A WAVELET THRESHOLDING APPLICATION ON PARTICULATE MATERIAL CONCENTRATION TIME SERIES
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https://doi.org/10.53612/recisatec.v1i2.7Palavras-chave:
ruído é extremamente importante na análise de dadosResumo
Denoising data is extremely important in data analysis to visualize patterns, estimate structural features present in the data and avoid misclassifications. In environmental field, it is of interest to identify days with high air pollutants levels that can impact on population health. Pollutants levels collected data in a certain region, as occur in any data collection procedure, have presence of random noise, which can lead to misclassifications of the real context of pollution at the considered region. In this sense, statistical denoising methods are welcome to reduce noise in the data. The present paper proposes the use of a soft wavelet thresholding with universal threshold policy for denoising particulate materials (PM) time series and identifying days with high concentration levels of these pollutants. The technique is applied on PM10 and PM2.5 time series collected by the São Paulo Environmental Company (CETESB) from Santos station during the period of 2018-2020.
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