基于PM2.5站点监测数据的京津冀AOD补值研究

以京津冀2020年318个地面监测站点的PM_2.5数据为估算因子,构建了时空线性混合效应模型(STLME)和时空嵌套线性混合效应模型(STNLME),为AOD数据的补值研究提供了一种新方法.结果表明:在有AOD-PM_2.5匹配数据的日期,上述两个模型估算精度相近,交叉验证后决定系数R²分别为0.868和0.874,均方根误差RMSE分别为0.112和0.109;在无AOD-PM_2.5匹配数据的日期,嵌套模型估算精度明显高于非嵌套模型,交叉验证后决定系数R²分别为0.63和0.26.经过模型补值后,研究区监测站点所在网格AOD数据空间维有效比率从原始数据的44.35%提高到99.35%,时间维有效比率从87.94%提高到100%;同时,每个站点的年均AOD值都有明显提高,弥补了高PM_2.5浓度条件下缺失的AOD数据,可以减少空气污染和健康研究中暴露评估的偏差.

Filling the missing data of AOD using the situ PM2.5 monitoring measurements in the Beijing-Tianjin-Hebei region

A spatiotemporal linear mixed effect model (STLME) and a spatiotemporal nested linear mixed effect model (STNLME) were presented using the PM_2.5 measurements of 318 ground monitoring stations in Beijing-Tianjin-Hebei (BTH) in 2020 to fill the missing data of AOD. The results indicated that the STLME and STNLME models in the days with AOD-PM_2.5 matchups showed similar performance with the cross-validation (CV) R² valued at 0.868 and 0.874, and the root mean square error (RMSE) valued at 0.112 and 0.109, respectively. However, the STNLME model with the CV R² valued at 0.63 outperforms STLME with the CV R² of 0.26 in the days without PM_2.5-AOD matchups. After models filling, the spatial valid value ratio of AOD data in the grid where the monitoring stations are located was increased from 44.35% to 99.35%, and the temporal valid value ratio was increased from 87.94% to 100%. Meanwhile, the annual mean AOD value of each station had increased significantly, and the missing AOD were filled under the condition of high PM_2.5 level, which could reduce the biases of exposure assessment in air pollution and health studies.