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

Sichen Wang, Xi Mu, Peng Jiang, Yanfeng Huo, Li Zhu, Zhiqiang Zhu and Yanlan Wu
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Sichen Wang: School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Xi Mu: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, China
Peng Jiang: School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Yanfeng Huo: Anhui Institute of Meteorological Sciences, Hefei 230031, China
Li Zhu: School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Zhiqiang Zhu: School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Yanlan Wu: School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

IJERPH, 2022, vol. 19, issue 12, 1-15

Abstract: 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.

Keywords: ozone pollution; deep learning; human exposure; eastern China; tropospheric monitoring instrument; long short-term memory network; data-driven model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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