10-year spatial and temporal trends of PM<sub>2.5</sub> concentrations in the southeastern US estimated using high-resolution satellite data

Abstract

Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5–AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 μg m−3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 μg m−3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 μgm−3, and RMSPE from 3.12 to 5.00 μgm−3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ∼20 %. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005.

Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements

“…fill the missing Aqua AOD by the available corresponding Terra AOD, and vice versa) and then averaged the two AOD products . The coefficients of determination (R 2 ) between the coexisting AOD products in the three regions were all greater than 0.8 for the cold season and greater than 0.72 for the warm season; these values are similar to the results obtained in the southeastern United States by Hu et al (2014).…”

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

“…In our model, the lowest R 2 in 2003 was 0.72 with a RMSPE 3.51 μg/m 3 . Recently, they expanded their study period for the same area 20 from a single year of 2003 to the multiple years from 2001 to 2010. Our study area covers vast additional areas in the southeastern U.S. by adding Florida, Mississippi, and the complete parts of other states.…”

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

“…Their R 2 values were generally lower, and varied between different areas. However, these listed models have been gradually optimized or integrated into other models, as with artificial neural networks (ANN, which incorporate LUR in the CTM) [52,61,68,110,111] and the two stage model (TSM, which combine the GWR with MEM) [80,81,119,121]. In recent years, with the development of the AOD-based mathematical model, many new methods have been developed, such as geographically and temporally weighted regression (GTWR) [107], support vector regression methods (SVR) [99] and machine learning regression (which is a combination of SVR, Gauss neural network processes, Decision trees, and Random forests) [28].…”