A Bayesian hierarchical approach for relating PM(2.5) exposure to cardiovascular mortality in North Carolina.

Considerable attention has been given to the relationship between levels of fine particulate matter (particulate matter < or = 2.5 microm in aerodynamic diameter; PM(2.5) in the atmosphere and health effects in human populations. Since the U.S. Environmental Protection Agency began widespread monitoring of PM(2.5) levels in 1999, the epidemiologic community has performed numerous observational studies modeling mortality and morbidity responses to PM(2.5) levels using Poisson generalized additive models (GAMs). Although these models are useful for relating ambient PM(2.5) levels to mortality, they cannot directly measure the strength of the effect of exposure to PM(2.5) on mortality. In order to assess this effect, we propose a three-stage Bayesian hierarchical model as an alternative to the classical Poisson GAM. Fitting our model to data collected in seven North Carolina counties from 1999 through 2001, we found that an increase in PM(2.5) exposure is linked to increased risk of cardiovascular mortality in the same day and next 2 days. Specifically, a 10- microg/m3 increase in average PM(2.5) exposure is associated with a 2.5% increase in the relative risk of current-day cardiovascular mortality, a 4.0% increase in the relative risk of cardiovascular mortality the next day, and an 11.4% increase in the relative risk of cardiovascular mortality 2 days later. Because of the small sample size of our study, only the third effect was found to have > 95% posterior probability of being > 0. In addition, we compared the results obtained from our model to those obtained by applying frequentist (or classical, repeated sampling-based) and Bayesian versions of the classical Poisson GAM to our study population.

Sampling frequency guidance for ambient air toxics monitoring.

The U.S. Environmental Protection Agency (EPA) is in the process of designing a national network to monitor hazardous air pollutants (HAPs), also known as air toxics. The purposes of the expanded monitoring are to (1) characterize ambient concentrations in representative areas; (2) provide data to support and evaluate dispersion and receptor models; and (3) establish trends and evaluate the effectiveness of HAP emission reduction strategies. Existing air toxics data, in the form of an archive compiled by EPA's Office of Air Quality Planning and Standards (OAQPS), are used in this paper to examine the relationship between estimated annual average (AA) HAP concentrations and their associated variability. The goal is to assess the accuracy, or bias and precision, with which the AA can be estimated as a function of ambient concentration levels and sampling frequency. The results suggest that, for several air toxics, a sampling schedule of 1 in 3 days (1:3) or 1:6 days maybe appropriate for meeting some of the general objectives of the national network, with the more intense sampling rate being recommended for areas expected to exhibit relatively high ambient levels.

Using continuous PM2.5 monitoring data to report an air quality index.

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area's (MSA's) AQI. Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality. This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency's (EPA's) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity. The chosen measure of model adequacy is r2, the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greensboro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r2 of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r2 of 0.86, based on 214 paired observations.

Development and evaluation of a quantitative video-fluorescence imaging system and fluorescent tracer for measuring transfer of pesticide residues from surfaces to hands with repeated contacts.

A video imaging system and the associated quantification methods have been developed for measurement of the transfer of a fluorescent tracer from surfaces to hands. The highly fluorescent compound riboflavin (vitamin B2), which is also water soluble and non-toxic, was chosen as the tracer compound to simulate the transfer from surfaces to hands of pesticide residues deposited on carpeted and laminate surfaces of a residence. The system was designed around the unique properties of riboflavin. Excitation energy was centered near 440 nm (in the blue region of the visible spectrum); emitted energy was measured at 600 nm (in the red/orange region), well beyond the significant fluorescence peak maximum of natural skin. A video camera system with an image intensifier was interfaced to an image processing analysis software system. Quantification utilized chemometric techniques to account for the non-linearity of pixel detectivity and non-linear excitation strength. Method quantification and detection limits were approximately 0.1 and 0.02 micro g/cm(2), respectively. The relative error was approximately 100% at the quantification limit, but <20% at higher levels. Transfer of riboflavin to hands, resulting in dermal loadings in the range 0.1-2.0 micro g/cm(2), were measured with this system from surfaces whose loadings approximated the pesticide levels that occur in homes after broadcast application.