R. F. Esswein

Improved retrieval of PM2.5 from satellite data products using non-linear methods

Satellite observations may improve the areal coverage of particulate matter (PM) air quality data that nowadays is based on surface measurements. Three statistical methods for retrieving daily PM2.5 concentrations from satellite products (MODIS-AOD, OMI-AAI) over the San Joaquin Valley (CA) are compared--Linear Regression (LR), Generalized Additive Models (GAM), and Multivariate Adaptive Regression Splines (MARS). Simple LRs show poor correlations in the western USA (R(2) ~/= 0.2). Both GAM and MARS were found to perform better than the simple LRs, with a slight advantage to the MARS over the GAM (R(2) = 0.71 and R(2) = 0.61, respectively). Since MARS is also characterized by a better computational efficiency than GAM, it can be used for improving PM2.5 retrievals from satellite aerosol products. Reliable PM2.5 retrievals can fill in missing surface measurements in areas with sparse ground monitoring coverage and be used for evaluating air quality models and as exposure metrics in epidemiological studies.

Improving retrievals of regional fine particulate matter concentrations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations

A combination of multiplatform satellite observations and statistical data analysis are used to improve the correlation between estimates of PM2.5 (particulate mass with aerodynamic diameter less that 2.5 µm) retrieved from satellite observations and ground-level measured PM2.5. Accurate measurements of PM2.5 can be used to assess the impact of air pollution levels on human health and the environment and to validate air pollution models. The area under study is Californias San Joaquin Valley (SJV) that has a history of poor particulate air quality. Attempts to use simple linear regressions to estimate PM2.5 from satellite-derived aerosol optical depth (AOD) have not yielded good results. The period of study for this project was from October 2004 to July 2008 for six sites in the SJV. A simple linear regression between surface-measured PM2.5 and satellite-observed AOD (from MODIS [Moderate Resolution Imaging Spectroradiometer]) yields a correlation coefficient of about 0.17 in this region. The correlation coefficient between the measured PM2.5 and that retrieved combining satellite observations in a generalized additive model (GAM) resulted in an improved correlation coefficient of 0.77. The model used combinations of MODIS AOD, OMI (Ozone Monitoring Instrument) AOD, NO2 concentration, and a seasonal variable as parameters. Particularly noteworthy is the fact that the PM2.5 retrieved using the GAM captures many of the PM2.5 exceedances that were not seen in the simple linear regression model. Implications: Particulate Mass (PM) in the air is a concern because of its effect on climate and human health. PM concentrations retrieved from satellite observations of aerosol optical depth can provide broad regional coverage that is not attained by surface sites. The techniques developed in this paper have resulted in greatly improved correlations between PM retrieved from satellite observations and PM from surface measurements in areas where the correlation is typically low. These improved retrievals can be used to fill in the gaps between surface sites and validate air quality models that are used for air quality forecasts and epidemiological studies. Supplemental Materials: Supplemental materials are available for this paper. Go to the publishers online edition of the Journal of the Air & Waste Management Association for information on the effects of grid size and MODIS data quality flags on the GAM results. The coefficients for the GAMs used in this study are also listed.

Monthly analysis of PM ratio characteristics and its relation to AOD

ABSTRACT Airborne particulate matter (PM) is derived from diverse sources—natural and anthropogenic. Climate change processes and remote sensing measurements are affected by the PM properties, which are often lumped into homogeneous size fractions that show spatiotemporal variation. Since different sources are attributed to different geographic locations and show specific spatial and temporal PM patterns, we explored the spatiotemporal characteristics of the PM2.5/PM10 ratio in different areas. Furthermore, we examined the statistical relationships between AERONET aerosol optical depth (AOD) products, satellite-based AOD, and the PM ratio, as well as the specific PM size fractions. PM data from the northeastern United States, from San Joaquin Valley, CA, and from Italy, Israel, and France were analyzed, as well as the spatial and temporal co-measured AOD products obtained from the MultiAngle Implementation of Atmospheric Correction (MAIAC) algorithm. Our results suggest that when both the AERONET AOD and the AERONET fine-mode AOD are available, the AERONET AOD ratio can be a fair proxy for the ground PM ratio. Therefore, we recommend incorporating the fine-mode AERONET AOD in the calibration of MAIAC. Along with a relatively large variation in the observed PM ratio (especially in the northeastern United States), this shows the need to revisit MAIAC assumptions on aerosol microphysical properties, and perhaps their seasonal variability, which are used to generate the look-up tables and conduct aerosol retrievals. Our results call for further scrutiny of satellite-borne AOD, in particular its errors, limitations, and relation to the vertical aerosol profile and the particle size, shape, and composition distribution. This work is one step of the required analyses to gain better understanding of what the satellite-based AOD represents. Implications: The analysis results recommend incorporating the fine-mode AERONET AOD in MAIAC calibration. Specifically, they indicate the need to revisit MAIAC regional aerosol microphysical model assumptions used to generate look-up tables (LUTs) and conduct retrievals. Furthermore, relatively large variations in measured PM ratio shows that adding seasonality in aerosol microphysics used in LUTs, which is currently static, could also help improve accuracy of MAIAC retrievals. These results call for further scrutiny of satellite-borne AOD for better understanding of its limitations and relation to the vertical aerosol profile and particle size, shape, and composition.