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

Abstract

Abstract. 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 × 1 km approximately). The pollutants included are CO2, NOx, SO2, CO, VOCs, 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. 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 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 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. 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.

PAPILA dataset: a regional emission inventory of reactive gases for South America based on the combination of local and global information

“…Although the latter improve the representativeness of the estimates, the groups that generate information on emissions in the region do not necessarily have the same objectives: some are mainly oriented towards the generation of input information for models and others towards mitigation measures that respond to air pollution concerns of their region. In this sense, it is worth mentioning that although the resources in the region often limit their growth, the capacities of the groups are growing, which is partly reflected in the development of local databases published in this same special issue (Puliafito et al, 2021;Álamos et al, 2021;Osses et al, 2021). In addition to individual advances, we want to emphasize the role of the PAPILA project and the EMISA initiative, which promote collaboration between groups in the region, enhancing efforts aimed at the development of appropriate and consistent surface emission inventories.…”

Sources of Air Pollution Health Impacts and Co‐Benefits of Carbon Neutrality in Santiago, Chile

“…We aggregate new national emissions for Chile from Inventario Nacional de EMisiones Antropogénicas (INEMA) (Alamos et al., 2021) (Figure 1) from the native 1 × 1 km resolution (76°W–56.3°W, 66°S–17°S) to the same resolution of HTAP. In any 0.1° × 0.1° grid cell, for each precursor species we overwrite HTAP emissions with values from INEMA when an INEMA sector has non‐zero emissions.…”

“…This domain ranges from 36.5°S, 81.9°W to 1.8°N, 32.2°W and includes many of the most populous cities in South America. In this updated simulation, we incorporated new regional anthropogenic emissions inventories for Santiago (Mazzeo et al., 2018) and Chile (Alamos et al., 2021), and global anthropogenic emissions from HTAP v3 (Crippa et al., 2023). We evaluate the performance of these model updates in Santiago by comparing daily simulated pollutant concentrations to ground level observations for PM 2.5 , O 3 , and NO 2 .…”