resolution satellite monitoring has begun to close the gap for developing-country cities. The most advanced example is provided by Heger et. al. (2018), whose research combines extensive air monitoring data for Cairo with vehicle counts obtained from ultra-high-resolution satellite imagery using a machine learning algorithm. Even for this study, significant problems remain: Spatial panels of vehicle counts must be pieced together from overlapping but non-identical satellite image frames; vehicle counts are sensitive to the technical characteristics of different satellite platforms; and pollution measures are limited to incomplete panels of particulate pollution at PM10 scale or low-resolution satellite measures of aerosol optical depth whose ability to measure particulate pollution remains controversial.

Differences in technical and institutional resources have produced bifurcated approaches to air pollution modeling in developed and developing countries. Some air quality modelers in developed countries have produced sophisticated atmospheric pollution transport models that link detailed emissions inventories from geo-located ground sources (Dommen et al. 2003) to pollution levels in three-dimensional atmospheric grids (Baertsch-Ritter et al. 2003; Keller et al. 2002; Khalid and Samson 1996; Jane et al. 1995). These exercises draw on scientifically-grounded models of atmospheric flow dynamics and photochemistry (Baertsch-Ritter et al. 2004; Russel and Dennis 2000; Gery et al. 1989). They also permit explicit incorporation of detailed meteorological data (Khalid and Samson 1996; Sillman and Samson 1995).

These models require so much data that their application is not widespread, even in high-income metropolitan regions. Where they are applied, they are not statistically fitted to local data because the available degrees of freedom (total observations minus the number of model parameters) are insufficient for robust nonlinear estimation. Even the most recent and sophisticated applications (e.g., Kim et al. 2018) are confined to verifying that predictions from imported atmospheric transport models are spatially correlated with gridded observations on local pollution.

Urban air quality modelers in developing countries have generally adopted more modest approaches tailored to limitations in data availability, technical resources and institutional constraints. Some of the more sophisticated statistical exercises in this domain have been applied in Nepal (Giri et al. 2008); Egypt (Elminir 2005); China (Zhang et al. 2015); Mexico (Csavina et al. 2014); Turkey (Ocak and Turalioglu 2008); Vietnam (Hien et al. 2002); the Islamic Republic of Iran (Hosseinibalam and Azadeh 2012); and India (Punithavathy et al. 2015). These studies relate air pollution near ground monitoring stations to actual and estimated emissions from nearby sources, with meteorological controls drawn from available measures of temperature, pressure, precipitation, wind speed and wind direction. However, air pollution data are typically drawn from a few ground monitoring stations that are located where pollution is expected to be highest. In addition, technical problems often create random, lengthy gaps in observations from individual monitoring stations. Appropriate spatial econometric panel estimators are difficult or impossible to apply under these conditions. In addition, the concentration of ground monitors in areas where pollution is expected to be high creates a severely-truncated sample of citywide air pollution.

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