An integrated approach for enhancing the overall performance of constructed wetlands in urban areas

A Copula-based spatiotemporal probabilistic model for heavy metal pollution incidents in drinking water sources

Water pollution incidents pose a significant threat to the safety of drinking water supplies and directly impact the quality of life of the residents when multiple pollutants contaminate drinking water sources. The majority of drinking water sources in China are derived from rivers and lakes that are often significantly impacted by water pollution incidents. To tackle the internal mechanisms between water quality and quantity, in this study, a Copula-based spatiotemporal probabilistic model for drinking water sources at the watershed scale is proposed. A spatiotemporal distribution simulation model was constructed to predict the spatiotemporal variations for water discharge and pollution to each drinking water source. This method was then applied to the joint probabilistic assessment for the entire Yangtze River downstream watershed in Nanjing City. The results demonstrated a significant negative correlation between water discharge and pollutant concentration following a water emergency. The water quantity-quality joint probability distribution reached the highest value (0.8523) after 14 hours of exposure during the flood season, much higher than it was (0.4460) during the dry season. As for the Yangtze River downstream watershed, five key risk sources (N1-N5) and two high-exposure drinking water sources (W3-W4; AEI=1) should be paid more attention. Overall, this research highlights a comprehensive mode between water quantity and quality for drinking water sources to cope with accidental water pollution.

Quantitative prediction of the hydraulic performance of free water surface constructed wetlands by integrating numerical simulation and machine learning

Quantitative prediction of the design parameter-influenced hydraulic performance is significant for optimizing free water surface constructed wetlands (FWS CWs) to reduce point and non-point source pollution and improve land utilization. However, owing to limitations of the test conditions and data scale, a quantitative prediction model of the hydraulic performance under multiple design parameters has not yet been established. In this study, we integrated field test data, mechanism model, statistical regression, and machine learning (ML) to construct such quantitative prediction models. A FWS CW numerical model was established by integrating 13 groups of trace data from field tests. Subsequently, training, test and extension datasets comprising 125 (5^3), 25 (L₂₅(5⁶)) and 16 (L₁₆(4⁴)) data points, respectively, were generated via numerical simulation of multi-level value combination of three quantitative design parameters, namely, water depth, hydraulic loading rate (HLR), and aspect ratio. The short circuit index (φ₁₀), Morrill dispersion index (MDI), hydraulic efficiency (λ) and moment index (MI) were used as representative hydraulic performance indicators. Training set with large samples were analyzed to determine the variation rules of different hydraulic indicators. Based on the control variable method, φ₁₀, λ, and MI grew exponentially with increasing aspect ratio whereas MDI showed a decreasing trend; with increasing water depth, φ₁₀, λ, and MI showed polynomial decreases whereas MDI increased; with increasing HLR, φ₁₀, λ, and MI slowly increased linearly whereas MDI showed the opposite trend. Finally, we constructed models based on multivariate nonlinear regression (MNLR) and ML (random forest (RF), multilayer perceptron (MLP), and support vector regression. The coefficients of determination (R²) of the MNLR and ML models fitting the training and test sets were all greater than 0.9; however, the generalization abilities of different models in the extension set were different. The most robust MLP, MNLR without interaction term, and RF models were recommended as the preferred models to hydraulic performance prediction. The extreme importance of aspect ratio in hydraulic performance was revealed. Thus, gaps in the current understanding of multivariate quantitative prediction of the hydraulic performance of FWS CWs are addressed while providing an avenue for researching FWS CWs in different regions according to local conditions.

Chemical and biological tracking in decentralized sanitation systems: The case of artificial constructed wetlands

Given that the social and economic sustainability of rural areas is highly based on the protection of natural resources, biodiversity and human health, simple-operated and cost-effective wastewater treatment systems, like artificial constructed wetlands (CWs), are widely proposed for minimizing the environmental and human impact of both water and soil pollution. Considering that the optimization of wastewater treatment processes is vital for the reduction of effluents toxic potential, there is imperative need to establish appropriate management strategies for ensuring CW performance and operational efficiency. To this end, the present study aimed to assess the operational efficiency of a horizontal free water surface CW (HFWS-CW) located in a world heritage area of Western Greece, via a twelve-month duration Toxicity Identification Evaluation (TIE)-like approach, including both chemical and biological tracking tools. Conventional chemical tracking, by means of pH, conductivity, total COD, and nitrogen-derived components, like nitrates and ammonia-nitrogen, were monthly recorded in both influents and effluents to monitor whether water quality standards are maintained, and to assess potent CW operational deficiencies occurring over time. In parallel, Whole Effluent Toxicity (WET) bioassays were thoroughly applied, using freshwater algae and higher plant species (producers), crustaceans and rotifers (consumers), as well as human lymphocytes (in terms of Cytokinesis Block Micronucleus assay) to evaluate the acute and short-term toxic and hazardous potential of both influents and effluents. The integrated analysis of abiotic (physicochemical parameters) and biotic (toxic endpoints) parameters, as well as the existence of "cause-effect" interrelations among them, revealed that CW operational deficiencies, mainly based on poorly removal rates, could undermine the risk posed by treated sewage. Those findings reinforce the usage of WET testing, thus giving rise to the importance of applying appropriate water management strategies and optimization actions, like oxygen enrichment of surface and bottom of HFWS-CW basins, expansion of the available land, the enhancement of bed depth and seasonal harvesting of plants, for ensuring sewage quality, in favor of water resources protection and sustainable growth in rural areas.

Spatiotemporal characteristics of hydraulic performance and contaminant transport in treatment wetlands

Wetlands have been proven to be efficient and cost-effective ecological treatment systems for municipal and domestic wastewater. It is essential to understand the hydrodynamic characteristics and the related contaminant transport process to optimize the treatment efficiency in free water surface wetlands. Thirty-six tracer experiments were conducted under different water depths and wetland configurations, such as installing static obstacles and dynamic disturbances. The particle image velocimetry and a novel color-concentration transform method were developed to reveal spatiotemporal flow velocity and residence time distribution. The flow fields are categorized into short-circuiting, circulation flow, dead zones, and the corresponding contaminant transport phenomena are flow advection, shear dispersion, and eddy diffusion. The flow circulation dominates the formation of the dead zone and decreases contaminant dissipation. The flow path could have effectively meandered, and the dead zone and short-circuiting could be reduced by increasing the length of the obstacles. The improved flow field is close to the plug flow, indicating enhanced hydraulic performance and treatment efficiency. The dynamic disturbance reflects the movement of fish in wetlands and provides momentum flux to promote the dissipation of pollutants in the circulation field and dead zone, alleviating the deterioration of water quality caused by pollutant accumulation. The findings of this study may provide a critical reference for the optimal design of wetlands.

Effects of design parameters, microbial community and nitrogen removal on the field-scale multi-pond constructed wetlands

Ecological multi-pond constructed wetlands (CWs) are an alternative wastewater treatment technology for nitrogen removal from non-point source pollution. As an important component of nitrogen cycles in the field-scale CWs, microorganisms are affected by design parameters. Nevertheless, the mechanism of design parameters affecting the distribution of microbial community and removal performance remains largely unexplored. In this study, satisfactory nitrogen removal performance was obtained in three multi-pond CWs. The highest mass removal rate per square meter (1104.0 mg/m²/day) and mass removal rate per cubic meter (590.2 mg/m³/day) for total nitrogen removal were obtained in the XY CW system during the wet season. The changes in seasonal parameters accounted for different removal performances and distributions of the microbial community. The combination of wastewater treatment technologies in the XY CW system consisting of ponds, CWs, and eco-floating treatment wetlands enriched the abundances of nitrogen-related functional genera. Correlation network analysis further demonstrated that longer hydraulic residence time and higher nitrogen concentration could intensify the enrichment of nitrogen-related functional genera. Regulating the combination of wastewater treatment technologies, the nitrogen concentration of influent, hydraulic loading rate, and water depth might promote the accumulation of microbial communities and enhance nitrogen removal. Macroscopical spatial/temporal regulation were proposed to enhance the treatment of non-point source pollution. The clarification of driving mechanism on design parameters, microbial community, and removal performance provided a novel perspective on the long-term maintenance of purification performance, practically sustainable applications, and scientific management of field-scale multi-pond CWs.

Urban Wetlands: A Review on Ecological and Cultural Values

Wetlands are a critical part of natural environments that offer a wide range of ecosystem services. In urban areas, wetlands contribute to the livability of cities through improving the water quality, carbon sequestration, providing habitats for wildlife species, reducing the effects of urban heat islands, and creating recreation opportunities. However, maintaining wetlands in urban areas faces many challenges, such as the reduction of hydrological functions, changed water regimes due to barriers, contamination by wastewater, habitat loss due to land-use change, and loss of biodiversity due to the entry of alien species. In this article, we review the theoretical background of wetlands in urban areas through the existing studies in the literature. We provide knowledge on urban wetlands and highlight the benefits of these wetlands in urban areas. These benefits include sustainability, biodiversity, urban heat islands, social perception, and recreation values. We also summarize the objectives, methodologies, and findings of the reviewed articles in five tables. In addition, we summarize the critical research gaps addressed in the reviewed articles. Our review study addresses the research gaps by performing a rigorous analysis to identify significant open research challenges, showing the path toward future research in the field. We further discuss and highlight the role of policymakers and stakeholders in preserving wetlands and finally present our conclusions.

Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models

Effectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa–Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rates, and fluid and cuttings properties. It is observed that, in many cases, the data-driven model significantly outperforms the mechanistic models, which provides a very promising direction for real-time drilling optimization and automation. After the neural network is proven to work effectively, an optimization attempt to estimate flow rate and pipe rotation speed is introduced using a genetic algorithm. The decision is made considering minimizing the required total energy for this process. This approach may be used as a design tool to identify the required flow rate and pipe rotation speed to acquire effective hole cleaning while consuming minimal energy.