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Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants

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Abstract

In order to address the growing problem of air pollution, it is necessary to implement innovative regulations and practical solutions to reduce and control its impact. Numerous studies have recommended using multivariate statistical methods to identify the connections and characteristics of atmospheric pollutants, which can provide valuable information about their generation, dispersion, and contribution to the deterioration of air quality. This study thoroughly examines the air quality in Salvador, Bahia, using the Self-Organizing Maps (SOM) technique. The data used in the analysis spans from 2011 to 2016 and comes from air quality monitoring stations. The dataset includes hourly measurements of pollutants such as SO2, CO, O3, particulate matter, and meteorological data (wind speed, ambient temperature, relative humidity, the standard deviation of wind direction, rainfall, and wind direction). The SOM analysis successfully identifies significant clusters, revealing associations between high concentrations of specific pollutants and environmental variables. For example, clusters with elevated SO2 concentrations are observed in areas that suggest the presence of local sources of pollution. The validation of the results using Principal Component Analysis strengthens the findings. These findings are essential for developing air quality management policies, as they highlight areas of concern and offer insights for mitigation strategies. This study demonstrates the effectiveness of the SOM technique in environmental analysis and emphasizes the importance of domain knowledge in comprehensively interpreting air pollution patterns.

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Data availability

The datasets generated and/or analyzed during the current study are available in the Mendeley Data repository, https://data.mendeley.com/datasets/rg7pfvgwv8/1.

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Acknowledgements

The authors wish to acknowledge the financial support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). We sincerely thank CETREL S.A. for providing us access to this valuable dataset.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001.

Author information

Authors and Affiliations

  1. InovAI Lab - IMD/nPITI, Federal University of Rio Grande do Norte, Natal, 59078-900, Brazil

    Emanoel L. R. Costa & Marcelo A. C. Fernandes

  2. Federal Institute of Education, Science, and Technology of Bahia, Salvador, 40301-015, Brazil

    Taiane Braga

  3. Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL, UK

    Leonardo A. Dias

  4. Department of Industrial Processes and Chemical Engineering, Federal Institute of Education, Science and Technology of Bahia, Salvador, 40301-015, Brazil

    Édler L. de Albuquerque

  5. Department of Computer and Automation Engineering, Federal University of Rio Grande do Norte, Natal, 59078-900, Brazil

    Marcelo A. C. Fernandes

Authors
  1. Emanoel L. R. Costa
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  4. Édler L. de Albuquerque
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  5. Marcelo A. C. Fernandes
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Contributions

ELRC, TB, ELA, and MACF contributed to conceptualization and methodology. ELRC, TB, LAD, ELA, and MACF helped in data curation, writing—original draft preparation, and writing—reviewing and editing. ELA and MACF helped in collecting, analyzing, writing, and editing data.

Corresponding author

Correspondence to Marcelo A. C. Fernandes.

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Costa, E.L.R., Braga, T., Dias, L.A. et al. Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants. Neural Comput & Applic 36, 11643–11657 (2024). https://doi.org/10.1007/s00521-024-09793-w

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