- Email: [email protected]
Estimation of landfill methane emissions using stochastic search methods
Atmospheric Pollution Research xxx (2016) 1e9
H O S T E D BY
Contents lists available at ScienceDirect
Atmospheric Pollution Research journal homepage: http://www.journals.elsevier.com/locate/apr
Estimation of landfill methane emissions using stochastic search methods Tarek Kormi a, b, Nizar Bel Hadj Ali a, b, *, Tarek Abichou c, Roger Green d Ecole Nationale d’Ing enieurs de Gab es, University of Gab es, Rue Omar Ibn-Elkhattab, 6029, Gab es, Tunisia LASMAP, Ecole Polytechnique de Tunisie, University of Carthage, B.P. 743, La Marsa 2078, Tunisia c Department of Civil and Environmental Engineering, Florida State University, Tallahassee, FL 32310, USA d Waste Management, Inc., 2956 Montana Avenue, Cincinnati, OH 45211, USA a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 September 2016 Received in revised form 12 December 2016 Accepted 15 December 2016 Available online xxx
Municipal solid waste (MSW) landfills significantly contribute to global methane emissions. In order to establish methane mitigation strategies, one important step is to quantify fugitive methane emissions resulting from organic waste decomposition. This paper presents a cost-effective method to estimate methane emissions using ambient air methane measurements taken within a landfill. Stochastic search techniques combined with the standard Gaussian dispersion model are employed to identify locations and emission rates of potential emission sources. Four stochastic search techniques are tested and compared. Results show effectiveness of the optimization-based emission estimation and sourcelocating scheme. Two hand-generated case-studies showed that methane flux estimation-error is lower than 15%. The method proves also useful in identifying locations of emission sources. Furthermore, when monitoring data of a real closed landfill are used, results showed that method results are comparable with those obtained using more established experimental Tracer-based technique. © 2016 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Keywords: Methane emission Solid waste landfill Gaussian dispersion model Stochastic search
1. Introduction 1.1. Background Greenhouse gases (GHG), such as methane (CH4), have a high warming potential and therefore are of growing concern. Global climate change (powerful hurricanes, rising sea level, flooding, etc …) is due in large part to the emission of greenhouse gases into the atmosphere. Many nations around the world are developing innovative policies to slow the rate of near-term global warming and reduce global air pollution. In this context, a global effort is being made to understand, quantify, and manage greenhouse gas emissions. Methane has become a target for emission reduction especially because it is, on a molar basis, 28 times more potent than carbon dioxide (CO2) (IPCC, 2013). Methane has also relatively short
* Corresponding author. LASMAP, Ecole Polytechnique de Tunisie, University of Carthage, B.P. 743, La Marsa 2078, Tunisia. E-mail address: [email protected] (N. Bel Hadj Ali). Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
decay time in the atmosphere: 9e10 years. Municipal Solid Waste (MSW) landfills are amongst the largest human-related sources of methane emissions. Landfill methane is the result of an anaerobic decomposition of the organic fraction in the waste. The amount of methane resulting of this process depends mainly on waste quantity, moisture content and temperature. MSW landfills are progressively replacing solid waste disposals, open dumping and burning practices. Waste management is estimated to be the third largest source of CH4 emissions in the United States (EPA, 2010). In Europe, an estimated 30% of anthropogenic methane emissions are caused by landfills (EEA, 2014). In 2015, China was the source of approximately 13% of global methane emissions making it the largest emitter of methane in the world. More than 5% of total methane emissions in China emerge from MSW landfills (EPA, 2014). In a global scale, methane emissions from landfills account for approximately 11% of total methane emissions (EPA, 2014). For all these reasons, estimation of CH4 emissions from landfills is becoming a major environmental concern.
http://dx.doi.org/10.1016/j.apr.2016.12.020 1309-1042/© 2016 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
2
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
1.2. Estimation methods of landfill methane emissions Several methods exist for the estimation of CH4 emissions from landfills. Estimation methods are either based on simulation models or on measurement methods. In model-based methods, methane emissions can be estimated using exiting biogas production mathematical models. One of the largely used model is the LandGEM (landfill gas emissions model) developed by the U.S Environmental Protection Agency (Alexander et al., 2005). Models also includes GasSim (GasSim2.5, 2014) developed by Golder Associates for the Environment Agency of England and Wales. Besides, in the frame of the European Pollutant Emission Register (EPER) various evolving version of the European EPER models are frequently used (Oonk, 2010; Rajaram et al., 2011). Comparative studies showed that emission models may give different results, even when employed input-data are the same. Jacobs and Scharff (2005) compared several methane emission models with methane emission measurements. They showed that emission models resulted into high divergent estimations. Jacobs and Scharff (2005) also stated that in the near future further development of methane emission measurement techniques may provide more reliable tools than modeling. Measurement techniques are having an increasing interest for landfill methane emission estimation. Surface methane emissions from landfills are determined using small to large-scale direct and indirect measurement approaches applied on a continuous or discrete basis. Closed chamber measurements are frequently employed both for monitoring methane emissions from small parts of a landfill as well as estimating overall emissions from an entire landfill (Abichou et al., 2011; Scheutz et al., 2009). Also micrometerological measurements are used for methane emission quantification (Lohila et al., 2007). Recently, several methodologies have been proposed in order to estimate methane emissions from landfills while delivering cost- and labor-effective results (Cambaliza et al., 2015; Foster-Wittig et al., 2015). These methods include: static and mobile plume measurement methods using tracer gas (Mønster et al., 2014, 2015; Scheutz et al., 2011), radial plume mapping (RPM) using optical remote sensing (ORS) by means of laser infrared radiation emissions (Goldsmith et al., 2011; Thoma et al., 2010), differential absorption light detection and ranging (LiDAR) (Babilotte et al., 2010) and inverse plume modeling (Mackie and Cooper, 2009; Oonk, 2010). The only technology that can be used directly to determine emission flux is the stationery enclosure technique. All of the other technologies provide concentrations, which can be used to estimate emission flux indirectly via analytical or numerical models. The direct flux measurement system is based on the static flux chamber enabling only point measurements. Several challenges and uncertainties arise when transferring these point measurements to total or areal estimates. The concentration based measurement methods are: tracer testing using inert gases as detectors, optical remote sensing using tunable diode laser spectroscopy, and optical remote sensing using Fourier transform infrared spectroscopy. Sensitivity of the optical remote sensing and tracer methods to micrometeorological conditions (including wind, rain, and fog) and high cost limit the full applicability of these measurement and monitoring systems.
Methane concentration monitoring is already frequently performed in many MSW landfills. Thus, it is important to develop efficient approaches to correlate surface concentrations to emission fluxes in order to allow estimating landfill emission fluxes (Mackie and Cooper, 2009). This study builds upon the approach developed by Figueroa et al. (2009) for estimating fugitive landfill methane emissions based on surface emission monitoring. An optimization-based approach using stochastic search techniques is employed to solve the inverse problem that consists on identifying source data (source locations and corresponding emission rates) having receptor locations and surface measurements along with meteorological conditions as input data. Four methods including Genetic Algorithms (GA), Simplex-Simulated Annealing Approach (SIMPSA), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Probabilistic Global Search Lausanne (PGSL) are tested in order to evaluate the performance of such methods in handling the methane emission estimation task. Optimization techniques are used along with the standard Gaussian dispersion equations to determine the methane emission sources and to estimate areal methane emissions. Three case studies are presented to show effectiveness of the proposed methodology. Results obtained with various stochastic search techniques are interpreted and compared.
2. Methane emission quantification methodology The proposed CH4 emission estimation technique exploits ambient air methane concentration measurements. Measurement locations are taken into account as receptor points. Assuming that a field survey has resulted in a vector of methane concentration measurements denoted Cmeasured performed at m defined points in a landfill. Measurement locations or receptors are thus represented by two coordinate vectors Xr and Yr, each having m components; related with the vector of gas concentration measurements. For this study, a source configuration defines a peculiar distribution of sources and emission rates. It is represented by the following data set: - Geographic positions of a number (n) of sources: source positions are represented by two coordinate vectors Xs and Ys, each having n components; - Point emission rates corresponding to the n sources of the model: this information is represented by a vector Q of n components corresponding to emission rates of the source configuration (expressed in micrograms per second: mg/s). Any generated source configuration is evaluated through calculating the corresponding methane concentrations at receptors points. This is done by backward application of atmospheric dispersion model. Gaussian dispersion equations are used for this task. The Gaussian model still the basic workhorse for dispersion calculations. According to this approach, gas concentration is given by Equation (1).
Cðx; y; z; HÞ ¼
1.3. Objectives In concentration-mapping techniques a field survey can be done by walking a predefined grid with a field gas analyzer. Surface gas concentrations are relatively inexpensive and easy to obtain. Mapping of methane concentration could be exploited to identify point sources of relatively high concentrations such as cracks in the landfill cover (Figueroa et al., 2009; Gonzalez-Valencia et al., 2014).
!( 1 y2 exp 2 s2y !) 1 ðz þ HÞ2 2 s2z
Q exp 2pusy sz þ exp
1 ðz HÞ2 2 s2z
!
(1) where C is the steady-state gas concentration (mg/m3) at a point (x, y, z), Q is the emission rate (mg/s), sy and sz are the horizontal and vertical spread parameters that are functions of the downwind
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
distance x (Fig. 1) and the atmospheric stability, u is the average wind speed at stack height (m/sec), y is the crosswind distance from ground level (m), z is the vertical distance above the ground (m), and H is the effective stack height (physical stack height plus plume rise expressed in m). In Equation (1) the second z-exponential term is added to account for the fact that pollutant cannot diffuse downward through the ground at z ¼ 0. A virtual source located at z ¼ eH below the ground will then begin to contribute to the aboveground concentrations. This “image” term is used to account for plume reflection at the ground (Wark et al., 1998). The Gaussian distribution equation uses relatively simple calculations requiring only two dispersion parameters (sy and sz) to identify the variation of gas concentrations away from the diffusion source. These dispersion coefficients, sy and sz, are functions of wind speed, cloud cover, and surface heating by the sun. Generally, the evaluation of the diffusion coefficients are based on atmospheric stability class (Pasquill and Smith, 1983). In this study, Pasquill-Gifford stability classes are employed and dispersion coefficients are calculated using Briggs model (Hanna et al., 1982). Details about dispersion coefficient evaluation are given in Appendix. For ground-level sources and receptors (z ¼ 0 and H ¼ 0), Equation (1) reduces to Equation (2):
C¼
Q
pmsy sz
" exp
1 y2 2 s2y
# (2)
Under defined atmospheric conditions and wind speed, dispersion parameters are obtained and the predicted methane concentration in a receptor point i (Ci,predicted) is calculated through summing up all contributions (Cij) of assumed source points j (j ¼ 1,…, n). This is expressed in Equation (3).
Ci;predicted ¼
n X
Ci;j
(3)
j¼1
Calculating predicted concentration for all receptor points (i ¼ 1,
…, m) results in a vector of predicted concentration (Cpredicted).
The main task is to estimate, from measured concentrations using ambient CH4 measurements, the sources (number and locations) and to estimate their emission rates. This task is formulated as an optimization problem that consists on residual minimization under bound constraints. The objective function R is thus defined as
N θ
Wind u
Source (xi, yj) y
x
Receptor
x y
Fig. 1. Source-receptor configuration.
3
the normalized estimation error between predicted and measured methane concentrations. The problem is presented as follows:
Pn Min RðX; Y; Q Þ ¼
i¼1
Ci; measured Ci; predicted 2 Pn i¼1 Ci; measured
2
under
(4)
xmin xj xmax
; j ¼ 1; :: n
ymin yj ymax
; j ¼ 1; :: n
Qmin Qj Qmax
; j ¼ 1; :: n
In Equation (4), the two first constraints define the geographic limits of source positions (Limits of the landfill). The third constraint defines the lower and the upper bounds of the source emission rates. An estimation of the number of potential emission sources is performed prior to optimization with stochastic search algorithms. The aim is to guide the search with an initial guess of the number of release points (through peak picking). This decreases the complexity of the optimization task through including data-driven information. A peak picking procedure is employed to treat measurement data. Peak picking allows for identification of high concentration points. The procedure employs a threshold value that represents the minimum peak height to be taken into account. Identifying peaks from measurement data is performed employing a Matlab® routine called findpeaks. This routine is able to find values and locations of local maxima in a set of data even if some peaks are very close to each other. 3. Optimization through stochastic search techniques The scientific and technical community is confronted with optimization tasks of growing complexity, which emerge in a wide variety of disciplines. Modern optimization techniques have aroused great interest because of their ability to solve problems with a non-linear and non-convex dependence of design variables. In the broadest sense, these techniques can be classified into exact and stochastic algorithms. Exact algorithms, such as branch and bound, linear or dynamic programming can be highly effective for small-size problems. For hard optimization problems with large and complex solution space, the use of stochastic algorithms becomes a reasonable alternative. Stochastic techniques involve randomness in a constructive way in order to efficiently and effectively exploring a search space. These are robust algorithms capable of traversing large search space to provide near-optimal solutions. Furthermore, even if stochastic search methods do not guarantee to obtain the optimal solution for whatever an optimization problem, these techniques are able to converge to nearoptimal solutions that are most of the time satisfactory from an engineering point of view, especially, when this is done within a reasonable amount of time. As formulated in this study, the optimization problem is characterized by a finite number of continuous variables. A source configuration involves positions and emission rates of a definite number of source points. The number of source configurations generated for an identification case increases rapidly with increasing estimated number of sources. Four stochastic search techniques amongst the most widely used search methods in engineering applications are employed and compared in this study. Optimization techniques include: Genetic Algorithms (GA), Simplex-Simulated Annealing Approach (SIMPSA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Probabilistic Global Search Lausanne (PGSL). The following paragraphs contain
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
4
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
brief descriptions of these search methods.
applications (Yaghini et al., 2012).
3.1. Genetic Algorithms (GA)
3.4. Probabilistic Global Search Lausanne (PGSL)
Genetic algorithms are global search methods that belong to the class of stochastic search algorithms. They were originally proposed by John Holland, whereas the success of the method owes much to the work of Goldberg (1989). GAs have been used efficiently since the early 1990s to solve hard optimization problems with discrete as well as continuous variables. GAs are modeled based on the principles of the evolution of generations via natural selection, mutation and crossover. Biological evolution is simulated by encoding potential solutions (a population of individuals in genetic terminology) using a chromosomelike data structure and the search is guided by the results of evaluating the objective function for each individual in the population (Raphael and Smith, 2013). New individuals are generated in the population through reproduction using crossover and mutation operators. Individuals that have higher fitness (i.e., represent better solutions) can be identified, and these are given more opportunity to breed. The genetic algorithm evolves, in successive generations, the composition of the population; enabling thus convergence towards near-optimal global solutions.
The Probabilistic Global Search Lausanne (PGSL) technique uses a probability density function (PDF) to explore the search space and select the best point (Raphael and Smith, 2003). The PDF represents the probability of finding a good solution at any given point in the search space. The PGSL method is based on the assumption that sets of better solutions are more likely to be found in the neighborhood of sets of good solutions and, therefore, intensifies search in regions that contain sets of good values. In PGSL, each search direction/axis is divided into a fixed number of intervals and a uniform probability distribution is considered. Probabilities and intervals are dynamically updated as the search for optimal solution progresses. Higher probability in regions contained good solutions are considered. The search space is thus gradually narrowed down and the convergence is achieved.
3.2. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) The CMA-ES is a stochastic method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. This method was proposed for the first time in the mid 1990s and has been considerably developed since then (Hansen et al., 2003; Hansen and Ostermeier, 2001) with many successful applications in single as well as multi-objective optimization (Igel et al., 2007). The CMA-ES method generates an initial population from a multivariate normal distribution and evaluates individual fitness values. Then, it updates the mean vector and covariance matrix of the multivariate normal distribution by using the information of the sampled points and their fitness values. Through the repeated sampling-evaluation-updating procedure, the method is likely to move the sampling distribution towards a neighborhood of the optimal solution. In CMA-ES, updating rules for the mean vector and the covariance matrix along with step-size adaptation are the basic operators of the optimization technique. These operators are designed to ensure reproducing successful search steps, as well as exploiting the evolution path of the distribution mean. A detailed description of the CMA-ES strategy can be found in (Hansen, 2006). 3.3. Simplex-Simulated Annealing Approach (SIMPSA) The Simulated Annealing method (Kirkpatrick et al., 1983) employs stochastic generation of solution vectors and transposes the physical process of annealing (i.e. melting a solid by heating it, followed by slow cooling and crystallization into a minimum free energy state). Slow cooling is implemented in the Simulated Annealing algorithm as a slow decrease in the probability of accepting worse solutions as it explores the solution space. Accepting worse solutions avoids local optimums and allows for a more extensive search for the optimal solution. Its ease of implementation and convergence properties have made it a popular technique over the past two decades (Henderson et al., 2003). In order to improve its ability to escape from local optima, SA is combined with the non-linear simplex method of Nelder and Mead (1965) giving rise to the SIMPSA (Press and Teukolsky, 1991). It is shown that the combination between the two strategies not only makes possible to easily escape from local optima but also to accelerate the search procedure, especially in high-dimensional
3.5. Efficiency of chosen optimization techniques Stochastic search algorithms have become established as an efficient alternative for exploring the optimal solutions of optimization problems that are too complex to be solved by exact methods, such as linear programming and gradient search. The GAs method is the first evolutionary-based technique introduced in the literature. GAs ability to reach near-optimum solutions is demonstrated through many applications in science and engineering. Despite their benefits, GAs may require long processing time for a near-optimum solution to evolve (Elbeltagi et al., 2005). The CMAES is relatively new compared with GAs. This method is typically applied to unconstrained or bounded constraint continuous optimization problems. The main advantages of CMA-ES lie in its invariance properties, which are achieved by carefully designed variation and selection operators, and in an efficient scheme for adapting mutation distribution (Hansen et al., 2003). The SIMPSA algorithm is based on the combination of the nonlinear simplex and Simulated Annealing algorithms. Compared with the classic Simulating Annealing algorithm, the performance of SIMPSA is largely improved due to the solution generation-scheme based on nonlinear simplex method (Banga et al., 2004). An advantage of the SIMPSA algorithm is that it requires little parameter tuning. PGSL is a stochastic search method where the main idea is to sample with higher probability in regions containing good solutions. Comparative studies showed that PGSL outperforms GAs and adaptive Simulated Annealing for most tested nonlinear benchmark problems (Raphael and Smith, 2005). PGSL is also shown particularly efficient for optimization tasks with large number of variables. 4. Case studies In order to illustrate the proposed approach for landfill methane emission estimation, three case studies are presented. The two first examples are hand-generated case studies with defined configurations of sources and receptors. Case studies simulate two landfill configurations with 12 and 16 sources, respectively. The number of sources is chosen in order to test effectiveness of the identification methodology for varying optimization variables. For the two cases, measurement data are generated through application of the Gaussian dispersion equations. The third case study is an actual municipal solid waste landfill for which a surface concentration measurement campaign was performed. For this MSW landfill Tracer-based emission results are also available as described by (Foster-Wittig et al., 2015; Green et al., 2012; SCS, 2010).
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
4.1. Test case with 12 sources For the first case study, twelve sources with predefined emission rates and 100 receptors are considered. Positions for sources are randomly generated assuming a square-shaped landfill having 1.5 km sides. Source emission rates are also randomly generated between zero and 106 mg/s. Receptor positions are uniformly distributed over the landfill area. The total emission rate for the entire landfill is equal to 4.43 g/s. Receptor concentrations are determined through forward application of the dispersion equation assuming a wind speed of 2 m/s and wind direction of 35 and stability class C. The Briggs model is employed for determining horizontal and vertical dispersion spread parameters (see supplementary material for complete data and more details about the Briggs model). 4.2. Test case with 16 sources For the second case study, 16 sources with predefined emission rates and 170 receptors are considered. Positions for sources are randomly generated assuming a square-shaped landfill having 2 km width. Source emission rates are also randomly generated between zero and 106 mg/s. Receptor positions are uniformly distributed over the landfill area (see supplementary material for complete data). The total emission rate for the entire landfill is equal to 8.6 g/s. Receptor concentrations are determined based on Gaussian dispersion model assuming a wind speed of 2 m/s and wind direction of 40 and stability class C. The Briggs model is employed for determining horizontal and vertical dispersion spread parameters. 4.3. SW landfill case The optimization-based identification methodology was applied to an existing MSW landfill named SW landfill in order to estimate methane emissions. The studied case is a closed landfill that covers an overall area of about 202,345 m2. Along with a surface emission measurement campaign, tracer test campaigns were performed in the same period for SW landfill as described by (Foster-Wittig et al., 2015; Green et al., 2012; SCS, 2010). This allowed comparing outcomes of the two methodologies. Approximately 2 h were needed for collecting surface methane concentrations across the walking path in 463 measurement locations. The temperature and the wind speed were measured twice: in the beginning and by the end of monitoring. A Flame Ionization Detector (a PhotoVac Micro-FID) was used to perform surface monitoring with data collected every 15 s. The average inlet flow rate of the FID was equal to 600 mL/min. The FID was calibrated at the beginning of monitoring event, prior to use, in accordance with the regulations. In order to screen surface methane concentrations a funnel-shaped probe was directly put on the landfill surface and via an integrated pump the emitted gas was drawn through the FID. A GPS unit was used to collect measurement points along the monitoring path. The surface emission monitoring route provided coverage of all waste disposal of the landfill. Additionally, if surface defects or passive vents were encountered, concentration readings were performed around such features. Table 1 shows general data and climate conditions for the SEM campaign. Based on monitoring conditions a stability class C is assigned for this case. As for earlier case studies the Briggs model is employed for determining horizontal and vertical dispersion spread parameters. For the SEM campaign, methane concentrations are measured in 463 points. Fig. 2 shows receptor positions and measured methane concentrations (as a color bar). The four stochastic search techniques are used to estimate methane emissions
5
Table 1 General conditions for the SEM in SW landfill (SCS, 2010). SEM conditions (SCS, 2010) Date Time Sky Ground Temperature ( C) Wind direction Wind speed (m/s) Pressure (Pa) Number of measurement points
September 17, 2010 18:05 to 20:00 overcast dry 15 NW 2.22 101795 463
and locate potential sources. Peak picking is first performed to obtain an estimation of the number of sources. Based on the peak picking procedure, the number of potential emission sources in the landfill is estimated to be 11 sources. 5. Results and discussion 5.1. Results for the test case with 12 sources The four optimization methods are employed and results are compared. For all case studies, the total number of fitness evaluation is fixed for all methods in order to ensure a consistent comparison of identification results. Moreover, the best solution generated over a sequence of 5 runs using different random seeds is considered as the optimal configuration. An overall description of optimization results is presented in Table 2. The total emission methane rates predicted by each of the optimization techniques are presented. Bold values in Table 2 correspond to best-fitness optimization results. With varying performances, all four techniques succeeded to approach the assumed emission rate in the landfill (4.43 g/s). Estimation errors range between 3 and 12% when only the best run is taken into account. However, when five-runs averaged results are taken into account, the estimation error decreases and ranges between 3 and 8%. The best-run results showed that CMA-ES and PGSL are capable of identifying the total emission rate with smaller discrepancy compared with GA and SIMPSA. CMA-ES and PGSL also outperform the other techniques when the five runs are accounted for. These results are confirmed by Fig. 3 comparing the assumed source positions to those predicted by the CMA-ES method. It is shown that 11 sources from 12 are located. The remaining source was not identified mainly because there are few receptors under the wind direction. Fig. 4 shows assumed and predicted emission rates for the twelve sources. Results show good agreement between simulated and predicted values. It is noticed that identification error for source fluxes depends upon source-positions. For instance, the relatively higher identification-error for the emission rates for source 6 and 7 could be explained by the proximity of the two sources. Most of the sources having high emission rates are well identified in terms of location and emission rate. 5.2. Results for the test case with 16 sources For the second case study, optimization is performed assuming a number of sources equal to 16 for all studied methods. The estimation of the number of sources is deliberately ignored here since the objective of this study is to compare efficiency of the identification procedure. An overall description of optimization results is presented in Table 3. The total emission methane rates predicted by studied
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
6
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
Fig. 2. Receptor positions for SW Landfill (SCS, 2010).
Table 2 Predicted emission rates for the 12 source case study. Optimization method
Total predicted emission for the best run [g/s] Best-run identification error Total predicted emission averaged over five runs [g/s] Five-runs averaged error
1.6
GA
CMA-ES
SIMPSA
PGSL
4.07 8.0% 4.70 6.1%
4.26 3.7% 4.28 3.3%
3.93 11.3% 4.05 8.6%
4.64 4.7% 4.43 0.0%
1
Assumed
1.4
Predicted
1 0.8
0.6 0.4
Predicted
0,8
Emission rate (g/s)
Y (km)
1.2
Assumed
0,9 0,7 0,6 0,5 0,4 0,3 0,2
0.2
0,1
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
X (km)
0 1
2
3
4
5
6
7
8
9
10
11
12
Source number
Fig. 3. Assumed and predicted sources locations by the CMA-ES method.
Fig. 4. Assumed and predicted sources fluxes by the CMA-ES method.
optimization techniques are presented. Bold values in Table 3 correspond to best-fitness optimization results. With varying performances, all four techniques succeeded to approach the assumed emission rate in the landfill (8.6 g/s). Estimation errors range between 1 and 16% when only the best run is taken into account. However, when five-runs averaged results are taken into account, the estimation error increases and ranges between 0 and 27%. The best-run results showed that PGSL, SIMPSA and GA are capable of identifying the total emission rate with smaller discrepancy compared with CMA-ES. The CMA-ES method shows
relatively lower efficiency in the identification task with a best-run error over 15% and an averaged five run error over 27%. Relatively high and comparable efficiency is obtained using PGSL, GA and SIMPSA. In addition, this case study showed that GA outperforms the other techniques when the five runs are accounted for. Optimization results obtained with the SIMPSA method are displayed in Figs. 5 and 6. These results are obtained with 16 sources as an estimated number of point sources in the landfill. Fig. 5 shows position of 16 predicted sources compared to assumed source positions. Fig. 6 shows assumed and predicted emission
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
7
Table 3 Predicted emission rates for the 16 source case-study. Optimization method
Total predicted emission for the best run [g/s] Best-run error Total predicted emission averaged over five runs [g/s] Averaged five-runs error
2.0
GA
CMA-ES
SIMPSA
PGSL
8.91 3.3% 8.65 0.0%
7.29 15.4% 6.29 27.0%
8.52 1.1% 8.01 7.0%
8.73 1.2% 8.27 4.0%
measurement data-sets to overcome insufficient information. Moreover, identification efficiency could be maximized if there is a mean to place the receptors at the most informative locations.
Assumed
1.8
Predicted
1.6
Y (km)
1.4
5.3. Results for the SW landfill case
1.2 1.0
0.8 0.6 0.4 0.2
0.0 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
X (km) Fig. 5. Assumed and predicted sources locations by the SIMPSA method.
1,2
Assumed
Emission rate (g/s)
1
Predicted
0,8 0,6 0,4
0,2 0 1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
Source number Fig. 6. Assumed and predicted sources fluxes by the SIMPSA method.
rates for all sources. The two figures show good agreement between simulated and predicted results. The identification errors for source positions are below 10%. Emission rates for most sources are also well identified. It is noticed that the two sources that are not identified are associated with relatively low emission rates. For the overall emission in the landfill, optimization results into a total value of 8.52 g/s which can be considered as an accurate approximation of the assumed total emission flux (8.6 g/s). A part from CMA-ES results, optimization results through GA, PGSL and SIMPSA showed that source positions and emission rates for most points are well identified. It is noticed that identification efficiency for some source point is influenced by the level of the local emission rate of the source. Furthermore, identification accuracy is also influenced by the position of the source in the landfill and also by the number of corresponding downward receptor points. These results emphasize the importance of having several
Starting from measurement data and meteorological conditions, identification through stochastic optimization is performed. For each generated source configuration, receptor concentrations are determined based on Gaussian dispersion model assuming a wind speed of 2.22 m/s, wind direction of 315 and stability class C. Plume reflection is taken into account when methane concentrations are calculated for each receptor position. This is achieved by simply doubling the simulated concentrations since both emission and measurements occur at ground level. For all cases, the identification procedure is performed assuming a number of sources equal to 11. An overall description of optimization results is presented in Table 4. The best-run fitness and the averaged fitness over five runs are presented for each of the studied methods. Fitness values show that SIMPSA is slightly outperforming PGSL, GA and CMA-ES. The total emission methane rates predicted with tested optimization techniques are presented. The best run with the lowest fitness value is obtained with the SIMPSA method with an emission rate of 12.83 g/s. In an overall scale, the emission rate for the SW landfill ranges between 10 and 14 g/s. In addition to the SEM campaign, an Acetylene Tracer-Based approach was performed in order to quantify methane emissions in the SW landfill. Tracer methods allow for quantitative measurements to be made using a single, mobile gas analyzer located in the far field of the source. In the SW landfill, mobile and stationary plume measurements were made on September 16, 2010 (Green et al., 2012; SCS, 2010). Mobile transect measurements were made by driving the analyzer along roads located around the landfill. Stationary measurements were performed by positioning the analyzer downwind from the landfill where concentrated measurements are performed (Foster-Wittig et al., 2015; Green et al., 2012). The mobile and stationary plume measurements resulted into an estimated mean emission rate of 12.6 g/s and 11.8 g/s, respectively. The standard deviations for the two measurements are equal to 1.27 g/s and 1.42 g/s, respectively. Comparing outcomes of the two methods shows that the discrepancy between estimation results is below 20% for all used stochastic search methods. These results confirm the potential of the proposed optimization-based methodology as a cost effective method for the estimation of landfill methane emissions. It is also supposed that the overall coverage of the landfill offered by surface emission monitoring enabled the proposed method to determine a more representative whole site methane emission. 5.4. Key discussion points Through the case studies presented in this work, the proposed
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
8
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
Table 4 Predicted emission rate obtained with various optimization methods. Optimization method
Total predicted emission for the best run [g/s] Best-run fitness Total predicted emission averaged over five runs [g/s] Averaged five-runs fitness
GA
CMA-ES
SIMPSA
PGSL
13.60 0.626 13.61 0.637
11.37 0.643 10.00 0.652
12.83 0.583 12.84 0.589
14.23 0.621 14.04 0.633
optimization-based treatment of surface concentration measurements confirms its potential as a reliable methane emission estimation method. The comparison between optimization techniques indicated that all four stochastic search methods show relatively equal performance with a slight advantage to GA, PGSL and SIMPSA over CMA-ES. However, the efficiency of the proposed technique still requires further verifications. In fact, in order to identify the best optimization technique among all tested ones, a larger number of case studies are needed. Results of the three case studies also showed the importance of having an overall coverage of all waste disposal of the landfill when surface concentration measurements are performed. It was noticed that identification efficiency for some source point is influenced by the level of the local emission rate of the source and also by the number of corresponding downward receptor points. Without prior knowledge of the most informative locations, results from this study emphasize the importance of having several measurement data-sets to overcome insufficient information. Additionally, the efficiency of the identification procedure is related to the variability of the meteorological conditions (wind speed and direction, etc.). The proposed method does not take into account uncertainties related to such parameters. Thus, it is highly advised to perform measurement campaigns in stable climate conditions with minimum variability during measurement rounds. Prior to optimization with stochastic search techniques, an estimation of the number of potential emission sources is performed. The number of sources could be added as an optimization variable in the identification procedure. However, it would increase the complexity of the optimization task and increase drastically the running time of the optimization code. Instead, a data-driven initial guess (through a peak picking procedure) of the number of release points is proposed to simplify the optimization task.
6. Conclusion Municipal solid waste (MSW) landfills emit significant amounts of methane. Estimating and reducing flux of this greenhouse gas to the atmosphere is becoming a major scientific and practical concern. Thus, development of reliable and cost-effective methods for measurements of landfill methane emissions is a challenge to the scientific community. Two hand-generated case studies showed effectiveness of the proposed methodology. One peculiar novelty of this work is the ability to locate major emission sources in a landfill. Furthermore, in the third case study, emission estimations obtained with the proposed methodology are compared with those obtained using Traced-based method. Results showed that the outcomes of the two methods are comparable confirming the effectiveness of the proposed methodology. The presented methodology shows that the estimation of the solid waste landfill's methane emissions can be made with very little extra effort if on-site monitoring data are already available; greenhouse gas inventories will improve by more accurately calculating methane fugitive emissions from a landfill. Therefore,
the efficiency of existing LFG collection systems would be enhanced by determining the emissions at a variety of points within a landfill and identifying areas of greater than normal losses. This should also improve the design and maintenance of landfill covers. The proposed methodology could be seen as a good step toward assisting landfill operators to reasonably estimate and locate major methane emissions. The authors believe that the described sourcelocating-scheme is promising and could be part of future endeavors. The proposed approach should be sufficiently field tested to resolve uncertainties and develop best implementation practices. Consistency and accuracy of the method need to be explored more intensively especially through different landfill case studies involving various geographical and meteorological conditions. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.apr.2016.12.020. References Abichou, T., Clark, J., Chanton, J., 2011. Reporting central tendencies of chamber measured surface emission and oxidation. Waste Manag. 31 (5), 1002e1008. Alexander, A., Burklin, C., Singleton, A., 2005. Landfill Gas Emissions Model (LandGEM) Version 3.02 User's Guide. US/EPA, Washington DC, USA. Babilotte, A., Lagier, T., Fiani, E., Taramini, V., 2010. Fugitive methane emissions from landfills: field comparison of five methods on a French landfill. J. Environ. Eng. 136 (8), 777e784. Banga, J.R., Moles, C.G., Alonso, A.A., 2004. Global Optimization of Bioprocesses Using Stochastic and Hybrid Methods. Frontiers in Global Optimization. C. A. Floudas and P. Pardalos. Boston, MA, Springer US, pp. 45e70. Cambaliza, M., Shepson, P., Bogner, J., Caulton, D., Stirm, B., 2015. Quantification and source apportionment of the methane emission flux from the city of Indianapolis. Elem. Sci. Anthropocene 3 (000037). EEA, 2014. Annual European Union Greenhouse Gas Inventory 1990-2011 and Inventory Report 2014. European Environment Agency. Elbeltagi, E., Hegazy, T., Grierson, D., 2005. Comparison among five evolutionarybased optimization algorithms. Adv. Eng. Inf. 19 (1), 43e53. EPA, 2010. Methane and Nitrous Oxide Emissions from Natural Sources. U.S. Environmental Protection Agency, Washington, DC, USA. EPA, 2014. Global Mitigation of Non-CO2 Greenhouse Gases: 2010-2030. U.S. Environmental Protection Agency, Washington, DC, USA. Figueroa, V.K., Mackie, K.R., Guarriello, N., Cooper, C.D., 2009. A robust method for estimating landfill methane emissions. J. Air Waste Manag. Assoc. 59 (8), 925e935. Foster-Wittig, T.A., Thoma, E.D., Green, R.B., Hater, G.R., Swan, N.D., Chanton, J.P., 2015. Development of a mobile tracer correlation method for assessment of air emissions from landfills and other area sources. Atmos. Environ. 102, 323e330. GasSim2.5, (2014), http://www.gassim.co.uk/index.html#. Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley. Goldsmith, C.D., Chanton, J., Abichou, T., Swan, N., Green, R., Hater, G., 2011. Methane emissions from 20 landfills across the United States using vertical radial plume mapping. J. Air Waste Manag. Assoc. 62 (2), 183e197. Gonzalez-Valencia, R., Magana-Rodriguez, F., Maldonado, E., Salinas, J., Thalasso, F., 2014. Detection of hotspots and rapid determination of methane emissions from landfills via a ground-surface method. Environ. Monit. Assess. 187 (1), 4083. Green, R.B., Swan, N.D., Thoma, E.D., Footer, T.L., Chanton, J., Hater, G.R., 2012. Measured and modeled methane emissions at closed MSW landfills without gas collection. In: Global Waste Management Symposium. At Phoenix, AZ, USA. Hanna, S.R., Briggs, G.A., Hosker, R.P.J., 1982. Handbook on Atmospheric Diffusion: Medium: ED; Size: Pages: 107. Hansen, N., 2006. Tutorial: Covariance Matrix Adaptation (CMA) Evolution Strategy. Institute of Computational Science, ETH Zurich.
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9 Hansen, N., Müller, S.D., Koumoutsakos, P., 2003. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMAES). Evol. Comput. 11 (1), 1e18. Hansen, N., Ostermeier, A., 2001. Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9 (2), 159e195. Henderson, D., Jacobson, S., Johnson, A., 2003. The theory and practice of simulated annealing. In: Handbook of Metaheuristics, vol. 57. F. Glover and G. Kochenberger, Springer, US, pp. 287e319. Igel, C., Hansen, N., Roth, S., 2007. "Covariance matrix adaptation for multi-objective optimization.". Evol. Comput. 15 (1), 1e28. IPCC, 2013. Climate Change 2013: the Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, vol. 1535. Jacobs, J., Scharff, H., 2005. Comparison of Methane Emission Models and Methane Emission Measurements. Workshop on Inventories and Projections of Greenhouse Gas Emissions from Waste. EEA, Copenhagen, Denmark. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., 1983. Optimization by simulated annealing. Science 220 (4598), 671e680. Lohila, A., Laurila, T., Tuovinen, J.-P., Aurela, M., Hatakka, J., Thum, T., Pihlatie, M., Rinne, J., Vesala, T., 2007. "Micrometeorological measurements of methane and carbon dioxide fluxes at a municipal landfill. Environ. Sci. Technol. 41 (8), 2717e2722. Mackie, K., Cooper, C., 2009. Landfill gas emission prediction using Voronoi diagrams and importance sampling. Environ. Model. Softw. 24 (10), 1223e1232. Mønster, J., Samuelsson, J., Kjeldsen, P., Scheutz, C., 2015. Quantification of methane emissions from 15 Danish landfills using the mobile tracer dispersion method. Waste Manag. 35 (0), 177e186. Mønster, J.G., Samuelsson, J., Kjeldsen, P., Rella, C.W., Scheutz, C., 2014. Quantifying methane emission from fugitive sources by combining tracer release and downwind measurements e a sensitivity analysis based on multiple field surveys. Waste Manag. 34 (8), 1416e1428. Nelder, J.A., Mead, R., 1965. A simplex method for function minimization. Comput. J.
9
7 (4), 308e313. Oonk, H., 2010. Literature review: methane from landfills. Innovations Environ. Technol. 75. Pasquill, F., Smith, F.B., 1983. Atmospheric Diffusion. Ellis Harwood. Press, W.H., Teukolsky, S.A., 1991. Simulated annealing optimization over continuous spaces. Comput. Phys. 5 (4), 451e469. Rajaram, V., Siddiqui, F.Z., Khan, M.E., 2011. From Landfill Gas to Energy: Technologies and Challenges. Taylor & Francis. Raphael, B., Smith, I., 2003. A direct stochastic algorithm for global search. Appl. Math. Comput. 146 (2), 729e758. Raphael, B., Smith, I., 2005. Engineering applications of a direct search algorithm. PGSL Comput. Civ. Eng. 2005, 1e9. Raphael, B., Smith, I.F.C., 2013. Engineering Informatics: Fundamentals of Computeraided Engineering, second ed. Wiley. Scheutz, C., Kjeldsen, P., Bogner, J.E., De Visscher, A., Gebert, J., Hilger, H.A., HuberHumer, M., Spokas, K., 2009. Microbial methane oxidation processes and technologies for mitigation of landfill gas emissions. Waste Manag. Res. 27 (5), 409e455. Scheutz, C., Samuelsson, J., Fredenslund, A.M., Kjeldsen, P., 2011. Quantification of multiple methane emission sources at landfills using a double tracer technique. Waste Manag. 31 (5), 1009e1017. SCS, 2010. WMI SEM Investigation Sites Nationwide, SCS Field Services. Waste Management, Inc, Bedford, TX, USA. Thoma, E., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M., Hashmonay, R., 2010. Development of EPA OTM 10 for landfill applications. J. Environ. Eng. 136 (8), 769e776. Wark, K., Warner, C.F., Davis, W.T., 1998. Air Pollution: its Origin and Control. Addison-Wesley. Yaghini, M., Momeni, M., Sarmadi, M., 2012. A Simplex-based simulated annealing algorithm for node-arc capacitated multicommodity network design. Appl. Soft Comput. 12 (9), 2997e3003.
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
H O S T E D BY
Contents lists available at ScienceDirect
Atmospheric Pollution Research journal homepage: http://www.journals.elsevier.com/locate/apr
Estimation of landfill methane emissions using stochastic search methods Tarek Kormi a, b, Nizar Bel Hadj Ali a, b, *, Tarek Abichou c, Roger Green d Ecole Nationale d’Ing enieurs de Gab es, University of Gab es, Rue Omar Ibn-Elkhattab, 6029, Gab es, Tunisia LASMAP, Ecole Polytechnique de Tunisie, University of Carthage, B.P. 743, La Marsa 2078, Tunisia c Department of Civil and Environmental Engineering, Florida State University, Tallahassee, FL 32310, USA d Waste Management, Inc., 2956 Montana Avenue, Cincinnati, OH 45211, USA a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 September 2016 Received in revised form 12 December 2016 Accepted 15 December 2016 Available online xxx
Municipal solid waste (MSW) landfills significantly contribute to global methane emissions. In order to establish methane mitigation strategies, one important step is to quantify fugitive methane emissions resulting from organic waste decomposition. This paper presents a cost-effective method to estimate methane emissions using ambient air methane measurements taken within a landfill. Stochastic search techniques combined with the standard Gaussian dispersion model are employed to identify locations and emission rates of potential emission sources. Four stochastic search techniques are tested and compared. Results show effectiveness of the optimization-based emission estimation and sourcelocating scheme. Two hand-generated case-studies showed that methane flux estimation-error is lower than 15%. The method proves also useful in identifying locations of emission sources. Furthermore, when monitoring data of a real closed landfill are used, results showed that method results are comparable with those obtained using more established experimental Tracer-based technique. © 2016 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Keywords: Methane emission Solid waste landfill Gaussian dispersion model Stochastic search
1. Introduction 1.1. Background Greenhouse gases (GHG), such as methane (CH4), have a high warming potential and therefore are of growing concern. Global climate change (powerful hurricanes, rising sea level, flooding, etc …) is due in large part to the emission of greenhouse gases into the atmosphere. Many nations around the world are developing innovative policies to slow the rate of near-term global warming and reduce global air pollution. In this context, a global effort is being made to understand, quantify, and manage greenhouse gas emissions. Methane has become a target for emission reduction especially because it is, on a molar basis, 28 times more potent than carbon dioxide (CO2) (IPCC, 2013). Methane has also relatively short
* Corresponding author. LASMAP, Ecole Polytechnique de Tunisie, University of Carthage, B.P. 743, La Marsa 2078, Tunisia. E-mail address: [email protected] (N. Bel Hadj Ali). Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
decay time in the atmosphere: 9e10 years. Municipal Solid Waste (MSW) landfills are amongst the largest human-related sources of methane emissions. Landfill methane is the result of an anaerobic decomposition of the organic fraction in the waste. The amount of methane resulting of this process depends mainly on waste quantity, moisture content and temperature. MSW landfills are progressively replacing solid waste disposals, open dumping and burning practices. Waste management is estimated to be the third largest source of CH4 emissions in the United States (EPA, 2010). In Europe, an estimated 30% of anthropogenic methane emissions are caused by landfills (EEA, 2014). In 2015, China was the source of approximately 13% of global methane emissions making it the largest emitter of methane in the world. More than 5% of total methane emissions in China emerge from MSW landfills (EPA, 2014). In a global scale, methane emissions from landfills account for approximately 11% of total methane emissions (EPA, 2014). For all these reasons, estimation of CH4 emissions from landfills is becoming a major environmental concern.
http://dx.doi.org/10.1016/j.apr.2016.12.020 1309-1042/© 2016 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
2
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
1.2. Estimation methods of landfill methane emissions Several methods exist for the estimation of CH4 emissions from landfills. Estimation methods are either based on simulation models or on measurement methods. In model-based methods, methane emissions can be estimated using exiting biogas production mathematical models. One of the largely used model is the LandGEM (landfill gas emissions model) developed by the U.S Environmental Protection Agency (Alexander et al., 2005). Models also includes GasSim (GasSim2.5, 2014) developed by Golder Associates for the Environment Agency of England and Wales. Besides, in the frame of the European Pollutant Emission Register (EPER) various evolving version of the European EPER models are frequently used (Oonk, 2010; Rajaram et al., 2011). Comparative studies showed that emission models may give different results, even when employed input-data are the same. Jacobs and Scharff (2005) compared several methane emission models with methane emission measurements. They showed that emission models resulted into high divergent estimations. Jacobs and Scharff (2005) also stated that in the near future further development of methane emission measurement techniques may provide more reliable tools than modeling. Measurement techniques are having an increasing interest for landfill methane emission estimation. Surface methane emissions from landfills are determined using small to large-scale direct and indirect measurement approaches applied on a continuous or discrete basis. Closed chamber measurements are frequently employed both for monitoring methane emissions from small parts of a landfill as well as estimating overall emissions from an entire landfill (Abichou et al., 2011; Scheutz et al., 2009). Also micrometerological measurements are used for methane emission quantification (Lohila et al., 2007). Recently, several methodologies have been proposed in order to estimate methane emissions from landfills while delivering cost- and labor-effective results (Cambaliza et al., 2015; Foster-Wittig et al., 2015). These methods include: static and mobile plume measurement methods using tracer gas (Mønster et al., 2014, 2015; Scheutz et al., 2011), radial plume mapping (RPM) using optical remote sensing (ORS) by means of laser infrared radiation emissions (Goldsmith et al., 2011; Thoma et al., 2010), differential absorption light detection and ranging (LiDAR) (Babilotte et al., 2010) and inverse plume modeling (Mackie and Cooper, 2009; Oonk, 2010). The only technology that can be used directly to determine emission flux is the stationery enclosure technique. All of the other technologies provide concentrations, which can be used to estimate emission flux indirectly via analytical or numerical models. The direct flux measurement system is based on the static flux chamber enabling only point measurements. Several challenges and uncertainties arise when transferring these point measurements to total or areal estimates. The concentration based measurement methods are: tracer testing using inert gases as detectors, optical remote sensing using tunable diode laser spectroscopy, and optical remote sensing using Fourier transform infrared spectroscopy. Sensitivity of the optical remote sensing and tracer methods to micrometeorological conditions (including wind, rain, and fog) and high cost limit the full applicability of these measurement and monitoring systems.
Methane concentration monitoring is already frequently performed in many MSW landfills. Thus, it is important to develop efficient approaches to correlate surface concentrations to emission fluxes in order to allow estimating landfill emission fluxes (Mackie and Cooper, 2009). This study builds upon the approach developed by Figueroa et al. (2009) for estimating fugitive landfill methane emissions based on surface emission monitoring. An optimization-based approach using stochastic search techniques is employed to solve the inverse problem that consists on identifying source data (source locations and corresponding emission rates) having receptor locations and surface measurements along with meteorological conditions as input data. Four methods including Genetic Algorithms (GA), Simplex-Simulated Annealing Approach (SIMPSA), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Probabilistic Global Search Lausanne (PGSL) are tested in order to evaluate the performance of such methods in handling the methane emission estimation task. Optimization techniques are used along with the standard Gaussian dispersion equations to determine the methane emission sources and to estimate areal methane emissions. Three case studies are presented to show effectiveness of the proposed methodology. Results obtained with various stochastic search techniques are interpreted and compared.
2. Methane emission quantification methodology The proposed CH4 emission estimation technique exploits ambient air methane concentration measurements. Measurement locations are taken into account as receptor points. Assuming that a field survey has resulted in a vector of methane concentration measurements denoted Cmeasured performed at m defined points in a landfill. Measurement locations or receptors are thus represented by two coordinate vectors Xr and Yr, each having m components; related with the vector of gas concentration measurements. For this study, a source configuration defines a peculiar distribution of sources and emission rates. It is represented by the following data set: - Geographic positions of a number (n) of sources: source positions are represented by two coordinate vectors Xs and Ys, each having n components; - Point emission rates corresponding to the n sources of the model: this information is represented by a vector Q of n components corresponding to emission rates of the source configuration (expressed in micrograms per second: mg/s). Any generated source configuration is evaluated through calculating the corresponding methane concentrations at receptors points. This is done by backward application of atmospheric dispersion model. Gaussian dispersion equations are used for this task. The Gaussian model still the basic workhorse for dispersion calculations. According to this approach, gas concentration is given by Equation (1).
Cðx; y; z; HÞ ¼
1.3. Objectives In concentration-mapping techniques a field survey can be done by walking a predefined grid with a field gas analyzer. Surface gas concentrations are relatively inexpensive and easy to obtain. Mapping of methane concentration could be exploited to identify point sources of relatively high concentrations such as cracks in the landfill cover (Figueroa et al., 2009; Gonzalez-Valencia et al., 2014).
!( 1 y2 exp 2 s2y !) 1 ðz þ HÞ2 2 s2z
Q exp 2pusy sz þ exp
1 ðz HÞ2 2 s2z
!
(1) where C is the steady-state gas concentration (mg/m3) at a point (x, y, z), Q is the emission rate (mg/s), sy and sz are the horizontal and vertical spread parameters that are functions of the downwind
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
distance x (Fig. 1) and the atmospheric stability, u is the average wind speed at stack height (m/sec), y is the crosswind distance from ground level (m), z is the vertical distance above the ground (m), and H is the effective stack height (physical stack height plus plume rise expressed in m). In Equation (1) the second z-exponential term is added to account for the fact that pollutant cannot diffuse downward through the ground at z ¼ 0. A virtual source located at z ¼ eH below the ground will then begin to contribute to the aboveground concentrations. This “image” term is used to account for plume reflection at the ground (Wark et al., 1998). The Gaussian distribution equation uses relatively simple calculations requiring only two dispersion parameters (sy and sz) to identify the variation of gas concentrations away from the diffusion source. These dispersion coefficients, sy and sz, are functions of wind speed, cloud cover, and surface heating by the sun. Generally, the evaluation of the diffusion coefficients are based on atmospheric stability class (Pasquill and Smith, 1983). In this study, Pasquill-Gifford stability classes are employed and dispersion coefficients are calculated using Briggs model (Hanna et al., 1982). Details about dispersion coefficient evaluation are given in Appendix. For ground-level sources and receptors (z ¼ 0 and H ¼ 0), Equation (1) reduces to Equation (2):
C¼
Q
pmsy sz
" exp
1 y2 2 s2y
# (2)
Under defined atmospheric conditions and wind speed, dispersion parameters are obtained and the predicted methane concentration in a receptor point i (Ci,predicted) is calculated through summing up all contributions (Cij) of assumed source points j (j ¼ 1,…, n). This is expressed in Equation (3).
Ci;predicted ¼
n X
Ci;j
(3)
j¼1
Calculating predicted concentration for all receptor points (i ¼ 1,
…, m) results in a vector of predicted concentration (Cpredicted).
The main task is to estimate, from measured concentrations using ambient CH4 measurements, the sources (number and locations) and to estimate their emission rates. This task is formulated as an optimization problem that consists on residual minimization under bound constraints. The objective function R is thus defined as
N θ
Wind u
Source (xi, yj) y
x
Receptor
x y
Fig. 1. Source-receptor configuration.
3
the normalized estimation error between predicted and measured methane concentrations. The problem is presented as follows:
Pn Min RðX; Y; Q Þ ¼
i¼1
Ci; measured Ci; predicted 2 Pn i¼1 Ci; measured
2
under
(4)
xmin xj xmax
; j ¼ 1; :: n
ymin yj ymax
; j ¼ 1; :: n
Qmin Qj Qmax
; j ¼ 1; :: n
In Equation (4), the two first constraints define the geographic limits of source positions (Limits of the landfill). The third constraint defines the lower and the upper bounds of the source emission rates. An estimation of the number of potential emission sources is performed prior to optimization with stochastic search algorithms. The aim is to guide the search with an initial guess of the number of release points (through peak picking). This decreases the complexity of the optimization task through including data-driven information. A peak picking procedure is employed to treat measurement data. Peak picking allows for identification of high concentration points. The procedure employs a threshold value that represents the minimum peak height to be taken into account. Identifying peaks from measurement data is performed employing a Matlab® routine called findpeaks. This routine is able to find values and locations of local maxima in a set of data even if some peaks are very close to each other. 3. Optimization through stochastic search techniques The scientific and technical community is confronted with optimization tasks of growing complexity, which emerge in a wide variety of disciplines. Modern optimization techniques have aroused great interest because of their ability to solve problems with a non-linear and non-convex dependence of design variables. In the broadest sense, these techniques can be classified into exact and stochastic algorithms. Exact algorithms, such as branch and bound, linear or dynamic programming can be highly effective for small-size problems. For hard optimization problems with large and complex solution space, the use of stochastic algorithms becomes a reasonable alternative. Stochastic techniques involve randomness in a constructive way in order to efficiently and effectively exploring a search space. These are robust algorithms capable of traversing large search space to provide near-optimal solutions. Furthermore, even if stochastic search methods do not guarantee to obtain the optimal solution for whatever an optimization problem, these techniques are able to converge to nearoptimal solutions that are most of the time satisfactory from an engineering point of view, especially, when this is done within a reasonable amount of time. As formulated in this study, the optimization problem is characterized by a finite number of continuous variables. A source configuration involves positions and emission rates of a definite number of source points. The number of source configurations generated for an identification case increases rapidly with increasing estimated number of sources. Four stochastic search techniques amongst the most widely used search methods in engineering applications are employed and compared in this study. Optimization techniques include: Genetic Algorithms (GA), Simplex-Simulated Annealing Approach (SIMPSA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Probabilistic Global Search Lausanne (PGSL). The following paragraphs contain
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
4
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
brief descriptions of these search methods.
applications (Yaghini et al., 2012).
3.1. Genetic Algorithms (GA)
3.4. Probabilistic Global Search Lausanne (PGSL)
Genetic algorithms are global search methods that belong to the class of stochastic search algorithms. They were originally proposed by John Holland, whereas the success of the method owes much to the work of Goldberg (1989). GAs have been used efficiently since the early 1990s to solve hard optimization problems with discrete as well as continuous variables. GAs are modeled based on the principles of the evolution of generations via natural selection, mutation and crossover. Biological evolution is simulated by encoding potential solutions (a population of individuals in genetic terminology) using a chromosomelike data structure and the search is guided by the results of evaluating the objective function for each individual in the population (Raphael and Smith, 2013). New individuals are generated in the population through reproduction using crossover and mutation operators. Individuals that have higher fitness (i.e., represent better solutions) can be identified, and these are given more opportunity to breed. The genetic algorithm evolves, in successive generations, the composition of the population; enabling thus convergence towards near-optimal global solutions.
The Probabilistic Global Search Lausanne (PGSL) technique uses a probability density function (PDF) to explore the search space and select the best point (Raphael and Smith, 2003). The PDF represents the probability of finding a good solution at any given point in the search space. The PGSL method is based on the assumption that sets of better solutions are more likely to be found in the neighborhood of sets of good solutions and, therefore, intensifies search in regions that contain sets of good values. In PGSL, each search direction/axis is divided into a fixed number of intervals and a uniform probability distribution is considered. Probabilities and intervals are dynamically updated as the search for optimal solution progresses. Higher probability in regions contained good solutions are considered. The search space is thus gradually narrowed down and the convergence is achieved.
3.2. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) The CMA-ES is a stochastic method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. This method was proposed for the first time in the mid 1990s and has been considerably developed since then (Hansen et al., 2003; Hansen and Ostermeier, 2001) with many successful applications in single as well as multi-objective optimization (Igel et al., 2007). The CMA-ES method generates an initial population from a multivariate normal distribution and evaluates individual fitness values. Then, it updates the mean vector and covariance matrix of the multivariate normal distribution by using the information of the sampled points and their fitness values. Through the repeated sampling-evaluation-updating procedure, the method is likely to move the sampling distribution towards a neighborhood of the optimal solution. In CMA-ES, updating rules for the mean vector and the covariance matrix along with step-size adaptation are the basic operators of the optimization technique. These operators are designed to ensure reproducing successful search steps, as well as exploiting the evolution path of the distribution mean. A detailed description of the CMA-ES strategy can be found in (Hansen, 2006). 3.3. Simplex-Simulated Annealing Approach (SIMPSA) The Simulated Annealing method (Kirkpatrick et al., 1983) employs stochastic generation of solution vectors and transposes the physical process of annealing (i.e. melting a solid by heating it, followed by slow cooling and crystallization into a minimum free energy state). Slow cooling is implemented in the Simulated Annealing algorithm as a slow decrease in the probability of accepting worse solutions as it explores the solution space. Accepting worse solutions avoids local optimums and allows for a more extensive search for the optimal solution. Its ease of implementation and convergence properties have made it a popular technique over the past two decades (Henderson et al., 2003). In order to improve its ability to escape from local optima, SA is combined with the non-linear simplex method of Nelder and Mead (1965) giving rise to the SIMPSA (Press and Teukolsky, 1991). It is shown that the combination between the two strategies not only makes possible to easily escape from local optima but also to accelerate the search procedure, especially in high-dimensional
3.5. Efficiency of chosen optimization techniques Stochastic search algorithms have become established as an efficient alternative for exploring the optimal solutions of optimization problems that are too complex to be solved by exact methods, such as linear programming and gradient search. The GAs method is the first evolutionary-based technique introduced in the literature. GAs ability to reach near-optimum solutions is demonstrated through many applications in science and engineering. Despite their benefits, GAs may require long processing time for a near-optimum solution to evolve (Elbeltagi et al., 2005). The CMAES is relatively new compared with GAs. This method is typically applied to unconstrained or bounded constraint continuous optimization problems. The main advantages of CMA-ES lie in its invariance properties, which are achieved by carefully designed variation and selection operators, and in an efficient scheme for adapting mutation distribution (Hansen et al., 2003). The SIMPSA algorithm is based on the combination of the nonlinear simplex and Simulated Annealing algorithms. Compared with the classic Simulating Annealing algorithm, the performance of SIMPSA is largely improved due to the solution generation-scheme based on nonlinear simplex method (Banga et al., 2004). An advantage of the SIMPSA algorithm is that it requires little parameter tuning. PGSL is a stochastic search method where the main idea is to sample with higher probability in regions containing good solutions. Comparative studies showed that PGSL outperforms GAs and adaptive Simulated Annealing for most tested nonlinear benchmark problems (Raphael and Smith, 2005). PGSL is also shown particularly efficient for optimization tasks with large number of variables. 4. Case studies In order to illustrate the proposed approach for landfill methane emission estimation, three case studies are presented. The two first examples are hand-generated case studies with defined configurations of sources and receptors. Case studies simulate two landfill configurations with 12 and 16 sources, respectively. The number of sources is chosen in order to test effectiveness of the identification methodology for varying optimization variables. For the two cases, measurement data are generated through application of the Gaussian dispersion equations. The third case study is an actual municipal solid waste landfill for which a surface concentration measurement campaign was performed. For this MSW landfill Tracer-based emission results are also available as described by (Foster-Wittig et al., 2015; Green et al., 2012; SCS, 2010).
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
4.1. Test case with 12 sources For the first case study, twelve sources with predefined emission rates and 100 receptors are considered. Positions for sources are randomly generated assuming a square-shaped landfill having 1.5 km sides. Source emission rates are also randomly generated between zero and 106 mg/s. Receptor positions are uniformly distributed over the landfill area. The total emission rate for the entire landfill is equal to 4.43 g/s. Receptor concentrations are determined through forward application of the dispersion equation assuming a wind speed of 2 m/s and wind direction of 35 and stability class C. The Briggs model is employed for determining horizontal and vertical dispersion spread parameters (see supplementary material for complete data and more details about the Briggs model). 4.2. Test case with 16 sources For the second case study, 16 sources with predefined emission rates and 170 receptors are considered. Positions for sources are randomly generated assuming a square-shaped landfill having 2 km width. Source emission rates are also randomly generated between zero and 106 mg/s. Receptor positions are uniformly distributed over the landfill area (see supplementary material for complete data). The total emission rate for the entire landfill is equal to 8.6 g/s. Receptor concentrations are determined based on Gaussian dispersion model assuming a wind speed of 2 m/s and wind direction of 40 and stability class C. The Briggs model is employed for determining horizontal and vertical dispersion spread parameters. 4.3. SW landfill case The optimization-based identification methodology was applied to an existing MSW landfill named SW landfill in order to estimate methane emissions. The studied case is a closed landfill that covers an overall area of about 202,345 m2. Along with a surface emission measurement campaign, tracer test campaigns were performed in the same period for SW landfill as described by (Foster-Wittig et al., 2015; Green et al., 2012; SCS, 2010). This allowed comparing outcomes of the two methodologies. Approximately 2 h were needed for collecting surface methane concentrations across the walking path in 463 measurement locations. The temperature and the wind speed were measured twice: in the beginning and by the end of monitoring. A Flame Ionization Detector (a PhotoVac Micro-FID) was used to perform surface monitoring with data collected every 15 s. The average inlet flow rate of the FID was equal to 600 mL/min. The FID was calibrated at the beginning of monitoring event, prior to use, in accordance with the regulations. In order to screen surface methane concentrations a funnel-shaped probe was directly put on the landfill surface and via an integrated pump the emitted gas was drawn through the FID. A GPS unit was used to collect measurement points along the monitoring path. The surface emission monitoring route provided coverage of all waste disposal of the landfill. Additionally, if surface defects or passive vents were encountered, concentration readings were performed around such features. Table 1 shows general data and climate conditions for the SEM campaign. Based on monitoring conditions a stability class C is assigned for this case. As for earlier case studies the Briggs model is employed for determining horizontal and vertical dispersion spread parameters. For the SEM campaign, methane concentrations are measured in 463 points. Fig. 2 shows receptor positions and measured methane concentrations (as a color bar). The four stochastic search techniques are used to estimate methane emissions
5
Table 1 General conditions for the SEM in SW landfill (SCS, 2010). SEM conditions (SCS, 2010) Date Time Sky Ground Temperature ( C) Wind direction Wind speed (m/s) Pressure (Pa) Number of measurement points
September 17, 2010 18:05 to 20:00 overcast dry 15 NW 2.22 101795 463
and locate potential sources. Peak picking is first performed to obtain an estimation of the number of sources. Based on the peak picking procedure, the number of potential emission sources in the landfill is estimated to be 11 sources. 5. Results and discussion 5.1. Results for the test case with 12 sources The four optimization methods are employed and results are compared. For all case studies, the total number of fitness evaluation is fixed for all methods in order to ensure a consistent comparison of identification results. Moreover, the best solution generated over a sequence of 5 runs using different random seeds is considered as the optimal configuration. An overall description of optimization results is presented in Table 2. The total emission methane rates predicted by each of the optimization techniques are presented. Bold values in Table 2 correspond to best-fitness optimization results. With varying performances, all four techniques succeeded to approach the assumed emission rate in the landfill (4.43 g/s). Estimation errors range between 3 and 12% when only the best run is taken into account. However, when five-runs averaged results are taken into account, the estimation error decreases and ranges between 3 and 8%. The best-run results showed that CMA-ES and PGSL are capable of identifying the total emission rate with smaller discrepancy compared with GA and SIMPSA. CMA-ES and PGSL also outperform the other techniques when the five runs are accounted for. These results are confirmed by Fig. 3 comparing the assumed source positions to those predicted by the CMA-ES method. It is shown that 11 sources from 12 are located. The remaining source was not identified mainly because there are few receptors under the wind direction. Fig. 4 shows assumed and predicted emission rates for the twelve sources. Results show good agreement between simulated and predicted values. It is noticed that identification error for source fluxes depends upon source-positions. For instance, the relatively higher identification-error for the emission rates for source 6 and 7 could be explained by the proximity of the two sources. Most of the sources having high emission rates are well identified in terms of location and emission rate. 5.2. Results for the test case with 16 sources For the second case study, optimization is performed assuming a number of sources equal to 16 for all studied methods. The estimation of the number of sources is deliberately ignored here since the objective of this study is to compare efficiency of the identification procedure. An overall description of optimization results is presented in Table 3. The total emission methane rates predicted by studied
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
6
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
Fig. 2. Receptor positions for SW Landfill (SCS, 2010).
Table 2 Predicted emission rates for the 12 source case study. Optimization method
Total predicted emission for the best run [g/s] Best-run identification error Total predicted emission averaged over five runs [g/s] Five-runs averaged error
1.6
GA
CMA-ES
SIMPSA
PGSL
4.07 8.0% 4.70 6.1%
4.26 3.7% 4.28 3.3%
3.93 11.3% 4.05 8.6%
4.64 4.7% 4.43 0.0%
1
Assumed
1.4
Predicted
1 0.8
0.6 0.4
Predicted
0,8
Emission rate (g/s)
Y (km)
1.2
Assumed
0,9 0,7 0,6 0,5 0,4 0,3 0,2
0.2
0,1
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
X (km)
0 1
2
3
4
5
6
7
8
9
10
11
12
Source number
Fig. 3. Assumed and predicted sources locations by the CMA-ES method.
Fig. 4. Assumed and predicted sources fluxes by the CMA-ES method.
optimization techniques are presented. Bold values in Table 3 correspond to best-fitness optimization results. With varying performances, all four techniques succeeded to approach the assumed emission rate in the landfill (8.6 g/s). Estimation errors range between 1 and 16% when only the best run is taken into account. However, when five-runs averaged results are taken into account, the estimation error increases and ranges between 0 and 27%. The best-run results showed that PGSL, SIMPSA and GA are capable of identifying the total emission rate with smaller discrepancy compared with CMA-ES. The CMA-ES method shows
relatively lower efficiency in the identification task with a best-run error over 15% and an averaged five run error over 27%. Relatively high and comparable efficiency is obtained using PGSL, GA and SIMPSA. In addition, this case study showed that GA outperforms the other techniques when the five runs are accounted for. Optimization results obtained with the SIMPSA method are displayed in Figs. 5 and 6. These results are obtained with 16 sources as an estimated number of point sources in the landfill. Fig. 5 shows position of 16 predicted sources compared to assumed source positions. Fig. 6 shows assumed and predicted emission
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
7
Table 3 Predicted emission rates for the 16 source case-study. Optimization method
Total predicted emission for the best run [g/s] Best-run error Total predicted emission averaged over five runs [g/s] Averaged five-runs error
2.0
GA
CMA-ES
SIMPSA
PGSL
8.91 3.3% 8.65 0.0%
7.29 15.4% 6.29 27.0%
8.52 1.1% 8.01 7.0%
8.73 1.2% 8.27 4.0%
measurement data-sets to overcome insufficient information. Moreover, identification efficiency could be maximized if there is a mean to place the receptors at the most informative locations.
Assumed
1.8
Predicted
1.6
Y (km)
1.4
5.3. Results for the SW landfill case
1.2 1.0
0.8 0.6 0.4 0.2
0.0 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
X (km) Fig. 5. Assumed and predicted sources locations by the SIMPSA method.
1,2
Assumed
Emission rate (g/s)
1
Predicted
0,8 0,6 0,4
0,2 0 1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
Source number Fig. 6. Assumed and predicted sources fluxes by the SIMPSA method.
rates for all sources. The two figures show good agreement between simulated and predicted results. The identification errors for source positions are below 10%. Emission rates for most sources are also well identified. It is noticed that the two sources that are not identified are associated with relatively low emission rates. For the overall emission in the landfill, optimization results into a total value of 8.52 g/s which can be considered as an accurate approximation of the assumed total emission flux (8.6 g/s). A part from CMA-ES results, optimization results through GA, PGSL and SIMPSA showed that source positions and emission rates for most points are well identified. It is noticed that identification efficiency for some source point is influenced by the level of the local emission rate of the source. Furthermore, identification accuracy is also influenced by the position of the source in the landfill and also by the number of corresponding downward receptor points. These results emphasize the importance of having several
Starting from measurement data and meteorological conditions, identification through stochastic optimization is performed. For each generated source configuration, receptor concentrations are determined based on Gaussian dispersion model assuming a wind speed of 2.22 m/s, wind direction of 315 and stability class C. Plume reflection is taken into account when methane concentrations are calculated for each receptor position. This is achieved by simply doubling the simulated concentrations since both emission and measurements occur at ground level. For all cases, the identification procedure is performed assuming a number of sources equal to 11. An overall description of optimization results is presented in Table 4. The best-run fitness and the averaged fitness over five runs are presented for each of the studied methods. Fitness values show that SIMPSA is slightly outperforming PGSL, GA and CMA-ES. The total emission methane rates predicted with tested optimization techniques are presented. The best run with the lowest fitness value is obtained with the SIMPSA method with an emission rate of 12.83 g/s. In an overall scale, the emission rate for the SW landfill ranges between 10 and 14 g/s. In addition to the SEM campaign, an Acetylene Tracer-Based approach was performed in order to quantify methane emissions in the SW landfill. Tracer methods allow for quantitative measurements to be made using a single, mobile gas analyzer located in the far field of the source. In the SW landfill, mobile and stationary plume measurements were made on September 16, 2010 (Green et al., 2012; SCS, 2010). Mobile transect measurements were made by driving the analyzer along roads located around the landfill. Stationary measurements were performed by positioning the analyzer downwind from the landfill where concentrated measurements are performed (Foster-Wittig et al., 2015; Green et al., 2012). The mobile and stationary plume measurements resulted into an estimated mean emission rate of 12.6 g/s and 11.8 g/s, respectively. The standard deviations for the two measurements are equal to 1.27 g/s and 1.42 g/s, respectively. Comparing outcomes of the two methods shows that the discrepancy between estimation results is below 20% for all used stochastic search methods. These results confirm the potential of the proposed optimization-based methodology as a cost effective method for the estimation of landfill methane emissions. It is also supposed that the overall coverage of the landfill offered by surface emission monitoring enabled the proposed method to determine a more representative whole site methane emission. 5.4. Key discussion points Through the case studies presented in this work, the proposed
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
8
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9
Table 4 Predicted emission rate obtained with various optimization methods. Optimization method
Total predicted emission for the best run [g/s] Best-run fitness Total predicted emission averaged over five runs [g/s] Averaged five-runs fitness
GA
CMA-ES
SIMPSA
PGSL
13.60 0.626 13.61 0.637
11.37 0.643 10.00 0.652
12.83 0.583 12.84 0.589
14.23 0.621 14.04 0.633
optimization-based treatment of surface concentration measurements confirms its potential as a reliable methane emission estimation method. The comparison between optimization techniques indicated that all four stochastic search methods show relatively equal performance with a slight advantage to GA, PGSL and SIMPSA over CMA-ES. However, the efficiency of the proposed technique still requires further verifications. In fact, in order to identify the best optimization technique among all tested ones, a larger number of case studies are needed. Results of the three case studies also showed the importance of having an overall coverage of all waste disposal of the landfill when surface concentration measurements are performed. It was noticed that identification efficiency for some source point is influenced by the level of the local emission rate of the source and also by the number of corresponding downward receptor points. Without prior knowledge of the most informative locations, results from this study emphasize the importance of having several measurement data-sets to overcome insufficient information. Additionally, the efficiency of the identification procedure is related to the variability of the meteorological conditions (wind speed and direction, etc.). The proposed method does not take into account uncertainties related to such parameters. Thus, it is highly advised to perform measurement campaigns in stable climate conditions with minimum variability during measurement rounds. Prior to optimization with stochastic search techniques, an estimation of the number of potential emission sources is performed. The number of sources could be added as an optimization variable in the identification procedure. However, it would increase the complexity of the optimization task and increase drastically the running time of the optimization code. Instead, a data-driven initial guess (through a peak picking procedure) of the number of release points is proposed to simplify the optimization task.
6. Conclusion Municipal solid waste (MSW) landfills emit significant amounts of methane. Estimating and reducing flux of this greenhouse gas to the atmosphere is becoming a major scientific and practical concern. Thus, development of reliable and cost-effective methods for measurements of landfill methane emissions is a challenge to the scientific community. Two hand-generated case studies showed effectiveness of the proposed methodology. One peculiar novelty of this work is the ability to locate major emission sources in a landfill. Furthermore, in the third case study, emission estimations obtained with the proposed methodology are compared with those obtained using Traced-based method. Results showed that the outcomes of the two methods are comparable confirming the effectiveness of the proposed methodology. The presented methodology shows that the estimation of the solid waste landfill's methane emissions can be made with very little extra effort if on-site monitoring data are already available; greenhouse gas inventories will improve by more accurately calculating methane fugitive emissions from a landfill. Therefore,
the efficiency of existing LFG collection systems would be enhanced by determining the emissions at a variety of points within a landfill and identifying areas of greater than normal losses. This should also improve the design and maintenance of landfill covers. The proposed methodology could be seen as a good step toward assisting landfill operators to reasonably estimate and locate major methane emissions. The authors believe that the described sourcelocating-scheme is promising and could be part of future endeavors. The proposed approach should be sufficiently field tested to resolve uncertainties and develop best implementation practices. Consistency and accuracy of the method need to be explored more intensively especially through different landfill case studies involving various geographical and meteorological conditions. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.apr.2016.12.020. References Abichou, T., Clark, J., Chanton, J., 2011. Reporting central tendencies of chamber measured surface emission and oxidation. Waste Manag. 31 (5), 1002e1008. Alexander, A., Burklin, C., Singleton, A., 2005. Landfill Gas Emissions Model (LandGEM) Version 3.02 User's Guide. US/EPA, Washington DC, USA. Babilotte, A., Lagier, T., Fiani, E., Taramini, V., 2010. Fugitive methane emissions from landfills: field comparison of five methods on a French landfill. J. Environ. Eng. 136 (8), 777e784. Banga, J.R., Moles, C.G., Alonso, A.A., 2004. Global Optimization of Bioprocesses Using Stochastic and Hybrid Methods. Frontiers in Global Optimization. C. A. Floudas and P. Pardalos. Boston, MA, Springer US, pp. 45e70. Cambaliza, M., Shepson, P., Bogner, J., Caulton, D., Stirm, B., 2015. Quantification and source apportionment of the methane emission flux from the city of Indianapolis. Elem. Sci. Anthropocene 3 (000037). EEA, 2014. Annual European Union Greenhouse Gas Inventory 1990-2011 and Inventory Report 2014. European Environment Agency. Elbeltagi, E., Hegazy, T., Grierson, D., 2005. Comparison among five evolutionarybased optimization algorithms. Adv. Eng. Inf. 19 (1), 43e53. EPA, 2010. Methane and Nitrous Oxide Emissions from Natural Sources. U.S. Environmental Protection Agency, Washington, DC, USA. EPA, 2014. Global Mitigation of Non-CO2 Greenhouse Gases: 2010-2030. U.S. Environmental Protection Agency, Washington, DC, USA. Figueroa, V.K., Mackie, K.R., Guarriello, N., Cooper, C.D., 2009. A robust method for estimating landfill methane emissions. J. Air Waste Manag. Assoc. 59 (8), 925e935. Foster-Wittig, T.A., Thoma, E.D., Green, R.B., Hater, G.R., Swan, N.D., Chanton, J.P., 2015. Development of a mobile tracer correlation method for assessment of air emissions from landfills and other area sources. Atmos. Environ. 102, 323e330. GasSim2.5, (2014), http://www.gassim.co.uk/index.html#. Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley. Goldsmith, C.D., Chanton, J., Abichou, T., Swan, N., Green, R., Hater, G., 2011. Methane emissions from 20 landfills across the United States using vertical radial plume mapping. J. Air Waste Manag. Assoc. 62 (2), 183e197. Gonzalez-Valencia, R., Magana-Rodriguez, F., Maldonado, E., Salinas, J., Thalasso, F., 2014. Detection of hotspots and rapid determination of methane emissions from landfills via a ground-surface method. Environ. Monit. Assess. 187 (1), 4083. Green, R.B., Swan, N.D., Thoma, E.D., Footer, T.L., Chanton, J., Hater, G.R., 2012. Measured and modeled methane emissions at closed MSW landfills without gas collection. In: Global Waste Management Symposium. At Phoenix, AZ, USA. Hanna, S.R., Briggs, G.A., Hosker, R.P.J., 1982. Handbook on Atmospheric Diffusion: Medium: ED; Size: Pages: 107. Hansen, N., 2006. Tutorial: Covariance Matrix Adaptation (CMA) Evolution Strategy. Institute of Computational Science, ETH Zurich.
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020
T. Kormi et al. / Atmospheric Pollution Research xxx (2016) 1e9 Hansen, N., Müller, S.D., Koumoutsakos, P., 2003. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMAES). Evol. Comput. 11 (1), 1e18. Hansen, N., Ostermeier, A., 2001. Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9 (2), 159e195. Henderson, D., Jacobson, S., Johnson, A., 2003. The theory and practice of simulated annealing. In: Handbook of Metaheuristics, vol. 57. F. Glover and G. Kochenberger, Springer, US, pp. 287e319. Igel, C., Hansen, N., Roth, S., 2007. "Covariance matrix adaptation for multi-objective optimization.". Evol. Comput. 15 (1), 1e28. IPCC, 2013. Climate Change 2013: the Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, vol. 1535. Jacobs, J., Scharff, H., 2005. Comparison of Methane Emission Models and Methane Emission Measurements. Workshop on Inventories and Projections of Greenhouse Gas Emissions from Waste. EEA, Copenhagen, Denmark. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., 1983. Optimization by simulated annealing. Science 220 (4598), 671e680. Lohila, A., Laurila, T., Tuovinen, J.-P., Aurela, M., Hatakka, J., Thum, T., Pihlatie, M., Rinne, J., Vesala, T., 2007. "Micrometeorological measurements of methane and carbon dioxide fluxes at a municipal landfill. Environ. Sci. Technol. 41 (8), 2717e2722. Mackie, K., Cooper, C., 2009. Landfill gas emission prediction using Voronoi diagrams and importance sampling. Environ. Model. Softw. 24 (10), 1223e1232. Mønster, J., Samuelsson, J., Kjeldsen, P., Scheutz, C., 2015. Quantification of methane emissions from 15 Danish landfills using the mobile tracer dispersion method. Waste Manag. 35 (0), 177e186. Mønster, J.G., Samuelsson, J., Kjeldsen, P., Rella, C.W., Scheutz, C., 2014. Quantifying methane emission from fugitive sources by combining tracer release and downwind measurements e a sensitivity analysis based on multiple field surveys. Waste Manag. 34 (8), 1416e1428. Nelder, J.A., Mead, R., 1965. A simplex method for function minimization. Comput. J.
9
7 (4), 308e313. Oonk, H., 2010. Literature review: methane from landfills. Innovations Environ. Technol. 75. Pasquill, F., Smith, F.B., 1983. Atmospheric Diffusion. Ellis Harwood. Press, W.H., Teukolsky, S.A., 1991. Simulated annealing optimization over continuous spaces. Comput. Phys. 5 (4), 451e469. Rajaram, V., Siddiqui, F.Z., Khan, M.E., 2011. From Landfill Gas to Energy: Technologies and Challenges. Taylor & Francis. Raphael, B., Smith, I., 2003. A direct stochastic algorithm for global search. Appl. Math. Comput. 146 (2), 729e758. Raphael, B., Smith, I., 2005. Engineering applications of a direct search algorithm. PGSL Comput. Civ. Eng. 2005, 1e9. Raphael, B., Smith, I.F.C., 2013. Engineering Informatics: Fundamentals of Computeraided Engineering, second ed. Wiley. Scheutz, C., Kjeldsen, P., Bogner, J.E., De Visscher, A., Gebert, J., Hilger, H.A., HuberHumer, M., Spokas, K., 2009. Microbial methane oxidation processes and technologies for mitigation of landfill gas emissions. Waste Manag. Res. 27 (5), 409e455. Scheutz, C., Samuelsson, J., Fredenslund, A.M., Kjeldsen, P., 2011. Quantification of multiple methane emission sources at landfills using a double tracer technique. Waste Manag. 31 (5), 1009e1017. SCS, 2010. WMI SEM Investigation Sites Nationwide, SCS Field Services. Waste Management, Inc, Bedford, TX, USA. Thoma, E., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M., Hashmonay, R., 2010. Development of EPA OTM 10 for landfill applications. J. Environ. Eng. 136 (8), 769e776. Wark, K., Warner, C.F., Davis, W.T., 1998. Air Pollution: its Origin and Control. Addison-Wesley. Yaghini, M., Momeni, M., Sarmadi, M., 2012. A Simplex-based simulated annealing algorithm for node-arc capacitated multicommodity network design. Appl. Soft Comput. 12 (9), 2997e3003.
Please cite this article in press as: Kormi, T., et al., Estimation of landfill methane emissions using stochastic search methods, Atmospheric Pollution Research (2016), http://dx.doi.org/10.1016/j.apr.2016.12.020