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• W4385717858 endingPage "120021" @default.
• W4385717858 startingPage "120021" @default.
• W4385717858 abstract "Carbon monoxide (CO) has notable effects on the atmospheric environment and human health. This study used Himawari-8 top-of-atmosphere radiation (TOAR) data, meteorological factors, and geographic information, combined with an interpretable deep forest (DF) model, to obtain high-spatiotemporal resolution near-surface CO concentrations (temporal resolution: 1 h; spatial resolution: 0.05°) in China from September 2015 to August 2021 and reveal the distribution and variation in city-level CO hotspots. Based on the feature importance method and the spectral absorption characteristics of pollutants, the optimal combination of Himawari-8 band for the TOAR-CO model was selected. The performance comparison of five machine learning models showed that the DF model was the most performant. The hourly average R2 value (using 10-fold cross-validation) of the TOAR-CO model ranged from 0.72 to 0.78 (10:00 to 15:00, Beijing time); the R2 values of daily, monthly, and annual averages were 0.79, 0.97 and 0.98, respectively. The feature importance of the model varied in the different seasons and regions, with TOAR and meteorological factors had significantly contributions to the model. During the study period, the CO concentration in most areas of China showed a decreasing trend, with an average decline rate of 7.2%·yr−1. Despite CO being a stable, long-lived gas, the TOAR-CO model still captured obvious intraday variation in CO, which reached a peak of 0.89 mg/m3 at 10:00 and dropped to 0.69 mg/m3 at 15:00. The spatiotemporal distribution of estimated CO was basically consistent with satellite and reanalysis data. The TOAR-CO model predictions reflected city-level CO pollution, and the distribution of CO hotspots was closely related to industrial structure and human activities. During the COVID-19 pandemic, CO concentration significantly dropped after the 4th week of control. The CO concentrations in the hotspot cities decreased more significantly, by over 50%. The research results provide some supports for the assessment of CO fine-scale pollution and related research." @default.
• W4385717858 created "2023-08-11" @default.
• W4385717858 creator A5007581371 @default.
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• W4385717858 date "2023-11-01" @default.
• W4385717858 modified "2023-10-12" @default.
• W4385717858 title "Exploring high-resolution near-surface CO concentrations based on Himawari-8 top-of-atmosphere radiation data: Assessing the distribution of city-level CO hotspots in China" @default.
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• W4385717858 doi "https://doi.org/10.1016/j.atmosenv.2023.120021" @default.
• W4385717858 hasPublicationYear "2023" @default.
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