Abstract
Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lower-Stratosphere-to-Troposphere (LST) ozone at a pressure of 100 hPa to the near surface by employing a deep learning Long Short-Term Memory (LSTM) model. We referred to a history of all the observed parameters (meteorological data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and the ozone profiles of the World Ozone and Ultraviolet Data Center (WOUDC)) between 2014 and 2018 for training the predictive models. Model–measurement comparisons for the monitoring sites of WOUDC for the period 2019–2020 show that the mean correlation coefficients (R2) in the Northern Hemisphere at high latitude (NH), Northern Hemisphere at middle latitude (NM), and Northern Hemisphere at low latitude (NL) are 0.928, 0.885, and 0.590, respectively, indicating reasonable performance for the LSTM forecasting model. To improve the performance of the model, we applied the LSTM migration models to the Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flights in the Northern Hemisphere from 2018 to 2019 and three urban agglomerations (the Sichuan Basin (SCB), North China Plain (NCP), and Yangtze River Delta region (YRD)) between 2018 and 2019. The results show that our models performed well on the CARIBIC data set, with a high R2 equal to 0.754. The daily and monthly surface ozone concentrations for 2018–2019 in the three urban agglomerations were estimated from meteorological and ancillary variables. Our results suggest that the LSTM models can accurately estimate the monthly surface ozone concentrations in the three clusters, with relatively high coefficients of 0.815–0.889, root mean square errors (RMSEs) of 7.769–8.729 ppb, and mean absolute errors (MAEs) of 6.111–6.930 ppb. The daily scale performance was not as high as the monthly scale performance, with the accuracy of R2 = 0.636~0.737, RMSE = 14.543–16.916 ppb, MAE = 11.130–12.687 ppb. In general, the trained module based on LSTM is robust and can capture the variation of the atmospheric ozone distribution. Moreover, it also contributes to our understanding of the mechanism of air pollution, especially increasing our comprehension of pollutant areas.
Highlights
Ozone (O3 ) is considered to be a significant trace gas in the Earth’s atmosphere, 90% of which is distributed in the stratosphere and 10% in the troposphere [1].Stratospheric ozone protects the Earth’s biota from harmful UV radiation
The module was trained, validated, and tested in three different regions using a history of all observed parameters (ERA5, satellite data, and World Ozone and Ultraviolet Data Center (WOUDC) ozone profiles) from 2014 to 2020—the data from 2014 to December 2017 were used for training (80% of total data), the data from January 2018 to December 2018 were used for validation (10% of total data), and the data from January 2019 to December 2020 were used as the test set
The reason why WOUDC datasets were divided into three parts is that the training sets were used to train the Long Short-Term Memory (LSTM) modules, the validation sets were used to adjust hyperparameters during training, and the testing sets were used to objectively evaluate the performance of the model
Summary
Ozone (O3 ) is considered to be a significant trace gas in the Earth’s atmosphere, 90% of which is distributed in the stratosphere and 10% in the troposphere [1]. Stratospheric ozone protects the Earth’s biota from harmful UV radiation. Ozone is a type of greenhouse gas [2,3] and is the main air pollutant endangering human health, agriculture, and natural ecosystems, and it traps heat in the Earth’s atmosphere and plays as an important role in atmospheric chemistry, impacting air quality and climate change [4,5]. The World Health Organization (WHO 2006 and 2017) recommended that the ozone level of the MDA8 should be within
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