# Spatiotemporal Calibration of Atmospheric Nitrogen Dioxide Concentration Estimates From an Air Quality Model for Connecticut

## TL;DR

This study developed a spatiotemporal calibration model to improve nitrogen dioxide concentration estimates from an air quality model, incorporating local covariates to enhance spatial resolution and accuracy across Connecticut.

## Contribution

The paper introduces a novel calibration approach that refines air quality model estimates using local covariates, significantly improving spatial resolution and bias correction.

## Key findings

- Calibration reduced mean squared error at monitor sites.
- Predicted NO₂ maps showed marked spatial resolution improvements.
- Traffic and land use covariates influenced NO₂ concentration patterns.

## 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 x 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.