Assessment of Black Carbon Impact on Himalayan Glacier Melting: Integrating Atmospheric Modelling and AI-Driven Predictive Analytics

Abstract- Black carbon (BC) particles from incomplete fossil fuel and biomass combustion accelerate Himalayan glacier retreat through atmospheric heating and surface darkening mechanisms. This research combines atmospheric transport modelling with artificial intelligence techniques to assess BC's contribution to glacial mass loss, examining 100,000 hourly meteorological and atmospheric observations. Results demonstrate BC accounts for approximately 13% of annual glacier melting, with pronounced seasonal variability showing winter-spring concentrations 2.3-2.8 times greater than summer levels, maximizing during March-April biomass burning periods. Anthropogenic sources contribute 66% of regional BC deposition, dominated by household fuel combustion, transportation emissions, and long-range transport from the Indo-Gangetic Plain. Daily concentration patterns exhibit dual peaks at 08:00 and 20:00, corresponding to traffic and domestic heating activities. Random Forest modelling achieved optimal predictive performance (R² = 0.895), identifying PM2.5 as the primary predictor variable (28.7% feature importance). Clear-sky conditions facilitate dry deposition processes (78% of total removal), with 62% of deposited BC existing as light-absorbing coated particles that enhance solar absorption. Monsoon rainfall effectively removes atmospheric BC through precipitation scavenging. Accelerating glacier retreat endangers water resources for over 750 million people while increasing flood hazards. Implementation of cleaner combustion technologies, improved emission standards, and sustainable agricultural practices could substantially reduce BC-induced glacial melting across this climatically sensitive region. Furthermore, establishing regional monitoring networks with high-resolution satellite observations and ground-based measurements would enable real-time tracking of BC concentrations and their immediate impacts on glacier surface albedo. Coordinated international policies addressing transboundary pollution transport are essential, particularly targeting emission reduction strategies in the Indo-Gangetic Plain where agricultural burning and industrial activities significantly contribute to upstream BC deposition. Keywords — Artificial Intelligence, Black Carbon, Glacier Melting, Predictive Analytics.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-07
DOI
https://doi.org/10.5281/zenodo.16762688
Primary Topic
Bacterial Identification and Susceptibility Testing
Type
article
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article

Assessment of Black Carbon Impact on Himalayan Glacier Melting: Integrating Atmospheric Modelling and AI-Driven Predictive Analytics

Zenodo (CERN European Organization for Nuclear Research)
Bacterial Identification and Susceptibility Testing
article

Assessment of Black Carbon Impact on Himalayan Glacier Melting: Integrating Atmospheric Modelling and AI-Driven Predictive Analytics

article en

Abstract

Abstract- Black carbon (BC) particles from incomplete fossil fuel and biomass combustion accelerate Himalayan glacier retreat through atmospheric heating and surface darkening mechanisms. This research combines atmospheric transport modelling with artificial intelligence techniques to assess BC's contribution to glacial mass loss, examining 100,000 hourly meteorological and atmospheric observations. Results demonstrate BC accounts for approximately 13% of annual glacier melting, with pronounced seasonal variability showing winter-spring concentrations 2.3-2.8 times greater than summer levels, maximizing during March-April biomass burning periods. Anthropogenic sources contribute 66% of regional BC deposition, dominated by household fuel combustion, transportation emissions, and long-range transport from the Indo-Gangetic Plain. Daily concentration patterns exhibit dual peaks at 08:00 and 20:00, corresponding to traffic and domestic heating activities. Random Forest modelling achieved optimal predictive performance (R² = 0.895), identifying PM2.5 as the primary predictor variable (28.7% feature importance). Clear-sky conditions facilitate dry deposition processes (78% of total removal), with 62% of deposited BC existing as light-absorbing coated particles that enhance solar absorption. Monsoon rainfall effectively removes atmospheric BC through precipitation scavenging. Accelerating glacier retreat endangers water resources for over 750 million people while increasing flood hazards. Implementation of cleaner combustion technologies, improved emission standards, and sustainable agricultural practices could substantially reduce BC-induced glacial melting across this climatically sensitive region. Furthermore, establishing regional monitoring networks with high-resolution satellite observations and ground-based measurements would enable real-time tracking of BC concentrations and their immediate impacts on glacier surface albedo. Coordinated international policies addressing transboundary pollution transport are essential, particularly targeting emission reduction strategies in the Indo-Gangetic Plain where agricultural burning and industrial activities significantly contribute to upstream BC deposition. Keywords — Artificial Intelligence, Black Carbon, Glacier Melting, Predictive Analytics.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 51%
Bacterial Identification and Susceptibility Testing
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Assessment of Black Carbon Impact on Himalayan Glacier Melting: Integrating Atmospheric Modelling and AI-Driven Predictive Analytics · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS