Evaluating the impact of hydrometeorological conditions on E. coli concentration in farmed mussels and clams: experience in Central Italy

Association between rainfall and Escherichia coli in live bivalve molluscs harvested in Sardinia, Italy

Rainfall is generally accepted as one of the most important factors associated with an increased level of E. coli in bivalve molluscs. Performing microbiological risk assessment is relevant to official control authorities to determine the sanitary status of harvesting areas and, therefore, develop monitoring strategies and identify management practices that could be used to improve the quality and safety of the final product. The present study aimed to investigate the impact of rainfall on the content of E. coli in bivalve molluscs farmed in Sardinia (Italy). Enumeration of E. coli was performed according to the Most Probable Number (MPN) method (ISO 16649-3) on 1,920 bivalve samples collected from 7 regional counties between 2018 and 2020. Bivalve molluscs samples included 955 mussels (Mytilus galloprovincialis), 500 oysters (Crassostrea gigas), 325 clams (Ruditapes decussatus), 94 warty venus (Venus verrucosa), and 46 lagoon cockles (Cerastoderma glaucum). Rainfall data were obtained by the Department of Meteorology of the ARPA Sardegna. For each sampling site, GPS coordinates were used to identify gauge stations within catchment areas. Cumulative rain (mm) was recorded 1, 3, 5, 7, and 15 days before sampling, among which the 7-day cumulative rain was the strongest predictor of E. coli counts. Several thresholds of 7-day cumulative rain (from <10 mm up to >300 mm) before sampling were used to estimate the chances of a non-compliant sample (E. coli levels above the limit for sanitary class A; 230 MPN/100 g). The 7-day cumulative rain was positively associated with the chances of non-compliance. When the 7-day cumulative rain before sampling was >300 mm, 80.5 % of the samples were non-compliant, and the odds of a non-compliant sample were 23.6 times higher, as compared to samples harvested when the 7-day cumulative rainfall was <10 mm. Precipitation data could be a useful tool for interpreting anomalous results from official control authorities and reduce the costs that originate from closure of production areas.

Evaluating Escherichia coli contamination in bivalve mollusks using the impedance method: a comparison with most probable number analyses and correlation with environmental parameters

The application of an electrochemical (impedance) tool for monitoring Escherichia coli contamination in shellfish was evaluated after 13 months of observation. The primary aim of the present study was to compare the standard most probable number (MPN) and μ-trac 4200 (log imped/100 g) for the assessment of E. coli contamination (log MPN/100 g) in non-depurated bivalve mollusks (BM) from five sampling areas of the Veneto-Emilian coast (Italy) (118 samples). The secondary aim was to evaluate the correlation between E. coli concentrations in BM and environmental factors on a large data set (690). The methods showed a moderate, positive correlation (0.60 and 0.69 Pearson and Spearman coefficients, respectively; P<0.01) in Ruditapes philippinarum. The McNemar test indicated analogous sample classification between methods, and the impedance method overestimated the most contaminated class (P=0.03; >4,600 MPN/100 g). The results highlighted the suitability of the impedance method for a faster evaluation and routine use especially in clams, while in Mytilus it seemed less effective. Different models built by multivariate permutational variance analysis and multinomial logistic regression selected the suitable environmental features able to predict the E. coli load. Overall, salinity and season affected the E. coli contamination, whereas locally it was mainly influenced by hydrometry and salinity. The application of the impedance method coupled with environmental data analysis could help purification phase management to adhere to legal limits and could represent an advantage for local control authorities to define actions, considering extreme meteorological events' effects as a proactive reaction to climate change.

Evaluating the Response of Hydrological Stress Indices Using the CHyM Model over a Wide Area in Central Italy

Central Italy is characterized by complex orography. The territorial response to heavy precipitation may activate different processes in terms of hydrogeological hazards. Floods, flash floods, and wet mass movements are the main ground effects triggered by heavy or persistent rainfall. The main aim of this work is to present a unique tool that is based on a distributed hydrological model, able to predict different rainfall-induced phenomena, and essential for the civil protection early warning activity. The Cetemps Hydrological Model is applied to the detection of hydrologically stressed areas over a spatial domain covering the central part of Italy during a weather event that occurred in 2014. The validation of three hydrological stress indices is proposed over a geographical area of approximately 64,500 km2 that includes catchments of varying size and physiography. The indices were used to identify areas subject to floods, flash floods, or landslides. Main results showed very high accuracies (~90%) for all proposed indices, with flood false alarms growing downstream to larger basins, but very close to zero in most cases. The three indices can give complementary information about the predominant phenomenon and are able to distinguish fluvial floods from pluvial floods. Nevertheless, the results were influenced by the presence of artificial reservoirs that regulated flood wave propagation, therefore, indices timing slightly worsen downstream in larger basins.