Using statistical regressions to identify factors influencing PM2.5 concentrations: The pittsburgh supersite as a case study

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, SO₂, O₃, and season of the year. Comparison with similar studies of PM2.5 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.