Work

Monitoring of Light Absorbing Impurities (LAIs) in Snow and Glaciers along the Central Andes

Year: 2024

Type: article

Abstract: Chile is one of the countries with the largest glacier area in the world, accounting for approximately 80% of the glaciers in South America and vast zones of snow cover area. Climate Change in some regions of South America is generally characterized by increasing temperatures and decreasing precipitation, which have accelerated glacier retreat and reduced snowfall. Furthermore, in areas of the country conterminous to populated centers, glaciers and snow cover areas are particularly vulnerable due to exposure to pollution from urban centers and industrial activities related to nearby mining. Emissions from such anthropogenic sources result in increased deposition of impurities on their surfaces. These contaminants, known as LAIs (Light Absorbing Impurities) such as Black Carbon (BC), significantly reduce ice and snow albedo. In this study, a monitoring platform was developed to integrate satellite imagery for observing Black Carbon on snow and glacier surfaces. Sentinel 2 satellite data, corresponding to the Level-2 surface reflectance product, were utilized. Two glaciers were selected for analysis: one located in the central region near an active mining operation, named Olivares Gamma Glacier and another located away from urban centers, the Tupungatito Glacier. Satellite imagery data were compared with in-situ measurements. Snow pit samples were processed under standard laboratory conditions to quantify Black Carbon concentrations using single-soot particle photometry (SP2). Results indicated elevated Black Carbon concentration levels in the samples from Olivares Gamma, located in closer proximity to anthropogenic emission sources. These results enable the modeling of LAIs using radiative transfer models to establish sensitivity thresholds for glaciers to contamination by anthropogenic actions. Snow and albedo from in-situ stations present a systematic drift with satellite information, which can be attributed to the localization of the stations.

Source: Recent advances in remote sensing.

DOI: 10.62880/rars25004