Anticipating the European Water Framework Directive (2000/60/EC), the Italian Government issued Legislative Decree n.152/99 which sets out rules for classifying the environmental status of national water bodies in order to achieve specific qualitative objectives by 2016. The most recent European Groundwater Directive (2006/118/EC), which was only recognized by Italy in early 2009 (Legislative Decree 30/09), requires such resources to be characterized from a qualitative standpoint and the risk of their being polluted by individual pollutants or groups of pollutants to be evaluated. This paper reports a simple methodology, based on easy-to-apply rules, for the rapid classification of groundwater, and the results of its application to the shallow aquifer of the plain of Tavoliere delle Puglie located in south Italy. Data collected during well-water monitoring campaigns carried out from 2002 to 2003 made it possible to assess the environmental status of the Tavoliere which, unfortunately, was found to be characterized by "significant anthropic pressures on quality and/or quantity of groundwater and necessitating specific improvement actions". © 2010 Springer Science+Business Media B.V.

Environmental time series are often affected by the "presence" of missing data, but when dealing statistically with data, the need to fill in the gaps estimating the missing values must be considered. At present, a large number of statistical techniques are available to achieve this objective; they range from very simple methods, such as using the sample mean, to very sophisticated ones, such as multiple imputation. A brand new methodology for missing data estimation is proposed, which tries to merge the obvious advantages of the simplest techniques (e.g. their vocation to be easily implemented) with the strength of the newest techniques. The proposed method consists in the application of two consecutive stages: once it has been ascertained that a specific monitoring station is affected by missing data, the "most similar" monitoring stations are identified among neighbouring stations on the basis of a suitable similarity coefficient; in the second stage, a regressive method is applied in order to estimate the missing data. In this paper, four different regressive methods are applied and compared, in order to determine which is the most reliable for filling in the gaps, using rainfall data series measured in the Candelaro River Basin located in South Italy.

Using reliable stochastic or deterministic methods, it is possible to rearrange an existing network by eliminating, adding or moving monitoring locations producing the optimal arrangement among any possible. In this paper, some spatial optimization methods have been selected as more effective among those reported in literature and implemented into a software M-Sanos able to carry out a complete redesign of an existing monitoring network. Both stochastic and deterministic methods have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information. Finally, an application to the existing regional groundwater level monitoring network of the aquifer of Tavoliere located in Apulia (south Italy) is presented.

This paper presents a general methodology for processing bioclimatic data in the temporal domain. Two different methods are used to assess the presence of temporal trends in the time-series of bioclimatic indices at each mea- surement station. A preliminary stage checks for the statistical homogeneity in the data set and for the presence of serial autocorrelation in the data, applying the proper methods to remove these effects. The methodology has been applied to a case study in Apulia, Italy, using the popular De Martonne index as a bioclimatic indicator.

This paper presents a general methodology for processing bioclimatic data in the spatial domain whose main goal is to derive indications related to the moisture/dryness level of a region and provide water management authorities with information about its irrigation requirements. The methodology uses point-scale measurements of weather related data to perform a detailed analysis of the spatial behavior of the corresponding bioclimatic indicators at the continuous regional scale. The proposed methodology, although more demanding in terms of computation resources, gives more accurate results than standard approximate approaches available in current GIS packages. This methodology has been applied to a particular case study using the well known De Martonne index as a bioclimatic indicator.

In the present paper, an extensive cross-validation procedure, based on the analysis of numerical indices and graphical tools, is described and discussed. The procedure has been implemented in a software application designed to support practitioners in the variogram model assessment. It provides an extensive report, which summarizes a large post-processing stage and suggests how to interpret the performed analysis to rate the model to be validated. Besides classical accuracy indices, two new integrated tools based on the variogram of residuals are introduced, which take the spatial nature of the dataset into account. Finally, inspecting the summary report, the user can decide whether the considered model is satisfactory for his/her goals or it needs to be improved. Finally, a case study is presented related to the variogram assessment of groundwater level measured in a porous shallow aquifer of the Apulia Region (South-Italy).

The water content (q) of the subsoil is an important parameter affecting all the processesthat occur in the vadose zone, playing a key role in the infiltration, aquifer recharge, andflow and transport of water and substances, as well as groundwater storage. Although thevadose zone is usually considered constituted of soil, it may also be comprised of rock.In the latter case, q cannot yet be operationally measured despite its unquestionableimportance. The aim of the present work was therefore to investigate the potential of anovel electrical impedance sensor for q measurement in rocks and to evaluate the effectsof frequency and salinity on the calibration of this device. In this study, only the resistance(R), i.e., the real part of the measured impedance, was investigated because of its directcorrelation with q. Calcarenite, a sedimentary porous rock, was used for the calibration inthe laboratory via the installation of penetration-type probes. The independence of themeasured R from the different frequencies set on the device was checked using a statisticalapproach. The q-R calibration functions obtained for the different saturation solutionshave a power-law dependence with a good degree of correlation. The results highlight boththe strong reproducibility of the experimental data (by using the penetration-type probes)and the suitability of the device for q measurement in calcarenite. The method could beadvantageous for field applications that involve rocks.

Temporary streams are characterised by specific hydrological regimes, which influence ecosystem processes, groundwater and surface water interactions, sediment regime, nutrient delivery, water quality and ecological status. This paper presents a methodology to characterise and classify the regime of a temporary river in Southern Italy based on hydrological indicators (HIs) computed with long-term daily flow records. By using a principal component analysis (PCA), a set of non-redundant indices were identified describing the main characteristics of the hydrological regime in the study area. The indicators identified were the annual maximum 30- and 90-day mean (DH4 and DH5), the number of zero flow days (DL6), flow permanence (MF) and the 6-month seasonal predictability of dry periods (SD6). A methodology was also tested to estimate selected HIs in ungauged river reaches. Watershed characteristics such as catchment area, gauging station elevation, mean watershed slope, mean annual rainfall, land use, soil hydraulic conductivity and available water content were derived for each site. Selected indicators were then linked to the catchment characteristics using a regression analysis. Finally, MF and SD6 were used to classify the river reaches on the basis of their degree of intermittency. The methodology presented in this paper constitutes a useful tool for ecologists and water resource managers in the Water Framework Directive implementation process, which requires a characterisation of the hydrological regime and a 'river type' classification for all water bodies.

Groundwater represents an essential water resource for human purposes, mainly in those areas characterised by a scarcity of surface water and dry climate. Consequently, tools for assessing the groundwater balance are fundamental for its suitable management. The conventional groundwater balance equation, which considers all the natural and human-induced terms of the balance, such as rainfall, withdrawals, irrigation, etc., sometimes lacks of some important terms. One of the terms of the balance that is most difficult assess is the volume of water exchanged with other neighbouring water bodies (subsurface inflow/outflow). In this case, the estimation must be considered as a poor approximation. In this paper, a novel methodology is proposed that is capable of significantly increasing the accuracy of the groundwater balance when subsurface inflows and outflows are unknown. The improvement is accomplished by comparing two corresponding time series of annual groundwater balances assessed by means of different balance models. The first time series is evaluated by means of the conventional balance equation and the second one by directly estimating the groundwater volumes by means of geostatistical methods. Both these models are supposed to lack specific, even though different, information. Their comparison through simple statistical tools allows them to be calibrated and to recover missing average information. A study case is presented considering the inflow/outflow term and the specific yield as missing information for the conventional and the geostatistical approaches, respectively. The study area is the shallow porous aquifer of the Tavoliere di Puglia (South Italy).

Starting from the literature concerning applications of Data Mining (DM) for the integrated management of environmental information, in the present work an application path referred to the smart use of spatial data is traced to encourage the adoption of sustainable practices in agriculture, overcoming the limitations to the productivity related to biological agriculture. In particular, this work focuses its attention on the class of DM algorithms called "supervised algorithms" addressed to the clustering of the cultivated area of farms, illustrating its potentiality to define the typical decision making of management and planning of interventions. This approach is particularly significant for the integrated management of regionalized and environmental data to the topographical and soil features of the area that we want to model and to the physical-chemical nature of soil, to the kind of cultivation used, to the availability and quality of the water, as well as economic and socio-economic aspects.

Due to the high wells drilling cost, monitoring sites are usually selected among existing wells; nevertheless, the resulting monitoring network must assure a good assessment of the main characteristics of the considered aquifer. Groundwater managers, need to find a good balance between two conflicting objectives: maximizing monitoring information and minimizing costs. In this paper, a couple of groundwater monitoring optimization methods are presented, related to the local shallow aquifer of the Alimini Lakes, located in Apulia (South-Eastern Italy) where a large number of existing wells have been pinpointed and the need of optimally reducing exists. The proposed methods differ each other for the required amount of prior information. The first proposed method, namely Greedy Deletion, just requires the geographical position of the available sites, while the second, the Simulated Annealing, also requires the knowledge of the spatial law of the considered phenomenon. The managerial need was to halve the number of monitoring sites minimizing the information loss.

Major concerns arise about marine pollution due to human and industrial activities. The quality of sensing system response depends upon the materials used to realize sensors. There are different techniques used for the purposes of this kind of research that makes easier to reveal main parameters such as: Temperature, salinity, waiving, wind, chlorophyll, oxygen, depth, etc. The paper presents an optical fiber sensor design using nanotechnology. The sensor will be used on floating buoys for autonomous marine detection. The sensor principle is based on photochromatography and to be realized using a waveguide for conducting light for multimodal approach.

The effective protection of the coastal ecosystem requires a detailed knowledge of the morphological evolution of the coastal environment. Several probabilistic models have been developed in the last decades to implement a reliable statistical forecasting of coastline dynamics. In this work, the non-linear Evolutionary Polynomial Regression (EPR) model has been used for the first time to evaluate the short-term dynamics of the shoreline from a set of measured shoreline positions in previous years. A comparison of the mean known shoreline positions with those predicted by the model, together with their confidence and prediction intervals, can be used to assess the reliability of the estimation by the EPR model.

One of the most often encountered modelling problems is that of handling missing data, i.e. the problem of intermediate data gaps, where data/observations before and after the missing observations are available. The gaps in data represent discontinuities, which can pose difficulties both for model construction and model application phases. Evolutionary Polynomial Regression (EPR-MOGA) is a data-driven hybrid technique, which combines the effectiveness of genetic programming with the numerical regression for developing simple and easily interpretable mathematical model expressions. Evolutionary Polynomial Regression takes advantage of the evolutionary computing approach that allows the construction of several model expressions based on training data and least squares methodology to estimate numerical parameters/coefficients. These models can then be verified on a test set and gaps can be in-filled in test datasets by using one selected model. Because of the pseudo-polynomial formulations achievable by EPR-MOGA, it requires fewer numbers of parameters to be estimated, which in turn requires shorter time series for training. Another advantage of the EPR-MOGA approach is the ability to choose objective functions pertaining accuracy and parsimony. In the present work, an application of EPR-MOGA is shown on some sites belonging to the Apulian meteo-climatic monitoring network.

Heavy fluctuations in wastewater composition, such as those typical of tourist areas, can lead to a deteri-orationintreatmentplantperformanceifnoactionistakeninadvance.Mathematicalmodelling,appliedto treatment plant performance prediction, can provide valuable information to address the stress issue.The present study shows that the evolutionary polynomial regression methodology (EPR) is able to pre-dicttheperformancesofanattachedgranularbiomasssystemsothatitispossibletomakethenecessaryoperatingchangesinadvance,avoidingdeteriorationinthequalityoftheeffluentdischarged.ThepresentpapershowstheresultsofEPRapplicationtogrossparametersofagranularattachedbiomassreactor.Foreach parameter, a model capable of predicting the effluent value was assessed, based on the knowledgeoftheinfluentcharacteristics.Coefficientsofdeterminationvalues(CoD)obtainedduringthemodelsval-idation phase, can be said to be more than satisfactory, varying between 84.2% and 94.6%. Moreover, theapplied tests showed typical behaviours commonly found when observed and predicted values are quitesimilar. This paper reports the first application attempt for modelling this kind of emerging treatmentsystem and gross parameters.

The Artificial Neural Networks by Multi-objectiveGenetic Algorithms (ANN-MOGA) model has been appliedto gross parameters data of a Sequencing Batch BiofilterGranular Reactor (SBBGR) with the aim of providing an effectivetool for predicting the fluctuations coming from touristicpressure. Six independent multivariate models, whichwere able to predict the dynamics of raw chemical oxygendemand (COD), soluble chemical oxygen demand (CODsol),total suspended solid (TSS), total nitrogen (TN), ammoniacalnitrogen (N-NH4+) and total phosphorus (Ptot), were developed.The ANN-MOGA software application has shown to besuitable for addressing the SBBGR reactor modelling. The R2found are very good, with values equal to 0.94, 0.92, 0.88,0.88, 0.98 and 0.91 for COD, CODsol, N-NH4+, TN, Ptot andTSS, respectively. A comparison was made between SBBGRand traditional activated sludge treatment plant modelling.The results showed the better performance of the ANNMOGAapplication with respect to a wide selection of scientificliterature cases.

In the present paper, the novel softwareGTest is introduced, designed for testing the normality of a userspecified empirical distribution. It has been implemented with two unusual characteristics; the first is the user option of selecting four different versions of the normality test, each of them suited to be applied to a specific dataset or goal, and the second is the inferential paradigm that informs the output of such tests: it is basically graphical and intrinsically self-explanatory. The concept of inference-byeye is an emerging inferential approach which will find a successful application in the near future due to the growing need of widening the audience of users of statistical methods to people with informal statistical skills. For instance, the latest European regulation concerning environmental issues introduced strict protocols for data handling (data quality assurance, outliers detection, etc.) and information exchange (areal statistics, trend detection, etc.) between regional and central environmental agencies. Therefore, more and more frequently, laboratory and field technicians will be requested to utilize complex software applications for subjecting data coming from monitoring, surveying or laboratory activities to specific statistical analyses. Unfortunately, inferential statistics, which actually influence the decisional processes for the correct managing of environmental resources, are often implemented in a way which expresses its outcomes in a numerical form with brief comments in a strict statistical jargon (degrees of freedom, level of significance, accepted/rejected H0, etc.). Therefore, often, the interpretation of such outcomes is really difficult for people with poor statistical knowledge. In such framework, the paradigm of the visual inference can contribute to fill in such gap, providing outcomes in self-explanatory graphical forms with a brief comment inthe common language. Actually, the difficulties experienced by colleagues and their request for an effective tool for addressing such difficulties motivated us in adopting the inference-by-eye paradigm and implementing an easy-to-use, quick and reliable statistical tool. GTest visualizes its outcomes as a modified version of the Q-Q plot. The application has been developed in Visual Basic for Applications (VBA) within MS Excel 2010, which demonstrated to have all the characteristics of robustness and reliability needed. GTest provides true graphical normality tests which are as reliable as any statistical quantitative approach but much easier to understand. The Q-Q plots have been integrated with the outlining of an acceptance region around the representation of the theoretical distribution, defined in accordance with the alpha level of significance and the data sample size. The test decision rule is the following: if the empirical scatterplot falls completely within the acceptance region, then it can be concluded that the empirical distribution fits the theo

The variogram incorporates the basic information for the geostatistical analysis of spatial data, making it one of the most popular and widespread approaches for the evaluation and assessment of environmental data. Fitting an experimental variogram with a proper model, capable of catching the main characteristics of the sampled data, is a critical stage of spatial analysis. This stage is generally carried out using a semi-Automatic approach, with different levels of user's contribution. Once a model and the related starting parameter values have been defined, they can be refined using a trial-And-error strategy and the impact of such changes can be evaluated through several calibration indices. In this paper, we present an overview of the main indices used for the refinement of the variogram model parameters, highlighting the specific information provided by each of them together with a few heuristic strategies for driving the trial-And-error refinement process.

Escherichia coli (E. coli) is one of the most commonly adopted indicators for the determination of themicrobiological quality in water and treated wastewater. Two main types of methods are used for theenumeration of this faecal indicator: membrane filtration (MF) and enzyme substrate tests. For bothtypes, several substrates based on the ?-D-glucuronidase activity have been commercialized. Thespecificity of this enzyme for E. coli bacteria has generated considerable use of methods that identifythe ?-D-glucuronidase activity as a definite indication of the presence of E. coli, without any furtherconfirmation. This approach has been recently questioned for the application to wastewater. Thepresent study compares two methods belonging to the above-mentioned types for the enumerationof E. coli in wastewater: MF with Tryptone Bile X-glucuronide agar and the Colilert®-18 test.Confirmation tests showed low average percentages of false positives and false negatives for bothenumeration methods (between 4 and 11%). Moreover, the counting capabilities of these twomethods were compared for a set of 70 samples of wastewater having different origins and degreesof treatment. Statistical analysis showed that the Colilert®-18 test allowed on average for asignificantly higher recovery of E. coli.

Starting from the literature concerning applications of Data Mining (DM) for the integrated managementof environmental information, in the present work an application path referred to the smart use of spatial data is tracedto encourage the adoption of sustainable practices in agriculture, overcoming the limitations to the productivityrelated to biological agriculture. In particular, this work focuses its attention on the class of DM algorithms called "supervisedalgorithms" addressed to the clustering of the cultivated area of farms, illustrating its potentiality to definethe typical decision making of management and planning of interventions. This approach is particularly significant forthe integrated management of regionalized and environmental data to the topographical and soil features of the areathat we want to model and to the physical-chemical nature of soil, to the kind of cultivation used, to the availabilityand quality of the water, as well as economic and socio-economic aspects.

When measurements of values that are less than the limit of detection are reported as not detected, the data are referred to as censored. The non-recording of values below the limit of detection is common in soil science research although modelling data affected by censoring can be problematic. This paper develops and tests a modified version of Spatial Simulated Annealing, called Simulated Annealing by Variogram and Histogram form, for drawing values for censored points given a mixed set of observed and censored data. The algorithm aims to maximise the goodness of fitting between the experimental and theoretical variograms (by allowing variation in its parameters) while the imputed values are constrained to a target histogram form. In practice, the experimental histogram is estimated by transforming the available data (interval and exact observations) to quantiles and fitting a plausible distribution. The theoretical distribution of the data is used to constrain the variogram fitting. The proposed simulated annealing method is designed to find the optimal spatial arrangement of values, given by the lowest errors in variogram and histogram fitting and kriging prediction. The accuracy of the method proposed is assessed on a simulated data set in which the censored point values are known and compared with the Spatial Simulated Annealing algorithm. According to the results obtained, the Simulated Annealing by Variogram and Histogram form (SAVH) approach can be recommended as a useful tool for the analysis of spatially distributed data with censoring.

Soil survey is generally time-consuming, labour-intensive and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute.Maps of bulk soil electrical conductivity (ECa) recorded with EMI sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk ECa survey, has been applied in an agricultural field in Apulia region (south-eastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries and preliminary observations. Three optimization criteria were used: the first criterion (MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid ECa data as weighting function, and the third criterion (MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil moisture estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time and the cooling law, which can be an invaluable support to the process of sampling design.The proposed approach has found the optimal solution in a reasonable computation time. The use of bulk ECa gradient as an exhaustive variable, known at any node of an interpolation grid, has allowed the optimization of the sampling scheme, distinguishing among areas with different priority levels.

Il Servizio di Protezione Civile della Regione Puglia garantisce il monitoraggio meteoclimatico attraverso un proprio sistema di rilevamento e trasmissione dei dati in telemisura, ex Ufficio Idrografico e Mareografico, integrato nel Centro Funzionale Decentrato. Gran parte dei dati termopluviometrici sono visibili ed acquisibili consultando il sito www.protezionecivile.puglia.it. Il Sevizio, d'intesa con l'Ufficio Statistico regionale ha istituito un gruppo tecnico di lavoro, in collaborazione con l'Istituto di Ricerca Sulle Acque del CNR (IRSA) sede di Bari, per la realizzazione di mappe tematiche, sempre più frequentemente richieste dalle Amministrazioni Pubbliche locali e da comunità e consorzi privati. Obiettivo della prima fase di lavoro, oggetto di questa presentazione, è stato quello di realizzare mappe di precipitazione e temperatura attraverso l'applicazione di tecniche geostatistiche, in grado di studiare e modellare la variazione spaziale e di fornire in ogni punto geografico due informazioni: stima e varianza di stima. L'implementazione dell'approccio attraverso le tecnologie GIS ha consentito di produrre tre mappe relative a precipitazione totale mensile e temperatura minima e massima mensile che rappresentano i valori stimati ed i corrispondenti estremi dell'intervallo di confidenza al 95%.

There is a need for a reliable sustainable option to effectively manage the landfill leachate generation. This study presents a simple procedure for the revegetation of the walls of closed landfills, employing the leachate as a fertirrigant. The native plants Lepidium sativum, Lactuca sativa, and Atriplex halimus, which suit the local climate, were chosen for this study in Southern Italy. The methodology was structured into three phases (i) early stage toxicity assessment phase (apical root length and germination tests), (ii) adult plant resistance assessment phase, and (iii) soil properties verification phase. The rationale of the proposed approach was first to look at the distinctive qualities and the potential toxicity in landfill leachates for fertigation purposes. Afterwards, through specific tests, the plants used were ranked in terms of resistance to the aqueous solution that contained leachate. Finally, after long-term irrigation, any possible worsening of soil properties was evaluated. The results demonstrated the real possibility of using blended leachate as a fertigant for the revegetation of the walls of closed landfills. In particular, the plants maintained good health when leachate was blended at concentrations of lower than 25 and 5 %, respectively for A. halimus and Lepidium sativum. Irrigation tests showed good resistance of the plants, even at dosages of 112 and 133.5 mm m(-2), at maximum concentrations of 25 and 5 %, respectively, for A. halimus and Lepidium sativum. The analysis of the total chlorophyll content and of aerial parts dried weight confirmed the results reported above.

In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study smallscale, short-term coastal morphodynamics, given its capability for treating a wide database of known information,non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance betweenthe computational load and reliability of estimations of the three models. In fact, even though it is easyto imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worseningof estimations can be accepted in exchange for the time saved in data organization and computational load. Themodels' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly betterestimation of the polynomial model with respect to the multilinear model, as expected. On the other hand,even though the data organization was identical for the two models, the multilinear one required a simplersimulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression modelwas used in order to make some conjecture about the uncertainty increase with the extension of extrapolationtime of the estimation. The overlapping rate between the confidence band of the mean of the known coast positionand the prediction band of the estimated position can be a good index of the weakness in producing reliableestimations when the extrapolation time increases too much. The proposed models and tests have been appliedto a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.

A regression tree model has been used to make predictions of six gross parameters (COD, CODsol, N-NH4 +, TN, Ptot and TSS) of an innovative SBBGR reactor. R2 values ranging from 0.94 to 0.97 were found forammonia and total phosphorus, respectively. This application showed its usefulness as a decision support system for wastewater treatment plants in tourist areas which typically operate under high stressconditions due to the sharp fluctuations of wastewater flow and composition. A forecast of the bioreactor's performance would help the plant manager to put in place the required practices and procedures.The regression tree model could be part of the automation and control system of the SBBGR plant, allowing the change of operating conditions to be carried out automatically and in an effective way to face thetouristic stress issue.

Mo.nalis.a is a conceptual model aimed atidentifying the most suitable local geothermal sourcesto match the nearest industrial thermal needs. The methodologicalapproach proposed is based on investigatingindustrial thermal processes and then identifying suitablegeothermal solution plants that match these thermalrequirements. The model was tested in Apulia (southernItaly) as a case study for assessing how the methodologycould contribute to reducing the use of conventionalenergy resources for the industrial heat supply sector.The medium thermal needs in Apulia are always higherthan 60 °C, and the main strategic industrial processesdiscussed into this work are Bpasta and flour production^Bwastewater treatment/sludge digestion^ and Bswimmingpool management^. In order to match these industrialthermal demands, the most suitable proposed plant isthe ground water heat pump system, limited to the first100 m, the depth involved in the heat exchange throughvertical probes of model. Finally,Mo.nalis.a identifies theApulian areas with a possible development of these threeactivities using geothermal resource: the Foggia province,Murge and Salento sectors.

Within the recent EU Water Framework Directive and the modification introduced into national water-related legislation, monitoring assumes great importance in the frame of territorial managerial activities. Recently, a number of public environmental agencies have invested resources in planning improvements to existing monitoring networks. In effect, many reasons justify having a monitoring network that is optimally arranged in the territory of interest. In fact, modest or sparse coverage of the monitored area or redundancies and clustering of monitoring locations often make it impossible to provide the manager with sufficient knowledge for decision-making processes. The above mentioned are typical cases requiring optimal redesign of the whole network; fortunately, using appropriate stochastic or deterministic methods, it is possible to rearrange the existing network by eliminating, adding, or moving monitoring locations and producing the optimal arrangement with regard to specific managerial objectives. This paper describes a new software application, MSANOS, containing some spatial optimization methods selected as the most effective among those reported in literature. In the following, it is shown that MSANOS is actually able to carry out a complete redesign of an existing monitoring network in either the addition or the reduction sense. Both model-based and design-based objective functions have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information and the managerial optimization objectives. Finally, two applications for testing the goodness of an existing monitoring network and the optimal reduction of an existing groundwater-level monitoring network of the aquifer of Tavoliere located in Apulia (South Italy), constrained to limit the information loss, are presented.

The data fusion is a growing research field, which finds a natural application in the remotesensing, in particular, for performing supervised classifications by means of multi-sensor data.From the theoretical standpoint, to address such an issue, the Bayesian setting provides an elegantand consistent framework. Recently, a methodology has been successfully proposed incorporatinga geostatistical non-parametric approach for improving the estimation of the prior probabilitiesin the scope of the supervised classification. In this respect, a limitation affecting the Bayescomputation in the multi-sensor data is the naïve approach, which considers independent all thesensor measurements. Obviously, such hypothesis is unsustainable in practice, because differentsensors can provide similar information. Therefore, an enhancement of the previous describedmethod is proposed, introducing the smooth multivariate kernel method in the Bayes frameworkto furtherly improve the probability estimations. A peculiar advantage of the smooth kernelapproach concerns the fact that it is inherently non-parametric and consequently overcomes themultinormality data hypotesis. A case study is presented based on the data coming from theAQUATER project.

Redesign of environmental monitoring networks is a field rich in practical applications, mainly in the light of recent EU environmental directives. In the present work, an optimisation software application has been applied to reduce the size of an existing monitoring network affecting in minimal fashion its descriptive capability and making its maintenance costs more sustainable at the same time. The optimal reduction of a long-term monitoring network is a balance issue. In fact, redundancies among monitoring sites often load the manager with costs providing a negligible contribution in the decision-making processes. However, on the other hand, discarding erroneously monitoring sites could reduce the representativeness of the monitored system and, consequently, distort its informative framework. To avoid such risk, downstream of the optimisation stage, a retrospective analysis should be carried out in order to verify the correctness of the decision about the number and the locations of sites to be discarded. A case study is presented that focuses on the optimisation of a groundwater monitoring network located in the southern part of Italy. The investigated case is of particular interest for the criticality of the considered resource and for the recognised importance of the groundwater monitoring in the Water Framework Directive.

Monitoring networks redesign is a field rich of practical applications, mainly at the light of recent EU environmental directives. A software application capable of redesigning a given Monitoring Network (MN) can rightly be considered a valuable decision support system. In fact, such software can support resource managers to improve the informative power of the MN and make more sustainable its maintenance costs. Redundancies and clustering of monitoring locations, over the considered area, often provide the manager with an insufficient knowledge for decision-making processes and pose the need for upsizing or downsizing the MN or relocating monitoring sites over the area. In this paper, a suitable software application able to carry out all the three tasks mentioned above and a related case study are presented.

Within recent WFD and the modification introduced into national water related legislation, monitoring assumes great importance in the frame of territorial managerial activities. Recently, a number of public environmental agencies invested resources in planning improvements on existing monitoring networks. A lot of reasons justify the optimal redesign of a monitoring network. In fact, a modest or sparse coverage of the monitored area or redundancies and clustering of monitoring locations often make impossible to provide the manager with a sufficient knowledge for decision-making processes. These are typical cases requiring an optimal redesign of the whole network; particular emphasis shall be devoted to quality groundwater monitoring network. Using reliable stochastic or deterministic methods, it is possible to rearrange the existing network by eliminating, adding or moving monitoring locations producing the most uniform arrangement among any possible. In this paper, some spatial optimization methods have been selected as more effective among those reported in literature and implemented in a software able to carry out a complete redesign of an existing monitoring network. Both stochastic and deterministic methods have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information. Finally, an application to the existing regional groundwater level monitoring network of the aquifer of Tavoliere located in Apulia (South Italy) is presented.

The redesign of environmental monitoring networks is a field rich of practical applications, in particular in light of recent EU environmental directives. When dealing with an existing monitoring network reduction, the easiest approach is to search for redundancies among the monitoring locations (clusters of locations) and to remove the most useless of them on the basis of the values of some quantitative parameter. This approach could lose some important features of the existing network, thus reducing its informative content. To avoid such a risk, the results of the network optimization should undergo a retrospective analysis capable to verify, by assessing some independent statistical indices, the acceptability of the reduced configuration with respect to specific managerial issues. The case study presented in this work focuses on the optimal downsizing of a groundwater monitoring network located in the Southern part of Italy. The MSANOS optimization software has been used to select the locations to remove from the existing network, with the goal to sustainably balance maintenance costs and information loss. The investigated case is of particular interest for both the critical issues of the considered resource and the recognized importance of the groundwater monitoring in the European Water Framework Directive.

Shallow water table levels can be predicted using several approaches, either based on climatic records, on field evidences based on soil morphology, or on the outputs of physically based models. In this study, data from a monitoring network in a relevant agricultural area of Northern Italy (ca. 12,000 Km2) were used to develop a data driven model for predicting water table depth in space and time from meteorological data and long-term water table characteristics and to optimize sampling density in space and time. Evolutionary Polynomial Regressions (EPR) were used to calibrate a predictive tool based on climatic data and on the records from 48 selected sites (N = 5,611). The model was validated against the water table depths observed in 15 independent sites (N = 1,739), resulting in a mean absolute error of 30.8 cm (R 2 = 0.61). The model was applied to the whole study area, using the geostatistical estimates of the average water table depth as input, to provide spatio-temporal maps of the water table depth. The impact of the degradation of data input in the temporal and spatial domain was then assessed following two approaches. In the first case, three different EPR models were calibrated based on 25 %, 50 % and 75 % of the available data, and the error indexes compared. In the second case, an increasing number of monitoring sites were removed from the initial data set, and the associated increased kriging standard deviation was assessed. Reducing the average sampling frequency from 1.5 per month to 1 every 40 days did not impact significantly on the prediction capability of the proposed model. Reducing the sampling frequency to 1 every 4 months resulted in a loss of accuracy <3 %, while removing more than half locations from the network, resulted in a global loss of information <15 %.

Nel presente studio, sono stati usati dati provenienti da una rete di monitoraggio situata in una vasta area dell'Italia settentrionale (ca. 12,000 Km2), per sviluppare un modello data-driven per la previsione della misura del livello piezometrico nello spazio e nel tempo. Il modello si basa sugli EPR (Evolutionary Polynomial Regressions). Il modello è stato applicato all'intera area di studio, utilizzando come valori di ingresso le stime del livello piezometrico medio ottenute mediante metodi geostatistici per fornire mappe con le variazioni del livello idrico nello spazio e nel tempo. La riduzione della frequenza di campionamento da 1.5 per mese ad 1 ogni 40 giorni, non influisce significativamente sulla capacità predittiva del modello proposto. La riduzione della frequenza di campionamento ad 1 ogni 4 mesi ha prodotto uno scarto quadratico medio (RMSE) minore di 40 cm, infine, dal punto di vista spaziale, la rimozione di oltre la metà delle locazioni di misura dalla rete di monitoraggio comporta una perdita di informazione globale di meno del 15%.

tHomogeneous aluminium species, obtained by dissolving AlCl3·6H2O into methanol, were characterisedand tested as catalysts into the direct esterification of free fatty acids with methanol. The nature andthe role of this catalyst was further investigated through ESI-MS and FTIR spectroscopy, by revealingan immediate exchange reaction between methanol and the water molecules originally bounded to thealuminium, producing a final mixed methanol-aquo-complex whose reactivity was found to be compa-rable to that of a methanolic solution of hydrogen chloride. Reaction conditions were optimised using thedesirability function applied on the response surface methodology analysis of a Box-Behnken factorialdesign of experiments. By carrying out the reaction at 72oC for 120 min and using a catalyst amount of1.5% (mol of Al respect to fatty acids), almost 94% of the starting acids were converted. At the end of thereaction, a biphasic system was obtained in which the upper methanolic phase, which contained mostof the starting catalyst, was separated from the heaviest phase, mainly composed of fatty acid methylesters. Such a distribution not only allowed the biodiesel to be easily separated, but also the catalystswere efficiently recovered and reused for at least four times, determining a total TON greater than 200,without revealing any loss of its activity.

Monitoring networks are essential tools for the effective management of vulnerable or limited environmental resources. Cost and logistics constraints often suggest to reduce the number of monitoring sites while minimizing the loss of information determined by these changes. The problem can be rigorously addressed through the optimization of one or more objective functions that rep- resent the managerial goals associated to the network. However, the use of objective functions is based on assumptions that in practical cases can be inaccurate. To overcome this problem, we have developed a retrospective analysis procedure that validates the degree of acceptability of the optimal reduced configuration at a local and global level. The procedure has been applied to a case study in Apulia, Italy, finding that the optimal reduced network was unable to recover the measured values of the monitored parameter of two discarded locations, making it unable to accomplish its monitoring goals.

The meteo-climatic datasets are at the basis of a great deal of studies on environmental state and its consequent management. In this frame, the completeness of meteo-climatic datasets is required for accurate and reliable analysis. Unfortunately, completeness is a rare in practice and, consequently, a preliminary treatment for filling in all gaps is needed. In this work, two intuitive and easy procedures for handling missing data are presented based on the "similarity station" concept. Finally, a comparison between the proposed methods and the Multiple Imputation Chained Equations, which is the state of the art in the field of missing data handling, has been carried out.

Space-time dependencies among monitoring network stations have been investigated to detect and quantify similarity relationships among gauging stations. In this work, besides the well-known rank correlation index, two new similarity indices have been defined and applied to compute the similarity matrix related to the Apulian meteo-climatic monitoring network. The similarity matrices can be applied to address reliably the issue of missing data in space-time series. In order to establish the effectiveness of the similarity indices, a simulation test was then designed and performed with the aim of estimating missing monthly rainfall rates in a suitably selected gauging station. The results of the simulation allowed us to evaluate the effectiveness of the proposed similarity indices. Finally, the multiple imputation by chained equations method was used as a benchmark to have an absolute yardstick for comparing the outcomes of the test. In conclusion, the new proposed multiplicative similarity index resulted at least as reliable as the selected benchmark.

Hydrological indicators (HIs) are commonly used in eco-hydrological studies (i.e. environmental flow and hydrological status assessment). Their computation is based on streamflow data, and if measured data are not available, hydrological models can be used to generate flow data. The present paper describes a study that aimed to predict streamflow in a temporary river and to analyze the general reliability of some hydrological indicators evaluated by using simulated data instead of measured flow data. The SWAT model was used to predict daily streamflow in a river section of the Celone river (Puglia, Italy). Several HIs characterizing the patterns of river flow or specific hydrological components were evaluated using observed and simulated streamflow. The results show that the SWAT model is able to simulate streamflow in temporary river systems, but its performance under extreme low flow conditions may be a weak point. When simulated streamflow time series were used, the replicability of the HIs evaluated using a rigorous statistical methodology ranged from good to limited. Good performance was found for the magnitude of discharge in wet months (average monthly flow from November to May), for the high flow indicators (annual maxima, 1-, 3-, 7-, 30-, 90-day mean flow) and timing, while limited performance was detected for low flow indicators (annual minimum 1-, 3-, 7-, 30-, 90-day mean flow) and the number of zero flow days. Better performance for low flow indicators was found after introducing the zero-flow threshold. This type of eco-hydrological study may contribute to characterizing the flow regime and its alterations in regions with scarce data.