Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011

Abstract

Numerous studies have demonstrated that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM2.5 to assess personal exposure; however, induces measurement error. Land use regression provides spatially resolved predictions but land use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM2.5 exposures. In this paper, we used AOD data with other PM2.5 variables such as meteorological variables, land use regression, and spatial smoothing to predict daily concentrations of PM2.5 at a 1 km2 resolution of the southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 through 2011. We divided the study area into 3 regions and applied separate mixed-effect models to calibrate AOD using ground PM2.5 measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors (RMSPE) of 2.89, 2.51, and 2.82 μg/m3 for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM2.5 concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM2.5. Our model results will also extend the existing studies on PM2.5 which have mostly focused on urban areas due to the paucity of monitors in rural areas.

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

“…The results are in line with other recent studies [52][53][54], which all indicated that land use information such as NDVI can help to predict the PM 2.5 concentration.…”

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

“…Included studies showed that R 2 value of MEM was higher than those of the other three models in the same area [17,87,104,136]. Moreover, MAIAC algorithms, which led to a highly accurate of AOD, were mostly used in MEM, significantly improving the R 2 value of the model [7,35,83,120,135]. On the global scale, CTM has been proven to be efficient for the mechanism of completing the prediction from using partial AOD data by AOD component analysis [57]; (2) Adjusting factors: The number of these factors has increased due to the development of prediction models.…”

“…In recognition of regional geographical differences, Lee et al [7] predicted PM 2.5 concentrations using IPW in seven southeastern states of the United States in 2016, and they obtained three coefficients of determination (0.770, 0.810, and 0.700) from three different geographical area types. They suggested that their PM 2.5 estimation methods could be applied from urban areas to rural areas.…”

“…R 2 values of time and spatial consistency were also high; they could reach up to 0.700 or higher among different regions. Besides, R 2 value could be greatly enhanced through the use of MAIAC algorithms [7,72,82]; (2) MEM could be widely applied to the prediction of PM 2.5 at a regional level by using different land use and meteorological variables for model calibration; (3) MEM can be used to predict daily PM 2.5 concentrations, and has been applied in studies on the acute and chronic health effects of PM 2.5 exposure in New England, the Mid-Atlantic and other regions of the United States. These studies can be extended to other regions in the future [15].…”

Assessing the Exposome with External Measures: Commentary on the State of the Science and Research Recommendations

“…Remote-sensing estimates have been used to assess associations between PM 2.5 and cardiovascular disease in epidemiological studies (12,14,17). Some studies have recently used 1-km estimates of PM 2.5 for the United States, which increase their utility for exposomics studies (55,101). Remote-sensing techniques have also been used to estimate an expanding list of environmental exposures, including nitrogen dioxide (NO 2 ) concentrations (35), green spaces (2, 70), temperature (19), the built environment (13,95), outdoor light at night (45), agricultural chemical exposure (60), land cover classifications (11), river plumes (3), water quality (26), and marine microorganisms (39), for example.…”