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Assessing the Impact of COVID-19 Regulations on Air Quality in Asian Cities: A Time Series Analysis

by Iqbal Ahmed, Tamanna Akther Mukta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 41
Year of Publication: 2023
Authors: Iqbal Ahmed, Tamanna Akther Mukta
10.5120/ijca2023923214

Iqbal Ahmed, Tamanna Akther Mukta . Assessing the Impact of COVID-19 Regulations on Air Quality in Asian Cities: A Time Series Analysis. International Journal of Computer Applications. 185, 41 ( Nov 2023), 22-30. DOI=10.5120/ijca2023923214

@article{ 10.5120/ijca2023923214,
author = { Iqbal Ahmed, Tamanna Akther Mukta },
title = { Assessing the Impact of COVID-19 Regulations on Air Quality in Asian Cities: A Time Series Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2023 },
volume = { 185 },
number = { 41 },
month = { Nov },
year = { 2023 },
issn = { 0975-8887 },
pages = { 22-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number41/32961-2023923214/ },
doi = { 10.5120/ijca2023923214 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:21.174530+05:30
%A Iqbal Ahmed
%A Tamanna Akther Mukta
%T Assessing the Impact of COVID-19 Regulations on Air Quality in Asian Cities: A Time Series Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 41
%P 22-30
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Air pollution represents a growing threat to ecosystems and the health of all living organisms, carrying risks of heart and respiratory diseases, as well as a spectrum of other health issues. The outbreak of the COVID-19 pandemic prompted worldwide lockdowns, effectively restricting outdoor activities, and imposing strict controls on vehicular movement. This period of stringent regulations led to discernible changes in air quality. This research leverages various time series analyses on Air Quality Index (AQI) data to assess the influence of COVID-19 on air quality. Our dataset comprises AQI records from nine major cities in Asia, spanning from January 2019 to September 2022. Rigorous data preprocessing procedures were applied to create a comprehensive dataset encompassing AQI readings from diverse urban centers. Through detailed analysis of AQI values and their temporal trends, we unveil the impacts of COVID-19 on air quality. To model and forecast AQI values, we employed three distinct approaches: ARIMA, Prophet, and LSTM. Model selection was guided by comprehensive performance comparisons. Utilizing the chosen model, we forecasted future AQI values, providing insights into the anticipated air quality trends. Our findings conclusively demonstrate that COVID-19 regulations positively influenced air quality in all analyzed cities. Among the modeling techniques, ARIMA emerged as the standout performer, boasting the highest R-squared score. By generating AQI forecasts using the ARIMA model, we underscore the potential of regulatory interventions during the COVID-19 pandemic to exert a quantifiable impact on air quality. This research endeavors to deliver valuable insights that can inform global strategies for combatting air pollution. Ultimately, we aspire to contribute to the global mission of reducing air pollution, safeguarding the health of our planet, and promoting a sustainable future for generations to come.

References
  1. Venter, Z.S., Aunan, K., Chowdhury, S., and Lelieveld, J. 2020. COVID-19 lockdowns cause global air pollution declines. Proc. Natl. Acad. Sci. U.S.A., 117 (32), 18984–18990
  2. Who emits the most CO2 today? Our World Data. https://ourworldindata.org/annual-co2-emissions . last accessed 2023/08/08.
  3. Bangladesh air worst in the world, again. https://www.thedailystar.net/environment/pollution/air-pollution/news/bangladesh-air-worst-the-world-again-2988236 . last accessed 2023/08/08.
  4. Ahuja, A. 2019. Air Pollution: What Is Air Quality Index, How Is It Measured And Its Health Impact. https://swachhindia.ndtv.com/air-pollution-what-is-air-quality-index-how-is-it-measured-and-its-health-impact-40387/ . last accessed 2023/08/08.
  5. Air Quality Index (AQI) - UPSC Notes. https://byjus.com/free-ias-prep/air-quality-index/, last accessed 2023/08/09.
  6. Saha, L., Kumar, A., Kumar, S., Korstad, J., Srivastava, S., and Bauddh, K. 2022. The impact of the COVID-19 lockdown on global air quality: A review. Environ. Sustain., 5 (1), 5–23.
  7. Mishra, A.K., Rajput, P., Singh, A., Singh, C.K., and Mall, R.K. 2021. Effect of Lockdown Amid COVID-19 on Ambient Air Quality in 16 Indian Cities. Front. Sustain. Cities, 3.
  8. Zaini, N., Ean, L.W., Ahmed, A.N., and Malek, M.A. 2022. A systematic literature review of deep learning neural network for time series air quality forecasting. Environ. Sci. Pollut. Res., 29 (4), 4958–4990.
  9. Espinosa, R., Palma, J., Jiménez, F., Kamińska, J., Sciavicco, G., and Lucena-Sánchez, E. 2021. A time series forecasting based multi-criteria methodology for air quality prediction. Appl. Soft Comput., 113, 107850.
  10. Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. 2014. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734.
  11. Hochreiter, S., and Schmidhuber, J. 1997. Long Short-Term Memory. Neural Comput., 9 (8), 1735–1780.
  12. Ho, T.K. 1995. Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, 1, 278–282.
  13. Tibshirani, R. 1996. Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58 (1), 267–288.
  14. Cortes, C., and Vapnik, V. 1995. Support-vector networks. Mach. Learn., 20 (3), 273–297.
  15. Wang, J., Li, H., and Lu, H. 2018. Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China. Appl. Soft Comput., 71, 783–799.
  16. Jephcote, C., Hansell, A.L., Adams, K., and Gulliver, J. 2021. Changes in air quality during COVID-19 ‘lockdown’ in the United Kingdom. Environ. Pollut., 272, 116011.
  17. Dey, T., Tyagi, P., Sabath, M.B., Kamareddine, L., Henneman, L., Braun, D., and Dominici, F. 2021. Counterfactual time series analysis of short-term change in air pollution following the COVID-19 state of emergency in the United States. Sci. Rep., 11 (1), 23517.
  18. Stellwagen, E., and Tashman, L. 2013. ARIMA: The models of Box and Jenkins. Foresight Int. J. Appl. Forecast., (30), 28–33.
  19. Gao, M., Yang, H., Xiao, Q., and Goh, M. 2022. COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts. Socioecon. Plann. Sci., 83, 101228.
  20. Du, S., Li, T., Yang, Y., and Horng, S.-J. 2021. Deep Air Quality Forecasting Using Hybrid Deep Learning Framework. IEEE Trans. Knowl. Data Eng., 33 (6), 2412–2424.
  21. Tiwari, A., Gupta, R., and Chandra, R. 2021. Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown.
  22. Lin, Y., Mago, N., Gao, Y., Li, Y., Chiang, Y.-Y., Shahabi, C., and Ambite, J.L. 2018. Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. Proc. 26th ACM SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst., 359–368.
  23. Freeman, B.S., Taylor, G., Gharabaghi, B., and Thé, J. 2018. Forecasting air quality time series using deep learning. J. Air Waste Manag. Assoc., 68 (8), 866–886.
  24. Al-qaness, M.A.A., Fan, H., Ewees, A.A., Yousri, D., and Abd Elaziz, M. 2021. Improved ANFIS model for forecasting Wuhan City Air Quality and analysis COVID-19 lockdown impacts on air quality. Environ. Res., 194, 110607.
  25. Liu, H., Yan, G., Duan, Z., and Chen, C. 2021. Intelligent modeling strategies for forecasting air quality time series: A review. Appl. Soft Comput., 102, 106957.
  26. Benhaddi, M., and Ouarzazi, J. 2021. Multivariate Time Series Forecasting with Dilated Residual Convolutional Neural Networks for Urban Air Quality Prediction. Arab. J. Sci. Eng., 46 (4), 3423–3442.
  27. Kolehmainen, M., Martikainen, H., and Ruuskanen, J. 2001. Neural networks and periodic components used in air quality forecasting. Atmos. Environ., 35 (5), 815–825.
  28. Cameletti, M. 2020. The Effect of Corona Virus Lockdown on Air Pollution: Evidence from the City of Brescia in Lombardia Region (Italy). Atmos. Environ., 239, 117794.
  29. Samal, K.K.R., Babu, K.S., Das, S.K., and Acharaya, A. 2019. Time Series based Air Pollution Forecasting using SARIMA and Prophet Model. Proc. 2019 Int. Conf. Inf. Technol. Comput. Commun., 80–85.
  30. Taylor, S.J., and Letham, B. 2018. Forecasting at scale. Am. Stat., 72 (1), 37–45.
  31. Google Colaboratory, https://colab.research.google.com/, last accessed 2023/08/11.
  32. Welcome to Python.org, https://www.python.org/, last accessed 2023/08/11.
  33. pandas - Python Data Analysis Library, https://pandas.pydata.org/, last accessed 2023/08/11.
  34. Matplotlib — Visualization with Python, https://matplotlib.org/, last accessed 2023/08/11.
  35. Introduction — statsmodels, https://www.statsmodels.org/stable/index.html, last accessed 2023/08/11.
  36. Prophet, http://facebook.github.io/prophet/, last accessed 2023/08/11.
  37. TensorFlow, https://www.tensorflow.org/, last accessed 2023/08/11.
  38. scikit-learn: machine learning in Python — scikit-learn 1.2.0 documentation, https://scikit-learn.org/stable/, last accessed 2023/08/11.
  39. US Embassies and Consulates | AirNow.gov, https://www.airnow.gov/international/us-embassies-and-consulates/, last accessed 2023/08/08.
Index Terms

Computer Science
Information Sciences

Keywords

Time Series Analysis COVID-19 Lockdown Air Quality Air Pollution Air Quality Index (AQI).

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