Using Statistical Regressions to Identify Factors Influencing PM_2.5 Concentrations: The Pittsburgh Supersite as a Case Study

Abstract

Using data from the Pittsburgh Air Quality Study, we find that temperature, relative humidity, their squared terms, and their interactions explain much of the variation in airborne concentrations of PM _2.5 in the city. Factors that do not appreciably influence the concentrations over a full year include wind direction, inverse mixing height, UV radiation, SO ₂ , O ₃ , and season of the year. Comparison with similar studies of PM _2.5 in other cities suggests that the relative importance of different factors can vary greatly. Temperature and relative humidity are important factors in both Pittsburgh and New York City, and synoptic scale meteorology influencing these two sites can explain much of the pattern in PM _2.5 concentrations which peak in the summer. However, PM _2.5 levels in other cities have different seasonal patterns and are affected by a number of other factors, and thus the results presented here cannot be generalized to other locations without additional study.

We acknowledge the generous assistance of Stan Benjamin of the NOAA/ESRL Global Systems Division in obtaining the RUC data and assisting with interpretation, and that of Louis Giardano of the National Weather Service office in Pittsburgh for his assistance in estimating mixing heights. We also acknowledge the assistance of Xiting Yang, Anna Anselmi, and David Hwang in earlier versions of this work. This research was conducted as part of the Pittsburgh Air Quality Study that was supported by US Environmental Protection Agency under contract R82806101 and the US Department of Energy National Energy Technology Laboratory under contract DE-FC26-01NT41017. This article has not been subject to EPA's required peer and policy review, and therefore does not necessarily reflect the views of the Agency. No official endorsement should be inferred.