Error Involved in Estimation of Site Sensitivity Index ... · of municipal solid waste disposal sites. The selected attributes are grouped into 7 categories viz. accessibility, receptor,

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

© Copyright 2010 All rights reserved Integrated Publishing Association

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

1Assistant 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]

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, socioeconomic, 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 (510%) 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, socioeconomic, 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 (00.25, 0.250.50, 0.500.75 and 0.751.00) 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.

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

Sr. No.

Attribute Weights 0.00.25 0.250.5 0.50.75 0.751.0

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 1020km 2025km > 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

6 Distance to nearest 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 12 Distance to nearest

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



775

17 Soil quality 30 Highly contaminate

Contaminated Average No contamination

Socioeconomic 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 1020 years 210 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 runoff 15 Not

susceptible Potential Moderate Severe

29 Physical characteristics of

rock

15 Massive Weathered Highly weathered

30 Depth of soil layer 30 > 5 m 25m 12m < 1m 31 Slope pattern 15 < 1% 12% 25% >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

i w =Weight of i th attribute; i s = sensitivity of i th attribute; n=no of attributes for calculating

SSI=32 ; and 1000 1

n w i i

∑ = =



776

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 nonavailability 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:

1 m

1

1

m

i i i m

i i

n w i 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:

m 100 SSI SSI Error X SSI

− = (3)

3. Case Study

To assess the errors involved due to nonavailability 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.



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

Padaw Karsada S. No.

Attribute w 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 2501000 0.6 30

4 Distance to nearest drinking water source

55 400 m 0.9 49.5 1000m 0.75 41.25


25 Moderate 0.5 12.5 Occasional 0.25 7.5


15 <500 0.75 11.25 5001500m 0.65 9.75

7 Land use/Zoning 35 Agricultural 0.50 17.5 Completely remote

0 0

8 Decrease in property value with respect to distance

15 No decrease in Property Value

0.1 1.5 No decrease in Property Value

0.1 1.5

9 Public utility facility 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

12 Distance to nearest 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 2501000 tonnes 0.35 15.75

23 Life of site 40 210 years 0.65 26 1020years 0.35 14 24 Precipitation

effectiveness index 25 31 to 63 0.35 8.75 3163 0.35 8.75



778

25 Climatic features contributing to Air pollution

15 No problem 0 0 No problem 0 0

26 Soil permeability 35 1x10 5 to 1x10 7 0.3 10.5 1x10 5 to 1x10 7 0.3 10.5

27 Depth to bedrock 20 >20m 0.1 2 >20m 0.1 2 28 Susceptibility to

erosion & runoff 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)

Kannahallo Arbitrary High Sensitive Site S. No.

Attribute w 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

4 Distance to nearest drinking water source

55 200 m 1 55 200 m 1 55


25 Not Used 0 0 Occasional 0.25 6.25


15 100 1 15 100 1 15

7 Land use/Zoning 35 completely Remote

0 0 Residential 0.8 28

8 Decrease in property value with respect to distance

15 No decrease in Property Value

0 0 Decrease in Property Value

0.7 10.5

9 Public utility facility 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

12 Distance to nearest 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



779

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

25 Climatic features 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 1040 m 0.3 6 1820 m 0.5 10 28 Susceptibility to

erosion & runoff 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 (EDSSMSWM) 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 EDSSMSWM also guides the user about the importance of the missing attributes.



780

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 & runoff, 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.



781

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 drinking water source

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



782

Health 40 Moderate 0.35 14 14 14 14 Life of site 40 210 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 1x105to 1x107

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 & runoff

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



783

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.

Figure 3: Variation of percentage error in SSI with decreasing number of attributes due to 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.



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Figure 4: Variation of percentage error in SSI with decreasing number of attributes due to 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, Socioeconomic related 2, and geological related 5) have weights in 1520 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.



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quality index—Do we dare? Water Sewage Works, 117(10), pp 339–343. 2. CPCB, 2003, Guidlines for the selection of site for landfilling, Central Pollution

Control Board, New Delhi, India.

3. Dinius, S. H., 1972, Social accounting system for evaluating water resources, Water Resources Research, 8(5), pp 1159–1177.

4. Gijanovic, N. S., 1999, Water quality evaluation by index in Dalmatia, Journal of Water Research, 33(16), pp 3423–3440.

5. Horton, R. K., 1965, An index number system for rating water quality. Journal of Water Pollution Control Federation, 37(3), pp 300–306.

6. Kumar, D. and Alappat, B. J., 2003, A technique to quantify landfill leachate pollution, Proc., 9th International Waste Management Landfill Symposium, Cagliari, Italy, Paper 400.

7. Kumar, D. and Alappat, B. J., 2005, Evaluating leachate contamination potential of landfill sites using leachate pollution index, Journal of Clean Technology and Environmental Policy, 7 (3), pp 190–197.

8. Kumar, D. and Alappat, B.J., 2009, NSFWater quality index: does it represent the experts’ Opinion? Practice Periodical of Hazardous, Toxic, and Radioactive Waste Management, 13 (1), pp 7579.

9. Ohri, A. and Singh, P.K., 2009, Landfill site selection using site sensitivity index a case study of Varanasi city in India. In: Proceedings of 1st International Conference on Solid Waste Management. Khudiram Anushilan Kendra (KAK) & Netaji Indoor Stadium(NIS), Kolkata, India, pp 3138.

10. Ohri, A. and Singh, P.K., 2010, Development of decision support system for municipal solid waste management in India: a review, International Journal of Environmental Sciences, 1 (4), pp 440453.

11. Prati, L., Pavanello, R. and Pesarin, F., 1971, Assessment of surface water quality by a single index of pollution. Journal of Water Research, 5 (5), pp 741–751.

12. Smith, D. G., 1990, A better water quality indexing system for rivers and streams. Journal of Water Research, 24(10), pp 1237–1244.

13. Walski, T. M. and Parker, F. L., 1974, Consumers water quality index. Journal of Environmental Engineering Division, 100 (3), pp 593–611.

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Error Involved in Estimation of Site Sensitivity Index ... · of municipal solid waste disposal sites. The selected attributes are grouped into 7 categories viz. accessibility, receptor,