Using Algal Virus Paramecium bursaria Chlorella Virus as a Human Adenovirus Surrogate for Validation of UV Treatment Systems

Systematic Review and Meta-analysis on the Inactivation Rate of Viruses and Bacteriophage by Solar Wavelength Radiation

Sunlight is a known biocide, and photodriven inactivation is an important avenue for controlling viruses in both natural and engineered systems. However, there remain significant unknowns regarding damage to viruses by sunlight, including the impact of wavelength and viral characteristics. Herein, a systematic review of the literature and meta-analysis was conducted to identify inactivation rate constants (k-values) when exposed to solar wavelengths (280-700 nm) for common human viruses and surrogates in natural and synthetic matrices. We identified 457 k-values, with 356 for nonenveloped viruses. Extracted rate constants were transformed into UV fluence-normalized k-values to isolate the most photobiologically relevant wavelengths in the solar spectrum and reported for the first time in terms of energy, rather than time, based units. Each spectral region was assessed independently, with UVB illumination reporting the highest inactivation rates, UVA contributing to inactivation both in the presence and absence of photosensitizers, and visible light demonstrating no biocidal activity. Inactivation mechanisms are reviewed identifying knowledge gaps in translating UVC mechanisms to longer wavelengths. The data compiled in this meta-analysis can be applied to inform the environmental transport of viruses, estimate solar disinfection performance in variable light conditions, or design disinfection systems based on UVA and UVB light.

Water Disinfection with Dual-Wavelength (222 + 275 nm) Ultraviolet Radiations: Microbial Inactivation and Reactivation

Emerging mercury-free ultraviolet (UV) sources, such as krypton chloride excimer (KrCl*) lamps and UV light emitting diodes (UV-LEDs), emit diverse wavelengths with distinct inactivation mechanisms. The combined application has the potential to improve disinfection effectiveness through synergism. In this study, a mini-fluidic photoreaction system equipped with a KrCl* lamp (222 nm) and a strip of UV-LEDs (275 nm) was developed, which could individually/simultaneously deliver accurate UV radiation(s) at 222 nm (0.32 mW cm-2) or/and 275 nm (0.50 mW cm-2). Dual-wavelength UV (DWUV) radiations demonstrated a substantial synergistic effect on Escherichia coli (E. coli) inactivation with synergistic coefficients reaching up to 1.92, while no synergistic effect was observed for PR772 bacteriophage (PR772) inactivation. Moreover, DWUV radiations significantly (p < 0.05) suppressed the reactivation of E. coli and PR772 in the subsequent light/dark treatment. For E. coli, the underlying mechanism could be ascribed to the increased level of reactive oxygen species induced by DWUV radiations, which not only enhanced inactivation by damaging proteins and lipids, but also suppressed the reactivation by damaging DNA repair enzymes. For PR772, although the DNA and protein damages caused by DWUV radiations did not yield a synergistic effect, the protein damage prevented the viral DNA from entering host cells for repair, thereby suppressing reactivation. This study helps develop more effective UV disinfection technologies by using DWUV radiations.

Quantitative Microbial Risk Assessment for Quantification of the Effects of Ultraviolet Germicidal Irradiation on COVID-19 Transmission

Quantitative microbial risk assessment (QMRA) is presented as a tool for evaluation of the effectiveness of ultraviolet germicidal irradiation (UVGI) systems for the disinfection of indoor air. The QMRA is developed in the context of UVGI system implementation for control of SARS-CoV-2 infection and comprises submodels to address problem formulation, exposure assessment, and health effects assessment, all of which provide input to a risk characterization submodel. The model simulations indicate that UVGI systems can effectively control the risk of infection associated with SARS-CoV-2 for low to moderate virus emission rates. The risk of disease transmission is strongly influenced by the rate of pathogen emission by an infected individual, the output power of UVGI fixtures and their configuration, the source of UV-C radiation implemented in the UVGI fixtures, and the characteristics of the heating, ventilation, and air conditioning (HVAC) system. The QMRA framework provides a quantitative link between UVGI/HVAC system characteristics and changes in the risk of disease transmission. The framework can be adapted to other airborne pathogens and provides a rational basis for the design, testing, and validation of UVGI systems.

A review of the fluence determination methods for UV reactors: Ensuring the reliability of UV disinfection

Ultraviolet (UV) is a green and effective technique that has been widely applied in water disinfection. The reliability of UV disinfection is an important issue, in which the aim is to ensure the delivery of adequate real-time fluence in a UV reactor. Unlike chemical disinfection systems whose disinfection dose can be directly measured with disinfectant residuals, UV is a physical process and the determination of fluence is complicated in practical reactors. To date, several fluence determination methods have been developed, including conventional methods such as biodosimetry and model simulation, as well as emerging methods such as dyed microsphere method and the model-detector method. However, a systematic and comprehensive review of these methods is still needed to discuss the attributes and application scenarios of each method. In this review, we summarized the principal theories, procedures, applications, and pros/cons of these fluence determination methods. Further, the selection and application of appropriate fluence determination methods were discussed based on different purposes (e.g., feedbacks for reactor design, evidence for third-party validation, as well as on-site determination and long-term monitoring of fluence). Overall, this review could provide useful information and new insights regarding the application of current fluence determination methods to ensure the reliability of UV disinfection.

Mapping of UV-C dose and SARS-CoV-2 viral inactivation across N95 respirators during decontamination

During public health crises like the COVID-19 pandemic, ultraviolet-C (UV-C) decontamination of N95 respirators for emergency reuse has been implemented to mitigate shortages. Pathogen photoinactivation efficacy depends critically on UV-C dose, which is distance- and angle-dependent and thus varies substantially across N95 surfaces within a decontamination system. Due to nonuniform and system-dependent UV-C dose distributions, characterizing UV-C dose and resulting pathogen inactivation with sufficient spatial resolution on-N95 is key to designing and validating UV-C decontamination protocols. However, robust quantification of UV-C dose across N95 facepieces presents challenges, as few UV-C measurement tools have sufficient (1) small, flexible form factor, and (2) angular response. To address this gap, we combine optical modeling and quantitative photochromic indicator (PCI) dosimetry with viral inactivation assays to generate high-resolution maps of "on-N95" UV-C dose and concomitant SARS-CoV-2 viral inactivation across N95 facepieces within a commercial decontamination chamber. Using modeling to rapidly identify on-N95 locations of interest, in-situ measurements report a 17.4 ± 5.0-fold dose difference across N95 facepieces in the chamber, yielding 2.9 ± 0.2-log variation in SARS-CoV-2 inactivation. UV-C dose at several on-N95 locations was lower than the lowest-dose locations on the chamber floor, highlighting the importance of on-N95 dose validation. Overall, we integrate optical simulation with in-situ PCI dosimetry to relate UV-C dose and viral inactivation at specific on-N95 locations, establishing a versatile approach to characterize UV-C photoinactivation of pathogens contaminating complex substrates such as N95s.

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection.