Monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction

Ana Luna; Alvaro Talavera; Hector Navarro; Luis Cano

doi:10.1007/978-3-030-11680-4_15

Monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction

Ana Luna
, Alvaro Talavera
, Hector Navarro
, Luis Cano

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

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 CO2 and SO2 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.

Original language	English
Title of host publication	Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
Editors	Denisse Muñante, Juan Antonio Lossio-Ventura, Hugo Alatrista-Salas
Pages	137-150
Number of pages	14
ISBN (Electronic)	9783030116798
DOIs	https://doi.org/10.1007/978-3-030-11680-4_15
State	Published - 1 Jan 2019
Event	Communications in Computer and Information Science - Duration: 1 Jan 2019 → …

Publication series

Name	Communications in Computer and Information Science
Volume	898
ISSN (Print)	1865-0929

Conference

Conference	Communications in Computer and Information Science
Period	1/01/19 → …

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities
SDG 13 Climate Action
SDG 15 Life on Land
SDG 17 Partnerships for the Goals

Keywords

Air pollution
Artificial neural networks
Electrochemical sensors

Access to Document

10.1007/978-3-030-11680-4_15

Cite this

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 D. Muñante, J. A. Lossio-Ventura, & H. Alatrista-Salas (Eds.), Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings (pp. 137-150). (Communications in Computer and Information Science; Vol. 898). https://doi.org/10.1007/978-3-030-11680-4_15

Luna, Ana ; Talavera, Alvaro ; Navarro, Hector et al. / Monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction. Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. editor / Denisse Muñante ; Juan Antonio Lossio-Ventura ; Hugo Alatrista-Salas. 2019. pp. 137-150 (Communications in Computer and Information Science).

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title = "Monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction",

abstract = "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 CO2 and SO2 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.",

keywords = "Air pollution, Artificial neural networks, Electrochemical sensors, Air pollution, Artificial neural networks, Electrochemical sensors",

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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 D Muñante, JA Lossio-Ventura & H Alatrista-Salas (eds), Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. Communications in Computer and Information Science, vol. 898, pp. 137-150, Communications in Computer and Information Science, 1/01/19. https://doi.org/10.1007/978-3-030-11680-4_15

Monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction. / Luna, Ana ; Talavera, Alvaro; Navarro, Hector et al.
Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. ed. / Denisse Muñante; Juan Antonio Lossio-Ventura; Hugo Alatrista-Salas. 2019. p. 137-150 (Communications in Computer and Information Science; Vol. 898).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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 CO2 and SO2 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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KW - Artificial neural networks

KW - Electrochemical sensors

KW - Air pollution

KW - Artificial neural networks

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Luna A , Talavera A, Navarro H, Cano L. Monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction. In Muñante D, Lossio-Ventura JA, Alatrista-Salas H, editors, Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. 2019. p. 137-150. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-11680-4_15

Monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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