Sub-city scale hourly air quality forecasting by combining models,
satellite observations, and ground measurements
Abstract
While multiple information sources exist concerning surface-level air
pollution, no individual source simultaneously provides large-scale
spatial coverage, fine spatial and temporal resolution, and high
accuracy. It is therefore necessary to integrate multiple data sources,
using the strengths of each source to compensate for the weaknesses of
others. In this paper, we propose a method incorporating outputs of
NASA’s GEOS Composition Forecasting model system with satellite
information from the TROPOMI instrument and ground measurement data on
surface concentrations. Although we use ground monitoring data from the
EPA network in the continental United States (US), the model and
satellite data sources used have the potential to allow for global
application. This method is demonstrated using surface measurements of
nitrogen dioxide as a test case in regions surrounding five major US
cities. The proposed method is assessed through cross-validation against
withheld ground monitoring sites. In these assessments, the proposed
method demonstrates major improvements over two baseline approaches
which use ground-based measurements only. Results also indicate the
potential for near-term updating of forecasts based on recent ground
measurements.