Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model

Abstract

Recently, the global background concentration of ozone (O₃) has demonstrated a rising trend. Among various methods, groun-based monitoring of O₃ concentrations is highly reliable for research analysis. To obtain information on the spatial characteristics of O₃ concentrations, it is necessary that the ground monitoring sites be constructed in sufficient density. In recent years, many researchers have used machine learning models to estimate surface O₃ concentrations, which cannot fully provide the spatial and temporal information contained in a sample dataset. To solve this problem, the current study utilized a deep learning model called the Residual connection Convolutional Long Short-Term Memory network (R-ConvLSTM) to estimate daily maximum 8-hr average (MDA8) O₃ over Jiangsu province, China during 2020. In this research, the R-ConvLSTM model not only provides the spatiotemporal information of MDA8 O₃, but also involves residual connection to avoid the problem of gradient explosion and gradient disappearance with the deepening of network layers. We utilized the TROPOMI total O₃ column retrieved from Sentinel-5 Precursor, ERA5 reanalysis meteorological data, and other supplementary data to build a pre-trained dataset. The R-ConvLSTM model achieved an overall sample-base cross-validation (CV) R² of 0.955 with root mean square error (RMSE) of 9.372 µg/m³. Model estimation also showed a city-based CV R² of 0.896 with RMSE of 14.029 µg/m³, the highest MDA8 O₃ in spring being 122.60 ± 31.60 µg/m³ and the lowest in winter being 69.93 ± 18.48 µg/m³.