A large body of evidence links ambient fine particulates (PM<sub>2.5</sub>) to chronic disease. Efforts continue to be made to improve large scale estimation of this pollutant for within-urban environments...A large body of evidence links ambient fine particulates (PM<sub>2.5</sub>) to chronic disease. Efforts continue to be made to improve large scale estimation of this pollutant for within-urban environments and sparsely monitored areas. Still questions remain about modeling choices. The purpose of this study was to evaluate the performance of spatial only models in predicting national monthly exposure estimates of fine particulate matter at different time aggregations during the time period 2000-2009 for the contiguous United States. Additional goals were to evaluate the difference in prediction between federal reference monitors and non-reference monitors, assess regional differences, and compare with traditional methods. Using spatial generalized additive models (GAM), national models for fine particulate matter were developed, incorporating geographical information systems (GIS)-derived covariates and meteorological variables. Results were compared to nearest monitor and inverse distance weighting at different time aggregations and a comparison was made between the Federal Reference Method and all monitors. Cross-validation was used for model evaluation. Using all monitors, the cross-validated R<sup>2</sup> was 0.76, 0.81, and 0.82 for monthly, 1 year, and 5-year aggregations, respectively. A small decrease in performance was observed when selecting Federal Reference monitors only (R<sup>2</sup> = 0.73, 0.78, and 0.80 respectively). For Inverse distance weighting (IDW), there was a significantly larger decrease in R<sup>2</sup> (0.68, 0.71, and 0.73, respectively). The spatial GAM showed the weakest performance for the northwest region. In conclusion, National exposure estimates of fine particulates at different time aggregations can be significantly improved over traditional methods by using spatial GAMs that are relatively easy to produce. Furthermore, these models are comparable in performance to other national prediction models.展开更多
文摘A large body of evidence links ambient fine particulates (PM<sub>2.5</sub>) to chronic disease. Efforts continue to be made to improve large scale estimation of this pollutant for within-urban environments and sparsely monitored areas. Still questions remain about modeling choices. The purpose of this study was to evaluate the performance of spatial only models in predicting national monthly exposure estimates of fine particulate matter at different time aggregations during the time period 2000-2009 for the contiguous United States. Additional goals were to evaluate the difference in prediction between federal reference monitors and non-reference monitors, assess regional differences, and compare with traditional methods. Using spatial generalized additive models (GAM), national models for fine particulate matter were developed, incorporating geographical information systems (GIS)-derived covariates and meteorological variables. Results were compared to nearest monitor and inverse distance weighting at different time aggregations and a comparison was made between the Federal Reference Method and all monitors. Cross-validation was used for model evaluation. Using all monitors, the cross-validated R<sup>2</sup> was 0.76, 0.81, and 0.82 for monthly, 1 year, and 5-year aggregations, respectively. A small decrease in performance was observed when selecting Federal Reference monitors only (R<sup>2</sup> = 0.73, 0.78, and 0.80 respectively). For Inverse distance weighting (IDW), there was a significantly larger decrease in R<sup>2</sup> (0.68, 0.71, and 0.73, respectively). The spatial GAM showed the weakest performance for the northwest region. In conclusion, National exposure estimates of fine particulates at different time aggregations can be significantly improved over traditional methods by using spatial GAMs that are relatively easy to produce. Furthermore, these models are comparable in performance to other national prediction models.
