Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity.

Subglacial discharge controls seasonal variations in the thermal structure of a glacial lake in Patagonia.

Water temperature in glacial lakes affects underwater melting and calving of glaciers terminating in lakes. Despite its importance, seasonal lake temperature variations are poorly understood because taking long-term measurements near the front of calving glaciers is challenging. To investigate the thermal structure and its seasonal variations, we performed year-around temperature and current measurement at depths of 58-392 m in Lago Grey, a 410-m-deep glacial lake in Patagonia.