percentiles, predictions from MODIS AOD and the Google Traffic congestion index come closer to the CAMS-3 observations than the Sentinel-5P aerosol index or NO2 measure.

Our PM2.5 results are basically replicated for NO2, with some notable differences. Google Traffic nearly matches CAMS-3 in the higher percentiles, while the predictions from Sentinel-5P are notably conservative beyond the 90th percentile. By implication, Sentinel-based inferences about hazard levels on the ground might be drawn from percentiles rather than absolute numbers. In any case, Sentinel-5P clearly has two useful roles to play: It can identify areas polluted by non-traffic sources, and it is available globally while Google Traffic is limited to major urban areas in some countries.

# **6.  Application Benefits and Costs**

To summarize, we find that Sentinel-5P, MODIS and Google Traffic all provide useful information for air pollution monitoring in the Dhaka metro region. MODIS, S5P and Google Traffic all track CAMS-3 PM2.5 with high significance; S5P and Google Traffic perform similarly for CAMS-3 NO2. Google Traffic can track air pollution in metro Dhaka at higher spatial resolution, but for vehicular sources only. MODIS and S5P capture the effects of emissions from other sources. The two satellite platforms also measure pollution at all locations, while Google Traffic is often confined to the major metro areas of developing countries.

Use of the three information sources does involve trade-offs in the dimensions of spatial resolution, update frequency and cost. The effective spatial resolution of Google traffic is 100 m. Outputs from Google Earth Engine are rasters with 1 km pixels, built from Sentinel-5P imagery with 5.5 km resolution and MODIS imagery with 250 m resolution. While the underlying MODIS imagery is higher-resolution, its repeat observation frequency is much lower than Sentinel’s. Information from all three sources is available at no acquisition cost, but translating the raw data into usable outputs requires knowledge of Javascript, GIS technology, and a modular programming language (Python or R).

Overall, the evidence from Dhaka suggests that these three information sources should be viewed as complements rather than substitutes. Taken together, they can provide a powerful adjunct to traditional information systems that provided much more limited coverage while imposing replacement and maintenance costs that are difficult for many urban governments to sustain.

# **7. Summary and Conclusions**

This paper has assessed the potential for low-cost augmentation of ground-based air quality monitoring in Dhaka, Bangladesh, with information from more comprehensive global information sources. At present, systematic local monitoring data are confined to areas near Dhaka’s Darussalam (CAMS-3) station (Figure 1). In this exercise, we have assessed the potential contributions of three global information sources: the European Space Agency’s Sentinel-5P, NASA’s MODIS, and Google Traffic.

Our tests for Sentinel-5P (S5P) and MODIS use daily measures for the .01-degree cell surrounding the CAMS-3 monitoring station. We match the satellite measures with CAMS-3 observations during the satellites’ overpass times for Dhaka. Our tests for the CAMS-3 measure of fine 16