1 research outputs found
Estimating a Representative Value and Proportion of True Zeros for Censored Analytical Data with Applications to Contaminated Site Assessment
This paper demonstrates a maximum
likelihood (ML)-based approach
to derive representative (“best guess”) contaminant
concentrations from data with censored values (e.g., less than the
detection limit). The method represents an advancement over existing
techniques because it is capable of estimating the proportion of measurements
that are true zeros and incorporating varying levels of censorship
(e.g., sample specific detection limits, changes through time in method
detection). The ability of the method to estimate the proportion of
true zeros is validated using precipitation data. The stability and
flexibility of the method are demonstrated with stochastic simulation,
a sensitivity analysis, and unbiasedness analysis including varying
numbers of significant digits. A key aspect of this paper is the application
of the statistical analysis to real site rock core contaminant concentration
data collected within a plume at two locations using high resolution
depth-discrete sampling. Comparison of the representative values for
concentrations at each location along the plume center-line shows
a larger number of true zeros and generally lower concentrations at
the downgradient location according to the conceptual site model,
leading to improved estimates of attenuation with distance and/or
time and associated confidence; this is not achievable using deterministic
methods. The practical relevance of the proposed method is that it
provides an improved basis for evaluating change (spatial, temporal,
or both) in environmental systems