Operation Of Wastewater Treatment Plants Research Articles

Enhanced treatment of synthetic wastewater by bioaugmentation with a constructed consortium

Bioaugmentation by adding well-functioning mixed microorganism consortia represents a potentially useful approach to improve contaminant removal in wastewater treatment plants (WWTPs). However, unfavorable environmental conditions (i.e., low temperatures) can severely inhibit microbial activity, drawing our attention to constructing cold-tolerant microorganism preparations and investigating their availability in practical applications. Here we screened four in situ functional isolates from the activated sludge of secondary sedimentation tanks in WWTPs to construct a psychrophilic microbial consortium, which was used to perform bioaugmentation for enhanced removal of nitrogen and phosphorus under low temperatures. The consortium was established by cocultivation of four isolates, characterized by 16 S rRNA as the COD-degrading bacterium Aeromonas sp. Z3, aerobic denitrifying bacterium Acinetobacter sp. HF9, nitrifying bacterium Klebsiella sp. X8, and polyphosphate-accumulating bacterium Pseudomonas sp. PC5 respectively. The microorganism preparation was composed of Z3, HF9, X8, and PC5 under the ratio of 1: 1: 3: 1, which can exert optimal pollutant removal under the conditions of 12 °C, 6.0–9.0 pH, 120–200 r‧min−1, and a dosage of 5% (V/V). A 30-day continuous operation of the bioaugmented and control sequencing batch reactors (SBRs) was investigated, and the bioaugmented SBR showed a shorter start-up stage and a more stable operating situation. Compared to the control SBR, the COD, NH4+-N, TN, and TP removal efficiency of the bioaugmented SBR increased by an average of 7.95%, 9.05%, 9.54%, and 7.45% respectively. The analysis of the microbial community revealed that the introduced isolates were dominant in the activated sludge and that functional taxa such as Proteobacteria, Bacteroidota, and Actinobacteria were further enriched after a period of bioaugmentation. The study provides some basis and guidance for the practical application of how to strengthen the stable operation of WWTPs under low temperatures.

Static calibration of wastewater treatment plant models: Investigating calibration processes and objective functions

Mathematical modeling provides a reliable tool for optimal management and operation of wastewater treatment plants (WWTPs). Accurate calibration is crucial for producing reliable results, and static calibration is a critical initial step for this purpose, ensuring enough accuracy and minimizing later difficulties. The two main areas to investigate in static calibration are the appropriate calibration process and objective function. This study proposes a three-stage methodology for static calibration of model parameters. Nine different calibration objective functions considering various target variables are investigated to evaluate how different objective functions can affect the calibration process accuracy. The proposed approach is applied in a real WWTP with a carousel oxidation ditch for secondary treatment. Benchmark Simulation Model No.2 (BSM2) is modified to be used for oxidation ditch modeling. The calibration results show less than 3 % prediction error for COD, TN, and TSS, and 6.7 % mean overall error. The validation resulted in 22.5 %, 10.6 %, and 31.5 % MARE (Mean Absolute Relative Error) for effluent COD, TN, and, TSS, respectively, indicating successful calibration. Comparing different objective functions indicates that in static calibration, the objective functions that consider more targets usually yield more accurate results in a relatively acceptable time.

Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment

The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP.

Understanding the risk of using herbicides for tree root removal into wastewater treatment plant performance

Adding herbicides to sewer lines, a common practice for controlling root intrusion in sewer pipes, may adversely impact downstream wastewater treatment by inhibiting nitrification and denitrification performance. This study investigated the effects of herbicides, namely diquat, triclopyr, and 2-methyl-4-chlorophenoxyacetic acid (MCPA)-dicamba, on these processes. Various parameters were monitored, including oxygen uptake rate (OUR), nutrients (NH3–N, TP, NO3–N, and NO2–N), chemical oxygen demand (COD), and herbicide concentrations. It was found that nitrification was not affected by OUR in the presence of each herbicide at various concentrations (1, 10, and 100 mg L−1). Additionally, MCPA-dicamba at various concentrations demonstrated minimal inhibition in the nitrification process compared to diquat and triclopyr. COD consumption was not affected by the presence of these herbicides. However, triclopyr significantly inhibited NO3–N formation in the denitrification process at various concentrations. Similar to nitrification process, both COD consumption and herbicide reduction concentration were not affected by the presence of herbicides during the denitrification process. Adenosine triphosphate measurements showed minimal impact on nitrification and denitrification processes when herbicides were present in the solution up to a concentration of 10 mg L−1. Tree root kill efficiency experiments were performed on Acacia melanoxylon. Considering the performance on nitrification and denitrification process, diquat emerged as the best herbicide option (concentration of 10 mg L−1), with a 91.24% root kill efficiency.

Improvement of air flowrate distribution in the nitrification reactor of the waste water treatment plant by effluent quality, energy and greenhouse gas emissions optimization via artificial neural networks models

Wastewater Treatment Plants (WWTPs) optimal operation is vital for sustainable wastewater management. Nonlinear autoregressive networks with exogenous inputs and architectures optimized by genetic algorithms (GA-NARX) were used to model energy consumption, effluent quality (EQ), and greenhouse gas (GHG) emissions. The GA-NARX were fed with 7 influent and operational variables, and were individually trained to predict 10 effluent variables and the performance indices. The mean absolute percentage error at prediction varied between 1.136 % and 21.460 %. The developed models were employed in WWTP operation optimization by unevenly distributing airflow in the aerated bioreactor. Multi-objective optimization of energy demand, EQ, and GHG emissions was used to search for the best values of the air distribution factors. Two optimization cases were investigated, the first working with EQ predicted values, while in the second case effluent concentrations were also predicted to calculate EQ and reveal the pollution loading limits violation. Selected solutions decreased the performance indices. The largest improvements were 2.01 %, 0.28 %, and 1.14 % for energy demand, EQ, and GHG emissions. The NARX models succeeded to efficiently model the WWTP main variables, predict performance indices and provide valuable support for operation optimization, leading to diminished energy consumption, improved effluent quality and reduced greenhouse gas emissions.

Life Cycle Assessment of a Domestic Wastewater Treatment Plant Simulated with Alternative Operational Designs

Life cycle assessment (LCA) is a powerful tool to evaluate the environmental impacts of domestic wastewater treatment plant (WWTP) operations. It involves a thorough evaluation of the main characteristics or components of the environment, human health, and resources. However, the literature to date is still lacking analysis on the widely varied designs and operational conditions of full-scale WWTPs. The aim here was to integrate analyses such as LCA, greenhouse gas (GHG) emissions, and energy consumption, when considering the environmental impacts of a full-scale WWTP, which can provide practical outputs to aid decision-making on optimum designs and operational conditions. The Russtmiya domestic WWTP, located in Iraq, was considered as the case study. Three operational alternatives were proposed as solutions to improve the WWTP’s performance, as follows: (1) conventional activated sludge with sand filter (CAS), (2) conventional activated sludge with sand filter and nitrogen removal (CAS-N), and (3) membrane bioreactor (MBR). The operation of such alternatives was investigated through modeling and simulation using GPS-X 8.0.1 software. The energy consumption of each alternative was estimated via GPS-X, while the GHG emissions were estimated using three different methods according to the intergovernmental panel on climate change (IPCC), the United States Environmental Protection Agency (USEPA), and GPS-X software. The OpenLCA software (1.10.3) was used to measure all impact categories at both the midpoint and endpoint levels using various methods. As a conclusion, comparing the three proposed alternatives indicated that: (1) the MBR alternative provided the lowest energy consumption and moderate GHG emissions, and (2) the CAS alternative provided the best environmental performance, particularly in aspects such as ozone depletion, global warming, and climate change, where the lowest GHGs emission values had the major contribution.

A Novel Current Condition Assessment System for Sustainable Management and Operation of Wastewater Treatment Plants

Wastewater treatment is important in terms of human and environmental health in the urban water management cycle. Appropriate methods should be applied based on accurate data in order to ensure efficiency. This study developed a novel current condition assessment system to evaluate the data quality and the implementation level of wastewater treatment plant (WWTP) management and operations practices. The developed system consists of 48 components including the basic processes used in the operation of WWTPs. A unique scoring system is proposed to evaluate the system. The developed system was tested in three pilot WWTPs. The performance of Utility 2 was better than that of the other utilities in terms of data quality and implementation level of methods. However, the scores of Utilities 1 and 2 generally were close to each other. The weaknesses and strengths were determined within the scope of WWTP management in each utility.

Geochemical evaluation of groundwater around Chromepet tannery belt, Southern India

The study appraises the geogenic and anthropogenic contamination of groundwater around tanneries located in Chromepet, Southern India. Thirty-six groundwater samples were examined for major anions, cations and trace metals concentration. The concentration of chromium (Cr), iron (Fe) and lead (Pb) in the study area ranges from 0.04 to 0.51 mg/L, 0.001–1.07 mg/L and 0.001–0.09 mg/L respectively. 97% of the samples exceeded the acceptable range for Cr whereas, lead and iron exceeded 89 and 8% of the samples respectively. The high concentrations of chromium, iron and lead in the groundwater are due to the influence of tannery effluents. The majority of the samples in the study area suggested silicate weathering. The piper plot reveals that 50% of the water samples have Na–Cl type water which is possibly due to anthropogenic sources. The Chadha plot revealed that silicate weathering and ion exchange govern the bulk of groundwater chemistry in freshwater aquifers owing to carbonate precipitation and mineral dissolution. Durov plot highlights that the bulk of the samples has no dominating anion or cation types, indicating simple dissolution or mixing. Saturation Index (SI) reveals that 50% of the samples were supersaturated due to calcite and dolomite precipitation. Principal Component Analysis (PCA) 1 suggested secondary salt discharge from geogenic and anthropogenic processes due to tanning industries. Increasing the performance of wastewater treatment plants is essential to improve the groundwater quality of the region under tannery influence.

Development of IoT-Based Real-Time Monitoring System and LFA to Improve the Efficiency and Performance of Wastewater Treatment Plant in Udayana University Hospital

Indonesia is one of the countries infected by the Coronavirus Disease 2019 (COVID-19) pandemic, which is caused by acute respiratory syndrome virus 2 (SARS-CoV-2). At the end of March 2020, the provincial government of Bali appointed Udayana University Hospital to handle COVID-19 patients because the province has experienced an increase in the number of positive cases. In September 2020, COVID-19 cases in Bali increased by more than 100%, resulting in a higher volume and content of hazardous liquid waste. Furthermore, hazardous liquid waste is the residue of activities that contain substances that can pollute and damage the environment and health, necessitating more efforts in managing the processing of hazardous wastewater produced by the hospital. Based on the background above, this study developed and applied an Internet of Things (IoT) based monitoring system to the Wastewater Treatment Plant (WWTP) in Udayana University Hospital. In principle, the IoT system can be used as a real-time monitoring tool and minimizes direct contact activities of officers’ WWTP sites. Moreover, the Liquid Film Aerator (LFA) was applied to improve the efficiency of WWTP. The developed IoT system successfully monitors pH, DO, and real-time temperature, and the monitoring results were presented in a web-based user interface. The result shows better power usage efficiency than conventional aeration. Furthermore, conventional aeration with a root blower requires 619.8 watts to produce 1 mg/L of DO, while LFA only requires 273.2 watts. The developed systems can be applied to other hospitals or similar wastewater plants that handle COVID-19 cases.

Coupling process-based modeling with machine learning for long-term simulation of wastewater treatment plant operations

Effective treatment of sewage by wastewater treatment plants (WWTPs) are essential to protecting water environment as well as people's health worldwide. However, operation of WWTPs is usually intricate due to precarious influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTPs can provide valuable decision-making support to facilitate their daily operations and management. In this study, we have built a novel hybrid model by combining a process-based WWTP model (GPS-X) with a data-driven machine learning model (Random Forest) to improve the simulation of long-term hourly effluent ammonium-nitrogen concentration of a WWTP. Our study results have shown that the hybrid GPS-X-RF model performs the best with a coefficient of determination (R2) of 0.95 and root mean squared error (RMSE) of 0.23 mg/L, followed by the GPS-X model with a R2 of 0.93 and RMSE of 0.33 mg/L and last the Random Forest model with a R2 of 0.84 and RMSE of 0.41 mg/L. Capable of incorporating wastewater treatment mechanisms and utilizing superior data mining capabilities of machine learning, the hybrid model adapts better to the large fluctuations in influent and operating conditions of the WWTP. The proposed hybrid modeling framework may be easily extended to WWTPs of various size and types to simulate their operations under increasingly variable environmental and operating conditions.

Optimization pollutants removals from wastewater treatment plant using artificial neural networks

Water quality, treatment plant management, and environmental concerns all affect how well a sewage treatment plant performs. Due to the high degree of nonlinearity in the plant as well as the nonuniformity and unpredictability of the influent amount, quality parameters, and operational conditions, modelling the sludge capacity index of the activated sludge method in municipal wastewater treatment plants is a challenging mission. To assess the effectiveness of the al-diwaniyah wastewater treatment plant (WWTP) operation and to estimate quality parameters, the study’s first goal is to improve the WWTP by using artificial neural networks (ANNs). Second, increasing the efficiency of the ANNs model to determine the best WWTP procedure. ANNS were created to predict the sludge volume index (SVI) using the al-diwaniyah WWTP operational and influent quality characteristics. The neural network’s best model for predicting SVI consists of an input node with six input variables, a hidden layer with five nodes, and an output layer with one variable, with an R2 value of 0.965. The outcomes show how effective the right neural network models are at predicting SVI. This is a highly helpful tool that WWTP operators may use in their daily management to improve the effectiveness of the treatment process and the dependability of the WWTP.

Highly efficient carbon assimilation and nitrogen/phosphorus removal facilitated by photosynthetic O2 from algal-bacterial aerobic granular sludge under controlled DO/pH operation

Reducing CO2 emission and energy consumption is crucial for the sustainable management of wastewater treatment plants (WWTPs). In this study, an algal-bacterial aerobic granular sludge (AGS) system was developed for efficient carbon (C) assimilation and nitrogen (N)/phosphorus (P) removal without the need for mechanical aeration. The photosynthetic O2 production by phototrophic organisms maintained the dissolved oxygen (DO) level at 3-4 mg/L in the bulk liquid, and an LED light control system reduced 10–30% of light energy consumption. Results showed that the biomass assimilated 52% of input dissolved total carbon (DTC), and the produced O2 simultaneously facilitated aerobic nitrification and P uptake with the coexisting phototrophs serving as a C fixer and O2 supplier. This resulted in a stably high total N removal of 81 ± 7% and an N assimilation rate of 7.55 mg/(g-MLVSS∙d) with enhanced microbial assimilation and simultaneous nitrification/denitrification. Good P removal of 92–98% was maintained during the test period at a molar ∆P/∆C ratio of 0.36 ± 0.03 and high P release and uptake rates of 10.84 ± 0.41 and 7.18 ± 0.24 mg/(g-MLVSS∙h), respectively. Photosynthetic O2 was more advantageous for N and P removal than mechanical aeration. This proposed system can contribute to a better design and sustainable operation of WWTPs using algal-bacterial AGS.

Enhancing biolipid production and self-flocculation of Chlorella vulgaris by extracellular polymeric substances from granular sludge with CO2 addition: Microscopic mechanism of microalgae-bacteria symbiosis

Microalgae-bacteria symbiotic systems were known to have great potential for simultaneous water purification and resource recovery, among them, microalgae-bacteria biofilm/granules have attracted much attention due to its excellent effluent quality and convenient biomass recovery. However, the effect of bacteria with attached-growth mode on microalgae, which has more significance for bioresource utilization, has been historically ignored. Thus, this study attempted to explore the responses of C. vulgaris to extracellular polymeric substances (EPS) extracted from aerobic granular sludge (AGS), for enhancing the understanding of microscopic mechanism of attached microalgae-bacteria symbiosis. Results showed that the performance of C. vulgaris was effectively boosted with AGS-EPS treatment at 12–16 mg TOC/L, highest biomass production (0.32±0.01 g/L), lipid accumulation (44.33±5.69%) and flocculation ability (20.83±0.21%) were achieved. These phenotypes were promoted associated with bioactive microbial metabolites in AGS-EPS (N-acyl-homoserine lactones, humic acid and tryptophan). Furthermore, the addition of CO2 triggered carbon flow into the storage of lipids in C. vulgaris, and the synergistic effect of AGS-EPS and CO2 for improving microalgal flocculation ability was disclosed. Transcriptomic analysis further revealed up-regulation of synthesis pathways for fatty acid and triacylglycerol that was triggered by AGS-EPS. And within the context of CO2 addition, AGS-EPS substantially upregulated the expression of aromatic protein encoding genes, which further enhanced the self-flocculation of C. vulgaris. These findings provide novel insights into the microscopic mechanism of microalgae-bacteria symbiosis, and bring new enlightenment to wastewater valorization and carbon-neutral operation of wastewater treatment plants based on the symbiotic biofilm/biogranules system.

Artificial Neural Networks Modelling For AL-Rustumiya Wastwater Treatment Plant in Baghdad

In the present research, Artificial Neural Networks (ANNs) were developed for modelling the performance of Al-Rustamiya wastewater treatment plant, Baghdad, Iraq. There were created two models and the outputs were the removal efficiency of BOD and COD parameters. Four main input parameters were selected for modelling, namely Total suspended solids (TSS), Total dissolved solids (TDS), chloride ion (Cl-), and pH. Influent and effluent concentrations of the parameters were collected from Mayoralty of Baghdad for the period from 2011 to 2021. The results of the modelling were in terms of mean square error (MSE) and correlation coefficient (R). The results indicated that the ANNs models were accurately able to predict the removal of the BOD, and COD, and the optimum topology of the ANNs is obtained at 13 neurons in the hidden layer for both with 3.09 MSE, 0.96 and 4.28 MSE, 0.96 R for BOD and COD respectively.

Analysing Pan Evaporation to Understand Wastewater Treatment Plant Performance, A Case Study in A Manufacturing Industry

Water loss has become a problem with the balance of a water system, including one in the industry. General opinion has considered evaporation as one of the main justifications for explaining the water loss, especially in the area with a higher daily temperature. A study was conducted on a wastewater treatment system owned by a manufacturing industry consisting of semi-open-air sedimentation and aeration ponds, which suddenly experienced a significant deficit in its water balance. Problem-solving was performed by 8-Step Problem Solving approach and root causes were confirmed by estimating water evaporation. The actual water evaporation rate (E) was approached by pan evaporation (Epan) using the partial pressure of the water vapour and the pan evaporation coefficient (Kpan). The study revealed that evaporation (1.67±0.59mm.d-1 and 1.72±0.62mm.d-1, for sedimentation pond and aeration pond, respectively) was not the main cause of sudden significant water loss (R2 = .490, p-value .05) and confirmed another root cause. In parallel, a water balance model was constructed and fitted the actual condition (R = .987). A countermeasure was performed against the confirmed root cause followed by a monthly evaluation of water loss using the constructed model with a 3σ threshold value (UCL = 9.55%) which showed the elimination of the problem.

Sewage sludge derived magnetic biochar effectively activates peroxymonosulfate for the removal of norfloxacin

As an undesirable by-product of the wastewater treatment plants (WWTPs), sewage sludge (SS) caused considerable stress on the normal operation of WWTPs, because of its expensive disposal costs. On the other hand, exploiting resource utilization of the residual SS seems to be an attractive approach to resolve this problem. In this study, SS was thermally converted into biochar catalysts (SBC) under oxygen-limiting conditions. By using peroxymonosulfate (PMS) as the chemical oxidant, norfloxacin (NOR) could be effectively removed and the reaction was significantly accelerated after the addition of SBC. Specifically, over 90% of NOR was eliminated by the SBC800/PMS process within 70 min. As evidenced by the comprehensive mechanistic investigations, 1O2 as well as electron-transfer regime primarily participated in the NOR degradation process, while radical species played minor roles. More importantly, the endogenous iron substances endowed SBC with magnetic properties, making the efficient separation of SBC from water available. Eventually, the possible intermediates were tentatively identified by liquid chromatography-mass spectrometry and the corresponding degradation pathways were proposed. Overall, this research developed a novel method to achieve the remediation of NOR-contaminated water and elimination of the potential risks of SS simultaneously.

Characteristics of greenhouse gases emission from wastewater treatment plants operation in China (2009–2016): A case study using operational data integrated method (ODIM)

The reduction of greenhouse gas (GHG) and achieving carbon neutrality in wastewater treatment plants (WWTPs) has gained significant research attention due to China's dual carbon goals. However, the temporal and spatial characteristics of GHG emissions and the reduction potential regarding GHG emissions from WWTPs in China remain significant uncertainties. This study aims to evaluate GHG emissions characteristics over the years using a set of computational methods, and assess the reduction potential from WWTPs based on operational parameters and economic considerations. This study evaluated GHG emissions of more than 1000 WWTPs in China from 2009 to 2016 using operational data integrated methods (ODIM) and emission factors method to estimate direct N2O, direct CH4 emissions and indirect electricity-induced GHG emissions. GHG emissions were analyzed from temporal, spatial and operational parameters aspects. Besides, data envelopment analysis (DEA) was used to analyze WWTPs' efficiency in 2017. The results show that GHG emissions increased from 8163 Gg in 2009 to 14,008 Gg in 2016, with emission intensity maintained in the range of 0.266 kgCO2-eq/m3 to 0.298 kgCO2-eq/m3. Indirect GHG emissions from electricity consumption were the main source, which accounted for 72%–80% of the total emissions. Remarkable differences in GHG emissions were observed among six different regions and 31 provinces. WWTPs with AAO process, large treatment capacity, high loading, low influent COD concentration and high effluent NH3–N concentration contributed to lower GHG emission intensity. Furthermore, the treatment process was correlated significantly with GHG emission intensity, whereas influent COD concentration had the least influence from regression analysis results. DEA evaluation indicated that the average efficiency score in 2016 was only up to 0.240 with 27 WWTPs reaching a full efficiency score (efficiency score above 1.000). Electricity consumption and ammonia nitrogen reduction were the most prioritized indicators which could be changed by −53.55% and 592.60%, respectively, to achieve a full efficiency score. The study provides a valuable reference for policymakers and stakeholders to reduce GHG emissions and achieve carbon neutrality in WWTPs, thus contributing to the larger goal of reducing GHG and mitigating climate change.

Effects of Temperature and Water Types on the Decay of Coronavirus: A Review

The analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) gene copy numbers in wastewater samples can provide quantitative information on Coronavirus Disease-19 (COVID-19) cases within a sewer catchment. However, many wastewater-based epidemiology (WBE) studies have neglected virus decay during the wastewater transportation process in sewers while back-calculating COVID-19 prevalence. Among various sewer condition parameters, wastewater temperature and dilution by fresh/saltwater infiltration may result in a significant change to the virus decay, in terms of both infectivity and Ribonucleic Acid (RNA). This paper reviewed the literature to identify and discuss the effects of temperature and water types (i.e., wastewater, freshwater, and seawater) on coronavirus decay based on the decay rate constants that were collected from published papers. To evaluate the importance of virus decay, a sensitivity analysis was then conducted with decay rates of SARS-CoV-2 RNA based on a WBE back-calculation equation. Finally, the decay rates of coronavirus in wastewater were also compared with those of other viruses to further understand the difference among virus species. The decay of SARS-CoV-2 RNA was found to be less impacted by temperature variation than viable coronaviruses. Nevertheless, WBE back-calculation was still sensitive to the RNA decay rates increased by warm wastewater (i.e., over 26 °C), which could lead to a two-times higher relative variance in estimated COVID-19 prevalence, considering the wastewater temperature variation between 4 and 37 °C in a sewer catchment with a 12-h hydraulic retention time. Comparatively, the sensitivity of the WBE estimation to the enveloped SARS-CoV-2 was greater than nonenveloped enteric viruses, which were less easily degradable in wastewater. In addition, wastewater dilution by stormwater inflow and accompanied cold weather might alleviate the decay of coronavirus infectivity, thus increasing the potential risk of COVID-19 transmission through wastewater. Overall, this paper aims to better understand the impact of in-sewer processes on coronavirus decay and its potential implications for WBE. The outcome could quantitatively inform WBE and improve awareness of the increased risk of COVID-19 infection via wastewater during heavy rainfall events. Given the identified scarcity of data available for coronavirus decay in salt water or with chemical additions, future research on the fate of SARS-CoV-2 subjected to chemical dosing for sewer or wastewater treatment plant operations is recommended.

Under-loaded operation of an anaerobic-anoxic-aerobic system in dry and wet weather dynamics to prevent overflow pollution: Impacts on process performance and microbial community

Effects of low hydraulic loading rate (HLR) in dry weather and high HLR in wet weather on pollutant removal, microbial community, and sludge properties of a full-scale wastewater treatment plant (WWTP) were extensively studied to explore the risk of under-loaded operation for overflow pollution control. Long-term low HLR operation had an insignificant effect on the pollutant removal performance of the full-scale WWTP, and the system could withstand high-load shocks in wet weather. Low HLR resulted in higher oxygen and nitrate uptake rate due to the storage mechanism under the alternating feast/famine condition, and lower nitrifying rate. Low HLR operation enlarged particle size, deteriorated floc aggregation and sludge settleability, and reduced sludge viscosity due to the overgrowth of filamentous bacteria and inhibition of floc-forming bacteria. The remarkable increase in Thuricola and the contract morphology of Vorticella in microfauna observation confirmed the risk of flocs disintegration in low HLR operation.

Improvement of the Treatment Performance of the Conventional Wastewater Treatment Plant: A Case Study of the Central Wastewater Treatment Plant in Ulaanbaatar, Mongolia

This study is carried out within the framework of the requirements of ensuring reliable operation of the Central Wastewater Treatment Plant of Ulaanbaatar to increase the treatment performance and mitigate the adverse impacts of the current poor performance on nature and ecology. The dynamic of the current influent was studied in order to find the best-suited solution as a means of improvement for the WWTP. As an immediate and fast-to-build solution, an equalization tank and pre-aeration facilities were proposed. The positive effect of the facilities on the biological treatment stage and the overall treatment performance was estimated both theoretically and empirically. The results showed that the overall performance of the treatment for BOD5 and TSS will be increased by up to 30% and 10%, respectively. Furthermore, the high fluctuation problems the central WWTP in terms of hydraulic and pollution load will be positively affected by the intervention, eliminating the main cause of the current poor treatment performance.