Spatially continuous mapping of hourly ground ozone levels assisted by Himawari-8 short wave radiation products

Zhang, Yixiao; Wang, Wei; He, Junchen; Jin, Zhili; Wang, Nan

doi:10.1080/15481603.2023.2174280

Cited by 19 publications

(8 citation statements)

References 72 publications

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“…As the day progresses, however, the contribution of radiation gradually weakens, the role of temperature undergoes a significant upswing, and RH remains a relatively stable impact factor. The primary cause lies in increased human activities (R. Zhang et al, 2004), with heightened radiation and elevated temperatures substantially stimulating the production of atomic oxygen and oxidants and increasing photochemical reactions (Bloomer et al, 2009;Zhao et al, 2016;Wei et al, 2022;Zhang et al, 2023). RH can also impact the reaction process by interacting with water vapor, atomic oxygen, and cloud cover, and also affecting the dissipation of surface O3 through dry deposition (Otero et al, 2018;Vautard et al, 2012).…”

Section: Diurnal Variations In Surface O3 and Driving Factorsmentioning

confidence: 99%

“…Accurate MDA8 calculations require 24 hours of retrievals, but few studies have addressed this issue (Y. Wang et al, 2022;Xue et al, 2022;Zhang et al, 2023). All of these studies have also…”

Section: Comparison With Related Studiesmentioning

confidence: 99%

“…Y. Zhang et al (2023) have applied a bagged-tree model to generate hourly (09:00-16:00 LT) ground-level O3 concentrations at a 5-km resolution over China by integrating the hourly Himawari-8 shortwave radiation product. B. built a deep-learning (DL) model to acquire hourly (10:00-15:00 LT) 5-km surface O3 concentrations from Himawari-8 top-of-the-atmosphere radiation.…”

Section: Introductionmentioning

confidence: 99%

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First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O 3) from space in China using artificial intelligence: diurnal variations and implications for air quality and phytotoxicity

Cheng,

Li,

Yang

et al. 2024

Preprint

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Surface ozone (O3) is a crucial ambient pollutant gas that poses substantial risks to both human health and ecosystems. Nonetheless, there is a scarcity of high-spatial-resolution hourly surface O3 data, particularly during the day when this information is needed due to the strong diurnal variations of O3. We thus determined a best-performing artificial intelligence model to derive 24-hourly 1-kmresolution surface O3 concentrations in China from a large array of satellite and surface data, which can portray well the diurnal variations of O3 concentration. The overall sample-based crossvalidated coefficients of determination (root-mean-square error) are 0.89 (15.74 μg/m 3 ), 0.91 (14.91 μg/m 3 ), and 0.85 (16.31 μg/m 3 ) during the full day (00:00-23:00 local time, or LT), daytime (08:00-17:00 LT), and nighttime (18:00-07:00 LT), respectively. The surface O3 level generally rises from sunrise, around 07:00 LT, reaching a peak at ~15:00 LT, then continuously declining overnight. The magnitude of the diurnal variation amounts to 180% relative to its diurnal mean level. During daytime, solar radiation in the ultraviolet and shortwave spectral bands, along with temperature, explain more than half (32% and 24%) of the diurnal variations using the interpretable SHapley Additive exPlanations (SHAP) method, while nighttime O3 levels are dominated by temperature (31%) and relative humidity (16%). In 2018, approximately 59%, 93%, and 100% of populated areas were susceptible to O3 exposure risk for at least one day, with the maximum daily average 8-h O3 levels surpassing the World Health Organization's recommended daily air quality standards of 160 µg/m³, 120 µg/m³, and 100 µg/m³, respectively. Approximately 65%, 70%, and 99% of vegetated areas in China exceed the minimum critical levels for O3 mixing ratios, as determined by the sum of all hourly values ≥ 0.06 μmol mol -1 (SUM06), the sigmoidally weighted sum of all hourly values (W126), and accumulates over the threshold of 40 nmol mol -1 (AOT40), respectively. Notably, gross primary productivity stands out as the most responsive indicator to surface O3 pollution across various vegetated types in China, especially concerning the Hourly O3 Accumulates without Threshold (AOT0, R = -0.37--0.53, p < 0.001).

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Section: Diurnal Variations In Surface O3 and Driving Factorsmentioning

confidence: 99%

Section: Comparison With Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O 3) from space in China using artificial intelligence: diurnal variations and implications for air quality and phytotoxicity

Cheng,

Li,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The observational data is archived in the World Data Centre for Greenhouse Gases. The heterogeneous geographic distribution of the ground-based data results in significant uncertainty in the reanalyzed data for regions with sparse distribution of monitoring stations (Pei et al, 2022;Liu et al, 2023;Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features

He,

Zheng,

Zhao

et al. 2024

Front. Earth Sci.

Self Cite

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Atmospheric methane is one of the major greenhouse gases and has a great impact on climate change. To obtain the polluted levels of atmospheric methane in the ground-level range, this study used satellite observations and vertical profile features derived by atmospheric chemistry model to estimate the ground methane concentrations in first. Then, the improved daily ground-level atmospheric methane concentration dataset with full spatial coverage (100%) and 5-km resolution in mainland China from 2019 to 2021 were retrieved by station-based observations and gaussian regression model. The overall estimated deviation between the estimated ground methane concentrations and the WDCGG station-based measurements is less than 10 ppbv. The R by ten-fold cross-validation is 0.93, and the R2 is 0.87. The distribution of the ground-level methane concentrations in the Chinese region is characterized by high in the east and south, and low in the west and north. On the time scale, ground-level methane concentration in the Chinese region is higher in winter and lower in summer. Meanwhile, the spatial and temporal distribution and changes of ground-level methane in local areas have been analyzed using Shandong Province as an example. The results have a potential to detect changes in the distribution of methane concentration.

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“…Firstly, traditional forest survey methods can provide high-accuracy forest height information; however, this method is time-consuming and laborious, which is impractical for achieving spatial mapping of forest canopy height at large scales [16]. However, remote sensing technology has enabled large-area continuous Earth observation [17] and is being increasingly applied to extensive forest canopy height mapping [18]. In particular, passive optical sensors can provide spectral feature information of a large-scale continuous surface and combine with field measurements to build a regression estimation model [19], or use the stereo imagery pair technique [20,21] to obtain the corresponding digital surface model and achieve the accurate extraction of continuous canopy height.…”

Section: Introductionmentioning

confidence: 99%

Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method

Sang,

Xiao,

Jin

et al. 2023

Remote Sensing

Self Cite

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Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height.

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Spatially continuous mapping of hourly ground ozone levels assisted by Himawari-8 short wave radiation products

Cited by 19 publications

References 72 publications

First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O 3) from space in China using artificial intelligence: diurnal variations and implications for air quality and phytotoxicity

First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O 3) from space in China using artificial intelligence: diurnal variations and implications for air quality and phytotoxicity

Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features

Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method

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