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

=== 6.6.2 Attribution of Temperature and Air Pollution Changes to Emissions Sectors and Regions ===

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Assessment of the temperature response to source emissions sectors is important for identifying priority mitigation measures and designing efficient mitigation strategies.

Temperature effects of emissions can be quantified for the historical contribution to the present temperature impact (Section 6.4.2), for idealized one-year pulses of emissions or for continued (sustained) emissions at present levels and for changes during a specific time period, for emissions from future scenarios with various hypotheses, giving complementary information to feed mitigation strategies. The AR5 assessed the net global temperature impact of source emissions sectors from a one-year pulse (a single year’s worth) of year 2008 emissions and found that the largest contributors to warming on 50–100-year time scales are the energy, industrial and on-road transportation sectors. Sectors that emit large amounts of methane (agriculture and waste management) and black carbon (residential biofuel) are important contributors to warming over short time horizons up to 20 years. Below, we discuss the effect on ERFs, temperature and air pollution of selected key sectors estimated to have large non-CO <sub>2</sub> forcing, including agriculture, residential and commercial, and transport (aviation, shipping, land transportation).

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==== 6.6.2.1 Agriculture ====

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According to the SRCCL assessment ( [[#Jia--2019|Jia et al., 2019]] ), agriculture, forestry and other land use (AFOLU) are a significant net source of GHG emissions ( ''high confidence'' ), with more than half of these emissions attributed to non-CO <sub>2</sub> gHGs from agriculture. With respect to SLCFs, agricultural activities are major global sources of methane and NH <sub>3</sub> (Section 6.2.1). The agriculture sector exerts strong near-term warming due to large methane emissions that is slightly offset by a small cooling from secondary inorganic aerosols formed notably from the NH <sub>3</sub> emissions ( [[#Heald--2016|Heald and Geddes, 2016]] ; [[#Lund--2020|Lund et al., 2020]] ). For present-day emissions, agriculture is the second largest contributor to warming on short time scales but with a small persisting effect on surface temperature (+0.0012°C ± 0.00028°C) after a pulse of current emissions (Figure 6.16, see detailed description in Section 6.6.2.3.4; [[#Lund--2020|Lund et al., 2020]] ). Aerosols produced from agricultural emissions, released after nitrogen fertilizer application and from animal husbandry, influence surface air quality and make an important contribution to surface PM <sub>2.5</sub> in many densely populated areas ( Figure 6.17; [[#Lelieveld--2015b|Lelieveld et al., 2015b]] ; [[#Bauer--2016|Bauer et al., 2016]] ).

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==== 6.6.2.2 Residential and Commercial Cooking and Heating ====

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The residential and commercial sector is associated with SLCF emissions of carbonaceous aerosols, CO and NMVOCs, SO <sub>2</sub> and NO <sub>x</sub> , and can be split by fuel type (biofuel or fossil fuel) where residential fossil fuel is also associated with CO <sub>2</sub> and methane emissions (Section 6.2.1).

The net effect of residential CO and NMVOC emissions is warming and that of SO <sub>2</sub> and NO <sub>x</sub> is cooling of the atmosphere. However, the sign of the net global radiative effects of carbonaceous aerosols from the residential sector and solid-fuel cookstove emissions (warming or cooling) is not well constrained based on evidence from recent global atmospheric modelling studies. Estimates of direct aerosol – radiation and aerosol – cloud effects from the global residential sector range from –20 to +60 mW m <sup>–2</sup> ( [[#Kodros--2015|Kodros et al., 2015]] ) and –66 to +21 mW m <sup>–2</sup> ( [[#Butt--2016|Butt et al., 2016]] ) and from –20 to +10 mW m <sup>–2</sup> ( [[#Kodros--2015|Kodros et al., 2015]] ) and –52 to –16 mW m <sup>–2</sup> ( [[#Butt--2016|Butt et al., 2016]] ), respectively. Uncertainties are due to assumptions about the aerosol emissions masses, size distribution, aerosol optical properties and mixing states (Section 6.3.5.3). Allowing BC to act as an INP in a global model leads to a much larger global forcing estimate from –275 to +154 mW m <sup>–2</sup> with a large uncertainty range due to uncertainty in the plausible range of maximum freezing efficiency of BC (Huang et al. , 2018). The residential biofuel sector is a major concern for indoor air quality (Bonjour et al. , 2013). In addition, several atmospheric modelling studies find that this sector is also important for outdoor air quality and even a dominant source of population-weighted outdoor PM <sub>2.5</sub> in India and China ( [[#Lelieveld--2015b|Lelieveld et al. 2015b]] , Silva et al. , 2016; Spracklen et al. , 2018; Reddington et al. , 2019).

The net climate effect of a one-year pulse of current emissions from the residential sector is warming in the near term of +0.0018°C ± 0.00084°C from fossil fuel use and +0.0014°C ± 0.0012°C from biofuel use. Over a 100-year time horizon, this warming is +0.0017°C ± 0.00017°C and +0.0001°C ± 0.000079°C, respectively ( [[#Lund--2020|Lund et al., 2020]] ). This is due to the effects of BC, methane, CO and NMVOCs, which add to that of CO <sub>2</sub> , but the uncertainty in the sign of carbonaceous aerosol net effects challenges overall quantitative understanding of this sector and leads to ''low confidence'' in this assessment. Residential sector emissions are an important source of indoor and outdoor air pollution in Asia and globally ( ''high confidence'' ).

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==== 6.6.2.3 Transportation ====

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===== 6.6.2.3.1 Aviation =====

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Aviation is associated with a range of SLCFs, in particular emissions of NO <sub>x</sub> and aerosol particles, alongside emissions of water vapour and CO <sub>2.</sub> The largest SLCF effects are those from the formation of persistent condensation trails (contrails) and NO <sub>x</sub> emissions. Persistent contrails are ice-crystal clouds, formed around aircraft soot particles (and water vapour from the engine), injected in ambient cold and ice-supersaturated atmosphere, which can spread and form contrail cirrus clouds. The ‘net NO <sub>x</sub> ’ effect arises from the formation of tropospheric ozone, counterbalanced by the destruction of ambient methane and associated cooling effects of reductions in stratospheric water vapour and background ozone. The AR5 assessed the radiative forcing from persistent linear contrails to be +0.01 [+0.005 to +0.03] W m <sup>–2</sup> for the year 2011, with ''medium confidence'' ( [[#Boucher--2013|Boucher et al., 2013]] ). The combined linear contrail and their subsequent evolution to contrail cirrus radiative forcing from aviation was assessed to be +0.05 [+0.02 to +0.15] W m <sup>–2</sup> , with ''low confidence'' . An additional forcing of +0.003 W m <sup>–2</sup> due to emissions of water vapour in the stratosphere by aviation was also reported ( [[#Boucher--2013|Boucher et al., 2013]] ). The aviation sector was also estimated to lead to a net surface warming at 20- and 100-year horizons following a one-year pulse emission. This net temperature response was determined by similar contributions from contrails, contrail cirrus and CO <sub>2</sub> over a 20-year time horizon, and dominated by CO <sub>2</sub> in a 100-year perspective (Figure 8.34 in AR5, [[#Myhre--2013|Myhre et al., 2013]] ).

Our assessment is built upon [[#Lee--2021|Lee et al. (2021)]] . Their study consists of an updated, comprehensive assessment of aviation climate forcing in terms of RF and ERF based on a large number of studies and the most recent air-traffic and fuel-use datasets available (for 2018), new calculations and the normalization of values from published modelling studies, and combining the resulting best estimates via a Monte-Carlo analysis. [[#Lee--2021|Lee et al. (2021)]] report a net aviation ERF for year-2018 emissions of +0.101 [0.055–0.145] W m <sup>–2</sup> with major contributions from contrail cirrus (0.057 W m <sup>–2</sup> ), CO <sub>2</sub> (0.034 W m <sup>–2</sup> ) and NO <sub>x</sub> (0.017 W m <sup>–2</sup> ). Contrails and aviation-induced cirrus yield the largest individual positive ERF followed by CO <sub>2</sub> and NO <sub>x</sub> emissions (Lee et al. 2021). The ''confidence'' level in ERF due to contrails and aviation-induced cirrus is assessed to be ''low'' in [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.2|Section 7.3.4.2]] ) due to potential missing processes. The formation and emission of sulphate aerosols yield a negative (cooling) term. SLCF forcing terms contribute about eight times more than CO <sub>2</sub> to the uncertainty in the aviation net ERF in 2018 ( [[#Lee--2021|Lee et al., 2021]] ). The largest uncertainty in assessing aviation climate effects is on the interactions of BC and sulphate aerosols on cirrus and mixed-phase clouds, for which no best estimates of the ERFs were provided ( [[#Lee--2021|Lee et al., 2021]] ).

One of the most significant changes between AR5 and AR6 in terms of aviation SLCFs is the explicit calculation of a contrail cirrus ERF found to be 35% of the corresponding RF (Bickel et al. , 2020), confirming the studies indicating smaller efficacy of linear contrails ( [[#Ponater--2005|Ponater et al., 2005]] ; [[#Rap--2010|Rap et al., 2010]] ). The net-NO <sub>x</sub> term is generally agreed to be a positive RF in the present day, although attribution in a non-linear chemical system is problematic ( [[#Grewe--2019|Grewe et al., 2019]] ), but [[#Skowron--2021|Skowron et al. (2021)]] point out that the sign of net NO <sub>x</sub> term is dependent on background conditions and could be negative under certain future scenarios.

The best estimate ERFs from aviation ( [[#Lee--2021|Lee et al., 2021]] ) have been used to calculate aviation-specific Absolute Global Temperature change Potential (AGTP) using the method described in [[#Lund--2020|Lund et al. (2020)]] and subsequently compute the effect of a one-year pulse of aviation emissions on global mean surface temperature on a 10- and 100-year time horizon (Section 6.6.2.3.4 and Figure 6.16). The effect of contrail-cirrus is most important for the estimated net-GSAT response after the first decade, followed by similar warming contributions from NO <sub>x</sub> and CO <sub>2</sub> emissions. At a 20-year time horizon, the net contribution from aviation to GSAT has switched from a positive to a small negative effect (see Supplementary Material 6.SM.4). This is due to the combination of rapidly decaying contrail-cirrus warming and the complex time variation of the net temperature response to NO <sub>x</sub> emissions, which changes sign between 10 and 20 years due to the balance between the positive short-lived ozone forcing and negative forcing from changes in methane and methane-induced changes in ozone and stratospheric water vapour. The net GSAT response to aviation emissions has previously been estimated to be positive on a 20-year time horizon (AR5, Chapter 8; [[#Lund--2017|Lund et al., 2017]] ). This difference in net GSAT after 20 years in AR5 compared to AR6, results primarily from a shorter time scale of the climate response in the underlying AGTP calculations in [[#Lund--2020|Lund et al. (2020)]] , which means the initial, strong impacts of the most short-lived SLCFs, including the warming by contrail-cirrus decay faster, in turn giving the net NO <sub>x</sub> effect a relatively higher importance after 20 years. On longer time horizons, the net GSAT response switches back to positive, as CO <sub>2</sub> becomes the dominating warming contribution.

In summary, the net aviation ERF is assessed to be +0.1 W m <sup>–2</sup> (±0.045) for the year 2018 ( ''low confidence)'' . This confidence level is largely a result of the fact that the SLCF-related terms, which account for more than half (66%) of the net aviation ERF, are the most uncertain terms. The climate response to SLCF-related aviation terms exhibits substantial spatio-temporal heterogeneity in characteristics ( ''high confidence'' ). Overall, cirrus and contrail cirrus warming, as well as NO <sub>x</sub> -induced ozone increase, induce strong but short-lived warming contributions to the GSAT response 10 years after a one-year pulse of present-day aviation emissions ( ''medium confidence'' ), while CO <sub>2</sub> both gives a warming effect in the near term and dominates the long-term warming impact ( ''high confidence'' ).

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===== 6.6.2.3.2 Shipping =====

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Quantifying the effects of shipping on climate is particularly challenging because (i) the sulphate cooling impact is dominated by aerosol–cloud interactions and (ii) ship emissions contain NO <sub>x</sub> , SO <sub>x</sub> and BC, which lead to mixed particles. Previous estimates of the sulphate radiative effects from present-day shipping span the range –47 to –8 mW m <sup>–2</sup> (direct radiative effect) and –600 to –38 mW m <sup>–2</sup> (indirect radiative effects) (Lauer et al. , 2007; Balkanski et al. , 2010; Eyring et al. , 2010; Lund et al. , 2012) . [[#Partanen--2013|Partanen et al. (2013)]] reported a global mean ERF for year-2010 shipping aerosol emissions of –390 mW m <sup>–2</sup> . The temperature change has been shown to be highly sensitive to the choice of aerosol–cloud parametrization ( [[#Lund--2012|Lund et al., 2012]] ). One year of global present-day shipping emissions, not considering the impact of recent low sulphur fuel regulation ( [[#IMO--2016|IMO, 2016]] ), are estimated to cause net cooling in the near term (–0.0024°C ± 0.0025°C) and slight warming (+0.00033°C ± 0.00015°C) on a 100-year horizon ( [[#Lund--2020|Lund et al., 2020]] ).

Shipping is also of importance for air pollution in coastal areas along the major trade routes, especially in Europe and Asia (Corbett et al. , 2007; H. Liu et al. , 2016, Figure 6.17; Jonson et al. , 2020). Jonson et al. (2020) estimated that shipping is responsible for 10% or more of the controllable PM <sub>2.5</sub> concentrations and depositions of oxidised nitrogen and sulphur for many coastal countries. Widespread introduction of low-sulphur fuels in shipping from 2020 ( [[#IMO--2016|IMO, 2016]] ) will lead to improved air quality and reduction in premature mortality and morbidity ( [[#Sofiev--2018|Sofiev et al., 2018]] ).

In summary, a year’s worth of present-day global shipping emissions (i.e., without the implementation of the 2020 clean fuel standards) cause a net global cooling (–0.0024 ± 0.0025°C) on 10–20 year time horizons ( ''high confidence'' ) but its magnitude is of ''low confidence'' .

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===== 6.6.2.3.3 Land transportation =====

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The on-road and off-road transportation sectors have a net warming impact on climate over all time scales ( [[#Berntsen--2008|Berntsen and Fuglestvedt, 2008]] ; [[#Fuglestvedt--2008|Fuglestvedt et al., 2008]] ; [[#Unger--2010|Unger et al., 2010]] ; [[#Lund--2020|Lund et al., 2020]] ). A one-year pulse of present-day emissions has a small net global temperature effect on short time scales (+0.0011°C ± 0.0045°C), predominantly driven by CO <sub>2</sub> and BC warming offset by NO <sub>x</sub> -induced cooling through methane lifetime reductions ( [[#Lund--2020|Lund et al., 2020]] ).

The vehicle tailpipe emissions profiles of diesel and gasoline are distinctly different. Diesel air pollutant emissions are dominated by BC and NO <sub>x</sub> whereas gasoline air pollutant emissions are dominated by CO and NMVOCs, especially when distribution and upstream losses are considered. Thus, the net radiative effect of the on-road vehicle fleets depends upon the share of different fuels used, in particular gasoline and diesel ( [[#Lund--2014|Lund et al., 2014]] ; [[#Huang--2020|Huang et al., 2020]] ). The net SLCF for year-2010 emissions from the global diesel vehicle fleet have been estimated to be +28 mW m <sup>–2</sup> ( [[#Lund--2014|Lund et al., 2014]] ). [[#Huang--2020|Huang et al. (2020)]] estimated net global radiative effects of SLCFs (including aerosols, ozone, and methane) from the gasoline and diesel vehicle fleets in the year 2015 to be +13.6 and +9.4 mW m <sup>–2</sup> , respectively, with similar fractional contributions of SLCFs to the total global climate impact including CO <sub>2</sub> on the 20‐year time scale (14–15%).

There is consensus that on‐road transportation sector emissions, including gasoline and diesel, are important anthropogenic contributors to elevated surface ozone and PM <sub>2.5</sub> concentrations (Chambliss et al. , 2014; [[#Lelieveld--2015b|Lelieveld et al., 2015b]] ; Silva et al. , 2016; Anenberg et al. , 2019) . At a global scale, land transportation has been estimated to be the dominant contributor to surface ozone concentrations in populated areas ( [[#Silva--2016|Silva et al., 2016]] ) and ozone-induced vegetation damages (Section 6.4.4; [[#Unger--2020|Unger et al., 2020]] ). Furthermore, it is now well established that real-world diesel NO <sub>x</sub> emissions rates are substantially higher, the so-called ‘excess NO <sub>x</sub> ’, in all regional markets than in laboratory tests, worsening air quality ( [[#Anenberg--2017|Anenberg et al., 2017]] ; [[#Jonson--2017|Jonson et al., 2017]] ; [[#Chossière--2018|Chossière et al., 2018]] ) and contributing to slightly larger warming on the scale of years and smaller warming at the decadal scale ( [[#Tanaka--2018|Tanaka et al., 2018]] ). Excess NO <sub>x</sub> emissions from key global diesel markets are estimated at 4.6 Tg yr <sup>–1</sup> in 2015, with annual mean ozone and PM <sub>2.5</sub> increases of 1 ppb and 1µg m <sup>–3</sup> across large regions of Europe, India and China ( [[#Anenberg--2017|Anenberg et al., 2017]] ).

In summary, the present-day global land-based transport pulse emissions cause a net global warming on all time scales ( ''high confidence'' ) and are detrimental to air quality ( ''high confidence'' ).

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==== 6.6.2.4 GSAT Response to Emissions Pulse of Current Emissions ====

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Figure 6.16 presents the GSAT response to an idealized pulse of year-2014 emissions of individual SLCF and LLGHG. The GSAT response is calculated for 11 sectors and 10 regions accounting for best available knowledge and geographical dependence of the forcing efficacy of different SLCFs ( [[#Lund--2020|Lund et al., 2020]] ). Two time horizons are shown (of 10 and 100 years) to represent near- and long-term effects (and a 20-year horizon is presented in Supplementary Material Figure 6.SM.3). Other time-horizon choices may affect the relative importance, and even sign in the case of the NO <sub>x</sub> effect, of the temperature response from some of the SLCFs, or be more relevant for certain applications. GSAT response is calculated using the concept of AGTP ( [[IPCC:Wg1:Chapter:Chapter-7#7.6.2.2|Section 7.6.2.2]] ). Further details of the AGTP emulator applied in Figure 6.16 are provided in [[#Lund--2020|Lund et al. (2020)]] and Supplementary Material 6.SM.4 ( [[IPCC:Wg1:Chapter:Chapter-7#7.6.1.2|Section 7.6.1.2]] , Cross-Chapter Box 7.1 and Supplementary Material 7.SM.7.2). As discussed by [[#Lund--2020|Lund et al. (2020)]] , the AGTP framework is primarily designed to study the relative importance of individual emissions and sources, but the absolute magnitude of temperature responses should be interpreted with care due to the linearity of the AGTP, which does not necessarily capture all the non-linear effects of SLCFs emissions on temperature.

Differences in the mix of emissions result in net effects on GSAT that vary substantially, in both magnitude and sign, between sectors and regions. SLCFs contribute substantially to the GSAT effects of sectors on short time horizons (10–20 years) but CO <sub>2</sub> dominates on longer time horizons (Figure 6.16). As the effect of the SLCFs decays rapidly over the first few decades after emission, the net long-term temperature effect is predominantly determined by CO <sub>2</sub> . N <sub>2</sub> O adds a small contribution to the long-term effect of agriculture. CO <sub>2</sub> emissions cause an important contribution to near-term warming that is not always fully acknowledged in discussions of LLGHGs and SLCFs ( [[#Lund--2020|Lund et al., 2020]] ).

The global sectoral ranking for near- and long-term global temperature effects is similar to the AR5 assessment despite regional reductions in aerosol precursor emissions between 2008 and 2014. This report has applied updated climate policy metrics for SLCFs and treatment of aerosol–cloud interactions for SO <sub>2</sub> , BC and OC ( [[#Lund--2020|Lund et al., 2020]] ). By far the largest 10-year GSAT effects are from the energy production (fossil fuel mining and distribution), agriculture and waste management sectors ( ''high confidence'' ). Methane is the dominant contributor in the energy production, agriculture and waste management sectors. On the 10-year time horizon, other net warming sectors are residential fossil fuel and energy combustion (dominated by CO <sub>2</sub> ) and aviation and residential biofuel (dominated by SLCFs and cloud) ( ''medium confidence'' ). The total residential and commercial sector, including biofuel and fossil fuels, is the fourth largest contributor to warming globally on short time horizons of 10–20 years. The energy combustion sector has considerable cooling from high emissions of SO <sub>2</sub> that result in a relatively small net GSAT temperature effect on short time horizons, despite the high CO <sub>2</sub> emissions from this activity. On the 10-year time horizon, global emissions from industry and shipping cause a net cooling effect despite a considerable warming from CO <sub>2</sub> emissions. On the 100-year time horizon, the net effects of agriculture and waste management are small, while energy combustion is the largest individual contributor to warming due to its high CO <sub>2</sub> emissions. The second largest driver of long-term temperature change is industry, demonstrating the importance of non-CO <sub>2</sub> emissions for shaping relative weight over different time frames. Transport contributes a small net warming on the 10-year time horizon that increases by a factor of three on the 100-year time horizon. In contrast, the aviation sector contribution to warming shrinks by about a factor of three between the 10- and 100-year time horizons. Results for the 20-year time horizon are provided in the Supplementary Material 6.SM.4. Compared to the 10-year time horizon, there are some changes in ranking, especially of sectors and regions with a strong SO <sub>2</sub> contribution, which decays substantially between 10 and 20 years. Aviation is the sector with the most distinct difference between 10- and 20-year time horizons, such that the net GSAT effect after 20 years becomes small but negative. This is due to a switch in sign for the NO <sub>x</sub> AGTP for this sector and the stronger effect of short-lived ozone response over these two short-term horizons in the case of aviation compared with other sectors.

In terms of source regions, the largest contributions to net short-term warming are caused by emissions in Eastern Asia, Latin America and North America, followed by Africa, Eastern Europe, West-Central Asia and South East Asia ( ''medium confidence'' ). However, the relative contributions from individual species vary. In Eastern Asia, North America, Europe and Southern Asia, the effect of current emissions of cooling and warming SLCFs approximately balance in the near term and these regions cause comparable net warming effects on 10- and 100-year time horizons (Figure 6.16). In Latin America, Africa, and South East Asia and Developing Pacific, methane and BC emissions are currently high while emissions of CO <sub>2</sub> and cooling aerosols are low compared to other regions, resulting in a net warming effect after 10 years that is substantially higher than that of CO <sub>2</sub> alone.

Overall, the global sectors that contribute the largest warming on short time scales are the methane-dominated sources, that is energy production (fossil fuel mining and distribution), and agriculture and waste management ( ''high confidence'' ). On short time scales, other net warming sectors are residential fossil fuel and energy combustion (dominated by CO <sub>2</sub> ), and aviation and residential biofuel (dominated by SLCFs) ( ''medium confidence'' ). On short time scales, global emissions from industry and shipping cause a net cooling effect despite a considerable warming from CO <sub>2</sub> emissions ( ''high confidence'' ). On longer time horizons, the sectors that contribute the largest warming are energy combustion and industry due to the large CO <sub>2</sub> emissions ( ''high confidence'' ).

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==== 6.6.2.5 Source attribution of regional air pollution ====

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The attribution of present-day surface PM <sub>2.5</sub> and ozone concentrations to sectors and regions (Figure 6.17) is based on 2014 CMIP6 emissions used in the TM5-FASST model ( [[#Van%20Dingenen--2018|Van Dingenen et al., 2018]] ) that has been widely applied to analyse air quality in regional and global scenarios (e.g., Van Dingenen et al. , 2009; Rao et al. , 2016, 2017; Vandyck et al. , 2018; Harmsen et al. , 2020b) . Regions with the largest year-2014 population-weighted annual average surface PM <sub>2.5</sub> concentrations are Southern Asia, Eastern Asia and the Middle East. The dominant anthropogenic source of ambient PM <sub>2.5</sub> in Southern Asia are the residential and commercial sectors (biomass and coal fuel-based cooking and heating) with secondary contributions from energy and industry. In Eastern Asia, the main anthropogenic sources of ambient PM <sub>2.5</sub> are energy, industry and residential sources. Natural sources, predominantly dust, are the most important PM <sub>2.5</sub> source in the Middle East, Africa and Eurasia, contributing about 40–70% of ambient annual average concentrations (Figure 6.17). Agriculture is an important contributor to ambient PM <sub>2.5</sub> in Europe and North America, while open biomass burning is a major contributor in South East Asia and Developing Pacific, North America as well as Latin America. These results are consistent with several global and regional studies, where contribution of emissions sources to ambient PM <sub>2.5</sub> or premature mortality was estimated at different scales (e.g., Guttikunda et al. , 2014; [[#Lelieveld--2015b|Lelieveld et al. 2015b]] ,; Amann et al. , 2017; Qiao et al. , 2018; Venkataraman et al. , 2018; Wu et al. , 2018) .

Natural sources contribute more than 50% to surface ozone in all regions except Southern Asia and South East Asia. Southern Asia, Eastern Asia and the Middle East experience the highest surface ozone levels of all regions. For ozone, the anthropogenic sectoral attribution is more uniform across regions than for PM <sub>2.5</sub> , except for Southern and South East Asia, where land transportation plays a larger role, and Eastern Asia, where the most significant contribution is from energy and industry. Land transportation and energy are the most important contributors to ozone across many of the regions, with smaller contributions from agriculture, biomass burning, waste management and industry. Open biomass burning is not a major contributor to surface ozone, except for in Africa, Latin America and South East Asia where its contribution is estimated at about 5–10% of anthropogenic sources. The relative importance of natural and anthropogenic emissions sources on surface ozone has been assessed in several studies ( [[#Uherek--2010|Uherek et al., 2010]] ; [[#Zare--2014|Zare et al., 2014]] ; [[#Mertens--2020|Mertens et al., 2020]] ; [[#Unger--2020|Unger et al., 2020]] ) and the results are comparable with the estimates of the TM5-FASST used here.

Residential and commercial cooking and heating are among the most important anthropogenic sources of ambient PM <sub>2.5</sub> , except in the Middle East and Asia-Pacific Developed ( ''high confidence'' ) and agriculture is the dominant source in Europe and North America ( ''medium confidence'' ). Energy and industry are important PM <sub>2.5</sub> contributors in most regions, except Africa ( ''high confidence'' ). Energy and land transportation are the major anthropogenic sources of ozone across many world regions ( ''medium to high confidence'' ).

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'''Figure 6.16 |''' '''Global mean temperature response 10 and 100 years following one year of present-day (year 2014) emissions.''' The temperature response is broken down by individual species and shown for total anthropogenic emissions (top), sectoral emissions (left) and regional emissions (right). Sectors and regions are sorted by (high-to-low) net temperature effect on the 10-year time scale. Error bars in the top panel show uncertainty (5–95% interval) in net temperature effect due to uncertainty in radiative forcing ''only'' (calculated using a Monte Carlo approach and best estimate uncertainties from the literature – see [[#Lund--2020|Lund et al. (2020)]] for details). CO <sub>2</sub> emissions are excluded from open biomass burning and residential biofuel use due to their unavailability in the Community Emissions Data System (CEDS) and uncertainties around non-sustainable emission fraction. Emissions for 2014 originate from the CEDS ( [[#Hoesly--2018|Hoesly et al., 2018]] ), except for HFCs which are from [[#Purohit--2020|Purohit et al. (2020)]] , open biomass burning from [[#van%20Marle--2017|van Marle et al. (2017)]] , and aviation H <sub>2</sub> O which is from [[#Lee--2021|Lee et al. (2021)]] . The split of fossil fuel production and distribution and combustion for energy and residential and commercial fuel use into fossil fuel and biofuel components is obtained from the GAINS model (ECLIPSE version 6b dataset). Open biomass burning emissions are not included for the regions. Emissions are aggregated into fossil fuel production and distribution (coal mining, oil and gas production, upstream gas flaring and gas distribution networks), agriculture (livestock and crop production), fossil fuel combustion for energy (power plants), industry (combustion and production processes, solvent-use losses from production and end use), residential and commercial (fossil fuel use for cooking and heating as well is HFCs leakage from A/C and refrigeration), waste management (solid waste, including landfills and open trash burning, residential and industrial waste water), transport (road and off-road vehicles, and HFC leakage from A/C and refrigeration equipment), residential and commercial (biofuels use for cooking and heating), open biomass burning (forest, grassland, savanna fires and agricultural waste burning), shipping (including international shipping), and aviation (including international aviation). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

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'''Figure 6.17 |''' '''Emissions source-sector attribution of regional population-weighted mean concentrations of PM''' <sub>2.5</sub> '''and ozone for present-day emissions (year 2014).''' Regional concentrations and source apportionment are calculated with the TM5-FASST model ( [[#Van%20Dingenen--2018|Van Dingenen et al., 2018]] ) for the 2014 emissions data from the Community Emissions Data System (CEDS) ( [[#Hoesly--2018|Hoesly et al., 2018]] ) and [[#van%20Marle--2017|van Marle et al. (2017)]] for open-biomass burning. Dust and sea salt contributions to PM 2.5 are monthly mean climatological averages over 2010–2018 from CAMS global reanalysis (EAC4) ( [[#Inness--2019|Inness et al., 2019]] ), generated using Copernicus Climate Change Service information (January 2020). Anthropogenic sectors are similar to those in Figures 6.2 and 6.16, except the grouping of fossil fuel production, distribution and combustion for energy under ‘Energy’ and the grouping of use of fossil fuel and biofuel use for cooking and heating under ‘Residential and Commercial’. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

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