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.

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.

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.

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.

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.

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.

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.

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.

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.

A multicenter study investigating SARS-CoV-2 in tertiary-care hospital wastewater. viral burden correlates with increasing hospitalized cases as well as hospital-associated transmissions and outbreaks.

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.

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.

Potential human health risks due to environmental exposure to nano- and microplastics and knowledge gaps: A scoping review.

Microplastics are an emerging global environmental contaminant that are affecting multiple spheres. Despite their ubiquity in all spheres of life and ecology, little is known about the health effects of microplastics exposure to humans. This scoping review explores the existing evidence on the potential human health effects of microplastics and subsequent knowledge gaps. An electronic search of published articles in PubMed, Scopus, EMBASE, Cochrane databases, and Google Scholar was conducted using a combination of subject headings and keywords relating to microplastics and human health effects. The initial search resulted in 17,043 published articles and grey literature documents. After a full review of published articles and their references, 129 publications were identified for further detailed review. These articles indicate that human exposure to microplastics can occur through ingestion, inhalation, and dermal contact due to their presence in food, water, air, and consumer products. Microplastics exposure can cause toxicity through oxidative stress, inflammatory lesions, and increased uptake or translocation. Several studies have demonstrated the potentiality of metabolic disturbances, neurotoxicity, and increased cancer risk in humans. Moreover, microplastics have been found to release their constituent compounds as well as those that are adsorbed onto their surface. Further research is needed to quantify the effects of microplastics on human health and their pathogenesis.

Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors.

Machine learning models provide an adaptive tool to predict the performance of treatment reactors under varying operational and influent conditions. Aerobic granular sludge (AGS) is still an emerging technology and does not have a long history of full-scale application. There is, therefore, a scarcity of long-term data in this field, which impacted the development of data-driven models. In this study, a machine learning model was developed for simulating the AGS process using 475 days of data collected from three lab-based reactors. Inputs were selected based on RReliefF ranking after multicollinearity reduction. A five-stage model structure was adopted in which each parameter was predicted using separate models for the preceding parameters as inputs. An ensemble of artificial neural networks, support vector regression and adaptive neuro-fuzzy inference systems was used to improve the models' performance. The developed model was able to predict the MLSS, MLVSS, SVI5, SVI30, granule size, and effluent COD, NH4-N, and PO43- with average R2, nRMSE and sMAPE of 95.7%, 0.032 and 3.7% respectively.

Degradation of pharmaceutical mixtures in aqueous solutions using UV/peracetic acid process: Kinetics, degradation pathways and comparison with UV/H2O2.

This paper presents an evaluation of UV/PAA process for degradation of four pharmaceuticals venlafaxine (VEN), sulfamethoxazole (SFX), fluoxetine (FLU) and carbamazepine (CBZ) with comparison to UV/H2O2 process. The effectiveness of combining PAA and H2O2 at various proportions while irradiating with UVC were also evaluated. UVC/PAA (λ = 254 nm) was effective in degrading all four pharmaceuticals and followed pseudo first-order kinetics. Increasing PAA dosage or UVC intensity resulted in a linear increase in pseudo-first order rate coefficient. Both PAA in dark conditions and UVA/PAA (λ = 360 nm) were marginally effective to degrade SFX and ineffective to degrade VEN, CBZ and FLU; indicating the need for UVC irradiation for activation of PAA. For similar oxidant dosages of 50 mg/L UVC/H2O2 was found to be faster than UV/PAA for VEN, CBZ and FLU by 55%, 75% and 33%, respectively. Under similar conditions, SFX was degraded 24% faster by UV/PAA. Increase in the proportion of H2O2 to PAA in UVC/PAA/H2O2 improved kinetics of degradation compared to PAA alone. Tests on TOC were conducted to determine the amount of acetic acid that is released to water when treatment by UVC/PAA is conducted. Results demonstrated that 70% of PAA by mass was ultimately converted to acetic acid and remained in the treated solutions. Hydroxyl radical attack is hypothesized to be the main mechanism of degradation by UV/PAA as degradation intermediates identified for all the target pharmaceuticals coincided with by-products identified during UV/H2O2 process.

Effect of UV dose on degradation of venlafaxine using UV/H2O2: perspective of augmenting UV units in wastewater treatment.

Many water and wastewater treatment plants (WWTPs) are fitted with a UV system that provides post treatment disinfection before the water is released to receiving water. This paper presents a study on expected removal for the pharmaceutical venlafaxine (VEN) in a typical UV unit at a municipal WWTP with analysis of removal rates of an advanced oxidation process using UV irradiation with injection of H2O2. The study is supported by bench scale degradation experiments on VEN. Results demonstrated that UV can completely degrade VEN, but the addition of H2O2 increased pseudo first order rate constant by up to 2.5 times. Extrapolations of the lab data indicated that removal rates of VEN at the UV disinfection unit of a typical municipal WWTP are approximately 0.4% at standard operating conditions. With the addition of 10 mg/L of H2O2, degradation of VEN can be increased by ten times over existing UV treatment. By studying the impact of adjusting parameters such as UV intensity, UV dosage, and H2O2 dosage, a framework is set to allow researchers and engineers to move forward with developing UV/H2O2 systems that meet their future design needs for pharmaceutical removal.

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.

Health Impact Assessment of Sulfolane on Embryonic Development of Zebrafish (Danio rerio).

Sulfolane is a widely used polar, aprotic solvent that has been detected by chemical analysis in groundwater and creeks around the world including Alberta, Canada (800 µg/mL), Louisiana, USA (2900 µg/mL) and Brisbane, Australia (4344 µg/mL). Previous research provided information on adverse effects of sulfolane on mammals, but relatively little information is available on aquatic organisms. This study tested the effects of sulfolane (0-5000 µg/mL) on early development of zebrafish larvae, using various morphometric (survival, hatching, yolk sac and pericardial oedema, haemorrhaging, spinal malformations, swim bladder inflation), growth (larval length, eye volume, yolk sac utilisation), behavioural (touch response, locomotor activity and transcript abundance parameters (ahr1a, cyp1a, thraa, dio1, dio2, dio3, 11ßhsd2, gr, aqp3a, cyp19a1b, ddc, gria2b and hsp70) for 120 h. Embryos were chronically exposed to sulfolane throughout the experimental period. For locomotor activity, however, we also investigated acute response to 2-h sulfolane treatment. Sulfolane sensitivity causing significant impairment in the observed parameters were different depending on parameters measured, including survival (concentrations greater than 800 µg/mL), morphometric and growth (800-1000 µg/mL), behaviour (500-800 µg/mL) and transcript abundance (10 µg/mL). The overall results provide novel information on the adverse health impacts of sulfolane on an aquatic vertebrate species, and an insight into developmental impairments following exposure to environmental levels of sulfolane in fish embryos.

Coaggregation of bacterial communities in aerobic granulation and its application on the biodegradation of sulfolane.

Aerobic granulation is regarded as the future technology for wastewater treatment that can replace conventional activated sludge. In this study, two approaches of forming sulfolane degrading aerobic granules (SDAG) were successfully developed and evaluated. These include adaptation of pre-grown granules to sulfolane environment and coaggregation of pre-grown granules with bacterial culture native to sulfolane contaminated site. The adaption method required a longer period to form robust SDAG compared to coaggregation method where degradation of sulfolane was observed within 24 h. Electronic images revealed dominant filamentous bacteria on the surface of granules while DNA analysis unveiled the complexity of the dynamic change of microbial community during aerobic granule formation. The rate of sulfolane degradation by coaggregated granules reduced as the concentration of carbon source increased, nevertheless, the rate increased with increased biomass. In addition, the presence of co-contaminants can slightly impact the ability of newly cultivated granules to degrade sulfolane. Finally, the stability and settleability of the new aerobic granules was investigated under different environmental conditions. About 30% of the aerobic granules were lost after 14 d of operation without any continuous supply of carbon sources. The surviving SDAGs continued to display an intact structure coupled with good settleability.

Degradation of sulfolane in aqueous media by integrating activated sludge and advanced oxidation process.

In this study, the performance of an integrated technology, combining biological treatment with advanced oxidation process in sequence, was evaluated for the degradation of sulfolane in aqueous media. In addition, the impact of biological process on AOP was also studied by assessing residual sulfolane, nutrient and total suspended solids (TSS) concentrations. The integration of activated sludge process with UVC/H2O2 resulted in more than 81% of sulfolane degradation in less than 24 h. It was observed that mineralization was much faster in biological system compared to AOP. Mechanistically, the process of degradation is different in the two processes as various by-products were identified during UVC/H2O2 but not during the biological process. The impact of residual sulfolane concentration on UVC/H2O2 was significant beyond a concentration of 30 mg L-1, while below 30 mg L-1 the rate of degradation was independent of sulfolane concentration. Residual nutrients from biological systems did not impact AOP performance. Nevertheless, presence of TSS >44 mg L-1 had a negative impact on the performance of UVC/H2O2 by reducing UV transmittance which led to retardation of sulfolane degradation. The application of UVC/H2O2 after biological treatment was an advantage as UVC/H2O2 could perform dual roles of oxidant and disinfectant.