Determination of waste decay rate for a large Finnish landfill by calibrating methane generation models on the basis of methane recovery and emissions

Abstract

The aim of this study was to determine the methane (CH₄) generation factor (k) and CH₄ generation potential (L) for bulk waste in order to calibrate a CH₄ generation model (USEPA Landgem 3.02) and provide information on the remaining CH₄ generation potential in a large (54 ha) municipal solid waste landfill located in a boreal climate. The CH₄ generation model was calibrated by actual CH₄ recovery and emission measurement data. Moreover, waste characterisation information from a previous study was considered. The appropriate k for bulk waste was 0.18 in the studied landfill, which indicated a higher rate of degradation than proposed by the Intergovernmental Panel on Climate Change as a default k value of 0.09 for wet conditions in boreal and temperate climes, whereas the calibrated L of 100 m³/t was lower than estimated on the basis of a previous waste characterisation study. The results demonstrate the importance of model calibration, as inappropriate model parameters may result in a large discrepancy (approximately 100 % or 119 million m³ having an energy equivalent of nearly 1.2 TWh) in cumulative CH₄ generation estimates within a 18-year timescale (2012–30) at the studied landfill.

Keywords

Methane landfill gas emissions modelling decay rate

Introduction

Landfilling has been the main method of municipal solid waste (MSW) disposal during the last few decades in Europe, as well as in the USA (Eurostat, 2011,; USEPA, 2009). Furthermore, landfilling continues to have an important role in MSW management in many countries, even though material recycling and waste treatment technologies have been increasingly developed and introduced. Over the years, different kinds of materials have been disposed in landfills, the waste properties of which [e.g. methane (CH₄) generation potential] vary in accordance with the development and design of products, consumption and local waste management practices. The proportion of biodegradable materials in landfilled MSW has been high, and remains so even though source segregation of bio-waste has increasingly been implemented in many countries. Consequently, landfills are very heterogeneous in their material composition and contain biodegradable material, which—in anaerobic landfill conditions—is converted to landfill gas consisting mainly of CH₄ and carbon dioxide (CO₂). CH₄, which is a strong greenhouse gas, can be recovered and used for energy production to replace fossil fuels, or it can be flared to reduce greenhouse gas emissions. Biodegradation of organic waste in landfill conditions is affected by several factors, for example climatic conditions, landfill structure, moisture and landfilling practices.

In order to quantify landfill gas as an energy potential, several landfill gas generation (LGG) models, such as the German European Pollutant Emission Register (EPER), TNO, Landgem and Scholl Canyon, have been introduced in order to estimate long-term landfill gas generation rates and potentials (Thompson, 2009). Most models are based on first order reaction kinetics of biological degradation using the CH₄ generation rate constant (k, year⁻¹), which determines the half-life (k = ln(2)/t_1/2, where t is in years) of the CH₄ generation potential (L m³/t) of the landfilled waste (Amini and Reinhart, 2011; Faour et al., 2007; Scharff and Jacobs, 2006; Thompson et al., 2009).

The CH₄ generation rate factor k is affected by several landfill-dependent variables, such as precipitation, waste composition, moisture, temperature, landfill depth, availability of nutrients and pH. The reported k values for bulk waste in US landfills have varied from 0.003 to 0.21 year⁻¹, while the regulations under the Clean Air Act suggest a default k of 0.05 year⁻¹ for conventional MSW landfills. In arid conditions the recommended k value is 0.02 year⁻¹ (USEPA, 2005). Recently, a value of 0.3 year⁻¹ was proposed for well designed wet landfills in the USA, if the acidogenic lag phase of anaerobic decomposition is minimised (Faour et al., 2007).

L is estimated preferably on the basis of the composition (e.g. analysed proportion of organic material) or the measured CH₄ generation potential (e.g. in batch assays) of the waste to be landfilled. The theoretical default L given in a landfill gas generation model for MSW varies from 96 to 170 m³/t waste (wet weight) (USEPA, 2005), while, on the basis of recovered CH₄, the empirical L of 100 m³/t waste (wet weight) might be appropriate in wet landfills (Faour et al., 2007). For a comparison Jokela et al. (2002) reported a biological CH₄ potential (biological methane potential (BMP), termed L in present study) of 130 m³/t total solids (TS) for approximately 10-year-old landfilled MSW in the Ämmässuo landfill (Espoo, Finland), while for unsorted fresh MSW CH₄ potentials from 78 to 152 m³/t TS have been presented (Barlaz et al., 1989). Residual MSW material (of which bio-waste, paper and cardboard, glass and metals are source-segregated) may have L (BMP) from 46 to 100 m³/t TS (Jokela et al., 2002).

As landfill conditions are highly affected by local climatic conditions and by other local factors, for example landfilling and waste management practices, the use of the proposed k and L values in the literature can be misleading. The difficulty of determining or selecting site-specific k values using values from literature has been generally discussed (Amini and Reinhart, 2011; Garg et al., 2006; Machado et al., 2009; Tolaymat et al., 2010,). Some information on k in warmer climatic conditions has been obtained in recent bioreactor landfill studies in the field (Benson et al., 2007; Faour et al., 2007; Machado et al., 2009; Yazdani et al., 2006; ), while information on determining k, in particular, for landfills in boreal climes, where a high proportion of the annual precipitation falls as snow, have not been presented.

LGG models using k and L values have generally been used for predicting gas generation rates and yields with limited site specific information on landfill gas generation, collection efficiency, CH₄ oxidation and emission. Recently, CH₄ recovery data obtained from landfill gas collection systems have been used to improve the estimation of future gas generation (Faour et al., 2007; Thompson et al., 2009). Recovered landfill gas data have been considered important to ensure model accuracy (Amini and Reinhart, 2011; Spokas et al., 2006). However, recovered landfill gas may be only a portion of the total volume of generated landfill gas, especially at sites that are not sealed with impermeable covers. The gas recovery rates are usually estimated to be in the range of 60 to 90% of the total gas generation (Spokas et al., 2006); thus, information on CH₄ emission is significant to determine appropriate k and L values. Moreover, CH₄ oxidation in the landfill cover layer may significantly reduce the CH₄ emissions (Einola et al., 2008); thus, it should be considered together with data on CH₄ recovery and emissions to improve model accuracy.

Recently, long-term and continuous emission measurements using micrometeorological methods have been introduced (Laurila et al., 2005), providing more comprehensive information also for studies on landfill gas generation. At Ämmässuo landfill extensive emission monitoring has been running continuously since 2006. During most of this time two measurement units have been used to monitor all subareas and surface types of the large landfill.

The objective of this study was to evaluate the use of the measured CH₄ recovery and CH₄ emission data to calibrate a LGG model for a large landfill in a post-landfill operation phase. The specific objective was to calibrate the first order kinetic factors CH₄ generation factor (k) and CH₄ generation potential (L) for a particular landfill and to evaluate such a calibration process in order to obtain more detailed data on CH₄ generation.

Materials and methods

The studied landfill

The study was performed using data from a MSW landfill (Ämmässuo), which is operated by the Helsinki Region Environmental Services Authority. Ämmässuo is the largest Scandinavian landfill facility, and accepts waste from the largest metropolitan area of Finland (approximately one million inhabitants). The landfill operated for 20 years (1987–2007) and is now closed. It contains approximately 9 million tons of MSW over an area of 54 ha. Moreover, it contains surplus soils, industrial and construction waste, as previously described by Sormunen et al. (2008). The annual amount of deposited waste at the site ranged from 300,000 to 600,000 Mg year⁻¹, with the practice of source segregation increasing since the 1990s for bio-waste, metals, glass, paper and cardboard. The mean annual precipitation is approximately 650 mm in the studied landfill area.

Landfill gas recovery systems have increasingly been implemented in the studied landfill from 1996 to 2004, when gas recovery systems included more than 200 vertical gas recovery wells (spaced at 50-m intervals) and 32 horizontal wells (lengths up to several hundreds of meters) located in the gas collection layer. The recovered landfill gas volumes measured at the gas recovery station during 1996–2011 were obtained from the landfill operator. The landfill was closed at the end of the 2007 in accordance with the European Union Landfill Directive (Council Directive 1999/31/EC, 1999). A sealed cover structure have been implemented gradually since 2002 and covered up to 46 ha by 2011 (Figure 1). Leachate recirculation (approximately 200 mm/a by 39 horizontal leachate infiltration fields, each of which consist of two infiltration pipes in lengths varying up to 100 m located at 20 m intervals in porous drainage material) was started on top the landfill (25 ha) in 2010 and enlarged (by 40 leachate recirculation canals with varying lengths mainly from 50 to 80 m) to the slope areas in 2011 to promote CH₄ generation and shorten the post-operation phase of landfill.

The leachate saturated portion of the landfill was up to 20–30 m from the bottom of landfill in 2011 and an increasing tendency of leachate saturation has been measured from gas recovery wells (data obtained from landfill operators). The maximum depth of the landfill in the mid-region is up to 37 m. A major part (55–80%) of the landfill is leachate saturated. The internal leachate has been high since at least 2003, when the saturated zone varied from 21 to 44% of landfill height, and an increasing trend was observed (Sormunen, 2008). Moreover, in 2011, temperatures increased up to 45–50°C in the internal leachate.

CH₄ emission measurements

The emitted CH₄ was measured using the micrometeorological eddy-covariance method (Baldocchi, 2003), which continuously measures vertical gas mass fluxes of CH₄, CO₂ and water vapour (H₂O) (Laurila et al., 2005; Lohila et al. 2007). A micrometeorological measurement system was used for CH₄, CO₂ and H₂O fluxes. It consisted of a three-dimensional (3D) sonic anemometer (Metek GmbH, Elmshorn, Germany), for measuring 3D wind velocity and air temperature, a CO₂ and H₂O analyser (LI-7000, Li-COR, Inc., Lincoln, NE, USA) and an off-axis integrated cavity CH₄ tuneable diode laser (RMT-200, Los Gatos Research, Inc., Mountain View, CA, USA). The measurement height, which depended on the size of the source area, was adjusted between 3.6 and 5.6 m. During 2007–09, two such units were used in order to obtain good spatial coverage of the large landfill area. The first unit used as a CH₄ sensor was a fast response flame ionization detector (FID) (Laurila et al., 2005), which was replaced by another off-axis laser spectrometer in 2009.

Concentration and anemometer data were collected at a frequency of 10 Hz on a computer using RS232 data transfer protocol. The fluxes were calculated on-line as 15-min averages. The software included coordinate rotation, de-trending of signals using block averaging, removal of time shifts between the signals from the anemometer and each gas analyser, and the correction for high-frequency flux loss. Air and soil temperatures, air humidity, air pressure, net radiation and photosynthetically active radiation (PAR) radiation were measured to support data analysis.

The source areas of micrometeorological flux observations extend several hundreds of metres upwind from the mast. The source area depends on the measurement height, roughness length, wind speed, friction velocity and atmospheric stability. Using a dispersion model, the relative source weight functions (flux footprints) were estimated for each flux observation. Flux observations were corrected for the footprint area to represent the landfill area only.

In data analysis only good quality data based on turbulence and source area characteristics were included. An observation was deleted if wind speed or friction velocity was too low or if the on-line lag optimisation routine was not able to find a unique lag between vertical wind speed and gas concentration. It was required that most of the foot print area, which was calculated for each observation, was within the target area. The observed emission was corrected for the missing data by dividing the observation by the ratio between foot print area within the landfill to the total footprint area. The total emission from the landfill was calculated from the average emissions per unit area according to the surface cover types. The total emission from the landfill is the sum of subareas, which is multiplied by its respective emission factor. The measured CH₄ and CO₂ emissions, and data on CH₄ and CO₂ recovery were used to calculate total CH₄ generation, efficiency of the gas recovery and to estimate CH₄ oxidation in the cover layer, as described previously (Laurila et al., 2005). However, this method is inexact whether the CO₂ is generated by CH₄ oxidation or aerobic respiration in the top layer of landfill, which may result in overestimation of CH₄ oxidation.

Landfill gas model

The landfill gas generation model, LGG, used in this study was the Landgem 3.02 [equation (1); USEPA, 2005].

Q_{C H 4} = \sum_{i = 1}^{n} \sum_{j = 0.1}^{1} k L (\frac{M i}{10}) e^{- k t_{i j}}

where QCH4 is the annual CH₄ generation in the year of calculation (m³ year⁻¹); i is a 1 year time increment; n is (year of calculation) – (initial year of waste acceptance); j is a 0.1 year time increment; k is the CH₄ generation rate (year⁻¹); L is the CH₄ generation capacity (m³/Mg), Mi is the mass of waste accepted in the i_th year (Mg); t_ij is the age of the j_th section of waste mass M_i accepted in the i_th year (decimal years).

The CH₄ generation factor (k values 0.12 – 0.2) tested were selected based on literature (Amini and Reinhart, 2011; Faour et al., 2007; Tolaymat et al., 2010; USEPA, 2005). The L value of 120 m³/t was used on the basis of a previous study (Sormunen et al., 2008) in which the studied landfill was characterised using 40 waste samples taken at different horizontal and vertical locations. Among the 40 samples the experimentally determined Ls ranged from 60 to 180 m³/t in MSW samples, while, initially, the CH₄ generation potential of the disposed MSW was probably higher than that measured for the 2–3-year-old waste samples.

Model calibration

Model calibration was based on the recovered CH₄ (m³/a) between 1996 and 2011, and CH₄ emissions (m³/a) in 2007–11. The calibration included sensitivity testing for determination of the k value describing the decay rate with L presented in previous study (Sormunen et al., 2008) to obtain similar trends for the CH₄ generation estimated by the Landgem model and measured CH₄ generation in 2007–11.

Results and discussion

Methane recovery and emission

In the studied landfill the annual CH₄ recovery increased from 3 million m³ in the late 1990s up to 34 million m³ in 2008 (Figure 2). The annual CH₄ emissions measured and estimated by micrometeorological methods were 18 million m³ in 2007 and decreased to 2 million m³ in 2011, while the annual CH₄ recovery was 30 million m³ and 22 million m³ in 2007 and 2011 respectively. Thus, the CH₄ recovery rate increased from approximately 62% to 94 % from 2007 to 2011. CH₄ recovery increased owing to implementation of gas recovery wells and construction of sealed cover layers. In 2007 the CH₄ recovery rate (62%) was approximately the same as previously suggested (65%) for temporarily covered landfills in the EPER, while the 97% recovery is similar to that reported for landfill cells with clay and geomembrane final covers (whose recovery efficiencies vary from 84% to 98%; Spokas et al., 2006). Evidently, enlargement of the covered areas had a major effect on the CH₄ recovery rate in 2007–09. Leachate recirculation was initiated early in 2010, but no increase in CH₄ generation was observed in 2010 and 2011. This suggests that the short-term effects of leachate recirculation are minor in the post-landfill operation phase, if it is assumed that internal conditions, such as a high water table and temperature, favour biodegradation. However, high internal leachate level is a risk to landfill stability and it may cause landfill failures (e.g. Jianguo et al., 2010), especially together with high rainfall or leachate recirculation due to accumulation of excess leachate (Blight, 2008).

According to landfill operators the volume of the recovered CH₄ by the 32 horizontal gas recovery wells located in the gas collection layer accounted for up to 15% of the total recovered CH₄ in 2011. It is probable that some of the vertical wells had limited CH₄ recovery ability owing to the high internal leachate level. However, 46 ha of the landfill was covered by the impermeable cover layer in 2011, which probably had such a major effect that a CH₄ recovery rate of more than 90% was obtained. The CH₄ emissions were observed only in the 8-ha area (covered by the surplus soils) located on top of the landfill in 2011.

A previous study demonstrated that the CH₄ oxidation varied from 4 to 29% of the CH₄ entering the temporary cover layer, while the gas recovery rate varied from 44 to 72% in 2003 (Laurila et al., 2005). The CH₄ oxidation was approximately same (from 11 to 24%) in 2007 as in 2003. In other words, the CH₄ emissions varied from 1510 to 1950 m³ h⁻¹, while average CH₄ oxidation varied from 90 to 500 m³ h⁻¹ in 2007 (Figure 3). However, CH₄ oxidation decreased after 2007 to 10 m³ h⁻¹ in 2011 owing to fact that less CH₄ was entering the cover layer when the gas recovery rate remained higher than 90%.

Sensitivity testing of CH₄ generation factor (k value)

First, CH₄ generation was modelled using LGG with k values between 0.05 and 0.2,presented in latest publications (Amini and Reinhart, 2011; Faour et al., 2007; IPCC, 2006; Tolaymat et al., 2010; USEPA, 2005) and L of 120 m³/t (Figure 2). The k values < 0.16 seemed to be too small, even if US Environmental Protection Agency offers a default k of 0.02 (precipitation < 635 mm) or 0.04 (precipitation > 635 mm; USEPA, 2004) value for decay rate estimations. However, a k value of 0.2 seemed to be too high while comparing obtained trends by model and CH₄ generation measured from 2007 to 2011. Thus, the best fitted k value proved to be 0.18, which resulted in model results that corresponded to measured CH₄ generation. A k value of 0.18 indicates that the CH₄ generation rate in the studied landfill is higher than that generally assumed for typical MSW landfills, in conditions where annual precipitation is approximately 600 mm. For example, default k values of 0.06 (range 0.05–0.08) and 0.09 (range 0.08–0.1) represent degradation of food waste in dry climates and bulk waste in wet climates respectively (IPCC, 2006). However, a default k of 0.185, range 0.1–0.2) is used for rapidly degrading waste in wet (mean annual precipitation > potential evapotranspiration) temperate conditions, which is similar to the value proposed in the present study (0.18) for landfilled bulk waste in the studied landfill. A major cause for such a high decay rate is probably the internal leachate saturation since 2003. The k of 0.18 is lower than the estimated k of 0.3 for recovered CH₄ in wet bioreactor landfill conditions (Faour et al., 2007). However, leachate recirculation was implemented after 23 years of landfill operation in the studied landfill. It is obvious that the decay rate in the studied landfill is lower than in well-controlled bioreactor conditions established in the early phases of landfill operation. The present wet conditions may be suitable for modelling using the Landgem model with a constant decay rate value over the post-operation phase of the landfill. However, leachate recirculation and the final capping layer may have a long-term effect on the moisture content in the landfill, probably leading to decay rate changes. Varying moisture conditions need modelling with changing k values over time, which is not possible with the Landgem model. A model with the ability to consider the nature of a landfill and landfilling practices in more detail, for example the IPCC model (IPCC, 2006) could be more relevant for further work, as changing k values seem to be important to calibrate landfill gas models over time.

A previous study (Sormunen et al., 2008) showed the high vertical and horizontal variability in the waste properties (e.g. moisture, organic material and biological CH₄ potential), and trends appeared in respect to sampling depth in the studied landfill. The internal landfill conditions (e.g. temperature, moisture, leachate saturation, pH), weather conditions and landfill practices (leachate recirculation and cover layer) may have a major effect on decay rate of landfill waste. This all indicates the difficulty in estimating a constant decay rate for the bulk waste with changing properties according to the varying internal landfill conditions and changing local waste management practices over the past decades.

Estimation of CH₄ generation potential (L)

The suitability of a previously determined L of 120 m³/t (Sormunen et al., 2008) was sensitivity tested by the Landgem model, comparing the modelled CH₄ generation curve with the measured CH₄ generation. The trend obtained by an L of 120 m³/t and by k values varying from 0.12 to 0.20 was higher than the actual measured CH₄ generation between 2007 and 2011 (Figure 2).

From 1998 until 2005 the recovered CH₄ and the trend obtained by L of 120 and k values varying from 0.12 to 0.20 increased in a similar manner. However, the CH₄ recovery rate was probably low (e.g. approximately 40–60%) before 2002, as gas collection was not fully implemented and a major part of the landfill was without surface structures. Since 2005 the recovered volume of CH₄ varied with a slightly increasing trend until 2008, and after this the volume of recovered CH₄ had been decreasing rather similarly to the trend obtained by Landgem model with an L of 120 and a k value of 0.18.

The measured CH₄ generation was approximately 7 million m³ year ⁻¹ (15 %) lower than determined by the calibrated model trend using an L of 120 and a k value of 0.18; thus, the L of 120 seems to be overestimated. According to sensitivity testing with lower L values (90, 100, 110) the L of landfilled waste seems to be approximately 100 m³/t. The trend generated by an L of 100 (with a k value of 0.18) was quite similar to the measured CH₄ generation during the last 5 years.

Importance of model calibration

The CH₄ generation rate (k) and methane potential (L) of MSW are critical factors when using LGG models to estimate gas generation rates for landfills. The gas generation estimates can be used, for example, for planning gas recovery and of utilisation (heat, power, vehicle fuel) systems, as well as for the inventory and trading of greenhouse gas emissions also used by IPCC (IPCC, 2006). The landfill gas generation models may rely on some site-specific data on CH₄ recovery, emission and oxidation, as well as on conditions, which are all important to calibrate or validate the landfill gas models. However, changing internal landfill conditions and post-landfill operations, as well as declining proportions of rapidly degrading waste fractions, cause uncertainties (such as continuously changing k values), which are difficult to consider in the landfill gas model parameters extending for several decades.

In the present study the first order kinetic factors (k and L) for CH₄ generation were determined on the basis of the experimentally determined values in the large MSW landfill, and their feasibility was determined by comparison of the modelled CH₄ generation with the CH₄ recovery in 1996–2006 and CH₄ generation in 2007–11. This methodology enables more precise CH₄ generation estimates compared with models based only on recovered CH₄. For example, the remaining cumulative CH₄ generation estimate is approximately 250 million m³ by using a k of 0.12 and an L of 120, while using calibrated model parameters (a k of 0.18 and an L of 100) the model results in cumulative CH₄ generation of approximately 130 million m³ from 2012 until the end of the 2030 (Figure 4). In most previous studies CH₄ generation factors (k, L) for modelling have been validated using recovered CH₄ (m³ CH₄) and using assumptions about the gas recovery rate, CH₄ oxidation and CH₄ emission (Scharff and Jacobs, 2006). In reality, these assumed values are not well known as long-term, full-scale landfill gas emissions are not available.

Conclusions

The determination of the CH₄ generation rate k and potential L are important in order to improve accuracy of first-order kinetic empirical landfill gas models, as these values seem to be very case- and target-dependent.

The appropriate k for bulk waste was 0.18 in the studied landfill, which indicates a higher rate of degradation than proposed by the default k value of 0.09 (IPCC, 2006) for wet conditions in a boreal and temperate climate.

An L value of 100 m³/t was determined for bulk MSW by model calibration on the basis of measured CH₄ generation, while an L of 120 m³/t estimated on the basis of a previous waste characterisation study proved to be too high for the studied landfill.

In this particular landfill the inappropriate model parameters may cause a difference of approximately 100% (of 119 million m³) in cumulative CH₄ generation estimates within an 18-year timescale (2012–30).

Controlling landfill gas emissions by representative measurements, such as the micrometeorological eddy-covariance method, are recommended for implementation and maintenance of gas recovery and utilisation facilities, as well as for calibration of landfill gas models.

Footnotes

Conflict of interest

The authors declare no conflict of interest.

Funding

This study was supported by the Finnish Funding Agency for Technology and Innovation (TEKES) and HSY Helsinki Region Environmental Services Authority.

References

Amini

Reinhart

(2011) Regional prediction of long-term landfill gas to energy potential. Waste Management 31: 2020–2026.

Baldocchi

(2003) Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global Change Biology 9: 479–492.

Barlaz

Ham

Schaefer

(1989) Mass-balance analysis of anaerobically decomposed refuse. Journal of Environmental Engineering 115: 1088–1102.

Benson

Barlaz

Lane

Rawe

(2007) Practice review of five bioreactor/recirculation landfills. Waste Management 27: 13–29.

Blight

(2008) Slope failures in municipal solid waste dumps and landfill: a review. Waste Management & Research 26: 448–463.

Council Directive 1999/31/EC (1999) Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste. Official Journal of the European Communities L182: 1–19.

Einola

Sormunen

Lensu

Leiskallio

Ettala

Rintala

(2008) Methane oxidation at a surface-sealed boreal landfill. Waste Management 29: 2105–2120.

Eurostat (2011) Waste statistics; treatment of municipal solid waste. Available at: http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Waste_statistics (accessed June 2011).

Faour

Reinhart

You

(2007) First order kinetic gas generation model parameters for wet landfills. Waste Management 27: 946–953.

10.

Garg

Achari

Joshi

(2006) A model to estimate the methane generation rate constant in sanitary landfills using fuzzy synthetic evalution. Waste Management & Research 24: 363–375.

11.

IPCC (2006) IPCC guidelines for national greenhouse gas inventories. Volume 5: Waste, Chapter 3, solid waste disposal, p. 40. Intergovernmental panel on climate change, National greenhouse gas inventories programme. Available at: http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/5_Volume5/V5_3_Ch3_SWDS.pdf (accessed December 2011).

12.

Jianguo

Yong

Shihui

Bin

Chang

(2010) Effects of leachate accumulation on landfill stability in humid regions in china. Waste Management 30: 848–855.

13.

Jokela

JPY

Kettunen

Rintala

(2002) Methane and leachate pollutant emission potential from various fractions of municipal solid waste (MSW): effect of source separation and aerobic treatment. Waste Management & Research 20: 424–433.

14.

Laurila

Tuovinen

J-P

Lohila

Hatakka

Aurela

Thum

(2005) Measuring methane emissions from a landfill using a cost-effective micrometeorological method. Geophysical Research Letters 32: L19808.

15.

Lohila

Laurila

Aurela

Thum

Tuovinen

J-P

Pihlatie

. (2007) Micrometeorological measurements of methane and carbon dioxide fluxes at a municipal landfill. Environmental Science and Technology 41: 2717–2722.

16.

Machado

Carvalho

Gourc

J-P

Vilar

do Nascimento

JCF

(2009) Methane generation in tropical landfills: Simplified methods and field results. Waste Management 29: 153–161.

17.

Scharff

Jacobs

(2006) Applying guidance for methane emission estimation for landfills. Waste Management 26: 417–429.

18.

Sormunen

Ettala

Rintala

(2008) Detailed internal characterisation of two Finnish landfills by waste sampling. Waste Management 28: 151–163.

19.

Sormunen

(2008) Characterisation of landfills for recovery of methane and control of emissions. PhD Thesis, University of Jyväskylä, Finland.

20.

Spokas

Bogner

Chanton

Morcet

Aran

Graff

. (2006) Methane mass balance at three landfill sites: What is the efficiency of capture by gas collection systems? Waste Management 26: 516–525.

21.

Tolaymat

Green

Hater

Barlaz

Black

Bronson

Powell

(2010) Evaluation of landfill gas decay constant for municipal solid waste landfills operated as bioreactors. Journal of Air and Waste Management Association 60: 91–97.

22.

Thompson

Sawyer

Bonam

Valdivia

(2009) Building a better methane generation model: Validating models with methane recovery rates from 35 Canadian landfills. Waste Management 29: 2085–2091.

23.

USEPA (United States Environmental Protection Agency) (2004) Direct emissions from landfilling municipal solid waste. Climate leaders greenhouse gas inventory protocol core module guidance. Available at: http://www.epa.gov/climateleadership/documents/resources/protocol-solid_waste_landfill.pdf (accessed December 2012).

24.

USEPA (United States Environmental Protection Agency) (2005) Landfill gas emission model (Landgem) version 3.0.2 users guide. EPA-600/R-05/047. Available at: http://www.epa.gov/ttn/catc/dir1/landgem-v302-guide.pdf (accessed December 2011).

25.

USEPA (United States Environmental Protection Agency) (2009) Municipal solid waste generation, recycling, and disposal in the United States: facts and figures for 2009. Available at: http://www.epa.gov/wastes/nonhaz/municipal/pubs/msw2009-fs.pdf (accessed June 2011).

26.

Yazdani

Kieffer

Sananikone

Augenstein

(2006) Full scale bioreactor landfill for carbon sequestration and greenhouse emission control. Final technical progress report. Available at: http://www.yolocounty.org/index.aspx?page=438 (accessed June 2011).

Determination of waste decay rate for a large Finnish landfill by calibrating methane generation models on the basis of methane recovery and emissions

Abstract

Keywords

Introduction

Materials and methods

The studied landfill

CH4 emission measurements

Landfill gas model

Model calibration

Results and discussion

Methane recovery and emission

Sensitivity testing of CH4 generation factor (k value)

Estimation of CH4 generation potential (L)

Importance of model calibration

Conclusions

Footnotes

Conflict of interest

Funding

References

CH₄ emission measurements

Sensitivity testing of CH₄ generation factor (k value)

Estimation of CH₄ generation potential (L)