Particle Pollution Estimation Based on Image Analysis

Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

doi:10.1371/journal.pone.0145955

Cited by 81 publications

(69 citation statements)

References 29 publications

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“…In order to understand which features can affect PM 2.5 concentrations when using image processing methods, previous research has pointed out that the PM 2.5 concentrations may affect image characteristics, including the distance, hazy model, entropy, contrast, sky color, and solar zenith angle. It was found that the distance is the feature that has the most influence [28]. This is consistent with the definition of visibility, and a previous study has also shown that visibility can be estimated using high-frequency information from an image [22].…”

Section: Introductionsupporting

confidence: 86%

“…This is consistent with the definition of visibility, and a previous study has also shown that visibility can be estimated using high-frequency information from an image [22]. The region of interest (RoI) has also been manually selected to estimate PM 2.5 concentrations [28]. However, the estimation performance might be degraded because of such a manually selected RoI.…”

Section: Introductionsupporting

confidence: 77%

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PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Liaw

Huang

Hsieh

et al. 2020

Sensors

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Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

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Section: Introductionsupporting

confidence: 86%

Section: Introductionsupporting

confidence: 77%

PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Liaw

Huang

Hsieh

et al. 2020

Sensors

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“…Liu et al [30] has proposed an image-based approach, while considering several image features, such as transmission, sky smoothness, image color, entropy, contrast, time, geographical location, sun, and weather condition to predict the PM 2.5 of the air. They have developed a regression model that is based on these features to predict PM level from photos of Beijing, Shanghai, and Phoenix over a one-year period.…”

Section: Related Workmentioning

confidence: 99%

“…This dataset contains 1954 real images, with dimensions 584 x 389 and RGB color space, of the Oriental Pearl Tower, Shanghai, China [30]. These images, which were collected from Archive of Many Outdoor Scenes (AOMS) dataset, are hourly images that are captured every hour from 08:00 to 16:00 hrs during May to December in 2014 [55].…”

Section: Shanghai City Datasetmentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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“…Apart from the applications mentioned, several studies were carried out regarding the estimation of air quality from images. In [21], the authors utilize six image features together with additional information such as the position of the sun, date, time, geographic information and weather conditions, etc., to estimate the amount of PM 2.5 (particles with aerodynamic diameter less than 2.5 micrometers) in the air. Experimental results have shown that the image analysis method is able to estimate the PM 2.5 index accurately.…”

Section: Related Workmentioning

confidence: 99%

Towards Improved Air Quality Monitoring Using Publicly Available Sky Images

Spyromitros-Xioufis

Moumtzidou

Papadopoulos

et al. 2018

Multimedia Tools and Applications for Environmental &Amp; Biodiversity Informatics

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Air pollution causes nearly half a million premature deaths each year in Europe. Despite air quality directives that demand compliance with air pollution value limits, many urban populations continue being exposed to air pollution levels that exceed by far the guidelines. Unfortunately, official air quality sensors are sparse, limiting the accuracy of the provided air quality information. In this chapter, we explore the possibility of extending the number of air quality measurements that are fed into existing air quality monitoring systems by exploiting techniques that estimate air quality based on sky-depicting images. We first describe a comprehensive data collection mechanism and the results of an empirical study on the availability of sky images in social image sharing platforms and on webcam sites. In addition, we present a methodology for automatically detecting and extracting the sky part of the images leveraging deep learning models for concept detection and localization. Finally, we present an air quality estimation model that operates on statistics computed from the pixel color values of the detected sky regions. 5.1 Introduction Environmental data are crucial both for human life and the environment. Especially, the environmental conditions related to air quality are strongly related to health issues (e.g. asthma) and to everyday life activities (e.g. walking, cycling). Thus, it is E. Spyromitros-Xioufis () • A

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Particle Pollution Estimation Based on Image Analysis

Cited by 81 publications

References 29 publications

PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Towards Improved Air Quality Monitoring Using Publicly Available Sky Images

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