Howard Chang, a former PhD student of mine now at Emory, just published a paper on a measurement error model for estimating the health effects of coarse particulate matter (PM). This is a cool paper that deals with the problem that coarse PM tends to be very spatially heterogeneous. Coarse PM is a bit of a hot topic now because there is currently no national ambient air quality standard for coarse PM specifically. There is a standard for fine PM, but compared to fine PM, the scientific evidence for health effects of coarse PM is relatively less developed.
When you want to assign a coarse PM exposure level to people in a county (assuming you don’t have personal monitoring) there is a fair amount of uncertainty about the assignment because of the spatial variability. This is in contrast to pollutants like fine PM or ozone which tend to be more spatially smooth. Standard approaches essentially ignore the uncertainty which may lead to some bias in estimates of the health effects.
Howard developed a measurement error model that uses observations from multiple monitors to estimate the spatial variability and correct for it in time series regression models estimating the health effects of coarse PM. Another nice thing about his approach is that it avoids any complex spatial-temporal modeling to do the correction.