A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information.
By Gongbo Chen, Shanshan Li, L. Knibbs, Nicholas A. S. Hamm, W. Cao, Tiantian Li, Jianping Guo, Hongyan Ren, M. Abramson, Yuming Guo. Science of the Total Environment, 2018
515 citations recorded by the source corpus.
BACKGROUND Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level. OBJECTIVES To estimate daily concentrations of PM2.5 across China during 2005-2016. METHODS Daily ground-level PM2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM2.5 across China with a resolution of 0.1° (≈10 km) during 2005-2016. RESULTS The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM2.5 [10-fold cross-validation (CV) R2 = 83%, root mean squared prediction error (RMSE) = 28.1 μg/m3]. At the monthly and annual time-scale, the explained variability of average PM2.5 increased up to 86% (RMSE = 10.7 μg/m3 and 6.9 μg/m3, respectively). CONCLUSIONS Taking advantage of a novel application of modeling framework and the most recent ground-level PM2.5 observations, the machine learning method showed higher predictive ability than previous studies. CAPSULE Random forests approach can be used to estimate historical exposure to PM2.5 in China with high accuracy.
Subfields: Atmospheric Science, Environmental Engineering, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/4264b10b109230b87a0ba726c19720b7507c3e11
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In Environmental Engineering
Citations in the corpus
- MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing (2021, background): “Eventually, it could be asserted that semantic segmentation in urban scenes is characterized by a strong intra-class variance and a reduced inter-class one [6,7].”