# **5.  Distributional Impact of NO2 Pollution**

# **5.1 Pollution-Vulnerable Residents of Dar es Salaam**

To assess the impact of vehicular NO2 pollution, we focus on two vulnerable populations: children (0-5 years) and elderly adults (65+ years). We incorporate poverty in a composite vulnerability index for each grid cell by multiplying population in the groups [0-5,65+] by the estimated proportions of cell residents living in MPI-defined poverty, below $1.25/day, and below $2.00/day. For each variable, we use raster databases from the previously-cited WorldPop program at 1 km resolution.

Figure 7 displays the vulnerability index for the three poverty definitions. In each case, highvulnerability areas form roughly concentric rings around the city center. Nevertheless, there are evident differences in the pattern across poverty definitions. For MPI-defined poverty, the vulnerability index is greatest in Azimio, Mbagala and Makangarwe divisions. For a poverty line of $1.25/day these divisions remain in the most critical group, but they are joined by Tandika division in the south and a cluster of divisions northwest of the city center that includes Kigogo, Mzimuni, Mburahati, Makurumla, Ndugumbi and Magomeni. For a poverty line of $2.00/day the southern cluster disappears but five of six divisions in the northwest cluster remain.

# **5.2  Pollution Exposure Paths**

Figure 7 displays areas where potential vulnerability is high, but actual vulnerability will vary with exposure. To assess this component, we produce pollution exposure paths for each month by tracing pollution measured by Sentinel-5P in each grid cell back along the wind vector to its traffic sources. Figure 4 indicates the general direction of these effects as the wind direction changes: Exposure paths shift toward the northwest in August and September; toward the west in October, November and December; and toward the southwest in February and March. The winds shift the paths toward the north in April and May, but weather-related factors lead to minimal pollution during that period.

We estimate the effects of these trends by assigning pollution in the destination cell of each vector strip (from our previously-explained methodology) to the cells that the strip intersects back to its origin at the most distant cell where traffic is measured. For each destination cell, we sum the assigned pollution values of all intersecting strips. We multiply by our three vulnerability measures (for MPI, $1.25/day and $2.00/day) and normalize to the range [0-100] to obtain indicators that incorporate both pollution vulnerability and actual pollution exposure.

Figure 8 displays the results for the three poverty criteria. In each case, high-vulnerability areas surround the core area where traffic volume is highest. However, the geography of vulnerability differs by poverty definition. For MPI-defined poverty, vulnerability is greatest in five divisions southwest of the city center: Tandika, Azimio, Yombo Vituka, Makangarawe and Mbagala. This area is also high-vulnerability if $1.25/day is used as the poverty line, but it is joined by a group of divisions northwest of the city center: Ndugumbi, Makurumla, Mzimuni, Kigogo, Mabibo and Mburahati. The locus shifts again for the poverty line of $2.00/day; the most critical northwest cluster for $1.25/day remains but the southern cluster is reduced to two divisions.

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