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

Abstract

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. Keywords Trajectories • HYSPLIT model • Air mass residence time • Air pollution 1 IntroductionAir pollution occurs when a substance present in the atmosphere, resulting from natural (Morris & Therivel, 2009) or anthropogenic activities, can cause detrimental effects on humans, animals, vegetation, and even materials (Kampa & Castanas, 2008;Lelieveld et al., 2015;Feng et al., 2019). Anthropogenic sources, such as fossil fuels, industrial processes, and construction, are the main causes of air pollution (Drumm et al., 2014;Seinfeld & Pandis, 2006). According to Fajersztajn et al. (2017), air pollution is among the main health risks worldwide, and, in 2050, deaths related to air pollution may overcome those due to lack of sanitation and malaria. Consequently, exposure to air pollutants has severe health effects, such as mortality Abstract 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-PM 10 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/m 3 , with a maximum concentration of 114.38 µg/m 3 . Results point to autumn, winter, and spring as the seasons with the highest number of episodes, accounting for

Organophosphate esters (OPEs) in atmospheric particulate matter in different Brazilian regions

“…Low wind speed, low relative humidity, and low height of the mixing boundary layer hinder pollutant dispersion. 40 TEHP also showed a signicant negative correlation (p < 0.05) with wind speed, differently from the correlation between PM 10 and TEHP.…”

“…38,39 In Limeira, intense vehicle traffic, industrial emissions and biomass burning are important contribution sources. 40 The OPEs found in the samples analyzed were TNBP, TDCIPP, TPHP, TBOEP, EHDPHP, and TEHP. While TCEP and TCIPP were probably not found in the PM as, according to the gas-particle partitioning models, these compounds should be predominantly present in the gaseous phase.…”

Brazilian truckers’ strike and particulate matter (PM10) concentration: Temporal trend and time series models

“…Besides, Limeira is close to an important pole of industrial ceramic production, especially in the city of Santa Gertrudes. The activities of extraction, processing, and transport of raw materials are important sources of PM in the city of Santa Gertrudes, causing significant impacts on air quality (CETESB, 2020;Nogarotto et al, 2021). Thus, both vehicular emissions and industrial emissions are relevant sources of air pollution in this city (Gonçalves et al, 2022).…”

“…PM 10 concentrations were obtained from the hourly measurements, totaling 24 measurements per day, made by automatic stations in the cities of Campinas (Figure 1 -green dot) and Limeira (Figure 1 -blue dot) between May 1 st and June 30 th , 2018. The automatic station of the city of Campinas is located within the Taquaral Park (South latitude 22°52'28'' and West longitude 47°03'3''); while in Limeira, it is located next to the City Park, the main park of Limeira (South latitude 22°33'49'' and West longitude 47°24'51'') (Nogarotto et al, 2021).…”

“…An increase in PM 10 may be associated with urban dynamics and meteorological variables (Ramírez et al, 2018;Nogarotto and Pozza, 2020). It can include the reduction of the boundary layer height (Singh et al, 2020), variability in humidity conditions (Singh et al, 2020), and the horizontal movement of air masses (Nogarotto et al, 2021).…”