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
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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
Containment None None None None None None
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
RASCL System mRASCL System
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
RASCL System mRASCL System
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
Containment None None None None None None
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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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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