Deciphering urban traffic impacts on air quality by deep learning and emission inventory  

作　　者：Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang 

机构地区：[1]School of Software Engineering,University of Science and Technology of China,Hefei 230026,China [2]Suzhou Institute for Advanced Research,University of Science and Technology of China,Suzhou 215123,China [3]Alibaba Inc.,Hangzhou 310052,China [4]School of Computer Science and Technology,University of Science and Technology of China,Hefei 230026,China [5]CAS Key Laboratory of Urban Pollutant Conversion,Department of environmental science and Engineering,University of Science and Technology of China,Hefei 230026,China [6]USTC-CityU Joint Advanced Research Center,Suzhou 215123,China

出　　处：《Journal of Environmental Sciences》2023年第2期745-757,共13页环境科学学报（英文版）

基　　金：supported by the Anhui Science Foundation for Distinguished Young Scholars (No.1908085J24);the Natural Science Foundation of China (No.62072427);the Jiangsu Natural Science Foundation (No. BK20191193)

摘　　要：Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future metropolises.Here,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality.Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other models.And identifies the ranking of major factors,local meteorological conditions have become a nonnegligible factor.Layer-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of Shanghai.Meanwhile,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 gradually.These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关 键 词：PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] X73[自动化与计算机技术—控制科学与工程]