Abstract
Air pollution is a problem that causes adverse effects, which tends to interfere with human comfort, health or well-being, and that may cause serious environmental damage. In this regard, this study aims to analyze large data sets generated by real-time wireless sensor networks that determine different air pollutants. Business Intelligence and Data Mining techniques have been applied in order to support subsequent decision-making strategies. For normalization and modeling, we applied the CRISP-DM methodology using the Pentaho Data Integration. Then, the Sap Lumira has been applied in order to acquire models of tables and views. For the data analysis, R-Studio has been used. For validation, Clustering has been applied using the k-means algorithm by the Jambu method, where it has been proceeded to check the consistency of these, being later stored and debugged in PostgreSQL. Results demonstrate that the increase in air pollutants is directly related to the traffic hours, which may cause an increase of asthma or sick related syndrome in the population. This analysis may also serve as a source of information to authorities for improving public policies in such matter.
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References
OMS (2018, May 2). Ambient (outdoor) air quality and health. http://www.who.int/en/news-room/fact-sheets/detail/ambient–air-quality-and-health
Brauer, M., et al.: Air pollution from traffic and the development of respiratory infections and asthmatic and allergic symptoms in children. Am. J. Respir. Crit. Care Med. 166(8), 1092–1098 (2002)
MacIntyre, E.A., Gehring, U., Mölter, A., Fuertes, E., Klümper, C., Krämer, U., Koppelman, G.H.: Air pollution and respiratory infections during early childhood: an analysis of 10 European birth cohorts within the ESCAPE Project. Environ. Health Perspect. 122(1), 107–113 (2013)
Larson, T.V., Koenig, J.Q.: Wood smoke: emissions and noncancer respiratory effects. Annu. Rev. Public Health 15(1), 133–156 (1994)
Guan, W.J., et al.: Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action. Lancet 388(10054), 1939–1951 (2016)
Boubiche, S., Boubiche, D., Bilami, A., Toral-Cruz, H.: Big data challenges and data aggregation strategies in WSN. IEEE Access 6, 20558–20571 (2018)
Ho, K., Hirai, H.W., Kuo, Y., Meng, H.M., Tsoi, K.K.F.: Indoor air monitoring platform and personal health reporting system: big data analytics for public health research. In: 2015 IEEE International Congress on Big Data, New York, NY, pp. 309–312 (2015)
Lopes, A.M., Abreu, P., Restivo, M.T.: Analysis and pattern identification on smart sensors data. In: 2017 4th Experiment@ International Conference (exp.at’17), Faro, pp. 97–98 (2017)
Chang, Y.S., Lin, K., Tsai, Y., Zeng, Y., Hung, C.: Big data platform for air quality analysis and prediction. In: 2018 27th Wireless and Optical Communication Conference, Hualien, pp. 1–3 (2018)
Ayyalasomayajula, H., Gabriel, E., Lindner, P., Price, D.: Air quality simulations using big data programming models. In: 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService), Oxford, pp. 182–184 (2016)
Chen, L., Xu, J., Zhang, L., Xue, Y.: Big data analytic based personalized air quality health advisory model. In: 2017 13th IEEE Conference on Automation Science and Engineering (CASE), Xi’an, pp. 88–93 (2017)
Mehta, Y., Pai, M.M.M., Mallissery, S., Singh, S.: Cloud enabled air quality detection, analysis and prediction - a smart city application for smart health. In: 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), Muscat, pp. 1–7 (2016)
Rios, L.G., Diguez, J.A.I.: Big data infrastructure for analyzing data generated by wireless sensor networks. In: 2014 IEEE International Congress on Big Data, Anchorage, AK, pp. 816–823 (2014)
Jardak, C., Riihijärvi, J., Oldewurtel, F., Mähönen, P.: Parallel processing of data from very large-scale wireless sensor networks. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC 2010), pp. 787–794. ACM, New York (2010). http://dx.doi.org/10.1145/1851476.1851590
Fan, T., Zhang, X., Gao, F.: Cloud storage solution for WSN based on internet innovation union. In: Proceedings of the 2nd International Conference on Cloud-Computing and Super-Computing, vol. 22, pp. 164–169 (2013)
Yuan, H., Wang, J., An, Q., Li, S.: Research of WSN and big data analysis based continuous pulse monitoring system for efficient physical training. In: 2016 Future Technologies Conference (FTC), San Francisco, CA, pp. 1137–1145 (2016)
Anezakis, V.D., Mallinis, G., Iliadis, L., Demertzis, K.: Soft computing forecasting of cardiovascular and respiratory incidents based on climate change scenarios. In: 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems, Rhodes, Greece, pp. 1–8 (2018)
Fotopoulou, E., Zafeiropoulos, A., Papaspyros, D., Hasapis, P., Tsiolis, G., Bouras, T., Mouzakatis, S., Zanetti, N.: Linked data analytics in interdisciplinary studies: the health impact of air pollution in urban areas. IEEE Access 4, 149–164 (2016)
Sacha, D., Kraus, M., Bernard, J., Behrisch, M., Schreck, T., Asano, Y., Keim, D.A.: Somflow: guided exploratory cluster analysis with self-organizing maps and analytic provenance. IEEE Trans. Visual. Comput. Graph. 25, 120–130 (2018)
Jambu, V., Provine, J., Ranganath, R., Rizvi, A.A.: U.S. Patent Application No. 15/391,697 (2018)
Guanochanga, B., Cachipuendo, R., Fuertes, W., Salvador, S., Benítez, D.S., Toulkeridis, T., Torres, J., Villacís, C., Tapia, F., Meneses, F.: Real-time air pollution monitoring systems using wireless sensor networks connected in a cloud-computing, wrapped up web services. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) 2018 Proceedings of the Future Technologies Conference (FTC). FTC 2018. Advances in Intelligent Systems and Computing, vol. 880. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02686-8_14
Acknowledgments
The authors would like to express their gratitude for the financial support of the Ecuadorian Corporation for the Development of Research and the Academy (RED CEDIA) during the development of this study, under Project Grant CEPRA-XI-2017-13.
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Fuertes, W., Cadena, A., Torres, J., Benítez, D., Tapia, F., Toulkeridis, T. (2019). Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_6
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