Real‐Time and Image‐Based AQI Estimation Based on Deep Learning

Zhang, Qiang; Tian, Lei; Fu, Fengchen; Wu, Huanyu; Wei, Wei; Liu, Xueyan

doi:10.1002/adts.202100628

Cited by 7 publications

(1 citation statement)

References 47 publications

(52 reference statements)

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“…Li et al (2023) examined the interplay between market segmentation and haze pollution in the urban agglomerations of China's Yangtze River Delta, finding that cities with high market segmentation and haze pollution could potentially reduce pollution through future market integration. Many scholars have predicted AQI by constructing models, such as Liu & Guo (2022) predicting the Air Quality Index (AQI) based on LSTM model and SSA algorithm, Zhang et al (2022) predicting AQI based on real-time images of deep learning, Duangsuwan et al (2022) conducted AQI mapping and data evaluation based on real-time air pollution monitoring using low altitude drones. Liu et al (2023b) developed an AQI prediction model based on a BP Neural Network, providing a reliable reference for governmental decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial and Seasonal Dynamics of Air Quality in the Beijing-Tianjin-Hebei Region: An Analysis Using K-means Clustering and BP Neural Networks

Wang,

Wu,

et al. 2023

OCS

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In the context of rapid economic development, air pollution has emerged as a critical environmental issue, particularly in the Beijing-Tianjin-Hebei region. This study, through the application of Air Quality Index (AQI) data and K-means clustering, investigates the seasonal variations and spatial distribution of air quality in this region. It has been identified that air pollution in this area is not only subject to seasonal fluctuation but also exhibits distinct patterns of local spatial aggregation. Utilizing a Back Propagation (BP) Neural Network model, this research predicts AQI values, offering foresight into the development and transformation of haze weather conditions. The findings of this investigation are instrumental in enhancing the understanding of air pollution dynamics, facilitating the formulation of effective air control strategies. Such strategies are vital for the issuance of accurate pollution warnings and reminders, thereby contributing to the mitigation of severe pollution impacts.

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Section: Literature Reviewmentioning

confidence: 99%

Spatial and Seasonal Dynamics of Air Quality in the Beijing-Tianjin-Hebei Region: An Analysis Using K-means Clustering and BP Neural Networks

Wang,

Wu,

et al. 2023

OCS

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Air quality monitoring using statistical learning models for sustainable environment

Imam,

Adam,

Dev

et al. 2024

Intelligent Systems with Applications

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Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

Mondal,

Islam,

Islam

et al. 2024

Sci Rep

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The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi.

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Real‐Time and Image‐Based AQI Estimation Based on Deep Learning

Cited by 7 publications

References 47 publications

Spatial and Seasonal Dynamics of Air Quality in the Beijing-Tianjin-Hebei Region: An Analysis Using K-means Clustering and BP Neural Networks

Spatial and Seasonal Dynamics of Air Quality in the Beijing-Tianjin-Hebei Region: An Analysis Using K-means Clustering and BP Neural Networks

Air quality monitoring using statistical learning models for sustainable environment

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

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