This study presents the first high-resolution national inventory
of anthropogenic emissions for Chile (Inventario Nacional de Emisiones Antropogénicas, INEMA). Emissions for the vehicular, industrial, energy, mining and residential sectors are estimated for the
period 2015–2017 and spatially distributed onto a high-resolution grid (approximately
The estimated annual average total national emissions of PM
This inventory (available at
Air pollution is one of the main environmental challenges in Chile; in 2018
more than 9 million of its people (out of a population of 17 million) were exposed to
concentrations of fine particulate matter (PM
The current air pollution and climate change problems are directly related to atmospheric emissions of criteria pollutants – which affect air quality – and greenhouse gases (GHGs). 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 other is key in the design of policies that allow for 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 nationally determined contributions (NDCs) as part of the
commitments of the parties to the United Nations Framework Convention on
Climate Change (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 the Environment (MMA from Spanish for Ministerio del Medio
Ambiente) responsible for the development and updating of the GHG emission
inventories, whereas the decontamination plans are prepared by consultants
hired on a case-by-case basis. Furthermore, while GHG inventories are
performed consistently over the years, urban emission inventories of
criteria pollutants are not necessarily consistent with previous versions
and/or emission inventories of other cities. Additionally, the Pollutant
Release and Transfer Register (RETC from Spanish for Registro de Emisiones y
Transferencia de Contaminantes) from the MMA gathers the emission
declaration from the industrial sector and combines it with emission
estimates from the residential and transport sectors from different state
agencies to build a national emission inventory. This information is
available to the public through a dedicated web platform
(
While the national GHG inventory provides annual emissions at a national and
regional scale, inventories of criteria pollutants provide annual emissions
at the communal The commune is the smallest administrative and
territorial unit in Chile and is equivalent to what is known in other
countries as a municipality.
This paper presents the first gridded national inventory of anthropogenic emission for Chile of criteria pollutants as well as GHGs (hereafter INEMA from Spanish for 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 Sect. 2, while in Sect. 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 Sect. 4. Finally, in Sect. 5 the main conclusions of this work are presented.
The INEMA inventory includes yearly emissions of carbon dioxide (
Pollutants and sectors considered in the Chilean inventory (INEMA) according to IPCC (2006a) classification.
Throughout this paper we will follow the EDGAR (Emission Database for Global Atmospheric Research) nomenclature and use the term
“sectors” to refer to emission activities (Crippa et al., 2018).
Furthermore, emissions of
The atmospheric emissions for each sector and pollutant are obtained by
weighting the total activity level by an emission factor (EMEP/EEA, 2016),
as shown in Eq. (1).
Chile spans from
Continental Chile highlighting three macrozones defined for the paper, namely the north (green), central (blue) and south zone (red). Divisions within each macrozone indicate limits of the 16 administrative regions. Population in each of these 16 regions is indicated in the orange ellipses.
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;
Annual fuelwood consumption in kilotons for 2017 by region according to UACH (2013, red), CDT (2015, green) and INFOR (2019, blue). Regions are colored according to the data source used in each region.
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 (
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,
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
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,
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,
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 industries with generator sets greater than 20 kW and industrial and heating
boilers with fuel energy consumption greater than 1 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 establishments corresponding to copper smelters and arsenic emitting sources
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
Map of Santiago at different scales: in blue the utilized
Residential emissions were initially estimated at the communal level and
distributed onto a regular
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
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.
Total national emissions remain mainly stable for most species between 2015
and 2017 with a slight increase for PM
Total national annual emissions distributed by sector for
pollutants VOC, PM
Emissions of
Average annual total emissions (
Given the large health impact associated with PM
Spatial distribution of the 2017 emissions of PM
Spatial distribution of the 2017 emissions of PM
In central and southern Chile emissions are largely dominated by the
residential sector and are consequently distributed along 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 PM
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
PM
Emissions of
Total 2015 emissions in kilotons by pollutant and sector according to this work and EDGAR v5.0. Sectors considered in both inventories correspond to the classification proposed in IPCC (2006a) presented in Sect. 2.
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 PM
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 PM
To examine and estimate emission uncertainties associated with the
residential sector, multiple emissions considering different levels of
activity and EFs are estimated (Sect. 2.6). For VOC, CO, BC and
particulate matter emissions, the range of possible residential-emission
estimations can reach differences of a factor of 84, 24, 13 and 13, respectively,
(Fig. 9a) between the upper and the lower estimation limits. 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. For the
residential sector in this study, the largest uncertainty in the estimated
magnitude is associated with the emission factor. In the case of PM
The final EFs used in this study (see Table A2) are obtained
by aggregating several EFs, each one corresponding to a specific emission
condition and/or fuel component. They determine the magnitude of the emitted
flux (see Table A1), by weighting each EF according to distribution
parameters estimated in household surveys. The most relevant parameters that
were considered when weighting the EFs are the quality and efficiency of the
technology used (appliance type), the humidity of firewood fuel and the
operating conditions of the 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 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 PM
Components that determine the final uncertainty of residential-emission estimations.
The emission database described is available at Zenodo
(
A high-resolution emission inventory (
Total national emissions remained mostly stable between 2015 and 2017 with
slight increases for PM
A comparison of the estimated emissions against the EDGAR v5.0 database (Crippa
et al., 2019, 2020) shows significant differences for several species. For
CO and VOC, EDGAR emissions double those of INEMA's, while
We note that what we call the “residential sector” in EDGAR corresponds to IPCC code 1A4 and therefore, in addition to including residential emissions, also includes emissions from commercial activities, agriculture, forestry, fishing and fish farms. Given that the residential sector in INEMA only considers residential emissions (IPCC code 1A4b) the difference between both inventories (INEMA and EDGAR) are actually larger than illustrated in this study. Nevertheless, future versions of INEMA need to estimate the emissions from all activities in IPCC code 1A4 and not only residential emissions. This means including not only emissions from commercial activity, agriculture, forestry, fishing and fish farms but also residential emissions from fuels other than biomass.
The dominant contribution of the residential sector to various pollutants,
especially particulate matter, highlights the importance of increasing
efforts to mitigate this source. Increasing the thermal efficiency of
dwellings, improving the firewood combustion quality by reducing the
humidity of burned woods, 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
a prerequisite to estimate emissions from the residential sector. This
requires the creation of an official database that characterizes firewood
consumption throughout the 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
considering there is no activity level data of the residential sector
collected regularly for the whole country. Furthermore, additional studies
need to be conducted to develop EFs for residential emissions of VOC, CO,
NMVOC and
This is the first version of a national gridded inventory and 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 measures. Further, future development of this inventory should consider, for instance, including the speciation of VOCs, the agriculture sector and off-road vehicle emissions as well as emissions from non-paved roads given that approximately 60 % of the national roads in Chile fall into this category and completing the industry sector by locating in the territory the non-documented sources. Nevertheless, this inventory provides policymakers, stakeholders and scientists with qualified scientific spatially explicit emission information to support air quality modeling and the development and further evaluation of policies to minimize (health- and climate-relevant) atmospheric pollutant emissions.
Emission factors (
Table A1 shows emission factors (
Table A2 shows the aggregated emission factors (
Aggregated emission factors (
Table B1 displays the total emissions (
Total emissions (
NA and NH led the study and wrote the original draft with contributions from all authors. NA, NH, MOpazo and SP prepared and curated the data. MOsses and NP generated and described the emissions from the transport sector. RR and AS participated in the processing of the residential activity level data. NA generated the residential-emission data, while NA and MOpazo prepared the industry, mining and energy emission estimates. HDvdG and NH designed the algorithm to distribute the residential emissions and together with RC provided feedback on the methodology used and the global consistency of the inventory. All the authors reviewed and edited the manuscript.
At least one of the (co-)authors is a member of the editorial board of
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the special issue “Surface emissions for atmospheric chemistry and air quality modelling”. It is not associated with a conference.
The authors would like to thank the Center for Climate and Resilience Research (Fondap no. 15110009)
for institutional support and funding. Thanks also go to the data providers of the EDGAR v5.0 database, available on the EDGAR air pollutant website (
The Center for Climate and Resilience Research (Fondap no. 15110009) provided financial support. Nicolás Huneeus was also partially funded by the Science, Technology, Knowledge and Innovation Ministry of Chile through the FONDECYT (grant no. N1181139) and Research and Innovation programs (grant agreement no. N870301, AQ-WATCH). Mauricio Osses was funded by Centro Científico Tecnológico de Valparaíso ANID PIA/APOYO AFB 180002.
This paper was edited by Nellie Elguindi and reviewed by two anonymous referees.