Identifying Likely PM_2.5 Sources on Days of Elevated Concentration: A Simple Statistical Approach

Abstract

A simple statistical method is described for identifying the likely importance of local sources of PM2.5 in a region on days when the National Ambient Air Quality Standard is exceeded. The method requires only PM2.5 mass concentration and wind direction data, and makes use of the EPA database on PM2.5 emissions in the local region of interest. The method has been illustrated using data from the Pittsburgh Air Quality Study, and suggests that local sources can be very important in affecting PM2.5 exceedances. The results have implications for many of the urban areas in the eastern United States downwind of large sources in the Midwest, and shows that simple statistical tests can be of value in identifying regions where further testing with sophisticated air quality dispersion models and source-receptor models is warranted.

Using Statistical Regressions to Identify Factors Influencing PM2.5Concentrations: The Pittsburgh Supersite as a Case Study

“…Unlike results of our earlier work (Chu et al 2009), the wind direction variables are not major contributors to predicting PM 2.5 . The inverse mixing height is also unimportant.…”

“…Thus the PM 2.5 concentration at any specified time is a very influential factor in predicting its value in the next time period. This also justifies averaging 10 min measurements to obtain 3-h average values as was done in our previous analysis (Chu et al 2009), since PM 2.5 concentrations in this study generally did not change dramatically in the time scale of a few hours. Table 4 shows that the explanatory variables Temp, (Temp) 2 , and Temp × Humidity are the most important in explaining PM 2.5 concentration, as was true in Table 2.…”

“…Although the site is in the middle of a high density urban area, there are no heavily traveled roads within a few hundred meters of the site. There are no major stationary sources within 2 km of the site except one source that has been shown statistically to have negligible influence (Chu et al 2009). PM 2.5 was measured with a Rupprecht and Patashnick Model 1400A Tapered Element Oscillating Microbalance (TEOM), as described in Chu et al (2009).…”

“…There are no major stationary sources within 2 km of the site except one source that has been shown statistically to have negligible influence (Chu et al 2009). PM 2.5 was measured with a Rupprecht and Patashnick Model 1400A Tapered Element Oscillating Microbalance (TEOM), as described in Chu et al (2009). Temperature and relative humidity were measured with a Campbell Scientific HMP45C Probe, while windspeed and wind direction data were obtained with a Met One 014A 3-cup anemometer and a Met One 024A wind vane, respectively.…”

“…In a previous paper (Chu et al 2009), we describe a simple statistical procedure to identify specific sources that are likely contributing to PM 2.5 on days of elevated concentration. The procedure involves determining an effective wind direction over an averaging period that depends on estimated transport times, and examining patterns in wind direction frequency associated with periods of high PM 2.5 concentration.…”