BackgroundEpidemiologic and health impact studies of fine particulate matter with diameter < 2.5 μm (PM2.5) are limited by the lack of monitoring data, especially in developing countries. Satellite...

http://fizz.phys.dal.ca/~atmos/publications/vanDonkelaar_2009_EHP.pdf

Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application

Three decades of rapid economic development is causing severe and widespread PM2.5(particulate matter (is) less than 2.5 ) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1 deg resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R(sup 2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM(sub 2.5) concentrations with little bias at the monthly (R(sup 2) = 0.73), regression slope = 0.91) and seasonal (R(sup 2) = 0.79), regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 micro-g/cu cm between 2004 and 2007 and a decrease of 0.46 micro-g/cu cm between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013

Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m3. Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that ex...

Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing

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.

https://www.tandfonline.com/doi/pdf/10.3155/1047-3289.59.6.645

Remote sensing of particulate pollution from space: have we reached the promised land?

[1] We assess the relationship of ground-level fine particulate matter (PM 2.5 ) concentrations for 2000-2001 measured as part of the Canadian National Air Pollution Surveillance (NAPS) network and the U.S. Air Quality System (AQS), versus remote-sensed PM 2.5 determined from aerosol optical depths (AOD) measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) satellite instruments. A global chemical transport model (GEOS-CHEM) is used to simulate the factors affecting the relation between AOD and PM 2.5 . AERONET AOD is used to evaluate the method (r = 0.71, N = 48, slope = 0.69). We find significant spatial variation of the annual mean ground-based measurements with PM 2.5 determined from MODIS (r = 0.69, N = 199, slope = 0.82) and MISR (r = 0.58, N = 199, slope = 0.57). Excluding California significantly increases the respective slopes and correlations. The relative vertical profile of aerosol extinction is the most important factor affecting the spatial relationship between satellite and surface measurements of PM 2.5 ; neglecting this parameter would reduce the spatial correlation to 0.36. In contrast, temporal variation in AOD is the most influential parameter affecting the temporal relationship between satellite and surface measurements of PM 2.5 ; neglecting daily variation in this parameter would decrease the correlation in eastern North America from 0.5-0.8 to less than 0.2. Other simulated aerosol properties, such as effective radius and extinction efficiency have a minor role temporally, but do influence the spatial correlation. Global mapping of PM 2.5 from both MODIS and MISR reveals annual mean concentrations of 40-50 ug/m 3 over northern India and China.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2005JD006996

Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing

An empirical model based on the regression between daily PM2.5 (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 PM2.5 concentrations. The root-mean-square error of the model was 6.2 μg/m3 with a corresponding average PM2.5 concentration of 13.8 μg/m3. When PM2.5 concentrations greater than 40 μg/m3 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 PM2.5 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 ...

/pdf/estimating-ground-level-pm2-5-in-the-eastern-united-states-1q22y2tdwc.pdf

Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing.

Epidemiological and human exposure studies have shown that exposure to particulate matter (PM) can result in a variety of adverse health responses. However, the spatial coverage of environmental data used in these studies is often spatially sparse and population exposures are typically obtained by averaging or interpolating PM concentrations over a large geographic area from a handful of sparsely located monitoring stations. This leaves substantial uncertainty as to the accuracy of these unobserved concentrations. To overcome these limitations, a spatio-temporal functional regression method was developed to predict PM 2.5 concentrations over a regular grid using satellite aerosol optical thickness (AOT) from NASA’s Moderate Imaging Spectroradiometer (MODIS). The inclusion of surface meteorological information from the Rapid Update Cycle (RUC) weather forecast/assimilation model improved the predictability of ground-level PM 2.5 concentrations from the satellite's total-column optical depth measure. Heterogeneity in scale and spatial misalignment were accounted for with this functional regression approach.

Yang Liu

Papers

Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing.

Prediction of ground level PM 2.5 from satellite measured aerosol optical thickness using functional regression for spatial data