Analysis and visualization of multidimensional time series: Particulate matter (PM10) from Sao Carlos-SP (Brazil)

Author(s)

Alexandrina, Eduardo Carlos ^[1] ; Ortigossa, Evandro S. ^[2] ; Lui, Elaine Schornobay ^[3] ; Silveira Goncalves, Jose Antonio ^[1] ; Correa, Nivaldo Aparecido ^[4] ; Nonato, Luis Gustavo ^[2] ; Aguiar, Monica Lopes ^[1]

Affiliation

^[1] Univ Fed Sao Carlos, Dept Chem Engn, Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil

^[3] Technol Fed Univ Parana, Dept Environm Engn, Curitiba, Parana - Brazil

^[4] Univ Sao Paulo, Dept Hydraul Engn & Sanitat, Sao Paulo - Brazil

• Document type: Journal article
• Source: ATMOSPHERIC POLLUTION RESEARCH; v. 10, n. 4, p. 1299-1311, JUL 2019.
• Web of Science Citations: 0

Abstract

Data visualization techniques have been successfully used in a time series context for many years. In this work, a web-based data visualization system was developed to support the evolutionary analysis of atmospheric particulate matter (PM10) behavior, collected in the central region of Sao Carlos, Sao Paulo State, Brazil. The samples were acquired following two patterns: daily in the first stage and on alternate days in the second stage. Each measurement covered a period of 23 h and 30 min, using a large volume sampler (high-volume). To interrogate the acquired data, an interactive visualization tool was developed in JavaScript and D3 library. The tool is portable and can be used in all modern web browsers, not requiring any software installation. The values obtained from Sao Carlos' PM10 monitoring campaign from 2014 to 2017 clearly showed a decrease of PM10 in the city atmosphere, in comparison with past data from the 1997 to 2005 campaign. This decreasing trend in PM10 concentrations could be due to an increase in the severity of law n degrees 997 of 05/31/1976 and decree 54.487 of 06/26/2009, which charges the anthropogenic agents with air pollution control. (AU)

• FAPESP's process: 12/14928-2 - Prediction of particulate matter in the short and medium term with the use of artificial neural networks.
• Grantee: Elaine Schornobay
• Support Opportunities: Scholarships in Brazil - Doctorate