An example of hydrochemical maps of unsteady contaminations

The present research has been aimed at the analysis of contaminated areas in which it is difficult to reconstruct the contamination with the classical techniques of interpretation of the hydrochemical data in time, for example when, at irregular intervals, secondary pollutants are separated from the main contamination and attracted by local piezometric depressions. To address the problem, an area near Milan (Lombardy, Italy) has been monitored for many years, characterized by very extensive contaminations, where there are secondary plumes that propagate to areas that are not normally affected by the main contamination, producing sudden worsening of the chemical state of the waters that make their effects felt for some time (even more than a year), until the moment they are exhausted. Therefore, by highlighting, through statistical processing, the behavior of these secondary contaminations, they have been outlined and their relationship with particular piezometric structures has been established. In this way, the most appropriate provisions have been proposed to avoid problems to the wells of the aqueducts, caused by this type of pollution. These provisions essentially consist in integrating the traditional hydrochemical cartography with the maps of the concentration trend over time, which provide the elements to distinguish the areas of origin and development of secondary contaminations. Once the sources and the exposed areas have been identified, interventions to regulate the withdrawal system of the wells can be implemented in order to avoid the formation of piezometric depressions channeling the secondary pollutants.

The present research has been aimed at the analysis of contaminated areas in which it is difficult to reconstruct the contamination with the classical techniques of interpretation of the hydrochemical data in time, for example when, at irregular intervals, secondary pollutants are separated from the main contamination and attracted by local piezometric depressions. To address the problem, an area near Milan (Lombardy, Italy) has been monitored for many years, characterized by very extensive contaminations, where there are secondary plumes that propagate to areas that are not normally affected by the main contamination, producing sudden worsening of the chemical state of the waters that make their effects felt for some time (even more than a year), until the moment they are exhausted. Therefore, by highlighting, through statistical processing, the behavior of these secondary contaminations, they have been outlined and their relationship with particular piezometric structures has been established. In this way, the most appropriate provisions have been proposed to avoid problems to the wells of the aqueducts, caused by this type of pollution. These provisions essentially consist in integrating the traditional hydrochemical cartography with the maps of the concentration trend over time, which provide the elements to distinguish the areas of origin and development of secondary contaminations. Once the sources and the exposed areas have been identified, interventions to regulate the withdrawal system of the wells can be implemented in order to avoid the formation of piezometric depressions channeling the secondary pollutants.


ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

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