Hans Jarett Ong

Hans Jarett Ong

PhD candidate at NAIST specializing in Causal AI. My research connects theory with practical applications to build robust and fair AI systems, drawing on my experience deploying ML models.

Using Mahalanobis Distance to Classify Aerosol in Southeast Asia based on AERONET-Retrieved Optical Properties

Published in Ateneo de Manila University, 2019

Aerosol types over Southeast Asia (SEA) were classified using Mahalanobis distance applied to AERONET-retrieved optical properties (AE, SSA, refractive index) for 25 observation sites.

Seven aerosol classes were identified:

  • Biomass burning white/dark smoke (BB-W, BB-D)
  • Polluted dust (PD)
  • Urban industrial (UI, UI-D)
  • Mineral dust (MD)
  • Marine aerosols

The majority of sites were dominated by PD and UI-D, with geographical trends in reflectivity and absorption aligning with regional pollution and combustion patterns.

Recommended citation: Ong, Hans Jarett J. (2019). "Using Mahalanobis Distance to Classify Aerosol in Southeast Asia based on AERONET-Retrieved Optical Properties." Bachelors Thesis, Ateneo de Manila University.
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