Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 1
Articles | Volume 17, issue 1
https://doi.org/10.5194/amt-17-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-17-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 1
Research article
 | 
15 Jan 2024
Research article |  | 15 Jan 2024

Research of low-cost air quality monitoring models with different machine learning algorithms

Gang Wang, Chunlai Yu, Kai Guo, Haisong Guo, and Yibo Wang

Viewed

Total article views: 1,448 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,093 305 50 1,448 40 61
  • HTML: 1,093
  • PDF: 305
  • XML: 50
  • Total: 1,448
  • BibTeX: 40
  • EndNote: 61
Views and downloads (calculated since 13 Oct 2023)
Cumulative views and downloads (calculated since 13 Oct 2023)

Viewed (geographical distribution)

Total article views: 1,448 (including HTML, PDF, and XML) Thereof 1,439 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Nov 2024
Download
Short summary
A low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM) and gas pollutants simultaneously. The performance of the different algorithms (RF, MLR, KNN, BP, GA-BP) with the parameters such as R2 and RMSE are compared and discussed. These measurements indicate the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution.
Read more
Special issue
In-depth study of the atmospheric chemistry over the Tibetan...