Inverse air pollution modelling of urban-scale carbon monoxide emissions

Abstract

A new recursive least-squares technique is developed to give spatial and temporal definition to the adjustments necessary in an emission inventory, to fit ambient concentration observations optimally. The CIT Photochemical Airshed Model is used to compute CO concentration distributions arising from 29 separate source domains in the South Coast Air Basin of California. A Kalman filter integrated within the model matches predictions with CO observations at 27 locations by superposing the computed distributions with optimal weighting factors. The filter structure allows control of the extent to which adjusted emission inventories are allowed to deviate from a base-case, which already has high spatial and temporal definition. Applied to the Southern California Air Quality Study, 27–29 August 1987, strong temporal dependence was noted in the necessary adjustment to the available CO emission inventory, with a peak factor of 3.0 at midday on weekdays. The spatial resolution of the technique revealed new high-emission zones for CO in a corridor between Pasadena and San Bernardino, in the Riverside-Corona area, and along the Pacific coast on Saturday. In this first such application to an urban environment, some success was also achieved in correcting the phasing of emissions for errors arising from the neglect of source-receptor lags in the inverse modelling technique.