Vito Felice Uricchio

A GIS tool for hydrogeological water balance evaluation on a regional scale in semi-arid environments

A GIS tool to evaluate hydrogeological water balance based on a mass-balance model applied to surface and subsurface systems is discussed. The tool is designed for managers responsible for groundwater resource planning during conditions of water shortage. In developing the tool, the natural groundwater recharge was evaluated through the application of a soil water balance equation, and defined as the difference between the inflows (rainfall, irrigation) and the outflows (plant evapotranspiration, surface run-off). A distributed approach was used in the soil water balance equation to account for the spatial variability of climate and landscape features. Conversely the groundwater balance was calculated on a watershed or aquifer scale, using a lumped water balance equation, in which withdrawals for different uses were estimated together with inflows from other water bodies and coastal outflows. The model was implemented on a GIS platform with an automatic routine that manages all the data sets required and allows for the forecasting of groundwater storage volumes. Furthermore, the model was able to evaluate agricultural water demands under different climatic and management scenarios. A tool which provides a summary of the results and performs a statistical analysis for any portion of the study area was also implemented. The model was applied to a coastal region of Southern Italy. The averaged groundwater balance calculated by the model was in agreement with the piezometric head and chlorine concentration trends measured in selected monitoring wells.

Source apportionment of groundwater pollutants in Apulian agricultural sites using multivariate statistical analyses: case study of Foggia province

BackgroundGround waters are an important resource of water supply for human health and activities. Groundwater uses and applications are often related to its composition, which is increasingly influenced by human activities.In fact the water quality of groundwater is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area. During the years 2004-2007 the Agricultural and Food Authority of Apulia Region has implemented the project “Expansion of regional agro-meteorological network” in order to assess, monitor and manage of regional groundwater quality. The total wells monitored during this activity amounted to 473, and the water samples analyzed were 1021. This resulted in a huge and complex data matrix comprised of a large number of physical-chemical parameters, which are often difficult to interpret and draw meaningful conclusions. The application of different multivariate statistical techniques such as Cluster Analysis (CA), Principal Component Analysis (PCA), Absolute Principal Component Scores (APCS) for interpretation of the complex databases offers a better understanding of water quality in the study region.ResultsForm results obtained by Principal Component and Cluster Analysis applied to data set of Foggia province it’s evident that some sampling sites investigated show dissimilarities, mostly due to the location of the site, the land use and management techniques and groundwater overuse. By APCS method it’s been possible to identify three pollutant sources: Agricultural pollution 1 due to fertilizer applications, Agricultural pollution 2 due to microelements for agriculture and groundwater overuse and a third source that can be identified as soil run off and rock tracer mining.ConclusionsMultivariate statistical methods represent a valid tool to understand complex nature of groundwater quality issues, determine priorities in the use of ground waters as irrigation water and suggest interactions between land use and irrigation water quality.

Pharmaceutical waste disposal: assessment of its effects on bacterial communities in soil and groundwater

A preliminary ecological characterisation of an open quarry that had been used for the disposal of pharmaceutical wastes from a factory producing antibiotics was performed. Pharmaceutical wastes and groundwater samples were collected and analysed in order to assess both the bacterial community structure and functioning, and the contamination by organic compounds, including antibiotics. Bacterial abundance measured using the epifluorescence direct count method, cell viability measured by using two fluorescent dyes, species diversity measured by assessing the bacterial community structure using fluorescence in situ hybridisation (FISH) and soil microbial activity based on dehydrogenase activity were used as microbiological indicators to evaluate the ‘quality state’ of the area studied. The overall results show that groundwater has a low-quality state in terms of bacterial viability, activity and diversity, associated with trace contamination by antibiotics and chlorinated volatile organics.

Plant-assisted bioremediation of a historically PCB and heavy metal-contaminated area in Southern Italy

A plant-assisted bioremediation strategy was applied in an area located in Southern Italy, close to the city of Taranto, historically contaminated by polychlorinated biphenyls (PCBs) and heavy metals. A specific poplar clone (Monviso) was selected for its ability to promote organic pollutant degradation in the rhizosphere, as demonstrated elsewhere. Chemical and microbiological analyses were performed at the time of poplar planting in selected plots at different distances from the trunk (0.25-1m) and at different soil depths (0-20 and 20-40cm), at day 420. A significant decrease in PCB congeners and a reduction in all heavy metals was observed where the poplar trees were present. No evidence of PCB and heavy metal reduction was observed in the non poplar-vegetated soil. Microbial analyses (dehydrogenase activity, cell viability, microbial abundance) of the autochthonous microbial community showed an improvement in soil quality. In particular, microbial activity generally increased in the poplar-rhizosphere and a positive effect was observed in some cases at up to 1m distance from the trunk and up to 40cm depth. The Monviso clone was effective in promoting both a general decrease in contaminant occurrence and an increase in microbial activity in the chronically polluted area a little more than one year after planting.

Enteric Viruses and Fecal Bacteria Indicators to Assess Groundwater Quality and Suitability for Irrigation

According to Italian Ministerial Decree No. 185 of 12 June 2003, water is considered suitable for irrigation if levels of fecal bacteria (i.e., Escherichia coli and Salmonella) are within certain parameters. The detection of other microorganisms is not required. The aim of this study is to determine the bacteriological quality of groundwater used for irrigation and the occurrence of enteric viruses (Norovirus, Enterovirus, Rotavirus, Hepatovirus A), and to compare the presence of viruses with the fecal bacteria indicators. A total of 182 wells was analyzed. Widespread fecal contamination of Apulian aquifers was detected (141 wells; 77.5%) by the presence of fecal bacteria (i.e., E. coli, Salmonella, total coliforms, and enterococci). Considering bacteria included in Ministerial Decree No. 185, the water from 35 (19.2%) wells was unsuitable for irrigation purposes. Among 147 wells with water considered suitable, Norovirus, Rotavirus, and Enterovirus were detected in 23 (15.6%) wells. No Hepatovirus A was isolated. Consequently, 58 wells (31.9%) posed a potential infectious risk for irrigation use. This study revealed the inadequacy of fecal bacteria indicators to predict the occurrence of viruses in groundwater and it is the first in Italy to describe the presence of human rotaviruses in well water used for irrigation.

Detecting soil organic carbon by CASI hyperspectral images

Soil organic carbon (SOC) plays an important role in soil quality definition. In fact, soil organic matter (SOM) decline is one of the most relevant land degradation processes [1]. Therefore, an innovative methodology able to monitoring this soil property, collecting data more rapidly and economically, is needed. In this regard, remote sensing technique can open new scenarios of research. In particular, few studies have shown the capability to accurately determine SOC contents from airborne-hyperspectral sensors [2], [3], [4]. With this work we demonstrate that is possible to evaluate the Soil Organic Carbon in a test site in Apulia Region, Italy, through hyperspectral measurements by the airborne sensor CASI 1500, achieving very promising results.

Direct analysis of polychlorinated biphenyls in heavily contaminated soils by thermal desorption/gas chromatography/mass spectrometry

A robust analytical method is presented for the direct determination of polychlorinated biphenyls in soil samples by thermal desorption/gas chromatography/mass spectrometry. The method is simple to perform (thermal desorption and analysis are performed in-line employing a limited amount of sample, 2 mg) and eliminates the need for any solvent and time-consuming extraction. The analytical procedure was optimized using a soil sample spiked with Aroclor 1254 and Aroclor 1260 and validated with a certified industrial soil sample for which the concentration of thirteen PCB congeners are known. Limits of detection were sensitive to matrix effects and varied substantially among analytes. The matrix effect resulted in a reduction of the limits of detection by 1.5–10 times. However, it was found that the matrix effect is not due to ion suppression but to the increase of the noise of selected ion monitoring (SIM) traces, indicating that no limitation exists with using a single surrogate standard. By employing a 13C-labelled PCB internal standard, limits of detection in the range of 0.8 to 10 µg g−1 of soil were obtained. The obtained experimental results demonstrated that the proposed analytical method can be conveniently applied for screening a large number of heavily contaminated soil samples thus avoiding the employment of harmful solvents and time-consuming extraction procedures.

A centralized management data to prevention of environmental crimes fight the Altamura case

One of the major emergencies in Europe is the presence of illegal waste dumps, where toxic elements have negative effects on the environment and human health. In order to take mitigation measures, IRSA has developed Perimsiti, a software application that allows perimeter, managing and monitoring through WEBGIS all data relating to illegal rubbish dumps. This paper shows an example of its application in the Apulia, region particularly affected by karst processes .

Detection of asbestos-containing materials in agro-ecosystem by the use of airborne hyperspectral CASI-1500 sensor including the limited use of two UAVs equipped with RGB cameras

ABSTRACT This study aims to identify asbestos-containing materials (ACM) through the use of innovative technology such as aerial hyperspectral sensors. The development of operational methodologies and ad hoc processing were also applied for the purpose of this study. The activity was part of the ICT Living Labs DroMEP project carried out by Water Research Institute of the National Research Council (IRSA-CNR) and Servizi di Informazione Territoriale S.r.l. (SIT Srl). This was funded by the Apulia Region to support the growth and development of specialized SMEs in offering digital content and services. Uncontrolled abandoned wastes pose a threat to the human health and ecosystem. The presence of harmful or dangerous substances released without any control can become a dangerous source of pollution. Many areas of the Apulia region generally, in southern Italy, are subjected to this type of phenomena because most often, these areas are not easily accessible to Authorities for the control and management of the territory. Land monitoring and characterization operations would be carried out in a very long time and would require significant financial resources and considerable effort if done by conventional methods. The project activities include the testing and integration of several technologies already available, but not engineered for specific purposes. The work has been focused on the development of a methodology with a defined and high reliability capable of identifying the presence of ACM in various piles of rubbish abandoned in agro-ecosystems. The developed methodology analyses the spectral behaviour of ACM highlighting and emphasizing certain features through the use of a procedure based on an if–then–else control structure. It also allows the selection of the most effective features to combine that significantly reduces the number of false positives.

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project “Improvement of the Regional Agro-meteorological Monitoring Network (2004–2007)”. LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.