# Emission Impacts from Sustainable Aviation Fuel Blends via Engine Plume Measurements and Predictive Modeling at the Airport Scale

## TL;DR

This study shows that using a 34% sustainable aviation fuel blend reduces harmful emissions during taxiing, improving air quality and supporting model development for real-world conditions.

## Contribution

The study provides real-world emission data from a 34% HEFA SAF blend under operational flight conditions, validating predictive models.

## Key findings

- SAF use reduced total particle emissions by about 10% and non-volatile particle emissions by about 40%.
- Field data from real flights supports the development and validation of predictive emission models.
- The study confirms the air quality benefits of SAF under real-world airport operations.

## Abstract

Sustainable aviation fuels (SAFs) are receiving increasingly
high
attention by science, industry, and politics as they are considered
an effective tool to reduce the impact of aviation on the climate
system and air quality. While numerous experimental studies on the
effects of SAFs were performed, these are often limited to specific
test scenarios or engine measurements. This work combines predictions
based on the DLR SimFuel platform with field data from the first campaign
to investigate a 34% hydrotreated esters and fatty acids (HEFA) SAF
blend under real-world operating conditions during regular passenger
flights. For this purpose, an Airbus A320-251N flying between Copenhagen
and Arlanda was fueled with conventional Jet A-1 for 30 flights during
1 week and with a 34% HEFA SAF blend for 85 flights in 2 weeks. The
corresponding exhaust gas plumes during taxiing were analyzed by the
DLR mobile lab. Equipped with state-of-the-art instruments, this analysis
contains total and non-volatile particle number concentrations and
size distribution, gas analytics (CO2 and NO
x
), and weather parameters. The results confirm the
beneficial effects of SAF usage toward the air quality by reducing
total particle emissions by about 10% and non-volatile particle emissions
by about 40%. Also, this data set obtained under real-world conditions
provides a valuable basis for model development and validation.