Article Sidebar

Submitted
Mar 13, 2026
Accepted
Apr 4, 2026
Published
Apr 16, 2026
Download
pdf
Statistic
Read Counter : 8 Download : 8

Downloads

Download data is not yet available.

Main Article Content

Abstract

This study aims to analyze the response of PM2.5 concentrations and rainfall to the El Niño and La Niña phases at the Global Atmosphere Watch Bukit Kototabang station, West Sumatra, using the Superposed Epoch Analysis method. Observational data of PM2.5 from the Beta Attenuation Monitor  1020, rainfall from the Automatic Agroclimate Weather Station, and sea surface temperature in the Niño 3.4 region were analyzed for the period October 2021 to May 2025. A total of 31 extreme rainfall events were identified as reference points (epochs) and classified into El Niño and La Niña periods. The results show that during the La Niña phase, an average rainfall increase of 41.1 mm effectively reduces PM2.5 concentration anomalies by approximately ±3.1 µg/m³. In contrast, during the El Niño phase, although rainfall increases by 33.5 mm, PM2.5 concentrations remain highly variable, with anomaly increases of approximately ±1.3 µg/m³, due to drier air masses and lower rainfall intensity. The combined results of SEA analysis and the Monte Carlo test indicate a 13-day SST lead during the El Niño period and a 9-day lag during the La Niña period. This study reveals that ENSO influences both rainfall and air quality at the GAW Bukit Kototabang station.

Keywords

ENSO PM2.5 Extreme rainfall GAW Bukit Kototabang

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Dodi Saputra, Nofri Yendri Sudiar, Dhiyaul Qalbi, Aditya Prapanca. Response of PM2.5 Concentrations at the GAW Bukit Kototabang Station to ENSO Phases Based on Superimposed Epoch Analysis. EKSAKTA [Internet]. 2026 Apr. 16 [cited 2026 Apr. 19];27(02):144-5. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/666

References

  1. [1] Sucitra, A.W., and Oemar, M.A.F. (2023). Korelasi antara Tren Curah Hujan dan ENSO di Makassar. J. Minfo Polgan, 12 (2), 2618–2623.
  2. [2] Turner, D., Hedrick, D., Starnes, S.L., and Van Haren, R.M. (2025). Looking through the particles: a narrative review of air pollution and lung cancer. J. Thorac. Dis., 17 (12), 11369–11376.
  3. [3] Li, Z., Zhang, X., Liu, X., and Yu, B. (2022). PM2. 5 pollution in six major Chinese urban agglomerations: spatiotemporal variations, health impacts, and the relationships with meteorological conditions. Atmosphere (Basel)., 13 (10), 1696.
  4. [4] Lim, E.Y., and Kim, G.-D. (2024). Particulate matter-induced emerging health effects associated with oxidative stress and inflammation. Antioxidants, 13 (10), 1256.
  5. [5] Okaem, T.T. (2022). Pengembangan instrumen pengukuran kualitas udara menggunakan sensor PMS7003. Megasains, 13 (1), 31–38.
  6. [6] Zulistyawan, K.A. (2023). Identifikasi Konsentrasi CO, CO2, NO2, SO2, dan PM10 yang Terukur di Stasiun GAW Bukit Kototabang Selama Mudik Lebaran Tahun 2019-2023. Megasains, 14 (2), 39–47.
  7. [7] Ariska, M., Suhadi, Supari, Irfan, M., and Iskandar, I. (2023). Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes. Atmosphere (Basel)., 14 (2), 300.
  8. [8] Plocoste, T., Carmona-Cabezas, R., Gutiérrez de Ravé, E., and Jiménez-Hornero, F.J. (2022). Wet scavenging process of particulate matter (PM10): A multivariate complex network approach. Atmos. Environ., 268, 118801.
  9. [9] Pancariniwati, S., Fadila, R., and Melinda, S. (2024). Pengaruh Madden Julian Oscillation (MJO) terhadap Sifat Hujan di 2x11 Kayu Tanam, Sumatera Barat. Bul. GAW Bariri, 5 (2), 50–59.
  10. [10] Hanifa, R., and Wiratmo, J. (2024). ENSO and IOD Influence on Extreme Rainfall in Indonesia: Historical and Future Analysis using CMIP6. Agromet, 38 (2), 88–101.
  11. [11] Muharsyah, R., Ratri, D.N., and Kussatiti, D.F. (2021). Improving prediction quality of sea surface temperature (SST) in Niño3. 4 region using Bayesian Model Averaging. IOP Conf. Ser. Earth Environ. Sci., 893 (1), 12028.
  12. [12] Santoso, A.B., Supriana, T., and Girsang, M.A. (2022). Precipitation impact on cassava yield in Indonesia.
  13. [13] Carslaw, D.C., and Ropkins, K. (2023). openair — An R package for air quality data analysis. Environ. Model. Softw.
  14. [14] (NOAA), N.O. and A.A., and Center, C.P. (2024). Cold & Warm Episodes by Season (Oceanic Niño Index, ONI).
  15. [15] Institute, C.C. (2026). Climate Reanalyzer.
  16. [16] Maruyama, F. (2024). Analysis of the annual number of tropical cyclones over Japan using the extreme value theory. Cont. Shelf Res., 282, 105341.
  17. [17] Berendrecht, W., Van Vliet, M., and Griffioen, J. (2023). Combining statistical methods for detecting potential outliers in groundwater quality time series. Environ. Monit. Assess., 195 (1), 85.
  18. [18] Wilks, D.S. (2020). Statistical Methods in the Atmospheric Sciences, Academic Press.
  19. [19] Arat, M.M. (2025). Detection of anomalous Nitrogen Dioxide concentration of Ankara: a Reconstruction-based approach. Politek. Derg., 28 (1), 101–114.
  20. [20] Sun, C., Huang, G., and Fan, Y. (2020). Multi-indicator evaluation for extreme precipitation events in the past 60 years over the Loess Plateau. Water, 12 (1), 193.
  21. [21] Yusof, K.A., Abdullah, M., Hamid, N.S.A., Ahadi, S., and Ghamry, E. (2021). Statistical global investigation of pre-earthquake anomalous geomagnetic diurnal variation using superposed epoch analysis. IEEE Trans. Geosci. Remote Sens., 60, 1–13.
  22. [22] Walton, S.D., and Murphy, K.R. (2022). Superposed epoch analysis using time-normalization: A Python tool for statistical event analysis. Front. Astron. Sp. Sci., 9, 1000145.
  23. [23] Gong, S., Liu, Y., He, J., Zhang, L., Lu, S., and Zhang, X. (2022). Multi-scale analysis of the impacts of meteorology and emissions on PM2. 5 and O3 trends at various regions in China from 2013 to 2020 1: Synoptic circulation patterns and pollution. Sci. Total Environ., 815, 152770.
  24. [24] Jia, Z., Doherty, R.M., Ordóñez, C., Li, C., Wild, O., Jain, S., and Tang, X. (2022). The impact of large-scale circulation on daily fine particulate matter (PM 2.5) over major populated regions of China in winter. Atmos. Chem. Phys., 22 (10), 6471–6487.
  25. [25] Pujadini, N.A., Zaki, S., Effendi, F., Aini, R.T., Vonnisa, M., Ramadhan, R., Yusnaini, H., Hashiguchi, H., Shimomai, T., and Marzuki, M. (2023). Seasonal variation of cloud layer over Sumatra from long-term ceilometer observation. Int. Conf. Geosci. Remote Sens. Technol., 135–143.
  26. [26] Moron, V., and Robertson, A.W. (2020). Tropical rainfall subseasonal-to-seasonal predictability types. npj Clim. Atmos. Sci., 3 (1), 4.
  27. [27] Ding, D., Gandy, A., and Hahn, G. (2020). A simple method for implementing Monte Carlo tests. Comput. Stat., 35 (3), 1373–1392.
  28. [28] Rodrigues, J.C., Facao, J., and Carvalho, M.J. (2024). Parameter identification and uncertainty evaluation in quasi-dynamic test of solar thermal collectors with Monte Carlo method. Renew. Energy, 236, 121403.
  29. [29] Parchami, A., Iranmanesh, H., and Gildeh, B.S. (2022). Monte Carlo statistical test for fuzzy quality. Iran. J. Fuzzy Syst., 19 (1), 115.
  30. [30] Sengottuvel, S., Devi, S.S., Sasikala, M., Satheesh, S., and Selvaraj, R.J. (2021). An epoch based methodology to denoise magnetocardiogram (MCG) signals and its application to measurements on subjects with implanted devices. Biomed. Phys. Eng. Express, 7 (3), 35006.
  31. [31] Zhang, T., and Ebihara, Y. (2022). Superposed epoch analyses of geoelectric field disturbances in Japan in response to different geomagnetic activities. Sp. Weather, 20 (5), e2021SW002893.