New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China

Ozone (O(3)), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O(3) is crucial for human exposure studies. We developed a deep learning model based on a long short-term memory (LSTM) network to estimate the daily maximum 8 h average (MDA8) O(3) across eastern China in 2020. The proposed model combines LSTM with an attentional mechanism and residual connection structure. The model employed total O(3) column product from the Tropospheric Monitoring Instrument, meteorological data, and other covariates as inputs. Then, the estimates from our model were compared with real observations of the China air quality monitoring network. The results indicated that our model performed better than other traditional models, such as the random forest model and deep neural network. The sample-based cross-validation R(2) and RMSE of our model were 0.94 and 10.64 μg m(−3), respectively. Based on the O(3) distribution over eastern China derived from the model, we found that people in this region suffered from excessive O(3) exposure. Approximately 81% of the population in eastern China was exposed to MDA8 O(3) > 100 μg m(−3) for more than 150 days in 2020.