SARS-CoV-2 can be detected in the feces of infected people, consequently in wastewater, and in bivalve mollusks, that are able to accumulate viruses due to their ability to filter large amounts of water. This study aimed to monitor SARS-CoV-2 RNA presence in 168 raw wastewater samples collected from six wastewater treatment plants (WWTPs) and 57 mollusk samples obtained from eight harvesting sites in Campania, Italy. The monitoring period spanned from October 2021 to April 2022, and the results were compared and correlated with the epidemiological situation. In sewage, the ORF1b region of SARS-CoV-2 was detected using RT-qPCR, while in mollusks, three targets—RdRp, ORF1b, and E—were identified via RT-dPCR. Results showed a 92.3% rate of positive wastewater samples with increased genomic copies (g.c.)/(day*inhabitant) in December–January and March–April 2022. In the entire observation period, 54.4% of mollusks tested positive for at least one SARS-CoV-2 target, and the rate of positive samples showed a trend similar to that of the wastewater samples. The lower SARS-CoV-2 positivity rate in bivalve mollusks compared to sewages is a direct consequence of the seawater dilution effect. Our data confirm that both sample types can be used as sentinels to detect SARS-CoV-2 in the environment and suggest their potential use in obtaining complementary information on SARS-CoV-2.

Legionella is a pathogen that colonizes soils, freshwater, and building water systems. People who are most affected are those with immunodeficiencies, so it is necessary to monitor its presence in hospitals. The purpose of this study was to evaluate the presence of Legionella in water samples collected from hospitals in the Campania region, Southern Italy. A total of 3365 water samples were collected from January 2018 to December 2022 twice a year in hospital wards from taps and showers, tank bottoms, and air-treatment units. Microbiological analysis was conducted in accordance with the UNI EN ISO 11731:2017, and the correlations between the presence of Legionella and water temperature and residual chlorine were investigated. In total, 708 samples (21.0%) tested positive. The most represented species was L. pneumophila 2–14 (70.9%). The serogroups isolated were 1 (27.7%), 6 (24.5%), 8 (23.3%), 3 (18.9%), 5 (3.1%), and 10 (1.1%). Non-pneumophila Legionella spp. represented 1.4% of the total. Regarding temperature, the majority of Legionella positive samples were found in the temperature range of 26.0–40.9 °C. An influence of residual chlorine on the presence of the bacterium was observed, confirming that chlorine disinfection is effective for controlling contamination. The positivity for serogroups other than serogroup 1 suggested the need to continue environmental monitoring of Legionella and to focus on the clinical diagnosis of other serogroups.

Air contamination in operating rooms (ORs) depends on the conditions of the room and on activities therein performed. Methodologies of air quality assessment in ORs are often inadequately described in the scientific literature, and the time required for a change in status in air quality is never taken into account. The purpose of this study was to determine the influence of the state and the presence of human operators on air quality by implementing a precise measurement protocol that also took into account the time required for changes in the room to affect air pollution. As the main indicators of air pollution, bacterial load and concentration of airborne dust were measured. The results showed that: the use of surgical masks by operators in the OR did not significantly affect bacterial load within a distance of 2 m; keeping OR doors open did not induce a significant increase in bacterial load and of 5 μm particles while 10 μm particles concentration was positively affected; and air pollution measured with open doors was not significantly different from that due to the presence of two staff members, whether or not they were wearing masks. The results clarified the role of some factors on air pollution in ORs.

Pseudomonas aeruginosa is a Gram-negative bacterium that can be found in pipes, and that mainly affects immunocompromised patients. From March to September 2023, 393 drinking water samples were collected and analysed from taps of 21 hospital and 26 non-hospital facilities of Campania region. Antimicrobial susceptibility test was conducted by broth microdilution method and the whole genome sequencing was carried out. The bacterium was detected in 47 samples (12.0%), 26 from hospitals and 21 from non-hospital facilities. All strains were resistant to first- and second-generation cephalosporin, ampicillin and ampicillin/sulbactam. Some strains were resistant to fourth-generation cephalosporin cefepime, and sixteen of the 47 isolates were multi drug resistant, ten from hospitals and six from non-hospital setting. Multidrug-resistant (MDR) strains were detected in 16 samples (34.0%). Thirteen (27,6 %) distinct sequence types (STs) have been highlighted in this study revealing a heterogeneous population. ST 309 was the most common observed genotype. It was observed the presence of the same clone in different wards of the same hospital. The presence of the same clone in different facilities indicates the P. aeruginosa diffusion in different pipelines. This study revealed P. aeruginosa presence in hospital and non-hospital settings, highlighting the importance of conducting environmental monitoring, especially in locations hosting vulnerable people. Routinely performing this monitoring (and extending it to other microorganisms that threaten public health) is important to prevent infections in humans.

Wastewater monitoring has been recognized as a valid tool for monitoring coronavirus disease 2019 (COVID-19) diffusion. In this paper we analyse a dataset composed by the measurements of SARS-CoV-2 RNA load in 605 raw wastewater samples collected from nine wastewater treatment plants (WWTPs) in the Campania region from October 2021 to May 2025. We analyse the correlation structure of the dataset using multivariate statistical techniques with the aim of identifying the most representative sentinel WWTPs and thus optimizing the number of samples. Results of spatial analysis showed that there are two isolated elements, SA3 and NA1, with the highest and lowest SARS-CoV-2 load values, respectively, and other two clusters (Cl1 and Cl2) from the other WWTPs. Temporal analysis showed that NA3 WWTP had a statistically significant difference in SARS-CoV-2 load from 2022 to 2023. Our method suggests limiting samplings to three sites, as follows: SA3 (which can act as a sentinel site because it is the first site that records variation in viral load) and two with the higher variation coefficients (CV%) belonging to the two clusters, as follows: CE1 for Cl1 and NA4 for Cl2. This data analysis procedure could allow to focus only on certain WWTPs for SARS-CoV-2 monitoring, to promptly identify outbreaks.