Preprints
https://doi.org/10.5194/essd-2022-428
https://doi.org/10.5194/essd-2022-428
 
21 Dec 2022
21 Dec 2022
Status: this preprint is currently under review for the journal ESSD.

Recurrent mapping of Hourly Surface Ozone Data (HrSOD) across China during 2005–2020 for ecosystem and human health risk assessment

Wenxiu Zhang1,, Di Liu1,, Hanqin Tian2, Naiqin Pan2,3, Ruqi Yang4, Wenhan Tang5, Jia Yang6, Fei Lu1, Buddhi Dayananda7, Han Mei8, Siyuan Wang1, and Hao Shi1 Wenxiu Zhang et al.
  • 1State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 10085, China
  • 2Schiller Institute of Integrated Science and Society, Boston College, Chestnut Hill, MA 02467, US
  • 3College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, US
  • 4Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, US
  • 5Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, US
  • 6Oklahoma State University, Natural Resource Ecology & Management, Stillwater, OK 74078, US
  • 7School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
  • 8Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong 999077, China
  • These authors contributed equally to this work.

Abstract. Surface ozone is an important air pollutant detrimental to human health and vegetation productivity. Regardless of its short atmospheric lifetime, surface ozone has significantly increased since the 1970s across the Northern Hemisphere, particularly in China. However, high temporal resolution surface ozone concentration data is still lacking in China, largely hindering accurate assessment of associated environmental and human health impacts. Here, we collected hourly ground ozone observations (over 6 million records), meteorological data, remote sensing products, and social-economic information, and applied the Long Short-Term Memory (LSTM) recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2005–2020. Benefiting from its advantage in time-series prediction, the LSTM model well captured the spatiotemporal dynamics of observed ozone concentrations, with the sample-based, site-based, and by-year cross-validation coefficient of determination (R2) values being 0.72, 0.65 and 0.71, and root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb), respectively. Air temperature, atmospheric pressure, and relative humidity were found to be the primary influencing factors. Spatially, surface ozone concentrations were high in northwestern China and low in the Sichuan Basin and northeastern China. Among the four megacity clusters in China, namely the Beijing-Tianjin-Hebei region, the Pearl River Delta, the Yangtze River Delta, and the Sichuan Basin, surface ozone concentration kept decreasing before 2016. However, it tended to increase thereafter in the former three regions, though an abrupt decrease in surface ozone concentrations occurred in 2020. Overall, the HrSOD provides critical information for surface ozone pollution dynamics in China and can support fine-resolution environmental impact and human health risk assessment. The data set is available at https://doi.org/10.5281/zenodo.7415326 (Zhang et al., 2022).

Wenxiu Zhang et al.

Status: open (until 22 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2022-428', Hui Zhang, 28 Dec 2022 reply
    • CC2: 'Reply on CC1', Wenxiu Zhang, 17 Jan 2023 reply
  • CC3: 'Comment on essd-2022-428', Ningpeng Dong, 23 Jan 2023 reply

Wenxiu Zhang et al.

Data sets

Hourly Surface Ozone data (HrSOD) across China during 2005-2020 Wenxiu Zhang; Di Liu; Hao Shi https://doi.org/10.5281/zenodo.7415326

Wenxiu Zhang et al.

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Short summary
High temporal resolution surface ozone concentration data is still lacking in China, so we used deep learning to generate hourly surface ozone data (HrSOD) during 2005–2020 across China. HrSOD showed that surface O3 in China tended to increase from 2016 to 2019, despite a decrease in 2020. HrSOD had high spatial and temporal accuracies, long time ranges and high temporal resolution, enabling it to be easily converted to various evaluation indicators for ecosystem and human health assessments.