A Probabilistic Evaluation of Surface Loading and Concentration as Metrics for Post Structural Fire Assessment Soot Sampling Data

Abstract

Surface sampling and laboratory analysis for soot/combustion particulates was conducted following a fire at an education/research facility in the southwest United States. This provided a bank of data by which to probabilistically evaluate the behavior of soot loading (counts/mm²) and relative soot concentration (percent ratio; %R) as useful metrics for quantifying differences in soot impact across a building. Surface tape sampling and analysis via light microscopy were conducted via industry standard protocols, and resulting data from various building zones were selected to construct various comparisons. The performance of counts/mm² and %R as metrics to identify differences in soot impact for each comparison was assessed by comparing inference generated by traditional Student’s t test, Mann Whitney U rank comparison (MW), and the directly calculated axiomatic probability associated with difference in detection (pΔf_d). The fourteen (14) comparisons in which a significant difference was inferred via pΔf_d was similarly indicated via Student’s t and/or MW in only four (4) instances. Further, approximately one half of the comparisons generated different inference via pΔf_d for counts/mm² and %R, with the former demonstrating better discriminatory ability. In broad view, the heuristic concept of comparing numerical “soot levels” (e.g., average) by either metric was not generally suitable for the distribution of the data. In contrast, pΔf_d avoids the statistical bias imposed by traditional statistical inference, and ultimately the efficacy of post fire comparative surface sampling is as dependent upon the metric and inference model utilized as it is on the sampling and laboratory analytical protocols.