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A Deep Learning Approach to Increase the Value of Satellite Data for PM2.5 Monitoring in China
Journal article   Open access   Peer reviewed

A Deep Learning Approach to Increase the Value of Satellite Data for PM2.5 Monitoring in China

Bo Li, Cheng Liu, Qihou Hu, Mingzhai Sun, Chengxin Zhang, Yizhi Zhu, Ting Liu, Yike Guo, Gregory Carmichael and Meng Gao
Remote sensing (Basel, Switzerland), Vol.15(15), 3724
01/01/2023
DOI: 10.3390/rs15153724
url
https://doi.org/10.3390/rs15153724View
Published (Version of record) Open Access

Abstract

Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R2 is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m−3). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions.
 Air Quality Atmospheric Aerosols Chemical Reactions Machine Learning Ozone Remote Sensing Datasets Deep learning Emission analysis Health risks Methods National parks Neural networks Night Nighttime Optical properties Outdoor air quality Particulate matter Protected areas Quality management Radiation Risk assessment Root-mean-square errors Rural areas Satellites Spatial data Spatial discrimination Spatial resolution

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