High resolution inventory of atmospheric emissions from transport, industrial, energy, mining and residential sectors of Chile

This study presents the first high-resolution national inventory of anthropogenic emissions for Chile (INEMA). Emissions for vehicular, industrial, energy, mining and residential sectors are estimated for the period 2015-2017 and spatially distributed onto a high-resolution grid (1 x 1 km approximately). The pollutants included are CO2, NOx, SO2, CO, VOCs, 15 NH3, and particulate matter (PM10 and PM2.5) for all sectors. CH4 and Black Carbon are included for transport and residential sources, while Arsenic, Benzene, Mercury, Lead, Toluene, and Polychlorinated dibenzo-p-dioxins and Furan (PCDD / F) are estimated for energy, mining and industrial sources. New activity data and emissions factors are compiled to estimate emissions, which are subsequently spatially distributed using census data and Chile ́s road network information. 20 The estimated total annual national emissions of PM10 and PM2.5 are 191 and 173 kilotonnes (Kt) , respectively. The residential sector is responsible for over 90% of these emissions. This sector also emits 81% and 87% of total CO and VOC, respectively. Additionally, the energy and industry sectors contribute significantly to NH3, SO2, CO2 emissions while the transport sector dominates NOx and CO2 emissions, and the Mining sector dominates SO2 emissions. In general, emissions of anthropogenic air pollutants and CO2 in northern Chile are dominated by mining activities as well as thermoelectric power plants while in 25 central chile the dominant sources are transport and residential emissions. The latter also mostly dominates emissions in southern Chile which has a much colder climate. Preliminary analysis revealed the dominant role of the emission factors in the final emission uncertainty. Nevertheless, uncertainty in activity also contributes, as suggested by the difference in CO2 emissions between INEMA and EDGAR. A comparison between these two inventories also revealed considerable differences for all pollutants in terms of magnitude and sectoral contribution, especially for the residential sector. EDGAR presents larger 30 emissions for most of the pollutants except for CH4 and PM2.5. The differences between both inventories can partly be explained by the use of different emission factors, in particular for the residential sector where emission factors incorporate information on firewood and local operation conditions. However, as mentioned above, differences in CO2 emissions between both inventories also points to biases in the quantification of the activity. https://doi.org/10.5194/essd-2021-216 O pe n A cc es s Earth System Science Data D icu ssio n s Preprint. Discussion started: 7 July 2021 c © Author(s) 2021. CC BY 4.0 License.


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This inventory (available at https://doi.org/10.5281/zenodo.4784286) (Alamos et al., 2021)) will support the design of policies that seek to mitigate climate change and improve air quality by providing policy makers, stakeholders and scientists with qualified scientific spatial explicit emission information.

Introduction
Air pollution is one of the main environmental challenges in Chile; in 2018 more than 9 million of its population (out of 17 40 million) were exposed to concentrations of fine particulate matter (PM2.5) above the national air quality standard, and around 3,640 cases of premature mortality were estimated due to cardiopulmonary diseases (MMA, 2019a). Urban areas of central and southern Chile are among the most polluted in Latin America with important consequences for human health (Romero-Lankao et al., 2013) including an increase in hospital admissions and mortality associated with cardiovascular and respiratory diseases (WHO, 2016). 45 The current air pollution and climate change problems are directly related to atmospheric emissions of criteria pollutantswhich affect air quality -and greenhouse gas (GHG). Identifying the origin and estimating the emissions of these pollutants by source type is a prerequisite for quantifying the impact of anthropogenic activity on air quality and climate, and thus developing effective mitigation strategies. Additionally, having GHG emissions and criteria pollutants consistent with each 50 other is key in the design of policies that allow addressing climate change and air quality in an integrated manner (Melamed et al., 2016).
Currently, emission inventories of GHG in Chile are produced within the framework of their national determined contributions (NDCs) as part of the commitments with the Parties to the United Nations Framework Convention on Climate Change 55 (UNFCCC). Emission inventories of criteria pollutants are developed for the most polluted cities within the framework of the decontamination plans to develop mitigation strategies to improve urban air quality. The national GHG emissions are prepared by a team of professionals from the Ministry of Environment (MMA from Spanish Ministerio del Medio Ambiente) responsible for the development and update of the GHG emission inventories whereas the latter are prepared by consultants hired on a case-by-case basis. Furthermore, while GHG inventories are performed consistently over the years, urban emission inventories 60 of criteria pollutants are not necessarily consistent with previous versions and/or emission inventories of other cities.
Additionally, the Register of Release and Transfer of Pollutants (RETC from Spanish Registro de Emisiones y Transferencia de Contaminantes) from the MMA gathers the emission declaration from the industrial sector and combines it with emission estimate from the residential and transport sector from different state agencies to build a national emission inventory. This information is available to the population through a dedicated web platform (www.retc.cl). While the national GHG inventory provides annual emissions at a national and regional scale, inventories of criteria pollutants provide emissions at a communal level (RETC) or for an entire city (Decontamination Plans). However, these inventories (GHG and/or criteria pollutants) do not have the spatial nor the temporal resolution necessary for air quality modelling.
Regional air quality (AQ) assessments in South America have relied on global emission inventories to understand the 70 interactions between emissions, air quality and public health (e.g. Longo et al., 2013;Rosario et al., 2013;Klimont et al., 2017;UNEP/CCAC, 2018). Furthermore, a comparison of global emission inventories against city-scale emission inventories for five South American cities (namely, Buenos Aires, Bogota, Lima, Rio de Janeiro and Santiago) revealed that although total emissions are in general comparable for these cities between the inventories, large differences exist for sectoral estimates (Huneeus et al., 2020). Given that mitigation of air quality depends on identifying the dominating emission sectors, using 75 global emission inventories is not recommended to define mitigation policies due to the risk of identifying the wrong target (Huneeus et al., 2020). Therefore, national inventories built on local data are needed to understand the contribution of human activity to air quality and climate change and design effective mitigation policies. This paper presents the first gridded national inventory of anthropogenic emission for Chile of criteria pollutants as well as 80 GHG (hereafter INEMA from Spanish Inventario Nacional de EMisiones Antropogénicas). The paper is structured as follows; the data and methodology used to estimate the emissions of each pollutant and sector are presented in section 2 while in section 3 the main results are shown, differentiating between the main pollutants and sectors that acquire relevance in the different regions of Chile. Discussion of the main results and uncertainty analysis of the estimated emissions are presented in section 4.
Finally, in section 5 the main conclusions of this work are presented. 85

Methodology and data
The INEMA inventory includes yearly emissions of Carbon Dioxides (CO2), Nitrogen Oxides (NOx), Sulfur Dioxides (SO2), The atmospheric emissions for each sector and pollutant are obtained by weighting the total fuel consumption (activity level) 100 by an emission factor (EMEP / EEA, 2016), as shown in eq. (1).
where is the total emission for species or pollutant i on year z and sector j, is the activity level of pollutant i, in sector j on year z, and is the emission factor for pollutant species i, type of source j. No interannual variability is assumed 105 for the EFs. The following subsections present detailed methodology and considerations for each sector

Study area.
Chile spans from 17 ° 29'57 "S to 56 ° 32'12" S and has a population of over 19 million inhabitants. The administrative political division is made up of 16 regions containing 56 provinces and 346 communes, presenting considerable differences in size and population density. Furthermore, each commune contains urban and rural areas, with the exception of some purely urban 110 communes in the larger cities. The territory can be broken down into 3 large macrozones with diverse climatic and geographical characteristics (Fig 1). The northern zone, with the regions of Arica and Parinacota, Tarapacá, Antofagasta, Atacama and Coquimbo, has an arid climate and includes the presence of the Atacama Desert, the driest desert outside polar regions (Rondanelli et al., 2015). Between 32-and 38-degrees south latitude is the central zone with the regions of Valparaíso,

Residential sector 125
Estimates from the residential sector include emissions produced by the combustion of biomass for heating, cooking and heating water. Firewood is acquired mostly through informal wood markets and the few regular and consistent information existing to characterize its consumption is collected through household surveys (REDPE, 2020). In this study, three studies (conducted in the last ten years) with regional representation are used to estimate the total firewood consumption in central and southern Chile. Despite their differences, these studies agree that the consumption increases towards the south -consistent with lower temperatures and the corresponding higher energy requirement of dwellings (Fig 2). For several individual regions, however, significant differences in wood consumption are found.

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Activity levels in INFOR19 are higher than the ones estimated in CDT15 and methodological shortcomings that potentially explain this underestimation have been identified (Reyes et al., 2018;Reyes 2017). For this reason, firewood consumption from CDT15 is only used for regions lacking alternative data (all regions north of Santiago). For the regions of O'Higgins, Maule, Bio-Bio, Ñuble, Araucanía and Los Ríos, the data reported in INFOR19 are used, whereas for regions Los Lagos, Aysén, and Magallanes, the information from UACH13 is selected over CDT15. Consumption estimates from UACH13 are 150 consistent with the results from INFOR19 for regions with data from both sources (Fig 2). We highlight that only INFOR19 provides firewood consumption at the communal level, the remaining studies estimate the firewood consumption at the regional level. Regardless of the spatial disaggregation, in each study an average household firewood consumption (AHFC) is computed at a communal level. For regions where the data are available at a regional level, the same average consumption is assumed for all communes contained in the administrative region. 155 A bottom-up approach is used to standardize the different information sources for the study period. The activity level of residential emissions is obtained at communal level (c), differentiating between urban and rural areas (a) as follows: Where , , is the residential fuelwood consumption for the year y in commune c for urban/rural area a; , , is the number

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The MDS conducts every two years the National Socio-Economic Characterization Survey (CASEN) for the entire country.
This survey contains information on the type of fuel used by households for heating, cooking food, and domestic hot water production, allowing to derive the penetration factor (PF) of biomass. For those isolated communes where this survey is not applied, the PF is taken for each region at urban or rural level considering the regional PF value from CDT (2015). In this study, we use the local EFs from SICAM (2014) and also used in MMA (2019b) to estimate emissions of NOx, PM2.5, 175 PM10, and SO2 (Table A1 in Supplementary Material) and those estimated from MMA (2019b) for CO2 and NH3. Among other factors, the EFs vary according to the efficiency of the technology used (e.g., fireplace, wood stove, simple heater, heater with temper, etc.), the humidity present in the wood, and the device's operating conditions 1 (Jimenez et al., 2017;Guerrero et al., 2019;Schuefftan et al., 2016). For CO and NMVOC's (Non-Methane VOCs) we use EFs for dry firewood from EMEP/EEA (2019;) while for CH4 the EFs estimated on the tier 1 approach from IPCC (2006) are used. However, EMEP/EEA and IPCC's 180 database do not present EFs for wet firewood and bad operation of heating devices. To compensate for this, we follow USACH (2014) and apply correction factors of 1.5 for wet firewood combustion and 3 for poor operation. Finally, we follow EMEP/EEA (2016) and consider BC to represent 10% of PM2.5 emissions.

Transport sector:
Estimated emissions from the transport sector consider exhaust emissions from vehicles traveling on public routes nationwide, 185 in urban and interurban areas, for years 2015 to 2017. Rail, air and sea modes are not included, nor are off-road machinery.
Emissions were calculated per region based on estimates of number of vehicles and their activity level. A more detailed description of the method applied to estimate transport emissions can be found in Osses et al. (2021, this issue) The different types of vehicles and their activity levels per region come from information obtained from official reports of 190 government agencies. This information includes statistics on fleet composition as the number of registered vehicles by region (INE 2017b), average annual mileage by vehicle type (SCSS, 2014;MAPS, 2013) and fuel sales for road transport by region (SEC, 2017). Vehicle categories considered are: light passenger, commercial and taxi vehicles; 12-and 18-meter buses; light, medium and heavy duty trucks; and two-wheeled vehicles. Each of these categories is subdivided according to the type of fuel used (gasoline or diesel) and the emission standard in its European equivalent (EURO standard). Total fuel consumption of 195 registered vehicles was estimated and compared to real fuel sales for each region. Thus, the number of active vehicles in a region was inferred and the number of vehicles per region was adjusted accordingly. The distribution of vehicles into urban and interurban activity per region was based on a proportional regional distribution provided by SCSS (2010). The combination of categories, fuels and emission standards generates a total of 70 types of vehicles for the emission analysis, distributed regionally and distinguishing between urban and interurban activity. 200 1 A bad operation condition occurs when combustion is carried out with the stove draft closed. 2 "Any copper smelter or any other industrial source emitting ash where a thermal treatment of mineral or metallurgical compounds of copper and gold is carried out, whose ash content in the feed is greater than 0.005% by weight on a monthly basis" taken from the Decree 28 on Emission Standard from copper smelters and arsenic emission sources.
where , , is the number of vehicles of type i in region j and road class k (urban or interurban); , , are the kilometers travelled per year by vehicles type i, in region j and road class k.
The estimate considers that all vehicles that enter Chile are required to comply with the European EURO regulations or their US equivalent. Consequently, the assignment of emission factors for each of the vehicle types was carried out by applying 210 COPERT V values (EMEP/EEA, 2019), adapted to the Chilean fleet (Gomez, 2020). Total emissions are calculated by multiplying VKT by an emission factor in grams per kilometer. The result is a regional emission database distinguishing by urban and interurban emissions, for CO, CO2, VOC, NOx, PM2.5, PM10 CH4 and BC.

Point sources: Energy, Mining and Industry sectors
Emissions from point sources and for species listed in table 1 are not estimated by our work but downloaded from the Register 215 of Emissions and Pollutant Transport (RETC from spanish Registro de Emisiones y Transporte de Contaminantes, https://datosretc.mma.gob.cl/group/emisiones-al-aire). from the Ministry of Environment. This register receives all selfreported emissions by the industrial facilities in accordance with current environmental regulations (MMA, 2019b). Industries are not obliged to declare CH4 and NMVOC but only total VOCs. Therefore, when analysing VOC emissions from the energy, mining and/or industry sector we will be referring to total VOCs. 220 The facilities that must declare their emissions are; • Pulp and Paper Production, Primary and Secondary Smelters, Thermoelectric Power Plants, Cement, Lime and Gypsum Production, Glass Production, Ceramic Production, Iron and Steel Industry, Petrochemical Industry, Asphalt Production.
• Industries with generator sets greater than 20kW, and industrial and heating boilers with fuel energy 225 consumption greater than 1 Mega Joule per hour.
• Establishments with electricity generation units, made up of boilers or turbines, with a thermal power greater than or equal to 50 MWt • Establishments whose fixed sources, made up of boilers or turbines, individually or as a whole, add a thermal power greater than or equal to 50 MWt 230 • Establishments corresponding to copper smelters and arsenic emitting sources In this work, emissions from point sources are differentiated between Energy, Mining and Industry sectors. The energy sector includes production and distribution of fuels and the generation of electric energy while Mining includes the production and smelting of metals. The remaining point sources will be aggregated into a single sector to which we will refer as Industry 235 henceforth.
This database includes more than 8324 point sources along the territory, most of which have associated coordinates. Those without a specified location were pinpointed on google earth using the facilities names and the address provided in their declaration if their contribution to the total communal emission was larger than 20%. Point sources without a geographic location contributing with emissions less than the above threshold were not explicitly included in the inventory, however their 240 emissions were distributed among the located sources. For a given species and sector and within each commune, emissions of located sources were scaled to fit total (located + non-located) emissions in that commune and sector.

Spatial distribution of emissions:
While point source emissions from Industry, Mining and Energy sector are spatially distributed using their coordinates (section 2.4), those from the transport and residential sectors are estimated at the regional or communal level and thus need to be 245 distributed to the final distribution of 0.01ºx0.01º (approximately 1x1 km) by means of a proxy (Fig 3).

Results: 270
Total national emissions increase for PM2.5, NH3, SO2, and CO2 between 2015 and 2017, whereas CO, VOC, PM10 and NOx decrease during that period (Fig 4). While PM10 decreases due to decreasing trend in industrial emissions and stable residential ones, NOx remains mostly constant due to a decrease in energy emissions and the slight increase in the rest of the sectors. included in this inventory and therefore not reflected in emissions of CH4 nor NH3. In general, the agriculture sector dominates NH3 and CH4 emissions and for any future study on these species, these sectors would need to be included. sector in both cases. This sector also makes the largest contribution in CO and VOC. SO2 has a greater presence in the northern part of the country, consistent with a larger mining activity. Given the large health impact associated toPM2.5 and its role in poor air quality in central and southern Chile, we focus now on this particular pollutant and its spatial emission distribution along the territory (Fig 5). More than 90% of the 158 (170) Tons of PM2.5 (PM10) total national emissions for 2017 originated from the residential sector (Fig 5). Emissions in the northern macrozone are mostly from the energy and industry sectors, which are generally located in urban areas. The Mejillones 300 commune concentrates more than 20% of all PM2.5 emissions in the northern macrozone (Fig 6a). More than 1,300 tons are emitted per year in this commune, of which 99% come from the energy sector (thermal power plants) concentrated in a few locations.
In Central and Southern Chile emissions are largely dominated by the residential sector and are consequently distributed along 305 the territory according to population, with a larger magnitude in locations with a greater number of dwellings and concentrated in the country's central valley. However, contrary to cities of Southern Chile (Fig 6c), significant contributions from other sources are observed in some areas of Central Chile. For instance, Santiago, the capital of Chile (Fig 6b), where more than 40% of the country's population resides, stands out in Central Chile. Although firewood burning for heating and cooking is prohibited in the metropolitan area, it is still the largest contributor to PM2.5 in the region due to its use in the outskirts and   Puliafito et al. (2017) and Huneeus et al. (2020) show that despite consistencies in the magnitude of total emissions of pollutants, global inventories have large discrepancies in sectoral contribution when compared to local or national inventories.

Comparison of total emissions by sector and pollutant with EDGAR inventory
We compare estimated emission for 2015 from the present inventory against the EDGAR v5.0 inventory (Crippa et al., 2019;. Global inventories, such as EDGAR, have been used in South America in the absence of a local inventory for AQ 330 assessments (Huneeus et al., 2020). Both inventories, EDGAR and this work, follow the sectoral classification proposed in IPCC (2006) allowing thus the direct comparison of sectoral emissions.
The differences for 2015 between both inventories for all pollutants are considerable in terms of magnitude and sectoral contribution, especially for the residential sector (Fig 7 and 8). Except for PM2.5 and CH4, EDGAR presents larger emissions 335 than INEMA. For CO, VOC and CO2, emissions are between 20 and 40% larger, for NOx differences are around 90%, while for PM2.5 INEMA emissions are 45% larger than those estimated on EDGAR. These differences can partly be explained due to the use of different emission factors in both inventories. The selection of emission factors for residential wood burning which include filterable PM only or filterable PM + condensable PM have an extremely large influence on the estimated emissions (e.g. Denier van der Gon et al.,2015). Moreover, these emission factors do not consider wet firewood and combustion 340 with poor operating conditions which considerably increases these pollutants' emissions (Schueftan et al., 2016;Guerrero et https://doi.org/10.5194/essd-2021-216 Open NOx the larger emissions in EDGAR are from the transport, energy and industry sector. While EDGAR and INEMA have comparable CO2 emissions for the transport, mining and residential sector, they differ significantly for the Energy and Industry sector. We note that the estimated emissions in this work for the energy and industry sectors are in line with what Chile reports 345 to the UNFCCC (34*10 3 KT for energy and 18*10 3 for industry) (MMA, 2020) suggesting a potential source of bias in the activity data used in EDGAR for the energy sector.
EDGAR VOC transport emissions are larger, due to evaporation emissions, which are not considered on INEMA inventory. Furthermore, smaller EDGAR emissions of PM2.5 and VOC are mostly due to differences in emissions from the residential 350 sector. Although the use of distinct EF by both inventories might explain this discrepancy, differences in estimating activity as highlighted by different CO2 emissions might also explain part of the difference.

Uncertainty and quality of estimations on the residential sector
Emissions represent a large source of uncertainty in air quality modelling (Thunis et al., 2016) of which uncertainty in emission factors dominate over the better-known activity data (Scarpelli et al. 2019). Consistently, for the residential sector in this study, the largest uncertainty in the estimated magnitude is associated with the emission factor. For VOC, CO, BC and particulate matter emissions, the range of possible estimations using varying information sources can reach differences of a factor 84, 24, 365 https://doi.org/10.5194/essd-2021-216 13 and 13 respectively (Fig 9a). For VOC and PM not all differences can be attributed to uncertainty, it is partly related to the choice of what is included in the definition of VOC or PM. In the case of PM2.5 emissions, differences in the estimated magnitude following the different emissions factors can reach up to factor 8 whereas differences in the activity data are less than a factor two (Fig 9b). However, the final uncertainty is even larger when considering the combined uncertainties from each parameter (Fig 9b). CO2, NH3, and SO2, have lower uncertainty ranges (Fig 9a) due to the greater consensus on their EFs 370 in the literature (MMA,2019b;IPCC, 2006;US EPA (1996a;b); SICAM, 2014) and the fact that these are single well-defined species whereas VOC and PM are container definitions; they include a variation of species or sizes. For CO2, NH3, and SO2 the possible estimations of the lower and upper limits differ by a factor 2, while for NOx this value can reach up to a factor 5. (groups of columns) and activity levels (colors). In yellow are the inventories constructed using CDT information (CDT, 2015), which provides the lowest possible activity level (AL) while in red are the activity levels used in this inventory (section 2.1).
The first group of columns represent estimated PM2.5 emissions based on EF from RETC while the second group correspond to the estimate based on EF used in the current inventory. The third and fourth group of columns correspond to the estimate 380 based on EF proposed by US EPA (1996a;b) and EMEP/EEA 2019, respectively.
The final EF's used in this study results from aggregating several EF each one of which corresponds to specific emission conditions, and/or fuel components that determine the magnitude of the emitted flux, by weighting each EF according to distribution parameters estimated on household surveys. The most relevant parameters considered are, among others, the quality and efficiency of the used technology (appliance type), the tree species, the humidity of firewood fuel, and the operating 385 conditions of devices (Jimenez et al., 2017;Guerrero et al., 2019;Schuefftan et al., 2016). Each of these EFs has its uncertainty, which depends on the quality and the number of laboratory tests carried out to determine its robustness (RTI International, 2007). Despite the magnitude of data and studies carried out, the uncertainty associated with EF estimation is considerable.
EMEP/EEA 2019 indicates that for a standard heater, the associated uncertainty to the estimated CO and PM2.5 EF can be larger than ten and four times, respectively. Additionally, estimating activity data has also sources of uncertainties associated.

Data availability
The emission database described is available at Zenodo (https://doi.org/10.5281/zenodo.4784286) (Alamos et al., 2021). The database consists of one .tar file for each year and sector, containing NetCDF (.nc) files for each pollutant. Each 400 NetCDF contains annual total emissions for the pollutant and year indicated per grid cell

Conclusions
A high-resolution emission inventory (0.01°x 0.01° degrees, approximately 1 x 1 km) of criteria pollutants, CH4 and CO2 from transport, industry, mining, energy, and residential sectors in Chile for the period 2015 to 2017 was developed. This is the first time a national gridded emission inventory with consistent CH4, CO2 and criteria pollutants was created for the entire 405 country. Urban and rural emissions from the residential sector are estimated based on firewood consumption data derived from different surveys conducted at the regional and communal level. The transport sector includes vehicles travelling on public urban and interurban routes nationwide. For mining, industry and energy sources, the self-reported emission estimates compiled by the environmental agency RETC are used. The dominant contribution of the residential sector to various pollutants, especially particulate matter, highlights the 430 importance of increasing efforts to mitigate this source. Increasing the thermal efficiency of dwellings, improving the firewood combustion quality by reducing the humidity of the burned wood or increasing the efficiency of combustion technologies, and implementing educational campaigns that ensure the correct use of the devices, are among the potential policies to achieve this goal. Nevertheless, a consistent and robust estimation of firewood consumption is prerequisite to estimate emissions from the residential sector. This requires the creation of an official database that characterizes firewood consumption throughout the 435 territory. Given the timeliness of the consumption data used in the present work, the absence of such an official database would prevent updating the present inventory in the near future. This is the first version of a national gridded inventory that will need to be further developed and continuously updated. It can be an important reference and benchmark for comparison in the future to track the impact of mitigation or other policy 440 measures. Further, future development of this inventory should consider, for instance including the agriculture sector and offroad vehicle emissions as well as completing the industry sector by locating in the territory the non-documented sources.
Nevertheless, this inventory provides policy makers, stakeholders and scientists with qualified scientific spatial explicit emission information to support air quality modelling and the development and further evaluation of policies to minimize (health and climate relevant) atmospheric pollutant emissions. 445

Author Contributions
NA and NH lead and write the original draft with contributions of all authors. NA, NH, MOpazo, SP prepare and curate the data, MOsses and NP generate and describe the emissions from transport sector. RR and AS participate in the processing of the residential activity level data. NA generates the residential emission data while NA and MOpazo prepared the industry, mining and energy emission estimate. HDvdG and NH designed the algorithm to distribute the residential emissions and 450 together with RC provide feedback on the methodology used and the global consistency of the inventory. All the authors review and edit the manuscript.
WHO. Health risk assessment of air pollutiongeneral principles, Copenhagen: WHO Regional Office for Europe, available at: https://www.euro.who.int/en/publications/abstracts/health-risk-assessment-of-air-pollution.  Table A1 shows emission factors for the residential sector by technology (appliance type), humidity and operation device conditions for PM10, PM2.5, NOx, and SO2. For CO and NMVOC's (Non-Methane VOCs) we use EFs for dry firewood from EMEP/EEA(2019;) while for CH4 the EFs estimated on the tier 1 approach from IPCC(2006) are used. 650 Table A2 shows the aggregated emission factors (g/kg) at a regional level for each pollutant used in the residential sector (eq A1), considering the distribution of technologies and humidity conditions of fuelwood estimated in CDT (2015) Where: is the emission factor for pollutant on region