Limits...
Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000.

Hart JE, Yanosky JD, Puett RC, Ryan L, Dockery DW, Smith TJ, Garshick E, Laden F - Environ. Health Perspect. (2009)

Bottom Line: Model performance was determined using a cross-validation (CV) procedure with 10% of the data.We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting.The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

View Article: PubMed Central - PubMed

Affiliation: Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA. Jaime.hart@channing.harvard.edu

ABSTRACT

Background: Epidemiologic studies of air pollution have demonstrated a link between long-term air pollution exposures and mortality. However, many have been limited to city-specific average pollution measures or spatial or land-use regression exposure models in small geographic areas.

Objectives: Our objective was to develop nationwide models of annual exposure to particulate matter < 10 microm in diameter (PM(10)) and nitrogen dioxide during 1985-2000.

Methods: We used generalized additive models (GAMs) to predict annual levels of the pollutants using smooth spatial surfaces of available monitoring data and geographic information system-derived covariates. Model performance was determined using a cross-validation (CV) procedure with 10% of the data. We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting.

Results: For PM(10), distance to road, elevation, proportion of low-intensity residential, high-intensity residential, and industrial, commercial, or transportation land use within 1 km were all statistically significant predictors of measured PM(10) (model R(2) = 0.49, CV R(2) = 0.55). Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides-emitting power plant were all statistically significant predictors of measured NO(2) (model R(2) = 0.88, CV R(2) = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

Conclusions: These models provide reasonably accurate and unbiased estimates of annual exposures for PM(10) and NO(2). This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.

Show MeSH

Related in: MedlinePlus

Annual GAM-predicted PM10 (A) and NO2 (B) values at the TrIPS cohort addresses at the beginning (1985), middle (1993), and end (2000) of follow-up.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2801201&req=5

f3-ehp-117-1690: Annual GAM-predicted PM10 (A) and NO2 (B) values at the TrIPS cohort addresses at the beginning (1985), middle (1993), and end (2000) of follow-up.

Mentions: Figure 2 shows the distribution of the pollution values for each year at the cohort addresses. The mean predicted levels of the two pollutants decreased over the follow-up period, although there was little change in the overall spread of the distributions. The spatial distributions of the predictions for both PM10 and NO2 are shown in Figure 3. At all three time points shown, PM10 values are higher in the western half of the United States than in the east. For NO2, however, the levels in all time periods are highest in major cities. To compare the two prediction methods, Figure 4 shows the cohort predictions for PM10 at base-line (1985), midpoint (1993), and last year of follow-up (2000). There is moderate correlation between the results of the GAM and IDW PM10 models, although the IDW models tend to be lower than the predictions of the GAMs (thus their lower slope of 0.76 vs. 0.94 for the GAM when both are compared with measured concentrations). The Spearman correlations between the two prediction types were 0.66 for 1985, 0.64 for 1993, and 0.77 for 2000. As shown in Figure 4, there is also moderate correlation between the GAM and IDW NO2 models. Specifically, the Spearman correlation is 0.63 for 1985, 0.53 for 1993, and 0.51 for 2000. Overall, the IDW models tend to be lower than the GAM predictions and tend to have less variance (heterogeneity).


Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000.

Hart JE, Yanosky JD, Puett RC, Ryan L, Dockery DW, Smith TJ, Garshick E, Laden F - Environ. Health Perspect. (2009)

Annual GAM-predicted PM10 (A) and NO2 (B) values at the TrIPS cohort addresses at the beginning (1985), middle (1993), and end (2000) of follow-up.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2801201&req=5

f3-ehp-117-1690: Annual GAM-predicted PM10 (A) and NO2 (B) values at the TrIPS cohort addresses at the beginning (1985), middle (1993), and end (2000) of follow-up.
Mentions: Figure 2 shows the distribution of the pollution values for each year at the cohort addresses. The mean predicted levels of the two pollutants decreased over the follow-up period, although there was little change in the overall spread of the distributions. The spatial distributions of the predictions for both PM10 and NO2 are shown in Figure 3. At all three time points shown, PM10 values are higher in the western half of the United States than in the east. For NO2, however, the levels in all time periods are highest in major cities. To compare the two prediction methods, Figure 4 shows the cohort predictions for PM10 at base-line (1985), midpoint (1993), and last year of follow-up (2000). There is moderate correlation between the results of the GAM and IDW PM10 models, although the IDW models tend to be lower than the predictions of the GAMs (thus their lower slope of 0.76 vs. 0.94 for the GAM when both are compared with measured concentrations). The Spearman correlations between the two prediction types were 0.66 for 1985, 0.64 for 1993, and 0.77 for 2000. As shown in Figure 4, there is also moderate correlation between the GAM and IDW NO2 models. Specifically, the Spearman correlation is 0.63 for 1985, 0.53 for 1993, and 0.51 for 2000. Overall, the IDW models tend to be lower than the GAM predictions and tend to have less variance (heterogeneity).

Bottom Line: Model performance was determined using a cross-validation (CV) procedure with 10% of the data.We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting.The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

View Article: PubMed Central - PubMed

Affiliation: Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA. Jaime.hart@channing.harvard.edu

ABSTRACT

Background: Epidemiologic studies of air pollution have demonstrated a link between long-term air pollution exposures and mortality. However, many have been limited to city-specific average pollution measures or spatial or land-use regression exposure models in small geographic areas.

Objectives: Our objective was to develop nationwide models of annual exposure to particulate matter < 10 microm in diameter (PM(10)) and nitrogen dioxide during 1985-2000.

Methods: We used generalized additive models (GAMs) to predict annual levels of the pollutants using smooth spatial surfaces of available monitoring data and geographic information system-derived covariates. Model performance was determined using a cross-validation (CV) procedure with 10% of the data. We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting.

Results: For PM(10), distance to road, elevation, proportion of low-intensity residential, high-intensity residential, and industrial, commercial, or transportation land use within 1 km were all statistically significant predictors of measured PM(10) (model R(2) = 0.49, CV R(2) = 0.55). Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides-emitting power plant were all statistically significant predictors of measured NO(2) (model R(2) = 0.88, CV R(2) = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

Conclusions: These models provide reasonably accurate and unbiased estimates of annual exposures for PM(10) and NO(2). This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.

Show MeSH
Related in: MedlinePlus