Modelling annual data of Pm10 atmospheric in Campinas city from probability density function
Marco Cremasco, Osvaldir Taranto, Guilherme Jose Castilho, Edson Tomaz
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Agradecimentos: The authors would like to thank DEPro/FEQ/Unicamp for the financial support
Inhalable particulates (PM10) are those ones that present diameter less than 10µ in the atmosphere. The effects of atmospheric levels can cause since fatigue, burning eyes, nose and throat to serious risk of respiratory and cardiovascular disease manifestation, and an increase in premature deaths of...
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Inhalable particulates (PM10) are those ones that present diameter less than 10µ in the atmosphere. The effects of atmospheric levels can cause since fatigue, burning eyes, nose and throat to serious risk of respiratory and cardiovascular disease manifestation, and an increase in premature deaths of sensitive groups (children, the elderly, and suffering from respiratory and cardiac diseases). Due to importance of this subject, the goal of this work is to verify the model, based on probability density function (PDF), that describes the annual concentrations of PM10, obtained from time series in the period of 2015 to 2017, provided from monitoring of two stations of Campinas City, Sao Paulo State, Brazil. Known the time series, it is necessary to group the original data into classes. The strategy employed in this study is to choose a representative statically bin, which it is evaluated by saturation of coefficient of variation with increase of the classes. After that, several PDFs were evaluated. The Kolmogorov-Smirnov and sum of the quadratic errors criteria were applied on the determination of the best fit
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Modelling annual data of Pm10 atmospheric in Campinas city from probability density function
Marco Cremasco, Osvaldir Taranto, Guilherme Jose Castilho, Edson Tomaz
Modelling annual data of Pm10 atmospheric in Campinas city from probability density function
Marco Cremasco, Osvaldir Taranto, Guilherme Jose Castilho, Edson Tomaz
Fontes
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Chemical engineering transactions Vol. 74 (2019), p. 349-354 |