What Does It Mean to Be Spatiotemporally Continuous
Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China
1
School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China
2
Observation and Research Station of Beijing Fangshan Comprehensive Exploration, Ministry of Natural Resources, Beijing 100083, China
3
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
4
Chinese Academy of Surveying and Mapping, Beijing 100830, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yong Ge, Xiaomei Yang and Lianfa Li
Received: 4 August 2022 / Revised: 31 August 2022 / Accepted: 4 September 2022 / Published: 6 September 2022
Abstract
Aerosol optical depth (AOD) observations have been widely used to generate wide-coverage PM2.5 retrievals due to the adverse effects of long-term exposure to PM2.5 and the sparsity and unevenness of monitoring sites. However, due to non-random missing and nighttime gaps in AOD products, obtaining spatiotemporally continuous hourly data with high accuracy has been a great challenge. Therefore, this study developed an automatic geo-intelligent stacking (autogeoi-stacking) model, which contained seven sub-models of machine learning and was stacked through a Catboost model. The autogeoi-stacking model used the automated feature engineering (autofeat) method to identify spatiotemporal characteristics of multi-source datasets and generate extra features through automatic non-linear changes of multiple original features. The 10-fold cross-validation (CV) evaluation was employed to evaluate the 24-hour and continuous ground-level PM2.5 estimations in the Beijing-Tianjin-Hebei (BTH) region during 2018. The results showed that the autogeoi-stacking model performed well in the study area with the coefficient of determination (R2) of 0.88, the root mean squared error (RMSE) of 17.38 µg/m3, and the mean absolute error (MAE) of 10.71 µg/m3. The estimated PM2.5 concentrations had an excellent performance during the day (8:00–18:00, local time) and night (19:00–07:00) (the cross-validation coefficient of determination (CV-R2): 0.90, 0.88), and captured hourly PM2.5 variations well, even in the severe ambient air pollution event. On the seasonal scale, the R2 values from high to low were winter, autumn, spring, and summer, respectively. Compared with the original stacking model, the improvement of R2 with the autofeat and hyperparameter optimization approaches was up to 5.33%. In addition, the annual mean values indicated that the southern areas, such as Shijiazhuang, Xingtai, and Handan, suffered higher PM2.5 concentrations. The northern regions (e.g., Zhangjiakou and Chengde) experienced low PM2.5. In summary, the proposed method in this paper performed well and could provide ideas for constructing geoi-features and spatiotemporally continuous inversion products of PM2.5. View Full-Text
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MDPI and ACS Style
Chu, W.; Zhang, C.; Zhao, Y.; Li, R.; Wu, P. Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China. Remote Sens. 2022, 14, 4432. https://doi.org/10.3390/rs14184432
AMA Style
Chu W, Zhang C, Zhao Y, Li R, Wu P. Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China. Remote Sensing. 2022; 14(18):4432. https://doi.org/10.3390/rs14184432
Chicago/Turabian Style
Chu, Wenhao, Chunxiao Zhang, Yuwei Zhao, Rongrong Li, and Pengda Wu. 2022. "Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China" Remote Sensing 14, no. 18: 4432. https://doi.org/10.3390/rs14184432
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Source: https://www.mdpi.com/2072-4292/14/18/4432
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