Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

Chuchro, Monika; Sarlej, Wojciech; Grzegorczyk, Marta; Nurzynska, Karolina

doi:10.3390/s21165483

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Previous investigations supporting the aforementioned conditions encompass the research by Chuchro et al [27], which delved into the utilization of photo texture analysis and weather data to evaluate air quality in relation to airborne PM10 and PM2.5 particulates. This study employed a texture analysis approach for each photo frame, and the results were employed in constructing regression and classification models.…”

Section: Introductionmentioning

confidence: 99%

The development of color histogram method to identify air quality index based on sky images

Enggari,

Sumijan,

Tajuddin

2024

IJEECS

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Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.

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Section: Introductionmentioning

confidence: 99%

The development of color histogram method to identify air quality index based on sky images

Enggari,

Sumijan,

Tajuddin

2024

IJEECS

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“…Generally, in atmospheric and air quality research, “PM X ” refers to particles with aerodynamic diameter less than or equal to x µm. PM 10 and PM 2.5 , etc., are commonly used to judge the air particulate matter (PM) pollution situation [ 1 , 2 , 3 , 4 ]. These particles will be deposited in different areas of the human respiratory tract after being inhaled by the human body, causing a great impact on human health [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Small-Angle Particle Counting Coupled Photometry for Real-Time Detection of Respirable Particle Size Segmentation Mass Concentration

Zhang

Zhao

2021

Sensors

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Respirable particulate matter air pollution is positively associated with SARS-CoV-2 mortality. Real-time and accurate monitoring of particle concentration changes is the first step to prevent and control air pollution from inhalable particles. In this research, a new light scattering instrument has been developed to detect the mass concentration of inhalable particles. This instrument couples the forward small-angle single particle counting method with the lateral group particle photometry method in a single device. The mass concentration of four sizes of inhalable particles in the environment can be detected simultaneously in a large area in real-time without using a particle impactor. Different from the traditional light scattering instrument, this new optical instrument can detect darker particles with strong light absorption, and the measurement results mainly depend on the particle size and ignore the properties of the particles. Comparative experiments have shown that the instrument can detect particles with different properties by simply calibrating the environmental density parameters, and the measurement results have good stability and accuracy.

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Airborne particulate matter measurement and prediction with machine learning techniques

Iwaszenko,

Smolinski,

Grzanka

et al. 2024

Sci Rep

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Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

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Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

Cited by 4 publications

References 35 publications

The development of color histogram method to identify air quality index based on sky images

The development of color histogram method to identify air quality index based on sky images

Small-Angle Particle Counting Coupled Photometry for Real-Time Detection of Respirable Particle Size Segmentation Mass Concentration

Airborne particulate matter measurement and prediction with machine learning techniques

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