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http://dx.doi.org/10.14249/eia.2022.31.2.83

A Study on Prediction of PM2.5 Concentration Using DNN  

Choi, Inho (Department of Environmental Science and Engineering, Kyung Hee University)
Lee, Wonyoung (Department of Environmental Science and Engineering, Kyung Hee University)
Eun, Beomjin (Department of Environmental Science and Engineering, Kyung Hee University)
Heo, Jeongsook (Department of Environmental Science and Engineering, Kyung Hee University)
Chang, Kwang-Hyeon (Department of Environmental Science and Engineering, Kyung Hee University)
Oh, Jongmin (Department of Environmental Science and Engineering, Kyung Hee University)
Publication Information
Journal of Environmental Impact Assessment / v.31, no.2, 2022 , pp. 83-94 More about this Journal
Abstract
In this study, DNN-based models were learned using air quality determination data for 2017, 2019, and 2020 provided by the National Measurement Network (Air Korea), and this models evaluated using data from 2016 and 2018. Based on Pearson correlation coefficient 0.2, four items (SO2, CO, NO2, PM10) were initially modeled as independent variables. In order to improve the accuracy of prediction, monthly independent modeling was carried out. The error was calculated by RMSE (Root Mean Square Error) method, and the initial model of RMSE was 5.78, which was about 46% betterthan the national moving average modelresult (10.77). In addition, the performance improvement of the independent monthly model was observed in months other than November compared to the initial model. Therefore, this study confirms that DNN modeling was effective in predicting PM2.5 concentrations based on air pollutants concentrations, and that the learning performance of the model could be improved by selecting additional independent variables.
Keywords
$PM_{2.5}$; air pollutants; machine learning; prediction model; DNN model;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Kim DS. 2016. Characteristics in Atmospheric Chemistry between NO, NO2 and O3 at an Urban Site during MAPS (Megacity Air Pollution Study)-Seoul, Korea, Journal of Korean Society for Atmospheric Environment 32(4): 422-434. http://dx.doi.org/10.5572/KOSAE.2016.32.4.422   DOI
2 Lee HK. 2020. A Study on the Seasonal Correlation between O3 and PM2.5 in Seoul in 2017. Journal of Korean Society for Atmospheric Environment 36(4): 533-542. https://doi.org/10.5572/KOSAE.2020.36.4.533   DOI
3 Lee HW. 1999. The Effect of Meteorological Factors on Variation and Temporal and Spatial Characteristics of NO2 Concentration in Pusan Area. Journal of Environmental Science International 8(4): 465-471.
4 Hwang SM, Shin DS, Lee BM, Kim HH, Cho HG, Moon GJ, Jeong SH. 2007. A Study on the Distribution Characteristics of Air Pollutants at PAMS in Seoul Metropolitan Area, Proceeding of 43th meeting of Korean Society for Atmospheric Environment in 2007, pp. 1396-1399.
5 Kim JS, Bais AL, Kang SH, Lee J, Park K. 2011. A semi-continuous measurement of gaseous ammonia and particulate ammonium concentrations in PM2.5 in the ambient atmosphere. J. Atmos. Chem. 68(3): 251-263. doi:10.1007/s10874-012-9220-y   DOI
6 Jeon BI. 2010. Characteristics of Spacio-Temporal Variation for PM10 and PM2.5 Concentration in Busan, Journal of Environmental Science International 19(8): 1013-1023. https://doi.org/10.5322/JES.2010.19.8.1013   DOI
7 Sung SH, Kim SJ, Ryu MH. 2020. A Comparative Study on the Performance of Machine Learning Models for the Prediction of Fine Dust: Focusing on Domestic and Overseas Factors. Korea Innovation Studies 15(4): 339-357.   DOI
8 Son SH, Kim JS. 2020. Evaluation and Predicting PM10 Concentration Using Multiple Linear Regression and Machine Learning. Korean Journal of Remote Sensing 36(6-3): 1711-1720.   DOI
9 Yoon SY. 2014. Effect of Precipitation Cleaning on Air Pollutants in Summer and Winter. Korean Meteorological Society, Proceedings of the Autumn Meeting of KMS, 2014: 146-147.
10 Shin HW. 2021. Changes of Air Pollutants Concentrations Associated with the Rates of Rainfall and Its Duration during Summertime in Korea. Journal of Korean Society for Atmospheric Environment 2014(10): 175.
11 Lim IH. 2013. A Study on the hourly Characteristics of Air Pollution in the Gwangyang Bay. 環境硏究論文集 13: 29-39.
12 Im DY. 2021. Temporal Analyses of PM Data, Estimation of the Past Unmonitored PM2.5 Data, and Assessment of the COVID-19 Effect at the Background Areas in Korea. Journal of Korean Society for Atmospheric Environment 37(4): 670-690. https://doi.org/10.5572/KOSAE.2021.37.4.670   DOI
13 Lee BK, Lee DS, Kim MG. 2001. Rapid time variations in chemical composition of precipitation in South Korea. Water, Air, and Soil Pollution 130(1-4): 427-432. doi:10.1023/A:1013845620642.   DOI
14 Ghim YS. 2013. Regional Trends in Short-Term High Concentrations of Criteria Pollutants from National Air Monitoring Stations. Journal of Korean Society for Atmospheric Environment 29(5): 545-552.   DOI
15 Cha J, Kim J. 2018. Development of Data Mining Algorithm for Implementation of Fine Dust Numerical Prediction Model. Journal of the Korea Institute of Information and Communication Engineering, 22(4), 595-601. https://doi.org/10.6109/JKIICE.2018.22.4.595   DOI
16 Won DJ. 2021. Prediction of Fine Dust in Gyeonggi-do Industrial Complex using Machine Learning Methods, Journal of KIISE 48(7): 764-773.   DOI