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Temporally and Spatially Resolved Air Pollution in Georgia Using Fused Ambient Monitor Data and Chemical Transport Model Results

  • Sheila A. Sororian
  • Heather A. Holmes
  • Mariel Friberg
  • Cesunica Ivey
  • Yongtao Hu
  • James A. MulhollandEmail author
  • Armistead G. Russell
  • Matthew J. Strickland
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Health data geo-coded with residential coordinates are being used to investigate the relationship between ambient air quality and pediatric emergency department visits in the State of Georgia over the time period 2000–2010. Two types of ambient air quality data – observed concentrations from ambient monitors and predicted concentrations from a chemical transport model (CMAQ) – are being fused to provide spatially resolved daily metrics of five air pollutant gases (CO, NO2, NOx, SO2 and O3) and seven airborne particulate matter measures (PM10, PM2.5, and PM2.5 constituents SO4 2−, NO3 , NH4 +, EC, OC). The observational data provide reliable temporal trends at and near monitors, but limited spatial information due to the sparse monitoring network; CMAQ data, on the other hand, provide rich spatial information but less reliable temporal information. Four data fusion techniques were applied to provide daily spatial fields of ambient air pollutant concentrations, with data withholding used to evaluate model performance. Two of the data fusion methods were combined to provide results that minimized bias and maximized correlation over time and space with withheld data. Results vary widely across pollutants. These results provide health researchers with complete temporal and spatial air pollutant fields, as well as with temporal and spatial error estimate fields that can be incorporated into health risk models. Future work will apply these methods to five cities for use in ongoing air pollution health studies and to examine strategies for incorporating land use regression variables for spatial downscaling of data fusion results.

Keywords

Data Fusion Chemical Transport Model Evaluate Model Performance Data Fusion Method Data Fusion Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This publication was made possible by USEPA grant R834799. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sheila A. Sororian
    • 1
  • Heather A. Holmes
    • 1
  • Mariel Friberg
    • 1
  • Cesunica Ivey
    • 1
  • Yongtao Hu
    • 1
  • James A. Mulholland
    • 1
    Email author
  • Armistead G. Russell
    • 1
  • Matthew J. Strickland
    • 2
  1. 1.School of Civil and Environmental EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Environmental HealthEmory UniversityAtlantaUSA

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