Assessment of spatio-temporal variability of faecal pollution along coastal waters during and after rainfall events

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Manini, E., Baldrighi, E., Ricci, F., Grilli, F., Giovannelli, D., Intoccia, M., Casabianca, S., Capellacci, S., Marinchel, N., Penna, P., Moro, F., Campanelli, A., Cordone, A., Correggia, M., Bastoni, D., Bolognini, L., Marini, M., & Penna, A. Assessment of spatio-temporal variability of faecal pollution along coastal waters during and after rainfall events. Water, 14(3), (2022): 502, https://doi.org/10.3390/w14030502.More than 80% of wastewaters are discharged into rivers or seas, with a negative impact on water quality along the coast due to the presence of potential pathogens of faecal origin. Escherichia coli and enterococci are important indicators to assess, monitor, and predict microbial water quality in natural ecosystems. During rainfall events, the amount of wastewater delivered to rivers and coastal systems is increased dramatically. This study implements measures capable of monitoring the pathways of wastewater discharge to rivers and the transport of faecal bacteria to the coastal area during and following extreme rainfall events. Spatio-temporal variability of faecal microorganisms and their relationship with environmental variables and sewage outflow in an area located in the western Adriatic coast (Fano, Italy) was monitored. The daily monitoring during the rainy events was carried out for two summer seasons, for a total of five sampling periods. These results highlight that faecal microbial contaminations were related to rainy events with a high flow of wastewater, with recovery times for the microbiological indicators varying between 24 and 72 h and influenced by a dynamic dispersion. The positive correlation between ammonium and faecal bacteria at the Arzilla River and the consequences in seawater can provide a theoretical basis for controlling ammonium levels in rivers as a proxy to monitor the potential risk of bathing waters pathogen pollution.This research was funded by WATERCARE project (Water management solutions for reducing microbial environment impact in coastal areas, project ID 10044130, https://www.italy-croatia.eu/web/watercare, accessed on 17 October 2021) funded by the European Union under the Interreg Italy–Croatia CBC Programme

Status of faecal pollution in ports: A basin-wide investigation in the Adriatic Sea

Ports are subject to a variety of anthropogenic impacts, and there is mounting evidence of faecal contamination through several routes. Yet, little is known about pollution in ports by faecal indicator bacteria (FIB). FIB spatio-temporal dynamics were assessed in 12 ports of the Adriatic Sea, a semi-enclosed basin under strong anthropogenic pressure, and their relationships with environmental variables were explored to gain insight into pollution sources. FIB were abundant in ports, often more so than in adjacent areas ; their abundance patterns were related to salinity, oxygen, and nutrient levels. In addition, a molecular method, quantitative (q)PCR, was used to quantify FIB. qPCR enabled faster FIB determination and water quality monitoring that culture-based methods. These data provide robust baseline evidence of faecal contamination in ports and can be used to improve the management of routine port activities (dredging and ballast water exchange), having potential to spread pathogens in the sea

Biogeochemistry, grain size and mineralogy of the Central and Southern Adriatic Sea sediments: a review.

Volume 26 Supplement 1, January 2010

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Three layers network influence on cloud data center performances

The effects of networks on the performances of cloud architectures are a very significant issue in designing a data center. The efficiency of data transfers and the overall traffic management are a critical factor that constitutes a potential performance bottleneck, potentially limiting the number of computing nodes that can be installed more than their cost issues. In this paper we present a modeling approach, based on Markovian agents, that allows a performance analysis of network effects in high scale cloud architectures

COVID-19 Spatial Diffusion: A Markovian Agent-Based Model

We applied a flexible modeling technique capable of representing dynamics of large populations interacting in space and time, namely Markovian Agents, to study the evolution of COVID-19 in Italy. Our purpose was to show that this modeling approach, that is based on mean field analysis models, provides good performances in describing the diffusion of phenomena, like COVID-19. The paper describes the application of this modeling approach to the Italian scenario and results are validated against real data available about the Italian official documentation of the diffusion of COVID-19. The model of each agent is organized similarly to what largely established in literature in the Susceptible-Infected-Recovered (SIR) family of approaches. Results match the main events taken by the Italian government and their effects

Performance evaluation of massively distributed microservices based applications

Microservice-based software architectures are a recent trend, stemming from solutions that have been designed and experimented in big software companies, that aims to support devops and agile development strategies. The main point is that software architectures, similarly to what happens in SOA, are decomposed into very elementary tasks, that can be developed, maintained and deployed in isolation by small independent teams, and that compose an application by means of simple interactions. The resulting architecture is advocated to be more maintainable, less prone to failures, more agile, but obviously impacts on performances. In this paper we provide a simulation based approach to explore the impact of microservicebased software architectures in terms of performances and dependability, given a desired configuration. Our approach aims at giving a first approximation estimation of the behavior of different classes of microservice-based applications over a given system configuration, to characterize the infrastructure from the point of view of the service provider under a randomly generated realistic overall workload: to the best of our knowledge, there is not any other analogous decision support tool available in literature