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A GIS-based modeling of environmental health risks in populated areas of Port-au-prince, Haiti

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par Myrtho Joseph
University of Arizona - Master in Natural Resources Information System 1987
  

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3.4.2.8 Proximity to high voltage power line

Power lines are considered a noticeable source of electromagnetic pollution to which extended exposure to some specific frequencies may result in cancer, birth defects, decreased immunity to disease, even new sicknesses (Michrowski 1991). A study conducted by Fews et al. (1999) observed an increased exposure to pollutant aerosols under high voltage power line. But another study conducted by Draper et al. (2005) and leading to the conclusion of association between the proximity of birth residence to high voltage power lines and children with leukemia (e.g.) has been the occasion of much debate (Day et al. 2005).

The delineation of this hazard was built on a uniform buffer of 500 meters from the power cables (line geometry) and 1000 meters around the distribution centers (point geometry). The distance ranges considered are shown in the Table 8.

Table 8: Distance to High Voltage Power Lines and Vulnerability level

Distance to Lines (in meters)

Distance to Centers

Vulnerability Level

0 - 150

0 - 300

4

151 - 300

301 - 500

3

301 - 400

501 - 700

2

> 400

> 700

1

Both distances were combined using the max function within the single output Map Algebra tool. In areas where the two grids intersect, the max function assigns the greatest value to the output grid. The use of this tool was necessary to put in evidence extreme cases of risk.

3.4.3 Linear Combination of the Variables

The Expert Opinion survey was conducted with the participation of ten local professionals having a certain familiarity with health and environmental issues in the study area. The respondents comprised 5 professionals in public health, 2 in environment, 2 economists and one agronomist. They were requested to assign a score from 1 to 9 (with 1 being very low and 9 very high) in order to rank the relative importance of the environmental variables of the model (see results in Table 15 of Appendix A). The ten scores were summed for each factor and divided by the total scores for all the factors. This provided a normalized weight comprised between 0 and 1. Hereafter this approach will be referred to Expert Opinion Weighting (EOW).

The second weighting approach assumed equal influence of the factors on health risks and assigned the same weight of 1/9 to each. The last weighting scheme was based on our own perception of the spatial extent and intensity of the different hazards of the model. Henceforth, either of these designations will be used for this approach: Own weighting or personalized weighting. Finally, to put in evidence those areas exposed uniquely to high risks for any factor, the maximum combination was applied. The max operator is a local function within the Spatial Analyst tools, which uses several input rasters to calculate the highest value on a cell-by-cell basis within the Analysis window.

Further the linear regression technique was used to validate the rationale under the Expert Opinion weighting and the personalized (own) weighting scheme. The intent was to discover the degree of correlation existing between the assigned coefficient in these two schemes and the factors in terms of area covered and the proportion of high and very high risks.

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