doi.org/10.5281/zenodo.15211604

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https://doi.org/10.5281/zenodo.15211604

A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)

Vertical profile observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profile data. This study introduces a high-time-resolution (approximately 15 minutes) dataset of vertical profile observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal variations and diurnal pattern of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing would facilitate the scientific community in exploring of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate and feedback mechanisms.



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A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)

https://doi.org/10.5281/zenodo.15211604

Vertical profile observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profile data. This study introduces a high-time-resolution (approximately 15 minutes) dataset of vertical profile observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal variations and diurnal pattern of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing would facilitate the scientific community in exploring of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate and feedback mechanisms.



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https://doi.org/10.5281/zenodo.15211604

A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)

Vertical profile observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profile data. This study introduces a high-time-resolution (approximately 15 minutes) dataset of vertical profile observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal variations and diurnal pattern of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing would facilitate the scientific community in exploring of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate and feedback mechanisms.

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      A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
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      A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
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      Vertical profile observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profile data. This study introduces a high-time-resolution (approximately 15 minutes) dataset of vertical profile observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal variations and diurnal pattern of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing would facilitate the scientific community in exploring of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate and feedback mechanisms.
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      A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
    • twitter:description
      Vertical profile observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profile data. This study introduces a high-time-resolution (approximately 15 minutes) dataset of vertical profile observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal variations and diurnal pattern of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing would facilitate the scientific community in exploring of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate and feedback mechanisms.
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