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Advanced Research on Micropollutants in Water

A special issue of Environments (ISSN 2076-3298).

Deadline for manuscript submissions: closed (25 October 2024) | Viewed by 18073

Special Issue Editor


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Guest Editor
Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials (LSRE-LCM), Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
Interests: water treatment; advanced oxidation processes; membrane technology; ozonation; catalysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Micropollutants have the capacity to disturb physiological processes, resulting in unfavorable neurological, immune, developmental and reproductive effects on both humans and wildlife. These substances are frequently detected in aquatic ecosystems and encompass active pharmaceutical ingredients (APIs), personal care products (PCPs), pesticides and microplastics. Understanding the sources, transport, and fate of micropollutants in the environment is crucial for developing effective strategies to mitigate their impacts. In natural waters exposed to sunlight (surface waters), solar-radiation-mediated degradation constitutes an important natural depuration process of micropollutants, especially those resistant to biological degradation. However, these natural processes might not be enough to remove such substances, and complementary remediation strategies must be explored. These strategies can include advanced wastewater treatment technologies, the development of best practices in agriculture and industry to reduce pollutant inputs, and policy measures to limit the release of micropollutants. This Special Issue seeks research papers dealing with advances in micropollutant detection, environmental fate and removal in waters, to provide a well-rounded and complete understanding of the topic.

Dr. Cátia Alexandra Leça Graça
Guest Editor

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Keywords

  • water quality monitoring
  • contaminant identification
  • wastewater management
  • environmental impact
  • discharge regulations
  • pollution control
  • water treatment
  • environmental destination
  • remediation technologies
  • integrated water management

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Published Papers (9 papers)

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Research

15 pages, 2330 KiB  
Article
Optimization of Capillary Electrophoresis by Central Composite Design for Separation of Pharmaceutical Contaminants in Water Quality Testing
by Eman T. Elmorsi and Edward P. C. Lai
Environments 2025, 12(1), 22; https://doi.org/10.3390/environments12010022 - 12 Jan 2025
Viewed by 474
Abstract
Many pharmaceutical active compounds are prepared as hydrochlorides for quick release in the gastrointestinal tract upon oral administration. Their inadvertent escape into the water environment requires efficient analytical separation for accurate quantitation to monitor their environmental fate. The purpose of this study is [...] Read more.
Many pharmaceutical active compounds are prepared as hydrochlorides for quick release in the gastrointestinal tract upon oral administration. Their inadvertent escape into the water environment requires efficient analytical separation for accurate quantitation to monitor their environmental fate. The purpose of this study is to demonstrate how best to optimize a capillary electrophoresis method for the separation of four model pharmaceutical hydrochlorides. Concentration of sodium dibasic phosphate in the background electrolyte solution, pH adjustment with HCl or NaOH, and applied voltage across the capillary were the three key factors chosen for optimization. The peak resolutions and total migration time were examined as the response indicators to complete a central composite design in response surface methodology. The examination revealed that CE separation was driven significantly by a linear regression model and minimally by a quadratic regression model, based on the coefficient of determination, the lack of fit, the total sum of squares, and the p values. Under optimal conditions of the background electrolyte concentration of 75 mM, pH 9, and the applied voltage of 10 kV, the model hydrochlorides were separated within five minutes in the migration order of metformin (first) > phenformin > mexiletine > ranitidine (last). The limits of UV detection/quantification attained under optimal CE conditions were 0.015/0.045, 0.020/0.060, 0.142/0.426, and 0.017/0.051, respectively. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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Figure 1

Figure 1
<p>Comparison of predicted versus actual data of RSM-CCD for (<b>A</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> (MET.HCl: PHEN.HCl), (<b>B</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> (PHEN.HCl: MEX.HCl), (<b>C</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (MEX.HCl: RAN.HCl), and (<b>D</b>) migration time.</p>
Full article ">Figure 2
<p>CE electropherogram of the separation of MET, PHEN, MEX, RAN, and MO at 20 °C, using hydrodynamic injection at 20 mbar for 10 sec: (<b>A</b>) unresolved PAC peaks before applying optimum conditions, (<b>B</b>) good separation of PAC peaks after applying optimum conditions. Background electrolyte concentration of 75 mM, pH 9, applied voltage of 10 kV, and UV detection at 200/254/420 nm.</p>
Full article ">Figure 3
<p>Response surface design of the effect of BGE concentration (mM) and pH versus (<b>A</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>B</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>C</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>R</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>, and (<b>D</b>) migration time (min). The third variable of applied voltage is kept constant at central points.</p>
Full article ">
23 pages, 6263 KiB  
Article
Submerged Membrane Bioreactor Configurations for Biological Nutrient Removal from Urban Wastewater: Experimental Tests and Model Simulation
by Javier A. Mouthón-Bello, Oscar E. Coronado-Hernández and Vicente S. Fuertes-Miquel
Environments 2024, 11(11), 260; https://doi.org/10.3390/environments11110260 - 20 Nov 2024
Viewed by 684
Abstract
Pilot-scale experimental measurements and simulations were utilised to evaluate the nutrient removal efficiency of three submerged membrane bioreactor designs. This study compared setups with post- and pre-denitrification processes. A 625 L pilot plant for treating primary effluent provided the operational data necessary for [...] Read more.
Pilot-scale experimental measurements and simulations were utilised to evaluate the nutrient removal efficiency of three submerged membrane bioreactor designs. This study compared setups with post- and pre-denitrification processes. A 625 L pilot plant for treating primary effluent provided the operational data necessary for calibrating the activated sludge model, specifically for chemical oxygen demand and nitrogen removal under steady-state flow. Identical influent conditions were maintained for all configurations while varying the sludge retention times (from 5 to 100 d), hydraulic retention times (ranging from 4 to 15 h), return activated sludge flow rates (between 0.1 and 3.0), and aerobic volume fractions (from 0.3 to 1.0). The pilot plant tests showed high COD and ammonia removal (above 90%) but moderate total nitrogen removal (above 70%). The simulation results successfully forecasted the effluent concentrations of COD and nitrogen for each configuration. There were noticeable variations in the kinetic parameters, such as mass transfer coefficients and biomass decay rates, related to the activated sludge model. However, increasing the sludge retention time beyond 20 d, hydraulic retention time beyond 8 h, return activated sludge rates above 2.0, or aerobic volume fractions beyond 0.4 did not significantly enhance nutrient removal. The post-denitrification setup showed a clear benefit in nitrogen removal but required a greater oxygen supply. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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<p>Schematic of the ASM model setup in GPS-X for Configurations (<b>a</b>) No. 1, (<b>b</b>) No. 2, and (<b>c</b>) No. 3.</p>
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<p>Configurations and runs of unit processes: (<b>a</b>) Configuration No. 1—Run 1; (<b>b</b>) Configuration No. 2—Run 2; (<b>c</b>) Configuration No. 2—Run 3; and (<b>d</b>) Configuration No. 3—Run 4.</p>
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<p>Effect of sludge retention time on simulated: (<b>a</b>) NH<sub>3</sub> effluent; (<b>b</b>) total heterotrophic biomass; (<b>c</b>) NO<sub>3</sub><sup>−</sup> effluent; and (<b>d</b>) total autotrophic biomass.</p>
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<p>Effect of sludge retention time on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
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<p>Effect of sludge retention time on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
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<p>Effect of hydraulic retention time on simulated: (<b>a</b>) NH<sub>3</sub> effluent; (<b>b</b>) total heterotrophic biomass; (<b>c</b>) NO<sub>3</sub><sup>−</sup> effluent; and (<b>d</b>) total autotrophic biomass.</p>
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<p>Effect of hydraulic retention time on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
Full article ">Figure A5 Cont.
<p>Effect of hydraulic retention time on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
Full article ">Figure A6
<p>Effect of return activated sludge on simulated: (<b>a</b>) NH<sub>3</sub> effluent; (<b>b</b>) total heterotrophic biomass; (<b>c</b>) NO<sub>3</sub><sup>−</sup> effluent; and (<b>d</b>) total autotrophic biomass.</p>
Full article ">Figure A7
<p>Effect of return activated sludge on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
Full article ">Figure A7 Cont.
<p>Effect of return activated sludge on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
Full article ">Figure A8
<p>Effect of aerobic fraction on simulated: (<b>a</b>) NH<sub>3</sub> effluent; (<b>b</b>) total heterotrophic biomass; (<b>c</b>) NO<sub>3</sub><sup>−</sup> effluent; and (<b>d</b>) total autotrophic biomass.</p>
Full article ">Figure A9
<p>Effect of aerobic fraction on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
Full article ">Figure A9 Cont.
<p>Effect of aerobic fraction on simulated: (<b>a</b>) oxygen uptake; (<b>b</b>) nitrification; and (<b>c</b>) denitrification.</p>
Full article ">
14 pages, 2641 KiB  
Article
From Waste to Resource: Evaluating Biomass Residues as Ozone-Catalyst Precursors for the Removal of Recalcitrant Water Pollutants
by Cátia A. L. Graça and Olívia Salomé Gonçalves Pinto Soares
Environments 2024, 11(8), 172; https://doi.org/10.3390/environments11080172 - 12 Aug 2024
Viewed by 1352
Abstract
Five different biomass wastes—orange peel, coffee grounds, cork, almond shell, and peanut shell—were transformed into biochars (BCs) or activated carbons (ACs) to serve as adsorbents and/or ozone catalysts for the removal of recalcitrant water treatment products. Oxalic acid (OXL) was used as a [...] Read more.
Five different biomass wastes—orange peel, coffee grounds, cork, almond shell, and peanut shell—were transformed into biochars (BCs) or activated carbons (ACs) to serve as adsorbents and/or ozone catalysts for the removal of recalcitrant water treatment products. Oxalic acid (OXL) was used as a model pollutant due to its known refractory character towards ozone. The obtained materials were characterized by different techniques, namely thermogravimetric analysis, specific surface area measurement by nitrogen adsorption, and elemental analysis. In adsorption experiments, BCs generally outperformed ACs, except for cork-derived materials. Orange peel BC revealed the highest adsorption capacity (Qe = 40 mg g−1), while almond shell BC showed the best cost–benefit ratio at €0.0096 per mg of OXL adsorbed. In terms of catalytic ozonation, only ACs made from cork and coffee grounds presented significant catalytic activity, achieving pollutant removal rates of 72 and 64%, respectively. Among these materials, ACs made from coffee grounds reveal the best cost/benefit ratio with €0.02 per mg of OXL degraded. Despite the cost analysis showing that these materials are not the cheapest options, other aspects rather than the price alone must be considered in the decision-making process for implementation. This study highlights the promising role of biomass wastes as precursors for efficient and eco-friendly water treatment processes, whether as adsorbents following ozone water treatment or as catalysts in the ozonation reaction itself. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Adsorption capacities Qe (mg g<sup>−1</sup>).</p>
Full article ">Figure 2
<p>OXL degradation profile promoted by BCs combined with O<sub>3</sub>. [OXL]<sub>0</sub> = 20 mg·L<sup>−</sup><sup>1</sup>, BCs = 50 mg·L<sup>−</sup><sup>1</sup>, [O<sub>3</sub>]<sub>gas</sub> = 50 g·Nm<sup>3</sup>.</p>
Full article ">Figure 3
<p>OXL degradation profile promoted by ACs combined with O<sub>3</sub>. [OXL]<sub>0</sub> = 20 mg·L<sup>−1</sup>, ACs = 50 mg·L<sup>−1</sup>, [O<sub>3</sub>]<sub>gas</sub> = 50 g·Nm<sup>3</sup>.</p>
Full article ">Figure A1
<p>TGA and DTG curves for orange peel.</p>
Full article ">Figure A2
<p>TGA and DTG curves for coffee ground.</p>
Full article ">Figure A3
<p>TGA and DTG curves for cork waste.</p>
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<p>TGA and DTG curves for almond shell.</p>
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<p>TGA and DTG curves for peanut shell.</p>
Full article ">
18 pages, 3779 KiB  
Article
Can Lagoons Serve as a Quaternary Treatment for Micropollutants in Wastewater Treatment Plants? Recent Implications for Compliance with the New Urban Wastewater Treatment Directive
by Lissette Díaz-Gamboa, Sofía Martínez-López, Luis Miguel Ayuso-García, Agustín Lahora and Isabel Martínez-Alcalá
Environments 2024, 11(6), 105; https://doi.org/10.3390/environments11060105 - 22 May 2024
Viewed by 2061
Abstract
This study explores the potential of storage lagoons as a quaternary treatment step in wastewater treatment plants (WWTPs), focusing on compliance with the recent European Urban Wastewater Treatment Directive (UWWTD), which mandates an 80% reduction in specific micropollutants. While conventional treatments effectively remove [...] Read more.
This study explores the potential of storage lagoons as a quaternary treatment step in wastewater treatment plants (WWTPs), focusing on compliance with the recent European Urban Wastewater Treatment Directive (UWWTD), which mandates an 80% reduction in specific micropollutants. While conventional treatments effectively remove residual nutrients and solids, the potential of storage lagoons as an additional treatment is not fully defined. This research aims to address this gap by assessing the efficacy of storage lagoons in refining the effluent quality at the Cabezo Beaza WWTP, considering recent UWWTD requirements. We conduct a comprehensive assessment of the water quality parameters and micropollutants, before and after the storage lagoon stage, at the Cabezo Beaza WWTP. The results indicate that this strategy of prolonged storage in lagoons manages to meet the reduction objectives established by the Directive, reaching elimination percentages greater than 80% for the majority of the analyzed micropollutants. Our findings suggest that lagoons significantly improve water quality and reduce contaminants beyond conventional treatments, offering environmental and economic benefits. This paper discusses the mechanisms behind these improvements, such as natural sedimentation, microbial activity, and potential phytoremediation. This study contributes to the research on advanced wastewater treatment and supports the integration of storage lagoons as a viable quaternary treatment solution that meets the UWWTD standards. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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Figure 1

Figure 1
<p>Location and image of the WWTP used in this study.</p>
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<p>Graphical representation of the Cabezo Beaza WWTP’s treatment stages and sampling sites.</p>
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<p>(<b>a</b>) Mean concentrations and (<b>b</b>) elimination percentages of micropollutants in category 1 from influent and effluent samples for both the WWTP secondary treatment and lagoon treatment, (n ≥ 5).</p>
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<p>(<b>a</b>) Mean concentrations and (<b>b</b>) elimination percentages of micropollutants in category 2 from influent and effluent samples for both the WWTP secondary treatment and lagoon treatment (n ≥ 5).</p>
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<p>Average removal percentages of six and twelve micropollutants.</p>
Full article ">
13 pages, 2275 KiB  
Article
Removal of Residual Chlorine from Stormwater Using Low-Cost Adsorbents and Phytoremediation
by Marina Valentukeviciene, Ieva Andriulaityte, Agnieszka Karczmarczyk and Ramune Zurauskiene
Environments 2024, 11(5), 101; https://doi.org/10.3390/environments11050101 - 12 May 2024
Cited by 4 | Viewed by 2162
Abstract
In recent decades, the pollution of water with micropollutants has become an increasing environmental concern. Since 2019, increased stormwater pollution from chlorine-based disinfectants has been recorded due to the COVID-19 pandemic. Runoff from disinfected areas and the residual chlorine present in stormwater are [...] Read more.
In recent decades, the pollution of water with micropollutants has become an increasing environmental concern. Since 2019, increased stormwater pollution from chlorine-based disinfectants has been recorded due to the COVID-19 pandemic. Runoff from disinfected areas and the residual chlorine present in stormwater are transported to surface water bodies, posing a risk to aquatic flora and fauna. The objectives of this study were (1) to evaluate the efficiency of different low-cost and recyclable filter materials in removing residual chlorine, and (2) to test plants’ ability to reduce residual chlorine concentrations through phytoremediation. Experiments were conducted in the laboratory (column and batch) and in the field (raised garden bed) to assess the efficiency of various filter materials (peat, wood chips, sawdust and the lightweight aggregates) in retaining residual chlorine to be implemented in green infrastructure. The best retainers of chlorine were sawdust (96%) and the LWA Leca (76%). No harmful effects of residual chlorine (changes in growth, color, leaf size, etc.) on plants (Tagetes patula or Pisum savitum) were observed and the residual chlorine in the leachate samples was below the equipment’s detection limit. Our research results will contribute to future studies aiming to remove various micropollutants from stormwater using remediation technologies. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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Figure 1
<p>Forms of chlorine in water.</p>
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<p>Filter materials used in the experiments: (<b>a</b>) peat; (<b>b</b>) wood chips; and (<b>c</b>) sawdust.</p>
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<p>Researched LWA materials: (<b>a</b>) Polski; (<b>b</b>) Leca; (<b>c</b>). Pollytag; and (<b>d</b>) Ceski.</p>
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<p>Batch test.</p>
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<p>Column test: 1—LWA (Pollytag); 2—peat (0.1–5 mm); 3—wood chips (20–50 mm); 4—sawdust (0.1–2 mm).</p>
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<p>Raised garden bed: 1. Plants; 2. Peat layer (15 cm); 3. Water-filtering layer (10 cm); 4. Drainage layer (LWA, 5 cm); 5. Wooden bed frame; and 6. Water tank.</p>
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<p>Fraction size evaluation.</p>
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13 pages, 4870 KiB  
Article
A Droplet-Based Microfluidic Impedance Flow Cytometer for Detection of Micropollutants in Water
by Mohammadreza Aghel, Somayeh Fardindoost, Nishat Tasnim and Mina Hoorfar
Environments 2024, 11(5), 96; https://doi.org/10.3390/environments11050096 - 6 May 2024
Cited by 2 | Viewed by 3775
Abstract
Microplastics as micropollutants are widely spread in aquatic areas that can have a toxic effect on aquatic life. To reduce the potential risk they pose, it is essential to detect the microplastics and the source of the contamination of the environment. Here, we [...] Read more.
Microplastics as micropollutants are widely spread in aquatic areas that can have a toxic effect on aquatic life. To reduce the potential risk they pose, it is essential to detect the microplastics and the source of the contamination of the environment. Here, we designed and developed a droplet-based microfluidic impedance flow cytometer for in situ detection of microplastics in water. Impedance spectroscopy enables the direct measurement of the electrical features of microplastics as they move in water, allowing for sizing and identification of concentration. To show the feasibility of the developed method, pure and functionalized polystyrene beads ranging from 500 nm to 6 μm in four size groups and different concentrations were used. Focusing on three different frequencies (4.4 MHz, 11 MHz, and 22.5 MHz), the changes in the signal phase at frequencies of 4.4 MHz and 11 MHz are a strong indicator of microplastic presence. In addition, the functionalized microplastics showed different magnitudes of the measured signal phase than the pure ones. A k-nearest neighbors classification model demonstrated our developed system’s impressive 97.4% sensitivity in accurately identifying microplastics based on concentration. The equivalent circuit model revealed that the double-layer capacity of water droplets is significantly impacted by the presence of the microplastics. Our findings show the potential of droplet-based microfluidic impedance flow cytometry as a practical method for detecting microplastics in water. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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Figure 1
<p>(<b>a</b>) The final device, (<b>b</b>) the dimension of the channel, and (<b>c</b>) the coplanar electrodes at the bottom of the microchannel.</p>
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<p>(<b>a</b>) The actual microfluidic chip, and (<b>b</b>) the measurement setup including syringe pump (left), the optical microscope (middle), and the lock in amplifier (right). The arrows show the magnified image of actual device under the optical microscope.</p>
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<p>Signal phase vs. signal magnitude for all the measurements for all droplet contents at (<b>a</b>) 0.05% solid particle concentration at 4.4 MHz, (<b>b</b>) 0.10% solid particle concentration at 4.4 MHz, (<b>c</b>) 0.20% solid particle concentration at 4.4 MHz, (<b>d</b>) 0.05% solid particle concentration at 11 MHz, (<b>e</b>) 0.10% solid particle concentration at 11 MHz, (<b>f</b>) 0.20% solid particle concentration at 11 MHz, (<b>g</b>) 0.05% solid particle concentration at 22.5 MHz, (<b>h</b>) 0.10% solid particle concentration at 22.5 MHz, (<b>i</b>) 0.20% solid particle concentration at 22.5 MHz.</p>
Full article ">Figure 4
<p>Signal phase at 11 MHz vs. signal phase at 4.4 MHz for all the droplet contents for (<b>a</b>) 0.05% solid particle content, (<b>b</b>) 0.10% solid particle content, (<b>c</b>) 0.20% solid particle content.</p>
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<p>Effect of solid particle concentration of the mean of the signal phase at (<b>a</b>) 4.4 MHz, (<b>b</b>) 11 MHz.</p>
Full article ">Figure 6
<p>The estimated sensitivity of the device using hold-out validation for different models. The device shows poor performance in the classification of carboxyl-modified MPs, while it can successfully classify the inert ones.</p>
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<p>An equivalent circuit is associated with the impedance sensor in (<b>a</b>) an empty channel and (<b>b</b>) a channel with a droplet. (<b>c</b>) The signal phase vs. frequency of inert microplastic at different concentration levels. R<sub>d</sub> and R<sub>o</sub>: Represent the resistance of droplets and oil, respectively. C<sub>dl,d</sub> and C<sub>dl,o</sub>: Represent the double-layer capacitors of the droplet and oil, respectively, which change due to the microbeads’ ability to store charge. C<sub>par</sub>: Represent the parasitic capacitance, which is also present in all measurements induced by the electric field between wires, PCB connections, and the gold pattern on the glass.</p>
Full article ">
13 pages, 2481 KiB  
Article
Detection and Screening of Organic Contaminants in A Riverine System of Georgia Using Non-Targeted Analysis
by Gayatri Basapuram, Srimanti Duttagupta and Avishek Dutta
Environments 2024, 11(5), 89; https://doi.org/10.3390/environments11050089 - 26 Apr 2024
Viewed by 1854
Abstract
Numerous organic chemicals exist within aquatic environments, yet effectively screening and prioritizing them is a huge challenge. This study provides a comprehensive investigation into the ecological dynamics of the North Oconee River within Athens-Clarke County, Georgia, with a specific focus on the distribution [...] Read more.
Numerous organic chemicals exist within aquatic environments, yet effectively screening and prioritizing them is a huge challenge. This study provides a comprehensive investigation into the ecological dynamics of the North Oconee River within Athens-Clarke County, Georgia, with a specific focus on the distribution of 33 identified compounds, including a prominent pesticide. The research, conducted in the riverine ecosystems proximal to the Firefly trail, employs advanced analytical techniques to elucidate potential contamination sources arising from agricultural and urban runoff. Intriguingly, the study reveals North Oconee River near the Firefly Trail as a notable site for heightened pesticide contamination, warranting a meticulous exploration of its origins. Furthermore, the investigation unveils the intricate microbial degradation processes of malathion within the North Oconee River, elucidating the pivotal role played by microbial activity in river water. The detection of degradant byproducts prompts the considerations of bioavailability and toxicity, associating potential implications for the river’s overall ecological health. Ongoing research endeavors to precisely quantify environmental risks and unravel indigenous microbial degradation pathways, presenting pivotal contributions to the scientific community’s understanding of complex riverine ecosystems. This research serves as a foundational piece in informing sustainable environmental management practices and emphasizes the urgency of comprehensive stewardship in safeguarding aquatic ecosystems. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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<p>Overview map of the study area and sampling sites, featuring representative images of Firefly Trail (FT), located in Athens, Georgia, USA.</p>
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<p>Heatmap illustrating the normalized responses of the identified organic compounds at the FT across all sampling events.</p>
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<p>Graph illustrating malathion concentration (ng/L) across five sampling events at FT.</p>
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<p>Graph depicting malathion concentration (ng/L) (plotted in the bar graph) and peak areas (×10<sup>3</sup>) of malaoxon (plotted in line graph) in four experimental sets. Set 1 includes type 1 ultrapure water spiked with 0.2% malathion, set 2 consists of river water from FT spiked with 0.2% malathion, set 3 features autoclaved river water from FT spiked with 0.2% malathion, and set 4 comprises river water from FT spiked with HgCl<sub>2</sub> (resulting final concentration of 0.1%) and 0.2% malathion.</p>
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28 pages, 7845 KiB  
Article
Linking Clusters of Micropollutants in Surface Water to Emission Sources, Environmental Conditions, and Substance Properties
by Tessa E. Pronk, Elvio D. Amato, Stefan A. E. Kools and Thomas L. Ter Laak
Environments 2024, 11(3), 46; https://doi.org/10.3390/environments11030046 - 28 Feb 2024
Cited by 1 | Viewed by 2198
Abstract
Water quality monitoring programs yield a wealth of data. It is often unclear why a certain substance occurs in higher concentrations at a certain location or time. In this study, substances were considered in clusters with co-varying concentrations rather than in isolation. A [...] Read more.
Water quality monitoring programs yield a wealth of data. It is often unclear why a certain substance occurs in higher concentrations at a certain location or time. In this study, substances were considered in clusters with co-varying concentrations rather than in isolation. A total of 196 substance clusters at 19 monitoring sites in the rivers Rhine and Meuse were identified. A total of nine clusters were found repeatedly with a similar composition at different monitoring sites. Several environmental conditions and substance properties could be linked to clusters. In addition, overlap with reference substance lists was determined. These lists group multiple substances according to emission sources, substance types, or type of use. The reference substance lists revealed that Rhine and Meuse are similarly affected. The nine ‘repeating clusters’ were analyzed in more detail to identify drivers. For instance, a repeating cluster with herbicides was specifically linked to high temperatures and a high number of hours in the sun per day, e.g., summer conditions. A cluster containing polychlorinated biphenyls, identified as persistent and with a high tendency to bind organic matter, was linked to high river discharge and attributed to a potential release from sediment resuspension. Not all substances could be clustered, because their concentration did not structurally vary in the same way as other substances. The presented explorative cluster analyses, along with the obtained relations with substance properties, local environmental conditions, and reference substance lists, may facilitate the reconstruction of the processes that lead to the observed variation in concentrations. This knowledge can subsequently be used by water managers to improve water quality. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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<p>Workflow for preprocessing of the data per location for hierarchical clustering analyses (HCA), yielding substance clusters per location. A selection for ‘significant’ clusters follows after this workflow (not shown).</p>
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<p>Visualization of the hypergeometric test for significance of enrichment of ‘reference list’ substances of any reference list in a cluster.</p>
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<p>Heatmap obtained using monitoring data from Nieuwegein (NGN). Rows: substances. Columns: weekly aggregated monitoring samples (see <a href="#environments-11-00046-f001" class="html-fig">Figure 1</a>) between 2017–2021. Some example clusters are highlighted by a colored box. The substances are indicated attached to the box. Darker colors indicate higher relative concentrations (Z-score).</p>
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<p>The association of recurring clusters (<a href="#environments-11-00046-t004" class="html-table">Table 4</a>) with substance properties. The green box at the bottom indicates the average property value of all the substances in the analysis. Clusters are indicated on the <span class="html-italic">y</span>-axis by a recurring cluster code (see <a href="#environments-11-00046-t004" class="html-table">Table 4</a>), a location code (see <a href="#environments-11-00046-t002" class="html-table">Table 2</a>), and a cluster number. Orange boxplots are significantly different from the average property value (<span class="html-italic">p</span> &lt; 0.01), blue boxplots are not. See the data package for substances associated with each of the coded clusters.</p>
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<p>The association of recurring clusters with conditions. The green box indicates the condition values at high concentration of all the substances in all clusters. Clusters are indicated on the <span class="html-italic">y</span>-axis by a recurring cluster code (see <a href="#environments-11-00046-t004" class="html-table">Table 4</a>), a location code (see <a href="#environments-11-00046-t002" class="html-table">Table 2</a>), and a cluster number. Orange boxplots are significantly different from the average condition value (<span class="html-italic">p</span> &lt; 0.01), blue boxplots are not. See the data package for substances associated with each of the coded clusters.</p>
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<p>Hierarchical clustering of percentages overlap of substances between reference substance lists. The height of the line in the dendrogram indicates the dissimilarity between clusters.</p>
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<p>Log Half-life values per cluster (d). Half-life values were obtained from Opera models [<a href="#B22-environments-11-00046" class="html-bibr">22</a>]. Orange boxplots differ significantly from the average (green boxplot), blue boxplots do not.</p>
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<p>Log Solubility values per cluster (mg/L) Log Solubility values were obtained from EpiSuite models [<a href="#B23-environments-11-00046" class="html-bibr">23</a>]. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Henry’s constant values per cluster. Henry’s constant values were obtained from EpiSuite models [<a href="#B23-environments-11-00046" class="html-bibr">23</a>] and recalculated by log10 (value * 101,325). Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Log <span class="html-italic">K<sub>OC</sub></span> values per cluster. Log <span class="html-italic">K<sub>OC</sub></span> values were obtained from EpiSuite kow models [<a href="#B23-environments-11-00046" class="html-bibr">23</a>]. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Log <span class="html-italic">K<sub>OW</sub></span> values per cluster. Log <span class="html-italic">K<sub>OW</sub></span> values were obtained from EpiSuite models [<a href="#B23-environments-11-00046" class="html-bibr">23</a>]. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Biodegradability values per cluster. Biodegradability values were obtained from EpiSuite model Biowin3 [<a href="#B23-environments-11-00046" class="html-bibr">23</a>]. Values range from very persistent (1) to very biodegradable (5). Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Average mass values per cluster. Average mass values were obtained from the CompTox Chemicals Dashboard v2.3.0. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Log vapor pressure values per cluster (mm Hg, 25 deg C). Log vapor pressure values were obtained from EpiSuite models [<a href="#B23-environments-11-00046" class="html-bibr">23</a>]. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Log KOA values per cluster. Log octanol air partition coefficients (KOA) values were obtained from Opera models [<a href="#B22-environments-11-00046" class="html-bibr">22</a>]. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Boiling point values per cluster (deg C). Boiling point values were obtained from Opera models [<a href="#B22-environments-11-00046" class="html-bibr">22</a>]. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Density values per cluster (g/cm<sup>3</sup>). Density values were obtained from EPA Toxicity Estimation Software Tool (TEST) prediction models via the CompTox Chemicals Dashboard v2.3.0. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>AOH values per cluster (cm<sup>3</sup>/molecule * sec). Atmospheric hydroxylation rate (AOH) values were obtained from Opera models [<a href="#B22-environments-11-00046" class="html-bibr">22</a>]. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Temperature values per cluster (C). Temperature values were obtained from the RIWA datasets. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Discharge values per cluster (m<sup>3</sup>/s). Discharge values were obtained from the RIWA datasets. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Oxygen values per cluster (mg/L). Oxygen values were obtained from the RIWA datasets. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Evaporation potential values per cluster (Makkink reference crop evaporation in 0.1 mm). Evaporation values were obtained from Dutch weather data (KNMI). Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>pH values per cluster. pH values were obtained from the RIWA datasets. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>DOC values per cluster (mg/L). DOC values were obtained from the RIWA datasets. Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Sun hour values per cluster. Sun hour values were obtained from Dutch weather data (KNMI). Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Precipitation values per cluster (0.1 mm/day). Precipitation values were obtained from Dutch weather data (KNMI). Orange boxplots differ significantly from the average values (green boxplot), blue boxplots do not.</p>
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<p>Flow diagram for determining significant clusters in the cluster significance method.</p>
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32 pages, 9751 KiB  
Article
Stream Chemistry and Forest Recovery Assessment and Prediction Modeling in Coal-Mine-Affected Watersheds in Kentucky, USA
by Oguz Sariyildiz, Buddhi R. Gyawali, George F. Antonious, Kenneth Semmens, Demetrio Zourarakis and Maya P. Bhatt
Environments 2024, 11(3), 40; https://doi.org/10.3390/environments11030040 - 21 Feb 2024
Cited by 1 | Viewed by 2359
Abstract
Kentucky is one of the largest coal-producing states; surface coal mining has led to changes in natural land cover, soil loss, and water quality. This study explored relationships between actively mined and reclaimed areas, vegetation change, and water quality parameters. The study site [...] Read more.
Kentucky is one of the largest coal-producing states; surface coal mining has led to changes in natural land cover, soil loss, and water quality. This study explored relationships between actively mined and reclaimed areas, vegetation change, and water quality parameters. The study site evaluated 58 watersheds with Landsat-derived variables (reclamation age and percentage of mining, reclaimed forest, and reclaimed woods) as well as topographic variables (such as elevation, slope, drainage density, and infiltration). Water samples were collected in spring (n = 9), summer (n = 14), and fall (n = 58) 2017 to study changes in water quality variables (SO42−, alkalinity, conductivity, Ca2+, Mg2+, Mn2+, Al3+, and Fe2+, Fe3+) in response to changes in land cover. Pearson correlation analyses indicated that conductivity has strong to very strong relationships with water quality variables related to coal mining (except Al3+, Fe2+, Fe3+, Mn2+, elevation, slope, and drainage density) and land cover variables. In addition, separate regression analyses were performed, with conductivity values based on samples collected in the fall. First, conductivity responses to mining percentage, reclamation age and topographic variables were examined (adjusted R2 = 0.818, p < 0.01). Next, vegetation cover change parameters were added to the same model, which yielded slightly improved R2 (adjusted R2 = 0.826, p < 0.01). Finally, reclamation age and mining percentages were used to explain the quantity of reclaimed forested areas as a percentage of watersheds. The model was significant (p < 0.01), with an adjusted R2 value of 0.641. Results suggest that the quantity (area as a percentage) of reclaimed forests may be a predictor of the mining percentage and reclamation age. This study indicated that conductivity is a predictable water quality indicator that is highly associated with Coal-Mine-Related Stream Chemistry in areas where agriculture and urban development are limited. Water quality is not suitable for various purposes due to the presence of contaminants, especially in mined sites. These findings may help the scientific community and key state and federal agencies improve their understanding of water quality attributes in watersheds affected by coal mining, as well as refine land reclamation practices more effectively while such practices are in action. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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<p>Effects of coal mining on stream water. Left side of valley (unmined): natural infiltration, precipitation infiltrated efficiently (e.g., trees intercept rain, roots create porosity, topsoil provides effective infiltration). Right side of valley (mined): poor infiltration, stream pollution proportionally with mined area, surface flow not tolerated properly (e.g., compacted soil, topsoil loss). The figure was created by Oguz Sariyildiz.</p>
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<p>Two Valley fills side by side near Chavies, Kentucky [37.373367, 83.351092]. Valley fill is an engineered earthen and rock structure where excess soil and rocks are deposited from surface mining or, in some cases, underground mining. They built in approximately 1995 (<b>right</b>) and 2013 (<b>left</b>). They are 350 m and 300 m deep, respectively. Source: Google Earth.</p>
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<p>Photos from the same scene (near Whitesburg, Kentucky [37.031036, −82.710169] in different years. It displays how surface mining affects the natural land cover and the appearance of land cover during recovery. Mined areas from 1995 turned to woods while mined areas from 2005 turned to grass and bush.</p>
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<p>Location of the research area.</p>
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<p>Research area (HUC10 watersheds), studied stream reach watersheds, and sampling locations (exit point of the stream reach watersheds).</p>
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<p>Water quality data analysis steps to assess variables.</p>
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<p>Active mining areas by years between 1986 and 2017.</p>
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<p>Mining percentage (independent variable) versus fall conductivity [µS/cm] (dependent variable) values (<span class="html-italic">n</span> = 58, <span class="html-italic">R</span> = 0.86, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Reclaimed forest percentage (independent variable) versus fall conductivity [µS/cm] (dependent variable) values (<span class="html-italic">n</span> = 58, <span class="html-italic">R</span> = −0.07, <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Mined-RF (mining percentage without reclaimed forest; independent variable) versus fall conductivity [µS/cm] (dependent variable) values (<span class="html-italic">n</span> = 58, <span class="html-italic">R</span> = 0.90, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Reclamation age [years] (independent variable) versus fall conductivity [µS/cm] (dependent variable) values (<span class="html-italic">n</span> = 58, <span class="html-italic">R</span> = −0.67, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Reclamation age [independent variable) versus normalized reclamation forest percentage (Dependent variable) values (<span class="html-italic">n</span> = 58, <span class="html-italic">R</span> = 0.50, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Log Mined (Independent variable) versus normalized reclamation forest percentage (dependent variable) values (<span class="html-italic">n</span> = 58, <span class="html-italic">R</span> = 0.45, <span class="html-italic">p</span> &lt; 0.01).</p>
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