First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O3) from space in China using artificial intelligence: Diurnal variations and implications for air quality and phytotoxicity,Remote Sensing of Environment

当前位置： X-MOL 学术 › Remote Sens. Environ. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O3) from space in China using artificial intelligence: Diurnal variations and implications for air quality and phytotoxicity
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2024-10-31 , DOI: 10.1016/j.rse.2024.114482
Fan Cheng, Zhanqing Li, Zeyu Yang, Ruohan Li, Dongdong Wang, Aolin Jia, Ke Li, Bin Zhao, Shuxiao Wang, Dejia Yin, Shengyue Li, Wenhao Xue, Maureen Cribb, Jing Wei

Surface ozone (O3) is a critical ambient pollutant that poses significant risks to both human health and ecosystems. However, there is a scarcity of high-spatial-resolution hourly surface O3 data, which is crucial for understanding its diurnal variations. In this study, we employed a best-performing spatiotemporal artificial intelligence (AI) model to estimate 24-hourly 1-km-resolution surface O3 concentrations across China, incorporating key photochemical processes responsible for O3 formation. Our model effectively captured diurnal O3 patterns, achieving average sample-based cross-validated coefficients of determination (root-mean-square errors) of 0.89 (16.35 μg/m3) for the full day (00:00–23:00 LT), 0.92 (15.72 μg/m3) during daytime (08:00–20:00 LT), and 0.82 (16.97 μg/m3) at nighttime (20:00–08:00 LT). Typically, surface O3 levels increase after sunrise, peak around 15:00 LT, and decrease overnight, with a diurnal variation magnitude of 62 % relative to the mean level. During the daytime, we found that solar radiation (in the ultraviolet and shortwave spectra) and surface temperature explained over 42 % of the diurnal variation, while nighttime O3 levels were mainly influenced by tropospheric nitrogen dioxide (16 %), temperature (13 %), and relative humidity (12 %). In 2019, approximately 61 %, 98 %, and 100 % of populated areas in China experienced O3 exposure risks for at least one day, with maximum daily 8-h average (MDA8) O3 levels exceeding 160, 120, and 100 μg/m3, respectively. Additionally, around 70 %, 82 %, and 100 % of vegetated areas exceeded the three minimum critical thresholds for cumulative hourly O₃ exposure, as indicated by the SUM06, W126, and AOT40 indices, respectively. Notably, gross primary productivity (GPP) was the most sensitive indicator of O3 pollution across various vegetation types, showing a strong negative correlation with AOT0 (R = −0.43 to −0.59, p < 0.001).

更新日期：2024-10-31

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南