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Table 1 Pollutants and sectors considered in the Chilean inventoryINEMA) according to IPCC (2006) classification

Table 1 Pollutants and sectors considered in the Chilean inventoryINEMA) according to IPCC (2006) classification

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Figure 1 Continental Chile highlighting three macrozones defined for the paper, namely the northern (green), central (blue) of Magellan where a tundra-type climate predominates (Sarricolea et al., 2017). and southern zone (red). Divisions within each macrozone indicate limits of the 16 administrative regions.
Figure 1 Continental Chile highlighting three macrozones defined for the paper, namely the northern (green), central (blue) of Magellan where a tundra-type climate predominates (Sarricolea et al., 2017). and southern zone (red). Divisions within each macrozone indicate limits of the 16 administrative regions.
Figure 2 Total fuelwood consumption on kilotonnes by region according to UACH (2013, red), CDT (2015, green) and
Figure 2 Total fuelwood consumption on kilotonnes by region according to UACH (2013, red), CDT (2015, green) and
Figure 3 Map of Santiago at different scales: in blue the utilized 0.01° x 0.01° grid (approximately 1x1 km), and in black t
Figure 3 Map of Santiago at different scales: in blue the utilized 0.01° x 0.01° grid (approximately 1x1 km), and in black t
NH3 and CH, emissions and for any future study on these species, these sectors would need to be included. included in this inventory and therefore not reflected in emissions of CH4 nor NH3. In general, the agriculture sector dominates
NH3 and CH, emissions and for any future study on these species, these sectors would need to be included. included in this inventory and therefore not reflected in emissions of CH4 nor NH3. In general, the agriculture sector dominates
Table 2 Average total emissions from Energy, Industry, Residential, and Transportation sectors [in kilotones] in the 2015- part of the country, consistent with a larger mining activity.
Table 2 Average total emissions from Energy, Industry, Residential, and Transportation sectors [in kilotones] in the 2015- part of the country, consistent with a larger mining activity.
Figure 5 Spatial distribution of the 2017 emissions of particulate matter 2.5 of the Energy (red), Industry (green), Residential (blue) and Transportation (purple) sectors in a grid of 0.01 ° x 0.01 ° on a map of Chile according to the macrozones defined for the country (Figure 1). Note that the Metropolitan region includes the capital Santiago and ~ 40% of the population. The pie charts indicate the relative contribution that each source makes to total PM2.5 emissions in each region.
Figure 5 Spatial distribution of the 2017 emissions of particulate matter 2.5 of the Energy (red), Industry (green), Residential (blue) and Transportation (purple) sectors in a grid of 0.01 ° x 0.01 ° on a map of Chile according to the macrozones defined for the country (Figure 1). Note that the Metropolitan region includes the capital Santiago and ~ 40% of the population. The pie charts indicate the relative contribution that each source makes to total PM2.5 emissions in each region.
Figure 6 Spatial distribution of the 2017 emissions of PM2.s5 of the Energy (red), Industry (green), Residential (blue) and Transportation (purple) sectors in a grid of 0.01 x 0.01 degrees (approximately 1x1 km) for Mejillones (a), Santiago (b) and Temuco and Padre Las Casas Cities (c). These cities are located in the Northern, Central and Southern zones, respectively. Temuco and Padre Las Casas Cities (c). These cities are located in the Northern, Central and Southern zones, respectively.
Figure 6 Spatial distribution of the 2017 emissions of PM2.s5 of the Energy (red), Industry (green), Residential (blue) and Transportation (purple) sectors in a grid of 0.01 x 0.01 degrees (approximately 1x1 km) for Mejillones (a), Santiago (b) and Temuco and Padre Las Casas Cities (c). These cities are located in the Northern, Central and Southern zones, respectively. Temuco and Padre Las Casas Cities (c). These cities are located in the Northern, Central and Southern zones, respectively.
Figure 7 Emissions of CH4, CO, CO2x, NMVOC, VOC, NOx, PM2,s for 2015 reported by EDGAR v5.0 (purple) relative to the present work (INEMA; pink). EDGAR emissions were normalized by the magnitude of the current INEMA inventory. as highlighted by different CO2 emissions might also explain part of the difference. EDGAR VOC transport emissions are larger, due to evaporation emissions, which are not considered on INEMA inventory
Figure 7 Emissions of CH4, CO, CO2x, NMVOC, VOC, NOx, PM2,s for 2015 reported by EDGAR v5.0 (purple) relative to the present work (INEMA; pink). EDGAR emissions were normalized by the magnitude of the current INEMA inventory. as highlighted by different CO2 emissions might also explain part of the difference. EDGAR VOC transport emissions are larger, due to evaporation emissions, which are not considered on INEMA inventory
Figure 8 Total 2015 emissions in kilotonnes by pollutant and sectors according to this work and EDGAR v5.0. Sector: | considered in both inventories correspond to the classification proposed in IPCC (2006) presented in section 2.
Figure 8 Total 2015 emissions in kilotonnes by pollutant and sectors according to this work and EDGAR v5.0. Sector: | considered in both inventories correspond to the classification proposed in IPCC (2006) presented in section 2.
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 PM25 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 SO, have lower uncertainty ranges (Fig 9a) due to the greater consensus on their EFs 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 SO 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. The final EF’s used in this study results from aggregating several EF each one of which corresponds to specific emission
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 PM25 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 SO, have lower uncertainty ranges (Fig 9a) due to the greater consensus on their EFs 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 SO 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. The final EF’s used in this study results from aggregating several EF each one of which corresponds to specific emission
} aggregated EFs and thus the magnitude of the estimated emissions (EMEP / EEA, 2016) Figure 10 Components that determine the final uncertainty of residential emission estimations
} aggregated EFs and thus the magnitude of the estimated emissions (EMEP / EEA, 2016) Figure 10 Components that determine the final uncertainty of residential emission estimations
5 Table Al emission factors for the residential sector by technology (appliance type), humidity and operation device conditions
5 Table Al emission factors for the residential sector by technology (appliance type), humidity and operation device conditions
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