Augmentation of field fluorescence measures for improved in situ contaminant detection.

This research proposes a new method that fuses data from the field and lab-based optical measures coupled with machine learning algorithms to quantify the concentrations of toxic contaminants found in fuels and oil sands process-affected water. Selected pairs of excitation/emission intensities at key wavelengths are inputs to an augmentation neural network (NN), trained using lab-based measurements, that generates synthetic high-resolution spectra. Then, an image processing NN is used to estimate the contaminant concentrations from the spectra generated from a few key wavelengths. The presented approach is tested using naphthenic acids, phenol, fluoranthene and pyrene spiked into natural waters. The spills or loss of containment of these contaminants represent a significant risk to the environment and public health, requiring accurate and rapid detection methods to protect the surrounding aquatic environment. Results were compared with models based on only the corresponding peak intensities of each contaminant and with an image processing NN using the original spectra. Naphthenic acids, fluoranthene and pyrene were easy to detect by all methods; however, performance for more challenging signals to identify, such as phenol, was optimized by the proposed method (peak picking with mean absolute error (MAE) of 30.48 µg/L, generated excitation-emission matrix with MAE of 8.30 µg/L). Results suggested that data fusion and machine learning techniques can improve the detection of contaminants in the aquatic environment at environmentally relevant concentrations.