Using radon-222 as an indicator of atmospheric mixing depth in ME-2 for PM2.5 source apportionment

Abstract

We isolated diurnal timescale contributions to a 6-year hourly radon record and incorporated them in ME-2 as a proxy for changes in atmospheric mixing depth in an attempt to improve the source apportionment of fine atmospheric particulate matter (PM2.5). Results from this radon-based implementation of ME-2 are directly compared with the more traditional ME-2 implementation where wind speed is used, as a proxy for changes in mixing depth. The radon-based version more accurately reproduced daily PM2.5 source contributions, as evidenced by better correlations with the results from the corresponding bi-linear model. The versions of ME-2 employed in this study were modified to account for calm wind conditions separately, and a recently updated solution approach was adopted. Source apportionment for the radon-based ME-2 implementation was most successful for the finer, primary emissions (Smoke, Autos, Industry) that are more easily suspended and whose concentrations are more directly tied to changes in atmospheric mixing depth. Incorporation of the diurnal radon signal in ME-2 improved the estimated source strength distributions of the Smoke, Autos and Industry sources with respect to the township of Muswellbrook. It also resulted in a more consistent anti-correlation between these 3 source types and atmospheric mixing depth than for the wind speed case. These results confirm that near surface radon concentration is more closely tied to atmospheric mixing depth (and therefore pollutant concentrations) than wind speed. The measurement site for this study is a small township in a rural setting, with nearby power stations and open-cut coal mines. Consequently, the distribution and characteristics of anthropogenic aerosol sources are very different than for a typical urban or industrial setting. This is reflected in lower correlation between the multi-linear models and the corresponding bi-linear models, indicating that the performance of multi-linear models is affected by the nature of the distribution of sources.