Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation

Abstract

The ability of regional air quality models to skilfully represent pollutant distributions throughout the atmospheric column is important to enabling their skilful prediction at the surface. This provides a requirement for model evaluation at elevated altitudes, though observation datasets available for this purpose are limited. This is particularly true of those offering sampling over extended time periods. To address this requirement and support evaluation of regional air quality models such as the UK Met Offices Air Quality in the Unified Model (AQUM), a long-term, quality-assured dataset of the three-dimensional distribution of key pollutants was collected over the southern United Kingdom from July 2019 to April 2022. Measurements were collected using the Met Office Atmospheric Survey Aircraft (MOASA), a Cessna 421 instrumented for this project to measure gaseous nitrogen dioxide, ozone, sulfur dioxide and fine-mode (PM2.5) aerosol. This paper introduces the MOASA measurement platform, flight strategies and instrumentation and is not intended to be an in-depth diagnostic analysis but rather a comprehensive technical reference for future users of these data. The MOASA air quality dataset includes 63 flight sorties (totalling over 150 h of sampling), the data from which are openly available for use. To illustrate potential uses of these upper-air observations for regional-scale model evaluation, example case studies are presented, which include analyses of the spatial scales of measured pollutant variability, a comparison of airborne to ground-based observations over Greater London and initial work to evaluate performance of the AQUM regional air quality model. These case studies show that, for observations of relative humidity, nitrogen dioxide and particle counts, natural pollutant variability is well observed by the aircraft, whereas SO2 variability is limited by instrument precision. Good agreement is seen between observations aloft and those on the ground, particularly for PM2.5. Analysis of odd oxygen suggests titration of ozone is a dominant chemical process throughout the column for the data analysed, although a slight enhancement of ozone aloft is seen. Finally, a preliminary evaluation of AQUM performance for two case studies suggests a large positive model bias for ozone aloft, coincident with a negative model bias for NO2 aloft. In one case, there is evidence that an underprediction in the modelled boundary layer height contributes to the observed biases at elevated altitudes.