Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited. In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters. For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made.

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Over the polar regions, snow is frequently lifted by wind and forms blowing snow (BLSN). Over large areas of East Antarctica BLSN occurs over 60% of the time during the winter months (e.g., Palm et al., 2011). It has been known that BLSN plays an important role in many aspects of the Earth's cryospheric processes, such as ice sheet surface mass balance and polar hydrologic processes (e.g.,

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