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KCI우수등재 학술저널

Simple Forecasting of Surface Ozone through a Statistical Approach

Simple Forecasting of Surface Ozone through a Statistical Approach

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Objectives: Ozone (O<sub>3</sub>) advisories are issued by provincial/prefectural and city governments in Korea and Japan when oxidant concentrations exceed the criteria of the related country. Advisories issued only after exposure to high O<sub>3</sub> concentrations cannot be considered ideal measures. Forecasts of O<sub>3</sub> would be more beneficial to citizens’ health and daily life than real-time advisories. The present study was undertaken to present a simplified forecasting model that can predict surface O<sub>3</sub> concentrations for the afternoon of the day of the forecast. Methods: For the construction of a simple and practical model, a multivariate regression model was applied. The monitored data on gases and climate variables from Japan s air quality networks that were recorded over nearly one year starting from April 2016 were applied as the subject for our model. Results: A well-known inverse correlation between NO<sub>2</sub> and O<sub>3</sub> was confirmed by the monitored data for Iksan, Korea and Fukuoka, Japan. Typical time fluctuations for O<sub>3</sub> and NO<sub>x</sub> were also found. Our model suggests that insolation is the most influential factor in determining the concentration of O<sub>3</sub>. CH<sub>4</sub> also plays a major role in our model. It was possible to visually check for the fit of a theoretical distribution to the observed data by examining the probability-probability (P-P) scatter plot. The goodness of fit of the model in this study was also successfully validated through a comparison (r=0.8, p<0.05) of the measured and predicted O<sub>3</sub> concentrations. Conclusions: The advantage of our model is that it is capable of immediate forecasting of surface O<sub>3</sub> for the afternoon of the day from the routinely measured values of the precursor and meteorological parameters. Although a comparison to other approaches for O<sub>3</sub> forecasting was not carried out, the model suggested in this tudy would be very helpful for the citizens of Korea and Japan, especially during the O<sub>3</sub> season from May to June.

I. Introduction

II. Methods

III. Results and Discussion

IV. Conclusions

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