Forecasting system for urban air quality with automatic correction and web service for public dissemination

Abstract

This article describes the forecasting system urbanAQF, which incorporates several developments to deal with the complexities of air pollution in cities, including the adaptation of high-resolution numerical weather prediction data to the urban canopy, the coupling with regional forecast data, and an interactive web service for public dissemination of urban air quality information. The system applies a unique bias correction algorithm that adjusts boundary conditions and traffic emissions to observations of the previous days. An evaluation of the air quality forecasts during 2021 for Hamburg, Germany, against a comprehensive dataset of the administrative monitoring network and meteorological data, demonstrated the system’s capability to describe space and time variations of NO2 and PM10. At traffic sites, the high number of missed alerts in relation to exceedance of the daily mean limit for NO2 indicates the need to improve the simulation of traffic emissions. The forecast of PM2.5 alerts was affected by the time lag of the automatic correction, leading to a low number of correct alerts. The overall performance for O3 was very good, despite frequent false alarms connected to the prediction of unstable atmospheric conditions. The urbanAQF system empowers policymakers to implement effective measures for improving air quality in cities.

Keywords

• Urban air pollution
• air quality forecasting
• air quality index
• chemistry transport models
• ozone alerts