This study aimed to verify the impact of inhalable particulate matter (PM 10 ) on cancer incidence and mortality in the city of São Paulo, Brazil. Statistical techniques were used to investigate the relationship between PM 10 on cancer incidence and mortality in selected districts. For some types of cancer (skin, lung, thyroid, larynx, and bladder)

A statistical study on the behavior of ground-level O 3

Objetivo. Este trabalho teve como objetivo geral verificar a influência do poluente atmosférico material particulado inalável (MP 10) na incidência e na mortalidade por câncer, na cidade de São Paulo. Métodos. Os dados de câncer foram coletados do Registro de Câncer de São Paulo e os dados do poluente provenientes da CETESB. Foram utilizadas técnicas estatísticas para verificar a relação do MP 10 sobre a incidência e a mortalidade por câncer (pele, pulmão, laringe, tireóide, estômago, próstata, colo do útero, mama, bexiga, cólon, esôfago e reto), nos distritos do Brás,

É expressamente proibida a comercialização deste documento tanto na sua forma impressa como eletrônica. Sua reprodução total ou parcial é permitida exclusivamente para fins acadêmicos e científicos, desde que na reprodução figure a identificação do autor, título, instituição, ano da tese. AGRADECIMENTOSAos meus pais pelo incentivo constante aos meus estudos e por estarem sempre ao meu lado. Introduction. The study and understanding of the effects of air pollution can contribute to the planning of pollutant emission control strategies and decision-making in relation to public health. Air pollution forecasting models are important, as they can anticipate precautions and actions of public action. Objetive. Develop and analyze tropospheric ozone forecasting models for the São Paulo Metropolitan Area (SPMA). Methods. Ozone forecasting models were adjusted using artificial neural networks (ANNs), called artificial intelligence techniques. The model input data were the weather, obtained from CPTEC -Weather and Climate Studies Prediction Center and INMETNational Meteorology Institute and the pollutant ozone data monitored by CETESB -São Paulo State Environmental Company. Were considered for ozone, the national standard of air quality (1 hour) and the state standard of air quality (8 hours). Data were distributed among the averages of the morning (07h to 12h) and the average of the afternoon (13h to 18h), obtaining as output the maximum concentrations of ozone to the afternoon. The study period was from 2008 to 2014. Results. Were conducted 311 tests distributed according to the standard of ozone air quality (O3-1h or O3-8h) and the source of meteorological data (CPTEC or INMET). The observed and estimated ozone values were shown to be very well correlated. For the settings using the CPTEC database, the best results of the verification statistics for O3-1h were: A= 90%; B=0.41; FAR=47%; POD=22%; r=0.60. Where A is the percentage of correct answers of forecasts in the events and not events; B indicates, on average, if the predictions are underestimated or overestimated; FAR is the percentage concentrations that were predicted high and that did not occur; POD is the ability to predict high concentrations (%) and r is the correlation coefficient between the observed value and the estimated value. To O3-8h: A=96%; B=0.1; FAR=14%; POD=6.5%; r=0.72. Considering the INMET database, the best results for O3-1h were: A=93%; B=0.54; FAR=29%; POD=38%, r=0.84. To O3-8h: A=95%; B=0.76; FAR=47%; POD=40%; r=0.85. The variables temperature and meridional wind were the most importante in the models. Conclusions. Overall, the simulated models with meteorological INMET data showed better results than those presented by the CPTEC data for both O3-1h, and for O3-8h. The simulated model with INMET data, considering O3-8h, presented better predictability. The models adjusted by neural networks showed up as good instruments to predict the ozone concentration in the SPMA.