Environ. Sci. Technol. 2000, 34, 4044-4050
Methane Fluxes from a Swedish Landfill Determined by Geostatistical Treatment of Static Chamber Measurements GUNNAR BO ¨ RJESSON,* A° S A D A N I E L S S O N , A N D B O H . S V E N S S O N Department of Water and Environmental Studies, Linko¨ping University, SE-581 83 Linko¨ping, Sweden
Methane emissions from a Swedish landfill were measured with a static chamber technique on three occasions during 1997. Methane flux rates ranged from -15.2 × 10-3 to 40 g of CH4 m-2 h-1, and the spatial variability was high (CV ) 343-386%). The spatial distribution of the emissions was estimated with the help of ordinary kriging, which is a spatial interpolation method. Three different approaches to estimate the total amounts were used: kriging on logarithm-transformed data, kriging with extremes excluded, and linear interpolation of measurements. These were compared between themselves and with the flux rates measured with a tracer gas technique. While the latter gave an estimate of 41 kg of CH4 h-1 from the landfill (with small variations), the highest possible estimate obtained with static chambers and geostatistical methods was 9.7 kg of CH4 h-1. The conclusion is that static chambers can hardly be trusted for making more than small-scale estimates of landfill gas emissions.
Introduction Methane is one of the most potent greenhouse gases, and its concentration is increasing in the atmosphere by 0.6% every year (1). One important source is landfills, where methane is produced in large quantities, as a result of anaerobic degradation of organic matter. Landfills are estimated to contribute approximately 20% of the methane from anthropogenic sources (1). The total methane flux from individual landfills can be described as the production minus gas recovery minus oxidation in cover soils. Through official statistics, reliable values are only available for gas recovery. In Sweden, landfill gas was extracted from over 60 of the 282 active sites in 1997, and the total energy production was 0.42 TWh during 1997 (2). The total amount of methane produced and oxidized remains unknown. Field measurements of methane emissions from landfills are few, and the most widely used method has been the static chamber technique. Some of these measurements have been combined with geostatistical methods yielding integrated flux rates for whole landfills (3-6). Czepiel et al. (5) also made comparisons of the static chamber method and a tracer gas technique, where the concentration of a known gas released from the landfill was used to estimate the methane flux. Their conclusion was that the two methods * Corresponding author e-mail:
[email protected]; telephone: +46 13 28 22 92; fax: +46 13 13 36 30. 4044
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FIGURE 1. Map of Sweden showing the location of the Falko1 ping Landfill. gave similar total flux estimates. Another comparison made by their group confirmed this finding (6). The current CH4 emission estimates from landfills are built upon data in which there are large uncertainties. More field measurements are needed to provide us with enough data to allow for a more proper estimation of the actual methane emissions occurring from landfills worldwide. In this respect, it is also important to study the consistency of fluxes over seasons. In this study, we wanted to see if the spatial pattern provided by the geostatistical method was consistent over a season. In addition, we wanted to collect data in order to evaluate the reliability of the total flux estimates obtained by the method. A comparison was also made between this method and a tracer gas technique that was conducted simultaneously (7).
Methodology Sampling Site. The landfill in this study, Falevi, is located outside the town Falko¨ping (58°15′ N, 15°30′ E) at 215 m above sea level (Figure 1). It serves approximately 34 000 inhabitants, collecting municipal solid waste, and has been active since around 1965. The total landfilled volume was 325 000 m3 when the main subsite was closed in May 1997, 10.1021/es991350s CCC: $19.00
2000 American Chemical Society Published on Web 08/17/2000
FIGURE 2. Map showing points (stations) for measuring methane fluxes on the Falko1 ping Landfill. See also explanatory notes in text. immediately prior to the start of our experiment. The landfill area is 25 ha, but methane is only produced in an area of approximately 3 ha. This subsite, formed as a 10-m-high hill with a large plateau (Figure 2) has been filled with building waste in the northern parts (above the transect A-B in Figure 2) and with household waste from the middle southwards. The southern part also contains a deposit for sewage sludge. The landfill received 18 000 m3 of household waste and 41 000 m3 of industrial waste during 1996 (K. Nilsson, personal communication). Landfill gas is collected through vertical wells and piped to a neighboring wastewater plant for conversion to heat, serving a local dairy. The annual delivery of energy from this landfill is 4000-4500 MWh. The covering cap was made up of approximately 40 cm of rough soil with concrete-like properties and sparse vegetation. After the stones (>2 mm, corresponding to 44 ( 4% of the total dry weight) were removed, the remaining fractions were 40 ( 10% coarse sand (0.2-2 mm), 36 ( 3% fine sand (20-200 µm), 12.5 ( 3.6% silt (2-20 µm), and 8.3 ( 2.8% clay ( 0.85 in linear regressions (corresponding to p < 0.10). Measurements were carried out three times during 1997: May 6 (81 points), July 2 (101 points), and October 21 (83 measuring points). The larger number of points in July was VOL. 34, NO. 18, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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used as an attempt to make the grid more narrow in the high-emitting areas found in May. On all three occasions, the whole sets of samples were taken within 5 h. Geostatistics: Kriging. Ordinary kriging was used to estimate the spatial distribution of methane emissions from the landfill. It relies on the theory of regionalization, where observations close in space are assumed more likely to be similar than those further apart (9, 10). A number of papers have compared different spatial interpolation methods to see which performs the best and under what conditions. Kriging has often been proven to give the best estimates (see ref 11 for a review). It has been used to give the spatial distribution, estimates of the interpolation errors, descriptions of the spatial scales, and optimized sampling procedures (e.g., refs 12-14). It has also been used in estimating the spatial distribution of methane from landfills (see, e.g., refs 3-5). The basic model is
Z(s0) ) µ + δ(s)
(2)
where Z is the variable of interest (here methane emissions, mg of CH4 m-2 h-1) to be interpolated at locations given in vector s0. The true population mean, µ, is estimated by the sample mean and is assumed to be identical all over the area, while the small-scale fluctuation, δ(s), depends on the spatial location. The predictor (Z(s0), in mg of CH4 m-2 h-1) is as a weighted average of observations of a variable within a neighborhood M: M
Z(s0) )
∑u Z(s) k
(3)
)1
(4)
k)1
with conditional weights M
∑u
k
k)1
to make it unbiased. These weights are decided by a variogram function, which describes the spatial correlation structure within the data. It will ensure that observations close to the estimation location will receive higher weights than those further away (10). The variogram is calculated according to
γ(h) )
{
1
N
∑[Z(s 2N(h)
p
+ h) - Z(sp)]2
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sampling date
quantiles maximum, 100.0% quartile, 75.0% median, 50.0% quartile, 25.0% minimum, 0.0% moments mean SD CV (%) std error mean positive rates (n) zero rates (n) negative rates (n) N
May 6
July 2
October 21
40698 6.0 0 0 -6.9
8213 49 0 0 -13.9
15858 193 2.0 0 -15.2
1775.5 6860.5 386 762.3 23 55 3 81
348.8 1197.8 343 119.2 43 51 7 101
589.7 2032.8 345 223.1 42 37 4 83
a Emissions from individual chambers on the three sampling occasions at the Falko¨ ping Landfill in 1997. Units are mg of CH4 m-2 h-1.
transformed, and the kriging estimates were then backtransformed using
Y(s0) ) exp(Z(s0) + 0.5var[Z(s0)])
(6)
where Y is the back-transformed methane flux estimate and Z is the kriging estimate. In the second approach, the extremes were excluded before the kriging (but added when estimating the total flux). The total emission was calculated by integrating over the spatial distribution (see, for example, ref 13). As a comparison to the kriging, a comparison was made using only the original values (lineary interpolated and then intergrated). This was made for the respective sampling occasions (i.e., May, July, and October). Climate Factors. Temperature was measured in air and in soil (at 25 cm depth) with precalibrated thermistors, described by Bo¨rjesson and Svensson (8). Soil moisture (030 cm depth) was measured gravimetrically in August 1997 and in February 1998. Data on gas recovery were kindly provided from the control station at the neighboring wastewater plant. Data on barometric pressure were taken from two nearby stations of the Swedish Meteorological and Hydrological Institute, Jo¨nko¨ping (57′46° N, 14′05° E) and Såtena¨s (58°26′ N, 12°42′ E).
(5)
p)1
where h (called the lag) is the geographical distance between two observations and N(h) is the total amount of pairs at lag h. The variogram function is the function best representing γ(h) versus h, described by three shape parameters; the nugget, the sill, and the range. The nugget is the value of γ(h) where the lag is close to zero. The range is the lag distance where the correlation of the methane emission between stations at a certain distance ceases to exist, and the sill is the corresponding value of γ(h). The parameters were determined using cross-validation (see, for example, ref 10). Kriging does not rely on an assumption of normally distributed data. But since it is a linear predictor, it is sensitive to outliers, which will have a large influence on the estimates. In this study, the data was highly positively distributed, with a few values having extremely high concentrations. Therefore, two different approaches were used in order to reduce the effect of the extremes. In the first, the data were logarithm4046
TABLE 1. Distributions of Methane Emission Ratesa
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Results Methane Flux Rates from Individual Chambers. The measured flux rates ranged from -6.9 × 10-3 to 40 g of CH4 m-2 h-1 on May 6, from -13.6 × 10-3 to 8.0 g of CH4 m-2 h-1 on July 2, and from -15.2 × 10-3 to 15.9 g of CH4 m-2 h-1 on October 21, 1997 (Table 1). The minimum detectable fluxes were -2.2 and 1.0 mg of CH4 m-2 h-1, for negative and positive fluxes, respectively. The distributions shown in Table 1 are to some extent biased by the choice of more points in the less well-covered slope during the two later sampling occasions. Nevertheless, it should be noted that positive fluxes were recorded from over 50% of the measuring points (42 out of 83 chambers) on October 21, while this percentage was only 28% (23 out of 81) on May 6 and 43% (43 out of 101) on July 2. Negative fluxes (consumption of atmospheric methane) were measured at 3, 7, and 4 points in May, July, and October, respectively. These negative fluxes seemed to be quite randomly distributed over the area.
In May there were three stations that described 72% of the entire amount of emissions measured. In July and October, three stations were responsible for 56% and 46%, respectively, although it was three different stations each time. The CV (coefficient of variation ) the ratio between standard deviation and the mean value) was between 343 and 386% on these three occasions (Table 1). The number of measurements required to estimate the average flux within 10% of its actual value can be calculated with the formula used by Lessard et al. (15):
N ) (tR × CV/0.1)2
(7)
where the required number N is given by the student’s statistic tR and CV. According to the results from this experiment, the required number of chambers would be between 4610 and 5900, i.e., a 2.5-m grid. Despite this degree of variation, the mean flux rate measured in May was significantly higher than that measured in July (t-test, p ) 0.0415; median test, p ) 0.0486) in a comparison of the mean flux rates obtained (data in Table 1). Spatial Distribution of Methane Flux Rates. In the first approach, the data was log-transformed before being kriged. Figure 3A-C shows the variogram functions and parameters for the respective sampling occasions. As seen in the figure, the correlation scale (range) is more or less equal for the three time points. For the first two (May and July), the variogram function fitted the points very well. At the shortest lag in July the divergent value is due to too few pairs and should therefore not be accounted for. For the last occasion (October), the function was less well-fitted, and it may therefore be seen as slightly less certain, which is reflected in the distribution. The spatial distributions of these backtransformed kriged estimates of methane fluxes are shown in Figure 4A-C. In general, areas with high emissions could be discerned in the middle of the landfill, while low flux rates were seen in the northwestern parts (Figure 4A-C). Three subareas with large fluxes were observed on May 6, the highest emitting in the slope of the west part, followed by an area close to the sludge deposit, and an area with thin cover just north of the middle. The slope also showed high emissions on the other two dates, while the other high fluxes seemed to have moved slightly and the borders between the subareas seemed less obvious. Instead, there were tendencies for bands of high fluxes stretching from the left to the right. It should also be noted that in the south part of the landfill, which was filled with waste and covered just before the first measurement in May, methane emissions increased between the sampling dates from 0 in May to 45.2 mg in July and up to 352 mg of CH4 m-2 h-1 in October. Care should be taken when interpreting the values on the border, since the maxima near the borders are partly an interpolation effect. Linear interpolation (calculating the distribution up to the border) will picture higher emissions toward the border when there is an increase in the observed rates when moving toward the border. The option to set the border as zero is doubtful, and since it will influence the distribution inside the area, it would probably also underestimate the emissions. Estimates of the Total Methane Flux Rates. The first approach, with the back-transformed kriged estimates, probably gives an underestimate as it smoothes (cuts the highest peak values). In the second approach (called “without spikes” in Table 2), the extreme values (three observations for each respective sampling occasion, see above) were excluded before performing the kriging analysis. The area was then integrated before adding these spikes again. In addition to the two kriging approaches, linear interpolation of the original values was used to get a distribution over the field, and this was then integrated to obtain the total amount
FIGURE 3. Variogram functions and parameters on sampling occasions (A) May 6, (B) July 2, and (C) October 21, 1997. The unit for γ(h) is mg of CH4 m-2 h-1. estimate (Table 2, “original”). This usually gives overestimates as locations close to the extreme values will be given too high values. The order of size is, for all three sampling events, as follows: original (largest), kriging, and without spikes. Simultaneously with our studies in late October 1997, Galle et al. (7) measured the total methane emission from this landfill using a tracer gas technique. They used an instrument based on Fourier transform infrared absorption spectroscopy placed downwind of the landfill, at 500 or 600 m distance, depending on wind direction. On top of the landfill, at two positions, a tracer gas (N2O) was released with a known flow rate (5.9 kg h-1). Concentrations of N2O and methane were VOL. 34, NO. 18, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Spatial distribution of back-transformed kriged estimates of methane flux rates from the Falko1 ping Landfill on sampling occasions (A) May 6, (B) July 2, and (C) October 21, 1997. then measured simultaneously by the system with high time resolution (13 measurements were averaged for every minute). Their flux estimate, each measured for approximately 20 h on October 21-22 and 24-25, 1997, averaged 41 kg of CH4 h-1 during both days, with minor variations (e10% for the ratio between CH4 and N2O). This value is more than 4 times higher than the original value of 9.7 kg of CH4 h-1 in our study (Table 2). Climate Factors. Temperatures were normal for the season (Table 2). Most variation occurred on July 2, which was a sunny day, while the other 2 days were cloudy. A cautious calculation based on these three points showed a 4048
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negative correlation between temperature and methane emissions, strongest between soil temperature and the kriged values in Table 2 (r 2 ) 0.9997, p ) 0.0115). Soil moisture was 2.0% of wet weight at 0-5-cm, 7.7% at 5-15-cm, and 13.0% at 15-30-cm depth interval in soil sampled August 20, 1997 (air temperature 26.8 °C). In soil sampled February 24, 1998 (air temperature 5.9 °C), the moisture contents were 14.6% of wet weight at 0-5-cm, 13.1% at 5-10-cm, and 14.8% at 10-15-cm depth interval. No exact measurements were done on the sampling dates, generally the conditions were dry in May and July and wet in October.
TABLE 2. Total Methane Emission Rates from Falko1 ping Landfill and Climatic Observations sampling date
emissionsa
May 6
July 2
October 21
26 2.3 1.7 11.2 984.4 f 984.9
4.3 1.6 1.0 6.5 1009.9 f 1011.6
9.7 3.0 2.0 12.8 1018.8 f 1015.6
10.9 f 12.8 9.3 f 10.5
13.8 f 22.2 17.6 f 18.7
3.8 f 6.6 1.9 f 2.4
h-1)
(kg of CH4 original (linear interpolation) kriging kriging without spikes gas recoveryb (kg of CH4 h-1) barometric pressurec (mbar) temperature (°C) air soil (20 cm depth)
a As estimated with different methods. Without spikes ) three extremes excluded for each occasion. b Calculated from energy delivery. c Interpolated from two nearby locations, measured 10 at a.m. and 4 p.m.
Barometric pressure showed almost identical patterns for the two stations chosen (Jo¨nko¨ping and Såtena¨s) between 10 a.m. and 4 p.m. on each day, i.e., when the measurements were done. After the values were averaged, it could be concluded that on May 6 and on July 2 the pressure increased slightly, while on October 21 there was a small decrease from 1018.8 to 1015.6 mbar (Table 2). The gas recovery was not optimal during the experimental period. The system produced 138 MWh in May, 80 MWh in July, and 5.1 MWh on October 21, 1997, which would correspond to 11.2, 6.5, and 12.8 kg of CH4 h-1 on the actual days (Table 2). The conversion from energy to gas was made assuming that 1 kWh corresponds to 60.4 g of CH4 in landfill gas (according to Gendebien et al. (16)). The normal capacity is probably around 12 MWh day-1, and during the winter of 1997-1998 some gas was flared. The low efficiency of the gas recovery system was probably also the reason that we did not see any clear effects of the distance between sampling points and gas wells.
Discussion The relatively short sampling time of 2-3 min used in this landfill experiment not only gave more significant positive methane flux rates than could have been expected but also enabled us to see significant negative fluxes. The main regulating factor of the methane flux from the landfill in this study is most likely the gas recovery, even though methane oxidation, governed by soil temperature (5, 8, 17), could also have mitigated the methane efflux (Tables 1 and 2). The number of negative flux values is also indicative of an active methane oxidation process, with the highest number of seven points occurring during the warmest date of sampling. The usual skewness in the distribution of methane emissions from landfills, as demonstrated by several authors (3-5, 18, 19), was very clearly expressed also in this experiment. This makes integration of the flux rates a rather difficult task, since a few points are responsible for almost the entire landfill flux. Coefficients of variation between 343 and 386% seem to represent a normal spatial variability for landfills. Boeckx et al. (20) reported CVs that ranged from 60.3 up to 315.6% for six chambers on a covered landfill site in Belgium, while Czepiel et al. (5) found a CV of 326% for 139 fluxes from a landfill in New Hampshire. The differences in the spatial patterns, with high-emitting areas not being consistent between dates, is likely to be explained by hydrological conditions. Water is added, above all from precipitation but also from water vapor in gas and from methane oxidation. The only difference with a more obvious reason is the gradually increasing flux rates in the southern part (just above D in Figure 2), which certainly was due to a rise of methane production in the newly buried refuse in this part.
With the use of static chambers, the estimated total amount of methane released from the landfill was at least four times smaller than was estimated with the tracer gas technique (7). Our result contradicts the studies by Czepiel et al. (5, 6), where it was concluded that the two methods gave comparable results. The success of their work with chambers is probably explained by the use of a portable GC, which enabled them to make immediate analyses and rapid adjustments of the grid. Since the landfill in their study was similar both in size (2 ha active area) and methane emissions (approximately 1.06 g of CH4 m-2 h-1 as compared to approximately 1.26 g of CH4 m-2 h-1 in our study) and the chamber size was almost the same, the only remaining explanation for the difference between our results is that we did not use a fine enough grid to target the most active points. Our 5-m grid, in the areas where we expected high rates, was also not enough. The potential for lateral migration and subsequent release of methane outside the landfill area, which has been reported to be more common than generally believed (21), is less likely to explain the difference between the methods. The tracer gas experiment (7) showed that the landfill acted as a point source of methane at 500-600 m distance and that the main flux must have occurred from the top of the landfill (close to the tracer release) or from its close vicinity. Most likely a strong source not covered by the grid, for example, a fissure, was present within the landfill area. In conclusion, to get reliable total flux estimates from this type of landfill, the static chamber method combined with ordinary kriging demands the use of many more measuring points than we used in this experiment in order to cover hot-spots and ensure a reliable estimate. This is laborious and expensive and probably not worth the effort as compared to the use of tracer techniques, especially not if the number of chambers required to estimate the average flux within 10% of its actual value is several thousands, thus requiring several days of manpower. The strength of the geostatistical method employed for methane flux measurements on landfills lies solely in qualitative studies of the spatial distribution, for example, in differentiating high-efflux from low-efflux areas.
Acknowledgments This research was funded in part by the Natural Swedish Research Council (Contract AFR 202/96) and by the Swedish National Energy Administration (Contract P10856-1). We are grateful to the Community Council in Falko¨ping, Stig Sa¨ll, Klas Nilsson, and Kenneth Karlsson in particular, for letting us use the landfill and for much useful information. Mattias von Bro¨mssen and Elisabet Bo¨rjesson assisted during the field campaigns. Mary McAfee did the linguistic revision. VOL. 34, NO. 18, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Literature Cited (1) Intergovernmental Panel on Climate Change. Climate Change 1995. The Science of Climate Change; Cambridge University Press: Cambridge, U.K., 1996. (2) Swedish Association of Waste Management (RVF). Avfallsanla¨ggningar med Deponering. Statistik 1997; RVF Report 98/9; RVF: Malmo¨, Sweden, 1998; (in Swedish). (3) Nozhevnikova, A. N.; Lifshitz, A. B.; Lebedev, V. S.; Zavarzin, G. A. Chemosphere 1993, 26, 401-417. (4) Pokryszka, Z.; Tauziede, C.; Cassini, P. In Proceedings of the Fifth International Landfill Symposium; CISA: Cagliari, Italy, 1995; pp 495-506. (5) Czepiel, P. M.; Mosher, B.; Harriss, R. C.; Shorter, J. H.; McManus, J. B.; Kolb, C. E.; Allwine, E.; Lamb, B. K. J. Geophys. Res. Atmos. 1996, 101 (D11), 16711-16719. (6) Mosher, B. W.; Czepiel, P. M.; Harriss, R. C.; Shorter, J. H.; Kolb, C. E.; McManus, J. B.; Allwine, E.; Lamb, B. K. Environ. Sci. Technol. 1999, 33, 2088-2094. (7) Galle, B.; Samuelsson, J.; Bo¨rjesson, G.; Svensson, B. H. Manuscript submitted for publication in Environ. Sci. Technol. (8) Bo¨rjesson, G.; Svensson B. H. Waste Manage. Res. 1997, 15, 33-54. (9) Matheron, G. Econ. Geol. 1963, 58, 1246-1266. (10) Cressie, N. A. C. Statistics for Spatial Data; John Wiley & Sons: New York, 1993. (11) Danielsson, Å. Ph.D. Dissertation, Linko¨ping University, Sweden, 1998. (12) Winkels, H. J.; Stein, A. J. Environ. Qual. 1997, 26, 933-946.
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Received for review December 6, 1999. Revised manuscript received June 13, 2000. Accepted June 14, 2000. ES991350S
