Emission strength validation using four-dimensional data assimilation: Application to primary aerosol and precursors to ozone and secondary aerosol

Three-dimensional air quality models (AQMs) represent the most powerful too l to follow the dynamics of air pollutants at urban and regional scales. Cu rrent AQMs can account for the complex interactions between gasphase chemis try, aerosol growth, cloud and scavenging processes, and transport. However , errors in model applications still exist due in part to limitations in th e models themselves and in part to uncertainties in model inputs. Four-dime nsional data assimilation (FDDA) can be used as a top-down tool to validate several of the model inputs, including emissions inventories, based on amb ient measurements. Previously, this FDDA technique was used to estimate adj ustments in the strength and composition of emissions of gas-phase primary species and O-3 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 appro ach consists of an iterative optimization procedure in which an AQM is coup led to an inverse model, and adjusting the emissions minimizes the differen ce between ambient measurements and model-derived concentrations. Here, the FDDA technique was applied to two episodes, with the modeling domain cover ing the eastern United States, to derive emission adjustments of domainwide sources of NOx, volatile organic compounds (VOCs), CO, SO2, NH3, and fine organic aerosol emissions. Ambient measurements used include gas-phase inor ganic and organic species and speciated fine PM. Results for the base-case inventories used here indicate that emissions of SO2 and CO appear to be es timated reasonably well (requiring minor revisions), while emissions of NOx , VOC, NH3, and organic PM with aerodynamic diameter less than 2.5 mum (PM2 .5) require more significant revision.