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A Probabilistic Model For Assessing Time-Varying Contaminant Levels.
Charles Stark Draper Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts :26 pages
A mathematical model was developed for a probabilistic method to assess airborne contaminants. The analysis was based on terminology appropriate to time inference analysis and applied for application to a wide variety of problems. The Poisson process was used to develop equations for discrete probabilities. The equations were developed for the density of probabilities of infinite time versus infinite time. The Poisson process was then generalized to a continuous function; a Poisson wave with the pulses having unit area. Proportionality was considered. Means and variance for the Poisson wave were developed. The probability density for temporal sampling was derived. Equations for the measurement of the autocorrelation function were developed. The author concludes that the mathematical abstraction of the model concept allows a wide range of applications. The model gives the probability of obtaining a particular measurement in a specified time interval given another measurement in a different time interval.
NIOSH-Contract; Safety-research; Analytical-models; Aerosol-particles; Mathematical-models; Air-quality; Exposure-levels; Industrial-medicine; Contract-HSM99-72-18;
NTIS Accession No.
Charles Stark Draper Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
Page last reviewed: February 11, 2022
Content source: National Institute for Occupational Safety and Health Education and Information Division