High downward surface solar radiation conducive to ozone pollution more frequent under global warming

Kou, Wenbin; Gao, Yang; Zhang, Shaoqing; Cai, Wenju; Geng, Guannan; Davis, Steven J.; Wang, Hong; Guo, Xinyun; Cheng, Wenxuan; Zeng, Xueying; Ma, Minglin; Wang, Houwen; Yao, Xiaohong; Gao, Huiwang; Wu, Lixin

doi:10.1016/j.scib.2023.01.022

Cited by 18 publications

(9 citation statements)

References 13 publications

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“…Consequently, the statistics for both periods do not When considering the temporal aspect, combining these two figures, we observed that the predicted ground-level O 3 concentrations as well as the standard deviation experienced a significant and quick increase from 09:00 to 13:00. This phenomenon was caused by the increasing solar radiation and temperature [46], which facilitate the chemical production of ground-level O 3 . Subsequently, the mean ground-level O 3 concentrations reached a steady state of approximately 105 µg/m 3 until sunset, as ground-level O 3 does not disperse quickly before dusk.…”

Section: Discussion Of Spatiotemporal Distribution Of Omentioning

confidence: 99%

High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning

Chen,

Dong,

Zhang

et al. 2023

Atmosphere

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High concentrations of ground-level ozone (O3) pose a significant threat to human health. Obtaining high-spatiotemporal-resolution information about ground-level O3 is of paramount importance for O3 pollution control. However, the current monitoring methods have a lot of limitations. Ground-based monitoring falls short in providing extensive coverage, and remote sensing based on satellites is constrained by specific spectral bands, lacking sensitivity to ground-level O3. To address this issue, we combined brightness temperature data from the Himawari-8 satellite with meteorological data and ground-based station data to train four machine learning models to obtain high-spatiotemporal-resolution information about ground-level O3, including Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Among these, the CatBoost model exhibited superior performance, achieving a ten-fold cross-validation R2 of 0.8534, an RMSE of 17.735 μg/m3, and an MAE of 12.6594 μg/m3. Furthermore, all the selected feature variables in our study positively influenced the model. Subsequently, we employed the CatBoost model to estimate averaged hourly ground-level O3 concentrations at a 2 km resolution. The estimation results indicate a close relationship between ground-level O3 concentrations and human activities and solar radiation.

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Section: Discussion Of Spatiotemporal Distribution Of Omentioning

confidence: 99%

High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning

Chen,

Dong,

Zhang

et al. 2023

Atmosphere

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“…Tropospheric O 3 is a secondary air pollutant formed from photochemical reactions 60 : where VOCs and NO x refer to volatile organic compounds and nitrogen oxides, respectively, hv represents the strength of SSR. Figure 3a demonstrates that the three regions experienced varied interannual solar shortwave radiation (SSR) from 2014 to 2021, which can be associated with the variability of surface O 3 61 – 63 . In China, the SSR difference between 2014 and 2021 was smaller than 100 Wm −2 , and higher SSR values were found during 2017–2019.…”

Section: Technical Validationmentioning

confidence: 99%

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations

Zhu,

Xu,

Zeng

et al. 2023

Sci Data

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This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012–2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 μg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

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“…Considerable studies have been dedicated to examining the phenomenon of elevated ozone pollution during the warm season in the PRD, where ozone concentrations frequently surpass national standards [17][18][19][20][21][22][23][24][25][26][27][28] . For example, the identification of key meteorological factors, such as low humidity and specific wind patterns, has provided insights into how these conditions can exacerbate O3 pollution through increased photochemical production 23,24 .…”

Section: Introductionmentioning

confidence: 99%

“…The examination of O3 diurnal variations has underscored the importance of meteorological processes in the temporal dynamics of O3 concentrations 18 . In addition, the role of biogenic volatile organic compounds (BVOCs), enhanced by extreme heat events, has been recognized as a critical factor in the formation of high O3 levels, which highlights the interplay between BVOCs and meteorological condition 26,28 . Overall, these findings advance our understanding of the underlying mechanisms responsible for the pronounced O3 pollution and also underscore the complexity of O3 pollution in the warm seasons in the PRD region.…”

Section: Introductionmentioning

confidence: 99%

Rapid increase in spring ozone in the Pearl River Delta, China during 2013-2022

Wang,

Cao,

Chen

et al. 2024

Preprint

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Surface ozone pollution is a severe air pollution problem and typically occurs in the warm season since it is driven by photochemistry. Recent studies showed a tendency for ozone pollution to spread into the cold season. Yet, less attention was paid to its evolution and causes. Here, we show general uptrends in surface ozone of 1.15 ppbv a-1 during the spring (March to May) over the past decade (2013 -2022) in the Pearl River Delta, China. Meteorological shift mainly contributed to the average rapid growth of 77%, as determined by the multiple linear regression model. The change in three meteorological factors, namely SSR, U10, and V850, especially in the stagnant weather conditions, are critical in driving spring ozone increase, and their relative importance differs between inland and coastal regions. These results show that the meteorological-induced springtime ozone pollution warrants vigilance, as spring ozone pollution control in climate-sensitive areas, will be more challenging towards climate change.

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High downward surface solar radiation conducive to ozone pollution more frequent under global warming

Cited by 18 publications

References 13 publications

High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning

High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations

Rapid increase in spring ozone in the Pearl River Delta, China during 2013-2022

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