A regression-based approach for estimating primary and secondary particulate matter intake fractions.
One of the common challenges for life cycle impact assessment and risk assessment is the need to estimate the population exposures associated with emissions. The concept of intake fraction (a unitless term representing the fraction of material or its precursor released from a source that is eventually inhaled or ingested) can be used when limited site data are available or the number of sources to model is large. Although studies have estimated intake fractions for some pollutant-source combinations, there is a need to quickly and accurately estimate intake fractions for sources and settings not previously evaluated. It would be expected that limited source or site information could be used to yield intake fraction estimates with reasonable accuracy. To test this theory, we developed regression models to predict intake fractions previously estimated for primary fine particles (PM2.5) and secondary sulfate and nitrate particles from power plants and mobile sources in the United States. Our regression models were able to predict pollutant-specific intake fractions with R2 between 0.53 and 0.86 and equations that reflected expected relationships (e.g., intake fraction increased with population density, stack height influenced the intake fraction of primary but not secondary particles). Further analysis would be needed to generalize beyond this case study and construct models applicable across source categories and settings, but our analysis demonstrates that inclusion of a limited number of parameters can significantly reduce the uncertainty in population-average exposure estimates.
Levy
Jonathan L
JL
Harvard School of Public Health, Department of Environmental Health and Center for Risk Analysis, Boston, MA 02215, USA. jilevy@hsph.harvard.edu
Wolff
Scott K
SK
Evans
John S
JS
D000393:Air Pollutants / Q000633:toxicity*; D004777:Environment; D004781:Environmental Exposure; D006801:Humans; D015993:Life Tables; D008962:Models, Theoretical; D009566:Nitrates / Q000633:toxicity; D011210:Power Plants; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D013431:Sulfates / Q000633:toxicity; D014481:United States
D000393:Air Pollutants; D009566:Nitrates; D013431:Sulfates