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Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

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Quantifying Aviation-Related Contributions to Ambient Ultrafine Particle Number Concentrations Using Interpretable

Sean C Mueller¹, Prasad Patil², Jonathan I Levy¹

¹Department of Environmental Health, Boston University School of Public Health, 715 Albany Street, Boston, Massachusetts 02118, United States.

Environmental Science & Technology

|September 11, 2025

Summary

This summary is machine-generated.

Machine learning accurately identified aircraft as a key source of ultrafine particles (UFP) near airports. Even aircraft not flying overhead significantly impact air quality, especially with crosswinds.

Keywords:

SHAP XGBoost air pollution aircraft interpretable machine learning landing and takeoff operations source apportionment ultrafine particulate matter

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Published on: May 22, 2020

Area of Science:

Environmental Science
Atmospheric Chemistry
Machine Learning Applications

Background:

Ultrafine particles (UFP) from aircraft are a significant air quality concern near airports.
Quantifying aircraft UFP contributions is difficult due to traffic and meteorological factors.
Traditional methods struggle to capture complex source interactions.

Purpose of the Study:

To develop and validate a machine learning model for hourly source attribution of particle number concentrations (PNC).
To interpret model predictions using SHapley Additive exPlanations (SHAP) for detailed source insights.
To assess the impact of aircraft activity on near-airport air quality.

Main Methods:

Applied an ensemble machine learning model to multi-year PNC data near Boston Logan International Airport.
Incorporated meteorological data, road traffic, and runway-specific aircraft activity.
Utilized SHAP for model interpretation, identifying feature contributions and interactions.

Main Results:

The ML model achieved strong performance (R² = 0.66), outperforming typical hourly PNC models.
SHAP analysis revealed aircraft arrivals on perpendicular runways had the most significant impact.
Crosswinds were shown to increase ground-level air quality impacts from aircraft not flying overhead.
Intermediate planetary boundary layer heights correlated with elevated PNC from aircraft.

Conclusions:

The ML-SHAP framework provides a novel and transferable method for UFP source attribution.
Aircraft operations, including those not directly overhead, substantially influence near-airport air quality.
This approach enhances characterization of aviation-related UFP exposure in communities.