Recurrent mapping of Hourly Surface Ozone Data (HrSOD) across China during 2005–2020 for ecosystem and human health risk assessment
- 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.
- 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).
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Wenxiu Zhang et al.
Status: open (until 22 Feb 2023)
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CC1: 'Comment on essd-2022-428', Hui Zhang, 28 Dec 2022
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The manuscript titled “Recurrent mapping of Hourly Surface Ozone Data (HrSOD) across China during 2005–2020 for ecosystem and human health risk assessment” by Zhang et al generates the surface ozone data across China. My biggest concern is that all your true air quality monitoring station data is during the period of 2015-2020, how did you predict the surface ozone before 2015? Did you build LSTM model based on data from 2015-2020 to predict results before 2015? If so, how do you assess the uncertainty in the data before 2015?
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CC2: 'Reply on CC1', Wenxiu Zhang, 17 Jan 2023
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We thank the reviewers for their helpful comments. Our responses to each reviewer are provided in the pdf attached.
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CC2: 'Reply on CC1', Wenxiu Zhang, 17 Jan 2023
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CC3: 'Comment on essd-2022-428', Ningpeng Dong, 23 Jan 2023
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This is a nice paper, and I have a few questions regarding the technical details. 1) Did the authors only used those grid cells with ozone observations for training, and how many of those grid cells are there? 2) Why did the authors chose a time window of 24 hours for training, is other time window possible? 3) The authors carried out a 10-fold cross validation for hyperparameter optimization, which should be followed by a model performance evaluation with the testing data. I might have missed the information on model testing, but how does the model perform with the testing data?
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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