ii Acknowledgment iv List of Figures vi 1 Introduction 1 2 Traditional Air Quality Event Characterization 3 2.1 April 2003 Kansas Smoke Characterization 3 2.2 Traditional Data Access 5 3 Service-Oriented Air Quality Analysis 7 3.1 DataFed 9 3.1.1 DataFed Wrappers 9 3.1.2 DataFed Metadata 10 3.1.3 DataFed Workflow 11 3.2 Social Media as an Air Quality Sensor 13 3.3 Combining Science Data and Social Media for AQ Event Analysis 18 4 Case Study: Real-time Event Analysis of August 2009 Southern California Fires .. 20 5 Case Study: Exceptional Event Analysis for May 2007 Georgia Swamp Fires........ 23 5.1 May 2007 Georgia Swamp Fires 24 5.1.1 A: Event Identification 25 5.1.2 B: Clear Causal Relationship between the Data and the Event 27 5.1.3 C: The Event is in Excess of the "Normal" Values 28 5.1.4 D. The Exceedance or Violation would not Occur, But For the Exceptional Event 30 5.1.5 Summary 31 6 Earth Observations Requirements 32 7 Future Work 35 7.1 Collaboration on AQ Event Analysis 35 7.2 Global Earth Observing System of Systems 35 References 37 Vita 37
[1]
Rudolf B. Husar,et al.
DataFed: Mediated web services for distributed air quality data access and processing.
,
2007,
2007 IEEE International Geoscience and Remote Sensing Symposium.
[2]
Rudolf B. Husar,et al.
DATAFED AND FASTNET: TOOLS FOR AGILE AIR QUALITY ANALYSIS
,
2006
.
[3]
Robert Frouin,et al.
Asian Dust Events of April 1998
,
2001
.
[4]
Edward M. Robinson,et al.
Enabling Tools and Methods for International, Inter-disciplinary and Educational Collaboration
,
2008
.
[5]
George Percivall,et al.
DataFed: An Architecture for Federating Atmospheric Data for GEOSS
,
2008,
IEEE Systems Journal.
[6]
Lorraine A. Remer,et al.
ARM Southern Great Plains Site Observations of the Smoke Pall Associated with the 1998 Central American Fires
,
2000
.