# Taking drift-diffusion analysis from the study of turbulent flows to the study of particulate matter smog and air pollutants dynamics

## TL;DR

This study adapts drift-diffusion analysis from turbulent flow physics to model the dynamics of particulate matter and air pollutants, revealing their time-dependent behavior in Chiangmai, Thailand.

## Contribution

It introduces the application of drift-diffusion analysis to air pollution data, demonstrating its effectiveness in modeling pollutant dynamics and seasonal variations.

## Key findings

- All three variants explain annual pollutant peaks.
- Pollutant parameters vary periodically throughout the year.
- Underlying models are explicitly time-dependent.

## Abstract

Drift-diffusion analysis has been introduced in physics as a method to study turbulent flows. In the current study, it is proposed to use the method to identify underlying dynamical models of particulate matter smog, ozone and nitrogen dioxide concentrations. Data from Chiangmai are considered, which is a major city in the northern part of Thailand that recently has witnessed a dramatic increase of hospitalization that are assumed to be related to extreme air pollution levels. Three variants of the drift-diffusion analysis method (kernel-density, binning, linear approximation) are considered. It is shown that all three variants explain the annual pollutant peaks during the first half of the year by assuming that the parameters of the physical-chemical evolution equations of the pollutants vary periodically throughout the year. Therefore, our analysis provides evidence that the underlying dynamical models of the three pollutants being considered are explicitly time-dependent.