Abstract
This paper presents a comparison between self-organizing maps (SOMs) and principal component analysis (PCA) on investigating the temporal variation characteristics of air pollution. The concentrations of particulate matters (PM10, PM2.5), sulfur dioxide (SO2), nitrogen oxides (NO2), ozone (O3) and carbon monoxide (CO) were measured by the monitoring station at Nilai, an industrial and residential area in Negeri Sembilan, Malaysia. The data were collected from January 2018 to December 2019. Both the SOM and PCA were employed to reveal the patterns of the monthly and hourly variability of air pollutants in 2018 and 2019, respectively, and then in both the two years. Generally, the scores plots of PCA and mapping plot of SOMs gave comparable clustering results. However, visualization of the patterns was much feasible in SOM than PCA when the number of samples increased. In conclusion, SOM appears to be much appropriate than PCA in revealing temporal characteristics of air quality data.
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References
Orellano P, Reynoso J, Quaranta N, Bardach A, Ciapponi A (2020) Short-term exposure to particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2) and ozone (O3) and all-cause and cause-specific mortality: systematic review and meta-analysis. Environ Int 142:105876
Sanchez-Balseca J, Perez-Foguet A (2020) Spatio-temporal air pollution modelling using a compositional approach. Heliyon 6:e04794
Bai X, Tian H, Liu X, Wu B, Liu S, Hao Y, Luo L, Liu W, Zhao S, Lin S, Hao J, Guo Z, Lv Y (2021) Spatial-temporal variation characteristics of air pollution and apportionment of contributions by different sources in Shanxi province of China. Atmos Environ 244:117926
Shen F, Zhang L, Jiang L, Tang M, Gai X, Chen M, Ge X (2020) Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China. Environ Int 137:105556
Chang F-J, Chang L-C, Kang C-C, Wang Y-S, Huang A (2020) Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Sci Total Environ 736:139656
Neme A, Hernandez L (2011) Visualizing patterns in the air quality in Mexico City with self-organizing maps. In: Laaksonen J, Honkela T (eds) Advances in self-organizing maps. Lecture Notes in Computer Science. Springer, Heidelberg, vol 6731, pp 318–327
Smeyers-Verbeke J, Den Hartog JC, Dehker WH, Coomans D, Buydens L, Massart DL (1984) The use of principal component analysis for the investigation of an organic air pollutants data set. Atmos Environ 18:2471–2478
Rupakheti D, Yin X, Rupakheti M, Zhang Q, Li P, Rai M, Kang S (2021) Spatio-temporal characteristics of air pollutants over Xinjiang, northwestern China. Environ Pollut 268:115907
Padoan S, Zappi A, Adam T, Melucci D, Gambaro A, Formenton G, Popovicheva O, Nguyen D-L, Schnelle-Kreis J, Zimmermann R (2020) Organic molecular markers and source contributions in a polluted municipality of north-east Italy: extended PCA-PMF statistical approach. Environ Res 186:109587
Hadeed SJ, O’Rourke MK, Burgess JL, Harris RB, Canales RA (2020) Imputation methods for addressing missing data in short-term monitoring of air pollutants. Sci Total Environ 730:139140
Wehrens R, Kruisselbrink J (2019) Supervised and unsupervised self-organizing maps, Ver. 3.0.10
Dupont MF, Elbourne A, Cozzolino D, Chapman J, Truong VK, Crawford RJ, Latham K (2020) Chemometrics for environmental monitoring: a review. Anal Methods 12:4597–4620
Kohonen T (1997) Self-organizing maps, 2nd edn. Springer, Berlin
Department of Environment Malaysia (DOE): Malaysia Environmental Quality Report 2011 Malaysia. Department of Environment, Malaysia (2011)
Azmi SZ, Latif MT, Ismail AS, Juneng L (2010) Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Qual Atmos Health 3:53–64
Nurul Adyani G, Nor Azam R, Ahmad Shukri Y, Noor Faizah FMDY, Nurulilyana S, Wesam Ahmed AM (2010) Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques. Environ Monitor Assessm 165:475–489
Chelani AB (2013) Study of Extreme CO, NO2 and O3 concentrations at a traffic site in Delhi: statistical persistence analysis and source identification. Aerosol Air Qual Res 13:377–384
Khan MF, Latif MT, Juneng L, Amil N, Nadzir MSMN, Hoque HMS (2015) Physicochemical factors and source of particulate matter at residential urban environment in Kuala Lumpur. J Air Waste Manag Assoc 65:958–969
Kalbarczyk R, Kalbarczyk E (2020) Meteorological conditions of the winter-time distribution of nitrogen oxides in Poznan: a proposal for a catalog of the pollutants variation. Urban Clim 33:100649
Das G, Chattopadhyay M, Gupta S (2016) A comparison of self-organising maps and principal components analysis. Int J Mark Res 58:815–834
Astel A, Tsakovski S, Barbieri P, Simeonov V (2007) Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Res 41:4566–4578
Acknowledgements
The authors acknowledge the Malaysian Department of Environment (DOE) for providing the air quality data. We would like to give our gratitude to Prof. Dr. Mohd Talib Latif from University Kebangsaan Malaysia (UKM) for the collaboration given in doing this research. This research is funded by the CRIM-UKM, GP-2019-K016373.
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Lee, L.C., Sino, H. (2021). Comparison Between Self-organizing Maps and Principal Component Analysis for Assessment of Temporal Variations of Air Pollutants. In: Kumar, S., Purohit, S.D., Hiranwal, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-3246-4_65
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