4.5 Sensitivity analysis
Though the comparison of the different aggregation schemes
constitutes a sort of analysis of sensitivity, this section introduces a more
in-depth consideration of this issue. To keep the comparison of the results
as simple as possible, reference was made only to the Expert Opinion weighting
using the geometric interval classification. The maps are displayed in
Appendix B, Figures 38 to 40.
Traffic Pollution Influence
The assumption of an increase of traffic pollution's weight to
30% results in decreasing the coefficient of all the other parameters of the
model while keeping the total weight to 1. Table 11 shows a substantial
alteration of the percent area in each risk level. The most significant change
was observed in the lower risk level which declined by 67%. At the opposite,
high and very high risk levels increased by 50% and 77% respectively. The
overall change is almost 60%. This reveals the strong influence traffic
pollution has on health risk in the study area. To this regard, traffic
pollution represents one of the most urgent issues that policies should
address. Let's recall that traffic pollution occurs in about 76% of the study
area.
Table 11: Increase in
traffic pollution weight compared to EOW
|
EOW
|
Increase in
Traffic weight
|
Difference (%)
|
Absolute
Difference
|
|
Low
|
36.0
|
15.8
|
-56.1
|
-20.2
|
|
Moderate
|
27.1
|
24.0
|
-11.5
|
-3.1
|
|
High
|
19.3
|
29.0
|
50.2
|
9.7
|
|
Very High
|
17.7
|
31.3
|
76.8
|
13.6
|
|
Sum of changes
|
|
|
59.4
|
|
Waste Pollution Influence
The geographical incidence of waste represents 95% of the area.
With the alteration of waste coefficient, the model records a decline in low
classes of about 54% while high and very high risk levels indicate a positive
joint change of 100%. The aggregate change is 45%, 15 points smaller than
changes induced by traffic pollution, though the physical occurrence of waste
is greater than traffic pollution (Table 12). Nevertheless, this influence on
the model is still substantial.
Table 12: Increase in waste
pollution weight compared to EOW
|
EOW
|
Increase in
waste weight
|
Difference (%)
|
Absolute
Difference
|
|
Low
|
36.0
|
19.1
|
-46.9
|
-16.9
|
|
Moderate
|
27.1
|
25.2
|
-7.0
|
-1.9
|
|
High
|
19.3
|
32.9
|
70.4
|
13.6
|
|
Very High
|
17.7
|
22.8
|
28.8
|
5.1
|
|
Sum of changes
|
|
|
45.3
|
|
4.5.3 Proportional Spatial Incidence of the factors
This weighting, brought out based on the relative physical extent
of each model's parameters, assigned a greater coefficient to housing density
(0.21), followed by waste (0.20), rivulets (0.19), and traffic pollution
(0.16). Unlike the previous two scenarios, the area incidence of high and very
high risk levels increased only by 60% and the aggregate change is less than
30% (Table 13). This weighting shrank the gap between risk levels and enables
a better balanced distribution than did EOW.
Table 13: Comparison of EOW
and Proportional Incidence Weighting Results
|
EOW
|
Proportional
Weighting
|
Difference (%)
|
Absolute
Difference
|
|
Low
|
36.0
|
23.8
|
-33.9
|
-12.2
|
|
Moderate
|
27.1
|
27.0
|
-0.3
|
-0.1
|
|
High
|
19.3
|
31.3
|
62.2
|
12
|
|
Very High
|
17.7
|
17.9
|
1.1
|
0.2
|
|
Sum of changes
|
|
|
29.1
|
|
To summarize, the alteration of some of the coefficients of the
model's parameters influenced the results in different ways. The increase of
the traffic pollution weighting had the greatest impact on the model, altering
drastically the categories' rank from top to bottom. This exercise definitely
illustrates the responsiveness of the model to changes of its parameters.
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