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

Soo-Min Choi and Hyo Choi ()
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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

IJERPH, 2022, vol. 19, issue 23, 1-22

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.

Keywords: artificial neural network model; COVID-19 pandemic; air quality; PM 10; PM 2.5; NO 2; root mean square error; coefficient of determination (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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