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Monitoring of Air Quality with Low-Cost Electrochemical Sensors and the Use of Artificial Neural Networks for the Atmospheric Pollutants Concentration Levels Prediction

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Information Management and Big Data (SIMBig 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 898))

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This paper shows the preliminary results of the monitoring and estimation of air pollutants at a strategic point within the district of San Isidro, Lima - Peru. Low-cost, portable, wireless and geo-locatable electrochemical sensors were used to capture reliable contamination levels in real-time which could be used not only to quantify atmospheric pollution exposure but also for prevention and control, and even for legislative purposes. For the prediction of \(CO{_2}\) and \(SO{_2}\) levels, computational intelligence algorithms were applied and validated with experimental data. We proved that the use of Artificial Neural Networks (ANNs) has a high potential as a tool to use it as a forecast methodology in the area of air pollution.

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The authors would like to thank the Universidad del Pacífico and in particular the Department of Engineering for the purchase of the air and sound pollution sensors; and of the SIM cards used for data transmission. The authors would also like to express their gratitude to Mrs. Jimena Sánchez Velarde, Director of and Pamela Olenka Peña, Manager of Sustainability of the Municipality of San Isidro, who authorized the use of the facilities within the Municipality, allowing the realization of this research.

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Correspondence to Ana Luna .

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Luna, A., Talavera, A., Navarro, H., Cano, L. (2019). Monitoring of Air Quality with Low-Cost Electrochemical Sensors and the Use of Artificial Neural Networks for the Atmospheric Pollutants Concentration Levels Prediction. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham.

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  • Print ISBN: 978-3-030-11679-8

  • Online ISBN: 978-3-030-11680-4

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