Forecasting Sensitivities: Is Adaptive, Short-Term Air Quality Management a Viable Option?

Abstract

Air quality managers have traditionally relied on deterministic and statistical approaches for forecasting concentrations. In some parts of the world, concentration forecasts are used to trigger urgent and sometimes drastic preventative measures in the hope that severe pollution episodes are alleviated or avoided altogether. These preventative measures, however, are often applied rather indiscriminately to all emission sources without accounting for specific influence of each individual source on the forecast outcome. This blanket approach fails to improve air quality in a cost-effective manner. Adjoint sensitivity analysis approach provides a unique framework for efficient forecast of the impact of individual sources on short-term air quality. We demonstrate how this method can be used for short-term emission behavior modification resulting in amelioration of pollution episodes. © Springer Science+Business Media Dordrecht 2014.