Preprints
https://doi.org/10.5194/amt-2024-127
https://doi.org/10.5194/amt-2024-127
16 Oct 2024
 | 16 Oct 2024
Status: this preprint is currently under review for the journal AMT.

Improving raw readings from ozone low cost sensors using artificial intelligence for air quality monitoring

Guillem Montalban-Faet, Eric Meneses-Albala, Santiago Felici-Castell, Juan J. Perez-Solano, and Jaume Segura-Garcia

Abstract. Ground level ozone (O3) is a highly oxidising gas with very reactive properties, harmful at high levels, generated by complex photochemical reactions when "primary" pollutants from combustion of fossil materials react with sunlight. Thus, its concentration serves as an indicator of the activity of other air pollutants and plays a key role in Air Quality monitoring systems in smart cities. To increase its spatial sampling resolution over the city map, ozone low cost sensors are an interesting alternative, but they have a lack of accuracy. In this context, artificial intelligence techniques, in particular ensemble machine learning methods, can improve the raw readings from these sensors taking into account additional environmental information. In this paper, we analyse, propose and compare different techniques, reducing the estimation error in around 94 %, achieving the best results using the Gradient Boosting algorithm and outperforming the related work using sensor approximately 10 times less expensive.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Guillem Montalban-Faet, Eric Meneses-Albala, Santiago Felici-Castell, Juan J. Perez-Solano, and Jaume Segura-Garcia

Status: open (until 27 Dec 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2024-127', Anonymous Referee #1, 31 Oct 2024 reply
Guillem Montalban-Faet, Eric Meneses-Albala, Santiago Felici-Castell, Juan J. Perez-Solano, and Jaume Segura-Garcia
Guillem Montalban-Faet, Eric Meneses-Albala, Santiago Felici-Castell, Juan J. Perez-Solano, and Jaume Segura-Garcia

Viewed

Total article views: 184 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
142 36 6 184 7 5
  • HTML: 142
  • PDF: 36
  • XML: 6
  • Total: 184
  • BibTeX: 7
  • EndNote: 5
Views and downloads (calculated since 16 Oct 2024)
Cumulative views and downloads (calculated since 16 Oct 2024)

Viewed (geographical distribution)

Total article views: 168 (including HTML, PDF, and XML) Thereof 168 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Nov 2024
Download
Short summary
WHO warns of ground-level ozone problems in urban areas, recommending increased monitoring for which low-cost sensors coupled with artificial intelligence are an excellent alternative.