Avaliação da influência das condições meteorológicas na concentração de material particulado fino (MP2,5) em Belo Horizonte, MG

Santos, Fábio Soares dos; Pinto, Janaina Antonino; Maciel, Felipe Marinho; Horta, Frederico Silva; Albuquerque, Taciana Toledo de Almeida; Andrade, María de Fátima

doi:10.1590/s1413-41522019174045

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…Studies show that meteorological factors such as TEMP, reduction in RH, and WS can impair the dispersion of PM 2.5 , increasing health-related risks (INPE, 2019;CETESB, 2019). The studies by Santos, Carvalho and Reboita (2016) and Santos et al (2019) confirm a significant difference between the concentration of PM 2.5 in dry and rainy periods, indicating the association between meteorological parameters and the pollutant.…”

Section: Stationmentioning

confidence: 84%

Application of machine learning algorithms to PM2.5 concentration analysis in the state of São Paulo, Brazil

Godoy

Silva

Bueno

et al. 2020

Rev. Bras. Ciênc. Ambient. (Online)

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Air quality monitoring data are useful in different areas of research and have varied applications, especially with a focus on the relationship between air pollution, respiratory problems, and other health hazards. The main atmospheric pollutants are: ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and particulate matter (PM). PM is one of the main objects of study when one intends to protect people from exposure to pollutants. This study contributes to the analysis of PM2.5 in 21 stations in the state of São Paulo monitored by the Environmental Company of São Paulo State (CETESB). It employs cluster analysis, a prominent data mining method for detecting patterns and discovering similarities which is important for assessing air pollution, especially in a geographically vast area such as that of the state of São Paulo, which does not follow a single pattern. Another data mining technique (association rules) supports the analysis of the relationship between pollutants and meteorological variables, as it allows identifying changes between elements that occur together, in a wide variety of data. Our objectives include determining stations with similar behaviors and exploring the temporal variety of the pollutant as it relates to the dominant meteorological factors in the periods of high concentration. The clustering algorithm automatically separates stations according to their monthly averages of PM2.5 concentration between 2017 and 2019. The clusters of stations that showed the highest pollution rates essentially included urban centers with emissions by industries and vehicles, while those with the lowest rates were located further inland. A cyclical behavior in pollutant variation was also observed in the three years under study and for both clusters. For the months with the highest concentration of PM2.5, association rule learning was applied to connect air temperature, relative humidity, and wind speed with PM2.5 and carbon monoxide (CO) concentrations. The obtained results are useful to analyze the temporal and geolocation profiles of pollution by particulate matter, since they identify the behavior of the meteorological factors that predominate in periods of greater concentration.

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Section: Stationmentioning

confidence: 84%

Application of machine learning algorithms to PM2.5 concentration analysis in the state of São Paulo, Brazil

Godoy

Silva

Bueno

et al. 2020

Rev. Bras. Ciênc. Ambient. (Online)

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“…Among the implications of a high total rainfall is the increase of scavenging air pollutants by wet deposition (Shukla et al 2008 ; Freitas and Solci 2009 ; Vieira-Filho et al 2013 ; Yoo et al 2014 ; Santos et al 2019 ). Thus, given the high precipitation at the end of February, the NO concentrations were altered and, therefore, the identified intervention points were off in comparison to the social isolation period.…”

Section: Resultsmentioning

confidence: 99%

São Paulo’s atmospheric pollution reduction and its social isolation effect, Brazil

Rosse

Pereira

Boari

et al. 2020

Air Qual Atmos Health

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Since January 2020, studies report reductions in air pollution among several countries due to social isolation measures, which have been adopted in order to contain the coronavirus outbreak progress (COVID-19). This study aims to evaluate the change in the atmospheric pollution levels by NO and NO 2 in São Paulo City for the social isolation period. The NO and NO 2 hourly concentrations were obtained through air quality monitoring stations from CETESB, from January 14, 2020 to April 12, 2020. Mann-Kendall and the Pettitt tests were performed in the air pollutant time series. We observed an overall negative trend in all stations, indicating a decreasing temporal pattern in concentrations. Regarding NO, the highest absolute decrease rates were observed in the Congonhas (− 6.39 μg m −3 month −1 ) and Marginal Tietê (− 6.19 μg m −3 month −1 ) stations; regarding NO 2 , the highest rates were observed in the Marginal Tietê (− 4.45 μg m −3 month −1 ) and Cerqueira César (− 4.34 μg m −3 month −1 ) stations. In addition, we identified a turning point in the NO and NO 2 series trends that occurred close to the start date of the social isolation period (March 20, 2020). Moreover, from statistical analysis, it was found that NO 2 is a suitable surrogate for monitoring economic activities during social isolation periods. Thus, we concluded that social isolation measures implemented on March 20, 2020 caused significant changes in the air pollutant concentrations in the city of São Paulo (as high as − 200% in NO 2 levels).

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“…Among them, we emphasize the high rainfall volume in the São Paulo city in February 2020 (496.7mm), which is a 248% increase from the climatological value (200 mm, period 1981 -2010) and also higher than the last 4 years (INMET, 2020). Among the implications of a high rainfall index, is the increase of scavenging air pollutants by wet deposition (Torres and MARTINS 2005;Shukla et al 2008;Freitas and Solci 2009;Vieira-Filho et al 2013;Yoo et al 2014;Santos et al 2019). Thus,…”

Section: No -Nitrogen Monoxide No2 -Nitrogen Dioxidementioning

confidence: 99%

Atmospheric Pollution Reduction in São Paulo City and the Social Isolation Effect

Rosse¹,

Pereira²,

Boari³

et al. 2020

Preprint

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Since January 2020, some studies have been reporting a reduction in air pollution in several countries due to social isolation measures, which have been adopted in order to contain the coronavirus outbreak progress (Covid-19). This study aims to evaluate the change in the atmospheric pollution levels by NO and NO2 in São Paulo city in the social isolation period. The NO and NO2 hourly concentrations were obtained through air quality monitoring stations from CETESB, from January 14 to April 12, 2020. Mann-Kendall and the Pettitt tests were performed in the air pollutants time series. We observed an overall negative trend in all stations, indicating a decreasing temporal pattern in concentrations. Regarding NO, the highest decrease rates were observed in Congonhas (-6.39 µg.m-3month-1) and Marginal Tietê (-6.19 µg.m-3month-1) stations; regarding NO2, the highest rates were observed in Marginal Tietê (-4.45 µg.m-3month-1) and Cerqueira César (-4.34 µg.m-3month-1) stations. In addition, we identified a turning point in the NO and NO2 series trends that occurred close to the start date of the social isolation period (March 20, 2020). Moreover, from statistical analysis, it was found that NO2 is a suitable surrogate for monitoring economic activities during social isolation period. Thus, we concluded that social isolation measures implemented in March 20, 2020 caused significant changes in the air pollutants concentrations in São Paulo city (as high as -200% in NO2 levels).

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Avaliação da influência das condições meteorológicas na concentração de material particulado fino (MP2,5) em Belo Horizonte, MG

Cited by 16 publications

References 8 publications

Application of machine learning algorithms to PM2.5 concentration analysis in the state of São Paulo, Brazil

Application of machine learning algorithms to PM2.5 concentration analysis in the state of São Paulo, Brazil

São Paulo’s atmospheric pollution reduction and its social isolation effect, Brazil

Atmospheric Pollution Reduction in São Paulo City and the Social Isolation Effect

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