Source Apportionment Assessment of Marine Sediment Contamination in a Post-Industrial Area (Bagnoli, Naples)

Abstract

The area of Bagnoli (Gulf of Naples, central Tyrrhenian Sea) has been heavily exposed to pollution for over a century due to the presence of industrial sites along its coastline. The aim of this study is to analyze contaminant concentrations (i.e., heavy metals and hydrocarbons) in seabed sediments through a statistical multivariate approach. Multivariate methods permit us to describe the pollution dynamics affecting the area and distinguish between anthropogenic and natural pollution sources. Additionally, the association between contamination patterns and the wave climate characteristics of the gulf (i.e., wave period, direction, height, power, and energy) is investigated. The study confirms that the main contamination source in the Bagnoli bay is anthropogenic activities (i.e., former steel plant and sewage discharges) for the majority of investigated pollutants. It also provides evidence, however, for the potential co-existence of multiple anthropogenic and geogenic sources of arsenic and other metals that may be originating also from the water-rock interaction and submarine volcanic emissions in the Phlegraean area.

Machine Learning Approach to Identify the Relationship Between Heavy Metals and Soil Parameters in Salt Marshes

“…The relationship between soil geochemistry and As concentrations is not yet fully understood [54], but Fe reduces the lability of As and effectively attenuates As in arsenic-polluted soils [55]. According to [25], As is not correlated with other elements available in marine sediments, but its spatial concentration pattern is a function of As concentration of groundwater and subaerial/ submarine geothermal spring. Fe reduces nearly fifty percent extractable As in soils [56].…”

“…Multiple linear regression method, generalized linear model, partial least squares regression, geographical weighted regression and linear mixed model are popular methods for their simplicity in both application and interpretation but not novel concepts [23,24]. For example, multivariate statistical methods were used to describe the pollution dynamics influencing marine sediments and distinguish between anthropogenic and natural sources of contamination [25]. Despite of this simplicity, these models lead to unsatisfactory performances [26,27].…”

Multi-Collocation-Based Estimation of Wave Climate in a Non-Tidal Bay: The Case Study of Bagnoli-Coroglio Bay (Tyrrhenian Sea)

“…The evaluation of wave climate here presented would provide the opportunity for careful eco-engineering solutions against storm control and for restoration purposes. In this vein, it is worth noting a first application of the method to provide wave data for a source apportionment assessment of marine sediment contamination in the study area [142]. Reliable nearshore wave assessment, in fact, makes it possible to assess restoration practices from the perspective of projected medium/long term changes in sea state characteristics; for instance, those stemming from climate change (both at global and local levels).…”

Coastal Vulnerability and Mitigation Strategies: From Monitoring to Applied Research

“…Giglioli et al [28] analyzed the contaminants' concentrations (i.e., heavy metals and hydrocarbons) in seabed sediments through a statistical multivariate approach in a post-industrial area, Bagnoli (Gulf of Naples, Southern Italy). The main contamination source was found related to anthropogenic activities but, concerning Arsenic and other metals, it was reported that the existence of multiple anthropogenic and geogenic sources might originate from the volcanic rocks present in the area.…”