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Author Notes:

Corresponding author: Ying Zhou: ying.zhou@emory.edu

YZ collected input data and carried out the atmospheric modeling, conducted the statistical analysis and drafted the manuscript.

JIL conceived of the study, helped refine the analysis and revised the manuscript.

All authors read and approved the final manuscript.

We thank Steven Melly, Philip Soucacos, Melissa Ohsfeldt, Ted Thrasher, Saravanan Arunachalam, Vlad Isakov and Breeze technical support for valuable input.

The authors declare that they have no competing interests.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA.


Research Funding:

This study was sponsored by the Federal Aviation Administration (FAA) through the Partnership for AiR Transportation Noise and Emissions Reduction (PARTNER) under Cooperative Agreement No. 03-C-NE-MIT-026, Subcontract Agreement No. 5710002069.

Between-airport heterogeneity in air toxics emissions associated with individual cancer risk thresholds and population risks

Journal Title:

Environmental Health


Volume 8, Number 22


, Pages 1-11

Type of Work:

Article | Final Publisher PDF


Background Airports represent a complex source type of increasing importance contributing to air toxics risks. Comprehensive atmospheric dispersion models are beyond the scope of many applications, so it would be valuable to rapidly but accurately characterize the risk-relevant exposure implications of emissions at an airport. Methods In this study, we apply a high resolution atmospheric dispersion model (AERMOD) to 32 airports across the United States, focusing on benzene, 1,3-butadiene, and benzo [a]pyrene. We estimate the emission rates required at these airports to exceed a 10-6 lifetime cancer risk for the maximally exposed individual (emission thresholds) and estimate the total population risk at these emission rates. Results The emission thresholds vary by two orders of magnitude across airports, with variability predicted by proximity of populations to the airport and mixing height (R2 = 0.74–0.75 across pollutants). At these emission thresholds, the population risk within 50 km of the airport varies by two orders of magnitude across airports, driven by substantial heterogeneity in total population exposure per unit emissions that is related to population density and uncorrelated with emission thresholds. Conclusion Our findings indicate that site characteristics can be used to accurately predict maximum individual risk and total population risk at a given level of emissions, but that optimizing on one endpoint will be non-optimal for the other.

Copyright information:

© 2009 Zhou and Levy; licensee BioMed Central Ltd.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 2.0 Generic License (http://creativecommons.org/licenses/by/2.0/).

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