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Development of intelligent system capable of performing ultrafine particulate matter sensing towards the formulation of enhanced air quality index.

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Title: Development of intelligent system capable of performing ultrafine particulate matter sensing towards the formulation of enhanced air quality index.
Authors: Ismail, Amer Syazwan, Andrew, Allan Melvin, Loong, Foo Khai, Ting, Sam Sung, Kamalraj, Subramaniam, Wahab, Mohamad Kahar Ab, Ragunathan, Santiagoo, Zain, Irdina Faqihah Mohd, Koilpillai, Charis Samuel Solomon, Muslim Tan, Erdy Sulino Mohd
Source: AIP Conference Proceedings; 2025, Vol. 3349 Issue 1, p1-10, 10p
Subject Terms: PARTICULATE matter, AIR quality indexes, NANOTECHNOLOGY, MACHINE learning, CARBONACEOUS aerosols, HAZE, ZINC oxide, DETECTORS
Geographic Terms: MALAYSIA
Abstract: Haze events frequently occur in northern Malaysia, primarily due to the widespread practice of crop burning, particularly rice straw. Among the hazardous components in haze are ultrafine particles (UFPs), which are typically not included in standard air quality assessments, raising concerns about the accuracy of these readings. While nitrogen dioxide (NO2), tropospheric ozone (O3), and larger particulate matter are usually measured, smaller particles like particulate matter 1 micron (PM1) are often overlooked. The lack of sensors for evaluating UFPs highlights an opportunity for technological advancement, especially in nanotechnology. In this research, carbon particles, a major component of UFPs, are selected for evaluation using a zinc oxide (ZnO)-based nanosensor. A device incorporating sensors to measure NO2, O3, particulate matter 10 microns (PM10), particulate matter 2.5 microns (PM2.5), and PM1 is developed to be integrated with the ZnO nanosensor. Data from the fully integrated device, collected in a controlled environment, is analyzed using a machine learning approach, with 2000 data points from each sensor and 2000 data points for ambient air. The dataset with the highest accuracy will be used for enhanced air quality index (EAQI) estimation. Based on the observations and testing, it can be seen that the proposed method provides good accuracy of 99.51%. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Haze events frequently occur in northern Malaysia, primarily due to the widespread practice of crop burning, particularly rice straw. Among the hazardous components in haze are ultrafine particles (UFPs), which are typically not included in standard air quality assessments, raising concerns about the accuracy of these readings. While nitrogen dioxide (NO<subscript>2</subscript>), tropospheric ozone (O<subscript>3</subscript>), and larger particulate matter are usually measured, smaller particles like particulate matter 1 micron (PM<subscript>1</subscript>) are often overlooked. The lack of sensors for evaluating UFPs highlights an opportunity for technological advancement, especially in nanotechnology. In this research, carbon particles, a major component of UFPs, are selected for evaluation using a zinc oxide (ZnO)-based nanosensor. A device incorporating sensors to measure NO<subscript>2</subscript>, O<subscript>3</subscript>, particulate matter 10 microns (PM10), particulate matter 2.5 microns (PM<subscript>2.5</subscript>), and PM1 is developed to be integrated with the ZnO nanosensor. Data from the fully integrated device, collected in a controlled environment, is analyzed using a machine learning approach, with 2000 data points from each sensor and 2000 data points for ambient air. The dataset with the highest accuracy will be used for enhanced air quality index (EAQI) estimation. Based on the observations and testing, it can be seen that the proposed method provides good accuracy of 99.51%. [ABSTRACT FROM AUTHOR]
ISSN:0094243X
DOI:10.1063/5.0299820