Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine...

Numerous modeling approaches to estimate concentrations of PM_2.5 components have been developed to derive better exposures for health studies, including geostatistical interpolation approaches, land use regression models and, models based on remote sensing technology. Recently, there have been some efforts to develop models based on machine learning algorithms. Each one of these exposure assessment methods has inherent uncertainties resulting in varying levels of exposure misclassification. To date, only a few studies have attempted to systematically compare exposure estimates from different PM_2.5 constituent models. Our research addresses this gap, by comparing the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM_2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM_2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The OK model performed poorest for all PM_2.5 components, with an R² under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, for which the R² value increased to 0.62 and 0.59, respectively. These elements presented the highest R² value from the hybrid model. The forest model presented the best performance, with R² values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R² value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with an R² value of 0.54, 0.37, and 0.44, respectively. The results presented here can be useful for the environmental health community to more accurately estimate PM_2.5 constituents over space.