Research of low-cost air quality monitoring models with different machine learning algorithms
Abstract 2.5 and PM10) and gas pollutants (SO2, NO2, CO and O3) simultaneously. The multi-input multi-output (MIMO) prediction model is developed based on the original signals of the sensors, ambient temperature (T R 2 R 2 2.5 and PM10 are within 2.36–18.68 and 4.55–45.05 µ g m- 3 2.5 and PM10 are within 1.44–12.80 and 3.21–23.20 µ g m- 3 R 2 3, CO and NO2) is within 0.70–0.99; the RMSE values for these pollutants are 4.05–17.79 µ g m- 3 - 3 µ g m- 3 µ g m- 3 - 3 µ g m- 3 R 2 2 is within 0.27–0.97, the RMSE value is in the range 0.64–5.37 µ g m- 3 µ g m- 3. These measurements are consistent with the national environmental protection standard requirement of China, and the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Research of low-cost air quality monitoring models with different machine learning algorithms ; volume:17 ; number:1 ; year:2024 ; pages:181-196 ; extent:16
Atmospheric measurement techniques ; 17, Heft 1 (2024), 181-196 (gesamt 16)
- Creator
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Wang, Gang
Yu, Chunlai
Guo, Kai
Guo, Haisong
Wang, Yibo
- DOI
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10.5194/amt-17-181-2024
- URN
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urn:nbn:de:101:1-2024011803250381572243
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:37 AM CEST
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Associated
- Wang, Gang
- Yu, Chunlai
- Guo, Kai
- Guo, Haisong
- Wang, Yibo
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