Examination of the Mathematical Models Used in Atmospheric  Source Apportionment Studies
{'Code': '06020104', 'Directorate': {'Abbreviation': 'GEO', 'LongName': 'Directorate For Geosciences'}, 'Division': {'Abbreviation': 'AGS', 'LongName': 'Div Atmospheric & Geospace Sciences'}}
{'SignBlockName': 'Jarvis L. Moyers', 'PO_EMAI': None, 'PO_PHON': None}
Identifying the various sources responsible for the                        composition of airborne particles at a given location is an                     important prerequisite to developing effective regulatory                       policies for  air quality.  During the past decade, there has                   been increasing use of elemental and chemical analytical                        methods to characterized airborne particulate matter and                        collected precipitation.  With the availability of these large                  data sets and new multivariate of statistical methods that have                 been made practical by the continuing increases in computer                     size and speed, there is an enhanced opportunity to extract                     information about atmospheric processes from data                               characterizing collected particles or precipitation.  Since                     some elements are strongly geochemically related, there is                      redundancy in the elemented data that limits the number of                      parameters that are influential in identifying sources.                         Another source of data is physical characterization (size,                      shape, texture) of individual particles by computer-controlled                  scanning electron microscopy.                                                        This project will examine several eigenvector-based                        statistical methods to investigate regional scale transport of                  airborne particulate matter.  By using principal components                     analysis, empirical orthogonal functions, and three-mode factor                 analysis, is it expected that the patterns of spatial and                       temporal variations in the aerosol compositions can be better                   understood and relationships with the regional meteorology                      defined.  In addition, methods will be developed for extracting                 quantitative measures of particle texture from images of                        individual particles obtained with a Scanning Electron                          Microscope.  Fractal dimension, eigenvector and Fourier                         analysis  methods will be explored.  The effects of chemical                    modification of the particle surface by reaction with gaseous                   species on the visual surface texture and results of the image                  analysis methods will be examined.
{'FirstName': 'Philip', 'LastName': 'Hopke', 'PI_MID_INIT': 'K', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Philip K Hopke', 'EmailAddress': 'hopkepk@clarkson.edu', 'PI_PHON': '3152683861', 'NSF_ID': '000123778', 'StartDate': '07/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'URBANA', 'ZipCode': '618013620', 'PhoneNumber': '2173332187', 'StreetAddress': '506 S WRIGHT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'IL13', 'ORG_UEI_NUM': 'Y8CWNJRCNN91', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ILLINOIS', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '1524', 'Text': 'Atmospheric Chemistry'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8810787.xml'}