suitability of hazard rating systems for air contamination · professor & head, department of...

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Suitability of Hazard Rating Systems for Air Contamination

from Municipal Solid Waste (MSW) Dumps and Improvements to Enhance Performance

Journal: Canadian Journal of Civil Engineering

Manuscript ID cjce-2016-0500.R1

Manuscript Type: Article

Date Submitted by the Author: 06-Feb-2017

Complete List of Authors: Kumar, Amit; Indian Institute of Technology Roorkee, Centre for

Transportation System (CTRANS) Datta, Manoj; Indian Institute of Technology Delhi, Civil Engineering Nema, A; Indian Institute of Technology Delhi Singh, R; HUDCO Ltd.

Is the invited manuscript for consideration in a Special

Issue? : N/A

Keyword:

municipal < Environ & Sanitary Eng., solid waste management < Environ & Sanitary Eng., environmental < MANUSCRIPT CLASSIFICATION, envir plan & impact asses < Environ & Sanitary Eng., environ & sanitary eng < Computer Applications

https://mc06.manuscriptcentral.com/cjce-pubs

Canadian Journal of Civil Engineering

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Suitability of Hazard Rating Systems for Air Contamination from Municipal Solid Waste (MSW) Dumps

and Improvements to Enhance Performance

Amit Kumar

National Post-Doctoral Fellow, CTRANS, Indian Institute of Technology (IIT) Roorkee, Roorkee 247667, India.

(Corresponding author). E-mail: [email protected]; Phone: +91-965-414-0757; Fax: +91-133-2275568

Manoj Datta

Professor & Head, Department of Civil Engineering, Indian Institute of Technology (IIT) Delhi, Hauz Khas,

New Delhi 110016, India. Email: [email protected]

Arvind K. Nema

Professor, Department of Civil Engineering, Indian Institute of Technology (IIT) Delhi, Hauz Khas, New Delhi

110016, India. Email: [email protected]

R. K. Singh

Joint General Manager (Projects), Housing and Urban Development Corporation Ltd., India Habitat Center,

Lodhi Rd., New Delhi 110003, India. Email: [email protected]

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Abstract:

Close vicinity of uncontrolled municipal waste sites (or ‘waste dumps’) to well-populated communities makes

the air contamination a prominent hazard from the waste dumps. The hazard rating systems, considered useful in

prioritizing these sites for remediation, are investigated for their suitability to assess air contamination of

municipal waste dumps. Out of the eight systems employed in the study, six rating systems respond well to

changes in site conditions when applied to hazardous waste sites. However for MSW sites, all eight rating

systems give scores in a narrow range and do not perform well. One system is selected for improvement by

modifying the indicators for waste quantity and rainfall and, introducing the indicators for waste composition

and fresh waste quantity using expert judgment. The modified system performs well for MSW dumps, produces

air contamination hazard ratings in a wider range and responds to higher number of scenarios in sensitivity

analysis, thus making it an appropriate tool for site prioritization for remediation.

Keywords: Municipal solid waste, Waste Dumps, Hazard Rating System, Prioritization, Air Contamination

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1 Introduction

In developing countries, municipal solid waste (MSW) is often disposed off in open (un-lined) dumps. Such a

scenario also exists in India. From amongst 7000 cities and towns having population in excess of 5000, well

designed engineered landfills have been started only in a dozen large metropolitan cities and disposal in open

dumps continues unabated due to financial constraints.

Though open dumps seriously impact public health through several pathways including groundwater

contamination and surface water contamination, the most often heard complaints from residents of local

communities are related to bad odour and air contamination due to harmful emissions such as toxic fumes,

smoke, dust etc.(Henshaw et al. 2006). Poor odour causes emotional stresses such as discomfort and depression

as well as physical symptoms such as vomiting, headaches and respiratory problems (Shusterman 1992).

Depression of real estate prices in nearby areas (Farber 1998) is also a significant fallout of the air

contamination.

To begin with, waste dumps are located well away from community boundaries. However, as cities and towns

grow, these dumps come close to or become engulfed by local communities. A recent study of waste dumps

(Datta and Kumar 2016) in 53 cities of India having population above 1 million reveals that over 60% dumps lie

within 0-500m distance from the community causing severe public outcry, time and again, due to bad odour and

other environmental issues.

Government bodies in India at the national level, state level and city level are according high priority for

remedial measures of MSW dumps. Control of odour and provision of aesthetic covers over these dumps receive

higher priority amongst residents in comparison to control of groundwater and surface water pollution. Remedial

measures are envisaged to be carried out in a phased manner with dumps causing larger impact receiving higher

priority. A system for prioritization of sites for remedial action for air contamination (amongst other measures) is

an important requirement for decision-makers. For site prioritization, hazard rating systems are useful tools.

Although these systems suffer from subjectivity, they are simple and quick to use in comparison to deterministic

approaches. This study attempts to evaluate suitability of existing hazard rating systems for determining air

contamination of municipal waste dumps in Indian cities having population more than one million.

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2 Objective and scope

The objectives of the present study are: (a) to review the existing hazard rating systems for air contamination, (b)

to assess their suitability for rating of MSW sites, (c) to make improvements for enhancing performance of a

suitable existing rating system and, (c) to apply the improved system to waste dumps in a few cities and assess

its performance.

The study has been carried out on the basis of data collected from Indian cities having population between 1

million and 18 million but its scope can be extended to smaller cities as well.

3 Assessment of Air Contamination Hazard Rating from Hazardous and Municipal Waste Sites

Air contamination from hazardous waste site is different from that of a MSW site (Young and Parker 1983).

While toxicity is the main concern for the emissions from a hazardous waste site, the emissions from a MSW site

which are a cause of concern are greenhouse gases (i.e. CH4 and CO2) (Amini and Reinhart 2011) and Odorous

emissions (i.e. trace amounts of NH3, H2S and volatile organic compounds) (ATSDR 2001). So for MSW sites,

main concerns are in terms of greenhouse effect and odor nuisance. Consequently, the important parameters for

hazardous waste sites are size and toxicity of the contaminant of concern. On the other hand, parameters of

significant importance for the MSW sites are landfill area, biodegradable fraction, annual rainfall and quantity of

fresh waste disposed per day While landfill area, annual rainfall and biodegradable fraction greatly influence the

quantity and quality of the gas generation at a landfill (Cooper et al. 1992), the emissions from fresh waste are a

number of times higher than the old waste (Sironi et al. 2005).

For the assessment of hazard/impact from waste disposal sites, a number of models and methodologies are

available based on a number of factors that affect release of contaminants from waste sites into the environment.

The approaches for hazard/impact assessment fall generally into one of three categories: deterministic water

balance analyses, stochastic simulation models, or relative hazard methodologies/ ranking systems. As

deterministic analyses and stochastic models need large amounts of data and are time-consuming, relative hazard

methodologies are generally preferred for hazard assessment leading to site prioritization. Ranking/rating

systems have application in a number of domains such as sustainability (Gupta et al. 2016).

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Hazard rating systems are often based on structured-value approach (National Research Council 1994). A

structured-value approach incorporates in a mathematical framework the major input factors that determine

impacts and risk, but it does so in a heuristic manner. Field data and qualitative judgment are used to assign

scores for different levels of the input factors, and these scores are combined mathematically to obtain an overall

score for a particular potential impact. In the situations when priority setting is lone objective, and the formal

risk analysis may prove time consuming and cost-intensive, the structured-value scoring methods are more

suitable.

The ranking systems follow two approaches to rank the waste disposal sites: index function or vectorial

approach. The index function approach yields a single number by aggregating the relevant parameters using a

suitable algorithm indicating the hazard of a site. For the vectorial approach, the parameters are not combined

into a single index and since all sites are not necessarily comparable by this approach, the ranking becomes

partial. As the vectorial approach is not very useful in site prioritization, only the site hazard rating systems that

are based on the index function approach are reviewed here.

Singh et al. (2010a) have reviewed eighteen existing hazard rating systems for ranking of hazardous and/or

municipal waste sites from literature. The existing systems evaluate a hazard score for one or more hazard

migration route(s), namely groundwater, surface water, air or soil. A number of these rating systems have been

assessed in peer-reviewed literature (Nixon and Murphy 1998; Thiessen and Achari 2011; Thiessen and Achari

2012). Nixon and Murphy 1998 summarized the methods of hazard assessment from waste disposal sites. While

Thiessen and Achari 2011 investigated the correlaton between NCS-2008 systems scores and preliminary

quantitative risk assessment, Thiessen and Achari 2012 evaluates the ability of the NCS-2008 systems to rank

the waste sites by comparing NCSCS score ranks to preliminary quantitative risk assessment (PQRA) result

ranks.

Out of these eighteen rating systems, nine systems i.e. HRS-1982 (USEPA 1982), HRS-1990 (USEPA 1990),

ERPHRS (Department of Natural Resources 2001), ISM (Solid Waste Management Board 2001), JENV (Joseph

et al. 2005), NPC (National Productivity Council 2003), WARM (Science Applications International

Corporation 1990), HR-FCP (Hagemeister et al. 1996) and RASCL (Ministry for the Environment (NZ) 2002)

have the mechanism to assess air contamination hazard from waste sites. NCS-2008, a rating system developed

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in Canada, has a component for assessment of hazard resulting from hazardous vapours migrated to adjacent soil

and groundwater, but not from air contamination. Hence, NCS-2008 will not be further discussed in the study.

4 Review of Existing Rating Systems

From amongst the nine rating systems that can assess air contamination hazard, six systems i.e. HRS-1982,

HRS-1990, ERPHRS, ISM, HR-FCP and WARM have been developed in USA. While RASCL was formulated

in New Zealand, two systems i.e. NPC and JENV have their origins in Asia. Three systems i.e. HRS-1982, HRS-

1990 and RASCL are applicable to hazardous as well as municipal waste sites. RASCL has been developed for

smaller landfills (less than 15000m3 in size). The applicability of three rating systems i.e. ERPHRS, ISM and

WARM is limited to hazardous waste sites. NPC and JENV systems are primarily intended to serve municipal

waste sites,

Out of the nine hazards rating systems, six rating systems i.e. HRS-1982, HRS-1990, ERPHRS, ISM, WARM

and RASCL evaluate three to four migration routes, and produce separate scores for all the routes. Other

systems i.e. NPC and JENV produce a combined score for all routes by assessing various routes of hazard

simultaneously. For such rating systems, the air contamination rating scores are derived using only the air route

parameters. Both the systems i.e. NPC and JENV employ an additive algorithm to aggregate their parameters

and hence it is easy to segregate parameters relevant for air contamination. However, for the remaining one

system, HR-FCP, it is not possible to separately calculate the air contamination hazard only, as it employs a

complex algorithm and hence this system will not be dealt with, further in the study

The existing hazard rating systems discussed here have been compared with respect to (a) parameters required

by the system for air route, (b) ease of availability of data, and (c) scoring algorithm.

4.1 System Parameters for Air Route

To compare the existing rating systems in terms of system parameters, the parameters considered relevant for

assessing the air contamination potential of waste disposal sites are identified from various systems. These

parameters have been grouped into three categories: source, pathway, and receptors (Table 1). The source

parameters describe magnitude and characteristics of the source, landfill geometry and climatic conditions

present in the region. The pathway parameters specify the presence and absence of the cover system and its

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effectiveness. Receptors mainly define the presence of human beings and sensitive environment in the vicinity of

the site.

Table 1: Air route parameters and scoring algorithms used by different existing systems

A total of 26 parameters are identified from various existing rating systems. Most of the parameters are used by

more than one rating system (Table 1); however, the number of parameters considered individually by various

systems lie in a wide range. The number of parameters used for air contamination hazard rating employed by

individual systems ranges from 6 to 12. RASCL uses the minimum number of parameters whereas JENV and

HRS-1990 use the maximum number of parameters of 12. Three systems i.e. HRS-1982, ERPHRS and ISM,

each use seven parameters whereas WARM and NPC use ten and eleven parameters respectively.

For the source category, most of the systems consider the following parameters: waste quantity, toxicity of the

contaminant of concern, reactivity and incompatibility of the compounds disposed. Under the pathway category,

only one parameter i.e. containment is taken into consideration. For the receptor category, the parameters

employed by most systems are land use/ population within 4−mile radius and distance to a sensitive

environment.

4.2 Ease of Availability of Data

The existing rating systems differ in the type and number of parameters being employed. Some of these

parameters are easy to obtain, whereas others may be cumbersome and time-consuming to acquire. A system

can estimate the hazard score of a site more precisely if it takes into account more information. While it is of

high significance to obtain the correct hazard score for a site, it is also essential that the data needs of a rating

system are not so extraordinary that an end user is distracted from it. Generally the acceptability of a system is

greatly reduced if it requires large data involving expensive and time-consuming investigations.

The ease of availability of data being employed by various rating systems for air route can be assessed in terms

of simple and complex parameters. Simple parameters are the ones which are easy to obtain (these include waste

quantity, depth of filling of waste, area of the dumpsite, type of waste (MSW/HW), quantity of fresh wastes

disposed, rainfall/annum, active period, design aspects (ranging from ‘no proper design’ to ‘Scientifically

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designed’), site operation and management (‘poorly operated’ to ‘scientifically operated’), containment, land

use/ population within ½ mile or 4−mile radius, distance to a sensitive environment, nearest habitation, distance

to populated area) whereas the complex parameters are much more time-consuming to obtain (Singh et al.

2010b). HRS-1990 uses the highest number of complex parameters (five) closely followed by JENV, which uses

four complex parameters (Table 1). Three systems i.e. NPC, WARM and RASCL each use one complex

parameter. Two complex parameters are employed by HRS-1982, ERPHRS and ISM each.

4.3 Scoring Algorithm

The sensitivity of a rating system to change in site conditions depends also on the type of algorithm employed to

aggregate the system parameters (Singh et al. 2009). Three types of scoring algorithms are in use by existing

systems: additive, multiplicative and additive-multiplicative. In general, additive algorithms exhibit least

sensitivity and multiplicative algorithms show the highest sensitivity. The sensitivity of Additive-multiplicative

stands in between the two. In a multiplicative system, significant change in even a single parameter can alter the

site ranking significantly. In an additive–multiplicative ranking system, the impact of a parameter on site hazard

depends on the type of algorithm used to integrate the parameter with the aggregate score.

For the eight rating systems under consideration, two systems i.e. JENV and NPC employ the additive algorithm,

five systems employ the additive–multiplicative algorithm and one system employs the multiplicative algorithm

(Table 1).

4.4 Overall Observation

HRS-1990 is the most detailed system, using twelve parameters (including seven complex ones) and uses an

additive-multiplicative algorithm. RASCL is the simplest system using six parameters (only one complex) and

employs a multiplicative algorithm.

5 Assessment of the Existing Systems

The performance of the rating systems can be measured in terms of spread in the rating scores obtained for the

waste sites with continuously varying characteristics (i.e. varying from best to worst scenario for the

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contamination hazard) (Singh et al. 2009). Another indicators for performance measurement are the clustering

index, a parameter for measuring uniformity of spread of scores across the range between the minimum and

maximum possible value of the scores (Kumar et al. 2016) and sensitivity analysis.

Clustering index, measures the clustering of scores for a given set of waste sites. it performs better than the

spread of scores because it takes into account the difference in the scores of the successive sites as well as the

range of scores (Kumar et al. 2016). While a clustering index of 0 shows no clustering among scores, the

clustering index of 1 indicates the maximum clustering. In addition, the sensitivity of rating systems to various

parameters is evaluated by performing sensitivity analysis.

5.1 Application to Hazardous Waste Sites

The existing rating systems have been applied to six conceptual hazardous waste (HW) sites referred to as HAC-

1 to HAC-6, all having no covers and liners (Table 2). Site HAC-1 is the least hazardous or best site whereas site

HAC-6 is most risky or the worst site. Others lie in between HAC-1 and HAC-6 in terms of air contamination

hazard. For the characteristics of these sites, values for area, waste height, annual rainfall and evapotranspiration

are based on Singh et al. (2010). The values for Fresh waste quantity are based on Datta and Kumar (2016). The

values for remaining parameters are based on existing rating systems. All the eight rating systems have been

applied to these sites and the results are presented in Table 3. Table 3 shows the scores after being normalized to

the scale of 0-1000.

Table2: Site characteristics of six conceptual hazardous waste sites with continuously increasing hazard

As indicated from site characteristics, the air contamination ratings increased consistently in all the systems as

one moved from site HAC-1 to HAC-6 (Table 3). However, for a particular hazardous waste site, the hazard

scores from different systems varied significantly. The scores from these rating systems were examined for the

variation of scores from the least hazardous site to the most hazardous site and, the clustering index.

While applying rating systems to the hazardous waste sites, each system produces six hazard ratings—each for a

site. A set of six sites’ scores evenly distributed on the scale of 0–1,000 (i.e., the lowest score being at zero, each

succeeding score being 200 more than the previous one, and the highest score being at 1,000) will show nil

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clustering, thus producing a score clustering index of zero. Another set of scores in which all the scores are equal

will show maximum clustering (i.e., a score clustering index of 1). Supplementary material exhibits calculations

performed for determining clustering index for HRS-1982 system.

Table 3: Scores for HW Landfills and corresponding clustering indices

While the scores from HRS-1990 and RASCL covered the range of 2-1000 and 95-1000 respectively, the scores

from JENV system were confined to a narrow range of 545-608 (Table 3, Fig. 1). NPC system was another

system having a narrow range of scores i.e. 547-835. Other rating systems e.g. HRS-1982, ERPHRS, ISM and

WARM also showed scores in wider range of 462-1000, 327-1000 and 25-704 respectively.

Figure 1: Ranges of scores for conceptual hazardous waste sites

The ranges of clustering indices for these rating systems vary from 0.37 (i.e. for WARM) to 0.94 (i.e. for JENV).

The clustering indices for HRS-1990, RASCL, HRS-1982, ERPHRS and ISM vary from 0.41 to 0.46 indicating

clustering in the moderate range. For the systems with narrowest ranges i.e. JENV system and NPC system,

clustering indices are very high i.e. 0.71 and 0.94 respectively. Although the range of HRS-1990 is the widest,

still the clustering index is the minimum for WARM. For WARM, five out of six sites are having scores spread

in the range of 25-704 whereas for HRS-1990, four out of six sites are concentrated within 2-169.

The rating systems showing wider range and lower clustering use additive-multiplicative and multiplicative

algorithm. While systems such as HRS-1982, HRS-1990, ERPHRS, ISM and WARM employs additive-

multiplicative algorithm to aggregate various parameters, RASCL uses multiplicative algorithm. On the other

hand, NPC system and JENV system having narrow range of scores and exhibiting clustering of scores make use

of additive scoring algorithm.

5.2 Suitability for MSW sites

MSW dumps are different from hazardous waste sites in terms of size and the contaminants present.

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In general, hazardous waste sites are smaller in size and quantity of waste. In India, the areas of hazardous waste

or contaminated sites vary from 0.1 ha to 6.6 ha in India (Datta and Kumar 2016). On the other hand, MSW

dumps are much larger in size and waste quantities than the hazardous waste sites. A recent study (Datta and

Kumar 2015) in Indian cities having more than a million population reveals that the area of MSW dumps in these

cities vary from 2 ha to 53 ha (interquartile range).

Generally, hazardous waste sites have a variety of industrial waste present on the site. The waste disposed is

generally a concomitant of hazardous substances consisting of heavy metals such as hexavalent chromium and

hydrocarbons. The rating system intended to assess hazardous waste sites such as HRS-1982, HRS-1990,

ERPHRS, ISM and WARM use toxicity of the most hazardous chemical present on-site for the site hazard

rating. On the other hand, the hazards posed by municipal waste dumps are attributed to the biodegradable

content of the municipal waste. The hazardous waste fraction is generally in miniscule quantities in municipal

waste (Sharma and Lewis 1994). Hence the waste characteristic for MSW which is important for contamination

rating is the biodegradable fraction. The rating systems developed for municipal waste dumps e.g. JENV system

and NPC system, specify waste composition in terms of the relative fraction of biodegradable and other

components. RASCL system, being applicable to both of the hazardous waste and municipal waste sites, provide

separate ratings for municipal waste and hazardous waste.

Amongst the existing rating systems, the systems such as JENV system, NPC system and RASCL rate a waste

site depending on the relative fraction present for biodegradable, non-biodegradable and hazardous waste. These

systems can be directly applied to MSW sites. Other systems such as HRS-1982, HRS-1990, ERPHRS, ISM and

WARM use toxicity of the most hazardous chemical present on-site.

The existing systems were applied to six conceptual MSW dump sites having continuously varying site

characteristics for air contamination (Table 4) (varying from best to worst scenario). While M-1 has the

characteristics leading to minimum hazard for air contamination, site M-6 poses the highest air contamination

hazard. For the site characteristics of these sites, the values for area, waste height, waste composition, annual

rainfall and evapotranspiration are based on Kumar et al. (2016). The values for Fresh waste quantities are based

on Datta and Kumar (2016). The values for remaining parameters are based on existing rating systems.

Table4: site characteristics of six conceptual MSW sites with continuously increasing hazard

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The results for the eight rating systems as applied are presented in Table5. For MSW dumps, almost all the

systems exhibit high clustering of scores. The values of clustering indices are in the range of 0.56 to 0.90. As

was the case for hazardous waste sites, hazard rating for a particular site varies considerably among various

rating systems.

Table5: Scores for MSW sites and corresponding clustering indices

HRS-1990 exhibits the widest range of 4-741, while the narrowest range of 17 to 116 is shown by WARM

(Table5, Fig. 2). While the clustering indices of HRS-1990 and RASCL are the lowest (0.56 to 0.58), the

clustering index of WARM is the maximum i.e. 0.90.

Figure 2: Ranges of scores for conceptual MSW sites

For all the other rating systems, clustered scores are obtained. For the systems with additive-multiplicative

algorithm i.e. HRS-1982, ERPHRS and ISM, the ranges of scores vary from 231 to 369 with clustering indices

in the range of 0.66 to 0.76. For the two additive systems i.e. JENV system and NPC system, the ranges of scores

were 215 and 285 respectively with corresponding clustering indices to be 0.78 and 0.71 respectively.

When the results for MSW dumps (Table5) are compared with that of hazardous waste sites (Table3), it is

observed that the scores for MSW dumps are on the lower side of 0-1000 scale and show higher clustering.

Hence improvements to an existing system are needed.

5.3 Sensitivity Analysis

To investigate the sensitivity of these rating systems to various parameters, important for air contamination,

sensitivity analysis of these systems is performed. The process of sensitivity analysis involves assuming a base

case of a waste site and, then recording the changes in hazard ratings of the base case in response to a specified

change in one parameter. The observed change in hazard rating of base case is evaluated in percent and forms

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the sensitivity of a rating system to a particular parameter. Sensitivity analysis has been carried out for the

parameters important for MSW dumps. The six parameters which are considered include area, waste height,

biodegradable fraction, annual rainfall, fresh waste disposed and land use/population within 4-mile radius. For

the base case (Table6), the values of all the parameters, i.e. site area, waste height, annual rainfall, biodegradable

fraction and population within 4-mile radius have been derived from (Singh et al. 2010a) and (Datta and Kumar

2016). The parameters in the source component including area, waste height, biodegradable fraction, annual

rainfall and fresh waste disposed are varied from -50% to +50%. The land use/ population with4-mile radius was

varied between its best and worst values i.e. remote (sparsely populated) and residential (densely populated).

Table6: Base case parameters for sensitivity analysis

The results of sensitivity are analyzed in terms of degree of sensitivity (Table 8). A change in hazard rating of

more than 20% constitutes a case of ‘significant’ degree of sensitivity (indicated by ‘S’); a change in hazard

rating between 10 and 20% indicates medium degree of sensitivity (indicated by ‘M’); a change of less than 10%

specifies low degree of sensitivity (indicated by ‘L’) and no change means no sensitivity (indicated by ‘N’).

Table 8 shows that the sensitivity of all the systems to majority of the parameters lie in the range of low to nil.

This highlights the need for an improved system for MSW dumps.

Table7: Summary of sensitivity analysis

6 Improvements to an Existing System to enhance performance for MSW Dumps

To improve an existing system so as to make it applicable to municipal waste sites, the first step was to select a

system suitable for the purpose.

The rating systems were considered for improvement based on their performance when applied to municipal

waste dumps and sensitivity analysis. For MSW sites, HRS-1990 and RASCL gave the least clustering index

being followed by HRS-1982. In sensitivity analysis also, RASCL performed marginally better than other

systems under comparison. Another advantage with RASCL is its ease of applicability to the MSW sites (M-1 to

M-6). So it was decided to make modifications to improve RASCL system.

According to RASCL, the hazard rating of a site is given by:

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HRi = Wqi × Mi×Wci × Ri × LUi ……(1)

Where, Wqi – rating for waste quantity; Mi – Mobility of the emission; Wci - Rating for waste composition;

Ri - Rating for annual rainfall; LUi – Rating for land use.

The system also takes into account the containment of the waste in terms of lining and cover system installed. As

the purpose of the study is to develop a rating system for waste dumps, the containment is not considered.

mRASCL, the modified RASCL gives the hazard rating of a site as:

HRi = Wqi,m × Wci,m × FWqi × Ri,m × LUi ……(2)

Where, Wqi,m – Modified rating for waste quantity; Wci,m - Modified rating for waste composition; FWqi,m –

Rating for fresh waste quantity; Ri,m - Modified rating for annual rainfall.

The modified and newly introduced ratings were based on judgment of twenty two experts including experts

from academic, research and regulatory institutions and, engineering firms. The experts were provided with best

(corresponding to minimum air contamination hazard) and worst values (corresponding to maximum air

contamination hazard) of the parameters for which ratings were to be decided. As the maximum rating for the

worst values was always kept as 1, experts were asked to provide the minimum rating corresponding to the best

value on a scale of 0-1. The mean of the inputs from the expert was then used to decide the range of ratings for a

particular parameter. For the values of a parameter between best and worst values, linear variation of the rating

was assumed.

There were four modifications made to the RASCL system (designated as mRASCL after modification). One

was the modified waste quantity indicator (Table8) to replace the existing quantity/size indicator. Other was the

introduction of waste composition rating based on biodegradable fraction (Tables9) to substitute the toxicity

indicator. The waste composition indicator gives ratings based on biodegradable fraction whereas the waste

quantity indicator was based on the waste quantities found in the MSW dumps in India. Yet another change was

the introduction of indicator for fresh waste quantity being disposed on-site (Table10). Also the existing rainfall

rating was modified to make it more sensitive to the changes in rainfall (Table11).

Table8: Rating for waste quantity in mRASCL

Table9: Rating for waste composition (biodegradable fraction) in mRASCL

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Table10: Rating for fresh waste disposed in mRASCL

Table11: Rating for rainfall in mRASCL

For the waste quantity ratings, the original system gives a single rating of 0.4 for all cases of waste quantities.

The modified system gives ratings in the range of 0. 4 – 1.0 for the waste quantities ranging between 0 and more

than 5 million tons. The ratings for waste composition lie in the range of 0.6 to 1.0 for both the RASCL and

mRASCL. However, RASCL considers three types of waste i.e. municipal waste only, municipal with 15%

industrial waste and industrial waste only. Generally municipal waste would have only about 1% of hazardous

waste. In comparison, the modified system considers different percentages of biodegradable fraction in the

municipal waste. The ratings of fresh waste quantity have been introduced in mRASCL only and RASCL did not

use this parameter. The ratings for annual rainfall vary between 0.8 – 1.0 for RASCL, whereas for RASCL, it

varies from 0.7 to 1.0. Furthermore, the range of rainfall being considered in RASCL is from less than 700 to

more than 2000 in three segments. The range of 700-2000 has a constant rating of 0.8; which is too broad a range

for the parameter and hence no variations are observed with change in annual rainfall in the range of interest for

the present study. On the other hand, mRASCL considers the range of less than 400 to more than 1000 in four

segments and is able to respond to change in values.

When the modified RASCL is applied to the MSW sites, an improved set of hazard rating scores is obtained

(Table 12). The scores from the modified system are in a wider range as compared to RASCL. The clustering

index also improves significantly from 0.58 to 0.21 (Table 12). The modified system is sensitive to all the six

scenarios as compared to two scenarios in case of original system (Table 13). For RASCL, total number of

‘significant’ and ‘medium’ scenarios was two whereas in case of the mRASCL, system has ‘significant’

sensitivity to all the six parameters (Table 13).

Table 12: Comparison of scores and clustering indices of MSW sites from RASCL and mRASCL

Table13: Comparison of sensitivity to various parameters for RASCL and mRASCL

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7 Application to MSW Dumps in Indian Cities

Six MSW dumps from Indian cities (Table 14) have been selected as case studies. The areas of these waste

dumps vary from 8 ha to 81 ha whereas average waste heights in these waste dumps vary from 6 m to 48 m. The

waste disposed per day varies from zero to 3500 tons/day. The annual rainfall in the regions of these waste

dumps varies from 700 mm to more than 1600 mm. The surrounding population density for these waste dumps

varies from insignificant to high.

Table 14: Site characteristic parameters for MSW dumps from Indian cities

All the nine (eight existing and one modified) rating systems are applied to these six waste dumps (Table 15).

For these MSW dumps, all the systems exhibit narrow range of scores (high clustering) except mRASCL. The

values of clustering indices are in the range of 0.64 to 0.92 for the existing systems whereas mRASCL, which

displays a much wider range of scores (Fig. 3), exhibits minimum clustering with clustering index of 0.35.

Table15: Scores for MSW dumps from Indian cities and corresponding clustering indices

Figure 3: Range of Scores for MSW dumps from Indian cities

Among the existing systems, the widest range of 108-540 is shown by RASCL, while improved mRASCL gives

much better range of 47 to 810.

Fig. 4 shows the response of 6 systems in terms of scores for the six Indian cities. The trends are by and large

similar but mRASCL gives more spread in scores. All systems indicate that D-3 and D-6 have high scores

whereas D-1 and D-4 have low scores. This is magnified by mRASCL.

Figure 4: Scores from existing and improved rating systems for waste dumps in Indian cities

For the results of mRASCL, the sites can be easily categorized into three different categories: low hazard sites

(hazard rating between 0-250), medium hazard sites (hazard rating between 250-500); high hazard sites (hazard

rating between 500-1000). Two waste dumps, D-1 and D-4 come in low hazard category. While D-1 is an old

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closed dump situated in a sparsely populated area, D-4 is large dump located in a remote area with insignificant

human population in vicinity.

The waste dumps with high hazard potential are D-3 and D-6. Both of these dumps are large dumps, located

within city limits, surrounded by very dense population with significant fresh waste disposed every day. Two

waste dumps, D-2 and D-5 are in medium hazard category. Both of the dumps are large size dumps situated in

areas with medium population density.

While D-3 and D-6 need immediate attention, dumps D-2 and D-5 would have to be remediated within short

time period and dumps D-1 and D-4 can be taken up for capping subsequently.

8 Conclusions

The study deliberates on the usefulness of existing hazard rating systems to assess the air contamination potential

of municipal waste dumps. The following can be summarized from the study:

a. Out of the existing eighteen hazard rating systems, only eight systems have the capability to assess the

hazard rating for air contamination from waste sites (hazardous and/or municipal).

b. These eight systems show good response when applied to hazardous waste sites as most have been

developed for such sites. Their performance for assessment of rating of air contamination from MSW

dumps is found to be inadequate.

c. From amongst the eight systems, one system (RASCL) is found to be more responsive to the conditions

of MSW dumps. Subsequently on the basis of the deficiencies identified during the assessment,

modifications have been suggested to make the system more responsive to changes in the site

conditions for MSW dumps.

d. The modifications in RASCL consist of modifying the waste quantity indicator and rainfall indicator,

substituting the toxicity indicator by introducing the indicator for waste composition and introducing

the indicator for fresh waste quantity.

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e. The improved system exhibits wider range of scores, lower clustering of scores and higher sensitivity in

comparison to existing systems and yield satisfactory results when applied to six MSW sites of Indian

cities.

9 Acknowledgements

The authors wish to thank the Science and Engineering Research Board, Department of Science and Technology,

Government of India for extending the financial support (#PDF/2016/000716) to this research, and the

anonymous reviewers for their valuable suggestions.

10 References

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LIST OF FIGURES CAPTIONS

Figure 1: Ranges of scores for conceptual hazardous waste sites

Figure 2: Ranges of scores for conceptual MSW sites

Figure 3: Range of Scores for MSW dumps from Indian cities

Figure 4: Scores from existing and improved rating systems for waste dumps in Indian cities

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Table 1: Air route parameters and scoring algorithms used by different existing systems

Parameters HRS-

1982

HRS-

1990

ERPHRS ISM JENV NPC WARM RASCL

SOURCE:

Waste Quantity � � � � � � �

Depth of filling of waste �

Area of the dumpsite � �

Type of waste (MSW/HW) � �

Quantity of fresh wastes disposed �

Hazardous contents in waste (%)* �

Biodegradable fraction of waste at site (%) *

�

Moisture of waste at site (%) * �

Toxicity$,* � � � � � �

Eco-Toxicity$,* �

Reactivity and Incompatibility* � � �

Mobility � �

Vapor Pressure$,* � �

Henry's constant$,* � �

Rainfall/annum � � �

Active Period �

Design Aspects �

Site operation and Mgmt. �

PATHWAY:

Containment / Effectiveness of Capping

� � � � � �

RECEPTOR: �

Land use/ Population Within ½ mile or 4−Mile Radius

� � � � � �

Distance to a Sensitive Environment

� � � � � �

Ambient air quality - CH4 (%)/* � �

Nearest habitation � �

Distance to populated area �

Other parameters 1 5 1 1 2

Total parameters 7 12 7 7 12 11 10 6

Simple Parameters 5 7 5 5 8 10 9 5

Complex parameters 2 5 2 2 4 1 1 1

Scoring Algorithm A-M A-M A-M A-M

ADD ADD A-M MUL

$ - easy to determine or NOT required for MSW site in case of WARM; A-M: Additive multiplicative; ADD: Additive; MUL: Multiplicative

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Table 2: Site characteristics of six conceptual hazardous waste sites with continuously increasing hazard

Site Parameter

HAC-1 HAC-2 HAC-3 HAC-4 HAC-5 HAC-6

Site area (ha) 1 2 5 5 10 10

Waste height (m) 2 4 4 6 8 10 Fresh waste disposed (ton/day)

5 10 20 40 80 100

Contaminant of concern

Xylene Xylene Benzene Benzene Vinyl Chloride

Vinyl Chloride

Reactivity /Incompatibility

Do not pose a hazard

Do not pose a hazard

may pose a future hazard

may pose a future hazard

posing an immediate hazard

posing an immediate hazard

Annual Precipitation (mm)

750 750 1200 1200 2000 2500

Containment None None None None None None

Land Use/ Surrounding population density

Remote/

Insignifi

cant

Agricultu

ral/ Low

Commerci

al/

medium

Commerci

al/

medium

Residentia

l/ high

Residentia

l/ very

high

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Table 3: Scores for HW Landfills and corresponding clustering indices

Site

System

HAC-1 HAC-2 HAC-3 HAC-4 HAC-5 HAC-6 C.I.

HRS-1982 208 600 706 780 1000 1000 0.421

HRS-1990 2 5 77 169 793 1000 0.433

ERPHRS 462 646 828 828 1000 1000 0.462

ISM 327 539 755 828 949 949 0.406

JENV 545 573 555 566 593 608 0.937

NPC 547 745 752 752 822 835 0.712

WARM 25 100 343 500 704 704 0.369

RASCL 95 180 630 630 900 1000 0.420

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Table 4: site characteristics of six conceptual MSW sites with continuously increasing hazard based on waste dumps in

Indian cities having population more than a million

Site Name

Parameter

M-1 M-2 M-3 M-4 M-5 M-6

Landfill area (ha) 5 10 15 20 25 30

Waste height (m) 5 10 15 15 20 20

Fresh waste

disposed (ton/day)

0 300 600 1200 1500 2000

Biodegradable

Fraction (%)

40 50 60 65 70 75

Annual

Precipitation (mm)

750 750 1200 1200 2000 2500


Land Use/

Surrounding

population density

Remote/

Insignifi

cant

Agricultura

l/ Low

Commer

cial/

medium

Commerci

al/

medium

Residen

tial/

high

Residenti

al/ very

high

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Table 5: Scores for MSW sites and corresponding clustering indices

Site

System M-1 M-2 M-3 M-4 M-5 M-6 C.I.

HRS-1982 139 369 415 462 508 508 0.662

HRS-1990 4 12 39 91 242 741 0.562 ERPHRS 277 369 462 462 508 508 0.769

ISM 231 369 415 462 508 508 0.723

JENV 456 553 594 627 664 670 0.785 NPC 492 664 682 687 753 777 0.715

WARM 17 50 97 116 116 116 0.901

RASCL 108 108 378 378 540 600 0.580

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Table 6: Base case parameters for sensitivity analysis

S. No. Waste site parameter Base case value

1 Waste fill area (ha) 15

2 Waste fill height / depth (m) 10

3 Annual precipitation at site (mm) 1000

4 Biodegradable waste fraction (%) 50

5 Fresh waste Disposed (tons/day) 500

6 Land Use/ Population density Commercial/ Medium

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Table 7: Summary of sensitivity analysis

System

Sensitivity

HRS-

1982

HRS-

1990

ERPHRS ISM JENV NPC WARM RASCL

Significant 1 1 1 1 0 0 1 1 Medium 0 0 0 0 0 1 0 1

Low 0 0 0 0 6 2 0 0 Nil 5 5 5 5 0 3 5 4

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Table 8: Rating for waste quantity in mRASCL

RASCL System mRASCL System

Waste quantity

(m3)

Rating Waste quantity (million

tons)

Rating

0 – 15000 0.4 0-1 0.4

1-3 0.6

3-5 0.8

>5 1.0

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Table 9: Rating for waste composition (biodegradable fraction) in mRASCL


Waste

Composition

Rating Biodeg. Fraction

(%)

Rating

Municipal 0.6 <=40 0.6

Municipal + 15% Industrial

0.8 40-60 0.8

Industrial 1.0 >60 1.0

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Table 10: Rating for fresh waste disposed in mRASCL

Fresh waste disposed (tons/day) Rating*

0-500 0.6

500-1500 0.8

1500-2500 0.9

>2500 1.0

*RASCL does not use the parameter “fresh waste disposed”

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Table 11: Rating for rainfall in mRASCL


Rainfall (mm) Rating Rainfall (mm) Rating

<700 0.8 <=400 0.7

700-2000 0.9 400 to 700 0.8

>2000 1 700 to 1000 0.9

>= 1000 1

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Table 12: Comparison of scores and clustering indices of MSW sites from RASCL and mRASCL

Site

System M-1 M-2 M-3 M-4 M-5 M-6 C.I.

RASCL 108 108 378 378 540 600 0.58

mRASCL 26 52 269 448 720 900 0.21

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Table 13: Comparison of sensitivity to various parameters for RASCL and mRASCL

Parameters Area Height Biodeg.

Fraction

Annual

Rain

Fresh waste

disposed

Land use/ Population

density

RASCL N N N M N S

mRASCL S S S S S S

N-Nil; L-Low; M-Medium; S-Significant

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Table 14: Site characteristic parameters for MSW dumps from Indian cities

Site Name

Parameter

D-1 D-2 D-3 D-4 D-5 D-6

State Madhya

Pradesh

Gujarat Tamilnadu West

Bengal

NCR NCR

Landfill area (ha) 8 28 81 21.4 13 29.8

Waste height (m) 16 24 6.4 24 48 32

Fresh waste

disposed (ton/day)

0 2000 2300 3500 600 3000

Biodegradable

Fraction (%)

43 43 41 51 45 61

Annual

Precipitation (mm)

950 803 1200 1650 721 721


Surrounding

population density

Low Medium High Nil Medium High

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Table 15: Scores for MSW dumps from Indian cities and corresponding clustering indices

Site

System D-1 D-2 D-3 D-4 D-5 D-6 C.I.

HRS-1982 292 369 369 185 369 508 0.677

HRS-1990 41 41 310 12 310 310 0.771

ERPHRS 415 462 508 231 508 508 0.723

ISM 415 415 508 231 508 508 0.723

JENV 416 564 581 529 535 602 0.814

NPC 596 719 819 561 611 760 0.804

WARM 82 110 116 33 116 116 0.917

RASCL 108 378 540 108 378 540 0.640

mRASCL 47 408 720 160 363 810 0.35

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HRS-1982

HRS-1990

ERPHRS

ISM

JENV

NPC

WARM

RASCL

0 100 200 300 400 500 600 700 800 900 1000

Rating Systems

Scores

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HRS-1982

HRS-1990

ERPHRS

ISM

JENV

NPC

WARM

RASCL

0 100 200 300 400 500 600 700 800 900 1000

Rating Systems

Scores

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HRS-1982

HRS-1990

ERPHRS

ISM

JENV

NPC

WARM

RASCL

mRASCL

0 100 200 300 400 500 600 700 800 900 1000

Rating Systems

Scores

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1

0

200

400

600

800

1000

D-1 D-2 D-3 D-4 D-5 D-6

Sco

res

Waste Dumps

HRS-1990

0

200

400

600

800

1000

D-1 D-2 D-3 D-4 D-5 D-6

Sco

res

Waste Dumps

ISM

0

200

400

600

800

1000

D-1 D-2 D-3 D-4 D-5 D-6

Sco

res

Waste Dumps

JENV

0

200

400

600

800

1000

D-1 D-2 D-3 D-4 D-5 D-6

Sco

res

Waste Dumps

NPC

0

200

400

600

800

1000

D-1 D-2 D-3 D-4 D-5 D-6

Sco

res

Waste Dumps

RASCL

0

200

400

600

800

1000

D-1 D-2 D-3 D-4 D-5 D-6

Sco

res

Waste Dumps

mRASCL

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Supplementary Material

Table S.1: Calculations for Clustering Analysis

Proposed

System

Increase in

Score

Z*-Increase

in Scores

139 - -

369 231 0.0

415 46 153.8

462 46 153.9

508 46 153.8

508 0 200.0

Average 132

Clustering Index

(=Average/Z*)

0.66

*Z = Difference between the scores when scores are evenly spread on the scale of 0-1000. In the present

study Z = 200;

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