A GIS-based modeling of environmental health risks in populated areas of Port-au-prince, Haiti
par Myrtho Joseph
University of Arizona - Master in Natural Resources Information System 1987
Vehicular traffic is recognized to be one of the main sources of air pollution in urban cities. Carbon monoxide, hydrocarbons, nitrogen oxides are the major air pollutants generated by motor vehicles, and are the underline causes of lung malfunction, lung cancer, cardiovascular diseases, respiratory symptoms, stroke, neurobehavioral problems, premature mortality, and possibly exacerbation of asthma, which ultimately results in deaths (UNEP 1994a, Watkiss et al. 2000, Maheswaran and Elliott 2003, Nafstad et al. 2003, Greene and Pick 2006). The main roads of Port-au-Prince are associated with high traffic concentration particularly at peak hours. Wargny (2004) describes the traffic situation in the downtown area in these terms: «The traffic jam is quasi permanent. The third-hand vans that provide public transportation spit black smoke, and the carbon monoxide aggregates to the fecal dust...» Refining measures of exposure to air pollution takes into account proximity to the source of pollution (WHO 2005). Other factors that exacerbate pollution concentration in Port-au-Prince are the occasional maintenance of the vehicles, the lack or absence of motor vehicle emission control, leaded gas consumption, and in general the deficient control of cars by the authorities. In addition to housing close to heavy traffic, which also creates indoor pollution, commercial activities take place for long hours in the streets. Therefore, vehicle traffic impact both outdoor and indoor pollution.
Among various approaches, the qualitative method is recommended to assess the exposure to air pollution from traffic (WHO 2005). Different studies used a distance analysis approach to model air pollution from traffic (Elliott et al. 2001, Hoek et al. 2001, 2002, Wilhelm and Ritz 2003, Ferguson et al. 2004, Schikowski et al. 2005). The final determination of traffic pollution was built upon the linear combination of four variables: land use, elevation, traffic density, and distance to roads with high traffic volume. This approach assumes that there is no spatial variability excluding elevation within the area and doesn't take into account different coping strategies and capacity for the households exposed. Likewise the mean level of concentration and the mean amount of time exposed were used in this model.
Though buffers applied differ from one study to another, depending on the situation at hand, a Euclidean distance grid was generated to the main roads and important traffic streets, and distances comprised between 0 and 300 meters were considered in the delimitation of four vulnerability levels ranging from low to very high. Afterward three raster surfaces were constructed for traffic density, elevation, and land use. The elevation raster was derived from the DEM itself created from the contours, the rivulets, and the VIPs. One thousand points were randomly generated for the study area and input as location data to be sampled for DEM values. The elevation points obtained were subsequently converted to raster using the Inverse Distance Weighting (IDW) within the Spatial Analyst tool. IDW was chosen for its simplicity of use and the large amount of points that were available for its creation (1000). Though IDW has the effect of flattering peaks and valleys, the utilization of the VIPs (representing sample of high and low elevation in the area) in the DEM surface creation, this disadvantage was minimized.
High risks were associated with distance less than 50 meters, high traffic density areas, high residential areas and low elevation as shown on Table 13 in Appendix A. These sub-variables were aggregated using weights in relation to their assumed implication in creating air pollution. No data, specific studies or expert opinions were available to support this assumption. We had to make a personal decision about these weights.
Greater weights (0.4 and 0.3) were assigned to the exposure factors (distance to road and traffic density) and a smaller weight (0.15) was applied to the two other factors as seen below:
Traffic Pollution Risk = 0.40*(Distance to Roads) + 0.30* (Traffic Density) + 0.15*(Land Use) + 0.15*(elevation)
The final step involved reclassifying the resulting grid into different thresholds using the geometric interval reclassification algorithm2(*). This scheme was used for its ability to deal both with the number of values in each class range and to establish consistent change between intervals (ESRI 1996). More details are provided in Appendix in Table 14.
* 2 The geometric interval was mostly used to aggregate the sub-variables.
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