A deep learning and image-based model for air quality estimation

Zhang, Qiang; Fu, Fengchen; Tian, Ruifeng

doi:10.1016/j.scitotenv.2020.138178

Cited by 59 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The particular complexity and non-linearity of the relationships associated with air pollution suggest the use of a more flexible tool than usual. Boznar et al [49] , Cigizoglu and Kisi [50] , Pasero and Mesin [51] , Zhang [52] , and Zhang et al [53] have shown that NNs are adaptive structures capable of correctly estimating environmental and energy problems. NNs are capable of “learning” the solution of the issues starting from known examples.…”

Section: Data Collection and Methodologymentioning

confidence: 99%

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

2020

View full text Add to dashboard Cite

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM 2.5 and PM 10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM 2.5 and PM 10 connected to COVID-19: 17.4 µg/m 3 (PM 2.5 ) and 29.6 µg/m 3 (PM 10 ) for Paris; 15.6 µg/m 3 (PM 2.5 ) and 20.6 µg/m 3 (PM 10 ) for Lyon; 14.3 µg/m 3 (PM 2.5 ) and 22.04 µg/m 3 (PM 10 ) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

show abstract

Section: Data Collection and Methodologymentioning

confidence: 99%

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

2020

View full text Add to dashboard Cite

show abstract

“…Based on outdoor photo data from Beijing, Shanghai and Phoenix, they used solar position, local date, local time, geographic location, and weather conditions as inputs and passed ROI regions to estimate PM2.5. In our previous research, [ 6 ] we proposed a deep ResNet model, AQC‐Net, by incorporating an SCA block to analyze the correlation of air quality features in environmental images. This model improved the accuracy of ResNet for image‐based air quality classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, it is necessary to develop real‐time, fast, and accurate AQI estimation methods. The previous studies on AQI estimation are mainly based on monitoring station experiments, [ 4 ] laboratory experiments, [ 5 ] traditional machine learning experiments, [ 6 ] neural network model experiments, [ 7 ] and image recognition experiments. [ 8 ] They tend to focus on model accuracy and pollutant transmission, but fail to optimize model running time and estimation efficiency.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Tian

et al. 2022

Advcd Theory and Sims

Self Cite

View full text Add to dashboard Cite

The real-time information on surrounding air quality index (AQI) is important for the public to protect themselves from air pollution. Traditional methods have some shortages regarding the estimation time and running efficiency. Consequently, the AQI results cannot meet the needs of personal protection and environmental management. With the popularity of smart terminals, it is easier to collect particular environmental images for AQI estimation tasks. Therefore, a real-time and image-based deep learning model named YOLO-AQI is proposed. Based on the object detection algorithms, the model has better performance regarding the AQI estimation speed. By optimizing the parameter transfer and network structure, the model takes an average of 0.0582 s to perform feature analysis and achieves 75.15% accuracy on AQI estimation tasks. Comparing YOLO-AQI with several image recognition models (VGG, AlexNet, GoogLeNet, MobileNet, and ResNet), it shows that YOLO-AQI outperforms other models by 14.8% on accuracy and by 71.1% on running speed. This method can provide real-time AQI level information for remote areas such as rural.

show abstract

“…In [10], the authors proposed a deep learning and imagebased model for air quality estimation. The model extracts feature information from scene images captured by camera equipment and then classifies them to estimate air quality levels.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approach for Forecasting Water Quality in IoT Systems

Nguyen¹,

Thanh-Hai²,

Ngôn³

2020

IJACSA

View full text Add to dashboard Cite

Global climate change and water pollution effects have caused many problems to the farmers in fish/shrimp raising, for example, the shrimps/fishes had early died before harvest. How to monitor and manage quality of the water to help the farmers tackling this problem is very necessary. Water quality monitoring is important when developing IoT systems, especially for aquaculture and fisheries. By monitoring the real-time sensor data indicators (such as indicators of salinity, temperature, pH, and dissolved oxygen-DO) and forecasting them to get early warning, we can manage the quality of the water, thus collecting both quality and quantity in shrimp/fish raising. In this work, we introduce an architecture with a forecasting model for the IoT systems to monitor water quality in aquaculture and fisheries. Since these indicators are collected every day, they becomes sequential/time series data, we propose to use deep learning with Long-Short Term Memory (LSTM) algorithm for forecasting these indicators. Experimental results on several data sets show that the proposed approach works well and can be applied for the real systems.

show abstract

A deep learning and image-based model for air quality estimation

Cited by 59 publications

References 27 publications

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

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

Deep Learning Approach for Forecasting Water Quality in IoT Systems

Contact Info

Product

Resources

About