Forecasting the Air Quality Index by Utilizing Several Meteorological Factors Using the ARIMAX Method (Case Study: Central Jakarta City)
DOI:
https://doi.org/10.35870/jtik.v8i3.2012Keywords:
Air Quality Index, Meteorology, ARIMAX, Central JakartaAbstract
Today's society really pays attention to air quality because the impact of exposure to pollutants in the air is starting to be felt. PM 2.5 pollutants are very dangerous because their small size can penetrate the alveoli of human lungs. The value calculation of the Air Quality Index (AQI) is important to prepare mitigation and defensive measures to reduce the negative impact of air quality and as a basis for future policymaking. Several method comparisons have been carried out by researchers to predict AQI. However, researchers have not studied much regarding the use of meteorological factors in the form of average air temperature (°C), average air humidity (percent), and average wind speed (m/s) in forecasting AQI values, even though meteorological factors have a significant link, according to previous researchers. This research forecasts AQI using the ARIMAX method, which includes meteorological factors as exogenous variables, using daily AQI PM 2.5 data in Central Jakarta. The best modeling of the data is ARIMA (1,1,1) without X and ARIMAX (1,1,1). Based on the calculation of AIC, BIC, RMSE, and MAPE values, ARIMAX (1,1,1) modeling produces better forecasting, so it can be concluded that forecasting involving meteorological factors can make forecasting more precise. Predicting AQI using ARIMAX with upcoming meteorological factors is beneficial, as precise prediction results can assist in policy-making to prevent the adverse impacts of air quality on public health. In future research, other meteorological factors could be studied and combined with other modeling besides ARIMA.
Downloads
References
Jhun, I., Coull, B. A., Schwartz, J., Hubbell, B., & Koutrakis, P. (2015). The impact of weather changes on air quality and health in the United States in 1994–2012. Environmental research letters, 10(8), 084009.
Ren, C., & Tong, S. (2008). Health effects of ambient air pollution–recent research development and contemporary methodological challenges. Environmental Health, 7, 1-10.
Qin, S., Liu, F., Wang, C., Song, Y., & Qu, J. (2015). Spatial-temporal analysis and projection of extreme particulate matter (PM10 and PM2. 5) levels using association rules: A case study of the Jing-Jin-Ji region, China. Atmospheric Environment, 120, 339-350. DOI: https://doi.org/10.1016/j.atmosenv.2015.09.006.
Kartiasih, F., & Setiawan, A. (2020). Aplikasi error correction mechanism dalam analisis dampak pertumbuhan ekonomi, konsumsi energi dan perdagangan internasional terhadap emisi CO2 di Indonesia. Media Statistika, 13(1), 104-115. DOI: https://doi.org/10.14710/medstat.13.1.104-115.
Kartiasih, F., & Pribadi, W. (2020). Environmental quality and poverty assessment in Indonesia. Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan (Journal of Natural Resources and Environmental Management), 10(1), 89-97. DOI: https://doi.org/10.29244/jpsl.10.1.89-97.
Hageneder, S., Bauch, M., & Dostalek, J. (2016). Plasmonically amplified bioassay–Total internal reflection fluorescence vs. epifluorescence geometry. Talanta, 156, 225-231.
Hu, D., & Jiang, J. (2014). PM 2.5 pollution and risk for lung cancer: a rising issue in China. Journal of Environmental Protection, 2014.
Doherty, R. M., Heal, M. R., & O’Connor, F. M. (2017). Climate change impacts on human health over Europe through its effect on air quality. Environmental Health, 16, 33-44.
Cobourn, W. G. (2010). An enhanced PM2. 5 air quality forecast model based on nonlinear regression and back-trajectory concentrations. Atmospheric Environment, 44(25), 3015-3023. DOI: https://doi.org/10.1016/j.atmosenv.2010.05.009.
Zhu, S., Lian, X., Liu, H., Hu, J., Wang, Y., & Che, J. (2017). Daily air quality index forecasting with hybrid models: A case in China. Environmental pollution, 231, 1232-1244. DOI: https://doi.org/10.1016/j.envpol.2017.08.069.
Hsu, H. H., Adamkiewicz, G., Houseman, E. A., Spengler, J. D., & Levy, J. I. (2014). Using mobile monitoring to characterize roadway and aircraft contributions to ultrafine particle concentrations near a mid-sized airport. Atmospheric Environment, 89, 688-695. DOI: https://doi.org/10.1016/j.atmosenv.2014.02.023.
Wen, T., Liu, Y., he Bai, Y., & Liu, H. (2023). Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model. Heliyon, 9(11).
Zhao, L., Li, Z., & Qu, L. (2022). Forecasting of Beijing PM2. 5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition. Heliyon, 8(12).
Aprianto, Y., Nurhasanah, N., & Sanubary, I. (2018). Prediksi Kadar Particulate Matter (PM10) untuk Pemantauan Kualitas Udara Menggunakan Jaringan Syaraf Tiruan Studi Kasus Kota Pontianak. Positron, 8(1), 15-20. DOI: http://dx.doi.org/10.26418/positron.v8i1.25470.
Jian, L., Zhao, Y., Zhu, Y. P., Zhang, M. B., & Bertolatti, D. (2012). An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Science of the Total Environment, 426, 336-345. DOI: https://doi.org/10.1016/j.scitotenv.2012.03.025.
Miri, A., Shirmohammadi, E., & Sorooshian, A. (2023). Influence of meteorological factors and air pollutants on bacterial concentration across two urban areas of the Sistan region of Iran. Urban Climate, 51, 101650. DOI: https://doi.org/10.1016/j.uclim.2023.101650.
Liu, X., & Guo, H. (2022). Air quality indicators and AQI prediction coupling long-short term memory (LSTM) and sparrow search algorithm (SSA): A case study of Shanghai. Atmospheric Pollution Research, 13(10), 101551. DOI: https://doi.org/10.1016/j.apr.2022.101551.
Wood, D. A. (2022). Local integrated air quality predictions from meteorology (2015 to 2020) with machine and deep learning assisted by data mining. Sustainability Analytics and Modeling, 2, 100002.
Zhang, J., & Li, S. (2022). Air quality index forecast in Beijing based on CNN-LSTM multi-model. Chemosphere, 308, 136180. DOI: https://doi.org/10.1016/j.chemosphere.2022.136180.
Udristioiu, M. T., Mghouchi, Y. E., & Yildizhan, H. (2023). Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning. Journal of Cleaner Production, 421, 138496. DOI: https://doi.org/10.1016/j.jclepro.2023.138496.
Luo, J., & Gong, Y. (2023). Air pollutant prediction based on ARIMA-WOA-LSTM model. Atmospheric Pollution Research, 14(6), 101761.
Sengupta, A., Govardhan, G., Debnath, S., Yadav, P., Kulkarni, S. H., Parde, A. N., ... & Ghude, S. D. (2022). Probing into the wintertime meteorology and particulate matter (PM2. 5 and PM10) forecast over Delhi. Atmospheric Pollution Research, 13(6), 101426. DOI: https://doi.org/10.1016/j.apr.2022.101426.
Hardini, M., Sunarjo, R. A., Asfi, M., Chakim, M. H. R., & Sanjaya, Y. P. A. (2023). Predicting air quality index using ensemble machine learning. ADI Journal on Recent Innovation, 5(1Sp), 78-86.
Wang, Y., Huang, L., Huang, C., Hu, J., & Wang, M. (2023). High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city. Environment International, 172, 107752.
Yang, R., & Zhong, C. (2022). Analysis on Spatio-Temporal Evolution and Influencing Factors of Air Quality Index (AQI) in China. Toxics, 10(12), 712.
Jia, N., Li, Y., Chen, R., & Yang, H. (2023). A review of global PM2. 5 exposure research trends from 1992 to 2022. Sustainability, 15(13), 10509.
Li, Y., Chiu, Y. H., & Lu, L. C. (2018). Energy and AQI performance of 31 cities in China. Energy Policy, 122, 194-202. DOI: https://doi.org/10.1016/j.enpol.2018.07.037.
Lunderberg, D. M., Liang, Y., Singer, B. C., Apte, J. S., Nazaroff, W. W., & Goldstein, A. H. (2023). Assessing residential PM2. 5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors. Proceedings of the National Academy of Sciences, 120(50), e2308832120. DOI: https://doi.org/10.1073/pnas.2308832120.
Ma, Q., Wang, J., Xiong, M., & Zhu, L. (2023). Air Quality Index (AQI) did not improve during the COVID-19 lockdown in Shanghai, China, in 2022, based on ground and TROPOMI observations. Remote Sensing, 15(5), 1295.
Raaschou-Nielsen, O., Antonsen, S., Agerbo, E., Hvidtfeldt, U. A., Geels, C., Frohn, L. M., ... & Pedersen, C. B. (2023). PM2. 5 air pollution components and mortality in Denmark. Environment International, 171, 107685.
Lin, J., Que, X., Ma, X., Salati, S., Chen, Q., LIU, J., & Fan, C. Spatiotemporal Heterogeneity of the Influence from Main Meteorological Factors on Air Quality Index in China Based on Stwr. Available at SSRN 4245535.
Zhao, M., Liu, Y., & Gyilbag, A. (2022). Assessment of meteorological variables and air pollution affecting COVID-19 Cases in urban agglomerations: evidence from China. International Journal of Environmental Research and Public Health, 19(1), 531.
Handhayani, T. (2023). An integrated analysis of air pollution and meteorological conditions in Jakarta. Scientific Reports, 13(1), 5798.
Kartikasari, D. (2020). Analisis Faktor-Faktor yang Mempengaruhi Level Polusi Udara dengan Metode Regresi Logistik Biner. Mathunesa: Jurnal Ilmiah Matematika, 8(1), 55-59. DOI: https://doi.org/10.26740/mathunesa.v8n1.p55-59.
Pratama, I., Permanasari, A. E., Ardiyanto, I., & Indrayani, R. (2016, October). A review of missing values handling methods on time-series data. In 2016 international conference on information technology systems and innovation (ICITSI) (pp. 1-6). IEEE. DOI: 10.1109/ICITSI.2016.7858189.
Ahn, H., Sun, K., & Kim, K. P. (2022). Comparison of missing data imputation methods in time series forecasting. Computers, Materials & Continua, 70(1), 767-779.
Adu, W. K., Appiahene, P., & Afrifa, S. (2023). VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends. Journal of Electrical Systems and Information Technology, 10(1), 12.
Ospina, R., Gondim, J. A., Leiva, V., & Castro, C. (2023). An overview of forecast analysis with ARIMA models during the COVID-19 pandemic: Methodology and case study in Brazil. Mathematics, 11(14), 3069.
Islam, F., & Imteaz, M. A. (2020). Use of teleconnections to predict Western Australian seasonal rainfall using ARIMAX model. Hydrology, 7(3), 52.
Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 1847979018808673. DOI: https://doi.org/10.1177/1847979018808673.
Alabdulrazzaq, H., Alenezi, M. N., Rawajfih, Y., Alghannam, B. A., Al-Hassan, A. A., & Al-Anzi, F. S. (2021). On the accuracy of ARIMA based prediction of COVID-19 spread. Results in Physics, 27, 104509.
Klabi, F. (2014). Forecasting non-residents' monthly entries to Tunisia and accuracy comparison of time-series methods. The Journal of North African Studies, 19(5), 770-791. DOI: https://doi.org/10.1080/13629387.2014.949694.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Naufal Fadli Muzakki, Azmi Zulfani Putri, Surya Maruli, Fitri Kartiasih
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
