Published Jan 27, 2020


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Javier Olaya

Diana Paola Ovalle-Muños

Cristhian Leonardo Urbano-León



Environmental pollution is harmful to human health, as it can lead to chronic respiratory diseases. In particular, fine particles suspended in the air (PM2.5) count among the most aggressive air pollutants. PM2.5 levels vary depending on local conditions. The goal of this work was to compare year-round airborne PM2.5 readings from three air quality surveillance stations in Cali (Colombia) to determine whether these show significant spatial and temporal variation. We subjected the obtained PM2.5 dataset to a functional analysis of variance. We observed that PM2.5 levels vary significantly among the three measurement sites on a temporal scale. Whereas in the morning hours PM2.5 levels among the three sites differed most, in the afternoon and evening hours, the corresponding PM2.5 levels were not significantly different.


Environmental pollution, Functional data, Airborne Particles

Al-Hamdan M, Crosson WL, Limaye AS, Rickman DL, Quattrochi DA, Estes Jr MG, Qualters JR, Sinclair AH, Tolsma D, Adeniyi KA, Niskar AS. Methods for characterizing fine particulate matter using ground observations and remotely sensed data: Potential use for environmental public health surveillance. Journal of the Air Waste Management Association, 2009.
doi: 10.3155/1047-3289.59.7.865

Bell M, Dominici F, Ebisu K, Zeger S, Samet J. Spatial and temporal variation in PM2.5 chemical composition in the united states for health effects studies. Environmental Health Perspectives, 7(115): 989-995, July 2007.
doi: 10.1289/ehp.9621

Estévez-Pérez G, Vilar J. Functional anova starting from discrete data: an application to air quality data. Environmental and Ecological Statistics, 20(3): 495-517, September 2013. ISSN 1573-3009.
doi: 10.1007/s10651-012-0231-2

Febrero-Bande M, Galeano P, González-Manteiga W. A functional analysis of NOx levels: location and scale estimation and outlier detection. Computational Statistics, 2007.
doi: 10.1007/s00180-007-0048-x

Febrero-Bande M, De la Fuente MO. Statistical computing in functional data analysis: The R package fda.usc. Journal of Statistical Software, 51(4), October 2012.
doi: 10.18637/jss.v051.i04

Ferraty F, Vieu P. Nonparametric Functional Data Analysis Theory and Practice. Springer Series in Statistics. Springer, 2006. doi: 10.1007/0-387-36620-2

Górecki T, Smaga Ł. Multivariate analysis of variance for functional data. Journal of Applied Statistics, 44(12): 2172-2189, 2017.
doi: 10.1080/02664763.2016.1247791

Górecki T, Smaga Ł. fdANOVA: An R software package for analysis of variance for univariate and multivariate functional data. Computational Statistics, 2018.
doi: 10.1007/s00180-018-0842-7

Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer, 2nd ed, 2009.
doi: 10.1007/BF02985802

James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning with Applications in R. Springer, 2013.
doi: 10.1007/978-1-4614-7138-7

Laden F, Neas LM, Dockery DW, Schwartz J. Association of fine particulate matter from different sources with daily mortality in six u.s. cities. Environmental Health Perspectives, 108(10), October 2000.
doi: 10.1289/ehp.00108941

Ramsay JO, Silverman BW. Functional Data Analysis. Springer, New York, 2nd ed, 2005.
doi: 10.1007/b98888

Ramsay JO, Wickham H, Graves S, Hooker S. Package “fda”. R Project, July 2018.

Ruiz-Medina MD. Functional analysis of variance for hilbert-valued multivariate fixed effect models. Statistics, 50(3): 689-715, 2016.
doi: 10.1080/02331888.2015.1094069

Zhang JT. Analysis of Variance for Functional Data. Chapman Hall, London, 2013.
How to Cite
Olaya, J., Ovalle-Muños, D. P., & Urbano-León, C. L. (2020). Functional analysis of variance of air pollution caused by fine particles. Universitas Scientiarum, 25(1), 1–16.
Ciencias ambientales/Environmental sciences/Ciências Ambientais