Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model
Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model
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DOI：
10.1016/j.rse.2017.06.001
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发表时间：
2017-09-01
影响因子：
13.5
通讯作者：
Zhang, Ziyin
中科院分区：
文献类型：
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作者：
Guo, Yuanxi;Tang, Qiuhong;Zhang, Ziyin
中国大部分按时间顺序排列的环境空气污染数据是通过每日空气质量指数（AQI）发布的。然而，很少有研究使用空气质量指数数据来校准基于卫星的细颗粒物（PM2.5，空气动力学直径不大于2.5 μ m的颗粒）浓度估计值，部分原因是空气质量指数导出的PM2.5不是每天连续获得的。以北京市为例，我们开发了一个地理和时间加权回归（GTWR）模型，可以考虑空间和时间变化的非连续空气质量指数推导的PM2. 5和卫星推导的气溶胶光学厚度（AOD）之间的关系。GTWR模型使用从中分辨率成像光谱仪（MODIS）获得的3 km空间分辨率的AOD值，气象场和土地利用变量作为预测因子，从2013年4月到2015年3月进行了季节性拟合。经与地面观测数据交叉验证后，PM2.5的决定系数（R-2）范围为0.36至0.75，平均值为0.58。GTWR模型优于几个传统的模型，如多元线性回归（MLR）模型，地理加权回归（GWR）模型，时间加权回归（TWR）模型和线性混合效应（LME）模型。与已有的两阶段（LME + GWR）时空模型相比，GTWR模型可能更具可行性。当日记录数>= 5时，预测精度没有明显差异（交叉验证R-2均为0.68）。然而，当每日记录的数量为
Most time-sequenced ambient air pollution data in China is published through daily Air Quality Index (AQI). However, few studies have used the AQI data to calibrate satellite-based estimates of fine particulate matter (PM2.5, particles no greater than 2.5 mu m in aerodynamic diameter) concentrations, partly because the AQI-derived PM2.5 is not continuously obtained each day. Taking Beijing as an example, we developed a geographically and temporally weighted regression (GTWR) model that can account for spatial and temporal variability in the relationship between the non-continuous AQI-derived PM2.5 and satellite-derived aerosol optical depth (AOD). The GTWR model, which uses AOD values with a 3-km spatial resolution obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological fields, and land-use variables as predictors, was fitted seasonally from April 2013 to March 2015. After being cross-validated against ground observations, the coefficient of determination (R-2) of PM2.5 ranged from 0.36 to 0.75, with a mean value of 0.58. The GTWR model outperforms several conventional models, such as the multiple linear regression (MLR) model, geographically weighted regression (GWR) model, temporally weighted regression (TWR) model, and linear mixed-effects (LME) model. Compared to a previous spatiotemporal model, the two-stage (LME + GWR) model, the GTWR model may be more feasible. When the number of daily records is >= 5, there is no obvious difference in prediction accuracy (cross-validated R-2 both valued at 0.68). However, when the number of daily records is