Record Display for the EPA National Library Catalog

RECORD NUMBER: 4 OF 10

OLS Field Name OLS Field Data
Main Title General Purpose Univariate Probability Model for Environmental Data Analysis.
Author Ott, Wayne R. ; Mage., David T. ;
CORP Author Environmental Protection Agency, Washington, D.C. Quality Assurance Div.
Year Published 1976
Report Number EPA/600/J-76/037;
Stock Number PB-265 306
Additional Subjects Mathematical models ; Water quality ; Air quality ; Data analysis ; Decision making ; Water pollution ; Air pollution ; Sulfur dioxide ; Ozone ; Carbon monoxide ; Particles ; Hydrocarbons ; Nitrogen dioxide ; Chlorides ; Sulfates ; Computerized simulation ; Coliform bacteria ; Diffusion ; Numerical analysis ; Biochemical oxygen demand ; monitoring ; Atmospheric composition ; Atmospheric composition ; Concentration(Composition) ; Law enforcement ; Assessments ; LN3C model
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB-265 306 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 06/23/1988
Collation 10p
Abstract
Analysis of environmental quality data for decision making purposes (evaluation of compliance with standards, examination of environmental trends, determination of confidence intervals) generally requires a suitable univariate probability model. It sometimes is difficult, when many probability models are available, to select the most appropriate one for a given data set. The underlying physical laws which generate pollutant concentrations--diffusion processes--offer insight into which model may be most appropriate for a variety of situations. Treating the diffusion equation as a stochastic differential equation, the time series of pollutant concentration data from diffusion phenomena is shown to have a distribution that is best approximated by the censored, 3-parameter lognormal probability model (LN3C). The model is applied to 10 air quality data sets (SO2, O3, CO, particulate, hydrocarbons, and NO2 from the United States, France, West Germany, and Denmark) and 9 water quality data sets (BOD, coliform, chloride, and sulfate from the Ohio River). The authors conclude that the LN3C probability model offers data analysts a superior, general purpose model suitable for a large variety of environmental phenomena.