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• W4317103711 endingPage "161675" @default.
• W4317103711 startingPage "161675" @default.
• W4317103711 abstract "The impact of aviation on climate change is reflected in increasing emissions of CO2 and other pollutants from fuel burning emitted at high altitudes, representing 2.9 % of total Greenhouse gases (GHG) emissions in 2019. However, mitigations options for decarbonization of aviation are difficult to implement given operational safety, technology maturity, energy density and other constraints. One alternative for mitigation is the use of certified sustainable aviation fuel (SAF) with lower carbon intensity than conventional jet fuel (CJF). This research presents an inventory of Argentine civil aviation emissions for its domestic and international flights, and analyzes the possibility of supplying SAF as a mitigation strategy given its abundant biomass production. Argentine aviation activity is presented as a monthly 4D (latitude, longitude, altitude and time) spatial inventory for the interval 2001-2021, based on origin and destination city pairs, aircraft types and airlines. Fuel consumption and pollutant emissions were calculated for landing-and-take-off and cruise phases. Monthly domestic ranged from 67 to 179 kt CO2eq (2001-2019). Annual peak values occurred in 2019 consuming 560 kt CJF and direct emitting of 1.77 Mt CO2eq. While Revenue-Passenger-Kilometer (RPK) grew almost 4 times (4.18 × 109 in 2001 to 16.42 × 109 in 2019), the number of flights changed only 1.5 times (from 98,000 in 2002 to 152,000 in 2019). The main efficiency indexes varied from 97 t CJF/RPK, 308 gCO2eq/RPK to 34 t CJF/RPK, 107 gCO2eq/RPK between 2001 and 2019, respectively, showing an average annual improvement of 3.5 % due to partial fleet renewal, especially from 2015 onwards. Emissions of other pollutants for 2019 reached total values of CO 14.14 kt; NOx 6.77 kt; PM tot 55.12 kt. For the period 2001-2019, international aviation consumed between 1 Mt - 1.5 Mt CJF, directly emitting between 3.30 and 4.80 Mt of CO2eq; RPKs went from 6.234 × 109 to 20.524 × 109; the efficiency indices ranged from 529 to 240 gCO2eq/RPK. The most important changes occurred with an optimization of routes and number of flights and the replacement of the four-engines (B747, A380) by more efficient twin-engines (B777, A330) aircraft. Argentina is not required to any offsetting regulatory program due to its small aviation market (approx. 0.22 % global market in 2019), nor has to date certified SAF production pathways, nevertheless it has potential for SAF availability based on actual biofuels production (ethanol, biodiesel and soybean oil) and biomass feedstock's existences. In this sense this studies proposes that 2019 domestic fuel consumption could be supplied using 79 % exportable amounts of sugarcane ethanol (257 ± 53 kt) (by Ethanol to Jet ETJ) and 34 % of exportable soybean oil (1079 ± 160 kt) (by hydroprocessed esters and fatty acids- HEFA) pathways. For this scenario average GHG emissions reached 1.321 ± 0.115 Mt CO2eq; which would imply a 62 % of the current emission value using CJF (2.17Mt CO2eq), or savings of about 838 kt CO2eq (38 %). At the 2019 level of harvest and biofuel production, up to 1.4 Mt of SAF could be produced from sugarcane ethanol/ETJ and soybean oil/HEFA mitigating up to 1.8 MtCO2eq. A 35 kt CO2eq annual sectoral national mitigation strategy could be reached by using 14 kt of SAF." @default.
• W4317103711 created "2023-01-18" @default.
• W4317103711 creator A5026732168 @default.
• W4317103711 date "2023-04-01" @default.
• W4317103711 modified "2023-10-06" @default.
• W4317103711 title "Civil aviation emissions in Argentina" @default.
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• W4317103711 cites W1987637049 @default.
• W4317103711 cites W2002421326 @default.
• W4317103711 cites W2004583029 @default.
• W4317103711 cites W2008098614 @default.
• W4317103711 cites W2018092837 @default.
• W4317103711 cites W2036065656 @default.
• W4317103711 cites W2049753235 @default.
• W4317103711 cites W2052372268 @default.
• W4317103711 cites W2060852319 @default.
• W4317103711 cites W2067246174 @default.
• W4317103711 cites W2068580013 @default.
• W4317103711 cites W2076722068 @default.
• W4317103711 cites W2079086890 @default.
• W4317103711 cites W2092126244 @default.
• W4317103711 cites W2093720814 @default.
• W4317103711 cites W2098489838 @default.
• W4317103711 cites W2104649576 @default.
• W4317103711 cites W2110604437 @default.
• W4317103711 cites W2116063627 @default.
• W4317103711 cites W2116508262 @default.
• W4317103711 cites W2121690346 @default.
• W4317103711 cites W2124008752 @default.
• W4317103711 cites W2124170107 @default.
• W4317103711 cites W2148647412 @default.
• W4317103711 cites W2151056285 @default.
• W4317103711 cites W2160031550 @default.
• W4317103711 cites W2200075590 @default.
• W4317103711 cites W2259084702 @default.
• W4317103711 cites W2311063823 @default.
• W4317103711 cites W2413227257 @default.
• W4317103711 cites W2496851420 @default.
• W4317103711 cites W2508171124 @default.
• W4317103711 cites W2594580073 @default.
• W4317103711 cites W2596998468 @default.
• W4317103711 cites W2599875507 @default.
• W4317103711 cites W2600399632 @default.
• W4317103711 cites W2770606259 @default.
• W4317103711 cites W2896892829 @default.
• W4317103711 cites W2911854501 @default.
• W4317103711 cites W2915352471 @default.
• W4317103711 cites W2950773247 @default.
• W4317103711 cites W2955480378 @default.
• W4317103711 cites W2979653715 @default.
• W4317103711 cites W3015685167 @default.
• W4317103711 cites W3043649933 @default.
• W4317103711 cites W3081879483 @default.
• W4317103711 cites W3115937518 @default.
• W4317103711 cites W3132009674 @default.
• W4317103711 cites W3133512799 @default.
• W4317103711 cites W3139337658 @default.
• W4317103711 cites W3146318241 @default.
• W4317103711 cites W3158954337 @default.
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• W4317103711 doi "https://doi.org/10.1016/j.scitotenv.2023.161675" @default.
• W4317103711 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36669658" @default.
• W4317103711 hasPublicationYear "2023" @default.
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