Assessment of Groundwater Contamination Risk in an Agricultural Area in North Italy

Accepted Manuscript

Assessment of groundwater contamination risk in an agricultural area in north Italy

Georgios Bartzas, Federico Tinivella, Luca Medini, Dimitra Zaharaki, Kostas Komnitsas

PII: S2214-3173(15)00031-1 DOI: http://dx.doi.org/10.1016/j.inpa.2015.06.004 Reference: INPA 28

To appear in: Information Processing in Agriculture

Received Date: 14 May 2015 Accepted Date: 19 June 2015

Please cite this article as: G. Bartzas, F. Tinivella, L. Medini, D. Zaharaki, K. Komnitsas, Assessment of groundwater contamination risk in an agricultural area in north Italy, Information Processing in Agriculture (2015), doi: http:// dx.doi.org/10.1016/j.inpa.2015.06.004

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Assessment of groundwater contamination risk in an agricultural area in north Italy `

Georgios Bartzas a, Federico Tinivella b, Luca Medini c, Dimitra Zaharaki d, Kostas Komnitsas d,* a National Technical University of Athens, School of Mining and Metallurgical Engineering, 15780 Athens, Greece b Centro di Sperimentazione e Assistenza Agricola, Regione Rollo 98, 17031 Albenga (SV), Italy c LABCAM s.r.l., Regione Rollo 98, 17031 Albenga (SV), Italy d Technical University Crete, School of Mineral Resources Engineering, 73100 Chania, Greece, * Corresponding author. Tel.: +30 28210 37686; fax: +30 28210 69554 Email addresses: [email protected] (K. Komnitsas), [email protected] (G. Bartzas)

ABSTRACT

In the present study a specific approach is followed, considering the Pesticide DRASTIC and Susceptibility Index (SI) methods and a GIS framework, to assess groundwater vulnerability in the agricultural area of Albenga, in north Italy. The results indicate “high” to “very high” vulnerability to groundwater contamination along the coastline and the middle part of the Albenga plain, for almost 49% and 56% of the total study area for Pesticide DRASTIC and SI methods, respectively. These sensitive regions depict characteristics such as shallow depth to groundwater, extensive deposits of alluvial silty clays, flat topography and intensive agricultural activities. The distribution of nitrates concentration in groundwater in the study area is slightly better correlated with the SI (0.728) compared to Pesticide DRASTIC (0.693), thus indicating that both methods are characterized by quite good accuracy. Sensitivity analysis was also performed to acknowledge statistical uncertainty in the estimation of each parameter used, assess its impact and thus identify the most critical parameters that require further investigation in the future. Depth to water is the parameter that exhibited the largest impact on the Pesticide DRASTIC vulnerability index followed by the impact of the vadose zone and topography. On the other hand, the SI method is more sensitive to the removal of the topography parameter followed by the aquifer media and the depth to water parameters.

Keywords: Agriculture, Albenga, DRASTIC, Groundwater Vulnerability, Nitrate

1. Introduction

Contamination of groundwater in agricultural areas has become today a global concern and limits its availability as a resource for crop irrigation. The presence of several organic and inorganic contaminants in groundwater used for irrigation may cause several health problems to humans and result in loss of soil fertility and income for farmers. The impacts of groundwater contamination are more noticeable in areas suffering from desertification, salinization or when groundwater is not sufficient to support intense agricultural activities [1]. Nitrates and pesticides are the most common non-point source contaminants detected in shallow alluvial aquifers in agricultural areas. Alluvial aquifers are especially vulnerable to nitrate contamination and salinity problems due to a number of factors including shallow water table, highly permeable alluvial deposits, interconnections between surface water and agricultural related land uses usually carried out on floodplain terraces along river banks, and sea water intrusion due to over-pumping of groundwater for irrigation [2,3]. The assessment of groundwater vulnerability offers a low cost alternative to traditional groundwater quality plans and can be used to evaluate changes of risk over time, caused either from changes in land uses or because contaminants such as nitrates have migrated via preferential hydraulic flow pathways. The vulnerability of groundwater needs to be assessed as it is not only a function of the intrinsic properties of groundwater flow system (hydraulic conductivity, porosity), but also of the proximity of contaminant sources and their particular characteristics (location, chemical interaction with surface water) that could potentially increase the load of specific contaminants to aquifer systems. However, the estimation of groundwater vulnerability is a complex procedure and depends on the temporal and spatial variability of contamination sources [4,5]. Several approaches involving the use of deterministic or stochastic methods can be used to assess soil and predict groundwater contamination in industrial and agricultural areas. Factors such as soil type, pollution load, depth of aquifer, mobility and fate of contaminants should be always taken into consideration [6-9]. During the past decades, several methods for assessing groundwater vulnerability using different evaluation factors and approaches have been developed, including GOD [10], SINTACS [11], AVI [12] and the PI method [13]. Apart from all these methods, the DRASTIC method, developed by the US Environmental Protection Agency (US EPA), remains one of the most frequently used approaches to assess vulnerability to groundwater contamination in porous aquifers [14,15]. DRASTIC uses seven parameters, namely Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic Conductivity as weighted layers to enable a reliable assessment of vulnerability [16-18]. Recent studies have revealed that land use is also a key issue that has to be taken into account when predicting potential future hydrological responses and the effect of anthropogenic

2 activities on groundwater quality [19,20]. Within this context, the Susceptibility index (SI), developed by Ribeiro [21], is a contemporary adaptation of the Drastic method and has also been applied in this study to assess the effect of the land use on groundwater vulnerability in an agricultural coastal plain where adverse land use changes are common [22]. The Sustainability Index enables an in-depth and comprehensive analysis pertinent to the impacts of continuous urban development against the shortage of land resources for agricultural purposes. Pesticide DRASTIC and SI methods may be combined with GIS technology and remote sensing to develop an integrated approach, especially for heterogeneous media, that considers geological, hydrological and geochemical data to improve the reliability of risk estimation [23-25]. The major advantage of GIS-based groundwater vulnerability mapping is the use of data layers and the consideration of spatial variability of the parameters used for risk estimation [26,27]. The resulting vulnerability maps can be easily used by local authorities, decision- and policy makers for designing groundwater protection and remediation strategies [28]. The objective of this paper is to estimate groundwater vulnerability to contamination in the agricultural area of Albenga, in north Italy, using two appropriate methods (Pesticide DRASTIC and SI) suitable for shallow alluvial aquifer systems and determine risk levels based on calculated GIS-based vulnerability indices. Special emphasis is given on testing the reliability of the approach followed, in order to delineate the most vulnerable areas in the proximity of the defined Vulnerable Zone in terms of nitrate contamination. Furthermore, sensitivity and statistical analyses were conducted to evaluate, compare and validate the obtained results in terms of subjectivity, degree of parameter independence and variation effect.

2. Study area description

2.1. Location and climate

The study site is an experimental farm with coordinates 44°04'05.54''N and 8°12'45.51''E that belongs to the Centre for Agricultural Experimentation and Assistance (CERSAA), in Italy (Fig. 1). It is located about 1.5 km north from Albenga, a town at the Ligurian coastal region in the province of Savona, belonging to the geographical zone of Ligurian Alps in the north Italy. The municipality of Albenga has a territory of 36.50 km2, 24,200 inhabitants (in 2013) and a high density of population (663 inh/km2). The size of the area selected for risk analysis is 59 km2, extends from Albenga to Ceriale and is characterized by a steep sandy coastal zone with numerous human settlements, intensive agricultural activities and low forest cover. The climate of the study area is typical Mediterranean, with mean summer temperature ranging between 16.9 and 21.2 oC, and mean winter temperature between 8.8 and 9.9 oC [29].The mean annual temperature over a 20 year period (1991-2010) is 15.4 oC. The annual precipitation

3 for the same period ranges from 280 to 1150 mm with its mean value being 664 mm/year. Three quarters of the precipitation falls between May and October. The mean precipitation for summer is less than 28.4 mm (June - August), and increases to 85.8 mm during winter. Sudden showers occur very often in autumn causing flood events. The most recent floods (November 1994 and October/November 2000) have caused great damages to settlements, particularly in the town centre of Albenga.

Fig. 1 ‒ Location of the study area.

The Albenga coastal plain is a characteristic example of shallow alluvial aquifer chronically affected by nitrate pollution from agricultural activities. In the last decades, the general trend towards more intensive and industrialised agriculture has led to the exploitation of almost the entire Albenga plain and the subsequent abandonment of traditional agricultural management practices. In addition, land-use changes due to the growing demand for urbanization and the pressure for touristic development, together with regional policies such as inadequate groundwater monitoring planning and inaccurate spatial establishment of the boundaries of nitrate vulnerable zones without a full and continuously updated evaluation of the related impacts, have resulted in gradual environmental degradation of this important natural ecosystem. Today, the Albenga coastal plain lacks a groundwater monitoring system capable to provide the required information in a timely and cost-effective manner.

2.2. Geology and hydrogeology

The study area is located in the easternmost segment of the Cretaceous Helminthoid unit at the border with the Briançonnais-Piedmont zone (Fig. 2). In general, the current state of the Albenga plain and its water resources is the result of the evolutionary history of the Alpine orogen created through a complex series of tectonic and metamorphic phenomena.

Fig. 2 ‒ Geological map of the study area.

The northern part of the study area is geologically characterized by the presence of limestones (Verano, Roca Liverna and Menosio) and dolomites (Monte Morena) belonging to the Arnasco-Castelbianco (Piemontese) unit [30,31]. This sedimentary unit is characterized by phenomena of plication associated with normal faulting as a result of Alpine tectonic penetration. Breccias of Monte Galero and radiolarites of Arnasco are also involved in the Arnasco- Castelbianco unit. The central-eastern part of the study area is characterized by a strongly erosive base consisting of sandstone facies and thick-bedded conglomerates deposited during the Middle- Lower Pliocene (Conglomerates of Monte Villa). For a distance of approximately 4 km from the coast, the Albenga floodplain is covered by recent (Padano) and former alluvial (Quaternary) deposits of the Torrente Neva, Pennavaire, Arroscia and Lerrone rivers which, west of Albenga, join to form the Centa River. In this area, lower Pliocene clays witnessing the marine Pliocene transgression of the coastal body are also

5 observed. The main axis of the plain hosting the Pliocene Albenga zone, is oriented toward a W-E direction, and consists of quartz sandstones, polygenic conglomerates, dolomitic limestones and marine shales. Therefore, it is evident that the Albenga plain is the result of slow synsedimentary subsidence attributed to tectonic activity with tilting movements and fragile deformations. Based on the ruins of the Roman age, found at a depth of about 13 m below the present sea level, an average uplift of 6.7 cm every 1000 years is estimated for the study area [32].

2.3. Topography and hydrography

The topographical and hydrographical characteristics of the study area are linked to the extreme variability of its geological features derived mainly from the interaction of the complex morphogenetic processes occurred on the floodplain of Albenga due to the genesis and dynamics of the Ligurian Sea and its adjacent continental shelf [33]. As a result, the study area presents a notable topographic contrast that can be divided in 3 parts, each one with a different altitude: the coastal plain (0 – 25 m a.s.l.) in the central, intensively cultivated with extensive residential coverage, the hilly terrain (25 - 200 m a.s.l.) of glacial origin in the south covered by low scrubs and rangelands and the chestnut mountains (500 - 800 m a.s.l.) in the north presenting an undulating relief with cone landforms which does not allow a consistent development of vegetation (Fig. 3).

Fig. 3 ‒ Simplified hydrographic network and altitude map of the study area.

The hydrographic network development in the study area, as a result of uplifting, is strongly controlled by brittle tectonic faults and fracture systems. The catchment area generally drains towards the coast and four major basins are identified. The one in the centre of the sequence, which corresponds to the Carenda basin (28 km2) is surrounded by the La Ligglia, Varatella and Centa basins (Fig.4). The watershed of the Carenda basin, starts from the coast to the edge of the west and goes clockwise, following the line along the mountains of Monte Pesalto (686.4 m), Pizzo Ceresa (710.2 m), Poggio Grande (812.7 m), Monte Acuto (748 m), Monte Croce (541.4 m), Bric Cianastre (316.4 m), Monte Piccaro (280.3 m), Poggio Barbera (276.4 m) and Monte Rosso (242.3 m). The Carenda basin is bordered to the south and west by the River Centa, and to the north by the River Varatella. The prominent Centa River originates from the west-central part of the Carenda basin at an altitude of about 435 m.a.s.l. and it is formed by the confluence of the Neva and Arroscia rivers. Both rivers drain almost 423 km2 (286 km2 for Arroscia and 137 km2 for Neva) up to their confluence and after 3 km flow into the Ligurian Sea at the town of Albenga (Cape Lena) (Relazione Centa) [34]. The average flow of the Centa River is about 10 m3/s [35] presenting for the last 3 km a uniform bed gradient (~1%).

Fig. 4 ‒ Basic hydrographic background in the study area.

2.4. Land use and protection areas

According to the Corine land cover classification system, about 49% of the study area is used for agricultural purposes, 45% is conserved as forest and semi natural lands and the remaining 6% corresponds to urban areas and other uses (Fig.5). The predominant land uses in the Albenga plain include irrigated and to a lesser degree non-irrigated agriculture, and take place in the alluvial deposits of the middle coastal region. Both agricultural areas include intensive cultivations, consisting mainly of fruit orchards, olive groves, horticultural crops, vineyards and arable lands, used to cultivate cereals (maize and wheat), and in a small area citrus and herbs. Areas of forest are present in the central-southern part of the Carenda basin representing a transition between the mixed and hardwood forests of the upper plains and foothills and the flat plain area. Residential/urban areas belong to the towns of Albenga and Ceriale along the coast, and inland to the town of Vilanova d’Albenga. Groundwater constitutes 72% of the total water supply in the study area. The annual water abstraction for irrigation is about 4.4 million m3. The demand for irrigated agricultural areas varies from a minimum of 65.000 m3 in April to a maximum of 1.32 million m3 in August. Table 1 shows the average annual water consumption for irrigation for each cultivated crop in the study area. It is important to note that horticultural crops, mainly cultivated in the central area of the Albenga plain, account for up to 74% of the total annual water consumption.

Table 1 ‒ Average annual water consumption for irrigation of each cultivated crop in the study area.

Crop cultivation Water consumption for irrigation (m3/year) Cereal 5,500 Vegetables/horticultural 3,249,477 Herbs and fodder 9,050 Grapevines 116,060 Olives 371,760 Citrus 29,960 Fruits 626,220 Total 4,408,027

The gradual abstraction of water for irrigation has altered the existing water balance, continuously lowering the shallow water table and simultaneously favouring the intrusion of saline waters [28]. In fact, the sea water intrusion within the highly permeable alluvial plain of Albenga,

8 has affected the entire coastline between Albenga and Ceriale and extends inland for about 1.5 - 2 km reaching the areas of Antognano and Carenda Pineo.

Fig. 5 ‒ Corine land cover map of the study area and the limits of the Nitrate Vulnerability Zone (NVZ).

Part of the study area that covers approximately 1350 ha of agricultural land (30.6 % of the total study area), is officially identified as "nitrate vulnerable zone (NVZ)" by the Liguria region, according to the requirements of the EU Directive 91/676 and Italian Law [36-38]. The NVZ occupies a flat and high permeable area which is defined by the administrative boundaries of the municipalities of Albenga (77.13 %), Ceriale (22.81 %) and a very small part of the Cisano Neva (0.06 %). The use of chemical fertilisers has resulted in elevated levels of nitrates in soil and groundwater. Studies regulated and authorized by local authorities have shown mean concentrations of nitrates in groundwater ranging between 57.4 and 61.7 mg/L for the period 2009 - 2012 [39]. Within this context, there exists a growing need to spatially predict nitrate contamination in the agricultural area of the coastal Albenga plain for a more detailed delineation of the NZV. By using GIS technology, groundwater vulnerability maps can be created for any point of interest in the study area in order to optimize the efficiency of the existing groundwater monitoring programme and propose additional protective measures against contamination diffusion and establishment of new protection areas, if needed.

3. Materials and methods

3.1. GIS-based vulnerability indices

The two GIS-based indices (Pesticide DRASTIC and SI) selected in this study for evaluating the groundwater vulnerability in the agricultural study area, are described below.

3.1.1. Pesticide DRASTIC index

The DRASTIC Index is often used to standardize the evaluation of groundwater pollution potential within various hydrogeological settings [40]. Its calculation assumes that: (1) the contaminant is introduced at the ground surface; (2) the contaminant is flushed into the groundwater by precipitation; (3) the contaminant has the mobility of water; and (4) the area evaluated is 0.4 km2 or larger [14,41]. The DRASTIC method calculates an index derived from ratings and weights assigned to the seven parameters mentioned earlier, namely Depth to water (D), net Recharge (R), Aquifer media (A), Soil media (S), Topography (T), Impact of vadose zone (I) and hydraulic Conductivity (C). The DRASTIC index is quantified by a linear combination of ratings and weights of the seven parameters and is expressed in equation (1):

= + + + + + + + . (1) where D, R, A, S, T, I and C are the acronyms of the seven parameters of the DRASTIC methodology and the subscripts w and r are the corresponding weights and ratings, respectively. The several classes of each parameter are gauged and assigned scores from 1 to 10, while the seven parameters are assigned weights ranging from 1 to 5 depending on their significance (Table 2). Even though the DRASTIC methodology provides two different weighting modes, one for normal conditions (Generic DRASTIC) and the other one for intense agricultural activity (Pesticide DRASTIC), in the present study the latter was chosen in order to obtain more reliable results. Once the Pesticide DRASTIC Index is evaluated, it is possible to identify areas that are more vulnerable to groundwater contamination. The higher values of the Pesticide DRASTIC Index, the greater the groundwater vulnerability to contamination.

Table 2 ‒ Parameters and weight settings in Pesticide DRASTIC method [14]

Parameter Acronym Weight Depth to water D 5 Net Recharge R 4 Aquifer media A 4 Soil media S 5 Topography T 3 Impact of vadose zone I 4 Hydraulic Conductivity C 2

3.1.2 Susceptibility Index (SI)

SI index [21,42] is an adaptation of the well-established DRASTIC method by including the additional parameter of land use and eliminating the DRASTIC parameters of soil (S), impact of vadose zone (I) and hydraulic conductivity (C). This additional parameter takes into account the impact of agricultural activities (such as fertilizer and pesticide application) on groundwater quality. Stigter [41] mentions that even though soil type can largely influence the attenuation potential of certain contaminants, its effect on groundwater vulnerability can be indirectly estimated by considering land use. This is because the quality of natural soils often changes during land cultivation. The SI is quantified by a linear combination of ratings and weights of the four parameters and is expressed using equation (2):

= + + + + . (2) where D, R, A, T and LU are the acronyms of the parameters used and the subscripts w and r are the corresponding weights and ratings, respectively. Table 3 presents the assigned weights for each of these parameters according to the SI method.

Table 3 ‒ Parameters and weight settings in SI method [21]

Parameter Acronym Weight Depth to water D 0.186 Net Recharge R 0.212 Aquifer media A 0.259 Topography T 0.121 Land use LU 0.222

The principal classes of land use and their assigned ratings according to the SI approach are shown in Table 4. It is important to note that definitions of land use classes are based on Corine Land Cover (Legend III) [43].

Table 4 ‒ Main land use (LU) occupation classes and their assigned SI ratings

Land use Rating Industrial discharge, landfill, mines 100 Irrigated perimeters, paddy fields, irrigated perimeters, paddy fields, 90 irrigated and non-irrigated annual culture Quarry, shipyard 80 Artificial covered zones, green zones, continuous urban zones 75 Permanent cultures (vines, orchards, olive trees, etc.) 70 Discontinuous urban zones 70 Pastures and agro-forest zones 50 Aquatic milieu (swamps, saline, etc.) 50 Forest and semi-natural zones 0

3.2. Data collection techniques and methodology

In this study, several data collection techniques and procedures were employed based on the specific requirements of each GIS-based index used (Pesticide DRASTIC and SI). These included various data sets of cartographic features based on geospatial data, which were obtained from public web sites maintained by national agencies and local authorities. Most of the datasets required for water table and nitrates were obtained from in-situ measurements carried out by local authorities and agencies such as the Province of Savona, the Region of Liguria and CERSAA. The flowchart that represents the general overview of the methodology followed is shown in Fig.6. The proposed methodology intends to combine aquifer vulnerability and actual groundwater pollution data.

Fig. 6 ‒ Schematic flowchart of the methodology adopted in this study.

ArcGIS 9.1 [44] was used because it is a widely applied software tool to manage, interpretate, and process geospatial data. Due to the large amount and variability of geospatial data involved, GIS environment provides useful options to solve spatial problems. All data layers created for the study area of Albenga were georeferenced within GIS environment using the UTM projection system (Zone 32N) and WGS84 datum. All vector data were converted into raster format with a cell size (pixel) of 30×30 m. This cell size was selected considering the spatial resolution of available data, computational considerations and site-specific conditions of peri-urban agricultural development.

3.3. Sensitivity analysis

The assessment of groundwater vulnerability requires additional experimental support to reduce subjectivity, increase reliability and therefore minimize doubts about the accuracy of the GIS-based methods used [45]. To this extent, sensitivity analysis serves to acknowledge

13 uncertainty, estimate variability and relative changes in the obtained results using different sets of input parameters, thus fully indicating the most important and influential parameters that critically affect the reliability of groundwater vulnerability. This statistical tool is essential both for scientists to construct a groundwater vulnerability map and policy- and decision makers to properly evaluate current land use practices and future land management planning [16]. In this study, map removal sensitivity analysis [46] and single-parameter sensitivity analysis [16,47] were carried out to assess the degree of uncertainty of the obtained results from Pesticide DRASTIC and SI methods. To this extent, different criteria and weighting scenarios were evaluated for each method used. More specifically, the map removal sensitivity analysis identifies the sensitivity of the groundwater vulnerability map towards removing one parameter or map from vulnerability analysis. The map removal sensitivity analysis is performed using equation (3):

− = × 100 . (3) where S is the sensitivity measure expressed in terms of variation index (%), Vi and Vxi are the unperturbed and the perturbed vulnerability indices, respectively; N and n denote the number of map layers used for the calculation of Vi and Vxi. The unperturbed vulnerability index (Vi) is obtained using Eq. (2) for the ith sub-area, while the vulnerability index (Vxi) is calculated for the ith sub-area excluding one map layer (x-parameter) at a time [16]. The single-parameter sensitivity analysis is performed to assess the influence of input parameters on the calculated groundwater vulnerability according to the methods used. Therefore, the real or effective weight of each parameter is compared with the assigned or theoretical weight in each polygon of the resulting groundwater vulnerability map [16]. The effective weight of each ith sub-area is obtained using the following equation:

= × 100 . (4)

where Wxi refers to the effective weight of each parameter, Xri and Xwi represent the rating and the weight assigned to a parameter (x) in ith sub-area, respectively; Vi is the overall vulnerability index (unperturbed) calculated.

4. Results and discussion

4.1. Creation of input parameter maps

Seven maps representing the seven parameters of DRASTIC were prepared using ArcGIS 9.1. An additional map of land use was prepared for the estimation of the groundwater vulnerability index according to SI method. Each map was classified and assigned ratings and weights according to DRASTIC methodology while SI standards were incorporated. The features used (raw input or classified) in the spatial analysis are presented by each raster layer in the following maps.

4.1.1. Depth to water

Depth to water represents the depth from the ground surface to the water table. As a result, deeper water table implies lesser chance for contamination in the aquifer. In this study, the map of depth to water (Fig. 7) was obtained through spatial interpolation of the elevation data obtained from 36 observation piezometer wells [34,48,49] covering the period 2009-2014. Generally, the depth to water in most of the study area (59.62%) covered by alluvial deposits, ranges from less than 30.4 m in the central area and gradually decreases to coastal boundaries (in a west to east direction) to less than 4.6 m. The depth to water table in the western and northern parts (40.38% of the study area) is very high (>30.4). A total of six depth to water classes are extracted and in each one a rating value from 1 and 9 has been assigned with regard to Drastic classification.

Fig. 7 ‒ Spatial distribution of Depth to Water input map layer of the study area.

4.1.2. Net recharge

Net recharge represents the amount of water per unit area of land which penetrates soil and reaches the water table. The mean precipitation and evapotranspiration over a 10 year period (2003 – 2013) were interpolated by ordinary kriging [9] based on meteorological data provided by the Province of Savona [29]. Fig. 8 shows the spatial distribution of Net recharge in the study area. Higher net recharge occurs in the north-central part of the Carenda Basin, an area underlain by alluvial deposits and highly permeable aquifer materials (sand and gravel), while lower net recharge occurs in the NE part of the study area as a result of the thick low-permeable dolomite and conglomerate formations.

Fig. 8 ‒ Spatial distribution of Net recharge in the study area.

4.1.3. Aquifer media

The map for aquifer media in the study area was prepared using integrated aquifer data obtained from the Province of Savona and the regional Agency for Environmental Protection of Liguria Region ARPAL [29,34,39,49]. The map was digitized and converted to raster format, on the basis of the assigned index values of DRASTIC method. Regarding “Aquifer media”, most part of the study area (47%) is covered by sand and gravel, followed by metamorphic/igneous units (25%) (Fig. 9). It is evident that the strips of alluvium, sand and gravel developing along streams/rivers are characterized by a higher vulnerability, with a rating of 6, in terms of potential for groundwater

16 contamination. Mountainous areas (northwest and southwest) with a rating of 3 have low groundwater contamination potential.

Fig. 9 ‒ Spatial distribution of Aquifer media input in the study area.

4.1.4. Soil media

The soil media is the uppermost part of the vadose zone (approximately 1-2 m thick) and indicates the recharge rate which can infiltrate soil and cause groundwater contamination. Coarse-textured soils that contain mostly sand and gravel allow more water to infiltrate downwards, thus in the coastal and central areas of the Albenga plain increased groundwater vulnerability is anticipated. For the classification of dominant soil textures in the study area, the point shapefile containing the soil textural data was converted to a polygon shapefile through Thiessen polygon tessellation [51]. A total of seven soil textural classes are extracted and to each one a rating value between 2 and 9 has been assigned. This vector layer was then converted into the grid format. Depending on the parent material, the soil textural classes of the area include: sand (5.99%), peat (23.25%), shrinking clay (19.91%), loam (12.65%), sandy loam (11.51%), clay loam (18.86%) and muck (7.83%) (Fig.10). Soils have been strongly influenced by fluvial dynamics in the Albenga plain and along all waterways of the investigated area. In general, the Carenda Basin located in the central Albenga plain which is fully covered by alluvial deposits, indicated higher soil-contamination

17 attenuation than the surrounding upper and lower mountainous areas which are composed of clay loam soils having lower attenuation.

Fig. 10 ‒ Spatial distribution of Soil media DRASTIC classification in the study area.

4.1.5. Topography

A topographic (Slope) map was prepared using the following steps: (1) digitization of elevation contours (25 m) from 1:25,000-scale topographic map [34,49]; (2) creation of a raster-based digital elevation model (DEM) utilizing this vector data; and (3) calculation of slope (%) of land surface from DEM. The final map was then reclassified into intervals and assigned ratings ranging from 1 to 10. The slope map of the study area, shown in Fig. 11, is highly variable and the slope percentage varies within a wide range, from 0 to 13.4%. In general, slopes range from 0-2% in the alluvial Albenga plain to over 6% along hillsides and surrounding mountainous areas.

Fig. 11 ‒ Spatial distribution of Topography (Slope) in the study area.

More specifically, the slope map indicates that flat slopes (0-2%) dominate the study area (48.8%) followed by shallow slopes (17.2%). Steep and very steep slopes cover about 15% of the area. The remaining part is rather flat and occupies 15% of the study site. The flat slope mapping of the Albenga and its surrounding area indicates a high risk for groundwater contamination mainly for the coastal and central plain.

4.1.6. Impact of Vadose zone

Impact of vadose zone parameter indicates the texture of the vadose zone which determines the migration potential and the time the contaminants need to reach groundwater. A vadose zone map was prepared based on sub-surface geology and available lithology (Fig. 12). This figure shows that most of the study area (47%) is assigned a rating of 9 in the DRASTIC rating system due to the presence of sand and gravel formations. In the upland areas, the vadose zone consists of limestone and shales and a rating of 6 and 3 was assigned, respectively.

Fig. 12 ‒ Spatial distribution of Impact of Vadose zone in the study area.

4.1.7. Hydraulic Conductivity

The map of hydraulic conductivity was obtained through spatial interpolation of data obtained from CERSAA and the corresponding map (1:10,000) from the official authority of the Province of Savona [34,49] (Fig. 13). In general, the study area is characterized by high hydraulic conductivity (1 cm/s) in the middle part (62.5%), and thus the high score of 9 was assigned. Permeability values based on mapping data vary between 0.01 cm/s in the north and less than 0.001 cm/s in the south.

Fig. 13 ‒ Spatial distribution of Hydraulic Conductivity in the study area.

4.1.8. Land use

The spatial distribution of the land use cover of the study area has been mapped from data obtained from the corresponding maps (1:10,000 and 1:100,000) obtained from the official authority of the Province of Savona [34,50] and the Corine Land Cover [51], respectively (Fig.14). According to SI classification, the lowest score of 0 (no effect on vulnerability) is assigned to about 48 % of the total area covered by forest land, crop land, and fallow land. On the other hand, the highest score (70-100) is assigned to the central and coastal parts of the study area, indicating that these urban and irrigated agricultural areas have the highest effect on vulnerability when the land use parameter is taken into account.

Fig. 14 ‒ Spatial distribution of Land use SI classification in the study area.

4.2. Pesticide DRASTIC and SI groundwater vulnerability maps

The Pesticide DRASTIC and SI groundwater vulnerability indices were calculated by selecting a maximum estimation probability for the input parameters (depth to water, net recharge, aquifer media, soil media, slope, impact of vadose zone, hydraulic conductivity and land use) according to Equations (1) and (2), respectively. The GIS-based index values were obtained for Pesticide DRASTIC and SI methods and then summed up separately on a raster cell-by-cell basis in order to create the groundwater vulnerability maps. These maps were then reclassified into five equal categories of relative risk ranging from “low” to “very high” to evaluate the spatial distribution patterns of the different groundwater contamination risk over the area under study. Higher degrees of contamination risk are indicated by the change of colour from green to red index values. The ranges and colours are presented in Pesticide DRASTIC and SI groundwater vulnerability maps to enable quick visual comparison and analysis. Overall, high groundwater vulnerability index values indicate areas with high risk and high spatial variance for the geospatial input parameters, suggesting that these areas need to be monitored more intensely. The groundwater vulnerability maps obtained for the study area after the application of the Pesticide DRASTIC and SI methods are shown in Fig. 15. Legend values in these figures are shown in five risk categories: No, Low, Medium, High and Very high. The groundwater vulnerability index values obtained from the pesticide DRASTIC method range from 65 to 232, whereas index

22 values obtained from the SI method vary from 15 to 83 (Table 5). The higher index values obtained from the Pesticide DRASTIC method are solely due to higher weighting values and number of parameters used.

Fig. 15 ‒ a) Pesticide DRASTIC and b) SI groundwater vulnerability map in the study area.

Table 5 ‒ Categories of contamination risk based on the Pesticide DRASTIC and SI methods

Frequency of Range of Groundwater vulnerability Index Index (%) Risk Category Pesticide DRASTIC SI 0-20 65 – 98 15 – 28 No 20-40 98 – 132 28 – 42 Low 40-60 132 – 165 42 – 56 Medium 60-80 165 – 199 56 – 70 High 80-100 199 – 232 70 – 83 Very high

Overall, similarities found in the results of Pesticide DRASTIC and SI methods include areas (55.80% and 49.13% of the total area, respectively) that are characterized by “high to “very high” risk along the coastline and the middle area, especially areas covered by alluvial deposits. According to the results of the Pesticide DRASTIC method, the risk for groundwater contamination in a substantial part (38.18%) of the Albenga - Ceriale coastal plain is characterized as “very high” as a result of its fairly low topographic gradient (<6%), shallow water table (<22.8 m), uniform distribution of hydraulic conductivity (1 cm/s) and medium to high net recharge (101.6 –177.8 mm). Similar results (33.95%) concerning “very high” potential for groundwater contamination have also derived from the application of the SI method, indicating that most of the vulnerable areas are located in the central region as well as in a small part of the west central region. Medium risk for groundwater contamination is mainly anticipated in the mid-area between the Albenga plain and the surrounding mountainous areas, occupying 19.53% and 14.97% of the study area as the Pesticide DRASTIC and SI methods indicate, respectively. In general, the Pesticide DRASTIC map for groundwater vulnerability exhibited a significantly positive correlation (0.896) with the SI vulnerability map.

Table 6 ‒ Categories of contamination risk based on the Pesticide DRASTIC and SI methods for the study area

Pesticide DRASTIC SI Risk Category Area (km2) Area (%) Area (km2) Area (%) No 5.16 9.22 5.07 9.06 Low 8.65 15.45 15.03 26.84 Medium 10.94 19.53 8.38 14.97 High 9.87 17.62 8.50 15.18 Very high 21.38 38.18 19.01 33.95 Total 56 100 56 100

Moreover, areas of “no” risk are restricted to only a small part of the study area (9.22% and 9.06% for the Pesticide DRASTIC and SI methods, respectively) in the NE edge between the Albenga coastal plain and the surrounding mountains of Monte Pesalto, Pizzo Ceresa and Poggio Grande, representing primarily areas of medium to high topographic gradients (6-18%) and low net recharge (50.8-101.6 mm). In general, the overall groundwater contamination risk increases from east to inland (W and SW), which corresponds well with the increasing clay fraction in soil and the decreasing impact of vadose zone along the same direction. Therefore, it is shown that both Pesticide DRASTIC and SI index values indicate “very high” risk of groundwater contamination especially in areas (coastal-central plain) where intensive agricultural activities are carried out, thus continuous monitoring is required in these areas. These monitoring activities could entail sampling of existing wells, boreholes and soils above aquifer media, drilling and sampling of new monitoring wells in hotspots, adopting groundwater monitoring programmes and protocols, minimizing groundwater irrigation activities and implementing pesticide management/control practices to reduce the risk for groundwater contamination.

4.3. Sensitivity analysis

4.3.1. Independence of parameters

Tables 7 and 8 present the statistical summary of the seven and five parameters used to calculate the Pesticide DRASTIC and SI indices, respectively. The highest contribution to the calculated Pesticide DRASTIC index in the study area originates from the parameters of topography (T), impact of vadose zone (I) and hydraulic conductivity (C) with mean values of 8.72, 7.66 and 6.99, respectively. On the other hand, net recharge – R (3.58) and soil media – S (5.84) have the lowest contribution to the risk of groundwater contamination in the study area.

Table 7 ‒ Statistical summary of the Pesticide DRASTIC parameter maps.

Value D R A S T I C Minimum 1 1 3 2 3 1 1 Maximum 9 6 8 9 10 10 10 Mean 6.21 3.58 6.18 5.84 8.72 7.66 6.99 Standard Deviation 2.80 1.72 2.03 2.17 2.01 2.28 4.00 (SD) Coefficient of 45.1 48.0 32.8 37.2 23.1 29.8 57.2 variation (CV) (%)

The analysis of the coefficient of variation (CV%) indicates that the higher contribution to the variation of Pesticide DRASTIC vulnerability index is due to hydraulic conductivity (57.2%),

25 followed by net recharge – R (48.0%) and depth to water – D (45.1%), while soil media (S), aquifer media (A) and impact of vadose zone have lower contribution (CV = 37.2%, 32.8% and 29.8%, respectively). The very low variability of the topography – T (CV = 23.1%) indicates low contribution of this parameter to the variation of the Pesticide vulnerability index across the study area.

Table 8 ‒ Statistical summary of the SI parameter maps.

Value D R A T LU Minimum 10 10 30 30 0

Maximum 90 60 80 100 100 Mean 62.06 35.77 61.77 87.21 39.21 Standard Deviation 27.97 17.22 20.30 20.08 37.80 (SD) Coefficient of 45.1 48.0 32.8 23.1 96.4 variation (CV) (%)

The statistical data (mean values) shown in Table 8 for SI input parameters, confirm the greater contribution of topography – T (87.21) followed by depth to water – D (62.06) and aquifer media – A (61.77). The lowest mean values obtained through parameter independence analysis revealed that land use – LU (39.21) and net recharge – R (35.77) had the smallest contribution to the risk of groundwater contamination in the study area for all parameters considered during the application of the SI method. The analysis of the coefficient of variation values indicates that a very high contribution to the variation of Pesticide DRASTIC vulnerability index is caused by land use (96.4%). The other SI parameters showed exactly the same mean values as in the case of the Pesticide DRASTIC method, since both methods are used in the same way for the calculation of the corresponding GIS-based indices.

4.3.2. Map removal sensitivity analysis

The statistical summary of the results of map removal sensitivity analysis that was performed by removing one input parameter layer at a time from the resulting Pesticide DRASTIC and SI maps is represented in Tables 9 and 10, respectively. In the Pesticide DRASTIC map, the highest variation of the vulnerability index is observed upon removal of the net recharge - R (1.42%), followed by the depth to water - D (1.23%). This can be attributed to the high theoretical weight assigned to these layers (4 and 5, respectively) and the shallow alluvial aquifer (<30.4 m) that dominates most of the study area (59.62%).

Table 9 ‒ Statistical summary of the Pesticide DRASTIC map removal sensitivity analysis.

Variation index (%) Parameter removed Standard Deviation Minimum Maximum Mean (SD) D 0.44 0.85 0.66 1.23 R 0.00 1.36 0.98 1.47 A 0.00 2.23 0.04 0.04 S 0.18 0.56 0.48 0.49 T 0.00 1.47 0.19 0.91

I 0.21 3.00 0.62 0.39 C 0.94 1.87 1.01 0.19

The Pesticide DRASTIC vulnerability index is moderately sensitive to the removal of the topography (T), soil media (S) and impact of vadose zone (I) layers from the entire calculation procedure, since these parameters exhibited relatively moderate mean values of variation index (0.91%, 0.49% and 0.39%, respectively) when removed from the resulting maps. The hydraulic conductivity (C) and aquifer media (A) parameters, both exhibiting very low mean values (0.19% and 0.04%, respectively), have the lowest variation index upon removal from the Pesticide DRASTIC map.

Table 10 ‒ Statistical summary of the SI map removal sensitivity analysis.

Variation index (%) Parameter Standard removed Minimum Maximum Mean Deviation (SD) D 0.55 2.47 0.75 0.88 R 0.46 2.05 1.28 2.23 A 1.95 7.14 2.60 1.20 T 0.09 0.65 0.41 2.12 LU 0.77 5.49 1.72 4.58

In the SI method, map removal sensitivity analysis showed that parameters aquifer media (A) and land use (LU) had the highest variation of 2.60% and 1.72%, respectively. High sensitivity to these two parameters indicates that the characteristics of lithology and human activities contribute most to the groundwater contamination in the study area. Upon removal of the net recharge (R) and depth to water (D) layers, considerable impact on the variation of the vulnerability index is observed. Mean variation indices for these parameters are 1.28 and 0.75%,

27 respectively. The least sensitive parameter is topography (T) for which the respective mean variation index showed the lowest variation (0.41%) among all SI parameters analysed.

4.3.3. Single parameter sensitivity analysis:

The single parameter sensitivity analysis was carried out for the seven input parameters of the Pesticide DRASTIC method (Table 11) and the five input parameters of the SI method (Table 12).

Table 11 ‒ Statistical summary of the single parameter sensitivity analysis (Pesticide

DRASTIC method)

Effective weight (%) Theoretical Theoretical Parameter Standard Weight Weight (%) Minimum Maximum Mean Deviation (SD) D 5 18.52 7.69 19.40 18.26 14.0 R 4 14.81 6.15 10.34 8.42 6.9 A 4 14.81 13.79 18.46 14.54 8.1 S 5 18.52 15.38 19.40 17.18 10.9 T 3 11.11 12.93 15.39 13.85 6.0 I 4 14.81 6.15 18.02 17.24 9.1 C 2 7.41 3.08 8.62 8.22 8.0

The effective weight factor results clearly indicate that the depth to water (D) parameter, with a mean effective weight of 18.26% against the theoretical weight of 18.52%, dominated the Pesticide Drastic vulnerability index. However, the net recharge - R showed the greatest difference between theoretical and effective weights in the Pesticide DRASTIC method. In particular, a negative 43% change in comparison to its theoretical weight (14.81%) was observed, thus indicating that this parameter showed the lowest impact in the estimation of the Pesticide DRASTIC groundwater vulnerability index. The hydraulic conductivity - C (8.22%) presented the lowest effective weight among all Pesticide DRASTIC parameters without any significant change in comparison to its theoretical weight (7.41%). The calculated effective weights for aquifer media – A (14.54%) and soil media - S (17.18) exhibited quite equal to their theoretical weights (14.81% and 18.52% respectively) assigned by the Pesticide DRASTIC method. Impact of vadose zone (I) and topography (T) displayed higher effective weight values (17.24% and 13.85, % respectively) than their theoretical ones (14.81 and 11.1 %). This positive change reflects the importance of these parameters for the Pesticide method and the need to obtain more accurate, detailed and representative data regarding their spatial distribution.

Table 12 ‒ Statistical summary of the single parameter sensitivity analysis (SI method)

Effective weight (%) Theoretical Theoretical Parameter Standard Weight Weight (%) Minimum Maximum Mean Deviation (SD)

D 0.186 18.60 12.40 21.38 20.17 5.2 R 0.212 21.20 14.04 15.33 14.13 3.7 A 0.259 25.90 24.96 51.80 29.63 5.3 T 0.121 12.10 14.58 24.20 19.54 2.4 LU 0.222 22.20 0.00 26.75 16.12 8.4

Table 12 reveals that topography (T) was the most effective parameter in the vulnerability assessment as its effective weight (19.54%) exceeded by 61.49%, the theoretical weight imposed by the SI method (12.10%). This statistical result shows the very high importance of the topography (T) in the resulting map of the SI method, thus indicating the need for more precise data on this parameter in order to address site-specific differential issues. The weights of the depth to water (D) and aquifer media (A) moderately increased by 8.44 and 14.40%, respectively. Finally, due to their low effective weight values compared to the theoretical weights, the net recharge (14.13%) and aquifer media (16.12%) were the least influential among all SI parameters considered.

4.4. Validation of the Pesticide DRASTIC and SI methods

The validation of the Pesticide DRASTIC and SI results was performed using available nitrate concentration values in groundwater. The selection of nitrate was based on the fact that the area is contaminated by nitrates, as a result of high application rates of nitrogenous fertilizers. Since both Pesticide DRASTIC and SI methods assume that contaminants are mobile and water soluble, nitrate satisfies these assumptions.

The values of nitrate concentrations used in the present study to validate results have been provided by CERSAA (from 12 sampling locations) and by an extensive search in public/government agencies (Province of Savona and ARPAL, Italy) (16 sampling locations), covering the entire area under study over a 3 year period (2009-2012) [34,39,48]. The nitrate values were classified into five classes based on its permissible (limit) concentration of 50 mg/L [52,53], as follows: Level 1: <2 mg/L; Level 2: 2–4.99 mg/L; Level 3: 5 –24.99 mg/L; Level 4: 25– 49.99 mg/L and Level 5: >50 mg/L.

Fig. 16 ‒ Actual concentration of nitrates in the groundwater of the study area

The actual concentration of nitrates in the groundwater of the study area is provided in Fig.16. The validation map clearly indicates that the interpolated nitrate concentrations using both Pesticide DRASTIC and SI indices are well correlated with actual concentration values for most of the study area. Areas with very high vulnerability show also elevated actual nitrate concentrations (>50 mg/L). The obtained correlation values between the Pesticide DRASTIC and SI indices with the actual nitrate concentration were 0.693 and 0.728, thus indicating that both methods are characterized by quite good accuracy. High levels of nitrates in groundwater can be attributed to the extensive and intensive agricultural activities in the central Carenda basin, which contribute to nitrate pollution as a result of the high hydraulic conductivity of the surface (alluvial deposits) and its flat topography.

Τhe nitrate concentrations in groundwater for the 23.21% of the total area belong to level 5 (>50 mg/L) and indicate very high risk for contamination. Based on the actual nitrate concentrations pattern, the risk for groundwater contamination increases from E to inland (W and SW), and correlates well with the obtained Pesticide DRASTIC and SI indices. However, in order to obtain a better validation of the DRASTIC risk mapping, data from additional sites (e.g monitoring wells), if available, need to be obtained and analysed [54].

5. Conclusions

In this study, the Pesticide DRASTIC and SI methods were applied in a GIS environment to assess the groundwater vulnerability in the agricultural area of the Albenga coastal plain in north Italy. The resulting Pesticide DRASTIC and SI vulnerability maps indicated that major parts of the study area (55.80% and 49.13% of the total area, respectively) are characterized by “high“ to “very high” risk. These parts are along the coastline and in the middle of the study area, are mainly covered by alluvial deposits, and are characterized by fairly low topographic gradients (<6%), shallow water table (<22.8 m), uniform distribution of hydraulic conductivity (1 cm/s) and medium to high net recharge (101.6 –177.8 mm). Medium risk for groundwater contamination is mainly evident in the mid-area between the Albenga plain and the surrounding mountainous areas, which occupies only 19.53% and 14.97% of the study area. The map removal sensitivity analysis showed that the groundwater vulnerability index was highly sensitive to the removal of the net recharge and depth to water parameters in the Pesticide DRASTIC map and to the aquifer media and land use parameters in the SI map. Furthermore, the results of the single-parameter sensitivity analysis indicated that the topography and the impact of the vadose zone are the most significant Pesticide DRASTIC parameters and dictate the high vulnerability of the shallow Albenga plain. On the other hand, topography was the most influential parameter in the SI vulnerability method followed by aquifer media and depth to water. The groundwater vulnerability analysis was validated using actual concentrations of nitrates in groundwater for the entire study area and the results showed significant and positive correlation values (0.693 and 0.728) for the Pesticide DRASTIC and SI indices respectively, thus indicating that both methods are characterized by quite good accuracy. The integration of the obtained results (maps, sensitivity analysis and validation) is particularly useful in terms of determining the most vulnerable areas that need detailed and frequent monitoring, especially in the context of delineating the Nitrate Vulnerable Zones. These monitoring activities could entail sampling of existing wells, boreholes and soils above aquifer media, drilling and sampling of new monitoring wells in hotspots, adopting groundwater monitoring programmes and protocols, minimizing groundwater irrigation activities and implementing pesticide management/control practices to reduce the risk of groundwater contamination. Finally, groundwater vulnerability maps created in this study are also useful for policy makers during the implementation and prioritization of policies for groundwater protection and management especially in areas where intensive agricultural (mainly greenhouse) activities are carried out.

Acknowledgements

The authors would like to acknowledge the financial support of LIFE10 ENV/GR/594 project “Best practices for Agricultural Wastes treatment and reuse in the Mediterranean countries” (Wastereuse, http://www.wastereuse.eu/).

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