Exploring total economic values in an emerging urban circular wastewater system.

Circular water management has the potential to close resource and material loops within and across value chains. In the water industry, circular municipal wastewater management through industrial urban symbiosis (IUS) is recognized as a solution to overcome water scarcity in urban environments. IUS involves collaboration between actors with different organizational backgrounds, which can lead to inherent risks of conflicting goals. This study explores how different values drive various organizations to participate in an emerging circular wastewater collaboration. The study comprises a literature review of 34 scientific articles and a case study of a potential circular wastewater system through IUS in Simrishamn, Sweden. It presents an interdisciplinary framework based on the total economic value concept and organizational archetypes for examining actor values in circular wastewater management. This framework provides a novel approach for assessing different values and how they may compete or align. It can also identify the absence of certain values, enabling the achievement of a minimum level of value coherence amongst different actors, and thereby increasing the sustainability and effectiveness of circular wastewater collaborations. Therefore, careful planning and stakeholder interaction, in accordance with economic value perspectives, can enhance the legitimacy and policy development of circular solutions.

Quantification of industrial wastewater discharge from the major cities in Sichuan province, China.

In this study, we used spatial autocorrelation, Environmental Kuznets Curve (EKC), and Logarithmic Mean Divisia Index model to study the spatial characteristics and driving factors of industrial wastewater discharge in Sichuan province (2003-2018). We showed that the amount of industrial wastewater discharge in Sichuan province for the period was reduced from 116,580 to 42,064.96 million tons as observed from the Moran index ranging from -0.310 to 0.302. We identified that the EKC type of Sichuan province was monotonically decreasing and six types of the EKC (monotonically decreasing, monotonically increasing, U, N, inverted U, and inverted N, shape) in 18 major cities. The technical effect (from -0.0964 to -8.8912) can reduce the discharge of industrial wastewater, while the economy effect (0.2948-5.882), structure effect (0.0892-4.5183), and population effect (from -0.0059 to 0.2873) can promote the industrial wastewater discharge. Our findings suggest that industrial wastewater discharge was reduced and changed from non-significant dissociation to non-significant agglomeration to non-significant dissociation during the study period. Furthermore, technical management upgrade is the primary driver in Sichuan province to reduce industrial wastewater discharge during this period.

Insights of environmental impact assessment reports for industrial parks: wastewater quantity prediction aspect.

While the contribution of industrial parks to the socio-economic aspect has been widely acknowledged over the past two decades in Vietnam, the problem of environmental pollution due to the wastes generated from industrial parks, especially wastewater, has still been an emerging issue, which places a great pressure on the Government. The wastewater generation from industrial parks was ordinarily required to report in all environmental impact assessment (EIA) reports as a base for the construction of on-site wastewater treatment plants within the industrial park. In Vietnam, this data was, however, often higher than the actual generated number due to inaccurate technical guidelines on predicting wastewater generation and many different methods to be applied. This study aimed to evaluate different approaches used to calculate the effluent in the industrial parks. The results showed that all of the four approaches (i.e., previous operation-based, water-based, wastewater-based, or experience-based methods) rendered significant gaps between predicted and actual values. None can be acceptable for use at present. A revision of technical guideline should be conducted to provide more detailed instruction for the better prediction. This shall minimize the investment capital and increase the efficiency of industrial parks in pollution control.

Monitoring Consumption of Common Illicit Drugs in Kuala Lumpur, Malaysia, by Wastewater-Cased Epidemiology.

Southeast Asian countries including Malaysia play a major role in global drug trade and abuse. Use of amphetamine-type stimulants has increased in the past decade in Malaysia. This study aimed to apply wastewater-based epidemiology for the first time in Kuala Lumpur, Malaysia, to estimate the consumption of common illicit drugs in urban population. Influent wastewater samples were collected from two wastewater treatment plants in Kuala Lumpur in the summer of 2017. Concentrations of twenty-four drug biomarkers were analyzed for estimating drug consumption. Fourteen drug residues were detected with concentrations of up to 1640 ng/L. Among the monitored illicit drugs, 3,4-methylenedioxy-methamphetamine (MDMA) or ecstasy had the highest estimated per capita consumptions. Consumption and dose of amphetamine-type stimulants (methamphetamine and MDMA) were both an order of magnitude higher than those of opioids (heroin and codeine, methadone and tramadol). Amphetamine-type stimulants were the most prevalent drugs, replacing opioids in the drug market. The prevalence trend measured by wastewater-based epidemiology data reflected the shift to amphetamine-type stimulants as reported by the Association of Southeast Asian Nations Narcotics Cooperation Center. Most of the undetected drug residues were new psychoactive substances (NPSs), suggesting a low prevalence of NPSs in the drug market.

Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network.

Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount of incoming wastewater in WWTPs, wastewater engineers face challenges arising from numerous complexities and uncertainties, such as the nonlinear precipitation-runoff relationships in combined sewer systems, unpredictability due to aging infrastructure, and frequently inconsistent data quality. To address such challenges, a time series analysis model (i.e., the autoregressive integrated moving average, ARIMA) and an artificial neural network model (i.e., the multilayer perceptron neural network, MLPNN) were developed for predicting wastewater inflow. A case study of the Barrie Wastewater Treatment Facility in Barrie, Canada, was carried out to demonstrate the performance of the proposed models. Fifteen-minute flow data over a period of 1 year were collected, and the resampled daily flow data were used to train and validate the developed models. The model performances were examined using root mean square error, mean absolute percentage error, coefficient of determination, and Nash-Sutcliffe efficiency. The results indicate that both models provided reliable forecasts, while ARIMA showed a slightly better performance than MLPNN in this case study. The proposed models can provide useful decision support for the optimization and management of WWTPs.

Forecasting China's wastewater discharge using dynamic factors and mixed-frequency data.

Forecasting wastewater discharge is the basis for wastewater treatment and policy formulation. This paper proposes a novel mixed-data sampling regression model, i.e., combination-MIDAS model to forecast quarterly wastewater emissions in China based on dynamic factors at different frequencies. The results show that a significant auto-correlation for wastewater emissions exists and that water consumption per ten thousand gross domestic product is the best predictor of wastewater emissions. The forecast performances of the combination-MIDAS models are robust and better than those of the benchmark models. Therefore, the combination-MIDAS models can better capture the characteristics of wastewater emissions, suggesting that the proposed method is a good method to deal with model misspecification and uncertainty for the control and management of wastewater discharge in China.

Wastewater-based epidemiology as a novel assessment approach for population-level metal exposure.

Pollution by heavy metals and metalloids is detrimental to human health due to their toxic, genotoxic, and carcinogenic effects. The traditional approach to assess the extent of environmental and occupational exposures of metals is human biomonitoring (HBM). This method has several limitations, including invasiveness, sampling bias, cost- and time-intensiveness, and ethical issues. This suggests the need for a more robust, non-invasive, epidemiological tool for assessment of exposure to metals and their public health effects. Recently, wastewater-based epidemiology (WBE) has been suggested and utilized as a novel approach to accurately determine the extent of exposure to multiple substances on the population level. We suggest the potential application of WBE to the study of metal exposure on the population level, including possible biomarkers for wastewater analysis of 10 metals belonging to three categories according to health effects and nutritional benefits, and its public health implications. Similar to previous studies of exposure to regulated or illegal drugs, unregulated legal substances, and pesticides, WBE can be applied to the study of metal exposure in a given community. Parental substance biomarkers (PSBs), metabolic substance biomarkers (MSBs), and non-substance biomarkers (NSBs) of 10 common metals are available for consideration in wastewater analysis. The use of WBE would allow for the interpretation of the relationship between metal exposure and population health, reveal synergistic effects of different health factors, and model public health risks under different scenarios.

Temporal profile of illicit drug consumption in Guangzhou, China monitored by wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has been widely used as a complementary method for estimating consumption of illicit drugs in the population. Temporal drug consumption estimates derived from WBE can provide important information for law enforcement and public health authorities in understanding changes in supply and demand of illicit drugs, but currently lacking in China. In this study, influent wastewater samples from a municipal sewage treatment plant in Guangzhou, China were collected for 8 weeks to investigate the temporal change in consumption of six illicit drugs in the catchment. The results indicated that methamphetamine and ketamine were the dominant illicit drugs in Guangzhou with the per capita use of 14.7-470.7 mg/day/1000 people and 64.9-673.7 mg/day/1000 people, respectively. No distinct weekly patterns were observed for illicit drug consumption in Guangzhou, indicating that drug users are likely to be regular ones. Further assessment about the impact of public holidays on the consumption behavior of drugs showed little impact for ketamine (p = 0.689), but higher consumptions of methamphetamine (p = 0.003) and cocaine (p = 0.027) were observed during public holidays than the control period. The considerable decrease in drug consumption observed in October 2017 compared with January and May 2017 was possibly the consequence of law enforcement action.

Constructed wetlands for winery wastewater treatment: A comparative Life Cycle Assessment.

A Life Cycle Assessment was carried out in order to assess the environmental performance of constructed wetland systems for winery wastewater treatment. In particular, six scenarios which included the most common winery wastewater treatment and management options in South-Western Europe, namely third-party management and activated sludge systems, were compared. Results showed that the constructed wetland scenarios were the most environmentally friendly alternatives, while the third-party management was the worst scenario followed by the activated sludge systems. Specifically, the potential environmental impacts of the constructed wetlands scenarios were 1.5-180 and 1-10 times lower compared to those generated by the third-party and activated sludge scenarios, respectively. Thus, under the considered circumstances, constructed wetlands showed to be an environmentally friendly technology which helps reducing environmental impacts associated with winery wastewater treatment by treating winery waste on-site with low energy and chemicals consumption.

A fuzzy multi-stakeholder socio-optimal model for water and waste load allocation.

This study proposes a fuzzy multi-stakeholder socio-optimal methodology for joint water and waste load allocation (WWLA) in river systems while addressing upstream flow uncertainty and different social choice rules (SCRs). QUAL2Kw, as the numerical river water quality model, is executed for various scenarios of water and waste loads to construct a comprehensive dataset of plausible settings, which is in turn used to train a meta-model in the form of multivariate linear regressions. The river upstream flow as the main uncertain parameter is assessed by fuzzy transformation method (FTM). Then, for different confidence levels of fuzzy uncertain input, the meta-model is linked with the non-dominated sorting genetic algorithm (NSGA-II) multi-objective optimization model to generate trade-off curves among the stakeholders' utility functions. Subsequently, five SCRs are utilized at each confidence level to determine the fuzzy interval solutions for each objective. Next, the possibility degree method is applied to rank the fuzzy interval solutions in each α-cut level. Finally, considering the priorities of all stakeholders, the fallback bargaining method is used to specify the most appropriate SCR in each confidence level. Application of the proposed methodology in Kor River, Iran, shows its efficacy to realize the socio-optimal WWLA scenario(s) among different stakeholders.

High rate filtration for local treatment of combined sewer overflow.

Combined sewer overflows (CSOs) pollute receiving waters and have a negative impact on ecosystem services. In urban areas rehabilitation of the sewer system to avoid CSOs is associated with high investment costs. Furthermore, not all CSOs can be closed due to the need for hydraulic reliability of the system. Local treatment of CSO with high rate filtration offers an alternative to rehabilitation of the sewer system that is flexible with respect to design and has lower investment cost than separating sewage and storm water runoff. Results from DESSIN, a 4-year EU demonstration project, are presented. The results showed on average 50% removal of particulate matter during CSO events, with higher removal (80%) in the initial first flush period. Other constituents, for example heavy metals, were removed through their association with particles. Potential impacts on ecosystem services in the catchment and the sustainability of the solution were assessed.

Scale and process design for sewage treatment plants in airports using multi-objective optimization model with uncertain influent concentration.

The treatment of airport sewage has posed many novel challenges because of its huge impact on the surrounding environment. This paper proposes a multi-objective decision model to optimize the scale design and process selection of sewage treatment plants in airports. In this model, we consider the conflict among the process cost, environmental protection, and benefits of recycled water. In addition, the uncertainty in influent concentration and passenger throughput is also incorporated. Airport sewage treatment has its own unique features, such as the concentration of airport sewage is higher than that of ordinary urban sewage, the change in passenger throughput impacts the volume of the airport sewage treatment, and the utilization rate of the entire sewage treatment plant must be higher than or equal to 70%. Only in this case can the airport sewage treatment plant pass the acceptance test. The Tianfu International Airport, the largest civil transportation hub airport project in southwestern China, is used to prove the efficiency of the proposed model. Finally, some significant insights are suggested for the design of wastewater treatment plants in airports.

Comparative study of different wavelet-based neural network models to predict sewage sludge quantity in wastewater treatment plant.

In this study, artificial neural networks (ANNs) including feed forward back propagation neural network (FFBP-NN) and the radial basis function neural network (RBF-NN) were applied to predict daily sewage sludge quantity in wastewater treatment plant (WWTP). Daily datasets of sewage sludge have been used to develop the artificial intelligence models. Six mother wavelet (W) functions were employed as a preprocessor in order to increase accuracy level of ANNs. In this way, a 4-day lags were considered as input variables to conduct training and testing stages for the proposed W-ANNs. To compare performance of W-ANNs with traditional ANNs, coefficient of correlation (R), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) were considered. In the case of all wavelet functions, it was found that W-FFBP-NN (R = 0.99 and MAE = 5.78) and W-RBF-NN (R = 0.99 and MAE = 6.69) models had superiority to the FFBP-NN (R = 0.9 and MAE = 21.41) and RBF-NN (R = 0.9 and MAE = 20.1) models. Furthermore, the use of DMeyer function to improve ANNs indicated that W-FFBP-NN (RMSE = 7.76 and NSE = 0.98) and W-RBF-NN (RMSE = 9.35 and NSE = 0.98) approaches stood at the highest level of precision in comparison with other mother wavelet functions used to develop the FFBP-NN and RBF-NN approaches. Overall, this study proved that application of various mother wavelet functions into architecture of ANNs led to increasing accuracy of artificial neural networks for estimation of sewage sludge volume in the WWTP.

Temporal variability of suspended solids in construction runoff and evaluation of time-paced sampling strategies.

The construction sites have been considered the type of land use with the highest pollution potential, especially due to the erosion of exposed soil surfaces. The runoff monitoring of the construction site was carried out since June 2011 through December 2015. Based on land use land cover (LULC) classification, the monitoring period was divided into active and post-construction phases. Total suspended solids (TSS) showed evident inter-phase variability in average annual event mean concentration (AAEMC) and wash-off pattern. We suggested that stringent runoff control measures should be adopted during active construction phase. Similarly, Personalized Computer Storm Water Management Model (PCSWMM) was applied to evaluate the performance of the time-paced discrete and composite sampling scheme in continuously changing LC scenario. It was found that even though the time-paced composite sampling scheme is more cost effective, it showed lower performance in EMC estimation when compared with the time-paced discrete sampling approach. The results also showed that the storm event monitored at a time discrete frequency of 5 min, 10 min, and 15 min, the maximum expected mean bias will be under the accepted level of 10% of the true EMC value. We concluded that construction phase-specific modifications in sampling scheme provides a view to generate near accurate estimates.

Distribution of polychlorinated biphenyls in effluent from a large municipal wastewater treatment plant: Potential for bioremediation?

This study involved an evaluation of the potential for bioremediation of polychlorinated biphenyls (PCBs) in the effluent from a large municipal wastewater treatment plant. It was focused on the presence of PCBs in two types of effluents: the continuous effluent present during dry weather conditions and the intermittently present effluent that was present during wet weather due to incoming stormwater. The annual discharge of PCBs for both types of effluent was calculated based on a five-year dataset (2011-2015). In addition, the toxicity and bioremediation potential of the PCBs in the effluent were also assessed. It was found that the continuous effluent was responsible for the majority of the discharged PCB into the receiving river (1821 g for five years), while the intermittent effluent contributed 260 g over the five years. The average number of chlorine per biphenyl for the detected PCB congeners showed a 19% difference between the two types of effluent, which indicated a potential for organohalide respiration of PCBs during the continuous treatment. This was further supported by a high level of tri-, tetra- and penta-chlorinated congeners accounting for 75% of the anaerobically respired PCBs. Potential for aerobic degradation and thus biomineralization of PCBs was identified for both effluents. Furthermore, toxicity of 12 dioxin-like PCBs showed that normal operation of the wastewater reduced the toxicity throughout the wastewater treatment plant.

Statistical regression modeling for energy consumption in wastewater treatment.

Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand (COD) reduction was the response variable of interest. Via the proposed approach, the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen (TN)-rich wastewater, and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.

Bioavailability of mercury in power plant wastewater and ambient river samples: Evidence that the regulation of total mercury is not appropriate.

Mercury (Hg) is a neurotoxin that can cause debilitating effects to human and environmental receptors under high exposure conditions. For industrial and municipal point sources that discharge Hg, wastewater limitations on total Hg (THg) concentrations or loads are typical. While this regulatory practice provides simplicity for regulated industry and water resource agencies (i.e., for analytical detection and reporting purposes), it ignores the important considerations of speciation and bioavailability. In this study, water samples were collected from multiple power plant wastewater, simulated mixing zone, and ambient river locations (N = 10 to 20) and were analyzed for bioavailable Hg forms (methylmercury and acid-labile Hg, or BHg), THg, and dissolved Hg. The median concentration of THg in wastewater, mixing zone, and ambient river samples was 7.1, 5.3, and 2.3 ng/L, respectively. The percentages of THg as BHg (median values) were 18.7%, 29.3%, and 8.5% for wastewater, mixing zone, and ambient river samples. The percentages of methylmercury (MeHg) as THg were not statistically different between paired ambient and mixing zone samples (P > 0.05); this result indicates that wastewater did not increase the MeHg fraction when mixed with ambient water. Multiple regression analysis indicated that variation in THg for combined wastewater and mixing zone samples could be adequately explained by pooled water quality parameters (total suspended solids, total dissolved solids, sulfate, total organic carbon, pH, specific conductivity; r2 = 0.51; P < 0.05); however, no significant regression relationships were apparent for the percentage of BHg. These results, at least for the wastewater samples evaluated, indicate that regulating THg is likely overly conservative, and mechanisms to regulate the bioavailable forms of Hg are needed. If Hg fish tissue monitoring data indicate that concentrations are less than consumption thresholds, metal translator methodologies or bioavailability-based criterion techniques (as currently used for non-Hg trace elements) should be allowed for Hg. Integr Environ Assess Manag 2019;15:142-147. © 2018 SETAC.