error involved in estimation of site sensitivity index ...to ‗1‘ (indicating a high potential...

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 1, No 5, 2011

© 2011 Anurag Ohri et al., licensee IPA- Open access - Distributed under Creative Commons Attribution License 2.0

Research article ISSN 0976 – 4402

Received on December, 2010 Published on January, 2011 772

Error Involved in Estimation of Site Sensitivity Index (SSI) for Land filling

of Municipal Solid Waste

Anurag Ohri.1 , P.K. Singh

2

1-Assistant Professor, Department of Civil Engineering, Institute of Technology,

Banaras Hindu University, Varanasi, India

2- Associate Professor, Department of Civil Engineering, Institute of Technology,

Banaras Hindu University, Varanasi, India

[email protected]

doi:10.6088/ijessi.00105020007

ABSTRACT

An environmental Index known as Site Sensitivity Index (SSI) was developed by Central

Pollution Control Board (CPCB) in association with National Environmental Engineering

Research Institute (NEERI), India to quantify and compare the sensitivity of different

sanitary landfill sites on the basis of accessibility, receptor, environmental, socio-economic,

waste management practices, climatological and geological criteria. The SSI integrates

parametric effects of all attributes about suitability of site for landfilling and generates a

single number expressing the sensitivity of the site for municipal solid waste disposal. The

index is based on the impact of 32 attributes and their relative significance as assessed by a

group of experts. It therefore necessitates ascertaining all 32 attributes for the selected sites

and then compare their respective suitability based on SSI. In case, data related with any or a

few attribute(s) are not available, comparing and finalization of landfill site is not possible

using this approach. This paper attempts to estimate and report the error introduced in the

value of SSI due to unavailability of such required data. An attempt has been made to classify

attributes into high, medium and low weight categories based upon their significance in

finding the SSI. The SSIs have been calculated for two sites of Varanasi, one site of

Bangalore and one arbitrarily selected most sensitive site. The analysis of this study indicates

that the error may be high (>10%) if more than two data of high weight category are not

available, whereas a marginal error (5-10%) is incurred if up to 10 parameters having low or

middle weights are not available. A software program has been developed in Visual Basic to

calculate SSI based on available data and guide the user about the importance of the missing

attributes.

Keywords: Environmental Index, Site Sensitivity Index (SSI), Municipal Solid Waste

Disposal, Landfill site selection, Error Estimation.

1. Introduction

A large number of environmental indices have been developed in last four decades. Various

indices are developed to quantify the pollution or quality of water and air. Usually, the

indices are formulated based either on studies conducted by the indices developers or on the

Delphi technique (Kumar and Alappat, 2009). An approach involving the selection of

variables, formulation of the sub index functions, and their aggregation by the developers

themselves has been adopted for Horton‘s Water Quality Index (WQI) (Horton 1965), Prati‘s

Implicit Index of Pollution (Prati et al. 1971), Dinius WQI (Dinius 1972), and Walski and

Parker‘s Index (Walski and Parker 1974). However, the Delphi technique has been used for

developing the National Sanitation Foundation NSF WQI (Brown et al. 1970), Dalmatia WQI

http://www.neeri.res.in/



Error Involved in Estimation of Site Sensitivity Index (SSI) for Landfilling of Municipal Solid Waste

Anurag Ohri, P.K.Singh

International Journal of Environmental Sciences Volume 1 No.5, 2011 773

(Gijanovic 1999), Water Quality Indexing System for Rivers and Streams (Smith 1990), and

Leachate Pollution Index (Kumar and Alappat, 2003).

Municipal solid waste management (MSWM) is one of the major environmental problems

throughout the world. Very few indices are developed so for to quantify the impacts of

different waste management activities. Kumar and Alappat (2003) developed a technique to

quantify the leachate contamination potential of sanitary landfills on a comparative scale in

terms of the leachate pollution index (LPI).

Landfill site selection is one of the important tasks for MSWM planners. Air, water and soil

pollution from the unscientifically selected disposal sites have been well known fact (Kumar

and Alappat, 2005). Central Pollution Control Board (CPCB) under the Ministry of

Environment and Forest (MoEF) with National Environmental Engineering Research

Institute (NEERI), Nagpur, India has developed a technique to quantify the suitability of site

for sanitary landfilling on a comparative scale in terms of the Site Sensitivity Index (SSI)

(CPCB, 2003). The SSI is an increasing scale index, wherein a lower value indicates that site

has less sensitivity to the impacts (preferable) and higher value indicates that site has high

sensitivity to the impacts (undesirable). The SSI has many possible applications including

ranking of potential landfill sites, prioritization of management plan initiatives and public

information. CPCB (2003) reported comparison and ranking of two potential municipal sites

at Kannahallo and Seegehalli in Banaglore based on SSI estimation following this approach,

Ohri and Singh (2009) attempted evaluation of two possible sites (Padaw and Karsada) in

Varanasi for landfill.

Kumar and Alappat (2005) considered estimation of errors involved in calculating Leachate

Pollution Index (LPI) due to non availability of data. The LPI is based on the concentration of

18 parameters, and the study reported the effect on LPI due to reducing number of available

parameters from 18 to 8. In case of landfill site selection, the SSI is an aggregated value

based on 32 attributes and their relative significance. Hence for calculating SSI, values of all

32 attributes are to be ascertained regardless of their high or low weight. It appears

reasonable therefore to assess the effect of non availability of some data on calculated value

of SSI in terms of error with respect to SSI estimated using all 32 attributes.

2. Methodology Adopted

2.1. Site Sensitivity Index (SSI)

CPCB (2003) has selected a set of 32 attributes for calculating an integrated index for ranking

of municipal solid waste disposal sites. The selected attributes are grouped into 7 categories

viz. accessibility, receptor, environmental, socio-economic, waste management practices,

climatological and geological. Sensitivity Index is a scale indicating degree of sensitivity of

individual attribute. This scale ranges from ‗0‘ (indicating low or very less potential hazard)

to ‗1‘ (indicating a high potential hazard). Thus, for each attribute a four level sensitivity

scale (0-0.25, 0.25-0.50, 0.50-0.75 and 0.75-1.00) has been considered. A numerical value

called weight has been assigned to each category, in accordance with the relative magnitude

of impact using a pair wise comparison technique. Within a category, the weight of each

attribute is assigned by following the same procedure of pair wise comparison. A total of

1000 point weights are assigned to all the 32 attributes grouped into 7 categories as shown in

Table 1.







Table1: Attributes and Calculation of Site Sensitivity Index for Landfilling (CPCB, 2003)

Sr.

No.

Attribute Weights 0.0-0.25 0.25-0.5 0.5-0.75 0.75-1.0

Accessibility Related ( No of Attributes 2, Total Weight 60)

1 Type of road 25 National

highway

State highway Local road No road

2 Distance from

collection area

35 < 10 km 10-20km 20-25km > 25km

Receptor Related (No of Attributes 8, Total Weight 250)

3 Population within

500 meters

50 0 to 100 100 to 250 250 to 1000 > 1000

4 Distance to nearest

drinking water

source

55 > 5000 m 2500 to

5000m

1000 to 2500

m

< 1000m

5 Use of site by nearby

residents

25 Not used Occasional Moderate Regular


building

15 > 3000 m 1500 to 3000

m

500 to 1500 m < 500m

7 Land use / Zoning 35 Completely

remote

(zoning not

applicable)

Agricultural Commercial

or industrial

Residential

8 Decrease in property

value with respect to

distance

15 > 500 m 2500 to

5000m

1000 to 2500

m

< 1000 m

9 Public utility facility

within 2 km

25 Commercial

and industrial

area

National

heritage

Hospital Air port

10 Public acceptability 30 Fully

accepted

Acceptance

with

suggestions

Acceptance

with major

changes

Non

acceptance

Environmental Related (No of Attributes 7, Total Weight 305)

11 Critical

environment

45 Not a critical

environment

Pristine

natural areas

Wetlands,

flood plains,

and preserved

areas

Major habitat

of endangered

or threatened

species


surface water

55 > 8000m 1500 to

8000m

500 to 1500m < 500 m

13. Depth to ground

water

65 > 30m 15 to 30m 5 to 15m < 5m

14 Contamination 35 Air, water or

food

contamination

Biota-

contamination

Soil

contamination

only

No

contamination

15 Water quality 40 Highly

polluted

Polluted Potable Confirming to

standard

16 Air quality 35 Highly

polluted

Polluted Confirming to

industrial

standards

Confirming to

residential

standards




17 Soil quality 30 Highly

contaminate

Contaminated Average No

contamination

Socio-economic Related (No of Attributes 4, Total Weight 110)

18 Health 40 No problem Moderate High Severe

19 Job opportunities 20 High Moderate Low Very low

20 Odour 30 No odour Moderate

odour

High odour Intensive foul

odour

21 Vision 20 Site not seen Site partly

seen (25%)

Site partly

seen (75%)

Site fully seen

Waste Management Practice Related (No of Attributes 2, Total Weight 85)

22 Waste quantity/ day 45 < 250 tons 250 to 1000

tons

1000 to 2000

tons

> 2000 tons

23 Life of site 40 > 20 years 10-20 years 2-10 years < 2 years

Climatological Related (No of Attributes 2, Total Weight 40)

24 Precipitation

effectiveness index*

25 < 31 31 to 63 63 to 127 >127

25 Climatic features

contributing to Air

pollution

15 No problem Moderate High Severe

Geological Related (No of Attributes 7, Total Weight 125)

26 Soil permeability 35 >1 x10-7

cm/sec

1x10-5

to

1x10-7

cm/sec

1x10-3

to

1x10-5

cm/sec.

< 1 x10-3

cm/sec.

27 Depth to bedrock 20 > 20m 10 to 20m 3 to 10 m < 3m

28 Susceptibility to

erosion and run-off

15 Not

susceptible

Potential Moderate Severe

29 Physical

characteristics of

rock

15 Massive Weathered Highly

weathered

30 Depth of soil layer 30 > 5 m 2-5m 1-2m < 1m

31 Slope pattern 15 < 1% 1-2% 2-5% >10%

32 Seismicity 20 Zone 1 Zone II Zone III Zone IV&V

*Precipitation effectiveness index is the ratio of annual precipitation to annual evaporation.

2.2 Variable aggregation

The weighted linear sum aggregation function has been used for the calculation of SSI and is

given by Equation 1:

1

n

i i

i

SSI w s

(1)

SSI= Total Score of Site Sensitivity Index

iw =Weight of ith

attribute; is = sensitivity of ith

attribute; n=no of attributes for calculating

SSI=32 ; and 10001

nwi

i




Based upon the actual measurement and the opinion of the experts, the aggregated SSI is

calculated for each site. Table 2 gives the decision criteria for landfill site selection based on

total score of SSI.

Table 2: Decision Criteria for a landfill site selection (CPCB, 2003)

Total Score of SSI Site Description

< 300 Less sensitive to the impacts (Preferable)

300 to 750 Moderate

> 750 Highly sensitive to the impacts (undesirable)

2.3. Errors involved in calculating site sensitivity index due to non-availability of data

When the data for any attribute included in SSI are not available, the normalized SSIm can be

calculated using the data set of the available attributes by using Equation 2:

1m

1

1

m

i i

i

m

i

i

nwi

i

w s

SSI X

w

(2)

where m number of attributes for which data are available, ( m<32)

and 1

m

i

i

w

<1000

The weights for the available attributes are normalized by proportionate redistribution so that

1

m

i

i

w

= 1000 and accordingly the SSI is calculated.

Error involved in calculated SSIm due to non availability of data can be calculated by using

Equation 3:

m100

SSI SSIError X

SSI

(3)

3. Case Study

To assess the errors involved due to non-availability of data in the estimated value of SSI,

case studies of two potential landfill sites of Varanasi (Padaw and Karsada), one of

Banagalore (Kannahallo) and one arbitrarily selected very sensitive site have been considered.

Based on judged attribute measurement (AM) for a site, the sensitivity index (SI) is assigned

with the help of Table 1 and the attribute score (AS) is calculated by multiplying SI with the

respective weight of that attribute. The SSI is the summation of all 32 attribute scores (ASs).

The results of such calculation of SSI for all sites are presented in table 3 and 4.




Table 3: SSI calculation for sites of Varanasi (Padaw and Karsada)

S.

No.

Attribute w Padaw Karsada

AM SI AS AM SI AS

1 Type of road 25 National Highway 0.15 3.75 Local Road 0.35 8.75

2 Distance from

collection point

35 9 km 0.25 8.75 14 km 0.4 14

3 Population within 500

meters

50 >1000 0.75 37.5 250-1000 0.6 30


drinking water source

55 400 m 0.9 49.5 1000m 0.75 41.25


residents

25 Moderate 0.5 12.5 Occasional 0.25 7.5


building

15 <500 0.75 11.25 500-1500m 0.65 9.75

7 Land use/Zoning 35 Agricultural 0.50 17.5 Completely

remote

0 0



distance

15 No decrease in

Property Value

0.1 1.5 No decrease in

Property Value

0.1 1.5


within 2 kms

25 No public utility 0 0 No public utility 0 0

10 Public acceptability 30 Acceptance with

major changes

0.75 22.5 Acceptance with

suggestions

0.3 9

11 Critical environments 45 Flood Plain 0.5 22.5 Not a critical

environment

0.1 4.5


surface water

55 850 m 0.6 33 900m 0.60 33

13 Depth to ground water 65 8m 0.7 45.5 12m 0.6 39

14 Contamination 35 Soil

contamination

0.6 21 No

contamination

0.9 26.25

15 Water quality 40 Polluted 0.5 20 Potable 0.75 30

16 Air quality 35 Confirming to

industrial

standards

0.6 21 Confirming to

residential

standards

0.8 28

17 Soil quality 30 Contaminated 0.25 7.5 Average 0.6 18

18 Health 40 Moderate 0.35 14 No problem 0.15 6

19 Job opportunities 20 Low 0.5 10 Low 0.5 10

20 Odour 30 High Odour 0.6 18 Moderate 0.25 7.5

21 Vision 20 Site partly seen

(75%)

0.75 15 Site partly seen

(25%)

0.3 6

22 Waste quantity/day 45 250 to 1000

tonnes

0.35 15.75 250-1000 tonnes 0.35 15.75

23 Life of site 40 2-10 years 0.65 26 10-20years 0.35 14

24 Precipitation

effectiveness index

25 31 to 63 0.35 8.75 31-63 0.35 8.75





contributing to Air

pollution

15 No problem 0 0 No problem 0 0

26 Soil permeability 35 1x10-5to 1x10

-7 0.3 10.5 1x10

-5to 1x10

-7 0.3 10.5

27 Depth to bedrock 20 >20m 0.1 2 >20m 0.1 2


erosion & run-off

15 Moderate 0.7 10.50 Moderate 0.6 9.00

29 Physical

characteristics of rock

15 Massive 0.2 3 Massive 0.2 3

30 Depth of soil layer 30 >5 m 0.1 3 >5 m 0.1 3

31 Slope pattern 15 <1% 0.1 1.5 <1% 0.1 1.5

32 Seismicity 20 Zone III 0.5 10 Zone III 0.5 10

Total Score 483.25 407.50

w=Weight, AM=Attribute Measurement, SI=Sensitivity Index, AS=Attribute Score

Table 4: SSI calculation for the site at Bangalore and one arbitrary site (CPCB, 2003)

S.

No.

Attribute w Kannahallo Arbitrary High Sensitive Site

AM SI AS AM SI AS

1 Type of road 25 SH 0.35 8.75 No road 0.85 21.25

2 Distance from

collection point

35 25 km 0.75 26.25 26 km 0.75 26.25

3 Population within 500

meters

50 100 0.25 12.5 1000 0.75 37.5



55 200 m 1 55 200 m 1 55


residents

25 Not Used 0 0 Occasional 0.25 6.25


building

15 100 1 15 100 1 15

7 Land use/Zoning 35 completely

Remote

0 0 Residential 0.8 28



distance

15 No decrease in

Property Value

0 0 Decrease in

Property Value

0.7 10.5


within 2 kms

25 No Public Utility 0 0 Hospital 0.75 18.75

10 Public acceptability 30 No complains 0.15 4.5 Non acceptance 0.75 22.5

11 Critical environments 45 Not a critical

environment

0.15 6.75 Preserved areas 0.85 38.25


surface water

55 1.5 km 0.5 27.5 300 m 0.8 44

13 Depth to ground water 65 5 m 0.75 48.75 2 m 1 65

14 Contamination 35 No contamination 1 35 No contamination 1 35




15 Water quality 40 Potable 0.75 30 Potable 1 40

16 Air quality 35 confirming to

residential

standards

1 35 confirming to

residential

standards

1 35

17 Soil quality 30 Average 0.75 22.5 Average 0.75 22.5

18 Health 40 Moderate 0.25 10 High 0.75 30

19 Job opportunities 20 Low 0.5 10 Very low 1 20

20 Odour 30 Moderate 0.35 10.5 Moderate 0.45 13.5

21 Vision 20 Site Partly Seen

(25%)

0.3 6 Site Partly Seen

(50%)

0.6 12

22 Waste quantity/day 45 1197 tonnes 0.6 27 1500 tonnes 0.8 36

23 Life of site 40 21 months 0.8 32 21 months 0.8 32

24 Precipitation

effectiveness index

25 31 to 63 0.5 12.5 31 to 63 0.5 12.5


contributing to Air

pollution

15 No problem 0 0 Moderate 0.5 7.5

26 Soil permeability 35 1X10-4

to 1X10-5

0.5 17.5 1X10-4

to 1X10-5

0.7 24.5

27 Depth to bedrock 20 10-40 m 0.3 6 18-20 m 0.5 10


erosion & run-off

15 Not susceptible 0 0 Potential 0.5 7.5

29 Physical characteristics

of rock

15 Weatherland 0.3 4.5 Weatherland 0.5 7.5

30 Depth of soil layer 30 0.3 to 3m 0.75 22.5 2 m 0.75 22.5

31 Slope pattern 15 2% 0.25 3.75 1% 0.25 3.75

32 Seismicity 20 Zone I 0 0 Zone II 0.5 10

Total Score 489.75 770.00

w=Weight, AM=Attribute Measurement, SI=Sensitivity Index, AS=Attribute Score

It is observed that based on SSI scores, both the potential sites of Varanasi as well as the

Kannahallo site of Bangalore fall in moderate impact ( 300< SSI score<750) category. The

unknown high sensitivity site, as expected achieves a SSI score of 770 (greater than 750).

To study the effect of non availability of data on estimated SSI, the whole range of attributes

were judiciously classified into three categories: Low, Middle and High, based on their

weight ranges as given in Table 5.

A software program has been developed in Visual Basic to calculate SSI based on available

data. This program is a part of our programe on development of Environmental Decision

Support System for Municipal Solid Waste Management (EDSS-MSWM) and discussed

elsewhere (Ohri and Singh, 2010). For studying the effect of non availability of data, the SSI

for each site is calculated by dropping one attribute from a category and the error introduced

in calculated SSIm (with respect to the SSI using all 32 attributes) is estimated. The procedure

is repeated by successively ignoring additional attributes of this weight category and

estimating the resultant error in calculated SSIm. Fig. 1 shows the snapshot of the program

for calculating SSI/SSIm along with number of missing data and % error due to non

availability of such data. The EDSS-MSWM also guides the user about the importance of the

missing attributes.




Table 5: Classification of attributes based on weight

Attributes No. of

Attributes

Weight

ranges

Weight

Category

Critical environments, Waste quantity/day, Population

within 500 meters, Distance to nearest drinking water

source, Distance to nearest surface water, Depth to

ground water

6 65 to 45 High

Public acceptability, Soil quality, Odour, Depth of soil

layer, Distance from collection point, Land use/Zoning,

Contamination, Air quality, Soil permeability, Water

quality, Health, Life of site

12 40 to 30 Middle

Distance to nearest building, Decrease in property value

with respect to distance, Climatic features contributing to

Air pollution, Susceptibility to erosion & run-off,

Physical characteristics of rock, Slope pattern, Job

opportunities, Vision, Depth to bedrock, Seismicity, Type

of road, Use of site by nearby residents, Public utility

facility within 2 kms, Precipitation effectiveness index

14 25 to 15 Low

Figure 1: Snapshot of developed software program for calculating SSI, report on suitability

of site and error due to missing data, if any.




3.1. Effect of non availability of attributes with high weight factors

If the data of ―depth to ground water‖ (Table 1, S. No. 13, weight= 65) is presumed to be

unknown, SSIm (m=31) is calculated using Eq.2. The percentage error in calculated SSIm is

estimated using Eq. 3. Subsequently, the next attribute (Distance to nearest drinking water

source, S. No. 4, weight= 55) is also presumed to be unknown. The SSIm (m=30) is similarly

calculated using Eq.2, and the percentage error in SSIm (m=30) with respect to SSI (n=32) is

estimated. The procedure is repeated till only 26 attributes data are assumed to be available

and all 6 low weight data are missing. The results of such analysis is shown in Fig. 2.

From Fig. 2 it is observed that when attributes of high weight are not available, the error is

negative indicating that the calculated SSIm is less than actual SSI, thereby projecting the site

to be less sensitive to impacts than actual. The percentage error varies from -2.08 (m=31) to a

maximum of -16.33 (m=28). The error exceeds 10%, if more than one attribute of high

weight category is missing.

Figure 2: Variation of percentage error in SSI with decreasing number of attributes due to

non availability of high weight parameters

Table 6: Estimating errors involved in calculated site sensitivity index due to non availability

of high weight attributes

Attribute wi AM SI (pi) AS

(n=32)

wi pi

AS

(n=30)

wi pi

AS

(n=28)

wi pi

AS

(n=26)

wi pi

Depth to ground water 65 8m 0.7 45.5 - - -

Distance to nearest


55 400 m 0.9 49.5 - - -

Distance to nearest surface

water

55 850 m 0.6 33 33 - -

Population within 500

meters

50 >1000 0.75 37.5 37.5 - -

Critical environments 45 Flood Plain 0.5 22.5 22.5 22.5 -

Waste quantity/day 45 250 to 1000

tonnes

0.35 15.75 15.75 15.75 -

Water quality 40 Polluted 0.5 20 20 20 20




Health 40 Moderate 0.35 14 14 14 14

Life of site 40 2-10 years 0.65 26 26 26 26

Distance from collection

point

35 9 km 0.25 8.75 8.75 8.75 8.75

Land use/Zoning 35 Agricultural 0.5 17.5 17.5 17.5 17.5

Contamination 35 Soil

contamination

0.6 21 21 21 21

Air quality 35 Confirming to

industrial

standards

0.6 21 21 21 21

Soil permeability 35 1x10-5to

1x10-7

0.3 10.5 10.5 10.5 10.5

Public acceptability 30 Acceptance

with major

changes

0.75 22.5 22.5 22.5 22.5

Soil quality 30 Contaminated 0.25 7.5 7.5 7.5 7.5

Odour 30 High Odour 0.6 18 18 18 18

Depth of soil layer 30 >5 m 0.1 3 3 3 3

Type of road 25 National

Highway

0.15 3.75 3.75 3.75 3.75

Use of site by nearby

residents

25 Moderate 0.5 12.5 12.5 12.5 12.5

Public utility facility

within 2 kms

25 No public

utility

0 0 0 0 0

Precipitation effectiveness

index

25 31 to 63 0.35 8.75 8.75 8.75 8.75

Job opportunities 20 Low 0.5 10 10 10 10

Vision 20 Site partly

seen (75%)

0.75 15 15 15 15

Depth to bedrock 20 >20 0.1 2 2 2 2

Seismicity 20 Zone III 0.5 10 10 10 10

Distance to nearest

building

15 <500 0.75 11.25 11.25 11.25 11.25

Decrease in property value

with respect to distance

15 >5000 0.1 1.5 1.5 1.5 1.5

Climatic features

contributing to Air

pollution

15 No problem 0 0 0 0 0

Susceptibility to erosion &

run-off

15 Moderate 0.7 10.5 10.5 10.5 10.5

Physical characteristics of

rock

15 Massive 0.2 3 3 3 3

Slope pattern 15 <1% 0.1 1.5 1.5 1.5 1.5

Summation 1000 483.25 388.25 317.75 279.5

Total weight 1000 880 775 685

Normalized SSIm 483.25 441.19 410.00 408.03

Percentage error 0 -8.7 -15.16 -15.57

w=weight, AM=Attribute Measurement, SI=Sensitivity Index, AS=Attribute Score




3.2. Effect of non availability of attributes with low weight factors

When a similar procedure is followed by removing attributes with low weights, starting with

―slope pattern‖ (S. No. 31, weight= 15) and % error in calculated SSIm (m<32) with respect to

SSI (m=32) is plotted with remaining number of attributes, the results are obtained as shown

in Fig 3.

From Fig. 3 it is observed that percentage error may increase from 0.75 to 17.58% when

attributes having low weights are ignored one by one up to 14. A positive % error in SSI

indicates a high calculated SSI with respect to the actual SSI for the site, suggesting that the

site is being projected more sensitive than really it is. The error is within 10% when up to 10

attributes having low weights are considered non available.


non availability of a few low weight parameters

3.3 Effect of non availability of attributes with middle weight factors

Following a similar procedure for middle weight attributes, starting with ―Public

Acceptability‖ (S. No. 10, weight= 30) and estimating the error in calculated SSIm (m<32)

with respect to SSI (m=32), a plot of % error with remaining number of attributes is obtained

as shown in Fig 4.

From Fig. 4, it is observed that when middle weights attributes are not available, the error

may be positive (indicating a site to be more sensitive than actual) as well as negative

(meaning a site is indicated less sensitive than actual). The maximum positive error is 7.46%

with all twelve middle weight parameter missing whereas the maximum negative error is

-6.07% with nine attributes non available.





non availability of middle weight parameters

4. Discussion

It is observed that the percentage error are positive and the estimated SSIm (m<32) are higher

than the actual SSI (m=32) when data for the attributes with low weights are missing. Thus

the site is reported as more sensitive to impacts than actually it is. This may give some factor

of safety for the considered site in terms of sensitivity. On the contrary, the percentage error

are negative and the estimated SSIm are lower than the actual SSI in case of high weights

attributes being non available, indicating a false sense of security on a vulnerable site. A

marginal positive error up to 10% is introduced in calculated SSI if ten attributes of low

weight category are considered not available. This means the actual SSI for a site can

reasonably be assessed using only 22 available attributes. Basically there are fourteen low

weight attributes in SSI calculation, out of which 10 (receptor related 2, climatological

related 1, Socio-economic related 2, and geological related 5) have weights in 15-20 range.

The weight of these 10 attributes is 170, which gets redistributed while normalizing the SSIm.

In case of high weight attributes, the error in calculated SSI exceeds 10% just with the non

availability of two attributes, and the error being negative a high sensitive site may be

reported less sensitive. Non availability of middle weight attributes may introduce either

positive or negative error in SSI, but remains within 10% with up to 10 attributes being not

available.

5. Conclusions

Site Sensitivity Index (SSI) calculated on the basis of 32 attributes as suggested by CPCB

(2003) serves as a useful basis for ranking the suitability of landfill site. In case of constraints

of time and resources, or non availability of data related with some attributes, a marginal

error up to 10% is introduced in calculated SSI if upto 10 attributes of low or middle weight

are missing. The high weight attributes must be available for SSI calculations in order to

reasonably assess the suitability of the site for landfill. Hence a classification of attributes in

high, medium and low categories appear desirable in order to prioritize the efforts for data

collection. A software program has been developed to calculate SSI, report on the suitability

of site for landfill, and number of missing data, if any, along with percentage error introduced

in calculated SSI due to such missing data.




References

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error involved in estimation of site sensitivity index ...to ‗1‘ (indicating a high potential...

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