Surveillance-image-based outdoor air quality monitoring  

作　　者：Xiaochu Wang Meizhen Wang Xuejun Liu Ying Mao Yang Chen Songsong Dai 

机构地区：[1]School of Geography,Nanjing Normal University,Nanjing,210023,China [2]Key Laboratory of Virtual Geographic Environment,Nanjing Normal University,Ministry of Education,Nanjing,210023,China [3]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing Normal University,Nanjing,210023,China

出　　处：《Environmental Science and Ecotechnology》2024年第2期60-69,共10页环境科学与生态技术（英文）

基　　金：supported by the National Key Research and Development Program of China[2021YFE0112300];the National Natural Science Foundation of China(NSFC)[41771420];the State Scholarship Fund from the China Scholarship Council(CSC)[201906865016];the Postgraduate Research&Practice Innovation Program of Jiangsu Province[KYCX21_1341].

摘　　要：Air pollution threatens human health,necessitating effective and convenient air quality monitoring.Recently,there has been a growing interest in using camera images for air quality estimation.However,a major challenge has been nighttime detection due to the limited visibility of nighttime images.Here we present a hybrid deep learning model,capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images.Our model,which integrates a convolutional neural network(CNN)and long short-term memory(LSTM),adeptly captures spatial-temporal image features,enabling air quality estimation at any time of day,including PM_(2.5) and PM10 concentrations,as well as the air quality index(AQI).Compared to independent CNN networks that solely extract spatial features,our model demonstrates superior accuracy on self-constructed datasets with R^(2)?0.94 and RMSE=5.11 mg m^(-3) for PM_(2.5),R^(2)=0.92 and RMSE=7.30 mg m^(-3) for PM10,and R^(2)=0.94 and RMSE?5.38 for AQI.Furthermore,our model excels in daytime air quality estimation and enhances nighttime predictions,elevating overall accuracy.Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model,reaffirming its applicability and superiority for air quality monitoring.

关 键 词：Outdoor air quality estimation Hybrid deep learning model Convolutional neural network Long short-term memory Image sequences 

分 类 号：X831[环境科学与工程—环境工程]