A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks.

Wastewater-based SARS-CoV-2 surveillance enables unbiased and comprehensive monitoring of defined sewersheds. We performed real-time monitoring of hospital wastewater that differentiated Delta and Omicron variants within total SARS-CoV-2-RNA, enabling correlation to COVID-19 cases from three tertiary-care facilities with >2100 inpatient beds in Calgary, Canada. RNA was extracted from hospital wastewater between August/2021 and January/2022, and SARS-CoV-2 quantified using RT-qPCR. Assays targeting R203M and R203K/G204R established the proportional abundance of Delta and Omicron, respectively. Total and variant-specific SARS-CoV-2 in wastewater was compared to data for variant specific COVID-19 hospitalizations, hospital-acquired infections, and outbreaks. Ninety-six percent (188/196) of wastewater samples were SARS-CoV-2 positive. Total SARS-CoV-2 RNA levels in wastewater increased in tandem with total prevalent cases (Delta plus Omicron). Variant-specific assessments showed this increase to be mainly driven by Omicron. Hospital-acquired cases of COVID-19 were associated with large spikes in wastewater SARS-CoV-2 and levels were significantly increased during outbreaks relative to nonoutbreak periods for total SARS-CoV2, Delta and Omicron. SARS-CoV-2 in hospital wastewater was significantly higher during the Omicron-wave irrespective of outbreaks. Wastewater-based monitoring of SARS-CoV-2 and its variants represents a novel tool for passive COVID-19 infection surveillance, case identification, containment, and potentially to mitigate viral spread in hospitals.

Tracking Emergence and Spread of SARS-CoV-2 Omicron Variant in Large and Small Communities by Wastewater Monitoring in Alberta, Canada.

Wastewater monitoring of SARS-CoV-2 enables early detection and monitoring of the COVID-19 disease burden in communities and can track specific variants of concern. We determined proportions of the Omicron and Delta variants across 30 municipalities covering >75% of the province of Alberta (population 4.5 million), Canada, during November 2021-January 2022. Larger cities Calgary and Edmonton exhibited more rapid emergence of Omicron than did smaller and more remote municipalities. Notable exceptions were Banff, a small international resort town, and Fort McMurray, a medium-sized northern community that has many workers who fly in and out regularly. The integrated wastewater signal revealed that the Omicron variant represented close to 100% of SARS-CoV-2 burden by late December, before the peak in newly diagnosed clinical cases throughout Alberta in mid-January. These findings demonstrate that wastewater monitoring offers early and reliable population-level results for establishing the extent and spread of SARS-CoV-2 variants.

Development of Land-Use/Land-Cover Maps Using Landsat-8 and MODIS Data, and Their Integration for Hydro-Ecological Applications.

The Athabasca River watershed plays a dominant role in both the economy and the environment in Alberta, Canada. Natural and anthropogenic factors rapidly changed the landscape of the watershed in recent decades. The dynamic of such changes in the landscape characteristics of the watershed calls for a comprehensive and up-to-date land-use and land-cover (LULC) map, which could serve different user-groups and purposes. The aim of the study herein was to delineate a 2016 LULC map of the Athabasca River watershed using Landsat-8 Operational Land Imager (OLI) images, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) images, and other ancillary data. In order to achieve this, firstly, a preliminary LULC map was developed through applying the iterative self-organizing data analysis (ISODATA) clustering technique on 24 scenes of Landsat-8 OLI. Secondly, a Terra MODIS-derived 250-m 16-day composite of 30 EVI images over the growing season was employed to enhance the vegetation classes. Thirdly, several geospatial ancillary datasets were used in the post-classification improvement processes to generate a final 2016 LULC map of the study area, exhibiting 14 LULC classes. Fourthly, an accuracy assessment was carried out to ensure the reliability of the generated final LULC classes. The results, with an overall accuracy and Cohen's kappa of 74.95% and 68.34%, respectively, showed that coniferous forest (47.30%), deciduous forest (16.76%), mixed forest (6.65%), agriculture (6.37%), water (6.10%), and developed land (3.78%) were the major LULC classes of the watershed. Fifthly, to support the data needs of scientists across various disciplines, data fusion techniques into the LULC map were performed using the Alberta merged wetland inventory 2017 data. The results generated two useful maps applicable for hydro-ecological applications. Such maps depicted two specific categories including different types of burned (approximately 6%) and wetland (approximately 30%) classes. In fact, these maps could serve as important decision support tools for policy-makers and local regulatory authorities in the sustainable management of the Athabasca River watershed.

The influence of biochar and compost mixtures, water content, and gas flow rate, on the continuous adsorption of methane in a fixed bed column.

Biofiltration is an excellent alternative for the treatment of diffuse emissions of methane (CH4) that cannot be treated by physical/chemical means or recovered for energy. Despite the advances on CH4 biological treatment technologies, they are limited by the low aqueous solubility of CH4 into the biofilm where CH4 mineralization occurs. In this study, the CH4 adsorption kinetics, adsorption capacity and transport behavior of CH4 was studied in batch experiments and in a fixed-bed column by varying the biochar and compost mixtures under 5-levels, 3 different water contents (dry, 15% and 30% water holding capacity), and 2 inlet flow rates. Experimental results were formally tested using analysis of variance (ANOVA) in order to draw objective conclusions based on statistical inference. As CH4 biofiltration requires water addition to maintain microbial activity, these results indicate adsorption capacity is not lost with water addition if biochar content is the dominant packing material. The Langmuir isotherm described the data best (R2 = 0.99). Maximum adsorption capacity by monolayer adsorption, or qmax, is relatively similar with or without the addition of water as long as the biochar component is the dominant material at 3.5 mg CH4/g medium for a 7:1 biochar: compost, v/v mixture. Empirical regression models for qo, kTh, (Thomas model) and τ and KYN (Yoon-Nelson model) were developed for the break through curves of CH4. The current work demonstrates the applicability of utilizing biochar, a relatively inexpensive adsorbent, can compensate for the low solubility of CH4 and overcome the rate-limiting step of mass transfer from the gas phase and into the methanotrophic biofilm. Further, biochar may be a reliable back-up system for CH4 storage especially for fluctuating inlet loads that may be encountered in industrial applications adsorbing up to 13 mg CH4/g biochar under dry conditions.

Fate of Zinc Oxide Nanoparticles in a Biosolid Slurry Characterized for Metal Complexation Characteristics.

Zinc oxide nanoparticles (NPs) present in domestic wastewaters may accumulate in biosolids used as fertilizer. In this paper, metal complexation by typical biosolids is explored using methods from the humics literature. Uptake of Zn from NPs in the biosolids is evaluated. Finally, the kinetics of release of Zn species are reported as a function of (i) pH and (ii) the presence of strong binding ligands (e.g., ion exchange resin promoting release). The investigation revealed that (i) metal binding sites of biosolids are analogs of humic substances, (ii) ZnO NPs do not survive in the digestion environment, and (iii) any ZnO NPs dissolve to aqueous Zn in <10 d. Kinetics of Zn in biosolids revealed that Zn release is a function of biosolid protonation. At pH 8, Zn is retained in the biosolids, whereas at pH 4.5, 10% of Zn is released from the biosolids. Adding a chelating resin to the system at pH 5.0 led to Zn release from the biosolids as per Noyes-Whitney kinetics, releasing 85% of the bound Zn in 360 h. Fifteen percent of Zn appeared to be irreversibly bound.

Design and evaluation of a UV LED Photocatalytic Reactor Using Anodized TiO2 Nanotubes.

A bench-scale flow-through photocatalytic reactor using light emitting diodes (LED) as light source and a TiO2 nanotube array (TN) as immobilized catalyst has been designed, fabricated and tested on commonly studied contaminants. The photoreactor is comprised of 144, 365-nm UV-LED lamps mounted along the inner periphery of an annular cylinder. An ordered array of TN, as catalyst, was immobilized by electrochemical anodization of a titanium cylinder and placed in the center of the reactor. Synthesized TN was characterized by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Laboratory investigations were conducted on the photoreactor to treat 4-chlorophenol (4-CP), atrazine and methylene blue. The performance of the photoreactor at different flow rates and at varying distances of photocatalyst from the light source was monitored. The photocatalytic reaction rates increased with bubbling oxygen into the reservoir. Significant improvement was observed when H2O2 was added and degradation to detection limits was observed.

Efficacy of UV-C photolysis of bisphenol A on transcriptome alterations of genes in zebrafish embryos.

The purpose of this study was to investigate the efficacy of UV-C direct photolysis of bisphenol A (BPA) as a remediation method of BPA contamination. We used zebrafish embryos as a model organism to test the toxicity and residual biological activity by measuring cytochrome P4501A1 (CYP1A), aromatase B (Aro B) and heat shock proteins (HSP-70) transcript levels. The mRNA levels of CYP1A gene increased about two fold while exposure of zebrafish embryos at 72 hpf resulted in significant induction (P = 0.048) of Aro B at 100 µg/L of BPA. Exposure of zebrafish embryos at 72 hpf to increasing concentrations of BPA resulted in significant induction (P = 0.0031) of HSP-70 transcript level. UV treatment of BPA resulted in a significant reduction in toxicity by reducing mortality of zebrafish embryos. The results suggest that UV-C direct photolysis may be an effective method for remediation of BPA contamination. Further studies will be necessary for better understanding of the identity and relative activity of the UV degradation by-products.

Some observations on the development of superior photocatalytic systems for application to water purification by the "adsorb and shuttle" or the interphase charge transfer mechanisms.

Adsorb and shuttle (A/S) and interfacial charge transfer are the two major strategies for overcoming recombination in photocatalysis in this era of nanoparticle composites. Their relationships are considered here. A review of key literature is accompanied by a presentation of three new experiments within the overall aim of assessing the relation of these strategies. The cases presented include: A/S by a high silica zeolite/TiO2 composite, charge transfer (CT) between phases in a TiO2/WO3 composite and both A/S and CT by composites of TiO2 with powered activated carbon (AC) and single-walled carbon nanotubes (SWCNT). The opportunities presented by the two strategies for moving toward photocatalysts that could support applications for the removal of contaminants from drinking water or that lead to a practical adsorbent for organics that could be regenerated photocatalytically link this discussion to ongoing research here.

Photocatalytic degradation of agricultural antibiotics using a UV-LED light source.

With a view to developing a UV-LED photocatalytic reactor for small and remote water systems, the degradation of three representative agriculturally produced contaminants, two antibiotics and an endocrine disruptor hormone, was evaluated in a bench 365 nm LED photoreactor using a slurry of the well-known Degussa P25® (TiO2) as photocatalyst. Use of an additional electron capture additives O2 and H2O2 was also assessed. Loss of the parent organic compounds was tracked by HPLC or UV absorbance and mineralization, where feasible, was studied with TOC analysis with conventional instrumentation. In all cases, degradation is significant with moderate light dose. Lab data suggest log reduction with light delivery less than 2.2 kWhr per cubic meter light delivery.

Dechlorination of polychlorinated biphenyls in transformer oil using UV and visible light.

A study on dechlorination of PCB138 in transformer oil (TO) and 2-propanol (IPA) using 254 nm ultraviolet (UV) light as well as dye sensitized visible light has been conducted. Studies on dechlorination of polychlorinated biphenyls (PCBs) in TO using visible light in the presence of methylene blue (MB) and triethylamine (TEA) (providing a 'photocatalytic' cycle) in both deaerated and aerated conditions have been conducted to determine effects of TO, MB and TEA on reaction rates. The results show that photolytic methods are effective in treating PCBs in TO, and that the oil plays a limited adverse role. Under UV irradiation, PCB 138 can be >99% dechlorinated in the presence 0.06% (w/w) TO in IPA within 1 h with a rate constant of 0.0853 min(-1), while 47% of PCB138 can be dechlorinated in 92.1% (w/w) TO in IPA within 2 h with a rate constant of 0.0051 min(-1). In the 'photocatalytic' system, 94% reduction of PCB 138 was achieved within 30 min with a rate constant of 0.0968 min(-1) when the solvent was 60.70% (w/w) TO in IPA, while 71% dechlorination of PCB138 was achieved within 30 min with a rate constant of 0.0382 min(-1) when 81.62% (w/w) TO was present. In treatment of 30-73 ppm PCBs in TO, the optimal concentration of MB and TEA were found to be 0.5 g/L and 58.08 g/L respectively. Because of quenching by oxygen, deaeration of the solution is necessary for an efficient reaction. The photocatalytic system is especially adapted for treating lower concentration of PCBs in TO.

Multi-antibiotics removal under UV-A light using sol-gel prepared TiO2: Central composite design, effect of persulfate addition and degradation pathway study.

The removal of three antibiotics i.e., metronidazole (MNZ), ciprofloxacin (CIP) and tetracycline (TET), from aqueous system via TiO2 photocatalysis under UV-A light was investigated. Photocatalyst(s) were prepared using sol-gel method under different calcination temperatures (400-800 °C) and water-alcohol ratio. The spherical shaped catalyst (mean particle size âˆ¼ 61 nm) was characterized via FTIR, XRD, BET, SEM, Raman, XPS, UV-DRS, and Fluorometry, and point of zero charge was also determined (pHPZC âˆ¼ 6.6). Batch photo-catalytic degradation studies have shown complete degradation of MNZ, CIP and TET after 50, 75 and 20 min with a TOC removal of 37%, 44% and 31%, respectively. The activity of sol-gel prepared TiO2 was comparatively higher than commercially available pure anatase TiO2 nanoparticles due to lesser mean particle size. The ratio of water to alcohol in the preparation of TiO2 catalyst was found to have significant effect on antibiotic removal. Moreover, persulfate (PS) addition of 0.1 g/L amplified the pseudo-first-order removal-rate constant by 2.75, 3.3 and 1.6 times for MNZ, CIP and TET, respectively. The higher initial pH values (8 and 10) have shown the best removal efficiency for all antibiotics. Subsequently, central composite design (CCD) experiments were conducted under multi-antibiotic conditions. Near complete removal of all antibiotics were observed within 120 min. Scavenging studies revealed that hydroxyl and superoxide radicals play major roles in photo-catalytic degradation of MNZ, CIP and TET. During photocatalysis, MNZ degradation was initiated by hydroxylation reaction, CIP by piperazine ring opening by hydroxyl attack and TET by multiple hydroxylation process. Overall, TiO2 showed good efficiency at degrading multiple antibiotics and has the potential for practical application on a larger scale.

A facile synthesis process of GCN/ZnO-Cu nanocomposite and the evaluation of the performance for the photocatalytic degradation of organic pollutants and the disinfection of wastewater under visible light.

In this research, graphitic carbon nitride/zinc oxide-copper denoted as GCN/ZnO-Cu nanocomposite photocatalysts were synthesized using a novel facile synthesis process, the co-exfoliation method involving ultrasonic exfoliation of the mixture of GCN and ZnO-Cu in ethanol and then thermal exfoliation. Different characterization techniques such as X-ray diffraction (XRD), mean crystallite size (MCS), BET surface area, transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), particle size distribution (PSD), Fourier transform-infrared spectroscopy (FT-IR), photoluminescence (PL) spectra, and ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) were conducted to study the crystallinity, morphology, elemental composition, chemical structure, and optoelectronic properties. The band gap was estimated using the UV-Vis DRS results and Tauc plots. The photocatalytic activity of the GCN/ZnO-Cu3% nanocomposites was evaluated in the degradation of 4-chlorophenol (4-CP), and the disinfection of wastewater primary influent under a narrowband visible light source, royal blue LED (λ = 450 nm). GCN/0.1ZnO-Cu3% nanocomposite showed the best performance in the degradation of 4-CP and the disinfection of municipal wastewater primary influent. For 4-CP degradation, GCN/0.1ZnO-Cu3% was 2.2 times better than GCN, 9.4 times better than ZnO-Cu3%, and 1.8 times better than the sum of the individual GCN and ZnO-Cu3%. A 5.5 log reduction was achieved for the disinfection of total coliforms in wastewater primary influent in 360 min. This enhanced photocatalytic activity of GCN/ZnO-Cu3% nanocomposite can be attributed to the synergistic of GCN and the ZnO-Cu3%, resulting in a large surface area and improved bandgap.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

An alternative process to treat boiler feed water for reuse.

A bench-scale process to treat boiler feed water for reuse in steam generation was developed. Industrial water samples from a steam-assisted gravity drainage plant in northern Alberta, Canada, were obtained and samples characterized. The technology, which consists of coagulation-settling to remove oil/grease and particulates followed by an advanced oxidative treatment, led to clean water samples with negligible organic carbon. Coagulation followed by settling removed most particulates and some insoluble organics. The advanced oxidative treatment removed any remaining color in the samples, decreased the organic content to near-zero, and provided water ready for reuse.

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region.

Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson's coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area.

Germline Biallelic Mismatch Repair Deficiency in Childhood Glioblastoma and Implications for Clinical Management.

We report a case of a 9-year-old boy with glioblastoma with a past history of colon cancer. Germline bi-allelic DNA-mismatch repair deficiency was diagnosed by a lack of immunohistochemical staining for PMS2 in the tumor and normal tissue. Family history was lacking. Sequencing confirmed compound heterozygous PMS2 mutations. A second hit in the DNA-polymerase-ε gene led to complete DNA-replication repair deficiency. This contributed to an ultra-hypermutated phenotype. Temozolomide was excluded from the treatment. PD-1 immunotherapy at recurrence contributed to extending post-relapse survival up to 11 months. Challenges included managing initial immune "flare" related to "pseudo-progression" and access to drug. Family screening diagnosed the sibling with Lynch syndrome. This is the first report of a child with a brain tumor treated with immunotherapy from India. Our report supports the routine inclusion of immunohistochemistry for mismatch repair proteins in the evaluation of pediatric high-grade glioma as this may directly impact the clinical care of these children and families.

Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population.

Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.44 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December 2020) and the third-wave of the COVID-19 pandemic (mid-April to May 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06% (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals' primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary's three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.

Enhancement of LED based photocatalytic degradation of sulfolane by integration with oxidants and nanomaterials.

In this study, oxidants and nanomaterials were used to improve titanium dioxide based photocatalytic degradation of sulfolane. Hydrogen peroxide (H2O2), sodium persulfate (SPS) and ozone (O3) were the oxidants studied and carbon nanotubes (CNT) and nanosized zero valent iron (nZVI) were used as the nanomaterials. The impact of these oxidants and nanomaterials was evaluated at various dosages in both Milli-Q water and groundwater. The results indicate that with a suitable dose of oxidants or nanomaterials, photocatalytic degradation of sulfolane in Milli-Q water can be enhanced. The addition of ozone contributed to a significant increase in sulfolane degradation rate in Milli-Q water. The experiments conducted in groundwater showed that oxidants (H2O2, SPS and O3) increased the degradation of sulfolane while the nanomaterials (CNT and nZVI) impeded sulfolane degradation in groundwater.