2008-12-15: Multi-Sensor Characterization of Aerosol Events AGU
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Title: Multi-Sensor Characterization of Aerosol Events
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HR: 10:20h AN: A22A-01
[edit] Multi-Sensor Characterization of Aerosol Events
R B Husar, R L Poirot, E M Robinson
Recent developments in surface and satellite sensing along with new information technologies now allow real- time, 'just-in-time' data analysis for the characterization and partial explanation of the of major air pollution events. The data from surface-based air pollution monitoring networks now provides routinely high grade, spatio-temporal and chemical patterns throughout the US for PM25 and ozone. Satellite sensors with global coverage and kilometer-scale spatial resolution now provide real-time snapshots which depict the pattern of haze, smoke and dust in stunning detail. The instantaneous 'horizontal' diffusion of information via the Internet now permits, in principle, the delivery of the right information to the right people at the right place and time. The resulting "data deluge" problem is especially acute for aerosol pollution, since aerosol systems are inherently complex and since there are so many different kinds of relevant data. Atmospheric aerosols are a 6-D system (x, y, z, t, size, composition) and full characterization requires 'filling' (observing or modeling) the entire 6D data-space. However, each sensor samples a small subset of the aerosol data space, while some sensors, like satellites provide only integral measures of the 6D aerosol pattern. Clearly, characterization requires the combination of the multi- sensory data and 6D models outputs. Complete aerosol chemical composition data are available only for limited sampling locations in few regions of the world but over the US, rich aerosol chemistry datasets are available from the IMPROVE aerosol chemistry network and from the EPA Speciation Trends Network. There is also an increasing array of Satellite data from MODIS, OMI, CALIPSO, GOES, AVHRR sensors. Aerosol characterization using the combination of these sensors over the US is in progress and will be reported. Recently, the multi-sensory characterization of aerosols during events has provided support to the air quality managers. The current challenge is to incorporate such support into the air quality management process in a more regular and robust way. Event analyses were performed as part of FASTNET (Poirot, et al, 2005), a data acquisition and analysis facility with particular emphasis on detailed real-time and post-analysis of major aerosol events including forest fire smoke and windblown dust. These have large emission rates over short periods of time, continental and global-scale impacts, and unpredictable sporadic occurrence.
