Sains Malaysiana 49(1)(2020): 201-209

http://dx.doi.org/10.17576/jsm-2020-4901-24

 

Air Pollutant Index Calendar-Based Graphics for Visualizing Trends Profiling and Analysis

(Indeks Pencemaran Udara berdasarkan Kalendar Grafik untuk Pemprofilan Tren Visualisasi dan Analisis)

 

NUR HAIZUM ABD RAHMAN1* & MUHAMMAD HISYAM LEE2

 

1Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia

 

2Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor Darul Takzim,Malaysia

 

Received: 3 July 2019/Accepted: 17 October 2019

 

ABSTRACT

Detection of air quality abnormality is important as an early warning system for air quality control and management. The information can raise citizens’ awareness towards current air quality status. By using time series plot, the data pattern can be identified but not able to exactly determine the abnormality due to overcrowded plot. Therefore, visualization data profiling was presented in this study by using seven years Malaysia daily air pollutant index to improve the detection. Result shown, the developed approach can simply identify the poor air quality across the month and year. Malaysia air quality was good and consistent between November and May. However, upward trend existed between June and October due to the forest fire happened in Sumatra. This visualization approach improved air pollution detection profiling and it is useful for related agencies to guide the control actions to be taken. This approach can be applied to any countries and data set to give more competent information.

 

Keywords: Air pollutant index; calendar; data visualization; profiling

 

ABSTRAK

Pengesanan kelainan kualiti udara adalah penting sebagai sistem amaran awal untuk kawalan dan pengurusan kualiti udara. Maklumat ini dapat meningkatkan kesedaran masyarakat terhadap status kualiti udara semasa. Dengan menggunakan plot siri masa, corak data dapat dikenal pasti tetapi tidak dapat menentukan secara tepat kelainan akibat plot yang sesak. Oleh itu, untuk meningkatkan pengesanan, pemprofilan data visualisasi telah dibincangkan dalam kajian ini dengan menggunakan indeks pencemaran udara harian di Malaysia selama tujuh tahun. Keputusan menunjukkan pendekatan yang digunakan dapat mengenal pasti kualiti udara yang tidak baik sepanjang bulan dan tahun. Kualiti udara di Malaysia adalah baik dan konsisten antara November dan Mei. Bagaimanapun, aliran menaik wujud antara bulan Jun dan Oktober akibat kebakaran hutan di Sumatra. Pendekatan profil visualisasi dapat mengesan pencemaran udara dan berguna kepada agensi berkaitan untuk membimbing tindakan kawalan yang akan diambil. Pendekatan ini boleh digunakan untuk mana-mana negara dan set data untuk memberikan maklumat yang lebih cekap.

 

Kata kunci: Indeks pencemaran udara; kalendar; pemprofilan; visualisasi data

 

REFERENCES

Afroz, R., Hassan, M.N. & Ibrahim, N.A. 2003. Review of air pollution and health impacts in Malaysia. Environmental Research 92(2): 71-77.

Ahmat, H., Yahaya, A.S. & Ramli, N.A. 2015. PM10 analysis for three industrialized areas using extreme value. Sains Malaysiana 44(2): 175-186.

Dilla, W.N. & Raschke, R.L. 2015. Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems 16: 1-22.

Gualtieri, G., Crisci, A., Tartaglia, M., Toscano, P., Vagnoli, C., Andreini, B.P. & Gioli, B. 2014. Analysis of 20-year air quality trends and relationship with emission data: The case of Florence (Italy). Urban Climate 10(P3): 530-549.

Hugine, A.L., Guerlain, S.A. & Turrentine, F.E. 2014. Education: Visualizing surgical quality data with treemaps. Journal of Surgical Research 191: 74-83.

Lanzafame, R., Scandura, P.F., Famoso, F., Monforte, P. & Oliveri, C. 2014. Air quality data for Catania: Analysis and investigation case study 2010-2011. Energy Procedia 45(2): 681-690.

Lee, S., Kim, E. & Monsen, K.A. 2015. Public health nurse perceptions of Omaha System data visualization. International Journal of Medical Informatics 84(10): 826-834.

Leh, O.L.H., Ahmad, S., Aiyub, K., Jani, Y.M. & Hwa, T.K. 2012. Urban air environmental health indicators for Kuala Lumpur city. Sains Malaysiana 41(2): 179-191.

Moustris, K.P., Ziomas, I.C. & Paliatsos, A.G. 2010. 3-day-ahead forecasting of regional pollution index for the pollutants NO2, CO, SO2, and O3 using artificial neural networks in Athens, Greece. Water, Air, and Soil Pollution 209(1-4): 29-43.

Sansuddin, N., Ramli, N.A., Yahaya, A.S., Yusof, N.F.F.M., Ghazali, N.A. & Madhoun, W.A. 2011. Statistical analysis of PM10 concentrations at different locations in Malaysia. Environmental Monitoring and Assessment 180(1-4): 573-588.

United States Environmental Protection Agency. 2014. Air quality index: A guide to air quality and your health. In EPA. https://doi.org/10.1023/A:1020370119096.

Wang, E., Cook, D. & Hyndman, R. 2018. Calendar-based graphics for visualizing people’s daily schedules. Journal of Statistical Software 10(2): 1-19. doi:10.18637/jss.v000.i00.

 

*Corresponding author; email: nurhaizum_ar@upm.edu.my

 

 
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