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Wildfire-impact identification in the Western USA using numerical techniques.

Siying Lu¹, Chiranjivi Bhattarai¹, W Patrick Arnott²

¹Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA.

Journal of Environmental Sciences (China)

|May 3, 2026

Summary

Wildfire smoke significantly alters aerosol particle size distribution and air pollutant concentrations. This study developed methods using k-means clustering and Random Forest classification to accurately identify smoke-influenced hours, aiding in detecting light wildfire smoke.

Keywords:

Air pollutants Machine learning Particle size distribution Random Forest Wildfire emissions k-means

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Area of Science:

Atmospheric Science
Environmental Science
Data Science

Background:

Aerosol particle size distribution (PSD) differs between wildfire smoke and clean conditions.
Assessing wildfire smoke impact requires analyzing air pollutant concentrations, PSD, and optical measurements.

Purpose of the Study:

To develop and validate methods for identifying smoke-influenced hours using air pollutant data.
To assess the effectiveness of k-means clustering and Random Forest classification in detecting wildfire smoke impacts.

Main Methods:

Applied k-means clustering using PM2.5, CO, O3, NOx concentrations, PSD, and optical measurements.
Developed a Random Forest classification system (RFCS) trained on k-means clusters and pollutant data.
Validated RFCS performance over 12 years of air pollutant concentration data.

Main Results:

K-means clustering effectively identified wildfire influences, showing larger particles and stronger PM2.5-CO correlations.
Wildfire clusters exhibited higher light extinction coefficients (βext), absorption Ångström exponent (AAE), and extinction Ångström exponent (EAE).
RFCS accurately identified smoke-influenced hours, with PM2.5 and CO concentrations 12x and 2.6x higher, respectively, and significantly elevated optical parameters.

Conclusions:

K-means clustering and RFCS provide robust methods for identifying smoke-influenced hours.
The developed methods are particularly useful for detecting light wildfire smoke.
This approach enhances air quality monitoring and wildfire impact assessment.