Multivariate analysis of historical data (2004–2013) in assessing the possible environmental impact of the Bellolampo landfill (Palermo)

  • Serena Indelicato
  • David Bongiorno
  • Nicola Tuzzolino
  • Maria Rosaria Mannino
  • Rosalia Muscarella
  • Pasquale Fradella
  • Maria Elena Gargano
  • Salvatore Nicosia
  • Leopoldo Ceraulo


Multivariate analysis was performed on a large data set of groundwater and leachate samples collected during 9 years of operation of the Bellolampo municipal solid waste landfill (located above Palermo, Italy). The aim was to obtain the most likely correlations among the data. The analysis results are presented. Groundwater samples were collected in the period 2004–2013, whereas the leachate analysis refers to the period 2006–2013. For groundwater, statistical data evaluation revealed notable differences among the samples taken from the numerous wells located around the landfill. Characteristic parameters revealed by principal component analysis (PCA) were more deeply investigated, and corresponding thematic maps were drawn. The composition of the leachate was also thoroughly investigated. Several chemical macro-descriptors were calculated, and the results are presented. A comparison of PCA results for the leachate and groundwater data clearly reveals that the groundwater’s main components substantially differ from those of the leachate. This outcome strongly suggests excluding leachate permeation through the multiple landfill lining.


Groundwater Leachate PCA Landfill Pollutants Environmental 


Bellolampo is a sanitary landfill (Fig. 1a) located uphill from Palermo (Italy). The landfill commenced operation in the 1990s. Today, the landfill has attained a total area of 600,000 m2. The landfill consists of six hollows enclosed by embankments or berms and is lined with a multiple barrier fitted with drainage piping (Fig. 1b, c). Only one hollow remains operational. It is currently exclusively used to dispose of municipal waste. The altitude of the dumpsite is between 420 and 475 m a.s.l. (that is, approximately 400 m above Palermo’s mean altitude).
Fig. 1

a Aerial view of the Bellolampo landfill (Palermo) and its facilities. b Drainage pipe installation. c The lining of the Bellolampo hollows: a double sheet of high-density polyethylene alternating with a double layer of clay

In the landfill, in the past, virtually unsorted waste was accepted and accumulated. Regrettably, this approach remains partially in practice although the municipality and the public waste management companies (RAP SpA, formerly AMIA SpA) are steadily extending and improving their source-separated collection services in Palermo.

Recently, three technological facilities have come into operation: (i) a power plant fueled with biogas, which annually produces approximately 30,000 MWh electric energy for Palermo; (ii) a shredding–screening plant with metal separation; and (iii) a large mechanical–biological treatment plant (MBT).

A severe need for additional space has driven the operator—upon permit—to pile orderly new layers of waste onto several already closed ones. Therefore, today, it is difficult to assign a definite age to a given bank.

From a geological perspective, the area occupied by the landfill is part of the carbonate reliefs belonging to the Monti di Palermo structure, a segment of the Apennine–Maghrebian Chain (Contino et al. 2001) characterized by a harsh morphology occasionally interrupted by planes of various extension.

The structure of Monti di Palermo consists of a series of superimposed tectonic units deriving from the deformation of the carbonate platform Panormide. The carbonate Panormide and Imerese domains, which belong to the Mesozoic age, constitute the main aquifers of Monti di Palermo. They are characterized by a high degree of fracturing and widespread karst formations produced by the chemical–physical action of percolating water. This highly permeable substrate has resulted in the formation of a deep aquifer, strategic for the water supply. The intense fracturing makes the aquifer vulnerable to potential infiltration of pollutants along preferential paths.

Geological studies have shown that the Bellolampo landfill rests on an extended direct system of faults and tectonic overlapping fronts. These circumstances result in significant problems with respect to establishing a clear geological model of the area (Indelicato et al. 2017). For the various identified hydro-geological units (Contino et al. 1998), a hydraulic exchange relationship involving different routes of infiltration of water and, hypothetically, pollutants from any source could be hypothesized.

Investigations performed on several of the landfill’s basins did not locate an aquifer down to a depth of 150 m from the landfill bottom. Given that in the wells located downhill the aquifer’s water table is detected at elevations less than 20 m a.s.l., for simple altitude considerations, in the landfill area, the same water table should be at least 400 m deep (RAP SpA, Internal Report 2013).

In waste landfills, both organic and inorganic substances are present in solid (waste), liquid (leachate), and gas phases. A bottom lining, a top covering, venting wells for gas, and drains for leachate are the practical means used to control flows across the boundaries of the waste banks. Normally, assuming a serious incident—in which all or several preventive measures failed and water-dissolved substances were released into the surrounding environment—the geological substratum remains the last barrier to migration.

Landfill leachate is generated by excess rainwater percolating through the waste layers and can be considered as a water solution of four groups of pollutants: dissolved organic matter and its decomposition products (including volatile fatty acids), inorganic macro-components, metals, and volatile organic compounds (VOCs).

Leachate composition varies significantly among landfills depending on waste composition, waste age, and landfilling technique. Obviously, there is a strong relationship between leachate composition and the decomposition progress.

At Bellolampo, the leachate should be contained at the bottom of the waste banks confined by a double barrier that was installed early in the site preparation phase. From here, leachate is periodically drawn and transferred to storage tanks before treatment. There are two storage tanks: the “North” and “South.” Periodically, tank samples are taken by the public company RAP SpA that operates the landfill.

In this research, any evidence of possible past seepage from the landfill site into the surrounding soils and groundwater was investigated. In particular, to establish if the landfill has affected groundwater quality, we evaluated a wealth of analytical data from environmental monitoring (collected from 2004 to 2013) and leachate analyses (2006 to 2013) using principal component analysis (PCA) (Ramanathan et al. 2001; Chidamabaram et al. 2002; Reghunath et al. 2002; Pujari and Deshpande 2005). Generally, we tried to identify the chemical and physical parameters that mostly affect groundwater quality and to consolidate these parameters. Evidence of relationships between leachate and groundwater or among groundwater samples was sought and discussed when found. The final goal of this research was to use historical analytical data as a tool to evaluate the impact of the landfill on the aquifer.

Material and methods

Monitoring network

To detect possible groundwater pollution due to the landfill (Eggen et al. 2010), existing wells around the landfill were monitored (Fig. 2 and Table 1). Notably, several of these wells are used for the drinking water supply. A hypothetical area flow model, albeit with uncertainties, is shown in Fig. 2 (adapted from Contino et al. 1998). This model indicates a groundwater flow primarily directed toward the north and northeast and several flows directed to the east toward the plain of Palermo.
Fig. 2

Principal hydro-geological flows. Several studies indicate that the groundwater flow is directed outward away from the Bellolampo landfill primarily in a N-N-E direction

Table 1

Monitored wells








Avg. flow rate

(l s−1)

Coordinates UTM 33N

East (m)

West (m)









Capaci Inf.







































































According to this model, the most important wells that should be considered unperturbed by landfill effects are the Susinna and Capaci-Infurnari wells (referred to as Capaci Inf.), both located west of the landfill. The wells Guggino, Nastri, and Benfratelli (northeast of the landfill); Celona, Bordonaro, and Sicomed (southeast of the landfill); and Bellolampo and Lorenzini (south of the landfill) could be considered useful in detecting pollutant leaks from the landfill (Christensen et al. 2001).

Analytical approach

Should a diffusion of unwanted compounds in the environment occur, it would remain unnoticed for a long time. It is therefore of utmost importance to designate a restricted set of physical or chemical properties—or of chemical species—that (a) are easy to measure and (b) behave as “tracers.”

The analytical methods adopted to determine these parameters were suggested by the US EPA and the APAT and IRSA-CNR reference methods (IRSA-CNR APAT 2003). Sampling and analysis were entrusted to laboratories and agencies in possession of quality certification.

Water samples

Water samples were collected every 3 months. When threshold values exceeded, bimonthly or monthly sampling was performed, as occurred in 2010 and 2011.

Overall, 328 samples were analyzed. In each sample, the following parameters were determined: pH and temperature (at the time of sample collection), electrical conductivity (EC; according to ASTM D1125-14 method), TOC according to ISO method 8245:1999, ammonia (as ammonium ion, NH4+), cations (Na+, K+, Ca2+, Mg2+, and NH4+ according to UNI EN ISO 14911:2001), anions (Cl, SO42−, NO2, NO3, and F according to ISO method 10304.1; CN according to EPA method 335.2), metals (Al, As, B, Cu, Cr, Fe, Hg, Mn, Ni, Pb, and Zn according to EPA method 6020), and organic micro-pollutants (such as phenols, according to EPA method 528; pesticides, according to EPA method 508.1; organo-halogenated compounds according to EPA method 502.2; polycyclic aromatic hydrocarbons (PAH) according to EPA method 550.1; and nitrogenated according to EPA method 8033 and/or chlorinated solvents according to EPA method 502.2). Given the large number of data and since there were no significant variations of the monitored parameters, in the following discussion, the results reported for each well refer to the seasonal average.

Leachate samples

The leachate samples were collected from the two storage tanks that hold the leachate before disposal. According to Legislative Decree no. 152/2006, the leachate is sampled every 3 months before being disposed of by suitable treatment plants. Overall, 68 samples were analyzed. In each sample, the following parameters were determined: pH, electrical conductivity, total suspended solids, BOD, COD, metals (Al, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Sn, Zn), ammonia, mineral oils, and organic micro-pollutants (such as phenols, aromatic and chlorinated solvents, and surfactants).

Statistical treatment of data

Multivariate analysis generally helps simplify the interpretation of the results from complex systems. Therefore, the large number of collected data was processed by PCA using XLSTAT 3.2 software (Addinsoft, USA) with Excel 2007 (Microsoft Corporate, USA). In PCA, the variables that describe the experimental data are processed into new variables—termed principal components—which are linear combinations of the original variables and whose most important feature is that each new variable is orthogonal to the others.

Because only the variables with similar characteristics (considering the variance of the initial data) are grouped into principal components, the analytical information is preserved. By this approach, it is possible to evaluate the correlation between the variables, to synthesize the description of the data, to reduce the dimensionality of the data, and to define a representation of the data model in an orthogonal space.

PCA involves a rotation of the original data to obtain a first new axis (the first principal component PC1), which is oriented in the direction of maximum variance of the data. The second (PC2) is perpendicular to the first and oriented in the direction of the next maximum data variance and so on for all new axes. The lower the number of principal components is, the higher the correlation between the variables used to construct the considered system (Box et al. 2005).

An aspect of substantial importance in the study of systems with many variables by PCA is the possibility to graphically display both the variables (i.e., the analytical parameters) and the objects (i.e., the analyzed water samples). The coordinates of the objects in the new reference system are termed scores, while the coefficients used in the linear combination that describes each major component, i.e., the contribution of each parameter to each PC, are termed loadings.

The graphic representation of the scores (score plot) facilitates the identification of groups of objects with similarities (i.e., objects in the score plot that lie close to one another). The corresponding loading plot facilitates identifying the parameters responsible for similarities or for differences between the samples identified in the score plot.

Thematic maps

The thematic maps were plotted with Surfer 12™. Among the several interpolation techniques offered by the program, kriging (Kemp, 2008) was chosen. The latter is a regression method used in spatial analysis (geo-statistics) that enables the interpolation of a magnitude in the space minimizing the mean square error.

This spatial interpolation is made assuming that the considered magnitude varies continuously in the space, i.e., the closest things are more similar than more distant things (Tobler 1970). In practice, this method produces visually appealing maps from irregularly spaced data.

Results and discussion

Before reporting the groundwater analyses, the leachate liquid from Bellolampo is described through a small number of synthetic indices.

Leachate composition

The results of the historical analysis (performed in the period 2006–2013) were first checked for general consistency. Over the entire period, the pH values remained at approximately 8 ± 0.5, all nitrogen was in ammonium form, and no nitrous nor nitric ions were found. COD and BOD were both high. In sum, these outcomes indicate a “young” leachate (Kuliowska and Klimiuk, 2008, Kjelsen et al. 2002).

Regarding the indices of organic load in these samples and its biodegradable part, Fig. 3 shows the relation between COD and BOD in the “south leachate tank” content. It is evident that (a) the absolute values are high, as one could expect since the banked-up waste was unsorted and one-third organic, and (b) organic matter dissolved in leachate is nearly 60% degradable, which is surprising because it is commonly assumed that leachate is barely degradable.
Fig. 3

Dissolved organic matter in the “south leachate tank” content. BOD is approximately 2/3 COD, much like common urban wastewater. In the “north tank,” the relation for 28 points of 32 is nearly identical (COD = 1.77 BOD + 2190)

The second measured and correlated index was electric conductivity EC (μS cm−1), which is an expression of the ionic species content as a whole. Unfortunately, the analysis performed on these samples did not include the inorganic fraction of total dissolved solids (i.e., the mineral content). However, EC shows a good correlation with chloride ion (Fig. 4).
Fig. 4

EC of the “south leachate tank” content. In the “north tank,” the relation, also for 28 points of 32, is nearly identical (EC = 157.7 Cl + 8589)

If chloride ions were the only anion in waste leachate, the intercept of the regression line on the ordinate should be zero. That it is over 7000 μS cm−1 is consistent with the obvious idea that chloride—although abundant—is not the only ion. If one can assume that 1 (meq l−1) either of cations or anions provides to water 80 (μS cm−1) EC, the most strongly saline samples should contain approximately (40,000/80) = 500 (meq l−1) ionic compounds of either sign. Among them is certainly ammonium, whose average concentration in Bellolampo’s leachate is 192 meq l−1.

As previously stated, no other ions—least of all volatile fatty acids (VFAs)—were analyzed and reported on. However, VFAs are certainly produced in organic matter hydrolysis. The formation of salts of such weak acids could explain the previously noted basicity of the leachate, whose pH values range from 7.5 to 8.5.

The stoichiometric alkalinity was in fact determined on a single sample of Bellolampo’s leachate. By the addition of 0.1 N HCl, pH was lowered from the initial 7.5 to 4.5. The result was 240 milliequivalents per liter. Thus, (a) this solution is strongly buffered, presumably by ammonium salts above all, and (b) alkalinity alone would account for approximately (240 × 80/40000) = 48% of the total conductivity.

Water samples analysis and PCA

The results of the water analyses performed on the samples collected from 2004 to 2013 were used to perform PCA. The chosen variables were subjected to auto-scaling (i.e., each value in a column was subtracted from the arithmetic average and divided by the standard deviation of the samples) to make the variances of the different variables comparable. Data auto-scaling was necessary because the variables were measured in different units.

PCA was performed on a data matrix of 192 rows × 12 columns (the rows represent the water samples, and columns represent the analyzed parameters). Several parameters, in particular EC, Na+, K+, Ca2+, Mg2+, Cl, SO42−, NO3, F, Mn, Fe, and Zn, were considered for PCA modeling. The other analytes were not considered because their values were below the quantitation limit of analytical methods.

The PCA results are reported in Table 2. The first three PCs account for 62% of the total variance contained in the data set. These PCs were retained as significant and are considered in the following discussion.
Table 2

Results of PCA performed on data set

No. of PC



Cumulative variance %





















The score plot of PC1 and PC2 is reported in Fig. 5, which shows the labels of each well together with the sampling period. To simplify reading of the graph, each well is labeled and represented with a different color. During the entire investigated period, each well occupies a precise area of the score plot. That is, the Bellolampo, Lorenzini, and Nastri wells are characterized by negative PC1 values and positive PC2 values; Bordonaro is characterized by positive PC1 and PC2 values; Benfratelli has positive PC1 values but PC2 values that are both positive and negative; Sicomed is characterized by positive PC1 values and negative PC2 values; Celona has negative PC1 and PC2 values; the Capaci-Infurnari, Guggino, and Susinna wells exhibit a greater dispersion in the score plot, which corresponds to a greater variability of results during the investigated period.
Fig. 5

Score plot of the entire data matrix (192 variables, 12 wells) reporting PC1 vs. PC2 results

The data were compared with the results of independent chemical analyses performed by ARPA Sicilia (Regional Agency for Environmental Protection) on water samples taken from the same wells in the same period. PCA was performed on a data matrix of 229 rows × 12 columns. The samples analyzed by ARPA (on the label) and the samples analyzed in this paper occupy the same areas of the score plot (Fig. 1 of the Supplemental Information (SI)), which highlights the coherence of all analytical results.

Taking into account that the first plot of the RAP results justifies approximately 50% of the variance, we consider it useful to examine the score plot of PC1 vs. PC3. An additional grouping of wells is confirmed by the score plot of PC1 and PC3 (Fig. 6). The graph highlights the similarity between the samples of the Capaci-Infurnari and Susinna wells. These well sites are geographically close to one another but far from the others. The score plot of PC1 vs. PC3 confirms the different characteristics of Bordonaro, Benfratelli, and Sicomed.
Fig. 6

Score plot of PC1 vs. PC3; 192 × 12 data matrix

The score plot in Fig. 5 highlights that several wells (Benfratelli, Bordonaro, Celona, and Sicomed) are far from one another, which indicates that these samples have different characteristics. To determine the peculiarities of these wells, we must compare the score plot in Fig. 5 with the loading plot in Fig. 7.
Fig. 7

Loading plot of PC1 vs. PC2; 192 × 12 data matrix

The loading plot determines whether there is a correlation between the parameters. Figure 7 shows that electrical conductivity is strongly correlated with sodium and chloride ions, and the sulfate ions are well connected with calcium ions and nitrates. Additionally, by comparing the score plot in Fig. 5 with the corresponding loading plot ion Fig. 7, one can identify the variables that are responsible for the analogies or differences detected for the samples in the score plot. In particular, Benfratelli is affected by high values of chloride, sodium, and electrical conductivity; Sicomed is characterized by high concentrations of NO3, SO42−, and Ca2+; and Celona is influenced by Fe, Mn, and Zn.

Evaluation of the characteristic analytical variables

Chloride ions

These ions were the most abundant anions among those determined in the water samples. Their concentration ranged between 17 and 241 mg L−1. The highest chloride concentrations were found in the Benfratelli well (Fig. 2SI). The chloride ion can be considered a non-reactive component of leachate and a conservative tracer of groundwater contamination by leachate (Christensen et al. 2001). Dilution, i.e., the interaction of the leachate flow in the aquifer with the flow of groundwater, is the only attenuation mechanism. The Cl concentration should have decreased as one moved away from the landfill. However, in this case, the spatial distribution of chloride ion (Fig. 2SI) exhibited an increase in concentration with increasing distance from the landfill.

The thematic maps in Fig. 2SI are relative to two different seasons, in particular summer and winter 2012. It is evident that the distribution of Cl ion does not undergo seasonal variation.

The groundwater flows northeast to the sea, which indicates that the high concentrations found probably are of natural origin and/or attributable to local human activity rather than to the leachate. Additionally, in the cases of the Sicomed and Benfratelli wells, closest to sea level, the entrance of brackish water cannot be excluded. This fact seems to be confirmed by the good correlation between the values for sodium ions and chloride of the Sicomed and Benfratelli samples compared with values typical for seawater, represented with a square in the logarithmic graph (Fig. 3SI).

Sodium ions

Similar to chlorides, these ions display an increase in concentration with increasing distance from the landfill, particularly near the Benfratelli, Bordonaro, and Sicomed wells. Thus, the origin of sodium ions is natural or due to human activity rather than to the landfill.

Because it is connected to human activity (e.g., the use of fertilizers) or to the geology of the land, the ammonium concentration in the water is a relevant pollution indicator. The ammonium ions are biodegraded to nitrite and then to nitrate. In nearly all examined cases, the NH4+ ion remained below the threshold of the analytical method.

Nitrate ions

These ions are important indicators of anthropogenic contamination and represent the last stage of oxidation of nitrogen compounds from the biological decomposition of organic substances. The primary sources of these contaminants therefore are domestic sewage and runoff from farmlands (Han et al. 2014).

The distribution of nitrates over time and space reveals that the trend of the concentration remained virtually constant throughout the investigated period. An increase in nitrate concentration was observed as the distance of the wells from the landfill increased (Fig. 4SI). Thus, the nitrates behaved similarly to the chlorides (Fig. 8). For the nitrate ions, it is also possible to assume that the contamination is of local origin since several plant nurseries are located in the area of the Benfratelli and Sicomed wells. The measured concentrations were between 5.4 and 60.2 mg L−1, with an average value of 24.3.
Fig. 8

Score plot of PC1 vs. PC2; 228 × 5 data matrix analyzing well water and leachate; the insert reports the corresponding loading plot

Sulfate ions

In the water samples, the measured sulfate ion concentrations were between 0.8 and 240 mg L−1, with an average value of 57.5 mg L−1. As shown in Fig. 5SI, Sicomed exhibits the maximum levels.

Calcium ions

In the water samples, calcium ion ranged between 19 and 189 mg L−1, with an average value of 118. The highest value was observed for the Sicomed well during winter 2004. Regarding chloride and nitrate ions, the calcium concentration increased as the distance of the wells from the landfill increased (Fig. 6SI). This element probably originates in the natural solubilization of the carbonate minerals in which the well was drilled (Pujari and Deshpande 2005; Talalaj 2014).

Organic micro-pollutants

In the water samples, the presence of these pollutants and in particular phenols, PAHs, halogenated organo-metallic compounds, pesticides, and nitrogenated or chlorinated aromatic organic solvents was determined. In all the analyzed samples, these compounds were always below the quantitation limits of the analytical method, with the notable exception of the chloroform level in the Benfratelli and Sicomed wells. In fact, from 2011 onward, the water samples taken from these wells had chloroform concentrations that exceeded the limit of 0.15 μg L−1 imposed by D.Lgs n.152, 2006 (Fig. 7SI).

The chloroform levels in the Benfratelli well ranged from 0.3 to 0.7 μg L−1, while in the Sicomed well, the corresponding levels ranged from a minimum of 0.4 μg L−1 to a maximum of 2.47 μg L−1. A high chloroform concentration in the Benfratelli and Sicomed wells might suggest leachate contamination. The results of VOC analysis performed on the leachate samples taken from the Bellolampo landfill indicate that this compound was not present.

Another option should be evaluated: chloroform in groundwater can be formed from dissolved organic compounds (DOCs), which are primarily constituted by humic substances reacting with chlorine used for disinfection (Hoekstra et al. 1998). Humic substances are commonly present in leachate or wastewater, and studies show that since the chlorine replaces the hydroxyl group of humic acids, the latter are favored as precursors to the formation of CHCl3. However, this hypothesis seems not to be viable because no sewerage or wastewater was present in the area of the previously mentioned wells. Therefore, the high chloroform concentration found in the Benfratelli and Sicomed wells remains to be explained. Additional studies have recently been initiated, and it is worth noting that the municipality of Palermo has entrusted the Sea and Land Sciences Department of Palermo University (DISTeM) to perform a preliminary study that aims to draft a characterization paper on the groundwater in the Palermo plain.


During the different surveys, the zinc seasonal concentration values ranged from 0.003 to 0.47 mg L−1, with an average value of 0.05. Nearly all of the analyzed samples showed the presence of this metal. However, in neither sample type did the value exceed the legal limit (3000 μg L−1).

In all the samples, the iron concentration ranged from 0.04 μg L−1 (Capaci-Infurnari well, autumn 2006) to 263 μg L−1 (Celona well, autumn 2013). Fe is already present in groundwater due to the runoff of rainwater or industrial waste. The water that percolates through soils containing iron-rich minerals brings Fe into solution in a bicarbonate form (Fe (HCO3)2) or linked to organic substances. Iron presence is not connected to water pollution but is of hygienic and food profile concern. In fact, ferrous deposits can promote iron bacteria development, which imparts an unpleasant taste to drinking water. Figure 8SI shows the iron spatial distribution in the 2010 summer and autumn seasons.

During the dry season, the iron concentration increases in the Celona well probably because of the low water level. A different behavior is reported during the autumn, when the concentration maintains a constant value. In fact, the collected data do not allow one to ascribe the iron levels found among the wells to the landfill.

Manganese (Mn) exhibits a concentration range from 0.4 μg L−1 (Sicomed well, spring 2013) to 57 μg L−1 (Susinna well, spring 2011), with an average value of 9.07. According to D.Lgs n.152, 2006, the contamination threshold concentration is 50 μg L−1. Mn by itself is naturally present in soil in all its oxidation states in the form of oxides and hydroxides. It occurs in groundwater much more rarely than iron but is often associated with iron’s presence. Groundwater in contact with manganese-rich soils is enriched in manganese as bicarbonate, which remains in solution. A Mn concentration in excess of 0.05 mg L−1 causes an unpleasant taste, turbidity, and deposits in the pipes. Although Mn is not a pollution indicator, its presence in water is undesirable for hygienic/food reasons.

Relation between groundwater contamination and landfills

To assess whether there had been contamination of groundwater due to the landfill, the results of the chemical analysis of the water and leachate samples were subjected to a further PCA. The leachate has a different chemical composition than the groundwater samples. In particular, it is characterized by high values of COD (between 1991 and 23,366 mg L−1), EC (from 7720 to 39,100 μS cm−1), and of total suspended solids (between 265 and 2667 mg L−1). Therefore, for the PCA modeling, five of the analyzed parameters were used: EC, Cl, Fe, Mn, and Zn. Then, a new PCA model was constructed using 163 groundwater and 64 leachate samples.

PCA was performed on a data matrix of 227 rows × 5 columns after an auto-scaling process. The results are reported in Table 3. The first two PCs account for 75% of the total variance contained in the data set, and these PCs have been retained as significant.
Table 3

Results for PCA performed on water and leachate results

No. of PC



Cumulative variance %





















The two footprints in the score plot of PC1 and PC2 (Fig. 8) clearly appear to be different. Thus, the analyzed wells and leachate samples have different characteristics. In particular, the water samples are characterized by Fe and Mn, while the leachate samples are characterized by Zn, EC, and chloride ions.

The divergence between the two groups suggests that the groundwater is not affected by any supposed leakage of leachate from the landfill.

It is to note that PCA is carried out on the same set of data issued from the chemical analysis and requires relatively little more time and effort to be performed. If the data obtained from leachate and sampled wells were demonstrated akin, it could be easily justified the heavy and time-requiring work of surveying, sampling, and modeling, after hydro-geo-chemical methods. On the other hand, if the two subsets exhibit little affinity or no affinity at all, further investigation on the aquifer appears unnecessary. Interestingly, in the case study presented here, the authors met with the second situation. Other studies reported for organic contaminants and determined in narrower timespan (Indelicato et al. 2017) are also consistent with our findings.


In this study, a retrospective analysis of several quality parameters of groundwater (2004–2013) and leachate (2006–2013) was conducted. A series of determined chemical macro descriptive parameters was individuated to characterize leachate samples. For leachate, only five parameters were correlated: BOD, COD, TDS, EC, and chloride ions. From the data plots, a good relation was observed for all these species. A high content of ammonium ions was also observed, which represents a symptom of recent generation and surprisingly high biodegradability.

Another series of chemical macro and micro parameters was selected using the multivariate statistical approach and applied to characterize the water and leachate samples. It has been demonstrated that PCA can be applied to large data sets to obtain information on the nature, similarity, and differences of numerous groundwater samples. In particular, groundwater collected at several sites (i.e., Bordonaro, Sicomed, Celona, and Benfratelli) located each of them in a well-defined zone in the plot. Notably, the grouping of the water samples that belonged to specific sites in narrow areas of the PCA plots indicated a stable composition of groundwater samples over the 10 years of observation. This outcome reveals that surface activity did not dramatically influence the chemical fingerprint of the sampled sites.

In addition to this evidence, the statistical approach enabled us to determine profound differences between the leachate and the groundwater fingerprints, which lie in different quadrants of the statistical plot. Based on this analysis conducted on inorganic substances and organic micro-pollutants, influences of leachate on groundwater samples can be reasonably excluded. Finally, these results that are also consistent with the findings of Indelicato et al., 2017evidence that the PCA can give researchers and professionals an address about the cases on which efforts may be spared, in order to go deeper only when facing to the most complex ones.



The authors are indebted to Dr. Giovanni Abbate and Dr. Vittoria Giudice of the Palermo Laboratory of ARPA Sicilia (Regional Agency for Environmental Protection), who provided the results of the chemical analysis of investigated wells. Their contribution is gratefully acknowledged.

Funding information

This research was supported by the EU-funded project SIGLOD (Repubblica Italiana, PON04a2_F).

Supplementary material

10661_2018_6594_MOESM1_ESM.docx (1.1 mb)
ESM 1 (DOCX 1122 kb)


  1. Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for experimenters. New York: Wiley.Google Scholar
  2. Chidamabaram, S., Ramanathan A.L., Srinivasamoorthy K., Ananthan P. (2002). A case study on the hydrogeochemistry of fluoride bearing groundwater of Erode District, Tamilnadu. Edited by V. Subramanian and A.L. Ramanathan. Vol. Ecohydrology. New Delhi: CPC.Google Scholar
  3. Christensen, T. H., Kjeldesen, P., Bjerg, P. L., Jensen, D. L., Christensen, J. B., Baun, A., Albrechtsen, A. J., & Heron, G. (2001). Biogeochemistry of landfill leachate plumes. Applied Geochemistry, 16, 659–718.CrossRefGoogle Scholar
  4. Contino, A., Cusimano, G., Di Cara, A., Di Maggio, C., Frias, F. A., & Hauser, S. (2001). Il ruolo del carsismo nella valutazione della vulnerabilità potenziale all’inquinamento degli acquiferi carbonatici dei monti di Palermo. Terrain, 323–332.Google Scholar
  5. Contino, A., Cusimano G., Frias Forcada A (1998). Modello Idrogeologico dei Monti di Palermo. 790° Congresso Nazionale Società Geologica Italiana (S.G.I.), 334–337.Google Scholar
  6. D.Lgs. n.152. (2006) «Decreto legislativo 3 aprile 2006, n. 152 - Norme in materia ambientale (T.U.: Testo Unico sull’Ambiente).» (Gazzetta Ufficiale della Repubblica Italiana), n. 88 (04 2006).Google Scholar
  7. Eggen, T., Moeder, M., & Arukwe, A. (2010). Municipal landfill leachates: a significant source for new and emerging pollutants. Science of the Total Environment, 408, 5147–5157.CrossRefGoogle Scholar
  8. Han, D., Tong, X., Currell, M. J., Cao, G., Jin, M., & Tong, C. (2014). Evaluation of the impact of an uncontrolled landfill on surrounding groundwater quality, Zhoukou, China. Journal of Geochemical Exploration, 136, 24–39.CrossRefGoogle Scholar
  9. Hoekstra, E. J., De Leer, E. W. B., & Brinkman, U. A. T. (1998). Natural formation of chloroform and brominated trihalomethanes in soil. Environmental Science and Technology., 32, 3724–3729.CrossRefGoogle Scholar
  10. Indelicato, S., Orecchio, S., Avellone, G., Bellomo, S., Ceraulo, L., Di Leonardo, R., Di Stefano, V., Favara, R., Gagliano Candela, E., La Pica, L., Morici, S., Pecoraino, G., Pisciotta, A., Scaletta, C., Vita, F., Vizzini, S., & Bongiorno, D. (2017). Effect of solid waste landfill organic pollutants on groundwater in three areas of Sicily (Italy) characterized by different vulnerability. Environmental Science and Pollution Research, 24, 16869–16882.CrossRefGoogle Scholar
  11. IRSA-CNR, APAT (2003) Metodi analitici per le acque (Sez. 1000, 2000, 3000) Manuali e Linee Guida, n. 29.Google Scholar
  12. Kemp, K. (2008). Encyclopedia of geographic information science (pp. 146–147). Los Angeles: SAGE.CrossRefGoogle Scholar
  13. Kjelsen, P., Barlaz, M. A., Rooker, A. P., Baun, A., Ledin, A., & Christensen, T. H. (2002). Present and long-term composition of MSW landfill leachate: a review. Critical Reviews in Environmental Science and Technology, 32(4), 297–336.CrossRefGoogle Scholar
  14. Kuliowska, D., & Klimiuk, E. (2008). The effect of landfill age on municipal leachate composition. Bioresource Technology, 99, 5981–5985.CrossRefGoogle Scholar
  15. Pujari, P. R., & Deshpande, V. (2005). Source apportionment of groundwater pollution around landfill site in Napgur, India. Environmental Monitoring and Assessment, 111, 43–54.CrossRefGoogle Scholar
  16. R.A.P., S.p.A. Piano di sorveglianza e controllo (PSC). Palermo, 2013;Google Scholar
  17. Ramanathan, A.L., Chidambaram S., Srinivasamoorthy K., Anandhan P. (2001). Dissolved ion concentrations in the surface and groundwaters of Neyvali mining regions. Edited by V. Subramanian and A.L. Ramanathan. Vol. Ecohydrology. New Delhi: CPC.Google Scholar
  18. Reghunath, R., Sreedhara Murty, T. R., & Raghavan, B. R. (2002). The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Research, 36, 2437–2442.CrossRefGoogle Scholar
  19. Talalaj, I. A. (2014). Assessment of groundwater quality near the landfill site using the modified water quality index. Environmental Monitoring and Assessment, 186, 3673–3683.CrossRefGoogle Scholar
  20. Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Serena Indelicato
    • 1
  • David Bongiorno
    • 2
  • Nicola Tuzzolino
    • 3
  • Maria Rosaria Mannino
    • 3
  • Rosalia Muscarella
    • 2
  • Pasquale Fradella
    • 4
  • Maria Elena Gargano
    • 4
  • Salvatore Nicosia
    • 5
  • Leopoldo Ceraulo
    • 2
  1. 1.Dipartimento DISTEM, Scuola delle Scienze di Base ed ApplicateUniversità degli Studi di PalermoPalermoItaly
  2. 2.Dipartimento STEBICEF, Scuola delle Scienze di Base ed ApplicateUniversità degli Studi di PalermoPalermoItaly
  3. 3.Agenzia Regionale Protezione dell’Ambiente (ARPA Sicilia) via San LorenzoPalermoItaly
  4. 4.RAP - Risorse Ambiente Palermo S.p.A.PalermoItaly
  5. 5.Dipartimento DICAM, Scuola PolitecnicaUniversità degli Studi di PalermoPalermoItaly

Personalised recommendations