Airborne exposure patterns from a passenger source in aircraft cabins

Abstract

Airflow is a critical factor that influences air quality, airborne contaminant distribution, and disease transmission in commercial airliner cabins. The general aircraft-cabin air-contaminant transport effect model seeks to build exposure-spatial relationships between contaminant sources and receptors, quantify the uncertainty, and provide a platform for incorporation of data from a variety of studies. Knowledge of infection risk to flight crews and passengers is needed to form a coherent response to an unfolding epidemic, and infection risk may have an airborne pathogen exposure component. The general aircraf-tcabin air-contaminant transport effect model was applied to datasets from the University of Illinois and Kansas State University and also to case study information from a flight with probable severe acute respiratory syndrome transmission. Data were fit to regression curves, where the dependent variable was contaminant concentration (normalized for source strength and ventilation rate), and the independent variable was distance between source and measurement locations. The data-driven model showed exposure to viable small droplets and post-evaporation nuclei at a source distance of several rows in a mock-up of a twin-aisle airliner with seven seats per row. Similar behavior was observed in tracer gas, particle experiments, and flight infection data for severe acute respiratory syndrome. The study supports the airborne pathway as part of the matrix of possible disease transmission modes in aircraft cabins.

Fig. 1

B767 mock-up at the University of Illinois.

Fig. 2

Aircraft cabin air quality research (lighter gray) in the context of disease pathways discussed at the 2009 TRB symposium.

Fig. 3

Solid particle injection and measurement.

Fig. 4

Release and collection of bacteria.

Fig. 5

Regression analysis of transformed data and 95% confidence and prediction bands (color figure available online).

Fig. 6

Two-segment regression on raw data that shows breakpoint between near and far fields.

Fig. 7

Summary data for KSU tracer gas experiments; geometric mean CO₂ concentrations within each distance bin fit a regression model closely.

Fig. 8

Two-segment piecewise regression yielded an R² value of 0.685 and a break point of 2.56 m between near and far fields.

Fig. 9

Non-linear regression using the model C = 14,150 exp (−0.487r) with R² value of 0.778.

Fig. 10

SARS cases on China Air flight.

Fig. 11

Infection rate versus distance measured as rows with a three-row grouping. Row distance is offset to one row in front of index passenger.

Fig. 12

Example of use of the GAATE model interactive graphic; relative exposure to an air contaminant from a source in seat 32B (color figure available online).