A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data

Abstract

Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

Ambient Fine Particulate Matter, Outdoor Temperature, and Risk of Metabolic Syndrome

“…Missing observations on the 1-km × 1-km grid for the period of October 2003-December 2011 were also replaced with measurements from the 10-km × 10-km grid. Out-of-sample cross-validation showed a good fit of the 10-km × 10-km model (R 2 = 0.81) and an excellent fit of the 1-km × 1-km model (R 2 = 0.87) (18,19).…”

“…We used the average PM 2.5 level during the year prior to each visit at each participant's residential address to assess the longterm association of air pollution with metabolic dysfunction. The model incorporates fine-scale local land-use regression analysis and satellite-measured aerosol optical depth (18 (18,19). Missing observations on the 1-km × 1-km grid for the period of October 2003-December 2011 were also replaced with measurements from the 10-km × 10-km grid.…”

“…To assign exposure we used a recently published, validated spatiotemporal hybrid model, developed by our group, to estimate daily PM 2.5 level (in μg/m 3 ) across the study area (18). We used the average PM 2.5 level during the year prior to each visit at each participant's residential address to assess the longterm association of air pollution with metabolic dysfunction.…”

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

“…Moreover, the sparse measurements in winter as well as other additional factors such as rainfall tend to limit the model prediction. In future research, we will attempt to incorporate other influential predictors into a mixed-effects model [62], which could generate daily PM 2.5 predictions to minimize the bias.…”

Estimating Air Particulate Matter Using MODIS Data and Analyzing Its Spatial and Temporal Pattern over the Yangtze Delta Region

“…Also, some studies found that the correlation between AOT and PM concentration is impacted by meteorological conditions [13][14][15][16][17][18][19], boundary layer height [20][21][22], land use [23] and geographical location, [24] etc. Recently, researchers developed approaches using various statistical methods, from linear regression models [25,26] to more complex multiple regression and neural network techniques [13,27], generalized additive mixed models (GAMs) [23,28], and mixed effects models [29,30].…”