ArticlePloS one2018
Short period PM2.5 prediction based on multivariate linear regression model.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Improving PMScientific reports · 2023Article
- Prediction of PMPeerJ · 2023Article
- PMEnvironmental challenges (Amsterdam, Netherlands) · 2021Article
- Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19.International journal of environmental research and public health · 2020Article
- Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China.PloS one · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A multivariate linear regression model was proposed to achieve short period prediction of PM2.5 (fine particles with an aerodynamic diameter of 2.5 μm or less). The main parameters for the proposed model included data on aerosol optical depth (AOD) obtained through remote sensing, meteorological factors from ground monitoring (wind velocity, temperature, and relative humidity), and other gaseous pollutants (SO2, NO2, CO, and O3). Beijing City was selected as a typical region for the case study. Data on the aforementioned variables for the city throughout 2015 were used to construct two regression models, which were discriminated by annual and seasonal data, respectively. The results indicated that the regression model based on annual data had (R2 = 0.766) goodness-of-fit and (R2 = 0.875) cross-validity. However, the regression models based on seasonal data for spring and winter were more effective, achieving 0.852 and 0.874 goodness-of-fit, respectively. Model uncertainties were also given, with the view of laying the foundation for further study.
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