Real-time image-based air quality estimation by deep learning neural networks
Journal
Journal of Environmental Management
Journal Volume
307
Date Issued
2022
Author(s)
Abstract
Air quality profoundly impacts public health and environmental equity. Efficient and inexpensive air quality monitoring instruments could be greatly beneficial for human health and air pollution control. This study proposes an image-based deep learning model (CNN?RC) that integrates a convolutional neural network (CNN) and a regression classifier (RC) to estimate air quality at areas of interest through feature extraction from photos and feature classification into air quality levels. The models were trained and tested on datasets with different combinations of the current image, the baseline image, and HSV (hue, saturation, value) statistics for increasing model reliability and estimation accuracy. A total of 3549 hourly air quality datasets (including photos, PM2.5, PM10, and the air quality index (AQI)) collected at the Linyuan air quality monitoring station of Kaohsiung City in Taiwan constituted the case study. The main breakthrough of this study is to timely produce an accurate image-based estimation of several pollutants simultaneously by using only one single deep learning model. The test results show that estimation accuracy in terms of R2 for PM2.5, PM10, and AQI based on daytime (nighttime) images reaches 76% (83%), 84% (84%), and 76% (74%), respectively, which demonstrates the great capability of our method. The proposed model offers a promising solution for rapid and reliable multi-pollutant estimation and classification based solely on captured images. This readily scalable measurement approach could address major gaps between air quality data acquired from expensive instruments worldwide. ? 2022
Subjects
Air quality index (AQI)
Convolutional neural network (CNN)
Deep learning
Image (photo) classification
PM10
PM2.5
air quality
atmospheric pollution
image classification
model
pollution monitoring
public health
regression analysis
air monitoring
article
classifier
convolutional neural network
deep learning
diagnostic test accuracy study
feature extraction
human
particulate matter 10
particulate matter 2.5
reliability
Taiwan
air pollutant
air pollution
city
reproducibility
Air
Kaohsiung
Niger [West Africa]
Air Pollutants
Air Pollution
Cities
Deep Learning
Humans
Neural Networks, Computer
Reproducibility of Results
Type
journal article