ABSTRACT Recently developed urban air quality sensor networks are used to monitor air pollutant concentrations at a fine spatial and temporal resolution. The measurements are however limited to point support. To obtain areal coverage in space and time, interpolation is required. A spatio-temporal regression kriging approach was applied to predict nitrogen dioxide (NO2) concentrations at unobserved space-time locations in the city of Eindhoven, the Netherlands. Prediction maps were created at 25 m spatial resolution and hourly temporal resolution. In regression kriging, the trend is separately modelled from autocorrelation in the residuals. The trend part of the model, consisting of a set of spatial and temporal covariates, was able to explain 49.2% of the spatio-temporal variability in NO2 concentrations in Eindhoven in November 2016. Spatio-temporal autocorrelation in the residuals was modelled by fitting a sum-metric spatio-temporal variogram model, adding smoothness to the prediction maps. The accuracy of the predictions was assessed using leave-one-out cross-validation, resulting in a Root Mean Square Error of 9.91 μg m−3, a Mean Error of −0.03 μg m−3 and a Mean Absolute Error of 7.29 μg m−3. The method allows for easy prediction and visualization of air pollutant concentrations and can be extended to a near real-time procedure.

中文翻译：

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用于模拟城市 NO2 浓度的时空回归克里金法

摘要 最近开发的城市空气质量传感器网络用于以精细的空间和时间分辨率监测空气污染物浓度。然而，测量仅限于点支撑。为了获得空间和时间的区域覆盖，需要进行插值。应用时空回归克里金法来预测荷兰埃因霍温市未观察到的时空位置的二氧化氮 (NO2) 浓度。预测地图是在 25 m 空间分辨率和每小时时间分辨率下创建的。在回归克里金法中，趋势是从残差中的自相关单独建模的。该模型的趋势部分由一组时空协变量组成，能够解释 2016 年 11 月埃因霍温 NO2 浓度的 49.2% 时空变异。残差中的时空自相关通过拟合和度量时空变异函数模型来建模，为预测图增加平滑度。使用留一法交叉验证评估预测的准确性，导致均方根误差为 9.91 μg m-3，平均误差为 -0.03 μg m-3，平均绝对误差为 7.29 μg m-3 －3。该方法可以轻松预测和可视化空气污染物浓度，并且可以扩展到近乎实时的程序。29 μg m-3。该方法可以轻松预测和可视化空气污染物浓度，并且可以扩展到近乎实时的程序。29 μg m-3。该方法可以轻松预测和可视化空气污染物浓度，并且可以扩展到近乎实时的程序。