The DELTA method uses a small pump to sample air (0.2 to 0.4 L min-1) in combination with a high-sensitivity gas meter to record sampled volume (Sutton et al., 2001c). Two citric acid coated denuders (10 cm long borosilicate glass tubes) in series are used to collect NH3 gas and to check the collection efficiency. A collection efficiency correction is applied to the measurement (Sutton et al., 2001d). The corrected air concentration is determined as χacorrected=χaDenuder1×11-χaχa(Denuder2)χa(Denuder1).

Typically, denuder collection efficiency is better than 90 % (Conolly et al., 2016). At 90 % collection efficiency, the correction represents 1 % of the corrected air concentration. Individual measurements with collection efficiency < 75 % (correction amounts to 11 % of the total at 75 %) are flagged as valid, but less certain (Tang and Sutton, 2003). Where less than 60 % of the total capture is recorded in the first denuder, the correction factor amounts to greater than 50 % and is not applied. The air concentration of (χa) of NH3 is then determined as the sum of NH3 in denuders 1 and 2: χa=χaDenuder1+χaDenuder2.

At sites where particulate NH4+ is also sampled, a 25 mm filter pack with a citric acid impregnated cellulose filter is added after the denuders to capture the NH4+. The calculated air concentrations (Υa) of NH4+ is corrected for incomplete capture of NH3 by the double denuder. The corrected air concentration of NH4+ is determined as Υa(correctedNH4+)=Υa(NH4+)-[((χa(correctedNH3)-[χa(Denuder1NH3)+χa(Denuder2NH3)])×(18/17))] For NH4+ sampling, loss of NH3 due to volatilization of NH4+ from the acid impregnated filter has been investigated, by adding a third citric acid coated denuder after the filter pack, which was found to be negligible. At DELTA sites where additional simultaneous sampling of acid gases and particulate phase components are made for AGANet, ion balance checks between anions and cations in the particulate phase are performed to provide an indication of the quality of the particulate measurements. For the acid and base particulate components, close coupling is expected between NH4+ and the sum of NO3- and SO42-, as NH3 is neutralized by HNO3 and H2SO4 to form NH4NO3 and (NH4)2SO4, respectively (Conolly et al., 2016).

At the Bush OTC site in Scotland (UK-AIR ID = UKA00128), duplicate DELTA measurements are made to assess the reproducibility of the method. For continuous monthly measurements between 1999 and 2014, the R2 between the duplicate systems was 0.96 for both NH3 and NH4+ (Supplement Fig. S1).

The NH3 membrane diffusion tubes deployed in the NAMN from 1996 to 2000 are hollow cylindrical tubes (FEP, 3.5 cm long). A cap at the top end holds in place two stainless steel grids coated with sulfuric acid. The lower air-inlet end of the tube is capped with a gas-permeable membrane (Sutton et al., 2001a; Tang et al., 2001a; Thijsse, 1996). In comparison, the ALPHA passive sampler is a badge-type high-sensitivity sampler with an uptake rate that is ∼ 20 times faster than the diffusion tube. It consists of a cylindrical low-density polyethylene body. An internal ridge supports a cellulose filter coated with citric acid, which is held in place with a polyethylene ring. The open end is capped with a PTFE membrane, providing a diffusion path length of 6 mm between the membrane and absorbent surface (Tang et al., 2001a).

Triplicate passive samplers are deployed for every measurement in the NAMN. Where the % coefficient of variation (CV) of the triplicate samplers is greater than 30 % for the diffusion tubes or greater than 15 % for the ALPHA samplers, the sample run is classed as failing the quality control test. Large discrepancies are most likely due to contamination of samples, and data from contaminated samples are excluded from the assessment in this paper.

The passive methods are calibrated against the DELTA method in the NAMN by ongoing comparison at several sites representing a wide range of ambient NH3 concentrations (see Sect. 2.2.4). Since 2009, the number of inter-comparison sites has been nine. These are Auchencorth (UKA00451), Bush OTC (UKA00128), Glensaugh (UKA00348), Lagganlia (UKA00290), Llynclys Common (UKA00270), Moorhouse (UKA00357), Rothamsted (UKA00275), Sourhope (UKA00347) and Stoke Ferry (UKA00317). The inter-comparison is used to establish a regression between the active and passive methods, with the DELTA samplers as the reference system, since the air volume sampled is accurately measured with high-sensitivity gas meters. The calibration is necessary to account for the fact that the sampling path length in the passive samplers is longer than the distance between the membrane and adsorbent, due to the additional resistance to molecular diffusion imposed by the turbulence damping membrane at the inlet and the presence of a laminar boundary layer of air on the outside of the sampler (Tang et al., 2001a). In addition, parallel measurements were made at a high NH3 concentration farm site (1998–2007) to extend the calibration range, and to ascertain linearity of response to high concentrations. To ensure that no bias is introduced in the sampling and to maintain the validity of long-term trends, the calibration is evaluated on an annual basis (Tang and Sutton, 2003; Conolly et al., 2016).

For the period up to 2000 when the diffusion tubes were implemented in the NAMN, their calibration (at 10 µg m-3) amounts to an average of 1.5 % compared with the DELTA system. The mean ALPHA sampler calibration (at 10 µg m-3), compared with the DELTA system, amounts to a correction of 10 % (ALP1: prototype 1, 1998–2000), 15 % (ALP2: injection mould 1, 2001–2005), 17 % (ALP3: injection mould 2, 2006), 34 % (ALP4: injection mould 2 + new membrane, 2007–2008) and 40 % (ALP5: injection mould 2 + new membrane + new lab/instrument FloRRia, 2010–2014), respectively. The new PTFE membrane (5 µm pore size) is supported on a regular polypropylene grid and is thicker (305 µm) than the earlier PTFE membrane (also 5 µm pore size, but 265 µm thickness) used, which was supported instead on a randomly arranged polypropylene support material. The difference in calibration was therefore due to the extra resistance to gas diffusion imposed by the new thicker membrane. The annual calibration of the methods shows both high precision and constancy between years (Fig. 2), which is important to support the detection of temporal trends in NH3 concentrations. There is no systematic trend over time in either of the passive method calibrations.

The comparison of monthly measurement data between the DELTA and calibrated passive measurements demonstrated a close agreement (Fig. 3). The correlation (R2) between DELTA and calibrated diffusion tubes was 0.91 (Fig. 3a), while the correlation between DELTA and calibrated ALPHA samplers was 0.92 (Fig. 3b). From the calibrated results, the intercept for the diffusion tubes was 0.10 µg NH3 m-3, while that for the ALPHA samplers was 0.03 µg NH3 m-3, demonstrating the improvement in sensitivity with the ALPHA samplers compared with the diffusion tubes (Fig. 3). In the present case the value of the intercepts, even for diffusion tubes, is much less than typical NH3 air concentrations (see Sect. 3). However, this cannot be assumed to be the case in other implementations of the same methods. Experience from other studies using the lower sensitivity diffusion tubes indicates a tendency to overestimate NH3 concentrations under clean conditions (RGAR, 1990; Thijsse et al., 1996; Tang et al., 2001a; Lolkema et al., 2015). This observation points to the need for any application of NH3 passive sampling for ambient monitoring to be accompanied by testing and calibration against a verified active sampling method. In independent assessments, for example in the USA (Puchalski et al., 2011), the ALPHA samplers performed well against a reference annular denuder method with a median relative percent difference of -2.4 %.

NH3 gas captured on the acid coating of the denuder (DELTA), grid (diffusion tubes) or filter paper (ALPHA), and particulate NH4+ captured on the DELTA aerosol filter, are extracted into deionized water and analysed for NH4+ on an ammonia flow injection analysis system. The analytical instrument has changed over the network's operational period from the AMFIA (ECN, NL) to the FloRRIA (Mechatronics, NL), an updated model based on AMFIA (Conolly et al., 2016). The principles of operation of both instruments are the same and are based on selective diffusion of NH4+ across a PTFE membrane at c. pH 13 into a counter-flow of deionized water, allowing selective detection of NH4+ by conductivity (Wyers et al., 1993). The extracted samples were analysed for NH4+ against a series of NH4+ standards and quality controls. Parallel analysis of laboratory and field blank (unexposed) samples were used to determine the amounts of NH4+ derived from NH3 and NH4+ in the atmosphere during transport and storage. The limit of detection (LOD) calculation of the ALPHA and DELTA methodologies are determined as 3 times the standard deviations of the laboratory blanks. For the DELTA method, the LODs were 0.01 µg m-3 for gaseous NH3 and 0.02 µg m-3 for particulate NH4+. For the ALPHA method, the LOD was determined as 0.03 µg m-3.

Measurement data are checked and screened, based on the quality management system applied in the UK air monitoring networks (Tang and Sutton, 2003). Data quality is assessed against the following set quality control criteria: (a) DELTA system: monitoring of the air flow rate and the use of two denuders in every sample to assess capture efficiency for NH3, and (b) passive samplers: use of triplicate samplers for monitoring NH3 concentrations at every site, to allow an assessment of sampling precision, and (c) ongoing calibration of passive samplers against the DELTA. Data flags are applied to the dataset; a full list of these is available from the EMEP website (http://www.nilu.no/projects/ccc/flags/index.html). Following the quality control checks and data flagging on the collected dataset, the annually ratified data from the NAMN are made publicly available on the Department for Environment, Food and Rural Affairs (Defra) UK-AIR website (https://uk-air.defra.gov.uk/; Tang et al., 2017) and are also in the process of being made available on the EMEP website (http://ebas.nilu.no/).

An inter-comparison of NH3 measurements by the RIVM AMOR system (hourly, Wyers et al., 1993) and the DELTA sampling system (monthly) have been carried out at the Zegweld site (ID 633) in the Dutch National Air Quality Monitoring Network (van Zanten et al., 2017) since July 2003. Since September 2012, ALPHA measurements have also been included. To compare results, monthly mean concentrations were derived from the average of hourly AMOR data for the corresponding DELTA and ALPHA monthly sampling periods with good agreement (Fig. S2).

Statistical trend analysis was conducted on the long-term dataset from the UK NAMN to identify trends (univariate monotonic, see e.g. Hirsch et al., 1991), estimate the rate of change and to address the question of whether trends in NH3 and NH4+ concentrations (if any) are consistent with the changes in estimated UK annual NH3 emissions (data downloaded from: http://naei.beis.gov.uk/data/data-selector-results?q=101505). The dataset is sufficiently long term (i.e. gaseous NH3: 17 years and particulate NH4+: 16 years) and collected by consistent methods to allow for effective statistical trend analyses to be carried out. Trend analyses were carried out using (i) linear regression (LR), (ii) the Mann–Kendall (MK) test (Gilbert, 1987) on annually averaged and monthly mean data, and (iii) the seasonal Mann–Kendall (SMK) test (Hirsch et al., 1982) on monthly data only. MK tests were performed using the “Kendall” package (McLeod, 2015) in the R software. Computation of the Sen slope and confidence interval (for non-seasonal Sen slope only) of the linear trend were performed using the R “Trend” package (Pohlert, 2016). Since concentrations of NH3 show strong seasonality, the SMK test was applied to identify the months that are driving the long-term trends in data. The SMK test (Hirsch et al., 1982) takes into account a 12-month seasonality in the time series data by computing the MK test on each of monthly “seasons” separately, and then combining the results. So for monthly “seasons”, January data are compared only with January, February only with February, etc. No comparisons are made across season boundaries.

The Sen slope is the fitted median slope of a linear regression joining all pairs of observations. For the SMK, an estimate of the seasonal Sen trend slope over time is computed as the median of all slopes between data pairs within the same season (i.e. January compared only with January etc.). Therefore, no cross-season slopes contribute to the overall estimate of the SMK trend slope. Parametric LR analysis are simple and straightforward to use and interpret monotonic trend assessment in environmental data (e.g. Kindzierski et al., 2009; Meals et al., 2011), but they require assumptions about normality of data and homogeneity of variance of data. The MK approach on the other hand is widely used in environmental time series assessments, e.g. long-term trends in precipitation (Serrano et al., 1999) and long-term trends in European air quality (Colette et al., 2016; Torseth et al., 2012). The main advantages, as discussed in the literature, of the MK approach over linear regression for trend assessments are that (i) it does not require normally distributed data, (ii) it is not affected by outliers, and (iii) it removes the effect of temporal auto-correlation in the data. However, linear trend assessment has been used in UK air quality monitoring network reports (e.g. Conolly et al., 2016). Therefore, both approaches were used in this paper, primarily as a quality assurance check.

To detect trends and to indicate the significance level of the trends in the long-term NAMN data, the non-parametric MK approach was used combined with the Sen slope method for estimating the trend and confidence interval of the linear trend (see Sect. 2.2.5). The classic MK test was used on the annually averaged data (datasets 1a, 2a, 3a), while both the classic MK and SMK tests were applied to the monthly averaged data (datasets 1b, 2b, 3b).

Results of the MK tests are summarized in Table 1. For each time series, the median annual trend (in units of µg NH3-1 yr-1) is estimated from the Sen slope and intercept of the MK linear trend. To assess the relative change over time, the % relative median change was calculated from the estimated NH3 concentration at the start (y0) and at the end (yi) of the selected time period (100 × [(yi-y0)/y0]) computed from the Sen slope and intercept. This approach was adopted instead of a direct comparison of actual observed NH3 concentrations at the start (y0) and at the end (yi) of the time series, since there is substantial inter-annual variability in the data (Figs. 10, 16). Using the estimated concentrations at the start and end from the fitted Sen slope allows using a reference that is less sensitive to inter-annual variability than the actual observed concentrations.

For the annually averaged NH3 concentrations across the UK, dataset 1a (1998–2014, 59 sites) show a small, but non-significant decreasing trend (relative median change =-6.3 %), while datasets 2a (1999–2014, 66 sites) and 3a (2000–2014, 75 sites) show no discernible trends (median relative change = 0.0 % for both) (Table 1). Results from the analysis of monthly data from all three different data groupings (1b, 2b, 3b) (relative median change -4.2 to -8.2 %) are similar to results for dataset 1a, based on analysis of annual data (Table 1). In the SMK tests on monthly data, two monthly “seasons” (January and April) in dataset 1b (1998–2014, 59 sites) are significant (p < 0.05), with a third monthly “season” (August) near-significant at p= 0.06. For datasets 2b (1999–2014, 66 sites) and 3b (2000–2014, 75 sites), August is the only monthly “season” in either time series to be close to significance at p= 0.06. Trends in individual monthly “seasons” are therefore weak and results between the MK and seasonal MK tests on monthly data are similar (Table 1).

The parametric linear regression time series trend analysis was also performed on the different data groupings. Results of the linear regression tests are summarized in Table 2, and a comparison of trends from the MK with the linear regression approach is provided in Fig. 12 for annual datasets 1a, 2a, 3a, and Fig. 13 for monthly datasets 1b, 2b, 3b. A similar approach to the MK was taken to assess the relative change, by calculating the % relative change from the estimated NH3 concentration at the start (y0) and at the end (yi) of the time series (100 × [(yi-y0)/y0]) computed from the linear regression slope and intercept. The different data groupings all show small, but non-significant decreasing trends (relative change =-2.4 to -5.3 %), similar to the trends and % relative median change from the MK and SMK analysis (Figs. 12, 13). This suggests that the errors in the NAMN data are normally distributed and that no or few outliers are present, since the results from the non-parametric MK tests are very similar to the parametric least squares linear regression.

Overall, the long-term NH3 concentration data from the UK NAMN suggests evidence of a small, but non-significant decreasing trend (Figs. 12 and 13). The level of reduction observed in the datasets is however less than the 16.3, 15.6 and 13.1 % reduction in estimated UK NH3 emissions over the periods 1998–2014, 1999–2014 and 2000–2014, respectively (Tables 1, 2). Inventories have inherent uncertainties such as uncertainties in activity data and emission factors, or may be missing emission sources. In terms of measurement data, it has already been shown in Sects. 3.1 and 3.3 that the annually averaged data mask considerable spatial and seasonal variability in NH3 concentrations. Drivers contributing to this variability include the influence of climate on emissions, variations in management practice for a particular emission source, and influence of local emission sources and interactions on concentrations at a site. In addition, once emissions have taken place, the resulting atmospheric NH3 concentrations are influenced by local deposition, which is in turn affected by receptor surfaces and by concentrations of interacting chemical species that affect atmospheric lifetime and transport distance of NH3 and physical dispersion (e.g. Bleeker et al., 2009; Sutton et al., 2013). In the following sections, we consider the possibility of interactions with climate, emission source type and chemical interactions as this may affect long-term trends in NH3 concentrations.

UK temperature and rainfall varied from year to year over the period 1998 to 2014 (Fig. 11), with no clear relationship with NH3 easily visible in the graph. Plotting the annual mean NH3 concentrations against the average temperature and rainfall however does show indicatively that elevated annual mean NH3 concentrations are observed in warmer years, and reduced annual mean NH3 concentrations are observed in wetter years (Fig. S4). This analysis for the full network is therefore consistent with the observation at a remote site (Inverpolly, Fig. 9). The thermodynamic equilibrium shifts NH3 from the aqueous (or particulate) phase to the gas phase with increased temperature, hence emissions from animal manures, soils and vegetation increase with increasing temperature (Asman et al., 1998; Sutton et al., 1993). Conversely, increases in precipitation decrease NH3 emissions because rain events dilute the available NH3 pool, while having the potential to wash urea and NHx in solution from the surface. As NH3 is soluble and washed out of the atmosphere by rainfall, this should also contribute to reduced NH3 concentrations during wet periods.

An exception to this relationship can occur where N is excreted as uric acid from birds (e.g. poultry). In this case, sufficient water is needed to allow hydrolysis to form NH3 (Riddick et al., 2014). In this situation, the arrival of rain promoted uric acid hydrolysis from seabird guano surfaces, which was limited in the absence of soil moisture. It is possible that this interaction could lead to NH3 emissions from field spreading of poultry litter to be larger in wetter years. In a recent trend analysis of NH3 concentrations from the Dutch Air Quality Monitoring Network, an attempt was also made to correct for meteorological (temperature and rainfall) influences for the eight monitoring stations, which broadly produced similar results with slightly enhanced statistical significance for the trends (van Zanten et al., 2017).

The inter- and intra-annual variability is also expected to be linked to influences from local emission source and activities. It has already been shown in Sect. 3.1 that the concentrations of NH3 in air are greatest in parts of the country with a large presence of livestock farming, particularly in areas of pig, poultry and cattle farming. Using the classification of NAMN sites according to dominant emission source sectors described in Sect. 3.1, the long-term change in NH3 concentrations at sites grouped into four different emission source sectors (background, sheep, cattle, and pigs & poultry) are compared in Fig. 14 (annual mean data) and Fig. 15 (monthly mean data). Results of the MK time series trend analysis are summarized in Table 3 and results of linear regression analysis are summarized in Table 4. A comparison of trends in measured NH3 concentrations with trends in NH3 emissions for the different source types then provided indicative evidence to support and inform the national emission inventory compilation. In Fig. 16, the relative changes in UK emissions between 1998 and 2014 are compared with relative changes in mean measured NH3 concentrations for all NAMN sites, and for grouped sites classified as dominated by cattle, pigs & poultry, and sheep.

For the 17 sites in cattle-dominated areas, there is an increasing, but non-significant trend. Overall, based on MK analysis of annual data, the relative change from 1998 to 2014 is a 12 % increase (Table 3, Fig. 14), compared with a smaller increase of 4 % from linear regression (Table 4, Fig. 14). With the monthly data, there is no discernible trend (-0.9 % (MK); 1.4 % (LR)). In the seasonal MK test on monthly data (% relative median change = 3.9 %), no monthly “seasons” are significant, with only January approaching significance at p= 0.07. The near-significant trend for January is likely to be due to unusually high NH3 concentrations recorded in January at some sites in the first few months of the time series, attributed to manure spreading activities taking place in the winter months when the ground was frozen (confirmed by local observations), in direct contravention of good farming practice.

Although the long-term trend in monitored NH3 concentrations at sites classified as dominated by cattle emissions shows a non-discernible or small increasing trend (non-significant), the opposite is happening with UK cattle NH3 emissions, which declined by an estimated 11 % over the same period (Fig. 16, Table 5). In principle, a signal (changes in atmospheric NH3 concentrations) related to substantial livestock changes associated with the 2000 outbreak of foot and mouth disease might have been expected. However, this outbreak was actually rather localized in northwest England and southwest England, and was followed by substantial restocking from 2001 (Sutton et al., 2006) and there was no detectable signal of foot and mouth disease in the average for cattle-dominated areas.

By contrast, in pig- and poultry-dominated areas (nine sites) there is a decreasing trend with significant reduction in measured NH3 concentrations between 1998 and 2014 (-22 % (MK), p= 0.02, Table 3; -21 % (LR), p= 0.06, Table 4) from analysis of annual data (Fig. 14). For the monthly data, the overall change based on linear regression is also a 22 % decrease (p= 0.02) (Table 4, Fig. 15), compared with a larger level of decrease based on MK analysis (-32 %, p= 0.01) (Table 3, Fig. 15). The SMK test also shows a significant decreasing trend (-11 %, overall p < 0.001), with 6 of the 12 monthly “seasons” showing significant trends (February, June, November, December: p < 0.05, October: p < 0.01, January: p < 0.001). A decrease in emissions from pig and poultry of 39 % between 1998 and 2014 (Fig. 16, Table 5) is therefore broadly supported, although not matched by a similar decrease in measured NH3 concentrations.

For sheep-dominated sites (four sites), there is an increasing trend in NH3 (MK: +16 %, p= 0.17, Table 3; LR: 20 %, p= 0.09, Table 4) between 1998 and 2014 in the annual data (Fig. 14). The monthly data also show a similar upward trend (Fig. 14) with relative change in concentrations of +19 % based on MK (p= 0.10) (Table 3) and +17 % based on LR (p= 0.14) (Table 4). The increasing trend at sheep sites is therefore in contrast to the estimated 24 % decrease in NH3 emissions from this sector since 1998 (Fig. 16, Table 5). For the SMK test, no individual monthly “seasons” were significant, although three of the monthly “seasons” approached the significance level (April, December: p= 0.08, October: p= 0.09). Overall, the increasing trend from the SMK test is significant at p < 0.01. While the Sen trend slope from both MK and SMK tests were comparable, at 0.0036 and 0.0033 µg NH3 yr-1, respectively, the % relative median change results computed from them are very different (MK = 16 % cf. SMK = 210 %), because the intercepts of the fitted Sen trend slopes are different (MK = 0.289 µg NH3 m-3 cf. SMK =-0.0267 µg NH3 m-3). Caution therefore needs to be exercised when interpreting the % relative change results, especially at sites with low NH3 concentrations, which must be examined together with the fitted trends.

At background sites (five sites where total NH3 emissions for the respective 5 km grid squares are estimated at < 1 kg N ha-1 yr-1), NH3 concentrations also appear to have increased (non-significant). Based on the MK analysis for the period 1998 to 2014, NH3 concentrations increased overall by 18 and 13 % from the analysis of annual and monthly data, respectively (Table 3). Results from linear regression were similar, with an overall increase of 13 and 12 % from analysis of the annual and monthly data, respectively (Table 4). Similar to sheep sites, the % relative median change estimated from the seasonal MK Sen slope and intercept (+49 %) is larger than from the classic MK Sen slope (+13 %) due to differences in the intercepts of the fitted trend lines (MK = 0.1528 µg NH3 m-3 cf. SMK = 0.0388 µg NH3 m-3) since the trend slopes are the same (0.0012 µg NH3 yr-1). Overall, the SMK test shows a significant increasing trend in the monthly data (p= 0.05). No individual monthly “seasons” were significant, with March, April and November monthly “seasons” approaching the significance level (p= 0.09).

As with the annual UK-wide long-term datasets (Sect. 3.5), it is useful to consider the significance of the NH3 trends for the groupings of sites according to dominant emission source sectors. Tables 3 and 4 show that neither the annual nor the monthly time series showed a significant change in NH3 concentrations for the cattle-dominated sites. In the case of pig- and poultry-dominated sites, the decrease in measured NH3 concentrations was significant for both the annual and monthly datasets. For sheep-dominated and backgrounds sites, the estimated increase in NH3 concentrations was not significant based on the MK and linear regression tests on the annual and monthly data, but was significant based on the SMK test of the monthly data. Overall, these statistics confirm significant differences between NH3 trends for sites dominated by different source types, with concentrations decreasing at pig- and poultry-dominated sites, concentrations increasing at sheep-dominated and background sites, and no significant trend at cattle-dominated sites (Table 5).

Other pollutants that affect NH3 concentrations in the atmosphere include SO2 and NOx emissions, which determine rates of secondary inorganic aerosol formation and therefore the lifetime of NH3 in the atmosphere. UK emissions of SO2 are estimated to have declined significantly by 81 % from 1.6 million tonnes in 1998 to 0.3 million tonnes in 2014 (Defra, 2015). Similarly, NOx emissions over the same period are estimated to have fallen by 50 % from 2 million tonnes to 1 million tonnes (Defra, 2015). The reaction of NH3 with H2SO4 to form (NH4)2SO4 is effectively irreversible (in the absence of in-cloud reprocessing), whereas an equilibrium exists between gaseous NH3 and particulate NH4NO3 and NH4Cl components which are appreciably volatile at ambient temperatures. A change in the particulate phase from (NH4)2SO4 to NH4NO3 suggests that NH3 will remain longer in the atmosphere, since NH4NO3 is volatile and releases NH3 in warm weather.

Elsewhere, a mismatch between reported trends in emissions and measurement data have similarly been investigated. The question of the “Ammonia Gap” in the Netherlands was debated over a number of years. There, the estimated reduction in emissions due to mitigation measures was not matched by expected decreases in measured NH3 concentrations in air and/or NH4+ in precipitation (Erisman et al., 2001; Bleeker et al., 2009; van Zanten et al., 2017). Similarly in Hungary, monitored NH3 concentrations from long-term measurements did not match the estimated reduction in NH3 emissions following the decline in agricultural livestock population and fertilizer usage after political changes in 1989 (Horvath and Sutton, 1998). This was subsequently attributed to a reduction in SO2 emissions over the same period, increasing the atmospheric lifetime of NH3 (Horvath et al., 2009).

Dry deposition of SO2 and NH3 are enhanced in the presence of both gases, an interaction referred to as “co-deposition” (Fowler et al., 2001). The acid-base neutralization by each of the gases provides an efficient sink for dry deposition on leaf surfaces, and deposition enhancement for each gas depends on the relative air concentrations of NH3 and SO2. For SO2, the dry deposition process has been shown to be strongly influenced by ambient concentrations of NH3 because the surface resistance is regulated mainly by uptake in moisture on foliar surfaces, which, in turn, is strongly influenced by the presence of NH3. The large reduction in SO2 emissions and ambient concentrations, compared with the relative stagnation in NH3 emissions and concentrations over the same period, has meant that the SO2 / NH3 ratio has decreased dramatically. This has led to a systematic decrease in canopy resistance to uptake of SO2 on surfaces, increasing dry deposition of SO2 in the UK (ROTAP 2012). The underlying cause of the decrease in surface resistance is that the ambient NH3 is sufficient to neutralize acidity from the solution and oxidation of deposited SO2, maintaining large rates of deposition.

Similar interactions are seen to be occurring in the UK based on the NAMN data, where the concurrent reduction in SO2 and NOx emissions over the same period (Fig. 18b) should theoretically lead to a longer atmospheric lifetime of NH3, thereby increasing NH3 concentrations in the UK, especially in remote areas. The interpretation of the NH3 and NH4+ measurement data can further be aided by comparison with particulate nitrate (NO3-) and sulfate (SO42-) data from the UK AGANet that are made concurrently with the NAMN NH3 and NH4+ measurements at 30 sites (see Sect. 2.2). There is close agreement between the aerosol components, with a near 1:1 relationship between NH4+ and the sum of NO3- and SO42-, lending support that particulate NH4+ in the UK is mainly derived from NH3 and acidic gases such as SO2 and NOx to form (NH4)2SO4 and NH4NO3, respectively (Conolly et al., 2016). For particulate NH4+, it has already been shown in Sect. 3.3 that this regional species has less of a relationship to the dominant NH3 source sectors; trend analysis was therefore undertaken using all NH4+ site data combined. As with the NH3 time series analysis, sites with incomplete data runs for particulate NH4+ due to reduced density of NH4+ measurements and site changes occurring from the period 2001–2006 were excluded (see Sect. 2.2.1).

Two data series for NAMN NH4+ data were selected for analysis: (i) 23 sites with complete NH4+ time series from 1999 to 2014, and (ii) 30 sites with complete NH4+ time series from 2006 to 2014. Both time series show a large significant downward trend in NH4+ (p < 0.01) (Table 6, Fig. S4). Overall, MK and LR tests show a significant decrease in NH4+ concentrations by 47 and 49 %, respectively, between 1999 and 2014 and by 44 and 43 %, respectively, between 2006 and 2014 (Table 6, Fig. S5). By contrast, concurrent NH3 data from the same sites over the same time periods showed a much smaller, non-significant downward trend between 2006 and 2014 (-17 %, MK; -18 %, LR), and no discernible trend between 1999 and 2014 (+ 3 %, MK and LR) (Table 6). This reduction in particulate NH4+ can be seen to be closely associated with parallel decreases in particulate SO42- and NO3- concentrations from AGANet (Table 7, Figs. 18a, S6), which are themselves associated with reductions in SO2 and NOx emissions (Table 7, Fig. 18b).

The comparisons above therefore suggest that reductions in SO2 and NOx emissions over the period have led to a lower formation of particulate NH4+ in the atmosphere. Further evidence in support of this is indicated by plotting the ratio of NH3 / NH4+ (Fig. 17b), which has increased from 1.8 in 1999 to 2.8 in 2014. This demonstrates how a larger fraction of the reduced N is staying in the gas phase as NH3, increasing its atmospheric residence time and maintaining NH3 concentrations at a higher level than solely based on NH3 emission trends. Although the overall changes in NH3 concentrations in the UK dataset are small and in many cases not significant for particular data groupings, they are consistent with similar phenomena observed in Hungary, the Netherlands and Denmark (Horvath et al., 2009; Erisman et al., 2001; Sutton et al., 2003; Bleeker et al., 2009).