Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021

This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format.

In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available.The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.