Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach

By Tongwen Li, Huanfeng Shen, Qiangqiang Yuan, Xuechen Zhang, Liangpei Zhang. 2017

376 citations recorded by the source corpus.

Fusing satellite observations and station measurements to estimate ground‐level PM2.5 is promising for monitoring PM2.5 pollution. A geo‐intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi‐DBN). Geoi‐DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi‐DBN performs significantly better than the traditional neural network. The out‐of‐sample cross‐validation R2 increases from 0.42 to 0.88, and RMSE decreases from 29.96 to 13.03 μg/m3. On the basis of the derived PM2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35 μg/m3. This study provides a new perspective for air pollution monitoring in large geographic regions.

Subfields: Atmospheric Science, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/0627acc18ca240386b5eb72c11ae5b76ce669339

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Huanfeng Shen
2. Qiangqiang Yuan
3. Alexander Y. Sun
4. Bridget R. Scanlon
5. Liangpei Zhang
6. Bo Du

In Environmental Engineering

1. Huanfeng Shen
2. Jianwei Ma
3. Siwei Yu
4. Alexander Y. Sun
5. Bridget R. Scanlon
6. Liangpei Zhang

Citations in the corpus

• PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data (2019, methodology): “The missing values in our study area were very high, and thus CNN and LSTM could not be applied.”
• Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Li et al. (2017a) proposed a geo-intelligent DL approach for the estimation of PM 2.5 across China.”
• Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “5 or PM10 levels by using satellite observations and station measurements (Li et al., 2017; Shen et al., 2018; Tang et al., 2018).”
• How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions (2019, methodology): “Li et al [176] used DBN to extend the spatial coverage of PM2.”