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Optimizing the collection of used paper from small businesses through GIS techniques: The Leganés case (Madrid, Spain)
Available online at www.sciencedirect.com
Waste Management 28 (2008) 282–293 www.elsevier.com/locate/wasman
Optimizing the collection of used paper from small businesses through GIS techniques: The Legane´s case (Madrid, Spain) J.V. Lo´pez Alvarez a
a,*
, M. Aguilar Larrucea b, S. Ferna´ndez-Carrio´n Quero a, A. Jime´nez del Valle a
Departamento de Forest Engineering, Escuela Te´cnica Superior de Ingenieros de Montes, Technical University of Madrid, Ciudad Universitaria s/n, 28040-Madrid, Spain b Environmental Council of Castilla-La Mancha, Guadalajara, Spain Accepted 26 February 2007 Available online 8 August 2007
Abstract This article deals with a methodology for the design of routes for the ‘‘bin to bin’’ (BTB) collection of paper and cardboard waste (PCB) from small businesses, as well as with the new location and calculation of the number of containers needed in the streets for both commercial and non-commercial use due to the large amount of PCB deposited in them. This study was carried out in five shopping areas of the city of Legane´s (Community of Madrid, Spain). One of the characteristics of the area is a high density of population and urban traffic. The tool used is the Geographical Information System (GIS-Arc-View). With it we can generate PCB points of high population density in commercial streets based on territorial analysis. We placed the special routes and the new container locations within a distance of 60 m of these collection points (CPT). The system calculates and optimizes six routes according to different urban restrictions. Finally, we provided service to 59% of the shops, which generate almost 82% of the PCB waste, using 160 min per day to collect 1027 kg of high quality PCB. If we compare the system with the system in place previously, we can conclude that the ‘‘bin to bin’’ (BTB) system improves the quality of the PCB in the containers, avoiding overflow and reducing the percentage of rejected material. Ó 2007 Elsevier Ltd. All rights reserved.
1. Introduction Blue containers (3 m3) for paper and cardboard (PCB) were designed for the selective collection of residential waste. Due to the non-existence of a specific service for the collection of used PCB, small businesses normally use the blue containers, but because of the nature of the packing used by these businesses, there are a number of deficiencies unjustly associated with these blue containers. These problems consist mainly of the presence of large cardboard boxes in the streets (Fig. 1) and the inefficiency in the filling of the blue containers, which become full and blocked with large, low density boxes. *
Corresponding author. Tel.: +34 91 3366411; fax: + 34 91 3367101. E-mail address: [email protected] (J.V. Lo´pez Alvarez).
0956-053X/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2007.02.036
A solution to the problem is to collect the PCB where it is produced, with a ‘‘bin to bin’’ (BTB) collection system. This service is offered in addition to the PCB collection using the blue containers. It is designed for zones with small shops that produce large amounts of PCB, in order to avoid the collapse of the igloo type container system used by the citizens, and in zones that cannot have a large number of containers due to their specific characteristics. It does not mean that every shopkeeper is going to have the PCB collection on their doorstep. The collection will be made in places previously determined by the collection service and shopkeepers. A BTB collection of the paper generated in schools and city offices can be made with the help of ecocontainers and other collection systems, with a schedule planned by the City Council.
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mada, El Carrascal, Legane´s Centro, La Fortuna and Legane´s Norte (Fig. 2), although we used the Zarzaquemada zone as an example for a later use on the rest of the city. 2. Background and rationale
Fig. 1. Container of PCB. Previous location in commercial district.
This current study focuses on the city of Legane´s (Community of Madrid), which belongs to the so-called ‘‘metropolitan crown’’ of the capital city. One of the characteristics of Legane´s is that it has a high population density: 184,841 inhabitants, over a surface area of 4324 ha and 65,802 dwellings (INE, 2004). It has more than 1300 small shops which generate, according to our information, an average of 1.2 kg/day/shop of packing and PCB waste. The study focused principally on five areas of the city: Zarzaque-
Nowadays the treatment of recycled waste depends almost exclusively on official policy and waste treatment plans (Read, 1999; Lave and Hendrickson, 1999). These policies nevertheless do not take into account the technical aspects for optimizing and increasing efficiency in waste collection. In some cases, either the real amount of waste generated is unknown or estimates are made based on sample techniques in waste collection plans or dumps (Klee, 1993). In this way many other studies have already been carried out in different fields: There are tendencies for the new location of containers to be decided according to socioeconomic parameters, such as for glass (Gonzalez-Torre and Adenso-Dı´az, 2005) or paper (Bach et al., 2004). The work which relates distance of the container from the user and the time the producer takes to bring the waste from his home is based on these references which, in turn, relate to other studies previously carried out. These studies help to clarify the recycling system from a social behaviour point of view (Everett, 1994) and other aspects about citizen participation and environmental education (Miller, 1999; Read, 1999; Moloney, 2002; Perrin and Barton,
Fig. 2. Distribution of the project by zones in Legane´s (Community of Madrid).
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2001), but they do not deal with the problems of the small businesses, which cause overfilling of the containers or the accessibility of the collection points (contribution areas) for the collection vehicles, which generates inefficiency in the collection system. Other studies concern the integrated territorial approach to the treatment of recycled waste, such as the ones carried out in Sussex (Woodard et al., 2004); London (Robinson and Read, in press); and Lancashire (Williams and Taylor, 2004), to give some recent examples. Both approaches, the socioeconomic and the territorial, offer acceptable solutions to the integrated treatment of domestic refuse, even when the collection cost (Hummel, 2001) or the important role played by the cost-efficiency variables caused by physical or territorial restrictions are taken into account (Chang et al., 1997). These authors demonstrate that it is necessary to look for a solution in the field of territorial logistics separately from the models of mathematical programming used to minimize costs in waste collection, transport, treatment and disposal. However, these policies leave aside one of the generators of the PCB waste, the small business. Small businesses are usually situated in the old city centres or in tourist areas. Their main characteristic is the daily generation of a very dense amount or cardboard (wrapping paper, test liner, corrugated cardboard). The collection problem is that either the containers cannot take these materials or the collection vehicles cannot get into these zones, most of which are pedestrianized. We can see that the average daily collection of PCB in Spain is about 46% in weight of the consumption (ASPAPEL, 2004). A large part of the paper generated by small businesses is overlooked. This could represent at least 20% more being collected. The use of the Geographical Information System (GIS) in these studies using the territorial approach already has antecedents in the design of dynamic generating models (Leao et al., 2001) or in rules or multicriteria decisions to explain the treatment of domestic refuse in the territory from the point of view of space (MacDonald, 1996). Detailed studies of the problem of optimizing routes for the collection of infectious waste from hospitals have already been made (Shih and Lin, 1999). However, the methodology is different because the origin and destination, and even the amount, are known. So the problem is a simple logistical one of optimization. The use of multiobject criteria in order to make decisions in the analysis of alternative routes for the waste collection through GIS (Chang et al., 1997) offer solutions before and after the collection, like a dynamic model. However, they do not deal with the possibility of placing PCB containers according to technical parameters, changing them according to an optimized deposit and collection route, thus avoiding the overfilling of some containers or containers being filled to less than half their capacity. We based the solution on the design of collection routes or the existing infrastructure (street and PCB containers) needing BTB collection with the GIS, taken from more complex studies that use algorithmic types of neural networks, fuzzy technology and genetic algorithms under conditions of incertitude or
incompleteness, for the use of infrastructure in general (Flintsch and Chen, 2004), which is not the case in the present paper. Finally we quote a study closely related to this one, based on the use of GIS for the location of light containers (Valeo et al., 1998). In this work we used the ‘‘maximum coverage distance’’ model (Manhattan distance), but we fixed the suitable places for the location of the containers a priori (shopping centres, city parking places, sidewalks of long streets) leaving aside the rest and quantifying the numbers and places for these containers. None of these studies give a complete solution to the problem we deal with here: we needed to know the distribution of the small businesses that generate PCB and to calculate the function of the generation density. Then we determined the possible points for a regrouping or nodal points. We drew the radius of the service coverage on them (as we can see below these cannot be fixed in advance) and in this way we were able to calculate the special routes. The GIS drew the best routes needed according to the city restrictions and finally, we estimated the number of containers and their location according to current and anticipated waste generation. 3. Objective The objective of this study was to create a new methodology for effecting a series of improvements for the collection of the PCB in the city of Legane´s (Spain), from a technical point of view. This methodology could be offered to other cities with similar characteristics. The objective was to find out the number of PCB containers, identify the needs of waste collection in the city’s small business zones and draw the best route that would link the main points of the small businesses’ PCB generation (contribution areas), along the main street of the city. The service offered by this route would improve the use of the 3-m3 containers, which could be used in zones with a higher density of homes, avoiding overflow or incorrect filling, which makes the PCB collection system inefficient and so optimize the collection of higher density materials already classified. 4. Methodology The methodology used was the following: (1) territorial analysis, (2) field work, (3) GIS analysis, and (4) service design. Below is a description of the different sections. In order to make the methodology description and results clearer, the results are shown initially in reference to Zarzaquemada, and are subsequently shown in relation to the rest of the city. 4.1. Territorial analysis This analysis was based on the identification of the needs and/or restrictions in the collection service. The city
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areas were identified according to their level of business activity. We needed to inspect and identify the collection needs of small businesses. The local authorities, who know the local traffic problems and shops timetables, informed us of the restrictions to take into account in the collection route. In order to do that we had to process and analyse information that was the basis for the proposed methodology. This consisted of:
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times it is cleaned a year), and suitability of the point according to these criteria:
Accessibility for pedestrians, Accessibility for vehicles, Visibility, Density of homes around it.
4.3. GIS analysis
Street Plan in digital format (scale 1:6.000), Ortophotos Community of Madrid (scale 1:1500), National Classification of Economic Activities, GPS, as basic material, to locate the small shops (which in turn were placed with name and street number) and the PCB containers.
4.2. Field work In order to establish the distribution of the shops, the PCB generation and attitude and preferences of the city shopkeepers, 300 interviews were carried out in situ. These obtained information about: type of shop and name, location (through GPS coordinates), size (number of employees), surface area, PCB generation (number of boxes), frequency and types:
Small Box: about 300 g/un, Medium Size Box: about 600 g/un, Large Box: about 900 g/un or more, Interest of the shopkeepers in the Collection system BTB and best time for waste deposit.
One of the main points in this study consisted of working out a system for classifying the small businesses in relation to their average daily PCB generation. A number of different typologies were created for 1200 shops, which allowed an accurate estimate of PCB generation in the shops that were not surveyed or did not give information about their waste generation (Table 1). Apart from the information obtained from the shopkeeper interviews, all of the shops in the area were identified, placing their geographical position on a street plan, for a later analysis in the GIS (Alpaydin and Gu¨rgen, 2004). At the same time, a detailed study of the present location of the containers was made in the city. An inventory of all of the containers was made in situ. They were found through GPS. In this way they could be analysed in the GIS in relation to the small businesses (Fig. 3). The inventory contained information about: places with PCB containers, number of containers, type (volume), collection frequency (days/week), capacity (litres, inhabitants/ year), distribution of containers according to population density, cleanliness around the container (high, medium, or low), maintenance of the blue container (number of
The use of the GIS in this study allowed us to create, arrange, treat and analyse the geographical information, organized by means of graphic and alphanumeric data (Richards, 1986). The GIS used is the Arc View 3.1, which enabled us to have a geographical reference for all of the points on the digital street map. In order to analyze the information through ArcView 3.1, the order followed in the study was the following: distribution of small businesses, estimate of the density of PCB waste generation (Devijver and Kittler, 1982; Fukunaga, 1990) possible regrouping points or waste collection, identifying the area covered by each collection (contribution) point, estimating the special routes (compulsory) for the collection vehicles and, finally, drawing the best collection route. We describe below the method and basis used in each step. 4.3.1. Small business distribution By entering the information obtained in the field work, we were able to quickly analyse the distribution of small businesses in the city of Legane´s. A priori we could easily distinguish the biggest commercial areas, that is the shopping streets. A PCB collection route could go along them. 4.3.2. Generation density By associating every shop seen on the map to the business typology and its waste generation, every point would have a specific value. By means of a GIS tool called ‘‘Density Calculation’’ we calculated the density according to their PCB generation. We then used an adaptable grouping method, in which we used two parameters which are related according to the following function (Tou and Gonzalez, 1974; Jain and Dubes, 1988; Cow, 1992): f ðxÞ ¼
1 d 2 1 pffiffiffiffiffi e2ðhÞ 2 2p h
where h is the distance threshold for creating groupings, d is the fraction of h which determines total confidence, according to the PCB generated or distance from any other x point in the space. This function generates spots (Fig. 4), being most intense at the point of observation and decreasing in relation to the distance from the point, more or less quickly, depending on the h. The curve approaches zero intensity asymptotically, without being totally extinguished. So the observations made close to a place in the space are very sig-
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Table 1 Classification of the small businesses according to generation parameters General type
Type in detail
Level
Size (m2)
Employees
Generation (g/day)
Generation (kg/day)
Category
Food
Nuts and dried fruit Groceries Butchers Fishmongers Frozen food Fruit shops Bakers Drinks
Small Medium Large Supermarket
<50 50–100 >100 >100
1 >1 P2 P4
1200 3000 5000 8000
1.2 3.0 5.0 8.0
Low Medium High Exceptional
Hotel Restaurant Coffee-shop
Bar Pub Cafeteria Restaurant Fast Food
Small Bar Medium Bar Large Bar Small Restaurant Large restaurant Small Fast Food Large Fast Food
<50 50 – 100 >100 <100 >100 680 >80
1 P2 P3 62 P3 62 >2
700 1400 2200 1500 2400 1500 4000
0.7 1.4 2.2 1.5 2.4 1.5 4.0
Low Low Medium Low Medium Low High
Textile Shoes Fashion Accessories
Clothes Shoes Fashion accessories Sportswear Haberdashery Jewellers
Small Medium Large
<75 75–120 >120
1 P2 P3
500 1800 7000
0.5 1.8 7.0
Low Low Exceptional
Home Furnishings
Furniture Mattresses Bathrooms Kitchen Electrical Appliances
Small Medium Large
<100 100–175 >175
P2 62 P3
300 800 3000
0.3 0.8 3.0
Low Low Medium
Decoration
Household Items H Lamps
Small Medium Large
<70 70–120 >120
1 2 to 3 P2
250 1200 3500
0.3 1.2 3.5
Low Low Medium
nificant, while the more distant observations make an infinitesimal contribution (Craig et al., 2002). The general lines of the algorithm are the following: 1. The essential part of the algorithm is to create groupings on the basis of the h distance (weighted by the d). The first group is established arbitrarily. 2. When a shop is ascribed to a group, the group’s centre is recalculated. This can change the status of other shops, either by them being removed from the group they belonged to before or by ascribing them to a new group. 3. Status changes were possible because we checked the total number of shops used for the grouping several times. This repetitive process ends when there are no more new ascriptions. The splitting is then considered fixed. 4. We use a part N of the total number of shops to be grouped, M, to define the groupings. So zones with a higher density generation take a definite shape as the possible points along a collection route. These high density spots merge to look like rivers (very busy
shopping streets) with their tributaries (entrance to shopping streets) with isolated points or fountains (isolated points with substantial density generation). So a zone with a large number of small shops very close to each other, but which do not generate significant amounts of PCB, will not be shown as a potential zone for a possible collection route, although it could seem so ‘‘a priori’’ (Fig. 4). 4.3.3. Collection area Once the collection points have been fixed because they are close to high generation spots along the potential route, we calculate, by means of the GIS, the number of shops each collection point can supply because of their proximity to the collection point, being within a 60-m area (Fig. 5). Apart from the number of shops supplied, it is interesting to calculate the average daily total volume for every collection point. The algorithm used in the GIS to calculate this covering distance is the ‘‘Euclidean distance’’ between x and y points (Wilson and Baetz, 2001), which is given in the expression: dððx1 ; x2 Þ; ðy 1 ; y 2 ÞÞ ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx1 y 1 Þ2 þ ðx2 y 2 Þ2
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It is interesting to know that there is more than one way to make an estimate of the distance between two points; in the Euclidean space it is made with the Pythagoras theorem, but for other spaces we could define the distance in a different way. That is why we think it is more useful to use the Euclidean distance as the calculation logarithm because we do not even know a priori what the final or ideal distribution of the containers or the contribution areas will be. It is good practice if we wish to maximize the service to the population; this decision must be based on distance, cost and social, environmental and political decisions. Without considering these last parameters, the Euclidean distance itself approaches the minimum cost of the service (Ramu and Kennedy, 1994).
Fig. 3. Distribution of shops (circles) and paper and cardboard containers (squares).
4.3.4. Special roads Due to the traffic difficulties for the vehicle used for collection, it is necessary to focus the analysis of the possible route by taking into account only those roads most suitable for small truck traffic, using topological and geometrical criteria (Rasdorf et al., 2002). These special roads for the collection route are marked on the street map, taking into account the restrictions in the traffic direction (Fig. 5). These special roads are divided into different sections in the GIS on the basis of crossroads or large housing blocks. In this way we can identify sections with high levels of business activity within any very long special road.
Fig. 4. Map of shops density according to generation.
where d is the Euclidean distance (m) between point x and y, (xl, yl) are the GPS coordinates in the territory of the collection point, and (x2, y2) are the GPS coordinates in the shops territory.
Fig. 5. Distribution of the preferential ways for the vehicles of selective collection. Daily withdrawal estimated in the suitable point (9.5–34.4 kg/ day).
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4.3.5. Possible route With the help of the GIS we carried out a space association in the shape of a reference node to identify which particular section of the route would supply each collection point, due to its proximity to the various collection points (Rasdorf et al., 2002). Every section was given an estimated amount of PCB generation, which is shown graphically in Fig. 6. In this way we can identify the ‘‘river’’ or street with the greatest amount of business activity and PCB generation. The route should go along this street. 4.4. Service design 4.4.1. Definition of the route With the information obtained with the help of the GIS regarding the possible routes, and having taken into account the restrictions to the road traffic, we defined the best route for the PCB business collection. The best route will be the one that offers a service to points with the largest amount of PCB generation. The route should go along those sections of the special road where there is substantial business activity. In this way the resources used for the collection, the length of the route and the time taken to complete the collection are minimized (Rasdorf et al., 2002). The algorithm used in the GIS is the MGT (Minimal Generator Tree) because we can combine the characteristics of distance and density (Duda and Hart, 1973) in the MGT. The algorithm is based on the estimate of an average distance between the groupings, defined as dminðS i ;S j Þ ¼
min fdðX ; X 0 Þg
X 2S i ;X 0 2S j
in such a way that for any of the two groupings Si and Sj, dminðS i ;S j Þ is the distance between the groupings’ two closest points. We must note that in this case, a grouping is not represented or characterized by its centre, but by a member of the grouping. In fact, instead of using the MGT as a starting point we use a main diameter of the MGT, which is defined as follows: the main diameter of an MGT is the one which is the most significant (Duda and Hart, 1973). The system determines the typological groupings and makes the routes pass along the centres of gravity of the generation nuclei previously decided. These routes are still fictitious and fit in the model of probabilities according to the type of vehicle, speed, and load and unload stops. Therefore they must be adapted to the street plan, street directions, their width, rush hours, etc. The groupings were obtained by repetition, taking areas of 20, 30 and 60 m (Fig. 4). As we can easily assume, it is possible to find several main diameters on the same MGT. Practically, and with the idea in mind of making the estimate computationally manageable, a main diameter is estimated taking into account the MGT branches, leaving aside the branches leading to less important routes. 4.4.2. New location for the blue containers The sections or zones with a high density of small businesses, but with low levels of PCB generation or those which are far away from the logistical optimization of the route, have been more effectively provided with 3-m3 containers after their relocation. This equipment needed was estimated by means of the methodology used to determine the contribution areas.
100.0 90.0 80.0 70.0 60.0 SMALL BUSINESSES (%) 50.0 40.0 30.0 20.0 10.0
% ACCUMULATED
0.0 % COVERED
0 - 20 m 20 - 40 m 40 - 60 m DISTANCE (m)
%
COVERED
% ACCUMULATED
DISTANCE 0 - 20 (m)
ROUTE
COVERED (%)
20 - 40 (m)
ACCUMULATED (%)
COVERED (%)
40 - 60 (m)
ACCUMULATED (%)
COVERED (%)
ACCUMULATED (%)
CENTRO EL CARRASCAL LA FORTUNA LEGANÉS NORTE ZARZAQUEMADA
81.8 51.4 25.6 74.0 31.5
81.8 51.4 25.6 74.0 31.5
14.1 21.4 69.4 20.0 38.5
95.9 72.9 95.0 94.0 70.0
4.1 20.0 5.0 6.0 24.9
100,0 92,7 100,0 100,0 94,9
LEGANÉS
62.4
62.4
23.0
85.4
12.4
97,8
Fig. 6. Level of service BTB for the small trade.
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5. Results and discussion 5.1. Field work analysis 5.1.1. Blue container The PCB containers in the city of Legane´s are the metal igloo type with a capacity of 3 m3, except in Zarzaquemada, where 18 out of its 64 igloos have a capacity of 2.5 m3. Altogether there are 262 containers; 153 of them are in the five study areas (Table 2). We observed that there were about 12 shops which were leaving their PCB waste in one container, which meant that it received about 14.5 kg/ day. With the arrangements we describe in this article, the contribution diminished to almost 3 kg/day, that is to say, about three shops for one container, leaving more room for the PCB from homes. 5.1.2. Description of the small businesses From the interviews held with the small shopkeepers, we obtained the following information: the shops could be grouped into 18 different types according to activity, the PCB generation for each activity. In the city the most common type of business is Hotel and Catering (HORECA), comprising 26.1% of the sample chosen. It is followed by Textile, Shoes Fashion and Accessories at 17.3%. This is followed by Food at 13.6% and Hairdressing and Health and Beauty salons at 7.0%. The information from the interviews shows that, in relation to the way the PCB generated by the small shopkeepers was disposed of, 56% of them stated that they used the blue container. The percentage of shops interested in the BTB service was greater among the shops with a significant level of PCB generation (1.5 kg/day or more), very close to the routes designed by the GIS (60 m or less). 5.2. GIS results Distribution of the small businesses. The zone with the greatest business activity is Legane´s Centro. Although it
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has a smaller number of shops than Zarzaquemada, these shops are bigger in area and number of employees, and on average produce a larger level of daily PCB generation (Table 3). Density of waste generation. The study of density shows very different patterns according to the particular zone. While in Zarzaquemada the density spots are distributed in the form of points in a more or less scattered distribution, in the Legane´s Norte zone the spots are distributed like currents or main rivers with some scattered points of high density. In the case of the La Fortuna district, the zones with a higher density of PCB generation are allocated along Avenida de la Libertad. By analysing the density in the El Carrascal district, we can see a level of PCB generation density lower than in Zarzaquemada. Finally in the Legane´s Norte zone, the small business density is near the axis, along Clara Janes and Petra Kelly streets. Possible collection points and special roads. The possible collection points, allocated to the spots with high generation density and next to the special roads, are shown by means of a graduated symbology according to the quantity which could be collected if they served the shops within 60 m. Possible routes. Fig. 7 shows those sections that are thought to have larger amounts of PCB. In Legane´s Centro, Juan Mun˜oz, Plaza de Parı´s, Teniente General Musiera and Sol streets are clearly the most suitable routes for a PCB business collection. 5.3. Service design Routes. Using the available information and the GIS to process it, six routes are defined (Fig. 9), one for Zarzaquemada, one for Barrio de la Fortuna, two for Legane´s Centro, one for El Carrascal and one for Legane´s Norte (Table 4). The routes previously mentioned only refers to the distance covered during the PCB collection; they do not include the distance between the routes. Details are given below: Distance between routes. Six routes were defined, with the characteristics given in Table 5.
Table 2 Previous situation and proposal for the location of containers Zone
Legane´s Centro (Town center) Zarzaquemada La Fortuna El Carrascal Legane´s Norte Total
Previous situation of the paper-cardboard containers
Proposal for the new distribution of the paper-cardboard containers
No. of papercardboard containers
No. of papercardboard containers
Shop/ container
Average generation per container (kg/day)
12
36
39.6
64 15 35 27
7 10 4 4 12.2
153
Shop/ container
Average generation per container (kg/day)
20
4
4.4
8.5 14.0 4.2 5.9
82 15 35 27
3 2 2 2
3.3 1.7 2.1 2.5
14.44
179
2.6
2.8
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Table 3 Generation of paper waste by shops in the city districts Zone
No. of shops
Total generation of the zone (kg/day)
Average generation (kg/shop/day)
Legane´s Centro Zarzaquemada La Fortuna El Carrascal Legane´s Norte
316
475
1.5
451 144 158 96
549 204 203 119
1.2 1.4 1.3 1.2
Fig. 8. Definitive location of containers. The darkest points show the businesses that must use the containers. The clearest points are the businesses to which one gives BTB service.
Fig. 7. Possible sections for the collection route, according to business generation of PCB waste.
Service parameters: collection duration. The length of time taken to collect the BTB was estimated to be 5 h for the six routes. The length of time for every route is given in Table 4. The length of time taken to drive from one route to another was estimated to be 45 min for the six routes (Table 5). Frequency. The PCB collection in the city should be made daily, from Monday to Saturday, in order to respond to the shopkeepers’ demands.
Timetable. From the information taken from the interviews and the City Council, the best collection time would be from 8 p.m. onwards, at the time when most of the shops close. Container location. For the final location of the PCB containers we firstly considered the zones that have a higher business concentration, trying to provide a service to the small businesses not covered by the routes of the BTB collection system (Fig. 8). By doing so, container overflow would be avoided and consequently the imbalance in the PCB container use which had limited their use by the domestic users. Since the 3 m3 blue container has been designed for domestic use, this calculation was made taking into account the highest density of dwellings. In order to identify the most suitable zones for the locations of the containers, we took into account the following aspects, mentioned in the field work methodology: the previous location of the PCB containers, accessibility for pedestrians and vehicles, residential density in the area, visibility and proximity to the small businesses. Some comparisons between the previous and optimized situations have been made using data collected during the last 6 mo and comparing this with information collected before the changes were made, taking into account that this work and the follow-up of the methodology is on-going
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Fig. 9. Final configuration of the BTB PCB collection routes.
Table 4 Graphic design of the route Zone
Route
Legane´s Centro Zarzaquemada El Carrascal Legane´s Norte La Fortuna
Route Route Route Route Route Route
Total
1 2 3 4 5 6
Length (m)
Shops supplied
Percentage shops in zone
Calculated collection (kg/day)
Percentage collection zone (kg)
Estimated length of time (min)
1.295 930 2.895 1.191 900 1.304
175 67 212 70 50 121
74 83 47 44 52 84
258 128 279 87 91 184
545 27 51 42 795 91
20 14 44 18 14 20
8515
695
59
1027
67
130
Table 5 Length and distance between routes Origin
Destination
Length (m)
Centre 1 Centre 2 Zarzaquemada El Carrascal Legane´s Norte
Centre 2 Zarzaquemada El Carrascal Legane´s Norte La Fortuna
460 1640 810 1050 5400 9360
Total
(Table 6). Broadly speaking, following what has been mentioned above, we can see a significant reduction in the amount of material placed in the containers which later has to be rejected (a reduction of 10% in weight for the domestic containers, a reduction of 30% for the domestic
Total no. of kg/day collected
No. of shops collected
Estimated time (min)
258 386 665 752 843
175 242 454 524 574
2 8 2 8 10
1027
695
30
and trade PCB containers), and above all, in the BTB containers (10–15% in weight). We have also seen that there is no container overflow. As the equipment is optimized according to the load they receive, the average filling is 65% for the home containers, 50% for the home and busi-
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Table 6 Comparative improvements before and after the implementation of the methodology (latest average information for 6 mo for 3 m3 containers) Container
Before %L
D D/C BTB
75–100 M –
After %R
65–75 45–65 –
% weight fraction PCB
%L
White
Newspaper
Card board
Mix
10 5 –
15 20 –
12 30 –
63 45 –
50–75 45–65 75–95
%R
50–60 25–35 10–15
% weight fraction PCB White
Newspaper
Card board
Mix
10 7 1
20 25 5
15 40 90
55 28 4
D: domestic container, D/C: domestic/business, BTB: business container, M: overflowing, L: Level of container, R: Reject material in the container.
ness ones and 80% for the BTB ones. Finally, although the load of the containers is smaller and the rejection percentage is smaller too, the quality of the collected product has increased very much in the BTB system. As we can see, this system receives 90% of its weight in cardboard, which supports the principle of ‘‘sorting at source’’. This research is now on-going, trying to improve the methodology, by adding elements of population consumption, and the material and quality of the containers, according to the city zones. 6. Conclusions The GIS methodology used, the method of point grouping (shops), plus the MGT algorithm seem to be suitable for the analysis of the territorial information regarding the city, without leaving aside the elements of distance different from the Euclidean distance. It represents a comparative research method which can compare real and empirical data from every system in the future. If we compare the situation before and after the proposal of a route for the BTB collection and the new location of the blue containers, we can see a significant reduction in the contribution of the shops in the blue containers. We therefore avoid overflow and offer more room for domestic PCB refuse. On the other hand, we provide a service to the shops that generate more waste, about 695 shops (food 410 kg/day; HORECA 380 kg/day and textile and shoes 235 kg/day). This represents 98% of need, leaving aside the 470 shops ‘‘out of the route’’ which deposit their waste in the special containers for domestic refuse because their waste generation is smaller. With the system we put forward (in shopping areas), we went from one container for 12 shops to one container for 3 shops, which means each container is filled with 2.8 kg/day instead 14.44 kg/day. The rest is for PCB of domestic origin and we prevent them from overflowing. Most of the shopkeepers are in favour of this system. They think the collection points are in the right place, within 60 m of their shops. The BTB system includes the so-called possible points or larger generation density points. The GIS designed six routes according to the special roads, taking into account both the possible points and traffic restrictions.
For the six routes designed, the BTB service takes 130 min, and 30 more minutes for the connection between the routes. The whole length of time with connections is 9360 min. It gives service to 695 shops (59% of the city) and loads 1027 kg/day at the possible points. The service is carried out from Monday to Saturday from 8 p.m. onwards, and has been well received by the shopkeepers. Finally, after the new location of the containers which service small businesses and homes outside of the route, we observe there is no overfilling and the quality of the material has increased, consisting of more dense paper and a higher proportion of newspapers (fewer rejections), while on the collection on the BTB routes the material is clean, made up of cardboard boxes, thin cardboard and wrapping paper (90% of the total weight of the container). To sum up, the tool described has proved to be a suitable and safe method for the selective collection of PCB, with a high degree of sorting at source, which leads to a lower cost in the handling of the raw material by the recycling staff. Now this pilot experience is being transferred to districts of large cities like Madrid and Barcelona (Spain). Acknowledgements This paper has been possible thanks to the funding of the BTB project by ASPAPEL. Equally we are grateful for the facilities given by Leganes’s Town hall. References Alpaydin, E., Gu¨rgen, F., 2004. Comparison of statistical and neural classifiers and their applications to optical character recognition and speech classification. Cap. 2 of Image Processing and Pattern Recognition. Academic Press, Leondes, CT. ASPAPEL, 2004. Memoria de sostenibilidad del Papel. Paper Sustainability Report, Madrid, 71 p. Bach, H., Mild, A., Natter, M., Webwe, A., 2004. Combining sociodemographic and logistic factors to explain the generation and collection of waste paper. Resour. Conserv. Recycl. 41 (1), 65–73. Chang, N., Lu, H., Wei, Y., 1997a. GIS technology for vehicle routing and scheduling in solid waste collection system. J. Environ. Engrg. 123 (9), 901–910. Chang, N., Chang, Y., Chen, Y., 1997b. Cost-effective and equitable workload operation in solid-waste management systems. J. Environ. Engrg. 123 (2), 178–190. Cow, S., 1992. Pattern Recognition and Image Preprocessing. Marcel Dekker Inc..
J.V. Lo´pez Alvarez et al. / Waste Management 28 (2008) 282–293 Craig, K. Johnston, S., Carvalho, A., Bruce, R., 2002. GIS application within the diagnostic modeling system. In: Stephen Garbaciak, Jr. (Ed.), 3rd Speciality Conference on Dredging and Dredged Material Disposal, Orlando, FL, USA, May 5–8. Devijver, P., Kittler, J., 1982. Pattern Recognition. A Statistical Approach. Prentice-Hall, Englewood Cliffs, NJ. Duda, R., Hart, P., 1973. Pattern Classification and Scene Analysis. John Wiley. Everett, J., 1994. Environmental collective action; residential recycling programs. J. Profl. Issues Engrg. Educat. Pract. 120 (2), 158–176. Flintsch, G., Chen, C., 2004. Soft computing applications in infrastructure management. J. Infrastruct. Syst. 10 (4), 157–166. Fukunaga, K., 1990. Introduction to Statistical Pattern Recognition. Academic Press. ´ az, B., 2005. Influence of distance on the Gonzalez-Torre, P., Adenso-Dı motivation and frequency of household recycling. Waste Manage. 25, 15–23. Hummel, J., 2001. What does it cost to recycle household waste? Warner Bull. 76, 8–10. Instituto Nacional De Estadistica. Espan˜a, 2004. Jain, A., Dubes, R., 1988. Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, NJ. Klee, A., 1993. New approaches to estimation of solid-waste quantity and composition. J. Environ. Engrg. 119 (2), 248–261. Lave, L., Hendrickson, C., 1999. Municipal solid waste recycling issues. J. Environ. Engrg. 125 (10), 944–949. Leao, S., Bishop, I., Evans, D., 2001. Assessing the demand of solid waste disposal in urban region by urban dynamics modelling in a GIS environment. Resour. Conservat. Recycl. 33 (4), 289–313. MacDonald, M.L., 1996. A multi-attribute spatial decision support system for solid waste planning. Comput. Environ. Urban Sust. 20 (1), 1–17. Miller Associates, 1999. Project INTEGRA: research, attitudes and behaviour. Report no. 1, Main Findings, Miller Associates, Southanpton, UK. Moloney, D., 2002. Research into householder participation in recycling schemes. Warner Bull. 85, 16–19.
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Perrin, D., Barton, J., 2001. Issues associated with transforming household attitudes and opinions into materials recovery: a review of two kerbside recycling schemes. Resour. Conserv. Recycl. 33, 61–74. Ramu, N., Kennedy, W., 1994. Heuristic algorithm to locate solid-waste disposal site. J. Urban Plng. Develop. 120 (1), 14–21. Rasdorf, W., Janisch, A., Robson, F., Tilley, C., 2002. Testing and evaluating alternative algorithms for location referencing system route generation. J. Comput. Civil. Engrg. 16 (3), 194– 205. Read, A., 1999. A weekly doorstep recycling collection: I had no idea we could-overcoming local barriers to participation. Resour. Conserv. Recycl. 26, 217–249. Richards, J., 1986. Remote Sensing Digital Image Analysis. SpringerVerlag, Berlin. Robinson, G., Read, A., in press. Recycling behaviour in a London Borough: result from large-scale household surveys. Resour. Conserv. Recycl. Shih, L., Lin, Y., 1999. optimal routing for infectious waste collection. J. Environ. Engrg. 125 (5), 479–484. Tou, J., Gonzalez, R., 1974. Pattern Recognition Principles. AddisonWesley, Reading, MA. Valeo, C., Baetz, B., Tsanis, I., 1998. Locatrion of recycling depots with GIS. J. Urban Plng. Develop. 124 (2), 93–99. Williams, I.D., Taylor, C., 2004. Maximising household waste recycling at civic amenity sites in Lancashire, England. Waste Management 24 (9), 861–874. Wilson, B., Baetz, B., 2001. Modeling municipal solid waste collection system using derived probability distributions. I: Model development. J. Environ. Engrg. 125 (11), 1031–1038. Wilson, B., Baetz, B., 2001. Modeling municipal solid waste collection system using derived probability distributions. II: Extensions and applications. J. Environ. Engrg. 127 (11), 1039–1047. Woodard, R., Bench, M., Harder, M.K., Stantzos, N., 2004. The optimisation of household waste recycling centres for increased recycling – a case study in Sussex, UK. Resour. Conserv. Recycl. 43 (1), 75–93.
Waste Management 28 (2008) 282–293 www.elsevier.com/locate/wasman
Optimizing the collection of used paper from small businesses through GIS techniques: The Legane´s case (Madrid, Spain) J.V. Lo´pez Alvarez a
a,*
, M. Aguilar Larrucea b, S. Ferna´ndez-Carrio´n Quero a, A. Jime´nez del Valle a
Departamento de Forest Engineering, Escuela Te´cnica Superior de Ingenieros de Montes, Technical University of Madrid, Ciudad Universitaria s/n, 28040-Madrid, Spain b Environmental Council of Castilla-La Mancha, Guadalajara, Spain Accepted 26 February 2007 Available online 8 August 2007
Abstract This article deals with a methodology for the design of routes for the ‘‘bin to bin’’ (BTB) collection of paper and cardboard waste (PCB) from small businesses, as well as with the new location and calculation of the number of containers needed in the streets for both commercial and non-commercial use due to the large amount of PCB deposited in them. This study was carried out in five shopping areas of the city of Legane´s (Community of Madrid, Spain). One of the characteristics of the area is a high density of population and urban traffic. The tool used is the Geographical Information System (GIS-Arc-View). With it we can generate PCB points of high population density in commercial streets based on territorial analysis. We placed the special routes and the new container locations within a distance of 60 m of these collection points (CPT). The system calculates and optimizes six routes according to different urban restrictions. Finally, we provided service to 59% of the shops, which generate almost 82% of the PCB waste, using 160 min per day to collect 1027 kg of high quality PCB. If we compare the system with the system in place previously, we can conclude that the ‘‘bin to bin’’ (BTB) system improves the quality of the PCB in the containers, avoiding overflow and reducing the percentage of rejected material. Ó 2007 Elsevier Ltd. All rights reserved.
1. Introduction Blue containers (3 m3) for paper and cardboard (PCB) were designed for the selective collection of residential waste. Due to the non-existence of a specific service for the collection of used PCB, small businesses normally use the blue containers, but because of the nature of the packing used by these businesses, there are a number of deficiencies unjustly associated with these blue containers. These problems consist mainly of the presence of large cardboard boxes in the streets (Fig. 1) and the inefficiency in the filling of the blue containers, which become full and blocked with large, low density boxes. *
Corresponding author. Tel.: +34 91 3366411; fax: + 34 91 3367101. E-mail address: [email protected] (J.V. Lo´pez Alvarez).
0956-053X/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2007.02.036
A solution to the problem is to collect the PCB where it is produced, with a ‘‘bin to bin’’ (BTB) collection system. This service is offered in addition to the PCB collection using the blue containers. It is designed for zones with small shops that produce large amounts of PCB, in order to avoid the collapse of the igloo type container system used by the citizens, and in zones that cannot have a large number of containers due to their specific characteristics. It does not mean that every shopkeeper is going to have the PCB collection on their doorstep. The collection will be made in places previously determined by the collection service and shopkeepers. A BTB collection of the paper generated in schools and city offices can be made with the help of ecocontainers and other collection systems, with a schedule planned by the City Council.
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mada, El Carrascal, Legane´s Centro, La Fortuna and Legane´s Norte (Fig. 2), although we used the Zarzaquemada zone as an example for a later use on the rest of the city. 2. Background and rationale
Fig. 1. Container of PCB. Previous location in commercial district.
This current study focuses on the city of Legane´s (Community of Madrid), which belongs to the so-called ‘‘metropolitan crown’’ of the capital city. One of the characteristics of Legane´s is that it has a high population density: 184,841 inhabitants, over a surface area of 4324 ha and 65,802 dwellings (INE, 2004). It has more than 1300 small shops which generate, according to our information, an average of 1.2 kg/day/shop of packing and PCB waste. The study focused principally on five areas of the city: Zarzaque-
Nowadays the treatment of recycled waste depends almost exclusively on official policy and waste treatment plans (Read, 1999; Lave and Hendrickson, 1999). These policies nevertheless do not take into account the technical aspects for optimizing and increasing efficiency in waste collection. In some cases, either the real amount of waste generated is unknown or estimates are made based on sample techniques in waste collection plans or dumps (Klee, 1993). In this way many other studies have already been carried out in different fields: There are tendencies for the new location of containers to be decided according to socioeconomic parameters, such as for glass (Gonzalez-Torre and Adenso-Dı´az, 2005) or paper (Bach et al., 2004). The work which relates distance of the container from the user and the time the producer takes to bring the waste from his home is based on these references which, in turn, relate to other studies previously carried out. These studies help to clarify the recycling system from a social behaviour point of view (Everett, 1994) and other aspects about citizen participation and environmental education (Miller, 1999; Read, 1999; Moloney, 2002; Perrin and Barton,
Fig. 2. Distribution of the project by zones in Legane´s (Community of Madrid).
284
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2001), but they do not deal with the problems of the small businesses, which cause overfilling of the containers or the accessibility of the collection points (contribution areas) for the collection vehicles, which generates inefficiency in the collection system. Other studies concern the integrated territorial approach to the treatment of recycled waste, such as the ones carried out in Sussex (Woodard et al., 2004); London (Robinson and Read, in press); and Lancashire (Williams and Taylor, 2004), to give some recent examples. Both approaches, the socioeconomic and the territorial, offer acceptable solutions to the integrated treatment of domestic refuse, even when the collection cost (Hummel, 2001) or the important role played by the cost-efficiency variables caused by physical or territorial restrictions are taken into account (Chang et al., 1997). These authors demonstrate that it is necessary to look for a solution in the field of territorial logistics separately from the models of mathematical programming used to minimize costs in waste collection, transport, treatment and disposal. However, these policies leave aside one of the generators of the PCB waste, the small business. Small businesses are usually situated in the old city centres or in tourist areas. Their main characteristic is the daily generation of a very dense amount or cardboard (wrapping paper, test liner, corrugated cardboard). The collection problem is that either the containers cannot take these materials or the collection vehicles cannot get into these zones, most of which are pedestrianized. We can see that the average daily collection of PCB in Spain is about 46% in weight of the consumption (ASPAPEL, 2004). A large part of the paper generated by small businesses is overlooked. This could represent at least 20% more being collected. The use of the Geographical Information System (GIS) in these studies using the territorial approach already has antecedents in the design of dynamic generating models (Leao et al., 2001) or in rules or multicriteria decisions to explain the treatment of domestic refuse in the territory from the point of view of space (MacDonald, 1996). Detailed studies of the problem of optimizing routes for the collection of infectious waste from hospitals have already been made (Shih and Lin, 1999). However, the methodology is different because the origin and destination, and even the amount, are known. So the problem is a simple logistical one of optimization. The use of multiobject criteria in order to make decisions in the analysis of alternative routes for the waste collection through GIS (Chang et al., 1997) offer solutions before and after the collection, like a dynamic model. However, they do not deal with the possibility of placing PCB containers according to technical parameters, changing them according to an optimized deposit and collection route, thus avoiding the overfilling of some containers or containers being filled to less than half their capacity. We based the solution on the design of collection routes or the existing infrastructure (street and PCB containers) needing BTB collection with the GIS, taken from more complex studies that use algorithmic types of neural networks, fuzzy technology and genetic algorithms under conditions of incertitude or
incompleteness, for the use of infrastructure in general (Flintsch and Chen, 2004), which is not the case in the present paper. Finally we quote a study closely related to this one, based on the use of GIS for the location of light containers (Valeo et al., 1998). In this work we used the ‘‘maximum coverage distance’’ model (Manhattan distance), but we fixed the suitable places for the location of the containers a priori (shopping centres, city parking places, sidewalks of long streets) leaving aside the rest and quantifying the numbers and places for these containers. None of these studies give a complete solution to the problem we deal with here: we needed to know the distribution of the small businesses that generate PCB and to calculate the function of the generation density. Then we determined the possible points for a regrouping or nodal points. We drew the radius of the service coverage on them (as we can see below these cannot be fixed in advance) and in this way we were able to calculate the special routes. The GIS drew the best routes needed according to the city restrictions and finally, we estimated the number of containers and their location according to current and anticipated waste generation. 3. Objective The objective of this study was to create a new methodology for effecting a series of improvements for the collection of the PCB in the city of Legane´s (Spain), from a technical point of view. This methodology could be offered to other cities with similar characteristics. The objective was to find out the number of PCB containers, identify the needs of waste collection in the city’s small business zones and draw the best route that would link the main points of the small businesses’ PCB generation (contribution areas), along the main street of the city. The service offered by this route would improve the use of the 3-m3 containers, which could be used in zones with a higher density of homes, avoiding overflow or incorrect filling, which makes the PCB collection system inefficient and so optimize the collection of higher density materials already classified. 4. Methodology The methodology used was the following: (1) territorial analysis, (2) field work, (3) GIS analysis, and (4) service design. Below is a description of the different sections. In order to make the methodology description and results clearer, the results are shown initially in reference to Zarzaquemada, and are subsequently shown in relation to the rest of the city. 4.1. Territorial analysis This analysis was based on the identification of the needs and/or restrictions in the collection service. The city
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areas were identified according to their level of business activity. We needed to inspect and identify the collection needs of small businesses. The local authorities, who know the local traffic problems and shops timetables, informed us of the restrictions to take into account in the collection route. In order to do that we had to process and analyse information that was the basis for the proposed methodology. This consisted of:
285
times it is cleaned a year), and suitability of the point according to these criteria:
Accessibility for pedestrians, Accessibility for vehicles, Visibility, Density of homes around it.
4.3. GIS analysis
Street Plan in digital format (scale 1:6.000), Ortophotos Community of Madrid (scale 1:1500), National Classification of Economic Activities, GPS, as basic material, to locate the small shops (which in turn were placed with name and street number) and the PCB containers.
4.2. Field work In order to establish the distribution of the shops, the PCB generation and attitude and preferences of the city shopkeepers, 300 interviews were carried out in situ. These obtained information about: type of shop and name, location (through GPS coordinates), size (number of employees), surface area, PCB generation (number of boxes), frequency and types:
Small Box: about 300 g/un, Medium Size Box: about 600 g/un, Large Box: about 900 g/un or more, Interest of the shopkeepers in the Collection system BTB and best time for waste deposit.
One of the main points in this study consisted of working out a system for classifying the small businesses in relation to their average daily PCB generation. A number of different typologies were created for 1200 shops, which allowed an accurate estimate of PCB generation in the shops that were not surveyed or did not give information about their waste generation (Table 1). Apart from the information obtained from the shopkeeper interviews, all of the shops in the area were identified, placing their geographical position on a street plan, for a later analysis in the GIS (Alpaydin and Gu¨rgen, 2004). At the same time, a detailed study of the present location of the containers was made in the city. An inventory of all of the containers was made in situ. They were found through GPS. In this way they could be analysed in the GIS in relation to the small businesses (Fig. 3). The inventory contained information about: places with PCB containers, number of containers, type (volume), collection frequency (days/week), capacity (litres, inhabitants/ year), distribution of containers according to population density, cleanliness around the container (high, medium, or low), maintenance of the blue container (number of
The use of the GIS in this study allowed us to create, arrange, treat and analyse the geographical information, organized by means of graphic and alphanumeric data (Richards, 1986). The GIS used is the Arc View 3.1, which enabled us to have a geographical reference for all of the points on the digital street map. In order to analyze the information through ArcView 3.1, the order followed in the study was the following: distribution of small businesses, estimate of the density of PCB waste generation (Devijver and Kittler, 1982; Fukunaga, 1990) possible regrouping points or waste collection, identifying the area covered by each collection (contribution) point, estimating the special routes (compulsory) for the collection vehicles and, finally, drawing the best collection route. We describe below the method and basis used in each step. 4.3.1. Small business distribution By entering the information obtained in the field work, we were able to quickly analyse the distribution of small businesses in the city of Legane´s. A priori we could easily distinguish the biggest commercial areas, that is the shopping streets. A PCB collection route could go along them. 4.3.2. Generation density By associating every shop seen on the map to the business typology and its waste generation, every point would have a specific value. By means of a GIS tool called ‘‘Density Calculation’’ we calculated the density according to their PCB generation. We then used an adaptable grouping method, in which we used two parameters which are related according to the following function (Tou and Gonzalez, 1974; Jain and Dubes, 1988; Cow, 1992): f ðxÞ ¼
1 d 2 1 pffiffiffiffiffi e2ðhÞ 2 2p h
where h is the distance threshold for creating groupings, d is the fraction of h which determines total confidence, according to the PCB generated or distance from any other x point in the space. This function generates spots (Fig. 4), being most intense at the point of observation and decreasing in relation to the distance from the point, more or less quickly, depending on the h. The curve approaches zero intensity asymptotically, without being totally extinguished. So the observations made close to a place in the space are very sig-
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Table 1 Classification of the small businesses according to generation parameters General type
Type in detail
Level
Size (m2)
Employees
Generation (g/day)
Generation (kg/day)
Category
Food
Nuts and dried fruit Groceries Butchers Fishmongers Frozen food Fruit shops Bakers Drinks
Small Medium Large Supermarket
<50 50–100 >100 >100
1 >1 P2 P4
1200 3000 5000 8000
1.2 3.0 5.0 8.0
Low Medium High Exceptional
Hotel Restaurant Coffee-shop
Bar Pub Cafeteria Restaurant Fast Food
Small Bar Medium Bar Large Bar Small Restaurant Large restaurant Small Fast Food Large Fast Food
<50 50 – 100 >100 <100 >100 680 >80
1 P2 P3 62 P3 62 >2
700 1400 2200 1500 2400 1500 4000
0.7 1.4 2.2 1.5 2.4 1.5 4.0
Low Low Medium Low Medium Low High
Textile Shoes Fashion Accessories
Clothes Shoes Fashion accessories Sportswear Haberdashery Jewellers
Small Medium Large
<75 75–120 >120
1 P2 P3
500 1800 7000
0.5 1.8 7.0
Low Low Exceptional
Home Furnishings
Furniture Mattresses Bathrooms Kitchen Electrical Appliances
Small Medium Large
<100 100–175 >175
P2 62 P3
300 800 3000
0.3 0.8 3.0
Low Low Medium
Decoration
Household Items H Lamps
Small Medium Large
<70 70–120 >120
1 2 to 3 P2
250 1200 3500
0.3 1.2 3.5
Low Low Medium
nificant, while the more distant observations make an infinitesimal contribution (Craig et al., 2002). The general lines of the algorithm are the following: 1. The essential part of the algorithm is to create groupings on the basis of the h distance (weighted by the d). The first group is established arbitrarily. 2. When a shop is ascribed to a group, the group’s centre is recalculated. This can change the status of other shops, either by them being removed from the group they belonged to before or by ascribing them to a new group. 3. Status changes were possible because we checked the total number of shops used for the grouping several times. This repetitive process ends when there are no more new ascriptions. The splitting is then considered fixed. 4. We use a part N of the total number of shops to be grouped, M, to define the groupings. So zones with a higher density generation take a definite shape as the possible points along a collection route. These high density spots merge to look like rivers (very busy
shopping streets) with their tributaries (entrance to shopping streets) with isolated points or fountains (isolated points with substantial density generation). So a zone with a large number of small shops very close to each other, but which do not generate significant amounts of PCB, will not be shown as a potential zone for a possible collection route, although it could seem so ‘‘a priori’’ (Fig. 4). 4.3.3. Collection area Once the collection points have been fixed because they are close to high generation spots along the potential route, we calculate, by means of the GIS, the number of shops each collection point can supply because of their proximity to the collection point, being within a 60-m area (Fig. 5). Apart from the number of shops supplied, it is interesting to calculate the average daily total volume for every collection point. The algorithm used in the GIS to calculate this covering distance is the ‘‘Euclidean distance’’ between x and y points (Wilson and Baetz, 2001), which is given in the expression: dððx1 ; x2 Þ; ðy 1 ; y 2 ÞÞ ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx1 y 1 Þ2 þ ðx2 y 2 Þ2
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It is interesting to know that there is more than one way to make an estimate of the distance between two points; in the Euclidean space it is made with the Pythagoras theorem, but for other spaces we could define the distance in a different way. That is why we think it is more useful to use the Euclidean distance as the calculation logarithm because we do not even know a priori what the final or ideal distribution of the containers or the contribution areas will be. It is good practice if we wish to maximize the service to the population; this decision must be based on distance, cost and social, environmental and political decisions. Without considering these last parameters, the Euclidean distance itself approaches the minimum cost of the service (Ramu and Kennedy, 1994).
Fig. 3. Distribution of shops (circles) and paper and cardboard containers (squares).
4.3.4. Special roads Due to the traffic difficulties for the vehicle used for collection, it is necessary to focus the analysis of the possible route by taking into account only those roads most suitable for small truck traffic, using topological and geometrical criteria (Rasdorf et al., 2002). These special roads for the collection route are marked on the street map, taking into account the restrictions in the traffic direction (Fig. 5). These special roads are divided into different sections in the GIS on the basis of crossroads or large housing blocks. In this way we can identify sections with high levels of business activity within any very long special road.
Fig. 4. Map of shops density according to generation.
where d is the Euclidean distance (m) between point x and y, (xl, yl) are the GPS coordinates in the territory of the collection point, and (x2, y2) are the GPS coordinates in the shops territory.
Fig. 5. Distribution of the preferential ways for the vehicles of selective collection. Daily withdrawal estimated in the suitable point (9.5–34.4 kg/ day).
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4.3.5. Possible route With the help of the GIS we carried out a space association in the shape of a reference node to identify which particular section of the route would supply each collection point, due to its proximity to the various collection points (Rasdorf et al., 2002). Every section was given an estimated amount of PCB generation, which is shown graphically in Fig. 6. In this way we can identify the ‘‘river’’ or street with the greatest amount of business activity and PCB generation. The route should go along this street. 4.4. Service design 4.4.1. Definition of the route With the information obtained with the help of the GIS regarding the possible routes, and having taken into account the restrictions to the road traffic, we defined the best route for the PCB business collection. The best route will be the one that offers a service to points with the largest amount of PCB generation. The route should go along those sections of the special road where there is substantial business activity. In this way the resources used for the collection, the length of the route and the time taken to complete the collection are minimized (Rasdorf et al., 2002). The algorithm used in the GIS is the MGT (Minimal Generator Tree) because we can combine the characteristics of distance and density (Duda and Hart, 1973) in the MGT. The algorithm is based on the estimate of an average distance between the groupings, defined as dminðS i ;S j Þ ¼
min fdðX ; X 0 Þg
X 2S i ;X 0 2S j
in such a way that for any of the two groupings Si and Sj, dminðS i ;S j Þ is the distance between the groupings’ two closest points. We must note that in this case, a grouping is not represented or characterized by its centre, but by a member of the grouping. In fact, instead of using the MGT as a starting point we use a main diameter of the MGT, which is defined as follows: the main diameter of an MGT is the one which is the most significant (Duda and Hart, 1973). The system determines the typological groupings and makes the routes pass along the centres of gravity of the generation nuclei previously decided. These routes are still fictitious and fit in the model of probabilities according to the type of vehicle, speed, and load and unload stops. Therefore they must be adapted to the street plan, street directions, their width, rush hours, etc. The groupings were obtained by repetition, taking areas of 20, 30 and 60 m (Fig. 4). As we can easily assume, it is possible to find several main diameters on the same MGT. Practically, and with the idea in mind of making the estimate computationally manageable, a main diameter is estimated taking into account the MGT branches, leaving aside the branches leading to less important routes. 4.4.2. New location for the blue containers The sections or zones with a high density of small businesses, but with low levels of PCB generation or those which are far away from the logistical optimization of the route, have been more effectively provided with 3-m3 containers after their relocation. This equipment needed was estimated by means of the methodology used to determine the contribution areas.
100.0 90.0 80.0 70.0 60.0 SMALL BUSINESSES (%) 50.0 40.0 30.0 20.0 10.0
% ACCUMULATED
0.0 % COVERED
0 - 20 m 20 - 40 m 40 - 60 m DISTANCE (m)
%
COVERED
% ACCUMULATED
DISTANCE 0 - 20 (m)
ROUTE
COVERED (%)
20 - 40 (m)
ACCUMULATED (%)
COVERED (%)
40 - 60 (m)
ACCUMULATED (%)
COVERED (%)
ACCUMULATED (%)
CENTRO EL CARRASCAL LA FORTUNA LEGANÉS NORTE ZARZAQUEMADA
81.8 51.4 25.6 74.0 31.5
81.8 51.4 25.6 74.0 31.5
14.1 21.4 69.4 20.0 38.5
95.9 72.9 95.0 94.0 70.0
4.1 20.0 5.0 6.0 24.9
100,0 92,7 100,0 100,0 94,9
LEGANÉS
62.4
62.4
23.0
85.4
12.4
97,8
Fig. 6. Level of service BTB for the small trade.
J.V. Lo´pez Alvarez et al. / Waste Management 28 (2008) 282–293
5. Results and discussion 5.1. Field work analysis 5.1.1. Blue container The PCB containers in the city of Legane´s are the metal igloo type with a capacity of 3 m3, except in Zarzaquemada, where 18 out of its 64 igloos have a capacity of 2.5 m3. Altogether there are 262 containers; 153 of them are in the five study areas (Table 2). We observed that there were about 12 shops which were leaving their PCB waste in one container, which meant that it received about 14.5 kg/ day. With the arrangements we describe in this article, the contribution diminished to almost 3 kg/day, that is to say, about three shops for one container, leaving more room for the PCB from homes. 5.1.2. Description of the small businesses From the interviews held with the small shopkeepers, we obtained the following information: the shops could be grouped into 18 different types according to activity, the PCB generation for each activity. In the city the most common type of business is Hotel and Catering (HORECA), comprising 26.1% of the sample chosen. It is followed by Textile, Shoes Fashion and Accessories at 17.3%. This is followed by Food at 13.6% and Hairdressing and Health and Beauty salons at 7.0%. The information from the interviews shows that, in relation to the way the PCB generated by the small shopkeepers was disposed of, 56% of them stated that they used the blue container. The percentage of shops interested in the BTB service was greater among the shops with a significant level of PCB generation (1.5 kg/day or more), very close to the routes designed by the GIS (60 m or less). 5.2. GIS results Distribution of the small businesses. The zone with the greatest business activity is Legane´s Centro. Although it
289
has a smaller number of shops than Zarzaquemada, these shops are bigger in area and number of employees, and on average produce a larger level of daily PCB generation (Table 3). Density of waste generation. The study of density shows very different patterns according to the particular zone. While in Zarzaquemada the density spots are distributed in the form of points in a more or less scattered distribution, in the Legane´s Norte zone the spots are distributed like currents or main rivers with some scattered points of high density. In the case of the La Fortuna district, the zones with a higher density of PCB generation are allocated along Avenida de la Libertad. By analysing the density in the El Carrascal district, we can see a level of PCB generation density lower than in Zarzaquemada. Finally in the Legane´s Norte zone, the small business density is near the axis, along Clara Janes and Petra Kelly streets. Possible collection points and special roads. The possible collection points, allocated to the spots with high generation density and next to the special roads, are shown by means of a graduated symbology according to the quantity which could be collected if they served the shops within 60 m. Possible routes. Fig. 7 shows those sections that are thought to have larger amounts of PCB. In Legane´s Centro, Juan Mun˜oz, Plaza de Parı´s, Teniente General Musiera and Sol streets are clearly the most suitable routes for a PCB business collection. 5.3. Service design Routes. Using the available information and the GIS to process it, six routes are defined (Fig. 9), one for Zarzaquemada, one for Barrio de la Fortuna, two for Legane´s Centro, one for El Carrascal and one for Legane´s Norte (Table 4). The routes previously mentioned only refers to the distance covered during the PCB collection; they do not include the distance between the routes. Details are given below: Distance between routes. Six routes were defined, with the characteristics given in Table 5.
Table 2 Previous situation and proposal for the location of containers Zone
Legane´s Centro (Town center) Zarzaquemada La Fortuna El Carrascal Legane´s Norte Total
Previous situation of the paper-cardboard containers
Proposal for the new distribution of the paper-cardboard containers
No. of papercardboard containers
No. of papercardboard containers
Shop/ container
Average generation per container (kg/day)
12
36
39.6
64 15 35 27
7 10 4 4 12.2
153
Shop/ container
Average generation per container (kg/day)
20
4
4.4
8.5 14.0 4.2 5.9
82 15 35 27
3 2 2 2
3.3 1.7 2.1 2.5
14.44
179
2.6
2.8
J.V. Lo´pez Alvarez et al. / Waste Management 28 (2008) 282–293
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Table 3 Generation of paper waste by shops in the city districts Zone
No. of shops
Total generation of the zone (kg/day)
Average generation (kg/shop/day)
Legane´s Centro Zarzaquemada La Fortuna El Carrascal Legane´s Norte
316
475
1.5
451 144 158 96
549 204 203 119
1.2 1.4 1.3 1.2
Fig. 8. Definitive location of containers. The darkest points show the businesses that must use the containers. The clearest points are the businesses to which one gives BTB service.
Fig. 7. Possible sections for the collection route, according to business generation of PCB waste.
Service parameters: collection duration. The length of time taken to collect the BTB was estimated to be 5 h for the six routes. The length of time for every route is given in Table 4. The length of time taken to drive from one route to another was estimated to be 45 min for the six routes (Table 5). Frequency. The PCB collection in the city should be made daily, from Monday to Saturday, in order to respond to the shopkeepers’ demands.
Timetable. From the information taken from the interviews and the City Council, the best collection time would be from 8 p.m. onwards, at the time when most of the shops close. Container location. For the final location of the PCB containers we firstly considered the zones that have a higher business concentration, trying to provide a service to the small businesses not covered by the routes of the BTB collection system (Fig. 8). By doing so, container overflow would be avoided and consequently the imbalance in the PCB container use which had limited their use by the domestic users. Since the 3 m3 blue container has been designed for domestic use, this calculation was made taking into account the highest density of dwellings. In order to identify the most suitable zones for the locations of the containers, we took into account the following aspects, mentioned in the field work methodology: the previous location of the PCB containers, accessibility for pedestrians and vehicles, residential density in the area, visibility and proximity to the small businesses. Some comparisons between the previous and optimized situations have been made using data collected during the last 6 mo and comparing this with information collected before the changes were made, taking into account that this work and the follow-up of the methodology is on-going
J.V. Lo´pez Alvarez et al. / Waste Management 28 (2008) 282–293
291
Fig. 9. Final configuration of the BTB PCB collection routes.
Table 4 Graphic design of the route Zone
Route
Legane´s Centro Zarzaquemada El Carrascal Legane´s Norte La Fortuna
Route Route Route Route Route Route
Total
1 2 3 4 5 6
Length (m)
Shops supplied
Percentage shops in zone
Calculated collection (kg/day)
Percentage collection zone (kg)
Estimated length of time (min)
1.295 930 2.895 1.191 900 1.304
175 67 212 70 50 121
74 83 47 44 52 84
258 128 279 87 91 184
545 27 51 42 795 91
20 14 44 18 14 20
8515
695
59
1027
67
130
Table 5 Length and distance between routes Origin
Destination
Length (m)
Centre 1 Centre 2 Zarzaquemada El Carrascal Legane´s Norte
Centre 2 Zarzaquemada El Carrascal Legane´s Norte La Fortuna
460 1640 810 1050 5400 9360
Total
(Table 6). Broadly speaking, following what has been mentioned above, we can see a significant reduction in the amount of material placed in the containers which later has to be rejected (a reduction of 10% in weight for the domestic containers, a reduction of 30% for the domestic
Total no. of kg/day collected
No. of shops collected
Estimated time (min)
258 386 665 752 843
175 242 454 524 574
2 8 2 8 10
1027
695
30
and trade PCB containers), and above all, in the BTB containers (10–15% in weight). We have also seen that there is no container overflow. As the equipment is optimized according to the load they receive, the average filling is 65% for the home containers, 50% for the home and busi-
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Table 6 Comparative improvements before and after the implementation of the methodology (latest average information for 6 mo for 3 m3 containers) Container
Before %L
D D/C BTB
75–100 M –
After %R
65–75 45–65 –
% weight fraction PCB
%L
White
Newspaper
Card board
Mix
10 5 –
15 20 –
12 30 –
63 45 –
50–75 45–65 75–95
%R
50–60 25–35 10–15
% weight fraction PCB White
Newspaper
Card board
Mix
10 7 1
20 25 5
15 40 90
55 28 4
D: domestic container, D/C: domestic/business, BTB: business container, M: overflowing, L: Level of container, R: Reject material in the container.
ness ones and 80% for the BTB ones. Finally, although the load of the containers is smaller and the rejection percentage is smaller too, the quality of the collected product has increased very much in the BTB system. As we can see, this system receives 90% of its weight in cardboard, which supports the principle of ‘‘sorting at source’’. This research is now on-going, trying to improve the methodology, by adding elements of population consumption, and the material and quality of the containers, according to the city zones. 6. Conclusions The GIS methodology used, the method of point grouping (shops), plus the MGT algorithm seem to be suitable for the analysis of the territorial information regarding the city, without leaving aside the elements of distance different from the Euclidean distance. It represents a comparative research method which can compare real and empirical data from every system in the future. If we compare the situation before and after the proposal of a route for the BTB collection and the new location of the blue containers, we can see a significant reduction in the contribution of the shops in the blue containers. We therefore avoid overflow and offer more room for domestic PCB refuse. On the other hand, we provide a service to the shops that generate more waste, about 695 shops (food 410 kg/day; HORECA 380 kg/day and textile and shoes 235 kg/day). This represents 98% of need, leaving aside the 470 shops ‘‘out of the route’’ which deposit their waste in the special containers for domestic refuse because their waste generation is smaller. With the system we put forward (in shopping areas), we went from one container for 12 shops to one container for 3 shops, which means each container is filled with 2.8 kg/day instead 14.44 kg/day. The rest is for PCB of domestic origin and we prevent them from overflowing. Most of the shopkeepers are in favour of this system. They think the collection points are in the right place, within 60 m of their shops. The BTB system includes the so-called possible points or larger generation density points. The GIS designed six routes according to the special roads, taking into account both the possible points and traffic restrictions.
For the six routes designed, the BTB service takes 130 min, and 30 more minutes for the connection between the routes. The whole length of time with connections is 9360 min. It gives service to 695 shops (59% of the city) and loads 1027 kg/day at the possible points. The service is carried out from Monday to Saturday from 8 p.m. onwards, and has been well received by the shopkeepers. Finally, after the new location of the containers which service small businesses and homes outside of the route, we observe there is no overfilling and the quality of the material has increased, consisting of more dense paper and a higher proportion of newspapers (fewer rejections), while on the collection on the BTB routes the material is clean, made up of cardboard boxes, thin cardboard and wrapping paper (90% of the total weight of the container). To sum up, the tool described has proved to be a suitable and safe method for the selective collection of PCB, with a high degree of sorting at source, which leads to a lower cost in the handling of the raw material by the recycling staff. Now this pilot experience is being transferred to districts of large cities like Madrid and Barcelona (Spain). Acknowledgements This paper has been possible thanks to the funding of the BTB project by ASPAPEL. Equally we are grateful for the facilities given by Leganes’s Town hall. References Alpaydin, E., Gu¨rgen, F., 2004. Comparison of statistical and neural classifiers and their applications to optical character recognition and speech classification. Cap. 2 of Image Processing and Pattern Recognition. Academic Press, Leondes, CT. ASPAPEL, 2004. Memoria de sostenibilidad del Papel. Paper Sustainability Report, Madrid, 71 p. Bach, H., Mild, A., Natter, M., Webwe, A., 2004. Combining sociodemographic and logistic factors to explain the generation and collection of waste paper. Resour. Conserv. Recycl. 41 (1), 65–73. Chang, N., Lu, H., Wei, Y., 1997a. GIS technology for vehicle routing and scheduling in solid waste collection system. J. Environ. Engrg. 123 (9), 901–910. Chang, N., Chang, Y., Chen, Y., 1997b. Cost-effective and equitable workload operation in solid-waste management systems. J. Environ. Engrg. 123 (2), 178–190. Cow, S., 1992. Pattern Recognition and Image Preprocessing. Marcel Dekker Inc..
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