Use of Trajectory Regression Analysis to Understand High-PM10 Episodes: a Case Study in Limeira, Brazil

Nogarotto, Danilo Covaes; de Souza, Felipe Lima Campos; Ribeiro, Flávia Noronha Dutra; Pozza, Simone Andréa

Use of Trajectory Regression Analysis to Understand High-PM10 Episodes: a Case Study in Limeira, Brazil

2021

Nogarotto, Danilo Covaes | de Souza, Felipe Lima Campos | Ribeiro, Flávia Noronha Dutra | Pozza, Simone Andréa

Emitted from vehicles, plant biomass combustion, and industries, particulate matter (PM) is an air pollutant widely studied by the scientific community due to its health effects (cardiorespiratory diseases, cancers, eye irritations, among others). The present study evaluates periods with high PM concentrations, defined as high-PM₁₀ episodes (daily concentrations above the 75th percentile), to define and assess the main possible sources of PM emission in the city of Limeira, São Paulo, Brazil. To determine the location of such sources, the trajectory regression analysis (TRA) statistical tool was used, based on trajectories obtained from the HYSPLIT model. The 75th percentile was calculated at 41.21 µg/m³, with a maximum concentration of 114.38 µg/m³. Results point to autumn, winter, and spring as the seasons with the highest number of episodes, accounting for 33, 91, and 49 episodes, respectively. April 2016 (20 episodes), July 2016 (25), and September 2017 (27), possibly due to the low precipitation rates, had the highest monthly totals. TRA showed that local sources (within a 500 km radius) were the ones contributing the most to PM concentration in the period studied, totaling 55%, which allows us to point to vehicle and industrial emissions near the city of Limeira as the main sources.

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