Evaluation of the field dissipation of fungicides and insecticides used on fruit bearing trees in northern Italy

The dissipation of the fungicides captan, cyprodinil, fludioxonil, dithianon, and tebuconazole and of the insecticides chlorpyrifos, fenitrothion, and malathion was studied, following a single treatment of different cultivars of pears, apples, and peaches. The study was conducted in northern Italy, over two successive growing seasons (2004 and 2005). The treatments were performed by the farmers involved, in line with their usual practice. At various time intervals from treatment to harvest, representative samples of fruit were collected and analyzed for pesticide residues. In some cases, concentrations lower than the maximum residue levels (MRLs) were found immediately after treatment. In all trials a rapid decline in pesticide concentrations was observed leading to residues at harvest greatly below the MRLs.     

Cokriging optimization of monitoring network configuration based on fuzzy and non-fuzzy variogram evaluation

A number of optimization approaches regarding monitoring networkdesign and sampling optimization procedures have been reported inthe literature. Cokriging Estimation Variance (CEV) is a usefuloptimization tool to determine the influence of the spatial configuration of monitoring networks on parameter estimations. Itwas used in order to derive a reduced configuration of a nitrateconcentration monitoring well network. The reliability of the reduced monitoring configuration suffers from the uncertainties caused by the variographer's choices and several inherent assumptions. These uncertainties can be described considering thevariogram parameters as fuzzy numbers and the uncertainties by means of membership functions.Fuzzy and non-fuzzy approaches were used to evaluate differencesamong well network configurations. Both approaches permitted estimates of acceptable levels of information loss for nitrate concentrations in the monitoring network of the aquifer of the Plain of Modena, Northern Italy. The fuzzy approach was found torequire considerably more computational time and numbers of wellsat comparable level of information loss.     

Optimal extension of the rain gauge monitoring network of the Apulian Regional Consortium for Crop Protection

The goal of this paper is to provide a methodology for assessing the optimal localization of new monitoring stations within an existing rain gauge monitoring network. The methodology presented, which uses geostatistics and probabilistic techniques (simulated annealing) combined with GIS instruments, could be extremely useful in any area where an extension of whatever existing environmental monitoring network is planned. The methodology has been applied to the design of an extension to a rainfall monitoring network in the Apulia region (South Italy). The considered monitoring network is managed by the Apulian Regional Consortium for Crop Protection (ARCCP), and, currently consists of 45 gauging stations distributed over the regional territory, mainly located on the basis of administrative needs. Fifty new stations have been added to the existing monitoring network, split in two groups: 15 fixed and 35 mobile stations. Two different methods were applied and tested: the Minimization of the Mean of Shortest Distances method (MMSD) and Ordinary Kriging (OK) whose related objective function is estimation variance. The MMSD, being a purely geometric method, produced a spatially uniform configuration of the gauging stations. On the contrary, the approach based on the minimization of the average of the kriging estimation variances, produced a less regular configuration, though a more reliable one from a spatial standpoint. Nevertheless, the MMSD approach was chosen, since the ARCCP's intention was to create a new monitoring network characterized by uniform spatial distribution throughout the regional territory. This was the most important constraint given to the project by the ARCCP, whose main objective was to accomplish a territorial network capable of detecting hazardous events quickly. A seasonal aggregation of the available rainfall data was considered. The choice of the temporal aggregation in quarterly averages allowed four different optimal configurations to be determined per season. The overlapping of the four configurations allowed a number of new station locations, which tended to remain fixed season after season, to be identified. Other stations, instead, changed their coordinates considerably over the four seasons. Constraints were defined in order to avoid placing new monitoring locations either near existing stations, belonging to other Agencies, or near the coast line.     

A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy)

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.     

Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation

In some previous papers a probabilistic methodology was introduced to estimate a spatial index of risk of groundwater quality degradation, defined as the conditional probability of exceeding assigned thresholds of concentration of a generic chemical sampled in the studied water system. A crucial stage of this methodology was the use of geostatistical techniques to provide an estimation of the above-mentioned probability in a number of selected points by crossing spatial and temporal information. In this work, spatial risk values were obtained using alternatively stochastic conditional simulation and disjunctive kriging. A comparison between the resulting two sets of spatial risks, based on global and local statistical tests, showed that they do not come from the same statistical population and, consequently, they cannot be viewed as equivalent in a statistical sense. At a first glance, geostatistical conditional simulation may appear to represent the spatial variability of the phenomenon more effectively, as the latter tends to be smoothed by DK. However, a close examination of real case study results suggests that disjunctive kriging is more effective than simulation in estimating the spatial risk of groundwater quality degradation. In the study case, the potentially ‘harmful event’ considered, threatening a natural ‘vulnerable groundwater system,’ is fertilizer and manure application.     

A methodology for rapid assessment of the environmental status of the shallow aquifer of ��Tavoliere di Puglia�� (Southern Italy)

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??.     

A Methodology for Space-Time Classification of Groundwater Quality

Safeguarding groundwater from civil, agricultural and industrial contamination is matter of great interest in water resource management. During recent years, much legislation has been produced stating the importance of groundwater as a source for drinking water supplies, underlining its vulnerability and defining the required quality standards. Thus, schematic tools, able to characterise the quality and quantity of groundwater systems, are of very great interest in any territorial planning and/or water resource management activity.This paper proposes a groundwater quality classification method which has been applied to a real aquifer, starting from several studies published by the Italian National Hydrogeologic Catastrophe Defence Group (GNDCI).The methodology is based on the concentration values of several parameters used as indexes of the natural hydro-chemical water condition and of potential man-induced modifications of groundwater quality. The resulting maps, although representative of the quality, do not include any information on its evolution in time. In this paper, this “stationary” classification method has been improved by crossing the quality classes with three indexes of temporal behaviour during recent years. It was then applied to data from monitoring campaigns, performed in spring and autumn, from 1990 to 1996, in the plain of Modena aquifer (central Italy). The results are reported in the form of space-time classification table and maps.