Improving Intra-Urban Prediction of Atmospheric Fine Particles Using a Hybrid Deep Learning Approach

被引:2
|
作者
Zhang, Zhengyu [1 ]
Ren, Jiuchun [1 ]
Chang, Yunhua [2 ]
机构
[1] Fudan Univ, Sch Informat Sci & Engn, Dept Commun Engn, Shanghai 200433, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, NUIST Ctr Atmospher Environm, Nanjing 210044, Peoples R China
基金
中国国家自然科学基金;
关键词
fine particles; deep learning; hybrid model; street-level pollution; PM2; 5; prediction; NETWORKS; EXPOSURE;
D O I
10.3390/atmos14030599
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Growing evidence links intra-urban gradients in atmospheric fine particles (PM2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM2.5 prediction, which has important implications for the intra-urban health assessment of PM2.5-related pollution exposures.
引用
收藏
页数:15
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