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
The United Nations Conference on Environment and Development (1992) pointed out that rapid urbanization and demographic concentration have shocking implications for shelter and sanitation, and especially the disposal of wastes. Waste is responsible for the transmission of agents of infectious disease from human and animal excreta, the breeding of disease vectors, and exposure to toxic chemicals in human and animal excreta (WHO 2006). Port-au-Prince is the perfect example of this statement. Waste production and disposal outweigh the institutional, structural, and managerial capacity of institutions in charge. Two problems arise from waste collection in Port-au-Prince. First, the collection of waste is absent in slums due to inaccessibility to the narrow streets and alleys (World Bank 2005). Second, where it occurs, waste collection is not reliable. For instance, a lack of fuel or money to buy it, broken or lack of vehicles, and employees' strikes may cause waste to store up for days, even weeks, spreading all over the streets, blocking vehicle transport, and giving off offensive smells. In fact the garbage is fully exposed to the air and the wind, along with mosquitoes, cockroaches, and rodents facilitating the spatial propagation of the pollutants. No less shocking is the presence of hogs in some neighborhoods streets where waste is dumped.
Though these factors could not be accounted in the determination of waste incidence, we assumed that the spatial extent of this hazard is inversely proportional to the proximity to the sources. The waste collection network was digitized based on information gathered from SMCRS (Service Metropolitain de Collecte des Résidus Solides). The geometry of the network being linear, we had to consider whether the hazard manifests across the network or in dumping at specific points. However, no such information was available: the collection locations may change at any time and, in spite of the presence of some established posts along the network, there is no way to control the emergence of new unplanned ones. After calculating the Euclidean distance of the waste network feature, the resulting raster was divided into 4 classes from 0 to 400 meters with increment of 100 between each threshold.
To take into consideration the inaccessibility of some areas to waste collection, neighborhoods located at least 400 meters from the waste collection network were also integrated into the model. In low-to-medium residential areas where mostly people of middle economic class or above reside, some private services ensure the pick up of garbage. But people living in high-to-extremely high density housing neighborhoods may be struggling to get rid of waste, may have used non-hygienic or non-conventional ways to eliminate their garbage (for instance burning, or depositing it in the streets when it rains, or dumping it into the channels) , and consequently create a hazard for human's health. Those areas away from the network were given values in reference to the housing density. Then, both distances less than 400 meters and greater 400 meters were summed.
Regarding the sanitation aspect of the neighborhoods, it would not be reasonable to assume that the waste network conditions are uniform everywhere. For instance, in areas involved with intensive commercial activities like informal markets places, waste production is far much greater than in areas with little activity. The waste network at La Saline does not weigh against Lalue's circuit. This consists of a different aspect influencing waste accumulation and conditions. Based on this evidence a raster that stands for waste conditions was created and was integrated into the waste factor determination.
Furthermore, elevation was believed to play a role in neighborhoods' exposure to waste effects. On one hand, waste from upslope is carried down by wastewater from the canals and by runoff; on the other hand, in the absence of an efficient collection system, waste accumulates in lower elevation and is mixed with water from artificial ponds and obstructed canals. The same rationale illustrated for the pollution from traffic vehicle may be also true for waste: better air circulation in higher elevation acts as barrier to attenuate the pollution's impact of the garbage.
The different factors were aggregated using weights of 0.5, 0.3 and 0.2 for distance from waste collection network, conditions, and elevation respectively. Once again those weights were personally chosen since I could not access any example in the literature review for the study area. The result was standardized to values comprised from 1 to 4 as displayed in Table 2.
Waste Risks = 0.5*Distance + 0.3*(Network conditions) + 0.2*Elevation
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