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The potential use of a statistical approach for the investigation of complex dissolved organic matter (DOM) sources in surface water within a recycled water system monitored by excitation-emission matrix (EEM) fluorescence spectroscopy is shown. The work in this manuscript utilize information extracted from EEM spectroscopy to characterize DOM in collected surface water samples along with a wastewater treatment plant to drinking water treatment plant, discussing that humic-like and protein-like DOM sources predominate in the investigated water samples. Five different fluorescent components were resolved, describing several different types of DOM with different excitation and emission spectra that were distinct among the watershed sampling sites and indicating the influences of anthropogenic impacts. In addition, these novel fluorescence parameters have potential to improve resolution to direct more targeted water quality monitoring approaches.

Bench-scale UV experiments are an inefficient and laborious process when using current flow-through and collimated beam technologies. Current technologies are also limited in their ability to custo...

A commercially-available UV surface disinfection system used for hospital room disinfection was characterized for use as a UV-inactivation system for N95 filtering facepiece respirator (FFR) respirators. The output of light was initially characterized and assessed for its ability to penetrate through the individual N95 respirator layers. Following characterization, disinfection performance was tested using a range of model pathogens. In a series of experiments, coupons of respirator material were inoculated with Escherichia coli, Staphylococcus aureus, Geobacillus stearothermophilus spores, Bacillus cereus, Pseudomonas aeruginosa, and influenza A virus. Inoculated coupons were then treated with UV light from a low-pressure UV disinfection system during specific treatment times. Microbial enumeration was performed pre- and post-UV treatment. It was found that respirator coupons inoculated with E. coli, influenza A, P. aeruginosa, B. cereus and G. stearothermophilus resulted in below detection counts when exposed to UV fluences between 410 to 2700 mJ cm−2. In addition, UV light was unable to sufficiently penetrate all layers of respirator material, highlighting the importance of flipping respirators when exposing them to UV light. This work suggests that with further validation, UV light will be a promising touchless treatment option for frontline professionals who need to repurpose N95 respirators for subsequent use.

The recent surge in the use of UV technology for personal protective equipment (PPE) has created a unique learning opportunity for the UV industry to deepen surface disinfection knowledge, especially on surfaces with complex geometries, such as the N95 filter facepiece respirators (FFR). The work outlined in this study addresses the interconnectedness of independent variables (e.g., UV Fluence, respirator material) that require consideration when assessing UV light efficacy for disinfecting respirators. Through electron microscopy and Fourier-transform infrared (FTIR) spectroscopy, we characterized respirator filter layers and revealed that polymer type affects disinfection efficacy. Specifically, FFR layers made from polypropylene (PP) (hydrophobic in nature) resulted in higher disinfection efficiency than layers composed of polyethylene terephthalate (PET-P) (hygroscopic in nature). An analysis of elastic band materials on the respirators indicated that silicone rubber-based bands achieved higher disinfection efficiency than PET-P bands and have a woven, fabric-like texture. While there is a strong desire to repurpose respirators, through this work we demonstrated that the design of an appropriate UV system is essential and that only respirators meeting specific design criteria may be reasonable for repurposing via UV disinfection.

The main psychoactive compound in Cannabis sativa, Δ9-tetrahydrocannabinol (THC), and its metabolites are emerging organic contaminants that have been detected in waste and surface waters around the globe. This study tested the efficacy of advanced oxidation processes (i.e., direct UV photolysis or UV/H2O2 processes) towards the treatment of the main human metabolite of THC, THC-COOH. Three different waters were selected to evaluate the photolysis and oxidation performance with polychromatic UV light in the range of 200–300 nm, and the effect of pH on the photooxidation of THC-COOH was also studied. The results indicated that direct UV photolysis followed pseudo first-order kinetics, with fluence-based rate constants ranging from 1.9(0.3)–2.7(0.2) × 10−3 cm2 mJ−1, 0.6(0.2)–1.0(0.1) × 10−3 cm2 mJ−1, and 0.6(0.2)–0.9(0.2) × 10−3 cm2 mJ−1 for THC-COOH prepared in deionised (DI) water, Suwannee River humic acid (SRHA) dissolved in DI water, and natural lake water, respectively. This study found that the UV/H2O2 process did not enhance the oxidation rate in comparison to direct photolysis, and no effect of pH from 5.6 ± 0.2 to 8.7 ± 0.2 was observed. Hence, the hydroxyl radical (HO˙) degradation pathway played a less important role in the overall oxidation of THC-COOH under the tested conditions (i.e., SRHA and lake water) than direct photolysis.

Fluorescence spectroscopy shows promise as a tool for monitoring regulated disinfection by-products (DBPs) online in water treatment applications. Prediction of DBP formation via fluorescence spectroscopy was investigated using drinking water treatment plant (WTP) samples and experimental data from bench-scale advanced oxidation processes applied to a natural water matrix. L1-Regularized linear regression (lasso), boosted regression tree ensembles, principal components regression, supervised principal components, and fluorescent regional integration models were applied to data comprising instantaneous haloacetic acid (HAA) and trihalomethane (THM) concentrations and DBP formation potentials (HAAfp and THMfp) paired with fluorescence excitation–emission matrices. L1-Regularized linear regression yielded the lowest mean absolute error (MAE), assessed by cross-validation, on HAA and HAAfp data collected at the WTP (7.7 μg L−1, N = 22). Boosted regression tree ensemble predictions had the lowest MAE on WTP THM and THMfp data (13.5 μg L−1, N = 37). L1-Regularized linear regression and supervised principal components, respectively, exhibited the greatest prediction accuracy (MAE 14.9 and 9.5 μg L−1, N = 60) for HAAfp and THMfp data generated via bench-scale advanced oxidation processes. Linear models based on either fluorescent regional integration or (unsupervised) principal components were consistently less accurate than the highest-performing methods for DBP prediction.