Challenges and opportunities of ML and explainable AI in large-sample hydrology.

Machine learning (ML) is a powerful tool for hydrological modelling, prediction, dataset creation and the generation of insights into hydrological processes. As such, ML has become integral to the field of large-sample hydrology, where hundreds to thousands of river catchments are included within a single ML model to capture diverse hydrological behaviours and improve model generalizability. This manuscript outlines recent advances in ML for large-sample hydrology.

Benefits of refined NH emission controls on PM mitigation in Central China.

Atmospheric ammonia (NH) is one of the most crucial precursors of secondary inorganic aerosols. However, its emission control is still weakness over China. NH emission inventories of 2015 with and without considering a set of refined emission reduction strategies covering seven major NH emission sources were constructed in Central China.