the sustainable city, c.a. brebbia a. ferrante, m ... · depot location problem [7]. ni-bin chang...

Integrated geographical information system

(GIS) for urban solid waste management

A.C. Caputo & P.M. PelagaggeDepartment of Energetics, University ofL'Aquila, Italy

Abstract

The general architecture of a GIS-based software tool for computer aided designof urban solid waste management systems is presented. In particular, thedeveloped software assists the user during the planning phase of separate wastecollection, including waste generation forecasting, logistics, vehicle routingissues and economic analysis. An important feature of the model is its ability tocalculate realistic waste generation rates and distribution starting fromgeographical and population density information. Moreover, it provides rapidinteractive analyses of alternative management scenarios for decision makers.

1 Introduction

Urban solid waste management is a critical issue in most countries, requiring anintegrated system approach to be faced in an effective manner. Solid wastemanagement systems have thus received wide attention from environmentalplanners because of their complex coordination of different managementstrategies. In fact, a correct balance among the various available technologicaloptions for waste treatment has to be found respecting also economicconstraints, existing local and state regulations, environmental issues and takinginto account public acceptance. In this framework, one of the most importantaspects is the planning of an effective waste collection strategy, which impactson both the overall cost of the waste management activity and the possibility ofeffective resource recovery and recycling as advocated by actual internationalguidelines. When planning a solid waste management system several goals maybe pursued, namely reduction of waste generation, recycling, reuse, andrecovery of materials and energy.

The Sustainable city, C.A. Brebbia A. Ferrante, M. Rodiguez & B.Terra (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-811-2

160 The Sustainable City

Currently, the most promising approach appears to be the separatecollection (SC) of different waste classes. In most developed countries, in fact, itis strongly suggested and widely practiced. As an example, in Italy the largemajority of urban solid waste is disposed of in landfills. However, existinglandfill space is nearing to completion and the opening of new ones is adversedby both local administrative boards and the population which is also contrary tolarge scale utilization of waste incineration. This asks for a strong effort towardswaste recycling, requiring separate collection of the different waste categories.Recently introduced regulation [1] sets the goal for 35% of the overall urbansolid waste production to be separately collected by 2003, while current averagelevel of separate collection in Italy lies between 5 and 10% [2]. The task oforganizing separate collections is devoted to municipal boards which need theavailability of accurate but easy to use computer aided tools to simulate thewaste generation process and to compare different management and collectionpolicies in order to comply at low costs and in short time with ever increasinglystringent regulation in an environmentally sustainable manner when relying onlimited budgets.

Several types of SC exist, the main difference consisting betweenmonomaterial and multimaterial collection. In the first case a single class ofwaste is separately collected in a dedicated container. Usually, many differentcontainers are utilized with identifiable colour and shape according to the typeof waste (glass, paper, aluminium etc.). In case of multimaterial collection twoor more waste categories share the same container, the simultaneous collectionof metals, plastics and glass being the most common. Paper is always collectedseparately. Another useful form of SC is the separation of the dry waste fractionfrom the wet one i.e. food organic waste and putrescibles. A wide choice ofcollection schemes are available, ranging from curbside hauled or stationarycontainers to door-to-door collection to conveyance in remote depot sites ortransfer stations. However, it is very difficult to a priori select the best solutionin a given urban setting from an economical and logistic view point.

In this kind of problems, software tools based on GeographicalInformation Systems (GIS) technology seem at the moment to represent themost interesting approach for environmental decision support systems. In fact,the use of mathematical models associated with the capability of spatial analysisin GIS is a new focus of recent research activity in solid waste managementliterature. Massie [3] applied GIS to improve solid waste management andrecycling programs, Cargin and Dwyer [4] used GIS for locating a low-levelradioactive waste disposal facility, Siddiqui et al. [5] and Kao et al. [6] solved asimilar problem for landfill siting, while Valeo et al. worked on the recyclingdepot location problem [7]. Ni-Bin Chang et al. utilized GIS for vehicle routingand scheduling in solid waste collection systems [8].

In order to assist researchers and public agencies in all phases of a SCplanning problem, in this study an integrated software tool based on GIStechnology has been developed. The adoption of this interactive designprocedure enables decision makers to analyze various waste management andcollection alternatives in a specific urban or geographical setting in order to


The Sustainable City 161

select the most cost-effective one, and to integrate in a single environmentmultiple data sources in order to correctly forecast waste production asdistributed on the territory.

The various tasks which are supported in an interactive manner are: theselection of the desired typology of SC, the location of waste containers over theterritory, the simulation of container filling process and the determination of thecollection interval, the planning of collection routes and logistics, and finally theeconomic analysis. In order to carry out such tasks a detailed geographicalcharacterization of the territory is previously performed. This representation isthen integrated with personal population data allowing for an accurateforecasting of waste production either overall and for each product class.

In this paper the general software architecture is presented with particularreference to the waste forecasting phase. Finally, an analysis of operatingcapabilities of the software tool is carried out.

2 Problem statement and architecture definition

Planning of waste collection activities follows the logical flow diagram depictedin figure 1, which also represents the main functional areas of softwarearchitecture, each module being integrated in the GIS programmingenvironment. A detailed geographical representation of the territory and itsdescription in terms of waste generating entities is carried out at first, in order toobtain an accurate forecast of waste production with reference to each wasteclass and generator type. This phase highlights the total volume of servicedemand and its distribution over the territory enabling the successive step ofassignment and location of collection resources. The user may then envisage awaste management system by deciding which kind of collection strategy is to beadopted and by locating on the territory the utilized resources (collection points,waste treatment or disposal facilities, collection vehicle garages etc.) accordingto the simulated waste generation scenario. The further simulation of resourcesutilization (i.e the progressive filling of waste containers) enables to check itsconsistency with hypothesized collection intervals. Then the collection logisticscan be planned. The analysis of the collection system behaviour in response tothe considered scenario assists the planner in correctly sizing the requiredresources in terms of manpower, number and location of required collectionpoints and vehicles also accounting for the collection routes which are selectedby the program user. This is an interactive phase where the planner may verifyin real time the effect of his choices and act accordingly through successiveapproximations in order to completely define the structure of the whole urbansolid waste management system. Finally, an economic analysis is performedwhich enables the user to evaluate the cost of the hypothesized wastemanagement system including revenues from collected recyclable materials.The decision maker may then select the best option, on the basis of itsconformity to the stated performance goals or performing a cost-effectivenessevaluation which enables also the proper choice of the tipping or taxation policy



to be adopted. In each step of the work the planner is assisted by a wide choiceof thematic views displaying homogeneous information. The user has alsoaccess to the multiple tables which make up the whole relational database inorder to make specific queries or to know the values of specific attributes ofeach spatial feature. Further, the selection of operating parameters values andcommands is performed resorting to customized masks and icons in a Windows-based environment

3 Waste production forecasting

GIS enable the computer collection, analysis and display of geographicalinformation by associating spatial features (points, lines and polygons) to ageographical relational database containing attributes related to such features.

In the present application, points are representative of waste treatmentplants or the location of waste containers in the urban setting, lines mayrepresent the road urban network or the routes of collection vehicles, whilepolygons represent buildings or areas where waste producers (households orcommercial activities) are located. Each feature is given an identification codewhich supports the link between the geographical information of the feature(coordinates, shape, dimension) and non-spatial information like properties andsymbols. The programming language accompanying GIS application packagesenables complete freedom in interactively operating either on the geographicand the database information. The geographic data may be acquired by manualdigitization of maps, by manual coordinate entering or by calculation, resultingin a vector space representation even if raster data may be accepted. Databaseinformation may be manually input or acquired by interfacing with external filesor databases. Multiple layers containing graphical features of the same classmay be superimposed to generate a single output thus easing the visualizationand decision making process.

CL Z=(Geographical datajcz _^> fI I ^ 1

/ Historical / |

vCreation of territorial model 1

VWaste generation modeling 1

-1Container management I

yCollection logistic I

+Cost analysis

—

Waste productionforecasting

Planning of wasteSeparate Collection

Figure 1: Flow chart of integrated waste collection planning activities.



3.1 Acquisition of territorial data

In case no ready-made digital maps are available for the examined territory,geographical data are accepted as existing maps or aerial and satellitephotographs digitized in raster format from an image scanner. Such data areconverted (manually or automatically entering their coordinates) in vectorformat by ESRI ARC/INFO GIS software on a Silicon Graphics Onyx2workstation, a RISC-based computer adopting Scalable Shared Memory Multi-Processing architecture. Data vectorialization includes georeferencing whereactual coordinates in space are associated to the vector image. The vector formatenables the recognition of geometrical features (lines, polygons etc.) whichwould be otherwise impossible in raster format. Moreover, memory usage isminimum depending on the number of objects rather than the size of pixels.Vector data created in the ARC/INFO environment are then exported to ESRIArc View which is a PC-based GIS environment enabling greater portability andease of use even for non experienced users. As a result, a data structurecontaining vector files and tables is generated where each geometric feature isassigned an identification code, georeferenced and linked to a tabulardescription of its characteristics, i.e. road names and length are assigned to linesegments, while surface area and street address are assigned to polygons. Theentire logical process is shown in Figure 2.

3.2 Integration with population data

In order to link the waste producers to the geographical representation of theterritory, a database containing population data is imported in the GISenvironment. Such data may be supplied for instance by the municipalityregistrar's office for households or by category associations for commercialbusiness activities.

'Geographical data:mapsaerial photossatellite images

Digitalization and georeferencing

Files export

Arclnfo on SGOnyx 2

workstation

/ d a t e / | Import files in ArcView [

' ^| Association of geographic and population data [

tI Generation of territory thematic maps [

Figure 2: Creation of the geographical database.



Required information include at least a) for households: name, address, numberof components; b) for commercial activities: name, address, type of activity(offices, restaurants, markets etc.), and a productivity parameter linked to wastegeneration (such as the number of daily meals served in a restaurants or thenumber of desks in a local market). Resorting to the address information eachproducer is automatically assigned to a polygon, also updating its associatedtable. The user may then evaluate the total populatior or, as an example,compute the number of inhabitants in each polygon and generate a thematic mapshowing the population density over the territory. By utilizing the geographicrepresentation of the urban environment (figure 3) and the thematic maps ofwaste generation the user may therefore plan, in an interactive manner, thewaste collection operations over the territory. As an example, referring to figure3, each polygon may represent a building or, in general, a facility where waste isgenerated, and a table is associated to each polygon which can be visualized toshow the details of its specific waste production activity.

3.3 Waste generation forecast

Once waste producers are located in the geographical representation of theterritory, information on waste generation rates are required. Such informationare entered in specific data structures enabling the utilization of any desiredcorrelation for waste generation. Although correlation of waste generation rateswith several parameters like country gross national product, personal income orelectrical energy consumption has been attempted, a strong evidence ofstatistical correlation has been proved only with the number of inhabitants of themunicipality [9-12]. However, correlations need specific data to be validatedand calibrated. Otherwise general production factors may be utilized as shownin Table 1 [13], which are also confirmed by experiments [14].

HBE3

Case arc .shp '

Roads

Buildingsor

facilities

Figure 3: Example of the graphical interface showing an urban environment.



Further, in order to estimate the specific amount of waste generated foreach class, the waste composition must be specified by the user according to thecategory of producer. Typical composition values are given in Table 2. Thecomputation of total generated waste amount is then carried out starting fromeach single producer, aggregating the data relevant to each polygon and finallyextending the computation over the whole territory. Thematic maps showing thegeographic distribution of waste production according to each waste category orproducer type may be then generated. The user may also evaluate the totalamount of waste generated or the amount produced by a single producer orpolygon.

Table 1. Waste generation factors.

CategoryHouseholdsMarketsRestaurantsCanteensOfficesCommercial activities (A= area)Food A <81m*)Food (A = 81-199 nf)Food markets (A > 199 m~)Nonfood (A < 81 m*)Nonfood (A = 81-199 nt)Nonfood (A > 199m')

ParameterPersonsDesksMealsMealsClerks

Factor70093002500.30

Unitg/(person*day)kg/(desk-day)g/mealg/mealkg/(clerk-day)

AreaAreaAreaAreaAreaArea

117104131251725

g/(nf-day)g/(nf'day)g/(nf-day)£/(W-day)g/(nf-day)g/(nf-day)

Table 2. Waste composition.

Waste classFoodUndersizePaper, cardboardPlasticsGlassInertsWoodMetalsAluminiumLeather, textilesHazardousOther

Households27.5721.0021.0610.6510.991.401.222.390.762.360.60

Food31.9

35.913.6

16.7

1.9

Non Food

805

5

10

Markets35.815.916.45.10.60.324.30.60.70.3

Restaurants45.817.518.15.28.4

2.22.3*

1.1

Canteens48.514.115.66.57.80.34.62.2*

0.4

" aluminium included

4 Planning of the separate collection

4.1 Container positioning

In this phase the user is prompted to start planning the waste collection system.At first he is asked to select the desired type of SC to be carried out, i.e. whichcategory of waste has to be collected separately and which waste classes may beinstead aggregated; at the two extremes we have each waste category collected



in separate containers or undifferenced waste collected in a single type ofcontainer. This choice affects the number and types of container to be located onthe territory and their filling rate, besides the collection logistics and economicresults. The user may obviously select the characteristics (cost, size) of eachtype of container and manually locate, in an interactive manner, the wastecollection containers over the territory. The user is also prompted to select theradius of an ideal circular influence area centered around each container (figure4). All waste producers falling inside this area are hypothesized to convey theirwaste in the corresponding container enabling the computation of the overallwaste mass collected by each single container according to its waste category. Acollection efficiency may be set by the user indicating the amount of wasteactually conveyed into the container with respect to the waste mass generatedfor each waste category. This features is important for SC because it simulatesthe imperfect behaviour of waste producers who are not willing to perfectlyseparate all their wastes according to the categories supported by the SC. Bydisplaying the influence circles the user may also visually check the territorycoverage. The filling level of each container may be displayed on screen in realtime and the emptying interval is automatically computed or imposed by theuser, who, at any time, may remove or relocate each container; all the databasetables being updated in real time.

4.2 Collection

In this phase the user is given the tools to organize the collection logistic systemand to determine the necessary number of vehicles or the personnelrequirements. The user interactively selects the position of the vehicle dispatchstation and the typology of vehicles including truck size, the presence ofcompactors and the number of operators. Among the data which may be inputby the user are the average vehicle speed, besides pickup, unloading and at-sitetimes which are utilized to estimate the total collection times as indicated instandard procedures [15]. The user then interactively chooses the sequence ofcontainers that are visited by each vehicle during its collection route. Theresulting route is displayed on screen (figure 4) while the program automaticallycomputes and visualizes the total route time and the progressive loading of thevehicle, as the containers are being emptied, according to the distance betweenthe selected containers (evaluated referring to actual road lengths) and theirfilling degree. When the specified daily shift time is expired or the vehicle isfully loaded it goes to the disposal facility and starts another route or returns tothe garage as desired. When the capacity of a vehicle is saturated, in terms ofavailable time utilization, new vehicles are added until all containers can beemptied respecting the required collection frequency. The program memorizesthe routes of each vehicle and the number of vehicles needed, while verifyingthat all containers have been emptied before they reach their maximum capacity.However, the user may delete or modify at any time each route, all collectioncomputations being updated in real time.



Circular influencearea Container location

Waste levelindicator

Collection route

Figure 4: Example of the logistic planning phase.

The same procedure is followed for each type of container, i.e. for each wastecategory which is separately collected. The software automatically detects ifeach container is unloaded by the right kind of vehicles and, in case of separatecollection, if different waste categories are not mixed. As a result the programgenerates a report showing the details of the planned logistic system, includingthe summary of resources requirements, the selected routes and duration and thenumber of trips. The amount of collected waste is memorized in specific tablesto compute revenues from sales and disposal costs.

4.3 Economic analysis

The cost of waste collection is a very large part of the expense for municipalsolid waste management. Therefore, the optimization of collection services mayyield large savings. Moreover a precise estimate of incurred costs is important todefine the requested tipping fee or the taxation amount.



The present tool may carry out a complete economic analysis of theadopted modality of SC on the basis of the results of the collection planningphase. Capital investment is automatically computed according to the number ofvehicles and refuse containers utilized, including any vehicle dispatch station.This cost is levelized on a time span selected by the user at a specified interestrate in order to obtain an equivalent annual cost which is summed to the annualoperating expenses (mainly labor, fuel and maintenance costs plus wastedisposal fees) to calculate the total annual cost. Annual labor and vehicle cost iscomputed on the basis of total mileage and total collection time as results fromthe planning phase. Revenues from sales of collected materials are included inthe computation. This aggregated economic result may be adopted to comparedifferent values of the tipping fee or taxation strategies in order to ensure theeconomic feasibility of the business process. The user is asked to enternumerical values for selected cost data and parameters such as fuel cost, wastemarket value, disposal cost, vehicles and containers cost, labor rate. Taxation(£/m ) and tipping (£/kg) coefficients are stored in appropriate tables and maybe specified according to producer category (household, commercial activityetc.) or waste class. As a result a report is generated indicating the net annualeconomic result, and the specific collection costs (£/kg). Resorting to suchinformation the user may modify the planned collection system or plan anentirely different one for sake of comparison repeating the process until asatisfying solution is obtained.

5 Conclusions and future work

The developed software tool based on GIS technology appears as a usefulframework in the organization of separate waste collection activities, as well asa decision support system for solid waste management planning.

An important feature of the model is its ability to calculate realistic wastegeneration rates and distribution starting from geographical and populationdensity information. The data management and display functions of the softwaretool may be used to create, manage and analyze many complex issues for thewaste collection system. In overall, the model is able to perform data entry,integration, analysis and display, to receive data from modeling systems ordatabase management systems, and to depict model results by generatingcartographic products. A menu format, coded with macroprogramming tool, hasbeen also developed where the user can perform database queries, cartographicdisplay, modeling analysis, and evaluate management planning scenariosinteractively.

As a matter of fact at the moment the presented software environmentshows ability as a useful computer aided planning system especially whenproper databases are easily available.

Future work will be aimed towards introducing autonomous decisionfunctions by using operation research optimization techniques and artificialintelligence (i.e. neural networks and fuzzy logic) as required by each specific



application. That is, starting from population and waste density characteristics,finding the optimal allocation of collection resources in the network will bepossible respecting economic constraints.

References

[1] D. L. 22/97, Gazzetta Ufficiale, Supplement ordinario alia "Gazzetta Ufficiale"n.38, 15.2.1997, Serie generate (in Italian).

[2] Cerpelloni, S., Indagine 1995 Raccolte differenziate - Banca DatiFEDERAMBIENTE - AMIA, 1995 (in Italian).

[3] Massie, K., Using GIS to Improve Solid Waste Management and RecyclingPrograms, Proc. 1995 ESRI User Conference, p. 18, 1995.

[4] Cargin, J., & Dwyer, J., Pennsylvania's Low-Level Radioactive Waste DisposalFacility Siting Project: Special GIS Operations, Proc. 1995 ESRI User Conference,p. 162, 1995.

[5] Siddiqui, M. Z., Everett, J. W. & Vieux, B. E., Landfill Siting Using GeographicInformation Systems: a Demonstration, Journal of Environmental Engineering,June, pp. 515-523, 1996.

[6] Kao, J. J. & Lin, H. Y., Multifactor Spatial Analysis for Landfill Siting, Journal ofEnvironmental Engineering, October, pp. 902-908, 1996.

[7] Valeo, C, Baetz, B.W. &, Tsanis, I.K., Location of Recycling Depots with GIS,Journal of Urban Planning and Development, Vol. 124, No.2, June, pp. 93-99,1998.

[8] Ni-Bin Chang, H. Y. Lu & Wei, Y. L., GIS Technology for Vehicle Routing andScheduling in Solid Waste Collection Systems, Journal of EnvironmentalEngineering, September, pp. 901-910, 1997.

[9] Cossu, R., Serra, R. & Granara, F., Studio Sperimentale sulle Correlazioni traProduzione di RSU ed Alcuni Fattori di Influenza, RS Rifluti Solidi, Vol. II, n.3, pp.212-223, 1988 (in Italian).

[10] Daskalopoulos, E., Badr, O. & Probert, S.D., Municipal Solid Waste: a PredictionMethodology for the Generation Rate and Composition in the European UnionCountries and the United States of America, Resources, Conservation andRecycling, Vol. 24, pp. 155-166, 1998.

[11] Serra, R., Correlazioni, Trend Evolutivi e Stagionalita delle Produzioni di RifiutiUrbani in Sardegna, RS Rifiuti Solidi, Vol. X, n.3, pp. 175-182, 1996 (in Italian).

[12] Serra, R., Analisi dell'Influenza di Variabili Economiche nella Produzione di RifiutiSolidi Urbani, RS Rifiuti Solidi, Vol. XII, n.2, pp. 79-86, 1998 (in Italian).

[13] Abollino, O., Barberis R. & Consiglio M., Le utenze produttrici di rifiuti solidiurbani. Caratterizzazione qualitativa e quantitativa, RS Rifiuti Solidi, vol. IX, n.4,pp. 226-230, 1995 (in Italian).

[14] Ass.ne Federcasalinghe, & Parco Scientifico e Tecnologico d'Abruzzo, ProgettoPilota Raccolta Differenziata del Rifiuti Solidi Urbani, Rapporto Finale, Novembre,1997 (in Italian).

[15] Tchobanoglous, G., Theisen, H. & Vigil, S.A., Integrated Solid Waste Management,McGraw Hill, New York, 1993.


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