Compounding one problem with another? A look at biodegradable microplastics.

Environmental concerns about microplastics (MPs) have motivated research of their sources, occurrence, and fate in aquatic and soil ecosystems. To mitigate the environmental impact of MPs, biodegradable plastics are designed to naturally decompose, thus reducing the amount of environmental plastic contamination. However, the environmental fate of biodegradable plastics and the products of their incomplete biodegradation, especially micro-biodegradable plastics (MBPs), remains largely unexplored. This comprehensive review aims to assess the risks of unintended consequences associated with the introduction of biodegradable plastics into the environment, namely, whether the incomplete mineralization of biodegradable plastics could enhance the risk of MBPs formation and thus, exacerbate the problem of their environmental dispersion, representing a potentially additional environmental hazard due to their presumed ecotoxicity. Initial evidence points towards the potential for incomplete mineralization of biodegradable plastics under both controlled and uncontrolled conditions. Rapid degradation of PLA in thermophilic industrial composting contrasts with the degradation below 50 % of other biodegradables, suggesting MBPs released into the environment through compost. Moreover, degradation rates of <60 % in anaerobic digestion for polymers other than PLA and PHAs suggest a heightened risk of MBPs in digestate, risking their spread into soil and water. This could increase MBPs and adsorbed pollutants' mobilization. The exact behavior and impacts of additive leachates from faster-degrading plastics remain largely unknown. Thus, assessing the environmental fate and impacts of MBPs-laden by-products like compost or digestate is crucial. Moreover, the ecotoxicological consequences of shifting from conventional plastics to biodegradable ones are highly uncertain, as there is insufficient evidence to claim that MBPs have a milder effect on ecosystem health. Indeed, literature shows that the impact may be worse depending on the exposed species and polymer type, and the ecosystem complexity.

Arsenic and fluoride in groundwater triggering a high risk: Probabilistic results using Monte Carlo simulation and species sensitivity distribution.

The widespread presence of arsenic (As) and fluoride (F-) in groundwater poses substantial risks to human health on a global scale. These elements have been identified as the most prevalent geogenic contaminants in groundwater in northern Mexico. Consequently, this study aimed to evaluate the human health and ecological risks associated with the content of As and F- in the Meoqui-Delicias aquifer, which is in one of Mexico's most emblematic irrigation districts. Concentrations of As and F- were measured in 38 groundwater samples using ICP-MS and ion chromatography, respectively. Overall, these elements showed a similar trend across the aquifer, revealing a positive correlation between them and pH. The concentration of As and F- in the groundwater ranged from 5.3 µg/L to 303 µg/L and from 0.5 mg/L to 8.8 mg/L, respectively. Additionally, the levels of As and F- surpassed the established national standards for safe drinking water in 92% and 97% of samples, respectively. Given that groundwater is used for both agricultural purposes and human activities, this study also assessed the associated human health and ecological risks posed by these elements using Monte Carlo simulation and Species Sensitivity Distribution. The findings disclosed a significant noncarcinogenic health risk associated with exposure to As and F-, as well as an unacceptable carcinogenic health risk to As through water consumption for both adults and children. Furthermore, a high ecological risk to aquatic species was identified for F- and high to medium risks for As in the sampling sites. Therefore, the findings in this study provide valuable information for Mexican authorities and international organizations (e.g., WHO) about the adverse effects that any exposure without treatment to groundwater from this region represents for human health.

A metagenomic study of antibiotic resistance genes in a hypereutrophic subtropical lake contaminated by anthropogenic sources.

Antibiotic resistance genes (ARGs) are a major threat to human and environmental health. This study investigated the occurrence and distribution of ARGs in Lake Cajititlán, a hypereutrophic subtropical lake in Mexico contaminated by anthropogenic sources (urban wastewater and runoff from crop and livestock production). ARGs (a total of 475 genes) were detected in 22 bacterial genera, with Pseudomonas (144 genes), Stenotrophomonas (88 genes), Mycobacterium (54 genes), and Rhodococcus (27 genes) displaying the highest frequencies of ARGs. Among these, Pseudomonas aeruginosa and Stenotrophomonas maltophilia showed the highest number of ARGs. The results revealed a diverse array of ARGs, including resistance to macrolides (11.55 %), aminoglycosides (8.22 %), glycopeptides (6.22 %), tetracyclines (4 %), sulfonamides (4 %), carbapenems (1.11 %), phenicols (0.88 %), fluoroquinolones (0.44 %), and lincosamides (0.22 %). The most frequently observed ARGs were associated with multidrug resistance (63.33 %), with MexF (42 genes), MexW (36 genes), smeD (31 genes), mtrA (25 genes), and KHM-1 (22 genes) being the most common. Lake Cajititlán is a recreational area for swimming, fishing, and boating, while also supporting irrigation for agriculture and potentially acting as a drinking water source for some communities. This raises concerns about the potential for exposure to antibiotic-resistant bacteria through these activities. The presence of ARGs in Lake Cajititlán poses a significant threat to both human and environmental health. Developing strategies to mitigate the risks of antibiotic resistance, including improving wastewater treatment, and promoting strategic antibiotic use and disposal, is crucial. This study represents a significant advancement in the understanding of antibiotic resistance dynamics in a hypereutrophic subtropical lake in a developing country, providing valuable insights for the scientific community and policymakers.

FlowSOM clustering - A novel pattern recognition approach for water research: Application to a hyper-arid coastal aquifer system.

Monitoring and understanding of water resources have become essential in designing effective and sustainable management strategies to overcome the growing water quality challenges. In this context, the utilization of unsupervised learning techniques for evaluating environmental tracers has facilitated the exploration of sources and dynamics of groundwater systems through pattern recognition. However, conventional techniques may overlook spatial and temporal non-linearities present in water research data. This paper introduces the adaptation of FlowSOM, a pioneering approach that combines self-organizing maps (SOM) and minimal spanning trees (MST), with the fast-greedy network clustering algorithm to unravel intricate relationships within multivariate water quality datasets. By capturing connections within the data, this ensemble tool enhances clustering and pattern recognition. Applied to the complex water quality context of the hyper-arid transboundary Caplina/Concordia coastal aquifer system (Peru/Chile), the FlowSOM network and clustering yielded compelling results in pattern recognition of the aquifer salinization. Analyzing 143 groundwater samples across eight variables, including major ions, the approach supports the identification of distinct clusters and connections between them. Three primary sources of salinization were identified: river percolation, slow lateral aquitard recharge, and seawater intrusion. The analysis demonstrated the superiority of FlowSOM clustering over traditional techniques in the case study, producing clusters that align more closely with the actual hydrogeochemical pattern. The outcomes broaden the utilization of multivariate analysis in water research, presenting a comprehensive approach to support the understanding of groundwater systems.

A review of metallurgical slags as catalysts in advanced oxidation processes for removal of refractory organic pollutants in wastewater.

With the rapid growth of the metallurgical industry, there is a significant increase in the production of metallurgical slags. The waste slags pose significant challenges for their disposal because of complex compositions, low utilization rates, and environmental toxicity. One promising approach is to utilize metallurgical slags as catalysts for treatment of refractory organic pollutants in wastewater through advanced oxidation processes (AOPs), achieving the objective of "treating waste with waste". This work provides a literature review of the source, production, and chemical composition of metallurgical slags, including steel slag, copper slag, electrolytic manganese residue, and red mud. It emphasizes the modification methods of metallurgical slags as catalysts and the application in AOPs for degradation of refractory organic pollutants. The reaction conditions, catalytic performance, and degradation mechanisms of organic pollutants using metallurgical slags are summarized. Studies have proved the feasibility of using metallurgical slags as catalysts for removing various pollutants by AOPs. The catalytic performance was significantly influenced by slags-derived catalysts, catalyst modification, and process factors. Future research should focus on addressing the safety and stability of catalysts, developing green and efficient modification methods, enhancing degradation efficiency, and implementing large-scale treatment of real wastewater. This work offers insights into the resource utilization of metallurgical slags and pollutant degradation in wastewater.

The effects of heavy rain on the fate of urban and agricultural pollutants in the riverside area around weirs using multi-isotope, microbial data and numerical simulation.

The increase in extreme heavy rain due to climate change is a critical factor in the fate of urban and agricultural pollutants in aquatic system. Nutrients, including NO3- and PO43-, are transported with surface and seepage waters into rivers, lakes and aquifers and can eventually lead to algal blooms. δ15N-NO3-, δ18O-NO3-, and δ11B combined with hydrogeochemical and microbial data for groundwater and surface water samples were interpreted to evaluate the fate of nutrients in a riverside area around weirs in Daegu, South Korea. Most of the ions showed similar concentrations in the groundwater samples before and after heavy rain while concentrations of major ions in surface water samples were diluted after heavy rain. However, Si, PO43-, Zn, Ce, La, Pb, Cu and a number of waterborne pathogens increased in surface water after heavy rain. The interpretation of δ11B, δ15N-NO3-, and δ18O-NO3- values using a Bayesian mixing model revealed that sewage and synthetic fertilizers were the main sources of contaminants in the groundwater and surface water samples. δ18O and SiO2 interpreted using the Bayesian mixing model indicated that the groundwater component in the surface water increased from 4.4 % to 17.9 % during the wet season. This is consistent with numerical simulation results indicating that the direct surface runoff and the groundwater baseflow contributions to the river system had also increased 6.4 times during the wet season. The increase in proteobacteria and decrease of actinobacteria in the surface water samples after heavy rain were also consistent with an increase of surface runoff and an increased groundwater component in the surface water. This study suggests that source apportionment based on chemical and multi-isotope data combined with numerical modeling approaches can be useful for identifying main hydrological and geochemical processes in riverside areas around weirs and can inform suggestions of effective methods for water quality management.

Worldwide risk assessment of phthalates and bisphenol A in humans: The need for updating guidelines.

Phthalates and bisphenol A (BPA) are compounds widely used as raw materials in the production of plastics, making them ubiquitous in our daily lives. This results in widespread human exposure and human health hazards. Although efforts have been conducted to evaluate the risk of these compounds in diverse regions around the world, data scattering may mask important trends that could be useful for updating current guidelines and regulations. This study offers a comprehensive global assessment of human exposure levels to these chemicals, considering dietary and nondietary ingestion, and evaluates the associated risk. Overall, the exposure daily intake (EDI) values of phthalates and BPA reported worldwide ranged from 1.11 × 10-7 to 3 700 µg kg bw-1 d-1 and from 3.00 × 10-5 to 6.56 µg kg bw-1 d-1, respectively. Nevertheless, the dose-additive effect of phthalates has been shown to increase the EDI up to 5 100 µg kg bw-1 d-1, representing a high risk in terms of noncarcinogenic (HQ) and carcinogenic (CR) effects. The worldwide HQ values of phthalates and BPA ranged from 2.25 × 10-7 to 3.66 and from 2.74 × 10-7 to 9.72 × 10-2, respectively. Meanwhile, a significant number of studies exhibit high CR values for benzyl butyl phthalate (BBP) and di(2-ethylhexyl) phthalate (DEHP). Moreover, DEHP has shown the highest maximum mean CR values for humans in numerous studies, up to 179-fold higher than BBP. Despite mounting evidence of the harmful effects of these chemicals at low-dose exposure on animals and humans, most regulations have not been updated. Thus, this article emphasizes the need for updating guidelines and public policies considering compelling evidence for the adverse effects of low-dose exposure, and it cautions against the use of alternative plasticizers as substitutes for phthalates and BPA because of the significant gaps in their safety.

Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook.

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3--N) concentrations in Mexico's groundwater. Four ML algorithms-Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)-were executed to model NO3--N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the 'Support Points-based Split Approach' during data pre-processing. The models considered initially 68 covariates and identified 13-19 significant predictors of NO3--N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3--N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3--N levels in groundwater. Moreover, the NO3--N hotspots align with reported NO3--N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3--N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

Prevalence of pharmaceuticals and personal care products, microplastics and co-infecting microbes in the post-COVID-19 era and its implications on antimicrobial resistance and potential endocrine disruptive effects.

The COVID-19 (coronavirus disease 2019) pandemic's steady condition coupled with predominance of emerging contaminants in the environment and its synergistic implications in recent times has stoked interest in combating medical emergencies in this dynamic environment. In this context, high concentrations of pharmaceutical and personal care products (PPCPs), microplastics (MPs), antimicrobial resistance (AMR), and soaring coinfecting microbes, tied with potential endocrine disruptive (ED) are critical environmental concerns that requires a detailed documentation and analysis. During the pandemic, the identification, enumeration, and assessment of potential hazards of PPCPs and MPs and (used as anti-COVID-19 agents/applications) in aquatic habitats have been attempted globally. Albeit receding threats in the magnitude of COVID-19 infections, both these pollutants have still posed serious consequences to aquatic ecosystems and the very health and hygiene of the population in the vicinity. The surge in the contaminants post-COVID also renders them to be potent vectors to harbor and amplify AMR. Pertinently, the present work attempts to critically review such instances to understand the underlying mechanism, interactions swaying the current health of our environment during this post-COVID-19 era. During this juncture, although prevention of diseases, patient care, and self-hygiene have taken precedence, nevertheless antimicrobial stewardship (AMS) efforts have been overlooked. Unnecessary usage of PPCPs and plastics during the pandemic has resulted in increased emerging contaminants (i.e., active pharmaceutical ingredients and MPs) in various environmental matrices. It was also noticed that among COVID-19 patients, while the bacterial co-infection prevalence was 0.2-51%, the fungi, viral, protozoan and helminth were 0.3-49, 1-22, 2-15, 0.4-15% respectively, rendering them resistant to residual PPCPs. There are inevitable chances of ED effects from PPCPs and MPs applied previously, that could pose far-reaching health concerns. Furthermore, clinical and other experimental evidence for many newer compounds is very scarce and demands further research. Pro-active measures targeting effective waste management, evolved environmental policies aiding strict regulatory measures, and scientific research would be crucial in minimizing the impact and creating better preparedness towards such events among the masses fostering sustainability.

Response of wastewater-based epidemiology predictor for the second wave of COVID-19 in Ahmedabad, India: A long-term data Perspective.

In this work, we present an eight-month longitudinal study of wastewater-based epidemiology (WBE) in Ahmedabad, India, where wastewater surveillance was introduced in September 2020 after the successful containment of the first wave of COVID-19 to predict the resurge of the infection during the second wave of the pandemic. The study aims to elucidate the weekly resolution of the SARS-CoV-2 RNA data for eight months in wastewater samples to predict the COVID-19 situation and identify hotspots in Ahmedabad. A total of 287 samples were analyzed for SARS-CoV-2 RNA using RT-PCR, and Spearman's rank correlation was applied to depict the early warning potential of WBE. During September 2020 to April 2021, the increasing number of positive wastewater influent samples correlated with the growing number of confirmed clinical cases. It also showed clear evidence of early detection of the second wave of COVID-19 in Ahmedabad (March 2021). 258 out of a total 287 samples were detected positive with at least two out of three SARS-CoV-2 genes (N, ORF- 1 ab, and S). Monthly variation represented a significant decline in all three gene copies in October compared to September 2020, followed by an abrupt increase in November 2020. A similar increment in the gene copies was observed in March and April 2021, which would be an indicator of the second wave of COVID-19. A lead time of 1-2 weeks was observed in the change of gene concentrations compared with clinically confirmed cases. Measured wastewater ORF- 1 ab gene copies ranged from 6.1 x 102 (October 2020) to 1.4 x 104 (November 2020) copies/mL, and wastewater gene levels typically lead to confirmed cases by one to two weeks. The study highlights the value of WBE as a monitoring tool to predict waves within a pandemic, identify local disease hotspots within a city, and guide rapid management interventions.

Prevalence of Endocrine Disrupting Chemicals in the urban wastewater treatment systems of Dehradun, India: Daunting presence of Estrone.

We quantified the occurrences and seasonal variations of the target endocrine disrupting chemicals (EDCs) at four (two major municipals, and two academic institutions) WWTPs in Dehradun city, Uttarakhand, India. The results showed estrone in higher concentrations at µgL-1 levels in influent among the WWTPs, compared to triclosan (TCS) at ngL-1 levels. An astounding concentration of 123.95 µgL-1 was recorded for the estrone in the influent, which is to date the highest ever recorded, globally. Statistical data treatment was performed to test the distribution of the data (Shapiro-Wilk, Anderson-Darling, Lilliefors, and Jarque-Bera tests), and the significant difference between the mean of the wastewater sample population (ANOVA: F statistics, p values, Mann-Whitney test, Tukey's and Dunn's post hoc analysis). Statistical data treatment indicated EDCs concentration with a bi-modal distribution. The Shapiro-Wilk, Anderson-Darling, Lilliefors, and Jarque-Bera tests elucidate a non-normal distribution for the EDCs sample data. A statistically significant difference (F = 8.46; p < 0.0001) in the seasonal data for the abundance of the target EDCs at the WWTPs have been observed. Highest and significantly different mean EDCs concentrations were recorded during the monsoon, compared to the spring (p = 0.025) and summer (p = 0.0004) seasons in the influent waters. The mean influent concentrations of TCS and estrone in monsoon were 66.45 ngL-1 and 78.02 µgL-1, respectively. Maximum removals were recorded for TCS, while maximum negative removal of ∼293% was observed for estrone in the WWTPs. Particularly, the high levels of estrone in the wastewater pose a significant threat as estrone presence could be led to feminization, dysregulation of reproduction in organisms, and carcinogenesis processes in the environment. This study critically highlights the limitation of the WWTPs in the treatment, degradation, and assimilation of EDCs leading to their hyperaccumulation at WWTP effluents, thereby posing a substantial threat to nearby aquatic ecosystems, human health, and the ecological balance of the region.

Influence of PFDA on the nutrient removal from wastewater by hydrogels containing microalgae-bacteria.

PFAS have demonstrated to affect some aerobic microorganisms applied for wastewater treatment. This study evaluated the nutrient removal of three types of hydrogels containing a consortium of microalgae-bacteria (HB), activated carbon (HC), or both (HBC) in presence of perfluorodecanoic acid (PFDA). The nutrients evaluated were ammonium nitrogen (NH4-N), nitrate nitrogen (NO3-N), phosphate (PO4), and chemical oxygen demand (COD). Fluorine (F-) concentration and the integrity of HB exposed to PFDA were also determined at the end of experiments to understand the potential sorption and effects of PFDA on hydrogel. The results indicated that the presence of PFDA did affect the nitrification process, 13% and 36% to HB and HBC, respectively. Mass balance confirmed negative impact of PFDA on nitrogen consumption in HB (-31.37%). However, NH4-N was removed by all types of hydrogels in a range of 61-79%, while PO4 was mainly removed by hydrogels containing activated carbon (AC), 37.5% and 29.2% for HC and HBC, respectively. The removal of both NH4 and PO4, was mainly attributed to sorption processes in hydrogels, which was enhanced by the presence of AC. PFDA was also adsorbed in hydrogels, decreasing its concentration between 18% and 28% from wastewater, and up to 39% using HC. Regarding COD concentration, this increased overtime but was not related to hydrogel structure, since Transmission Electron Microscopy imaging revealed that their structure was preserved in presence of PFDA. COD increasement could be attributed to soluble algal products as well as to PVA leaching from hydrogels. In general, the presence of AC in hydrogels can contribute to mitigate the toxic effect of PFDA over microorganisms involved in biological nutrient removal, and hydrogels can be a technique to partially remove this contaminant from aqueous matrices.

Adsorptive removal of ciprofloxacin and sulfamethoxazole from aqueous matrices using sawdust and plastic waste-derived biochar: A sustainable fight against antibiotic resistance.

We produced carbon-negative biochar from the pyrolysis of sawdust biomass alone (SB) and from the co-pyrolysis of sawdust and plastic waste (SPB). The co-pyrolysis approach in this study was driven by several hypothetical factors, such as increased porosity, surface chemistry, stability, as well as waste management. We applied pyrolyzed and co-pyrolyzed biochars for the removal of ciprofloxacin (CFX) and sulfamethoxazole (SMX). Due to its more alkaline and amorphous nature, SB showed better removal efficiencies compared to SPB. The maximum removals of CFX and SMX with SB were observed as ∼95% and >95%, respectively whereas with SPB were 58.8%, and 34.9%, respectively. The primary mechanisms involved in the adsorption process were H-bonding, electrostatic and π-π electron donor-acceptor interactions. Homogenously and heterogeneously driven adsorption of both antibiotics followed the pseudo-second-order kinetic model, implying electron sharing/transfer (chemisorption) mediated adsorption. The work is highly pertinent in the context of emerging concerns related to drivers that promote antimicrobial resistance.

Development of a surface plasmon resonance based immunosensor for diclofenac quantification in water.

A Surface Plasmon Resonance (SPR) biosensor based on an inhibition immunoassay was developed for the detection of diclofenac (DCF) in aqueous solution. Due to the small size of DCF, an hapten-protein conjugate was produced by coupling DCF to bovine serum albumin (BSA). DCF-BSA conjugate formation was confirmed via MALDI-TOF mass spectrometry. The resulting conjugate was immobilized onto the surface of a sensor fabricated via e-beam deposition of a 2 nm chromium adhesion layer followed by a 50 nm gold layer onto precleaned BK7 glass slides. Immobilization onto the nano thin gold surface was accomplished by covalent amide linkage through a self-assembled monolayer. Samples were composed of a mixture of antibody at a fixed concentration and DCF at different known concentrations in deionized water, causing the inhibition of anti-DCF on the sensor. The DCF-BSA was obtained with a ratio of 3 DCF molecules per BSA. A calibration curve was performed using concentrations between 2 and 32 µg L-1. The curve was fitted using the Boltzmann equation, reaching a limit of detection (LOD) of 3.15 µg L-1 and limit of quantification (LOQ) of 10.52 µg L-1, the inter-day precision was calculated and an RSD value of 1.96% was obtained; and analysis time of 10 min. The developed biosensor is a preliminary approach to the detection of DCF in environmental water samples, and the first SPR biosensor developed for DCF detection using a hapten-protein conjugate.

Determination of thermal degradation behavior and kinetics parameters of chemically modified sun hemp biomass.

Sun hemp fibers are natural fibers obtained from plants grown in India and nearby countries. It is lignocellulosic biomass having the complex structure of hemicelluloses, cellulose and lignin. Chemical treatment of natural fibers is in practice to enhance the properties being used as reinforcement. Alkaline-treated fiber was sampled and thermal stability along with kinetic parameters was assessed with thermo gravimetric data at heating rates 10, 20 and 30 °C/min using four model-free methods Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO), Friedman (FM), Starink (STAR) along with Distributed activation energy model (DAEM) to calculate pre-exponential factor. The calculated activation energy Ea by these model-free methods were in the range of 93.3-104.8 kJ/mol and pre-exponential factor (A) was observed between the range 46.6 x103-90.5 x106/min by the DAEM method. The standard deviation (σ) calculated from average activation energy using all four methods was 4.5 kJ/mol, which showed the consistency in the methods employed to determine the activation energy of sun hemp.

Optical Biosensors and Their Applications for the Detection of Water Pollutants.

The correct detection and quantification of pollutants in water is key to regulating their presence in the environment. Biosensors offer several advantages, such as minimal sample preparation, short measurement times, high specificity and sensibility and low detection limits. The purpose of this review is to explore the different types of optical biosensors, focusing on their biological elements and their principle of operation, as well as recent applications in the detection of pollutants in water. According to our literature review, 33% of the publications used fluorescence-based biosensors, followed by surface plasmon resonance (SPR) with 28%. So far, SPR biosensors have achieved the best results in terms of detection limits. Although less common (22%), interferometers and resonators (4%) are also highly promising due to the low detection limits that can be reached using these techniques. In terms of biological recognition elements, 43% of the published works focused on antibodies due to their high affinity and stability, although they could be replaced with molecularly imprinted polymers. This review offers a unique compilation of the most recent work in the specific area of optical biosensing for water monitoring, focusing on both the biological element and the transducer used, as well as the type of target contaminant. Recent technological advances are discussed.

Potential arsenic-chromium-lead Co-contamination in the hilly terrain of Arunachal Pradesh, north-eastern India: Genesis and health perspective.

In the recent times, multi-metal co-contamination in the groundwater of various parts of the globe has emerged as a challenging environmental health problems. While arsenic (As) has been reported with high fluoride and at times with uranium; and Cr & Pb are also found in aquifers under high anthropogenic impacts. The present work probably for the first time traces the As-Cr-Pb co-contamination in the pristine aquifers of a hilly terrain that are under relatively less stress from the anthropogenic activities. Based on the analyses of twenty-two (n = 22) groundwater (GW) samples and six (n = 6) sediment samples, it was found that Cr being leached from the natural sources as evident from 100% of samples with dissolve Cr exceeding the prescribed drinking water limit. Generic plots suggests rock-water interaction as the major hydrogeological processes with mixed Ca2+-Na+-HCO3- type water. Wide range of pH suggests localized human interferences, as well as indicative of both calcite and silicate weathering processes. In general water samples were found high only with Cr and Fe, however all sediment samples were found to contain As-Cr-Pb. This implies that the groundwater is under-risk of co-contamination of highly toxic trio of As-Cr-Pb. Multivariate analyses indicate that the changing pH as the causative factor for Cr leaching into the groundwater. This is a new finding for a pristine hilly aquifers, and we suspect such condition may also be present in other parts of globe, and thus precautionary investigations are needed to prevent this catastrophic situation to arise, and to alert the community in advance.

Estimation of nutrient sources and fate in groundwater near a large weir-regulated river using multiple isotopes and microbial signatures.

The excessive input of nutrients into groundwater can accelerate eutrophication in associated surface water systems. This study combined hydrogeochemistry, multi isotope tracers, and microbiological data to estimate nutrient sources and the effects of groundwater-surface water interactions on the spatiotemporal variation of nutrients in groundwater connected to a large weir-regulated river in South Korea. δ11B and δ15N-NO3- values, in combination with a Bayesian mixing model, revealed that manure and sewage contributed 40 % and 25 % respectively to groundwater nitrate, and 42 % and 27 % to nitrate in surface water during the wet season. In the dry season, the source apportionment was similar for groundwater while the sewage contribution increased to 52 % of nitrate in river water. River water displayed a high correlation between NO3- concentration and cyanobacteria (Microcystis and Prochlorococcus) in the wet season. The mixing model using multiple isotopes indicated that manure-derived nutrients delivered with increased contributions of groundwater to the river during the wet season governed the occurrence of cyanobacterial blooms in the river. We postulate that the integrated approach using multi-isotopic and microbiological data is highly effective for evaluating nutrient sources and for delineating hydrological interactions between groundwater and surface water, as well as for investigating surface water quality including eutrophication in riverine and other surface water systems.

Seawater intrusion pattern recognition supported by unsupervised learning: A systematic review and application.

Seawater intrusion is among the world's leading causes of groundwater contamination, as salty water can affect potable water access, food production, and ecosystem functions. To explore such contamination sources, multivariate analysis supported by unsupervised learning tools has been used for decades to aid in water resource pattern recognition, clustering, and water quality data variability characterization. This study proposes a systematic review of these techniques applied for supporting seawater intrusion identification based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and subsequent bibliometric analysis of 102 coastal hydrogeological studies. The most relevant identified methods, including principal components analysis (PCA), hierarchical clustering analysis, K-means clustering, and self-organizing maps, are explained and applied to a case study. Although 74 % of the studies that applied dimensional reduction methods, such as PCA, associated most of the database variance with the salinization process, 77 % of the studies that applied clustering methods associated at least one water sample cluster with the influence of seawater intrusion. Based on the review and a practical demonstration using the open-source R software platform, recommendations are made regarding data preprocessing, research opportunities, and publishing information necessary to replicate and validate the studies.

EI/IOT of PFCs: Environmental impacts/interactions, occurrences, and toxicities of perfluorochemicals.

Various studies have been conducted on the perfluorochemicals (PFCs) family over the years. These compounds have been sought in various industrial aspects involving the synthesis of everyday utilities due to their broad range of applications. As a result, PFCs have built up in the environment, causing concern. The presence of PFCs in various environmental media, such as terrestrial and marine settings, as well as the mechanisms of transport, bioaccumulation, and physio-chemical interactions of PFCs within plants, aquatic organisms, microplastics, and, ultimately, the human body, are discussed in this review, which draws on a variety of research publications. The interaction of PFCs with proteins, translocation, and adsorption by hydrophobic interactions were observed, and this had an impact on the natural functioning of biological processes, resulting in events such as phylogenic clustering, competitive inhibition, and many others, posing potential hazards to human health and other relevant organisms in the ecosystem. However, further research is needed to have a better knowledge of PFCs and their interactions so that low-cost treatments can be developed to eliminate them. It is therefore, future research should focus on the role of soil matrix as a defensive mechanism for PFCs, as well as the impact of PFC chain length rejection.