Visual Analytics for the Reduction of Air Pollution on Real-Time Data Derived from WSN
Nowadays, the contaminated and poor air quality that is inhaled by the city population in industrialized cities around the world has led to one of the main causes of premature death due to respiratory diseases. Therefore, the improvement of air quality becomes a priority. In this sense, the current study aimed to design and implement a visual analytics tool, in order to process large data sets, which have been generated by wireless sensor networks (WSN), which measured different environmental pollutants in real time. Hereby, the phases of the CRISP-DM methodology have been applied as a reference to guide the process. In the data preparation phase, programs have been implemented using Python. Then, the results have been stored in collections within a MongoDB database. Furthermore, for the modeling and visual exploration of the data, the Tableau tool has been used. The evaluation of the results allowed to demonstrate certain behavior of air pollutants around the city, such as the increased air pollution levels during daylight hours. Similarly, we discovered that the presence of particulate material PM10 and PM2.5 is directly related to the increase of the Air Quality Index for the city of Quito (IQCA). This leads to the conclusion that our analysis may be useful as a support tool in the decision-making of public policies for the reduction of air pollution.
KeywordsAir pollution Data mining Wireless sensor networks Visual analytics
The authors would like to thank the financial support of the Ecuadorian Corporation for the Development of Research and the Academy (RED CEDIA) in the development of this study, within the Project Grant CEPRA-XI-2017-13.
- 2.WHO: Ambient air pollution: A global assessment of exposure and burden of disease. World Health Organization, Geneva, Switzerland (2016)Google Scholar
- 3.Liao, Z., Peng, Y., Li, Y., Liang, X., Zhao, Y.: A web-based visual analytics system for air quality monitoring data. In: 2014 22nd International Conference on Geoinformatics (GeoInformatics), pp. 1–6. IEEE (2014)Google Scholar
- 5.Zheng, Y., Liu, F., Hsieh, H.: U-air: When urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1436–1444. ACM (2013)Google Scholar
- 9.Ahlers, D., Kraemer, F., Braten, A., Liu, X., Anthonisen, F., Driscoll, P., Krogstie, J.: Analysis and visualization of urban emission measurements in smart cities. In: Proceedings of the 21st International Conference on Extending Database Technology (EDBT) (2018)Google Scholar
- 13.Keim, D., Andrienko, G., Fekete, J.D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: Definition, process, and challenges. In: Information visualization, pp. 154–175. Springer, Berlin (2008)Google Scholar
- 14.Fuertes, W., Alyssa, C., Torres, J., Benítez, D.S., Tapia, F., Toulkeridis, T.: Data analytics on real-time air pollution monitoring system derived from a wireless sensor network (2019)Google Scholar
- 15.Marbán, Ó., Mariscal, G., Segovia, J.: A data mining and knowledge discovery process model. In: Data Mining and Knowledge Discovery in Real Life Applications. InTech (2009)Google Scholar
- 16.WHO.: Air Quality Guidelines: Global Update 2005. Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide. World Health Organization (2006)Google Scholar
- 17.Secretaría del Ambiente: índice Quiteño de la Calidad del Aire (2004)Google Scholar
- 18.del Ambiente, Secretaría: Informe de la Calidad del Aire del Distrito Metropolitano de Quito 2017. Tech. rep, Secretaría del Ambiente Alcaldía (2018)Google Scholar