Waste Management 59 (2017) 3–13

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Waste Management

journal homepage: www.elsevier .com/locate /wasman

Assessment strategies for municipal selective waste collection schemes

http://dx.doi.org/10.1016/j.wasman.2016.10.0440956-053X/� 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author at: Centro de Investigação de Tecnologias Agro-Ambien-tais e Biológicas (CITAB), Escola de Ciências da Vida e Ambiente (ECVA), Univer-sidade de Trás-os-Montes e Alto Douro (UTAD), Quinta de Prados, Apartado 1013,5001-801 Vila Real, Portugal.

E-mail addresses: mmferrei@utad.pt (F. Ferreira), cavelino@utad.pt (C. Avelino),ibentes@utad.pt (I. Bentes), crismato@utad.pt (C. Matos), cafonso@utad.pt(C.A. Teixeira).

Fátima Ferreira a, Catarina Avelino a, Isabel Bentes b, Cristina Matos b, Carlos Afonso Teixeira c,⇑aCMAT-UTAD, CEMAT-IST-UL, Universidade de Trás-os-Montes e Alto Douro, UTAD, Vila Real, PortugalbC-MADE UBI/UTAD, Universidade de Trás-os-Montes e Alto Douro, UTAD, Vila Real, PortugalcCITAB/UTAD, Universidade de Trás-os-Montes e Alto Douro, UTAD, Vila Real, Portugal

a r t i c l e i n f o a b s t r a c t

Article history:Received 28 June 2016Revised 6 October 2016Accepted 27 October 2016Available online 12 November 2016

Keywords:Selective collectionWaste managementSolid waste collectionCollection schemesPerformance indicatorsPerformance índex

An important strategy to promote a strong sustainable growth relies on an efficient municipal wastemanagement, and phasing out waste landfilling through waste prevention and recycling emerges as amajor target. For this purpose, effective collection schemes are required, in particular those regardingselective waste collection, pursuing a more efficient and high quality recycling of reusable materials.This paper addresses the assessment and benchmarking of selective collection schemes, relevant to

guide future operational improvements. In particular, the assessment is based on the monitoring and sta-tistical analysis of a core-set of performance indicators that highlights collection trends, complementedwith a performance index that gathers a weighted linear combination of these indicators. This combinedanalysis underlines a potential tool to support decision makers involved in the process of selecting thecollection scheme with best overall performance. The presented approach was applied to a case studyconducted in Oporto Municipality, with data gathered from two distinct selective collection schemes.

� 2016 Elsevier Ltd. All rights reserved.

1. Introduction

A linear economy is being progressively replaced by a circulareconomy that ideally seeks for a zero waste framework (Loughlinand Barlaz, 2006; Jenkins et al., 2009; Sidique et al., 2010; Cruzet al., 2012; Park and Chertow, 2014; Ghisellini et al., 2016). Thelinear economy is a basic structured model that relies on theextraction of raw materials and their processing into products,which, after used, are treated as waste and mainly disposed intolandfills. During the last decades, this model has been consideredas a successful model, driving to a lot of products at very lowprices, boosting the economies of developed industrialized coun-tries, and encouraging consumption. However, such ‘‘take-make-dispose” approach resulted in a massive increase of waste pro-duced, in the extensive use of non-renewable raw materials andin the emergence of inherent environmental problems, quicklypointing to circular economy practices, where all the materialsshould be reused yielding new products through reuse, recoveryand recycling, for an infinite number of cycles (Zhijun and

Nailing, 2007; EMF, 2012). In this context, waste recycling mustachieve higher targets in the near future.

Currently, Directive 2008/98/EC establishes, as a goal for 2020,that waste reuse and recycling reach 50% of the total waste pro-duced. In Portugal, the Strategic Plan for Municipal Solid Waste(PERSU 2020, 2014) provides the same target, highlighting thatthe reference value in 2012 was 25%. European Commission goeseven further, proposing as a target level to be attained on 2030 thatat least 70% of the municipal waste is reused or recycled (EC, 2014).

Despite the huge benefits of recycling, the costs associated withrecycled waste management are significantly high (Naustdalslid,2014), where the waste collection, transfer and transport comprisethe major part of the total cost, reaching 70% of the overall man-agement expenses (Tavares et al., 2009).

Many of waste management systems are not financially sus-tainable, particularly due to the high collection costs (Ferreiraet al., 2014). In order to reduce these costs, that will increase withthe intensification of recyclable waste production and with thenumber of materials to separate, the management of selectivewaste collection systems must engage a cautious analysis ofdesigns and equipment to reach efficient and effective systems.

This problem is a challenge and an incentive to innovation,technological progress and research to support the design of newequipment and materials, new technologies and new managementmodels (Rada et al., 2013; Ranieri et al., 2014), but also in the sys-tems performance improvement that, among others, comprises theselective collection costs reduction.

Table 1Core-set performance indicators IDe, ITe and IFe.

Performanceindicator

Definition Unit Description

Effective collectiondistance

IDe ¼ DeMSWc

km t�1 Distance travelled by thecollection vehicle per unit ofselective waste collected

Effective collectiontime

ITe ¼ TeMSWc

h t�1 Time spent per unit ofselective waste collected

Effective fuelconsumption

IFe ¼ FcMSWc

l t�1 Amount of fuel consumedby the collection vehicle perunit of selective wastecollected

4 F. Ferreira et al. /Waste Management 59 (2017) 3–13

In most part of the European countries the waste separationmodel is not mandatory nor unique (Ragazzi and Rada, 2008;Alvarez et al., 2009; Passarini et al., 2011), and Portugal is not anexception. Nevertheless, the traditional process relies on sourceseparation that reveals the largest achievements in collection ratesand ensures the quality of the recyclable material (EC, 2015). Therecycling model is basically organized in ecopoints, which havethree different colors of containers according to the type of mate-rial to be collected, namely paper/cardboard, light packaging andglass. These containers are emptied according to a frequency stip-ulated by the management entity, and the waste is transported tosorting units.

There is a wide range of containers that differ regarding theirshape, capacity and emptying process. The emptying process –hereinafter designated by collection scheme – requires that thecollection vehicle owns a special configuration able to maneuverthe containers. The types of collection schemes most used in Por-tugal are: (i) the Street-Side collection – that load the containerthrough the rear of the vehicle, with hydraulic lifting, usually witha three workers team; (ii) the Drop-off collection – that lift the con-tainer through a crane to the top of the vehicle, with a team of twoor three workers.

The efficiency and inherent cost of the collection service heavilyrely on several factors, including the selected collection scheme,with implications on the expended time to maneuver the vehicleand empty the container, on the distance travelled in each circuitand on the fuel consumption, among others. Easier containersmaneuver, with better coordination between the container andthe collection vehicle, result in smaller discharge times, increasinglabor efficiency. Fuel consumption significantly depends on thecollection time and distance, but it is also an intrinsic characteristicof each collection vehicle, according to the power required to per-form the containers emptying operation. These factors are decisivein the efficiency and costs of the systems. Thus, performance eval-uation of collection schemes should be a factor to be taken intoaccount in the selection of equipment (containers/vehicles), whichis traditionally based on its acquisition cost and its suitability forthe territory (Bing et al., 2014). The performance evaluation of col-lection schemes may also encourage the equipment manufacturersto improve their products.

A current practice on performance evaluation of waste collec-tion systems is based on the use of performance indicators(Sanjeevi and Shahabudeen, 2015; Woon and Lo, 2016; Halkosand Papageorgiou, 2016; Rigamonti et al., 2016;). However, theapplication of indicators has some limitations, mainly related withthe number of indicators used. While a single indicator only allowsa partial and restrictive analysis of the problem, a large number ofindicators lead to a complex and ineffective analysis. There areadditional difficulties related to data availability for the indicatorscalculation and also persists a lack of a systematic and standard-ized culture to perform a gathering of proper data and its subse-quent processing (Gamberini et al., 2013).

Teixeira et al. (2014) have developed and implemented a per-formance assessment methodology for mixed waste collectionbased on a statistical analysis of a core-set of three performancerelevant indicators, namely the effective collection distance, theeffective collection time and the fuel consumption. As the selectivewaste collection is a multi-material collection, in order to optimizeequipment resources, the same collection scheme must be used forthe different materials collected. In such case, the overall perfor-mance evaluation involving more than one indicator could reveala difficult inconclusive task if the indicators for different materialsindicate better performance for a specific material and worse toanother.

The aim of this study is to contribute to the development of per-formance evaluation methodologies to be applied in the selective

collection schemes. As the selective collection focuses on multiplerecyclable materials, the methodology developed in Teixeira et al.(2014), for the undifferentiated collection performance assess-ment, has been adapted to analyze the efficiency of selective wastecollection along a performance comparison at operational level ofthe described collection schemes. The analysis has been performedthrough several performance indicators, evaluated for each type ofrecyclable materials. As the performance indicators do not showthe same trend for the different materials, the analysis was furthercomplemented with the proposal of a performance index to enablean integrated analysis of the results obtained by the indicators.Such performance index consists in a proper weighted linear com-bination, reflecting the relative importance of each indicator in theanalysis of the different collection schemes.

The presented framework was tested through a case study con-ducted in Oporto Municipality, Portugal, that uses two distinctschemes in the waste selective collection. Nevertheless, thismethodology is sufficiently general and flexible to be easily appliedor adapted to other selective collection schemes andmunicipalities.

2. Methodology

The main goal of this study consists in a reliable decision-making of a best collection strategy taking into account the analy-sis of different selective collection schemes. In a first stage data iscollected, treated and statistically analyzed, based on selected vari-ables and performance indicators. This phase can provide conclu-sive information if selective waste collection behaviors andtrends follow in the same direction for all type of selective wastecollected in each collection scheme. Otherwise, it is necessary asecond stage, where a global performance index is established,combining the performance indicators, properly weighted. Thismethodology provides a useful tool to support decisions concern-ing the selection of better collection schemes and is easily adaptedto other different systems and locations.

The first stage of the methodology is similar to the onedescribed in Teixeira et al. (2014), in particular the variables sur-veyed and the core-set indicators framework adopted. In the col-lection circuits, the vehicle drivers register information that issubsequently processed into the variables collection distance(De), collection time (Te), fuel consumption (Fc) and amount ofselective waste collected (MSWc). A sampling method was chosenin order to minimize the interference in the normal operating ser-vice, nevertheless insuring a representative sample. At each collec-tion circuit, these data measures begin with the stop to load at thefirst collection point and end with the final container emptying.The selected performance indicators are the Effective CollectionDistance (IDe), Effective Collection Time (ITe) and Effective FuelConsumption (IFe) (cf. Table 1). This indicators core-set arises fromthe normalization of the variables De, Te and Fc by the variableMSWc, allowing a more effective benchmarking.

F. Ferreira et al. /Waste Management 59 (2017) 3–13 5

The statistical analysis performed aims to obtain crucial infor-mation to support waste collection performance assessment. Itcombines the use of descriptive statistics for data preliminaryassessment and inferential statistics to subsequent hypothesistesting and screening of results.

For each selective waste type, the main objective was to evalu-ate if statistical significant different performance patterns areobserved among the different collection systems. In this sense, apreliminary inspection of the random nature of the variables instudy was assessed through an exploratory data analysis, present-ing the typical summarizing measures of center (mean, medianand quartiles) and spread (minimum, maximum, standard devia-tion and range), along with data graphic representations (his-tograms and box-and-whisker plots). Mean, median and standarddeviation were further estimated with 95% confidence level inter-vals. Descriptive statistical analysis allowed not only to revealunexpected or misleading patterns in the raw data (that was con-veniently corrected) but also to discover patterns in the data to fos-ter the development of statistical hypothesis of interest forpopulations. The statistical significance (or non-significance) ofsuch conjectures was further evaluated using suitable inferentialprocedures. Normality of distributions was tested through theAnderson-Darling test. Due to the general non-normality observedand unbalanced group dimensions, the nonparametric Mann–Whitney U rank sum test was used to stochastically compare the(group) population distributions. As most of the variables exhibitskewed patterns, when significant differences were detected, theranks and the confidence intervals for medians were used to decidewhich group is stochastically larger, i.e., more plausible to havegreater outcomes. Correlations among performance indicatorswere also inspected.

Fig. 1. Collection scheme and waste type material by c

Fig. 2. MSW distribution by collection

The statistical analysis was conducted through the statisticalsoftware packages SPSS version 23.0 (Carver and Nash, 2012) andR version 2.15.0 (R Core Team, 2012). All the tests considered inthe paper were performed at a significance level of 5%.

Finally, it is proposed a global performance index that gathers,in a weighted form, the indicators and selective waste types, andattempts to provide an effective decision support tool. The weightsconsidered in this index rely on each specific system location. Forinstance, characteristics as population density and type of locationare determinant for the importance level to assign to the fuel con-sumption or collection distance indicators.

3. Case study

3.1. Study area

The methodology was applied on selective collection service(paper/cardboard, light packaging and glass) at Municipality ofOporto. Located in the north of Portugal, Oporto is an urban region(>20% of the total population) with 263,131 inhabitants, a surfacearea of 41.66 km2 and an average population density of 6337.4inhabitants km�2 (INE, 2011). Municipal solid waste (MSW) gener-ation approaches 124,968 tons per year, corresponding to a dailyaverage rate of 1.3 kg per inhabitant. 117,815 tons of the totalMSW annual amount are associated with mixed waste generationand the remaining 7153 tons concerns to source separated recy-clable materials (paper/cardboard, light packaging – low and highdensity polyethylene, polyethylene terephthalate, liquid packagingboard, and ferrous and non-ferrous metals – and glass).

Currently, the selective collection at Oporto Municipality isperformed along two distinct schemes: (i) manual rear loading

ollection scheme absolute frequency distributions.

scheme and waste type material.

Table2

Descriptive

statisticalmea

sures(intons

)fortheam

ount

ofMSW

colle

cted

byco

llectionsche

mean

dwaste

type

.

Collectionschem

e

Street-side[SSc](1.1

m3)

Drop-off[SUc]

(2.5

m3)

Min

Perc.2

5Med

ian

Mea

nPe

rc.7

5Max

Ran

geStd.

dev.

Min

Perc.2

5Med

ian

Mea

nPe

rc.7

5Max

Ran

geStd.

dev.

Pape

r/cardbo

ard

1.46

2.52

3.02

(2.66;3.56

)3.19

(2.95;3.45

)3.88

5.42

3.96

0.97

(0.81;1.09

)0.64

1.30

1.98

(1.76;2.12

)2.13

(1.98;2.29

)2.70

6.02

5.38

1.03

(0.89;1.16

)Ligh

tpa

ckag

ing

0.38

0.69

0.86

(0.78;0.90

)0.88

(0.81;0.97

)1.01

3.16

2.78

0.35

(0.18;0.54

)0.22

0.44

0.56

(0.52;0.58

)0.63

(0.59;0.67

)0.76

1.64

1.42

0.28

(0.24;0.31

)Glass

4.40

5.50

7.00

(5.94;8.41

)7.27

(6.53;8.04

)8.64

11.56

7.16

1.96

(1.45;2.37

)1.30

3.16

4.24

(4.00;4.41

)4.11

(3.86;

4.37

)4.86

8.72

7.42

1.26

(1.02;1.50

)

6 F. Ferreira et al. /Waste Management 59 (2017) 3–13

vehicles (20 m3), handling street-side containers (SSc; 1.1 m3) withopen lids, and (ii) crane loading vehicles (20 m3), handling drop-offand underground containers (SUc; 2.5 m3). The gathering is con-ducted for each selective waste type through 12 different circuitswith drop-off (9 circuits) and street-side containers (3 circuits).All collection vehicles employ teams of three workers from Mon-day to Saturday, 8 h per day. The disposal site is located 5 km out-side the municipal limits.

Within this case study, performed through an extensive moni-toring of the selective waste collection, a major challenge relieson the analysis of behaviors and trends for different schemes thatwould enable a decision-making support tool for the selection of abest collection strategy. The methodology is similar to the onedescribed in Teixeira et al. (2014), applied to a source-separatedcollection system, complemented with a final performance indexthat gathers the results for the different materials collected. Thismethodology is easily adapted to other case studies or source-separated collection systems.

3.2. Data collection

Data acquisition followed the methodology previously appliedin Teixeira et al. (2014). The primary goal was to obtain a represen-tative sample able to produce accurate generalizations about thecollection system performance. Data was collected through directmeasurement from municipal sources during one year, one (ran-domly selected) week per month, at both selective collection sys-tems and all type of source-separated materials. In each datacollection day, a random sample of different circuits scheduledwas observed. These observations were manually recorded by thevehicle drivers, gathering information related to the completewaste load along each collection circuit. Whenever possible, itwas collected a large number of observations to ensure a higherstatistical accuracy and confidence level for the achievements. Ineach selective collection route, the variables measured includedthe registry of vehicle number, circuit, type of container, type ofwaste collected, load weight, fuel consumption, distance and timeat route start, in the first and last collection points, in the disposalsite (in and out) and finally in the parking again. A preliminaryanalysis of the raw data collected was conducted in order to detectand eliminate data abnormalities, ensuring correct and useful data.During such data processing, several missing values have beenidentified and filled in, outliers were detected and identified as reg-istry errors that have been corrected, and collection circuits withnotorious data errors that could not be corrected, or with missingobservations that could not be completed, were removed from theoriginal data. After raw data correction procedure, the processeddata yielded 631 observations of different types of containersand different types of source-separated materials, whose absolutefrequency distributions are illustrated in Fig. 1.

Thereafter, the processed data was used to compute the totalamount of recyclable materials collected (MSWc, in tons), the dis-tance travelled (De, in kilometers), the time spent (Te, in hours)and the fuel consumption (Fc, in liters), from the first to the lastcontainer, further normalized to derive the core-set indicators –the effective collection distance (IDe, in kilometers per tons), theeffective collection time (ITe, in hours per tons) and the effectivefuel consumption (IFe, in liters per tons) – that aim to access sys-tem performance trends. Descriptive and inferential statistics wereused for the data analysis.

3.3. Statistical analysis

We begin to analyze the amount of MSW collected during thecollection period of this case study. Concerning the recorded data,1350.24 tons of MSW have been collected selectively. As expected,

F. Ferreira et al. /Waste Management 59 (2017) 3–13 7

and taking into account the number of the observations by wastetype, the glass contributes for the majority of the total weight ofwaste in percent. The amount of glass and paper/cardboard largelyexceeds the amount of light packaging collected (Fig. 2).

This is not surprising and is well explained by the waste typedensities and their respective volume inside containers. In fact,glass has high density when compared to light packaging andpaper/cardboard, and usually breaks in small pieces that occupysmall volume inside containers. In turn, although light packagingmay have higher density than paper/cardboard, in general, it tendsto occupy higher volumes, as frequently plastic bottles, tetra-packsand metal cans are not compressed before introducing in thecontainers.

Attempting to assess selective collection global performance, apreliminary statistical descriptive analysis was conducted to theprocessed survey variables (MSWc, Te, De and Fc). For each vari-able and both collection schemes – SSc and SUc, descriptive mea-sures were obtained along with box-and-whisker plots andhistograms, of which we present in Table 2 and Fig. 3 those forthe amount of MSW collected.

The presented measures reveal some evidence that significantdifferences may exist between the amounts of waste collected onthe two types of containers, for each type of (selective) waste col-lected. Surprisingly, despite their smaller capacities (1.1 m3), SSccontainers circuits tend to achieve higher amounts of waste col-lected, compared with SUc containers (2.5 m3) circuits, for allwaste types. In particular, summary measures clearly assumegreater values for SSc containers, and it can be observed that:

– the minimum amount of glass collected in circuits with SSccontainers (4.4 ton) exceeds the median and mean (4.24 and4.11 tons) values for SUc, and the maximum achieved forSUc (8.72 ton) only slightly exceeds the 3rd quartile (8.64ton) for SSc;

– the minimum amount of paper/cardboard collected in circuitswith SSc containers (1.46 ton) exceeds the 1st quartile (1.3

Fig. 3. Boxplots and histograms for the MSW amount (in

ton) for SUc, and the mean and all quartiles are slightly largerfor SSc containers (mean, 1st, 2nd and 3rd quartiles: 3.19,2.52, 3.02 and 3.88 tons versus 2.13, 1.3, 1.98 and 2.7 tons);

– the 1st quartile for the amount of light packaging collected inSSc (0.69 ton) exceeds the corresponding median and mean(0.56 and 0.63 tons) for SUc.

Although the amount of waste collected provides relevant infor-mation for the different collection schemes, it must be comple-mented with additional information to drawn reliableconclusions on the overall systems performance comparison. Infact, despite the empirical evidence that SSc containers schemeperforms better in terms of the amount of selective waste col-lected, the analysis of the De, Te and Fc variables (see Fig. 4)revealed that SSc containers also tends to highlight larger collec-tion distances, times, and fuel consumptions, that may lead to amore expensive system collection. Therefore, to compare the effi-ciency of both collection schemes, it is crucial to combine thisinformation in an integrated view, and resort to a performanceindicators analysis. Accordingly, a preliminary statistical descrip-tive analysis was also undertaken over the core-set of performanceindicators – IDe, ITe and IFe, gathering baseline data, to guide thecomparison between the two groups of circuits, drop-off (SUc)and street-side (SSc) containers.

Observing descriptive measures of Table 3, along with the box-and-whisker plots and histograms presented in Fig. 5, it is worth-while to note that unusual high extreme values are observed, espe-cially for SUc containers. We realize that both type of collectionschemes show similar right skewed distributions patterns forIDe, ITe and IFe indicators, revealing the existence of several extre-mely inefficient waste collection results.

Despite the collection time in SSc containers have higher valuescomparing to SUc containers, for all selective waste types, weobserve that, as they lead to higher amounts of MSW collected,the former containers present smaller collection times by toncollected, which reveals a better efficiency in terms of MSW

tons) stratified by collection scheme and waste type.

Fig. 4. Boxplots for the De, Te and Fe stratified by collection scheme and waste type.

8 F. Ferreira et al. /Waste Management 59 (2017) 3–13

collected by unit of time. SSc containers have the drawback ofinvolving, in general, greater collection distances and consequentlyhigher diesel consumption by ton of MSW collected.

Before checking if statistical significant differences exist on theMSW amount, IDe, ITe or IFe, within both types of containers, weneed previously to verify whether the group distributions undercomparison could or not be considered normal. For this purpose,Anderson-Darling normality test was performed for each group

variable distribution and the test statistics results and p-valuesare presented in Table 4.

For the large majority of the variables tested, near zero p-valueswere attained, strongly confirming the non-normality of such dis-tributions. Exception made for MSW amount, IDe and ITe distribu-tions for paper/cardboard and SSc containers, and MSW amountdistribution for glass and SSc containers, cases where no statisticalevidence exist to reject the normality assumption (p-value > 0.05).

Table3

Descriptive

statisticalmea

suresfortheindicators

core-set.

Collectionschem

e

Street-side[SSc](1.1

m3)

Drop-off[SUc]

(2.5

m3)

Min

Perc.2

5Med

ian

Mea

nPe

rc.7

5Max

Ran

geStd.

Dev

.Min

Perc.

25Med

ian

Mea

nPe

rc.

75Max

Ran

geStd.

Dev

.

Pape

r/cardbo

ard

IDe

6.14

11.67

14.76(13.25

;15.94

)15

.18(13.90

;16.57

)18

.42

30.00

23.86

5.15

(4.13;6.02

)2.42

7.87

11.30(10.34

;12.19

)12

.79(11.78

;13.81

)15

.91

42.31

39.89

6.94

(5.83;7.80

)ITe

0.73

1.00

1.41

(1.31;1.54

)1.43

(1.32;1.55

)1.63

2.74

2.01

0.45

(0.36;0.52

)0.68

1.32

1.84

(1.62;1.98

)1.94

(1.82;2.06

)2.51

4.42

3.74

0.83

(0.74;0.91

)IFe

4.23

6.94

9.02

(8.24;10

.44)

9.89

(8.86;10

.91)

11.35

24.55

20.32

4.11

(3.05;5.05

)1.03

3.96

5.73

(5.20;6.00

)6.47

(5.86;7.08

)7.29

29.64

28.61

4.09

(3.13;5.03

)

Ligh

tpa

ckag

ing

IDe

15.82

43.08

53.85(48.84

;58.24

)55

.56(51.22

;60.24

)65

.33

120.69

104.87

18.15(12.82

;22.98

)6.25

27.17

38.73(36.21

;40.74

)41

.36(38.28

;44.52

)47

.83

130.56

124.31

22.63(18.95

;25.84

)ITe

1.50

4.04

4.92

(4.44;5.37

)5.09

(4.73;5.46

)6.06

9.87

8.37

1.50

(1.17;1.79

)1.84

4.05

5.88

(5.48;6.25

)6.03

(5.68;6.39

)7.61

15.53

13.69

2.49

(2.21;2.79

)IFe

9.46

24.37

32.95(29.11

;35.37

)35

.08(31.65

;38.85

)39

.78

99.23

89.78

15.28(10.28

;19.77

)2.14

11.26

18.81(17.19

;20.71

)21

.39(19.42

;23.46

)27

.16

101.75

99.62

14.39(11.74

;17.28

)

Glass

IDe

3.46

4.53

5.27

(4.62;5.59

)5.56

(5.00;6.16

)6.55

9.48

6.02

1.55

(1.02;1.91

)1.63

4.49

5.79

(5.14;7.24

)7.35

(6.51;8.35

)8.44

28.30

26.67

4.57

(3.26;5.75

)ITe

0.35

0.42

0.48

(0.45;0.55

)0.52

(0.47;0.57

)0.61

0.81

0.46

0.13

(0.09;0.16

)0.36

0.71

0.80

(0.77;0.86

)0.91

(0.84;0.98

)1.03

2.05

1.69

0.34

(0.27;0.41

)IFe

1.25

2.68

2.98

(2.78;3.73

)3.42

(2.90;3.93

)4.55

7.46

6.21

1.34

(0.84;1.77

)0.49

2.37

3.15

(2.69;3.55

)4.01

(3.53;4.58

)4.68

13.34

12.85

2.60

(1.99;3.14

)

F. Ferreira et al. /Waste Management 59 (2017) 3–13 9

As a consequence, for each type of collected waste, non-parametricMann–Whitney U tests for independent samples (Mann andWhitney, 1947) were conducted to study the effect of the type ofselective collection system on the amount of waste collected andon each performance indicator (IDe, ITe and IFe). The results,depicted in Table 5, allow to conclude that, for the glass collection,the type of container has no significant effect on the values of IDeand IFe indicators.

In all other cases, we conclude, with strong significance, thatstatistical differences exist between (group) variable distributionsfor SUc and SSc containers. Furthermore, taking into account themean ranks and the confidence intervals for medians and means(cf. Tables 2 and 3), we may assert that:

– for all selective waste types, the SSc collection system leads tohigher amounts of waste collected and statistically significantlower effective collection time;

– SSc collection system leads to statistically significant highereffective collection distance and fuel consumption on paper/-cardboard and light packaging collections, while no significantdifferences exist between SSc and SUc collections with respectto these two indicators in the glass collection.

The indicators values show that SSc collection scheme is fasterthan the SUc. This characteristic recommends its application inheavy traffic districts and in neighbourhoods with large numberof containers. This scheme is appropriate in urban sectors thatrequire collection celerity.

3.4. Global performance index

Despite the statistical analysis developed in the previous sec-tion may lead to relevant conclusions on benchmarking collectionsystems, in the particular case of selective waste studies, involvingdifferent types of materials and also different indicators, it may notbe possible to use it as an isolated tool. In fact, in such multi-criteria decision problems, frequently some collection schemesreveal better performance for some indicators and waste materials,and worst to others, leading to inconclusive judgments of the bestcollection scheme option. In those situations, it is worthwhile todefine an index that, for each type of collection scheme, is ableto aggregate, in a single value, the contributions of all selected indi-cators for the distinct type of materials, providing a quantitativeassessment of the selective collection schemes under comparison.

In the present work, a weighted index is suggested for assessingthe performance of each collection scheme, from which, in an obvi-ous way, a comparison between collection systems is made (thehigher the index, the better the collection scheme is). As the indi-cators are expressed in different units and scales, a previous nor-malization was provided to convert indicators into comparablevalues. In particular, we used an inverse min-max normalizationto convert indicators into values between 0 and 1. Specifically,for each collection scheme s and type of collected material i, thenormalization for indicator j presented in Eq. (1) is used,

NIsij ¼Imaxij �eIsij

Imaxij � Imin

ij

ð1Þ

where eIsij denotes the sample median value of indicator j, for the

type of material i, in collection scheme s, and Imaxij and Imin

ij denote,respectively, the maximum and minimum values recorded for indi-cator j and type of material i, within the k collection schemes undercomparison,

Iminij ¼ min

16s6kIsij and Imax

ij ¼ max16s6k

Isij:

Fig. 5. Boxplots and histograms of IDe, ITe and IFe stratified by collection scheme and waste type.

10 F. Ferreira et al. /Waste Management 59 (2017) 3–13

Table 4Anderson-Darling normality test.

Anderson-Darling normality test H0: the group distribution is normalH1: the group distribution is not normal

MSW amount IDe ITe IFe

Paper/cardboardStreet-side [SSc] A = 0.6839 A = 0.4186 A = 0.64 A = 2.6103

p-value = 0.07056 p-value = 0.3185 p-value = 0.09087 p-value = 1.18e�06Drop-off [SUc] A = 3.5901 A = 4.018 A = 2.1778 A = 2.6103

p-value = 5.34e�09 p-value = 4.882e�10 p-value = 1.506e�05 p-value = 1.18e�06

Light packagingStreet-side [SSc] A = 3.4175 A = 1.2731 A = 0.846 A = 2.6103

p-value = 1.223e�08 p-value = 0.002412 p-value = 0.02793 p-value = 1.18e�06Drop-off [SUc] A = 7.1423 A = 4.9662 A = 1.0962 A = 2.6103

p-value = 2.2e�16 p-value = 2.528e�12 p-value = 0.006973 p-value = 1.18e�06

GlassStreet-side [SSc] A = 0.3767 A = 0.7825 A = 0.7617 A = 2.6103

p-value = 0.3849 p-value = 0.03669 p-value = 0.04144 p-value = 1.18e�06Drop-off [SUc] A = 0.9091 A = 6.034 A = 4.78 A = 2.6103

p-value = 0.0198 p-value = 6.037e�15 p-value = 6.359e�12 p-value = 1.18e�06

Table 5Mann-Whitney U test.

Mann-Whitney U test H0: the two group distributions are equalH1: the two group distributions are not equal

MSW amount IDe ITe IFe

Paper/cardboardW = 8690.5 W = 7208.5 W = 3499.5 W = 8530p-value = 1.605e�11 p-value = 3.31e�4 p-value = 1.814e�05 p-value = 1.587e�10

Light packagingW = 10,489 W = 10,081 W = 5282.5 W = 10852.5p-value = 2.345e�11 p-value = 2.824e�09 p-value = 0.006005 p-value = 2.158e�13

GlassW = 2346 W = 1023 W = 235.5 W = 1251p-value = 1.838e�11 p-value = 0.1412 p-value = 2.144e�10 p-value = 0.9531

F. Ferreira et al. /Waste Management 59 (2017) 3–13 11

The performance index for collection scheme s, described in Eq.(2), cumulatively aggregates the information of m indicators for ndifferent types of materials.

Indexs ¼Xni¼1

wsi

Xmj¼1

ajNIsij ð2Þ

Once some indicators are operationally more relevant thanothers on the decision-makers perspective, the coefficient aj

weights the relative significance of indicator j in the overall serviceassessment. This coefficient may assume values according toexperts’ criteria, in particular, supporting the decision on Delphimethod. Due to their different specific density characteristics, dis-tinct types of material are naturally collected in different propor-tions. Such relevant factor in the collection circuit’s managementand optimization is incorporated in the index formula throughthe ws

i coefficient that weights the proportion of waste material iover all the waste collected in the collection scheme s. Considering0 6 aj 6 1, with

Pmj¼1aj ¼ 1, and 0 6 ws

i 6 1, withPn

i¼1wsi ¼ 1, it

follows that index values lie between 0 and 1.Such weighted sum model index approach is widely used in

multi-criteria decision problems arising in several areas (Coelhoet al., 2012; Stossel et al., 2015).

Concerning the application of this index in the Oporto selectivecollection system, through two different collection schemes,street-side containers (SSc) and drop-off containers (SUc), themunicipality stakeholders consider the effective collection timeas the main critical factor to take into account. In fact, faster collec-tion schemes reduce the probability of undesirable traffic conges-tion, which should be strongly avoided in urban high-density

areas. In this sense, stakeholders suggested to assign for the effec-tive collection time a a weight of 50% and 25% for the remainingtwo indicators. For each collection scheme, the performance indexwas computed normalizing the indicators with the respective min-imum, median and maximum values, presented in Table 3, andconsidering the proportions of each waste material collected, pre-sented in Fig. 2.

The performance index value obtained for the street-side anddrop-off containers collection was 0.80 and 0.76, respectively,allowing to conclude that, in this case study and for the weightsdetermined by the stakeholders, the SSc collection scheme pre-sents the best overall performance. The difference obtainedbetween the index values for both collection schemes is small,not evidencing significantly performance differences, although itmay represent larger differences in the indicators before standard-ization. Initially a better general performance for SSc collectionwas not evident, however this result seems consistent given that,for all types of selected waste, this collection scheme performs bet-ter concerning the most valued indicator, the effective collectiontime. In urban areas, where the waste collection may lead to trafficjams, specially when it is done during the day, it can be inferredthat smaller containers are easier to handle and, although theyexist in larger quantity, overall they are more effective where amore faster collection scheme per ton is valued.

4. Conclusions

The selection of deposition and collection equipment is a rele-vant issue in the design of new municipal waste collection servicesor when the replacement of existing equipment is required.

12 F. Ferreira et al. /Waste Management 59 (2017) 3–13

Monitoring the performance among different implemented collec-tion schemes provides meaningful information to guideline futuredecisions. It is particularly important to base the equipment selec-tion also in operational aspects and not only on the traditional cri-teria of acquisition costs and territory suitability. In this sense, thispaper highlights efficiency differences between distinct selectivecollection schemes, an important benchmarking issue to stimulatethe discussion among the stakeholders involved in the process ofimproving waste management strategies.

For each collection scheme and type of selective material col-lected, it was proposed the monitoring of three performance indi-cators considered decisive for the efficiency and costs of thesystems (effective collection distance, effective collection timeand effective fuel consumption), followed by an appropriate statis-tical analysis to highlight each collection system trends and toestablish a comparative analysis among collection schemes. Thisanalysis was further complemented by a performance index thatcaptures the values of each indicator, for each type of material col-lected, weighted by their relative importance, aggregating in a sin-gle value a quantitative assessment for each selective collectionscheme. With suitable weighting adjustments for the selective col-lection schemes and locations under study, this methodologyreveals flexibility and adaptability, being a useful and effective toolin the decision-making process.

We considered the three indicators used in this study as themost relevant from an operational point of view. Nevertheless,similar analysis could be developed using other indicators. More-over, concerning the weights assigned to the indicators in the per-formance index, further studies could also be performed. Theapplication of this model to other territories, including other Euro-pean countries, could give a more generalized idea of the perfor-mance and suitability of different collection schemes andencourage industry to enhance the design of collection equipmentin a perspective of efficiency improvements.

The proposed methodology was applied to a case study under-taken in Oporto Municipality, involving two different selectivewaste collection frameworks (street-side and drop-off containers).The statistical analysis, conducted with data collected, during oneyear, a week per month, allowed to establish a comparative analy-sis of the performance of these two collection schemes. It wasobserved that the street-side containers scheme is the one attain-ing higher amounts of each type of selective waste collected,exhibiting better effective collection times per ton of material col-lected, being thus suitable to be used in urban areas, especially inareas with high traffic intensity. The combined analysis displayedthe street-side containers as the equipment with the best overallperformance for the Oporto Municipality.

Acknowledgements

The authors would like to thank the Municipality of Oporto forall technical support and promptness while developing the currentwork. This work was supported by the FCT (Portuguese Foundationfor Science and Technology) through the projects PEst-OE/ECI/UI4082/2013 (C-MADE), UID/MAT/00013/2013 and UID/Multi/04621/2013.

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