Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model.

Joonho Jang,Seungjae Shin,Il-Chul Moon,Hyunjin Lee

doi:10.3390/s20143845

Joonho Jang, Seungjae Shin + Show 2 more

Open Access

https://doi.org/10.3390/s20143845

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Abstract

Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.

Highlights

The population of Seoul was affected by fine dust or particulate matter (PM) in the atmosphere [1]
Whereas most studies focused on investigating the relation between PM concentrations and input features, such as meteorological data, this study focused on estimating PM2.5 concentration that are not observable owing to the lack of the monitoring sites
Using the prior function defined over the continuous domain on space and time, we introduce a Gaussian Process Regression (GPR) that plays a crucial role in estimating the PM concentration

Summary

Introduction

The population of Seoul was affected by fine dust or particulate matter (PM) in the atmosphere [1]. In addition to the problem of the sources, the dynamics of PM needs to be modeled to aid the prediction of the concentration of PM to address the exposure to the population. As we cannot determine the main source of PM, the model needs to consider PM generation from a holistic perspective and the factors of the dynamics of the PM concentration. These perspectives and factors are not limited to a single domain of expertise such as traffic, chemistry, meteorology, and environmental studies. We present the relative significances of the factors with regard to Seoul

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