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- Title
Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants.
- Authors
Costa, Emanoel L. R.; Braga, Taiane; Dias, Leonardo A.; de Albuquerque, Édler L.; Fernandes, Marcelo A. C.
- 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.
- Subjects
BAHIA (Brazil : State); EL Salvador; AIR quality monitoring stations; SELF-organizing maps; AIR quality management; POLLUTANTS; AIR pollution; PARTICULATE matter; DISPERSION (Atmospheric chemistry)
- Publication
Neural Computing & Applications, 2024, Vol 36, Issue 19, p11643
- ISSN
0941-0643
- Publication type
Article
- DOI
10.1007/s00521-024-09793-w