Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application.

A study on aerosols in the Brazilian subequatorial Amazon region, Tangará da Serra (TS) and Alta Floresta (AF) was conducted and compared to findings in an additional site with background characteristics (Manaus, AM). TS and AF counties suffer from intense biomass burning periods in the dry season, and it accounts for high levels of particles in the atmosphere. Chemical characterization of fine and coarse particulate matter (PM) was performed to quantify water-soluble ions (WSI) and black carbon (BC). The importance of explanatory variables was assessed using three machine learning techniques. Average concentrations of PM in AF and TS were similar (PM2.0, 17±10 µg m-3 (AF) and 16±11 µg m-3 (TS) and PM10-2.0, 13±5 µg m-3 (AF) and 11±7 µg m-3 (TS)), but higher than the background site. BC and SO4 2- were the prevalent components as they represented 27%-68% of particulates chemical composition. The combination of the machine learning techniques provided a further understanding of the pathways for PM concentration variability, and the results highlighted the influence of biomass burning for key sample groups and periods. PM2.0, BC, and most WSI presented higher concentrations in the dry season, providing further support for the influence of biomass burning.