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Spatiotemporal estimation of PM2.5 using attention-based deep neural network

  • role： First author第一作者
  • Affiliation：

    College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China

  • Email：bj_chen@zju.edu.cn
  • Introduction：陈镔捷，研究方向为环境与资源遥感。E-mail：bj_chen@zju.edu.cn
CHEN Binjie1,  
  • Affiliation：

    College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China

YE Yang1,  
  • Affiliation：

    College of Environment, Zhejiang University of Technology, Hangzhou 310014, China

LIN Yi2,  
  • Affiliation：

    College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China

YOU Shixue1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China

    Zhejiang Ecological Civilization Academy, Anji 313300, China

    Center for Intelligent Ecology and Sustainability, Zhejiang University, Hangzhou 310058, China

  • Email：jsong_deng@zju.edu.cn
  • Introduction：邓劲松，研究方向为环境与资源遥感。E-mail：jsong_deng@zju.edu.cn
DENG Jinsong134*,  
  • Affiliation：

    College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China

    Center for Intelligent Ecology and Sustainability, Zhejiang University, Hangzhou 310058, China

YANG Wu14,  
  • Affiliation：

    College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China

WANG Ke1

resumen

PM2.5, as the primary indicator of environmental quality, not only affects the occurrence of haze but also is closely related to public health and has raised great attention recently. Although PM2.5 ground monitoring stations are expanding, they are still on the sparse side to identify the spatiotemporal heterogeneity of PM2.5 concentrations. With the development of remote sensing technology, satellite-based Aerosol Optical Depth (AOD) data provide an effective way to estimate large-scale PM2.5 concentrations. This study aims to develop a novel deep neural network model for estimating PM2.5 concentrations in the Yangtze River Delta (YRD).In addition to satellite remote sensing AOD data, meteorological factors, digital elevation model data, normalized different vegetation index data, and the lunar calendar day representing Chinese production and living habits were integrated into the proposed attention-based Self-Adaptive Deep Neural Network (SADNN) in this study to estimate PM2.5 concentrations in the YRD region from 2015 to 2020. Five-fold cross-validation was executed to evaluate the estimation accuracy of the SADNN. The multiple linear regression and random forest models were applied to compare with the SADNN.The cross-validation results showed the proposed SADNN model had a high coefficient of determination value of 0.85 and a slope of 0.86, which were highly consistent with ground-level observations. The results also showed better performance than those of multiple linear regression and random forest models. The results for the spring festival in 2016 demonstrated the effectiveness of integrating the lunar calendar day and attention module into the model. The spatiotemporal patterns of PM2.5 in the YRD were as follows: PM2.5 concentrations were high in the north and low in the south, and the coastal and mountainous areas were better than inland and plain areas, respectively. On seasonal scales, winter was the most polluted season, while summer was the best. The overall PM2.5 concentration in the YRD showed a decreasing trend from 2015 to 2020, especially in Shanghai Municipality, with the decreasing speed of 3.30 μg/(m3·a), following the Jiangsu Province (2.65 μg/(m3·a)). Zhejiang Province and Anhui Province had a lower decreasing speed of less than 2 μg/(m3·a), and Anhui Province needed more effort and attention to improve the air quality due to its overall high PM2.5 concentrations.In conclusion, applying satellite remote sensing data and the proposed SADNN model to accurately estimate spatially continuous PM2.5 concentrations can greatly make up for the lack of ground monitoring stations and scientific guide for environmental policy planning and implementation.

palabra clave

remote sensing;aerosol optical depth (AOD);deep learning;Attention module;the Yangtze River Delta;air quality

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