Reconsidering landfill monitoring with the aid of multivariate data analysis

Transcription

1 Reconsidering landfill monitoring with the aid of multivariate data analysis M. van Praagh & K. M. Persson Division of Water Resources Engineering, Lund University, Sweden Abstract Leachate constitutes the main potential long-term environmental risk of landfill emissions. The EU landfill directive 1999/31/EC requires minimum monitoring during the active landfill life and the after-care phase. Due to time and resource limitations, monitoring of leachate quality is a compromise between information density and applicability. Samples from passively collected leachate are suspect to mixing with groundwater and surface water. This is especially true for the widely used parameter conductivity. The objective of this work is to identify the extent of major external influences on conductivity monitoring data and its implications for future monitoring programs. A complete eight year conductivity monitoring data set from a municipal solid waste landfill in Southern Sweden is analysed with the help of multivariate data analysis. With the aid of a principle component analysis (PCA) the influences of surface water, groundwater and recycled leachate on the complete measurements variability is investigated. The impact of final capping on the variability is clearly reflected. A primary model is developed for the dependence of conductivity variability on water levels variability with the help of a canonical correlation analysis (CCA). On the one hand, the results allow identification of surplus sampling points. On the other hand, crucial sampling points are highlighted, which are intensively subject to processes other than mixing with surface water. Consequently, these results make way for tailor-made monitoring for comparable landfill situations. Keywords: landfill monitoring, leachate analysis, multivariate data analysis, electrical conductivity, final capping, leachate recirculation. 1 Introduction Landfilling of waste is the major waste management option in the EU. The main potential long-term environmental risk is posed by leachate emissions.

2 450 Waste Management and the Environment II Consequently, the EU landfill directive 1999/31/EC requires a minimum landfill monitoring program. Regularly, the leachate and surface water compositions have to be analysed quarterly during the active phase of landfills and every six months during the after-care phase. Conductivity in the leachate has to be measured at least once a year (EU Landfill directive [1]). In Sweden, for example, basic leachate characterisation is recommended to comprise 22 parameters only (Öman et al. [2]), including measurements of ph, conductivity, group-parameters such as COD and TOC, as well as inorganic parameters (ammonium-n, nitrate-n and phosphate) and salts (total salinity, chloride, fluoride and sulphate). The variety of emission paths for different gaseous and liquid substances to leave a landfill constitutes a major challenge for operators and competent authorities. Often surface landfills cover a significant land area and are subject to influences by rainfall, snowfall, by wind and groundwater infiltration. Bengtsson et al. [3] found similarities in evaporation performance of a municipal solid waste landfill with a not thin soil cover compared to a nearby river basin. The interaction between surface waters, groundwater and leachate plumes can cause difficulties in interpreting analysed parameters, though. Samples from passively collected leachate might especially be subject to mixing with groundwater and/or surface water. Due to time and resource limitations, monitoring of leachate becomes a compromise between information density and applicability. Operators and competent authorities have to decide on sampling points, sampling occasions and sampling parameters. Theoretically, interdependences exist for different sampling locations and between sampling parameters. Sampling locations might be non-representative and at the same time produce a variety of parameters with the same information content. Recently, multivariate data analysis, a tool widely used in scientific topics with complex data input, such as psychology and meteorology, has been applied to waste water and landfill leachate analysis data in order to detect interdependences between parameters (Ecke et al. [4], Kylefors [5], Pantsar-Kallio et al. [6]). The objective of this work is to identify the extent of major external influences on conductivity monitoring data and its implications for future monitoring programs. For this purpose, a complete eight year monitoring data set from a municipal solid waste landfill in Southern Sweden is analysed with multivariate data analysis. 2 Methods and material 2.1 The Rönneholm landfill The conductivity data is taken from regular monitoring analyses of leachate at seven locations in and around the Rönneholm landfill. The landfill is situated near the settlement Stehag in Scania, Southern Sweden. It is operated by the community owned company MERAB, under which supervision the monitoring samples are taken with a handheld conductivity meter. Conductivity is measured monthly at specific locations. Mainly municipal solid waste has been disposed of

3 Waste Management and the Environment II 451 at the Rönneholm landfill since the late 60s. A part of the landfill has been provided with a final cap (composted sludge from waste water treatment and soil cover) in the summer of The passively collected leachate and surface runoff is collected in inner ditches and let to a pond. During the growing season the leachate is irrigated onto energy forest, which partly covers the landfill itself. Thus, apart from plant uptake and evaporation, the leachate is partly recirculated, fig 1. Figure 1: Rönneholm Landfill; sampling points 37 to 44. Irrigation vaults are indicated (dark numerous rectangles). 2.2 Multivariate Data analysis Multivariate Data analysis (MVDA) allows the detection of linear relationships between parameters in a data matrix. It reduces noise and provides a new matrix

4 452 Waste Management and the Environment II with preferably less parameters. The original data matrix is projected onto a new coordinative system (eigenvectors), in which the parameters can be chosen to be linearly independent of each other but still contain a major portion of the variability of the original data set. Prerequisites for MVDA are a normal distribution of the input data set, limited missing values and preferably a time series with constant intervals. In the first part of this work Principle Component Analysis (PCA), also called Empirical Orthogonal Function (EOF) is applied to monthly conductivity measurements from different sampling locations, fig. 1. In the second part a Canonical Correlation Analysis (CCA) is used to investigate the cross co-variance between conductivity (matrix X, the predictand) and water level measurements (matrix Y, the predictor) at the same sampling locations. CCA can be regarded as an extension of PCA, where after having reduced the noise in the original data sets with a principle component analysis, the interdependence of the matrices is revealed. The analyses cover measurements from January 1996 to December 2002, respectively. The measured conductivity values varied between 301 and 5700 ms/m. Missing data mounted to 0.3% and was replaced by linear interpolated values. Calculations were computed with commercially available numeric software (MATLAB). Functions for executions as well as graphical displays are standard. The background of the Principle Component Analysis is derived from Jackson [7]. CCA theory is outlined in Barnett and Preissendorfer [8]. The input data was standardised by normalising and extracting the mean. Eigenvector plots where obtained by kriging through interpolation with commercially available software (SURFER). Figure 2: 1 st, 2 nd and 3 rd mode (principal component) of the PCA analysis. 3 Analytical results 3.1 Principal Component Analysis (PCA) The first 7 principal components, also called modes, correspond to the first seven columns of the new data matrix and contain 99.99% of the variance. 77% of the original data variance was explained by the first 3 modes, with the first principal

5 Waste Management and the Environment II 453 component explaining 45% of the variance. The 1 st mode shows a rather low variability over all seven sampling stations (numbers 1 to 7 corresponding to sampling locations 37, 38, 39, 41, 42, 43, 44, respectively), whereas the 2 nd and 3 rd mode change sign from station to station, fig. 2. Figure 3: Projected time series of 3 rd mode (principal component). Figure 4: Score plot of the 1 st principle component (PC1) versus the 2 nd (PC2).

6 454 Waste Management and the Environment II The projection of the input time series by the first 2 modes gave inconsistent results in both cases. The time series of the 3 rd mode (explaining 14.7% of the total variance) projected onto the new axes resulted in a reoccurring pattern month 0 to 52, fig. 3, and a distinct anomaly after the 52 nd month, the latter coinciding with the completion of the final cap on the landfill. The interdependence or independence of variables for the 1 st and 2 nd mode is shown by a score plot, fig. 4. Score points far from the centre have comparably great influence on the data variability. Note the overlapping sampling points 3 and 7 (corresponding to 39 and 44 in fig. 1), indicating reproduction of the same information. The 2-dimensional plot of mode 1 shows a pattern similar to the topography of the landfill, fig. 5a and b, indicating an increasing influence of run-off with low conductivity variability on the measured electric conductivity along the foot of the landfill mountain. The sampling locations are indicated by diamonds. Explained variance 45% Ronneholm isolines height variables (a) (b) Figure 5: (a) PCA mode 1. (b) Topography. The plot of mode 2 shows two distinct lines where the algebraic sign of variability changes, indicating a different behaviour of the northern and western part from the eastern and north-western part of the landfill, fig 6a. A comparable pattern is indicated by the groundwater-flow at the site, which is compiled by Börjesson [9], fig. 6b. The plot of mode 3 shows a distinct line around the landfill, fig. 7a, following the shape of the area which is supplied with recirculated leachate during spring to autumn, fig. 7b. The very distinct sprinkler application with leachate from the collection pond is regarded influencing the conductivity variability in the irrigation area. The non-irrigated areas should, as indicated, reveal a different behaviour.

7 Waste Management and the Environment II 455 Explained variance 17.5% (a) (b) Figure 6: (a) PCA mode 2. (b) Groundwater isoclines. Explained variance 14.7% (a) Figure 7: (b) (a) PCA mode 3. (b) Irrigation area. In order to verify the two-dimensional patterns of the principle component plots, the squared correlation between the new time series after the PCA and the original data set was carried out for every sampling point and principle

8 456 Waste Management and the Environment II component, respectively. Their contour plots showed the same patterns as above, and thus serve as a confirmation of the calculated principle components. In the above calculation of the principle components the eigenvectors are chosen to be orthogonal to one another. A loosening of this constraint in an additional PCA run, so called rotating, did not add to the interpretability of the variability. The resulting modes showed a similar spatial pattern and a similar distribution of explained variance among them. g map 1 expl. variance 48% 42 (a) h map 1 expl. variance 45% (b) Figure 8: (a) Time series of the 1 st canonical vector. (b) 1 st CCA vector predictand. (c) 1 st CCA vector predictor. (c)

9 Waste Management and the Environment II Canonical Correlation Analysis (CCA) The CCA run was computed with 4 modes each of the initial PCA for the predictor and for the predictand, covering 89.6 and 86.4% of the explained variance, respectively. The results are graphically displayed by a combined plot of the projected time series of the predictor and the predictand, fig. 8a, and the so called h- and g-maps, which display the correlations between the new time series (canonical vectors) and the reconstructed series after the PCA, fig. 8b and fig. 8c. The sampling spots are shown as diamonds, dark shaded areas are significant with p<0.05. The resulting 1 st canonical vector shows a good correlation between predictor and predictand, fig. 8a. The time show a positive correlation, but the correlations of the time series at specific locations (g and h maps, fig. 8b and c) have opposite signs over the whole areas, resulting in a negative correlation between predictor and predictand. The physical explanation for this can be found in the influence of raising/decreasing leachate levels due to surface water and groundwater with high volumes and relatively low conductivity variability. This leads to relative decreases/increases in electrical conductivity measurements. 4 Conclusion In this work Principle Component Analysis (PCA) demonstrated to be a useful tool to analyse co-variance in a large conductivity data set, with the focus laid on variance between locations rather than different parameters. It has served to initially distinguish main factors influencing conductivity in landfill leachate measurements. Through the PCA analysis a possible surplus conductivity sampling location (either location 39 or 44) is clearly identified, although these locations have originally been planned to reflect different parts of the landfill. Landfill topography seems to have the highest impact on conductivity variability, followed by locally varying influence of groundwater. The impact of final capping on conductivity is reflected in the third component, explaining 14.7% of the variability. It can be interpreted that the cover shows low total impact on conductivity variability. The CCA computation allowed the modelling of conductivity through level measurements with good correlations. Thus, it reveals strong influence of water table changes inside the landfill on analytical results. This work enables future research on verification of surplus sampling and the identification of crucial locations, which are less suspect to surface and groundwater influence and reflect the change in dissolution processes inside the landfill rather than mixing phenomena with external flows. Thus, it can be used to develop tailor-made monitoring programs. This requires a distinction, though, whether the detection of biochemical processes inside a landfill are to be targeted by a sampling program or the environmental impact in respect to the surrounding environment.

10 458 Waste Management and the Environment II Acknowledgements Financial support by SYSAV Utveckling AB, the Swedish Association of Waste Management (rvf) and MERAB Waste Management is gratefully acknowledged, as are the helpful discussions with Cintia Uvo at the Division of Water Resources Engineering, Lund University. References [1] Council Directive 1999/31/EC, Council directive on the landfilling of waste. Official Journal of the European Communities, L 182/1, Appendix III, [2] Öman, C., Malmberg, M. & Wolf-Watz, C., Guide on characterisation of leachate (Handbok för lakvattenbedömning, in Swedish), IVL report, B 1354, IVL Swedish Environmental Research Institute Ltd., Stockholm, [3] Bengtsson, L., Bendz, D., Hogland, W., Rosqvist, H. & Åkesson, M., Water Balance for landfills of different age, Journal of Hydrology, 158, pp , [4] Ecke, H., Bergmann, A. & Lagerkvist A., Multivariate Data Analysis (MVDA) in Landfill Research, The Journal of Solid Waste Technology and Management, 25:1, 33-9, [5] Kylefors, K., Evaluation of leachate composition by multivariate data analysis (MVDA), Journal of Environmental Management, 68:4, pp , [6] Pantsar-Kallio, M., Majunen, S.-P., Hatzimihalis, G., Koutoufides, P., Minkkinen, P., Wilkie, P. J., Connor, M. A., Multivariate data analysis of key pollutants in sewage samples: a case study, Analytica Chimica Acta, 393, pp , [7] Jackson, J. E., A user s guide to principle components, Wiley & Sons, Inc., [8] Barnett, T. P., Preissendorfer, R., Origins and Levels of Monthly and Seasonal Forecast Skill for United States Surface Air Temperatures Determined by Canonical Correlation Analysis, Monthly Weather Review, 115, pp , American Meteorological Society, [9] Börjesson M., Monitoring Program for landfills an evaluation (in Swedish), Masters Thesis, Geology Department, Lund University, Sweden, 1996.