APPENDIX A
Literature Review
Current approaches to converting total column aerosol optical depth (AOD) retrieved from satellites into estimates of ground-level PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 microns) concentrations have various limitations. First, retrieved total column AODs from satellites have errors associated with some issues. Such issues include excessive spatial averaging that mixes urban and rural areas and thus underestimates AOD in the more polluted urban areas (for example, Hu, Waller, Lyapustin, Wang, Al-Hamdan, and others 2014; Hu, Waller, Lyapustin, Wang, and Liu 2014; Lyapustin and others 2011); reflective surfaces, such as desert dust, that make it more difficult to separate the reflection of sunlight by aerosols from the surface reflection (for example, Sorek-Hamer and others 2015); “patchwork” surfaces in urban areas, where several different land-use types, each with different albedos, may all be present within a single satellite pixel (for example, Oo and others 2010); and the presence of undetected thin clouds, which can also create a positive bias in AOD retrievals (for example, Sun and others 2011). These limitations in satellite AOD products are consistent with some of the errors identified in using satellite observations to determine ground-level PM2.5 concentrations. For example, the high estimates in Egypt, Qatar, and Saudi Arabia are consistent with overestimates of AOD in regions with highly reflective surfaces. These problems can be mitigated by careful analysis and identification of the satellite observations and retrieval algorithms that give the most accurate and precise estimates of AOD for a given region, rather than trying to find one satellite product that works sufficiently well for all conditions around the globe. A deep understanding of the strengths and weaknesses of the different satellite AOD products is thus essential to determining which product will be most useful for a given set of geographic and meteorological conditions. Most previous validation work on the use of satellite observations to estimate ground-level PM2.5 has been performed in developed countries with extensive, well-calibrated, long-term ground-level-monitoring (GLM) observations of PM2.5 (Type V countries, using the proposed typology in chapter 1). However, the performance of the statistical models and chemical transport models (CTMs) used to derive these relationships is not as well characterized in many low- and middle-income countries (LMICs), where GLM is infrequent or absent and few
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