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Simultaneous statistical bias correction of multiplePM2.5 species from a regional photochemical grid model
Citation:
Crooks, J. AND H. Ozkaynak. Simultaneous statistical bias correction of multiplePM2.5 species from a regional photochemical grid model. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 95:126-141, (2014).
Impact/Purpose:
This paper presents a novel method of generating speciated PM2.5 ambient concentration estimates for use in medium-term exposure (multi-week or -month) epidemiological studies. Using a spatio-temporal spline basis, CMAQ output is bias-corrected using ambient monitor measurements thereby allowing ambient estimates far from monitor sites. The method explicitly assumes mass conservation so that information from the widespread PM2.5 mass network can be used in addition to the more limited speciated monitor data.
Description:
In recent years environmental epidemiologists have begun utilizing regionalscale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to monitoring data from central sites. The advantages of using such models include better spatiotemporal coverage and the capability to predict concentrations of unmonitored pollutants. However, there are also drawbacks, chief among them being that these models can exhibit systematic spatial and temporal biases. In order to use these models in epidemiological investigations it is very important to bias-correct the model surfaces. We present a novel statistical method of spatio-temporal bias correction for the Community Multi-scale Air Quality (CMAQ) model that allows simultaneous bias adjustment of PM2.5 mass and its major constituent species using publically available speciated data from ambient monitors. The method uses mass conservation and the more widespread unspeciated PM2.5 mass observations to constrain the sum of the PM2.5 species’ concentrations in locations without speciated monitors. We develop the model in the context of an epidemiological study investigating the association between PM2.5 species’ ambient concentrations and birth outcomes throughout the state of New Jersey. Since our exposures of interest are multi-month averages we focus specifically on modeling seasonal bias trends rather than daily biases. As one would expect, our bias-corrected CMAQ results are more accurate than the original CMAQ output. More interestingly, using a cross-validation study we find that our model’s predictions are improved by enforcing mass conservation, and furthermore that our model is competitive with kriging in a comparison in which the latter has the advantage.