Microwave pretreatment of wastewater sludge technology-a scientometric-based review.

This manuscript presents a scientometric review of recent advances in microwave pretreatment processes for sewage sludge, systematically identifying existing gaps and prospects. For this purpose, 1763 papers on the application of microwave technology to sludge pretreatment were retrieved from the Web of Science (WoS) using relevant keywords. These publications were then analyzed using diverse scientometric indices. The results show that research in this field encompasses applications based on the non-thermal effects of microwaves, enhanced effectiveness of anaerobic digestion (AD), and the energy balance of this pretreatment system. Overcoming existing technical challenges, such as the cleavage of extracellular polymers, reducing microwave energy consumption, understanding the non-thermal effects of microwaves, promoting AD of sludge in combination with other chemical and physical methods, and expanding the application of the technology, are the main scientific focuses. Additionally, this paper thoroughly examines both the constraints and potential of microwave pretreatment technology for wastewater treatment.

Analysis of environmental factors influencing endemic cholera risks in sub-Saharan Africa.

The recurring cholera outbreaks in sub-Saharan Africa are of growing concern, especially considering the potential acceleration in the global trend of larger and more lethal cholera outbreaks due to the impacts of climate change. However, there is a scarcity of evidence-based research addressing the environmental and infrastructure factors that sustain cholera recurrence in Africa. This study adopts a statistical approach to investigate over two decades of endemic cholera outbreaks and their relationship with five environmental factors: water provision, sanitation provision, raising temperatures, increased rainfall and GDP. The analysis covers thirteen of the forty-two countries in the mainland sub-Saharan region, collectively representing one-third of the region's territory and half of its population. This breadth enables the findings to be generalised at a regional level. Results from all analyses consistently associate water provision with cholera reduction. The stratified model links increased water provision with a reduction in cholera risk that ranged from 4.2 % to 84.1 % among eight countries (out of 13 countries) as well as a reduction of such risk that ranged from 9.8 % to 68.9 % when there is increased sanitation provision, which was observed in nine countries (out of 13). These results indicate that the population's limited access to water and sanitation, as well as the rise in temperatures, are critical infrastructure and environmental factors contributing to endemic cholera and the heightened risk of outbreaks across the sub-Saharan region. Therefore, these are key areas for targeted interventions and cross-border collaboration to enhance resilience to outbreaks and lead to the end of endemic cholera in the region. However, it is important to interpret the results of this study with caution; hence, further investigation is recommended to conduct a more detailed analysis of the impact of infrastructure and environmental factors on reducing cholera risk.

Synthetic musks in the natural environment: Sources, occurrence, concentration, and fate-A review of recent developments (2010-2023).

Synthetic musks (SMs) have served as cost-effective substitutes for natural musk compounds in personal care and daily chemical products for decades. Their widespread use has led to their detection in various environmental matrices, raising concerns about potential risks. Despite numerous studies on SM levels in different natural environments, a systematic review of their contemporary presence is lacking. This review aims to address this gap by summarising recent research developments on SMs in diverse natural environments, including river water, lake water, seawater, estuarine water, groundwater, snow, meltwater, sediments, aquatic suspended matter, soils, sands, outdoor air, and atmospheric particulate matter. Covering the period from 2010 to 2023, the review focuses on four SM categories: nitro, polycyclic, macrocyclic, and alicyclic. It systematically examines their sources, occurrences, concentrations, spatial and temporal variations, and fate. The literature reveals widespread detection of SMs in the natural environment (freshwater and sediments in particular), with polycyclic musks being the most studied group. Both direct (e.g., wastewater discharges) and indirect (e.g., human recreational activities) sources contribute to SM presence. Levels of SMs vary greatly among studies with higher levels observed in certain regions, such as sediments in Southeast Asia. Spatial and temporal variations are also evident. The fate of SMs in the environment depends on their physicochemical properties and environmental processes, including bioaccumulation, biodegradation, photodegradation, adsorption, phase exchange, hydro-dilution effects. Biodegradation and photodegradation can decrease SM levels, but may produce more persistent and eco-toxic products. Modelling approaches have been employed to analyse SM fate, especially for indirect processes like photodegradation or long-distance atmospheric transport. Future studies should further investigate the complex fate if SMs and their environmental influence. This review enhances understanding of SM status in the natural environment and supports efforts to control environmental contamination.

Deciphering the synergistic effects of photolysis and biofiltration to actuate elimination of estrogens in natural water matrix.

The presence of estrogens in water environments has raised concerns for human health and ecosystems balance. These substances possess potent estrogenic properties, causing severe disruptions in endocrine systems and leading to reproductive and developmental problems. Unfortunately, conventional treatment methods struggle to effectively remove estrogens and mitigate their effects, necessitating technological innovation. This study investigates the effectiveness of a novel sequential photolysis-granular activated carbon (GAC) sandwich biofiltration (GSBF) system in removing estrogens (E1, E2, E3, and EE2) and improving general water quality parameters. The results indicate that combining photolysis pre-treatment with GSBF consistently achieved satisfactory performance in terms of turbidity, dissolved organic carbon (DOC), UV254, and microbial reduction, with over 77.5 %, 80.2 %, 89.7 %, and 92 % reduction, respectively. Furthermore, this approach effectively controlled the growth of microbial biomass under UV irradiation, preventing excessive head loss. To assess estrogen removal, liquid chromatography-tandem mass spectrometry (LC-MS) measured their concentrations, while bioassays determined estrogenicity. The findings demonstrate that GSBF systems, with and without photolysis installation, achieved over 96.2 % removal for estrogens when the spike concentration of each targeted compound was 10 µg L-1, successfully reducing estrogenicity (EA/EA0) to levels below 0.05. Additionally, the study evaluated the impact of different thicknesses of GAC layer filling (8 cm, 16 cm, and 24 cm) and found no significant difference (p>0.05) in estrogen and estrogenicity removal among them.

A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty quantification.

Accurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today's more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects.

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining.

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method. The proposed model was compared to other commonly used ensemble models in a real-world urban drainage system in the UK. The results show that the proposed model achieves a higher hit rate compared to other benchmark models, with a hit rate of around 85% vs 70 % for the next 3 h of forecasting. Additionally, the proposed smart model can accurately classify various timesteps of flood or non-flood events without significant lag times, resulting in fewer false alarms, reduced unnecessary risk management actions, and lower costs in real-time early warning applications. The findings also demonstrate that two features, "antecedent precipitation history" and "seasonal time occurrence of rainfall," significantly enhance the accuracy of flood forecasting with a hit rate accuracy ranging from 60 % to 10 % for a lead time of 15 min to 3 h.

Dual function of magnetic field in enhancing antibiotic wastewater treatment by an integrated photocatalysis and fluidized bed biofilm reactor (FBBR).

The integrated photocatalysis and fluidized bed biofilm reactor (FBBR) is an attractive wastewater treatment technique for managing wastewater containing antibiotics. However, the fast recombination of photoinduced charge and low microbial activity limit the degradation and mineralization efficiency for antibiotics. To address this, we attempt to introduce magnetic field (MF) to the integrated system with B-doped Bi3O4Cl as the photocatalysts to effectively improve removal and mineralization of ciprofloxacin (CIP). As a consequence, the degradation rate reaches 96% after 40 d in integrated system with MF. The biofilm inside the integrated system with MF carrier can mineralize the photocatalytic products, thereby increasing the total organic carbon (TOC) degradation rate by more than 32%. The electrochemical experiment indicates the Lorentz force generated by MF can accelerate charge separation, increasing the electron concentration. Simultaneously, the increased amounts of electrons lead to the generation of more ·OH and ·O2-. MF addition also results in increased biomass, increased biological respiratory activity, microbial community evolution and accelerated microbial metabolism, enabling more members to biodegrade photocatalytic intermediates. Therefore, applied MF is an efficient method to enhance CIP degradation and mineralization by the integrated system.

Antibiotic resistance genes and the association with bacterial community in biofilms occurring during the drinking water granular activated carbon (GAC) sandwich biofiltration.

The granular activated carbon (GAC) sandwich modification to slow sand filtration could be considered as a promising technology for improved drinking water quality. Biofilms developed on sand and GAC surfaces are expected to show a functional diversity during the biofiltration. Bench-scale GAC sandwich biofilters were set-up and run continuously with and without antibiotic exposure. Surface sand (the schmutzdecke) and GAC biofilms were sampled and subject to high-throughput qPCR for antibiotic resistance gene (ARG) analysis and 16 S rRNA amplicon sequencing. Similar diversity of ARG profile was found in both types of biofilms, suggesting that all ARG categories decreased in richness along the filter bed. In general, surface sand biofilm remained the most active layer with regards to the richness and abundance of ARGs, where GAC biofilms showed slightly lower ARG risks. Network analysis suggested that 10 taxonomic genera were implicated as possible ARG hosts, among which Nitrospira, Methyloversatilis and Methylotenera showed the highest correlation. Overall, this study was the first attempt to consider the whole structure of the GAC sandwich biofilter and results from this study could help to further understand the persistence of ARGs and their association with the microbial community in drinking water biofiltration system.

Microplastics and nanoplastics in haemodialysis waters: Emerging threats to be in our radar.

Microplastics are present in the environment, in drinking water, in human blood and there is evidence of nanoplastics in tap water. The objective of this work was to analyze the possibility of hemodialysis patients being contaminated by micro and nanoplastics (MNPs) during dialysis treatment. The motivation for this investigation is the fact that hemodialysis patients use about 300-600 L of drinking water per week, which may be contaminated by MNPs. A literature review, a field investigation in a London hospital and an estimation of MNPs intake in patients were carried out. The results showed potential points of risk of contamination of patients by MNPs in hemodialysis. It was also estimated that for a filtration efficiency of 99 % for MNPs, the amount of microplastics that can penetrate the kidneys of patients is 0.0021-3768 particles/week. The assessment concludes that hemodialysis patients are at high risk of MNP contamination.

A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran.

Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves.

A rapid systematic scoping review of research on the impacts of water contaminated by chemicals on very young children.

Low-income countries are struggling with the health impacts of both surface and groundwater chemical contamination. Although the impact of biological contaminants on children's health is acknowledged, the long-term effects of these and emerging contaminants on young children may be underestimated. To map the existing evidence on health impacts of water contaminated with chemicals on young children (<5 years), we conducted a scoping review to select and organize relevant literature. Of the 98 studies in the review, 24 revealed that the hazard ratio of arsenic, nitrates, cadmium, and fluoride (all of which are on the World Health Organisation's list of 10 chemicals of public health concern) was higher in very young children than in older age groups. Anthropogenic activities (textile manufacturing, waste disposal, and intensified agriculture) are leading contributors to the release of chemicals to groundwater used for drinking. Three major pathways for chemical contamination exposure in young children were confirmed: maternal transmission during pregnancy and breastfeeding, and early school years. Children exhibited acute and chronic disruptions to their neurological, skeletal, reproductive, and endocrine systems, as well as cumulative carcinogenic risks, amongst other life-altering consequences. The lack of research on emerging contaminants' effects on young children in low-income countries is worrisome, as their increased use may compound the issues caused by the existing problem of "legacy chemicals." Precautionary principle should regulate the operation of industries producing these chemicals in a robust manner. Evidence from major producers and exporters in high-income countries is sufficient to warrant action, even without waiting for direct harm to be observed in low-income countries. Literature recommends prioritising prevention of contamination over demand side treatment or finding alternative water sources, especially in water-scarce areas affected by climate change. Local and transnational efforts are required to enforce safer industry practices and prevent further water quality deterioration in low-income countries.

Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant.

Despite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis. The suggested framework was demonstrated by its application on a real micro-AD plant in London. The NARX model was developed for predicting yielded biogas based on the feeding data over preceding days in which their lag times were fine-tuned using the SFLA. The optimal daily feeding pattern to obtain maximum biogas generation was determined using the SFLA. The results show that the developed framework can improve the productivity of biogas in optimal operation strategy by 43 % compared to business as usual and the average biogas produced can raise from 3.26 to 4.34 m3/day. The optimal feeding pattern during a four-day cycle is to feed over the last two days and thereby reducing the operational costs related to the labour for feeding the plant in the first two days. The results of the sensitivity analysis show the optimised biogas generation is strongly influenced by the content of oats and catering waste as well as the optimal allocated day for adding feed to the main digester compared to other feed variables e.g., added water and soaked liner.

Enhanced antibiotic wastewater degradation by intimately coupled B-Bi3O4Cl photocatalysis and biodegradation reactor: Elucidating degradation principle systematically.

Intimately coupled photocatalysis and biodegradation (ICPB) is an emerging technology that has potential applications in the degradation of bio-recalcitrant pollutants. However, the interaction principles between photocatalysts and biofilms in ICPB have not been well developed. This article covers a cooperative degradation scheme coupling photocatalysis and biodegradation for efficient degradation and mineralization of ciprofloxacin (CIP) using ICPB with B-doped Bi3O4Cl as the photocatalyst. In consequence, a removal rate of ∼95 % is reached after 40 d. The biofilms inside the ICPB carriers can mineralize the photocatalytic products, thus improving the removal rate of total organic carbon (TOC) by more than 20 %. Interior biofilms are not destroyed by CIP or photocatalysis, and they adapt to ICPB of CIP by enriching in Pseudoxanthomonas, Ferruginibacter, Clostridium, Stenotrophomonas and Comamonas and reconstructing their microbial communities using energy produced by the light-excited photoelectrons. Furthermore, this research gives new opinion into the degradation principles of the ICPB system.

Insights into the removal of microplastics and microfibres by Advanced Oxidation Processes.

Water treatment plants' effluents are hotspots of microplastics (MPs) and microfibres (MFs) released into the aquatic environment because they were not designed to capture these particles. Special attention should be given to MFs, since they mainly come from laundry and are related to one of the main MP shapes detected in water and wastewater treatment plants. In this sense, Advanced Oxidation Processes (AOPs) could be a feasible solution for tackling MP and MF pollution, however, it is still premature to extract conclusions due to the limited number of studies on the degradation of these particles (specifically MFs) using AOPs. This review addresses the impacts of AOPs on MPs/MFs, focusing on their degradation efficiency, toxicity, and sustainability of the processes, among other aspects. The review points out that polyamide MFs can achieve mass loss >90% by photocatalytic system using TiO2. Also, the low oxidation of MPs (<30 %) by conventional Fenton process affects mainly the surface of the MPs. However, other Fenton-based processes can provide better removal of some types of MPs, mainly using temperatures >100 °C, reaction time ≥ 5 h, and initial pH ≤ 3, achieving MP weight loss up to 96 %. Despite these results, better operating conditions are still required for AOPs since the ones reported so far are not feasible for full-scale application. Additionally, ozonation in treatment plants has increased the fragmentation of MPs (including MFs), leading to a new generation of MPs. More attention is needed on toxicity effects of intermediates and methods of analysis employed for the analysis of MPs/MFs in wastewater effluent should be standardized so that studies can be compared effectively. Future research should focus on the sustainability of the AOP for MP removal in water treatment (power consumption, chemicals consumed and operational costs) for a better understanding of full-scale applicability of AOP adapted to MP treatment.

Investigating how COVID-19 has challenged the Eurocentric concept of 'development': a case for sustainable food systems in the UK.

The COVID-19 health crisis has imposed extensive shocks to many global systems, particularly the UK food production chains, further challenging Eurocentric development discourses and stereotypes. Thus, this paper investigates how the pandemic has challenged the UK's development status by analysing how the pandemic has impacted the country's food industry. A literature review was conducted and used to identify, select and critically appraise publications between 2000 and 2021 discussing the challenges in the UK food system. The findings reveal that the UK's food industry is unsustainable as there are significant flaws in the system, that is food insecurity and food waste that go unaddressed. The impact of the pandemic has exacerbated the social and economic impacts of operating with such a system. Compounded with the geopolitical adjustments caused by Brexit, the UK is faced with the challenge of restructuring and developing new frameworks such as policies, regulations, schemes and partnerships to support the food industry's sustainability. Lastly, the findings reinforce that 'developed' and 'developing' nations encounter similar food challenges, which manifest differently in various landscapes and contexts. Therefore, the world (not just the UK) needs to shift away from Eurocentrism, moving towards a universal but equally personalised development outlook. This review provides an outline of the major problem areas in the UK food system and presents potential solutions aimed at helping guide the government's decision-making process.

Sand and sand-GAC filtration technologies in removing PPCPs: A review.

Concerns have been raised about the risks that pharmaceuticals and personal care products (PPCPs) in aquatic environments posed to humans and the environment. In recent years, sand filtration has been used to potentially remove these emerging contaminants from water. However, there has been no review of the effectiveness of this technology to date. This paper presents a brief introduction of sand filtration types, reviews the current progress in PPCPs removal through sand filtration, and discusses the mechanisms behind this process and the combination of granular activated carbon (GAC) and sand as an enhanced sand-GAC filtration technology. Sand filtration achieves a reasonable but highly variable degree of PPCPs removal. Biodegradation and adsorption are the two main mechanisms of PPCPs removal, in particular the biodegradation since adsorption capacity of sand is relatively low. Other processes, such as bio-sorption and indirect adsorption, may also contribute to PPCPs removal. To compensate for the inadequate PPCPs removal through sand filtration, porous GAC has been combined with sand to develop sand-GAC filtration technologies. Serial, dual, and sandwich filters have been investigated, and significant removal enhancement has been observed, due to the strengthened adsorption capacity, suggesting the applicability of these variants. Future research focus, such as investigating the influence of different operational conditions on sand filter performance, obtaining a deeper understanding of the various removal mechanisms, and investigating of long-term performance of the filter used for PPCPs removal, are suggested.

Algae, biochar and bacteria for acid mine drainage (AMD) remediation: A review.

Acid mine drainage (AMD) is a global issue and causes harmful environmental impacts. AMD has high acidity and contains a high concentration of heavy metals and metalloids, making it toxic to plants, animals, and humans. Traditional treatments for AMD have been widely used for a long time. Nevertheless, some limitations, such as low efficacy and secondary contamination, have led them to be replaced by other methods such as bio-based AMD treatments. This study reviewed three bio-based treatment methods using algae, biochar, and bacteria that can be used separately and potentially in combination for effective and sustainable AMD treatment to identify the removal mechanisms and essential parameters affecting AMD treatment. All bio-based methods, when applied as a single process and in combination (e.g. algae-biochar and algae-bacteria), were identified as effective treatments for AMD. Also, all these bio-based methods were found to be affected by some parameters (e.g. pH, temperature, biomass concentration and initial metal concentration) when removing heavy metals from AMD. However, we did not identify any research focusing on the combination of algae-biochar-bacteria as a consortium for AMD treatment. Therefore, due to the excellent performance in AMD treatment of algae, biochar and bacteria and the potential synergism among them, this review provides new insight and discusses the feasibility of a combination of algae-biochar-bacteria for AMD treatment.

Photodegradation of free estrogens driven by UV light: Effects of operation mode and water matrix.

Estrogens are endocrine disrupting chemicals that have been frequently detected in diverse water matrices (e.g. surface water, wastewater and drinking water) and caused a series of health risks. This study was aimed at investigating the photochemical degradation of free estrogens estrone (E1), 17ß-estradiol (E2), estriol (E3), and 17α-ethyl estradiol (EE2) upon the monochromatic irradiation (253.7 nm). Concerning the practical installation of photolysis treatment, exposing the impacts of photoreactor operation mode (stationary or up-flow) and the water matrix (ultrapure water or natural surface water) on the photolytic behaviour of estrogens was of high importance. The pseudo-first-order rate constants showed that E1 was the most susceptible to UV radiation among chosen estrogens due to its high molar absorption coefficient of 402.4 M-1 cm-1 and quantum yield of 0.065 mol E-1 at λ = 253.7 nm. Moreover, the up-flow mode and the surface water matrix collected from a lake in Regent's Park (London) were found to favour the photodegradation of estrogens due to the introduction of more dissolved oxygens and promotion of reactive oxygen species (ROS) formation. These findings may shed light on the photochemical behaviour of estrogens in some specific scenarios.

Bioaccumulation of metals by algae from acid mine drainage: a case study of Frongoch Mine (UK).

In Frongoch Mine (UK), it is unclear the distribution of metals on indigenous algae and whether these species of algae can accumulate metals. This study aimed to investigate the role of indigenous algae for metal removal from acid mine drainage and understand if metals can be adsorbed on the surface of algae or/and bioaccumulated in algae. A sequential extraction procedure was applied for algae samples collected from acid mine drainage (AMD) water to identify the forms in which metals are found in algae. Concentrations of Fe, Pb, Zn, Cu and Cd were evaluated in the algae and AMD samples were collected in June and October 2019. AMDs samples had a pH value ranging between 3.5 and 6.9 and high concentrations of Zn (351 mg/L) and Pb (4.22 mg/L) that exceeded the water quality standards (Water Framework Directive, 2015). Algae Ulothrix sp. and Oedogonium sp. were the two main species in the Frongoch AMDs. The concentrations of metals in algae ranged from 0.007 to 51 mg/g, and the bioconcentration factor of metals decreased in the following order: Fe > > Pb > > Cu > Cd > Zn. It was found that Zn, Cu and Cd were adsorbed onto the surface of and bioaccumulated in the algae, while Pb and Fe were mainly bioaccumulated in the algae. Indigenous algae can be considered as a biogeochemical barrier where metals are accumulating and can be used in bioremediation methods. Also, indigenous algae could be used as a bioindicator to assess water pollution at Frongoch Mine and other similar metal mines.

The impact of micropollutants on native algae and cyanobacteria communities in ecological filters during drinking water treatment.

An attractive alternative for drinking water production is ecological filtration. Previous studies have reported high removal levels of pharmaceutical and personal care products (PPCPs) by this technology. Algae and cyanobacteria play an important role in the biological activity of ecological filters. The aim of this study was to characterize and identify the community of algae and cyanobacteria in relation to its composition, density and biovolume from 22 ecological filters that received spikings of 2 µg L-1 PPCPs. For algae and cyanobacteria species, triplicate samples were collected before and 96 h after each spiking from the interface between the top sand layer of the ecological filters and the supernatant water. Results show that Chlorophyceae and Cyanobacteria were present in high numbers of taxa and abundance. The specie Lepocinclis cf. ovum (Euglenophyceae) had the highest percentage occurrence/abundance and frequency into the filters, indicating a possible tolerance by Lepocinclis cf. ovum to the concentration of selected PPCPs. Although the concentration of PPCPs did not affect the treated water quality, they did affect the algae and cyanobacteria community. No differences were detected between filters that received a single PPCP and filters that received a mixture of the six compounds. Also, changes in the composition of algae and cyanobacteria communities were observed before and 96 h after the spikings.