Emission Strength Validation Using Four-Dimensional Data Assimilation: Application to Primary Aerosol and Precursors to Ozone and Secondary Aerosol

ABSTRACT

Three-dimensional air quality models (AQMs) represent the most powerful tool to follow the dynamics of air pollutants at urban and regional scales. Current AQMs can account for the complex interactions between gas-phase chemistry, aerosol growth, cloud and scavenging processes, and transport. However, errors in model applications still exist due in part to limitations in the models themselves and in part to uncertainties in model inputs. Four-dimensional data assimilation (FDDA) can be used as a top-down tool to validate several of the model inputs, including emissions inventories, based on ambient measurements. Previously, this FDDA technique was used to estimate adjustments in the strength and composition of emissions of gas-phase primary species and O₃ precursors.

In this paper, we present an extension to the FDDA technique to incorporate the analysis of particulate matter (PM) and its precursors. The FDDA approach consists of an iterative optimization procedure in which an AQM is coupled to an inverse model, and adjusting the emissions minimizes the difference between ambient measurements