Reply on RC2

Kaixu Bai

doi:10.5194/essd-2021-404-ac2

Abstract

Abstract. Developing a big data analytics framework for generating the Long-term Gap-free High-resolution Air Pollutant 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 a daily 1âkm resolution covering the period of 2000â2020 in China was generated. Specifically, data gaps in daily AOD imageries from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra were reconstructed based on a set of AOD data tensors acquired from diverse satellites, numerical analysis, and inÂ situ air quality measurements 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 then estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in good agreement with inÂ situ AOD observations from the Aerosol Robotic Network (AERONET), with an R of 0.91 and RMSE equaling 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R values of 0.95 and 0.94 and RMSEs of 12.03 and 19.56âÂµgâmâ3, respectively. The LGHAP provides a suite of long-term gap-free gridded maps with a high resolution to better examine aerosol changes in China over the past 2Â decades, from which three major variation periods of haze pollution in China were revealed. Additionally, the proportion of the population exposed to unhealthy PM2.5 increased from 50.60â% in 2000 to 63.81â% in 2014 across China, which was then reduced drastically to 34.03â% in 2020. Overall, the generated LGHAP dataset has 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 are publicly available 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. Monthly and annual datasets can be acquired from 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 are 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

Introduction

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

Methods

Results

Conclusion