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Predicting Atmospheric Particle Phase State Using an Explainable Machine Learning Approach Based on Particle Rebound
Yanting Qiu1, Yuechen Liu1, Zhijun Wu1,2
1State Joint Key Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
Environmental Science & Technology
|September 29, 2023
Summary
A new machine learning model accurately predicts atmospheric particle phase state using aerosol composition and humidity. This research provides crucial insights into particle behavior in urban environments, impacting air quality and climate studies.
Area of Science:
- Atmospheric Chemistry
- Environmental Science
- Computational Science
Background:
- Particle phase state is critical for atmospheric processes like gas-particle partitioning and ice nucleation.
- Characterizing the atmospheric phase state of particles is a significant scientific challenge.
Purpose of the Study:
- To develop a highly accurate machine learning model for predicting particle phase state.
- To assess the particle phase state in various urban environments using the developed model.
Main Methods:
- Developed a machine learning (ML) model using measured aerosol chemical composition and ambient relative humidity (RH).
- The model predicts the particle rebound fraction (f) as an indicator of particle phase state.
- Applied the ML model to predict particle phase state across different seasons and urban regions.
Main Results:
- The ML model achieved high accuracy (R² = 0.952) and robustness (RMSE = 0.078).
- Aerosols were found to be in a liquid state in mid-high latitude cities and a semisolid state in semiarid regions year-round.
- East Asian megacities showed liquid states in spring/summer and semisolid states in other seasons.
- Increased nitrate content promoted a liquid particle state even at lower RH.
Conclusions:
- A novel, accurate ML-based approach for predicting atmospheric particle phase state was established.
- The study highlights regional and seasonal variations in particle phase state, influenced by composition (e.g., nitrate) and RH.
- Findings enhance understanding of atmospheric particle behavior and its implications for air quality and climate.
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