Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port

Hyunsu Hong,Hyungjin Jeon,Cheong Youn,Hyeonsoo Kim

doi:10.3390/atmos12091172

Hyunsu Hong, Hyungjin Jeon + Show 2 more

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https://doi.org/10.3390/atmos12091172

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Abstract

Air pollution sources and the hazards of high particulate matter 2.5 (PM2.5) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to manage seaport air quality. However, air pollution prediction models rarely consider shipping emissions. Here, the PM2.5 concentrations of the Busan North and Busan New Ports were predicted using a recurrent neural network and long short-term memory model by employing the shipping activity data of Busan Port. In contrast to previous studies that employed only air quality and meteorological data as input data, our model considered shipping activity data as an emission source. The model was trained from 1 January 2019 to 31 January 2020 and predictions and verifications were performed from 1–28 February 2020. Verifications revealed an index of agreements (IOA) of 0.975 and 0.970 and root mean square errors of 4.88 and 5.87 µg/m3 for Busan North Port and Busan New Port, respectively. Regarding the results based on the activity data, a previous study reported an IOA of 0.62–0.84, with a higher predictive power of 0.970–0.975. Thus, the extended approach offers a useful strategy to prevent PM2.5 air pollutant-induced damage in seaports.

Highlights

The World Health Organization (WHO) recommended an average annual PM2.5 concentration of 10 μg/m3 in South Korea
In contrast to previous studies that have mostly predicted air quality mainly based on air quality monitoring data and meteorological data, we considered the activity of ships when employing the recurrent neural network (RNN)-long short-term memory (LSTM) model in the present study
We modeled the PM2.5 concentrations for Busan North Port and Busan New Port using three models for each location

Summary

Introduction

The World Health Organization (WHO) recommended an average annual PM2.5 concentration (i.e., concentration of particles with a diameter less than 2.5 μm) of 10 μg/m3 in South Korea. PM2.5 is a class 1 carcinogen designated by the WHO and can cause lung cancer if the particles enter the lungs of the human body. There are reports of more harm to the human body than other pollutants such as NO2 and SO2 [1,2,3]. The Department of Environment and other related government departments jointly prepared the “Special Measures for PM Management” in August 2017 and announced the PM Management General Plan (2020–2024) in November 2019 as an effort to reduce air pollution [4]. Such efforts are being made across seaports. The ability to accurately predict air pollution levels is, arguably, as important as the efforts to reduce air pollution

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