scholarly journals Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution

2017 ◽  
Vol 51 (0) ◽  
Author(s):  
Luciano Eustáquio Chaves ◽  
Luiz Fernando Costa Nascimento ◽  
Paloma Maria Silva Rocha Rizol
Keyword(s):  
Particulate Matter ◽  
Sulfur Dioxide ◽  
Fuzzy Model ◽  
Sao Paulo ◽  
Variable Number ◽  
Actual Data ◽  
Apparent Temperature ◽  
São Paulo ◽  
Input Variables ◽  
Linguistic Approach

ABSTRACT OBJECTIVE Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State. METHODS This is a computational model using fuzzy logic based on Mamdani’s inference method. For the fuzzification of the input variables of particulate matter, ozone, sulfur dioxide and apparent temperature, we considered two relevancy functions for each variable with the linguistic approach: good and bad. For the output variable number of hospitalizations for asthma and pneumonia, we considered five relevancy functions: very low, low, medium, high and very high. DATASUS was our source for the number of hospitalizations in the year 2007 and the result provided by the model was correlated with the actual data of hospitalization with lag from zero to two days. The accuracy of the model was estimated by the ROC curve for each pollutant and in those lags. RESULTS In the year of 2007, 1,710 hospitalizations by pneumonia and asthma were recorded in São José dos Campos, State of São Paulo, with a daily average of 4.9 hospitalizations (SD = 2.9). The model output data showed positive and significant correlation (r = 0.38) with the actual data; the accuracies evaluated for the model were higher for sulfur dioxide in lag 0 and 2 and for particulate matter in lag 1. CONCLUSIONS Fuzzy modeling proved accurate for the pollutant exposure effects and hospitalization for pneumonia and asthma approach.

scholarly journals Physical-chemical characterization of the particulate matter inside two road tunnels in the São Paulo Metropolitan Area

2013 ◽  
Vol 13 (8) ◽  
pp. 20839-20883 ◽  
Author(s):  
J. Brito ◽  
L. V. Rizzo ◽  
P. Herckes ◽  
P. C. Vasconcellos ◽  
S. E. S. Caumo ◽  
...  
Keyword(s):  
Trace Elements ◽  
Particulate Matter ◽  
Real Time ◽  
Metropolitan Area ◽  
Single Scattering ◽  
Sao Paulo ◽  
Single Scattering Albedo ◽  
Fuel Composition ◽  
São Paulo ◽  
Road Tunnels

Abstract. The notable increase in biofuel usage by the road transportation sector in Brazil during recent years has significantly altered the vehicular fuel composition. Consequently, many uncertainties are currently found in particulate matter vehicular emission profiles. In an effort to better characterize the emitted particulate matter, measurements of aerosol physical and chemical properties were undertaken inside two tunnels located in the São Paulo Metropolitan Area (SPMA). The tunnels show very distinct fleet profiles: in the Jânio Quadros (JQ) tunnel, the vast majority of the circulating fleet are Light Duty Vehicles (LDVs), fuelled on average with the same amount of ethanol as gasoline. In the Rodoanel (RA) tunnel, the particulate emission is dominated by Heavy Duty Vehicles (HDVs) fuelled with diesel (5% biodiesel). In the JQ tunnel, PM2.5 concentration was on average 52 μg m−3, with the largest contribution of Organic Mass (OM, 42%), followed by Elemental Carbon (EC, 17%) and Crustal elements (13%). Sulphate accounted for 7% of PM2.5 and the sum of other trace elements was 10%. In the RA tunnel, PM2.5 was on average 233 μg m−3, mostly composed of EC (52%) and OM (39%). Sulphate, crustal and the trace elements showed a minor contribution with 5%, 1% and 1%, respectively. The average OC:EC ratio in the JQ tunnel was 1.59 ± 0.09, indicating an important contribution of EC despite the high ethanol fraction in the fuel composition. In the RA tunnel, the OC:EC ratio was 0.49 ± 0.12, consistent with previous measurements of diesel fuelled HDVs. Besides bulk carbonaceous aerosol measurement, Polycyclic Aromatic Hydrocarbons (PAHs) were quantified. The sum of the PAHs concentration was 56 ± 5 ng m−3 and 45 ± 9 ng m−3 in the RA and JQ tunnel, respectively. In the JQ tunnel, Benzo(a)pyrene (BaP) ranged from 0.9 to 6.7 ng m−3 (0.02–0.1‰ of PM2.5) in the JQ tunnel whereas in the RA tunnel BaP ranged from 0.9 to 4.9 ng m−3 (0.004–0.02‰ of PM2.5), indicating an important relative contribution of LDVs emission to atmospheric BaP. Real-time measurements performed in both tunnels provided aerosol size distributions and optical properties. The average particle count yielded 73 000 cm−3 in the JQ tunnel and 366 000 cm−3 in the RA tunnel, with an average diameter of 48 nm in the former and 39 nm in the latter. Aerosol single scattering albedo, calculated from scattering and absorption observations in the JQ tunnel, showed a minimum value of 0.4 at the peak of the morning rush hour, reached 0.6 around noon and stabilized at 0.5 in the afternoon and evening. Such single scattering albedo range is close to other tunnel studies results, despite significant biofuel usage. Given the exceedingly high Black Carbon loadings in the RA tunnel, real time light absorption measurements were possible only in the JQ tunnel. Nevertheless, using EC measured from the filters a single scattering albedo of 0.32 for the RA tunnel has been estimated. The results presented here characterize particulate matter emitted from nearly 1 million vehicles fuelled with a considerable amount of biofuel, providing an unique experimental site worldwide.

scholarly journals Data based model for predicting COVID-19 morbidity and mortality in metropolis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Demian da Silveira Barcellos ◽  
Giovane Matheus Kayser Fernandes ◽  
Fábio Teodoro de Souza
Keyword(s):  
Environmental Variables ◽  
Methodological Approach ◽  
Sao Paulo ◽  
São Paulo ◽  
Health Authorities ◽  
Scientific Analysis ◽  
Public Health Authorities ◽  
Forecast Models ◽  
Input Variables ◽  
Epidemic Diseases

AbstractThere is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.

scholarly journals Influence of particulate matter and meteorological conditions on multiple sclerosis relapse: a preliminary study in São Paulo, Brazil

2017 ◽  
Vol 6 (8) ◽  
Author(s):  
Fernanda Rodrigues Diniz ◽  
Ana Lúcia Frony-Macedo ◽  
Marina Piacenti-Silva
Keyword(s):  
Multiple Sclerosis ◽  
Particulate Matter ◽  
Relative Humidity ◽  
Linear Models ◽  
Hospital Admissions ◽  
Demyelinating Disease ◽  
Inflammatory Responses ◽  
Sao Paulo ◽  
São Paulo ◽  
Linear Regression Models

Introduction: Multiple sclerosis (MS) is a chronic, inflammatory, and demyelinating disease of the central nervous system, which in some cases may be characterized by recurrent relapses of inflammation that cause mild to severe neurological disability. Some studies around the world have associated the increase of systemic inflammatory responses and neuro-inflammation of patients with exposure to high levels of particulate matter (PM10) and certain conditions of temperature and humidity. Materials and methods: The objective of this study was to verify the influence of the concentration of PM10 and meteorological variables (air temperature and relative humidity) on the number of hospitalizations forMS in the city of São Paulo. Data from 2008 to 2016, which passed through descriptive statistics and inferences, were used as multiple linear regression models. Results: The models obtained indicated a positive relation (p < 0.01) in the number of hospitalizations with the increase of PM10and relative humidity, showing that 31.23% of hospital admissions can be explained by these variables. Conclusion: These results are important, since there are no other studies from Brazil that correlate meteorological and air quality variables with MS.Descriptors: Air Pollution; Multiple Sclerosis; Linear Models.

scholarly journals Association between COVID-19, mobility and environment in São Paulo, Brazil

2021 ◽  
Author(s):  
Sergio Ibarra-Espinosa ◽  
Edmilson Dias de Freitas ◽  
Karl Ropkins ◽  
Francesca Dominici ◽  
Amanda Rehbein
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Relative Humidity ◽  
Residential Mobility ◽  
Additive Model ◽  
Sao Paulo ◽  
São Paulo ◽  
Ambient Particulate Matter ◽  
Mobility Index ◽  
Small Increments

AbstractBackgroundBrazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to control quarantines and prevent the transmissions of SARS-CoV2.In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, BrazilObjectivesTo estimate the associations between daily residential mobility index (RMI), air pollution, and meteorology, and daily cases and deaths for COVID-19 in São Paulo, Brazil.MethodsWe applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between residential mobility index and cases and deaths due to COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 3-21 days and 2) the association between exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility.ResultsWe found an RMI of 45.28% results in 1,212 cases (95% CI: 1,189 to 1,235) and 44 deaths (95% CI: 40 to 47). Reducing mobility 5% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 μg·m-3 of PM2.5 risk of 1.140 (95% CI: 1.021 to 1.274) for cases and of 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively.DiscussionWe compared our results with observations and literature review, finding well agreement. These results implicate that authorities and policymakers can use such mobility indices as tools to support social distance activities and assess their effectiveness in the coming weeks and months. Small increments of air pollution pose a risk of COVID-19 cases.ConclusionSpatial distancing is a determinant factor to control cases and deaths for COVID-19. Small increments of air pollution result in a high number of COVID-19 cases and deaths. PM2.5 has higher relative risks for COVID-19 than O3.

scholarly journals Association between particulate matter air pollution and monthly inhalation and nebulization procedures in Ribeirão Preto, São Paulo State, Brazil

2012 ◽  
Vol 28 (8) ◽  
pp. 1591-1598 ◽  
Author(s):  
Estela Cristina Carneseca ◽  
Jorge Alberto Achcar ◽  
Edson Zangiacomi Martinez
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Sao Paulo ◽  
Respiratory Morbidity ◽  
São Paulo ◽  
Random Factor ◽  
Paulo State ◽  
São Paulo State ◽  
The Impact ◽  
The Relationship

The study was designed to investigate the impact of air pollution on monthly inhalation/nebulization procedures in Ribeirão Preto, São Paulo State, Brazil, from 2004 to 2010. To assess the relationship between the procedures and particulate matter (PM10) a Bayesian Poisson regression model was used, including a random factor that captured extra-Poisson variability between counts. Particulate matter was associated with the monthly number of inhalation/nebulization procedures, but the inclusion of covariates (temperature, precipitation, and season of the year) suggests a possible confounding effect. Although other studies have linked particulate matter to an increasing number of visits due to respiratory morbidity, the results of this study suggest that such associations should be interpreted with caution.

scholarly journals Public health implications of particulate matter inside bus terminals in Sao Paulo, Brazil

2020 ◽  
Vol 711 ◽  
pp. 135064 ◽  
Author(s):  
Thiago Nogueira ◽  
Prashant Kumar ◽  
Adelaide Nardocci ◽  
Maria de Fatima Andrade
Keyword(s):  
Public Health ◽  
Particulate Matter ◽  
Sao Paulo ◽  
São Paulo ◽  
Health Implications
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