Editing IPCC:AR6/WGI/Chapter-6 (section)

=== 6.3.3 Precursor Gases ===

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==== 6.3.3.1 Nitrogen Oxides (NO <sub>x</sub> ) ====

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The distribution of tropospheric NO <sub>x</sub> is highly variable in space and time owing to its short lifetime coupled with highly heterogeneous emission and sink patterns. NO <sub>x</sub> undergoes chemical processing, including the formation of nitric acid (HNO <sub>3</sub> ), nitrate (NO <sup>–</sup> <sub>3</sub> ), and organic nitrates (e.g., alkyl nitrate and peroxyacyl nitrate), atmospheric transport, and deposition. Despite challenges in retrieving quantitative information from satellite observations ( [[#Duncan--2014|Duncan et al., 2014]] ; [[#Lin--2015|Lin et al., 2015]] ; [[#Lorente--2017|Lorente et al., 2017]] ; [[#Silvern--2018|Silvern et al., 2018]] ), improved accuracy and resolution of satellite-derived tropospheric NO <sub>2</sub> columns over the past two decades have advanced understanding of the global distribution, long-term trends and source attribution of NO <sub>x</sub> . Long-term average tropospheric NO <sub>2</sub> column based on multiple satellite-borne instruments (Figure 6.6a) reveals the highest NO <sub>2</sub> levels over the most populated, urbanized and industrialized regions of the world corresponding to high NO <sub>x</sub> emissions source regions ( [[#Krotkov--2016|Krotkov et al., 2016]] ; [[#Georgoulias--2019|Georgoulias et al., 2019]] ). Enhanced but highly variable NO <sub>2</sub> columns are also associated with biomass-burning regions as well as areas influenced by lightning activity ( [[#Miyazaki--2014|Miyazaki et al., 2014]] ; [[#Tanimoto--2015|Tanimoto et al., 2015]] ).

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[[File:54fa0416445da6c9329b02a4769f09b2 IPCC_AR6_WGI_Figure_6_6.png]]

'''Figure 6.6 |''' '''Long-term climatological mean (a) and time evolution (b) of tropospheric nitrogen dioxide (NO''' <sub>2</sub> ''') vertical column density.''' Data are from the merged GOME/SCIAMACHY/GOME-2 (TM4NO2A version 2.3) dataset for the period 1996–2016 ( [[#Georgoulias--2019|Georgoulias et al., 2019]] ). Time evolution of NO <sub>2</sub> column shown in panel (b) is normalized to the fitted 1996 levels for the 10 regions shown as boxes in panel (a). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Observational constraints derived from the isotopic composition of atmospheric nitrate inferred from ice cores provide evidence of increasing anthropogenic NO <sub>x</sub> sources since pre-industrial times ( [[#Hastings--2009|Hastings et al., 2009]] ; [[#Geng--2014|Geng et al., 2014]] ). Global NO <sub>x</sub> emissions trends in bottom-up inventories (Section 6.2.1) as well as model simulations of nitrogen deposition ( [[#Lamarque--2013a|Lamarque et al., 2013a]] ) are in qualitative agreement with these observational constraints. CMIP6 ESMs exhibit stable NO <sub>x4</sub> burden over the first half of the 20th century and then a sharp increase driven by a factor of three increase in emissions, however, the magnitude of this increase remains uncertain due to poor observational constraints on pre-industrial concentrations of NO <sub>x</sub> <sub></sub> ( [[#Griffiths--2021|Griffiths et al., 2021]] ).

The AR5 reported NO <sub>2</sub> decreases by 30–50% in Europe and North America, and increases by more than a factor of two in Asia, over the 1996–2011 period based on satellite observations ( [[#Hartmann--2013|Hartmann et al., 2013]] ). Extension of this analysis covering the time period up to 2015 reveals that NO <sub>2</sub> has continued to decline over the USA, Western Europe and Japan ( [[#Schneider--2015|Schneider et al., 2015]] ; [[#Duncan--2016|Duncan et al., 2016]] ; [[#Krotkov--2016|Krotkov et al., 2016]] ) because of effective fossil fuel NO <sub>x</sub> emissions controls (Section 6.2), although this rate of decline has slowed down post-2011 ( [[#Jiang--2018|Jiang et al., 2018]] ). Satellite observations also reveal a 32% decline in NO <sub>2</sub> column over China after peaking in 2011 (Figure 6.6b), consistent with declining NO <sub>x</sub> emissions (Section 6.2) due to the implementation of emissions-control strategies (de Foy et al. , 2016; Irie et al. , 2016; F. Liu et al. , 2016) . Over Southern Asia, tropospheric NO <sub>2</sub> levels have grown rapidly with increases of 50% during 2005–2015, largely driven by hotspot areas in India experiencing rapid expansion of the power sector ( [[#Duncan--2016|Duncan et al., 2016]] ; [[#Krotkov--2016|Krotkov et al., 2016]] ). Further analysis indicates that many parts of India have also undergone a reversal in NO <sub>2</sub> trends since 2011 that has been attributed to a combination of factors, including a slowdown in economic growth, implementation of cleaner technologies, non-linear NO <sub>x</sub> chemistry, and meteorological variability ( [[#Georgoulias--2019|Georgoulias et al., 2019]] ). Satellite data reveals spatially heterogeneous NO <sub>2</sub> trends over the Middle East with an overall increase over 2005–2010 and a decrease over large parts of the region after 2011–2012. The reasons for trend reversal within individual areas are diverse, including warfare, imposed sanctions, and air-quality controls ( [[#Lelieveld--2015a|Lelieveld et al., 2015a]] ; [[#Georgoulias--2019|Georgoulias et al., 2019]] ). Satellite-derived tropospheric NO <sub>2</sub> levels over Africa and Latin America do not show a clear trend; both increasing and decreasing trends are observed over large agglomerations in these regions since the early 2000s ( [[#Schneider--2015|Schneider et al., 2015]] ; [[#Duncan--2016|Duncan et al., 2016]] ).

In summary, global tropospheric NO <sub>x</sub> abundance has increased from 1850–2015 ( ''high confidence'' ). Satellite observations of tropospheric NO <sub>x</sub> indicate strong regional variations in trends over 2005–2015. There is ''high confidence'' that NO <sub>2</sub> has declined over the USA and Western Europe since the mid-1990s and increased over China until 2011. NO <sub>2</sub> trends have reversed (declining) over China beginning in 2012 and NO <sub>2</sub> has increased over Southern Asia by 50% since 2005 ( ''medium confidence'' ).

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'''Table 6.4 |''' '''Summary of the global CO trends based on model estimates and observations.'''

{| class="wikitable"
|-
! '''Analysis Period'''

! '''Trends: Regions'''

! '''Reference/Methodology'''

|-
| colspan="3"| '''Global/Hemispheric'''

|-
| 2003–2015

| –0.86% yr <sup>–1</sup>

| [[#Flemming--2017|Flemming et al. (2017)]] Model assimilating MOPITT

|-
| 2002–2013

| –1.4% yr <sup>–1</sup>

| [[#Gaubert--2017|Gaubert et al. (2017)]]

Model assimilating MOPITT

|-
| 2002–2018

| –0.50 ± 0.3% yr <sup>–1</sup> : 60°N–60°S (MOPITT)

–0.56 ± 0.3% yr <sup>–1</sup> ; <sup>–</sup> 0.61 ± 0.2% yr <sup>–1</sup> : 0°–60°N

–0.35 ± –0.3% yr <sup>–1</sup> ; -0.33±0.3% yr <sup>–1</sup> : 0°–60°S

| [[#Buchholz--2021|Buchholz et al. (2021)]]

Satellite Observations

MOPITT; AIRS

|-
| 2000–2017

| –0.32 ± 0.05% yr <sup>–1</sup>

| [[#Zheng--2019|Zheng et al. (2019)]]

Satellite Observations

MOPITT

|-
| 2003–2014

| around –2.5 to 0.5 ppb yr <sup>–1</sup> : Northern Hemisphere around –0.5 to 0 ppb yr <sup>–1</sup> : Southern Hemisphere

| [[#Flemming--2017|Flemming et al. (2017)]] NOAA Carbon Cycle Cooperative Global Air Sampling Network

|-
| 2001–2013

| –2.19 to –0.80 ppb yr <sup>–1</sup> : Northern Hemisphere (Upper Troposphere/Tropopause Layer)

| [[#Cohen--2018|Cohen et al. (2018)]] IAGOS Airborne

|-
| colspan="3"| '''Pacific/Tropics'''

|-
| 2004–2013

(Spring Mean)

| –2.9 ± 2.6 ppb yr <sup>–1</sup> : Mauna Loa (19.54°N, 155.58°W)

| [[#Gratz--2015|Gratz et al. (2015)]] Ground-based

|-
| 2004–2013

(Spring Mean)

| –2.6 ± 1.8 ppb yr <sup>–1</sup> : Sand Island Midway (28.21°N, 177.38°W)

| [[#Gratz--2015|Gratz et al. (2015)]] Ground-based

|-
| colspan="3"| '''Europe'''

|-
| 1996–2006

| –0.45 ± 0.16% yr <sup>–1</sup> : Jungfraujoch (46.6°N, 8.0°E)

–1.00 ± 0.24% yr <sup>–1</sup> : Zugspitze (47.4°N, 11.0°E)

–0.62 ± 0.19% yr <sup>–1</sup> : Harestua (60.2°N, 10.8°E)

0.61 ± 0.16% yr <sup>–1</sup> : Kiruna (67.8°N, 20.4°E)

| [[#Angelbratt--2011|Angelbratt et al. (2011)]] Ground-based

|-
| 2001–2011

May to Sep

| –3.1 ± 0.30 ppb yr <sup>–1</sup> : Pico Mt. Obs (38.47°N, 28.40°W) –1.4 ± 0.20 ppb yr <sup>–1</sup> : Mace Head, Ireland

| [[#Kumar--2013|Kumar et al. (2013)]] Ground-based

|-
| 2002–2018

| –-0.89 ± 0.1% yr <sup>–1</sup> : Europe (45°N–55°N, 0°E–15°E)

| [[#Buchholz--2021|Buchholz et al. (2021)]]

Satellite Observations

MOPITT

|-
| colspan="3"| '''North America'''

|-
| 2001–2010

| –2.5 ppb yr <sup>–1</sup> : Thompson Farm (43.11°N, 70.95°W)

–2.3 ppb yr <sup>–1</sup> : Mt. Washington (44.27°N, 71.30°W)

+2.8 ppb yr <sup>–1</sup> : Castle Springs (43.75°N, 71.35°W)

–3.5 ppb yr <sup>–1</sup> : Pack Monadnock (42.86°N, 71.88°W)

–2.8 ppb yr <sup>–1</sup> : Whiteface Mountain (44.40°N, 73.90°W)

–4.3 ppb yr <sup>–1</sup> : Pinnacle State Park (42.09°N, 77.21°W)

| [[#Zhou--2017|Zhou et al. (2017)]] Ground-based

|-
| 2004–2013

(Spring Mean)

| –3.2 ± 2.9 ppb yr <sup>–1</sup> : Mt. Bachelor Observatory

| [[#Gratz--2015|Gratz et al. (2015)]] Ground-based

|-
| 2004–2012

(Spring Mean)

| –2.8 ± 1.8 ppb yr <sup>–1</sup> : Shemya Island (55.21°N, 162.72°W)

| [[#Gratz--2015|Gratz et al. (2015)]] Ground-based

|-
| 2002–2018

| –0.85 ± 0.1%yr <sup>–1</sup> : Eastern USA (35°N–40°N, –95°E–75°E)

| [[#Buchholz--2021|Buchholz et al. (2021)]]

Satellite Observations

MOPITT

|-
| colspan="3"| '''Asia'''

|-
| 2005–2018

| –0.46 ± 0.14% yr <sup>–1</sup> : Eastern Asia

| [[#Zheng--2018a|Zheng et al. (2018a)]]

WDCGG Ground-based

|-
| 2005–2018

| –0.41 ± 0.09% yr <sup>–1</sup> : Eastern Asia

| [[#Zheng--2018a|Zheng et al. (2018a)]]

MOPITT

|-
| 2002–2018

| –1.18 ± 0.3% yr <sup>–1</sup> : (Northeast China 30°E–40°E, 110°E–123°E) –0.28 ± 0.2% yr <sup>–1</sup> : (North India 20°N–30°N, 70°E–95°E)

| [[#Buchholz--2021|Buchholz et al. (2021)]]

Satellite Observations

MOPITT

|}

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==== 6.3.3.2 Carbon Monoxide (CO) ====

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About half of the atmospheric CO burden is due to its direct emissions and the remainder is due to the atmospheric oxidation of methane and NMVOCs. Reaction with OH is the primary sink of CO with a smaller contribution from dry deposition.

Since AR5, advances in satellite retrievals (e.g., Worden et al. , 2013; Warner et al. , 2014; Buchholz et al. , 2021) , ground-based column observations (e.g., Zeng et al. , 2012; Té et al. , 2016) , airborne platforms (e.g., [[#Cohen--2018|Cohen et al., 2018]] ; [[#Petetin--2018|Petetin et al., 2018]] ), surface measurement networks (e.g., Andrews et al. , 2014; Schultz et al. , 2015; Prinn et al. , 2018; Pétron et al. , 2019) and assimilation products (e.g., [[#Deeter--2017|Deeter et al., 2017]] ; [[#Flemming--2017|Flemming et al., 2017]] ; [[#Zheng--2019|Zheng et al., 2019]] ) have resulted in better characterization of the present-day atmospheric CO distribution. Typical annual mean surface CO concentrations range from around 120 ppb in the Northern Hemisphere to around 40 ppb in the Southern Hemisphere ( [[#Pétron--2019|Pétron et al., 2019]] ). The sub-regional patterns in CO reflect the distribution of emissions sources. Seasonal hotspots are linked to areas of biomass burning in tropical South America, equatorial Africa, South East Asia and Australia. A study using data assimilation techniques estimates a global mean CO burden of 356 ± 27 Tg over the 2002–2013 period ( [[#Gaubert--2017|Gaubert et al., 2017]] ).

Global models generally capture the global spatial distribution of the observed CO concentrations but have regional biases of up to 50% (e.g., [[#Emmons--2020|Emmons et al., 2020]] ; [[#Horowitz--2020|Horowitz et al., 2020]] ). Despite updated emissions datasets, the global multi-model and single-model simulations persistently underestimate observed CO concentrations at northern high and mid-latitudes as well as in the Southern Hemisphere, but with smaller biases compared with that in the Northern Hemisphere (Naik et al. , 2013; Stein et al. , 2014; Monks et al. , 2015; Strode et al. , 2015). Models are biased high in the tropics, particularly over highly polluted areas in India and Eastern Asia ( [[#Strode--2016|Strode et al., 2016]] ; [[#Yarragunta--2017|Yarragunta et al., 2017]] ).

Estimates of global CO burden simulated by global models generally fall within the range of that derived from data assimilation techniques, though the spread across the models is large (Naik et al. , 2013; Stein et al. , 2014; Zeng et al. , 2015; Myriokefalitakis et al. , 2016) . There is a large diversity in model-simulated CO budget driven by uncertainties in CO sources and sinks, particularly those related to in situ production from NMVOCs and loss due to reaction with OH ( [[#Stein--2014|Stein et al., 2014]] ; [[#Zeng--2015|Zeng et al., 2015]] ; [[#Myriokefalitakis--2016|Myriokefalitakis et al., 2016]] ). Global CO budget analysis from a multi-model ensemble for more recent years, including results from the CMIP6 model runs, are not yet available.

Reconstructions of CO concentrations based on limited ice-core samples in the Northern Hemisphere high latitudes suggest CO mole fractions of about 145 ppb in the 1950s, which rose by 10–15 ppb in the mid- 1970s, and then declined by about 30–130 ppb by 2008 ( [[#Petrenko--2013|Petrenko et al., 2013]] ). The negative trends since the 1990s are often attributed to emissions regulations from road transportation in North America and Europe. Due to limited observations prior to the satellite era, long-term global CO trends are based on estimates from models. An increase of global CO burden of about 50% for the year 2000 relative to 1850 is found in CMIP6 ( [[#Griffiths--2021|Griffiths et al., 2021]] ).

The AR5 reported a global CO decline of about 1% yr <sup>–1</sup> based on satellite data from 2002–2010, but biases in instruments rendered ''low confidence'' in this trend. The AR5 also indicated a small CO decrease from in situ networks but did not provide quantitative estimates. New analysis of CO trends performed since AR5 and based on different observational platforms and assimilation products show a decline globally and over most regions during the last one to two decades with varying amplitudes partly depending on the period of analysis (Table 6.4). Inversion-based analysis attributes the global CO decline during the past two decades to decreases in anthropogenic and biomass-burning CO emissions despite probable increase in atmospheric CO chemical production (Gaubert et al. , 2017; Jiang et al. , 2017; Zheng et al. , 2019). Furthermore, [[#Buchholz--2021|Buchholz et al. (2021)]] report a slowdown in global CO decline in 2010–2018 compared to 2002–2010, although the magnitude and sign of this change in the trend varies regionally. Global models prescribed with emissions inventories developed prior to the CMIP6 inventory capture the declining observed CO trends over North America and Europe but not over Eastern Asia ( [[#Strode--2016|Strode et al., 2016]] ). CMIP6 models driven by CMIP6 emissions simulate a negative trend in global CO burden over the 1990–2020 period ( [[#Griffiths--2021|Griffiths et al., 2021]] ), however the simulated trends have not yet been evaluated against observations.

In summary, our understanding of present-day global CO distribution has increased since AR5 with newer and improved observations and reanalysis. There is ''high confidence'' that global CO burden is declining since 2000. Evidence from observational CO reanalysis suggests this decline is driven by reductions in anthropogenic CO emissions, however this is yet to be corroborated by global ESM studies with the most recent emissions inventories.

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==== 6.3.3.3 Non-Methane Volatile Organic Compounds (NMVOCs) ====

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NMVOCs encompass thousands of compounds with lifetimes from hours to days to months, and abundances and chemical composition highly variable with respect to space and time. Although the biogenic source (Section 6.2.2) dominates the global NMVOC budget, anthropogenic activities are the main driver of long-term trends in the abundance of many compounds.

Information on the global distribution of individual NMVOCs is scarce, except for the less reactive compounds having lifetimes of several days to months. Based on measurements from polar firn air samples and ground-based networks, AR5 reported that the abundances of the predominantly anthropogenic light alkanes (C <sub>2</sub> -C <sub>5</sub> ) increased until 1980 and declined afterwards. The decline was attributed to air-quality emissions controls and to fugitive emissions decreases following the collapse of the Soviet Union ( [[#Simpson--2012|Simpson et al., 2012]] ). Since AR5, scarce ground-based measurements have shown that the decline in C <sub>2</sub> -C <sub>3</sub> alkanes ended around 2008 and their abundances are since growing again, which is primarily attributed to increasing North American emissions (Section 6.2.1). Furthermore, since AR5 the evolution of ethane levels during the past millennium was made accessible by analysis of ice-core samples ( [[#Nicewonger--2016|Nicewonger et al., 2016]] ). The large observed interpolar ratio of ethane in pre-industrial times (3.9) corroborates a large geologic source of ethane previously put forward by ( [[#Etiope--2009|Etiope and Ciccioli, 2009]] ), and narrows down its likely global magnitude ( [[#Nicewonger--2018|Nicewonger et al., 2018]] ) ( ''low to medium confidence'' ). The incorporation of geologic emissions in CCMs is not yet systematic though a one-model study has shown improved agreement of the results with observations ( [[#Dalsøren--2018|Dalsøren et al., 2018]] ).

Formaldehyde (HCHO) is a short-lived, high-yield product of NMVOC oxidation, and formaldehyde column data from satellite instruments can therefore inform on trends in anthropogenic NMVOC abundances over very industrialized regions. The AR5 reported significant positive trends in formaldehyde between 1997 and 2009 over northeastern China (4% yr <sup>–1</sup> ) and negative trends over northeastern US cities. Since AR5, there is ''robust evidence'' and ''high agreement'' of an upward trend of HCHO over eastern China, though large regional disparities exist in the trends ( [[#De%20Smedt--2015|De Smedt et al., 2015]] ; [[#Shen--2019|Shen et al., 2019]] ) with a possible negligible or decreasing trend over Beijing and the Pearl River Delta. In other world regions, in particular North America, there is ''limited'' to ''medium evidence'' for significant changes in the HCHO columns, except in regions where the trend is particularly strong (e.g., the Houston area: –2.2% yr <sup>–1</sup> over 2005–2014) and the Alberta oil sands (+3.8% yr <sup>–1</sup> ; <sup></sup> [[#Zhu--2017|Zhu et al., 2017]] ). Over the northeastern USA, even the sign of the trend differs between studies (De Smedt et al. , 2015; Zhu et al. , 2017) for reasons that are unclear.

In summary, after a decline between 1980 and 2008, abundances of light NMVOCs have increased again over the Northern Hemisphere due to the extraction of oil and gas in North America ( ''high confidence'' ). Trends in satellite HCHO observations, used as a proxy of anthropogenic NMVOC over industrialized areas, show a significant positive trend over eastern China ( ''high confidence'' ) but also indicate large regional disparities in the magnitude of the trends over China and even in their signs over North America.

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==== 6.3.3.4 Ammonia (NH <sub>3</sub> ) ====

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Ammonia is the most abundant alkaline gas in the atmosphere. Its present-day source is dominated by livestock and crop production (Section 6.2). Ammonia reacts with nitric acid and sulphuric acid to produce ammonium sulphate and ammonium nitrate, which contribute to the aerosol burden (Section 6.3.5.2), promotes aerosol nucleation by stabilizing sulphuric acid clusters ( [[#Kirkby--2011|Kirkby et al., 2011]] ), and contributes to nitrogen deposition (Section 6.4.4; [[#Sheppard--2011|Sheppard et al., 2011]] ; [[#Flechard--2020|Flechard et al., 2020]] ). Trends in NH <sub>3</sub> were not assessed in AR5.

Considerable expansion of satellite (Clarisse et al. , 2009; [[#Shephard--2015|Shephard and Cady-Pereira, 2015]] ; Warner et al. , 2016) and ground-based observations (Miller et al. , 2014; Y. Li et al. , 2016; Pan et al. , 2018) has improved our understanding of the spatial distribution and seasonal to interannual variability of ammonia, and advanced its representations in models (e.g., [[#Zhu--2015|Zhu et al., 2015]] ). Regionally, peak NH <sub>3</sub> concentrations are observed over large agricultural (e.g., northern India, the USA Midwest and Central Valley) and biomass-burning regions, in good qualitative agreement with emissions inventories (Van Damme et al. , 2015, 2018) . However, several large agricultural and industrial hotspots have been found to be missing or greatly underestimated in emissions inventories (Van Damme et al. , 2018) . NH <sub>3</sub> exhibits a strong vertical gradient, with a maximum in the boundary layer ( [[#Schiferl--2016|Schiferl et al., 2016]] ), and can be transported into the upper troposphere and lower stratosphere (UTLS), particularly in the Asian Monsoon region, as indicated by observations ( [[#Froyd--2009|Froyd et al., 2009]] ; [[#Höpfner--2016|Höpfner et al., 2016]] , 2019) and theoretical considerations (Ge et al. , 2018) . There is a large range in the present-day NH <sub>34</sub> burden (from 0.04–0.7 TgN) simulated by CCMs, highlighting deficiencies in the process-level representation of NH <sub>3</sub> in current global models ( [[#Bian--2017|Bian et al., 2017]] ). The underestimate of surface NH <sub>3</sub> concentrations ( [[#Bian--2017|Bian et al., 2017]] ) further highlights such deficiencies and the limitations in comparing site-specific observations with relatively coarse-resolution models.

Observations show that NH <sub>3</sub> concentration has been increasing in recent decades in the USA (Butler et al. , 2016; Warner et al. , 2016; Yu et al. , 2018) , Western Europe (van Zanten et al. , 2017; Warner et al. , 2017; Wichink Kruit et al. , 2017; Tang et al. , 2018) , and China ( [[#Warner--2017|Warner et al., 2017]] ; M. [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ). This trend has been attributed to a combination of increasing ammonia emissions ( [[#Sutton--2013|Sutton et al., 2013]] ; [[#Fowler--2015|Fowler et al., 2015]] ) and decreases in the chemical reaction of NH <sub>3</sub> with nitric and sulphuric acids associated with reductions in SO <sub>2</sub> and NO <sub>x</sub> emissions whose rate depends on the region ( [[#Warner--2017|Warner et al., 2017]] ; [[#Yao--2019|Yao and Zhang, 2019]] ). Over longer time scales, CCMs simulate an increase of the NH <sub>34</sub> burden by a factor of two to seven since pre-industrial conditions ( [[#Xu--2012|Xu and Penner, 2012]] ; [[#Hauglustaine--2014|Hauglustaine et al., 2014]] ).

In summary, progress has been made in the understanding of the spatio-temporal distribution of ammonia, though representation of NH <sub>3</sub> remains rather unsatisfactory due to process-level uncertainties. Evidence from observations and models suggests that ammonia concentrations have been increasing over recent decades due to emissions and chemistry. There is ''high confidence'' that the global NH <sub>34</sub> burden has increased considerably from the pre-industrial period to the present day, although the magnitude of the increase remains uncertain.

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<span id="sulphur-dioxide-so-2"></span>
==== 6.3.3.5 Sulphur Dioxide (SO <sub>2</sub> ) ====

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The AR5 did not assess trends in SO <sub>2</sub> concentrations. Trends in SO <sub>2</sub> abundances are consistent with the overall anthropogenic emissions changes as presented in Section 6.2 and Figure 6.18. Long-term surface-based in situ observations in North America and Europe show reductions of more than 80% since the measurements began around 1980 (Table 6.5). Europe had the largest reductions in the first part of the period while the highest reduction came later in North America. Observed trends are qualitatively reproduced by global and regional models over North America and Europe during the period 1990–2015 for which emissions changes are well quantified (Table 6.5; [[#Aas--2019|Aas et al., 2019]] ).

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'''Table 6.5 |''' '''Summary of changes or trends in atmospheric abundance of sulphur dioxide (SO''' <sub>2</sub> ''') and sulphate (SO''' <sub>4</sub> <sup>2–</sup> ''') aerosols based on in situ and satellite observations.'''

{| class="wikitable"
|-
| '''Analysis Period'''

| '''Trends in SO''' <sub>2</sub>

| '''Trends in Particulate SO''' <sub>4</sub> <sup>2–</sup>

| '''Reference'''

|-
| colspan="4"| Global Models/Assimilated Models

|-
| 1990–2000

| –8.54 ± 1.40% yr <sup>–1</sup> (EU, 43 sites)

–2.63 ± 0.30% yr <sup>–1</sup> (NA, 53 sites)

| –5.23 ± 1.17% yr <sup>–1</sup> (EU, 41 sites)

–1.94 ± 0.43% yr <sup>—</sup> (NA 101 sites)

| [[#Aas--2019|Aas et al. (2019)]]

|-
| 2000–2015

| –0.41 ± 0.92% yr <sup>–1</sup> (EA, 19 sites)

–4.86 ± 1.31% yr <sup>–1</sup> (EU, 47 sites)

–4.40 ± 0.93% yr <sup>–1</sup> (NA, 77 sites)

| 0.02 ± 0.91% yr <sup>–1</sup> (EA, 13 sites)

–3.26 ± 0.85% yr <sup>–1</sup> (EU, 36 sites)

–3.18 ± 0.66% yr <sup>–1</sup> (NA, 218 sites)

| [[#Aas--2019|Aas et al. (2019)]]

|-
| colspan="4"| Ground-based In Situ Observations

|-
| 1980–1990

| –5.03 ± 2.04% yr <sup>–1</sup> (EU, 20 sites)

–2.5% yr <sup>–1</sup> (US)

| –2.56 ± 3.10% yr <sup>–1</sup> (EU, 16 sites)

–1.80 ± 4.09% yr <sup>–1</sup> (US SO <sub>4</sub> <sup>2–</sup> in precipitation, 78 sites)

| [[#Aas--2019|Aas et al. (2019)]]

[https://www.epa.gov/air-trends/sulfur-dioxide-trends US EPA] <sup>a</sup>

|-
| 1990–2000

| –7.56 ± 1.81% yr <sup>–1</sup> (EU, 43 sites)

–3.27 ± 1.69% yr <sup>–1</sup> (NA, 53 sites)

| –5.16 ± 2.11% yr <sup>–1</sup> (EU, 41 sites)

–2.08 ± 1.44% yr <sup>–1</sup> (NA, 101 sites)

| [[#Aas--2019|Aas et al. (2019)]]

|-
| 2000–2015

| –0.14 ± 5.32% yr <sup>–1</sup> (EA, 19 sites)

–3.89 ± 2.16% yr <sup>–1</sup> (EU, 47 sites)

–4.69 ± 1.35% yr <sup>–1</sup> (NA, 77 sites)

| 2.68 ± 9.41% yr <sup>–1</sup> (EA, 13 sites)

–2.67 ± 2.03% yr <sup>–1</sup> (EU, 36 sites)

–3.15 ± 1.30% yr <sup>–1</sup> (NA, 218 sites)

| [[#Aas--2019|Aas et al. (2019)]]

|-
| colspan="4"| Change Based on Satellite Observations

|-
| 2005–2015

| ca –80% (Eastern US)

|
| [[#Krotkov--2016|Krotkov et al. (2016)]]

|-
| 2005–2015

| ca –60% (Eastern EU)

|
| [[#Krotkov--2016|Krotkov et al. (2016)]]

|-
| 2005–2015

| 200 ± 50% (India)

|
| [[#Krotkov--2016|Krotkov et al. (2016)]]

|-
| 2005 (and 2012) –2015

| ca –50% (The North China Plain)

|
| [[#Krotkov--2016|Krotkov et al. (2016)]]

|}

<sup>a</sup> https://www.epa.gov/air-trends/sulfur-dioxide-trends

In situ observations over other parts of the world are scattered. However, the limited in situ observations in Eastern Asia indicate an increase in atmospheric SO <sub>2</sub> up to around 2005 and then a decline ( [[#Aas--2019|Aas et al., 2019]] ). This is confirmed by satellite observations ( [[#Krotkov--2016|Krotkov et al., 2016]] ), which further reveal a rapid decline in SO <sub>2</sub> since around 2012 or 2013 ( [[#Krotkov--2016|Krotkov et al., 2016]] ; [[#Zheng--2018b|Zheng et al., 2018b]] ). In India, on the other hand, SO <sub>2</sub> levels have doubled between 2005 and 2015 ( [[#Krotkov--2016|Krotkov et al., 2016]] ).

In summary, surface and satellite observations indicate strong regional variations in trends of atmospheric SO <sub>2</sub> abundance. The SO <sub>2</sub> concentrations in North America and Europe have declined over 1980–2015 with slightly stronger reductions in North America (70 ± 20%) than in Europe (58 ± 32%) over 2000–2015, though Europe had larger reductions than the US in the prior decade (1990–2000). In Asia, the SO <sub>2</sub> trends are more scattered, though there is ''medium confidence'' that there was a strong increase up to around 2005, followed by a steep decline in China, while over India, the concentrations are increasing steadily.

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