School of Geosciences and Info-Physics, Central South University, China.
School of Geosciences and Info-Physics, Central South University, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, China.
Environ Pollut. 2021 Sep 1;284:117116. doi: 10.1016/j.envpol.2021.117116. Epub 2021 Apr 10.
Numerous statistical models have established the relationship between ambient fine particulate matter (PM, with an aerodynamic diameter of less than 2.5 μm) and satellite aerosol optical depth (AOD) along with other meteorological/land-related covariates. However, all the models assumed that all covariates affect the PM concentration at the same scale, and none could provide a posterior uncertainty analysis at each regression point. Therefore, a multiscale geographically and temporally weighted regression (MGTWR) model was proposed by specifying a unique bandwidth for each covariate. However, the lack of a method for predicting values at unsampled points in the MGTWR model greatly restricts its corresponding application. Thus, this study developed a method for inferring unsampled points and used the posterior uncertainty assessment value to improve the model accuracy. With the aid of the high-resolution satellite multi-angle implementation of atmospheric correction (MAIAC) AOD product, daily PM concentrations with a 1 km × 1 km resolution were generated over the Beijing-Tianjin-Hebei region between 2013 and 2019. The coefficient of determination (R) and root mean square error (RMSE) of the fitted MGTWR results vary from 0.90 to 0.94 and from 10.66 to 25.11 μg/m, respectively. The sample-based and site-based cross-validation R and RMSE vary from 0.81 to 0.89 and from 14.40 to 34.43 μg/m respectively, demonstrating the effectiveness of the proposed inference method at unsampled points. With the uncertainty constraint, the sample-based and site-based validated MGTWR R results for all years are further improved by approximately 0.02-0.04, demonstrating the effectiveness of the posterior uncertainty assessment constraint method. These results suggest that the inference method proposed in this study is promising to overcome the defects of the MGTWR model in inferring the prediction values at unsampled points and could consequently enhance the wide applications of MGTWR modeling.
许多统计模型已经建立了环境细颗粒物（PM，空气动力学直径小于 2.5μm）与卫星气溶胶光学深度（AOD）以及其他气象/土地相关协变量之间的关系。然而，所有模型都假设所有协变量在同一尺度上影响 PM 浓度，并且没有一个模型能够在每个回归点提供后验不确定性分析。因此，通过为每个协变量指定独特的带宽，提出了一种多尺度地理和时间加权回归（MGTWR）模型。然而，MGTWR 模型缺乏在未采样点预测值的方法，极大地限制了其相应的应用。因此，本研究提出了一种推断未采样点的方法，并使用后验不确定性评估值来提高模型精度。借助高分辨率卫星多角度大气校正（MAIAC）AOD 产品，生成了 2013 年至 2019 年京津冀地区每天 1km×1km 分辨率的 PM 浓度。拟合的 MGTWR 结果的决定系数（R）和均方根误差（RMSE）分别在 0.90 到 0.94 之间和 10.66 到 25.11μg/m 之间变化。基于样本和基于站点的交叉验证 R 和 RMSE 分别在 0.81 到 0.89 之间和 14.40 到 34.43μg/m 之间变化，表明了该推断方法在未采样点的有效性。在不确定性约束下，所有年份的基于样本和基于站点验证的 MGTWR R 结果都进一步提高了约 0.02-0.04，表明了后验不确定性评估约束方法的有效性。这些结果表明，本研究提出的推断方法有望克服 MGTWR 模型在推断未采样点预测值方面的缺陷，从而增强 MGTWR 建模的广泛应用。
