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Machine learning models based on satellite remote sensing have gained widespread use in estimating ground-level air pollutant concentrations, which overcome the limitations of the discontinuous spatial distribution of ground monitoring stations. However, due to the interdisciplinary nature of environmental modeling, atmospheric researchers may overlook some important issues when using machine learning. In this review, we summarize and discuss the overlooked but important issues in data preparation, model development, validation, and prediction, including feature engineering, imbalanced data, validation strategy, and model interpretation, which are critical for model generalizability. Firstly, we provide considerations and recommendations in obtaining, selecting, and using data of the main variables in machine learning for air quality mapping. Secondly, sufficient introduction and discussion are provided on …