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An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China
- Source :
- Atmospheric Environment. 211:120-127
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Modeling fine-scale spatial and temporal patterns of air pollutants can be challenging. Advanced spatio-temporal modeling methods were used to predict both long-term and short-term concentrations of six criteria air pollutants (particulate matter with aerodynamic diameter less than or equal to 10 and 2.5 μm [PM10 and PM2.5], SO2, NO2, ozone and carbon monoxide [CO]) in Beijing, China. Monitoring data for the six criteria pollutants from April 2014 through December 2017 were obtained from 23 administrative monitoring sites in Beijing. The dimensions of a large array of geographic covariates were reduced using partial least squares (PLS) regression. A land use regression (LUR) model in a universal kriging framework was used to estimate pollutant concentrations over space and time. Prediction ability of the models was determined using leave-one-out cross-validation (LOOCV). Prediction accuracy of the spatio-temporal two-week averages was excellent for all of the pollutants, with LOOCV mean squared error-based R2 (R2mse) of 0.86, 0.95, 0.90, 0.82, 0.94 and 0.95 for PM10, PM2.5, SO2, NO2, ozone and CO, respectively. These models find ready application in making fine-scale exposure predictions for members of cohort health studies and may reduce exposure measurement error relative to other modeling approaches.
- Subjects :
- Pollutant
Atmospheric Science
010504 meteorology & atmospheric sciences
Mean squared error
Air pollution
010501 environmental sciences
Particulates
Atmospheric sciences
medicine.disease_cause
01 natural sciences
Beijing
Criteria air contaminants
Kriging
Partial least squares regression
medicine
Environmental science
0105 earth and related environmental sciences
General Environmental Science
Subjects
Details
- ISSN :
- 13522310
- Volume :
- 211
- Database :
- OpenAIRE
- Journal :
- Atmospheric Environment
- Accession number :
- edsair.doi...........cc1069fa67093c1ab4786b5f2f1cdf15