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SARS-CoV-2 has been detected in wastewater and its abundance correlated with community COVID-19 cases, hospitalizations and deaths. We sought to use wastewater-based detection of SARS-CoV-2 to assess the epidemiology of SARS-CoV-2 in hospitals. Between August and December 2020, twice-weekly wastewater samples from three tertiary-care hospitals (totaling >2100 dedicated inpatient beds) were collected. Hospital-1 and Hospital-2 could be captured with a single sampling point whereas Hospital-3 required three separate monitoring sites. Wastewater samples were concentrated and cleaned using the 4S-silica column method and assessed for SARS-CoV-2 gene-targets (N1, N2 and E) and controls using RT-qPCR. Wastewater SARS-CoV-2 as measured by quantification cycle (Cq), genome copies and genomes normalized to the fecal biomarker PMMoV were compared to the total daily number of patients hospitalized with active COVID-19, confirmed cases of hospital-acquired infection, and the occurrence of unit-specific outbreaks. Of 165 wastewater samples collected, 159 (96%) were assayable. The N1-gene from SARS-CoV-2 was detected in 64.1% of samples, N2 in 49.7% and E in 10%. N1 and N2 in wastewater increased over time both in terms of the amount of detectable virus and the proportion of samples that were positive, consistent with increasing hospitalizations at those sites with single monitoring points (Pearson's r=0.679, P<0.0001, Pearson's r=0.799, P<0.0001, respectively). Despite increasing hospitalizations through the study period, nosocomial-acquired cases of COVID-19 (Pearson's r =0.389, P<0.001) and unit-specific outbreaks were discernable with significant increases in detectable SARS-CoV-2 N1-RNA (median 112 copies/ml) versus outbreak-free periods (0 copies/ml; P<0.0001). Wastewater-based monitoring of SARS-CoV-2 represents a promising tool for SARS-CoV-2 passive surveillance and case identification, containment, and mitigation in acute- care medical facilities.

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A ranking system for contaminated sites based on comparative risk methodology using fuzzy Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) was developed in this article. It combines the concepts of fuzzy sets to represent uncertain site information with the PROMETHEE, a subgroup of Multi-Criteria Decision Making (MCDM) methods. Criteria are identified based on a combination of the attributes (toxicity, exposure, and receptors) associated with the potential human health and ecological risks posed by contaminated sites, chemical properties, site geology and hydrogeology and contaminant transport phenomena. Original site data are directly used avoiding the subjective assignment of scores to site attributes. When the input data are numeric and crisp the PROMETHEE method can be used. The Fuzzy PROMETHEE method is preferred when substantial uncertainties and subjectivities exist in site information. The PROMETHEE and fuzzy PROMETHEE methods are both used in this research to compare the sites. The case study shows that this methodology provides reasonable results.

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A detailed performance evaluation of a simple high intensity LED based photoreactor exploiting a narrow wavelength range of the LED to match the spectrum of a dye in a photocatalysis system is reported. A dye sensitized (coumarin-343, lambda max = 446 nm) TiO 2 photocatalyst was used for the degradation of 4-chlorophenol (4-CP) in an aqueous medium using the 436 nm LED based photoreactor. The LED reactor performed competitively with a conventional multilamp reactor and sunlight in the degradation of 4-CP. Light intensities entering the reaction vessel were measured by conventional ferrioxalate actinometry. The results can be fitted by approximate first order kinetic behavior in this system. Hydroxyl radicals were detected by spin trapping EPR, and effects of OH radical quenchers on kinetics suggest that the reaction is initiated by these radicals or their equivalents. LEDs operating at competitive intensities offer a number of advantages to the photochemist or the environmental engineer via long life, efficient current to light conversion, narrow bandwidth, forward directed output, and direct current power for remote operation. Matching light source spectrum to chromophore is a key.

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This paper presents a model using fuzzy synthetic evaluation to estimate the methane generation rate constant, k, for landfills. Four major parameters, precipitation, temperature, waste composition and landfill depth were used as inputs to the model. Whereas, these parameters are known to impact the methane generation, mathematical relationships between them and the methane generation rate constant required to estimate methane generation in landfills, are not known. In addition, the spatial variations of k within a landfill combined with the necessity of site-specific information to estimate its value, makes k one of the most elusive parameters in the accurate prediction of methane generation within a landfill. In this paper, a fuzzy technique was used to develop a model to predict the methane generation rate constant. The model was calibrated and verified using k values from 42 locations. Data from 10 sites were used to calibrate the model and the rest were used to verify it. The model predictions are reasonably accurate. A sensitivity analysis was also conducted to investigate the effect of uncertainty in the input parameters on the generation rate constant.