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Interpretation:For both dumps, interpretation of the results was based on TP excavations as well as resistivity values of some mate- rials common in landﬁll and dump environments as presented in Table 2. For buried glass hotspots in particular, interpretation was also based on the ﬁndings from Madesjö open glass dump. 3-D resistivity visualisation (fence diagrams):Cross-sections of the lines on each site were combined using the software EriViz 1.0 (Lund University) to generate three-dimensional (3-D) resistiv- ity proﬁle images known as fence diagrams. Although fence dia- grams do not give a full picture of the whole dump, they are advantageous enough to just gather proﬁle images without using interpolation which may add artefacts (Leroux et al., 2007). 2.4.3.2. Statistical analysis.Statistical analysis of waste composition and PSD data was achieved using GraphPad Prism version 7.0c for Mac (GraphPad Software Inc.). Descriptive statistics (minimum, maximum, mean and standard deviation) was calculated while assuming a Gaussian distribution and at p < 0.05. Furthermore, some datasets were subjected to One-way ANOVA and Tukey’s multiple comparison tests. 3. Results and discussions 3.1. Electrical resistivity Tomography (ERT) 3.1.1. ERT on Madesjö open glass dump Resistivity sections of the two lines at Madesjö glass dump are shown in Fig. 3. The colour progression on the resistivity scales from dark blue to dark red corresponds to resistivity from low to high resistivity. Resistivity over 40,000 Xm was registered, which ﬁts with the expectation of high glass resistivity (Giancoli, 1998). The exceptionally high contact resistance at the dump was clearly caused by the highly resistive glass that is exposed on the surface, which can be very problematic for galvanic methods such as ERT. The resistivity contrast among the materials was in fact dif- ﬁcult for the inversion software to handle, which is probably the main reason for the relatively high mean residuals in Table 1 (10.5% and 11.1%). This calls for caution in data interpretation, since the inversion process is known for potential to generate unrealistic variations in model resistivity values, which can lead to over-shooting or under-shooting of the model resistivities on either side of the high contrast transition, uncharacteristic of actual geological features (Jolly et al., 2011). As presented in Table 2, pre- vious ERT studies on landﬁlls have attributed resistivity <70 Xm (dark to light blue in Fig. 3) to leachate or decomposed wastes, whereas resistivities >348 Xm (could be 10–2000 Xm depending on degree of saturation and weathering) have been attributed to demolition waste (Çinar et al., 2015; Boudreault et al., 2010). This interpretation, however, could not be adopted for the Madesjö case since veriﬁcation TPs were not excavated deep enough to reach beneath the glass pile due to unstable ground posing machine safety risks. Further attemps at literature-based interpretations were hindrered by the high likelihood for introduction of artefacts in the results due to very high discrepancy in resistivity. However, the data quality was good, which is judged to be a result of the survey design with separated electrode cables for cur- rent transmission and potential measurements in combination with careful ﬁeld procedures with gel used to enhance electrode- to-ground contact. Therefore, the near-surface data at this site was conﬁdently interpreted both through visual inspection and veriﬁcation TPs (Fig. 4c). Near-surface data showed some relatively high resistivity (1000–2500 Xm) regions indicated by yellow–light orange in Fig. 3. Based on visual inspection of materials heaped on the sur- face, inspection of the region between 4 and 8 m on line ML1 revealed demolition waste such as concrete and asbestos rooﬁng sheets as shown in Fig. 4a. This was in line with literature values for demolition waste as presented in Table 2. On both lines (ML1 and ML2), the glass heap registered resistivity >8000 Xm (dark orange to dark red). Resistivity of SiO2 glass at atmospheric tem- perature and pressure was not found in literature, although it has been estimated as ranging between 8000–6.3 108 Xm depending on temperature (CRC Press, 2001). TPs excavated across the dump (Fig. 4c) veriﬁed glass waste as the source of the high resistivity recorded. Although data beneath the glass pile in Fig. 3 was cautiously omitted from inversion results interpretation to avoid mistaking artefacts for actual geological features, one study objective of testing the glass heap to understand ‘pure’ glass (un- buried glass) resistivity was successfully achieved for application at Alsterfors dump where glass was buried. According to generated fence diagrams in Fig. 3c, the inverted proﬁles match quite well at the intersections and the data in the 3-D model agree with each other very well, thus conﬁrming a cer- tain level of conﬁdence in the results (Johansson et al., 2016). As such, the Madesjö ﬁndings could be reliably used as a ‘guide’ in identiﬁcation of buried glass hotspots at Alsterfors dump. 3.1.2. ERT on covered glass hotspots Resistivity sections for Alsterfors dump are shown in Fig. 5. Lines AL1,AL2 and AL3 are presented in Fig. 5a, b and c respectively. To avoid misinterpretations, knowing that all data comes with a degree of uncertainty in the results, and that the creation of arte- facts during the inversion process is a well-known phenomenon (Johansson et al., 2019; Jolly et al., 2011), data interpretation for Alsterfors was mainly based on excavation of veriﬁcation TPs, literature-based values of material resistivities as presented in Table 2, and resistivity observations from Madesjö glass dump (for glass hotspots). The resistivity scale was aggregated into six categories during interpretation: dark blue zones (<30 Xm), blue zones (30–70 Xm), green to dark green zones (60–530 Xm), light green zones (500–1100 Xm), yellow to light orange zones (1000–4600 Xm), and dark orange to dark red zones (>8000 Xm). The proﬁle images in Fig. 5 provided crucial information about glass hotspots, and guided TP excavations for veriﬁcation of observed resistivity against set hypotheses.Fig. 5a gives a clear indication of the dump base as shown by the yellow underlying structure (1000–2200 Xm), which is believed to be part of the bed- rock, since the bedrock at the site lies about 3–10 m below the sur- face (SGU, 2019). Dark blue zones (<30 Xm) such as between 28 and 33 m and around 5–8 m depths in Fig. 5b were interpreted as wet zones con- taining either decomposed waste or leachate or both, as they are Table 2 Parameters for ERT interpretations (modiﬁed from Abdulrahman et al., 2016). Material Parameter Value Granite SiO2 content 72.04% (Blatt and Tracy, 1996)*1 Glass (silicate) 00 74% (Shelby, 2005) Granite Resistivity 1000 – 1 106 Xm(Palacky, 1987) Glass (general) 00 8000 – 6.3 108 Xm(CRC Press, 2001)*2 Saturated (wet) soil 00 30 – 150 Xm(Guérin et al., 2004; Dahlin et al., 2010) Unsaturated (dry) soil 00 >1000 Xm(Leroux et al., 2007) Demolition waste 00 348 – 2000 Xm(Boudreault et al., 2010; Çinar et al., 2015) Decomposed waste 00 1–40Xm(Çinar et al., 2015) *1 World average. *2 Temperature-dependent, although not the value at standard temperature and pressure (STP). R.N. Mutafela et al./Waste Management 106 (2020) 213–225 217