Intrinsic groundwater vulnerability assessment: issues, comparison of different
methodologies and correlation with nitrate concentrations in NW Italy
Manuela Lasagna1*, Domenico Antonio De Luca1, Elisa Franchino1
Earth Sciences Department, Turin University Via Valperga Caluso 35, 10125 Turin (Italy) e-mail: manuela.lasagna@unito.it
*Corresponding author (phone: +390116705171) Abstract
The concept of aquifer vulnerability is certainly useful in the field of groundwater protection. Nevertheless, within the scientific community, the definition of vulnerability is still under debate and lacks standardisation. As a consequence, the methods for evaluating the vulnerability degree are numerous and often lead to conflicting results. Thus, in this study, three methods that are commonly used in groundwater vulnerability assessments due to their easy application (namely, DRASTIC, GOD and TOT) were utilised in four areas of the Piedmont region (NW Italy). The results obtained by the different methods were compared and correlated with the nitrate concentrations in the groundwater. The aims of the study were i) to evaluate the effectiveness of the adopted methods and their comparability, ii) to discuss the limits of the intrinsic vulnerability methods, and iii) to verify the applicability of nitrate as a tracer in the assessment of groundwater vulnerability or explain the reasons why it is not applicable.
It was observed that the three intrinsic vulnerability methods are not able to uniquely identify the most or least vulnerable areas. Additionally, the comparison of the intrinsic vulnerability indexes only occasionally showed a reasonable correlation. Furthermore, there was no clear correlation between the vulnerability indexes and nitrate concentrations in the groundwater. These results could be explained by several reasons, including (1) the methods are mostly based on the level of groundwater protection provided by the overlaying lithologies and do not consider the physical processes occurring in the aquifer; (2) the intrinsic vulnerability methods only consider vertical pathways for contaminants, but a pre-existing contaminant could be present in the aquifer; (3) groundwater nitrate concentrations are affected by the nitrate input and surplus; and (4) nitrates are subject to physical and biological attenuation in aquifers and cannot necessarily be considered stable tracers in the assessment of groundwater vulnerability.
Keywords
Nitrate; intrinsic vulnerability; parametric methods; dilution; denitrification; Italy
1. Introduction
The concept of aquifer pollution vulnerability was introduced in Europe in the early 1970s (Albinet and Margat 1970). Since the late 1970s, different definitions have been proposed, and related mapping systems have been developed. The authors attempted to represent a complex system in a simple, scientifically based way. Among numerous others, the proposed definition by Albinet and Margat (1970) described vulnerability as the penetrating and spreading abilities of the pollutants in aquifers, according to the nature of the surface layers and the hydrogeological conditions. Foster (1987) described aquifer pollution vulnerability as the intrinsic characteristics which determine the sensitivity of various parts of an aquifer to being adversely
affected by a contaminant load. The National Research Council (NRC, 1993) defined groundwater vulnerability as the tendency or likelihood for contaminants to reach a specific position in the groundwater system after their introduction at some location above the uppermost aquifer. Vrba and Zoporozec (1994) defined groundwater vulnerability as an intrinsic property of a groundwater system depending on the sensitivity of that system to human and/or natural impacts. According to the ASTM Standard Guide D6030-96 (ASTM, 2002), vulnerability is the potential for groundwater or an aquifer to become contaminated based on intrinsic hydrogeological characteristics. In the European COST Action 620, a logical terminology based on the work of Brouyère et al. (2001) and Frind et al. (2006) was proposed. Accordingly, intrinsic vulnerability was defined as the vulnerability of groundwater to contaminants, taking into account the inherent geological, hydrological, and hydrogeological characteristics of the system, but independent of the nature of the contaminants, while specific vulnerability additionally considers the chemical behaviour of the contaminants and the vulnerability of the groundwater to a particular contaminant or group of contaminants.
A detailed overview of groundwater vulnerability definitions and assessment methods is reported in Wachniew et al. (2016).
Thus, two vulnerability concepts can be distinguished from the reported definitions:
-
vulnerability as the likelihood of a generic contaminant to arrive in groundwater; this vulnerability can be defined as "vulnerability to a contaminant arrival”.-
vulnerability as the likelihood of exceeding a contaminant threshold concentration in groundwater; this vulnerability can be defined as “vulnerability to pollution”.In this study, the second concept was considered. Indeed, local managers and stakeholders are generally interested in knowing whether the groundwater in a specific area can be damaged by an excessive amount of a contaminant, not just its arrival in the aquifer.
Due to the abundance of the available definitions of groundwater vulnerability, this concept is perceived as ambiguous and not clear and conclusive (Daly et al., 2002; Frind et al., 2006, Stigter et al., 2006, Sorichetta, 2010; Stevenazzi et al. 2017). The lack of standardisation of the vulnerability concept is the reason for the numerous methods of vulnerability assessment, which sometimes lead to conflicting results or even lack scientific evidence.
In this study, three methods for assessing intrinsic vulnerability were applied in four areas of the Piedmont Plains (NW). Two of the applied methods, DRASTIC (Aller et al. 1987) and GOD (Foster 1987; Foster et al. 2002), are parametric methods, and they are among the best known and used methods in the literature. TOT (Zampetti 1983) is a numeric method that returns an evaluation of the time of travel of a generic contaminant in groundwater.
To evaluate the effectiveness of these methods and their comparability, which are often criticised by authors (Yu et al. 2010), a comparison of the obtained results was performed. Moreover, these results were validated using nitrate concentrations. Indeed, nitrate has been recognised as the most widespread shallow groundwater contaminant in the Piedmont Plains (Lasagna et al. 2015; Lasagna & De Luca 2016; Lasagna et al. 2016b). Furthermore, nitrate is a widespread pollutant in groundwater in many countries, especially in agricultural areas, and it has been proposed as a representative indicator of groundwater quality (US Environmental Protection Agency 1996). For all these reasons, nitrate can potentially be a good indicator of aquifer vulnerability. The validation using nitrate was performed to verify the applicability of this compound as
Finally, the results of the aforementioned assessment methods and nitrate validation were examined and discussed in order to highlight the limits of intrinsic vulnerability methods.
2. Previous studies
Several studies have tried to correlate intrinsic vulnerability assessments and the nitrate concentrations in groundwater (Evans & Maidment 1995; Stigter et al. 2006; Panagopoulos et al. 2006; Debernardi et al. 2008; Masetti et al. 2008; Sener et al. 2009). Nitrate was selected as a pollution index because it was considered a sufficiently stable contaminant not influenced by significant degradation or attenuation phenomena. However, the application of the most-used intrinsic vulnerability methods yielded good results in only a few cases. Among them, Sener et al. (2009) assessed groundwater vulnerability in the Senirkent-Uluborlu basin (Turkey) by applying the DRASTIC method. They created a vulnerability map and verified the vulnerability indexes by the nitrate concentrations in the groundwater. Their results showed that areas with high nitrate concentrations can be correlated with the DRASTIC index. In several other studies, there is a lack of correlation between intrinsic vulnerability indexes and nitrate concentrations in groundwater. Evans and Maidment (1995) compared nitrate concentration values in groundwater with vulnerability maps of Texas obtained with the DRASTIC index. They found that the probability of measuring high nitrate concentrations in groundwater was lower in areas of Texas where the DRASTIC index was high than in other areas. On the other hand, the probability of finding high nitrate concentrations in groundwater was higher in sectors where the DRASTIC index was low. In addition, the authors tried to use the nitrate concentrations in groundwater as a substitute for vulnerability. More specifically, they chose the probability of finding a nitrate concentration in groundwater higher than 0.1, 1, 5 or 10 mg/l as a measure of vulnerability. In their opinion, this method has some advantages compared to parametric methods because, by using concentrations, vulnerability values are more quantitative. Stigter et al. (2006) created vulnerability maps for two areas in southern Portugal with the DRASTIC intrinsic vulnerability method. Their results showed little correlation between the vulnerability indexes and contaminated areas. According to the author, this is mainly a result of the underestimation of the dilution process and overemphasis of the attenuating potential of the unsaturated zone and aquifers.
In recent studies, some authors have tried to implement parametric intrinsic vulnerability methods to improve the representativeness of the indexes. Panagopoulos et al. (2006) applied a modified DRASTIC method in the Trifilia region of Greece. They performed a revision of the rating and weighting factors of the DRASTIC parameters by simple statistical and geostatistical techniques. Then, they compared the correlation coefficient of each parameter with the nitrate concentrations in the groundwater. Based on their statistical significance, some parameters were subtracted from the DRASTIC index equation, while land use was considered as an additional DRASTIC parameter. Their study showed that some factors of intrinsic vulnerability assessments, such as hydraulic conductivity and soil type, did not seem to be related to nitrate concentrations. However, the addition of the contaminant loadings to the equation, based on the land use distribution, improved the correlation between vulnerability and nitrate concentrations. Antonakos & Lambrakis (2007) developed and checked three hybrid methods, combining the DRASTIC method categorisation and rating with statistical procedures such as descriptive statistics, logistic regression and weights of evidence. The comparison was based on the correlation of their results to nitrate pollution occurrences or the probability of exceeding a specific nitrate concentration in groundwater. They asserted
that the original DRASTIC model can be used as a theoretical background for the application of hybrid methods for groundwater pollution risk assessment if a good hydrochemical database is available. The statistical methods applied produced relatively poor estimates in areas where few or no sample points existed. Javadi et al. (2010) asserted that the DRASTIC method needs to be calibrated and corrected for specific aquifers. In their study, the DRASTIC parameter rates were corrected using the relationships between each parameter and the nitrate concentrations in the groundwater. They applied the proposed methodology to the Astaneh Aquifer in northern Iran. The correlation between the DRASTIC values and nitrate concentrations were calculated based on the Pearson correlation factor. Their results showed that the modified DRASTIC method allowed a better vulnerability assessment than the original method for diffuse pollution sources in the agricultural study areas.
Debernardi et al. (2008) provided a comparison between the nitrate concentrations measured in wells and piezometers and the GOD and TOT indexes assessed in the Piedmont Region plain (Bove et al. 2005). The GOD method was originally formulated for use in areas with limited data availability (Foster 1987). However, for preliminary studies, the GOD method provides good preliminary tools (Belmonte-Jiménez 2005). Indeed, it is a recommended method for the quick assessment of intrinsic vulnerability, especially in areas where no intensive human activity is present (Dragoi & Popa 2007). Debernardi et al. (2008) found a seemingly significant correlation between the nitrate and GOD indexes; more specifically, when the GOD index increased, the maximum nitrate concentrations in the shallow aquifer generally increased. However, there is not always a direct correlation between nitrate concentrations and all GOD indexes, as there is a wide range of nitrate concentrations in groundwater. Thus, the impact of nitrates in groundwater depends on various parameters, which are not taken into consideration by this method.
The TOT method represents the time required for a contaminant to move through the vadose zone from a specific point to the aquifer. For the GOD method, Debernardi et al. (2008) performed a comparison between nitrate concentrations and the TOT values assessed in the Piedmont region plain. These two variables seemed to be linked by a negative exponential trend: the higher the nitrate concentrations, the lower the TOT values. Nevertheless, very different nitrate concentrations can be present in groundwater for low TOT values due to various hydrogeological and land use situations. Hence, according to the authors, TOT values cannot fully explain the phenomenon of groundwater nitrate contamination, even if they have a good correlation with maximum nitrate concentrations.
3. Methods
In this paper, three intrinsic groundwater vulnerability methods (DRASTIC, GOD and TOT) were used in four study areas of the Piedmont region in north-western Italy. These four sample areas were located in the plains, particularly the Cuneo plain, Vercelli plain, and Alessandria plain, and on the Poirino plateau.
3.1 DRASTIC method
The DRASTIC method (Aller et al. 1987) is based on seven parameters including the depth to water (D), annual effective recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone media (I) and hydraulic conductivity (C). The vulnerability index is defined as a weighted sum of the scores assigned to these parameters according to equation 1:
where Dr, Rr, Ar, Sr, Tr, Ir, and Cr are the parameter scores, based on the characteristics of a specific area, and D, R, A, S, T, I, and C represent the corresponding weights of the score values.
Because Aller et al. (1987) did not propose any classification for DRASTIC results, the vulnerability ranges of the DRASTIC index used in this study are based on those of Civita and De Regibus (1995) and Corniello et al. (1997). More specifically, the vulnerability degree is very low for a DRASTIC index value less than 80, low for a value between 80 and 120, moderate for a value between 120 and 160, high for a value between 160 and 200, and very high for a value greater than 200.
3.2 GOD method
The GOD method (Foster 1987; Foster et al. 2002) is a simple parametric scoring method. The intrinsic vulnerability index is calculated by multiplying the values of each parameter, according to equation 2:
GOD Index = G × O × D eq. 2
where G represents the degree of groundwater hydraulic confinement, O is the overlying strata (lithology and consolidation), and D is the depth to the groundwater table for unconfined aquifers or the strike for confined aquifers.
The degree of confinement (G), whose values range from 0 to 1, can be classified into six classes. The lithological characteristics and the degree of consolidation (O) can range from 0.4 to 1. Finally, values between 0.6 and 1 can be assigned to the parameter D for both unconfined and confined aquifers.
The vulnerability degree is considered negligible with a GOD index value between 0 and 0.1, low for a value between 0.1 and 0.3, moderate for a value between 0.3 and 0.5, high for a value between 0.5 and 0.7 and extreme for a value between 0.7 and 1.
3.3 TOT method
The TOT method (Zampetti 1983) was developed in the late 1970s by Division XI of the European Community Commission under a research project aimed at mapping the quality of water resources. This method is based on the evaluation of the time taken by a pollutant, which physically and chemically behaves like water, to cover the distance between the ground surface and the upper aquifer boundary (defined as the time of travel). This time is calculated by applying the following relationship (equation 3) to each layer of the unsaturated zone:
TOT = b*ne / k*i eq. 3
where b is the thickness of the layer, ne is the effective porosity, k is the hydraulic conductivity and i is the hydraulic gradient. The time of travel through the aquifer is calculated by assuming the precautionary conditions in the evaluation of vulnerability, i.e., the saturation of the unsaturated zone and unit hydraulic gradient. Given these assumptions, the total time of travel is the summation of the times needed to cross each layer of the unsaturated zone, according to equation 4:
TOT = Σbne/k eq. 4
The vulnerability degree is considered extremely high if the TOT is lower than 1 day, very high if it is between 1 day and 1 week, high if it is between 1 week and 1 year, moderate if it is between 1 year and 10 years, low if it is between 10 and 20 years, and very low if it is higher than 20 years (Zampetti 1983, De Luca and Verga 1991).
3.4 Databases and data elaboration
In this paper, stratigraphic logs and well test data from a database of the Earth Science Department of Turin University were used to define the hydrogeological framework and the hydrogeological parameters. The hydraulic conductivity was derived from the transmissivity values, which were calculated from the well tests, and the aquifer thickness. The depth to the groundwater table was determined from the maps presented in Bove et al. (2005). The net recharge values of the four studied areas were estimated values derived from regional databases (Regione Piemonte 2007). The type of soil was obtained by consulting the Piedmont Region Soil Map (Regione Piemonte 2010).
The nitrate concentrations in the groundwater were obtained from the Regional Agency for Environmental Protection (ARPA Piedmont) database (http://www.regione.piemonte.it/monitgis/jsp/cartografia/mappa.do). The nitrate data were from the 2012 spring campaign, which was conducted at 380 points in the Piedmont shallow aquifer monitoring network. The use of only one sample campaign is due to the general similarity of the spring and autumn sampling campaign data. The data were elaborated in a GIS environment using a kriging interpolation. Moreover, the statistical interpolation was modified according to previous data (local nitrate concentration maps, reports, and papers) in order to refine and improve the elaborated map. In addition, ammonium and nitrite were considered for the shallow aquifer. However, the ammonium concentration was below the detection limit (0.04 mg/L) in 94% of the 380 monitored points. In the other 6% of the sampling points, the concentration is mostly less than 0.5 mg/L. Nitrite was analysed in 111 groundwater samples, and in most of the samples, the concentration was lower than the detection limit (0.01 mg/L). For these reasons, the data elaboration was conducted only using nitrate levels.
The vulnerability indexes, evaluated using the GOD, DRASTIC and TOT methods, were first obtained by creating a thematic map for each considered parameter. The data were interpolated using kriging via the software package SURFER (Golden software, Golden, CO, USA).
Then, a regular net was created, and a database of points was produced. In detail, the indexes were estimated at 553 points in the Cuneo area, 882 points in the Poirino area, 878 points in the Alessandria area and 1001 points in the Vercelli area.
4. Study area
The Piedmont region is situated in NW Italy. It is the second largest Italian region, with an area of approximately 25400 km². The Piedmont region is mostly mountainous (43.3%), but it also has extended hilly areas (30.3%) and plains (26.4%). This paper focuses on the plains, which are the largest and most important groundwater reservoir in the region (Debernardi et al. 2008; De Luca et al. 2014). In the Piedmont plains, five different hydrogeological complexes (Fig. 1) can be recognised (Lasagna et al. 2013) and are hereafter described from the top to the bottom:
1) Alluvial deposit complex: the alluvial deposits are characterised by gravelly sandy texture with subordinate silty-clayey intercalations (Late Pleistocene to Mid-Holocene). The grain size is variable and normally decreases from the mountains to the low plains along the Po River. This unit has a thickness generally ranging between 20 and 50 m, and it hosts a shallow unconfined aquifer.
2) “Villafranchiano” complex: the alluvial deposit unit lays on the ‘‘Villafranchiano’’ unit (Late Pliocene – Early Pleistocene), which consists of fluvial-deltaic deposits characterised by alternations of silty-clayey and
gravelly sandy horizons, which form a multi-layered confined and semi-confined aquifer system. This unit is irregularly found beneath the Piedmont plains.
3) Pliocene marine complex: the marine unit (Early Pliocene – Pliocene) consists of "Argille di Lugagnano", with fine texture sediments, and "Sabbie di Asti", formed by fine sands that can host confined aquifers. 4) Oligo-Miocene marine succession: this unit consists of fine-grained Pre-Pliocene marine deposits, which are impermeable or locally have a low fracture permeability.
5) Alpine basement unit: this unit consists mostly of crystalline rocks that are impermeable or have low fracture permeability; locally, karstic circuits can exist in calcareous rocks.
Figure 1 - Hydrogeological scheme of the Piedmont region (simplified from Aquater/Regione Piemonte, 1988) and litho-stratigraphical sections (modified from Irace et al., 2009).
Four sample areas were selected in the Piedmont plain. (Fig. 2). More specifically, one area is located in Turin Province, specifically on the Poirino plateau (Poirino area). The other areas are situated in the Cuneo
plain, Vercelli plain and Alessandria plain. Table 1 shows the main characteristics of the study areas. Because this research analysed the situation in the shallow aquifer, the hydraulic conductivity referred to the Alluvial deposit complex, with an aquifer media generally consisting of gravel and sand (Castagna et al. 2015a). The Poirino plateau demonstrates a unique situation: indeed, different from most of the Piedmont plain, the Alluvial deposit complex in this sector consists of silt and clay, with rare sandy–gravelly levels ( De Luca et al. 2018). In this area, the shallow aquifer has a small thickness and low productivity.
The soil media, for all areas, are composed of loam, sandy loam and silty loam.
Each area was chosen by following a flow tube from the recharge area to the drainage area (Bove et al. 2005; Castagna et al. 2015b; Bucci et al. 2017; Lasagna et al. 2014). This is the reason for the different forms and dimensions of the four sample areas.
All the sample areas are mainly supplied by direct rainfall and rivers at the outlet of the valleys in the plain. The base of the shallow aquifer is generally well marked due to the textural variability of the deposits (Bove et al. 2005). More specifically, the base is usually identified by the presence of thick and relatively continuous layers of silt or clay-rich deposits.
The land use in the plain can be identified as the cause of groundwater nitrate contamination, especially for the shallow aquifer (Lasagna and De Luca 2017; Martinelli et al. 2018).
Study area Analysed area(km2)
Depth to groundwater
table (m)
Hydraulic
conductivity (m/s) Dominantland use Poirino area 160 1-30 5.36*10Mean= 7.70*10-5 - 5.10*10-4-3 Crop fields
Vercelli area 227 1-37 1.37*10Mean= 3.39*10-4 - 6.15*10-4-4 Rice fields
Cuneo area 209 1-51 5.72*10Mean= 5.55*10-5 - 2.72*10-4-3 Crop fields
Alessandria area 172 1-18 2.79*10Mean= 4.45*10-6 - 3.67*10-4-3 Corn/wheatcrops fields
Table 1. Main features of the study areas.
5. Results and discussion
5.1 Intrinsic vulnerability indexes comparison
In Table 2, the variation ranges of the GOD index, DRASTIC index, and TOT are reported for the four study areas. The lowest values of the GOD (0.2) index were estimated for the Poirino and Alessandria areas, whereas the highest values (0.8) were estimated for the Cuneo area. Using the DRASTIC method, the lowest values (120) were estimated for the Vercelli area and the highest values were estimated (207) for the Cuneo area, which agreed with the results of the GOD method. The TOT method indicated the longest time of travel (113 days) for the Alessandria area; for all study areas, the minimum time of travel was less than half day.
GOD index DRASTIC index TOT (days)
range mean median range mean median range mean median
Poirino area 0.2-0.7 0.4 0.4 143-189 156 168 0.4-87 19 14
Vercelli area 0.3-0.7 0.5 0.5 120-189 162 160 0.1-43 6 4
Cuneo area 0.3-0.8 0.6 0.6 156-207 186 187 0.1-64 4 0.6
Alessandria
area 0.2-0.7 0.5 0.5 146-196 176 177 0.1-113 12 5
Table 2. Summary of the GOD and DRASTIC index values, TOT results and statistical data for the four study areas.
Furthermore, the comparison between the intrinsic vulnerability indexes only occasionally shows a reasonable correlation. Fig. 3 shows the biplot analysis of the DRASTIC indexes vs the GOD indexes. The Vercelli plain areas comprise moderate to high GOD vulnerability degrees and low to high DRASTIC vulnerability degrees. The Poirino and Alessandria plains show vulnerability degrees ranging from low to high with the GOD method and moderate to high with the DRASTIC method. Finally, the Cuneo plain shows the highest vulnerability according to both the GOD and DRASTIC methods, with a degree ranging from moderate to very high.
The GOD and DRATIC vulnerability indexes show a positive linear correlation; indeed, as the DRASTIC index increases, the GOD index generally also increases. Although the trend is recognisable, the correlation is not always clear. In fact, the coefficients of determination (R2) of linear regression analysis are, for all the
area: R2=0.24). In Fig. 4 and Fig. 5, the correlation between TOT and, respectively, the GOD and DRASTIC
indexes are reported. For the TOT values, the most populated interval is clearly between 0 and 20 days. In general, as the TOT increases, the GOD and DRASTIC indexes decrease or otherwise vary widely. The biplots of Fig. 4 and Fig. 5 highlight a nonlinear correlation with a curved hyperbolic trend. Moreover, in Fig. 5, the DRASTIC index trend becomes linear and parallel to the x-axis after a TOT value of 40 days.
Regarding the vulnerability degree, according to the TOT method, it ranges from high to extremely high for all the study areas. This result is in contrast with those of the other methods, which indicated a vulnerability degree from low to extremely high (GOD method, Fig. 4) and from moderate to very high (only for a few points in the Cuneo plain, DRASTIC method, Fig. 5).
Thus, these comparisons show that the different intrinsic vulnerability methods are not able to uniquely identify the most or least vulnerable areas.
Figure 4 – GOD and TOT vulnerability degrees and correlation.
Figure 5 – DRASTIC and TOT vulnerability degrees and correlation.
5.2 Comparison between intrinsic vulnerability and nitrate concentration
Intrinsic vulnerability methods generally provide support for monitoring and environmental protection plans. To evaluate their effectiveness, the DRASTIC, GOD and TOT indexes previously evaluated were validated using the nitrate concentrations in the groundwater. The considered methods should provide a system response that works for any type of contaminant due to the intrinsic nature of their vulnerability assessments. Theoretically, there should be a positive correlation between the intrinsic vulnerability assessment and the nitrate concentrations in groundwater, especially in predominantly agricultural areas. However, this
correlation depends on the nitrate input to the system. More specifically, in an area with a constant nitrate input, increasing the degree of vulnerability should also increase the nitrate concentrations. The theoretical trend for this particular correlation between the vulnerability degree and nitrate concentration is reported in Fig. 6A. When the nitrate input is not evenly spread (e.g., in large areas with different crops type), it is possible to have different nitrate concentrations in areas with the same vulnerability degree. This situation is outlined in Fig. 6B, in which points represent nitrate concentrations depending on both the vulnerability and nitrate input. These points are randomly distributed in the biplot, but it possible to draw an envelope curve connecting the maximum nitrate concentration for each vulnerability degree and observe a rising trend.
Figure 6 - Expected correlation between the groundwater nitrate concentration and aquifer vulnerability. (A) Area with a constant nitrate input: the red line represents a theoretical trend between the vulnerability degree and nitrate concentration. (B) Area with an uneven nitrate input: the points represent the concentrations depending on both the vulnerability degree and nitrate input; the black line represents the envelope curve connecting the maximum nitrate concentrations.
After applying the three vulnerability methods in the study areas, little correlation between the nitrate concentration in the groundwater and the vulnerability indexes was observed (Fig. 7), considering both all data and the data for single study areas.
In Fig. 7A and Fig. 7B, the DRASTIC and GOD indexes are correlated to the nitrate concentrations.
The analysis of the maximum value enveloping the nitrate concentrations highlights a bell-shaped trend. Thus, an increase in the nitrate content due to an increase in the vulnerability index was not found for the DRASTIC and GOD methods. In general, a large range of nitrate concentrations (0-80 mg/L) is located in areas with a vulnerability degree ranging from moderate to high for both the DRASTIC and GOD methods. High nitrate concentrations (between 50 mg/L and 125 mg/l) are located in areas characterised by a vulnerability degree ranging from moderate to high according to the DRASTIC method and from moderate to extreme according to the GOD method. For both of the methods, the areas with the highest degrees of vulnerability were sampling points with nitrate levels between 40 and 60 mg/L.
Analysing the single study areas, the highest vulnerability degrees were located in the Cuneo plain, both for the GOD and DRASTIC methods, and the monitoring wells show nitrate levels between 40 mg/L and 60
In Fig. 7C, where the TOT values are plotted against the nitrate concentrations, a nonlinear correlation occurs, if only the maximum nitrate values are considered. More specifically, as the TOT value decreases, the maximum nitrate concentration relative to each vulnerability degree increases. Furthermore, all the sampling points with nitrate levels from 0 to 125 mg/L are located in areas with a vulnerability degree ranging from high to extremely high. However, the highest nitrate concentrations (90-125 mg/L) are in areas with high vulnerability degrees, whereas concentrations ranging from a few mg/L to 90 mg/L are in areas with vulnerability degrees ranging from very high to extremely high.
Considering the individual study areas, the value distribution is highly variable. In the Poirino and Cuneo areas, the comparison between the TOT and nitrate concentration values shows a trend very similar to the theoretical trend (Fig. 6B). Indeed, the maximum nitrate concentration increases with the increase in the vulnerability. In the Alessandria area, the TOT vs nitrate concentration comparison is characterised by a trend in which decreases in the time of travel are related to weak increases in the maximum nitrate level relative to each vulnerability degree. The Vercelli area shows no correlation between vulnerability and nitrate levels in the aquifer.
5.3 Possible reasons for the lack of correlation between the intrinsic vulnerability assessments and nitrate concentrations in groundwater
Figure 6 is a conceptual simplification of how to carry out a correlation between aquifer nitrate concentrations and vulnerability. However, the nitrate input is not the only factor that can influence this theoretical correlation. Different factors can occur.
5.3.1 Nitrate inputs and nitrogen surplus
A criticism of the correlation between the intrinsic vulnerability indexes and nitrate concentrations is that the correlation disregards the effective input of nitrate in the groundwater of sample areas. The amount of nitrates that could potentially reach the groundwater system can be defined in terms of the nitrogen surplus (N surplus). The N surplus represents the difference between the amount of nitrogen input into the soil as fertiliser and that removed by agricultural crops and other degradation processes in the soil; this factor provides an indication of the potential contributions of nitrate leaching into the groundwater. Therefore, a correlation between the N surplus and maximum nitrate concentrations in groundwater is conceivable, with higher nitrate concentrations in areas with an elevated N surplus, given an equal degree of vulnerability (Fig. 8A). In cases of significant variations in nitrate vulnerability, such as studies at a regional scale (Fig. 8B), a cloud of points that represent the nitrate concentrations depending on both the vulnerability and N surplus is expected. These points are randomly distributed in the biplot, but it is possible to draw an envelope curve connecting the maximum nitrate concentrations for each N surplus and thereby observe a rising trend.
Figure 8 - Expected correlation between the nitrate concentrations in groundwater and the N surplus. (A) Area with a constant degree of vulnerability; the red line represents the theoretical trend between the N surplus and nitrate concentration. (B) Area characterised by different degrees of vulnerability; the points represent the concentrations depending on both the vulnerability and N surplus; the black line represents the envelope curve connecting the maximum nitrate concentrations.
Burigana et al. (2003) analysed the surplus of nitrogen at a farm scale, and they found a good regression between leaching and the N-surplus and suggested that the surplus is a reliable indicator of agricultural impacts on water. Other authors (Lasagna et al 2016a) tried to compare the nitrate distribution and N-surplus
in the Turin plain and highlighted many discrepancies. In detail, they found low and medium N-surplus values not only where the nitrate concentrations were low but also in areas where the nitrate levels were high and very high. They suggested that the lack of correlation between the N-surplus and nitrate indicated the presence of other processes in the aquifer.
In this paper, the N surplus was compared with the nitrate levels in four sample areas. The N surplus values were evaluated by AGROSELVITER (Department of Agricultural, Forest and Food Sciences and Technologies of Turin University) in a project performed in collaboration with Regione Piemonte on the entire Piedmont plain (Grignani & Sacco 2005). The N surplus is defined as the algebraic value obtained by the difference between the N-inputs to the soil and the N-outputs of crop products. It is expressed as a quantity unit per hectare per year. More specifically, the N-inputs for the study areas are represented by mineral nitrogen, which is derived from synthetic N-fertilisers, and organic nitrogen, which is derived from zootechnical effluents. The N surplus evaluations were made in homogeneous agricultural zones, defined on the basis of soil type, farms typology, crop type and productivity.
A clear correlation between the surplus results, which presented a wide range of values, and the nitrate concentrations at a regional scale was not found (Fig. 9). Instead, some correlations were found in each individual sample area. The Alessandria area shows a positive linear trend when considering the maximum nitrate concentration values. The Vercelli area shows a positive linear trend only above a nitrogen surplus of 50 kg/ha/yr. The Poirino area shows a downward trend from the nitrogen surplus value of 60 kg/ha/yr. The Cuneo area shows a constant trend for the maximum nitrate concentration values. Therefore, the nitrate surplus does not solely justify the nitrate concentrations in groundwater.
Figure 9 – Correlation between the nitrate concentrations in the groundwater and the annual nitrogen surplus.
5.3.2 Contaminant pathway
within an unconfined aquifer in a simple hydrogeological system (Fig. 10). On the ground surface, sources of nitrate include corn crops (S1) or livestock (S2). Applying the cited vulnerability methods, the vulnerability of the point P would be evaluated based on the intrinsic characteristics of the unsaturated zone. In this way, only the nitrate input from S1, placed vertically over the point, is considered for the intrinsic vulnerability assessments. Contributions from source S2, which first follow a vertical pathway (nitrate leaching into groundwater) and then tend towards a horizontal pathway (groundwater transported according to the hydraulic gradient) are not taken into account. Thus, there is an underestimation of the possible vulnerability of point P. In fact, the vulnerability of P should be determined by the sum of two different assessment tools (De Luca 1990):
a vertical vulnerability assessment a horizontal vulnerability assessment
Figure 10- Schematic and simplified possible contaminant pathways in a groundwater system.
5.3.3 Contaminant attenuation in aquifers: the dilution process
In aquifers, a dilution process occurs that affects all types of contaminants and consists of a combination of diffusion and mechanical dispersion processes. A contaminant flux arrives at a mixing zone within the aquifer, resulting in a lower contaminant concentration value. Thus, the contaminant concentrations and the degree of vulnerability to pollution decrease, increasing the dilution process.
Agricultural and farming areas are characterised by diffuse nitrate contamination in groundwater due to the ubiquitous use of synthetic N-fertilisers and zootechnical effluents, and the dilution phenomena can weaken the net recharge of nitrate in the groundwater (Stigter et al. 2011).
The standard intrinsic vulnerability assessment methods usually do not consider the quantitative processes of concentration attenuation that occur in aquifers. In this framework, intrinsic vulnerability assessments could potentially be useful assess the vulnerability associated with the arrival of contaminants but not the vulnerability to pollution.
Moreover, the dilution process, in contrast to the process of denitrification, is not affected by the chemical environment of the groundwater. Thus, dilution might play a very important role in nitrate concentration attenuation in shallow aquifers (Bekesi and McConchie 2002; Stigter et al. 2006; Lasagna et al. 2013; Lasagna et al. 2016a).
Lasagna et al. (2016a) tried to correlate the different nitrate concentrations in the Turin Po Plain with the qu
values (Lasagna et al. 2013) describing the degree of dilution in an aquifer. A trend was highlighted, in that higher nitrate concentrations were observed in zones with lower qu values and vice versa.
5.3.4 Contaminant degradation processes
Nitrate, as is the case with most contaminants, cannot be considered conservative because it can be subjected to degradation processes such as denitrification while travelling from the topsoil to groundwater. Denitrification is a multi-step process that involves various nitrogen oxides (N2O, NO), intermediate
compounds resulting from the chemically or biologically mediated reduction of nitrates to N2 (Korom 1992).
This degradation process has been observed, especially in deep, generally anoxic aquifers and in the deepest portions of shallow aquifers (Gillham and Cherry 1978; Postma et al. 1991; Starr and Gillham 1993). Indeed, microbial denitrification requires reducing conditions, and it does not occur in the presence of significant amounts of oxygen. However, recent studies have also highlighted the denitrification process in shallow aquifers (Toda et al. 2002; Sànchez-Pérez et al. 2003
)
.Studies on the role of chemical–biological attenuation processes, particularly denitrification, were conducted in the shallow aquifer of the Turin-Cuneo plain (NW Italy) using isotopic techniques.
These studies emphasised a denitrification process in the shallow aquifer, particularly in the Poirino plateau, probably due to the presence of deposits with a low permeability, which can enhance denitrification (Lasagna et al 2016a; Lasagna and De Luca, 2017).
In the light of these considerations, denitrification processes should be taken into account in the validation of intrinsic vulnerability maps with nitrate and in specific vulnerability assessments.
6 Conclusions
The studies in scientific literature show that the main advantages of parametric methods are related to their easy application and the presentation of thematic maps. However, these methods are often ineffective and difficult to validate. Moreover, intrinsic vulnerability methods, such as parametric methods, rely on parameter scores and weights based on expert opinions and are not objective and quantitative assessment processes. For example, Yu et al. (2010) asserted that DRASTIC was ineffective and hard to validate because it was built on experiences rather than studies of the quantitative physical processes in groundwater systems. Moreover, these authors highlighted that parametric methods for vulnerability assessments could not meet the demands of modern groundwater management and that quantitative techniques for groundwater vulnerability assessment are necessary.
In this study, three intrinsic groundwater vulnerability methods (DRASTIC, GOD and TOT) were used and compared in four areas of the Piedmont region (NW Italy). Moreover, the results obtained by the different methods were correlated with the nitrate concentrations in the groundwater.
The results show that the different intrinsic vulnerability methods are not able to uniquely identify the most or the least vulnerable areas. Moreover, the comparison between the intrinsic vulnerability indexes only occasionally showed a reasonable correlation. More specifically, the comparison between the DRASTIC and GOD indexes showed a recognisable trend (as the DRASTIC index increases, the GOD index generally also increases) but an unclear correlation. Furthermore, the correlation between TOT and, individually, the GOD and DRASTIC indexes highlighted a nonlinear correlation, although generally, as the TOT values increased, the GOD and DRASTIC index values decreased.
The validation of intrinsic methods using nitrate concentrations shows that only a small correlation between the nitrate in groundwater and vulnerability indexes is present. Accordingly, parametric methods for intrinsic vulnerability assessments are not validated by a comparison with this specific contaminant in groundwater. The lack of correlation between nitrate and vulnerability could be related to different reasons. One reason could be the different nitrate input/surplus levels of the different areas, which are reflected in various nitrate concentrations in the groundwater. Nevertheless, the nitrate surplus does not solely justify the nitrate concentrations in the groundwater.
Another reason could be related to the intrinsic vulnerability methods, which only consider vertical contaminant pathways, whereas a distinction between vertical and horizontal vulnerability assessments should be properly defined. Indeed, the pollutant levels of one point in the aquifer are due to the sum of contaminant concentrations following both vertical and horizontal pathways.
Moreover, in the intrinsic vulnerability methods, some parameters are not taken into consideration. For example, these methods do not consider, in a quantitative way, the attenuation processes that occur in a hydrogeological system. The contaminant concentration in groundwater is a dynamic condition; in shallow aquifers, contaminant inputs are renewed every year (in particular, widespread contaminants in agricultural areas). Notably, dilution in aquifers is a process that affects all contaminants to different degrees, decreases the concentrations and obscures a correlation between the contaminant input on the topsoil and the concentration values in the groundwater. For nitrate, denitrification also has a key role in its attenuation in aquifers. However, these processes are often neglected in studies investigating groundwater vulnerability, both in the validation of intrinsic vulnerability maps with nitrate concentrations and in specific vulnerability assessments.
Thus, although groundwater intrinsic vulnerability assessment methods play an important role in groundwater protection and, as reported by Foster et al. (2002), provide a basis for the adoption of safety measures and are normally the first step of a pollution risk assessment, the validity and application frameworks of these methods must be further discussed and developed.
Moreover, validation of vulnerability with nitrate concentrations is possible, but it is essential to consider the different nitrate inputs to the aquifer, the pre-existent nitrate level in the aquifer, and the processes of dilution and degradation that can occur, to different degrees, in aquifers. In fact, nitrate cannot be considered as a stable tracer in groundwater.
This discussion, although not exhaustive, aimed to focus on some concepts that are often neglected but pose serious problems in vulnerability assessments if neglected. In this sense, the considerations of this paper are useful for future studies and applications that will analyse aquifer vulnerability in a more extensive way.
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