HOSTED BY Available at www.sciencedirect.com

INFORMATION PROCESSING IN AGRICULTURE 2 (2015) 109–129

journal homepage: www.elsevier.com/locate/inpa

Assessment of groundwater contamination risk in an agricultural area in north

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 (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

ARTICLE INFO ABSTRACT

Article history: In the present study a specific approach is followed, considering the Pesticide DRASTIC and Received 14 May 2015 Susceptibility index (SI) methods and a GIS framework, to assess groundwater vulnerability Accepted 19 June 2015 in the agricultural area of Albenga, in north Italy. The results indicate ‘‘high’’ to ‘‘very high’’ Available online 26 July 2015 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 Keywords: methods, respectively. These sensitive regions depict characteristics such as shallow depth Agriculture to groundwater, extensive deposits of alluvial silty clays, flat topography and intensive agri- Albenga cultural activities. The distribution of nitrates concentration in groundwater in the study DRASTIC area is slightly better correlated with the SI (0.728) compared to Pesticide DRASTIC (0.693), Groundwater vulnerability thus indicating that both methods are characterized by quite good accuracy. Sensitivity anal- Nitrate ysis 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. 2015 China Agricultural University. Production and hosting by Elsevier B.V. All rights reserved.

1. Introduction and inorganic contaminants in groundwater used for irriga- tion may cause several health problems to humans and result Contamination of groundwater in agricultural areas has in loss of soil fertility and income for farmers. The impacts of become today a global concern and limits its availability as groundwater contamination are more noticeable in areas suf- a resource for crop irrigation. The presence of several organic fering from desertification, salinization or when groundwater is not sufficient to support intense agricultural activities [1]. * Corresponding author. Tel.: +30 28210 37686; fax: +30 28210 Nitrates and pesticides are the most common non-point 69554. source contaminants detected in shallow alluvial aquifers in E-mail addresses: [email protected] (G. Bartzas), agricultural areas. Alluvial aquifers are especially vulnerable [email protected] (K. Komnitsas). to nitrate contamination and salinity problems due to a num- Peer review under the responsibility of China Agricultural ber of factors including shallow water table, highly permeable University. http://dx.doi.org/10.1016/j.inpa.2015.06.004 2214-3173 2015 China Agricultural University. Production and hosting by Elsevier B.V. All rights reserved. 110 Information Processing in Agriculture 2 (2015) 109– 129

alluvial deposits, interconnections between surface water The objective of this paper is to estimate groundwater and agricultural related land uses usually carried out on vulnerability to contamination in the agricultural area of floodplain terraces along river banks, and sea water intrusion Albenga, in north Italy, using two appropriate methods due to over-pumping of groundwater for irrigation [2,3]. (Pesticide DRASTIC and SI) suitable for shallow alluvial aquifer The assessment of groundwater vulnerability offers a low systems and determine risk levels based on calculated cost alternative to traditional groundwater quality plans and GIS-based vulnerability indices. Special emphasis is given on can be used to evaluate changes of risk over time, caused testing the reliability of the approach followed, in order to either from changes in land uses or because contaminants delineate the most vulnerable areas in the proximity of the such as nitrates have migrated via preferential hydraulic flow defined Vulnerable Zone in terms of nitrate contamination. pathways. The vulnerability of groundwater needs to be Furthermore, sensitivity and statistical analyses were assessed as it is not only a function of the intrinsic properties conducted to evaluate, compare and validate the obtained of groundwater flow system (hydraulic conductivity, poros- results in terms of subjectivity, degree of parameter ity), but also of the proximity of contaminant sources and independence and variation effect. their particular characteristics (location, chemical interaction with surface water) that could potentially increase the load of specific contaminants to aquifer systems. 2. Study area description However, the estimation of groundwater vulnerability is a complex procedure and depends on the temporal and spatial 2.1. Location and climate variability of contamination sources [4,5]. Several approaches involving the use of deterministic or stochastic methods can The study site is an experimental farm with coordinates be used to assess soil and predict groundwater contamination 4404005.5400N and 812045.5100E that belongs to the Centre for in industrial and agricultural areas. Factors such as soil type, Agricultural Experimentation and Assistance (CERSAA), in pollution load, depth of aquifer, mobility and fate of contam- Italy (Fig. 1). It is located about 1.5 km north from Albenga, a inants should be always taken into consideration [6–9]. town at the Ligurian coastal region in the province of During the past decades, several methods for assessing , belonging to the geographical zone of groundwater vulnerability using different evaluation factors in the north Italy. The municipality of Albenga has a territory and approaches have been developed, including GOD [10], of 36.50 km2, 24,200 inhabitants (in 2013) and a high density of SINTACS [11],AVI[12] and the PI method [13]. Apart from all population (663 inh/km2). The size of the area selected for risk these methods, the DRASTIC method, developed by the US analysis is 59 km2, extends from Albenga to and is Environmental Protection Agency (US EPA), remains one of characterized by a steep sandy coastal zone with numerous the most frequently used approaches to assess vulnerability human settlements, intensive agricultural activities and low to groundwater contamination in porous aquifers [14,15]. forest cover. DRASTIC uses seven parameters, namely Depth to water, The climate of the study area is typical Mediterranean, with net recharge, aquifer media, soil media, topography, impact mean summer temperature ranging between 16.9 and 21.2 of vadose zone and hydraulic conductivity as weighted layers C, and mean winter temperature between 8.8 and 9.9 to enable a reliable assessment of vulnerability [16–18]. C [29]. The mean annual temperature over a 20 year period Recent studies have revealed that land use is also a key (1991–2010) is 15.4 C. The annual precipitation for the same issue that has to be taken into account when predicting period ranges from 280 to 1150 mm with its mean value being potential future hydrological responses and the effect of 664 mm/year. Three quarters of the precipitation falls between anthropogenic activities on groundwater quality [19,20]. May and October. The mean precipitation for summer is less Within this context, the Susceptibility index (SI), developed than 28.4 mm (June–August), and increases to 85.8 mm during by Ribeiro [21], is a contemporary adaptation of the Drastic winter. Sudden showers occur very often in autumn causing method and has also been applied in this study to assess flood events. The most recent floods (November 1994 and the effect of the land use on groundwater vulnerability in October/November 2000) have caused great damages to settle- an agricultural coastal plain where adverse land use changes ments, particularly in the town centre of Albenga. are common [22]. The Sustainability index enables an The Albenga coastal plain is a characteristic example of in-depth and comprehensive analysis pertinent to the shallow alluvial aquifer chronically affected by nitrate impacts of continuous urban development against the pollution from agricultural activities. In the last decades, the shortage of land resources for agricultural purposes. general trend towards more intensive and industrialized Pesticide DRASTIC and SI methods may be combined with agriculture has led to the exploitation of almost the entire GIS technology and remote sensing to develop an integrated Albenga plain and the subsequent abandonment of traditional approach, especially for heterogeneous media, that considers agricultural management practices. In addition, land-use geological, hydrological and geochemical data to improve the changes due to the growing demand for urbanization and reliability of risk estimation [23–25]. The major advantage of the pressure for touristic development, together with regional GIS-based groundwater vulnerability mapping is the use of policies such as inadequate groundwater monitoring planning data layers and the consideration of spatial variability of the and inaccurate spatial establishment of the boundaries of parameters used for risk estimation [26,27]. The resulting nitrate vulnerable zones without a full and continuously vulnerability maps can be easily used by local authorities, updated evaluation of the related impacts, have resulted in decision- and policy makers for designing groundwater gradual environmental degradation of this important protection and remediation strategies [28]. natural ecosystem. Today, the Albenga coastal plain lacks a Information Processing in Agriculture 2 (2015) 109– 129 111

Fig. 1 – Location of the study area.

groundwater monitoring system capable to provide the activity with tilting movements and fragile deformations. required information in a timely and cost-effective manner. 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 2.2. Geology and hydrogeology 6.7 cm every 1000 years is estimated for the study area [32].

The study area is located in the easternmost segment of the 2.3. Topography and hydrography Cretaceous Helminthoid unit at the border with the Brianc¸o nnais– zone (Fig. 2). In general, the current state of The topographical and hydrographical characteristics of the the Albenga plain and its water resources is the result of study area are linked to the extreme variability of its geologi- the evolutionary history of the Alpine orogen created through cal features derived mainly from the interaction of the a complex series of tectonic and metamorphic phenomena. complex morphogenetic processes occurred on the floodplain The northern part of the study area is geologically of Albenga due to the genesis and dynamics of the Ligurian characterized by the presence of limestones (Verano, Roca Sea and its adjacent continental shelf [33]. As a result, the Liverna and Menosio) and dolomites (Monte Morena) belong- study area presents a notable topographic contrast that can ing to the - (Piemontese) unit [30,31]. be divided in 3 parts, each one with a different altitude: the This sedimentary unit is characterized by phenomena of coastal plain (0–25 m a.s.l.) in the central, intensively culti- plication associated with normal faulting as a result of Alpine vated with extensive residential coverage, the hilly terrain tectonic penetration. Breccias of and radiolarites (25–200 m a.s.l.) of glacial origin in the south covered by low of Arnasco are also involved in the Arnasco-Castelbianco unit. scrubs and rangelands and the chestnut mountains (500–800 The central-eastern part of the study area is characterized by a m a.s.l.) in the north presenting an undulating relief with strongly erosive base consisting of sandstone facies and thick- cone landforms which does not allow a consistent develop- bedded conglomerates deposited during the Middle-Lower ment of vegetation (Fig. 3). Pliocene (Conglomerates of Monte Villa). The hydrographic network development in the study area, For a distance of approximately 4 km from the coast, the as a result of uplifting, is strongly controlled by brittle tectonic Albenga floodplain is covered by recent (Padano) and former faults and fracture systems. The catchment area generally alluvial (Quaternary) deposits of the Torrente , drains towards the coast and four major basins are identified. Pennavaire, Arroscia and Lerrone rivers which, west of The one in the centre of the sequence, which corresponds to Albenga, join to form the River. In this area, lower the Carenda basin (28 km2) is surrounded by the La Ligglia, Pliocene clays witnessing the marine Pliocene transgression and Centa basins (Fig. 4). The watershed of the of the coastal body are also observed. The main axis of the Carenda basin, starts from the coast to the edge of the west plain hosting the Pliocene Albenga zone, is oriented toward and goes clockwise, following the line along the mountains a W–E direction, and consists of quartz sandstones, polygenic of Monte Pesalto (686.4 m), Pizzo Ceresa (710.2 m), Poggio conglomerates, dolomitic limestones and marine shales. Grande (812.7 m), Monte Acuto (748 m), Monte Croce (541.4 Therefore, it is evident that the Albenga plain is the result m), Bric Cianastre (316.4 m), Monte Piccaro (280.3 m), Poggio of slow synsedimentary subsidence attributed to tectonic Barbera (276.4 m) and Monte Rosso (242.3 m). The Carenda 112 Information Processing in Agriculture 2 (2015) 109– 129

Fig. 2 – Geological map of the study area.

Fig. 3 – Simplified hydrographic network and altitude map of the study area. Information Processing in Agriculture 2 (2015) 109– 129 113

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

basin is bordered to the south and west by the River Centa, and Groundwater constitutes 72% of the total water supply in to the north by the River Varatella. The prominent Centa River the study area. The annual water abstraction for irrigation is originates from the west-central part of the Carenda basin at about 4.4 million m3. The demand for irrigated agricultural an altitude of about 435 m.a.s.l. and it is formed by the conflu- areas varies from a minimum of 65.000 m3 in April to a maxi- ence of the Neva and Arroscia rivers. Both rivers drain almost mum of 1.32 million m3 in August. Table 1 shows the average 423 km2 (286 km2 for Arroscia and 137 km2 for Neva) up to annual water consumption for irrigation for each cultivated their confluence and after 3 km flow into the Ligurian Sea at crop in the study area. It is important to note that horticultural the town of Albenga (Cape Lena) (Relazione Centa) [34]. The crops, mainly cultivated in the central area of the Albenga plain, average flow of the Centa River is about 10 m3/s [35] presenting account for up to 74% of the total annual water consumption. for the last 3 km a uniform bed gradient (1%). The gradual abstraction of water for irrigation has altered the existing water balance, continuously lowering the shallow 2.4. Land use and protection areas water table and simultaneously favouring the intrusion of saline waters [28]. In fact, the sea water intrusion within the According to the Corine land cover classification system, highly permeable alluvial plain of Albenga, has affected the about 49% of the study area is used for agricultural purposes, entire coastline between Albenga and Ceriale and extends 45% is conserved as forest and semi natural lands and the inland for about 1.5–2 km reaching the areas of Antognano remaining 6% corresponds to urban areas and other uses and Carenda Pineo. (Fig. 5). The predominant land uses in the Albenga plain Part of the study area that covers approximately 1350 include irrigated and to a lesser degree non-irrigated agricul- ha of agricultural land (30.6% of the total study area), is offi- ture, and take place in the alluvial deposits of the middle cially identified as ‘‘nitrate vulnerable zone (NVZ)’’ by the coastal region. Both agricultural areas include intensive region, according to the requirements of the EU cultivations, consisting mainly of fruit orchards, olive groves, Directive 91/676 and Italian Law [36–38]. The NVZ occupies a horticultural crops, vineyards and arable lands, used to flat and high permeable area which is defined by the adminis- cultivate cereals (maize and wheat), and in a small area citrus trative boundaries of the municipalities of Albenga (77.13%), and herbs. Areas of forest are present in the central-southern Ceriale (22.81%) and a very small part of the Cisano Neva part of the Carenda basin representing a transition between (0.06%). The use of chemical fertilisers has resulted in elevated the mixed and hardwood forests of the upper plains and levels of nitrates in soil and groundwater. Studies regulated foothills and the flat plain area. Residential/urban areas and authorized by local authorities have shown mean concen- belong to the towns of Albenga and Ceriale along the coast, trations of nitrates in groundwater ranging between 57.4 and and inland to the town of Vilanova d’Albenga. 61.7 mg/L for the period 2009–2012 [39]. 114 Information Processing in Agriculture 2 (2015) 109– 129

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

Within this context, there exists a growing need to spatially 3.1.1. Pesticide DRASTIC index predict nitrate contamination in the agricultural area of the The DRASTIC index is often used to standardize the coastal Albenga plain for a more detailed delineation of the evaluation of groundwater pollution potential within various NZV. By using GIS technology, groundwater vulnerability maps hydrogeological settings [40]. Its calculation assumes that: can be created for any point of interest in the study area in (1) the contaminant is introduced at the ground surface; (2) order to optimize the efficiency of the existing groundwater the contaminant is flushed into the groundwater by monitoring programme and propose additional protective precipitation; (3) the contaminant has the mobility of water; measures against contamination diffusion and establishment and (4) the area evaluated is 0.4 km2 or larger [14,41]. The of new protection areas, if needed. DRASTIC method calculates an index derived from ratings and weights assigned to the seven parameters mentioned 3. Materials and methods earlier, namely depth to water (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose 3.1. GIS-based vulnerability indices zone (I) and hydraulic conductivity (C). The DRASTIC index is quantified by a linear combination of ratings and weights The two GIS-based indices (Pesticide DRASTIC and SI) of the seven parameters and is expressed in Eq. (1): selected in this study for evaluating the groundwater vulner- DRASTIC index ¼ D D þ R R þ A A þ S S þ T T ability in the agricultural study area, are described below. r w r w r w r w r w þ IrIw þ CrCw ð1Þ

Table 1 – Average annual water consumption for irrigation where D, R, A, S, T, I and C are the acronyms of the seven of each cultivated crop in the study area. parameters of the DRASTIC methodology and the subscripts w and r are the corresponding weights and ratings, Crop cultivation Water consumption respectively. for irrigation (m3/year) The several classes of each parameter are gauged and Cereal 5500 assigned scores from 1 to 10, while the seven parameters Vegetables/horticultural 3,249,477 are assigned weights ranging from 1 to 5 depending on their Herbs and fodder 9050 significance (Table 2). Even though the DRASTIC methodology Grapevines 116,060 Olives 371,760 provides two different weighting modes, one for normal Citrus 29,960 conditions (Generic DRASTIC) and the other one for intense Fruits 626,220 agricultural activity (Pesticide DRASTIC), in the present study Total 4,408,027 the latter was chosen in order to obtain more reliable results. Information Processing in Agriculture 2 (2015) 109– 129 115

Table 2 – Parameters and weight settings in Pesticide Table 4 – Main land use (LU) occupation classes and their DRASTIC method [14]. assigned SI ratings.

Parameter Acronym Weight Land use Rating

Depth to water D 5 Industrial discharge, landfill, mines 100 Net recharge R 4 Irrigated perimeters, paddy fields, irrigated 90 Aquifer media A 4 perimeters, paddy fields, irrigated Soil media S 5 and non-irrigated annual culture Topography T 3 Quarry, shipyard 80 Impact of vadose zone I 4 Artificial covered zones, green zones, 75 Hydraulic conductivity C 2 continuous urban zones Permanent cultures (vines, orchards, 70 olive trees, etc.) Discontinuous urban zones 70 Once the Pesticide DRASTIC index is evaluated, it is possible Pastures and agro-forest zones 50 to identify areas that are more vulnerable to groundwater Aquatic milieu (swamps, saline, etc.) 50 contamination. The higher values of the Pesticide DRASTIC Forest and semi-natural zones 0 index, the greater the groundwater vulnerability to contamination. each GIS-based index used (Pesticide DRASTIC and SI). 3.1.2. Susceptibility index (SI) These included various data sets of cartographic features SI index [21,42] is an adaptation of the well-established based on geospatial data, which were obtained from public DRASTIC method by including the additional parameter of web sites maintained by national agencies and local authori- land use and eliminating the DRASTIC parameters of soil ties. Most of the datasets required for water table and nitrates (S), impact of vadose zone (I) and hydraulic conductivity (C). were obtained from in-situ measurements carried out by local This additional parameter takes into account the impact of authorities and agencies such as the Province of Savona, the agricultural activities (such as fertilizer and pesticide Region of Liguria and CERSAA. application) on groundwater quality. Stigter [41] mentions The flowchart that represents the general overview of the that even though soil type can largely influence the methodology followed is shown in Fig. 6. The proposed attenuation potential of certain contaminants, its effect on methodology intends to combine aquifer vulnerability and groundwater vulnerability can be indirectly estimated by actual groundwater pollution data. 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 Eq. (2):

SI index ¼ DrDw þ RrRw þ ArAw þ TrTw þ LUrLUw ð2Þ 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. The principal classes of land use and their assigned ratings according to the SI approach are shown in Table 4. It is impor- tant to note that definitions of land use classes are based on Corine Land Cover (Legend III) [43].

3.2. Data collection techniques and methodology

In this study, several data collection techniques and proce- dures were employed based on the specific requirements of

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 Fig. 6 – Schematic flowchart of the methodology adopted in Land use LU 0.222 this study. 116 Information Processing in Agriculture 2 (2015) 109– 129

ArcGIS 9.1 [44] was used because it is a widely applied soft- different criteria and weighting scenarios were evaluated for ware tool to manage, interpretate, and process geospatial each method used. data. Due to the large amount and variability of geospatial More specifically, the map removal sensitivity analysis data involved, GIS environment provides useful options to identifies the sensitivity of the groundwater vulnerability solve spatial problems. All data layers created for the study map towards removing one parameter or map from vulnera- area of Albenga were georeferenced within GIS environment bility analysis. The map removal sensitivity analysis is per- using the UTM projection system (Zone 32N) and WGS84 formed using Eq. (3):

V V datum. All vector data were converted into raster format with i xi S ¼ N n 100 ð3Þ a cell size (pixel) of 30 · 30 m. This cell size was selected Vi considering the spatial resolution of available data, where S is the sensitivity measure expressed in terms of computational considerations and site-specific conditions of variation index (%), Vi and Vxi are the unperturbed and the peri-urban agricultural development. perturbed vulnerability indices, respectively; N and n denote

the number of map layers used for the calculation of Vi and 3.3. Sensitivity analysis Vxi. The unperturbed vulnerability index (Vi) is obtained using

Eq. (2) for the ith sub-area, while the vulnerability index (Vxi) The assessment of groundwater vulnerability requires addi- is calculated for the ith sub-area excluding one map layer tional experimental support to reduce subjectivity, increase (x-parameter) at a time [16]. reliability and therefore minimize doubts about the accuracy The single-parameter sensitivity analysis is performed to of the GIS-based methods used [45]. To this extent, sensitivity assess the influence of input parameters on the calculated analysis serves to acknowledge uncertainty, estimate variabil- groundwater vulnerability according to the methods used. ity and relative changes in the obtained results using different Therefore, the real or effective weight of each parameter is sets of input parameters, thus fully indicating the most compared with the assigned or theoretical weight in each important and influential parameters that critically affect polygon of the resulting groundwater vulnerability map [16]. the reliability of groundwater vulnerability. This statistical The effective weight of each ith sub-area is obtained using tool is essential both for scientists to construct a groundwater the following equation: vulnerability map and policy- and decision makers to prop-  X X erly evaluate current land use practices and future land man- W ¼ ri wi 100 ð4Þ xi V agement planning [16]. i

In this study, map removal sensitivity analysis [46] and where Wxi refers to the effective weight of each parameter, Xri single-parameter sensitivity analysis [16,47] were carried out and Xwi represent the rating and the weight assigned to a to assess the degree of uncertainty of the obtained results parameter (x) in ith sub-area, respectively; Vi is the overall from Pesticide DRASTIC and SI methods. To this extent, vulnerability index (unperturbed) calculated.

Fig. 7 – Spatial distribution of depth to water input map layer of the study area. Information Processing in Agriculture 2 (2015) 109– 129 117

Fig. 8 – Spatial distribution of net recharge in the study area.

4. Results and discussion 4.1.2. Net recharge Net recharge represents the amount of water per unit area of 4.1. Creation of input parameter maps land which penetrates soil and reaches the water table. The mean precipitation and evapotranspiration over a 10 Seven maps representing the seven parameters of DRASTIC year period (2003–2013) were interpolated by ordinary kriging were prepared using ArcGIS 9.1. An additional map of land [9] based on meteorological data provided by the Province of use was prepared for the estimation of the groundwater Savona [29]. Fig. 8 shows the spatial distribution of Net vulnerability index according to SI method. Each map was recharge in the study area. Higher net recharge occurs in classified and assigned ratings and weights according to the north-central part of the Carenda Basin, an area underlain DRASTIC methodology while SI standards were incorporated. by alluvial deposits and highly permeable aquifer materials The features used (raw input or classified) in the spatial anal- (sand and gravel), while lower net recharge occurs in the NE ysis are presented by each raster layer in the following maps. part of the study area as a result of the thick low-permeable dolomite and conglomerate formations. 4.1.1. Depth to water Depth to water represents the depth from the ground surface 4.1.3. Aquifer media to the water table. As a result, deeper water table implies les- The map for aquifer media in the study area was prepared ser chance for contamination in the aquifer. In this study, the using integrated aquifer data obtained from the Province of map of depth to water (Fig. 7) was obtained through spatial Savona and the regional Agency for Environmental interpolation of the elevation data obtained from 36 observa- Protection of Liguria Region ARPAL [29,34,39,49]. The map tion piezometer wells [34,48,49] covering the period 2009– was digitized and converted to raster format, on the basis of 2014. Generally, the depth to water in most of the study area the assigned index values of DRASTIC method. Regarding (59.62%) covered by alluvial deposits, ranges from less than ‘‘Aquifer media’’, most part of the study area (47%) is covered 30.4 m in the central area and gradually decreases to coastal by sand and gravel, followed by metamorphic/igneous units boundaries (in a west to east direction) to less than 4.6 (25%) (Fig. 9). It is evident that the strips of alluvium, sand m. The depth to water table in the western and northern parts and gravel developing along streams/rivers are characterized (40.38% of the study area) is very high (>30.4). A total of six by a higher vulnerability, with a rating of 6, in terms of poten- depth to water classes are extracted and in each one a rating tial for groundwater contamination. Mountainous areas value from 1 and 9 has been assigned with regard to Drastic (northwest and southwest) with a rating of 3 have low classification. groundwater contamination potential. 118 Information Processing in Agriculture 2 (2015) 109– 129

4.1.4. Soil media 1:25,000-scale topographic map [34,49]; (2) creation of a The soil media is the uppermost part of the vadose zone raster-based digital elevation model (DEM) utilizing this vec- (approximately 1–2 m thick) and indicates the recharge rate tor data; and (3) calculation of slope (%) of land surface from which can infiltrate soil and cause groundwater contamina- DEM. The final map was then reclassified into intervals and tion. Coarse-textured soils that contain mostly sand and assigned ratings ranging from 1 to 10. The slope map of the gravel allow more water to infiltrate downwards, thus in the study area, shown in Fig. 11, is highly variable and the slope coastal and central areas of the Albenga plain increased percentage varies within a wide range, from 0% to 13.4%. In groundwater vulnerability is anticipated. For the classification general, slopes range from 0% to 2% in the alluvial Albenga of dominant soil textures in the study area, the point plain to over 6% along hillsides and surrounding mountain- shapefile containing the soil textural data was converted to ous areas. a polygon shapefile through Thiessen polygon tessellation More specifically, the slope map indicates that flat slopes [51]. A total of seven soil textural classes are extracted and (0–2%) dominate the study area (48.8%) followed by shallow to each one a rating value between 2 and 9 has been assigned. slopes (17.2%). Steep and very steep slopes cover about 15% This vector layer was then converted into the grid format. of the area. The remaining part is rather flat and occupies Depending on the parent material, the soil textural classes 15% of the study site. The flat slope mapping of the Albenga of the area include: sand (5.99%), peat (23.25%), shrinking clay and its surrounding area indicates a high risk for groundwater (19.91%), loam (12.65%), sandy loam (11.51%), clay loam contamination mainly for the coastal and central plain. (18.86%) and muck (7.83%) (Fig. 10). Soils have been strongly influenced by fluvial dynamics in the Albenga plain and along 4.1.6. Impact of vadose zone all waterways of the investigated area. In general, the Impact of vadose zone parameter indicates the texture of the Carenda Basin located in the central Albenga plain which vadose zone which determines the migration potential and is fully covered by alluvial deposits, indicated higher the time the contaminants need to reach groundwater. A soil-contamination attenuation than the surrounding upper vadose zone map was prepared based on sub-surface geology and lower mountainous areas which are composed of clay and available lithology (Fig. 12). This figure shows that most of loam soils having lower attenuation. the study area (47%) is assigned a rating of 9 in the DRASTIC rating system due to the presence of sand and gravel 4.1.5. Topography formations. In the upland areas, the vadose zone consists of A topographic (slope) map was prepared using the following limestone and shales and a rating of 6 and 3 was assigned, steps: (1) digitization of elevation contours (25 m) from respectively.

Fig. 9 – Spatial distribution of aquifer media input in the study area. Information Processing in Agriculture 2 (2015) 109– 129 119

4.1.7. Hydraulic conductivity recharge, aquifer media, soil media, slope, impact of vadose The map of hydraulic conductivity was obtained through spa- zone, hydraulic conductivity and land use) according to Eqs. tial interpolation of data obtained from CERSAA and the cor- (1) and (2), respectively. The GIS-based index values were responding map (1:10,000) from the official authority of the obtained for Pesticide DRASTIC and SI methods and then Province of Savona [34,49] (Fig. 13). In general, the study area summed up separately on a raster cell-by-cell basis in order is characterized by high hydraulic conductivity (1 cm/s) in the to create the groundwater vulnerability maps. These maps middle part (62.5%), and thus the high score of 9 was were then reclassified into five equal categories of relative risk assigned. Permeability values based on mapping data vary ranging from ‘‘low’’ to ‘‘very high’’ to evaluate the spatial dis- between 0.01 cm/s in the north and less than 0.001 tribution patterns of the different groundwater contamina- cm/s in the south. tion risk over the area under study. Higher degrees of contamination risk are indicated by the change of color from 4.1.8. Land use green to red index values. The ranges and colors are pre- The spatial distribution of the land use cover of the study area sented in Pesticide DRASTIC and SI groundwater vulnerability has been mapped from data obtained from the corresponding maps to enable quick visual comparison and analysis. maps (1:10,000 and 1:100,000) obtained from the official Overall, high groundwater vulnerability index values indicate authority of the Province of Savona [34,50] and the Corine areas with high risk and high spatial variance for the geospa- Land Cover [51], respectively (Fig. 14). According to SI classifi- tial input parameters, suggesting that these areas need to be cation, the lowest score of 0 (no effect on vulnerability) is monitored more intensely. assigned to about 48% of the total area covered by forest land, The groundwater vulnerability maps obtained for the crop land, and fallow land. On the other hand, the highest study area after the application of the Pesticide DRASTIC score (70–100) is assigned to the central and coastal parts of and SI methods are shown in Fig. 15. Legend values in these the study area, indicating that these urban and irrigated agri- figures are shown in five risk categories: No, Low, Medium, cultural areas have the highest effect on vulnerability when High and Very high. The groundwater vulnerability index the land use parameter is taken into account. values obtained from the Pesticide DRASTIC method range from 65 to 232, whereas index values obtained from the SI 4.2. Pesticide DRASTIC and SI groundwater vulnerability method vary from 15 to 83 (Table 5). The higher index values maps obtained from the Pesticide DRASTIC method are solely due to higher weighting values and number of parameters used. The Pesticide DRASTIC and SI groundwater vulnerability Overall, similarities found in the results of Pesticide indices were calculated by selecting a maximum estimation DRASTIC and SI methods include areas (55.80% and 49.13% probability for the input parameters (depth to water, net of the total area, respectively) that are characterized by ‘‘high

Fig. 10 – Spatial distribution of soil media DRASTIC classification in the study area. 120 Information Processing in Agriculture 2 (2015) 109– 129

Fig. 11 – Spatial distribution of topography (slope) in the study area.

Fig. 12 – Spatial distribution of impact of vadose zone in the study area. Information Processing in Agriculture 2 (2015) 109– 129 121

Fig. 13 – Spatial distribution of hydraulic conductivity in the study area. to ‘‘very high’’ risk along the coastline and the middle area, vadose zone along the same direction. Therefore, it is shown especially areas covered by alluvial deposits. According to that both Pesticide DRASTIC and SI index values indicate ‘‘very the results of the Pesticide DRASTIC method, the risk for high’’ risk of groundwater contamination especially in areas groundwater contamination in a substantial part (38.18%) of (coastal-central plain) where intensive agricultural activities the Albenga–Ceriale coastal plain is characterized as ‘‘very are carried out, thus continuous monitoring is required in high’’ as a result of its fairly low topographic gradient (<6%), these areas. These monitoring activities could entail sampling shallow water table (<22.8 m), uniform distribution of hydrau- of existing wells, boreholes and soils above aquifer media, lic conductivity (1 cm/s) and medium to high net recharge drilling and sampling of new monitoring wells in hotspots, (101.6–177.8 mm) (see Table 6). adopting groundwater monitoring programmes and protocols, Similar results (33.95%) concerning ‘‘very high’’ potential minimizing groundwater irrigation activities and implement- for groundwater contamination have also derived from the ing pesticide management/control practices to reduce the risk application of the SI method, indicating that most of the vul- for groundwater contamination. nerable areas are located in the central region as well as in a small part of the west central region. Medium risk for ground- 4.3. Sensitivity analysis water contamination is mainly anticipated in the mid-area between the Albenga plain and the surrounding mountainous 4.3.1. Independence of parameters areas, occupying 19.53% and 14.97% of the study area as the Tables 7 and 8 present the statistical summary of the seven and Pesticide DRASTIC and SI methods indicate, respectively. In five parameters used to calculate the Pesticide DRASTIC and SI general, the Pesticide DRASTIC map for groundwater vulnera- indices, respectively. The highest contribution to the calcu- bility exhibited a significantly positive correlation (0.896) with lated Pesticide DRASTIC index in the study area originates the SI vulnerability map. from the parameters of topography (T), impact of vadose zone Moreover, areas of ‘‘no’’ risk are restricted to only a small (I) and hydraulic conductivity (C) with mean values of 8.72, 7.66 part of the study area (9.22% and 9.06% for the Pesticide and 6.99, respectively. On the other hand, net recharge – R DRASTIC and SI methods, respectively) in the NE edge between (3.58) and soil media – S (5.84) have the lowest contribution the Albenga coastal plain and the surrounding mountains of to the risk of groundwater contamination in the study area. Monte Pesalto, Pizzo Ceresa and Poggio Grande, representing The analysis of the coefficient of variation (CV%) indicates primarily areas of medium to high topographic gradients (6– that the higher contribution to the variation of Pesticide 18%) and low net recharge (50.8–101.6 mm). In general, the DRASTIC vulnerability index is due to hydraulic conductivity overall groundwater contamination risk increases from east (57.2%), followed by net recharge – R (48.0%) and depth to to inland (W and SW), which corresponds well with the water – D (45.1%), while soil media (S), aquifer media (A) increasing clay fraction in soil and the decreasing impact of and impact of vadose zone have lower contribution (CV 122 Information Processing in Agriculture 2 (2015) 109– 129

Fig. 14 – Spatial distribution of land use SI classification in the study area.

= 37.2%, 32.8% and 29.8%, respectively). The very low variabil- assigned to these layers (4 and 5, respectively) and the shal- ity of the topography – T (CV = 23.1%) indicates low contribu- low alluvial aquifer (<30.4 m) that dominates most of the tion of this parameter to the variation of the Pesticide study area (59.62%). vulnerability index across the study area. The Pesticide DRASTIC vulnerability index is moderately The statistical data (mean values) shown in Table 8 for SI sensitive to the removal of the topography (T), soil media (S) input parameters, confirm the greater contribution of topogra- and impact of vadose zone (I) layers from the entire calcula- phy – T (87.21) followed by depth to water – D (62.06) and tion procedure, since these parameters exhibited relatively aquifer media – A (61.77). The lowest mean values obtained moderate mean values of variation index (0.91%, 0.49% and through parameter independence analysis revealed that land 0.39%, respectively) when removed from the resulting maps. use – LU (39.21) and net recharge – R (35.77) had the smallest The hydraulic conductivity (C) and aquifer media (A) parame- contribution to the risk of groundwater contamination in the ters, both exhibiting very low mean values (0.19% and 0.04%, study area for all parameters considered during the application respectively), have the lowest variation index upon removal of the SI method. The analysis of the coefficient of variation val- from the Pesticide DRASTIC map. ues indicates that a very high contribution to the variation of In the SI method, map removal sensitivity analysis showed Pesticide DRASTIC vulnerability index is caused by land use that parameters aquifer media (A) and land use (LU) had the (96.4%). The other SI parameters showed exactly the same highest variation of 2.60% and 1.72%, respectively. High sensi- mean values as in the case of the Pesticide DRASTIC method, tivity to these two parameters indicates that the characteris- since both methods are used in the same way for the calcula- tics of lithology and human activities contribute most to the tion of the corresponding GIS-based indices. groundwater contamination in the study area. Upon removal of the net recharge (R) and depth to water (D) layers, consid- 4.3.2. Map removal sensitivity analysis erable impact on the variation of the vulnerability index is The statistical summary of the results of map removal sensi- observed. Mean variation indices for these parameters are tivity analysis that was performed by removing one input 1.28 and 0.75%, respectively. The least sensitive parameter is parameter layer at a time from the resulting Pesticide topography (T) for which the respective mean variation index DRASTIC and SI maps is represented in Tables 9 and 10, showed the lowest variation (0.41%) among all SI parameters respectively. analyzed. In the Pesticide DRASTIC map, the highest variation of the vulnerability index is observed upon removal of the net 4.3.3. Single parameter sensitivity analysis recharge – R (1.42%), followed by the depth to water – D The single parameter sensitivity analysis was carried out for (1.23%). This can be attributed to the high theoretical weight the seven input parameters of the Pesticide DRASTIC method Information Processing in Agriculture 2 (2015) 109– 129 123

Fig. 15 – (a) Pesticide DRASTIC and (b) SI groundwater vulnerability map in the study area. 124 Information Processing in Agriculture 2 (2015) 109– 129

Table 5 – Categories of contamination risk based on the Pesticide DRASTIC and SI methods. Frequency of index (%) Range of groundwater vulnerability 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

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

Risk category Pesticide DRASTIC SI 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

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 (SD) 2.80 1.72 2.03 2.17 2.01 2.28 4.00 Coefficient of variation (CV) (%) 45.1 48.0 32.8 37.2 23.1 29.8 57.2

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 (SD) 27.97 17.22 20.30 20.08 37.80 Coefficient of variation (CV) (%) 45.1 48.0 32.8 23.1 96.4

(Table 11) and the five input parameters of the SI method effective weight among all Pesticide DRASTIC parameters (Table 12). without any significant change in comparison to its theoreti- The effective weight factor results clearly indicate that the cal weight (7.41%). The calculated effective weights for aquifer depth to water (D) parameter, with a mean effective weight of media – A (14.54%) and soil media – S (17.18) exhibited quite 18.26% against the theoretical weight of 18.52%, dominated equal to their theoretical weights (14.81% and 18.52% the Pesticide Drastic vulnerability index. However, the net respectively) assigned by the Pesticide DRASTIC method. recharge – R showed the greatest difference between theoret- Impact of vadose zone (I) and topography (T) displayed higher ical and effective weights in the Pesticide DRASTIC method. effective weight values (17.24% and 13.85,% respectively) In particular, a negative 43% change in comparison to its the- than their theoretical ones (14.81 and 11.1%). This positive oretical weight (14.81%) was observed, thus indicating that change reflects the importance of these parameters for the this parameter showed the lowest impact in the estimation Pesticide method and the need to obtain more accurate, of the Pesticide DRASTIC groundwater vulnerability index. detailed and representative data regarding their spatial The hydraulic conductivity – C (8.22%) presented the lowest distribution. Information Processing in Agriculture 2 (2015) 109– 129 125

Table 9 – Statistical summary of the Pesticide DRASTIC map removal sensitivity analysis. Parameter removed Variation index (%) Minimum Maximum Mean Standard deviation (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

Table 10 – Statistical summary of the SI map removal sensitivity analysis. Parameter removed Variation index (%) Minimum Maximum Mean Standard 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

Table 11 – Statistical summary of the single parameter sensitivity analysis (Pesticide DRASTIC method). Parameter Theoretical weight Theoretical weight (%) Effective weight (%) Minimum Maximum Mean Standard 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

Table 12 reveals that topography (T) was the most effective application rates of nitrogenous fertilizers. Since both parameter in the vulnerability assessment as its effective Pesticide DRASTIC and SI methods assume that contami- weight (19.54%) exceeded by 61.49%, the theoretical weight nants are mobile and water soluble, nitrate satisfies these imposed by the SI method (12.10%). This statistical result assumptions. shows the very high importance of the topography (T) in the The values of nitrate concentrations used in the present resulting map of the SI method, thus indicating the need for study to validate results have been provided by CERSAA (from more precise data on this parameter in order to address 12 sampling locations) and by an extensive search in site-specific differential issues. The weights of the depth to public/government agencies (Province of Savona and ARPAL, water (D) and aquifer media (A) moderately increased by Italy) (16 sampling locations), covering the entire area under 8.44% and 14.40%, respectively. Finally, due to their low effec- study over a 3 year period (2009–2012) [34,39,48]. The nitrate tive weight values compared to the theoretical weights, the values were classified into five classes based on its permissi- net recharge (14.13%) and aquifer media (16.12%) were the ble (limit) concentration of 50 mg/L [52,53], as follows: Level 1: least influential among all SI parameters considered. <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. 4.4. Validation of the Pesticide DRASTIC and SI methods The actual concentration of nitrates in the groundwater of the study area is provided in Fig. 16. The validation map The validation of the Pesticide DRASTIC and SI results was clearly indicates that the interpolated nitrate concentrations performed using available nitrate concentration values in using both Pesticide DRASTIC and SI indices are well corre- groundwater. The selection of nitrate was based on the fact lated with actual concentration values for most of the study that the area is contaminated by nitrates, as a result of high area. Areas with very high vulnerability show also elevated 126 Information Processing in Agriculture 2 (2015) 109– 129

Table 12 – Statistical summary of the single parameter sensitivity analysis (SI method). Parameter Theoretical weight Theoretical weight (%) Effective weight (%) Minimum Maximum Mean Standard 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

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

actual nitrate concentrations (>50 mg/L). The obtained corre- (e.g. monitoring wells), if available, need to be obtained and lation values between the Pesticide DRASTIC and SI indices analyzed [54]. with the actual nitrate concentration were 0.693 and 0.728, thus indicating that both methods are characterized by quite 5. Conclusions good accuracy. High levels of nitrates in groundwater can be attributed to the extensive and intensive agricultural In this study, the Pesticide DRASTIC and SI methods were activities in the central Carenda basin, which contribute to applied in a GIS environment to assess the groundwater vul- nitrate pollution as a result of the high hydraulic conductivity nerability in the agricultural area of the Albenga coastal plain of the surface (alluvial deposits) and its flat topography. in north Italy. The resulting Pesticide DRASTIC and SI vulner- The nitrate concentrations in groundwater for the 23.21% ability maps indicated that major parts of the study area of the total area belong to level 5 (>50 mg/L) and indicate very (55.80% and 49.13% of the total area, respectively) are charac- high risk for contamination. Based on the actual nitrate terized by ‘‘high’’ to ‘‘very high’’ risk. These parts are along the concentrations pattern, the risk for groundwater contamina- coastline and in the middle of the study area, are mainly cov- tion increases from E to inland (W and SW), and correlates ered by alluvial deposits, and are characterized by fairly low well with the obtained Pesticide DRASTIC and SI indices. topographic gradients (<6%), shallow water table (<22.8 However, in order to obtain a better validation of m), uniform distribution of hydraulic conductivity (1 the DRASTIC risk mapping, data from additional sites cm/s) and medium to high net recharge (101.6–177.8 Information Processing in Agriculture 2 (2015) 109– 129 127

mm). Medium risk for groundwater contamination is mainly Turkey) using fuzzy clustering, multivariate statistics and GIS evident in the mid-area between the Albenga plain and the techniques. J Hydrol 2012;414–415:435–51. surrounding mountainous areas, which occupies only [3] Nisi B, Vaselli O, Delgado Huertas A, Tassi F. Dissolved nitrates in the groundwater of the Cecina Plain (Tuscany, 19.53% and 14.97% of the study area. Central-Western Italy): clues from the isotopic signature of The map removal sensitivity analysis showed that the NO3. Appl Geochem 2013;34:38–52. groundwater vulnerability index was highly sensitive to the [4] Etteieb S, Kawachi A, Elayni F, Han J, Tarhouni J, Isoda H. removal of the net recharge and depth to water parameters Environmental risk assessment of groundwater quality and in the Pesticide DRASTIC map and to the aquifer media and treated wastewater reuse on aquifer recharge: a case study in land use parameters in the SI map. Furthermore, the results Korba, Tunisia. Desalin Water Treat 2014;52:2032–8. of the single-parameter sensitivity analysis indicated that [5] Khedidja A, Boudoukha A. Risk assessment of agricultural pollution on groundwater quality in the high valley of the topography and the impact of the vadose zone are the Tadjenanet–Chelghoum Laid (Eastern Algeria). Desalin Water most significant Pesticide DRASTIC parameters and dictate Treat 2014;52:22–4. the high vulnerability of the shallow Albenga plain. On the [6] Chaouki M, Zeddouri A, Hadj-Said S. Study of the behavior of other hand, topography was the most influential parameter some pollutants and the vulnerability to chemical in the SI vulnerability method followed by aquifer media contamination of groundwater in the region of Ouargla and depth to water. (Southeast Algeria). Energy Proc 2013;36:1043–9. [7] Chen SK, Jang CS, Peng YH. Developing a probability-based The groundwater vulnerability analysis was validated model of aquifer vulnerability in an agricultural region. J using actual concentrations of nitrates in groundwater for Hydrol 2013;486:494–504. the entire study area and the results showed significant and [8] Gay JR, Korre A. A spatially-evaluated methodology for positive correlation values (0.693 and 0.728) for the Pesticide assessing risk to a population from contaminated land. DRASTIC and SI indices respectively, thus indicating that both Environ Pollut 2006;142:227–34. methods are characterized by quite good accuracy. [9] Komnitsas K, Modis K. Geostatistical risk estimation at waste The integration of the obtained results (maps, sensitivity disposal sites in the presence of hot spots. J. Hazard. Mater. 2009;164:1185–90. analysis and validation) is particularly useful in terms of [10] Foster S., Fundamental concepts in aquifer vulnerability, determining the most vulnerable areas that need detailed pollution risk and protection strategy. In: van Duijvenbooden and frequent monitoring, especially in the context of W, van Waegeningh HG, editors. Vulnerability of soil and delineating the Nitrate Vulnerable Zones. These monitoring groundwater to pollutants, International conference activities could entail sampling of existing wells, boreholes (Noordwijk aan Zee, The Netherlands), proceedings and and soils above aquifer media, drilling and sampling of new information; 1987, 38, p. 69–86. monitoring wells in hotspots, adopting groundwater [11] Civita M. Le carte della vulnerabilita degli acquiferi all’inquinamiento: teoria e pratica Contamination monitoring programmes and protocols, minimizing vulnerability mapping of the aquifer: theory and practice. groundwater irrigation activities and implementing pesticide Quaderni di Tecniche di Protezione Ambientale, Pitagora, management/control practices to reduce the risk of ground- Italy; 1999, p. 344. water contamination. Finally, groundwater vulnerability [12] Van Stempvoort D, Ewert L, Wassenaar L. Aquifer maps created in this study are also useful for policy makers vulnerability index (AVI): a GIS compatible method for during the implementation and prioritization of policies for groundwater vulnerability mapping. Can Water Resour J 1993;18:25–37. groundwater protection and management especially in areas [13] Goldscheider N, Klute M, Strum S, Hotzl H. The PI method e a where intensive agricultural (mainly greenhouse) activities GIS based approach to mapping groundwater vulnerability are carried out. with special consideration on karst aquifers. Z Angew Geol 2000;46(3):157–66. Acknowledgements [14] Aller L, Bennet T, Lehr JH, Petty RJ. DRASTIC: a standardized system for evaluating groundwater pollution potential using hydro geologic settings. USEPA document no. EPA/600/2-85- The authors would like to acknowledge the financial support 018; 1987, p. 622. of LIFE10 ENV/GR/594 project ‘‘Best practices for Agricultural [15] Panagopoulos GP, Antonakos AK, Lambrakis NJ. Optimization Wastes treatment and reuse in the Mediterranean countries’’ of the DRASTIC method for groundwater vulnerability (Wastereuse, http://www.wastereuse.eu/). assessment via the use of simple statistical method and GIS. Hydrogeol J 2006;14(6):894–911. [16] Babiker IS, Mohamed MA, Hiyama T, Kato K. A GIS-based DRASTIC model for assessing aquifer vulnerability in REFERENCES Kakamigahara Heights, Gifu Prefecture, Central Japan. Sci Total Environ 2005;345:127–40. [17] Fijani E, Nadiri AA, Moghaddam AA, Tsai FTC, Dixon B. [1] Komnitsas K, Modis K, Doula M, Kavvadias V, Sideri D, Optimization of DRASTIC method by supervised committee Zaharaki D. Geostatistical estimation of risk for soil and machine artificial intelligence to assess groundwater water in the vicinity of olive mill wastewater disposal sites. vulnerability for Maragheh–Bonab plain aquifer, Iran. J Desalin Wat Wat Treat 2015. http://dx.doi.org/10.1080/ Hydrol 2013;503:89–100. 19443994.2014.983988. [18] Rajasooriyar LD, Boelee E, Prado MC, Hiscock KM. Mapping [2] Gu¨ ler C, Kurt M, Alpaslan M, Akbulut C. Assessment of the the potential human health implications of groundwater impact of anthropogenic activities on the groundwater pollution in southern Sri Lanka. Water Res Rural Develop hydrology and chemistry in Tarsus coastal plain (Mersin, SE 2013;1–2:27–42. 128 Information Processing in Agriculture 2 (2015) 109– 129

[19] Kazakis N, Voudouris KS. Groundwater vulnerability and sites/default/files/allegati/pianidibacino/04_relazione_ pollution risk assessment of porous aquifers to nitrate: generale.pdf; 2003 [accessed 16.01.15]. modifying the DRASTIC method using quantitative [35] Capello M, Cutroneo L, Ferranti MP, Budillon G, Bertolotto RM, parameters. J Hydrol 2015;525:13–25. Ciappa A, et al. Simulations of dredged sediment spreading [20] Teixeira J, Chamine´ HI, Espinha Marques J, Carvalho JM, on a Posidonia oceanica meadow off the Ligurian coast, Pereira AJSC, Carvalho MR, et al. A comprehensive analysis Northwestern Mediterranean. Mar Pollut Bull of groundwater resources using GIS and multicriteria tools 2014;79:196–204. (Caldas da Cavaca, Central Portugal): environmental issues. [36] Decreto legislativo 152/99. Disposizioni sulla tutela delle Journal Environ Earth Sci 2014;73(6):2699–715. acque dall’inquinamento e recepimento della direttiva 91/ [21] Ribeiro L. Um novo´ndice ı de vulnerabilidade especı´fico de 271/CEE concernente il trattamento delle acque reflue urbane aquı´feros. Formulac¸a˜o e aplicac¸o˜ es. [SI: a new index of e della direttiva 91/676/ CEE relativa alla protezione delle aquifer susceptibility to agricultural pollution]. Internal acque dall’inquinamento provocato dai nitrati provenienti da report, ERSHA/CVRM, Instituto Superior Tecnico, Lisbon, fonti agricole. Gazzetta Ufficiale della Repubblica Italiana, Portugal; 2000, p 12. Spp.to Ord. G.U. n. 124; 1999. [22] Anane M, Abidi B, Lachaal F, Limam A, Jellali S. GIS-based [37] D. Lgs. 258/2000. Decreto Legislativo 18 Agosto 2000, n. 258. DRASTIC, Pesticide DRASTIC and the Susceptibility Index (SI): ‘‘Disposizioni correttive ed integrative del D.Lgs. 11/05/1999 n. comparative study for evaluation of pollution potential in the 152 in material di tutela delle acque dall’inquinamento, a Nabeul–Hammamet shallow aquifer, Tunisia. Hydrogeol J norma dell’articolo 1, comma 4, della legge’’, Gazzetta 2013;21(3):715–31. Ufficiale n. 218 del 18/09/2000, Supplemento Ordinario n. 153; [23] Baalousha H. Assessment of a groundwater quality 2000. monitoring network using vulnerability mapping and [38] D.G.R. n.1256/2004. Individuazione, nei comuni di Albenga e geostatistics: a case study from Heretaunga Plains, New Ceriale, di una zona vulnerabile da nitrati di origine agricola, Zealand. Agric Water Manage 2010;97:240–6. ai sensi dell’art.19, comma 3, del decreto legislativo 152/1999 [24] Bai L, Wang Y, Meng F. Application of DRASTIC and extension e successive modifiche ed integrazioni. Bollettino Ufficiale N theory in the groundwater vulnerability evaluation. Water 47; 2004. Environ J 2012;26(3):381–91. [39] Regione Liguria. Il Regione Liguria – Programma regionale di [25] Wang J, He J, Chen H. Assessment of groundwater Sviluppo Rurale – Relazione annuale di esecuzione, http:// contamination risk using hazard quantification, a modified www.agriligurianet.it/it/impresa/sostegno-economico/ DRASTIC model and groundwater value, Beijing Plain, China. programma-di-sviluppo-rurale-psr-liguria/psr-2007- Sci Total Environ 2012;432:216–26. 2013/comitatodisorveglianza/relazioni-annuali- [26] Hamza SM, Ahsan A, Imteaz MA, Rahman A, Mohammad TA, diesecuzioneconsolidate/item/download/3372_ Ghazali AH. Accomplishment and subjectivity of GIS-based 1c8df76bac8e0da1ba67322 4ec9941ae.html; 2013 [accessed DRASTIC groundwater vulnerability assessment method: a 19.11.15]. review. Environ Earth Sci 2015;73(7):3063–76. [40] Croskrey A, Groves CG. Groundwater sensitivity mapping in [27] Nobre RCM, Rotunno Filho OC, Mansur WJ, Nobre MMM, Kentucky using GIS and digitally vectorized geologic Cosenza CAN. Groundwater vulnerability and risk mapping quadrangles. Environ Geol 2008;54:913–20. using GIS, modeling and a fuzzy logic tool. J Contam Hydrol [41] Neukum C, Ho¨ tzl H, Himmelsbach T. Validation of 2007;94(3–4):277–92. vulnerability mapping methods by field investigations and [28] Arthur JD, Wood HAR, Baker AE, Cichon JR, Raines GL. numerical modeling. Hydrogeol J 2008;16(4):641–58. Development and implementation of a Bayesian-based [42] Stigter TY, Riberio L, Dill AMMC. Evaluation of an intrinsic aquifer vulnerability assessment in Florida. Nat Resour Res and a specific vulnerability assessment method in 2007;16(2):93–107. comparison with groundwater salinisation and nitrate [29] Agrillo G, Bonati V. Atlante Climatico Della Liguria a cura di contamination levels in two agricultural regions in the south Arpal – Centro Funzionale Meteoidrologico di Protezione of Portugal. Hydrogeol J 2006;14:79–99. Civile, http://www.arpal.gov.it/contenuti_statici//clima/ [43] JRC-EEA, Corine land cover updating for the year 2000: image atlante/Atlante_climatico_della_Liguria.pdf; 2013 [accessed 2000 and CLC2000. In: Lima V, editor. Products and Methods. 06.12.14]. Report EUR 21757 EN. JRC-Ispra; 2005. [30] Boni P, Peloso GF, Vercesi PL. Nuovi dati e considerazioni sulla [44] ESRI. Environmental Systems Research Institute (ESRI) stratigrafia del bacino Pliocenico de Albenga (Alpi Marittime). ArcGIS, Version 9.1 Environmental Systems Research Mem Soc Geol It 1984;28:385–96. Institute, Incorporated, Redlands, California, USA; 2005. [31] Foeken JPT. Tectono-morphology of the Ligurian Alps and [45] Gogu R, Dassargues A. Current trend and future challenges in adjacent basins (NW Italy). Vrije Universitet Amsterdam; groundwater vulnerability assessment using overlay and 2004, p. 1–192 [PhD thesis]. index methods. Environ Geol 2000;39(6):549–59. [32] Pozzani R. Piano Comunale di Emergenza Speditivo [46] Lodwik WA, Monson W, Svoboda L. Attribute error and Finalizzato al Contrasto del Rischio Sismico nel Territorio del sensitivity analysis of maps operation in geographical di Albenga, Comune di Albenga, Genova, http:// information systems-sustainability analysis. Int J Geogr Inf www.comune.albenga.sv.it/upload/albenga_ecm8/ Syst 1990;4:413–28. gestionedocumentale/Piano%20Sismico%20Speditivo_ [47] Napolitano P, Fabbri AG. Single-parameter sensitivity Albenga_784_8696.pdf; 2014 [accessed 23.01.15]. analysis for aquifer vulnerability assessment using DRASTIC [33] Brandolini P, Cevasco A, Firpo M, Robbiano A, Sacchini A. and SINTACS. HydroGIS 96. IAHS Pub 1996;235:559–66. Geo-hydrological risk management for civil protection [48] Regione Liguria. Programma di Azione Locale di lotta a siccita` purposes in the urban area of Genoa (Liguria, NW Italy). Nat e alla desertificazione (PAL) – Relazione finale, Dipartimento Hazards Earth Syst Sci 2012;12:943–59. Ambiente, http://www.minambiente.it/ sites/default/ files/ [34] Provincia di Savona. Relazione Centa, Piano di Bacino Straclio archivio/allegati/desertificazione/PAL REGIONE_LIGURIA.pdf; sul Rischio Idrogeologico (ai sensi dell’art.1, comma 1, del 2010 [accessed 12.12.14]. D.L. 180/1998 convertito in L. 267/1998) Caratteristiche [49] Provincia di Savona, Capitolo 3 – Analisi dell’utilizzo della idrauliche e geologiche del territorio Valutazione del rischio risorsa idrica, A.T.O. SAVONESE – Autorita` d’Ambito per la idraulico e geomorfologico, http://www.provincia.savona.it/ gestione del Servizio Idrico Integrato Piano d’Ambito Information Processing in Agriculture 2 (2015) 109– 129 129

Provinciale in materia di organizzazione del Servizio Idrico [52] EU. Council Directive of 12 December 1991 Concerning the Integrato, http://www.provincia.savona.it/ sites/default/files/ Protection of Waters Against Pollution Caused by Nitrates allegati/pagina_allegati/Cap.%203.pdf [accessed 13.10.14]. from Agricultural Sources (91/676/EEC). European Union, [50] Regione Liguria. Il Programma di sviluppo rurale 2007–2013 http://eurlex.europa.eu/legalcontent/EN/TXT/PDF/?uri= della Regione Liguria e` stato approvato dal Comitato sviluppo CELEX:31991L0676&from= EN; 1991 [accessed 04.11.14]. rurale della Commissione europea il 24 ottobre 2007. [53] EC (European Commission). Council Directive 98/83/EC of 3 Cartografia, http://www.agriligurianet.it/media/com_ November 1998 on the quality of water intended for human publiccompetitions/docsrepository/ allf_cartografia_1_354. consumption. Doc Off J Eur Union 1998; L330:32–54. pdf; 2013 [accessed 13.11.14]. [54] Banton O, Villeneuve JP. Evaluation of groundwater [51] Corine Land Cover (CLC) Italy, http://www.sinanet. vulnerability to pesticides: a comparison between the isprambiente.it/it/sia-ispra/downl oad-mais/corine-land- pesticide drastic index and the PRZM leaching quantities. J cover; 2012 [accessed 17.10.14]. Contam Hydrol 1989;4(3):285–96.