domain: sdu sde

text:
Nicolas Huneeus

Hugo Denier Van Der Gon

Paula Castesana

Camilo Menares

Claire Granier

Louise Granier

Marcelo Alonso

Maria De

Fatima Andrade

Laura Dawidowski

Laura Gallardo

Dario Gomez

Zbigniew Klimont

Greet Janssens- Maenhout

Rachel Hoesly

Mauricio Osses

Enrique Puliafito

Nestor Rojas

Odón Sánchez-Ccoyllo

Sebastián Tolvett

Rita Yuri Ynoue

Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale

Keywords: South America, anthropogenic emissions, air pollutants, Argentina, Brazil, Chile, Colombia, Peru, city emission inventories

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction and Rationale

Over the last decades, environmental problems such as acidification, eutrophication, air pollution and climate change have caused significant adverse impacts on the environment, human health and vegetation [START_REF] Steffen | Planetary boundaries: Guiding human development on a changing planet[END_REF][START_REF] Htap | Hemispheric Transport of Air Pollution 2010 -Part A: Ozone and Particulate Matter[END_REF][START_REF] Schneidemesser | Chemistry and the Linkages between Air Quality and Climate Change[END_REF]. These environmental problems are directly related to the atmospheric emissions of greenhouse gases (GHG), air pollutants and their precursors.

Reliable emission inventories are a prerequisite to understand these environmental issues including the impact of anthropogenic activity on air quality and climate, and to develop effective mitigation options. Furthermore, emission knowledge of GHG and air pollutants are key in the development of integrated policies addressing climate change and/or air quality (AQ) and reducing unintended consequences [START_REF] Melamed | Sustainable policy-key considerations for air quality and climate change[END_REF][START_REF] Schmale | Air pollution: Clean up our skies[END_REF][START_REF] Reis | From Acid Rain to Climate Change[END_REF].

Emission inventories developed in different South American (SA) countries at national level typically focus on GHGs as part of the obligations of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC) within the framework of their national communications [START_REF] Baumgardner | Short-Lived Climate Pollutants: Drivers, Regional Emissions and Measurements[END_REF]. Emission inventories other than for GHG in SA focus mainly on megacities in an effort to understand the interactions and feedback mechanisms between emissions, AQ and public health [START_REF] Alonso | An urban emissions inventory for South America and its application in numerical modeling of atmospheric chemical composition at local and regional scales[END_REF]Gallardo et al., 2012a). Except for Argentina [START_REF] Castesana | Ammonia emissions from the agriculture sector in Argentina, 2000-2012[END_REF][START_REF] Puliafito | High resolution inventory of GHG emissions of the road transport sector in Argentina[END_REF][START_REF] Stohl | [END_REF], no national emission inventory for air pollutants is available with the spatial and temporal detail needed for AQ modelling, analysis and policy support. Therefore, the emission inventories currently used for national, regional or continental scale AQ assessments in SA are derived from global data sets (e.g. [START_REF] Longo | The Chemistry CATT-BRAMS model (CCATT-BRAMS 4.5): a regional atmospheric model system for integrated air quality and weather forecasting and research[END_REF]Rosario et al., 2013;[START_REF] Rodrigo | Application of WRF-Chem Model to Simulate PM10 Concentration over Bogota[END_REF]UNEP/CCAC, 2018).

South America is a continent that spans over the northern and the southern hemisphere, from the very cold Tierra del Fuego, close to Antarctica, to the equator and beyond to the Caribbean Sea. Its climate exhibits tropical, subtropical as well as extratropical features [START_REF] Garreaud | Present-day South American climate[END_REF]. It includes the world's largest rainforest, what is considered the driest desert outside polar regions [START_REF] Rondanelli | The Atacama Surface Solar Maximum[END_REF] and the Andes mountain range peaking well above 6000 meters, introducing east-west climate asymmetries [START_REF] Garreaud | Present-day South American climate[END_REF]. In short, a very diverse collections of ecosystems, physical landscapes and climate zones. While SA countries experienced similar population growth in the last 20 years, there have been large differences in economic development.

Emissions sources of air pollutants dominating impact on air quality also vary largely from country to country from residential combustion in central and southern Chile [START_REF] Saide | Air quality forecasting for winter-time PM2.5 episodes occurring in multiple cities in central and southern Chile[END_REF][START_REF] Mazzeo | Impact of residential combustion and transport emissions on air pollution in Santiago during winter[END_REF] to transport in Colombia [START_REF] Gonzalez | Relative impact of on-road vehicular and point-source industrial emissions of air pollutants in a medium-sized Andean city[END_REF]Pachon et al., 2018). Furthermore, emission conditions also vary largely, in particular among main metropolitan areas; while pollutants are emitted at sea level in Buenos Aires (Argentina), those in La Paz (Bolivia) are emitted at approximately 3600 m above sea level. This altitude can have an important impact on vehicle emissions with different emissions due to altitude [START_REF] He | Emission Characteristics of a Heavy-Duty Diesel Engine at Simulated High Altitudes[END_REF][START_REF] Ni | A research of the effects of altitude on the emissions and fuel consumption of a gasoline vehicle[END_REF][START_REF] Szedlmayer | Effect of Altitude Conditions on Combustion and Performance of a Multi-Cylinder Turbocharged Direct-Injection Diesel Engine[END_REF][START_REF] Wang | The real driving emission characteristics of light-duty diesel vehicle at various altitudes[END_REF].

The available global emission inventories for selected countries are analyzed by source-sector in this paper. These countries are highly urbanized, except for Peru, urbanization is above 80% and more than 50% of the urban population of these countries live in cities larger than 300.000 inhabitants (UN, 2018). Air quality in the SA cities is a major problem. Measurements of particulate matter of 2.5 microns or less (PM2.5) are available for 37 cities in these countries, 35 of them (representing approximately 15% of SA population) experience annual averaged concentrations exceeding the level recommended by the World Health Organization (WHO, 2017). This paper seeks to evaluate, give guidance and provide insight into the differences and similarities among emissions inventories for five selected SA countries, and possible reasons for such discrepancies. This analysis focuses foremost on air pollutants. The global emission inventories and local emission inventories for selected cities in the five SA countries together with the sources considered are presented in section 2. Sections 3 and 4 present a comparative analysis of the estimated emissions between the five countries investigating the reasons for discrepancies. A discussion of the main results is presented in section 5 followed by conclusions in section 6.

2 Methods and data

General country information and data

South America has an area of 17,840,000 km 2 and its population in 2010 is estimated at 395 Million (UN PD, 2017) (Table 1). The five countries selected in this study for analysis of the available emissions data are Argentina, Brazil, Chile, Colombia and Peru.

They cover 80% of the land area, and 84% of the population of South America ( The population of the selected countries is estimated at 330 million in 2010. The demographic development of the countries is similar, in all five countries the population doubled since 1970. When we focus on the last 20 years , the population has grown by about 30% and again the pattern for the 5 countries is similar. The development in gross domestic product (GDP), however, has been less similar and less gradual (Figure 1). Since Brazil is by far the largest country, its GDP follows the average rather closely but Colombia and Peru are clearly below the average GDP, while Chile and Argentina are above the average. Moreover, while the GDP for Chile has mostly increased since beginning of the 1980's, Argentina experienced a decrease in GDP from 1998 until 2003

and Peru mostly shows an increase in the last decade. A common feature for all SA countries is the pervasive inequity (e.g., [START_REF] Solimano | The Paradoxes of Chilean Economic Development: Growth, Inequal-ity, De-industrialization and Sustainability Risks[END_REF]PNUD, 2017), which is also relevant when considering consumption, emission and exposure patterns (e.g., Gallardo et al, 2012b;[START_REF] Carpenter | A Comparison of Economic and Social Implications of Rapid Urbanization in Lima and Santiago de Chile[END_REF]. Figure 1: Gross Domestic Product (GDP) per capita ($US/capita in constant 2010 prices) for the same five selected countries in SA. In addition, the GDP corresponding to Latin America and the Carribbean is also included.

Global emission data

Emission inventories providing data for SA countries from 1970 to present are collected and compared for the pollutants of primary concern: nitrogen oxides (NOx), sulfur dioxide (SO2), Black Carbon (BC), particulate Organic Carbon (OC), particulate matter of 10 microns or less (PM10), particulate matter of 2.5 microns or less (PM2.5), carbon monoxide (CO), and ammonia (NH3). Methane (CH4) is important in atmospheric chemistry (e.g., ozone formation) but this pollutant is explicitly included in the GHG emission inventories and is therefore not discussed in this study. A comparison of the data from the various inventories by pollutant and by country for the time period of 1970 to 2010 is given in section 3.1.

Although the total number of (global) emission inventories that provide information on SA is substantial ( [START_REF] Crippa | Forty years of improvements in European air quality: regional policy-industry interactions with global impacts[END_REF]. Source sector specification follows the IPCC 1996 code, as was also done for EDGAR v4.2).

Eclipsev5

The ECLIPSE emission data set was created with the GAINS (Greenhouse gas -Air pollution Interactions and Synergies; http://gains.iiasa.ac.at) model [START_REF] Amann | Cost-effective control of air quality and greenhouses gases in Europe: Modeling and policy applications[END_REF]

City emission data in South America

Local emission inventories are compiled for megacities in South America to address deteriorated air quality and implement measures to reduce concentration of air pollutants in these cities [START_REF] D'angiola | On-road traffic emissions in a megacity[END_REF]Gallardo et al., 2012a).

For each of the five countries, one city was selected to evaluate the local inventory against emissions from down-scaled global inventories. A city inventory contains the emissions occurring within the territory of the city (Table 3) and therefore the corresponding emissions from global inventories are extracted based on the squared area surrounding each city. To assess the impact of the choice of the area surrounding the city, two domains are considered when extracting the city emissions from global inventories; a small relatively tight domain following the city limits and a somewhat larger one to test if potential sources that in fact belong to the city are not included in the smaller domain. This method is rather crude and may introduce errors in the estimate, which are discussed in section 4.

Source sector definitions

The various global inventories used in this paper do not all share the same source sector definitions. Therefore, we have aggregated several sectors to come to a common sector breakdown (Table 4). This implies that individual inventories may have a more detailed sector breakdown than we use here. For example, EDGAR provides emission data for 12 IPCC 1996 main source categories whereas we aggregate the data into 8 different sectors. a) Landing and take off 3 Results

Source sector

Emission inventories by pollutant and by country

The SO2 and BC emissions from the different global emission inventories (Table 2) S06). The main observation is that emission estimates from different inventories widely vary and do not show a trend to converge. For some countries the early years vary widely (e.g. Argentina SO2), while for others the discrepancy increases with time (e.g. Peru SO2).

Overall, discrepancies of a factor 2-3 are present for all countries and pollutants. Some of the inventories shown in but the overall overview is useful because these inventories were used in important global assessments and it is not unlikely that those results may be sensitive to such differences in emission data. We will not discuss all these discrepancies in detail. In general, it can be stated that discrepancies between the emission inventories have multiple reasons including:

1. Emission factors may have been updated and improved over time.

2. Activity data may have been updated and improved over time.

3. New sources may have been identified over time.

4. The definition of which sources to be included in the inventory may differ between inventories. document that the major change between their ECLIPSE v5 and previous ECLIPSE versions is that IEA and FAO statistical data were reimported for the period 1990-2010, international shipping was included and that their global BC numbers are higher than previously published owing primarily to inclusion of new sources. Another, often overlooked issue is the inclusion or exclusion of semi-anthropogenic sources like forest fires or agricultural waste burning. Compare for example HTAPv2.2 and EDGARv4.3.1 in Figure 3 for Brazil. The emissions for all source sectors are equal but HTAPv2.2 excludes agricultural waste burning, and EDGARv4.3 includes it. In SA this is a relevant source and therefore HTAPv2.2 and EDGARv4.3.1 appear different inventories but the difference is simply a result of inclusion or exclusion of agricultural waste burning. Out of simplicity, the version of each inventory will be omitted henceforth and thus inventories will be referred to as EDGAR, ECLIPSE and CEDS. It is also important to note that emission inventories may show comparable patterns for one country and significantly diverse patterns for another country. Differences are seen in terms of trend and/or magnitude and this will be mostly related to different key source contributions in different countries. For SO2 these differences are seen both in terms of trends for Argentina and magnitude for Colombia and Peru (Figure 4). Whereas for NOx and BC, differences are mostly in terms of magnitude; see for example Argentina and Colombia for NOx and Brazil and Chile for BC (Figure 4) (See figures S7 to S10 for equivalent figures of the other pollutants considered in this work). We highlight that for most species the SA year-to-year variability (lower panels) is dominated by emissions in Brazil except for SO2 and NOx. For SO2 annual variability is also determined by emissions in Chile and Peru resulting from their mining activities. Argentina also contributes to the SA year-to-year variability for NOx emissions.

In addition to the spread in total emissions presented above, the EIs also differ in the relative contribution of each sector (Table S1 in supplement material). While the diversity between the emission inventories is small for SO2 and NH3 it is considerable for the other species. A more detailed description of this diversity in sectorial emissions between inventories is provided in the supplement material.

Particulate matter emissions

Particulate matter and its components, BC and OC are important species for air quality, health and climate change [START_REF] Bond | Bounding the role of black carbon in the climate system: A scientific assessment[END_REF]IPCC, 2018). Next to BC and OC, PM2.5 and/or PM10 can contain other components like sulphates, nitrates and mineral particles but in combustion emissions the bulk is BC + OC. Therefore, looking at BC+OC in relation to PM provides information on the type of emission factors used. Moreover, theoretically BC+OC cannot exceed PM2.5 as BC and OC emissions are smaller than 2.5 µm. The contribution of BC and BC+OC to PM2.5 as well as the contribution of PM2.5 to PM10 is analysed in more detail in Table 5. In addition, we pay attention to the ratio of CO to NOx (Table 5), which may provide insights into the role of traffic emissions [START_REF] Monks | Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer[END_REF]Gallardo et al, 2012a;[START_REF] Vivanco | Validation of the emission inventory in the São Paulo metropolitan area of Brazil based on ambient concentrations ratios of CO, NMOG and NOx and on a photochemical model[END_REF]. Note that the present analysis and EDGAR has in general larger BC content in PM2.5 than ECLIPSE except for Chile. In both inventories this contribution increases from 1995 to 2010 except for Argentina, and this increase is larger in ECLIPSE than EDGAR. Although BC is the product of incomplete combustion, such a trend can be explained by better combustion at higher temperatures, which first causes less particulate OC formation, and in a later, further advanced stage may also reduce the formation of BC. For the fraction of BC+OC in PM2.5, EDGAR presents the same features as for BC; decreasing trend in Argentina and increasing one in the other countries and the largest increase in Chile. This similarity between both ratios is likely due to the use of the same drivers to estimate the emissions. However, ECLIPSE does not seem to use the same drivers to estimate BC and OC emissions; while for the ratio BC/PM2.5 the country with the largest growth was Colombia, for the ratio (BC + OC)/PM2.5 Chile and Peru presented the largest increase. We note that both, Argentina and Peru, have unrealistic fractions larger than 1 in EDGAR (Table 5) 1 The use of independent OC emission factors in EDGAR (that is, independent from PM2.5) could explain these unrealistic values and allow BC+OC to be larger than PM2.5. In addition, EDGAR considers that all emitted PM10 for road transport is also PM2.5 while ECLIPSE estimates the coarse PM fraction (PM10-PM2.5) to be approximately 10%, and gradually increasing over time from approximately 8% to 11%. Finally, the CO/NOx ratio decreases over time with a comparable decrease in both inventories. When comparing the ratios CO/NOx by country and across the years we see a consistent lower CO/NOx ratio for ECLIPSE compared to EDGAR. Yet, while in EDGAR, both Argentina and Chile show an increasing trend since 2005; in ECLIPSE only Chile presents this trend. The CO/NOx emission ratios are important for atmospheric chemistry and composition. If better national data would be available, this discrepancy should be analysed and reconciled or at least better understood as it highlights a fundamental difference. One city per country was selected; namely Bogota, Buenos Aires, Lima, Rio de Janeiro and Santiago (Table 3). Unfortunately, the local city inventories differ substantially in terms of sectors included, period and pollutants considered (Table 3). Furthermore, while both global EIs are available for the year 2010, the local city EIs are only available for specific years and the year nearest to 2010 was selected (Table 3). The corresponding emission from the global EIs for a given city was derived by considering emissions within a squared domain surrounding the city. To assess the sensitivity of the method, two domains are considered; a small one with its limits in the vicinity of the city and a larger one considering potential sources excluded in the smaller domain. Maps illustrating each domain as well as the emissions included in each one are provided in the supplement material (Figure S11 to S13).

The total emissions from Santiago and Buenos Aires (the only two cities with emission estimates for all sectors) will be analyzed first and then the analysis will be extended to sectorial emissions for the remaining cities. We focus on two pollutants; NOx and PM10, since the goal of this analysis is not to validate either inventory but to highlight differences between them and identify the impact of local information in the estimate. In

Table 6 an overview is given for the cut-out from EDGAR and ECLIPSE that represents the Santiago and Buenos Aires metropolitan areas by sector and how it relates to national total emissions. Knowing that over 1/3 of Argentina's and Chile's population live in their corresponding capitals (Buenos Aires and Santiago, respectively) their share of Transport emissions appears small but an in-depth analysis by inventory would be needed to fully understand this. Furthermore, regardless of the domain considered, ECLIPSE attributes a larger fraction of the total national transport emissions to both cities than EDGAR while the opposite occurs for industrial emissions. For residential and total emissions, percentages between both inventories and for both domains are comparable. In terms of NOx emissions, for Buenos Aires both inventories assign comparable percentages of the total emissions to transport but differ for residential and industrial emissions. In spite of these differences, both inventories allocate comparable fractions of the national total emissions to Buenos Aires. On the contrary, for Santiago both inventories present comparable percentages for transport and industrial emissions but differ in residential and total emissions. Detailed, spatial explicit national inventories for Argentina and Chile would be extremely useful to elucidate the reasons for the discrepancies highlighted above. For EDGAR the geospatial proxy data have been disclosed in Janssens-Maenhout et al. ( 2017) for the sake of transparency but exactly why higher or lower shares of emissions are attributed to the city remains unclear.

The relative magnitude of total NOx emissions for both cities depend on the domain or regions considered to estimate the city emission from global inventories. EDGAR presents for Buenos Aires comparable emissions to the local inventory when the smaller domain is considered and larger emissions when using the larger domain, whereas the contrary is observed for Santiago where the estimate based on the larger domain is comparable to the local inventory and the one based on the smaller domain presents smaller emissions than the local one (Figure 5). Spatial distribution of the emission in each city explains this opposite behavior; while for Buenos Aires most of the city NOx emissions are distributed within the city domain enclosed by the smaller domain, for Santiago the emissions are distributed beyond the city limits and fall thus outside of the smaller domain but are captured by the larger one (Figure S12e and S12n in the supplement material).

Contrary to EDGAR for Santiago, ECLIPSE presents larger emissions than the local inventory regardless of the domain used to estimate the city emissions. For Buenos Aires however, the estimate based on the larger domain is comparable to the local inventory while the estimate based on the smaller domain is smaller than the local inventory. The local inventories in both cities estimate smaller emissions for the Transport sector (Figure 5). The contrasting estimates between Transport sector and the Residential and Industrial sectors between the Global EIs and the local city EIs may be partly related to the distribution proxies used in the global inventories but the impact on total emissions attributed to the cities is large (Figure 5).

Except for Transport, both EIs (and regardless of the domain used to estimate the city emissions) present larger PM10 emissions than local inventories for both cities. Finally, for total emissions both inventories present comparable emissions for Buenos Aires whereas for Santiago EDGAR presents larger emissions than ECLIPSE per domain. Although the magnitude of the estimated emissions from the global inventories for each city depends on the region considered, the results with respect to the local inventories are in general independent of the selected domain. and NOx in 2010 and the fraction of national total emissions located in the city domain for 2010 4 Discussion.

Brazil is the largest emitter of all analyzed species, except for SO2 where its emissions are comparable with those from Chile and Peru. This is not surprising as it is the largest and most populated country in SA. However, when emissions are considered per capita, i.e. they are normalized by population, this domination of Brazil disappears (Figure 6 and7). To limit the amount of data, we only present normalized data for the inventories ECLIPSE and EDGAR, including more inventories would not give significantly different insights. We only normalize sectors by population if population has a direct link with activities causing emissions, such as heating of houses (residential), commuting (transport) and industrial heat/power use. For sectors like agriculture and industry, population is not a good proxy to normalize emissions. Normalization by population of EDGAR emissions reveals that Chile is, in general, the country with the largest emissions per capita among these five countries. Chile stands out as the largest emitter per capita for SO2, PM10 and OC mostly related to the residential sector and power generation (Figure 6). For BC, Chile is also dominating due to transport and residential emissions. Finally, emissions of CO and NOx are dominated by the transport sector where again Chile has the largest emissions per capita, but those from Argentina and Brazil follow with smaller differences than for the other species. Normalization of ECLIPSE emissions by population presents similar figures as EDGAR for the power and residential sector with larger implied emission factors (EF) for EDGAR in the power sector but in general, smaller ones in the residential sector (Figure 7). Largest differences between both inventories are seen in the transport sector. While Chile is the largest emitter per capita for PM10, BC, OC and NOx according to EDGAR, the largest emitter according to ECLIPSE is Argentina. Part of the differences can be understood from geographical data. Chile is the country with a substantial amount of the population living in the South with cold winters and residential emission will be much higher in a cold climate, hence higher per capita emissions for residential sector. Another explaining factor is the economic development, in a relatively affluent country like Chile (see Fig 1) more people will own a car or have access to transportation and as a result more vehicle km (vkm) and transport emissions per person can be expected. This, however, already becomes more difficult to generalize because a more affluent country will have a more modern car fleet with less emission per vkm, but more vkm still create more emissions. So, while per capita transport emissions in Chile rank high (Figure 6 and7), per vkm they would probably be lower than some of the other countries. The data for power are most difficult to interpret. For SO2 it is related to the S content in the fuel used but more important we may have to correct for (small) industrial power use and we lack the data to properly do this. Also climatic differences may play a role. In general the normalization in Figure 6 and 7 suggests we may expect that per capita emissions from Colombia and Peru will still grow. Global Inventories use generic statistical country data to estimate emissions by combining for example transportation fuel sales data with emissions factors (e.g. Klimont et al. 2017, Crippa et al., 2018). Using road transport as an example, it is known that he emission factors are strongly dependent on the engine technology, type of fuel and fuel quality. The latter is highly variable and rapidly changing in SA. Global scale inventories try to capture this change over time but can only do so in a rather generic way, at best for individual countries but often by grouping multiple countries in similar "stage of technology development" classes. However, for air quality modelling and exposure in major cities the patterns in, and within, individual countries are important. This is illustrated with diesel fuel in Argentina as an example (Table 7). Argentina has three grades of diesel; Grade 1 (Agrodiesel or Gasoil Agro), is intended mainly for agricultural equipment. Grade 2 (Gasoil Común; common diesel fuel), is intended for the bulk of diesel fuelled vehicles and the sulphur limit of 500 ppm is not strictly enforced. Grade 3 diesel fuel, also known as Gasoil Ultra, is the highest quality diesel fuel. Another complication is that a policy or resolution may be accepted but the delay in implementation can be substantial; as seen in Table 7; a resolution for low sulphur fuels was accepted in 2016 but availability on the market is not expected until 3 years later. This kind of details are important but often only known by local or national experts. In the supplement material we provide additional information on sulphur contents in fuel and its evolution in SA (see Table S2), illustrating the importance of national and subnational policies. However, from Table 7 we can already see how challenging proper incorporation and spatial distribution will be for global emission inventories. Various grades may exist next to each other and will be implemented differently across the country, severely affecting the emissions in urban population centers. The example of the complex temporal and spatial implementation of the sulphur fuel standards shows the importance of working with local and national teams, especially when high resolution and spatial distribution is important to predict exposure and define mitigation measures. For global scale studies these details will be of limited importance but when understanding the air pollution in South American cities or regions it may be crucial. Also, when assessing climate impacts and mitigation options this may become increasingly important. The analysis of local city emission estimates against results from global inventories for the corresponding domain strongly highlight the importance of including local information. The emission estimates are dramatically different. If emissions are to be applied to forecast air quality and/or to develop mitigation measures the results from downscaled global inventories as compared to local city data will be entirely different. To come to more general conclusions we have calculated the ratio by source sector for the downscaled emissions from the global inventories and the local city inventories (Table 8). In this table a value smaller than 1 indicates that the local estimate is higher (green shading), a value higher than 1 indicates the down-scaled estimate is higher (orange shading). The sectors industry and power had to be grouped because in the city inventories this is often one category. A comparison for total emissions could only be made for Santiago and Buenos Aires because other city inventories are not complete. However, for these two cities the sectors transportation, RCO and Industry and power represent 62 ± 10% and 79 ± 9% of the emissions for PM10 and NOx, respectively. Hence, the comparison of the other cities is likely to cover at least the most important sectors and emissions.

Except for Lima, for each global inventory the magnitude of the estimated emission between the two domains (large and small) is mostly different, yet the relative magnitude to the local inventory is in general independent of the selected domain with the exception of total NOx Emissions in Buenos Aires from EDGAR and transport PM10 emissions in Bogota and NOx emissions in Lima from ECLIPSE. Therefore, the following analysis will focus on the dominant features between the local inventory and the equivalent estimate from each global inventory.

The result show that transportation emissions are estimated to be higher in local city inventories in most of the cities, except for Rio de Janeiro where global inventories are higher than the local inventory. Sometimes the result is close to 1 (e.g. PM10 ECLIPSE for Lima) but more often the discrepancies are large up to a factor of 5 (e.g. NOx ECLIPSE for Santiago) or even up to a factor of 10 when the NOx ECLIPSE estimate for Lima based on the small domain is considered. This is remarkable because the selected cities represent a large fraction of the national population and transport emissions are assumed to be in some way proportional to population, and in the case of Santiago 40% of the Chilean population lives in the Santiago Metropolitan region. (2017) for Argentina by comparing their national inventory (GEAA) with EDGAR.

Although the estimated CO2 emissions were very close (within 1%), large differences were found for the air pollutants with the national inventory being 1.7, 1.1, 1.4 higher for NOx, PM10 and PM2.5 but only 0.4 times the EDGAR estimate for SO2. In line with our results

in Table 8, the attribution to source sectors was often dramatically different partly masking the difference in the totals. Moreover, [START_REF] Puliafito | High-resolution atmospheric emission inventory of the argentine energy sector. Comparison with Edgar global emission database[END_REF] conclude that the spatial distribution by EDGAR was not adequate for Argentina, especially for transport and residential sectprs, leading to an overestimation in rural areas and an underestimation in urban areas. We refer to [START_REF] Puliafito | High-resolution atmospheric emission inventory of the argentine energy sector. Comparison with Edgar global emission database[END_REF], for a more detailed analysis.

In addition to the differences of the fraction of national emissions allocated to the different cities in the global inventories, we note that the spatial distribution of these emissions vary largely not only between the inventories but also with respect to the physical location of the actual city (Figure S12 and S13 in the Supplement material). For example, ECLIPSE locates most of the emissions of Santiago outside the city limits extending even over the Andes to the border with Argentina. Similarly, EDGAR although partially distributes the emissions within the city, they are centred on the northern border and an important fraction is outside the city limits.

Conclusions

Air quality modeling and exposure assessment needs, next to a validated chemistry transport model, two critical inputs; meteorological data and emissions data. National emission inventories in South America have focused on GHG as part of their obligations to the UNFCC. Only for Argentina a national emission inventory of air pollutants has recently been released [START_REF] Castesana | Ammonia emissions from the agriculture sector in Argentina, 2000-2012[END_REF][START_REF] Puliafito | High resolution inventory of GHG emissions of the road transport sector in Argentina[END_REF][START_REF] Puliafito | High-resolution atmospheric emission inventory of the argentine energy sector. Comparison with Edgar global emission database[END_REF]. Hence, studies assessing air quality at national, regional or continental scale in SA have been relying on global emission data sets. Several global emission inventories provide estimates for the selected SA countries (Argentina, Brazil, Chile, Colombia and Peru) for the past and present. These have been compiled and compared here with a further analysis focusing on three recent inventories (EDGAR 4.3.1,ECLIPSEv5,and CEDSv3) as they include data at least up to 2010 and can be compared with recent national or city scale emissions estimates.

Emissions from these global inventories are compared against available city-scale inventories for a major city in each country by selecting the spatial domain from the gridded data of the global inventory. Large discrepancies are found both between the global datasets as well as when comparing downscaled global emissions data with local/national city emissions data for the same domain. A direct conclusion of these discrepancies is that it is not recommended to use a global emission inventory to derive city emission as input for AQ modelling. The local situation appears to be not properly represented and the results of air quality modelling will significantly depend on the choice of inventory. Moreover a ranking of potential efficient mitigation measures would also depend on the choice of emission inventory. Without more detailed national emission data, a clear conclusion on the origin of discrepancies cannot be made although we have given several suggestions in the discussion. In some cases it is clear that emission factors differ substantially and this may need further attention in the future. At the same time spatial allocation can be another cause for large discrepancies. Simply said, the lack of consistent national inventories prohibits a further analysis. Efforts to create such national gridded inventories in South America are urgently needed.

The emissions analysed in this study, although limited to five countries out of the 12 existing countries in SA, are representative of most of the territory of South America as well as most of its population. This work seeks to increase the understanding of strength and weaknesses of emissions data available for South America by focusing on these five countries. The intention for the future is to include other countries not only from South America but also Central America and the Caribbean.

  figures for other pollutants are presented in Supplement material section 1 (Figures S01 to

  Fig. 3 are no longer considered up to date

Figure 2 :

 2 Figure 2: Total annual sulphur dioxide (SO2) emissions [Tg/yr] for the period 1970 to 2010 for global emission inventories: MACCity, EDGARv2, EDGARv3, HTAPv2, ECLIPSEv4, ECLIPSEv5, RCP2.6, RCP4.5, RCP6, RCP8.5, CEDSv1, CEDSv2 and CEDSv3. See figure legend for colours and symbols associated to each inventory.

Figure 3 :

 3 Figure 3: Same as Figure 2 but for black carbon (BC) and global inventories: MACCity, PKU, Junker-Liousse, HTAPv2, ECLIPSEv4, ECLIPSEv5, RCP2.6, RCP4.5, RCP6, RCP8.5, EDGARv3 and CEDSv3.

Figure 4 :

 4 Figure 4: Total annual emissions of SO2 (left), BC (centre) and NOx (right) [Tg/yr] for global inventories MACCity (green), EDGAR4.3 (black), ECLIPSEv5 (red) and CEDSv3 (blue) for the period 1995 to 2010.

Figure 5 :

 5 Figure 5: Total and sector emissions (Transport, Residential and Industry) of NOx and PM10 for Buenos Aires (a and b, respectively) and Santiago (c and d, respectively) from local EIs (blue) as well as global EIs EDGAR (red) and ECLIPSE (green). Estimates for a large (filled) and small (shaded) domain are included for each global EI. See figure S01 for limits of each domain and city boundaries.

Figure 6 :

 6 Figure 6: Normalized (by population) national emissions of SO2, CO, PM10, BC, NOx and OC from the global inventory EDGAR for Transport (yellow), RCO (green) and Power (blue) sector for the five selected countries (Argentina, Brazil, Chile, Colombia and Peru).

Figure 7 :

 7 Figure 7: Same as Figure 6 but for global inventory ECLIPSE.

Table

Table 1 :

 1 ). Selected generic data of the selected five countries in South America

	Country	ISO3 code Area ( km 2 ) a)	Total Population (10 6 ) b)	Urban Population [%] c)
	Argentina	ARG	2736690	41.2	92
	Brazil	BRA	8459420	196.8	87
	Chile	CHL	743800	17	88
	Colombia	COL	1109500	45.9	81
	Peru	PER	1280000	29.4	78
	Sum of 5 countries		14329410	330.3	86
	Total South America		17840000	395.3	84

a) Source FAOstat accessed through https://unstats.un.org/ b) UN PD (2017) c) UN PD (2018)

Table 2 )

 2 , a more in-depth analysis is made for a selection of few recent inventories. The selection of inventories is based on;

	1. Inventories need to include the recent period 2000-2010 for comparison with other local
	data sources such as SA city inventories
	2. From every "family" of EIs we take the latest version at the time of the start of our
	investigation; e.g. we look in detail to EDGAR 4.3 and not version 4.2.
	3. The inventory needs to have sectorial emissions information.
	Based on these criteria our further analysis focuses on EDGAR 4.3.1, ECLIPSE v5,
	CEDSv3 and MACCity. The MACCity inventory for years after 2000 is a projection based
	on the RCP8.5 projection by Riahi et al. (2007). As it was used in several studies in support
	of IPCC AR5 analysis, we include it for reference but will not analyze the implied emission
	factors or discrepancies in trends compared to the other bottom-up inventories. A brief
	introduction to the selected inventories is provided below; we refer to the provided
	references for more details. The emission inventories are accessible through the ECCAD
	server (https://eccad.aeris-data.fr/)

Table 2 :

 2 Overview of selected global emission data sets containing data for anthropogenic emissions for

	South America.
	2.2.1 EDGAR v4.3.1 (January 2016)
	The Emissions Database for Global Atmospheric Research (EDGAR) provides global
	historic anthropogenic emissions of GHG and air pollutants by country, and sector.
	EDGAR uses a bottom-up methodology with international activity data and emission
	factors (Janssens-Maenhout et al., 2017; Crippa et al., 2018). The estimated national
	emissions by sector are distributed on a 0.1ºx0.1º grid using geospatial proxy data.. In
	EDGARv4.3.1 emissions are calculated for gaseous and particulate air pollutants per sector
	and country for the time series 1970-2010. This version v4.3.1 is the official release of the
	EDGAR database used for PEGASOS scenarios

Table 3 :

 3 Overview of cities data (city, year(s), pollutants, reference) -see section 3.1 (Please add reference to

	each inventory)

Table 4 :

 4 The aggregated source sectors used for intercomparison of selected inventories and city inventories.

Note that in this work, Agriculture does not include savanna burning, but does include agricultural waste burning. Furthermore, CEDS includes agriculture waste burning under Waste -not under agriculture; ECLIPSE includes flaring emissions.

Table 2

 2

and Fig 2 and

Table 5

 5 are only for Transport emissions.

Table 5 :

 5 Road transport emission fraction of BC in PM2.5, BC+OC in PM2.5, fraction of PM2.5 in PM10 and CO/NOx ratio for EDGAR and ECLIPSE.Note: the color scale only applies to CO/NOx one can see that it becomes lighter from 1995 to 2010 and that EDGAR CO/NOx ratio tends to be higher than ECLIPSE3.3 Comparison of city emission data with global inventoriesEmission estimates from local and global inventories are intercompared for total emission as well as individual source sectors (Transport, Industry and Residential) whenever available. To keep the comparison transparent only EDGAR and ECLIPSE are considered in this analysis because CEDS partly relies on EDGAR and other inventories.

								EDGAR4.3.1-
	Transp.		EDGAR4.3.1-Ref		ECLIPSEv5	Ref	ECLIPSEv5
		BC/PM2.5 (BC+OC)/	PM2.5/PM10 BC/PM2.5 (BC+OC)/	PM2.5/PM10
	1995		PM2.5			PM2.5		CO/NOx	CO/NOx
	Argentina	0.71	1.18	1.00	0.45	0.82	0.94	14.28	5.51
	Brazil	0.52	0.86	1.00	0.45	0.82	0.93	9.57	6.31
	Chile	0.38	0.63	1.00	0.44	0.84	0.91	16.77	7.76
	Colombia	0.48	0.80	1.00	0.20	0.78	0.91	28.71	12.26
	Peru	0.65	1.08	1.00	0.36	0.81	0.92	12.57	5.88
	2000
	Argentina	0.69	1.14	1.00	0.48	0.84	0.94	9.70	4.01
	Brazil	0.53	0.89	1.00	0.48	0.84	0.92	8.86	5.51
	Chile	0.51	0.81	1.00	0.48	0.87	0.90	17.87	6.76
	Colombia	0.48	0.79	1.00	0.33	0.80	0.89	23.14	10.17
	Peru	0.63	1.04	1.00	0.44	0.85	0.92	10.43	5.46
	2005
	Argentina	0.67	1.09	1.00	0.50	0.83	0.93	8.16	3.28
	Brazil	0.50	0.83	1.00	0.50	0.84	0.91	7.22	4.81
	Chile	0.46	0.73	1.00	0.54	0.90	0.89	11.36	5.08
	Colombia	0.52	0.84	1.00	0.39	0.81	0.89	17.66	8.13
	Peru	0.71	1.15	1.00	0.48	0.86	0.91	9.27	4.55
	2010
	Argentina	0.69	1.13	1.00	0.53	0.85	0.92	13.02	3.13
	Brazil	0.54	0.88	1.00	0.53	0.87	0.90	6.11	3.56
	Chile	0.50	0.79	1.00	0.57	0.93	0.86	12.01	5.42
	Colombia	0.52	0.84	1.00	0.46	0.82	0.89	11.51	5.88
	Peru	0.84	1.32	1.00	0.51	0.88	0.89	7.52	3.74

Table 6 :

 6 Extracted city domain emissions of small (large) domain for Buenos Aires and Santiago for PM10

			EDGAR			ECLIPSE
		Transport	RCO	Industry	All sectors	Transport	RCO	Industry All sectors
	PM10
	(tons/yr)
	Buenos Aires	580 (868)	2491 (3192)	12643 (13424)	31020 (36836)	11805 (13943)	5146 (6295)	3553 (4647)	28655 (36814)
	Percentage of	6%	26%	28%	8%	24%	31%	8%	11%
	national total	(8%)	(33%)	(30%)	(9%)	(29%)	(38%)	(11%)	(15%)
	Santiago	618 (751)	28976 (36834)	13170 (15989)	60396 (74994)	3994 (5842)	21635 (36640)	5398 (8182)	39611 (62920)
	Percentage of	4%	31%	38%	25%	27%	25%	10%	21%
	national total	(5%)	(39%)	(47%)	(31%)	(39%)	(43%)	(15%)	(34%)
	NOx (tons/yr)
	Buenos Aires	19386 (28285)	16664 (18545)	64053 (68106)	137160 (161879)	38348 (47182)	8760 (10442)	35508 (41205)	116671 (138942)
	Percentage of	10%	68%	75%	29%	12%	45%	51%	23%
	national total	(15%)	(75%)	(80%)	(34%)	(14%)	(54%)	(59%)	(28%)
	Santiago	13125 (15299)	4420 (5564)	15398 (18769)	40774 (49008)	12005 (16360)	3644 (6546)	48420 (52740)	80195 (94801)
	Percentage of	13%	51%	47%	17%	9%	39%	53%	30%
	national total	(15%)	(64)%	(57%)	(21)%	(13%)	(70%)	(58%)	(35%)

Table 7 :

 7 Stepwise changes in diesel Sulphur limits in Argentina since 2006. ) Sold in high population zones (more than 90,000 inhabitants, since 2008 50,000 inhabitants)

a) Date of resolution, market availability is expected in 2019.

b

Table 8 :

 8 Ratio of city domain inventory over local city inventory for PM10 and NOx emissions for transport, RCO and industry & power. A comparison of total emissions could only be done for Santiago and Buenos factor 70 for ECLIPSE PM10 from the Residential sector in Lima. An overestimation in down-scaled global inventories for RCO emissions is not surprising as they are likely to use population density as a proxy, whereas national or cities information may have better data on which fuels are used where in the country; e.g. more in rural or colder mountainous areas. As a result the ratio for the total emission (Table8, right column) comes again closer to 1 but the unbalance in individual sectors suggests that this is a case of a better fit for the wrong reasons. Moreover, for air quality modelling emission height and emission timing which vary by source sector are important and for mitigation measures the sector information is crucial. While we highlight that the local information is crucial, it should be acknowledged that this is often lacking (only 2 cities provide a complete inventory) and the use of downscaled global inventories is the only option. A deeper understanding is further hampered by the scarcity of national total emission inventories. For example if the national total emission for transport between EDGAR or ECLIPSE and a national inventory would be similar but the ratio for the major city would be really different, we would know that this is due to spatial distribution and much less likely a fundamental difference in activity and emission factors used. So, national country wide emission inventories are not only needed to model air quality on the national scale but also to place city emissions in perspective. Such an analysis was made byPuliafito et al.

	City	Inventory	Transport	Residential	Industry	Total
			PM10	NOx	PM10	NOx	PM10	NOx	PM10	NOx
	Bogota	Local	1 a)	1	NA a)	NA	1	1	NA	NA
		EDGAR-S	0.44	0.19			5.5	3.1
		EDGAR-L	0.54	0.24			8.1	4.3
		ECLIPSE-S	0.96	0.07			1.2	3.1
		ECLIPSE-L	2.25	0.22			3.1	3.8
	Lima	Local	1	1	1	1	1	1	NA	NA
		EDGAR-S	0,30	0.52	49	5.4	8.7	32
		EDGAR-L	0.35	0.60	50	5.6	8.7	32
		ECLIPSE-S	1.1	0.17	76	8.2	14.2	39
		ECLIPSE-L	1.1	0.17	76	8.2	14.2	39
	Rio de	Local	1	1	NA	NA	NA	NA	NA	NA
	Janeiro*	EDGAR-S	0.60	1.3
		EDGAR-L	0.96	1.9
		ECLIPSE-S	3.0	0.44
		ECLIPSE-L	5.2	1.1
	Santiago Local	1	1	1	1	1	1	1	1
		EDGAR-S	0.21	0.33	13	2.5	18	3.1	9.2	0.79
		EDGAR-L	0.26	0.39	17	3.1	22	3.8	11	0.95
		ECLIPSE-S	1.4	0.31	10	2.0	7.3	9.8	6.0	1.6
		ECLIPSE-L	2.0	0.42	17	3.7	11	11	9.6	1.8
	Buenos	Local	1	1	1	1	1	1	1	1
	Aires	EDGAR-S	0.13	0.25	6.2	3.2	32	12	4.1	0.97
		EDGAR-L	0.19	0.37	8.0	3.6	34	13	4.9	1.2
		ECLIPSE-S	2.6	0.50	13	1.7	8.9	6.7	3.8	0.83
		ECLIPSE-L	3.0	0.62	16	2.0	12	7.7	4.9	0.98
	Aires.
	a) NA = Not Available; A ratio of 1 for the local inventory indicates the source sector is estimated, if no sector
	estimate is available in the local inventory, a ratio for EDGAR or ECLIPSE could not be calculated.

*PM as TSP in local

In contrast with the emissions from the transportation sector, the emissions from other sectors (Residential and Industry & Power) are overwhelmingly larger in the downscaled global inventories. The discrepancies can easily amount to a factor 10 or more with an

This is now corrected in version EDGARv4.3.2 where the PM2.5biofuel, PM2.5fossil and BC and OC have been revised and a ratio smaller than 1 is guaranteed for the sum of BC and OC over the sum of PM2.5fossil and PM2.5biofuel.

Acknowledgments

This work was conducted within the framework of the FONDECYT Project of International Networking 150043. The first author was partially funded by FONDECYT Regular projects 1150873 and 1181139. This work was also supported by the Center for Climate Research and Resilience (CR2) (FONDAP 15110009). The research leading to these results has received funding from the European Union H2020 programme PAPILA (GA 777544).