The meteorological and chemical processes influencing UK air quality investigated using satellite observations and modelling

Poor UK air quality has important social and economic impacts with ~29,000 premature deaths and costs to society of ~£20 billion, annually. It is important to understand the controlling factors and be able to forecast it. Therefore, the operational UK Met Office Air Quality in the Unified Model (AQUM), a short-range forecast model of atmospheric chemistry and aerosols, has been designed to predict hazardous air quality events. This study presents the first evaluation of AQUM using satellite observations of trace gases. These satellite observations, in comparison with AQUM, have also been used to investigate the influence of synoptic weather on UK air quality.
Satellite data are prone to large random, systematic and smoothing errors. An algorithm has been developed to calculate and reduce the random error component of time-averaged Differential Optical Absorption Spectroscopy (DOAS) NO2 retrievals. It reduces the time-averaged tropospheric NO2 errors by 30-70% through the cancellation of random errors, which allows for a more critical evaluation of regional models.
Comparisons with the processed observations of tropospheric column NO2 for 2006 show that AQUM overestimates column NO2 over northern England and Scotland in summer and across the domain in winter. Sensitivity experiments suggest that the model’s treatment of NOx point source (power station) emissions and missing N2O5 heterogeneous chemistry are the cause of these column NO2 overestimations.
Satellite column NO2 composited under daily classifications of UK synoptic weather indicate that unstable (stable) cyclonic (anticyclonic) conditions lead to column NO2 transport (accumulation) away from (over) UK source regions. Wind direction influences column NO2 as source region leeward transport can be detected from space. AQUM, composited using the same methodology, successfully captures these air quality - synoptic weather relationships, giving confidence in its ability to forecast air pollution under different synoptic conditions.