Concentrated Wastewater Research Articles

Wastewater Monitoring During the COVID-19 Pandemic in the Veneto Region, Italy: Longitudinal Observational Study

Abstract Background As the COVID-19 pandemic has affected populations around the world, there has been substantial interest in wastewater-based epidemiology (WBE) as a tool to monitor the spread of SARS-CoV-2. This study investigates the use of WBE to anticipate COVID-19 trends by analyzing the correlation between viral RNA concentrations in wastewater and reported COVID-19 cases in the Veneto region of Italy. Objective We aimed to evaluate the effectiveness of the cumulative sum (CUSUM) control chart method in detecting changes in SARS-CoV-2 concentrations in wastewater and its potential as an early warning system for COVID-19 outbreaks. Additionally, we aimed to validate these findings over different time periods to ensure robustness. Methods This study analyzed the temporal correlation between SARS-CoV-2 RNA concentrations in wastewater and COVID-19 clinical outcomes, including confirmed cases, hospitalizations, and intensive care unit (ICU) admissions, from October 2021 to August 2022 in the Veneto region, Italy. Wastewater samples were collected weekly from 10 wastewater treatment plants and analyzed using a reverse transcription–quantitative polymerase chain reaction. The CUSUM method was used to detect significant shifts in the data, with an initial analysis conducted from October 2021 to February 2022, followed by validation in a second period from February 2022 to August 2022. Results The study found that peaks in SARS-CoV-2 RNA concentrations in wastewater consistently preceded peaks in reported COVID-19 cases by 5.2 days. Hospitalizations followed with a delay of 4.25 days, while ICU admissions exhibited a lead time of approximately 6 days. Notably, certain health care districts exhibited stronger correlations, with notable values in wastewater anticipating ICU admissions by an average of 13.5 and 9.5 days in 2 specific districts. The CUSUM charts effectively identified early changes in viral load, indicating potential outbreaks before clinical cases increased. Validation during the second period confirmed the consistency of these findings, reinforcing the robustness of the CUSUM method in this context. Conclusions WBE, combined with the CUSUM method, offers valuable insight into the level of COVID-19 outbreaks in a community, including asymptomatic cases, thus acting as a precious early warning tool for infectious disease outbreaks with pandemic potential.

Optimization of Solar Corrosion Fenton Reactor for the Recovery of Textile Wastewater: In Situ Release of Fe2+

A Solar Corrosion Fenton reactor (SCFr) was developed by packing an iron-carbon steel filament inside the reactor to enable the in situ release of Fe2+. A Box–Behnken experimental design was used to optimize the effect of HRT (20, 30, and 40 min), the mass ratios of the packed filament inside the reactor with respect to volume (0.1, 0.2, 0.3 w/v), and the peroxide dosage added (500, 1000, and 1500 mg/L), the response variables were the percentage removal of COD, color, and turbidity. The optimum conditions for SCFr were an HRT of 24.5 min, a ratio of 0.16 (0.0032 m2/L), and a peroxide dose of 1006.9 mg/L. The removal was 91.8%, 98.4%, and 87.3% COD, color, and turbidity, respectively. Without solar radiation, the percentage removal was reduced by 16.3%, 47.9%, and 34.0% in terms of COD, color, and turbidity, respectively. The concentration of Fe2+ released was 25.4 mg/L of Fe2+. Prolonged HRT increases Fe2+ concentration and turbidity, which increase COD. The oxidation kinetics were fitted to a Behnajady–Modirshahla–Ghanbery (BMG) model, which indicated a high oxidation rate that is reflective of low treatment times. The w/v ratio was the most significant factor; the release of Fe2+ was stimulated by UV radiation and the chloride concentration of wastewater, which prevents the formation of an oxide layer, thus allowing its continuous release, taking advantage of solar radiation and the pH and chloride concentration of the raw sample.

Utilization of borax production waste in zinc (Zn+2) adsorption from industrial wastewater

Borax production waste materials were determined as a possible adsorbent to eliminate the zinc (Zn+2) concentration in industrial wastewater. To investigate the adsorption mechanism, reaction parameters were optimized such as solution pH, dilution ratio (initial concentration) and contact time. The effect of pH had a vital role on the adsorption efficiency. The optimum solution pH was selected as 9 and contact time was selected as 90 mins. In the isothermal and kinetic modeling, Temkin isotherm (R2: 0.9851) and Lagergren pseudo-second-order kinetic method (R2: 0.9999 − 9.9945) were determined as the best fitted functions. The results indicated the weak interaction between molecules of adsorbent and adsorbate that related to the physical adsorption. Rate constants of k2 were found between −16.5017 and 48.7805 whereas the values of qe were estimated in the range of 49.5050 and 96.1538 for the different dilution ratios. The results also proved that industrial wastes can be an option for water treatment processes.

Localised wastewater SARS-CoV-2 levels linked to COVID-19 cases: A long-term multisite study in England.

Wastewater-based surveillance (WBS) can monitor for the presence of human health pathogens in the population. During COVID-19, WBS was widely used to determine wastewater SARS-CoV-2 RNA concentration (concentrations) providing information on community COVID-19 cases (cases). However, studies examining the relationship between concentrations and cases tend to be localised or focussed on small-scale institutional settings. Few have examined this relationship in multiple settings, over long periods, with large sample numbers, nor attempted to quantify the relationship between concentrations and cases or detail how catchment characteristics affected these. This 18-month study (07/20-12/21) explored the correlation and quantitative relationship between concentrations and cases using censored regression. Our analysis used >94,000 wastewater samples collected from 452 diverse sampling sites (259 Sewage Treatment Works (STW) and 193 Sewer Network Sites (SNS)) covering ~65% of the English population. Wastewater concentrations were linked to ~6 million diagnostically confirmed COVID-19 cases. High correlation coefficients were found between concentrations and cases (STW: median r=0.66, IQR: 0.57-0.74; SNS: median r=0.65, IQR: 0.54-0.74). The quantitative relationship (regression coefficient) between concentrations and cases was variable between catchments. Catchment and sampling characteristics (e.g. size of population and grab vs automated sampling) had significant but small effects on correlation and regression coefficients. During the last six months of the study correlation coefficients reduced and regression coefficients became highly variable between catchments. This coincided with a shift towards younger cases, a highly vaccinated population and rapid emergence of the variant Omicron. The English WBS programme was rapidly introduced at scale during COVID-19. Laboratory methods evolved and study catchments were highly diverse in size and characteristics. Despite this diversity, findings indicate that WBS provides an effective proxy for establishing COVID-19 dynamics across a wide variety of communities. While there is potential for predicting COVID-19 cases from wastewater concentration, this may be more effective at smaller scales.

Nanofluid-peroxydisulfate integrated volumetric solar interfacial evaporation system for water evaporation and organic pollutant removal

Solar evaporation exhibits significant potential for the treatment of high-salt organic wastewater. However, it's also confronted with challenges due to the accumulation of organic pollutants and salts in the concentrated wastewater following evaporation, which compromises the long-term stability of evaporation unit and complicates subsequent treatment processes. To address these challenges, a volumetric solar interfacial evaporation (V-SIE) system by integrating Fe3O4-H2O nanofluids and peroxydisulfate (PDS) were proposed in this study. In V-SIE system, Fe3O4 magnetic nanoparticles (NPs) were prepared as solar receivers to form a volume-absorbing solar energy interface and enhance evaporation efficiency. The results show that the evaporation rate was 1.412 kg/(m2·h) and the solar efficiency reached 93.75% as the temperature rose to 57.2 ℃. Additionally, the high thermal conductivity of Fe3O4 facilitated the effective heat transfer to the fluid and provided sufficient thermal energy to activate PDS, thereby removing 99.3% of Rhodamine B (RhB). Fe3O4 NPs effectively promoted the generation of reactive species including SO4•−, •OH, O2•− and 1O2 from PDS and the four main stages including N-de-ethylation, chromophore cleavage, ring-opening, and mineralization were proposed as the possible degradation pathway of RhB. This study provides a reference for developing V-SIE system and highlights the positive effect of nanofluids in advanced oxidation processes.

Microelectricity enhances aerobic granular sludge granulation and sulfamethazine degradation: Performance, mechanism, antibiotic resistance genes and microbial community.

With the widespread use of typical antibiotics such as sulfamethazine (SMT), it leads to their accumulation in the environment, increasing the risk of the spread of antibiotic resistance genes (ARGs). Aerobic granular sludge (AGS) has shown great potential in treating antibiotic wastewater. However, the long cultivation period of AGS, the easy disintegration of particles and the poor stability of degradation efficiency for highly concentrated antibiotic wastewater are still urgent problems that need to be solved, and it is important to explore the migration and changes of ARGs and microbial diversity in AGS systems. In this study, a microelectrically enhanced pelletizing reactor (MEPR) was innovatively constructed using a microbial electrolysis cell (MEC) coupled with an AGS system, and a comparative study was carried out using a conventional sequential batch reactor (SBR). The results showed that the AGS obtained from MEPR culture was smooth white spherical, with rich internal microbial phase and good sludge activity. The microelectric condition shortened the AGS culture cycle by 10 days, with smaller AGS particle size, denser structure, and better pollutant degradation ability, and the average removal rate of SMT by MEPR (74.3 %) was much higher than that of SBR (3.13 %). The microelectrical properties reduced the sludge pressure to a certain extent, induced the reasonable secretion of extracellular polymeric substances (EPS), and kept the MEPR in a strong stable state. High-throughput sequencing and detection of ARGs indicated that MEPR had a richer microbial community structure, which significantly controlled the enrichment of ARGs. This study provides a theoretical reference for enhanced sludge granulation and biological treatment of high concentration antibiotic wastewater.

Wastewater surveillance of Candida auris in Baltimore

Candida auris (C. auris), an opportunistic fungus causing disease, poses a growing global health concern due to its significant mortality rate, resistance to antifungal treatment, and ability to persist in healthcare settings. Over a span of 47 weeks, untreated wastewater samples were regularly gathered from two wastewater treatment plants (referred to as WWTP-A and WWTP-B) in Baltimore, from July 27, 2022, to June 23, 2023. Throughout the study period, 110 primary influent (PI) samples were collected from both WWTP-A and WWTP-B. A quantitative polymerase chain reaction (qPCR) method was used to analyze C. auris captured on filters (0.45 μm) from these samples. Positive C. auris detection (11.81%) occurred in the influent samples, with a higher frequency of detection in WWTP-B. Influent wastewater concentrations ranged from 1.2 to 7.9 log10 gene copies per liter (gc/L). Interestingly, seasonal analysis showed that C. auris presence in wastewater was more pronounced during the spring season, indicating a sustained existence of the pathogen as seasons changed. Integration of wastewater surveillance and clinical data reveals temporal correlations in C. auris dynamics. The resulting Pearson correlation coefficient of 0.27 reveals a weak positive correlation between the number of new C. auris cases in Baltimore and the quantity of detected gene copies in wastewater. This study marks the first instance of detecting C. auris in Baltimore's wastewater. The results emphasize that wastewater monitoring could serve as an additional early warning tool for anticipating and managing future outbreaks of C. auris.

Development of a powdered activated charcoal sodium alginate hydrogel bead concentration method for detecting viruses in wastewater

Developing a simple and cost-effective wastewater concentration method using powdered activated charcoal sodium alginate (PAC-NaA) hydrogel beads, enhanced for capturing viruses.

Molecular Mechanisms of Humic Acid in Inhibiting Silica Scaling during Membrane Distillation.

Membrane distillation (MD) efficiently desalinizes and treats high-salinity water as well as addresses the challenges in handling concentrated brines and wastewater. However, silica scaling impeded the effectiveness of MD for treating hypersaline water and wastewater. Herein, the effects of humic acid (HA) on silica scaling behavior during MD are systematically investigated. The interaction mechanism between typical components of HA and active silica was evaluated by molecular dynamics simulations. We find that the addition of HA alleviated the significant decrease in water flux, with recoveries surpassing 60% and 80% at 10 and 20 ppm of HA, respectively. Quantum chemical calculations indicate that the presence of HA greatly raised the free-energy barriers of silica polymerization compared to the system without HA (489.7 vs 45.1 kJ mol-1). Moreover, the interaction between HA molecules and silica significantly weakened the diffusion capacity of silica scale in water (diffusion coefficient from 1.04 × 10-5 to 0.08 × 10-5 cm2 s-1), consequently decreasing the likelihood of contact between silica scale and the hydrophobic membrane. Finally, a neural network analysis model for the HA and silica interaction was developed to design effective inhibitors for silica polymerization. Overall, this study develops nanoscale modeling and simulations to understand how HA inhibits silica scaling in membrane processes, guiding the formation of new approaches to enhance MD performance.

A calibration framework toward model generalization for bacteria concentration estimation in water resource recovery facilities

Reduced bacteria concentrations in wastewater is a key indicator of the efficacy of water resource recovery facilities (WRRFs). However, monitoring the presence of bacterial concentrations in real time at each stage of the WRRF is challenging as it requires taking and processing water samples offline. Although few studies have been proposed to predict bacterial concentrations using data-driven models, generalizing these models to unseen data from different WRRFs remains challenging. This paper proposes a calibration approach based on neural networks to adapt the optimal models across various WRRFs in Saudi Arabia for bacterial estimation at the influent and effluent stages. The calibration relies on the out-of-distribution (OOD) framework of the physiochemical water parameters (e.g., pH, COD, TDS, turbidity, conductivity) with a design threshold chosen based on the data distribution of the received unseen samples. We propose a calibration framework that continues updating the trained neural network model for accurate bacterial concentration estimation upon receiving new samples. We tested the effectiveness of the proposed calibration scheme on four WRRF datasets in Saudi Arabia, comparing the results with before and after calibration without the OOD. Before calibration model was based on a traditional and optimal neural network approach, typically considered the conventional method for building neural networks. After calibration without OOD, the model continued retraining without explicitly checking for OOD condition. The results showed that the proposed calibration framework of the selected baseline WRRF with the OOD scheme improved and of the worst-case influent bacteria concentration before calibration and after calibration without OOD, respectively. Similarly, the worst-case effluent bacteria concentration estimation was enhanced by before calibration and after calibration without the OOD. Our findings highlight the importance of integrating the calibration framework with neural network approaches to achieve model generalization.

Temporal, spatial, and methodological considerations in evaluating the viability of measles wastewater surveillance.

Measles is a highly transmissible disease of increasing concern due to waning vaccination contributing to a significant rise in measles cases, with 283 reported cases and 16 outbreaks in the U.S. as of November 7, 2024. Early identification of measles cases is thus critical to disease containment and control. Wastewater-based epidemiology (WBE) represents a potential strategy for the efficient identification of measles outbreaks. We investigated the suitability of WBE for measles outbreak identification by using a model-based approach to elucidate the relationship between measles shedding, wastewater concentration, and detectability. The model reveals conditions for effective detection, specifying the optimal timing, location, and methodology needed to achieve a specific probability of detection, including accounting for geographic variability of wastewater generation and measles case rates. Measles RNA shedding, primarily from urine, contributes an average of 8.72 log10 genome copies (GC) daily per infection into sewage. At the average U.S. wastewater treatment plant (WWTP), achieving a 50% probability of detection requires approximately 78 cases per 100,000 people with a process limit of detection (PLOD) of 3.0 log10 GC/L. At a PLOD of 3.0 log10 GC/L, over half of all WWTPs in the world can detect a single hypothetical measles case at a 10% probability of detection. However, achieving a 50-90% detection rate is challenging, especially with a higher PLOD, except in areas with the highest measles cases. Some locations require case levels consistent with a complete lack of vaccination for feasible measles detection in wastewater. Future work exploring measles shedding, variable shedding behavior, and local case rates can enhance model predictions. Overall, this analysis suggests that WBE detection of measles in most locations remains challenging without a significant increase in case rates or technical improvements decreasing the PLOD.

Assessment of Heavy Metal Concentrations (Cu, Cd, Pb, and Zn) in Wastewater from Gusii Treatment Plant in Kisii County, Kenya

The concentrations of heavy metals were determined from wastewater samples collected from the Gusii wastewater treatment plant, from May to July, 2021. Heavy metal analysis was done using a flame atomic absorption spectrophotometer, model AA 7000 Shimadzu, Japan. The results showed that the concentrations of Zinc and Cadmium were below the detection limit for all the sampling sites. The concentrations of Lead and Copper (Mean ± SE) ranged between 0.34 ± 0.06 mg/L and 0.86 ± 0.08 mg/L and 0.25 ± 0.05 and 0.34 ± 0.01 mg/L respectively. The month of July exhibited a higher mean Cu concentration of 0.35 ± 0.004 mg/L compared to the mean Cu concentration (0.2 ± 0.02 mg/L) of May. Likewise, the mean lead concentration of May (0.60 ± 0.04 mg/L) was higher than the mean (0.53 ± 0.05 mg/L.) of July. The independent sample t-test showed that mean Cu concentration difference was significant between the sampling months (t (34) = 21.58; p < 0.05) while for Pb it was not significant between the sampling months (t (30) = 1.241; p = 0.274). The percentage removals of Copper and Lead were generally low at 12.61 % and 6.27 %, respectively. The continued discharge of effluent into River Riana may lead to accumulation of heavy metals in the environment, which in turn poses health risks to the general public. Therefore, the study recommends that Gusii Water and Sanitation Company continue monitoring and assessing the levels of heavy metals in the treatment plant for its sustainability.

Effect of Light Intensity on Ammonium Removal and Biomass Growth in Different Levels of Aquaculture Effluent Using Duckweed (Lemna perpusilla)

Cultivating duckweed in aquaculture effluent offers a viable approach to eliminating contaminants. The duckweed biomass obtained can be utilized for the generation of bioenergy. However, elevated levels of ammonium (NH4+) in aquaculture effluent, combined with variations in light intensity, can hinder biomass formation. The precise mechanisms underlying this inhibition remain incompletely elucidated. The study assessed the efficacy of duckweed (Lemna perpusilla) as a treatment agent for wastewater from catfish farms. The objective was to evaluate the growth response of duckweed and its efficacy in reducing ammonium levels. The research demonstrated that daily light intensity fluctuated using shade nets and that the ammonium concentration of aquaculture wastewater varied according to the age of the fish. The shade nets, which blocked 25% of the sunlight and had an average daily light intensity of 3433.34–15199.56 lux, demonstrated a slightly elevated NH4+ removal efficiency and duckweed productivity of 69.34% and 0.050 kg/m²/day, respectively. However, these values were not statistically significant when compared to conditions without shade nets, which had a removal efficiency of 63.97% and duckweed productivity of 0.042kg/m2/day (P<0.05). The implementation of shade structures that effectively decrease solar exposure by 25% shows promise for enhancing duckweed productivity and optimizing nutrient reduction in wastewater from fish cultivation systems. This approach contributes to the promotion of sustainable integrated aquaculture.

A High Sensitivity Refractive Index Based Sensor for Detection of Acetone Concentration in Wastewater

A High Sensitivity Refractive Index Based Sensor for Detection of Acetone Concentration in Wastewater

Kinetics of manganese extraction from pyrolusite using reduction leaching of acidic wastewater from steel plants

ABSTRACT Acidic wastewater from steel works was selected to reduce pyrolusite. The effects of leaching parameters such as the ratio of FeCl2 to MnO2, leaching temperature, liquid–solid ratio and initial HCl concentration in acidic wastewater on the leaching rate of manganese were investigated. The results show that under the conditions including the molar ratio of FeCl2 and MnO2 ( m FeC l 2 / m Mn O 2 ) at 2.2, the leaching temperature of 363 K, a leaching time of 180 min, an L/S ratio of 11:1, and an initial HCl concentration in acidic wastewater of 2.5 mol/L, the leaching rate of manganese in pyrolusite was 97.14%. An unreacted shrinking core model was employed to explore the kinetics of manganese in pyrolusite arising from the reduction leaching of acidic wastewater from steel plants. The results imply that the rate-determining step changed in the leaching process; among which, the first stage (0–100 min) of leaching was subject to the control of interfacial chemical reactions; the second stage (100–180 min) was controlled by internal diffusion. The apparent activation energy of the two stages was 15.57 and 125.48 kJ/mol respectively, and the apparent rate equations were respectively: 1 – ( 1 – r ) 1 / 3 = 0.39 ( m FeC l 2 / m Mn O 2 ) 1.18 [ HCl ] 0.21 ( V L / M S ) 0.62 exp ( − 15.57 / RT ) t 1 – 3 [ ( 1 – r ) / ( 1 – r 1 ) ] 2 / 3 + 2 [ 1 – ( 1 – r 1 ) ( r – r 1 ) 1 / 3 ] = 2.22 × 10 16 ( m FeC l 2 / m Mn O 2 ) 5.81 [ HCl ] exp ( − 125.48 RT ) t

Wastewater-based effective reproduction number and prediction under the absence of shedding information.

Estimating effective reproduction number (Re) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring Re from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of Re estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in wastewater in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and real-world data. In simulations, we found wastewater-based Re (Reww) closely aligns with instantaneous clinical Re when shedding dynamics are rapid. For more prolonged shedding, Reww approximates a smoothed Re over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and Reww accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found Reww for SARS-CoV-2 showed similar trend and values to clinically-based Re. Reww for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported Re. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables near real-time monitoring of approximated Re and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater.

Adsorption performance and mechanism of MgO-loaded tobermorite for Mn2+ with high concentration in wastewater

Magnesium oxide (MgO) nanoparticles were incorporated on the tobermorite (TOB) surface via the sol-gel method to realize the preparation of TOB-MgO composite. This sample exhibited remarkable adsorption performance for Mn2+ with high concentration in wastewater when compared to the reported relevant adsorbents. TOB-MgO could achieve a removal efficiency of nearly 100 % for 500 mg/L of Mn2+ by using a dosage of 1.0 g/L. The optimum efficiency was maintained over a wide pH range (pH >5.0), attributed to its pH self-regulating capability. The adsorption data exhibited excellent agreement with the pseudo-second-order kinetic model and the Langmuir isotherm model, implying that Mn2+ ions were removed by monolayer chemisorption. Besides, this process was verified to be spontaneous and thermodynamically favorable. TOB-MgO, with a calculated maximum adsorption capacity of 549.73 mg/g for Mn2+, was also demonstrated to have good stability and regeneration ability, and be effective in the treatment of actual wastewater. Different characterizations showed that aided by the mesoporous structure of TOB-MgO, Mn2+ ions were first adsorbed by electrostatic attraction, and then exchanged with Mg2+/Ca2+ released from TOB-MgO. With the participation of the dissolved CO2 in solutions, surface precipitation further anchored Mn2+ to the TOB-MgO surface. TOB-MgO presents promising potential in being a universal adsorbent for many heavy metal ions with high concentration.

Extraction of estrogenic hormones in wastewater samples by using rotating disk and modified clays with ionic liquids

Both synthetic and natural hormones—17α-ethinylestradiol (EE2), 17β-estradiol (E2), and estriol (E3)—are present in wastewater samples in concentrations typically expressed in ng L-1. These concentrations are significant, as the presence of such hormones in water sources poses risks to both living organisms and the environment. Steroidal hormones from human sources are categorized as emerging pollutants and endocrine-disrupting compounds (EDCs), which constitute a health hazard.In this study, the Rotating Disk Sorption Extraction (RDSE) method was used with modified clays incorporating ionic liquids to detect estrogenic hormones in hospital wastewater treatment plant (HWWTP) samples. Sodium montmorillonite was modified to enhance its extractive capacity by intercalating ionic liquids ([C16MIM+][anion-], where anion = Br-, OH-, or BF4-) between its interlayers. Following extraction, the concentrated analytes were measured using liquid chromatography coupled with photodiode array detection (HPLC-PDA). The method achieved absolute recoveries of 61% for EE2, 41% for E2, and 20% for E3; these recoveries were lower than those obtained with a commercial C18 sorbent. However, the technique achieved enrichment factors of 13, 23, and 32 for E3, EE2, and E2, respectively. In wastewater, the intra-disk reproducibility (RSD) was below 12%. The method also provided limits of detection and quantification in HWWTP samples of 0.18–0.42 ng mL-1 for E3, 0.15–0.37 ng mL-1 for E2, and 0.07–0.16 ng mL-1 for EE2 when using the modified adsorbents. Average concentrations in hospital wastewater were measured at 4.30, 3.85, and 7.23 ng mL-1 in the influent (before treatment) and 3.81, 1.33, and 4.00 ng mL-1 in the effluent (after treatment) for E3, E2, and EE2, respectively.

Viral load-dependent masking effect by activated sludge during the aeration process

Emission of bioaerosols from wastewater treatment processes is a significant risk for the workers involved and the residents around, but the driving factors of viral aerosol emission and are not clear. In this study, four model viruses (Phi6, MS2, PhiX174, and T7) were used to investigate the effects of activated sludge on the emission of viral aerosols. It was found that more than 95% of the viruses could be adsorbed by the activated sludge within three hours. During aeration, when the activated sludge was present, the virus-containing aerosols became larger compared to no sludge situation. This implies that some viruses were adsorbed by the sludge particles during the aerosolization. The viral aerosol concentrations in air were proportion to the virus concentrations in the sludge-free wastewater, but much lower when aerosolized from the sludge suspension. This result showed a masking effect by the sludge. However, the masking effect was gradually weakened or even disappeared, when the spiked virus concentrations were less than 107 copies/mL, which are much closer to the presence of pathogenic viruses in the real wastewater. One possible explanation could be that small sludge particles might adsorb viruses preferentially compared to large ones and could help the aerosolization of viruses. This was demonstrated by the increased emission of viral aerosols after reducing the particle size of the activated sludge by sonication. This work reveals evidence that the sludge will actually not mask the aerosolization of pathogenic viruses in the wastewater treatment plant as the concentrations of most viruses are very low, and health risks always exist.

Estimating effective reproduction numbers using wastewater data from multiple sewersheds for SARS-CoV-2 in California counties.

The effective reproduction number serves as a metric of population-wide, time-varying disease spread. During the early years of the COVID-19 pandemic, this metric was primarily derived from case data, which has varied in quality and representativeness due to changes in testing volume, test-seeking behavior, and resource constraints. Deriving nowcasting estimates from alternative data sources such as wastewater provides complementary information that could inform future public health responses. We estimated county-aggregated, sewershed-restricted wastewater-based SARS-CoV-2 effective reproduction numbers from May 1, 2022 to April 30, 2023 for five counties in California with heterogeneous population sizes, clinical testing rates, demographics, wastewater coverage, and sampling frequencies. We used two methods to produce sewershed-restricted effective reproduction numbers, both based on smoothed and deconvolved wastewater concentrations. We then population-weighted and aggregated these sewershed-level estimates to arrive at county-level effective reproduction numbers. Using mean absolute error (MAE), Spearman's rank correlation (ρ), confusion matrix classification, and cross-correlation analyses, we compared the timing and trajectory of our two wastewater-based models to: (1) a publicly available, county-level ensemble of case-based estimates, and (2) county-aggregated, sewershed-restricted case-based estimates. Both wastewater models demonstrated high concordance with the traditional case-based estimates, as indicated by low mean absolute errors (MAE ≤ 0.09), significant positive Spearman correlation (ρ ≥ 0.66), and high confusion matrix classification accuracy (≥ 0.81). The relative timings of wastewater- and case-based estimates were less clear, with cross-correlation analyses suggesting strong associations for a wide range of temporal lags that varied by county and wastewater model type. This methodology provides a generalizable, robust, and operationalizable framework for estimating county-level wastewater-based effective reproduction numbers. Our retrospective evaluation supports the potential usage of real-time wastewater-based nowcasting as a complementary epidemiological tool for surveillance by public health agencies at the state and local levels. Based on this research, we produced publicly available wastewater-based nowcasts for the California Communicable diseases Assessment Tool (calcat.cdph.ca.gov).