Sc

GMECa

b

c

d

e

f

g

h

i

j

a

ARRA

KMSTM

1

rspaa

w

UT

h0

Resources, Conservation and Recycling 89 (2014) 22–30

Contents lists available at ScienceDirect

Resources, Conservation and Recycling

jo u r n al homep age: www.elsev ier .com/ locate / resconrec

easonal variation of municipal solid waste generation andomposition in four East European cities

intaras Denafasa,∗, Tomas Ruzgasb, Dainius Martuzeviciusa, Sergey Shmarinc,ichael Hoffmannd, Valeriy Mykhaylenkoe, Stanislav Ogorodnik f, Mikhail Romanovg,

katerina Neguliaevag, Alexander Chusovg, Tsitsino Turkadzeh, Inga Bochoidzeh,hristian Ludwig i,j

Department of Environmental Technology, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Applied Mathematics, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Environmental Engineering, National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv, UkraineCentre of Ecological Monitoring of Ukraine at Taras Shevchenko National University of Kyiv, Kyiv, UkraineFaculty of Geography, Taras Shevchenko National University of Kyiv, Kyiv, UkraineFaculty of Management and Ecology, Kyiv State Municipal Engineering Academy, Kyiv, UkraineDepartment of Civil Engineering and Applied Ecology, St. Petersburg State Polytechnic University, St. Petersburg, RussiaDepartment of Chemical Technology, Akaki Tsereteli State University, Kutaisi, GeorgiaÉcole Polytechnique Fédéral de Lausanne, EPFL-ENAC-IIE, Lausanne, SwitzerlandPaul Scherrer Institute, PSI-ENE-LBK, Villigen, Switzerland

r t i c l e i n f o

rticle history:eceived 20 November 2013eceived in revised form 3 June 2014ccepted 3 June 2014

eywords:

a b s t r a c t

The quality of recyclable and residual municipal solid waste (MSW) is, among other factors, stronglyinfluenced by the seasonal variation in MSW composition. However, a relatively marginal amount ofpublished data on seasonal MSW composition especially in East European countries do not providesufficient information on this phenomenon. This study provides results from municipal waste composi-tion research campaigns conducted during the period of 2009–2011 in four cities of Eastern European

SW generation and compositioneasonal variationime series analysesodeling

countries (Lithuania, Russia, Ukraine and Georgia). The median monthly MSW generation values rangedfrom 18.70 in Kutaisi (Georgia) to 38.31 kg capita−1 month−1 in Kaunas (Lithuania). The quantitative esti-mation of seasonal variation was performed by fitting the collected data to time series forecasting models,such as non-parametric seasonal exponential smoothing, Winters additive, and Winters multiplicativemethods.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

The composition of municipal solid waste (MSW) is a result ofegional and cultural aspects as well as social behavior, and it istrongly influenced by economic factors. The changes in MSW com-osition may strongly influence the quality of the waste, whichffects emissions from landfills, the quality of incineration residues

nd other parameters of waste management systems.

The generation and composition of MSW in various parts of theorld, including East-Europe (EE) is well researched, e.g. Cyprus

∗ Corresponding author at: Department of Environmental Technology, Kaunasniversity of Technology, Radvilenu pl. 19, LT-50254, Kaunas, Lithuania.el.: +370 37 300180; fax: +370 37 300152.

E-mail address: gintaras.denafas@ktu.lt (G. Denafas).

ttp://dx.doi.org/10.1016/j.resconrec.2014.06.001921-3449/© 2014 Elsevier B.V. All rights reserved.

(Koufodimos and Samaras, 2002), Russia (Negulyaeva and Chusov,2005), Crete, Greece (Gidarakos et al., 2006); Ukraine (Sustainable,2007; Study, 2008), Estonia (Moora, 2008), Poland (Den Boer et al.,2010), Serbia (Vujic et al., 2010). These studies mainly aimed at theassessment of yearly MSW generation and composition, but rarelyprovide sufficient information on the seasonal variation of MSWgeneration and composition.

Seasonal changes in the generation of mixed MSW were alsomentioned in multiple earlier investigations, although very fewstudies aimed to address this issue specifically. Rhyner (1992) hasresearched monthly quantities of residential, commercial, indus-trial and other wastes generated in the period of 1985–1989 in

Brown County, Wisconsin, USA. It was discovered that the monthlyquantities of residential and commercial waste were lower thanaverage in winter months (up to 19.8%) and higher than average(up to 23.8%) in summer months. Zeng et al. (2005) have published

rvatio

dCwotcmssc(

aaweoTtsacsifl

itMtramtmoav

wftsi

fswtnmeat

t

W +

MM

MMSW

G. Denafas et al. / Resources, Conse

ata from the sorting actions in the Sanitary Landfill of Columbiaity during each of the four seasons of 1996. A substantial differenceas discovered among the researched seasons for fractions such as

rganic fraction, paper, and metal. Gidarakos et al. (2006) has regis-ered the increase of solid waste generation and the changes in theomposition (namely increase of packaging waste) during the sum-er period on the island of Crete. Gómez et al. (2009) conducted a

tudy of the characteristics of MSW composition for the differenteasons, represented by the months April, June and January, in theity of Chihuahua, Mexico. The waste generation values for Januarylow temperature season) were 28% lower than in April.

The seasonal changes in MSW generation and compositionre usually discussed through the identification of main factorsffecting these changes. These factors are strongly associatedith the social and economic development of the countries. For

xample, Gómez et al. (2009) has discovered strong dependencef waste generation on the economic welfare of the settlement.his factor also influenced the composition of waste throughouthe year. Zeng et al. (2005) have identified holiday activities,pecial summer events and the transition in student populations the possible key factors affecting seasonal variation of MSWomposition. Gidarakos et al. (2006) has revealed that in theettlements where the influence of tourism to the local economys strong, MSW generation and composition also depends on theows of tourists throughout the year.

The above-listed cases mainly aimed at determining variationsn waste flows and composition, but the data were not used forhe quantitative assessment and forecasting of the behavior of

SW streams at various locations. Considering that variations inhe composition of waste may cause further impacts on the envi-onment and affect the energy content of waste, it is necessary topproach this issue from the modeling perspective. Generally, theathematical representation of phenomena of the fluctuation of

otal waste quantity and composition would allow a better manage-ent, based on the quantitative assessment and forecasting. Some

f the advanced techniques applied for the forecasting of total MSWmount included models such as neuro-fuzzy, artificial neural andector machine models (Noori et al., 2009, 2010; Abbasi et al., 2013).

In this paper we firstly present the results of an extensiveaste data study focusing on seasonal MSW composition in at

our locations in four different EE countries. We secondly aimedo present a possible approach to model the times series of sea-onal waste composition changes. Extrapolating of the time-seriesnto the future is a possible and interesting approach for seasonal

orecasting considering important indicators of some industrialectors such as fisheries (Koutroumanidis et al., 2006). Concerningaste management, times series methods were formerly applied to

ypical MSW streams on the basis of nonparametric seasonal expo-ential smoothing (SES) and seasonal autoregressive integratedoving average (sARIMA) (Navarro-Esbrí et al., 2002; Rimaityte

t al., 2012). However, MSW fractions such as paper, plastics, glass

Ci common = MMSW × Ci MMS

Ci

nd food waste may follow different mathematical expressionshus worth examining separately.

This manuscript presents the results of MSW composi-ion research in Georgia (Kutaisi), Lithuania (Kaunas), Russia

n and Recycling 89 (2014) 22–30 23

(St. Petersburg) and Ukraine (Boryspil) obtained in the frame-work of the research project “Seasonality of Municipal WasteGeneration and Composition and Corresponding Fluctuations ofVarious Environmental Indicators for Waste Management andTreatment Facilities” (SWC-ENV-IND) conducted during the periodof 2010–2013 and including field investigations of the years2009–2011.

2. Methods

2.1. Estimation of MSW composition as generated

A unified methodology for determining the composition ofunprocessed MSW was developed to be utilized in all four selectedcities. This methodology was based on main standard test methods,such as ISO 14780 “Solid biofuels – Method for sample prepara-tion”; ASTM D5231-92 “Standard Test Method for Determination ofthe Composition of Unprocessed MSW”; ASTM D4687-95 “StandardGuide for General Planning of Waste Sampling” and the experienceof the Solid Waste Research Group at Kaunas University of Technol-ogy. The participating research groups were jointly trained beforethe field studies. Each SWC-ENV-IND project team has chosen a

city or region for the research of MSW composition. Multifamilyapartment complexes (block houses) and single family homes wereselected as target areas within cities. The main characteristics ofstudy locations are summarized in Table 1.

Depending on the local situation, the bins with contents of400–600 kg of MSW from selected residential areas were takento university laboratory (Kutaisi), from waste transfer stations(Kaunas and St. Petersburg), and from a landfill (Boryspil) whereit was hand-sorted to 11 fractions and weighed. In addition,500–600 kg of separately collected fractions from the same area(paper and cardboard, plastics, glass, metals) were examined inKaunas every month. Such action has not been performed in Kutaisi,Boryspil, and St. Petersburg because paper, plastics, metals, glassand yard waste were not separately collected with subsequentrecycling/composting in these cities.

The MSW composition data obtained during the manual sortingcampaigns were utilized to recalculate the generalized compo-sition of total generated MSW (kg capita−1 month−1). Additionaldata for such recalculations were collected from the municipalwaste collection companies including the monthly amounts of col-lected mixed MSW and separately collected fractions (paper andcardboard, glass, plastics, metals, yard waste). The calculations ofgeneralized content for every fraction and sub-fraction have beenobtained using the following equations:

PAPER × Ci PAPER + GLASS × Ci GLASS + PLAST MET × Ci PLAST MET

SW + PAPER + GLAS + PLAST MET + YARD(1)

= INHAB IND × Ci MMSW IND + INHAB BLOCK × Ci MMSW BLOCK

INHAB(2)

where

Ci common – content of i waste fraction in common flow of MSW,including separately collected;Ci MMSW – content of i waste fraction in common flow of MMSW;Ci MMSW IND – content of i waste fraction in flow of MMSW for indi-vidual houses (single family homes);Ci MMSW BLOCK – content of i waste fraction in flow of MMSW forblock-houses (multifamily apartments);

Ci PAPER, GLASS, PLAST MET – content of i waste fraction in flow of sepa-rately collected MSW (bins for paper, glass and plastics & metals);MMSW – monthly amount of mixed municipal solid waste;PAPER – monthly amount of collected waste in the bins for paper;

24 G. Denafas et al. / Resources, ConservatioTa

ble

1R

elev

ant

par

amet

ers

of

the

fou

r

stu

die

d

citi

es.

Cit

y,

cou

ntr

y

Pop

ula

tion

, th

ousa

nd

s

An

nu

al

GD

P

at

the

end

ofex

per

imen

tal c

amp

aign

, in

2011

, USD

per

cap

ita

(Cou

ntr

y,

2010

)

Geo

grap

hic

alco

ord

inat

esM

ean

air

tem

per

atu

re

ran

geD

escr

ipti

on

of

was

te

orig

inu

sed

for

sam

pli

ng

Ori

gin

of

MSW

coll

ecti

ond

ata

avai

labl

e

for

stat

isti

cal

anal

ysis

Du

rati

on

and

freq

uen

cyof

exp

erim

enta

lca

mp

aign

Ku

tais

i,

Geo

rgia

194.

171

(Nat

ion

al, 2

013)

5600

42◦ 1

5′ 0′′ N

42◦ 4

2′ 0′′ E

Win

ter:

4–12

◦ CSu

mm

er:

17–2

8◦ C

Mix

ed

MSW

flow

from

mu

ltif

amil

y

apar

tmen

tco

mp

lexe

s,

sin

gle

fam

ily

hom

es, o

ffice

buil

din

gs, s

tree

ts

Dat

a

of

mu

nic

ipal

com

pan

y“C

lean

Cit

y”

for

com

mon

mix

ed

MSW

flow

May

2010

–Ap

ril 2

011

(on

ce

a m

onth

)

Kau

nas

, Lit

hu

ania

343.

69

(Sta

tist

ics,

2013

)17

,900

54◦ 5

3′ 50′′ N

23◦ 5

3′ 10′′ E

Win

ter:

−8

to

0◦ C

Sum

mer

:

16–2

2◦ C

Mix

ed

MSW

flow

from

mu

ltif

amil

y

apar

tmen

tco

mp

lexe

s,

mix

ed

MSW

flow

from

sin

gle

fam

ily

hom

es

and

offi

ce

buil

din

gs;

sep

arat

eco

llec

ted

pap

er, p

last

ics/

met

als

from

mu

ltif

amil

y

apar

tmen

tco

mp

lexe

s

Dat

a

of

JSC

“Kau

no

svar

a”fo

r

com

mon

mix

ed

MSW

flow

, sep

arat

e

coll

ecte

dp

aper

, pla

stic

s/m

etal

s,gl

ass

and

yard

was

te.

Jan

uar

y

2009

–Dec

embe

r20

10

(on

ce

a

mon

th)

St. P

eter

sbu

rg,

Ru

ssia

4600

.26

(Offi

cial

, 201

3)

16,2

00

59◦ 5

7′ N30

◦ 18′ E

Win

ter:

−8

to−3

◦ CSu

mm

er:

12–2

3◦ C

Mix

ed

MSW

flow

from

mu

ltif

amil

y

apar

tmen

tco

mp

lexe

s,

sin

gle

fam

ily

hom

es, o

ffice

buil

din

gs, s

tree

ts

Dat

a

of

mu

nic

ipal

com

pan

y“S

pet

ztra

ns”

for

mix

edM

SW

flow

Jan

uar

y

2010

–Dec

embe

r20

10

(on

ce

a

mon

th)

Bor

ysp

il, U

krai

ne

59.2

(Sis

ter,

2013

)

7300

50◦ 2

1′ N30

◦ 57′ E

Win

ter:

−6

to

0◦ C

Sum

mer

:

14–2

6◦ C

Mix

ed

MSW

flow

s

from

mu

ltif

amil

y

apar

tmen

tco

mp

lexe

s,

sin

gle

fam

ily

hom

es, o

ffice

buil

din

gs, s

tree

ts

Dat

a

of

LLC

“Eco

serv

is”

for

mix

ed

MSW

flow

Jun

e

2010

–May

2011

(on

ce

a

mon

th)

n and Recycling 89 (2014) 22–30

GLASS – monthly amount of collected waste in the bins for glass;PLAST MET – monthly amount of collected waste in the bins forplastics and metals;YARD – monthly amount of collected yard waste in the city forcomposting;INHAB IND – number of inhabitants to be living in individualhouses;INHAB BLOCK – number of inhabitants to be living in block-houses.

2.2. Assessment and forecasting of the seasonal variation in MSWcomposition

The quantitative extent of seasonal variation was determinedby methods of descriptive statistics. The amount of the variation inwaste management data was assessed by calculating the variationthe mean percentage by which the monthly quantities differ fromthe average.

Time series analyses were applied to the MSW fraction gen-eration data with the aim to describe and forecast its seasonalbehavior. Since noise (residuals) of most time series of waste frac-tions were not stationary to satisfy the Ljung–Box Chi-square testfor white noise (Ljung and Box, 1978) and KPSS test (Kwiatkowskiet al., 1992) when samples are small- and medium-sized, non-parametric methods such as Simple Exponential Smoothing,Double Exponential Smoothing, Seasonal Exponential Smoothing,Linear Exponential Smoothing, Linear Damped-Trend ExponentialSmoothing, Winters Additive method and Winters Multiplicativemethod (Winters, 1960; Brown, 1962) were tested. The highestaccuracy of the researched waste fractions data was achievedby applying Simple Exponential Smoothing (SimpleES), SeasonalExponential Smoothing (SES), Damped Trend Exponential Smooth-ing (DTES), Winters Additive (WA) and Winters Multiplicative(WM) methods.

The outliers for MSW fractions time series were tested. Alsoaccuracy of used models was described by adjusted coefficient ofdetermination (R2

adj), root mean squared error (RMSE), and meanabsolute percentage error (MAPE). The assumptions for normallydistribution of residuals have been not tested, however the satis-faction of these assumptions could be characterized by R2

adj values.The following expression was established for SES for monthly

MSW fraction (Yt) generation for the time t (month) during inves-tigated period p (12 months):

Yt = Lt + St−p + εt, (3)

where Lt – average monthly generation for MSW fraction forinvestigated period (time-varying mean term); St−p – function asso-ciating time t with one of p seasonal factors; εt – error term has anormal distribution with a mean of zero and variance �2.

The k-step prediction equation for the simple SES model illus-trates the use of updating equations that employ two smoothingweights:

Yt+k = Lt + St−p+k, (4)

where Yt+k is predicted value, Lt = ω(Yt − St−p) + (1 − ω)Lt−1 islevel component that estimates Lt with level smoothing weightω, and St = ı(Yt − Lt) + (1 − ı)St−p is seasonal component that esti-mates St−p with seasonal smoothing weight ı.

WA method starts with the usual trend model and adds a sea-sonal component:

Yt = Lt + Ttt + St−p + εt, (5)

where Lt represents the time-varying mean term, Tt represents thetime-varying slope and St−p represents the time-varying seasonalcontribution for one of the p seasons.

rvation and Recycling 89 (2014) 22–30 25

Y

wm

c

Y

a

Y

tosing

pvedbTtitdsbmeecedadbro

3

3

3

vT(i(AF(t4wdiSe

al

par

amet

ers

of

the

seas

onal

vari

atio

n

of

MSW

gen

erat

ion

(kg

cap

ita−1

mon

th−1

).

Kau

nas

, Lit

hu

ania

Ku

tais

i,

Geo

rgia

St. P

eter

sbu

rg, R

uss

iaB

orys

pil

, Ukr

ain

eR

hyn

er

(199

2)G

idar

akos

et

al. (

2006

)G

ómez

et

al. (

2009

)

asu

rem

ents

Febr

uar

y

2009

–Dec

embe

r

2010

May

2010

–Ap

ril 2

011

Jan

uar

y 20

10–D

ecem

ber

2010

Jun

e

2010

–May

2011

1985

–198

9

2003

–200

4

2006

–200

7SE

S

WA

WA

WA

SES

DTE

S

WA

hly

gen

erat

ion

, kg

cap

ita−1

mon

th−1

MSW

mon

thly

gen

erat

ion

37.4

18.6

25.4

28.0

32.0

37.9

18.3

%

CL

for

med

ian

31.6

16.6

21.8

27.7

29.8

34.6

15.7

%

CL

for

med

ian

44.5

20.7

29.3

28.6

4

35.0

1

41.2

20.8

938

.3

18.7

25.7

28.2

33.5

37.5

18.0

nos

tic

stat

isti

cs0.

996

0.98

3 0.

995

0.88

0

0.86

5

0.58

7

0.78

10.

823

1.13

3 0.

882

1.40

8

5.15

3

3.92

1

4.57

40.

371

0.26

1

0.26

1

0.50

2

2.09

8

1.86

0

1.04

3

G. Denafas et al. / Resources, Conse

The k-step prediction equation is

ˆt+k = Lt + kTt + St−p+k (6)

here Tt = �(Lt − Lt−1) + (1 − �)Tt−1, is a smoothed trend that esti-ates Tt, � is a trend smoothing weight.WM method multiplies the seasonal component and the trend

omponent:

t = (Lt + Ttt)St−p + εt (7)

The smoothing equations are the same as in the additive model,nd the k-step prediction equation is

ˆt+k = (Lt + kTt)St−p+k (8)

The components of the above presented k-step prediction equa-ions for monthly generation of the particular MSW fraction dependn their own and the other components with 1 month lag andeasonal 1 year lag. Smoothing weights of components define themportance of time series values or previously estimated compo-ents. Large smoothing weights provide higher impact on recenteneration rates of monthly MSW fractions.

In order to assess the seasonal component in the time seriesrediction models it is essential to utilize 2 or more years of obser-ations. In the framework of this study, not all cities have collectedqual amount of data, as outlined in Table 2. In such cases, missingata for different MSW fractions may be generated using statisticalootstrapping technique based on historical data from other cities.he values of 2009 for all cities were approximated by the clus-ered data averages of 2010. The simplest estimator of time seriess mean. The clustering of samples values to the homogeneous clus-ers allows better to determine the properties from approximatingistribution. The missing values of specific MSW fractions in theelected city till full data set of 2-years have been approximatedy average clusters of really observed values. The correspondingonths have been caught to these clusters. Also, the yearly influ-

nce was evaluated as the multiplicative element of the trendstimation. The later multipliers for MSW fractions in the differentities have been calculated by evaluation of annual linear influ-nce for waste means in Kaunas, also in consideration to relativeifferences between fractions in Kaunas and actual city. Periods forvailable data are presented in Table 1. It must be born in mind thaturing evaluation of times series the Kaunas data for 2009 haveeen replaced by data to be generated by above described algo-ithm, but really observed 2-year data have been used for checkingf the validity of created models.

. Results and discussions

.1. The generation and composition of MSW

.1.1. Kaunas, LithuaniaWaste sorting actions lasted 2 full years in Kaunas, pro-

iding the largest set of data among participating cities.he pattern of seasonal variation was similar in both yearsFigs. 1 and 2a). In 2009 the generation of MSW was lowestn February (26.1 kg capita−1 month−1) and highest in September42.4 kg capita−1 month−1, mostly due to food waste fraction).ccordingly, in 2010 the generation of MSW was lowest inebruary (24.8 kg capita−1 month−1) and highest in September46.0 kg capita−1 month−1, mostly due to food waste frac-ion). A specific increase was observed in April for 2009 –2.4 kg capita−1 month−1, and for 2010 – 43.2 kg capita−1 month−1,hich was attributed to the spring clean-up activities and seasonal

isposal of unwanted household items. These actions have mostly

nfluenced the increase of inorganic (incombustible) materials.uch activities are common in many EE countries, but in Kaunas theffect was most pronounced. The increase in the late summer/early Ta

ble

2M

ain

stat

isti

c

Tim

e

of

me

Mod

el

MSW

mon

tA

vera

ge

Low

er

95U

pp

er

95M

edia

n

Mod

el

dia

gR

2 adj

MA

PE

RM

SE

26 G. Denafas et al. / Resources, Conservation and Recycling 89 (2014) 22–30

n and

aaiiai

FB

Fig. 1. Monthly variation of MSW generatio

utumn may be attributed to the harvesting activates, whichffected the generation of food waste. Several characteristicncreases also included other organic (combustible) waste fractions

n the period from May till September, the yard waste in spring andutumn, paper and cardboard in March–April and autumn, plasticsn April, as well as glass in March and September.

ig. 2. Monthly variation of MSW generation and composition in (a) Kaunas, Lithuania, 2oryspil, Ukraine, June 2010–May 2011.

composition in Kaunas, Lithuania in 2009.

3.1.2. Kutaisi, GeorgiaIn Kutaisi, MSW monthly generation was the lowest in

February (15.4 kg capita−1 month−1), the highest in August−1 −1

(21.7 kg capita month ). A specific increase was observed in

January (19.2 kg capita−1 month−1), mostly due to food wastefraction. The generation of paper, glass, other organic and other

010; (b) Kutaisi, Georgia, May 2010–April 2011; (c) St. Petersburg, Russia, 2010; (d)

G.

Denafas

et al.

/ R

esources, Conservation

and R

ecycling 89

(2014) 22–30

27

Table 3Forecasting models, parameters, and values of accuracy criteria estimated for MSW fractions data in the target cities.

Estimates for models of MSW fractions

Paper andcardboard

Plastics Ferrous metals Other metals Glass Tetra packs Food waste Yard waste Wood Other burnable Other unburnable Total MSW

Kaunas, Lithuania, 2009–2010Model WA WA WA WA WA WA WA WA SES WA SESLevel smoothing weight ω 0.139 0.154 0.064 0.174 0.113 0.143 0.164 0.155 0.142 0.123 0.154Trend smoothing weight � 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001Seasonal smoothing weight ı 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001Smoothed level L0 5.6019 4.9471 0.4779 0.4836 3.6121 0.2910 10.9233 3.1286 6.0320 1.6963 37.5Smoothed trend T0 0.0907 0.0291 −0.0048 0.0129 0.0081 0.0011 −0.1403 0.0565 −0.0705

Kutaisi, Georgia, May 2010–April 2011Model WA SES WA WA WM WA WM WA WA WM WA WALevel smoothing weight ω 0.001 0.231 0.001 0.143 0.001 0.001 0.047 0.001 0.001 0.001 0.001 0.029Trend smoothing weight � 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.999 0.001 0.001Seasonal smoothing weight ı 0.922 0.999 0.929 0.999 0.911 0.902 0.994 0.944 0.886 0.853 0.929 0.001Smoothed level L0 2.1395 2.3380 0.2205 0.1731 0.7376 0.0740 7.6038 1.4767 0.5904 1.4044 1.1790 18.5Smoothed trend T0 0.0327 −0.0016 0.0055 0.0005 0.0005 −0.0973 0.0365 0.0028 0.0121 −0.0597 −0.0591

St. Petersburg, Russia, 2010Model WA WA WA WA WA WA WA WA SES SES WA WALevel smoothing weight ω 0.115 0.283 0.999 0.116 0.079 0.120 0.126 0.114 0.099 0.114 0.113 0.095Trend smoothing weight � 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001Seasonal smoothing weight ı 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001Smoothed level L0 5.0084 3.7580 0.5250 0.2001 2.2456 0.6227 1.4294 0.1835 0.1856 5.7379 4.7596 24.9Smoothed trend T0 0.0823 0.0230 −0.0050 0.0057 0.0051 0.0028 −0.0194 0.0048 −0.1954 −0.0801

Boryspil, Ukraine, June 2010–May 2011Model WA WA WA WA WA WA WA WA WA WM WA WALevel smoothing weight ω 0.001 0.001 0.001 0.001 0.001 0.142 0.001 0.001 0.001 0.114 0.001 0.067Trend smoothing weight � 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.321 0.001 0.999Seasonal smoothing weight ı 0.817 0.829 0.911 0.922 0.894 0.870 0.866 0.926 0.881 0.751 0.928 0.001Smoothed level L0 2.5052 3.5254 0.3879 0.0836 5.7076 0.1959 5.9395 3.8846 0.4363 3.3505 1.9842 28.2Smoothed trend T0 0.0318 0.0033 −0.0036 0.0022 0.0164 0.0024 −0.0720 0.0864 0.0035 0.1125 −0.0904 0.0721

28 G. Denafas et al. / Resources, Conservation and Recycling 89 (2014) 22–30

Fig. 3. Time series of MSW fractions (kg capita−1 month−1) (a, paper and cardboard; b, plastics; c, ferrous metals; d, other metals; e, glass; f, tetra packs; g, food waste; h,yard waste; i, other organic with wood; j, other inorganic) in Kaunas, Lithuania. The actual data points marked as asterisks (*), the solid line represents predicted data andthe dotted line – confidence interval of the forecast.

iy

ib

norganic fractions was the highest in January, plastics in June,

ard waste and wood in August (Fig. 2b).

The contribution of food waste fraction to the MSW generationn Kutaisi was much higher compared to Boryspil and St. Peters-urg, but similar to Kaunas. This phenomenon may be explained

by the geography and climate in Georgia, which leads to the more

intensive harvesting and utilization of food products in summerand autumn.

Significantly lower content of other organic waste was influ-enced by social-economic factors. The extent of reuse (secondary

rvatio

arp

3

iowsmr

tpfoisfr

3

F(fwwd

aiM

Koc

spitmtg3eGpl

3

wadd

siLtTs

MSW generation and composition allow further studying of rela-

G. Denafas et al. / Resources, Conse

pplication) of clothing, shoes, and other consumer products isather large. The uncontrolled incineration of organic fraction inrivate households is also widely spread.

.1.3. St. Petersburg, RussiaThe largest amount of MSW was generated in autumn with max-

mum in October (32.0 kg capita−1 month−1), mostly due to otherrganic and other inorganic fractions, and the least in summerith minimum in June (18.0 kg capita−1 month−1) (Fig. 2c). Sea-

onal variations of total waste amount were mainly associated withigration of inhabitants in summer and clean-ups of yards and

oads in autumn and spring.The generation of food waste fraction was the highest from July

ill October, paper – in February, March, October and November,lastics in October, glass in September–November, other organicraction (combustibles) in September and October. A large amountf other organic and other inorganic fractions occurred due to mod-fied protocol utilized by Russian partners, where a MSW fractionmaller than 8 cm, consisting of mixture of dust, sweepings, gravel,ood wastes, plastic and paper particles was considered as insepa-able fraction in manual sorting.

.1.4. Boryspil, UkraineThe generation of MSW (Fig. 2d) was the lowest in

ebruary (24.3 kg capita−1 month−1), and the highest in April30.8 kg capita−1 month−1), mostly due to glass and other organicraction as an outcome of spring clean-ups. The specific increaseas observed in July (28.6 kg capita−1 month−1), mostly due to foodaste and glass, and October (28.9 kg capita−1 month−1), mostlyue to yard waste.

The generation of yard waste fraction was highest in Octobernd November (clean-ups of gardens and parks), food wasten July–September and January–February, paper and plastics in

arch, glass and other organics (combustible) in April.Significantly lower content of food waste in comparison to

utaisi and Kaunas may be explained by the fact that Boryspil isnly partly an industrial city. Also, a large portion of organic waste isomposted (utilized) by inhabitants themselves in their backyards.

Table 2 presents main statistical parameters for the MSWeasonal investigation results in this study as well as statisticalarameters to be determined by the same models for MSW seasonal

nvestigation in several cited articles. In addition to the descrip-ive statistics, the estimates for the most accurate time series

odels are presented. The corresponding data for main MSW frac-ions are presented in Tables S1–S9. The variation of average MSWeneration values from this study (median values from 18.70 to8.31 kg capita−1 month−1) corresponds well with the data fromarlier studies (17.98–37.49 kg capita−1 month−1) (Rhyner, 1992;idarakos et al., 2006; Gómez et al., 2009). It is evident, that therevailing factors such as economic development and geographical

ocation play a major role in the waste generation statistics.

.2. Short-term forecasting of the generation of MSW fractions

The results of the MSW composition and generation researchere employed for the short-term time series forecasting. This step

llowed the quantification of the variation components in the MSWata as well as research on overall feasibility of utilization of suchata for the time series forecasting.

Table 3 presents the main quantitative parameters of the timeeries forecasting model: level smoothing weight ω, trend smooth-ng weight � , seasonal smoothing weight ı, average smoothed level

0 and average smoothed trend T0. Smoothed seasonal factors St,o be determined for each month of investigation, are presented inables S10–S13. No outliers were observed in MSW fractions timeeries. The adjustment of the time series models for forecasting

n and Recycling 89 (2014) 22–30 29

of MSW fractions revealed that WA model is most accurate, withexception of several cases where SES and WM models were supe-rior. The accuracy validation of the criteria values deteriorated, butremained within reasonable bounds: for example adjusted R2 inmost cases amounted to high values. It means also that the assump-tion of residuals normality is satisfied.

The presented parameters may be employed for calculation ofMSW generation and composition values for each month. It is evi-dent that parameters of forecasting models are not comparable foreach target city, since they represent varying patterns of seasonalvariation as principle functions of regional social–economical indi-cators. The seasonal weight having a value close to unity impliesthat a non-seasonal model might be more appropriate. At the sametime, the seasonal weight close to zero implies that deterministicseasonal factors might be present.

The graphical representation of time series variation for Kaunascity is presented in Fig. 3. The data for other cities are presentedin Figs. S1–S3. The adequacy of the model was checked by vali-dation survey using historical observed data for 2009–2010 fromKaunas city with results of back- and forecasting. The seasonal vari-ation patterns are well represented by the model. At the same time,it is evident that the trend may not be adequately representedfrom the quantity of data that was available. The forecasting resultsshow the growing trends for paper, plastics and yard waste, declin-ing trends for food waste and steady trend for glass. Our earlierstudy (Rimaityte et al., 2012) has indicated that setting trends intime series forecasting is very sensitive to the selected method. Inorder to increase the significance of the trend, time series modelingrequires higher amount of year-to-year data, and the models mustbe updated each time when new data appear in order to update theforecasting with a possibly more accurate model.

4. Conclusions

This manuscript presents the results of MSW seasonal (monthly)composition research in Georgia (Kutaisi), Lithuania (Kaunas), Rus-sia (St. Petersburg) and Ukraine (Boryspil), conducted during theperiod of 2010–2011 (for Kaunas also in the period of 2009). Despitethe fact that in the selected cities the population number shows sig-nificant differences, waste generation per capita corresponds to theaverage of the whole country. The range of MSW generation values(median values from 18.7 in Kutaisi to 38.3 kg capita−1 month−1 inKaunas) suggest that the prevailing factors such as economic devel-opment and geographical latitude (which corresponds to differentclimate conditions) play the major role in the waste generationstatistics.

The MSW generation and composition data was employed totime series models with the aim to quantitatively assess the sea-sonal variation. Among the tested models, the WA model revealedthe highest accuracy in most cases. It is expected that estimatedparameters of forecasting models may be attributed to vary-ing regional social-economic indicators, however this could be asubject for further studies. The constructed time series modelswere able to represent the seasonal variation of MSW genera-tion with a high accuracy. However, more year-to-year data arerequired for the verification of the significance and robustness ofthe prediction method which we have developed in this studyfor the estimation of the trend component of the future MSWgeneration.

The obtained quantitative estimates of seasonal variation of

tionships of various fractions as a function of time. The resultsmay also be employed for decision-making process, especiallyonce higher amount of MSW composition data are collected overyears.

3 rvatio

A

NaRcbZgw

A

i2

R

A

B

C

D

G

G

K

K

0 G. Denafas et al. / Resources, Conse

cknowledgments

This research project was financially supported by the Swissational Science Foundation (project IZ73Z0 128178/1). Theuthors wish to thank the Swiss honour consul in Lithuania Mr.oberto Rossi and JSC “Kauno svara” (Kaunas, Lithuania) for theirollaboration and valuable discussions. We also thank the mem-ers of the Ukrainian project team Garry G. Martin and Olena V.huravel for constructive cooperation and support. Authors arerateful to the students and PhD-Students of partner universitiesho enthusiastically participated in the field studies.

ppendix A. Supplementary data

Supplementary data associated with this article can be found,n the online version, at http://dx.doi.org/10.1016/j.resconrec.014.06.001.

eferences

bbasi M, Abduli MA, Omidvar B, Baghvand A. Results uncertainty of support vectormachine and hybrid of wavelet transform-support vector machine models forsolid waste generation forecasting. Environ Prog Sustain Energy 2013;33:220–8.

rown RG. Smoothing, forecasting and prediction of discrete time series. New York:Prentice-Hall; 1962.

ountry comparison: GDP per capita; 2010, Available at https://www.cia.gov/library/publications/the-world-factbook/rankorder/2004rank.html[28.02.2013].

en Boer E, Jedrczak A, Kowalsky Z, Kulczycka J, Szpadt R. A review of municipal solidwaste composition and quantities in Poland. Waste Manage 2010;30:369–77.

idarakos E, Havas G, Ntzamilis P. Municipal solid waste composition determinationsupporting the integrated solid waste management system in the island of Crete.Waste Manage 2006;26:668–79.

ómez G, Meneses M, Ballinas L, Castells F. Seasonal characterization of munic-ipal solid waste (MSW) in the city of Chihuahua, Mexico. Waste Manage2009;29:2018–24.

oufodimos G, Samaras Z. Waste management options in southern Europe usingfield and experimental data. Waste Manage 2002;22:47–59.

outroumanidis T, Iliadis L, Sylaios GK. Time-series modeling of fishery landingsusing ARIMA models and fuzzy expected intervals software. Environ ModelSoftware 2006;21:1190–7.

n and Recycling 89 (2014) 22–30

Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. Testing the null hypothesis of sta-tionarity against the alternative of a unit root. J Economet 1992;54:159–78.

Ljung GM, Box GEP. On a measure of lack of fit in time series models. Biometrika1978;65:297–303.

Moora H. Analysis of composition and amount of municipal solid waste in Estonia,Contract 18-20/583. SEI-Tallinn; 2008.

National Statistics Office of Georgia; 2013, Available at http://www.geostat.ge/index.php?action=0&lang=eng [28.02.2013].

Navarro-Esbrí J, Diamadopoulos E, Ginestar D. Time series analysis and forecast-ing techniques for municipal solid waste management. Resour Conserv Recycl2002;35(3):201–14.

Negulyaeva E, Chusov AN. Development of the environmental action plan for munic-ipal solid waste management in St. Petersburg. In: Technical report of LIFE02TCY/ROS/039 project MS WASTE in SPb, SPbSPU, RU; 2005. p. 250.

Noori R, Abdoli MA, Farokhnia A, Abbasi M. Results uncertainty of solid wastegeneration forecasting by hybrid of wavelet transform-ANFIS and wavelettransform-neural network. Expert Syst Appl 2009;36(6):9991–9.

Noori R, Karbassi A, Sabahi MS. Evaluation of PCA and Gamma test techniqueson ANN operation for weekly solid waste prediction. J Environ Manage2010;91(3):767–71.

Rhyner ChR. Monthly variations in solid waste generation. Waste Manage Res1992;10:67–71.

Rimaityte I, Ruzgas T, Denafas G, Racys V, Martuzevicius D. Application and eval-uation of forecasting methods for municipal solid waste generation in aneastern-European city. Waste Manage Res 2012;30(1):89–98.

Sister City Boryspil, Ukraine; 2013, Available at http://www.hopkinsmn.com/sistercity/ [28.02.2013].

Statistics Lithuania. Population at the beginning of the year by age, adminis-trative territory and year; 2013, Available at http://db1.stat.gov.lt/statbank/selectvarval/saveselections.asp? [28.02.13].

Study of MSW Quality Parameters at Kyiv and Rivne Cities. Scientific and Techni-cal Report (Ukrainian). Kyiv, UA: Sci. Research Institute of Urban Management;2008 [Доcлiджehhя якichих хɑрɑкteриctик TПВ м. КиC-- вɑ i Рiвhe. (2008):Нɑyково-teхhiчho звit, викоhɑhо НИКTИ �Х м. КиC-- вɑ].

Sustainable consumption and production in South East Europe and EasternEurope, Caucasus and Central Asia. In: Joint UNEP-EEA report on the oppor-tunities and lessons learned. EEA Report No 3/2007; 2007, Available at:http://reports.eea.europa.eu/eea report 2007 3/en

The official web-site of Saint-Petersburg Administration; 2013, http://gov.spb.ru/helper/day/ [28.02.13].

Vujic G, Jovicic N, Redzic N, Jovicic G, Batinic B, Stanisavljevic N, et al. A fast methodfor the analysis of municipal solid waste in developing countries – case study ofSerbia. Environ Eng Manage J 2010;9(8):1021–9.

Winters PR. Forecasting sales by exponentially weighted moving averages. ManageSci 1960;6:324–42.

Zeng Y, Trauth KM, Peyton RL, Banerji SK. Characterization of solid waste dis-posed at Columbia Sanitary Landfill in Missouri. Waste Manage Res 2005;23(1):62–71.