# **7. Summary and Conclusions**

In this paper, we have used new databases for the Dar es Salaam metro region to investigate the spatial dynamics of traffic congestion, regional air pollution, and impacts on vulnerable residents. We use Google Traffic to develop high-resolution traffic measures, which provide the first temporally- and spatially-comprehensive view of congestion in the metro area. We assess the spatial dynamics of air pollution using high-resolution nitrogen dioxide (NO2) measures from the European Space Agency’s new Sentinel-5P satellite platform. Our information on the spatial distribution of vulnerable residents uses demographic and poverty maps assembled from numerous sources by the WorldPop project of the School of Geography and Environmental Sciences, University of Southampton. We combine these information sources in a spatial grid with 1-km cells for the metro region.

Our empirical modeling highlights the impact of weather factors on the spatial distribution and intensity of NO2 pollution from the emissions of motor vehicles in the region. We identify critical weather dimensions in two econometric analyses. The first uses daily NO2 measures from Sentinel-5P and weather data from World Weather Online to measure the effects of temperature, rainfall, humidity and wind speed on air pollution, as well as the effect of cloudiness on measurement by Sentinel-5P. We aggregate regression-based NO2 predictions by month and find a strong seasonal pattern of weather impacts on pollution. In the second econometric exercise, we estimate a model that relates spatial displacement of NO2 pollution from its traffic source locations to the locations observed by Sentinel-5P. Our analysis clearly identifies wind direction as the critical driving variable and, like the other weather factors, wind direction is strongly seasonal. Taken together, these weather variables account for a major portion of spatial pollution dynamics in the Dar es Salaam region. We confirm the strong seasonality of wind direction in a 10-year analysis for the region.

In the second part of the paper, we explore the distributional implications of our findings. Using our methodology for analysis of spatial pollution displacement, we quantify NO2 pollution exposure for residents in the displacement path. To index relative vulnerability, we use WorldPop digital maps to estimate the number of children (0-5 years) and elderly adults (65+) in poor households in each grid cell. We develop separate indicators for three measures of poverty incidence based on the global multi-dimensional poverty index (MPI); a poverty line of $US 1.25/day; and a poverty line of $US 2.00/day.

We combine our pollution exposure and pollution vulnerability measures to produce a composite pollution impact measure for each grid cell. We aggregate our measures for level-3 administrative divisions and map the results to assess the implications. We find three elements of primary significance. First, areas of the city have highly-varied “environmental endowments” driven by strongly-seasonal weather effects on the spatial distribution and intensity of NO2 pollution. Pollution exposure is worst for areas that are in the wind path from high-traffic areas in seasons when pollution is maximized by weather factors. The second critical element is the spatial juxtaposition of environmental endowments and vulnerability. Some highly-vulnerable areas have poor environmental endowments while others do not, with potentially-critical significance for pollution impact analysis. The third element relates to the definition of poverty. Vulnerability 20