An improved method for retrieving aerosol optical depth using the ground-level meteorological data over the South-central Plain of Hebei Province, China

• Published in: Atmospheric pollution research Vol. 13; no. 3; p. 101334
• Main Authors: Li, Fuxing, Zhang, Lingyun, Wei, Qiang, Yang, Yi, Han, Fang, Li, Weimiao, Zhao, Chunli, Wang, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: Aerosol optical depth; Aerosol scale height; Ground-level meteorological data; South-central plain of hebei province; South-central plain of hebei province; Ground-level meteorological data; Aerosol optical depth; Aerosol scale height
• ISSN: 1309-1042; 1309-1042
• DOI: 10.1016/j.apr.2022.101334

To retrieve the aerosol optical depth (AOD) from ground-level meteorological measurements at regional scale, a new method, the revised Elterman's retrieval model (R-ERM), was developed based on the meteorological observations to retrieve the AOD. The aerosol scale height (ASH1) algorithm might introduce significant biases into AOD retrieval. Thus, the model enhances the AOD retrieval precision by redefining the ASH1 algorithm. The model was evaluated and validated against the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD data with a 1-km spatial resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) collected over the South-central Plain of Hebei Province region, China for the period of 2016–2017. Results indicate that, with the redefinition of the ASH1 algorithm, the overall the Pearson's correlation coefficient is 0.69 in 2017 between R-ERM and MAIAC AOD, and root mean squared error and the relative error (RE) are 0.20 and 23%, respectively. The evaluation proves that the R-ERM performs previous models, such as Elterman's retrieval model (ERM) with an overall validation R of 0.11 and Qiu's retrieval model (QRM) with an overall validation R of 0.35. The spatial patterns of the retrieved AOD after ordinary Kriging interpolation are consistent with those of the MAIAC datasets. Adding the water vapor pressure parameter significantly improved the estimation accuracy of ASH1, which is a key factor to the AOD retrieval results. The findings from the study demonstrate the great potential and value of the R-ERM for regional AOD retrieval. •An improved model for AOD estimation based on the meteorological variables is proposed.•ASH1 algorithm is revised to enhance the model's performance.•The proposed model with an over-all R = 0.78 outperforms previous studies.