AAAR 34th Annual Conference
October 12 - October 16, 2015
Hyatt Regency
Minneapolis, Minnesota, USA
Abstract View
Climatology of PM10 Metals in St. Louis from Hourly Data
Clara Veiga Ferreira de Souza, JAY TURNER, Washington University in St. Louis
Abstract Number: 729 Working Group: Urban Aerosols
Abstract The commercialization of semicontinuous monitors for ambient measurements of particulate matter elemental composition is an important advancement towards meeting the need for continuous, high time resolution measurement of air quality parameters. This presentation will summarize our experience with the Cooper Environmental Services (CES) Ambient Metals Monitor (Xact 620) which is one in a series of XRF-based monitors for stack, fence-line and ambient measurements. In 2008 the Missouri Department of Natural Resources purchased a Xact 620 that was tuned for measurement of air toxics metals – especially arsenic and lead – in ambient air in urban and remote environments. Starting in November 2012 the instrument has been deployed at the City of St. Louis (MO) Blair Street station which is both an NCore site and a National Air Toxics Trends Station (NATTS). This presentation will briefly summarize Xact performance by comparing to NATTS 24-hour integrated PM10 air toxics metals data, and then focus on insights into the climatology of more than a dozen elements through the analysis of more than 13,000 valid one-hour samples collected over a twenty-month period. High time resolution (e.g. hourly) measurements better align the data to the timescales for variability in surface winds and thereby dramatically improve the ability to resolve local sources. The high data density permits analyses that condition the data on time (e.g. hour of day, weekday/weekend) and surface wind directions, or air mass transport patterns. The various measured species exhibit distinct patterns that reflect contributions ranging from local emissions to regional-scale transport. Weekday enhancement above weekend concentration values is most prevalent for the daytime hours. Nonparametric wind regression for data stratified into weekdays and weekends identifies bearings for sources which operate on both weekdays and weekends (e.g., iron from the local steelworks) and other sources that have reduced or no operations on weekends.