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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research of low-cost air quality monitoring models with different machine learning algorithms
Gang Wang
CORRESPONDING AUTHOR
Faculty of Engineering, Huanghe Science and Technology College, Zhengzhou 450063, China
Hanwei Electronics Group Corporation, Zhengzhou 450001, China
Zhengzhou Key Laboratory of Intelligent Measurement Techniques and Applications, Zhengzhou 450063, China
Chunlai Yu
Faculty of Engineering, Huanghe Science and Technology College, Zhengzhou 450063, China
Zhengzhou Key Laboratory of Intelligent Measurement Techniques and Applications, Zhengzhou 450063, China
Kai Guo
Hanwei Electronics Group Corporation, Zhengzhou 450001, China
Haisong Guo
Faculty of Engineering, Huanghe Science and Technology College, Zhengzhou 450063, China
Zhengzhou Key Laboratory of Intelligent Measurement Techniques and Applications, Zhengzhou 450063, China
Yibo Wang
Hanwei Electronics Group Corporation, Zhengzhou 450001, China
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Cited
16 citations as recorded by crossref.
- Experimental study on the application of mobile sensing in wireless sensor networks development: Node placement planning and on-site calibration J. Ren et al. https://doi.org/10.1016/j.snb.2025.137338
- Miniaturized Electronic Noses for Food Quality and Safety: Applications in Lipid Oxidation Monitoring M. Floch et al. https://doi.org/10.3390/molecules31193424
- Validation Design Governs the Apparent Performance of Machine Learning Calibration for Low-Cost Optical Particle Counters: A PM1 Study C. Sahin et al. https://doi.org/10.3390/atmos17100947
- A review of extreme air pollution measurement and modeling techniques with applications K. Rautela et al. https://doi.org/10.1016/j.rser.2026.117178
- First long-term air quality assessment in Luanda, Angola: Performance evaluation of a low-cost monitoring station against reference equipment A. da Silva et al. https://doi.org/10.1016/j.apr.2025.102746
- Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning Y. Colléaux et al. https://doi.org/10.3390/s25051423
- Benchmarking Low-Cost Particulate Matter Sensors: Evaluating Performance Under Controlled Environmental Conditions Using Low-Cost Experimental Setups A. Alvarez Cruz et al. https://doi.org/10.3390/atmos16020172
- Calibration of Low-Cost Sensors for PM10 and PM2.5 Based on Artificial Intelligence for Smart Cities R. Gómez et al. https://doi.org/10.3390/s26030796
- Time-generative AI-enabled temporal fusion transformer model for efficient air pollution sensor calibration in IIoT edge environments S. Henna et al. https://doi.org/10.3934/environsci.2025024
- Improving raw readings from low-cost ozone sensors using artificial intelligence for air quality monitoring G. Montalban-Faet et al. https://doi.org/10.5194/amt-18-4357-2025
- A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting J. Bernacki & R. Scherer https://doi.org/10.3390/s25196044
- Low-cost sensors for atmospheric NO2 measurement: A review C. Ayvaz et al. https://doi.org/10.1016/j.envpol.2025.126418
- Comparative Evaluation of Machine Learning and Hyperparameter Optimization Methods for Low-Cost CO2 Sensor Calibration in Terms of Performance and Computational Cost E. Karsu Asal et al. https://doi.org/10.3390/s26123671
- Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring K. Alhasa et al. https://doi.org/10.1007/s12145-025-01927-5
- Probabilistic Clustering for Data Aggregation in Air Pollution Monitoring System V. Shakhov & O. Sokolova https://doi.org/10.3390/s25237285
- AI and Machine Learning for Optimizing Waste Management and Reducing Air Pollution K. Rautela et al. https://doi.org/10.1061/JHTRBP.HZENG-1483
16 citations as recorded by crossref.
- Experimental study on the application of mobile sensing in wireless sensor networks development: Node placement planning and on-site calibration J. Ren et al. https://doi.org/10.1016/j.snb.2025.137338
- Miniaturized Electronic Noses for Food Quality and Safety: Applications in Lipid Oxidation Monitoring M. Floch et al. https://doi.org/10.3390/molecules31193424
- Validation Design Governs the Apparent Performance of Machine Learning Calibration for Low-Cost Optical Particle Counters: A PM1 Study C. Sahin et al. https://doi.org/10.3390/atmos17100947
- A review of extreme air pollution measurement and modeling techniques with applications K. Rautela et al. https://doi.org/10.1016/j.rser.2026.117178
- First long-term air quality assessment in Luanda, Angola: Performance evaluation of a low-cost monitoring station against reference equipment A. da Silva et al. https://doi.org/10.1016/j.apr.2025.102746
- Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning Y. Colléaux et al. https://doi.org/10.3390/s25051423
- Benchmarking Low-Cost Particulate Matter Sensors: Evaluating Performance Under Controlled Environmental Conditions Using Low-Cost Experimental Setups A. Alvarez Cruz et al. https://doi.org/10.3390/atmos16020172
- Calibration of Low-Cost Sensors for PM10 and PM2.5 Based on Artificial Intelligence for Smart Cities R. Gómez et al. https://doi.org/10.3390/s26030796
- Time-generative AI-enabled temporal fusion transformer model for efficient air pollution sensor calibration in IIoT edge environments S. Henna et al. https://doi.org/10.3934/environsci.2025024
- Improving raw readings from low-cost ozone sensors using artificial intelligence for air quality monitoring G. Montalban-Faet et al. https://doi.org/10.5194/amt-18-4357-2025
- A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting J. Bernacki & R. Scherer https://doi.org/10.3390/s25196044
- Low-cost sensors for atmospheric NO2 measurement: A review C. Ayvaz et al. https://doi.org/10.1016/j.envpol.2025.126418
- Comparative Evaluation of Machine Learning and Hyperparameter Optimization Methods for Low-Cost CO2 Sensor Calibration in Terms of Performance and Computational Cost E. Karsu Asal et al. https://doi.org/10.3390/s26123671
- Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring K. Alhasa et al. https://doi.org/10.1007/s12145-025-01927-5
- Probabilistic Clustering for Data Aggregation in Air Pollution Monitoring System V. Shakhov & O. Sokolova https://doi.org/10.3390/s25237285
- AI and Machine Learning for Optimizing Waste Management and Reducing Air Pollution K. Rautela et al. https://doi.org/10.1061/JHTRBP.HZENG-1483
Saved (final revised paper)
Latest update: 13 Oct 2026
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.
A low-cost multi-parameter air quality monitoring system (LCS) based on different machine...