Modelling of airborne particulate matter concentration in underground stations using a two size-class conservation...

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Transport Engineering

Background:

• Underground railway stations are environments with significant airborne particulate matter (APM).
• Understanding APM dynamics is crucial for public health and air quality management in these confined spaces.
• Existing models may not fully capture the complex interplay of factors influencing APM in such settings.

Purpose of the Study:

• To develop a predictive model for airborne particulate matter concentrations in underground railway stations.
• To represent aerosol dynamics using ordinary differential equations based on key influencing factors.
• To validate the model's performance against experimental data and existing literature.

Main Methods:

• Development of a mathematical model using ordinary differential equations for two particle size classes.
• Incorporation of variables such as train traffic, ventilation rates, and deposition.
• Numerical parameter identification using a genetic algorithm.
• Quantitative validation with experimental data from a Paris underground station.
• Qualitative comparison with findings from relevant scientific literature.

Main Results:

• The genetic algorithm successfully identified model parameters within expected orders of magnitude.
• Quantitative comparison showed good accordance between the model's numerical results and experimental data.
• Qualitative assessment aligned with existing literature, supporting the model's validity.

Conclusions:

• The proposed ordinary differential equation model provides a reliable method for predicting airborne particulate matter in underground railway environments.
• The model effectively captures the influence of train traffic, ventilation, and deposition on particle concentrations.
• This predictive capability can aid in developing strategies for improving air quality in subterranean transport systems.