Gabriele Buttafuoco

Assessment of lead pollution in topsoils of a southern Italy area: Analysis of urban and peri-urban environment.

Exposure to lead (Pb) may affect adversely human health. Mapping soil Pb contents is essential to obtain a quantitative estimate of potential risk of Pb contamination. The main aim of this paper was to determine the soil Pb concentrations in the urban and peri-urban area of Cosenza-Rende to map their spatial distribution and assess the probability that soil Pb concentration exceeds a critical threshold that might cause concern for human health. Samples were collected at 149 locations from residual and non-residual topsoil in gardens, parks, flower-beds, and agricultural fields. Fine earth fraction of soil samples was analyzed by X-ray Fluorescence spectrometry. Stochastic images generated by the sequential Gaussian simulation were jointly combined to calculate the probability of exceeding the critical threshold that could be used to delineate the potentially risky areas. Results showed areas in which Pb concentration values were higher to the Italian regulatory values. These polluted areas were quite large and likely, they could create a significant health risk for human beings and vegetation in the near future. The results demonstrated that the proposed approach can be used to study soil contamination to produce geochemical maps, and identify hot-spot areas for soil Pb concentration.

Drought events at different timescales in southern Italy (Calabria)

This study reports an analysis of the spatial drought patterns for a region of southern Italy (Calabria) based on a homogenous monthly precipitation data set of 129 rain gauges for the period 1916–2006. Drought was expressed using the Standardized Precipitation Index (SPI), and drought events were analyzed using both the short-time (3 and 6 months) and the long-time (12 and 24 months) SPI. In particular, in order to characterize the SPI spatial pattern, index data of the three most severe drought events were interpolated and mapped using a geostatistical approach. Results show that these heavy drought episodes have widely affected the Calabria region and the drought that occurred in 2002 was the worst in terms of spatial extent both at short- and long-time scales.

Using Digital Elevation Model to Improve Soil pH Prediction in an Alpine Doline

Abstract Among spatial interpolation techniques, geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations. A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics, to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging. Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging. Further, 110 pits were located using spatial simulated annealing (SSA) method. The interpolation techniques were multi-linear regression analysis (MLR), ordinary kriging (OK), regression kriging (RK), kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc). A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best. RK and KED yielded better results than the more complex CKmc and OK. The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.

Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing

Soil survey is generally time-consuming, labor-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 electromagnetic induction (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 (southeastern 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 (minimization of mean of the shortest distances, 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 (minimization of weighted mean of the shortest distances, 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 (mean of average ordinary kriging variance, MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil water content 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.

Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field

The paper proposes a geostatistical approach for delineating management zones (MZs) based on multivariate geostatistics, showing the use of polygon kriging to compare durum wheat yield among the different MZs (polygons). The study site was a durum wheat field in southern Italy and yield was measured over three crop seasons. The first regionalized factor, calculated with factorial cokriging, was used to partition the field into three iso-frequency classes (MZs). For each MZ, the expected value and standard deviation of yield were estimated with polygon kriging over the three crop seasons. The yield variation was only in part related to soil properties but most of it might be ascribable to different patterns of meteorological conditions. Both components of variation (plant and soil) in a cropping system should then be taken into account for an effective management of rainfed durum wheat in precision agriculture. The proposed approach proved multivariate Geostatistics to be effective for MZ delineation even if further testing is required under different cropping systems and management.

Assessment of Groundwater Salinisation Risk Using Multivariate Geostatistics

The risk assessment at regional scale requires modelling spatial variability of environmental variables. Traditional approach, based on estimating point environmental indicators, cannot be considered satisfactory for this purpose, because it does not take into account spatial dependence between variables. We propose the application of an approach to the problem of groundwater salinisation, in which multivariate geostatistics and GIS are combined to integrate primary information with exhaustive secondary information. The dataset consisted of 454 private wells used for irrigation and located in Apulia region (south Italy). Three variables were processed: concentration of chlorides and nitrates, as primary variables, and the distance from the coast, as auxiliary variable. The approach highlighted the widespread degradation of water resources in the Apulian groundwater. The maps of the global indicator allowed us to delineate the zones at high risk of groundwater contamination and also to identify those parameters most responsible for water degradation, so that a wiser management of water resources could be planned. This approach can be used as operational support to a wide range of activities and in decision making among several remediation alternatives.

Soil loss assessment in the Turbolo catchment (Calabria, Italy)

Soil loss caused by accelerated erosion is a growing problem in the Mediterranean belt in general, and in many parts of the Calabrian region (Southern Italy), in particular. It is due to the combination of peculiar geomorphological, pedological and climatic features, very often exacerbated by unsuitable land management. The aim of this study is to analyze and map soil loss by water-induced soil erosion at the catchment scale. Soil loss was quantified using the revised universal soil loss equation (RUSLE) model implemented in a geographical information system. The RUSLE is an empirical model which estimates the average annual soil loss that would generally result from splash, sheet and rill erosion. The analysis shows that total soil loss estimated in the study area is 16,470.88 t yr−1 with an average annual soil loss of 5.65 t ha−1 yr−1. Spatial variation and rates of soil erosion are mainly linked to land use, and the rate of soil erosion varies from less than 1 t ha−1 yr−1 in wooded areas to more than 40 t ha−1 yr−1 in barren land. In addition, the comparison between soil loss and slope maps shows that ∼47% of the estimated soil loss involves slopes with a gradient >20°. The map shows seven classes of soil loss, with 8% in the upper three classes and 51% in the lowest class.

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

Modelling seasonal variations of natural radioactivity in soils: A case study in southern Italy

The activity of natural radionuclides in soil has become an environmental concern for local public and national authorities because of the harmful effects of radiation exposure on human health. In this context, modelling and mapping the activity of natural radionuclides in soil is an important research topic. The study was aimed to model, in a spatial sense, the soil radioactivity in an urban and peri-urban soils area in southern Italy to analyse the seasonal influence on soil radioactivity. Measures of gamma radiation naturally emitted through the decay of radioactive isotopes (potassium, uranium and thorium) were analysed using a geostatistical approach to map the spatial distribution of soil radioactivity. The activity of three radionuclides was measured at 181 locations using a high-resolution ?-ray spectrometry. To take into account the influence of season, the measurements were carried out in summer and in winter. Activity data were analysed by using a geostatistical approach and zones of relatively high or low radioactivity were delineated. Among the main processes which influence natural radioactivity such as geology, geochemical, pedological, and ecological processes, results of this study showed a prominent control of radio-emission measurements by seasonal changes. Low natural radioactivity levels were measured in December associated with winter weather and moist soil conditions (due to high rainfall and low temperature), and higher activity values in July, when the soil was dry and no precipitations occurred.

Organic carbon and total nitrogen topsoil stocks, biogenetic natural reserve ‘Marchesale’ (Calabria region, southern Italy)

ABSTRACT It is essential estimating the spatial distribution of soil organic carbon (SOC) and soil total nitrogen (STN) stocks and their spatial-temporal variations to understand the role of soil in ecosystem services and in the global cycles of carbon and nitrogen. This work was aimed to quantify and map the stocks of SOC and STN in topsoils in an area of the Biogenetic Natural Reserve ‘Marchesale’ (Calabria region, southern Italy). Forest soil samples (0–20 cm depth) were collected at 231 locations and analysed in laboratory for SOC and STN. Moreover, in all samples, bulk density (BD) and soil coarse fragments (SCFs) were determined. Geostatistics was used to map all soil properties (SOC, STN, BD and SCFs) and the stocks of SOC and STN. The mean stock values were 86.3 Mg ha−1 for SOC and 5.1 Mg ha−1 for STN. The total amounts stored in the study area (33.2 ha) were 2865.2 Mg for SOC and 170.1 Mg for STN. Although only the topsoil was considered, the accompanying maps (1:4000 scale) will be useful for the sustainable management of the Biogenetic Natural Reserve ‘Marchesale’ and for undertaking appropriate conservation plans to mitigate the emissions of greenhouse gases.