Estimating Ground-Level PM_2.5 in the Eastern United States Using Satellite Remote Sensing

Abstract

An empirical model based on the regression between daily PM 2.5 (particles with aerodynamic diameters of less than 2.5 µm) concentrations and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM 2.5 concentrations. The root-meansquare error of the model was 6.2 µg/m 3 with a corresponding average PM 2.5 concentration of 13.8 µg/m 3 . When PM 2.5 concentrations greater than 40 µg/m 3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites, were found to influence the association between PM 2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for determining ground-level particle concentrations.

Estimating the global abundance of ground level presence of particulate matter (PM2.5)

“…Many studies have shown that the relationship between PM 2.5 and AOD is a multi-variate function of a large number of parameters (Liu et al, 2005;Lyamani et al, 2006;Choi et al, 2008;Natunen et al, 2010;Liu and Harrison, 2011). Further, many of these relationships are non-linear, some are of unknown functional form and many have non-Gaussian distributions.…”

“…Studies have shown that the relationship between PM 2.5 and AOD is not always suitable for simple regression models. Rather it is determined by a multi-variate function of a large number of parameters, including: humidity, temperature, boundary layer height, surface pressure, population density, topography, wind speed, surface type, surface reflectivity, season, land use, normalised variance of rainfall events, size spectrum and phase of cloud particles, cloud cover, cloud optical depth, cloud top pressure and the proximity to particulate sources releasing PM 2.5 (Liu et al, 2005;Lyamani et al, 2006;Choi et al, 2008;Paciorek et al, 2012;). The picture is further complicated by the biases present in satellite AOD products (e.g.…”

“…PM 2.5 estimates using this approach have since been used in a variety of health studies (van Donkelaar et al, 2010a;Anderson et al, 2012;Brauer et al, 2012;Crouse et al, 2012;Hystad et al, 2012). Meanwhile, Liu et al estimated the ground level abundance of PM 2.5 by using scaling factors from the GEOS-Chem, GOCART models and AOD from the MISR (Liu et al, 2004(Liu et al, , 2005(Liu et al, , 2007a(Liu et al, ,b,c, 2009a.…”

Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration

“…AOT data can be used to reveal the spatial heterogeneity characteristics of PM 2.5 concentration with the help of linear regression models (Wang and Christopher, 2003), but this data can reflect neither different temporal scales (seasonal, monthly and daily changes) of PM 2.5 distribution (Hoff et al 2009) nor spatial distribution of near-ground PM 2.5 ). All these studies were carried out with PM 2.5 observations of less than 100 mg/m 3 , because higher PM 2.5 concentrations will lead to biased and inaccurate predictions (Liu et al 2005), which happens in Beijing. Besides, missing values frequently occur in AOT data, especially when it is cloudy or hazy (Gupta et al 2006).…”

New algorithms and their application for satellite remote sensing of surface PM2.5 and aerosol absorption

“…The Total Ozone Mapping Spectrometer (TOMS) aerosol index at the ultraviolet wavelength is sensitive to absorbing aerosols so that it can be used to detect the cabonaceous and dust particles Torres et al, 1998). The relationship between column Aerosol Optical Depth(AOD) and surface PM2.5 has been explored over 4 United States using satellite AOD data (Wang and Christopher, 2003;Liu et al, 2005). They find that MODIS or MISR AOD has good correlations with hourly or monthly PM2.5 measurements.…”