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.

中文翻译：

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从康涅狄格州的空气质量模型估算大气中二氧化氮浓度的时空校准

使用时空校准和分辨率细化模型来校准来自社区多尺度空气质量（CMAQ）模型的二氧化氮（\（\ hbox {NO} _2 \））浓度估计值，并使用\（\ hbox { NO} _2 \）的时空分辨率不同。为了完善CMAQ模型估算值的空间分辨率，我们利用了附加的局部协变量来利用信息，包括2公里内的总交通量，人口密度，海拔和土地利用特征。通过\（\ hbox {NO} _2 \）处的均方误差（MSE）低得多，该模型的预测大大改善了CMAQ估计中的偏差。监视站点。最终模型用于预测像素大小为300 \（\ times \） 300 m的网格上康涅狄格州整个州的环境\（\ hbox {NO} _2 \）的日浓度。预测图与类似图的CMAQ估计值的比较显示出空间分辨率的显着提高。局部协变量的影响在更精细的空间分辨率图中显而易见，其中主要公路上的交通对周围\（\ hbox {NO} _2 \）浓度的贡献突出。还提供了一个动画来显示1994和1995年环境\（\ hbox {NO} _2 \）的浓度随时间和空间的变化。