Quantification of ATOFMS data by multivariate methods

Aerosol time-of-flight mass spectrometry (ATOFMS) is capable of measuring t he sizes and chemical compositions of individual polydisperse aerosol parti cles in real time. A qualitative estimate of the particle composition is ac quired in the form of a mass spectrum that must be subsequently interpreted in order to draw conclusions regarding atmospheric relevance. The actual p roblem involves developing a calibration that allows the mass spectral data to be transformed into estimates of the composition of the atmospheric aer osol. A properly calibrated ATOFMS system should be able to quantitatively determine atmospheric concentrations of various species. Ideally, it would be able to accomplish this more rapidly, accurately, with higher size and t ime resolution, and at a far lower marginal cost than the manual sampling m ethods that are currently employed. Attempts have already been made at usin g ATOFMS and similar techniques to extract the bulk chemical species concen tration present in an ensemble of particles. This study represents the use of a multivariate calibration method, two-dimensional partial least-squares analysis, for calibrating single-particle mass spectral data. The method p resented here is far less labor-intensive than the univariate methods attem pted to date and allows for less observer bias. Because of the labor saving s, this is also the most comprehensive calibration performed to date, resul ting in the quantification of 44 different chemical species.