Browsing by Author "Lopez Noreña, Ana"

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    CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories (resumen)
    (2024-06-22) Berna Peña, Lucas; Lopez Noreña, Ana; Puliafito, Enrique
    Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologi- cally consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific infor- mation and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1 ◦ 0.1 × ◦ ) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB- ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff (36.7 Gt) and CO2bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions gener- ally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations.
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    High-resolution seasonal and decadal inventory of anthropogenic gas-phase and particle emissions for Argentina (resumen)
    (2021-10-01) Puliafito, Enrique; Bolaño Ortíz, Tomás; Berna, Lucas; Pascual Flores, Romina; Urquiza, Josefina; Lopez Noreña, Ana; Tames, María
    This work presents the integration of a gas-phase and particulate atmospheric emission inventory (AEI) for Argentina in high spatial resolution (; approx. 2.5 km×2.5 km) considering monthly variability from 1995 to 2020. The new inventory, called GEAA-AEIv3.0M, includes the following activities: energy production, fugitive emissions from oil and gas production, industrial fuel consumption and production, transport (road, maritime, and air), agriculture, livestock production, manufacturing, residential, commercial, and biomass and agricultural waste burning. The following species, grouped by atmospheric reactivity, are considered: (i) greenhouse gases (GHGs) – CO2, CH4, and N2O; (ii) ozone precursors – CO, NOx (NO+NO2), and non-methane volatile organic compounds (NMVOCs); (iii) acidifying gases – NH3 and SO2; and (iv) particulate matter (PM) – PM10, PM2.5, total suspended particles (TSPs), and black carbon (BC). The main objective of the GEAA-AEIv3.0M high-resolution emission inventory is to provide temporally resolved emission maps to support air quality and climate modeling oriented to evaluate pollutant mitigation strategies by local governments. This is of major concern, especially in countries where air quality monitoring networks are scarce, and the development of regional and seasonal emissions inventories would result in remarkable improvements in the time and space chemical prediction achieved by air quality models. Despite distinguishing among different sectoral and activity databases as well as introducing a novel spatial distribution approach based on census radii, our high-resolution GEAA-AEIv3.0M shows equivalent national-wide total emissions compared to the Third National Communication of Argentina (TNCA), which compiles annual GHG emissions from 1990 through 2014 (agreement within ±7.5 %). However, the GEAA-AEIv3.0M includes acidifying gases and PM species not considered in TNCA. Temporal comparisons were also performed against two international databases: Community Emissions Data System (CEDS) and EDGAR HTAPv5.0 for several pollutants; for EDGAR it also includes a spatial comparison. The agreement was acceptable within less than 30 % for most of the pollutants and activities, although a >90 % discrepancy was obtained for methane from fuel production and fugitive emissions and >120 % for biomass burning. Finally, the updated seasonal series clearly showed the pollution reduction due to the COVID-19 lockdown during the first quarter of year 2020 with respect to same months in previous years.
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    Influence of emission inventory resolution on the modeled spatio-temporal distribution of air pollutants in Buenos Aires, Argentina, using WRF-Chem (resumen)
    (2021-11-01) Lopez Noreña, Ana; Berná, Lucas; Tames, Florencia; Puliafito, Enrique; Fernandez, Rafael
    The temporal and spatial resolution of the emission inventory included into an air quality model plays a key role in the appropriate representation of air pollution events and background atmospheric chemistry. Here, we use the Weather Research and Forecasting coupled with Chemistry (WRF-Chem v4.0) model to perform highresolution air quality simulations over the city of Buenos Aires, Argentina, with two different anthropogenic emissions datasets: the High-resolution Emissions Inventory of Argentina (GEAA-AEI) and the Emissions Database for Global Atmospheric Research - Hemispheric Transport of Air Pollution (EDGAR-HTAP). A local optimized configuration considering 3 nested domains with a horizontal grid size of 20 × 20 km, 4 × 4 km, and 1.3 × 1.3 km and the MOZART chemical scheme was used. The model performance for NO2, PM10, PM2.5, and O3 concentrations was validated against measurements from the existing air quality monitoring stations in the Buenos Aires Metropolitan Area (AMBA) during austral fall 2018. Our results show that the daytime concentrations of air pollutants are influenced by the shape and shift of the hourly emissions profile, especially for NO2 where the reduction in nighttime emissions decreased the mean model bias by ~50%. PM10 and PM2.5 generally satisfied the model performance criteria, but underestimation tended to occur in the GEAA-AEI simulations and overestimation for the EDGAR-HTAP case. Comparison with TROPOMI-derived tropospheric NO2 columns showed a high positive correlation (r > 0.75) and a positive bias. We found large discrepancies between the spatial distribution patterns of the simulations within the innermost high-resolution domain centered on AMBA, mostly in suburban areas where no observations are available. We propose additional monitoring sites to address such differences and determine the size and shape of the main pollutant plume. We conclude that high-resolution air quality modeling is important within underdeveloped or developing South American cities that lack continuous air quality measurements, as it represents a powerful tool in supporting the design of governmental monitoring networks and air pollution mitigation policies.