Mechanistic understanding of the pollutant removal and transformation processes in the constructed wetland system

Constructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): Evidence, mechanisms, and modelling

Per- and poly-fluoroalkyl substances (PFAS) have emerged as newly regulated micropollutants, characterised by extreme recalcitrance and environmental toxicity. Constructed wetlands (CWs), as a nature-based solution, have gained widespread application in sustainable water and wastewater treatment and offer multiple environmental and societal benefits. Despite CWs potential, knowledge gaps persist in their PFAS removal capacities, associated mechanisms, and modelling of PFAS fate. This study carried out a systematic literature review, supplemented by unpublished experimental data, demonstrating the promise of CWs for PFAS removal from the influents of varying sources and characteristics. Median removal performances of 64, 46, and 0 % were observed in five free water surface (FWS), four horizontal subsurface flow (HF), and 18 vertical flow (VF) wetlands, respectively. PFAS adsorption by the substrate or plant root/rhizosphere was deemed as a key removal mechanism. Nevertheless, the available dataset resulted unsuitable for a quantitative analysis. Data-driven models, including multiple regression models and machine learning-based Artificial Neural Networks (ANN), were employed to predict PFAS removal. These models showed better predictive performance compared to various mechanistic models, which include two adsorption isotherms. The results affirmed that artificial intelligence is an efficient tool for modelling the removal of emerging contaminants with limited knowledge of chemical properties. In summary, this study consolidated evidence supporting the use of CWs for mitigating new legacy PFAS contaminants. Further research, especially long-term monitoring of full-scale CWs treating real wastewater, is crucial to obtain additional data for model development and validation.

A nature-based closed-loop wastewater treatment system at vehicle-washing facilities: From linear to circular economy

Pakistan, among the top five most water-stressed nations globally, grapples with water scarcity owing to inadequate treatment infrastructure and groundwater overextraction. We demonstrate a successful nature-based closed-loop system to treat wastewater from urban vehicle-washing facilities, previously reliant on groundwater. An eco-friendly integrated system containing floating treatment wetlands (FTWs), subsurface flow constructed wetlands (SSF-CWs), and sand filtration (SF) was designed and installed at three vehicle-washing facilities for wastewater treatment and reuse in a loop. While the system is still operational after years, a consistent and significant reduction in water quality indicators is recorded, successfully meeting the national environmental quality standards of Pakistan. By reducing per unit water treatment costs to as low as $0.0163/m³ and achieving payback periods under a year, the embrace of these closed-loop strategies vividly underscores the imperative of transitioning to a circular economy in the domains of wastewater treatment and resource conservation.

An experimental and prediction modeling study on water lettuce (Pistia stratiotes L.) assisted heavy metals removal from glass industry effluent

The glass manufacturing industry produces hazardous effluent that is difficult to manage and causes numerous environmental problems when disposed of in the open. In this study, an attempt was made to study the phytoremediation feasibility of water lettuce (Pistia stratiotes L.), a free-floating aquatic macrophyte, for the removal of six heavy metals from glass industry effluent (GIE) at varying concentrations (0, 25, 50, 75, and 100%). After a 40-day experiment, the results showed that 25% GIE dilution showed maximum removal of heavy metals i.e., Cu (91.74%), Cr (95.29%), Fe (86.47%), Mn (92.95%), Pb (87.10%), and Zn (91.34%), respectively. The bioaccumulation, translocation, and Pearson correlation studies showed that the amount of heavy metals absorbed by vegetative parts of P. stratiotes was significantly correlated with concentrations. The highest biomass production, chlorophyll content, relative growth rate, and biomass productivity were also noted in the 25% GIE treatment. Moreover, the multiple linear regression models developed for the prediction of heavy metal uptake by P. stratiotes also showed good performance in determining the impact of GIE properties. The models showed a high coefficient of determination (R2 > 0.99), low mean average normalizing error (MANE = 0.01), and high model efficiency (ME > 0.99) supporting the robustness of the developed equations. This study outlined an efficient method for the biological treatment of GIE using P. stratiotes to reduce risks associated with its unsafe disposal.

Evaluating the potential of Abelmoschus esculentus, Solanum melongena, and Capsicum annuum spp. for nutrient and microbial reduction from wastewater in hybrid constructed wetland

Utilizing engineered wetlands for the cultivation of vegetables can help to overcome the problems of water and food scarcity. These wetlands are primarily designed for wastewater treatment, and their efficiency and effectiveness can be improved by selecting an appropriate substrate. To investigate the potential for nutrient and microbial removal, the Abelmoschus esculentus, Solanum melongena, and Capsicum annuum L. plants were selected to grow in a hybrid constructed wetland (CW) under natural conditions. The removal efficiency of the A. esculentus, S. melongena, and C. annuum L. in the CW system varied between 59.8 to 68.5% for total phosphorous (TP), 40.3 to 53.1% for ammonium (NH4+), and 33.6 to 45.1% for total nitrogen (TN). The influent sample contained multiple pathogenic bacteria, including Alcaligenes faecalis, Staphylococcus aureus, and Escherichia coli, with Capsicum annuum exhibiting a positive association with 7 of the 11 detected species, whereas microbial removal efficiency was notably higher in the S. melongena bed, potentially attributed to temperature variations and plant-facilitated oxygen release rates. While utilizing constructed wetlands for vegetable cultivation holds promising potential to address the disparity between water and food supply and yield various environmental, economic, and social benefits, it is crucial to note that the wastewater source may contain heavy metals, posing a risk of their transmission to humans through the food chain.

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

Microplastics in landfill leachate: Occurrence, health concerns, and removal strategies

Landfills are commonly used to manage solid waste, but they can contribute to microplastic (MPs) pollution. As plastic waste degrades in landfills, MPs are released into the surrounding environment, contaminating soil, groundwater, and surface water. This poses a threat to human health and the environment, as MPs can adsorb toxic substances. This paper provides a comprehensive review of the degradation process of macroplastics into microplastics, the types of MPs found in landfill leachate (LL), and the potential toxicity of microplastic pollution. The study also evaluates various physical-chemical and biological treatment methods for removing MPs from wastewater. The concentration of MPs in young landfills is higher than in old landfills, and specific polymers such as polypropylene, polystyrene, nylon, and polycarbonate contribute significantly to microplastic contamination. Primary treatments such as chemical precipitation and electrocoagulation can remove up to 60-99% of total MPs from wastewater, while tertiary treatments such as sand filtration, ultrafiltration, and reverse osmosis can remove up to 90-99%. Advanced techniques, such as a combination of membrane bioreactor, ultrafiltration, and nanofiltration (MBR + UF + NF), can achieve even higher removal rates. Overall, this paper highlights the importance of continuous monitoring of microplastic pollution and the need for effective microplastic removal from LL to protect human and environmental health. However, more research is needed to determine the actual cost and feasibility of these treatment processes at a larger scale.

Global distribution of pesticides in freshwater resources and their remediation approaches

The role of pesticides in enhancing global agricultural production is magnificent. However, their unmanaged use threatens water resources and individual health. A significant pesticide concentration leaches to groundwater or reaches surface waters through runoff. Water contaminated with pesticides may cause acute or chronic toxicity to impacted populations and exert adverse environmental effects. It necessitates the monitoring and removing pesticides from water resources as prime global concerns. This work reviewed the global occurrences of pesticides in potable water and discussed the conventional and advanced technologies for the removal of pesticides. The concentration of pesticides highly varies in freshwater resources across the globe. The highest concentration of α-HCH (6.538 μg/L, at Yucatan, Mexico), lindane (6.08 μg/L at Chilka lake, Odisha, India), 2,4, DDT (0.90 μg/L, at Akkar, Lebanon), chlorpyrifos (9.1 μg/L, at Kota, Rajasthan, India), malathion (5.3 μg/L, at Kota, Rajasthan, India), atrazine (28.0 μg/L, at Venado Tuerto City, Argentina), endosulfan (0.78 μg/L, at Yavtmal, Maharashtra, India), parathion (4.17 μg/L, at Akkar, Lebanon), endrin (3.48 μg/L, at KwaZuln-Natl Province, South Africa) and imidacloprid (1.53 μg/L, at Son-La province, Vietnam) are reported. Pesticides can be significantly removed through physical, chemical, and biological treatment. Mycoremediation technology has the potential for up to 90% pesticide removal from water resources. Complete removal of the pesticides through a single biological treatment approach such as mycoremediation, phytoremediation, bioremediation, and microbial fuel cells is still a challenging task, however, the integration of two or more biological treatment approaches can attain complete removal of pesticides from water resources. Physical methods along with oxidation methods can be employed for complete removal of pesticides from drinking water.

Phytoremediation of nitrogen and phosphorus pollutants from glass industry effluent by using water hyacinth (Eichhornia crassipes (Mart.) Solms): Application of RSM and ANN techniques for experimental optimization

The present study aimed to assess the efficiency of the water hyacinth (Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus pollutants from glass industry effluent (GIE) as batch mode phytoremediation experiments. For this, response surface methodology (RSM) and artificial neural networks (ANN) methods were adopted to evidence the optimization and prediction performances of E. crassipes for total Kjeldahl's nitrogen (TKN) and total phosphorus (TP) removal. The control parameters, i.e., GIE concentration (0, 50, and 100%) and plant density (1, 3, and 5 numbers) were used to optimize the best reduction conditions of TKN and TP. A quadratic model of RSM and feed-forward backpropagation algorithm-based logistic model (input layer: 2 neurons, hidden layer: 10 neurons, and output layer: 1 neuron) of ANN showed good fitness results for experimental optimization. Optimization results showed that maximum reduction of TKN (93.86%) and TP (87.43%) was achieved by using 60% of GIE concentration and nearly five plants. However, coefficient of determination (R²) values showed that ANN models (TKN: 0.9980; TP: 0.9899) were superior in terms of prediction performance as compared to RSM (TKN: 0.9888; TP: 0.9868). Therefore, the findings of this study concluded that E. crassipes can be effectively used to remediate nitrogen and phosphorus loads of GIE and minimize environmental hazards caused by its unsafe disposal.

Pesticide sorption and mitigation efficiency of a detention pond in a Champagne vineyard catchment

Detention ponds (DPs) are used to reduce the pesticide inputs from runoff to surface water. This study aimed to assess the role of the sorption process in the mitigation of a DP made up of four successive units and built at the outlet of a vineyard catchment in Champagne (France) to treat runoff waters. Sorption kinetics and isotherms were studied for four pesticides with contrasting properties, cyazofamid (CYA), fludioxonil (FLX), fluopicolide (FLP) and oryzalin (ORY), in the presence of copper in sediments and four emergent macrophyte roots and rhizomes sampled in the DP units 2 (photodegradation) and 3 (phytoremediation). The adsorption equilibrium time (from 24 to 96 h) was less than the hydraulic residence times in the two units (6 and 18 days on average) between November 2016 and November 2017. Sorption equilibrium could then be reached in situ in 85 % of cases. The K_d coefficients of the four pesticides were overall greater in plant roots (14–6742 L kg⁻¹) than in sediments (6–163 L kg⁻¹) because of their affinity for organic matter and the molecular and porous structure of the plant matrices. Typha latifolia and Iris pseudacorus exhibited greater K_d coefficients than Mentha aquatica and Phragmites australis, probably due to their greater specific surface area. The pesticide adsorption capacity in sediments and in T. latifolia and I. pseudacorus roots (ORY ≥ FLX > CYA > FLP) was linked to their K_ow. The estimated total annual amounts of the four pesticides adsorbed in situ were determined to be 1236 mg for unit 2 and 1570 mg for unit 3. The four plants improved the removal efficiency of the unit 3 by 33%. Thus, the establishment of suitable and effective plants should be promoted to optimize sorption processes and DP efficiency in reducing water pollution.

•

Pesticide sorption on various substrates of a vineyard detention pond was assessed.

•

The measured equilibrium time was less than the in situ hydraulic residence time.

•

Plant roots and rhizomes showed greater pesticide adsorption capacity than sediments.

•

Sorption capacity was higher on cattail and iris than on mint and reed.

•

Affinity of selected pesticides for roots and sediments was related to their Kow.

Two constructed wetlands within a Mediterranean natural park immersed in an agrolandscape reduce most heavy metal water concentrations and dampen the majority of pesticide presence

The water concentrations of 12 heavy and other metals/metalloids were analyzed seasonally along two horizontal-flow constructed wetlands (CWs) (Tancat Mília-TM and Tancat l'Illa-TLI) located within the Mediterranean Albufera de València Natural Park during 2020-2021. A wide-scope screening of pesticides present in waters was also performed. The two CWs were created to improve water quality and increase biodiversity. They currently receive effluent waters from two different tertiary-treatment wastewater plants, and the water flows along the CWs before being discharged into the main lagoon and a smaller lagoon in TM and TLI, respectively. TLI manages to reduce (Mn) or maintain the concentration of most of the studied elements (Zn, Ni, Hg, Cr, Fe Cd, Cu) at the same level as outside (67%). Only Al, Pb, B, and As remain at a higher concentration. TM also reduces Zn and Cu and keeps the concentration of Cr, Cd, and Hg (representing 42%). Al, Pb, B, and As remain at higher concentrations, as in TLI, but Ni, Fe, and Mn are also at higher concentrations. Although both CWs vary in their ability to remove elements, no risks to human health or the environment have been detected due to the low metal concentration in their outlets, all of them (except Hg) below the legal limits for environmental quality in the European Union. With the detection of 71 compounds in water in each CW area (26 herbicides, 26 insecticides, and 19 fungicides in TLI, and 29 herbicides, 23 insecticides, and 19 fungicides in TM), we also provide evidence of the impact of pesticides, which depends on the application method (helicopter, tractor), originated from areas with high agricultural pressure (chiefly rice crops) on systems (mainly TM) created to preserve biodiversity. Nevertheless, both systems provide crucial environmental services in water quality in this agrolandscape.

Greenhouse Gases Trade-Off from Ponds: An Overview of Emission Process and Their Driving Factors

Inland water bodies (particularly ponds) emit a significant amount of greenhouse gases (GHGs), particularly methane (CH4), carbon dioxide (CO2), and a comparatively low amount of nitrous oxide (N2O) to the atmosphere. In recent decades, ponds (<10,000 m2) probably account for about 1/3rd of the global lake perimeter and are considered a hotspot of GHG emissions. High nutrients and waterlogged conditions provide an ideal environment for CH4 production and emission. The rate of emissions differs according to climatic regions and is influenced by several biotic and abiotic factors, such as temperature, nutrients (C, N, & P), pH, dissolved oxygen, sediments, water depth, etc. Moreover, micro and macro planktons play a significant role in CO2 and CH4 emissions from ponds systems. Generally, in freshwater bodies, the produced N2O diffuses in the water and is converted into N2 gas through different biological processes. There are several other factors and mechanisms which significantly affect the CH4 and CO2 emission rate from ponds and need a comprehensive evaluation. This study aims to develop a decisive understanding of GHG emissions mechanisms, processes, and methods of measurement from ponds. Key factors affecting the emissions rate will also be discussed. This review will be highly useful for the environmentalists, policymakers, and water resources planners and managers to take suitable mitigation measures in advance so that the climatic impact could be reduced in the future.

Removal of Copper, Nickel, and Zinc Ions from an Aqueous Solution through Electrochemical and Nanofiltration Membrane Processes

Heavy metal contamination in water is a major health concern, directly related to rapid growth in industrialization, urbanization, and modernization in agriculture. Keeping this in view, the present study has attempted to develop models for the process optimization of nanofiltration (NF) membrane and electrocoagulation (EC) processes for the removal of copper, nickel, and zinc from an aqueous solution, employing the response surface methodology (RSM). The variable factors were feed concentration, temperature, pH, and pressure for the NF membrane process; and time, solution pH, feed concentration, and current for the EC process, respectively. The central composite design (CCD), the most commonly used fractional factorial design, was employed to plan the experiments. RSM models were statistically analyzed using analysis of variance (ANOVA). For the NF membrane, the rejection of Zn, Ni, and Cu was observed as 98.64%, 90.54%, and 99.79% respectively; while the removal of these through the EC process was observed as 99.81%, 99.99%, and 99.98%, respectively. The above findings and a comparison with the conventional precipitation and adsorption processes apparently indicate an advantage in employing the NF and EC processes. Further, between the two, the EC process emerged as more efficient than the NF process for the removal of the studied metals.