A field portable, single exposure excitation-emission matrix (EEM) fluorometer has been constructed and used in conjunction with parallel factor analysis (PARAFAC) to determine the sub part per billion (ppb) concentrations of several aqueous polycyclic aromatic hydrocarbons (PAHs), such as benzo(k)fluoranthene and benzo(a)pyrene, in various matrices including aqueous motor oil extract and asphalt leachate. Multiway methods like PARAFAC are essential to resolve the analyte signature from the ubiquitous background in environmental samples. With multiway data and PARAFAC analysis it is shown that reliable concentration determinations can be achieved with minimal standards in spite of the large convoluting fluorescence background signal. Thus, rapid fieldable EEM analyses may prove to be a good screening method for tracking pollutants and prioritizing sampling and analysis by more complete but time consuming and labor intensive EPA methods.

Independently emerging fluorescence profiles of unknown, photochemically induced degradation products of several naturally non-fluorescent pesticides were monitored using single exposure excitation-emission fluorescence spectroscopy. Three-way parallel factor analysis (PARAFAC) was employed to uniquely resolve the pure fluorescent spectra of the overlapping photolysis products. The quantitative utility of EEM photolysis-based determinations was demonstrated by employing four-way PARAFAC models built from EEM time cubes of multiple fenvalerate samples. The 4-way PARAFAC models were then used to predict original pesticide concentrations resulting in conservative limit of detection and root mean square errors of calibration (RMSEC) of 3 microM each.

A field-portable, single-exposure excitation-emission matrix (EEM) fluorometer is used in conjunction with parallel factor analysis (PARAFAC) for sub-ppb polycyclic aromatic hydrocarbon (PAH) determinations in the presence of spectral interferents. Several strategies for bringing multiway calibration methods such as PARAFAC into the field were explored. It was shown that automated methods of PARAFAC model selection can be as effective as manual selection. In addition, it was found that there is not always a single best model to employ for prediction. Second, the effect that reducing data density by systematically decreasing calibration set size and spectral resolution has on PARAFAC speed and prediction accuracy was investigated. By decreasing data density, the computational intensity of the PARAFAC algorithm can be reduced to increase the plausibility of on-the-fly data analysis. It was found that reducing eight sample PAH calibration sets to two or three calibration standards significantly decreased computation intensity yet generated adequate predictions. It was also found that spectral resolution can be decreased to reach an optimal compromise between calibration accuracy and analysis speed while minimizing instrumental requirements.

The application of photocatalysis enhancement to calibration of fluorescence excitation-emission matrixes (EEMs) with parallel factor (PARAFAC) analysis is described. In this study, three- and four-way PARAFAC analysis was employed to extract the fluorescent species' spectra from overlapping EEMs. Time-dependent photocatalysis degradation of the polycyclic aromatic hydrocarbons (PAHs) was employed to create an additional dimension for analysis. The consequent four-dimension degradation-EEM data cubes have greater selectivity for each PAH than do three-dimension EEM data cubes alone. On a scale of 0 to 1, with 0 being completely collinear spectra and 1 being orthogonal spectra, including the time-dependent measurements increased the selectivity an average of 21%, from 0.73 to 0.87.

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