Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5333-2021
© Author(s) 2021. 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-14-5333-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Aug 2021
Research article |
| 04 Aug 2021
| 04 Aug 2021
Estimation of PM2.5 concentration in China using linear hybrid machine learning model
Zhihao Song
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Bin Chen
CORRESPONDING AUTHOR
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Yue Huang
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Li Dong
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,
China
Tingting Yang
Gansu Seed General Station, Lanzhou 730030, China
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Cited
23 citations as recorded by crossref.
- Investigating the impact of pretreatment strategies on photocatalyst for accurate CO2RR productivity quantification: A machine learning approach Y. Liu et al. 10.1016/j.cej.2023.145255
- A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents M. Rahman & G. Thurston 10.1016/j.envint.2022.107233
- Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms B. Chen et al. 10.1016/j.scitotenv.2021.150338
- Combining Himawari-8 AOD and Deep Forest Model to Obtain City-Level Distribution of PM <sub>2.5</sub> in China Z. Song et al. 10.2139/ssrn.3937107
- Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS) M. Rahman et al. 10.1016/j.envpol.2023.122568
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- Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al. 10.1016/j.atmosenv.2024.120347
- Using satellite data on remote transportation of air pollutants for PM2.5 prediction in northern Taiwan G. Kibirige et al. 10.1371/journal.pone.0282471
- Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations T. Mi et al. 10.1016/j.gsf.2023.101686
- Estimation of Near-Surface High Spatiotemporal Resolution Ozone Concentration in China Using Himawari-8 AOD Y. Wang et al. 10.3390/rs17030528
- Long short-term memory network model to estimate PM2.5 concentrations with missing-filled satellite data in Beijing S. Jia et al. 10.1007/s00477-022-02253-8
- Unveiling the Role of Carbonate Radical Anions in Dust‐Driven SO2 Oxidation Y. Liu et al. 10.1029/2023JD040017
- Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A B. Chen et al. 10.1029/2021JD036393
- Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations A. Samad et al. 10.1016/j.atmosenv.2023.119987
- Prediction of Air Quality Index using genetic programming Q. Chu Thi et al. 10.54939/1859-1043.j.mst.91.2023.85-95
- Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19 B. Chen et al. 10.3389/fenvs.2021.806094
- Spatiotemporal Estimation and Analysis of PM2.5 Concentrations in Wuhan Utilizing Multisource Remote Sensing Data and NOx as Inputs for Machine Learning Models J. Song et al. 10.1109/JSEN.2024.3523046
- Spatiotemporal estimation of 6-hour high-resolution precipitation across China based on Himawari-8 using a stacking ensemble machine learning model S. Zhou et al. 10.1016/j.jhydrol.2022.127718
- Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations J. Brence et al. 10.1007/s10994-022-06155-2
- Estimation of Regional Ground-Level PM2.5 Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model Y. Feng et al. 10.3390/rs14112714
- Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data B. Chen et al. 10.1016/j.scitotenv.2022.160928
- Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China Z. Song et al. 10.1016/j.envpol.2022.118826
- Accuracy Analysis of Machine Learning Methods for Predicting PM Concentration Y. Kim & K. Lee 10.5572/KOSAE.2023.39.2.149
22 citations as recorded by crossref.
- Investigating the impact of pretreatment strategies on photocatalyst for accurate CO2RR productivity quantification: A machine learning approach Y. Liu et al. 10.1016/j.cej.2023.145255
- A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents M. Rahman & G. Thurston 10.1016/j.envint.2022.107233
- Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms B. Chen et al. 10.1016/j.scitotenv.2021.150338
- Combining Himawari-8 AOD and Deep Forest Model to Obtain City-Level Distribution of PM <sub>2.5</sub> in China Z. Song et al. 10.2139/ssrn.3937107
- Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS) M. Rahman et al. 10.1016/j.envpol.2023.122568
- Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data J. Feng et al. 10.5194/acp-23-375-2023
- Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al. 10.1016/j.atmosenv.2024.120347
- Using satellite data on remote transportation of air pollutants for PM2.5 prediction in northern Taiwan G. Kibirige et al. 10.1371/journal.pone.0282471
- Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations T. Mi et al. 10.1016/j.gsf.2023.101686
- Estimation of Near-Surface High Spatiotemporal Resolution Ozone Concentration in China Using Himawari-8 AOD Y. Wang et al. 10.3390/rs17030528
- Long short-term memory network model to estimate PM2.5 concentrations with missing-filled satellite data in Beijing S. Jia et al. 10.1007/s00477-022-02253-8
- Unveiling the Role of Carbonate Radical Anions in Dust‐Driven SO2 Oxidation Y. Liu et al. 10.1029/2023JD040017
- Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A B. Chen et al. 10.1029/2021JD036393
- Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations A. Samad et al. 10.1016/j.atmosenv.2023.119987
- Prediction of Air Quality Index using genetic programming Q. Chu Thi et al. 10.54939/1859-1043.j.mst.91.2023.85-95
- Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19 B. Chen et al. 10.3389/fenvs.2021.806094
- Spatiotemporal Estimation and Analysis of PM2.5 Concentrations in Wuhan Utilizing Multisource Remote Sensing Data and NOx as Inputs for Machine Learning Models J. Song et al. 10.1109/JSEN.2024.3523046
- Spatiotemporal estimation of 6-hour high-resolution precipitation across China based on Himawari-8 using a stacking ensemble machine learning model S. Zhou et al. 10.1016/j.jhydrol.2022.127718
- Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations J. Brence et al. 10.1007/s10994-022-06155-2
- Estimation of Regional Ground-Level PM2.5 Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model Y. Feng et al. 10.3390/rs14112714
- Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data B. Chen et al. 10.1016/j.scitotenv.2022.160928
- Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China Z. Song et al. 10.1016/j.envpol.2022.118826
1 citations as recorded by crossref.
Latest update: 01 Apr 2025
Short summary
The linear hybrid machine learning model achieves the expected target well. The overall inversion accuracy (R2) of the model is 0.84, and the RMSE is 12.92 µg m−3. R2 was above 0.7 in more than 70 % of the sites, whereas RMSE and mean absolute error were below 20 and 15 µg m−3, respectively. There was severe pollution in winter with an average PM2.5 concentration of 62.10 µg m−3. However, there was only slight pollution in summer with an average PM2.5 concentration of 47.39 µg m−3.
The linear hybrid machine learning model achieves the expected target well. The overall...
