Chile spans from 17∘29′57′′ 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/or rural areas, with the exception of some purely urban communes in larger cities. The territory can be broken down into three large macrozones with distinctive climatic, geographical and demographic characteristics (Fig. 1). The north 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∘ S is the central zone with the regions of Valparaíso, Metropolitan, O'Higgins, Maule, Ñuble and Biobío. A Mediterranean climate with rainy winter and dry summer seasons prevails in this area. The regions of Araucanía, Los Ríos, Los Lagos, Aysén and Magallanes are located in the south zone, characterized by a temperate rainy climate, where low temperatures and abundant rainfall stand out. These conditions are accentuated further south, although the rainfall drastically decreases in the highest mountainous areas and south of the Strait of Magellan, where a tundra-type climate predominates (Sarricolea et al., 2017).

Emissions from residential sources come from the combustion of all fuels used inside of homes, such as gasoline, kerosene and biomass, among others. However, in this first version of INEMA, the residential sector will focus on emissions from firewood combustion only given its dominant role in air quality in central and southern Chile (Saide et al., 2016; Huneeus et al., 2020b). The inclusion of additional fuels is left for future versions of this inventory.

Estimates for the residential sector include emissions from biomass combustion for heating, cooking and heating water. Firewood is acquired mostly through informal wood markets, and the few regular and consistent pieces of information that exist to characterize its consumption are collected through household surveys (REDPE, 2020). In this article, three studies with regional representation (conducted in the last 10 years) are used to estimate total firewood consumption in central and southern Chile.

The first one of the three aforementioned studies was conducted by the Universidad Austral de Chile (UACH). Firewood consumption in the residential sector was estimated based on existing studies for the years between 2005 and 2012 for each region in southern and central Chile (UACH, 2013; hereafter UACH13). Another study was mandated by the Ministry of Energy to the Corporation of Technological Development (CDT from Spanish for Corporación de Desarrollo Tecnológico; https://www.cdt.cl, last access: 30 June 2020), a private non-profit organization created in 1989 by the Chilean Construction Chamber. This study collected information on firewood consumption from the residential, commercial, public-service and industrial sectors for the entire Chilean territory for the year 2014. In each of the 16 Chilean regions, a total of 300 households in urban areas and 65 in rural ones were surveyed (CDT, 2015; hereafter CDT15). Finally, the most recent survey was done by the Forestry Institute (INFOR from Spanish for Instituto Forestal) by collecting samples of between 300 and 500 households for a single year between 2015 and 2018 in the six central Chilean regions considered in the study (INFOR, 2019; hereafter INFOR19) (Fig. 2). Despite their differences, these studies agree that consumption increases towards the south – consistent with lower temperatures and the corresponding higher energy requirement of dwellings (Fig. 2). However, significant differences in wood consumption are found between these datasets within several individual regions.

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, Biobío, Ñuble, Araucanía and Los Ríos, the data reported in INFOR19 are used, whereas for the regions Los Lagos, Aysén and Magallanes, the information from UACH13 is selected over CDT15. Consumption estimates from UACH13 are consistent with the results from INFOR19 for regions with data from both sources (Fig. 2). It is worth noting that only INFOR19 provides firewood consumption at the communal level; the remaining studies estimate the firewood consumption at the regional scale. Regardless of the spatial disaggregation, in each study average household firewood consumption (AHFC) is computed at the communal level. For regions where the data are available at the regional level (see Fig. 2, those with information from CDT15 and UACH13), the same average consumption is assumed for all communes contained within the same administrative region.

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 the communal level (c), differentiating between urban and rural areas (a) as follows: 2ALy,c,a=HNy,c,a⋅PFy,c,a⋅AHFCc,a, where ALy,c,a is the activity level corresponding to residential fuelwood consumption for year y in commune c for urban/rural area a, HNy,c,a is the number of total dwellings in year y in commune c in area a registered in INE (2019), PFy,c,a is the “penetration factor” representing the percentage of houses that use biomass in year y in commune c and area a obtained from the Chilean Ministry of Social Development and Family (MDS from Spanish for Ministerio de Desarrollo Social, 2015, 2017), and AHFCc,a is average household firewood consumption in commune c and area a.

The MDS conducts the National Socio-Economic Characterization Survey (CASEN) every 2 years 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 for deriving the penetration factor (PF) of biomass. For the isolated communes where this survey is not applied, the PF is taken for each region at an urban or rural level considering the regional PF value from CDT (2015).

Emission factors for residential combustion of firewood vary, among other factors, according to the efficiency of the technology used (e.g., fireplace, wood stove, simple heater, catalytic stove, etc.), the humidity present in the wood and the device's operating conditions

A bad operation condition occurs when combustion is carried out with the stove draft closed.

Estimated emissions from the transport sector consider exhaust emissions from vehicles traveling on public routes nationwide, in urban and interurban areas, for the years 2015 to 2017. Neither rail, air and sea modes nor off-road machinery is included. Also, resuspended dust from paved and non-paved roads are not considered in this analysis. Approximately 60 % of the national roads in Chile are non-paved. Emissions were calculated per region based on estimates of the 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 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 m 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). Estimates of total fuel consumption from registered vehicles were compared to real fuel use for each region, using information on sales of diesel and gasoline for the transportation sector, by political region, provided by the Electricity and Fuel Superintendence (SEC from Spanish for Superintendencia de Electricidad y Combustibles, https://www.sec.cl, last access: 30 June 2020). A correction factor to the total number of registered vehicles in each region is applied to make these two fuel consumptions equal, correcting for those vehicles that are registered but do not contribute to actual driving activity. Thus, the number of active vehicles in a region was inferred and 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.

Activity level was expressed in VKT (vehicle kilometers traveled) calculated as the sum of the number of vehicles per mileage per type of vehicle (Eq. 3) expressed as follows 3VKT=∑v,r,kN⋅Nv,r,k⋅KMv,r,k, where Nv,r,k is the number of vehicles of type v in region r and road class k (urban or interurban) and KMv,r,k is the kilometers traveled per year by vehicle type v, in region r 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 COPERT 5 values (EMEP/EEA, 2020), 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 differentiated by urban and interurban emissions for CO, CO2, VOC, NOx, PM2.5, PM10 CH4 and BC.

Emissions from point sources and for species listed in Table 1 are not estimated by our work but downloaded from the Pollutant Release and Transfer Register (RETC from Spanish for Registro de Emisiones y Transporte de Contaminantes, https://datosretc.mma.gob.cl/group/emisiones-al-aire, last access: 30 August 2020) from the Ministry of the Environment. Every year, this register receives self-reported emissions from industrial facilities in accordance with current environmental regulations (MMA, 2019b). Verification and quality control of the declared emission estimates are neither conducted by the ministry nor done in this work. Although self-reported emissions estimates could have strong biases, they are currently the sole estimates of industrial emissions and are therefore used in this work. This limitation will be considered when analyzing and discussing the results. Industries are obligated to declare neither CH4 nor NMVOC but only total VOCs. Therefore, when analyzing VOC emissions from the energy, mining and/or industry sector we will be referring to total VOCs.

Establishments with economic activities (given by their International Standard Industrial Classification of All Economic Activities or ISIC designation) that meet any of the following criteria are subject to declare their atmospheric emissions to RETC:

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, and asphalt production

Agricultural emissions, thermoelectric plants that are a part of cogeneration processes and other activity sectors not mentioned explicitly above or that do not meet one of the above criteria are not obligated to declare unless they are in a geographical zone with an existing atmospheric decontamination plan.

Emissions from point sources are differentiated between the energy, mining and industry sectors. The energy sector includes the 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 henceforth.

This database includes more than 8324 point sources along the territory, most of which have associated coordinates; however, a large number of sources exist in the database where only the commune of emission is known (along with additional information such as the company name, activity type and description) but not their coordinates. Approximate coordinates of these sources without a specified location and whose contribution to their respective commune was larger than 20 % were obtained by pinpointing them on Google Earth using the information provided in their declaration. The remainder of the point sources without a geographic location were not explicitly included in the inventory; however, their emissions were distributed among the located sources (including those manually georeferenced) within the same commune. For a given species, sector and commune, the spatial distribution of the emissions of located sources was scaled to fit the total (located and non-located) emissions.

While point source emissions from the industry, mining and energy sectors are spatially distributed using their coordinates (Sect. 2.4), those from the transport and residential sectors are estimated at the regional or communal level and thus need to be distributed to the final grid of 0.01∘×0.01∘ (approximately 1km×1 km) (Fig. 3).

Residential emissions were initially estimated at the communal level and distributed onto a regular 0.01∘×0.01∘ grid (approximately 1km×1 km) based on population density from the last census conducted in 2017 (INE, 2017a). The information is available at the census block scale, which is the smallest territorial scale for which relevant information from the census exists; it consists of a group of adjoining or separate dwellings, buildings, establishments and/or properties, delimited by geographical, cultural and natural features. The population distribution was obtained by projecting the 0.01∘×0.01∘ lat–long grid (EPSG:4326, WGS 84) onto the information contained in the census blocks and retrieving the aggregated information. We used the population density to distribute the emissions with the assumption that firewood is consumed similarly among the population despite the limitations it presents. This was due to not having information available at the census block scale that would allow for making a finer spatial disaggregation according to population characteristics such as income. Furthermore, the CDT15 database reveals that households with higher incomes have higher firewood consumption levels per capita but fewer dwellings using firewood for heating and cooking, making it difficult to establish a clear relationship between income and firewood consumption.

The spatial distribution of transport emissions within each region was performed by projecting the road network of each region onto a latitude–longitude grid of 0.01∘×0.01∘ (approximately 1km×1 km). The QGIS open-source software, the official database for Chile's road network and regional limits (BCN, 2020), was used to characterize the Chilean road network and was complemented with information from OpenStreetMap (OSM, 2020) to organize it into a hierarchy comprising freeways, arterial roads, collectors and local roads of 77 800 km of both rural lands and main cities. Road vehicle flow per type of road was estimated by applying a road weight factor, based on toll barrier vehicle counts at interurban roads and origin–destiny surveys on urban roads. Average weight factors are 54 % for freeways, 23 % for arterial roads, 16 % for collectors and 7 % for local roads. In each region, urban emissions were distributed among cities based on population (INE, 2017a) first, and then within each city emissions were distributed by applying the aforementioned weight factors. Thus, urban emissions in each cell depend on the population and the roads in the cell.

Emissions represent a large source of uncertainty in air quality modeling (Thunis et al., 2016), of which uncertainty in emission factors dominate over the better-known activity data (Scarpelli et al., 2019). To assess the uncertainty of the residential sector, we construct a range of possible estimates using different sources of information for the level of activity and emission factors. Two possible activity levels were considered; the lower limit is given by the CDT15 information for the whole country (CDT, 2015), while the upper limit considers the activity levels used in this inventory (Sect. 2.1). For emission factors we consider four possible datasets. The upper estimates are based on the EFs used in the RETC database until 2014, while the lower limit considers the EFs estimated based on IPCC (2006b) and EMEP/EEA (2019) for different species. Also, EFs used in the current inventory and those proposed by US EPA (1996a, b) are considered. Eight possible residential-emission levels were estimated by considering all possible combinations between the two activity level estimations and the four EF datasets. These eight emissions estimates are then normalized by INEMA's emissions, and therefore, for a given species, a resulting value of 4 represents an estimate 4 times larger than INEMA, while a value of 0.2 corresponds to an estimate 5 times smaller than INEMA, and the corresponding range of uncertainty of the estimated emission of the given species would be a factor of 20. The analysis (Sect. 4) focuses on the largest (considering INEMA's activity level and RETC EFs) and lowest (considering CTD15 activity level and EMEP/EEA EFs) emission estimate.

Puliafito et al. (2017) and Huneeus et al. (2020a) 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. We compare estimated emissions for 2015 from the present inventory against the EDGAR v5.0 inventory 2015 emissions (Crippa et al., 2019, 2020). Global inventories, such as EDGAR, have been used in South America in the absence of a local inventory for AQ assessments (Huneeus et al., 2020a). Both inventories, EDGAR and this work, follow the same sectoral classification proposed in IPCC (2006a) with the exception of the residential sector. While for INEMA the residential sector corresponds to IPCC code 1A4b with only firewood combustion, the residential sector in EDGAR corresponds to IPCC code 1A4, including residential emissions as well as emissions from commercial activities, agriculture, forestry, fishing and fish farms. In spite of these differences in activities represented in the residential sector between both inventories, INEMA presents larger total PM2.5 and sectoral residential emissions for all species (Figs. 7 and 8).

The differences for 2015 between both inventories for all pollutants are considerable in terms of magnitude and sectoral contribution, especially for the residential sector (Figs. 7 and 8). Except for PM2.5 and CH4, EDGAR presents larger emissions than INEMA. For CO, NMVOC and VOC, EDGAR emissions are more than double that of INEMA's; for NOx differences are around 90 %; and for CO2 they are around 40 %. While for PM2.5 INEMA emissions are 45 % larger than those estimated in EDGAR. These differences can partly be explained due to the use of different emission factors in both inventories. Emission factors used in EDGAR do not consider wet firewood and combustion with poor operating conditions which considerably increases these pollutants' emissions (Schueftan et al., 2016; Guerrero et al., 2019). In contrast, the current inventory (INEMA) considers emission factors for PM10, PM2.5, NOx and SO2 that take these conditions into account. For NOx the larger emissions in EDGAR are from the transport, energy and industry sectors. While EDGAR and INEMA have comparable CO2 emissions for the transport, mining and residential sectors, they differ significantly for the energy and industry sectors. We note that the estimated emissions in this work for the energy and industry sectors are in line with what Chile reports to the UNFCCC (34 000 and 18 000 kt for energy and industry, respectively; see INGEI – from Spanish for Inventario Nacional de Gases de Efecto Invernadero – on CO2 graph of Fig. 8; MMA, 2020), suggesting a potential source of bias in the activity data used in EDGAR for the energy sector.

EDGAR NMVOC and CO transport emissions are larger, due to evaporation emissions, which are not considered in the INEMA inventory. Furthermore, smaller EDGAR emissions of PM2.5 are mostly due to differences in emissions from the residential sector. Although the use of distinct EFs by both inventories might explain this discrepancy, differences in estimating activity as highlighted by different CO2 emissions might also explain part of the difference.