Investigating the reliability of estimating real-time air exchange rates in a building by using airborne particles, including PM1.0, PM2.5, and PM10: A case study in Suzhou, China

Fu, Nuodi; Kim, Moon Keun; Huang, Long; Liu, Jiying; Chen, Bing; Sharples, Stephen

doi:10.1016/j.apr.2023.101955

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…In Indonesia, the responsibility for determining the air quality level lies with the Ministry of Environment and Forestry (KLHK) of the Republic of Indonesia (RI). The KLHK RI measures air particles smaller than 10 microns (micrometers), commonly known as particulates (PM10) [8]- [11]. The presence of these particulate elements can lead to complications in the respiratory system.…”

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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Residential VOC from building materials: Exposures, health risks, and ambient hazards

Fan,

Hu,

Wang

et al. 2025

Sustainable Cities and Society

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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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Investigating the reliability of estimating real-time air exchange rates in a building by using airborne particles, including PM1.0, PM2.5, and PM10: A case study in Suzhou, China

Cited by 3 publications

References 60 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

Residential VOC from building materials: Exposures, health risks, and ambient hazards

Airborne particulate matter measurement and prediction with machine learning techniques

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