Application of Probability Density Functions in Modelling Annual Data of Atmospheric NOx Temporal Concentration
Prieto, W.
Cremasco, M.
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Prieto W., Cremasco M., 2017, Application of Probability Density Functions in Modelling Annual Data of Atmospheric NOx Temporal Concentration, Chemical Engineering Transactions, 57, 487-492.
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Currently it is observed, in many countries, an increasing concern by environmental agencies to monitor and control the air pollutants levels and, in this scenario, nitrogen oxides mainly arising from combustion processes deserve special attention. In large cities, the concentration and dispersion of NOx should be monitored not only by its toxicity, but also to be associated with photochemical production of tropospheric ozone, fine particulates, and its participates in the production of free radicals in atmosphere. In this context, the importance of understanding this phenomenon is grounded not only for to understand the complex dynamics involved in air pollution, but also the indispensability of the study of modeling and forecasting methodologies that can provide information for decision making with regard to the control of this compound in atmosphere. Thus, the present study aims to model, by probability density functions (PDF), the annual concentrations of NOx obtained in the period of 2010 to 2015 at the monitoring station of Ibirapuera Park, Sao Paulo, belonging to the Environmental Sanitation Technology Company of the State of Sao Paulo, Brazil. Initially, temporal data were exported directly from the electronic platform of Sao Paulo’s agency of air pollution control. The variation of annual NOx concentration is expressed in time series, with 1 hour of acquisition frequency and a total of 8,600 points/year. After obtaining the time series, the original data were organized into classes, and the maximum and minimum intervals determined by Sturges rule. In order to choose the most representative statically bin, it was evaluated the coefficient of variation of the mean to determine the point from which there are no more significant variations of the mean values of concentration of each time series. After this step, fourteen probability density functions were evaluated, and the fitting of the models were assessed by the Kolmogorov-Smirnov test. From the analyzes, it was concluded that the evaluated data showed clear positive displacement and leptokurtic distribution, indicating the Gumbel probability density function as the most representative among those evaluated in this study.
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