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Artificial Neural Network Modeling on PM 10 , PM 2.5 , and NO 2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020

Author

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  • Soo-Min Choi

    (Department of Computer Engineering, Konkuk University, Chungju 27478, Republic of Korea)

  • Hyo Choi

    (Atmospheric and Oceanic Disaster Research Institute, Gangneung 25563, Republic of Korea)

Abstract

The mutual relationship among daily averaged PM 10 , PM 2.5 , and NO 2 concentrations in two megacities (Seoul and Busan) connected by the busiest highway in Korea was investigated using an artificial neural network model (ANN)-sigmoid function, for a novel coronavirus (COVID-19) pandemic period from 1 January to 31 December 2020. Daily and weekly mean concentrations of NO 2 in 2020 under neither locked down cities, nor limitation of the activities of vehicles and people by the Korean Government have decreased by about 15%, and 12% in Seoul, and Busan cities, than the ones in 2019, respectively. PM 10 (PM 2.5 ) concentration has also decreased by 15% (10%), and 12% (10%) in Seoul, and Busan, with a similar decline of NO 2 , causing an improvement in air quality in each city. Multilayer perception (MLP), which has a back-propagation training algorithm for a feed-forward artificial neural network technique with a sigmoid activation function was adopted to predict daily averaged PM 10 , PM 2.5 , and NO 2 concentrations in two cities with their interplay. Root mean square error (RMSE) with the coefficient of determination (R 2 ) evaluates the performance of the model between the predicted and measured values of daily mean PM 10 , PM 2.5 , and NO 2, in Seoul were 2.251 with 0.882 (1.909 with 0.896; 1.913 with 0.892), 0.717 with 0.925 (0.955 with 0.930; 0.955 with 0.922), and 3.502 with 0.729 (2.808 with 0.746; 3.481 with 0.734), in 2 (5; 7) nodes in a single hidden layer. Similarly, they in Busan were 2.155 with 0.853 (1.519 with 0.896; 1.649 with 0.869), 0.692 with 0.914 (0.891 with 0.910; 1.211 with 0.883), and 2.747 with 0.667 (2.277 with 0.669; 2.137 with 0.689), respectively. The closeness of the predicted values to the observed ones shows a very high Pearson r correlation coefficient of over 0.932, except for 0.818 of NO 2 in Busan. Modeling performance using IBM SPSS-v27 software on daily averaged PM 10 , PM 2.5 , and NO 2 concentrations in each city were compared by scatter plots and their daily distributions between predicted and observed values.

Suggested Citation

  • Soo-Min Choi & Hyo Choi, 2022. "Artificial Neural Network Modeling on PM 10 , PM 2.5 , and NO 2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020," IJERPH, MDPI, vol. 19(23), pages 1-22, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16338-:d:994870
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    References listed on IDEAS

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    1. Wen Ming & Zhengqing Zhou & Hongshan Ai & Huimin Bi & Yuan Zhong, 2020. "COVID-19 and Air Quality: Evidence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(10), pages 2422-2442, August.
    2. Man Tat Lei & Joana Monjardino & Luisa Mendes & David Gonçalves & Francisco Ferreira, 2020. "Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19," IJERPH, MDPI, vol. 17(14), pages 1-19, July.
    3. Shasha Liu & Gaowen Kong & Dongmin Kong, 2020. "Effects of the COVID-19 on Air Quality: Human Mobility, Spillover Effects, and City Connections," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 635-653, August.
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    1. Mei-Hsin Chen & Yao-Chung Chen & Tien-Yin Chou & Fang-Shii Ning, 2023. "PM2.5 Concentration Prediction Model: A CNN–RF Ensemble Framework," IJERPH, MDPI, vol. 20(5), pages 1-13, February.

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