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Data Mining Paradigm in the Study of Air Quality

  • Natacha Soledad RepresaEmail author
  • Alfonso Fernández-Sarría
  • Andrés Porta
  • Jesús Palomar-Vázquez
Review Article

Abstract

Air pollution is a serious global problem that threatens human life and health, as well as the environment. The most important aspect of a successful air quality management strategy is the measurement analysis, air quality forecasting, and reporting system. A complete insight, an accurate prediction, and a rapid response may provide valuable information for society’s decision-making. The data mining paradigm can assist in the study of air quality by providing a structured work methodology that simplifies data analysis. This study presents a systematic review of the literature from 2014 to 2018 on the use of data mining in the analysis of air pollutant measurements. For this review, a data mining approach to air quality analysis was proposed that was consistent with the 748 articles consulted. The most frequent sources of data have been the measurements of monitoring networks, and other technologies such as remote sensing, low-cost sensors, and social networks which are gaining importance in recent years. Among the topics studied in the literature were the redundancy of the information collected in the monitoring networks, the forecasting of pollutant levels or days of excessive regulation, and the identification of meteorological or land use parameters that have the most substantial impact on air quality. As methods to visualise and present the results, we recovered graphic design, air quality index development, heat mapping, and geographic information systems. We hope that this study will provide anchoring of theoretical-practical development in the field and that it will provide inputs for air quality planning and management.

Keywords

Air quality Environmental management Air pollution Data mining 

Notes

Acknowledgments

The authors gratefully acknowledge the support of the Argentinean Scientific and Technical Research Council, the Polytechnic University of Valencia, Spain, and the National University of La Plata, Argentina. National Agency for Scientific and Technological Promotion funded this project through the PICT 2015-0618.

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Authors and Affiliations

  1. 1.Centro de Investigaciones del MedioambienteNational University of La Plata (UNLP)La PlataArgentina
  2. 2.Geo-Environmental Cartography and Remote Sensing GroupPolytechnic University of ValenciaValenciaSpain

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