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A model-based approach for imputing censored data in source apportionment studies
Jenna R Krall1, Charles H Simpson2, Roger D Peng3
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, Tel.: 410-502-5870.
Environmental and Ecological Statistics
|December 8, 2015
Summary
Model-based imputation improves particulate matter (PM) source estimation from censored chemical data, outperforming common methods. Proper adjustment of censored data is crucial for accurate PM source and health effect assessments.
Area of Science:
- Environmental Chemistry
- Atmospheric Science
- Data Science
Background:
- Particulate matter (PM) source apportionment relies on chemical constituent concentrations.
- Censored data below minimum detection limits (MDL) pose challenges for traditional models.
- Current methods like MDL substitution or data removal yield suboptimal results.
Purpose of the Study:
- To assess the impact of censoring adjustment methods on PM source estimation.
- To compare model-based imputation with commonly used censoring adjustment techniques.
- To provide guidance on handling censored PM data in source apportionment.
Main Methods:
- Review of censoring adjustment methods for PM data.
- Application of two common source apportionment models.
- Simulation study comparing imputation and traditional methods.
- Estimation of PM sources in New York City using different adjustment techniques.
Main Results:
- Model-based multiple imputation frequently yields superior source estimation compared to conventional methods.
- Censoring adjustment methods significantly impact PM source distribution estimates.
- Differences in source estimation were observed between methods in the NYC case study.
Conclusions:
- Model-based imputation offers a more robust approach for handling censored data in PM source apportionment.
- Effective adjustment of censored data is essential for accurate PM source identification and health impact analysis.
- Guidance is provided for optimal data handling in PM source apportionment models.
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