Abstract
Developing a big data analytics framework for generating a Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor flow based data fusion method, a gap-free aerosol optical depth (AOD) dataset with daily 1-km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from MODIS aboard Terra were reconstructed based on a set of AOD data tensors acquired from satellites, numerical analysis, and in situ air quality data via integrative efforts of spatial pattern recognition for high dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high resolution AOD dataset in China, from which spatially contiguous PM2.5 and PM10 concentrations were estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in a good agreement with in situ AOD observations from AERONET, with R of 0.91 and RMSE equaling to 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R of 0.95 and 0.94 and RMSE of 12.03 and 19.56 μg m−3, respectively. Overall, the LGHAP provides a suite of long-term gap free gridded maps with high-resolution to better examine aerosol changes in China over the past two decades, from which three distinct variation periods of haze pollution were revealed in China. Additionally, the proportion of population exposed to unhealthy PM2.5 was increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then drastically reduced to 34.03 % in 2020. Overall, the generated LGHAP aerosol dataset has a great potential to trigger multidisciplinary applications in earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM2.5, and PM10 datasets can be publicly accessed at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Meanwhile, monthly and annual mean datasets can be found at https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, Matlab, R, and IDL codes were also provided to help users read and visualize these data.
Highlights
Atmospheric aerosols impact regional climate by changing the Earth radiation budget but significantly influence air quality at the ground level (Fuzzi et al, 2015; Gao et al, 2018; Shen et al, 2020; Sun et al, 2015)
The current study developed a big data analytics framework for generating a Long-term Gap-free Highresolution Air Pollutants concentration dataset providing Aerosol optical depth (AOD), PM2.5 and PM10 concentration with a daily 1-km resolution in China from 2000 to 2020
The results shown here clearly highlight the success of big data analytics in generating the LGHAP AOD dataset via integrative efforts from diversified data sources
Summary
Atmospheric aerosols impact regional climate by changing the Earth radiation budget but significantly influence air quality at the ground level (Fuzzi et al, 2015; Gao et al, 2018; Shen et al, 2020; Sun et al, 2015). Monitoring aerosol loading in the atmosphere is of great significance for climate change attribution and haze pollution assessment. Compared with sparsely distributed ground aerosol monitoring stations (e.g., AERONET), satellite instruments can provide better AOD observations because of vast spatial coverage and high sampling frequency. Due to negative impacts of bright surface (e.g., snow cover) and clouds, as well as algorithmic restrictions, satellite AOD retrievals often suffer from extensive data gaps, significantly reducing the downstream application potential such as mapping particulate matter (PM) concentrations at the ground surface (e.g., Bai et al, 2019; Wei et al, 2021a). Filling data gaps in satellite-based AOD products is still a challenge due to extraordinary nonrandom missing values and high aerosol dynamics in space and time
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