Estimation of PM2.5 Emission Rates Using the Pollutant Standards Index and Python-Based Modeling

Abstract

PM2.5 concentration (particulate matter with a diameter of ≤2.5 micrometers) is a critical indicator of air quality, especially during wildfire or peatland fire events. This fine particulate matter poses serious health risks due to its ability to penetrate deep into the respiratory tract. In emission modeling, PM2.5 concentration is often used as a key input variable for estimating the emission rate—defined as the amount of pollutant released from a source per unit of time. Emission rate is typically expressed in units of mass per second (e.g., µg/s or g/s) and is calculated by integrating PM2.5 concentrations with meteorological data such as wind speed, wind direction, and atmospheric dispersion coefficients. Within a meso-scale modeling framework, PM2.5 values—derived either from direct measurements or converted from the Pollutant Standards Index (PSI)—can be used in pollutant transport equations (such as modified Gaussian plume models or steady-state advection-diffusion equations) to estimate spatially distributed emission rates. Estimating emission rates based on PM2.5 concentrations is vital for pollution risk assessment, dispersion model validation, and the formulation of fire management and air quality policies. By integrating PM2.5 data from monitoring stations with meteorological parameters, Python-based modeling tools can generate dynamic and spatially-resolved emission rate estimates. This provides essential support for early warning systems and environmental emergency response strategies.