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Article summary:
1. This paper presents an estimation result of PM2.5 concentration across China using a geospatial-temporal joint code to capture the influence of spatial-temporal information.
2. The proposed method outperforms the state-of-the-art method with higher R2, RMSE, and MAE values.
3. Partial dependence plots are used to visualize the complicated relationship between satellite-based aerosol products and PM2.5 concentrations.
Article analysis:
The article is generally reliable and trustworthy in its presentation of the research findings and conclusions. The authors provide a detailed description of their methodology, including the use of a geospatial-temporal joint code to capture the influence of spatial-temporal information, as well as partial dependence plots to visualize the complicated relationship between satellite-based aerosol products and PM2.5 concentrations. The authors also provide evidence for their claims by citing relevant studies in the field and providing experimental results that demonstrate the superiority of their proposed method over existing methods.
The only potential bias in this article is that it does not explore any counterarguments or present both sides equally; however, this is not necessarily a problem since this article is focused on presenting one particular approach to estimating PM2.5 concentrations rather than exploring all possible approaches or debating which approach is best. Additionally, there are no promotional content or partiality present in this article, nor does it fail to note any potential risks associated with its proposed approach; thus, overall, this article can be considered reliable and trustworthy in its presentation of research findings and conclusions regarding estimating PM2.5 concentrations using end-to-end gradient boosting models with geographical and temporal encoding features.