Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
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Published:2023-09-13
Issue:17
Volume:16
Page:5251-5263
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Gil JunsuORCID, Lee MeehyeORCID, Kim Jeonghwan, Lee Gangwoong, Ahn Joonyoung, Kim Cheol-HeeORCID
Abstract
Abstract. Nitrous acid (HONO) plays an important role in the
formation of ozone and fine aerosols in the urban atmosphere. In this study,
a new simulation approach is presented to calculate the HONO mixing ratios
using a deep neural technique based on measured variables. The Reactive
Nitrogen Species using a Deep Neural Network (RND) simulation is implemented
in Python. The first version of RND (RNDv1.0) is trained, validated, and
tested with HONO measurement data obtained in Seoul, South Korea, from 2016 to 2021.
RNDv1.0 is constructed using k-fold cross validation and evaluated with
index of agreement, correlation coefficient, root mean squared error, and
mean absolute error. The results show that RNDv1.0 adequately represents the
main characteristics of the measured HONO, and it is thus proposed as a
supplementary model for calculating the HONO mixing ratio in a polluted
urban environment.
Funder
National Research Foundation of Korea Korea Institute of Science and Technology
Publisher
Copernicus GmbH
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