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.
Highlights
Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research (NIER), Incheon, South Korea
We aimed to develop a user-friendly Reactive Nitrogen species simulation model using simple Deep Neural Network (DNN) (RND) based on ground measurements in a highly polluted urban area
Since this is the first attempt to calculate HONOsca × F1 (HONO) mixing ratios using a first version of RND model (RNDv1.0), we describe the entire modeling process and evaluate the model results by comparing them with the measurements
Summary
The development of RNDv1.0 model follows the systematic steps including collecting data, preprocessing data, building the DNN, training and validating the model, and testing the performance of the model (Figure 1).
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