THE AIR QUALITY GROUP

The Air Quality Group is a research group composed of professors, professional research scientists, staff research associates, and graduate and undergraduate students from the combined disciplines of Physics, Chemistry, Atmospheric Science and Ecology. The group is supported by extramural funding and grants and/or contracts from many sources, the largest being the National Park Service (NPS), the Environmental Protection Agency (EPA), and the U.S. Department of Agriculture (USDA).

NPS

The National Park Service supports a large research effort named IMPROVE (Interagency Monitoring for Protected Visual Environments) focused on particulate monitoring throughout the United States and the Virgin Islands. The Air Quality Group is responsible for the particulate analysis for the IMPROVE program by providing both collection and elemental analysis of particulate samples gathered in the Class 1 Areas. Determination of chemical composition can help distinguish air masses and identify different types of pollutant sources. Techniques to non-destructively analyze air filters are now routinely used at CNL for gravimetric mass, optical absorpt ion, elemental composition by proton-induced x-ray emission (PIXE) and X-ray fluorescence (XRF), and hydrogen by proton elastic scattering analysis (PESA). These techniques quantify the elements sodium to lead in the periodic table, plus hydrogen. The inorganic composition accounts for only a fraction of the particulate matter on an air filter however. The remaining components are highly complex organic materials that are more difficult to analyze.

USDA

Evaluation of dust emission from agricultural practices in the San Joaquin Valley was funded by the US Department of Agriculture and by Cotton Incorporated. The flux of PM10 emissions from the various stages in the harvesting of almonds, walnuts, figs and cotton is the emphasis of the work and emission rates for clay; silty and sandy soils are determined.

LIDAR

(Light Detection and Ranging) technology provides an on-site, detailed image of the aerosol relative concentrations within the scanned region. CNL researchers are using LIDAR to understand non-point source PM emissions from agricultural operations in the San Joaquin Valley.

REMOTE SENSING

Remote Sensing can be the source of accurate and current Geographic Information System (GIS) data layers. Our research focuses on the extraction of information from digital spectral image data to provide GIS data layers such as land cover type, urban boundary layers, roads, rivers and other lines of communication, and vegetation stress. Research on assessing the accuracy of extracted data layers assists in characterizing the reliability of the data for its inclusion in data models. Our research emphasizes techniques for assessing and representing the accuracy of derived spatial data.