a Spatial distribution of UK NAEI NH3emissions are in Figure 3. b Contributors to natural emissions, according to GEOS-Chem, are soils, vegetation and the ocean (together 18.7 Gg) and seabirds (3.1 Gg). c Other is industrial and domestic combustion (2.9 Gg) and solvent use (1.3 Gg).
Inversion of column densities of NH3 to estimate top-down surface emissions can be complicated by dependence of NH3 abundance on acidic sulfate aerosols formed from oxidation of SO2 and acidic nitrate formed from uptake of nitric acid from NOx sources. UK SO2emissions are dominated by large industrial and energy sector point sources, ships, domestic and industrial combustion, and traffic (Ricardo, 2018b). UK NOx 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) SO2 concentrations and those from the model driven with the NAEI (Figure S3). The model normalized mean bias (NMB) is >600% for modelled SO2 > 2 µg m-3 at sites influenced by point sources in Yorkshire and 205% for modelled SO2 < 2 µg m-3. Modelled sulfate is also greater than the observations (NMB of 17%) (Figure S3). This would enhance partitioning of NH3 to acidic aerosols to form ammonium, leading to a positive bias in the relative amount of NHx (NH3 + ammonium) present as ammonium.
Positive model biases in both SO2 and sulfate (Figure S3) suggest an overestimate in NAEI SO2 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 SO2is emitted from tall stacks, a reported positive bias in mainland Europe SO2 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 SO2 and sulfate. Details of these simulations and the effect on SO2 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 SO2 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). SO2 emissions from biomass combustion depend on fuel sulfur content and combustion efficiency. Reported emission factors range widely from 1 to 110 mg SO2 MJ-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 SO2 emissions on GEOS-Chem simulation of abundance of sulfate and NH3, we decrease land-based gridded (0.1° × 0.1°) NAEI SO2emissions by a factor of 3 for grids dominated by point sources (identified as grids with SO2 emissions > 10 g m-2 a-1) and by a factor of 1.3 for all other land-based grids. This reduces the original NAEI SO2 emissions over land by 49% from 164 Gg to 84.1 Gg. With shipping, the updated annual NAEI SO2 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 SO2 emissions in all subsequent simulations.
5 Top-down NH3 emissions and comparison to bottom-up estimates
We calculate gridded satellite-derived 24-hour monthly mean top-down NH3 emissions (E 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 Ωsat is satellite observations of NH3 multiyear monthly mean columns from IASI (Figure 1) or CrIS (Figure 2), and (E /Ω)model is the GEOS-Chem ratio of 24-hour monthly mean NH3 emissions (E ) to 3-hour monthly mean columns (Ω) during the satellite overpass. Model ratios ((E /Ω)model) are interpolated to 0.1° × 0.1°. Regression of midday vs morning values of Ωmodel result in slopes that exceed unity (1.6-2.2), indicative of midday enhancements in NH3 due to warmer temperatures and greater NH3 emissions. Intercepts are small and slightly negative (-0.1 to -0.7 × 1015molecules cm-2). Regression of CrIS vs IASI Ωsat yield a similar range in slopes (1.3-2.2) to the model, but large positive intercepts (0.2-5.4 × 1015molecules cm-2). This suggests that larger Ωsat 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 NH3 from the source. The global mean lifetime of NH3 is ~15 h (Hauglustaine et al., 2014), ranging from ~2 h near large sources (Dammers et al., 2019) to ~36 h far from emission sources (Van Damme et al., 2018). The displacement length, the horizontal distance for the target compound to decay to ~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 NH3 emissions over the UK of 10-12 km for calm conditions (wind speeds of 5-6 km h-1) typical of the UK in summer (Figure A1f.3 of BEIS (2016)) and a short NH3 lifetime typical of large sources (2 h). At slightly windier conditions (7 km h-1) and over regions with lower emissions and a longer NH3 lifetime (15 h), the displacement length increases to 105 km.