Comparison of the Space-Time Signatures of Air Quality Data From Different Monitoring Networks

  • Edith L. Gego
  • Christian Hogrefe
  • P. Steven Porter
  • John S. Irwin
  • S. Trivikrama Rao
Conference paper

Abstract

Ambient air quality in the United States is measured by several regional air quality monitoring networks. Yet, differences in sampling protocol between the networks may not allow joint use of the data reported by different networks. In this study, we compare the space-time signatures of sulfate and nitrate fine particle mass concentrations reported by the Clean Air Status and Trend Network (CASTNet) and the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE). First, a spectral decomposition technique was used to separate the low and high frequency variations in time series of pollutant concentrations at collocated IMPROVE and CASTNet sites. Through Principal Component Analysis (PCA) and Varimax orthogonal rotation, we determined the number of significant sulfate and nitrate modes of variation identifiable with both networks, and identify the mode of variation characterizing each monitoring site. In the case of sulfate, both networks allow identification of seven distinct modes of variation, each of which corresponds to a well-defined geographic area. PCA also suggests the existence of seven modes of variation for nitrate but, in contrast to sulfate, these modes of variations could not be linked to any unified geographic area. A combination of spectral decomposition and PCA reveals that the long-term fluctuations in sulfate at both networks are virtually identical — when they are averaged in homogeneous regions defined by PCA — between both networks.

Keywords

Combustion Covariance Ozone Nylon 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Briggs, G. A., 1985, Analytical Parameterizations of Diffusion: The Convective Boundary Layer. J. Clim. Appl. Met. 24, 1167–1186.CrossRefGoogle Scholar
  2. Cenedese, A.; Cosemans, G., Erbrink, H.; Stubi, R., 1998, Vertical profiles of wind, temperature and turbulence. In: Harmonisation of the pre-processing of meteorological data for atmospheric dispersion models. COST action 710 Final report. Office for Official Publications of the European Communities.Google Scholar
  3. Gryning, S.-E.; Holtslag, A.A.M.; Irwin, J.S.; Sivertsen, B., 1987, Applied Dispersion Modelling Based on Meteorological Scaling Parameters. Atmos. Environ. 21, 79–89CrossRefGoogle Scholar
  4. Rotach, M. W., 2001, Simulations of urban-scale dispersion using a Lagrangian stochastic dispersion model. Boundary-Layer Meteorology, 99, 379–410.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Edith L. Gego
    • 1
  • Christian Hogrefe
    • 2
  • P. Steven Porter
    • 3
  • John S. Irwin
    • 1
  • S. Trivikrama Rao
    • 4
  1. 1.NOAA Atmospheric Sciences Modeling DivisionOn Assignment to the U.S. Environmental Protection AgencyUSA
  2. 2.Atmospheric Sciences Research CenterUniversity at AlbanyAlbanyUSA
  3. 3.Department of Civil EngineeringUniversity of IdahoIdaho FallsUSA
  4. 4.U.S. Environmental Protection AgencyRoom

Personalised recommendations