The CEDS system has five key procedural steps, illustrated in Fig. 1. After the collection of input data in Step 0, Step 1 calculates default global emission estimates (Em) for each chemical compound using a bottom-up approach shown in Eq. (1). In Eq. (1), emissions are calculated using relevant activity (A) and emission factor (EF) data for each country (c) and year (y) as a function of 52 detailed working sectors (s) (sub-sectors used for intermediate steps in the CEDS system) and nine working fuel types (f) (Table 2). CEDS conducts these calculations for two types of emission categories: (1) fuel combustion sources (e.g., electricity production, industrial machinery, on-road transportation, etc.) and (2) process sources (e.g., metal production, chemical industry, manure management, etc.). We note that the distinction between these source categories is reflective of both sector definition and CEDS methodology, as described further in Sect. S2.1 in the Supplement. This results in some working sectors that include emissions from combustion, such as waste incineration and fugitive petroleum and gas emissions, to be characterized in the CEDS system as process-level sources (further details in Sect. S2.1). In contrast to CEDS combustion source emissions, which are calculated in Eq. (1) as a function of eight fuel types, emissions from CEDS process-level sources are combined into a single “process” category, as described in Sect. 2.4. Table 2 provides a complete list of CEDSGBD-MAPS working sectors and fuel types as well as source category distinctions. 1Emspeciescountry, sector, fuel, year=Ac,s,f,y×EFspeciesc,s,f,y For emissions from CEDS combustion sources, annual activity drivers in Eq. (1) primarily include country-, fuel-, and sector-specific energy consumption data from the International Energy Agency (IEA, 2019). Sector- and compound-specific emission factors are typically derived from energy use and total emissions reported from other inventories, including from the GAINS model (Klimont et al., 2017; IIASA, 2014; Amann et al., 2015), Speciated Pollutant Emission Wizard (SPEW) (Bond et al., 2007), and the US National Emissions Inventory (NEI) (NEI, 2013). For international shipping, IEA activity data are supplemented with consumption data and EFs from the International Maritime Organization (IMO), as described in Hoesly et al. (2018) and its supplement. In contrast, default emissions (Em) for CEDS process sources are directly taken from other inventories, including from the EDGAR v4.3.2 global emission inventory (EC-JRC, 2018; Crippa et al., 2018). “Implied emission factors” are then calculated for these process sources in Eq. (1) using global population data (UN, 2019, 2018) or pulp and paper consumption (FAOSTAT, 2015) as the primary activity drivers. For years without available emissions, default estimates for CEDS process sources are calculated in Eq. (1) from a linear interpolation of the “implied emission factors” and available activity data (A) for that year. Supplement Sects. S2.1 and S2.2 provide additional details regarding the input datasets for activity drivers and emission factors used for both CEDS combustion and process source categories.

While CEDS Step 1 is designed to provide a complete set of historical emission estimates, CEDS Step 2 scales these total default emission estimates to existing, authoritative global-, regional-, and national-level inventories. As described in Hoesly et al. (2018), CEDS uses a “mosaic” scaling approach to retain detailed fuel- and sector-specific information across different inventories while maintaining consistent methodology over space and time. The development and use of mosaic inventories has been recently increasing as they provide a means to utilize detailed local emissions while harmonizing this information across large regional or global scales (C. Li et al., 2017; Janssens-Maenhout et al., 2015). The CEDS approach, however, differs from previous mosaic inventories (e.g., Janssens-Maenhout et al., 2015), in that local and regional inventories in CEDSGBD-MAPS are used to scale sectoral emissions at the national level rather than merge together spatially distributed gridded estimates.

The first step in the scaling procedure is to derive a time series of scaling factors (SFs) for each scaling inventory using Eq. (2), calculated as a function of chemical compound, country, sector, and fuel type (where available). Due to persistent differences and uncertainties in the underlying activity data and sectoral definitions in each scaling inventory, CEDS emissions are scaled to total emissions within aggregate scaling sectors (and fuels, where applicable). These aggregate scaling groups are defined for each scaling inventory and are chosen to be broad in order to improve the overlap between CEDS emission estimates and those reported in other inventories. For example, the sum of CEDS emissions from working sectors 1A4a_Commercial-institutional, 1A4b_Residential, and 1A4c_agriculture-forestry-fishing are scaled to the aggregate 1A4_energy-for-buildings sector in the EDGAR v4.3.2 inventory. Sections 2.2 and S2.3 provide further details about this scaling procedure and the scaling inventories used to develop the 1970–2017 CEDSGBD-MAPS inventory. 2SFspeciesc,s,f,y=scaling  inventory Emspeciesc,s,f,ydefault  CEDS  Emspeciesc,s,f,y After SFs are calculated in Eq. (2), the second step in the scaling procedure is to extend these SFs forward and backward in time to fill years with missing data. For these time periods, the nearest available SF is applied. If a particular sector or compound is not present in a scaling inventory, default CEDS estimates are not scaled. For BC and OC emissions, the default procedure in the CEDS v2019-12-23 system was to retain all default BC and OC emission estimates due to limited availability of historical BC and OC emissions. In the CEDSGBD-MAPS inventory, these species are now scaled to available regional- and national-level inventories (further details in Sect. 2.2). For all other species, the CEDSGBD-MAPS system uses a sequential scaling methodology where total default emissions for each country are first scaled to available global inventories (primarily EDGAR v4.3.2) and then scaled to regional- and national-level inventories, many of which have been updated in this work (Sect. 2.2 and Table 3). This process results in final CEDSGBD-MAPS emissions that reflect the inventory last used to scale the emissions for that country (Fig. 2). Figure S2 in the Supplement provides a time series of implied emission factors after the scaling procedure for select sector and fuel combinations that dominate emissions of each compound in the top 15 emitting countries. Sections 2.2 and S2.3 describe further details and updates to this scaling procedure.

CEDS Step 3 extends the scaled emission estimates from 1970 back in time to 1750. This process is necessary as reported emission estimates and energy data are not typically reported with the same level of sectoral and fuel type detail prior to 1970. Hoesly et al. (2018) provide a detailed description of this historical extension procedure, which is used to derive pre-1970 emissions in the CEDSHoesly inventory. The new CEDSGBD-MAPS inventory only reports more contemporary emissions after 1970 and therefore does not utilize this historical extension.

CEDS Step 4 aggregates the scaled country-level CEDSGBD-MAPS emissions into 17 intermediate gridding sectors (defined in Table 2). In the CEDS v2019-12-23 system, Step 4 additionally aggregated sectoral emissions from all fuel types. In contrast, the CEDSGBD-MAPS system retains sectoral emissions from the combustion of total coal (hard coal + coal coke + brown coal), solid biofuel, the sum of liquid oil (light oil + heavy oil + diesel oil) and natural gas, and all CEDS process-level emissions (Table 2). Sections 2.4 and 4.2.4 describe the CEDSGBD-MAPS fuel-specific emissions in further detail.

Lastly, CEDS Step 5 uses normalized spatial-distribution proxies to allocate annual country-level emission estimates onto a 0.5∘ × 0.5∘ global grid. Annual emissions from the 17 intermediate gridding sectors and four fuel groups are first distributed spatially using compound-, sector-, and year-specific spatial proxies, primarily from the gridded EDGAR v4.3.2 inventory. Supplement Table S7 provides a complete list of sector-specific gridding proxies. Details about the general CEDS gridding procedure are provided in Feng et al. (2020), with additional details specific to the CEDSGBD-MAPS system in Sect. S2.5. Second, gridded emission fluxes (units: kg m-2 s-1) are aggregated into 11 final sectors (Table 2) and distributed over 12 months using sectoral and spatially explicit monthly fractions from the ECLIPSE project (IIASA, 2015) and EDGAR inventory (international shipping only). Relative to CEDS v2019-12-23, the new CEDSGBD-MAPS inventory retains detailed sub-sector emissions from the aggregate RCO (now RCO-residential, RCO-commercial, and RCO-other) and TRA (now on-road and non-road) sectors; separate sectoral emissions from process sources; and combustion sources that utilize coal, solid biofuel, and the sum of liquid fuels and natural gas. Table 2 contains a complete breakdown of the definitions of CEDS working, intermediate gridding, and final sectors. Gridded total NMVOCs are additionally disaggregated into 25 VOC classes following sector- and country-specific VOC speciation maps from the RETRO project (HTAP2, 2013), which are different from those used in the recent EDGAR v4.3.2 inventory (Huang et al., 2017). Similar to the gridding procedure, the same VOC speciation and monthly distributions are applied to sectoral emissions associated with each fuel category.

Final products from the CEDSGBD-MAPS system include total annual emissions from 1970–2017 for each country as well as monthly global gridded (0.5∘ × 0.5∘) emission fluxes, both as a function of 11 final source sectors and four fuel categories (total coal, solid biofuel, liquid fuel + natural gas, and remaining process sources). Section 5 provides additional details on the dataset availability and file formats.

As described above, default emission estimates for each compound are scaled in CEDS Step 2 to existing authoritative inventories as a function of emission sector and fuel type (where available). In the scaling procedure, annual emissions and EFs for each country are first scaled to available global inventories, then to available regional- and national-level inventories, assuming that the latter use local knowledge to derive more accurate regional estimates. Final CEDSGBD-MAPS emission totals for each country therefore reflect the inventory last used to scale each compound and sector. Many of these inventories are updated annually and, where available, have been updated in this work relative to the CEDS v2019-12-23 system (Table 3). For example, global CEDSGBD-MAPS combustion source emissions of NOx, total NMVOCs, CO, and NH3 are first scaled to EDGAR v4.3.2 country-level emissions as a means to incorporate additional country-specific information relative to default estimates derived using more regionally aggregate EFs from GAINS. CEDSGBD-MAPS emissions from European countries are then scaled to available EMEP (European Monitoring and Evaluation Programme) (EMEP, 2019) and UNFCCC (United Nations Framework Convention on Climate Change) (UNFCCC, 2019) inventories that extend to 2017, while CO, NMVOCs, NOx and SO2 emissions from the US, Canada, and Australia are scaled to emissions that extend to 2017 from the US NEI (US EPA, 2019), Canadian APEI (Air Pollutant Emissions Inventory) (ECCC, 2019), and Australian NPI (National Pollutant Inventory) (ADE, 2019), respectively. In addition, emissions of all seven compounds from China are scaled to emissions for 2008, 2010, and 2012 from C. Li et al. (2017), followed by subsequent scaling to emissions between 2010 and 2017 from Zheng et al. (2018). Relative to the CEDS v2019-12-23 system, regional inventories have also been added to scale CEDSGBD-MAPS emissions from India and Africa as described below. Updates to additional regional scaling inventories, including South Korea, Japan, and other European and Asian countries, are not available relative to those used in the CEDS v2019-12-23 system. Table 3 provides a complete list of the inventories used to scale CEDSGBD-MAPS default emissions, with additional details in Sect. S2.3.

Relative to the CEDS v2019-12-23 system, the CEDSGBD-MAPS system adds scaling inventories for two rapidly changing regions, Africa and India. First, CEDSGBD-MAPS emissions from Africa for select sectors are now scaled to the Diffuse and Inefficient Combustion Emissions in Africa (DICE-Africa) inventory from Marais and Wiedinmyer (2016). This inventory provides gridded (0.1∘ × 0.1∘) emissions for NOx (= NO + NO2), SO2, 25 speciated VOCs, NH3, CO, BC, and OC for 2006 and 2013 for select anthropogenic sectors and fuels. In this work, default CEDS emissions are scaled to total DICE-Africa emissions from each country and later re-gridded in CEDS Step 5 using source-specific spatial proxies described in Sect. 2.1. Following the CEDS v2019-12-23 scaling procedure (Supplement Sect. S2.3), a set of aggregate scaling sectors and fuels are defined to ensure that CEDSGBD-MAPS emissions are scaled to emissions from consistent sectors and fuel types within the DICE-Africa inventory (Table S3). Briefly, CEDSGBD-MAPS 1A3b_Road and 1A4b_Residential emissions are scaled to DICE-Africa emissions from diesel- and gasoline-powered cars and motorcycles as well as biomass and oil combustion associated with residential charcoal, crop residue, fuelwood, and kerosene use. The DICE-Africa inventory also includes emission estimates from gas flares across Africa and ad hoc oil refining in the Niger Delta, fuelwood use for charcoal production and other commercial enterprises, and gas and diesel use in residential generators. Marais and Wiedinmyer (2016) state that these particular sources are missing or not adequately captured in existing global inventories. Therefore, depending on the source sector and inventory details, they recommend that these emissions be added to existing global inventories for formal industry and on-grid energy production in Africa (DICE-Africa, 2016). Due to uncertainties in the representation of these sectors in the default CEDS Africa emissions, these sources are not included in the scaling process here. Default CEDSGBD-MAPS emissions from the 1B2_fugitive_pert_gas (gas flaring) sector (derived from the ECLIPSE and EDGAR inventories) are larger than DICE-Africa gas flaring emissions in 2013, suggesting that this source may be accurately represented in the default CEDSGBD-MAPS estimates. As described in Sect. S2.3.2, however, residential generator and fuelwood use for charcoal production and other commercial activities are not explicitly represented in CEDS and will be accounted for only to the extent that these sources are included in the underlying IEA activity data and EDGAR process emission estimates. In the event that the DICE-Africa emissions from these sources are missing in the default CEDS estimates, total 2013 CEDSGBD-MAPS emissions from Africa for each compound may be underestimated by up to 11 % (Sect. S2.3, Table S5). These values range from 0.7 % for SO2 to 11 % for CO (Table S5) and all fall within the range of uncertainties typically reported from regional bottom-up inventories (> 20 %; Sect. 4.2.3). Final emissions from additional sectors or species in CEDS that are not included in the DICE-Africa inventory are set to CEDSGBD-MAPS default values.

Second, emissions from India for select sectors are now scaled to the Speciated Multi-pollutant Generator Inventory described by Venkataraman et al. (2018) (hereafter called SMoG-India). This inventory includes gridded emissions (0.25∘ × 0.25∘) of NOx (as NO2), SO2, total NMVOCs, CO, BC, and OC for the year 2015 from select anthropogenic sectors and fuels (SMoG-India, 2019). Similar to DICE-Africa emissions, the final spatial distribution in the SMoG-India and CEDSGBD-MAPS inventories will differ as country-level emissions are scaled to country totals and spatially re-allocated using CEDS proxies in Step 5. SMoG-India emissions for each compound are available for 17 sectors and nine fuel types (coal, fuel oil, diesel, gasoline, kerosene, naphtha, gas, biomass, and fugitive or process). Similar to the DICE-Africa inventory, aggregate scaling groups have been defined to scale consistent sectors and fuels between inventories, as described in Sect. S2.3. Briefly, default CEDSGBD-MAPS emissions for the 1A4c_Agriculture-forestry-fishing sector are scaled to the sum of SMoG-India emissions for agricultural pumps and tractors; 1A4b_Residential emissions are scaled to the sum of SMoG-India emissions from residential lighting, cooking, diesel generator use, and space and water heating; 1A1a electricity and heat generation sectors are scaled to SMoG-India thermal power plant emissions; 1A3b road and rail sectors are scaled to the respective SMoG-India road and rail emissions; and CEDSGBD-MAPS industrial working sectors are allocated and scaled to four SMoG-India industrial sectors: light industry (e.g., mining and chemical production), heavy industry (e.g., iron and steel production), informal industry (e.g., food production), and brick production. Calculated scaling factors for these sectors are held constant before and after 2015. CEDSGBD-MAPS emissions do not include contributions from open burning and are not scaled to SMoG-India open burning emissions. In cases where SMoG-India emissions are not reported (e.g., power generation from oil combustion), default CEDSGBD-MAPS emissions are retained. Section S2.3.3 provides additional details.

To examine the changes in CEDSGBD-MAPS emissions associated with the incorporation of the SMoG-India and DICE-Africa scaling inventories as well as the updated underlying input datasets, Fig. 3 compares the total and sectoral distribution of CEDSGBD-MAPS and CEDSHoesly emissions for these two regions in 2014 (year with latest overlapping data). For the Africa comparison, panel a in Fig. 3 shows that total NOx, BC, and OC emissions are generally lower in the CEDSGBD-MAPS inventory than in CEDSHoesly. Lower NOx and OC emissions are largely associated with smaller contributions from on-road transport and residential combustion, respectively, while lower BC emissions are associated with both lower residential and on-road transport contributions. Lower emissions of NOx from the transport sector result from the lower EF used for diesel vehicles in the DICE-Africa inventory (Marais et al., 2019). Compared to GAINS (2010) and EDGAR v4.3.2 (2012), on-road emissions from African countries in CEDSGBD-MAPS are up to 2.5 Tg lower for NOx but within 0.1 Tg for BC. In contrast to NOx, larger EFs in the DICE-Africa inventory for on-road emissions of CO and OC result in CEDSGBD-MAPS emissions from this sector that are up to 14.8 and 0.3 Tg higher than previous estimates. Figure S2 shows that after scaling, the implied emission factors of CO from oil and gas combustion in the on-road transport sector for four African countries range from 0.19–0.28 g g-1, slightly smaller than the range of 0.029–0.380 g g-1 used in the DICE-Africa inventory. Emissions from the residential and commercial sectors in Africa are generally lower in CEDSGBD-MAPS than in CEDSHoesly due to both lower biofuel consumption and a lower assumed EF in the DICE-Africa inventory (Marais and Wiedinmyer, 2016). Residential BC and OC emission estimates are also lower than those from GAINS (Klimont et al., 2017). The difference in biofuel consumption is due to different data sources. The DICE-Africa inventory uses residential wood fuel consumption estimates from the UN, while CEDSHoesly uses data from the IEA. Both of these sources consist largely of estimates for African countries because there is little country-reported biofuel consumption data available. The estimation methodologies for both the UN and IEA estimates are not well documented, which adds to the uncertainty in these values (Sect. 4.2). After scaling, the implied EFs for residential biofuel emissions of OC are ∼ 0.001–0.002 g g-1 in three African countries (Fig. S2), within the range of EFs of 0.0007–0.003 g g-1 implemented in the DICE-Africa inventory. Total CEDSGBD-MAPS emissions of NMVOCs are larger, primarily due to increased contributions from solvent use in the energy sector associated with changes in the EDGAR v4.3.2 inventory, while total emissions of CO, SO2, and NH3 are relatively consistent between the two CEDS versions.

For the India comparison, panel b of Fig. 3 shows that total emissions of NOx, CO, SO2, NMVOCs, and OC are lower in CEDSGBD-MAPS. Relative reductions in NOx emissions are largely associated with on-road transport. Scaled CEDSGBD-MAPS transport emissions are 5 Tg smaller than NOx emissions in CEDSHoesly, largely as a result of lower fuel consumption levels for gas, diesel, and compressed natural gas (CNG) on-road vehicles used to develop SMoG-India estimates (Sadavarte and Venkataraman, 2014). Figure S2 shows that the implied emission factor for NOx emissions from oil and gas combustion in the on-road transport sector in India is ∼ 0.015 g g-1 in 2015, which falls within the range of values of 0.0026–0.046 g g-1 used for various vehicles and fuel type in Venkataraman et al. (2018). Similarly, NOx transport emissions are also lower in CEDSGBD-MAPS relative to the EDGAR and GAINS inventories. Causes of other reductions relative to the CEDSHoesly are mixed. For example, lower emissions of SO2 and NMVOCs are largely associated with the energy sector, while reductions in the industry sector contribute to reduced CO emissions. For SO2, Fig. S2 shows that the implied EF for coal combustion in the energy sector is ∼ 0.004 g g-1, slightly lower than the range of 0.0049–0.0073 g g-1 used for the SMoG-India inventory.

To further examine the CEDSGBD-MAPS inventory in these regions, Fig. 4 compares final CEDSGBD-MAPS and CEDSHoesly emissions for India and Africa to total emissions from two widely used global inventories: GAINS (ECLIPSE v5a) and EDGAR (v4.3.2). First, Fig. 4 shows the percent difference between the CEDSGBD-MAPS inventory and the GAINS and EDGAR inventories on the y axis against the percent difference between the CEDSHoesly inventory and GAINS and EDGAR emissions on the x axis. Percent differences are calculated from total emissions from Africa (left) and India (right) for the year 2012 for the comparison with EDGAR and for 2010 for the comparison to GAINS (most recent years with overlapping data). The green shaded areas indicate regions where the updated CEDSGBD-MAPS inventory has improved agreement with EDGAR or GAINS relative to the CEDSHoesly inventory. This comparison shows that the additional scaling of CEDSGBD-MAPS emissions to the SMoG-India inventory generally improves agreement with both the EDGAR and GAINS inventories relative to CEDSHoesly for all species except black carbon (BC). Scaling to the DICE-Africa inventory generally improves CEDSGBD-MAPS agreement with the EDGAR inventory but not with GAINS (except for OC). Further comparisons to these two inventories are discussed in Sect. 4. While uncertainties in emissions from these inventories are expected to be at least 20 % for each compound (discussed in Sect. 3.3), this comparison provides an illustration of the changes between the two CEDS versions relative to two widely used global inventories.

Relative to the CEDS v2019-12-23 system, the second-largest change to the CEDSGBD-MAPS system is the added scaling of BC and OC emissions in CEDS Step 2. In the v2019-12-23 system, OC and BC were not scaled due to a lack of historical BC and OC emission estimates in regional and global inventories. Due to the focus of the CEDSGBD-MAPS inventory on more recent years, these two compounds are now scaled to available regional- and country-level estimates (Table 3) following the same scaling procedure described above for the reactive gases. Unlike the reactive gases, however, BC and OC emissions are not scaled to the global EDGAR v4.3.2 inventory due to the large reported uncertainties in this inventory (ranging from 46.8 % to 153.2 %; Crippa et al., 2018).

To examine the impact of the new BC and OC emissions scaling, in addition to the updated IEA energy consumption data, Figs. 5 and S3–S4 show time series of global BC and OC emissions from CEDSGBD-MAPS compared to emissions from the CEDSHoesly inventory. In 2014, respective global annual emissions of BC and OC are 21 % and 28 % lower than the CEDSHoesly inventory and have total global annual emissions in 2017 of 6 and 13 Tg C yr-1 for BC and OC, respectively. These reductions in global emissions are largely due to the added scaling of emissions from China, Africa, Japan, and other countries in Asia included in the REAS inventory (Figs. S3–S4). Figures 5 and S3–S4 additionally compare CEDSGBD-MAPS emissions to those from the GAINS (ECLIPSE v5a) and EDGAR (v4.3.2) inventories, which generally show improved agreement in BC and OC emissions with the GAINS inventory. CEDSGBD-MAPS emissions between 1990 and 2015 are now 7 %–14 % lower than GAINS BC emissions, while CEDSGBD-MAPS emissions of OC remain 12 %–25 % higher than GAINS estimates. Further discussion of CEDSGBD-MAPS BC and OC emissions and comparisons to EDGAR and GAINS inventories are below in Sect. 4.1.2. As an additional point of comparison, Bond et al. (2013) report global BC and OC values for the year 2000, derived from averages of energy-related burning emissions from SPEW and GAINS. Reported global estimates of BC and OC are 5 and ∼ 11–14 Tg C (16 Tg organic aerosol reported; organic-mass-to-organic-carbon ratio = 1.1–1.4), respectively (Bond et al., 2013). These also have improved agreement with the CEDSGBD-MAPS estimates of BC and OC in 2000 relative to those in the CEDSHoesly inventory. Lastly, we note plans for an upcoming update to the core CEDS system to improve historical trends in carbonaceous aerosol by incorporating reported inventory values for total PM2.5 and its ratio with BC and OC emissions.

Prior to gridding, CEDSGBD-MAPS Step 4 combines total country-level emissions for each of the 52 working sectors and nine fuel groups into 17 aggregate sectors and four fuel groups: total coal (hard coal + brown coal + coal coke), solid biofuel, the sum of liquid fuels (heavy oil + light oil + diesel oil) and natural gas, and all remaining “process” emissions (Table 2). In contrast, the CEDS v2019-12-23 system aggregates all fuel-specific emissions and reports inventory values as a function of sector only. In CEDSGBD-MAPS, country-total emissions from these aggregate sectors and fuel groups are distributed across a 0.5∘ × 0.5∘ global grid using spatial gridding proxies, as discussed in Sect. 2.1 (Table S7). During gridding, the same spatial proxies are applied to all fuel groups within each sector. In practice, this requires that the gridding procedure be repeated 4 times for each of the fuel groups. After gridding in CEDS Step 5, both annual country-total and gridded emission fluxes from each fuel group are aggregated to 11 final sectors. Figure S5 demonstrates the level of detail available in the new CEDSGBD-MAPS gridded emission inventory by illustrating global BC emissions in 2017 from (1) all source sectors, (2) the residential sector only, (3) residential biofuel use only, and (4) residential coal use only. Additional uncertainties associated with the CEDSGBD-MAPS fuel-specific emissions in both the country-total and annual gridded products are discussed further in Sect. 4.2.4

Figures 6 and 7 show time series from 1970–2017 of global annual CEDSGBD-MAPS emissions for each emitted compound. Global CEDSGBD-MAPS emissions for reactive gases in 2017 are 122 Tg for NOx (as NO2), 538 Tg for CO, 79 Tg for SO2, 175 Tg C for total NMVOCs, and 61 Tg for NH3. Global 2017 emissions of carbonaceous aerosol are 13 and 6 Tg C for OC and BC, respectively. The time series in Figs. 6 and 7 additionally show the contributions to global emissions from each of the 11 source sectors (Fig. 6) and four fuel groups (Fig. 7). Each panel in Fig. 6 additionally shows a pie chart with the fractional contribution of each sector to total global emissions in 2017 (outside), while the inner pie chart shows the fractional contributions from each of the fuel groups to each source sector. Numerical values for these fractional contributions are in Table S8. Global totals for 2017 are provided in the center of each pie chart. Global emissions from each compound are additionally split into contributions from 11 world regions (defined in Table S9) in Fig. 8 to aid in the interpretation of global trends below.

For global 2017 emissions of NOx, Fig. 6 and Table S8 show that 60 % of NOx emissions are associated with the energy generation (22 %), industry (15 %), and on-road transportation (23 %) sectors. These sectors have the largest contributions from emissions from coal combustion (> 46 % for the energy and industry emissions) and the combined combustion of liquid fuels (oil) and natural gas (with these two fuels accounting for 100 % of NOx on-road emissions). Time series of regional contributions to global emissions in Fig. 8 additionally show that 50 % of global 2017 NOx emissions are from the combined Other Asia/Pacific region (Table S9) (13 Tg), China (24 Tg), and international shipping (25 Tg). For global 2017 emissions of remaining gas-phase pollutants, 67 % of CO emissions are from the on-road (100 %: oil + gas) and residential (86 %: biofuel) sectors; 78 % of SO2 emissions are from the energy generation (63 %: coal) and industry (38 % coal, 36 % process, 25 % oil + gas) sectors; 89 % of NH3 emissions are from the agriculture (100 %: process) and waste (100 %: process) sectors; and emissions of NMVOCs have the largest single contribution (36 %) from the energy sector, 99 % of which are associated with CEDSGBD-MAPS process sources (Table 2). For carbonaceous aerosol in 2017, 58 % of global BC emissions are from the residential (70 %: biofuel) and on-road (100 %: oil + gas) sectors, while 67 % of global OC emissions are from the residential (92 %: biofuel) and waste (100 %: process) sectors. Figure 8 shows that in 2017, China is the dominant source of global CO (144 Tg, 27 % of global total), SO2 (12 Tg, 15 % of global total), NH3 (12 Tg, 20 % of global total), OC (2.7 Tg C, 20 % of global total), and BC (1.4 Tg C, 24 % of global total). In contrast, Africa is the dominant source of global NMVOCs in 2017 (48 Tg C, 27 % of global total), and international shipping is the dominant source of global NOx emissions (25 Tg, 20 % of global total).

As discussed above in Sect. 2 and below in Sect. 4.2.4, the distinction between CEDS combustion- and process-level source categories for all species may result in the underrepresentation of emissions from combustion sources relative to those from CEDS process-level sectors. As shown in Table 2, for example, some combustion emissions from the energy, industry, and waste sectors, such as fossil fuel fires and waste incineration, are categorized as CEDS “process-level” source categories (Table 2). These emissions are allocated to the final CEDS process category rather than the CEDS total coal, biofuel, or oil and gas categories.

Historical emission trends between 1970 and 2017 in Figs. 6 and 7 indicate that global emissions of each compound generally follow three patterns: (1) global CO and SO2 emissions peak prior to 1990 and generally decrease until 2017; (2) global emissions of NOx, BC, and OC peak much later, around 2010, and then decrease until 2017; and (3) global emissions of NH3 and NMVOCs continuously increase throughout the entire time period. These trends generally reflect the sector-specific regulations implemented in dominant source regions around the world. For example, global emissions of CO generally decrease after the incorporation of catalytic converters in North America and Europe around 1990 (Figs. S7 and S8). Despite, however, continued reductions in these regions, global emissions of CO slightly increase between 2002 and 2012 due to simultaneous increases among the energy, industry, and residential sectors in China, India, Africa, and the Other Asia/Pacific region (Figs. S9–S12). Global CO emissions then decrease by 9 % between 2012 and 2017, largely due to reductions in industrial coal, residential biofuel, and process energy sector emissions in China (Figs. S9, S17–S18, S20), associated with the implementation of emission control strategies (reviewed in Zheng et al., 2018) as well as continued reductions in on-road transport emissions in North America and Europe (Figs. S7–S8). Similarly, global SO2 emissions decrease after peaking in 1979, largely due to emission control policies in the energy and industry sectors in North America and Europe (Figs. S7–S8). While simultaneous increases in emissions from coal use in the energy and industry sectors in China result in a brief increase in global SO2 emissions between 1999 and 2004 (Figs. 6, S9), global SO2 emissions decline by 32 % between 2004 and 2017 due to the implementation of stricter emission standards for the energy and industry sectors after 2010 in China (Zheng et al., 2018) as well as continued reductions in North America and Europe (Figs. S7–S8). Regional SO2 emission trends are particularly large with a factor of 9.5 decrease in total SO2 emissions in North America between 1973 and 2017, a factor of 6.9 decrease in Europe between 1979 and 2017, and a factor of 5.9 increase in China between 1970 and 2004, followed by a factor of 2.6 decrease after 2011 (Fig. 8). While China is the largest global contributor to SO2 emissions between 1994 and 2017, these large regional reductions, coupled with increasing SO2 emissions in the Other Asia/Pacific region, African countries, and India (Fig. 8), indicate that future global SO2 emissions will increasingly reflect activities in these other rapidly growing regions.

In contrast to historical emissions of SO2 and CO, global emissions of NOx, BC, and OC peak later, between 2011 and 2013. Global emissions then decrease by 7 %, 9 %, and 7 %, respectively, by 2017 (Fig. 6). These trends also reflect the sector-specific regulations implemented in dominant source regions. For NOx for example, global emissions between 1970 and 2017 are dominated by the combustion of coal, oil, and gas in the on-road transportation, energy generation, industry, and international shipping sectors (Figs. 6, 8). Global on-road transportation emissions are generally flat between 1988 and 2013 due to competing trends across world regions. While more stringent vehicle emission standards result in more than a factor of 2 decrease in on-road transportation NOx emissions in North America and Europe between 1992 and 2017 (Figs. S7–S8), on-road transport emissions in China, India, and the Other Asia/Pacific region simultaneously experience between a factor of 1.3 and 2.8 increase (Figs. S9–S11). Subsequent reductions between 2013 and 2017 in global on-road emissions correspond to a 12 % reduction in on-road transportation emissions in China due to the phase-in of stricter emission standards (Zheng et al., 2018), coupled with a continued decrease in emissions from North America and Europe. Global NOx emissions from the energy and industry sectors increase by up to a factor of 6 between 1970 and 2011 due to regional increases in China, India, the Other Asia/Pacific region, and African countries, with reductions between 2011 and 2017, again largely from reductions in China from stricter emissions control policies for coal-fired power plants and coal use in industrial processes (Zheng et al., 2018; Liu et al., 2015). Global emissions of NOx from waste combustion and agricultural activities also increased by 2 % and 65 %, respectively, between 1970 and 2017, also contributing to the offset of recent reductions in emissions from regulated combustion sources (Fig. 6). Similar to global NOx emissions, trends in historical BC and OC emissions reflect a balance between emission trends in North America, Europe, and other world regions, with reduction between 2010 and 2017 largely driven by reductions in emissions from China (Figs. 8, S9). In contrast to NOx emissions, however, BC and OC emissions are dominated by contributions from biofuel combustion in the residential sector as well as on-road transportation, industry, and energy sectors for BC and the waste sector for global OC (Fig. 6). Though emissions of BC and OC have a higher level of uncertainty relative to other compounds (Sect. 4), emissions from African countries and the Other Asia/Pacific region experience growth in BC and OC emissions from these sectors. The exceptions are in China and India, both of which experience a plateau or reduction in BC and OC emissions from the residential, energy (China only), industry, and on-road transportation sectors between 2010 and 2017. In India, reductions in BC and OC emissions from the residential and informal industry sectors are expected to continue under policies to switch to cleaner residential fuels and energy sources, while BC emissions from on-road transport may increase due to increased transport demand (Venkataraman et al., 2018). Similar to trends in SO2 emissions, increasing trends in total OC and BC emissions from Africa, India, Latin America, the Middle East, and the Other Asia/Pacific region, coupled with large decreases in emissions from China, North America, and Europe (Fig. 8), indicate that global emissions will increasingly reflect activities in these rapidly growing regions.

Trends in historical emissions of NMVOCs and NH3 differ from other pollutants in that they continuously increase between 1970 and 2017. Global emissions of NH3 increase by 81 % between 1970 and 2017 and are largely associated with emissions from agricultural practices (75 % in 2017) and waste disposal and handling (14 % in 2017) (Fig. 6, Table S8). Unlike emissions from combustion sources, there are no large-scale regulations outside of Europe targeting NH3 emissions from agricultural activities, such as livestock manure management. As a result, global agricultural emissions of NH3 increase between 1970 and 2017 by 82 %, driven by increases in all regions other than Europe (Figs. 6, S6–S12). Similarly, global NH3 emissions from the waste sector increase by 77 % between 1970 and 2017, driven by increases in Latin America, the Other Asia/Pacific region, Africa, and India (Figs. S10–S12). Global emissions of NMVOCs increase by 40 % between 1970 and 2017 and are largely associated with emissions from the on-road transport, residential, energy, industry, and solvent use sectors (Fig. 6). In contrast to other emitted pollutants, Africa is the largest global source of NMVOC emissions between 2010 and 2017, largely due to large contributions and continued increases in emissions from the residential (factor of 2.7) and energy (factor of 4) sectors (Fig. S12). Increases in energy sector emissions after 2003 are largely driven by increases in fugitive emissions from select African countries, including Nigeria, Kenya, Angola, and Mozambique. Emissions from China are the second-largest global NMVOC source between 1996 and 2017 (Fig. 8), while the Other Asia/Pacific region is the third-largest source between 1999 and 2017. Total NMVOCs in China increase by a factor of 3.4 between 1970 and 2017 due to activity increases in the solvent, energy, and industry sectors (Zheng et al., 2018), while targeted emission controls for the residential and on-road transport sectors result in their reduced contributions to NMVOC emissions between 2012 and 2017 (Fig. S9). Total emissions of NMVOCs in Europe and North America decrease by up to a factor of 2.4 between 1970 and 2017 due to reductions in all source sectors, except for energy emissions in North America, which increase between 2007 and 2011 and remain flat through 2017 (Fig. S7).

To provide a fuel-centric perspective of global historical emissions trends, Fig. 7 illustrates the contributions from the combustion of coal, solid biofuel, the sum of liquid fuel and natural gas, and all remaining CEDS “process-level” sources (Table 2) to total global emissions between 1970 and 2017. Reductions discussed above between 2010 and 2017 for global emissions of NOx, CO, SO2, BC, and OC are largely associated with reductions in coal combustion from the energy, industry, and residential sectors associated with emission control policies and residential fuel replacement in China as well as coal-fired power plant reductions in North America and Europe (Figs. 7, S13, S17–S18). Despite large reductions in emissions, China is still the single largest source of global emissions from coal combustion in 2017 (23 %–64 % for each compound except NH3). Figure S17, however, also shows that emissions from coal combustion are simultaneously increasing in India, the Other Asia/Pacific region, and Africa. Specifically, SO2 emissions from coal combustion in India are set to surpass those from China by 2018 if recent CEDSGBD-MAPS trends hold. For solid biofuel combustion, global emissions of all compounds are primarily associated with the residential sector (Fig. S14), with recent reductions in biofuel CO, SO2, BC, and OC emissions largely from reductions in China (Fig. S18). In contrast, biofuel emissions from all other regions remain relatively flat or increase between 1970 and 2017, though biofuel emissions of NMVOCs, CO, SO2, and OC in India as well as SO2 emissions in North America both decrease between 2010 and 2017 (Fig. S18). In 2017, biofuel emissions of all compounds are dominated by emissions from either Africa (NOx, SO2, NH3, NMVOC, BC) or India (OC). For oil and gas combustion, global emissions of all compounds are primarily associated with on-road transportation, international shipping, and energy and industry (SO2 only) sectors, with general decreases in associated emissions in North America and Europe between 1970 and 2017 and increases in other regions (Fig. S19). In contrast to other combustion sectors and fuels, emissions of NOx, CO, NMVOCs, BC, and OC from the combustion of liquid fuels and natural gas in China remain relatively flat or slightly decrease between 2010 and 2017. Dominant global regions vary by compound (Fig. S19) and include international shipping (NOx, SO2), Africa (OC), India (BC), North America (CO, NH3), and the Other Asia/Pacific region (NMVOCs). Global CEDS process source emissions, which include contributions from some fuel combustion processes (Table 2), decrease between 2010 and 2017 for CO, SO2, BC, and OC. These trends are primarily associated with reductions in emissions from the energy and industry sectors. In contrast, process source contributions to NOx, NH3, and NMVOCs increase over this same time period due to increases in non-combustion agricultural and solvent use emissions as well as emissions from waste disposal and energy generation and transformation (Fig. S16). Increases in emissions from these sectors between 1970–2017 drive the continuous increases in global NH3 and NMVOCs, discussed above. Dominant source regions in 2017 of these process-level emissions include China (NOx, CO, NH3, BC, OC), India (SO2), and African countries (NMVOCs) (Fig. S20).

The level and sources of uncertainty in the CEDSGBD-MAPS inventory are similar to those in the CEDSHoesly inventory, which are largely a function of uncertainty in the activity data, emission factors, and country-level inventories. As these uncertainties have been previously discussed in Hoesly et al. (2018), we have not performed a formal uncertainty analysis here but rather provide a brief summary of the sources of uncertainty associated with this work. We note plans for a robust uncertainty analysis in an upcoming release of the CEDS core system. While this section highlights many of the challenges associated with estimating comprehensive and accurate global bottom-up emission inventories, such inventories remain vital for their use in chemistry and climate models and for the development and evaluation of future control and mitigation strategies.