Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM_2.5Concentrations across the Continental United States

Abstract

Background: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data.Objective: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation.Methods: We developed a space–time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals.Results: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates.Conclusions: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China

“…Additionally, we also found that CV IS errors of PM 2.5 Optimal tended to be lower at the testing sites, which were surrounded by more training sites ( Figure 5). Similar findings have been reported in previous studies, which introduced spatial or spatiotemporal autocorrelations into PM 2.5 modeling [61]. Spatiotemporally autocorrelation benefits prediction of PM 2.5 , especially at the unmeasured locations but makes troubles for model evaluation.…”

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

“…Additionally, in a comparison between IDW-adjusted CTM and MLR, IDW-adjusted CTM (R 2 value = 0.510 per year) performed better than MLR (R 2 value = 0.330 per year) [62,67]. Lee et al [63] made a comparison between the Kriging method and CTM in the United States. Although both methods gave consistent results, CTM had better applicability and higher accuracy, especially in areas with few ground level monitoring sites.…”

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

“…Approaches that have been developed to simulate the distribution of the concentration of air pollutants usually include geostatistical interpolation [30], land-use regression [31], dispersion [32], and hybrid [33] models. "Dispersion models use information on emissions, source characteristics, chemical and physical properties of the pollutants, topography, and meteorology to model the transport and transformation of gaseous or particulate pollutants through the atmosphere to predict, e.g., ground level concentrations" [34].…”