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2016 Abstracts

A Framework for Validating Modeled Air Quality Data in Health Research

Nicole Burnett, Peter Mo, Naresh Rajan, Randy Madsen, Ram Gouripeddi, and Julio Facelli, University of Utah

Life Sciences


In the Salt Lake Valley there are three permanent Environmental Protection Agency (EPA) certified air quality monitoring stations that intake air samples and produce results of the air quality in the proximity of the monitor station. Due to the fact that the monitors only represent a small area of the 500- square-mile Salt Lake Valley, there are spatial gaps when using the air quality monitoring data for epidemiological studies. Since for certain studies health researchers may require a higher resolution spatiotemporal air quality grid [1], we need to devise new approaches to provide air quality data that could meet the epidemiological studies requirements.

Research Methodology

Modeled air quality data available from the EPA, has higher spatial and temporal resolution than data from monitoring stations, but it needs experimental validation and uncertainty quantification (UQ) in the Salt Lake Valley. We can achieve these validation and UQ goals through statistical comparisons of measured air quality data at the same location and time as the modeled air quality data. The air quality model that we primarily used is the one that the EPA has developed for the Centers for Disease Control and Prevention’s (CDC) National Environmental Public Health Tracking Network [2]. This is a model that uses a Hierarchical Bayesian Space Time Modeling approach [3]. This model was validated on the east coast of the United States so it is unknown how effective is in taking into account the terrain of the Salt Lake Valley. Modeled PM2.5 data in a 12×12 kilometer continuous grid resolution for the years 2007 and 2008 were compared against measured data of the same timeframe and location. The measured data was obtained from EPA’s Air Quality System (AQS) Datamart [4]. The statistical comparisons performed using these the two data sets were done using daily and monthly PM2.5 averages for the years 2007 and 2008 using MySQL, MATLAB and R.

Conclusion & Significance

We have developed a prototype for comparing and validating modeled air quality data against measured air quality data for the Salt Lake Valley. We found the modeled data fits the measured data fairly well. We will expand our work by developing a validating framework that will include a library of data modeling algorithms such as, The Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS) [5] and Yanosky’s [6], which could be selected by the user. The framework will be developed using OpenFurther, and then integrated with biomedical data [7]. The framework will be integrated into the PRISMS project [8] as part of the informatics infrastructure for studying the effects of air quality on pediatric asthma.


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  4. “AirData | US Environmental Protection Agency.” [Online]. Available: [Accessed: 20-Oct-2015].
  5. Environmental Protection Agency, “Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose.” Environmental Protection Agency, 09-Nov-2005.
  6. J. D. Yanosky, C. J. Paciorek, F. Laden, J. E. Hart, R. C. Puett, D. Liao, and H. H. Suh, “Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors,” Environ. Health, vol. 13, no. 1, p. 63, Aug. 2014.
  7. R. Gouripeddi, N. Sundar Rajan, R. Madsen, P. Warner, and J. C. Facelli, “Federating Air Quality Data with Clinical Data,” presented at the 2014 AMIA Annual Symposium Proceedings, 2014.
  8. “Pediatric Research Using Integrated Sensor Monitoring Systems | National Institute of Biomedical Imaging and Bioengineering.” [Online]. Available: [Accessed: 20-Oct- 2015]. Acknowledgements Grants: UU Air Quality Program, U54EB021973, NCRR/NCATS UL1RR025764, 3UL1RR025764-02S2, AHRQ R01 HS019862, DHHS 1D1BRH20425, UU Research Foundation. CHPC at UU.