Development and Application of a Supervised Pattern Recognition Algorithm for Identification of Fuel-Specific Emissions Profiles

Wildfires have increased in frequency, duration and size in the western United States (U.S.) over the past decades. These trends are projected to continue, with negative consequences for air quality across the U.S. Wildfires emit large quantities of particles and gases that serve as air pollutants and their precursors, and can lead to severe air quality conditions over large spatial and long temporal scales. Characterization of the chemical constituents in smoke as a function of combustion conditions, fuel type, and fuel component is an important step towards improving the prediction of air quality effects from fires and evaluating mitigation strategies. Building on the comprehensive characterization of gaseous non-methane organic compounds (NMOCs) identified in laboratory and field studies, a supervised pattern recognition algorithm was developed that successfully identified unique chemical speciation profiles among similar fuel types common in western coniferous forests. The algorithm was developed using laboratory data from single fuel species and tested on simplified synthetic fuel mixtures. The fuel types in the synthetic mixtures were differentiated but as the relative mixing proportions became more similar, the differentiation became poorer. Using the results from the pattern recognition algorithm, a classification model based on linear discriminant analysis was trained to differentiate smoke samples based on the contribution(s) of dominant fuel type(s). The classification model was applied to field data and despite the complexity of contributing fuels, and the presence of fuels "unknown" to the classifier, the dominant sources/fuel types were identified correctly. The pattern recognition and classification algorithms are a promising approach for identifying the types of fuels contributing to smoke samples and facilitating selection of appropriate chemical speciation profiles for predictive air quality modeling, using a highly reduced suite of measured NMOCs. Utility and performance of the pattern recognition and classification algorithms can be improved by expanding the training and test sets to include data from a broader range of single and mixed fuel types.

Trace-element XAFS sensitivity: a stress test for a new XRF multi-detector

X-ray absorption fine-structure (XAFS) spectroscopy can assess the chemical speciation of the elements providing their coordination and oxidation state, information generally hidden to other techniques. In the case of trace elements, achieving a good quality XAFS signal poses several challenges, as it requires high photon flux, counting statistics and detector linearity. Here, a new multi-element X-ray fluorescence detector is presented, specifically designed to probe the chemical speciation of trace 3d elements down to the p.p.m. range. The potentialities of the detector are presented through a case study: the speciation of ultra-diluted elements (Fe, Mn and Cr) in geological rocks from a calcareous formation related to the dispersal processes from Ontong (Java) volcanism (mid-Cretaceous). Trace-elements speciation is crucial in evaluating the impact of geogenic and anthropogenic harmful metals on the environment, and to evaluate the risks to human health and ecosystems. These results show that the new detector is suitable for collecting spectra of 3d elements in trace amounts in a calcareous matrix. The data quality is high enough that quantitative data analysis could be performed to determine their chemical speciation.