Junyu Zheng

Works (5)

Updated: July 5th, 2023 15:59

2006 journal article

Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models

JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY, 16(6), 491–506.

By: A. Mokhtari n, H. Frey  n & J. Zheng n

Contributors: A. Mokhtari n

author keywords: SHEDS models; sensitivity analysis; variance-based methods; sampling-based methods; risk assessment
MeSH headings : Analysis of Variance; Environmental Exposure / analysis; Environmental Pollutants / analysis; Fourier Analysis; Humans; Models, Theoretical; Pesticides / analysis; Risk Assessment; Sampling Studies; Sensitivity and Specificity; Statistics, Nonparametric; Stochastic Processes; United States; United States Environmental Protection Agency / standards
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2005 journal article

Quantitative analysis of variability and uncertainty with known measurement error: Methodology and case study

RISK ANALYSIS, 25(3), 663–675.

By: J. Zheng n & H. Frey  n

Contributors: J. Zheng n

author keywords: bootstrap pair; error-free data; measurement error; observed data; uncertainty; variability
TL;DR: A methodology for improving the characterization of variability and uncertainty with known measurement errors in data is demonstrated based on an observed data set, known measurement error, and a measurement-error model and a key finding is that total uncertainty in mean can be properly quantified even if measurement and random sampling errors cannot be separated. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2004 journal article

Quantification of variability and uncertainty using mixture distributions: Evaluation of sample size, mixing weights, and separation between components

RISK ANALYSIS, 24(3), 553–571.

By: J. Zheng n & H. Frey

Contributors: J. Zheng n

author keywords: bootstrap simulation; mixture distributions; parameter estimation; uncertainty; variability
TL;DR: To evaluate the robustness of using mixture distribution as a basis for estimating both variability and uncertainty, 108 synthetic data sets generated from selected population mixture log-normal distributions were investigated, and properties of variability and uncertainties estimates were evaluated with respect to variation in sample size, mixing weight, and separation between components of mixtures. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2002 journal article

Probabilistic analysis of driving cycle-based highway vehicle emission factors

ENVIRONMENTAL SCIENCE & TECHNOLOGY, 36(23), 5184–5191.

By: H. Frey  n & J. Zheng n

Contributors: H. Frey  n

MeSH headings : Air Pollutants / analysis; Atmospheric Pressure; Automobile Driving; Carbon Monoxide / analysis; Forecasting; Hydrocarbons / analysis; Models, Statistical; Nitrogen Oxides / analysis; Regression Analysis; Temperature; Vehicle Emissions / analysis
TL;DR: A probabilistic methodology for quantifying intervehicle variability and fleet average uncertainty in highway vehicle emission factors is developed and demonstrated for tailpipe carbon monoxide, hydrocarbon, and nitrogen oxides emissions for a selected light-duty gasoline vehicle technology. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2002 journal article

Quantification of variability and uncertainty in air pollutant emission inventories: Method and case study for utility NOx emissions

JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 52(9), 1083–1095.

By: H. Frey  n & J. Zheng n

Contributors: H. Frey  n & J. Zheng n

MeSH headings : Air Pollutants / analysis; Cost Savings; Environment; Environmental Monitoring / economics; Environmental Monitoring / standards; Nitrogen Oxides / analysis; Policy Making; Reproducibility of Results; Sensitivity and Specificity; Vehicle Emissions / analysis
TL;DR: A general methodology for the quantification of variability and uncertainty in emission factors, activity factors, and emission inventories (EIs) is described, featuring the use of bootstrap simulation and related techniques. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

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