In this work, we investigated the predictive power of graph machine learning and featurebased models in order to estimate environmental properties of chemicals. We first observed that although NeuralFP may outperform ECFP-based models, the best feature-based model may be more desirable when appropriate chemical features are selected, e.g. Mordred for solubility-related prediction tasks. We therefore recommended the best-performing featurebased models as a new baseline. Compared with baseline feature-based approaches, O-GNN achieved state-of-the-art predictive accuracy on all tested tasks of solubility, bioconcentration, metabolism, and contaminant reactivity. By analyzing the data efficiency of the baseline and graph neural networks, we can conclude that O-GNN outperforms the baseline significantly when an enough amount of data is provided, while conventional approaches reduce the prediction error in the low-data regime. Lastly, we thoroughly evaluated the model predictions from the two approaches based on parity plots, residual analysis and the PCA plots of Mordred descriptors and O-GNN-extracted features. O-GNN demonstrated a higher predictive power by distinguishing the environmental properties, e.g. Cl int , by molecule structures. We envision future works can be conducted as follows. In the low data regime, emerging ML methods may offer additional improvement, including multitask learning, 11 transfer learning, one-shot learning, and self-supervised learning. Where more data is available, modern graph machine learning models outperform the more commonly used ECFP fingerprint and feature-based models and should be the method of choice where prediction accuracy is prioritized.