This paper reports a systematic assessment of the information content of remote sensing measurements with regard to the chemical composition of aerosols. The focus is on ground-based, airborne, or space-borne lidar observations. The results are exploited in an air-quality forecasting model by incorporating measurements into the model in such a way that one maximises the use of available information. The goal of this work is to improve air-quality forecasting for suspended particulate matter.