A characterization scheme based on landfill leachate chemical signatures could support studies of leachate evolution over time, liner performance, and help confirm or disprove potential leachate contamination of groundwater. Wide variations in single constituents across time, sites, and site practices, and inconsistencies related to common bivariate measures suggest a robust, multivariate analysis could be useful. A variant Stiff diagram approach (a subjective analytical comparison of soluble salts) has been developed, and supports graphical depictions of multiple samples. The hypothesis is that leachates with similar chemistry form clusters, and this was tested using a data set of 652 samples from 26 distinct liner systems collected from a Long Island (New York, USA) landfill over more than 20 years. Most (75%) of diagrams were classified into three general leachate groupings that associated with the kinds of wastes received in the particular landfill module (90% if  early  leachate results are not considered)

Tonjes, David J

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Stony Brook University - SUNY

Stony Brook UniversityAcademic CommonsTechnology & Society Faculty Publications Technology and Society2013A Classification Methodology for LandfillLeachatesDavid J. TonjesDepartment of Technology and Society, david.tonjes@stonybrook.eduFollow this and additional works at: https://commons.library.stonybrook.edu/techsoc-articlesPart of the Environmental Engineering Commons, Environmental Indicators and ImpactAssessment Commons, Environmental Monitoring Commons, and the Water ResourceManagement CommonsThis Article is brought to you for free and open access by the Technology and Society at Academic Commons. It has been accepted for inclusion inTechnology & Society Faculty Publications by an authorized administrator of Academic Commons. For more information, please contactmona.ramonetti@stonybrook.edu.Recommended CitationTonjes, David J., "A Classification Methodology for Landfill Leachates" (2013). Technology & Society Faculty Publications. 20.https://commons.library.stonybrook.edu/techsoc-articles/201 A Classification Methodology for Landfill Leachates 1 David J. Tonjes, Aff.M.ASCE* 2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18 *David J. Tonjes 19 Assistant Professor 20 Department of Technology and Society 21 Stony Brook University 22 Stony Brook, NY 11794-3760 23 david.tonjes@stonybrook.edu 24 25 2 Abstract 26 A characterization scheme based on landfill leachate chemical signatures could support 27 studies of leachate evolution over time, liner performance, and help confirm or disprove potential 28 leachate contamination of groundwater. Wide variations in single constituents across time, sites, 29 and site practices, and inconsistencies related to common bivariate measures suggest a robust, 30 multivariate analysis could be useful. A variant Stiff diagram approach (a subjective analytical 31 comparison of soluble salts) has been developed, and supports graphical depictions of multiple 32 samples. The hypothesis is that leachates with similar chemistry form clusters, and this was 33 tested using a data set of 652 samples from 26 distinct liner systems collected from a Long Island 34 (New York, USA) landfill over more than 20 years. Most (75%) of diagrams were classified into 35 three general leachate groupings that associated with the kinds of wastes received in the 36 particular landfill module (90% if "early" leachate results are not considered).  37  38  39 Subject headings: sanitary landfills, monitoring, liners, chemical compounds, waste 40 management 41 42 3 Introduction 43 Reliable, meaningful landfill leachate contaminant signatures could support analyses of 44 landfill processes, and help determine liner performance and identify groundwater contamination 45 sources, depending on the signature relationship to landfill sections, waste types, or leachate age. 46 Univariate characterizations (e.g., ammonia, COD, or chloride concentrations), while often 47 useful, necessarily oversimplify leachate variability (Majone et al. 1998; Marttinen et al. 2002), 48 and are subject to large concentration ranges (Kjeldsen and Christopherson 2001). Bivariate 49 comparisons, especially BOD:COD ratios, have been used to classify leachate types (beginning 50 with Chian and DeWalle 1976); some find these measures inconsistent (Lo 1996; Armstrong and 51 Rowe 1999; Statom et al. 2004), and limited to descriptions of waste stability (Kjeldsen et al. 52 2002).  53 Stiff diagrams (Stiff 1951) subjectively describe ionic solutions, and have been used in 54 investigations of landfill-related groundwater contamination (beginning with Kimmel and Braids 55 1980). I have developed a multivariate analysis that modifies traditional Stiff diagrams, and 56 applies principal component analysis (PCA) to the resulting diagram components. This creates a 57 unique analytical approach that supports grouping of leachate results by major ion chemistry, 58 which appears to be a function of waste type and age.  59 Setting 60 The Town of Brookhaven landfill on Long Island, New York has operated since 1976. 61 All of its cells have liner systems with leachate collection. Cell 5 (1995-2003) and Cell 6 62 (2003+) were constructed in phases with “double composite” liner systems. Wastes received 63 have varied, affected mostly by a ban on landfilling of MSW (enacted in 1983, effective 1990, 64 fully enforced 1995). Landfilling post-1995 has been restricted to construction and demolition 65 4 debris ("C&D"), “car fluff,” waste-to-energy incinerator ash, and some other materials classified 66 by New York State as either "products of resource recovery" or "inert" (Tonjes and Swanson 67 1994). Disposal rates have increased from ~250,000 tonnes yr-1 (1976) to 1.1 million tonnes yr-1 68 (including wastes used as cover). 69 Materials and Methods 70  Sampling and Analysis 71 Unfiltered leachate samples are collected either from manholes or taps on pump lines. 72 Samples were collected irregularly until 1993 when semi-annual sampling was established. 73 Samples are analyzed by contract laboratories holding the highest New York state certifications 74 (at that time). Beginning in 1993, analytes included "leachate indicators" (nutrients, salts, and 75 general water quality indicators), metals, volatile organic compounds (VOCs), semi-volatile 76 organic compounds, herbicides, pesticides, and dioxins and furans. Prior to 1993, samples 77 typically were analyzed for leachate indicators, and sometimes metals and VOCs. 78  Data Analysis 79 Stiff diagrams (Stiff, 1951) were drawn using Grapher (Golden Software, Golden, CO) to 80 determine similarities and differences among the sample results. Stiff diagrams depict major 81 cations and anions from aqueous samples, measured in meq. The cations (Na++K+, Ca2+, Mg2+, 82 NH4+) and anions (HCO3-, SO4-2, Cl-, NO3-) selected here follow mid-1980s USGS practices 83 (e.g., Wexler 1988). These Stiff diagrams were modified, however, using a normalization 84 process based on the greatest value for any diagram parameter, so that diagrams are bounded by 85 values of ~1 (using two significant figures, meaning maximum values range 0.95-1.05) (Tonjes 86 et al. 1995), with the intention that concentration differences do not obscure diagram shape 87 similarity. Diagram shapes were classified (Table 1) using exemplars of primary leachate types. 88 5 Figure 1 is an example of the signature shape associated with landfilled MSW, illustrating a 89 shape archetype found for 65 other leachate samples.  90 PCA using correlational matrices was applied to confirm diagram classifications 91 (Statistix 9, Analytical Software, Tallahasee, FL). The value of the PCA is its potential for 92 identification of similar diagram shapes ,insofar as each diagram vertex is in a somewhat similar 93 position relative to the same parameter vertex in other diagrams. PCA can collapse 94 dimensionality, and so mapping the transformed data points into a 2-dimensional PCA graph 95 approximates distance relations in a theoretical 8-dimensional space -- similar shapes should plot 96 close together. The percent of variance explained is the conservation of overall distance 97 relations.  98 Results 99 Samples tended to be classified consistently, so that nearly 75% (482 of 652) were 100 identified as a signature pattern or similar to one. Liner systems eventually tended to be 101 classified consistently: 9 of 26 systems had “mature” results classified into only one of the 102 signature patterns. Four of 16 remaining systems had only 1 or 2 mature samples that did not 103 share a single signature pattern. The liner systems where only inert materials (mostly C&D) were 104 landfilled at first and then ash was added (Cell 5 Phases 4, 5, 8 and 9 and Cell 6) tended to first 105 be classified with the C&D pattern, and then the ash pattern. Only one-third (54 of 170) of 106 “other” patterns occurred after an identification of a signature leachate pattern (a “mature” liner 107 system), and 16 had relatively low concentrations (<100 meq. for a sum of the 9 Stiff diagram 108 parameters), lower than associated with the signature patterns. Thus, once a signature pattern was 109 detected, 93% of following results were also a signature pattern (except where contamination 110 decreased below signature pattern levels). 111 6 A single graph of all 652 results in the PCA is difficult to interpret (Figure 2). Although 112 Figure 2 is not a random scatterplot, and results concentrate in certain areas and are sparse in 113 others, no clear clusters emerge. However, mapping the signature classifications alone (Figure 3 114 illustrates the MSW signature set) appears to justify the subjective results groups. The MSW-115 signature results in Figure 3 create a fairly tight cluster in the lower left-hand corner of the plot, 116 and a halo of other results, described as varying from signature plot in one characteristic 117 description, is associated with the cluster. When similar isolations are made of the other two 118 signature shapes, the C&D signature, not as precisely defined as the other two, is more diffuse, 119 and overlaps considerably with the ash classifications (as it should, as many characteristics are 120 the same in Table 1). “Other” diagrams almost all map away from the signature centroids. The 121 two-dimensional PCA accounts for nearly 60% of the variance. 122 Discussion 123 The most widely referenced classification process for leachates, BOD:COD ratios, 124 reflects the overall state of biological degradation  with a low BOD:COD ratio (<0.4) defining 125 “mature” or “aged” leachate in this accounting (Kjeldsen et al. 2002). Molecular weights of 126 organic compounds has also been used to define similar classes (Calace et al. 2001). Chian and 127 DeWalle (1976) defined bivariate ratio sets to create leachate groupings, using broad bands 128 instead of linear trends due to large variabilities and changes in ratios with landfill age, creating 129 classes of leachate sorted by age. Gibbons et al. (1999) created a discriminant function to 130 separate VOCs results for MSW, hazardous waste, and co-disposal facilities. Isotopes have been 131 used to differentiate leachate contamination from other impacts on groundwater (beginning with 132 Baedecker and Back 1979) but although Bogner et al. (1996) used deuterium and stable C 133 7 isotopes to understand methane formation in fills, there has not been any use of isotopes to 134 distinguish leachate types to date.  135 Kylefors (2003) analyzed all data from 5 rounds (one year) of leachate sampling using 136 PCA, which echoes the approach used here. She did not normalize her parameters. Three 137 samples grouped nicely, and two others did not. Others (such as Barker et al. 1988) have used 138 PCA as a means of discerning leachate impacts from other contaminant sources, with a variety of 139 successes and failures. 140 Here, I found patterns in leachate quality despite its chemical complexity and variability, 141 and the changes in leachate quality were associated with differing waste streams for different 142 landfill systems. Although Stiff diagram analyses have always depended upon subjective 143 comparisons, use of principal component analysis and normalized diagrams can make the 144 process more quantitative and objective, and provide better justifications for determinations of 145 diagram shape similarity.  146 The diagram shapes and their clustering in the PCA depiction has been previously used to 147 track groundwater contamination and to identify contamination sources (Tonjes et al. 1995). 148 Close analysis of changes in diagram shapes may be useful in determining relationships between 149 dissolution (and other chemical reactions) and decomposition, and changes that occur with aging 150 of the landfilled material. Differences in diagram shapes and scaling factors appear to distinguish 151 primary and secondary liner chemistries and concentrations, in many cases, which holds promise 152 for understanding liner performance. 153 Conclusions 154 More than 600 leachate samples collected over 20 years from 26 different liner systems 155 were analyzed using a Stiff diagram variant, and further compared using principal component 156 8 analysis. These data were parsed so that 75% of all results were classified into three general 157 categories; once a landfill system leachate was classified as belonging to one group, 90% of 158 subsequent samples also fell into the same groupings. Thus, this method sorted monitoring data 159 samples into like and unalike sets. The data are generated from types of chemical analyses that 160 can be accurately and consistently accomplished with less technical requirements than some 161 other approaches, and the data processing is not very difficult to accomplish. Although 162 BOD:COD ratios are especially useful in determining treatment options (Renou et al. 2008), the 163 signature pattern approach clearly has more utility to track contamination in groundwater. The 164 subjectivity of signature pattern identification is offset by validation associated with waste 165 inputs, and clustering in PCA diagrams. Use of PCA can be daunting, but growing use of 166 statistical packages makes this feasible to consider. 167 Acknowledgements 168 Some of this work has been supported by a grant from the Town of Brookhaven (NY). Although 169 the Town of Brookhaven supported the work described in this article, it does not necessarily 170 reflect the view of the Town and no official endorsement should be inferred. The Town makes 171 no warranties as to the usability or suitability of the materials and the Town shall be under no 172 liability for any use made therefrom. Matthew Henigman helped create the figures. 173 References 174 Armstrong, M. D., and Rowe, R. K. (1999) "Effect of landfill operations on the quality of 175 municipal solid waste leachate." Proc. Sardinia 99, 7th Int. Waste Manage. Landfill 176 Symp., CISA, Cagliari, It. Vol. II, 81-88. 177 Baedecker, M. J., and Back, W. (1979) “Hydrological processes and chemical reactions at a 178 landfill.” Ground Water, 17(5), 429-437. 179 9 Barker, J. F., Barbash, J. E., and LaBonte, M. (1988) "Groundwater contamination at a landfill 180 sited on fractured carbonate and shale." J. Contam. Hydrol., 3, 1-25.  181 Bogner, J. E., Sweeney, R. E., Coleman, D., Huitric, R., and Ririe, G. T. (1996) "Using isotopic 182 and molecular data to model landfill gas processes." Waste Manage. Res., 14, 367-376. 183 Calace, N., Liberatori, A., Pertonio, B. M., and Pietroletti, M. (2001) "Characteristics of different 184 molecular weight fractions of organic matter in landfill leachate and their role in soil 185 sorption of heavy metals." Environ. Pollut. 113, 331-339. 186 Chian, E. S. K., and DeWalle, F. P. (1976) “Sanitary landfill leachates and their treatment.” J. 187 Env. Eng. Div. – ASCE, 102(EE2), 411-431. 188 Gibbons, R. D., Dolan, D. G., May, H., O’Leary, K., and O’Hara, R. (1999) “Statistical 189 comparison of leachate from hazardous, codisposal, and municipal landfills.” Ground 190 Water Monit. R., 19(3), 57-72. 191 Kimmel, G. E., and Braids, O. C. (1980) Leachate plumes in ground water from the Babylon and 192 Islip landfills, Long Island, New York. United States Geological Survey Professional 193 Paper 1085, United States Geological Survey, Washington, DC. 194 Kjeldsen, P., Barlaz, M. A., Rooker, A. P. Baun, A., Ledin, A., and Christensen, T. H. (2002) 195 "Present and long-term composition of MSW leachate: a review." Crit. Rev. Env. Sci. 196 Tec., 32(4), 297-332. 197 Kjeldsen, P., and Christopherson, M. (2001) “Composition of leachate from old landfills in 198 Denmark.” Waste Manage. Res., 19, 249-256. 199 Kylefors, K. (2003) "Evaluation of leachate composition by multivariate data analysis 200 (MVDA)." J. Environ. Manage., 68, 367-376. 201 10 Lo, I. M.-C. (1996) "Characteristics and treatment of leachates from domestic landfills." 202 Environ. Int., 22(4), 433-442. 203 Majone, M., Petrangeli Papini, M., and Rolle, E. (1998) “Influence of metal speciation in landfill 204 leachates on kaolinite sorption.” Wat. Res., 32(3), 882-890. 205 Marttinen, S. K., Kettunen, R. H., Sormunen, K. M., Soimasuo, R. M., and Rintala, J. A. (2002) 206 “Screening of physical-chemical methods for removal of organic material, nitrogen and 207 toxicity from low strength landfill leachates.” Chemosphere, 46, 851-858. 208 Renou, S., Givaudan, J. G., Poulain, S., Dirassouyan, F., and Moulin, P. (2008) “Landfill 209 leachate treatment: review and opportunity.” J. Hazard Mater., 150, 468-493. 210 Statom, R. A., Thyne, G. D., and McCray, J. E. (2004) "Temporal changes in leachate chemistry 211 of a municipal solid waste landfill cell in Florida, USA." Environ. Geol., 45, 982-991. 212 Stiff, H. A. (1951) "The interpretation of chemical water analysis by means of patterns." J. 213 Petrol. Technol., 3(10), 15-17. 214 Tonjes, D. J., Heil, J. H., Black, J. A. (1995) "Sliding-scale Stiff diagrams: A sophisticated 215 groundwater analytical tool." Ground Water Monit. R., 15(2), 147-155. 216 Tonjes, D. J, and Swanson, R.L. (1994) "Long Island's solid waste perplexities." J. Urban 217 Technol., 1(3), 21-46. 218 Wexler, E. J. (1988) Ground-water flow and solute transport at a municipal landfill site on Long 219 Island, New York: Part 1: Hydrogeology and water quality. Water-Resources 220 Investigation Report 86-4070, United States Geological Survey, Syosset, NY.   221 222 11 Table 1. Leachate Classifications 223 Classification Diagram Characteristics MSW Signature Na++K+, HCO3- ~1 Cl-, NH4+ major constituents (but < HCO3-, Na++K+) Ca+2 ~ Mg+2, << Na++K+ NO3-, SO4-2 ~ 0 Ash Signature Cl- ~1 Ca+2 ~ Na++K+ , major constituents but <1 (usually <<1) Mg+2, NH4+, HCO3- NO3-, SO4-2 ~ 0 C&D Signature Cl- ~1 Na++K+>Ca+2 but <1 NH4+, HCO3- noticeably >0 Mg+2, NO3-, SO4-2 ~ 0 MSW-similar One characteristic rule not followed (completely) but diagram appears similar Ash-similar One characteristic rule not followed (completely) but diagram appears similar C&D similar One characteristic rule not followed (completely) but diagram appears similar Other Distinct from the 3 major signature shapes  224 225 12  List of Figures 226 Figure 1. MSW Stiff diagram example; the y-axis indicates the relative scaling factor for each 227 cation and anion. The maximum strength of any one ion was 82 meq.  228 Figure 2. Scatterplot of the first two axes of the principal component analysis of the 8 modified 229 Stiff diagram parameters from 652 leachate samples from the Brookhaven landfill 230 Figure 3. Clustering of the 66 MSW ( ) and 179 MSW-similar diagrams ( ) from the 231 scatterplot of the first two axes of the principal component analysis of the 8 modified Stiff 232 diagram parameters from 652 leachate samples from the Brookhaven landfill (see Figure 2) 233 

A Classification Methodology for Landfill Leachates

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A Classification Methodology for Landfill Leachates

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