• Corpus ID: 119444732

Validation approaches for satellite-based PM2.5 estimation: Assessment and a new approach

@article{Li2018ValidationAF,
  title={Validation approaches for satellite-based PM2.5 estimation: Assessment and a new approach},
  author={Tongwen Li and Huanfeng Shen and Qiangqiang Yuan and Liang-pei Zhang},
  journal={arXiv: Atmospheric and Oceanic Physics},
  year={2018}
}

• Tongwen Li, Huanfeng Shen, +1 author Liang-pei Zhang
• Published 1 December 2018
• Environmental Science
• arXiv: Atmospheric and Oceanic Physics

Satellite-derived aerosol optical depth (AOD) has been increasingly employed for the estimation of ground-level PM2.5, which is often achieved by modeling the relationship between AOD and PM2.5. To evaluate the accuracy of PM2.5 estimation, the cross-validation (CV) technique has been widely used. There have been several CV-based validation approaches applied for the AOD-PM2.5 models. However, the applicable conditions of these validation approaches still remain unclear. Additionally, is there space to develop better validation approaches for the AOD-PM2.5 models? The contributions of this study can be summarized as two aspects. Firstly, we comprehensively analyze and assess the existing validation approaches, and give the suggestions for applicable conditions of them. Then, the existing validation approaches do not take the distance to monitoring station into consideration. A new validation approach considering the distance to monitoring station is proposed in this study. Among the existing validation approaches, the sample-based CV is used to reflect the overall prediction ability; the site-based CV and the region-based CV have the potentials to evaluate spatial prediction performance; the time-based CV and the historical validation are capable of evaluating temporal prediction accuracy. In addition, the validation results indicate that the proposed validation approach has shown great potentials to better evaluate the accuracy of PM2.5 estimation. This study provides application implications and new perspectives for the validation of AOD-PM2.5 models.