Spatiotemporal continuous PM2.5 concentrations inversion based on multisource data and hybrid model

Li Wang; Lili Xu; Zhiyong Li; Ziyu Ren; Shurui Fan

doi:10.1117/12.3035494

10 July 2024 Spatiotemporal continuous PM_2.5 concentrations inversion based on multisource data and hybrid model

Li Wang, Lili Xu, Zhiyong Li, Ziyu Ren, Shurui Fan

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132232F (2024) https://doi.org/10.1117/12.3035494
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

Air pollution affects public health, destroys ecological environment and even aggravates climate change, and PM_2.5 is an important factor causing pollution. Therefore, accurate and continuous spatiotemporal PM_2.5 concentration inversion is of great significance for air pollution control. Nevertheless, current high temporal resolution inversions are predominantly conducted using Top-of-atmosphere reflectance, nighttime concentration inversion cannot be realized in the visible band. Moreover, existing hybrid models often rely on linear integrated, increasing the risk of overfitting. Therefore, we propose an improved stacking model that contains XGBoost and CatBoost model, stacked by elastic net regression. By fusing features from multi-source data and accounting for spatiotemporal heterogeneity, the model enables the simultaneous inversion of daytime and nighttime PM_2.5 concentrations. Compared to using Top-of-atmosphere reflectance alone, the daytime inversion results demonstrate a 1.08% increase in R² and a reduction of 1.34μg/m³ in RMSE. Meanwhile, the nighttime inversion results show a 2.04% improvement in R² and reduce the MAE to 9.54μg/m³ when spatiotemporal features are used. Applying the model to the Beijing-Tianjin-Hebei region enables spatiotemporal continuous inversion of PM_2.5 concentrations in the region.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Wang, Lili Xu, Zhiyong Li, Ziyu Ren, and Shurui Fan "Spatiotemporal continuous PM_2.5 concentrations inversion based on multisource data and hybrid model", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132232F (10 July 2024); https://doi.org/10.1117/12.3035494

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KEYWORDS

Data modeling

Satellites

Atmospheric modeling

Machine learning

Meteorology

Performance modeling

Reflectivity

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