Efeitos da exposição a poluentes do ar na saúde das crianças de Cuiabá, Mato Grosso, Brasil

Machín, Adrian Blanco; Nascimento, Luiz Fernando Costa

doi:10.1590/0102-311x00006617

Cited by 26 publications

(20 citation statements)

References 21 publications

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“…20 The direct effects of this exposure may occur through agents that cross the pulmonary epithelium in the circulation, such as gases and possibly ultrafine particles (< 0.1 µ), along with soluble constituents Subsequently, this may contribute towards a systemic inflammatory state, which may, in turn, be able to activate hemostatic pathways, impair vascular function and accelerate atherosclerosis. 25 The present study had some limitations, and the way in which pollutant concentrations were estimated was one of them. This was done through mathematical modeling and may have provided incorrect data.…”

Section: Discussionmentioning

confidence: 90%

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Fine particulate matter and ischemic heart diseases inrelation to sex. An ecological time series study

et al. 2019

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BACKGROUND: Exposure to some air pollutants is associated with cardiovascular diseases. The objective of this study was to quantify the effect of exposure to fine particulate matter in hospitalizations due to ischemic heart disease and the costs to the healthcare system. DESIGN AND SETTING: Time-series ecological study conducted in Taubaté, Brazil. METHODS: Data on hospitalizations due to ischemic heart diseases (ICD I-20 to I-24) in the municipality of Taubaté (SP), Brazil, among adults of both sexes aged 40 years and over, from August 2011 to July 2012, were obtained from DATASUS. Fine particulate matter (PM 2.5) concentrations were estimated from a mathematical model. Poisson regression was used in statistical analyses to estimate the relative risks of exposure to PM 2.5 for both sexes and after stratification according to sex. The excess of hospitalizations and consequent excess expenditure for the healthcare system were calculated. RESULTS: There were 1040 admissions, among which 382 had ischemic heart diseases (257 males). The mean PM 2.5 concentration was 13.2 µg/m 3 (SD = 5.6). Significant effects from exposure were noted 4 and 5 days after exposure (lag 4 and lag 5) for both sexes and for male sex; for female sex, the effect was 2 days after exposure (lag 2). There were 59 excess hospitalizations for an increase in PM 2.5 concentration of 5 µg/m 3 and excess expenditure of US$ 150,000 for the National Health System. CONCLUSIONS: An excess of hospital admissions due to ischemic heart disease, with excess expenditure, was identified consequent to PM 2.5 exposure.

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Section: Discussionmentioning

confidence: 90%

“…Nonetheless, use of data estimated through the CCATT-BRAMS model, which has already been implemented in other studies, may form an alternative for studies on the effects of exposure to fine particulate matter on human health, such as in relation to respiratory and cardiovascular diseases. [22][23][24][25]…”

Section: Discussionmentioning

confidence: 99%

Fine particulate matter and ischemic heart diseases inrelation to sex. An ecological time series study

et al. 2019

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“…Considering that the periods with the highest concentration of PM 2.5 are the ones that present the greatest risk to the population and that meteorological factors have an influence on the increase in pollutant concentration, the rules presented in Table 6 could give warning indications for the increase in pollutant concentration. In Brazil, the studies by César et al (2016) and Machin and Nascimento (2018) show the influence of the 5 μg/m 3 increase in the concentrations of PM 2.5, resulting in increases between 20 and 38% in the risk of hospitalization due to pulmonary complications.…”

Section: Stationmentioning

confidence: 99%

“…According to the Environmental Company of São Paulo State (CETESB, 2019), the main air pollutants regulated by the National Environment Council (CONAMA) are: coarse inhalable particles (PM 10 ), fine inhalable particles (PM 2.5 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), ozone (O 3 ), total suspended particles (TSP), smoke (SMO), and lead (Pb), the latter three being monitored only in specific situations. Studies on the effects of pollution on health (POLEZER et al, 2018;MACHIN;NASCIMENTO, 2018;SEINFELD;PANDIS, 2016;NODARI;SALDANHA, 2016) show that exposure to fine particulate matter (PM 2.5 ) can cause respiratory problems and even premature deaths, since it penetrates deeply into the respiratory system, reaching the pulmonary alveoli and the bloodstream.…”

Section: Introductionmentioning

confidence: 99%

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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“…Recent studies have evaluated the adverse effects of air pollutants on the health of the population, including mortality rates, hospital admissions, and emergency room visits in hospitals because of cardiovascular, respiratory, and other diseases (2 –9). Other studies have shown that high levels of air pollution increase morbidity and mortality rates (10).…”

Section: Introductionmentioning

confidence: 99%

Effects of exposure to fine particulate matter in elderly hospitalizations due to respiratory diseases in the South of the Brazilian Amazon

Machín

Nascimento

Mantovani

et al. 2019

Braz J Med Biol Res

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Exposure to air pollution is an important cause of hospital admissions due to respiratory diseases. Nevertheless, few studies use pollutant concentration data estimated by mathematical models. A time-series ecological study was developed, using data from hospitalizations due to respiratory diseases in people over 60 years of age, residents of Cuiabá, Brazil, during 2012, obtained from the Brazilian Ministry of Health. The independent variables were the concentrations of fine particulate matter (PM2.5) and carbon monoxide (CO) estimated by mathematical modeling, minimum temperature, and relative humidity (obtained from the Brazilian Meteorological Agency), and the number of forest fires. The generalized linear regression model of Poisson was used, with lags of 0 to 7 days. The coefficients obtained were transformed into relative risk of hospitalization, with respective 95% confidence intervals; alpha=5% was adopted. In that year, 591 hospitalizations were evaluated, with a daily average of 1.61 (SD=1.49), the PM2.5 average concentration was 15.7 µg/m3, and the CO average concentration was 144.2 ppb. Significant associations between exposure to these contaminants and hospitalizations in lags 3 and 4 in 2012 were observed. There was a hospitalization risk increase of 31.8%, with an increase of 3.5 µg/m3 of PM2.5 concentrations and an increase of 188 in the total number of hospitalizations, with an expense of more than ≈US$ 96,000 for the Brazilian Public Health System. This study provided information on the cost of air pollution to the health system and the feasibility of using a mathematical model to estimate environmental concentration of air pollutants.

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Efeitos da exposição a poluentes do ar na saúde das crianças de Cuiabá, Mato Grosso, Brasil

Abstract: Efeitos da exposição a poluentes do ar na saúde das crianças de Cuiabá, Mato Grosso, BrasilEfectos de la exposición a contaminantes del aire en la salud de los niños de Cuiabá, Mato Grosso, Brasil

Cited by 26 publications

References 21 publications

Fine particulate matter and ischemic heart diseases inrelation to sex. An ecological time series study

Fine particulate matter and ischemic heart diseases inrelation to sex. An ecological time series study

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

Effects of exposure to fine particulate matter in elderly hospitalizations due to respiratory diseases in the South of the Brazilian Amazon

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