Modelling of greenhouse gas emissions from municipal solid waste disposal in Africa

International Journal of Greenhouse Gas Control 5 (2011) 1443–1453

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International Journal of Greenhouse Gas Control journal homepage: www.elsevier.com/locate/ijggc

Modelling of greenhouse gas emissions from municipal solid waste disposal in Africa R. Couth a , C. Trois a,∗ , S. Vaughan-Jones b a b

University of KwaZulu-Natal, CRECHE, School of Civil Engineering, Surveying and Construction, Durban 4041, South Africa SLR Consulting Limited, Mytton Mill, Forton Heath, Shropshire SY4 1HA, United Kingdom

a r t i c l e

i n f o

Article history: Received 11 March 2011 Received in revised form 27 July 2011 Accepted 1 August 2011 Available online 1 September 2011 Keywords: Africa Waste disposal Greenhouse gas emissions Prediction models Landfills Waste management

a b s t r a c t Data on waste management in Africa are poor. There is uncertainty over the quantity of greenhouse gas (GHG) emissions from waste management, notably from waste disposal. Data have been collected on solid waste management for territories in Africa and a multi-phase first order decay (FOD) model has been prepared to calculate GHG emissions from waste disposal in accordance with Intergovernmental Panel on Climate Change (IPCC) guidance. The multi-phase FOD model calculates the GHG emissions from waste disposal as 8.1% of the total GHG emissions in Africa in 2010. This is similar to the last published figure of 6.8% for 2004 data but considerably more than the world average figure of 3% GHG emissions from landfill. Probability modelling of the data used to calculate the multi-phase FOD model demonstrates that the data are variable, with a high standard deviation. The GHG emission rate from waste disposal in African territories will increase leading to further climate change as the population increases and becomes more urbanised. Whilst the UNFCCC is dedicated to minimise climate change globally, this paper demonstrates the need for the creation, at the African level, of a waste management body to assess the situation country by country with the objective to elaborate country specific recommendations for waste management. © 2011 Elsevier Ltd. All rights reserved.

1. Introduction Solid waste generation rates and composition vary across Africa in relation to local economy, industrial development, waste management system and lifestyle applicable to each country (IPCC, 2006). The availability and quality of data on solid waste generation and management in Africa is very poor or non-existent (IPCC, 2006; Couth and Trois, 2011). The collection of waste related data and statistics has improved substantially in many African territories during the last decade, e.g. Republic of South Africa; but at present there is a lack of comprehensive waste data for Africa (Couth and Trois, 2010). It is also acknowledged that waste management activities vary significantly within individual territories, let alone across the continent. This paper presents modelling results for GHG emissions from waste disposal in Africa. It is part of a study into the assessment of carbon dioxide equivalent emissions (CO2 e) from waste management activities in Africa. The objective of the study is to develop a strategy for sustainable waste management in developing countries. A summary of the statistics and data collected in previous aspects of this study is provided in Table 1.

∗ Corresponding author. Tel.: +27 31 260 3065/3055; fax: +27 31 260 1411. E-mail address: [email protected] (C. Trois). 1750-5836/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijggc.2011.08.001

The motivation for this study is to improve the sustainability of waste management activities in Africa. This includes reducing local and global impacts from landfill gas emissions, gaining funding for the provision and operation of waste management facilities; and providing employment and training for people. The objectives of this study are to obtain and correlate details of waste management in Africa to develop a model to calculate greenhouse gas emissions from waste disposal. Waste management data has not been collected, correlate and modelled to date and there is a gap in the knowledge for greenhouse gas emissions from waste disposal in Africa. The majority of GHG emissions from waste management activities are from disposal and anaerobic biodegradation of wet waste in landfills. This result in the generation and emission of landfill gas, primarily methane (CH4 ) and carbon dioxide (CO2 ) (IPCC, 1990). Organic waste degradation and landfill gas production is a fivephase process (Environment Agency, 2004): (1) aerobic (hydrolysis and aerobic degradation converting readily degradable carbohydrates to simple sugars, CO2 and H2 O); (2) acidogenic (hydrolysis and fermentation of simple sugars to soluble volatile acids); (3) acetogenic (conversion of soluble acids to acetic acids, H2 and CO2 ); (4) methanogenic (methane generation bacteria metabolize acetate and form CH4 and CO2 ); (5) aerobic (oxidation with the re-establishment of aerobic conditions). There have been many studies since the early 1980 s to calculate emissions from SWDS (solid waste disposal sites) (Rees,

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Table 1 Information on Africa and waste management in African countries. Category and Item 1. Africa 1.1. Number of territories 1.2. Population 1.3. Mean gross domestic product (GDP) 1.4.Population percentage 1.4.1.Rural 1.4.2.Urban 1.5.Population growth 1.5.1.Rural 1.5.2.Urban 1.5.3.Mean 1.6.Percentage of world population 1.6.1.Africa 1.6.2.Sub-Saharan Africa 2.Greenhouse gas (GHG) emissions 2.1.GHG emissions per capita/year (Couth and Trois, 2009a) 2.1.1.USA 2.1.2.UK 2.1.3.World mean 2.1.4.North Africa 2.1.5.Africa average 2.1.6.Sub-Saharan Africa 2.2.GHG emission data (Couth and Trois, 2009b) 2.2.1.World ceiling to control global warming 2.2.2.Percentage of world GHG emissions 2.2.2.1.Africa 2.2.2.2.Sub-Saharan Africa 2.3.Landfill gas data 2.3.1.CH4 global warming potential (GWP) 2.3.2.CH4 content of landfill gas 2.3.3.CH4 generated per tonne of wet MSW 2.3.4.CH4 oxidation in surface of SWDS 3. Waste production 3.1.MSW production per capita (Couth and Trois, 2011) 3.1.1.USA (2007) 3.1.2.UK (2007) 3.1.3.Africa 3.1.3.1.IPCC 3.1.3.2.Study 3.2.Urban organic MSW waste production in Africa 3.3.MSW waste composition 3.3.1.Africa 3.3.1.1.IPCC 3.3.1.2.Study

Quantity 61 934,046,079 (≈ 1 Billion) $ 4,186 60% 40% 0% 6.6% 2.6% 15% 12%

19.48 t CO2 e per capita/year 9.79 t CO2 e per capita/year 4.44 t CO2 e per capita/year 4.56 t CO2 e per capita/year 1.33 t CO2 e per capita/year 0.98 t CO2 e per capita/year

2.256 t CO2 e per capita/year

6% 3% 21× CO2 GWP 50% 243 m3 None

730 kg/capita/year 552 kg/capita/year 292 kg/capita/year 230 kg/capita/year 50 Mtpa

58% organic 56% organic

1980). Since then many models have been developed to calculate landfill gas generation, oxidation and emissions as summarised in the 2010 review by Oonk (2010). The data collected for waste management in Africa from this study have been used in the preparation of the multi-phase first order decay (FOD) model to quantify GHG emissions from SWDS. A FOD model is used to describe nonlinear microbial kinetics. The model calculates GHG emissions from municipal solid waste (MSW) disposal sites for the urban population in Africa and evaluates how these may increase over the next 10 years. A base case FOD model has been prepared together with secondary spreadsheets for a correlated range in statistics and waste management data as detailed in Table 2. The model also seeks to assess how GHG emissions could be effectively reduced through improved sustainable waste management. The objective of this paper is to describe the FOD model that has been prepared for landfill gas emissions in Africa, together with the results and the sensitivity of the results to the input data. After this Introduction, the paper provides Background information on GHG emissions from waste management, and methods and models used to assess GHG emissions. The paper then describes the Method-

ology used to calculate GHG emissions, and this is followed by a Model Description. The Results of the modelling are then described for the FOD model and the @RISK simulations. The sensitivities of the FOD results are assessed using @RISK and commentary is provided against how they compare against published UNFCCC figures (UNFCCC, 2005). Results are summarized for current GHG emissions and the potential increase in GHG emissions from SWDS in Africa over the next 10 years. The Conclusions to the paper summarize the modelling results for viable solutions to reduce GHG emissions from SWDS in Africa, and propose a way forward for waste management and UNFCCC waste GHG emission reduction projects in Africa. 2. Background All countries that are Parties to the Kyoto Protocol (United Nations, 1997) are required to regularly submit GHG emissions data to the UNFCCC Secretariat. The UNFCCC has a ‘Kyoto Protocol Reference Manual on Accounting of Emissions and Assigned Amounts’ (United Nations, 2008), which details how GHG inventories should be calculated. Industrialized (Annex I) Parties are requested to submit their detailed GHG inventories, including CO2 data, annually. Developing (non-Annex I) Parties should submit GHG data periodically as part of their national communications. The UNFCCC Secretariat publishes these inventories on the website: http://unfccc.int/ghg emissions data/items/3800.php. A summary of the National Inventory Reports (NIR) for Africa from the UNFCCC sixth compilation and synthesis of initial national communications from Parties not included in Annex 1 to the Convention is provided in Table 3 (United Nations, 2005). Table 3 indicates that 6.8% of GHG emissions from territories in Africa are attributable to waste management, primarily solid waste disposal sites (SWDS), compared to 4.2% in other non-Annex 1 countries (Couth and Trois, 2009a). Landfill emissions are the largest anthropogenic source of atmospheric CH4 in many countries (Bogner and Matthews, 2003; Spokas et al., 2006; Huber-Humer, 2007). However, in terms of the total global GHG emissions, methane produced from landfills contributes around 3% of the annual total (Metz et al., 2007; Jeon et al., 2007). It is therefore recognised that the waste sector represents a significant opportunity to reduce CO2 e emissions, which has yet to be fully exploited, particularly in developing countries (ISWA, 2009). The mean carbon dioxide equivalent emission rate from African territories in 2004 was 1.33 t CO2 e/capita (Couth and Trois, 2009a). The mean for North Africa was 4.44 t CO2 e/capita, compared with a mean of 0.98 t CO2 e/capita for sub-Saharan Africa (UNFCCC, 2005; Couth and Trois, 2009b). These rates are on the increase due to population growth and increasing urbanisation across Africa (UNPDA, 2007). The Intergovernmental Panel on Climate Change (IPCC) was established in 1988 by the World Meteorological Organization (WMO) and by the United Nations Environment Programme (UNEP) as a source of relevant information on climate change to governs and interested parties (Saundry, 2008). The IPCC guidelines for national GHG inventories for waste have a three-tier hierarchy to calculate estimates of CH4 emissions from SWDS (IPCC, 2006). The greater the quality of the data available, the higher the tier to be used, and the more accurate the estimate of GHG emissions from SWDS: Tier 1: ‘The estimations of the Tier 1 methods are based on the IPCC FOD method mainly based on default activity data and default parameters’ (verbatim from IPCC, 2006). Tier 2: ‘Tier 2 methods use the IPCC FOD method and some default parameters, but are based on good quality country-specific activity

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Table 2 Base case and variable model parameters (UNFCCC, 2008). Parameter

Symbol

Base case

Variation

Maximum emissions

Intermediary emissions

Minimum emissions

Model correction factor Fraction of methane captured per tonne waste Global Warming Potential Oxidation factor Fraction of methane in SWDS gas Fraction of degradable organic carbon that can decompose Methane correction factor

ϕ f GWPCH4 OX F DOCf

0.9 0.0025 21 0 0.5 50%

None Extraction/compost

– 0.0025 23 0 – –

– 0.5 – – – –

– 0.980 21 0.1 – –

MCF

0.8

1

0.5

0.4

Waste type

j

–

–

–

Waste production Amount of organic waste type j prevented from disposal

W Wj,x

See composition spreadsheet 230 kg/hd/year 230 kg/hd/year

Managed to unmanaged SWDS –

Fraction of degradable organic carbon by weight in waste type j Decay rate for waste type j

DOCj

–

0.23 See composition spreadsheet –

0.2875 See composition spreadsheet –

kj

–

–

–

–

Urban population growth

–

±25%

0.00495

0.066

0.0825

Waste growth due to life style Boreal and Temperate: dry Boreal and Temperate: wet Tropical: dry Tropical: wet Pulp, paper and cardboard (other than sludge), textiles Wood and wood products and straw Food, food waste, sewage sludge, beverages and tobacco

– – – – – –

DOCj-kj spreadsheet DOCj-kj spreadsheet 6.6%/year for 5 years, 3% thereafter 5 kg/p/year 19% 8% 34% 39% 7.4

0.1725 See composition spreadsheet –

– – – – –

– – – – – 25

0.005 – – – –

– – – – –

– –

14.1 53.9

Managed SWDS None None

±25% ±25%

15 23

Table 3 Carbon emissions from waste in Africa (Couth and Trois, 2009b). Total (without LUCF) Gg Max. Min. Average Total

379,837 256 37,486 1,612,904

Total (without LUCF) Gg South Africa Seychelles – –

Total (with LUCF) Gg 952,799 −494,351 43,667 1,201,794

data on current and historical waste disposal at SWDS. Historical waste disposal data for 10 years or more should be based on country-specific statistics, surveys or other relevant sources’ (verbatim from IPCC, 2006). Tier 3: Tier 3 methods are based on good quality countryspecific activity data (see Tier 2) and use either the FOD methods with (1) nationally developed key parameters, or (2) empirical country-specific parameters. The inventory compiler may use country-specific methods that are of equal or higher quality to the above-defined FOD based Tier 2 method. Key parameters should include the half-life, and either methane generation potential (Lo ) or degradable organic carbon (DOC) content in waste and the putrescible fraction of DOC (DOCf ) (modified from IPCC, 2006). The Tiers 1, 2 or 3 data that is available can be input into a landfill gas model to calculate carbon emissions. There is a number of established multi-phase FOD models that calculate the quantity of methane that can be generated in SWDS (Scharff and Jacobs, 2006; Kamalan et al., 2011) including: GasSim: UK Environment Agency (EA) GasSim2 model (Gregory and Rosevear, 2005). This is a Monte Carlo landfill gas simulation (GasSim) model developed for the UK EA as a risk assessment tool to be used to evaluate the impacts of landfill gas emissions. It is

Total (with LUCF) Gg

Waste

Waste

% waste

% waste

Tanzania Gabon – –

44,004 6.75 2546 95.32

Nigeria Togo – –

72.9 0.1 6.8 6.8

Gambia Togo – –

designed to calculate emissions from SWDS designed to European Union standards. LandGEM: US Environmental Protection Agency (EPA) Landfill Gas Emissions model (LandGEM) (US EPA, 2005). It is an automated estimation tool with a Microsoft Excel interface that can be used to estimate emission rates for total landfill gas and its individual components from municipal SWDS. It is designed to calculate emissions from SWDS designed to US EPA standards. EpE: Protocol of the Quantification of Greenhouse Gas Emissions from Waste Management Activities (EpE, 2010). This quantification protocol for GHG emissions from waste management activities provides guidelines for European waste management companies to calculate a GHG emissions inventory. It calculates emissions associated with waste management services (operation) over a limited time period (usually one year).

There is a reasonable number of other multi-phase FOD models which have been applied across the world, e.g. IPCC model (Weitz et al., 2007); World Bank model (World Bank, 2007); French and German EPER models (Scharff and Jacobs, 2006). This study has sought to develop a generic Tier 2 method model for Africa based upon a review of waste data compiled throughout the continent (Couth and Trois, 2010, 2011). There is not currently a Tier 2 model with input data for landfill gas emissions from Africa.

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The IPCC model was chosen to develop this FOD model for Africa because it is available on the IPCC web site free of charge (the GasSim model is only commercially available), it is universally used by developing countries, and its function is to calculate specifically GHG emissions. The objective of the modelling is to quantify the current percentage of GHG emissions attributed to waste disposal in Africa; assess how it is likely to increase; assess how it may be reduced; and compare this with emissions from waste disposal in other continents.

3. Methodology It was decided by the authors to test the FOD model using the UNFCCC methodological tool and expanding it to introduce country-specific parameters (UNFCCC, 2008). This tool calculates baseline emissions of methane in t CO2 e from waste disposed at a SWDS. The model differentiates between the different types of waste, assigning relative decay rates and different organic fractions to each one. The UNFCCC model calculates methane generation based on the actual waste streams disposed in each year, starting with the first year after the commencement of the project activity. The calculation which is carried out is as follows (sourced from · UNFCCC, 2008):BECH4,SWDS,y = ϕ · (1 − f ) · GWPCH4 · (1 − OX) · 16 12 F · DOCf

y  x=1

Wj, x · DOCj · e−kj(y−x) · (1 − e−kj )where

j

BECH4,SWDS,y

ϕ f GWPCH4 OX

F DOCf Wj,x

DOCj kj j x

y

=Methane emissions from waste disposal at the solid waste disposal site (SWDS) during the period from the start of the project activity to the end of the year y (t CO2 e) =Model correction factor to account for model uncertainties =Fraction of methane captured at the SWDS and flared, combusted or used in another manner =Global Warming Potential (GWP) of methane (IPCC, 2007a), valid for the relevant commitment period =Oxidation factor (reflecting the amount of methane from SWDS that is oxidised in the soil or other material covering the waste) (Bogner and Spokas, 1997) =Fraction of methane in the SWDS gas (volume fraction) (0.5) (World Bank, 2007) =Fraction of degradable organic carbon (DOC) that can decompose =Amount of organic waste type j prevented from disposal in the SWDS in the year x (tons) through separate waste collection, recycling and/or treatment =Fraction of degradable organic carbon (by weight) in the waste type j =Decay rate for the waste type j =Waste type category (index). Inert, slow, medium or rapidly degrading =Year during the crediting period: x runs from the first year of the first crediting period (x = 1) to the year y for which emissions are calculated (x = y) =Year for which methane emissions are calculated

The model has a methane correction factor (MCF) for the type of SWDS, as explained in the model description below. It is considered that for the current IPCC waste model ‘the main source of uncertainty lies in the selection of values for parameters for the model, rather than the methodology of the model in itself’ (IPCC, 2006). The data compiled for the model are very mixed, and it is considered that the quality of much of the data available for Africa is questionable (Couth and Trois, 2010, 2011). The methodology has been to prepare a base case model and then test this for variations to each input parameters to access the sensitivity of the model output to each input parameter. As there is uncertainty for many of the input parameters, the model has been developed using Palisade’s @RISK software (Vaughan-Jones et al., 2009) to report on probability distributions and uncertainties in the output. Risk analysis is the systematic use of available information to determine how often specified events may occur and the

magnitude of their consequences. @RISK performs quantitative risk analysis using Monte Carlo simulation to show many possible outcomes in a Microsoft Excel spreadsheet, and calculates how likely they are to occur. Quantitative (deterministic) risk analysis attempts to assign numeric values to risks, either by using empirical data or by quantifying qualitative assessments. A Monte Carlo simulation is a stochastic, non-deterministic process that can be used to perform risk analysis by building models of probable results by substituting values with probability distributions (Vaughan-Jones et al., 2009). The Monte Carlo simulation is then repeated according to the number of iterations specified, each time using a different set of random values taken from within each of the probability functions. The UK GasSim model also uses the Monte Carlo method to evaluate risks from the migration and emission of landfill gas (Gregory and Rosevear, 2005). The @RISK software has various probability distributions. ‘RiskTriang’ requires the definition of minimum, maximum and most probable values. The minimum and maximum values are derived from the minimum and maximum values used within any of the modelled scenarios, and the most probable values are derived through calculating the ‘Mode’ average across all modelled scenarios (where the most probable was not identified previously). The resultant probability distributions are triangular. ‘RiskNormal’ utilises the mean average and standard deviation of a given data set and provides a more normal statistical probability distribution. The output from the @RISK modelling is probability distributions for resultants and Tornado diagrams for sensitivity analysis. 4. Model description The landfill gas generation model which has been prepared for Africa comprises a series of Microsoft Excel spreadsheets which record and calculate input data; set up the multi-phase FOD base case model; set up multi-phase FOD spreadsheets for variable input parameters; summarize the outputs from all multi-phase FOD models and identify the key input variables; and use @RISK to assess the sensitivity of the results and the most probable output from the multi-phase FOD models for the key input variables. The FOD model calculates landfill gas generation and emissions from 2000 to 2019, with the base year being 2010. It is considered that the vast majority of landfill gas produced from waste disposed in Africa before 2000 will already have been emitted (Couth et al., 2011). African waste typically has a high biodegradable content, which degrades quickly and produces a spiked landfill gas profile. The base case and the variable input parameters are listed in Table 2. Inputs are varied to calculate maximum, intermediate and minimum emissions. The basis for the parameters in Table 2 is as follows: Model correction factor (ϕ): 0.9 for model uncertainties as set in the UNFCCC methodology, Fraction of methane captured at the SWDS (f): few SWDS in Africa have landfill gas extraction and flaring systems, landfill gas is simply vented to atmosphere at the majority of sites. The base case sets 1 in 20 SWDS in Africa with landfill gas extraction and flaring systems, which capture 50% of the landfill gas generated (f = 0.025). The World Bank assessed the delivery of carbon finance projects and concluded that, from a group of 15 landfill gas projects, the actual rate of landfill gas recovery was 47.3% of that predicted prior to the project being implemented (World Bank, 2007). The base case calculates the maximum emissions. Intermediate emissions are calculated assuming all landfills in Africa are engineered and have landfill gas extraction and flaring systems (f = 0.5). Minimum emissions are calculated for the scenario of informal recycling of dry waste, and composting of wet organic waste. Paper in dry

R. Couth et al. / International Journal of Greenhouse Gas Control 5 (2011) 1443–1453 Table 4 Average African urban municipal waste composition (Couth and Trois, 2011). Mean Food waste Paper and card Textiles Plastic Grass/Wood Leather Rubber Glass Metal Other Total Inert/non-combustible Organic/biodegradable/combustible

48.9 10.6 2.1 8.4 6.7 0.8 1.4 3.5 2.6 15.1 100.0 17.2 56.0

waste will generate small amounts of methane in a landfill as it degrades over a long period. The scenario assumes 2% anaerobic gas emissions from the composting activities, with the remaining dry fossil carbon waste (e.g. plastic and synthetic fabric) stored in landfills (f = 0.98). Global Warming Potential of methane (GWPCH4 ). The IPCC First Assessment Report (IPCC, 1990) set a GWPCH4 of 21. This has been adopted by the UNFCCC and is currently used for Clean Development Mechanism (CDM) projects. Academics argue over this figure and it is considered a minimum (Jardine et al., 2006). Subsequent IPCC reports assign a GWPCH4 of 23 (IPCC, 2001) and 25 (IPCC, 2007b). A GWPCH4 of 21 has been used in the base case model. Oxidation factor (OX). The base case assumes that there is little cover to SWDS in Africa with no oxidation (0%) of methane at the surface. A conservative scenario has taken an oxidation figure of 10% based on research for shallow cover landfills, although higher figures are reported (Bogner and Spokas, 1997; Scheutz et al., 2003; Fourie and Morris, 2004; Couth and Trois, 2010). Fraction of methane in SWDS gas (F). The UNFCCC standard model figure of 0.5 (i.e. 50% CH4 ) is used. Fraction of degradable organic carbon that can decompose (DOCf ). The UNFCCC standard model figure of 50% is used. Waste type (j). The waste composition as detailed in Table 4, which is based on recent research (Couth and Trois, 2011) has been used as the base case. Table 5 calculates percentages and tonnages for the model waste types in accordance with the waste composition in Table 4. The mean urban waste composition for Africa is taken as 14.1% of the waste being slowly biodegradable (garden refuse, wood, wood products and straw); 7.1% being degrading at a medium biodegradable rate (pulp, paper and cardboard, textiles); and 53.9% being readily biodegradable (food, food waste, putrescibles, sewage sludge, beverages and tobacco). The IPCC default of 15% slowly, 25% mean and 23% rapidly degrading has been modelled as a sensitivity check. A 25% maximum and minimum variation in the organic percentage (slowly and rapidly degrading) has been embedded into the model. European landfill gas models generally have a time lag of one year from disposal to methanogenesis but in Africa methanogenic conditions can occur within 4–8 weeks of the disposal of the waste, and hence no time lag is included in the FOD model (Trois et al., 2001). Waste production for the amount of organic waste type (j) prevented from disposal in year (x) (Wj,x ). A base case of 230 kg/head/year has been taken from previous research into waste production in Africa (Couth and Trois, 2011), with a 25% maximum and minimum variation as detailed in Table 5. Fraction of degradable organic carbon by weight in waste type j (DOCj ). UNFCCC default values for wet and dry waste have been used as detailed in Table 5. Decay rate for waste type j (kj ). Default k-values that represent the rate of degradation in the IPCC, 2006 Guidelines were used for

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four climate zones: (1) moist and wet tropical, (2) dry tropical, (3) moist boreal and temperate and (4) wet boreal and temperate, as detailed in Table 6. Climate. The model includes a climate spreadsheet detailing the mean annual rainfall and mean annual evapotranspiration for the 61 territories in Africa to define whether they are boreal and temperate and wet or dry, or tropical and wet or dry. Of the sixty one territories: ten are boreal and temperate and dry with 19% of the population and waste arisings; two are boreal and temperate and wet with 8% of the population and waste arisings; twenty five are tropical and dry with 34% of the population and waste arisings; and twenty four are tropical and wet with 39% of the population and waste arisings. Some 53% of African population has a dry climate and some 47% has a wet climate. Urban population growth. An urban population growth of 6.6% is used (Couth and Trois, 2009b), but the sensitivity of a 3% urban population growth is tested. From the data in the climate spreadsheet, another section of the model calculates the waste production for each of the four climatic areas. This waste production spreadsheet was formatted detailing the reported 2005 population census for the 61 territories: with an urban population of 40% of the total; an urban population growth of 6.6% per annum; and sensitivities for an urban population growth of 3% per annum. Waste growth due to lifestyle. A waste growth of 5 kg/head/year is taken due to lifestyle change due to the urban population becoming more affluent. Accurate data are not available for urban MSW growth for most territories in Africa, and 5 kg/capita/year is based upon waste production increasing by 50% over the next generation to 345 kg/head/year (Couth and Trois, 2011). This is also 50% of the United Kingdom’s (UK) waste reduction goal (Defra, 2007) Methane correction factor (MCF). The UNFCCC methodology lists a range of MCFs: 1. anaerobic managed SWDS. Fully engineered landfills where waste is directed to dedicated disposal areas through a weighbridge, scavenging and open fires are controlled and that are characterised by: (i) cover material; (ii) mechanical compacting; or (iii) levelling of the waste; 2. 0.8 unmanaged SWDS – deep and/or with high water table. This comprises all SWDS not meeting the criteria of managed/engineered landfills and ‘which have depths of greater than or equal to 5 metres and/or high water table at near ground level’; 3. 0.5 semi-aerobic managed SWDS. These must have controlled disposal and will include aeration of the waste body through: (i) permeable cover material; (ii) leachate drainage system; (iii) regulating pondage; and (iv) gas ventilation system; and 4. 0.4 unmanaged-shallow SWDS. This comprises all SWDS not meeting the criteria of managed SWDS and which have depths of less than 5 metres. A base case with a maximum MCF of 0.8 has been taken, with an intermediary case of 0.5 and a minimum emissions case of 0.4. A base case spreadsheet was prepared in the FOD model for the parameters described above and listed in Table 2. The sensitivity of these base case parameters was then tested for the variables listed in Table 2 by preparing further spreadsheets through modification of the base case model for: 1. IPCC waste composition default parameters; 2. ±25% increase and decrease of the organic fraction (i.e. 53.9 ± 25%); 3. ±25% increase and decrease of the urban waste per capita (i.e. 230 kg/head/year ± 25%); 4. 50% landfill gas collection and combustion through engineered landfills and landfill gas management. This figure may range

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Table 5 (DOCj ) Fraction of degradable organic carbon (by weight) in the waste type j (IPCC, 2006). Waste type j

Wood and wood products Pulp, paper and cardboard (other than sludge) Food, food waste, beverages and tobacco (other that sludge) Textiles Garden, yard and park waste Glass, plastic, metal, other inert waste

% waste type j

j tonnage kg/p/year

j tonnage kg/p/year −25%

j tonnage kg/p/year +25%

50 44 38

7.4 11.7 53.9

17.02 26.91 123.97

21.28 33.64 154.96

12.77 20.18 92.98

30 49 0

2.4 0 24.6

5.52 0 56.58

6.90 0.00 70.73

4.14 0.00 42.44

DOCj

DOCj

(% wet waste)

(% dry waste)

43 40 15 24 20 0

Table 6 kj Decay rate for waste type j (IPCC, 2006). Waste type j

Boreal and Temperate (MAT≤20 ◦ C)

Slowly degrading

Moderately degrading

Rapidly degrading

5.

6. 7. 8. 9. 10.

Pulp, paper and cardboard (other than sludge), textiles Wood and wood products and straw Other (non-food) organic putrescible garden and park waste Food, food waste, sewage sludge, beverages and tobacco

Tropical (MAT > 20◦ C)

Dry (MAP/PET < 1)

Wet (MAP/PET > 1)

Dry (MAP < 1000 mm)

Wet (MAP > 1000 mm)

% Waste type j

0.04

0.06

0.045

0.07

14.1

0.02

0.03

0.025

0.035

7.4

0.05

0.1

0.065

0.17

0

0.06

0.185

0.085

0.4

from 30 to 40% for an open landfill to greater than 70% for a contained landfill depending on the site management; 98% reduction of the total landfill gas emissions due to the composting of the wet organic waste and the landfill storage of the dry fossil carbon waste; GWPCH4 of 25 times CO2 ; MCFs of 0.8, 0.5 and 0.4; base case for the full Africa population of 1 billion people; 3% increase in urban population per annum; and 6.6% population growth per annum but without waste growth per capita.

5. Results 5.1. FOD model The base case calculates 55 Mt CO2 e GHG emissions from waste disposal from the urban African population in 2010. The model calculates an increase of 140% from 55 Mt CO2 e in 2010 to 132 Mt CO2 e in 2019 (Fig. 1). The percentage variation in GHG emissions for sensitivity differences in input parameters against the base case is shown in Fig. 2. A negative number in Fig. 2 is a reduction in CO2 e emissions, and a positive number is an increase in CO2 e emissions. It is agreed that the maximum increase of 106% in Fig. 2 for the whole population in Africa (Wj,x ) having CO2 e emissions from MSW equal to the urban population is unrealistic as the rural population does not have the same waste disposal profile. The 98% maximum reduction for composting all organic MSW (f) is also unrealistic as not all organic waste in Africa will be treated in this manner. The 49% reduction for all landfills in Africa having operational landfill gas extraction and combustion is equally unrealistic as the composting scenario. None the less, these percentages are quoted to represent the best-case scenario. However, they do illustrate the GHG emission reduction benefit of composting organic waste against extracting and combusting landfill gas. What is considered more realistic is an increase of up to 56% for 25% per annum

53.9

waste growth and 25% organic waste increase (Wj,x ), or a reduction of 37% to 50% for urban MSW disposed to semi-aerobic landfills or landfills which are unmanaged and shallow (MCF of 0.5 and 0.4 respectively). The remainder of the variations in CO2 e emissions in Fig. 2 are 25% or less about the base case and are considered realistic. For example, there is a 19% variation for a 25% variation in the organic waste percentage (j). It is interesting that using the IPCC default waste composition parameters under calculates the CO2 e emissions by 19%, whilst increasing the GWP of methane from 21 to 25 increases the CO2 e emissions by 19%. The variation in parameters in Fig. 2 illustrates that the CO2 e emissions could vary by at least 25% about the base case. The base case parameters have also been input into the GasSim model for comparison. The GasSim modelling calculated GHG emissions 25% less than the FOD model in 2010 for moist waste from Sub-Saharan Africa. This is a close comparison. One reporting criterion for GHG inventories is CO2 eequivalent per capita (Friedrich and Trois, 2008). The base case calculates an average 0.107 t CO2 e per capita from methane from waste disposal. This equates to 8.1% of the reported GHG emissions for Africa; 11% of the reported GHG emissions for sub-Saharan Africa; 2.4% of the world mean; and 4.2% of the world target to control global warming. The percentage GHG emissions per capita for waste disposal in developing territories in Africa, notably sub-Saharan territories, is considerably greater than developed countries.

5.2. @RISK model The calculation of GHG emissions in the model vary by an order of magnitude due to the range in input data (Table 2). The parameters which result in a variation of greater than 25% in the predicted GHG emissions for SWDS in Africa in the FOD model are the fraction of methane captured at the SWDS (f); methane correction factor (MCF); waste production (W); urban population growth; waste growth due to lifestyle change; percentage of medium degrading

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Table 7 Parameters in Palisade @RISK analysis (http://www.palisade.com/risk/risk analysis.asp). Parameter

Symbol

Base case

Variation

Maximum emissions

Intermediary emissions

Minimum emissions

Fraction of methane captured per tonne waste Methane correction factor

f MCF

0.0025 0.8

0.0025 1

0.5 0.5

0.980 0.4

Waste production Urban population growth

W –

0.1725 6.6% growth continuous

0.23 0.066

0.2875 3% growth continuous

Waste growth due to life style Pulp, paper and cardboard (other than sludge), textiles Food, food waste, sewage sludge, beverages and tobacco

– – –

230 kg/head/year 6.6%/year for 5 years, 3% thereafter 5 kg/hd/year 7.4 53.9

Extraction/compost Managed to unmanaged SWDS ±25% ±25%

– 25 23

0.005

0

waste; and percentage of rapidly degrading waste (Fig. 2). The @RISK assessment therefore considers probability distributions for these parameters as detailed in Table 7. The ‘RiskTriang’ function in the @RISK model has been applied to establish probability distributions for the parameters: fraction of methane capture per tonne (f); methane correction factor (MCF); waste production (W); urban population growth; and waste growth due to life style change. The @RISK function of ‘RiskNormal’ was used for the food fraction and paper fraction parameters. For the FOD model input data, Fig. 3a–f from the @RISK t CO2 e output data illustrate:

3.

4.

5.

Carbon emissions x 10-6tCO2

1. A high standard deviation for the fraction of methane captured at the SWDS (f) (Fig. 3a). There could be a significant variation in methane emissions depending how the waste is managed. There is a maximum reduction in methane emissions for the composting scenario. 2. A medium standard deviation for the methane correction factor (MCF) (Fig. 3b). There is a reasonable variation in methane

6.

–

emissions depending on how waste is disposed. The majority of waste in Africa is disposed in unmanaged SWDS, but if these are semi-aerobic then the emissions are reduced. The emissions will increase if SWDS are managed, but the landfill gas is not extracted and combusted. A low standard deviation for waste production rate (W) (Fig. 3c). The model does not consider significant variation for the waste production rate. A low standard deviation for urban population growth (Fig. 3d). The model does not consider significant variation for urban population growth. A medium standard deviation for medium degrading waste fraction (i.e. paper) due to the input parameters (Fig. 3e). The probability distribution is fairly normal for the medium degrading waste fraction. A low standard deviation for rapid degrading waste fraction (i.e. food) due to the input parameters (Fig. 3f). The probability distribution is fairly normal for the rapid degrading waste fraction.

Landfill Carbon Emissions, Africa: Base Case

140 120 100 80 60 40 20 0 2010

2011

2012

2013

2014

2015

2016

2017

2018

Year Fig. 1. Base case 2010–2019 Africa waste disposal carbon dioxide equivalent emissions.

Fig. 2. Percentage variation on base case.

2019

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Fig. 3. (a) Probability distribution, fraction of methane captured. (b) Probability distribution, methane correction factor. (c) Probability distribution, waste production. (d) Probability distribution, urban population growth. (e) Probability distribution, paper waste fraction. (f) Probability distribution, food waste fraction.

The impact assessed within the model is the resultant CO2 e summary probability distribution for the above key parameters. The summary probability distribution for CO2 e emissions for the key parameters for 2010 is as shown in Fig. 4. The @RISK analyses of the data for the key input parameters in the model calculate the 2010 mean GHG emissions from waste disposal in Africa as 31 Mt CO2 e with a standard deviation of 13 Mt CO2 e. The shape of the probability distribution curve and the difference in the mean from the base case demonstrate the uncertainty of the data. For example, the base case takes a MCF of 0.8 for the mean result of 55 Mt CO2 e. If a MCF of 0.5 is used for semiaerobic landfills in the base case, then the mean result is 35 Mt CO2 e, similar to the @RISK results. The probability distribution for the GHG emissions from African landfills for 2010–2019 is presented in Fig. 5. The @RISK modelling gives a mean result of 483 Mt CO2 e for GHG emissions for solid waste disposal in Africa from 2010 to 2019, with a standard deviation of 204 Mt CO2 e. The resultant probability distribution illustrates the variation of the input data. @RISK enables

Fig. 4. Probability distribution for CO2e emissions for the key parameters for 2010.

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of one standard deviation to the food waste fraction would result in approximately half the GHG emissions increase of one standard deviation increase to the MCF. The regression mapped values summary illustrates that medium-rate degrading paper factions, urban population growth and waste production parameters are less sensitive to resultant GHG emissions (Fig. 6).

6. Conclusions

Fig. 5. Probability distribution for the GHG emissions from African landfills for 2010–2019.

Fig. 6. @RISK ranking/prioritising of the most sensitive inputs.

Fig. 7. @RISK 2010–2019 Africa waste disposal carbon dioxide equivalent emissions.

the ranking/prioritising of the most sensitive inputs; Fig. 6 ranks the six identified inputs. The regression mapped value illustrates the impact on the resultant probability distribution for a single standard deviation increase. The base case FOD model calculates a 140% increase in GHG emissions from waste disposal in Africa between 2010 and 2019 (Fig. 1). The temporal profile of increasing GHG emissions from waste disposal from the @RISK modelling is shown in Fig. 7. Fig. 7 illustrates the mean, one standard deviation about the mean, and 5% to 95% probability about the mean. The fraction of methane captured per tonne (f) is identified as having the greatest impact on the resultant probability distribution. If the generation of methane can be avoided through the composting of the wet organic waste then all of the methane can be effectively captured (f). The next most sensitive parameter is the methane correction factor (MCF), and this illustrates that an increase of one standard deviation for this variable would result in an increase of 100 t CO2 e in GHG emissions. An increase

The annual GHG emissions ceiling for the world, in order to influence global warming, is considered to be 2.256 t CO2 e per capita (14.5 Gt CO2 e) (UNFCCC, 2005; Couth and Trois, 2010). This is less than half of the actual 2004 emissions of 4.56 t CO2 e per capita (29 Gt CO2 e) (UNSD, 2008). For African territories, the mean carbon dioxide equivalent emissions in 2004 were 1.33 t CO2 e per capita. The mean carbon dioxide equivalent emissions for North African territories were 4.44 t CO2 e per capita which is close to the world average of 4.56 t CO2 e. The mean carbon dioxide equivalent emissions for sub-Saharan territories in 2004 were 0.98 t CO2 e per capita, approximately 10% of the UK at 9.79 t CO2 e per capita and 5% of the USA at 19.48 t CO2 e per capita respectively, but only 38% of the emissions figure needed to control global warming. The multi-phase FOD model for GHG emissions from urban waste disposal for African territories calculates a base case of 0.064 t CO2 e per capita for 2004. Compared to reported figures, this equates to 4.8% of the mean t CO2 e per capita for Africa, 6.6% of the mean t CO2 e per capita for Sub-Saharan, and 2.8% of the world ceiling target to control climate change. For the urban waste and population growth researched by this study, the urban waste GHG emissions in 2010 are calculated as 0.107 t CO2 e per capita. This equates to some 8.1% of the 2004 mean GHG emissions for African territories. The United Nations Statistics Division (UNSD) recorded that there was a 44% to 69% increase in GHG emissions from North African territories between 1994 and 2004. A greater 222% increase in CO2 e emissions from sub-Saharan countries for the same period was recorded by the UNSD, whilst an increase of 307% was recorded by the Carbon Dioxide Information Analysis Center (CDIAC). (Couth and Trois, 2009a). The multi-phase FOD for Africa used in this study calculates that GHG emissions from urban municipal solid waste will increase by 140% over the next 10 years unless measures are taken to improve the management of this waste. The statistical range in this increase is illustrated in Fig. 7. By 2020, between 75 and 250 million people in Africa are projected to be exposed to increased stress due to climate change (IPCC, 2007b). Urban waste production per capita in African territories are around half of waste production in developed (Annex I) countries. GHG emissions from waste per capita in African territories are greater than those in developed Annex I countries, although the total GHG emissions per capita are significantly greater in Annex I countries than African territories. The higher impact from GHG emissions from waste per capita in African territories is primarily because the waste is dumped and not managed in many territories. There is however a paradox in that if MSW is disposed in engineered landfills without landfill gas extraction and combustion, the CO2 e emissions are higher than if the waste is disposed in open dumps. The CO2 e emissions from waste disposal in Africa are only going to increase with urban population growth, waste growth and unmanaged waste disposal. Whilst the UNFCCC is dedicated to climate change and how to reduce global warming, the findings of this paper suggest that there is the need for the creation in Africa of a waste management body, working in collaboration with UNFCCC, dedicated to elaborate country specific recommendations for waste management and greenhouse gas emissions reduction strategies. There is an urgent need for an African waste management body to

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initiate new measures to manage waste in Africa territories. These measures should be focused on the waste hierarchy, with the prevention of waste, followed by waste recycling and composting, with only the remaining fossil carbon waste disposed. MSW composting is only feasible where the waste is sorted to remove dry recyclables to allow a compost to be produced, which can be applied to land. The intention of this paper is to demonstrate to national authorities the most important issues to address and polices to be implemented in order to reduce GHG emissions from SWDSs. In particular, it highlights the main role that can be played by avoidance of direct landfilling of biodegradable fractions, and therefore the importance of composting and low cost mechanical biological pre-treatment. The less controllable parameters, such as urban population growth and waste production, are less sensitive to resultant GHG emissions. The @RISK ranking/prioritising of the most sensitive inputs clearly highlights those issues that should be targeted to reduce GHG from waste. The current means to reduce GHG emissions from waste activities in developing countries is the Clean Development Mechanism (CDM); hence, and a replacement is urgently needed when the Kyoto Protocol expires for new projects from December 2012. Acknowledgement The authors wish to thank Mr. Grant Pearson of SLR Consulting Ltd. for his valuable review of this paper.

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