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International Journal of Civil Engineering and Technology (IJCIET) ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 1, Number 1, May - June (2010), © IAEME

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GIS BASED SECONDARY STORAGE AND

TRANSPORTATION SYSTEM PLANNING FOR

MUNICIPAL SOLID WASTE

Anurag Ohri

Assistant Professor, Department of Civil Engineering IT-BHU, Varanasi

Email: [email protected]

Dr. P.K.Singh

Associate Professor, Department of Civil Engineering IT-BHU, Varanasi

Email: [email protected]

ABSTRACT

Rapid urban growth has brought with it an inevitable problem of municipal solid

waste management. A major portion of total expenditure on solid waste management

(SWM) is spent on collection and transportation of waste. Optimization of route is always

a challenging task for city planners. Geographical Information System (GIS) is an

important tool to solve the problems of waste transportation from collection to landfill

site based upon least cost. The cost factor is affected by the labor cost, vehicle cost, time,

distance and type of road among others. ArcGIS software may be used for digitization of

spatial data in GIS environment. Network Extension module is used for finding the route

with least cost. The proposed methodology can be used as a tool by urban local bodies to

make work schedule for efficient collection of solid waste. This methodology has been

validated for the city of Varanasi (India). It is observed that the present waste storage and

collection system in Varanasi is inadequate and unplanned. An initial investment of

approx. Rs. 76.45 million is estimated for improvement of waste storage and

transportation system and approximately Rs. 52.40 million is required per annum for

managing these two services efficiently.

Keyword: Municipal solid waste, GIS, waste collection, transportation, route planning,

optimization.

International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print) ISSN 0976 – 6316(Online) Volume 1 Number 1, May - June (2010), pp. 108-130

© IAEME, http://www.iaeme.com/ijciet.html

IJCIET

© IAEME


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1. INTRODUCTION

The management of municipal solid waste (MSW) is a high priority issue for

many communities throughout the world including India. Problems of solid waste

management are growing with rapid urbanization and change in the lifestyle of the

people. The situation is becoming critical in many cities with the passage of time. A

World Bank report estimated that in the year 2000 urban India produced approximately

100,000 metric tons of MSW daily or approximately 35 million metric tons of MSW

annually (Hanrahan et al., 2006). Various studies reveal that about 90% of MSW in India

is at present disposed off unscientifically in open dumps and landfills, creating problems

to public health and the environment (Kansal, 2002; Rathi, 2006; Sharholy et al., 2008;

Ohri and Singh, 2011). Most of the municipalities are currently unable to fulfill their

obligation to ensure environmentally sound and sustainable handling of solid wastes in

terms of collection, storage, transportation, treatment, and disposal. To improve upon the

situation and develop a proper infrastructure for SWM, Government of India sanctioned

2500 Crores (approx. US$500 million) exclusively for solid waste management from the

12th Finance Commission grants. Starting from December 2005, it has also earmarked Rs

100,000 Crores (approximately US$20 billion) over a period of seven years for

development of infrastructure in 63 cities under Jawaharlal Nehru National Urban

Renewal Mission (JNNURM). Thus, at least now finance does not appear to be a

constraint in proper solid waste management. However, the growing complexity of the

issues involved in integrated solid waste management demands advanced knowledge

based tools such as Decision Support System (DSS) and GIS application to improve the

complete solid waste management system.

Among all the component of municipal solid waste management (MSWM),

transportation takes a major portion of budget and unplanned transportation system may

cause huge loss of funds. Transportation of the wastes at regular intervals is essential to

ensure that garbage bins/containers do not overflow and waste is not seen littered on

streets. Hygienic conditions can be maintained in cities/towns only if regular clearance of

waste from temporary waste storage depots (bins) is ensured. Transportation system has

to be so designed that it is efficient, yet cost effective. For efficient planning,

transportation system must be synchronized with primary collection and storage of waste;


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otherwise it is liable to fail. Hence primary collection, storage and transportation must be

considered holistically to develop a waste management plan of a city.

Many works have been done all over the world for optimizing collection and

transportation systems for municipal solid waste. Chang et al. (1997) developed a multi

objective, mixed-integer programming model for collection vehicle routing and

scheduling for solid waste management systems synthesized within a GIS environment.

Tung and Pinnoi (2000) used operations research technique in waste collection activities.

Angelelli and Speranza (2002) presented a model for estimating the operational costs of

waste collection, which are strictly related to the distance traveled to collect the waste

and deliver it to the disposal points. The model was applied to traditional, side-loader,

side-loader with demountable body types of collection systems to compare the

performance. Viotti et al. (2003) used Genetic algorithms as a promising tool for

optimization of the MSW collection routes. The objectives of the study included

reduction in fuel consumption, labor cost, vehicle maintenance expenditures and

improved traffic conditions in urban areas. Ghose et al. (2006) used GIS in planning an

effective solid waste management system and an optimal routing model was proposed to

determine the minimum cost/distance and efficient collection paths for transporting the

solid wastes to the landfill. The model needs information on population density, waste

generation capacity, road network and the types of road, storage bins and collection

vehicles, etc. Lakshumi et al. (2006) has developed a GIS based model to calculate

optimal routes for secondary waste collection vehicles based on distance and time

criteria. A comparison is made between the cost of the fuel for the proposed routes and

the fuel consumed on the existing routes. Karadimas et al. (2007) used the Ant Colony

System (ACS) algorithm in the GIS environment for the identification of optimal routes

for MSW collection. Apaydin and Gonullu (2008) developed shortest path model using

GIS software MapInfo in order to optimize solid waste collection/hauling processes to

minimize emission. Li et al. (2008) considered a truck scheduling problem for solid waste

collection in the City of Porto Alegre, Brazil. They attempted designing “good” daily

truck schedules as compared to a set of previously defined collection trips, on which the

trucks used to collect solid waste in fixed routes and empty loads in one of several

operational recycling facilities. The major objective was to minimize the total operating


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and fixed truck costs. Tavares et al. (2009) proposed the use of GIS 3D route modeling

software which adds one more degree of freedom to the system and allows routes to be

optimized for minimum fuel consumption for waste collection and transportation. The

model takes into account the effects of road inclination and vehicle weight.

Kanchanabhan et al. (2010) have proposed a GIS based collection and transportation

model for MSWM. The parameters considered included population density, waste

generation capacity, road network, storage bins and collection vehicles. Optimization of

the routing system for collection and transport of solid waste is an important component

of an effective solid waste management system.

2. STUDY AREA PROFILE

Varanasi is the fourth largest city of Uttar Pradesh state of India, situated at

25�14' North latitude and 83�03’ East longitude (Fig 1). The city is known for its

mystic Ghats and rich architectural heritage. The city developed in the area falling

between two rivers, "Varuna" and "Assi" and is therefore known as Varanasi. It is

believed to be more than 3000 years old and considered as the oldest living city in the

world. River Ganga passes through the east of the city. As per census 2001, Varanasi city

has a population of 1202443 and an area of 79.79 Sq. Km. It is divided into 90

administrative wards. Average household size of Varanasi is 7.3 which is very high as

compared to the national average of 5 persons per household and state average of 6.3

persons per household. The average household size in slums is of 10 members which is

much higher than National and State averages. Varanasi has some 227 slums either on

government or private lands spread around the city. Total population in slums is

estimated as 453,222 which is about 37.7% of the total population. Majority of slums

have very poor water supply and sanitation facilities; and solid wastes are directly

dumped into nearby open nallas. The average floating population of the city is estimated

to be around 2 lakhs people per day (VNN, 2007).


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Figure 1 Study area on Indian map

3. STATUS OF MUNICIPAL SOLID WASTE MANAGEMENT IN

VARANASI

According to Varanasi Nagar Nigam (VNN) report, for sanitary purpose, Varanasi

is divided into 7 zones and 14 sanitary wards. In 2007, the city generated around 600

metric tons (MT) of municipal solid waste per day at the rate of 0.41 kg /capita/ day. The

corporation has a road length of 1247.00 km for street sweeping. The composition of

MSW in Varanasi is given in Table1.

Table 1 Composition of MSW in Varanasi (Source: VNN, 2007)


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The Municipal Solid (Management and Handling) Rules, 2000 of Government of

India demands proper compliance of all the seven steps for integrated solid waste

management. However, present status of municipal solid waste management in the city is

not very satisfactory (Figure 2).

Figure 2 Status of compliance of Municipal Solids (Management and Handling) Rules,

2000 in Varanasi (VNN, 2007) Segregation of recyclable from wastes is non-existent. Door to door collection

appears just introduced in the city. The situation of street sweeping is reasonably good.

At present Varanasi have around 1650 sanitary workers for road sweeping. Around 70%

of the streets are cleaned every day, around 15% on alternate days and 10% twice a week

.Drain cleaning is done by separate sweepers and around 400 workers are engaged in this

task. The facility for secondary storage in the city is very poor. Table 2 gives the details

of available secondary storage system in Varanasi.

Table 2 Secondary storage facilities in Varanasi (Source: VNN, 2007)


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Clearly there is insufficient number of waste storage depots and majority of them

are open and unhygienic. The metal containers (designed as dumper-placers) are not

emptied regularly. Rag-picking from depots and the secondary storage containers often

result in spreading of waste outside leading to further littering. The depots are not in

synchronization with the primary collection system and multiple handling of waste is

practiced. Transportation of 75% of total waste is done on a day to day basis by mostly

uncovered vehicles. Municipal Corporation does not have any treatment or scientific

disposal facility of wastes at present.

Transportation work is not scientifically designed. No work is done on Sundays

and public holidays, which give rise to accumulation of waste and consequent backlog.

Waste transportation in open trucks results in lot of littering. There is practically no route

planning and waste transportation is managed on adhoc and requirement basis. The

number and types of vehicles available with the municipal corporation is given in Table3.

Table 3 Available solid waste transport vehicles in Varanasi

(Source VNN, 2007)

The corporation has been using mostly open vehicles for transportation of waste.

The transportation efficiency is 85%. The vehicles are used in two shifts and on an

average tractor make two trips, whereas dumpers, hoppers and dumper placers make four

trips in one shift. The corporation has shortage of drivers for MSW transportation works.

A municipal workshop is there in the central district of the city and the repair equipments

include grinder, hand grill, air compressor, etc. Around 63% of tippers and about 88% of

available tractors in the fleet are more than 10 years old and need frequent repairs. The

city corporation does not have any facility for processing of municipal solid waste. The

entire waste of the city is disposed of in low lying area or undesignated dumping grounds.

The total number of open dumps either abandoned or in use in the city is 74. Of these, 35

sites are on private lands and 39 sites are on government land. The corporation is


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planning to close these sites with cover and bunding as per site condition in order to

protect public health, ground water and Ganga river pollution.

This study presents a conceptual model for the waste management practices to be

followed taking case study of this city and define the methodology for an engineered

system of waste collection, transportation and disposal.

4. METHODOLOGY

The framework of the waste management plan proposed in this study primarily

consists of:

4.1 Estimation of amount of waste in the study area

In order to find the requirement of secondary storage in an area, an estimate of

total quantity of waste generated in each ward is essential. For the purpose, ward wise

population data of 2001 was obtained and projected for the year 2011. For planning

purpose, the geometric progression method (Eq.1) has been used to forecast the

population of individual wards within the municipality.

(1)

Here Pt = population at time t; = population at time t + n; n= the number of

time periods; and r = the average percentage change in population over the past time

period. With waste generation rate increasing at a rate of 1.33% per year (Pappu et al.,

2007; Shekdar, 1999; Bhide and Shekdar, 1998; NEERI; Gupta et al. 1998; Sharholy et

al.,2008), the rate of generation in 2011 is likely to be 0.430kg/capita/day with respect to

0.410 kg/capita/day in the year 2007. The projected population in 2011 (P2011) and

waste generation rate are used to find the total quantity of waste on ward wise basis, as

shown in Fig 3. The type and number of storage bins or containers have to be decided

based on this quantity and frequency of waste collection plan.


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Figure 3 Ward map of Varanasi city with amount of waste expected (2011)

4.2 Evaluation of appropriate method for secondary storage of waste:

Door to door collection (DTDC) system has been proposed from year 2010 in

Varanasi. As per the recommendations of different studies (CPHEEO, 2000; Ghosh et al.,


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2006), the volume of container(s) has to be at least one and half to two times the volume

of waste expected daily in order to prevent over flow of bins due to exigencies. As far as

practicable, large sized closed body containers may be placed at the temporary waste

storage depot to minimize the cost of transportation. Considering a waste density of to

400kg/m3 in Varanasi (VNN, 2007), a storage volume of 2.5 m3/ton of waste is required.

Providing 100% extra capacity, the secondary storage will need 5 m3/ton of MSW. It will

be more convenient and practical to put two containers of equal capacity at each storage

site so that one is used for normal inflows of waste and other remains ready for extra load

or occasional exigencies. For instance, if two tons of waste per day is expected at any

location, it will need around 5 m3 of storage volume in normal situations. Proving 100%

extra volume, two containers of 5.0 m3 may be placed at that location so that there will be

no over flow of waste in the form of litters on the street. Each container may be lifted on

alternative days in general when it is fully filled to reduce the transportation cost. A

public awareness program may help to educate people to use full capacity of a working

container before the waste is put in second one. For Varanasi, as the roads and lanes are

narrow, medium sized containers having capacity 4.5m3 appears ideal. For slum areas,

small container may be used and it should be emptied regularly by using handcart to large

container kept outside the slum.

4.3 Secondary Collection and transportation of waste:

As there is at present no processing unit for MSW in Varanasi, the waste is

collected from secondary storage points and directly transported to disposal site. A

sanitary landfill is being planned and developed near village Karsada around 17 km away

from city centre. A 9m3 capacity dumper placer is proposed to be used for transportation

of solid waste. Each vehicle will work in two shifts of 7.5 hrs each excluding the time of

lunch and will carry two containers at a time to maximize utilization of fleet.

Hauled container systems (HCS) are usual choice where large quantities of waste

are generated and medium or large size containers are used. This avoids large scale

human handling of waste and reduces the time of collection and transportation. In

Varanasi, this system is recommended based on the quantity and pattern of solid waste

management activities. In this system, each filled container is picked up from its location

and transported to the treatment facility, transfer station or disposal site where it is


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emptied. The empty container is then returned to its original location. Alternatively swap

container system may be used in which container is deposited at the next pick-up location

on the route in exchange for a full container. Swap container system is normally more

economical (Peavy et al., 1985).

In HCS, the time required per trip, which also corresponds to the time required

per container, is equal to the sum of pick up at site and haul time, and is given by Eq.2

(Peavy et al., 1985):

THCS = PHCS + s + a + bx (2)

Where THCS= time per trip for hauled container system, h/trip

PHCS = pick up time per trip for hauled container system, h/trip

s = at site time per trip, h/trip

(0.127 -0.133 h/trip or 7.6-8.0 minutes/trip for mechanical loading of tilt frame vehicles)

a = an empirical haul constant, h/trip

b = empirical haul constant, h/km

x = round trip haul distance, km/trip.

The pick up time per trip is given by Eq.3.

PHCS = pc + uc + dbc (3)

Where pc = time required to pick up loaded container, h/trip

uc = time required to unload empty container, h/trip

dbc = average time spent driving between container locations, h/trip

On an average (pc + uc) is taken as 0.4h/trip (24 min) and the value of dbc is determined

locally.

Typical values of haul constant coefficients a and b are given in Table 4

Table 4 Haul Constant Coefficients, a and b (Peavy et al., 1985)

*Extrapolated values


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In order to find the values of these constants for lower speeds of vehicles, curves

for their variation with speed were drawn, as shown in Figure 4. The extrapolated values

of a and b at 20 km/h speed has been noted in Table 4.

Figure 4 Speed vs. Time curve for Haul Constant Coefficients, a and b The

number of trips that can be made per vehicle per day with a hauled container system

including a factor to account for off route activities is calculated using Equation 4.

ND = [H (1 - W) - (t1 + t2)] / THCS (4)

where ND is the number of trips made per day, trip/d;

H is the length of the workday, h/d;

t1 is the time in hours required for driving from the dispatch site at the beginning of

day to first container location, h; and

t2 is the time in hours required for driving from last container location to the garage

at the end of the day, h;

W is the off-route factor (normally taken as 0.15) (Peavy et al., 1985).

It is recommended that each vehicle has to fit with GPS navigation device and

connected to central server by GSM modem to transfer the real time location of vehicle to

transport mangers for better management of fleet and stopping malpractices in

transportation.

4.4 Generation of GIS data base for the study:

GIS represents a system of hardware and software used for storage, retrieval,

mapping, and analysis of geographic data. Different thematic layers of road network,

ward maps, waste disposal sites, rivers etc are prepared in ArcGIS software. Roads are


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divided into 3 categories namely Highways, Major District Roads and Streets. Centre

point of each ward is mapped as waste generation node for that ward. This nodal point is

used in calculating the average distance of disposal site from the ward. ArcGIS network

Analyst of ArcGIS 8.1 is used in finding shortest routes and travel directions from

landfill site to different waste generation nodes and is shown in Figure 5.

Figure 5 Route map showing least cost path from landfill site to different wards of

Varanasi

5. RESULTS AND DISCUSSION:

Based on the data of 1991 and 2001, a population growth rate of 23.63% per

decade has been estimated and used for forecasting ward wise population of Varanasi in


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the year 2011 by geometric progression method (Eq.1). With the projected per capita

waste generation rate of 0.430 kg/d, the quantity of MSW in each ward is estimated.

First, second third, fourth and fifth columns of Table 5 shows the ward number (W.N.),

Projected population in year 2011 (P2011) , Area of ward in sq.km (A) , waste generated in

each ward in year 2011 (W) and total volume of containers required to store waste

generated in any particular ward (V) respectively.

5.1 Design of secondary storage system

Closed containers of capacity 4.5m3 and 1m3are proposed to be used for

secondary storage of waste. The number of containers required in each word is calculated

based on the criterion given in section 4.2. Since containers are used in pair, even number

of containers is placed in each word. The detailed calculations of secondary storage

required and transportation vehicles are presented in Table 5. Columns 1, 2, 3, 4 and 5

give ward number (W.N), projected population in 2011 (P2011), area (A), total quantity

(W) and total volume (V) of waste generated per day respectively. The capacity of each

container (CV) to be put in a ward, number of container required (CR) and distance

between the containers (DAVC) are inter- related. If larger capacity containers are used,

depending on the area of ward, the distance between containers will become higher,

which may not be acceptable for practical reasons of keeping them not beyond 250 m of

walk by collecting staff. Giving too small containers will mean a very short distance

separation between them, thus increasing the operational costs. Based on preliminary

calculations, 4.5 m3, 3.0m3 or 1.0 m3 capacity containers are placed in a ward so that the

average distance between the containers does not exceed 500m in general. Column 6, 7

and 8 give optimum container size (CV) for the area, number of container required (CR)

be placed, and distance between the containers (DAVC). In order to meet the mandatory

requirement of keeping 100% extra capacity of secondary storage (CPHEEO, 2000), the

number of containers to be placed (CP) is doubled in each ward, with respect to

containers required (CR). Column 9 gives the number of containers to be place (CP) in

each ward. Based on the amount of solid waste generated (W) and the total number and

capacity of containers placed in the ward, the time of filling (TF) of storage provided has

been tabulated in Column 10. The frequency of collection (F) is accordingly decided as

given in Column11.


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5.2 Design of Transportation system:

The distance of the centroid of each ward from the disposal site (X) has been

given in Column 12. Time to pickup (pc) and unloading the container at any storage site

(uc) is equal to 0.4 h. Based on average distance (dbc) between the containers in a ward,

average speed of the transportation vehicle, pick up time per trip (PHCS) is estimated using

Eq. 3, as given in Column 13.

If the time to fill up the container is one days then the vehicle will lift one

container every day from each location. The total number of 4.5m3, 3m3, 1m3 capacity

containers required for the city is equal to 607, 106, 286 respectively which are placed in

500 different locations. Since area of the city is approx.80 sq. km., average distance

between the containers comes out to be 400 m.

Time spent at disposal site in weighing, waiting and unloading the waste is

approximately equals to 0.133 h/ trip. As Varanasi is very congested city, average speed

of vehicle is taken as 20 km/h. The values of empirical constants a and b of Eq. 2 are

taken from Table 4 accordingly. In order to find the number of trips per day by a vehicle,

as per Eq. 4, time spent driving from the dispatch site at the beginning of day (t1) and

driving back to dispatch site in the end of the day (t2) is approximately taken as 0.5h.

Value of off-route factor W is taken as 0.15 (Peavy et al., 1985). In Varanasi waste is

transported in two shifts and working time of each shift is 7.5 h excluding lunch, thus,

total working hour (H) is 15 h. The time to complete one trip to disposal site from each

ward (THCS) calculated from Eq. 2 is given in Column 14. The number of trips per day

(ND) by the vehicle taking waste from a ward is calculated from Eq.4 and shown in

Column15. Number of vehicles (Nv) required for each ward is calculated based on the

total number of container (CR), volume of each container (CV),volumetric carrying

capacity of transportation vehicle (9 m3 in this case) and the number of trip (ND) by the

vehicle from the ward per day. The values of Nv are given in Column 16. In order to

utilize full capacity and time of the transportation vehicle, one vehicle is suitably

assigned with two or more wards or vice versa. The vehicle number (VN) assigned to any

ward for transporting its waste from containers to the disposal site at Karsada village in

Varanasi is given in Column 17 and is accordingly shown in Figure 6. Taking 30% extra

as standby for any unforeseen circumstance, total number of vehicle with 9 m3 carrying


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capacity required in Varanasi come out to be 55. With each vehicle, if one driver and one

sweeper is required in each trip, in double shift 110 drivers and equal number of sweepers

may be required for transportation system.

Table 5 Requirement of secondary storage containers and transportation vehicles


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125


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Figure 6 Ward map showing the Vehicle number assigned to a ward

6. COST AND EXPENDITURE

Being a public utility and an essential service, investment in solid waste

management does not require justification in terms of positive return on investment or

minimum profits. Such an investment, however, needs to be justified on the basis of


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being technologically feasible option for achieving the required degree of efficiency with

the least cost. Based on unit price of equipments/vehicles adopted for solid waste

management planning by Varanasi Nagar Nigam (VNN, 2007) the total cost for the

designed storage and transportation system as proposed in this study, is estimated to be

around Rs. 76.45 million, as given Table 6. Annual operation and maintenance cost

comes out to be Rs. 52.40 million and is shown in Table 7.

Table 6 Investment costs of storage and transportation system for Varanasi

Table 7 Annual Operation and maintenance of storage and transportation system

for Varanasi


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7. CONCLUSION

In the present study, an attempt has been made to design and develop an

appropriate storage and transportation plan for the Varanasi Municipal Corporation, Uttar

Pradesh, India. The proposed model suggests the number of secondary storage containers

required on ward wise bases and transportation plan to the disposal site using GIS. The

hauled container system proposed in the work uses 4.5 m3, 3 m3, and 1 m3 capacity

containers with hydraulic loading/unloading attachments. 9 m3 capacity vehicle (Dumper

placer/tipper) are suggested for transportation of waste from waste storage point to

disposal site. It is observed that the present waste storage and collection system in

Varanasi is inadequate and unplanned. An initial investment of approx. Rs. 76.45 million

is estimated for improvement of waste storage and transportation system and

approximately Rs. 52.40 million is required per annum for efficiently manage these two

services. A GIS-based optimal routing model was used to develop a route map for

efficient transport planning. The proposed model can be used as a decision-support tool

by the municipal authorities for efficient management of the daily operations for

transporting solid waste, load distribution within vehicles, managing fuel consumption

and generating work schedules for the sanitary workers and vehicles.

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10 GIS BASED SECONDARY STORAGE AND ...iaeme.com/MasterAdmin/UploadFolder/GIS BASED SECONDARY...between two rivers, "Varuna" and "Assi" and is therefore known as Varanasi. It is believed