Influence of a Biomass-Burning Event in PM2.5 Concentration and Air Quality: A Case Study in the Metropolitan Area of São Paulo

Moreira, Gregori de Arruda; Andrade, Izabel da Silva; Cacheffo, Alexandre; Lopes, Fábio; Yoshida, Alexandre Calzavara; Gomes, Antonio Arleques; Silva, Jonatan João da; Landulfo, Eduardo

doi:10.3390/s21020425

Cited by 11 publications

(11 citation statements)

References 38 publications

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“…The next stage of this work consisted of processing the multiparametric data from the three bicycle routes to obtain simplified information on the state of the pollutants. Other studies such as [ 12 , 36 , 37 ] calculated an air-quality index to express the concentration values in a way that could be understood by all users. In our case, once the calibration algorithm was trained and validated, it was applied to the data collected by the device on the three routes in the city of Badajoz as described above.…”

Section: Resultsmentioning

confidence: 99%

A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists

Gómez-Suárez

Arroyo

Alfonso³

et al. 2022

Sensors

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We present a device based on low-cost electrochemical and optical sensors, designed to be attached to bicycle handlebars, with the aim of monitoring the air quality in urban environments. The system has three electrochemical sensors for measuring NO2 and O3 and an optical particle-matter (PM) sensor for PM2.5 and PM10 concentrations. The electronic instrumentation was home-developed for this application. To ensure a constant air flow, the input fan of the particle sensor is used as an air supply pump to the rest of the sensors. Eight identical devices were built; two were collocated in parallel with a reference urban-air-quality-monitoring station and calibrated using a neural network (R2 > 0.83). Several bicycle routes were carried out throughout the city of Badajoz (Spain) to allow the device to be tested in real field conditions. An air-quality index was calculated to facilitate the user’s understanding. The results show that this index provides data on the spatiotemporal variability of pollutants between the central and peripheral areas, including changes between weekdays and weekends and between different times of the day, thus providing valuable information for citizens through a dedicated cloud-based data platform.

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Section: Resultsmentioning

confidence: 99%

A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists

Gómez-Suárez

Arroyo

Alfonso³

et al. 2022

Sensors

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“…To investigate long‐range transport, CO and aerosols (i.e., PM 2.5 and PM 10 ) serve as useful indicators because they are pollutants strongly emitted by biomass combustion processes (e.g., Andreae, 2019; Yokelson et al., 2008). Their long atmospheric lifetimes (typically exceeding a week) allow them to be often transported over long‐range to São Paulo (e.g., Duarte et al., 2021; Martins et al., 2018; Moreira et al., 2021; Pereira et al., 2021; Squizzato et al., 2021). We compare the observed and modeled concentrations of CO and PM 2.5 (Figure 5), as well as of PM 10 and of the PM 2.5 /PM 10 ratio to estimate the proportion of fine aerosols compared to coarse aerosols (Figure S4 in Supporting Information ).…”

Section: Assessment Of the Regional Air Quality Model Ensemblementioning

confidence: 99%

“…Their long atmospheric lifetimes (typically exceeding a week) allow them to be often transported over long-range to São Paulo (e.g., Duarte et al, 2021;Martins et al, 2018;Moreira et al, 2021;Pereira et al, 2021;Squizzato et al, 2021). We compare the observed and modeled concentrations of CO and PM 2.5 (Figure 5), as well as of PM 10 and of the PM 2.5 /PM 10 ratio to estimate the proportion of fine aerosols compared to coarse aerosols (Figure S4 in Supporting Information S1).…”

Section: Long-range Transport Of Pollutionmentioning

confidence: 99%

“…The proportion of secondary organic aerosols and black carbon in the PM 2.5 concentration is high due to the composition of the fuels used (Albuquerque et al., 2019). In addition, the air quality of the metropolitan area is often affected by the transport of biomass burning pollutants from remote areas (e.g., Duarte et al., 2021; Martins et al., 2018; Moreira et al., 2021; Pereira et al., 2021; Squizzato et al., 2021). Despite the emission reduction measures implemented since the 1970s, the air quality in São Paulo is neither good for PM 2.5 , with about 25 exceedances per year of the WHO air quality standard (guidelines used before 2021), nor for O 3 , with about 100 exceedances per year (Andrade et al., 2017), and without decreasing trends (e.g., Carvalho et al., 2015; Chiquetto et al., 2020; Schuch et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Air Quality Models in a Megacity: Toward an Operational Ensemble Forecasting System for São Paulo

Deroubaix,

Hoelzemann,

Ynoue

et al. 2024

JGR Atmospheres

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An intercomparison of four regional air quality models is performed in the tropical megacity of São Paulo with the perspective of developing a forecasting system based on a model ensemble. Modeled concentrations of the main regulated pollutants are compared with combined observations in the megacity center, after analyzing the spatial scale of representativeness of air monitoring stations. During three contrasting periods characterized by different types of pollution events, the hourly concentrations of carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM2.5 and PM10) modeled by the ensemble are in moderate agreement with observations. The median of the ensemble provides the best performance (R ≈ 0.7 for CO, 0.7 for NOx, 0.5 for SO2, 0.5 for PM2.5, and 0.4 for PM10) because each model has periods and pollutants for which it has the best agreement. NOx concentration is modeled with a large inter‐model variability, highlighting potential for improvement of anthropogenic emissions. Pollutants transported by biomass burning events strongly affect the air quality in São Paulo and are associated with significant inter‐model variability. Modeled hourly concentration of ozone (O3) is overestimated during the day (≈20 ppb) and underestimated at night (≈10 ppb), while nitrogen dioxide (NO2) is overestimated at night (≈20 ppb). The observed O3 concentration is best reproduced by the median of the ensemble (R ≈ 0.8), taking advantage of the variable performance of the models. Therefore, an operational air quality forecast system based on a regional model ensemble is promising for São Paulo.

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“…During the dry period (mainly between July and October), the central region of Brazil suffers from biomass burning, which releases large quantities of gases and particles into the atmosphere [16]. Plumes of biomass burning from the interior of Brazil can reach São Paulo, worsening the air quality at certain times [17,18]. In August 2019, smoke plumes reached the MASP and caused high concentrations of pollutants and poor air quality for two consecutive days [19].…”

Section: Introductionmentioning

confidence: 99%

Chemical Characterization and Optical Properties of the Aerosol in São Paulo, Brazil

Vieira,

do Rosario,

Yamasoe

et al. 2023

Atmosphere

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Air pollution in the Metropolitan Area of São Paulo (MASP), Brazil, is a serious problem and is strongly affected by local sources. However, atmosphere column composition in MASP is also affected by biomass burning aerosol (BB). Understanding the impacts of aerosol particles, from both vehicles and BB, on the air quality and climate depends on in-depth research with knowledge of some parameters such as the optical properties of particles and their chemical composition. This study characterized fine particulate matter (PM2.5) from July 2019 to August 2020 in the eastern part of the MASP, relating the chemical composition data obtained at the surface and columnar optical parameters, such as aerosol optical depth (AOD), Ångström Exponent (AE), and single-scattering albedo (SSA). According to the analyzed data, the mean PM2.5 concentration was 18.0 ± 12.5 µg/m3; however, daily events exceeded 75 times the air quality standard of the World Health Organization (15 µg/m3). The mean black carbon concentration was 1.8 ± 1.5 µg/m3 in the sampling period. Positive matrix factorization (PMF) identified four main sources of aerosol: heavy vehicles (42%), followed by soil dust plus local sources (38.7%), light vehicles (9.9%), and local sources (8.6%). AOD and AE presented the highest values in the dry period, during which biomass burning events are more frequent, suggesting smaller particles in the atmosphere. SSA values at 440 nm were between 0.86 and 0.94, with lower values in the winter months, indicating the presence of more absorbing aerosol.

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Influence of a Biomass-Burning Event in PM2.5 Concentration and Air Quality: A Case Study in the Metropolitan Area of São Paulo

Cited by 11 publications

References 38 publications

A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists

A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists

Intercomparison of Air Quality Models in a Megacity: Toward an Operational Ensemble Forecasting System for São Paulo

Chemical Characterization and Optical Properties of the Aerosol in São Paulo, Brazil

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