Quantification of ATOFMS data by multivariate methods

Aerosol time-of-flight mass spectrometry (ATOFMS) is capable of measuring the sizes and chemical compositions of individual polydisperse aerosol particles in real time. A qualitative estimate of the particle composition is acquired in the form of a mass spectrum that must be subsequently interpreted in order to draw conclusions regarding atmospheric relevance. The actual problem involves developing a calibration that allows the mass spectral data to be transformed into estimates of the composition of the atmospheric aerosol. 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 time resolution, and at a far lower marginal cost than the manual sampling methods that are currently employed. Attempts have already been made at using ATOFMS and similar techniques to extract the bulk chemical species concentration 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 presented here is far less labor-intensive than the univariate methods attempted to date and allows for less observer bias. Because of the labor savings, this is also the most comprehensive calibration performed to date, resulting in the quantification of 44 different chemical species.