The concept of a “green hospital” is used in reference to a hospital that includes the environment as part of its quality services and one that pays attention to the sustainable design of buildings. Waste disposal represents a potential risk for the environment; therefore, waste collection from healthcare centers is a key environmental issue. Our study aims to systematically review the experiences acquired in worldwide nosocomial settings related to the management of healthcare waste. Nineteen studies, selected between January 2020 and April 2022 on Scopus, MEDLINE/PubMed and Web of Science databases were included in our systematic narrative review. Operating room and hemodialysis activities seem to be the procedures most associated with waste production. To deal with waste production, the 5Rs rule (reduce, reuse, recycle, rethink and research) was a common suggested strategy to derive the maximum practical benefit while generating the minimum amount of waste. In this context, the COVID-19 pandemic slowed down the greening process of nosocomial environments. Waste management requires a multifactorial approach to deal with medical waste management, even considering the climate change that the world is experiencing. Education of health personnel and managers, regulation by governmental institutions, creation of an “environmental greening team”, and awareness of stakeholders and policymakers are some of the measures needed for the greening of healthcare facilities.

Although direct contact is considered the main mode of transmission of SARS-CoV-2, environmental factors play an important role. In this study, we evaluated the presence of SARS-CoV-2 on bus and train surfaces. From the buses, we took samples from the following areas: handrails used to enter or exit the bus, stop request buttons and handles next to the seats. From the trains, the sampled surfaces were handrails used to enter or exit the train, door open/close buttons, handles next to the seats, tables and toilet handles. SARS-CoV-2 was detected on 10.7% of the tested surfaces overall, 19.3% of bus surfaces and 2% of train surfaces (p < 0.0001). On the buses, the most contaminated surfaces were the handles near the seats (12.8%), followed by door open/close buttons (12.5%) and handrails (10.5%). Of the five analyzed transport companies, bus companies were the most contaminated, in particular, companies C (40%) and B (23.3%). A greater number of positive samples were found among those taken at 10:00 a.m. and 10:55 a.m. (45% and 40%, respectively). The presence of the virus on many bus surfaces highlights how the sanitation systems on public transport currently in use are not sufficient to limit the spread of SARS-CoV-2.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) emerged in March 2020 in Italy, leading to the pandemic of coronavirus disease 2019 (COVID-19) that continues to cause high global morbidity and mortality in human populations. Numerous studies have focused on the spread and persistence of the virus in the hospital setting. New scientific evidence shows that SARS-CoV-2 is present in different community environments. Although aerosol is one of the main routes of transmission for SARS-CoV-2, indirect contact through virus-contaminated surfaces could also play a key role. The survival and persistence of SARS-CoV-2 on surfaces appear to be influenced by the characteristics of the material, temperature, and humidity. In this study, we investigated the presence of SARS-CoV-2 RNA on surfaces in 20 supermarkets throughout the Apulia region during the lockdown period. We collected a total of 300 swab samples from various surfaces including supermarket scales, trolley handles, refrigerator and freezer handles, and keyboards. In total, 13 (4.3%) surfaces were positive for SARS-CoV-2 RNA contamination, with shopping trolley handles being the most frequently contaminated. This study showed that contamination in public spaces can occur, so we remark the importance to adopt adequate preventive measures, including environment ventilation, careful surfaces sanitation, hand hygiene, and correct usage of masks, to reduce the likelihood of virus transmission.

The quantitative microbial risk assessment (QMRA) framework is used for assessing health risk coming from pathogens in the environment. In this paper, we used QMRA to evaluate the infection risk of L. pneumophila attributable to sink usage in a toilet cabin on Italian long-distance public transportation (LDT). LDT has water distribution systems with risk points for Legionella proliferation, as well as premise plumbing for drinking water, but they are not considered for risk assessment. Monitoring data revealed that approximately 55% of water samples (217/398) were positive for L. pneumophila, and the most frequently isolated was L. pneumophila sg1 (64%, 139/217); therefore, such data were fitted to the best probability distribution function to be used as a stochastic variable in the QMRA model. Then, a sink-specific aerosolization ratio was applied to calculate the inhaled dose, also considering inhalation rate and exposure time, which were used as stochastic parameters based on literature data. At L. pneumophila sg1 concentration ≤100 CFU/L, health risk was approximately 1 infection per 1 million exposures, with an increase of up to 5 infections per 10,000 exposures when the concentrations were ≥10,000 CFU/L. Our QMRA results showed a low Legionella infection risk from faucets on LDT; however, it deserves consideration since LDT can be used by people highly susceptible for the development of a severe form of the disease, owing to their immunological status or other predisposing factors. Further investigations could also evaluate Legionella-laden aerosols from toilet flushing.

Understanding the actual distribution of differentLegionellaspecies in water networks would help prevent outbreaks. Culture investigations followed by serological agglutination tests, with poly/monovalent antisera, still represent the gold standard for isolation and identification ofLegionellastrains. However, also MALDI-TOF andmip-gene sequencing are currently used. This study was conducted to genetically correlate strains ofLegionella non pneumophila(L-np) isolated during environmental surveillance comparing different molecular techniques. Overall, 346 water samples were collected from the water system of four pavilions located in a hospital of the Apulia Region of Italy. Strains isolated from the samples were then identified by serological tests, MALDI-TOF, andmip-gene sequencing. Overall, 24.9% of water samples were positive forLegionella, among which the majority wereLegionella pneumophila(Lpn) 1 (52.3%), followed byLpn2-15 (20.9%),L-np(17.4%),Lpn1 + Lpn2-15 (7.1%), andL-np + Lpn1 (2.3%). Initially,L-npstrains were identified asL. bozemaniiby monovalent antiserum, while MALDI-TOF andmip-gene sequencing assigned them toL. anisa. More cold water than hot water samples were contaminated byL. anisa(p < 0.001). PFGE, RAPD, Rep-PCR, and SAU-PCR were performed to correlateL. anisastrains. Eleven out of 14 strains identified in all four pavilions showed 100% of similarity upon PFGE analysis. RAPD, Rep-PCR, and SAU-PCR showed greater discriminative power than PFGE.

Legionellosis is a severe pneumonia caused by the inhalation of aerosols containing Legionella, Gram-negative bacteria present in the water systems of touristic-recreational facilities. The purpose of this study was to develop a scoring tool to predict the risk of both environmental contamination and Legionnaires' disease cases in such facilities in the Apulia region of southern Italy. We analyzed 47 structural and management parameters/risk factors related to the buildings, water systems, and air conditioning at the facilities. A Poisson regression model was used to compute an overall risk score for each facility with respect to three outcomes: water samples positive for Legionella (risk score range: 7-54), water samples positive for Legionella with an average load exceeding 1000 colony-forming units per liter (CFU/L) (risk score range: 22-179,871), and clinical cases of Legionnaire's disease (risk score range: 6-31). The cut-off values for three outcomes were determined by receiver operating characteristic curves (first outcome, samples positive for Legionella in a touristic-recreational facility: 19; second outcome, samples positive for Legionella in a touristic-recreational facility with an average load exceeding 1000 CFU/L: 2062; third outcome, clinical cases of Legionnaire's disease in a touristic-recreational facility: 22). Above these values, there was a significant probability of observing the outcome. We constructed this predictive model using 70% of a large dataset (18 years of clinical and environmental surveillance) and tested the model on the remaining 30% of the dataset to demonstrate its reliability. Our model enables the assessment of risk for a touristic facility and the creation of a conceptual framework to link the risk analysis with prevention measures.