Integration Of Multi-Sensory Earth Observations For Characterization Of Air Quality Events

In order to characterize air quality events, such as dust storms or smoke events from fires, a wide variety of Earth observations are needed from satellites, surface monitors and models. Traditionally, the burden of data access and processing was placed on the data user. These challenges of finding, accessing and merging data are overcome through the principles of Service Oriented Architecture. This thesis describes the collaborative, service-oriented approach now available for air quality event analysis, where datasets are turned into services that can be accessed by tools through standard queries. This thesis extends AQ event evidence to include photos, videos and personal observations gathered from social media websites such as Flickr, Twitter and YouTube. In this thesis, the service-oriented approach is demonstrated using two case studies. The first explains the benefits of data reuse in real-time event analysis focusing on the 2009 Southern California Smoke event. The second case study highlights post-event analysis for EPAΓÇÖs Exceptional Event Rule. The thesis concludes with a first attempt to quantify the benefits of data reuse by identifying all of the different user requirements for Earth observation data. We found that the real-time and post-event analysis had 68 unique Earth observation requirements making it an ideal example for illustrating the benefits of service oriented architecture for air quality analysis. While this thesis focuses on the air quality domain, the tools and methods can be applied to any area that needs distributed data.

Name thesis:degree_name

Master of Arts (MA)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Energy, Environmental and Chemical Engineering

Year dc:date.available

2010

Author dc:creator

• Robinson, Erin

Contributors dc:contributor

• Rudolf Husar

• Engineering
• Environmental
• Environmental Informatics
• Air Quality
• Information Science
• Web 2.0

Language dc:language

English (en)

DOI

https://doi.org/10.7936/K79W0CJQ

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:etd-1456