# **2.3  Socioeconomic Dimensions of Pollution Impact**

An extensive empirical literature has considered the interrelationships between air pollution, the demographic and socioeconomic characteristics of impacted neighborhoods, and health outcomes for their inhabitants. Some studies feature impacts on the elderly (Morris et al. 2005; Naess et al. 2007; Martins et al. 2004) or children (Brauer et al. 2002; Ciccone et al. 1998; Gauderman et al. 2004, 2005, 2007; Gehring et al. 2002; McConnell et al. 2002, 2003, 2006; Morgenstern et al. 2007; Pandey et al. 2005; Pikhart et al. 2001; van Vliet et al. 1997). Many other studies feature impacts on poor households (Hejat et al. 2015; Evans and Kantrowitz 2002; Laurent et al. 2007; Molitor et al. 2011; Rooney et al. 2012; Fan et al. 2012; Havard et al. 2009; Fann et al. 2011; Dionisio et al. 2010; Forastiere et al. 2007; Næss et al. 2007).

Studies that consider these groups serve one of two policy objectives, at least implicitly. First, highlighting their vulnerability can help promote measures for general reduction of traffic and pollution. Examples include stricter pollution controls for motor vehicles and fuel price increases to reduce motor fuel use. A second approach features targeted intervention to reduce pollution in areas where vulnerable groups are concentrated. In this context, many associational studies have linked proximity to heavily-traveled urban roads to respiratory and cardio-pulmonary problems associated with vehicular emissions (Ciccone et al. 1998; Gauderman et al. 2007; McConnell et al. 2006; van Vliet et al. 1997). Frequently-adopted measures include reserved bus lanes, new subway lines, and improved peripheral roads to divert traffic from congested areas. Extensive research has assessed the impact of mass transit measures on traffic and pollution (Adler et al. 2016; Anderson 2014; Basagaña et al. 2018; Gendron-Carrier et al. 2018; Goel and Gupta 2015; Tabatabaiee and Rahman 2011; Yang et al. 2017). Although magnitudes vary considerably, these studies all find significant traffic and/or pollution impacts.

# **2.4 Traffic Congestion and Air Pollution in Dar es Salaam**

Limited research has documented the rapid growth of motor vehicle traffic in Dar es Salaam, and its consequences for traffic congestion. Kiunsi (2013) draws on Tanzania Revenue Authority records to report that a total of 1,010,732 cars registered between 2003 and 2011, with Dar es Salaam accounting for 60% to 70% of the total. Similarly, Elinaza (2012) reports that Dar es Salaam absorbed most of the 245,180 motorcycles and 7,408 motorized tricycles registered in 2010 and 2011.

One major consequence of rapid growth in motor vehicle use has been severe traffic congestion. A 2007 JAICA study finds major slowdowns on primary traffic arteries under peak morning and evening conditions (Kiunsi, 2013). Congestion has in turn generated significant economic losses. Katala (2019) estimates a total loss of T Sh 655 billion per year from congestion-related delays and extra fuel use. Elinaza (2010) cites a Confederation of Tanzania Industries study which finds that traffic congestion reduces business profits by about 20 percent. Othman (2010) and Lupala (2010) identify congestion as a major source of air pollution in the city. Recent years have witnessed major investments to address these problems in Dar es Salaam, particularly an extensive bus rapid transit (BRT) system. Chengula and Kombe (2017) report that the BRT has provided significant benefits in time-saving, convenience and comfort. Nevertheless, serious congestion has persisted in Dar es Salaam as the number of motor vehicles has continued to grow. Aside from 5