Development and application of a robotic chemical mass balance model for source apportionment of atmospheric particulate matter

An advanced computational procedure is presented for the source apportionment (SA) of airborne particulate matter (PM) using chemical mass balance (CMB) receptor modeling. The so-called “Robotic Chemical Mass Balance” model (RCMB) minimizes personal judgment, by leading straight-forwardly to the best-fit combination of the source profiles that are included in a set of input data. RCMB involves application of an established least squares fitting method to every one of the possible combinations that can be made from the source profiles, without any human interference, in contrast with previous CMB modeling software. Any successful applications of the fitting method are automatically ranked according to performance measures, common in multiple linear regression (MLR). By maximizing an overall fitting index, the proposed computational procedure provides a unique solution to the conventional CMB problem, which cannot be questioned readily, unless additional information becomes available about the study area. This explicit advantage of RCMB is illustrated by a comparison with the original CMB analysis of the Crows, California PM2.5 data from the San Joaquin Valley Air Quality Study (SJVAQS).