Spatiotemporal calibration of atmospheric nitrogen dioxide concentration estimates from an air quality model for Connecticut

Owais Gilani, Lisa A. McKay, Timothy G. Gregoire, Yongtao Guan, Brian P. Leaderer, Theodore R. Holford

Research output: Contribution to journalArticle

Abstract

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (NO 2) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on NO 2 that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the NO 2 monitor sites. The final model was used to predict the daily concentration of ambient NO 2 over the entire state of Connecticut on a grid with pixels of size 300 × 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient NO 2 concentration stands out. An animation was also provided to show the change in the concentration of ambient NO 2 over space and time for 1994 and 1995.

Original languageEnglish (US)
Pages (from-to)325-349
Number of pages25
JournalEnvironmental and Ecological Statistics
Volume26
Issue number4
DOIs
StatePublished - Dec 1 2019
Externally publishedYes

Keywords

  • Ambient air pollution
  • CMAQ
  • Integrated exposure modeling
  • Kalman filter
  • Resolution refinement
  • SCARR model

ASJC Scopus subject areas

  • Statistics and Probability
  • Environmental Science(all)
  • Statistics, Probability and Uncertainty

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