Derechos de autor 2024 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Monitoreo de la calidad del aire urbano utilizando análisis de datos funcionales
Corresponding Author(s) : Rafael Meléndez Surmay
Investigación e Innovación en Ingenierías,
Vol. 12 Núm. 1 (2024): Enero-Junio
Resumen
Objetivo: Comparar la variación espacio temporal de los niveles de partículas suspendidas PM10 y de dióxido de nitrógeno NO2 en ciudades chilenas durante el periodo del 2021 a través del enfoque del Análisis de Datos Funcionales ADF. Metodología: A partir de la base de datos de PM10 y NO2 se aplicaron pruebas de hipótesis para datos funcionales para contrastar entre ciudades y métodos gráficos funcionales para detectar curvas atípicas. Resultados: Se evidencia diferencias significativas entre las curvas medias de NO2 entre la ciudad de Valparaíso y la Región Metropolitana de Santiago RMS, mientras que la prueba ANOVA funcional muestra significancia de las curvas medias de PM10 entre las cuatro ciudades estudiadas. Finalmente, los diagramas de cajas funcionales identificaron dos estaciones atípicas de PM10 (El Bosque y Pudahuel) en la RMS. Conclusiones: Utilizar el análisis de datos funcionales permite tener una visión global del problema al considerar cada objeto funcional como unidad de estudio y facilita identificar aquellas estaciones donde hay producción extrema de particulado PM10 evidenciando un factor de riesgo de morbilidad y mortalidad.
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Referencias
P.J. García-Nieto, Parametric study of selective removal of atmospheric aerosol by coagulation, condensation and gravitational settling, Int. J. Environ. Health Res, vol 11, pp. 151–162, 2001.
P.J. García-Nieto, Study of the evolution of aerosol emissions from coal-fired power plants due to coagulation, condensation, and gravitational settling and health impact, J. Environ. Manage, vol 79, no 4, pp.372–382, 2006.
T. Godish, Air Quality, Lewis Publishers, Boca Raton, Florida, 2004.
L.K. Wang, N.C. Pereira, Y.T. Hung, Air Pollution Control Engineering, Humana Press, New York, 2004.
M.E Héroux, H.R. Anderson, et al, Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project. Int. J. Publ. Health, vol 60, pp. 619–627, 2015, https://doi.org/10.1007/s00038-015-0690-y.
K.H Kim, E. Kabir, S. Kabir, A review on the human health impact of airborne particulate matter., Environ. Int, vol 74, pp. 136–143, 2015, https://doi.org/10.1016/j.envint.2014.10.005.
A.J. Badyda, J. Grellier, P. Da̧browiecki, Ambient PM2.5 exposure and mortality due to lung cancer and cardiopulmonary diseases in polish cities. Adv. Exp. Med. Biol, vol 944, pp. 9-17, 2017, https://doi.org/10.1007/5584_2016_55
UNION, PEAN, et al. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Official Journal of the European Union, 2008.
R. de Medrano, V. de Buen Remiro, J. L. Aznarte, SOCAIRE: Forecasting and monitoring urban air quality in Madrid, Environmental Modelling & Software,Volume 143,2021.
J. Martínez, Á. Saavedra, P.J. García-Nieto, J.I. Piñeiro, C. Iglesias, J. Taboada, J. Sancho, J. Pastor, Air quality parameters outliers detection using functional data analysis in the Langreo urban area (Northern Spain), Applied Mathematics and Computation, Volume 241,2014.
A. C. Comrie, J. E. Diem, Climatology and forecast modeling of ambient carbon monoxide in Phoenix, Arizona, Atmospheric Environment, Volume 33, Issue 30,1999.
J. Sancho, J.J. Pastor, J. Martínez, M.A. García, Evaluation of Harmonic Variability in Electrical Power Systems through Statistical Control of Quality and Functional Data Analysis, Procedia Engineering, Volume 63,2013.
R. J. Hyndman y H. L. Shang, Rainbow Plots, Bagplots, and Boxplots for Functional Data, 2008Journal of Computational and Graphical Statistics, vol 19, no 1, 2008, DOI:10.2307/25651298.
J.T. Zhang, Analysis of variance for functional data, Chapman & Hall, London, 2014.
Q. Shen, y J. Faraway, An F Test for linear models with functional responses, Statistica Sinica, vol 14, no 4, pp. 1239-1257, 2004.
A. Cuevas, M. Febrero, y R. Fraiman, On the use of the bootstrap for estimating functions with functional data, Computational Statistics and Data Analysis, In press, 2006.
O. A. Vsevolozhskaya, M. C. Greenwood, S. Powell, D. Zaykin, Resampling-based multiple comparison procedure with application to point-wise testing with functional data. Environ Ecol Stat. 2015.
D.D Cox, J.S. Lee, Pointwise testing with functional data using the Westfall–Young randomization method. Biometrika, vol 95, no 3, pp. 621–634, 2008.
J.O.,Ramsay, B. W. Silverman, Functional Data Analysis, Second ed. Springer 2005.
T. Gorecki, y L. Smaga, Comparison of tests for the one-way anova problem for functional data, Comput. Stat, vol 30, no 4, pp. 987–1010, 2015.
A. Maluf Blanco y E. S. Michelena Fernández, “Estudio de factores que afectan la integración de los sistemas de gestión en el Centro Nacional de Biopreparados, Cuba”, Investigación e Innovación en Ingenierías, vol. 8, n.º 1, pp. 37–53, ene. 2020. DOI: https://doi.org/10.17081/invinno.8.1.3592
R. J. Hyndman y M. S. Ullah, “Robust forecasting of mortality and fertility rates: A functional data approach”, Computational Statistics & Data Analysis, vol 51, no 10, pp. 4942-4956. 2007,http://ideas.repec.org/a/eee/csdana/v51y2007i10p4942-4956.html