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


A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (\(\hbox {NO}_2\)) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on \(\hbox {NO}_2\) 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 \(\hbox {NO}_2\) monitor sites. The final model was used to predict the daily concentration of ambient \(\hbox {NO}_2\) over the entire state of Connecticut on a grid with pixels of size 300 \(\times \) 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 \(\hbox {NO}_2\) concentration stands out. An animation was also provided to show the change in the concentration of ambient \(\hbox {NO}_2\) over space and time for 1994 and 1995.

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Correspondence to Owais Gilani.

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Handling Editor Pierre Dutilleul.

The authors thank Dr. Lance Waller for useful feedback on the manuscript, as well as the anonymous reviewers whose comments helped improve the manuscript. This research was partially funded by Grant R01ES017416 from the National Institutes of Health.

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Gilani, O., McKay, L.A., Gregoire, T.G. et al. Spatiotemporal calibration of atmospheric nitrogen dioxide concentration estimates from an air quality model for Connecticut. Environ Ecol Stat 26, 325–349 (2019).

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