias is wet deposition. The more efficient wet deposition
scheme of Luo et al. (2019) leads to an 11\% decrease in sulfate
concentrations.

Errors in NAEI SO\textsubscript{2} emissions could be due to
uncertainties in emissions from domestic and industrial biomass
combustion. The third of six generating units at the 3.9 GW generating
capacity Drax power station in Yorkshire transitioned from burning coal
to biomass in 2016 (Simet, 2017). SO\textsubscript{2} emissions from
biomass combustion depend on fuel sulfur content and combustion
efficiency. Reported emission factors range widely from 1 to 110 mg
SO\textsubscript{2} MJ\textsuperscript{-1} (Boersma et al., 2008;
Paulrud et al., 2006; EMEP, 2019) and so offer limited constraints. To
reduce the influence of a possible bias in SO\textsubscript{2} emissions
on GEOS-Chem simulation of abundance of sulfate and NH\textsubscript{3},
we decrease land-based gridded (0.1\selectlanguage{ngerman}° × 0.1°) NAEI
SO\textsubscript{2}emissions by a factor of 3 for grids dominated by
point sources (identified as grids with SO\textsubscript{2} emissions
\textgreater{} 10 g m\textsuperscript{-2} a\textsuperscript{-1}) and by
a factor of 1.3 for all other land-based grids. This reduces the
original NAEI SO\textsubscript{2} emissions over land by 49\% from 164
Gg to 84.1 Gg. With shipping, the updated annual NAEI
SO\textsubscript{2} emissions for the domain shown in Figure 3 total
94.5 Gg. The March-September modelled sulfate NMB changes from +17\%
(Figure S3) to -8.8\%. We use the scaled SO\textsubscript{2} emissions
in all subsequent simulations.

5 Top-down NH\textsubscript{3} emissions and comparison to bottom-up
estimates

We calculate gridded satellite-derived 24-hour monthly mean top-down
NH\textsubscript{3} emissions (\emph{E} \textsubscript{sat}) as follows:

\begin{equation}
\mathbf{E}_{\mathbf{\text{sat}}}\mathbf{=}\mathbf{\Omega}_{\mathbf{\text{sat}}}\mathbf{\times}\left(\frac{\mathbf{E}}{\mathbf{\Omega}}\right)_{\mathbf{\text{model}}}\mathbf{\text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }}\left(\mathbf{1}\right)\mathbf{,}\nonumber \\
 \end{equation}

where \selectlanguage{greek}Ω\selectlanguage{english}\textsubscript{sat} is satellite observations of
NH\textsubscript{3} multiyear monthly mean columns from IASI (Figure 1)
or CrIS (Figure 2), and (\emph{E} /\selectlanguage{greek}Ω\selectlanguage{english})\textsubscript{model} is the
GEOS-Chem ratio of 24-hour monthly mean NH\textsubscript{3} emissions
(\emph{E} ) to 3-hour monthly mean columns (\selectlanguage{greek}Ω\selectlanguage{english}) during the satellite
overpass. Model ratios ((\emph{E} /\selectlanguage{greek}Ω\selectlanguage{english})\textsubscript{model}) are
interpolated to 0.1\selectlanguage{ngerman}° × 0.1°. Regression of midday vs morning values of
\selectlanguage{greek}Ω\selectlanguage{english}\textsubscript{model} result in slopes that exceed unity (1.6-2.2),
indicative of midday enhancements in NH\textsubscript{3} due to warmer
temperatures and greater NH\textsubscript{3} emissions. Intercepts are
small and slightly negative (-0.1 to -0.7 \selectlanguage{ngerman}×
10\textsuperscript{15}molecules cm\textsuperscript{-2}). Regression of
CrIS vs IASI \selectlanguage{greek}Ω\selectlanguage{english}\textsubscript{sat} yield a similar range in slopes
(1.3-2.2) to the model, but large positive intercepts (0.2-5.4 \selectlanguage{ngerman}×
10\textsuperscript{15}molecules cm\textsuperscript{-2}). This suggests
that larger \selectlanguage{greek}Ω\selectlanguage{english}\textsubscript{sat} for CrIS than IASI is not just due to
differences in midday and morning environmental conditions.

The mass-balance approach that we use in Eq. (1) to infer emissions can
be susceptible to spatial misattribution of emissions due to
displacement of NH\textsubscript{3} from the source. The global mean
lifetime of NH\textsubscript{3} is \textasciitilde{}15 h (Hauglustaine
et al., 2014), ranging from \textasciitilde{}2 h near large sources
(Dammers et al., 2019) to \textasciitilde{}36 h far from emission
sources (Van Damme et al., 2018). The displacement length, the
horizontal distance for the target compound to decay to
\textasciitilde{}63\% of the original concentration of the emission
source, provides a measure of the spatial smearing or localization error
of the satellite-derived emissions (Marais et al., 2012; Palmer et al.,
2003). We estimate a smearing length for satellite-derived
NH\textsubscript{3} emissions over the UK of 10-12 km for calm
conditions (wind speeds of 5-6 km h\textsuperscript{-1}) typical of the
UK in summer (Figure A1f.3 of BEIS (2016)) and a short
NH\textsubscript{3} lifetime typical of large sources (2 h). At slightly
windier conditions (7 km h\textsuperscript{-1}) and over regions with
lower emissions and a longer NH\textsubscript{3} lifetime (15 h), the
displacement length increases to 105 km.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} IASI-derived NH\textsubscript{3} emissions for
March-September. Maps are 24-hour total emissions at 0.1\selectlanguage{ngerman}° × 0.1°. Inset
values are monthly emissions that sum to 271.5 Gg.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} CrIS-derived NH\textsubscript{3} emissions for
March-September. Maps are 24-hour total emissions at 0.1\selectlanguage{ngerman}° × 0.1°. Inset
values are monthly emissions that sum to 389.4 Gg.

Maps of the resultant top-down monthly NH\textsubscript{3} emissions are
shown in Figure 4 for IASI and Figure 5 for CrIS. Qualitatively, both
estimates exhibit spatial patterns similar to the NAEI (Figure 3). This
includes relatively low emissions along the Welsh border, and peak
emissions in Northern Ireland, the northern portion of the English side
of the Welsh border, and in Norfolk in the east. Emissions for retained
grid squares total 271.5 Gg for IASI, whereas these are 43\% more from
CrIS (389.4 Gg). CrIS monthly emissions are 20-38\% more than IASI for
March-July. This is similar in magnitude to the 25-50\% low bias in IASI
columns, though for an earlier IASI product (Dammers et al., 2017;
Whitburn et al., 2016a). The percentage difference increases to 57\% for
August and \textgreater{}100\% for September. The large difference in
September is due to 5.4 × 10\textsuperscript{15} molecules
cm\textsuperscript{-2} greater background NH\textsubscript{3} in CrIS,
even after correcting for the baseline trend (Section 2.2, Figure S1).
CrIS emissions excluding September are 33\% more than IASI. Differences
in sampling periods (2008-2018 for IASI, 2013-2018 for CrIS) only has a
small effect on satellite-derived emissions, but leads to data gaps over
Scotland and Northern England. IASI-derived emissions obtained for
2013-2018 are only 6\% more (288.3 Gg) than those in Figure 4.

For comparison of monthly top-down and bottom-up emissions, we estimate
monthly bottom-up emissions as the product of the annual NAEI emissions
in Figure 3 and GEOS-Chem seasonality. The latter we obtain as ratios of
GEOS-Chem monthly to annual 24-hour NH\textsubscript{3} emissions
interpolated onto the 0.1° × 0.1° grid. Figure 6 shows the resultant
monthly bottom-up NH\textsubscript{3} emissions for April and July. The
other months are in the supplementary (Figure S4). The bottom-up
emissions peak in April (\textasciitilde{}14\% of the annual total)
coincident with fertilizer application (Hellsten et al., 2007; Paulot et
al., 2014). The gridded difference between top-down and bottom-up
emissions are also shown in Figure 6 for April and July and in Figure S4
for the other months. Locations where bottom-up emissions exceed those
from the top-down approach (red grids) mostly occur where emissions are
low. The largest difference is in July when top-down emissions are 30 Gg
more (IASI) and 46 Gg more (CrIS) than the bottom-up emissions.
Pronounced regional differences include lower bottom-up values in
eastern England, particularly in April, where fertilizer use and pigs
and poultry farming are dominant sources, as well as in western England
and Northern Ireland, particularly in July, where dairy cattle farming
dominates (Hellsten et al., 2008). The spatial correlation between
top-down and bottom-up gridded emissions in general ranges from
\emph{R}= 0.5 to \emph{R} = 0.7, except for IASI in September (\emph{R}
= 0.34) when dynamic range in emissions is low.

The bottom-up emissions for March-September total 198.7 Gg. This is 27\%
less than IASI and 49\% less than CrIS. It is unlikely that the
relatively low bottom-up emissions is due to the time period (1981-2020)
of the 30-year meteorology used to determine agricultural
NH\textsubscript{3} emissions for the NAEI. We find that 2-metre
temperature from the NASA long-term consistent relanalysis product,
Modern-Era Retrospective analysis for Research and Applications Version
2 (MERRA-2), is similar for 1981-2010 (282.750 K) and 1991-2020 (282.957
K). Bottom-up emissions in March-September are 67\% of the annual total,
similar to \textasciitilde{}60\% for the monthly bottom-up
NH\textsubscript{3} emissions estimated by Hellsten et al. (2007). If we
use this relative contribution (60-67\%) to scale IASI and CrIS to
annual totals, this suggests annual NH\textsubscript{3} emissions of
405-453 Gg according to IASI and 581-664 Gg according to CrIS.
Subtracting the UK annual natural NH\textsubscript{3} emissions of
\textasciitilde{}22 Gg (Section 3) yields top-down annual anthropogenic
NH\textsubscript{3} emissions of 383-431 Gg according to IASI and
559-642 Gg according to CrIS. Both top-down estimates exceed annual
total anthropogenic emissions from the NAEI of 276 Gg (Section 3) and
the Gothenburg protocol emissions ceiling of 297 Gg.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6.} Comparison of bottom-up and top-down
NH\textsubscript{3} emissions for April and July. Panels are bottom-up
emissions (left), and the difference between top-down and bottom-up
emissions for IASI (middle) and CrIS (right) in April (top row) and July
(bottom row). Grids are blue for bottom-up \textless{} top-down and red
for bottom-up \textgreater{} top-down. Values inset are bottom-up total
(left) and differences in (middle and right) monthly emissions and the
Pearson's spatial correlation (\emph{R} ) between top-down and bottom-up
emissions.

Figure 7 compares regional seasonality in UK
NH\textsubscript{3}emissions from bottom-up and top-down estimates as
the percent change in emissions in each month relative to those in June.
Regional seasonality in the top-down emissions is very similar in
March-August in all regions except Northern Ireland. The mismatch
between IASI and CrIS in September is due to the at least 2-times
greater CrIS than IASI columns in that month (Section 2). The July peak
in emissions in Northern Ireland is more pronounced in IASI than CrIS.
This is also apparent in the seasonality in the column densities (Figure
S5). Northern Ireland has experienced dramatic changes in agricultural
activity that includes increases in livetock numbers of 45\% for pigs
and 42\% for table chickens and a decline in nitrogen fertilizer of 37\%
from 2000 to 2016 (DEFRA, 2020b). We find though that the that top-down
emissions estimates are relatively insensitive to differences in
temporal coverage of the two sensors (2008-2018 for IASI, 2013-2018 for
CrIS). All emission estimates exhibit a spring peak in April due to
intensive fertilizer and manure application in March-April (Hellsten et
al., 2007). Paulot et al. (2014) also identified this April peak in
NH\textsubscript{3} emissions inferred from ammonium wet deposition
measurements, though a recent study questions the utility of these
measurements for constraining NH\textsubscript{3} emissions (Tan et al.,
2020). A second summer peak in the top-down emissions in July that is
not present in the bottom-up emissions could be due to the timing of
manure spreading, dairy farming practices, or enhanced volatilization
and suppressed dry deposition due to warm summer temperatures (Hellsten
et al., 2007; Sutton et al., 1994). Spatial consistency between the July
top-down emissions (Figures 4 and 5) and locations dominated by
emissions from dairy cattle (Hellsten et al., 2008) suggests a it is due
to dairy farming, but this requires further investigation.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 7.} Regional seasonality in March-September
NH\textsubscript{3} emissions. Points are the percentage change in
emissions in each month relative to those in June for top-down emissions
from IASI (black) and CrIS (blue), and from the bottom-up emissions
(red). Regions sampled are in Figure 3. Inset values are March-September
totals for each region from each estimate.

In Figure 8, we compare March-September 2016 mean modelled and observed
surface concentrations of NH\textsubscript{3} to determine if the model
driven with NAEI NH\textsubscript{3} emissions and prior assumptions of
NH\textsubscript{3} seasonality and diurnal variability corroborates the
results obtained with the satellite observations. Monthly means from
model grids coincident with the surface sites are reasonably spatially
consistent with the surface observations (\emph{R} = 0.54) and the model
is 38.3\% less than the observations. This is midway between the NAEI
comparison to the top-down emissions of 27\% less than IASI and 49\%
less than CrIS. There are also low-cost passive sampler measurements of
NH\textsubscript{3} concentrations at 39 rural sites, but these have
relatively low precision, are not as extensively distributed as the
observations in Figure 8, and are only reliable (within \selectlanguage{ngerman}±10\% of
reference measurements) at NH\textsubscript{3} \selectlanguage{english}[?] 2 \selectlanguage{greek}µ\selectlanguage{english}g
m\textsuperscript{-3} (Martin et al., 2019; Sutton et al., 2001). Even
so, the model is similarly biased low (by 41.5\%) compared to these
measurements (not shown).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 8.} Comparison of observed and modelled surface
concentrations of NH\textsubscript{3}. 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 (\emph{R} ) and the model NMB for coincident monthly means.

6 Error analysis of the top-down emissions

The reported relative error for NAEI NH\textsubscript{3} emissions is
31\% (Ricardo, 2018b). Quantifiable random errors that contribute to
total March-September satellite-derived emissions include uncertainties
in retrieval of NH\textsubscript{3}, and in the modelled relationship
between NH\textsubscript{3} 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 NH\textsubscript{3} emissions. IASI
NH\textsubscript{3}retrieval errors for columns [?] 2 \selectlanguage{ngerman}×
10\textsuperscript{15} molecules cm\textsuperscript{-2} range from
0.7-34\%. Retrieval errors larger than 34\% do occur, but are in
locations with very low emissions. The CrIS NH\textsubscript{3} column
errors across all grids range from 0.2-25\%. Errors due to uncertainties
in the magnitude and variability in SO\textsubscript{2} and
NO\textsubscript{x} emissions that affect abundance of sulfate and
nitrate aerosols and hence the abundance and vertical distribution of
NH\textsubscript{3} 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 SO\textsubscript{2}
emissions for sulfate and NO\textsubscript{x}emissions for nitric acid.
The percent change in top-down emissions from a 50\% decrease in
SO\textsubscript{2} emissions is 4-5\%. A 50\% increase in
NO\textsubscript{x} emissions increases nitric acid by 14\%, aerosol
nitrate by 11\%, and satellite-derived NH\textsubscript{3}emissions by
8-9\%. The limited sensitivity to sulfate and nitrate in the UK is
because NH\textsubscript{3} is in excess due to the success of emission
controls targeting SO\textsubscript{2} and NO\textsubscript{x}sources
and absence of these for NH\textsubscript{3} sources. This would not
occur in regions and times with large unregulated SO\textsubscript{2}
and NO\textsubscript{x} sources. We find that (\emph{E}
/\selectlanguage{greek}Ω\selectlanguage{english})\textsubscript{model} used to convert satellite observations of
column densities to emissions (Eq. (1)) is relatively insensitive to
pertubations in NH\textsubscript{3} emissions, so is relatively
unaffected by errors in the spatial and temporal variability of
NH\textsubscript{3} emissions in GEOS-Chem. A 50\% increase in
NH\textsubscript{3} emissions only causes a small (3-4\%) decrease in
satellite-derived NH\textsubscript{3} 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 \selectlanguage{ngerman}± 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 NH\textsubscript{3} 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 NH\textsubscript{3} 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
NH\textsubscript{3} 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
(\selectlanguage{greek}Ω\selectlanguage{english}\textsubscript{model} 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 ((\emph{E} /\selectlanguage{greek}Ω\selectlanguage{english})\textsubscript{model} in Eq.
(1)) are sampled under all-sky conditions, though there would likely be
compensating effects of sampling clear-sky conditions on (\emph{E}
/\selectlanguage{greek}Ω\selectlanguage{english})\textsubscript{model}. Warmer temperatures and absence of clouds
increase \selectlanguage{greek}Ω \selectlanguage{english}by suppressing the amount of NH\textsubscript{3} that
partitions to the aqueous phase (Stelson \& Seinfeld, 1982; Walters et
al., 2018), but \emph{E} \textsubscript{l} also increases in response to
warmer temperatures (Sutton et al., 2013). Preferentially sampling
clear-sky conditions likely has the largest impact on
\selectlanguage{greek}Ω\selectlanguage{english}\textsubscript{sat}. 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 5.6\selectlanguage{ngerman}ºC during the morning overpass and
5.3ºC during the afternoon overpass. According to Sutton et al. (2013),
5°C warmer temperatures increase NH\textsubscript{3} 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
NH\textsubscript{3}emissions 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 \textgreater{} 0.1). NH\textsubscript{3} emissions in
GEOS-Chem also do not include changes in farming practices in response
to shifts in meteorology.

7 Conclusions

Emissions of ammonia (NH\textsubscript{3}) in the UK are mostly
(\textgreater{}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
NH\textsubscript{3} 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 (\textasciitilde{}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 NH\textsubscript{3} concentrations from
GEOS-Chem driven with the NAEI. We infer UK top-down annual
anthropogenic NH\textsubscript{3} 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
NH\textsubscript{3} 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 SO\textsubscript{2}, NO\textsubscript{x} and
NH\textsubscript{3} emissions, as NH\textsubscript{3} is in excess and
the relationship between modelled NH\textsubscript{3} columns and
emissions is near-linear.

Our study demonstrates the tremendous potential to use satellite
observations to derive NH\textsubscript{3} 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 O\selectlanguage{english}ffice for
Scienti\selectlanguage{english}fic, Technical and Cultural A\selectlanguage{english}ffairs (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 NH\textsubscript{3}
data are created by Environment and Climate Change Canada and hosted by
the Meteorological Service of Canada (MSC) Datamart. Access to the CrIS
NH\textsubscript{3} data can be requested from MWS
(mark.shephard@canada.ca). The IASI NH\textsubscript{3} data are
publicly available from the IASI data catalogue
(https://iasi.aeris-data.fr/nh3/).

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