Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?

Abstract

The recent literature on satellite remote sensing of air quality is reviewed. 2009 is the 50th anniversary of the first satellite atmospheric observations. For the first 40 of those years, atmospheric composition measurements, meteorology, and atmospheric structure and dynamics dominated the missions launched. Since 1995, 42 instruments relevant to air quality measurements have been put into orbit. Trace gases such as ozone, nitric oxide, nitrogen dioxide, water, oxygen/tetraoxygen, bromine oxide, sulfur dioxide, formaldehyde, glyoxal, chlorine dioxide, chlorine monoxide, and nitrate radical have been measured in the stratosphere and troposphere in column measurements. Aerosol optical depth (AOD) is a focus of this review and a significant body of literature exists that shows that ground-level fine particulate matter (PM2.5) can be estimated from columnar AOD. Precision of the measurement of AOD is +/-20% and the prediction of PM2.5 from AOD is order +/-30% in the most careful studies. The air quality needs that can use such predictions are examined. Satellite measurements are important to event detection, transport and model prediction, and emission estimation. It is suggested that ground-based measurements, models, and satellite measurements should be viewed as a system, each component of which is necessary to better understand air quality.

Optical Calibration and Equivalence of a Multiwavelength Thermal/Optical Carbon Analyzer

“…Multiwavelength aethalometers (Park et al, 2006) and multi-spectral remote sensors (Hoff and Christopher, 2009) have shown the value of aerosol absorption data, especially related to radiative transfer and aerosol climate forcing. Deming (1943) regression with zero intercept and standard error (σ).…”

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

“…Large-scale research has been based on satellite observation, such as aerosol optical thickness (AOT). 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.…”

Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis

“…Previous researchers have shown that surface PM 2.5 concentrations can be derived from satellite based aerosol optical thickness (AOT) observation through the use of linear regression (Wang and Christopher, 2003). However, the correlation between AOT and surface PM 2.5 varies for different seasons and locations (Hoff and Christopher, 2009), and AOT hardly reflects the spatial pattern of surface PM 2.5 (Paciorek and Liu, 2008). What is more, all this research was carried out at PM 2.5 observations less than 100 μg/m 3 , because higher PM 2.5 concentrations lead to biased and inaccurate predictions (Gupta et al, 2006), which is always the case in Beijing.…”