Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China
文献类型：期刊论文
| 作者 | Zhang, Kainan3,4; de Leeuw, Gerrit3; Yang, Zhiqiang4; Chen, Xingfeng2,3; Su, Xiaoli1; Jiao, Jiashuang4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-11-02 |
| 卷号 | 11期号:22页码:22 |
| 关键词 | VIIRS AOD PM2.5 Guanzhong Basin Geographically weighted regression Generalized additive model |
| DOI | 10.3390/rs11222679 |
| 通讯作者 | Yang, Zhiqiang(yang_gps@chd.edu.cn) |
| 英文摘要 | Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 mu m) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km x 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R-2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, -0.378 mu g/m(3), and 12.746 mu g/m(3), respectively, and the model cross-validation results were 0.703, 1.451 mu g/m(3), and 15.731 mu g/m(3), respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research. |
| WOS关键词 | GROUND-LEVEL PM2.5 ; AEROSOL OPTICAL DEPTH ; FINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; HAZE EPISODE ; TIME-SERIES ; DESERT DUST ; MODIS ; XIAN ; MORTALITY |
| 资助项目 | Central Universities Fund[310826175027] ; China Scholarship Council Fund[201806560027] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000502284300084 |
| 资助机构 | Central Universities Fund ; China Scholarship Council Fund |
| 源URL | [http://ir.ieecas.cn/handle/361006/13113]  |
| 专题 | 地球环境研究所_粉尘与环境研究室 |
| 通讯作者 | Yang, Zhiqiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Earth Environm, Xian 710075, Shaanxi, Peoples R China 2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China 3.Finnish Meteorol Inst, Climate Res Dept, Helsinki 00560, Finland 4.Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Kainan,de Leeuw, Gerrit,Yang, Zhiqiang,et al. Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China[J]. REMOTE SENSING,2019,11(22):22. |
| APA | Zhang, Kainan,de Leeuw, Gerrit,Yang, Zhiqiang,Chen, Xingfeng,Su, Xiaoli,&Jiao, Jiashuang.(2019).Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China.REMOTE SENSING,11(22),22. |
| MLA | Zhang, Kainan,et al."Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China".REMOTE SENSING 11.22(2019):22. |
入库方式： OAI收割
来源：地球环境研究所
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