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Title
The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Author
Chen, Jun

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
the occurrence possibility, which is difficult to communicate
with the non-expert. In this part, a methodology for evaluating
risk assessment by linguistic value and fuzzy relation analysis
is utilized subsequently. As described previously, the use of
fuzzy sets will allow an analyst to communicate degree of
health risk of individual elements to people in a readily
understood language term. Once these individual risk elements
are communicated, fuzzy set theory would then permit an
evaluation of the risk of human health to contaminated waste in
linguistic variable.
Thus, we have fuzzy relation matrix:
R = {r ij | i = l,2,3,4; j = 1,2,3,4,5} (18)
where r- is the membership function of contaminant i versus
different cancer risk level j, which is a function of contaminant
concentration and risk level criteria.
The groundwater monitoring data for four Volatile Organic
Petroleum Contaminants (VOPC) can be presented as C¡,.
The membership grade between c and the polluted level
grade j can be calculated, according to v the criterion for
pollutant i at polluted level j.
Table 3. Criteria of Risk Levels Under Different Concentrations
for each contaminant (pg/L)
Clean Slightly Contaminated Significantly Extremely
Contaminated Contaminated Contaminated
Benzene 0 1.32 ~ 2.55 3.77 5
EthylBenzene 13.2 174.9 186.6 347.7 700
Toluene 40 230 530 780 1000
Xylenes 8.8 116.6 224.4 332.2 10QQ00
(1) when C, 6 [V,.
_lgc,-igv,,_y (19)
/lgv # -lgv 1J4
(2) when C, s K,,,v„ >+1 ]
Based on the previously established criteria, consideration of
public health, federal and statewide regulatory limits for
groundwater, and well-accepted drinking water quality
standards, making use of the survey results from experts in the
environmental field, sets of linguistic value-supported and more
detailed risk level criteria according to the allowed ingestion
dose were established. Here, our study is mainly focused on
the age group 5.
Fig. 3 Membership grade of polluted level of Benzene
The overall fuzzy risk assessment can be preliminarily
processed by defining the sets for petroleum contaminants U
and criteria of risk levels V respectively, this definition
procedure is actually as same as what we previously did.
JgV,-lgC,/ (20|
" /Igvr'g''«
3) when Cj < Vy or C ( > v ( - - +1
4 = 0, Vi,* (21)
Thus a parameter of fuzzy related matrix can be obtained as
follows:
R ± =\r*\i = WAJ = V>~5\
Similarly, a weighting set for petroleum contaminants and the
max-* composition methodology of set W and R defined
previously are utilized
For this case, B= (0.23654, 0.05364, 0, 0, 0.71)
V={u¡ |V, } (15)
And MaxB = 0.71 (Extremely Contaminated)
V = {v 7 J } (16)
where ui represents contaminant i, and Vj represents risk level.
Putting in the linguistic terms, sets U and V can be specified
as:
U={ Benzene, EthylBenzene, Toluene, Xylenes}
V={Clean, Practically-not risky, slightly-risky, risky, highly-risky}
A fuzzy subset of UxV, which is a binary fuzzy relation from set
U to V, can be characterized through the following membership
function:
R .UxV [0,1] (17)
Therefore, we can determine the risk level for the sampling
well is extremely contaminated, high demands exist for the
future remediation.
3. GIS SUPPORTED RISK ASSESSMENT
Integrating GIS with environmental risk models can provide a
more meaningful interpretation of the problem within a
georeferenced environment. A few studies have focused on
using GIS for environmental risk assessment. Chen et al.
(1998) provided linking GIS with a groundwater model and
decision support system for the purpose of a petroleum
contaminated site assessment. Miller et al. (1996) conducted a
project using GIS to calculate human health risks at a large
military facility. For example, spatial and temporal attributes of
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