Variation of total alpha and beta activities and Rn-222 concentrations in the water supply system of an Italian volcanic region: How safe is tap water for human consumption?

Total alpha and beta activities and Rn-222 concentrations were determined in water from different sections of seven aqueducts belonging to the water supply system of Campania region (Italy), known worldwide for its volcanism. Statistical analysis was performed on data to account for their variability across the aqueduct sections, and results were discussed considering the geology of reservoirs, the potential mixing processes occurring along the pipe network, the building/constituting materials of the aqueduct sections, and the integrity of the infrastructure. Guidelines proposed by Italian and international regulation entities were considered to determine if total alpha and beta activities and Rn-222 concentrations found at the taps of the different aqueducts should be considered detrimental to public health. Based on a deterministic and a stochastic approach, a health risk assessment was also tested for Rn-222, assuming direct ingestion and showering as potential exposure pathways. Results showed that applying guidelines returned an absence of hazard, whereas risk assessment returned a high probability of exposure to unacceptable Rn-222 doses for some aqueducts. Beyond the usefulness of obtained results to plan actions to improve the safety of drinking water in Campania, our outcomes represent a warning for bodies dealing with public health at any level: the use of guidelines can bring an underestimation of the risks exerted by the exposure to Rn-222 on human health. Further, using a probabilistic approach in risk assessment accounting for uncertainty can favor risk forecasts based on more "realistic" scenarios.

Screening and optimization of interpolation methods for mapping soil-borne polychlorinated biphenyls.

There is yet no scientific consensus, and for now, on how to choose the optimal interpolation method and its parameters for mapping soil-borne organic pollutants. Take the polychlorinated biphenyls (PCBs) for instance, we present the comparison of some classic interpolation methods using a high-resolution soil monitoring database. The results showed that empirical Bayesian kriging (EBK) has the highest accuracy for predicting the total PCB concentration, while root mean squared error (RMSE) in inverse distance weighting (IDW) is among the highest in these interpolation methods. The logarithmic transformation of non-normally distributed data contributed to enhance considerably the semivariogram for modeling in kriging interpolation. The increasing of search neighborhood reduced IDW's RMSE, but slightly affected in ordinary kriging (OK), while both of them resulted in over smooth of prediction map. The existence of outliers made the difference between two points increase sharply, and thereby weakening spatial autocorrelation and decreasing the accuracy. As predicted error increased continuously, the prediction accuracy of different interpolation methods reached unanimity gradually. The attempt of the assisted interpolation algorithm did not significantly improve the prediction accuracy of the IDW method. This study constructed a standardized workflow for interpolation, which could reduce human error to reach higher interpolation accuracy for mapping soil-borne PCBs.

Wildfires as a Source of Potentially Toxic Elements (PTEs) in Soil: A Case Study from Campania Region (Italy).

The worldwide increase in fire events has attracted global attention, as potentially toxic elements (PTEs) have been widely recognised within the produced ash. Ash is transported, dispersed by wind, and deposited into the soil and surficial waters even far from fires. Considering that their composition can be enriched in PTEs, they represent a potential hazard for humans and other animals exposed to airborne particles and, afterwards, to resuspended matter, even at a considerable distance from the source. This study aimed to assess the environmental impact of fire events that occurred during the 2017 summer season at two different sites in the Campania region (Southern Italy). One of the fires affected a waste disposal site west of Caserta, and one involved a forest on the slopes of Mt. Somma-Vesuvius, a few kilometres southeast of Naples, the regional capital. Changes to the PTE concentration in the topsoil in the surroundings of both sites, after the fire events, were investigated. Enrichment factors (EFs) of a selection of PTEs were determined by comparing geochemical data from two sampling campaigns, one completed before and one after the fire events. A combined application of multivariate statistics (based on robust principal component analysis; RPCA) and geospatial analysis was used to determine the materials affected by the fire on the slopes of Mt. Somma-Vesuvius, and roughly locate their place. Specifically, a statistically significant enrichment of Hg was identified in the topsoil of both study areas. In addition, in soil samples collected at Mt. Somma-Vesuvius, more PTEs showed significant changes in their concentration. For both areas, Hg enrichments were related to the deposition of ash proceeding from waste burning; furthermore, as regards the soil of the Vesuvian area, Cr and Cd enrichments were associated with the fallout of ash generated during biomass combustion, and the increase in Cu and Zn concentrations was linked to the burning of crops on cultivated lands. Apart from the specific results obtained, concerning the examined case studies, the methods applied can be seen as a reliable option to determine the compositional characteristics of materials burned during a fire event, even with the prospect of improving the eventual assessment process of the related environmental hazards.

Assessment on the source of geochemical anomalies in the sediments of the Changjiang river (China), using a modified enrichment factor based on multivariate statistical analyses.

Rivers can be sinks for potential toxic elements (PTEs) inputted in their systems by both natural and anthropic processes. Many indices have been proposed to assess the contamination degree of sediments and the environmental conditions of surficial water bodies. Above all, enrichment factor (EF) is the most used tool, but also it is the most debated for its limitations. The need for a reference element and for a background/baseline composition makes the EF method dependent on the researcher's expertise, implying that its repeatability may not be granted. Starting from the awareness that geochemical processes, bringing to compositional changes in the environmental matrices, involve multiple elements rather than individual variables, we developed a modified EF (mEF) based on the use of elemental associations. Different multivariate statistical methods (i.e. Robust Principal Component Analysis and Fuzzy Clustering), in a compositional data analysis (CoDA) perspective, were used to set all the terms of the mEF. The mEF was applied to 101 sediment samples collected from a 2 m-long core, covering a sedimentation period of about 150 years (1850-2007), located in the lower Changjiang River (China). The method resulted effective in recognizing most of the signals proceeding from the main natural and anthropogenic events which affected the lower river basin in the considered timespan. The largest geochemical variations recorded fit well the flooding events occurred; besides, the effects produced on the system by the recent socio-economic development (following the end of the civil war in 1949 and the beginning of economic reforms in 1978) and the start-up of the Three Gorges Dam (the world's largest power station since 2012) were also intercepted. The proposed method represents a step forward to enhance the effectiveness of the EF in discriminating geochemical anomalies that may be significant to assess the human historical impact on the environment.

Radon flux estimates, from both gamma radiation and geochemical data, to determine sources, migration pathways, and related health risk: The Campania region (Italy) case study.

An empirical method was applied to estimate the 222Rn fluxes distribution across the Campania region (Italy) by using both gamma-rays and U, Th, K concentrations in soils. As a first step, K, Th and U soil concentrations and 4 K, 238U and 232Th activity have been converted into their own specific activity to calculate the Terrestrial Gamma Dose Rate (TGDR). This latter has been then used to determine the 222Rn fluxes across the region. Regardless of the radiometric or geochemical origin, 222Rn fluxes reached, as expected, their maximum values in correspondence with the volcanic centres of Campania (Mt. Somma-Vesuvius, Phlegrean Fields, Mt. Roccamonfina). However, comparing the results obtained from the two different datasets, it was also possible to infer the existence of contributions to surficial 222Rn fluxes proceeding from both some underlying geological bodies and active seismogenic sources. In line with some national regulations, the 222Rn flux esteemed from gamma radiations was also used to assess the possible regional distribution of risk deriving from the indoor environmental exposure to 222Rn; results were compared with standardized incidence rates (SIRs) of lung cancer for an area on the south-western sector of Mt. Somma-Vesuvius showing a potential spatial relationship among flux data and SIRs.

The first application of compositional data analysis (CoDA) in a multivariate perspective for detection of pollution source in sea sediments: The Pozzuoli Bay (Italy) case study.

In the last decades, investigating geochemistry of sea sediments has been challenging in the eastern sector of Pozzuoli Bay, source of the metal(loid)s has been a matter of debate and the proposed origin of potentially toxic elements (PTEs) has been occasionally inconsistent. In this study, compositional data analysis (CoDA) was used because the results are independent of the measurement unit, the selected subgroup of elements and the order of chemicals in the dataset. The robust variant of principal component analysis (PCA) indicated that Hg, Cd, Cu, Pb and Zn were positively correlated with mud and organic matter in the sediments deposited in front of the former industrial site. Concentrations of these metals decrease along the cores and in the distal zone. Nevertheless, Al, As, V, Fe, Cr, Ni and sand form an association along the coast which strengthens with increasing distance from fumaroles in the proximal zone. It suggests that arsenic was mainly originated from the pyroclastic deposits of Campi Flegrei and some of the seepages with hydrothermal component, supported by low contribution of the variables in robust PCA of the sediments from distal zone. Therefore, this pioneering article suggests CoDA as a powerful tool for answering the long-lasting questions over sediment geochemistry in polluted areas.