Chemistry and Microphysics of Small Particles
EMSL Project ID
25632
Abstract
This proposal is a continuation of research under EMSL User Proposal #17498. Atmospheric aerosol particles impact the global climate by scattering solar radiation to space and in their role as cloud condensation nuclei. How much light is scattered to space and how effective are particles at nucleating clouds are strong functions of the aerosol particle sizes and compositions. The data thus far reveal a complex picture with respect to particle compositions. Field measurements show that most particles are internally mixed composed of a wide range of inorganic and organic compounds. The data persistently show that the organic fraction, including a wide range of compounds with a broad spectrum of properties constitute over 50% of the particle mass. To understand the sources, behavior and fate of particles it is important to be able to quantitatively and accurately characterize the organic fraction of these particles. We have developed a very sensitive instrument that can measure the size and composition of individual particles we call SPLAT. SPLAT is particularly designed to address the organic fraction properly. Here we propose to build on our unparalleled capabilities to use this tool and the data it produces to advance our understanding of atmospheric particles. We propose to develop an improved quantification of the relative amounts of organics and inorganics by adding to the measures of each particles size and mass spectra a measure of its density and hygroscopicity, both of which are strong functions of the particle relative composition. It is important to keep in mind that the organic fraction is susceptible to reactions with ozone and OH. To properly understand the sources and fates we will quantify the rates of oxidation of organic compounds in particles. To make efficient use of the detailed data we produce, we will extend SpectraMiner - our data classification, visualization and mining software to change the approach we use to classify the data. The idea is to provide the researcher with tools to sculpt the data to allow the classification to yield expert steered results. We have named a test version of this approach ClusterSculptor. Preliminary tests revealed a significant improvement of the data classification process.
Project Details
Project type
Large-Scale EMSL Research
Start Date
2007-06-06
End Date
2010-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members