Preprints
https://doi.org/10.5194/gmd-2021-347
https://doi.org/10.5194/gmd-2021-347
Submitted as: model description paper
10 Dec 2021
Submitted as: model description paper | 10 Dec 2021
Status: this preprint is currently under review for the journal GMD.

Simulation Model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RND v1.0

Junsu Gil1, Meehye Lee1, Jeonghwan Kim2, Gangwoong Lee2, and Joonyoung Ahn3 Junsu Gil et al.
  • 1Department of Earth and Environmental Science, Korea University, Seoul, South Korea
  • 2Department of Environmental Science, Hankuk University of Foreign Studies, Yongin, South Korea
  • 3Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research (NIER), Incheon, South Korea

Abstract. Nitrous acid (HONO), one of the reactive nitrogen oxides (NOy), plays an important role in the formation of ozone (O3) and fine aerosols (PM2.5) in the urban atmosphere. In this study, a simulation model of Reactive Nitrogen species using Deep neural network model (RND) was constructed to calculate the HONO mixing ratios through a deep learning technique using measured variables. A Python-based Deep Neural Network (DNN) was trained, validated, and tested with HONO measurement data obtained in Seoul during the warm months from 2016 to 2019. A k-fold cross validation and test results confirmed the performance of RND v1.0 with an Index Of Agreement (IOA) of 0.79 ~ 0.89 and a Mean Absolute Error (MAE) of 0.21 ~ 0.31 ppbv. The RNDV1.0 adequately represents the main characteristics of HONO and thus, RND v1.0 is proposed as a supplementary model for calculating the HONO mixing ratio in a high- NOx environment.

Junsu Gil et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-347', Anonymous Referee #1, 13 Feb 2022
  • RC2: 'Comment on gmd-2021-347', Anonymous Referee #2, 14 Feb 2022

Junsu Gil et al.

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Short summary
In this study, the framework for calculating Reactive Nitrogen oxides mixing ratio in the atmosphere using Deep neural network (RND) was developed. It works through simple python codes and provides high-accuracy reactive nitrogen oxides data. As the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role to produce O3 and fine aerosol.