A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China
https://orcid.org/0000-0003-4243-079X
https://orcid.org/0000-0002-0499-1767
https://orcid.org/0000-0001-5799-549X
https://orcid.org/0000-0003-0041-4038
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 (R2 mse) 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.
Graphical abstract Display Omitted
Highlights Spatial-temporal models can do predictions over both space and time. Use all of the information of geographic covariates for modeling. Provide predictions at a point that can be a resident's address in a cohort.
An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China
https://orcid.org/0000-0003-4243-079X
https://orcid.org/0000-0002-0499-1767
https://orcid.org/0000-0001-5799-549X
https://orcid.org/0000-0003-0041-4038
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 (R2 mse) 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.
Graphical abstract Display Omitted
Highlights Spatial-temporal models can do predictions over both space and time. Use all of the information of geographic covariates for modeling. Provide predictions at a point that can be a resident's address in a cohort.
An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China
https://orcid.org/0000-0003-4243-079X
https://orcid.org/0000-0002-0499-1767
https://orcid.org/0000-0001-5799-549X
https://orcid.org/0000-0003-0041-4038
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 (R2 mse) 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.
Graphical abstract Display Omitted
Highlights Spatial-temporal models can do predictions over both space and time. Use all of the information of geographic covariates for modeling. Provide predictions at a point that can be a resident's address in a cohort.
An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China
Xu, Jia (author) / Yang, Wen (author) / Han, Bin (author) / Wang, Meng (author) / Wang, Zhanshan (author) / Zhao, Zhiping (author) / Bai, Zhipeng (author) / Vedal, Sverre (author)
Atmospheric Environment ; 211 ; 120-127
2019-04-02
8 pages
Article (Journal)
Electronic Resource
English