首页|Surveillance-image-based outdoor air quality monitoring

Surveillance-image-based outdoor air quality monitoring

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  • NETL
  • NSTL
  • 万方数据
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 PM2.5 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 R2=0.94 and RMSE=5.11 μg m-3 for PM2.5,R2=0.92 and RMSE=7.30 μg m-3 for PM10,and R2=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 appli-cability and superiority for air quality monitoring.

Outdoor air quality estimationHybrid deep learning modelConvolutional neural networkLong short-term memoryImage sequences

Yurong Zhang、Xudong Bu、Yajun Wang、Zhenyu Hang、Zhiqiang Chen、Songsong Dai

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School of Civil Engineering,Lanzhou University of Technology,Lanzhou,730050,China

Shaanxi Key Laboratory of Environmental Engineering,Xi'an University of Architecture and Technology,Xi'an,710055,China

State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(SKLUWRE,HIT),Harbin,150090,China

School of Geography,Nanjing Normal University,Nanjing,210023,China

Key Laboratory of Virtual Geographic Environment,Nanjing Normal University,Ministry of Education,Nanjing,210023,China

Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing Normal University,Nanjing,210023,China

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国家重点研发计划国家自然科学基金State Scholarship Fund from the China Scholarship Council(CSC)Postgraduate Research & Practice Innovation Program of Jiangsu Province

2021YFE011230041771420201906865016KYCX21_1341

2024

10.1016/j.ese.2023.100319

环境科学与生态技术(英文)

环境科学与生态技术(英文)

ISSN：
年,卷(期)：2024.18(1)
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