# Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}, and in the past it was characterized by a dense agglomeration of both residential buildings and industrial activities, such as automotive, refineries, chemical plants, stills, and tire production, which have been historically located in this area since the 1960s [12]. This territory is characterized by a natural hydrographic network as well as a man-made one. The first is made up of the Seveso River on the west side and the Lambro River on the east. The latter includes the Naviglio Martesana on the south-east side and the Villoresi Canal on the north.

^{−4}to 10

^{−3}m/s) and transmissivity (usually higher than 10

^{−2}m

^{2}/s). The underlying Group B consists of gravel and medium-coarse sand in a sandy matrix with discontinuous confining layers of clay and silt, limited to the lower part of the aquifer, that determine the presence of semi-confined and confined aquifers. Group B has a thickness of 50–60 m, and, due to the higher presence of clay and silt, it is characterized by lower values of hydraulic conductivity (ranging from 10

^{−5}to 10

^{−3}m/s) and transmissivity (ranging from 10

^{−3}and 10

^{−2}m

^{2}/s) with respect to Group A. Group C and Group D contain the deepest aquifers, which were not analyzed in this study. The regional flow is mainly oriented north-south, and the groundwater depth decreases from north to south. In the study area, the groundwater depth of the shallow aquifer ranges between about 3 m and 50 m below the ground surface. In the northern sector of the plain, Group A and Group B constitute a unique shallow aquifer, whereas from the northern part of Milan southwards, the two aquifers are separated by an aquitard (thickness of 5 m and hydraulic conductivity of 10

^{−8}m/s), of which the thickness and lateral continuity increase southwards [16,17,18]. This study focuses on the shallow aquifer, which is the one most exposed to the release of contaminants coming from the surface. This means we are considering data concerning the downgradient of Group A only with respect to the boundary of the aquitard, and the upgradient of both Group A and Group B with respect to the boundary of the aquitard (solid golden line in Figure 1).

## 3. Materials and Methods

#### 3.1. The WofE Modelling Technique: Classic and Alternative Approach

#### 3.2. Training Points

#### 3.3. Evidential Themes

^{2}was used for both the thematic and final maps. In this analysis, the occurrence of high PCE concentrations in groundwater within the study area was related to both natural and anthropogenic factors. Five explanatory variables were chosen as evidential themes in all ten WofE models (Figure 4):

- groundwater depth,
- hydraulic conductivity of the vadose zone,
- groundwater velocity,
- percentage of “potential sources zone” (PSZ) extent in 2000, including industrial, artisanal, and commercial settlements,
- PSZ variation during the period 2000–2012.

^{−8}to 5.7 × 10

^{−2}m/s, and in general, its distribution is rather irregular in the study area.

^{−7}to 1.3 × 10

^{−5}m/s. Higher values occupy a portion of territory which extends from the north-western sector to the central part of the study area.

^{2}cell. To take into account the influence of both the groundwater flow direction and the impact of PSZs located upgradient of each cell, a procedure called focal method was applied. The algorithm in the focal method considers both the value of each cell and the values of the surrounding cells with a deterministic mathematic function (i.e., arithmetic mean). By using this technique, it was possible to recalculate the percentage of PSZ extent in each cell by assigning the mean of the values in the surrounding cells located upgradient. As shown in Figure 5, among the surrounding cells, only the cells contained in a buffer of 250 × 550 m (colored in light blue) were considered in the calculation.

## 4. Results

#### 4.1. Contrasts of the Generalized Evidential Themes

#### 4.2. Predictive Responses and Susceptibility Map

#### 4.3. Reliability and Validation

^{2}= 0.78). Nevertheless, this validation method pointed out some anomalies related to class 2, characterized by an average concentration value lower than class 1, and to class 4, showing an average concentration value lower than class 3.

^{2}= 0.90). In this case, class 5 represents an anomaly, since it is characterized by a frequency of TPs lower than class 4 (Figure 8c) and by a frequency of control points higher than class 4 (Figure 8d).

#### 4.4. Evaluation of the Efficiency of the Monitoring Network

## 5. Discussion

#### Comparison between the Spatial Statistical Approach and the Numerical Stochastic Approach

## 6. Conclusions

- spatially evaluate the likelihood of having active MPS in a contaminated groundwater area;
- relate their impact on the shallow aquifer to the hydrogeological features of the area;
- highlight the presence of diffuse contamination due to both “historical” (that is, already removed) and recent MPS;
- test the efficiency of the monitoring network in properly characterizing the contamination in the aquifer, both for reducing the uncertainty in identifying the contaminated area and for eliminating unnecessary monitoring points.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**(

**a**) Location of the study area (grey square); (

**b**) study area with municipality borders, main rivers, and canals. Milan, Sesto San Giovanni, and Monza are the main cities in the study area. (

**c**) Piezometric levels of the shallow aquifer (m a.s.l.). The golden line approximates the northern border of the aquitard: the main shallow aquifers (A and B in the hydrogeological section) are hydraulically divided southwards (golden area), whereas the aquifers are not separated northwards (A + B in the hydrogeological section); (

**d**) north-south hydrogeological section.

**Figure 2.**Flow chart representing the classic and the alternative Weights of Evidence (WofE) procedures.

**Figure 4.**Natural and anthropogenic factors influencing groundwater contamination: (

**a**) groundwater depth; (

**b**) groundwater velocity; (

**c**) hydraulic conductivity of the vadose zone; (

**d**) potential sources zone (PSZ) extent in 2000; (

**e**) variation of PSZ extent between 2000 and 2012.

**Figure 5.**Focal method. Each cell is a 50 m × 50 m square (0.25 km

^{2}). Cells colored in light blue correspond to those considered in the calculation of the mean percentage of PSZ extent, attributed to the cell colored in red.

**Figure 6.**Contrasts of the statistically significant classes of each evidential theme: (

**a**) groundwater depth; (

**b**) groundwater velocity; (

**c**) hydraulic conductivity of the vadose zone; (

**d**) PSZ extent in 2000; (

**e**) variation of PSZ extent between 2000 and 2012.

**Figure 7.**(

**a**) Map representing the distribution of susceptibility classes within the study area; (

**b**) map representing the coefficient of variation (%) overlaid on the susceptibility map (in transparency). The degree of susceptibility increases from green (class 1) to red (class 5) colors.

**Figure 8.**(

**a**) area-under-the-curve (AUC) values for the ten post probability outputs. Histograms of the average PCE concentration (

**b**), the frequency of the training points (

**c**), and the control points (

**d**) in each susceptibility class of the map in Figure 6a. The susceptibility increases from class 1 to class 5.

**Figure 9.**Histogram representing the most influential training points (TPs), corresponding to the peaks highlighted by the highest values of the delta post probability.

**Figure 10.**Location of the most influential TPs (blue dots) in the study area, overlaid on the susceptibility map (in transparency). Susceptibility degree increases from green (class 1) to red (class 5) colors.

**Figure 11.**Map (

**a**) represents the contributing diffuse contamination areas defined by the occurrence frequency of particles backtracked within the study area. High percentages of frequency express a high probability of finding sources responsible for diffuse PCE contamination (red areas). Map (

**b**) shows the distribution of the normalized posterior probability. High percentages of normalized posterior probability correspond to a high degree of susceptibility (red areas).

**Table 1.**Percentages of area covered by each susceptibility class and each category of coefficient of variation within the study area.

Susceptibility Classes | 1 | 2 | 3 | 4 | 5 | Sum ^{1} | |
---|---|---|---|---|---|---|---|

Coefficient of Variation Categories | |||||||

0–25% | 0.1 | 0.1 | 0.9 | 0.2 | 0.0 | 1.3 | |

25–50% | 13.1 | 10.6 | 12.4 | 10.8 | 1.5 | 48.5 | |

50–75% | 3.5 | 1.2 | 7.0 | 11.8 | 3.1 | 26.7 | |

75–100% | 1.7 | 2.3 | 4.2 | 3.4 | 1.2 | 12.8 | |

≥100% | 4.0 | 1.9 | 2.8 | 1.6 | 0.3 | 10.6 | |

Sum ^{2} | 22.4 | 16.1 | 27.4 | 27.9 | 6.2 | 100 |

^{1}Sum of areas corresponding to the same category of coefficient of variation (in percentages).

^{2}Sum of areas corresponding to the same susceptibility class (in percentages).

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## Share and Cite

**MDPI and ACS Style**

Pollicino, L.C.; Masetti, M.; Stevenazzi, S.; Colombo, L.; Alberti, L. Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas. *Water* **2019**, *11*, 1211.
https://doi.org/10.3390/w11061211

**AMA Style**

Pollicino LC, Masetti M, Stevenazzi S, Colombo L, Alberti L. Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas. *Water*. 2019; 11(6):1211.
https://doi.org/10.3390/w11061211

**Chicago/Turabian Style**

Pollicino, Licia C., Marco Masetti, Stefania Stevenazzi, Loris Colombo, and Luca Alberti. 2019. "Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas" *Water* 11, no. 6: 1211.
https://doi.org/10.3390/w11061211