A stochastic regression approach to analyzing thermodynamic uncertainty in chemical speciation modeling

Environmental Science and Technology

Volumn 40, Issue 12, 2006, Pages 3872-3878

  Weber, Christopher L ^a   Vanbriesen, Jeanne M ^a   Small, Mitchell S ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN MARKOV CHAIN MONTE CARLO (MCMC) ESTIMATION METHODS; CHELATING AGENT; CHEMICAL SPECIATION MODELING; UNCERTAINTY MODELS;

COMPUTER SIMULATION; DECISION MAKING; MONTE CARLO METHODS; PARAMETER ESTIMATION; REGRESSION ANALYSIS; STOCHASTIC CONTROL SYSTEMS; UNCERTAIN SYSTEMS;

CHEMICAL ENGINEERING;

ALUMINUM; CALCIUM ION; CHELATING AGENT; COBALT; CUPRIC ION; EDETIC ACID; FERRIC ION; INORGANIC COMPOUND; LEAD; MAGNESIUM ION; MANGANESE; METAL ION; NICKEL; RIVER WATER; ZINC ION;

CHEMICAL ENGINEERING; COMPUTER SIMULATION; DECISION MAKING; MONTE CARLO METHODS; PARAMETER ESTIMATION; REGRESSION ANALYSIS; STOCHASTIC CONTROL SYSTEMS; UNCERTAIN SYSTEMS;

BAYESIAN ANALYSIS; BIOAVAILABILITY; CHELATING AGENT; IONIC COMPOSITION; MARKOV CHAIN; MONTE CARLO ANALYSIS; REGRESSION ANALYSIS; RIVER WATER; SPECIATION (CHEMISTRY); STOCHASTICITY; THERMODYNAMICS; UNCERTAINTY ANALYSIS;

ARTICLE; BAYES THEOREM; BIOAVAILABILITY; CHEMICAL MODEL; DECISION MAKING; IONIC STRENGTH; MONTE CARLO METHOD; PROBABILITY; QUANTITATIVE ANALYSIS; REGRESSION ANALYSIS; STATISTICAL MODEL; STOCHASTIC MODEL; THERMODYNAMICS;

BAYES THEOREM; EDETIC ACID; MARKOV CHAINS; MODELS, CHEMICAL; MONTE CARLO METHOD; RIVERS; STOCHASTIC PROCESSES; THERMODYNAMICS; UNCERTAINTY;

EID: 33745170364     PISSN: 0013936X     EISSN: None     Source Type: Journal    
DOI: 10.1021/es0523035     Document Type: Article

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