Triangulating residential biomass burning impacts on PM2.5 via PMF, satellite observation-based emission inventory, and WRF-Chem

Abstract

In agricultural regions of northern China, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) pollution driven by biomass burning remains a critical environmental challenge, yet uncertainties persist in source apportionment due to methodological limitations and insufficient multi-method validation in agriculturally intensive areas. This study synergistically applied receptor modeling (PMF, Positive Matrix Factorization), emission inventory, and WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations to quantify biomass burning contributions in Siping City, Jilin Province, using year-round PM2.5 compositional data (July 2021–June 2022) and localized emission parameters. The PMF model resolved six sources, identifying biomass burning as the dominant contributor (35.10 %), corroborated by emission inventory revisions incorporating satellite-derived burned area mapping (50.73 km2), which attributed 37.50 % of total PM2.5 emissions to biomass burning. WRF-Chem simulations, driven by the revised inventory, demonstrated that biomass burning sources collectively contributed 40.13 % to ambient PM2.5 during an episode, with residential and open burning of biomass accounting for 28.12 % and 12.01 %, respectively. These multi-method results consistently highlight biomass burning’s dominance (one-third of total emissions). The findings necessitate prioritizing residential biomass emission controls through clean energy transitions, stricter enforcement of combustion regulations, and integrated management strategies to mitigate air quality degradation in agricultural regions.

Keywords

• Biomass burning
• Multi-model source apportionment
• WRF-Chem
• Burned area
• Emission inventory