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Environments, Volume 12, Issue 1 (January 2025) – 33 articles

Cover Story (view full-size image): This study explored a novel soil washing process combined with air-bubbling to remove PFAS from soils with high organic carbon (OC) content. Air-bubbling with high-OC soil did not enhance PFBS or PFOA removal due to their low surface activity. However, PFOS extraction improved by ~56% with bubbling, consistent with its higher surface activity. PFDA was strongly adsorbed to the high-OC soil and was not efficiently removed by either method. A slight improvement in PFDA removal (~13%) occurred with a co-surfactant or when the OC content was reduced to 4%. Results indicate that soil washing alone is sufficient for short-chain PFAS removal. While bubbling mildly affected some long-chain PFAS removal from the solution, it did not improve the overall PFAS removal from high-OC soils, highlighting treatment challenges. Immobilization may be ideal for managing such sites. View this paper
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20 pages, 8720 KiB  
Article
Impacts of an Intermittent Bus Lane on Local Air Quality: Lessons from an Effectiveness Study
by Neelakshi Hudda, Isabelle S. Woollacott, Nisitaa Karen Clement Pradeep and John L. Durant
Environments 2025, 12(1), 33; https://doi.org/10.3390/environments12010033 - 20 Jan 2025
Viewed by 379
Abstract
Bus lanes with intermittent prioritization (BLIPs) have been proposed as a way to reduce traffic burden and improve air quality along busy urban streets; however, to date, the impacts of BLIPs on local-scale air quality have not been thoroughly evaluated, due in part [...] Read more.
Bus lanes with intermittent prioritization (BLIPs) have been proposed as a way to reduce traffic burden and improve air quality along busy urban streets; however, to date, the impacts of BLIPs on local-scale air quality have not been thoroughly evaluated, due in part to challenges in study design. We measured traffic-emission proxies—black carbon aerosol and ultrafine particles—before and after the installation of a BLIP in the Boston area (Massachusetts, USA) in 2021, and compared our data with traffic measurements to determine whether changes in air quality were attributable to changes in traffic patterns. We used both stationary and mobile monitoring to characterize temporal and spatial variations in air quality both before and after the BLIP went into operation. Although the BLIP led to a reduction in traffic volume (~20%), we did not find evidence that this reduction caused a significant change in local air quality. Nonetheless, substantial spatial and temporal differences in pollutant concentrations were observed; the highest concentrations occurred closest to a nearby highway along a section of the bus lane that was in an urban canyon, likely causing pollutant trapping. Wind direction was a dominant influence: pollutant concentrations were generally higher during winds that oriented the bus lane downwind of or parallel to the highway. Based on our findings, we recommend in future studies to evaluate the effectiveness of BLIPs that: (i) traffic and air quality measurements be collected simultaneously for several non-weekend days immediately before and immediately after bus lanes are first put into operation; (ii) the evaluation should be performed when other significant changes in motorists’ driving behavior and bus ridership are not anticipated; and (iii) coordinated efforts be made to increase bus ridership and incentivize motorists to avoid using the bus lane during the hours of intermittent prioritization. Full article
(This article belongs to the Special Issue Advances in Urban Air Pollution)
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<p>The bus lane (shown in green) is on the south side of Mystic Avenue and extends from the intersection of Main Street and Mystic Avenue in Medford (to the north) to the intersection of Wheatland Street and Mystic Avenue in Somerville (to the south). Traffic monitoring was performed near the intersections with 1. Reardon Road; 2. Billings Avenue; 3. Bonner Avenue; 4. Moreland Street; 5. Shore Drive; and 6. Grant Street. Mobile monitoring was performed along the entire length of the bus lane (both directions), as well as on the road segments (black lines) extending from each end. The stationary monitoring site is marked by the blue star (this figure was created using ArcGIS (version 10.8.2) with data layers acquired from MassGIS).</p>
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<p>Box plots of traffic counts (15-min intervals) at six sites along Mystic Avenue in Medford and Somerville between 06:00 and 09:00 on days before (red) and after (blue) the bus lane became operational. The <b>upper</b> panel shows the total counts in both southbound lanes (n.b., the bus lane was southbound only); the <b>lower</b> panel shows the total counts in both northbound lanes. The horizontal line in each box represents the median. ‘Before’ data were collected on November 5, 2020, at all six sites; ‘after’ data were collected on September 21, 2021 (Medford) and on August 5, August 11, August 21, December 8, and December 9, 2021 (Somerville) (see <a href="#environments-12-00033-t0A1" class="html-table">Table A1</a>). The northbound counts were generally much higher at the three Somerville sites (Moreland Road, Shore Drive, Grant Street) compared with the three Medford sites (Reardon Road, Billings Avenue, Bonner Avenue) due to northbound vehicles in Somerville turning onto I-93 north at the interchange near Moreland Street, the Somerville–Medford border (<a href="#environments-12-00033-f001" class="html-fig">Figure 1</a>).</p>
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<p><b>Left</b> column: time series of traffic counts (15-min intervals) and hourly average traffic speed measured at Moreland Street one day before (black line) and three days after (red, orange, and purple lines) the installation of the bus lane. <b>Right</b> column: On August 5, 2021, measurements were collected in the southbound lanes with separate traffic monitors on the left lane and the right lane (i.e., the bus lane).</p>
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<p>Mobile measurements of PNC, BC, and NO<sub>x</sub> collected along the southbound lanes of Mystic Avenue between 06:15 and 06:28 on December 8, 2021. <b>Upper</b> panels show pollutant timeseries plots; the panels <b>below</b> show the same data on maps. The highconcentration spike of each pollutant at ~06:25 occurred when the mobile lab passed the I-93 on/off ramp. The black arrows on the maps show the direction that the mobile lab was heading when the measurements were taken.</p>
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<p>Measurements of PNC, BC, and NO<sub>x</sub> collected during 9 mobile monitoring runs along the southbound and 11 runs along the northbound lanes of Mystic Avenue between 0600 and 0900 on December 8, 2021. The <span class="html-italic">x</span>-axis is shown as latitude with north on the left (Main St. end of route) and south on the right (Route 28 end of route). Black vertical lines indicate the traffic monitoring sites along Mystic Avenue: south of Moreland Street (I-93 on/off ramp), south of Shore Drive (Shore), and north of Grant Street (Grant). The numbers at the top of each vertical line indicate the 15-min-average traffic counts in each direction at the three sites between 06:00 and 09:00.</p>
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<p>Boxplots of particle number concentration (PNC) and black carbon (BC) concentration measured during mobile monitoring on Mystic Avenue (both lanes) before and after the bus lane became operational.</p>
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<p>(<b>A</b>) Comparison of one month of BC measurements from the stationary site before and after the installation of the bus lane (December 2020 vs. 2021) in the form of frequency distributions. (<b>B</b>) A polar plot of the same data, with wind direction around the circumference and BC concentration (ng/m<sup>3</sup>) along the radial axis. (<b>C</b>) Fine temporal resolution comparison of Mystic Avenue measurements with the regulatory site that is considered to represent the urban background in the region. (<b>D</b>) BC concentrations were monitored at the stationary site and at two regulatory sites in the Boston area.</p>
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<p>Box plots of traffic speed (15-min averages) measured at six sites along Mystic Avenue in Medford and Somerville between 06:00 and 09:00 on days before (red) and after (blue) the bus lane became operational. The <b>upper</b> panel shows traffic speed in both southbound lanes; the <b>lower</b> panel shows the traffic speed in both northbound lanes. The horizontal line in each box represents the median. ‘Before’ data were collected on November 5, 2020, at all six sites; ‘after’ data were collected on September 21 (Medford) and on August 5, August 11, August 21, December 8, and December 9, 2021 (Somerville) (see <a href="#environments-12-00033-t0A1" class="html-table">Table A1</a>).</p>
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<p>Time series of traffic counts (15-min intervals) and hourly average traffic speed measured at all sites on both southbound lanes of Mystic Avenue. Measurement dates before the bus lane went into service are indicated in blue; the dates in red indicate data collected after the bus lane went into service.</p>
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<p>Time series of traffic counts (15-min intervals) and hourly average traffic speed measured at all sites on both northbound lanes of Mystic Avenue. Measurement dates before the bus lane went into service are indicated in blue; the dates in red indicate data collected after the bus lane went into service.</p>
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<p>Hourly average black carbon (BC) concentrations measured at the stationary site on different days between October 18 and November 14, 2021, after the installation of the bus lane separated by weekdays and weekend days. Each color represents a different day. The top panels show all 24 h of data and the bottom panels show data for the bus lane hours (06:00–09:00; black box) and two more hours before and after the bus lane hours to show the temporal trend during the morning hours.</p>
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<p>Hourly average particle number concentrations (PNCs) measured at stationary site between October 18 and November 14, 2021, after the installation of the bus lane separated by weekdays and weekend days. Each color represents a different day. The top panels show all 24 h of data and the bottom panels show data for the bus lane hours (06:00–09:00; black box) and two more hours before and after the bus lane hours to show the temporal trend during the morning hours.</p>
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15 pages, 1727 KiB  
Article
Oxygen Consumption and Carbon Budget in Groundwater-Obligate and Surface-Dwelling Diacyclops Species (Crustacea Copepoda Cyclopoida) Under Temperature Variability
by Tiziana Di Lorenzo, Agostina Tabilio Di Camillo, Sanda Iepure, Diana M. P. Galassi, Nataša Mori and Tatjana Simčič
Environments 2025, 12(1), 32; https://doi.org/10.3390/environments12010032 - 20 Jan 2025
Viewed by 564
Abstract
This study explores the metabolic response and carbon budget of two cyclopoid copepod species, Diacyclops belgicus Kiefer, 1936 (a stygobitic, groundwater-adapted species) and Diacyclops crassicaudis crassicaudis (Sars G.O., 1863) (a stygophilic, predominantly surface-associated species). We measured oxygen consumption rates (OCRs), carbon requirements (CRs), [...] Read more.
This study explores the metabolic response and carbon budget of two cyclopoid copepod species, Diacyclops belgicus Kiefer, 1936 (a stygobitic, groundwater-adapted species) and Diacyclops crassicaudis crassicaudis (Sars G.O., 1863) (a stygophilic, predominantly surface-associated species). We measured oxygen consumption rates (OCRs), carbon requirements (CRs), ingestion (I) rates, and egestion (E) rates at 14 °C and 17 °C, representing current and predicted future conditions in the collection habitats of the two species. Diacyclops belgicus displayed OCRs (28.15 and 18.32 µL O2/mg DW × h at 14 and 17 °C, respectively) and carbon budget (CR: 0.14 and 0.10 µg C/mg × d at 14 and 17 °C) lower than those of D. crassicaudis crassicaudis (OCR: 55.67 and 47.93 µL O2/mg DW × h at 14 and 17 °C; CR: 0.3 and 0.27 µg C/mg × d at 14 and 17 °C). However, D. belgicus exhibited metabolic rates and carbon requirements comparable to those of other epigean species, challenging the assumption that low metabolic rates are universal among stygobitic species. Temperature variations did not significantly affect the metabolic responses and carbon requirements of the two species, suggesting that they may cope with moderate temperature increases. Full article
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<p>Phraetobiological net used to collect <span class="html-italic">Diacyclops belgicus</span> (<b>a</b>) and <span class="html-italic">Diacyclops crassicaudis crassicaudis</span> (<b>b</b>).</p>
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<p>The respirometric setup for oxygen consumption measurements of CV copepodids of <span class="html-italic">Diacyclops belgicus</span> and <span class="html-italic">Diacyclops crassicaudis crassicaudis</span> at 14 °C and 17 °C. One hour after collection, the specimens were kept in darkness for 21 days, transitioning through three media: 100% bore water (black beaker), a 50% bore and standard water mix (green beaker), and 100% standard water (blue beaker). Individual CV copepodids were placed in 80 μL glass wells with oxygen sensor spots, housed in a microplate, and monitored for oxygen levels over 18 h. At the end of the measurements, the specimens were measured, and their body volume was computed based on body dimensions.</p>
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<p>A heatmap of oxygen consumption rates (OCRs; μL O<sub>2</sub>/mg DW × h) of freshwater cyclopoid species across a temperature gradient: (<b>a</b>) all stages; (<b>b</b>) nauplii; (<b>c</b>) copepodids; (<b>d</b>) adults. Colour intensity indicates OCR magnitude. Mvi: <span class="html-italic">Megacyclops viridis</span> (Jurine, 1820); Mbr: <span class="html-italic">Mesocyclops brasilianus</span> Kiefer, 1933; Ese: <span class="html-italic">Eucyclops serrulatus serrulatus</span> (Fischer, 1851); Eag: <span class="html-italic">Eucyclops agilis agilis</span> (Koch, 1838); Dcr: <span class="html-italic">Diacyclops crassicaudis crassicaudis</span> (Sars G.O., 1863); Dbi: <span class="html-italic">Diacyclops bicuspidatus bicuspidatus</span> (Claus, 1857); Mva: <span class="html-italic">Microcyclops varicans varicans</span> (Sars G.O., 1863); Cvi: <span class="html-italic">Cyclops vicinus vicinus</span> Uljanin, 1875; Dbe: <span class="html-italic">Diacyclops belgicus</span> Kiefer, 1936.</p>
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21 pages, 3112 KiB  
Article
Environmental and Economic Life Cycle Impacts of Using Spent Mushroom Substrate as a Soil Improver
by Hasler Iglesias, Ana Paredes Ortiz, José M. Soriano Disla and Andrés J. Lara-Guillén
Environments 2025, 12(1), 31; https://doi.org/10.3390/environments12010031 - 20 Jan 2025
Viewed by 521
Abstract
The cultivation of white button mushrooms (Agaricus bisporus) generates significant quantities of spent mushroom substrate (SMS), a byproduct traditionally treated as waste despite its nutrient- and organic-carbon-rich composition. The EU-funded project FER-PLAY identified SMS as one of the most promising circular [...] Read more.
The cultivation of white button mushrooms (Agaricus bisporus) generates significant quantities of spent mushroom substrate (SMS), a byproduct traditionally treated as waste despite its nutrient- and organic-carbon-rich composition. The EU-funded project FER-PLAY identified SMS as one of the most promising circular fertilizers (i.e., those produced from waste streams, transforming them into value-added products). Within the project, a life cycle assessment (LCA) and life cycle costing (LCC) analysis of SMS were conducted with a cradle-to-gate-to-grave scope across three European regions, comparing it to a non-renewable mix with equivalent N, P, K, and C inputs. The LCA results reveal substantial environmental benefits of SMS over the non-renewable baseline, particularly in land use, fossil resource depletion, freshwater ecotoxicity and climate change, which together account for 98% of total impacts. Although SMS exhibits higher water consumption, it represents only 2% of total impacts. LCC highlights the critical effects of fresh mushroom substrate composition on yield, economies of scale, and revenue generation. Overall, this study highlights the significant environmental and economic potential of repurposing SMS as a soil improver, offering a compelling case for its integration into agricultural systems as part of a sustainable, circular economy. Full article
(This article belongs to the Special Issue Waste Management and Life Cycle Assessment)
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<p>Spent mushroom substrate (SMS) process diagram and system boundaries.</p>
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<p>Distribution of environmental impacts of SMS and the non-renewable baseline.</p>
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<p>Land use impacts of SMS and the non-renewable baseline.</p>
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<p>Fossil resource depletion’s impacts on SMS and the non-renewable baseline.</p>
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<p>The freshwater ecotoxicity impacts of SMS and the non-renewable baseline.</p>
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<p>Water use impacts of SMS and the non-renewable baseline.</p>
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<p>Climate change impacts of SMS and the non-renewable baseline.</p>
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<p>Weight of environmental impacts per stage.</p>
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<p>Life cycle costing results for SMS across the three target regions.</p>
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17 pages, 2610 KiB  
Article
Ecotoxicological Effects of the Herbicide Metribuzin on Tenebrio molitor Hemocytes
by Maria Luigia Vommaro, Michela Guadagnolo, Martina Lento and Anita Giglio
Environments 2025, 12(1), 30; https://doi.org/10.3390/environments12010030 - 19 Jan 2025
Viewed by 482
Abstract
Herbicides are synthetic chemicals that are extensively employed in agricultural practices with the objective of enhancing crop yield and quality. Despite their selectivity for plant systems and being generally regarded as non-toxic to animals, there is a paucity of understanding surrounding the sublethal [...] Read more.
Herbicides are synthetic chemicals that are extensively employed in agricultural practices with the objective of enhancing crop yield and quality. Despite their selectivity for plant systems and being generally regarded as non-toxic to animals, there is a paucity of understanding surrounding the sublethal effects on non-target organisms, including animals. This gap underscores the necessity for ecotoxicological research that prioritizes the identification of suitable models and develops reliable biomarkers for the early assessment of environmental impact. In this context, hemocytes—circulating immune cells found in invertebrates—have been identified as a crucial system for assessing sublethal toxicological effects, given their role in immune defense and overall organism health. Tenebrio molitor, a beetle pest of stored grain, was used as a model for the assessment of the effects of a metribuzin-based herbicide (MTB, Feinzin DF 70, 70% metribuzin, 0.25 kg ha−1). Following a 96 h exposure to MTB, the males (7–10 days post-eclosion) were examined for multiple biomarkers in their hemocytes, including cell density, phagocytic activity, lysosomal membrane stability, and cytological changes. Although no mortality was observed, exposure to MTB resulted in a reduction in the phagocytic index and an increase in blast-like cells, indicating the potential for immunotoxicity. Lysosomal membrane stability was reduced, though no significant changes in hemocyte density or nuclear morphology were observed. These responses indicate potential immune system impairment, which could affect the beetle’s fitness and reproductive potential. This study highlights the potential of hemocytes for assessing sublethal herbicide effects, raising concerns about the ecological impact of herbicides in agroecosystems and their potential risks to both wildlife and human health. Full article
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<p>Experimental design. Adult, mature, virgin males of <span class="html-italic">Tenebrio molitor</span> (N = 150) were exposed to a metribuzin-based herbicide (MTB; N = 75) for a period of 96 h at the field rate, while a control group (CTRL; N = 75) was not exposed to the chemical. At the conclusion of the exposure period, the hemolymph was extracted and the hemocyte was evaluated for density, lysosome membrane stability, and nuclear alterations. The phagocytic index was assessed in vivo by injecting a solution of latex beads into the hemocoel and subsequently quantifying the number of engulfing cells two hours post non-self immune challenge.</p>
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<p>(<b>a</b>) Kaplan–Meier survival curves for <span class="html-italic">T. molitor</span> exposed to MTB-based herbicide for 96 h (N = 75) and control (N = 75). (<b>b</b>,<b>c</b>) Violin plot of total hemocyte counts (<b>b</b>): CTRL, N = 27; MTB, N = 27) and phagocytic index following in vivo non-self immune challenge with latex beads (<b>c</b>): CTRL, N = 15; MTB, N = 12) at 96 h post-treatment.</p>
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<p>Nuclear alterations in <span class="html-italic">Tenebrio molitor</span> hemocyte smears at 96 h post-treatment. Experimental groups include CTRL (control, N = 15) and MTB (metribuzin-treated beetles, N = 12). (<b>a</b>) Violin plot shows the percentage of blast-like cells. (<b>b</b>) Blast-like cells (black arrowhead) in May–Grunwald–Giemsa smears of MTB-exposed beetles. (<b>c</b>) Violin plots depicting the percentage of apoptotic, kidney-shaped (KN), lobed and polymorphic (LN, PN), pyknotic and karyorrhectic (PK, KR), segmented (SN), and vacuolated (VN) nuclei (CTRL). Bars: 10 μm.</p>
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<p>Hemocytes from <span class="html-italic">T. molitor</span> control (<b>a</b>–<b>e</b>) and exposed to MTB (<b>f</b>–<b>o</b>), May–Grünwald–Giemsa staining. (<b>a</b>,<b>b</b>) Plasmatocytes. (<b>c</b>) Early plasmatocytes. (<b>d</b>) Granulocytes. (<b>e</b>) Prohemocytes. (<b>f</b>) Blast-like cell. (<b>g</b>) Phagocytic granulocytes (white arrowhead: latex beads). (<b>h</b>,<b>i</b>) Vacuolated cells (black arrowheads). (<b>j</b>,<b>k</b>) Cells with lobed and polymorphic nuclei. (<b>l</b>) Mitotic cell. (<b>m</b>) Cell with cytoplasmic basophilic granules. (<b>n</b>) Granulocytes. (<b>o</b>) Apoptotic cell. Bars: 5 μm (<b>a</b>–<b>e</b>,<b>g</b>,<b>i</b>,<b>j</b>,<b>l</b>–<b>o</b>) 10 μm (<b>f</b>,<b>h</b>,<b>k</b>).</p>
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<p>Violin plots shows neutral red retention time (<b>a</b>) and lysosomal membrane stability (<b>b</b>) of circulating hemocytes in <span class="html-italic">Tenebrio molitor</span> at 96 h post-treatment for the control (CTRL) and metribuzin-treated (MTB) groups. (<b>c</b>) Neutral red-stained hemocytes at 10, 40, 70, 110, and 155 min, showing increased lysosomal dilation over time and the presence of enlarged, leaky lysosomes and red cells (CTRL, N = 14; MTB, N = 15). Bars: 5 μm.</p>
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<p>Star plot of integrated biological response of the morpho-functional characteristics in <span class="html-italic">Tenebrio molitor</span> circulating hemocytes at 96 h post-treatment for the metribuzin-treated (MTB) groups (solid yellow curve). The reference condition is represented by the black dotted curve (CTRL). LMS: lysosomal membrane stability; THC: total hemocyte counts.</p>
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15 pages, 3699 KiB  
Article
Contrasting Perfluorooctanoic Acid Removal by Calcite Before and After Heat Treatment
by Zhaohui Li, Yating Yang, Yaqi Wen, Yuhan Li, Jeremy Moczulewski, Po-Hsiang Chang, Stacie E. Albert and Lori Allen
Environments 2025, 12(1), 29; https://doi.org/10.3390/environments12010029 - 17 Jan 2025
Viewed by 348
Abstract
Calcites before and after calcination at 1000 °C were evaluated for their potential removal of perfluorooctanoic acid (PFOA) from water. After heat treatment, the PFOA sorption capacity increased by 25%, from 3.2 to 3.9 mg g−1, and the affinity increased by [...] Read more.
Calcites before and after calcination at 1000 °C were evaluated for their potential removal of perfluorooctanoic acid (PFOA) from water. After heat treatment, the PFOA sorption capacity increased by 25%, from 3.2 to 3.9 mg g−1, and the affinity increased by 2.7 times, from 0.03 to 0.08 L mg−1. Kinetically, the initial rate, rate constant, and equilibrium sorption were 8.7 mg g−1 h−1, 2.6 g mg−1 h−1, and 1.8 mg g−1 for heat treated calcite, in comparison to 6.4 mg g−1 h−1, 3.1 g mg−1 h−1, and 1.4 mg g−1 for calcite without heat treatment. X-ray diffraction analyses showed phase changing from calcite to calcium oxide after calcination. However, after contact with PFOA solutions for 24 h, the major phase changed back to calcite with a minute amount of Ca(OH)2. These results suggest that using raw cement materials derived from heat treatment of limestone may be a good option for the removal of PFOA from water. Thus, further studies are needed to confirm this claim. Full article
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<p>Sorption of PFOA on Cal and HCal. The solid lines are the Langmuir model fitting of the observed data. The right <span class="html-italic">y</span>-axis with solid symbols is the percentage of PFOA sorbed.</p>
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<p>Sorption kinetics of PFOA on Cal and HCal without pH adjustment under an initial concentration of 100 mg L<sup>−1</sup>. The solid lines are pseudo-second-order model fitting of the observed data.</p>
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<p>PFOA removal by Cal as affected by equilibrium solution pH under an initial PFOA concentration of 100 mg L<sup>−1</sup>.</p>
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<p>PFOA removal by Cal and HCal as affected by equilibrium solution ionic strength under an initial PFOA concentration of 100 mg L<sup>−1</sup>.</p>
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<p>PFOA removal by Cal and HCal as affected by equilibrium solution temperature under an initial PFOA concentration of 100 mg L<sup>−1</sup>.</p>
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<p>FTIR spectra of Cal and HCal after in contact with different initial PFOA concentrations (H represents HCal), respectively. The numbers are the initial PFOA concentrations in mg L<sup>−1</sup>.</p>
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<p>XRD patterns of Cal (<b>a</b>) and HCal (<b>b</b>) after equilibrated with different initial concentrations of PFOA (numbers in mg L<sup>−1</sup>), and the standard samples of Ca(OH)<sub>2</sub> and CaO.</p>
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<p>TG (<b>a</b>) and DTG (<b>b</b>) analyses of Cal and HCal. The number is the initial PFOA concentration in mg L<sup>−1</sup>.</p>
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<p>SEM images of Cal (<b>a</b>) and HCal (<b>c</b>) and their SEM images after their sorption of PFOA from an initial concentration of 200 mg L<sup>−1</sup>, respectively (<b>b</b>,<b>d</b>). The EDS spectra of face and point scans of samples after in contact with PFOA solution for 24 h (<b>e</b>).</p>
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16 pages, 6877 KiB  
Article
Accumulation of Nanoplastics in Biomphalaria glabrata Embryos and Transgenerational Developmental Effects
by Leisha Martin, Carly Armendarez, Mackenzie Merrill, Chi Huang and Wei Xu
Environments 2025, 12(1), 28; https://doi.org/10.3390/environments12010028 - 17 Jan 2025
Viewed by 345
Abstract
(1) Background: Nanoplastics are emerging environmental pollutants with potential toxic effects on aquatic organisms. This study investigates the toxicity of NPs in Biomphalaria glabrata, a freshwater snail species widely used as a bioindicator species in ecotoxicology studies.; (2) Methods: We exposed three [...] Read more.
(1) Background: Nanoplastics are emerging environmental pollutants with potential toxic effects on aquatic organisms. This study investigates the toxicity of NPs in Biomphalaria glabrata, a freshwater snail species widely used as a bioindicator species in ecotoxicology studies.; (2) Methods: We exposed three generations (F0–F2) of B. glabrata snail embryos to different sizes of polystyrene nanoparticles and assessed responses.; (3) Results: We observed severe effects on F0 to F2 B. glabrata embryos, including size-dependent (30 to 500 nm) increases in mortality rates, size and dosage-dependent (1 to 100 ppm) effects on hatching rates with concentration-dependent toxicity in the 30 nm exposure group. The F2 generation embryos appear to be most responsive to detoxification (CYP450) and pollutant metabolism (HSP70) at 48-h-post-treatment (HPT), while our developmental marker (MATN1) was highly upregulated at 96-HPT. We also report a particle-size-dependent correlation in HSP70 and CYP450 mRNA expression, as well as enhanced upregulation in the offspring of exposed snails. We also observed significant reductions in hatching rates for F2.; (4) Conclusions: These findings indicate that F2 generation embryos appear to exhibit increased stress from toxic substances inherited from their parents and grandparents (F1 and F0). This study provides valuable insights into the impact of plastic particulate pollution on multiple generations and highlights the importance of monitoring and mitigating plastic waste. Full article
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Graphical abstract
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<p>Fluorescent images comparing treatments: particle size increases with row vs. exposure time increasing with columns from right to left. All treatment concentrations are 1 ppm. Fluorescent signals appear beginning at 24 HPT, and bioaccumulation continues through 96 HPT.</p>
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<p>Fluorescence microscopy image of juvenile snail treated with 10 ppm of 30 nm NPs during first 6-days of embryonic development, emerged from egg with fluorescent emission (<b>left image</b>) and brightfield image of the same snail (<b>right image</b>).</p>
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<p>Confocal microscopy images of snail embryos showing full-scan still images of the center of the embryo (<b>top row</b>) and bottom-view scans of the embryo (<b>lower row</b>); both locations show still images of the fluorescence (<b>left column</b>), a brightfield image of the embryo (<b>center column</b>), and merged images (<b>right column</b>). Fluorescent particles clearly accumulated within the embryo and not just on the outside of the egg.</p>
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<p>Dose-dependent reduction in hatching rates for 30 nm NP exposures.</p>
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<p>(<b>A</b>) The hatching rate of F0 generation snails showed significantly reduced hatching in the 1 µm group on days 6–10 and continued with slight reductions through day 15 (<span class="html-italic">p</span> &lt; 0.001). We also saw slight reductions in the hatching rates for the 500 nm treatment group on day 8 only. (<b>B</b>) The hatching rate of F2 generation snails showed statistically significant reduced hatching rates (<span class="html-italic">p</span> &lt; 0.001) at all timepoints for snails treated with particles. All treatment concentrations were 1 ppm. Error bars are 95% CI.</p>
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<p>Mortality rates show dose-dependence for the 30 nm treatments groups in the F2 generation.</p>
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<p>Survival curves for F0 (<b>upper graph</b>) and F2 (<b>lower graph</b>), shows a spike in mortalities in the F0 generation on day 8 that is absent from the F2 curves. We also see that most mortalities are in the 1.0 µm group in the F0, and the 500 nm group in F2. Dotted line error bars are 95% CI. Neither survival curve reached statistical significance, as evidenced by overlap of the error bars. Historical averages are ~5–10% mortalities for controls.</p>
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<p>Relative Gene Expression 2<sup>ΔΔCt</sup> for HSP70 (heat shock protein) leftmost column, CYP450 (cytochrome oxidase enzyme) center column, and MATN1 matrilin protein, rightmost column for F0 generation (<b>top row</b>) and F2 generation (<b>bottom row</b>). We see upregulation of HSP70 in both F0 and F2 and 48-h. This is more pronounced in the F2 generation. We also see significant upregulation of CYP450 in F0 at both 48 and 96-h, and in F2 at 96 h, once again this upregulation is higher in the F2 generation. We also observe significant upregulation of MATN1 in F0 at 96-h and in F2 both at 48-h as well as 96-h (<span class="html-italic">n</span> = 6). Error bars represent the standard deviation (SD).</p>
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31 pages, 11303 KiB  
Article
Integrated Surface and Tropospheric Column Analysis of Sulfur Dioxide Variability at the Lamezia Terme WMO/GAW Regional Station in Calabria, Southern Italy
by Francesco D’Amico, Teresa Lo Feudo, Daniel Gullì, Ivano Ammoscato, Mariafrancesca De Pino, Luana Malacaria, Salvatore Sinopoli, Giorgia De Benedetto and Claudia Roberta Calidonna
Environments 2025, 12(1), 27; https://doi.org/10.3390/environments12010027 - 16 Jan 2025
Viewed by 428
Abstract
Sulfur dioxide (SO2) can be of natural and anthropogenic origin and is one of the sulfur compounds present in the atmosphere. Among natural sources, volcanoes contribute with relevant annual outputs, and major eruptions lead to spikes in these outputs. In the [...] Read more.
Sulfur dioxide (SO2) can be of natural and anthropogenic origin and is one of the sulfur compounds present in the atmosphere. Among natural sources, volcanoes contribute with relevant annual outputs, and major eruptions lead to spikes in these outputs. In the case of anthropogenic pollution, SO2 emissions are mostly correlated with the sulfur content of fuels, which has been the focus of specific emission mitigation policies for decades. Following other examples of cyclic and multi-year evaluations, an analysis of SO2 at the Lamezia Terme (code: LMT) WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) station in Calabria, Southern Italy, was performed. The coastal site is characterized by wind circulation patterns that result in the detection of air masses with low or enhanced anthropic influences. The presence of the Aeolian Arc of active, quiescent, and extinct volcanoes, as well as Mount Etna in Sicily, may influence LMT observations with diffused SO2 emissions. For the first time in the history of the LMT, a multi-year analysis of a parameter has been integrated with TROPOMI data gathered by Sentinel-5P and used to test total tropospheric column densities at the LMT itself and select coordinates in the Tyrrhenian and Ionian seas. Surface and satellite data indicate that SO2 peaks at the LMT are generally linked to winds from the western–seaside wind corridor, a pattern that is compatible with active volcanism in the Tyrrhenian Sea and maritime shipping to and from the Gioia Tauro port located in the same region. The findings of this research provide the basis for enhanced source apportionment, which could further differentiate anthropogenic sources in the area from natural outputs. Full article
(This article belongs to the Special Issue Advances in Urban Air Pollution: 2nd Edition)
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<p>(<b>A</b>) Location of Lamezia Terme’s observation site (LMT) in the Mediterranean Basin on NOAA’s ETOPO1 DEM (Digital Elevation Model) [<a href="#B97-environments-12-00027" class="html-bibr">97</a>,<a href="#B98-environments-12-00027" class="html-bibr">98</a>]. (<b>B</b>) EMODnet DEM map [<a href="#B99-environments-12-00027" class="html-bibr">99</a>] showing the location of the LMT in central Calabria and the SO<sub>2</sub> emission hotspots present in the area: the Gioia Tauro Port, Stromboli and Vulcano in the Aeolian Islands, and Mount Etna in Sicily. The main underwater volcanoes in the arc are marked with dark blue labels. “Lametini” refers to the two twin underwater volcanoes (LamN and LamS) named after the municipality of Lamezia Terme. Local maps showing sources of pollution in the Lamezia Terme municipality area with greater detail are available from D’Amico et al. (2024a) [<a href="#B78-environments-12-00027" class="html-bibr">78</a>], (2024b) [<a href="#B100-environments-12-00027" class="html-bibr">100</a>], (2024c) [<a href="#B94-environments-12-00027" class="html-bibr">94</a>].</p>
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<p>(<b>A</b>) Location of Lamezia Terme’s observation site (LMT) in the Mediterranean Basin on NOAA’s ETOPO1 DEM (Digital Elevation Model) [<a href="#B97-environments-12-00027" class="html-bibr">97</a>,<a href="#B98-environments-12-00027" class="html-bibr">98</a>]. (<b>B</b>) EMODnet DEM map [<a href="#B99-environments-12-00027" class="html-bibr">99</a>] showing the location of the LMT in central Calabria and the SO<sub>2</sub> emission hotspots present in the area: the Gioia Tauro Port, Stromboli and Vulcano in the Aeolian Islands, and Mount Etna in Sicily. The main underwater volcanoes in the arc are marked with dark blue labels. “Lametini” refers to the two twin underwater volcanoes (LamN and LamS) named after the municipality of Lamezia Terme. Local maps showing sources of pollution in the Lamezia Terme municipality area with greater detail are available from D’Amico et al. (2024a) [<a href="#B78-environments-12-00027" class="html-bibr">78</a>], (2024b) [<a href="#B100-environments-12-00027" class="html-bibr">100</a>], (2024c) [<a href="#B94-environments-12-00027" class="html-bibr">94</a>].</p>
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<p>(<b>A</b>) Wind rose of frequency counts and wind speed thresholds, based on hourly data gathered at the LMT with Vaisala WXT520 equipment between 2016 and 2023. The bars have an angle of 8 degrees each. (<b>B</b>) Locations of the TYR1–4 (Tyrrhenian) and ION1 (Ionian) points used for the comparison of surface mole fractions of SO<sub>2</sub> with satellite tropospheric column data.</p>
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<p>(<b>A</b>) Daily cycle of SO<sub>2</sub> at the LMT based on the year of evaluation. (<b>B</b>) Seasonal daily cycle. (<b>C</b>) Daily cycle differentiated by wind corridor, using both SO<sub>2</sub> and meteorological data.</p>
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<p>Total (<b>top</b>) and seasonal (<b>bottom</b>) wind rose scatter plots of observed SO<sub>2</sub> mole fractions at Lamezia Terme (LMT), with respect to wind speeds and directions. In order to optimize visualization, the color scale bar’s maximum value of 0.2+ ppb is defined based on the average reported value of 0.217 ppb (see <a href="#sec3dot5-environments-12-00027" class="html-sec">Section 3.5</a>).</p>
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<p>Total (<b>top</b>) and seasonal (<b>bottom</b>) wind rose scatter plots of observed SO<sub>2</sub> mole fractions at Lamezia Terme (LMT), with respect to wind speeds and directions. In order to optimize visualization, the color scale bar’s maximum value of 0.2+ ppb is defined based on the average reported value of 0.217 ppb (see <a href="#sec3dot5-environments-12-00027" class="html-sec">Section 3.5</a>).</p>
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<p>SO<sub>2</sub> concentrations and wind speed variability on a wind corridor basis. (<b>A</b>) Western “seaside” (240–300 °N); (<b>B</b>) northeastern “continental” (0–90 °N); (<b>C</b>) total data, including those falling outside the two wind direction filters.</p>
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<p>Weekly cycle evaluation of surface and tropospheric column SO<sub>2</sub> at the LMT and the TYR-ION coordinates, with the dotted horizontal lines showing the averages. (<b>A</b>) Surface, western “seaside” corridor (240–300 °N); (<b>B</b>) surface, northeastern “continental” corridor (0–90 °N); (<b>C</b>) surface, total data; (<b>D</b>) column, at LMT; (<b>E</b>) column, based on averages of TYR points; (<b>F</b>) column, ION1.</p>
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<p>Weekly cycle evaluation of surface and tropospheric column SO<sub>2</sub> at the LMT and the TYR-ION coordinates, with the dotted horizontal lines showing the averages. (<b>A</b>) Surface, western “seaside” corridor (240–300 °N); (<b>B</b>) surface, northeastern “continental” corridor (0–90 °N); (<b>C</b>) surface, total data; (<b>D</b>) column, at LMT; (<b>E</b>) column, based on averages of TYR points; (<b>F</b>) column, ION1.</p>
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<p>Multi-year variability of surface SO<sub>2</sub> concentrations at the Lamezia Terme WMO/GAW station differentiated by wind corridor. (<b>A</b>) Yearly averages between 2016 and 2023. (<b>B</b>) Seasonal cycle. (<b>C</b>) Monthly averages.</p>
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<p>Multi-year variability of tropospheric column data. (<b>A</b>) Monthly variability. (<b>B</b>) TCSO<sub>2</sub> monthly means measured at TYR4 and ION1, and their respective differences. (<b>C</b>) Direct comparison between LMT surface and column SO<sub>2</sub> concentrations. (<b>D</b>) Variability of the column data from the three TYR points in the Tyrrhenian Sea, located close to the Aeolian Arc.</p>
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<p>Multi-year variability of tropospheric column data. (<b>A</b>) Monthly variability. (<b>B</b>) TCSO<sub>2</sub> monthly means measured at TYR4 and ION1, and their respective differences. (<b>C</b>) Direct comparison between LMT surface and column SO<sub>2</sub> concentrations. (<b>D</b>) Variability of the column data from the three TYR points in the Tyrrhenian Sea, located close to the Aeolian Arc.</p>
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<p>Multi-year variability of tropospheric column data. (<b>A</b>) Monthly variability. (<b>B</b>) TCSO<sub>2</sub> monthly means measured at TYR4 and ION1, and their respective differences. (<b>C</b>) Direct comparison between LMT surface and column SO<sub>2</sub> concentrations. (<b>D</b>) Variability of the column data from the three TYR points in the Tyrrhenian Sea, located close to the Aeolian Arc.</p>
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<p>SO<sub>2</sub> column number density (DU) for 2020–2023 created using COBRA.</p>
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<p>SO<sub>2</sub> column number density (DU) for 2020–2023 created using COBRA.</p>
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30 pages, 2183 KiB  
Review
Biobased Strategies for E-Waste Metal Recovery: A Critical Overview of Recent Advances
by Diogo A. Ferreira-Filipe, Armando C. Duarte, Andrew S. Hursthouse, Teresa Rocha-Santos and Ana L. Patrício Silva
Environments 2025, 12(1), 26; https://doi.org/10.3390/environments12010026 - 16 Jan 2025
Viewed by 1041
Abstract
The increasing e-waste volumes represent a great challenge in the current waste management landscape, primarily due to the massive production and turnover of electronic devices and the complexity of their components and constituents. Traditional strategies for e-waste treatment focus on metal recovery through [...] Read more.
The increasing e-waste volumes represent a great challenge in the current waste management landscape, primarily due to the massive production and turnover of electronic devices and the complexity of their components and constituents. Traditional strategies for e-waste treatment focus on metal recovery through costly, energetically intensive, and environmentally hazardous processes, such as pyrometallurgical and hydrometallurgical approaches, often neglecting other e-waste constituents. As efforts are directed towards creating a more sustainable and circular economic model, biobased alternative approaches to these traditional techniques have been increasingly investigated. This critical review focuses on recent advances towards sustainable e-waste treatment, exclusively considering studies using e-waste sources. It addresses, from a critical perspective, approaches using inactive biomass, live biomass, and biogenic compounds, showcasing the diversity of strategies and discussing reaction parameters, advantages and disadvantages, challenges, and potential for valorization of generated by-products. While ongoing research focuses on optimizing operational times and metal recovery efficiencies, bioprocessing approaches still offer significant potential for metal recovery from e-waste. These approaches include lower environmental impact by reducing energy consumption and effluent treatments and the ability to recover metals from complex e-waste streams, paving the way for a more circular economy in the electronics industry. Full article
(This article belongs to the Special Issue Deployment of Green Technologies for Sustainable Environment III)
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<p>Overview of e-waste generation by region, including the respective proportions of documented and undocumented treatment. Examples of end-of-life treatment methods are also provided. Data and information sourced from references [<a href="#B1-environments-12-00026" class="html-bibr">1</a>,<a href="#B4-environments-12-00026" class="html-bibr">4</a>].</p>
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<p>Potential management of electrical and electronic equipment (EEE) and e-waste constituents under a circular economic model. Abbreviation list: polybrominated diphenyl ether (PBDE); polycyclic aromatic hydrocarbon (PAH); polyhalogenated aromatic hydrocarbon (PHAH). Information sourced from references [<a href="#B23-environments-12-00026" class="html-bibr">23</a>,<a href="#B24-environments-12-00026" class="html-bibr">24</a>].</p>
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<p>Main reaction types of metal bioleaching employed by fungal and bacterial species described in recent studies concerning e-waste, including examples of lixiviant compounds (as per <a href="#environments-12-00026-t002" class="html-table">Table 2</a>).</p>
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<p>Comparison between direct bioleaching, two-step bioleaching, and spent medium bioleaching approaches regarding the inclusion and removal of biomass and waste in/from the leaching solution at different stages during the process.</p>
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18 pages, 6918 KiB  
Article
Assessing Water Temperature and Dissolved Oxygen and Their Potential Effects on Aquatic Ecosystem Using a SARIMA Model
by Samuel Larance, Junye Wang, Mojtaba Aghajani Delavar and Marwan Fahs
Environments 2025, 12(1), 25; https://doi.org/10.3390/environments12010025 - 14 Jan 2025
Viewed by 487
Abstract
Temperature and dissolved oxygen (DO) are of critical importance for sustainable aquatic ecosystem and biodiversity in the river systems. This study aims to develop a data-driven model for forecasting water quality in the Athabasca River using a seasonal autoregressive integrated moving average model [...] Read more.
Temperature and dissolved oxygen (DO) are of critical importance for sustainable aquatic ecosystem and biodiversity in the river systems. This study aims to develop a data-driven model for forecasting water quality in the Athabasca River using a seasonal autoregressive integrated moving average model (SARIMA) for forecasting monthly DO and water temperature. DO and water temperature observed at Fort McMurray and Athabasca from 1960 to 2023 were used to train and test the model. The results show the satisfied model performance of DO with a coefficient of determination (R2) value of 0.76 and an RMSE value of 0.79 for training and 0.67 and 0.92 for testing, respectively, at the Fort McMurray station. At the Town of Athabasca station, the RMSE and R2 of DO were 0.92 and 0.72 for training and 0.77 and 0.86 for testing, respectively. For the modeled temperature, RMSE and R2 were 2.7 and 0.87 for training and 2.2 and 0.95 for testing, respectively, at Fort McMurray and were 2.0 and 0.93 for training and 1.8 and 0.97 for testing, respectively, in the Town of Athabasca. The results show that DO concentration is inversely proportional to the temperature. This implies that the DO could be related to water temperature, which, in turn, is correlated with air temperature. Therefore, the SARIMA model performed reasonably well in representing the dynamics of water temperature and DO in the cold climate river. Such a model can be used in practice to reduce the risk of low DO events. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)
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<p>Athabasca River and monitoring stations: Fort McMurray and the Town of Athabasca. Land use classes: URML: urban medium density, PAST: pasture; RNGE: range shrub land; AGRL: agricultural land; RNGB: grassland/herbaceous; FRSD: deciduous forest; FRSE: evergreen forest; FRST: mixed forest; WATR: water; BARR: barren land; SWRN: southwestern rangeland.</p>
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<p>A raw dataset of DO at U/S Fort McMurray.</p>
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<p>Dickey–Fuller value, autocorrelation plot, and partial autocorrelation plot for the temperature time series with a non-seasonal differentiation.</p>
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<p>Performance of the SARIMA model between actual and forecasted temperature at Fort McMurray (trained on 96% of the dataset): (<b>a</b>) correlation figure and linear regression of the temperature time series (training portion) (NSE = 0.87, slope = 0.87, intercept = 1.01) and (<b>b</b>) time series.</p>
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<p>A comparison of actual and forecasted temperature in the Town of Athabasca (trained on 96% of the dataset): (<b>a</b>) correlation figure and linear regression of the temperature time series (training portion) (NSE = 0.95, slope = 0.86, intercept = 0.91) and (<b>b</b>) time series.</p>
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<p>A comparison of actual and forecasted DO at Fort McMurray (training on 96% of the dataset): (<b>a</b>) correlation figure and linear regression of the temperature time series (training portion) (NSE = 0.79, slope = 0.81, intercept = 2.19) and (<b>b</b>) time series.</p>
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<p>Effects of water temperature on DO at Fort McMurray (2016–2022).</p>
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<p>A comparison of actual and forecasted DO in the Town of Athabasca (trained on 96% of the dataset): (<b>a</b>) correlation figure and linear regression of the temperature time series (training portion) (NSE = 0.72, slope = 0.73, intercept = 2.74) and (<b>b</b>) time series.</p>
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10 pages, 455 KiB  
Article
Energy Use and Environmental Impact of Three Lithium-Ion Battery Factories with a Total Annual Capacity of 100 GWh
by Ákos Kuki, Csilla Lakatos, Lajos Nagy, Tibor Nagy and Sándor Kéki
Environments 2025, 12(1), 24; https://doi.org/10.3390/environments12010024 - 14 Jan 2025
Viewed by 440
Abstract
The rapid evolution of Li-ion battery technologies and manufacturing processes demands a continual update of environmental impact data. The general objective of this paper is to publish up-to-date primary data on battery manufacturing, which is of great importance to the scientific community and [...] Read more.
The rapid evolution of Li-ion battery technologies and manufacturing processes demands a continual update of environmental impact data. The general objective of this paper is to publish up-to-date primary data on battery manufacturing, which is of great importance to the scientific community and decision-makers. The environmental impacts have been calculated and estimated based on publicly available data disclosed under Hungarian government regulations and official decrees. The gate-to-gate energy use, greenhouse gas (GHG) emissions, water consumption, and N-methyl-2-pyrrolidone (NMP) consumption are estimated for three battery factories in Hungary, with a total annual capacity of approximately 100 GWh. The factories use around 30–35 kWh energy per kWh of battery capacity and the associated GHG emissions are around 10 kgCO2eq per kWh of cell production. The water consumption varies considerably among factories, with one plant using 28 L per kWh and the other two using 56 and 67 L per kWh. The specific consumption of NMP was calculated for two factories, resulting in close values of 0.51–0.56 kg per kWh of cell production. As a new approach, we distinguish between global and local GHG emissions related to battery production. The main component of the latter is carbon dioxide from the combustion of natural gas, but the local transport related to the battery factories is also a source of emissions. Our estimations include not only the consumptions required directly for the manufacturing technology, but also those for social purposes (e.g., heating offices), giving a more complete picture of the factory’s environmental impact. We believe that up-to-date primary data are crucial for ensuring transparency and holds significant value for both the scientific community and decision-makers. Full article
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<p>Local greenhouse gas (GHG) emissions of three Li-ion battery factories in Hungary, (CATL in Debrecen, Samsung SDI in Göd, and SK Innovation in Iváncsa): (<b>a</b>) in total, and (<b>b</b>) specific per kWh battery production.</p>
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18 pages, 6063 KiB  
Article
Seasonal Dynamics and Environmental Drivers of Phytoplankton in the Albufera Coastal Lagoon (Valencia, Spain)
by Juan Víctor Molner, Isabel Mellinas-Coperias, Clara Canós-López, Rebeca Pérez-González, María D. Sendra and Juan M. Soria
Environments 2025, 12(1), 23; https://doi.org/10.3390/environments12010023 - 13 Jan 2025
Viewed by 624
Abstract
The Albufera of Valencia is a hypereutrophic, oligohaline lagoon that has experienced significant changes in phytoplankton composition and state in recent decades due to human activities. These activities affect phytoplankton biomass and community structure, which are key indicators of ecosystem health. In this [...] Read more.
The Albufera of Valencia is a hypereutrophic, oligohaline lagoon that has experienced significant changes in phytoplankton composition and state in recent decades due to human activities. These activities affect phytoplankton biomass and community structure, which are key indicators of ecosystem health. In this study, phytoplankton samples from the lagoon were analyzed to identify dominant groups and genera, and their seasonal cycles were determined using biovolume measurements with the Utermöhl method. Various environmental variables were also measured. Diversity was assessed using richness, equitability, and the Shannon–Wiener index. Canonical Correspondence Analysis (CCA) and Pearson correlation revealed that temperature and phosphorus significantly influence phytoplankton abundance. A species that exhibited seasonal abundance, resulting in a change in the lagoon’s color from green to brown, was identified. Water quality was assessed using the trophic state index, indicating that the lagoon is in poor condition and hyper-eutrophic. Cyanobacteria were the most dominant group, peaking in November, contrary to previous studies, followed by Chlorophyceae and Bacillariophyceae. Phytoplankton are vital bioindicators for assessing ecosystem health, underscoring the need for further research in this area. Full article
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<p>Location of the sampling point used during the samplings conducted (39.34600 N &amp; −0.33660 W) in Albufera lagoon. Sentinel-2 image of 17 September 2023 in enhanced vegetation color mode.</p>
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<p>Stacked bar diagram of temporal evolution of photosynthetic pigments (μg/L) over the study period. Chla = chlorophyll-a; Car = carotenoids; PC = phycocyanin.</p>
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<p>Annual evolution of phytoplankton density by main taxonomic groups. BACILLA = Bacillariophyceae <span class="html-italic">s.l.</span>; CHRYSO = Chrysophyceae; CHLORO = Chlorophyceae; CYANOB = cyanobacteria; CRYPTO = Cryptophyceae.</p>
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<p>Annual evolution of phytoplankton biovolume by main taxonomic groups. BACILLA = Bacillariophyceae <span class="html-italic">s.l.</span>; CHRYSO = Chrysophyceae; CHLORO = Chlorophyceae; CYANOB = cyanobacteria; CRYPTO = Cryptophyceae.</p>
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<p>×60 microscope image of <span class="html-italic">Desmodesmus</span> sp. (1) with abundant presence of <span class="html-italic">Unknown</span> sp. (2) in a sample collected on 16 November 2023. Also included are the following: <span class="html-italic">Pseudanabaena</span> sp. (3), <span class="html-italic">Aphanothece</span> sp. (4), and <span class="html-italic">Merismopedia</span> sp. (5).</p>
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<p>Pearson correlation matrix between phytoplankton density and physicochemical variables; only significant correlations are plotted. TEMP = temperature, COND = conductivity, ZSD = Secchi disk depth, TSM = total suspended matter, LOI = organic solids, PTOT = total phosphorus; CHLA = chlorophyll-a; CAR = carotenoids; PC = phycocyanin; BACILLA = Bacillariophyceae <span class="html-italic">s.l.</span>; CHRYSO = Chrysophyceae; CHLORO = Chlorophyceae; CONJUG = Conjugatophyceae; CYANOB = cyanobacteria; CRYPTO = Cryptophyceae; DINOPHY = Dinopyta; EUGLE = Euglenophyta; XANTHO = Xantophyceae.</p>
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<p>CCA analysis. Colors square points represent sampling dates and seasons (green = spring; red = summer; brown = autumn; cyan = winter). Blue circle = phytoplankton groups. TEMP = temperature, COND = conductivity, ZSD = Secchi disk depth, TSM = total suspended matter, LOI = organic solids, PTOT = total phosphorus; CHLA = chlorophyll-a; CAR = carotenoids; PC = phycocyanin; BACILLA = Bacillariophyceae <span class="html-italic">s.l.</span>; CHRYSO = Chrysophyceae; CHLORO = Chlorophyceae; CONJUG = Conjugatophyceae; CYANOB = cyanobacteria; CRYPTO = Cryptophyceae; DINOPHY = Dinopyta; EUGLE = Euglenophyta; XANTHO = Xantophyceae.</p>
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<p>Cluster analysis according to the Unweighted Pair Group Method with Arithmetic (UPGMA). Colour text represents the season as in <a href="#environments-12-00023-f007" class="html-fig">Figure 7</a>.</p>
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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 633
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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<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>
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<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>
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<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>
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19 pages, 4709 KiB  
Article
Performance of Colilert-18 and qPCR for Monitoring E. coli Contamination at Freshwater Beaches in Michigan
by James N. McNair, Richard R. Rediske, John J. Hart, Megan N. Jamison and Shannon Briggs
Environments 2025, 12(1), 21; https://doi.org/10.3390/environments12010021 - 12 Jan 2025
Viewed by 504
Abstract
Fecal contamination is a common cause of impairment of surface waters. In monitoring studies, it is usually assessed by measuring concentrations of fecal indicator bacteria such as Escherichia coli (E. coli), a common monitoring target in freshwater systems. In this study, [...] Read more.
Fecal contamination is a common cause of impairment of surface waters. In monitoring studies, it is usually assessed by measuring concentrations of fecal indicator bacteria such as Escherichia coli (E. coli), a common monitoring target in freshwater systems. In this study, we assess the advantages and disadvantages of two common and previously validated methods for monitoring E. coli concentrations at freshwater beaches: Colilert-18®, with a turnaround time of ca. 18 h, and real-time quantitative PCR (qPCR), with a turnaround time of ca. 3–4 h. Based on data comprising 3081 pairs of Colilert-18 and qPCR estimates of E. coli concentrations in split samples from Michigan’s annual beach monitoring program in 2019 and 2020, we found that qPCR monitoring detected a high percentage of exceedances of the state’s water quality standard for E. coli contamination that went undetected on the day of sampling with Colilert-18 monitoring because qPCR concentration estimates were available on the day of sampling but Colilert-18 estimates were not. However, Colilert-18 data were more useful than qPCR data for the statistical comparison of contamination levels at different beaches, probably in part because Colilert-18 data showed a much lower percentage of concentration estimates outside the method’s range of quantification. Full article
(This article belongs to the Special Issue Monitoring of Contaminated Water and Soil)
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Graphical abstract
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<p><span class="html-italic">E. coli</span> concentrations as <math display="inline"><semantics> <msub> <mo form="prefix">log</mo> <mn>10</mn> </msub> </semantics></math> colony-forming units (CFU) per 100 mL for day <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> (vertical axis) versus day <span class="html-italic">t</span> (horizontal axis) at three locations (Sunnyside Beach 17 W, 18 W, and 19 W) on a Lake Ontario beach in Toronto, Canada. Data digitized from <a href="#environments-12-00021-f002" class="html-fig">Figure 2</a> of Saleem et al. [<a href="#B20-environments-12-00021" class="html-bibr">20</a>].</p>
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<p>Locations of Michigan recreational beaches for which paired Colilert-18 and qPCR beach monitoring data were available. Red dots: inland-lake beaches. Blue dots: coastal beaches. Base map: Michigan Geographic Framework.</p>
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<p>Relationship between Colilert-18 (vertical axis) and qPCR (horizontal axis) <span class="html-italic">E. coli</span> concentrations in split samples from Michigan beaches in 2019 and 2020. Blue dots are true positives ((<b>upper right</b>) quadrant) and true negatives ((<b>lower left</b>) quadrant). Orange upright triangles are false negatives ((<b>upper left</b>) quadrant) and yellow inverted triangles are false positives ((<b>lower right</b>) quadrant).</p>
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<p>Turnbull distribution functions for coastal and inland-lake beaches based on Colilert-18 data (<b>left</b>) and qPCR data (<b>right</b>). The results of log-rank tests are shown for the null hypothesis of no difference between distributions against the alternative hypothesis of a difference for at least one concentration. LLOQ: lower limit of quantification, RWQS: Michigan’s recreational water quality standard for total-body contact recreation, qTV: Michigan’s proposed qPCR threshold value.</p>
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<p>Turnbull distribution functions for coastal and inland-lake beaches, plotted separately for the four Michigan counties with sufficient data for both classes of beaches. Each row of panels corresponds to one county and shows distribution functions based on Colilert-18 data (<b>left</b>) and on qPCR data (<b>right</b>), plotted and annotated as in <a href="#environments-12-00021-f004" class="html-fig">Figure 4</a>.</p>
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12 pages, 1884 KiB  
Article
Air Bubbling Assisted Soil Washing to Treat PFAS in High Organic Content Soils
by Kaushik Londhe and Arjun K. Venkatesan
Environments 2025, 12(1), 20; https://doi.org/10.3390/environments12010020 - 12 Jan 2025
Viewed by 446
Abstract
The soil-washing technique has been successfully utilized for the remediation of PFAS-contaminated soils. Prior studies have shown that the organic carbon (OC) content and grain size of soil determined the efficiency of PFAS removal during washing. However, most of the past studies have [...] Read more.
The soil-washing technique has been successfully utilized for the remediation of PFAS-contaminated soils. Prior studies have shown that the organic carbon (OC) content and grain size of soil determined the efficiency of PFAS removal during washing. However, most of the past studies have focused on soils with a low OC content, typically ranging from 0–3%. In this study, we explored the use of a novel process where soil washing was combined with air bubbling (or foam fractionation) to aid in the removal of PFAS from high OC-content soils (~4–20%). Treatment with air bubbling of high OC soil (~20%) with perfluorobutane sulfonate (PFBS) and perfluorooctanoate (PFOA) did not enhance their removal, as they featured low surface activity. However, we observed an improvement in the extraction of perfluorooctane sulfonate (PFOS) from 27% to 42% with bubbling, consistent with the higher surface activity of PFOS compared to PFOA and PFBS. Perfluorodecanoic acid (PFDA) was irreversibly adsorbed to the high OC soil and was not removed efficiently by both bubbling and soil washing. A slight improvement in PFDA removal (6–13%) was observed when a co-surfactant (cetyltrimethylammonium chloride) was added and when the OC content was reduced to ~4% by the addition of nonorganic sand to the contaminated soil prior to soil washing. This suggested that the interaction of PFDA with OC was the dominant factor determining its extraction from soil. In conclusion, our results indicated that soil washing alone was sufficient for the removal of short-chain PFAS from soil. Although bubbling had a mild effect on the removal of some long-chain PFAS from the solution, it did not help in the overall removal of PFAS from high OC soils, highlighting the difficulty in the treatment of high OC-content soils and that immobilization of PFAS would be an ideal approach in managing such contaminated sites. Full article
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<p>Schematic of the batch column setup for air-bubbling extraction of PFAS from soil.</p>
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<p>Removal percentages of PFOA, PFOS, and PFDA when present as single solute after 60 min of soil washing. Initial concentration 0.1 μg/g. Error bars represent variations in duplicate samples.</p>
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<p>Removal percentages of PFDA-contaminated soils for (<b>a</b>) varying pHs for the wash solution, (<b>b</b>) 1 mg/L CTAC addition (pH 7.3), and (<b>c</b>) at reduced OC content of soil (soil/sand ratio of 25:75), pH 7.3. Error bars represent variations in duplicate samples. Numbers above the bars represent the average percentage of PFAS accounted for after treatment.</p>
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<p>Extraction efficiency of (<b>a</b>) PFBS, (<b>b</b>) PFOA, (<b>c</b>) PFOS, and (<b>d</b>) PFDA from soil spiked with 0.5 nmol/g of each PFAS. Error bars represent variations in duplicate samples.</p>
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<p>Mass balance of PFAS in various fractions before and after treatment: (<b>a</b>) PFBS, (<b>b</b>) PFOA, (<b>c</b>) PFOS, and (<b>d</b>) PFDA. Blue bar represents the mass of PFAS contained in untreated soil. After treatment: The gray fraction is the mass of PFAS contained in the washing (aqueous) solution, green fraction is the mass of PFAS associated with unsettled solids (fines), and yellow fraction represents the mass of PFAS associated with the apparatus due to sorption.</p>
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25 pages, 3076 KiB  
Article
A Comparison Between Industrial Energy Efficiency Measures in Guatemala and the United States
by Radhika Khosla, Angel Martinez Rodriguez, Ryan J. Milcarek and Patrick E. Phelan
Environments 2025, 12(1), 19; https://doi.org/10.3390/environments12010019 - 12 Jan 2025
Viewed by 423
Abstract
Energy auditing has been cited as a key tool in closing the gap between the actual energy consumption in industrial facilities and what should be at an environmentally sustainable level. Several factors affect the likelihood that energy audits will be effective in closing [...] Read more.
Energy auditing has been cited as a key tool in closing the gap between the actual energy consumption in industrial facilities and what should be at an environmentally sustainable level. Several factors affect the likelihood that energy audits will be effective in closing that gap, and more analysis is needed to understand these factors, especially for developing nations. This study compares three energy efficiency measures (EEMs) frequently recommended in both the United States and Guatemala, namely, installing solar panels to generate electricity, installing higher-efficiency lighting, and upgrading to premium efficiency motors. The implementation of each of these EEMs contributes to more sustainable energy consumption, and each of these EEM’s payback periods is affected by capital costs, energy costs, and other local factors analyzed in this study. Projected payback periods for each EEM based on Guatemalan and U.S. capital cost and energy cost ranges are assessed via EEM-specific payback period calculations and compared to the energy audit data from each country. While lower capital costs incentivize EEM implementation and reduce payback periods, there is an interplay between energy cost and capital cost that impacts the trends in the U.S. and Guatemala. As in the case of the solar panel installation EEM, though Guatemalan companies pay ~110% more for electricity than U.S. companies, when Guatemalan capital costs are lower, payback periods are lower than in the U.S. Conversely, in cases where Guatemalan capital costs are higher—as for higher-efficiency lighting and motor installation—Guatemalan payback periods are roughly the same as those in the U.S. because of the higher Guatemalan energy costs. Full article
(This article belongs to the Special Issue Environments: 10 Years of Science Together)
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<p>Map of Guatemalan departments showing: (<b>A</b>) the overall percentage of households in each department with the highest levels of poverty in 2014 and (<b>B</b>) the change in poverty percentage of households in each department between 2006 and 2014 [<a href="#B22-environments-12-00019" class="html-bibr">22</a>] (© 2021 Henry, C. L.; Baker, J. S.; Shaw, B. K.; Kondash, A. J.; Leiva, B.; Castellanos, E.; Wade, C. M.; Lord, B.,; van Houtven, G.; and Redmon, J. H. Published by Elsevier B.V. Link to the license: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> (accessed on 30 March 2024)).</p>
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<p>CO<sub>2</sub> emissions per sector in the U.S. in 2021 [<a href="#B30-environments-12-00019" class="html-bibr">30</a>].</p>
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<p>CO<sub>2</sub> emissions per sector in Guatemala in 2021 [<a href="#B30-environments-12-00019" class="html-bibr">30</a>].</p>
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<p>Histogram showing payback period distribution for the recommended EEM to install solar panels to generate electricity from Guatemalan and U.S. energy audit data [<a href="#B20-environments-12-00019" class="html-bibr">20</a>,<a href="#B21-environments-12-00019" class="html-bibr">21</a>].</p>
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<p>Histogram showing payback period distribution for the recommended EEM to install higher-efficiency lighting from Guatemalan and U.S. energy audit data [<a href="#B20-environments-12-00019" class="html-bibr">20</a>,<a href="#B21-environments-12-00019" class="html-bibr">21</a>].</p>
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<p>Histogram showing payback period distribution for the recommended EEM to replace old motors with premium efficiency motors from Guatemalan and U.S. energy audit data [<a href="#B20-environments-12-00019" class="html-bibr">20</a>,<a href="#B21-environments-12-00019" class="html-bibr">21</a>].</p>
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<p>Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to install solar panels, plotted with Guatemalan and U.S. energy audit data [<a href="#B20-environments-12-00019" class="html-bibr">20</a>,<a href="#B21-environments-12-00019" class="html-bibr">21</a>]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.</p>
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<p>Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to replace current lighting with higher-efficiency lighting, plotted with Guatemalan and U.S. energy audit data [<a href="#B20-environments-12-00019" class="html-bibr">20</a>,<a href="#B21-environments-12-00019" class="html-bibr">21</a>]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.</p>
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<p>Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to replace old motors with premium efficiency equipment, plotted with Guatemalan and U.S. energy audit data [<a href="#B20-environments-12-00019" class="html-bibr">20</a>,<a href="#B21-environments-12-00019" class="html-bibr">21</a>]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.</p>
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18 pages, 644 KiB  
Article
Adaptive Degenerate Space-Based Method for Pollutant Source Term Estimation Using a Backward Lagrangian Stochastic Model
by Omri Buchman and Eyal Fattal
Environments 2025, 12(1), 18; https://doi.org/10.3390/environments12010018 - 10 Jan 2025
Viewed by 362
Abstract
A major challenge in accidental or unregulated releases is the ability to identify the pollutant source, especially if the location is in a large industrial area. Usually in such cases, only a few sensors provide non-zero signal. A crucial issue is therefore the [...] Read more.
A major challenge in accidental or unregulated releases is the ability to identify the pollutant source, especially if the location is in a large industrial area. Usually in such cases, only a few sensors provide non-zero signal. A crucial issue is therefore the ability to use a small number of sensors in order to identify the source location and rate of emission. The general problem of characterizing source parameters based on real-time sensors is known to be a difficult task. As with many inverse problems, one of the main obstacles for an accurate estimation is the non-uniqueness of the solution, induced by the lack of sufficient information. In this study, an efficient method is proposed that aims to provide a quantitative estimation of the source of hazardous gases or breathable aerosols. The proposed solution is composed of two parts. First, the physics of the atmospheric dispersion is utilized by a well-established Lagrangian stochastic model propagated backward in time. Then, a new algorithm is formulated for the prediction of the spacial expected uncertainty reduction gained by the optimal placement of an additional sensor. These two parts together are used to construct an adaptive decision support system for the dynamical deployment of detectors, allowing for an efficient characterization of the emitting source. This method has been tested for several scenarios and is shown to significantly reduce the uncertainty that stems from the insufficient information. Full article
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<p>The concentration map (normalized by the source total emission) generated by a source located at (5000 m × 5000 m) is shown. The positions of the two detectors for the first four cases are also shown, each case represented by different markers (plus marker for case 1, circle for case 2, star for case 3 and triangle for case 4).</p>
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<p>Case 1 analysis: each row describes a cycle of the algorithm, starting from two detectors located at (6500, 5000) and (8000, 5000). In the left side of each row, the expected reduction according to the criterion described in Equation (<a href="#FD12-environments-12-00018" class="html-disp-formula">12</a>) is shown for all possible locations of the new detector. The already deployed detectors, used for the construction of the degenerate space in the beginning of the cycle, are designated by black crosses. The degenerate space before (blue) and after (black) the deployment of the new detector can be seen in the right panel of every row. The actual reduction gained by the procedure can be seen by the difference between the two colors. After two cycles, the degenerate space is reduced almost entirely to the correct source parameters, represented by the red dot.</p>
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<p>Case 4 analysis: the same analysis as described in the previous figure, starting from two detectors located at (6500, 5000) and (8000, 5700).</p>
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<p>The actual reduction, averaged over all possible hypotheses in the first cycle of the first and second cases, is shown for different points along the line (x, 5000) and (x, 5500) and for the preferred point (located at <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>6000</mn> <mo>,</mo> <mn>5300</mn> <mo>)</mo> </mrow> </semantics></math> in the first case and at <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>6200</mn> <mo>,</mo> <mn>4600</mn> <mo>)</mo> </mrow> </semantics></math> in the second case). Note that the best performance is achieved for the preferred point (green point).</p>
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18 pages, 5042 KiB  
Article
The Influence of Different Land Uses on Tungstate Sorption in Soils of the Same Geographic Area
by Gianniantonio Petruzzelli and Francesca Pedron
Environments 2025, 12(1), 17; https://doi.org/10.3390/environments12010017 - 8 Jan 2025
Viewed by 393
Abstract
The growing use of tungsten (W) in industrial applications has made it a critical element in modern production processes. This increasing demand is also contributing to the element’s wider dispersion in the environment, including in soil. In addition to mining areas, it is [...] Read more.
The growing use of tungsten (W) in industrial applications has made it a critical element in modern production processes. This increasing demand is also contributing to the element’s wider dispersion in the environment, including in soil. In addition to mining areas, it is necessary to evaluate the possible environmental effects of tungsten even in non-contaminated areas. The mobility and bioavailability of W in soil are essentially determined by the sorption processes that regulate its distribution between the liquid and solid phases of the soil. In this study, the effect of different land uses—natural, agricultural, and urban—on the sorption of W in soils of the same geographical area was addressed. The results showed that the maximum sorption can be found in natural soils, with a value of 528 mg/kg, while for agricultural and urban soils, the mean values are 486 and 392 mg/kg, respectively. Anthropic interventions seem to reduce this capacity in agricultural soils by about 8%, probably due to agronomic practices, and by even more, 26%, in urban soils, where the use of different materials can modify the original characteristics of the soils. These results show that variations in some of the main characteristics of soils, such as pH and organic matter content, also derived from different land uses, influence the sorptive properties of the soils. Full article
(This article belongs to the Special Issue Environments: 10 Years of Science Together)
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<p>Map of the study area.</p>
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<p>Boxplots representing the distribution of pH values and organic matter content in the soils from different land uses, with central lines marking the median values. Values followed by different lowercase letters are significantly different at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Boxplots representing the distribution of clay, sand, and Fe content (%) in the soils from different land uses, with central lines marking the median values.</p>
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<p>Sorption isotherms for the different soil uses. Data refer to the soil samples with the highest qmax values for each land use: N, A, and U.</p>
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<p>Relationship between the soils’ pH and the sorption maxima of all the sampled soils.</p>
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<p>Relationship between the SOM content and sorption maxima of all the sampled soils.</p>
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<p>Relationship between the soils’ clay, sand, and total Fe content and the sorption maxima of all the sampled soils.</p>
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<p>Kd trends of N, A, and U soils versus equilibrium concentrations (C<sub>e</sub>).</p>
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20 pages, 2981 KiB  
Article
Purification and Biochemical Characterization of Trametes hirsuta CS5 Laccases and Its Potential in Decolorizing Textile Dyes as Emerging Contaminants
by Guadalupe Gutiérrez-Soto, Carlos Eduardo Hernández-Luna, Iosvany López-Sandin, Roberto Parra-Saldívar and Joel Horacio Elizondo-Luevano
Environments 2025, 12(1), 16; https://doi.org/10.3390/environments12010016 - 7 Jan 2025
Viewed by 472
Abstract
This study explores the purification, characterization, and application of laccases from Trametes hirsuta CS5 for degrading synthetic dyes as models of emerging contaminants. Purification involved ion exchange chromatography, molecular exclusion, and chromatofocusing, identifying th ree laccase isoforms: ThIa, ThIb, and ThII. Characterization included [...] Read more.
This study explores the purification, characterization, and application of laccases from Trametes hirsuta CS5 for degrading synthetic dyes as models of emerging contaminants. Purification involved ion exchange chromatography, molecular exclusion, and chromatofocusing, identifying th ree laccase isoforms: ThIa, ThIb, and ThII. Characterization included determining pH and temperature stability, kinetic parameters (Km, Kcat), and inhibition constants (Ki) for inhibitors like NaN3, SDS, TGA, EDTA, and DMSO, using 2,6-DMP and guaiacol as substrates. ThII exhibited the highest catalytic efficiency, with the lowest Km and highest Kcat. Optimal activity was observed at pH 3.5 and 55 °C. Decolorization tests with nine dyes showed that ThII and ThIa were particularly effective against Acid Red 44, Orange II, Indigo Blue, Brilliant Blue R, and Remazol Brilliant Blue R. ThIb displayed higher activity towards Crystal Violet and Acid Green 27. Among substrates, guaiacol showed the highest Kcat, while 2,6-DMP was preferred overall. Inhibitor studies revealed NaN3 as the most potent inhibitor. These results demonstrate the significant potential of T. hirsuta CS5 laccases, especially ThIa and ThII, as biocatalysts for degrading synthetic dyes and other xenobiotics. Their efficiency and stability under acidic and moderate temperature conditions position them as promising tools for sustainable wastewater treatment and environmental remediation. Full article
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<p>SDS-PAGE analysis. Lane 1 corresponds to molecular weight markers, 2 corresponds to ThIa laccase, 3 to ThIb, and 4 to ThII isoform.</p>
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<p>Optimum pH determination.</p>
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<p>pH stability. (<b>A</b>) corresponds to ThIa isoform, (<b>B</b>) to ThIb laccase, and (<b>C</b>) to ThII isoform.</p>
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<p>Optimal temperature determination.</p>
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<p>Temperature stability.</p>
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<p>Effect of some inhibitors. (<b>A</b>) corresponds to sodium azide (NaN<sub>3</sub>), (<b>B</b>) to Sodium Dodecyl Sulfate (SDS), (<b>C</b>) to thioglycolic acid (TGA), (<b>D</b>) to ethylenediamine tetraacetic acid (EDTA), and (<b>E</b>) to dimethyl sulfoxide (DMSO).</p>
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<p>Decolorization kinetics graphs of laccase of <span class="html-italic">Trametes hirsuta</span> CS5 on nine synthetic dyes. (<b>A</b>) corresponds to Acid Red 44, (<b>B</b>) Orange II, (<b>C</b>) Reactive Black 5, (<b>D</b>) Blue Indigo, (<b>E</b>) Poly R-478, (<b>F</b>) Remazol Black B, (<b>G</b>) Violet Crystal, (<b>H</b>) Remazol Brilliant Blue R, and (<b>I</b>) Acid Green 27.</p>
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19 pages, 2156 KiB  
Article
Associations of Exposure to 24 Endocrine-Disrupting Chemicals with Perinatal Depression and Lifestyle Factors: A Prospective Cohort Study in Korea
by Nalae Moon, Su Ji Heo and Ju Hee Kim
Environments 2025, 12(1), 15; https://doi.org/10.3390/environments12010015 - 6 Jan 2025
Viewed by 519
Abstract
During pregnancy, reproductive hormonal changes could affect the mental health of women, such as depression and anxiety. Previous studies have shown that exposure to endocrine disrupting chemicals (EDCs) is significantly associated with mental health symptoms; however, the results were inconsistent. We aimed to [...] Read more.
During pregnancy, reproductive hormonal changes could affect the mental health of women, such as depression and anxiety. Previous studies have shown that exposure to endocrine disrupting chemicals (EDCs) is significantly associated with mental health symptoms; however, the results were inconsistent. We aimed to examine the association between 24 endocrine-disrupting chemicals (EDCs) in maternal urine and perinatal depression and their association with dietary and lifestyle factors. Participants were recruited from the “No Environmental Hazards for Mother–Child” cohort in Korea. Structured questionnaires asking dietary and lifestyle factors and evaluation of depressive symptoms were administered during antepartum (14 weeks of gestation) and postpartum (within four weeks after birth) periods. Urine samples were collected from 242 and 119 women during antepartum and postpartum periods, respectively. To assess perinatal depression, we used the Center for Epidemiological Studies-Depression Scale and the Edinburgh Postnatal Depression Scale. Antepartum depression and mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) (1.50, 1.01–2.23) and 1-hydroxypyrene (1-OHP) (0.05, 0–0.89) showed significant positive association. Additionally, postpartum depression showed significant associations with mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) (2.78, 1.00–7.70), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) (2.79, 1.04–7.46), 2-hydroxynaphthalene (2-NAP) (7.22, 1.43–36.59), and 2-hydroxyfluorene (2-FLU) (<0.01, 0–0.004). Some dietary factors (consumption of fish, fermented foods, cup noodles, and popcorn) and consumer product factors (use of skin care, makeup, perfume, antibiotics, sunscreen, nail polish, new furniture, plastic tableware, detergent, polish, paint, and pesticide) were associated with the concentration level of chemicals. We found that exposure to several EDCs during pregnancy and the postpartum period was associated with perinatal depression and dietary–lifestyle factors. Women in childbirth need to actively seek out information about exposure to EDCs and make efforts to avoid them for their own and fetal health. Full article
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<p>Concentration changes of chemicals in the ante- and postpartum periods. All boxplots show statistically significant higher median, mean, IQR, minimum, maximum, and outliers of chemicals in the antepartum period versus postpartum period. MnBP, mono-N-butyl phthalate; MEP, mono ethyl phthalate; MMP, mono(2-methylpropyl) phthalate; MECPP, mono(2-ethyl-5-carboxypentyl) phthalate; BPA, Bisphenol A; BPF, Bisphenol F; BPS, Bisphenol S; TCS, Triclosan; MP, Methylparaben; PP, Propylparaben; BP, Butylparaben; 1-OHP, 1-hydroxypyrene; 1-PHE, 1-hydroxyphenanthrene; BMA, benzylmercapturic acid. Extreme outliers (&lt;Q1 − 5*IQR, &gt;Q3 + 5*IQR) have been omitted.</p>
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<p>Difference of 24 non-persistent endocrine disrupting chemicals (EDCs) in maternal urine at the points of the 1st trimester and one month after delivery (n = 119). MnBP, mono-N-butyl phthalate; MEP, mono ethyl phthalate; MCPP, Mono(3-carboxypropyl) phthalate; MBzP, monobenzyl phthalate; MMP, mono(2-methylpropyl) phthalate; MEOHP, mono(2-ethyl-5-oxohexyl) phthalate; MEHHP, mono(2-ethyl-5-hydroxyhexyl) phthalate; MECPP, mono(2-ethyl-5-carboxypentyl) phthalate; MiBP, mono-isobutyl phthalate; BPA, Bisphenol A; BPF, Bisphenol F; BPS, Bisphenol S; MP, Methylparaben; EP, Ethylparaben; BP, Butylparaben; PP, Propylparaben; TCS, Triclosan; BP-3, Benzophenon-3; 1-OHP, 1-hydroxypyrene; 2-NAP, 2-hydroxynaphthalene; 1-PHE, 1-hydroxyphenanthrene; 2-FLU, 2-hydroxyfluorene; t,t-MA, trans, trans-muconic acid; BMA, benzylmercapturic acid.</p>
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<p>Bayesian Kernel Machine Regression between phthalates, bisphenols, parabens, PAHs, and other chemicals and antepartum and postpartum depression scores. (<b>a</b>) The overall effects of bisphenols; parabens; polycyclic aromatic hydrocarbons (PAHs); and other chemicals (TCS, BP-3, and VOCs) on the antepartum depression score were estimated using the Bayesian Kernel Machine Regression (BKMR) adjusted for maternal age, early-pregnancy body mass index, education, income, parity, gestational weeks, maternal urine cotinine level, and neonatal sex; ln: natural log. (<b>b</b>) Exposure–response relationship for the association between each chemical and antepartum depression score, fixing other chemical levels at their median. (<b>c</b>) The red, green, and blue bars and dots shows the estimated effect and 95% confidence intervals of an interquartile range change of each urinary chemical and antepartum depression score when the level of each of the other chemicals was held at their corresponding 25th, 50th, and 75th percentiles, respectively. (<b>d</b>) The overall effects of bisphenols; parabens; polycyclic aromatic hydrocarbons (PAHs); and other chemicals (TCS, BP-3, and VOCs) on the postpartum depression score were estimated using the Bayesian Kernel Machine Regression (BKMR) adjusted for early pregnancy depression score, maternal age, early-pregnancy body mass index, education, income, parity, gestational weeks, maternal urine cotinine level, and neonatal sex; ln: natural log. (<b>e</b>) Exposure–response relationship for the association between each chemicals and postpartum depression score, fixing other chemical levels at their median. (<b>f</b>) The red, green, and blue bars and dots shows the estimated effect and 95% confidence intervals of an interquartile range change of each urinary chemical and postpartum depression score when the level of each of the other chemicals was held at their corresponding 25th, 50th, and 75th percentiles, respectively.</p>
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<p>Bayesian Kernel Machine Regression between phthalates, bisphenols, parabens, PAHs, and other chemicals and antepartum and postpartum depression scores. (<b>a</b>) The overall effects of bisphenols; parabens; polycyclic aromatic hydrocarbons (PAHs); and other chemicals (TCS, BP-3, and VOCs) on the antepartum depression score were estimated using the Bayesian Kernel Machine Regression (BKMR) adjusted for maternal age, early-pregnancy body mass index, education, income, parity, gestational weeks, maternal urine cotinine level, and neonatal sex; ln: natural log. (<b>b</b>) Exposure–response relationship for the association between each chemical and antepartum depression score, fixing other chemical levels at their median. (<b>c</b>) The red, green, and blue bars and dots shows the estimated effect and 95% confidence intervals of an interquartile range change of each urinary chemical and antepartum depression score when the level of each of the other chemicals was held at their corresponding 25th, 50th, and 75th percentiles, respectively. (<b>d</b>) The overall effects of bisphenols; parabens; polycyclic aromatic hydrocarbons (PAHs); and other chemicals (TCS, BP-3, and VOCs) on the postpartum depression score were estimated using the Bayesian Kernel Machine Regression (BKMR) adjusted for early pregnancy depression score, maternal age, early-pregnancy body mass index, education, income, parity, gestational weeks, maternal urine cotinine level, and neonatal sex; ln: natural log. (<b>e</b>) Exposure–response relationship for the association between each chemicals and postpartum depression score, fixing other chemical levels at their median. (<b>f</b>) The red, green, and blue bars and dots shows the estimated effect and 95% confidence intervals of an interquartile range change of each urinary chemical and postpartum depression score when the level of each of the other chemicals was held at their corresponding 25th, 50th, and 75th percentiles, respectively.</p>
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37 pages, 5371 KiB  
Article
Coupling Advanced Geo-Environmental Indices for the Evaluation of Groundwater Quality: A Case Study in NE Peloponnese, Greece
by Panagiotis Papazotos, Maria Vlachomitrou, Despoina Psarraki, Eleni Vasileiou and Maria Perraki
Environments 2025, 12(1), 14; https://doi.org/10.3390/environments12010014 - 4 Jan 2025
Viewed by 905
Abstract
Water and its management have played a pivotal role in the evolution of organisms and civilizations, fulfilling essential roles in personal use, industry, irrigation, and drinking from ancient times to the present. This study seeks to evaluate groundwater quality for irrigation and drinking [...] Read more.
Water and its management have played a pivotal role in the evolution of organisms and civilizations, fulfilling essential roles in personal use, industry, irrigation, and drinking from ancient times to the present. This study seeks to evaluate groundwater quality for irrigation and drinking in the Northern Peloponnese region, specifically the wells of Loutraki and Schinos areas and the springs of the Gerania Mountains (Mts.), using geo-environmental indices and ionic ratios. For the first time, geo-environmental indices have been applied to a region where groundwater serves multiple purposes, addressing the challenge of understanding their dynamics to optimize their application in environmental science and groundwater pollution research. To achieve this, 68 groundwater samples from the study area were utilized, and a total of 25 geo-environmental indices were calculated to assess water quality. These indices examined: (i) drinking suitability (NPI, RI, PIG, WQI, and WPI), (ii) irrigation suitability (SAR, KR, %Na, PS, MAR, RSC, SSP, TH, PI, IWQI, and TDS), (iii) potentially toxic element (PTE) loadings (Cd, HEI, and HPI), and (iv) major hydrogeochemical processes, expressed as ionic ratios (Ca/Mg, Ca/SO4, Ca/Na, Cl/NO3, Cl/HCO3, and Si/NO3). Data processing involved descriptive statistics, hydrogeochemical bivariate plots, Spearman correlation coefficients, and multivariate statistical analyses, including factor analysis (FA) and R-mode hierarchical cluster analysis (HCA). Results revealed that all groundwater samples (100%) from the Loutraki area and the Gerania Mts. were of good quality for both drinking and irrigation purposes. In contrast, groundwater from the Schinos area exhibited lower quality, with most samples (93.9%) considered suitable only for irrigation. The deterioration in the coastal aquifer of the Schinos area is attributed to elevated concentrations of Cl, Na+, NO3, As, and Cr resulting from salinization and relatively limited anthropogenic influences. The study highlights that relying on individual geo-environmental indices can yield misleading results due to their dependence on factors such as researcher expertise, methodological choices, and the indices’ inherent limitations. Consequently, this research emphasizes the necessity of combining indices to enhance the reliability, accuracy, and robustness of groundwater quality assessments and hydrogeochemical evaluations. Last but not least, the findings demonstrate that calculating all available geo-environmental indices is unnecessary. Instead, selecting a subset of indices that either reflect the impact of specific elemental concentrations or can be effectively integrated with others is sufficient. This streamlined approach addresses challenges in optimizing geo-environmental index applications and contributes to improved groundwater resource management. Full article
(This article belongs to the Special Issue Research Progress in Groundwater Contamination and Treatment)
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Graphical abstract

Graphical abstract
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<p>A simplified geological map of the study area [<a href="#B34-environments-12-00014" class="html-bibr">34</a>,<a href="#B35-environments-12-00014" class="html-bibr">35</a>] with the groundwater sampling sites. An enlarged image of the Schinos area is given in (A) [<a href="#B30-environments-12-00014" class="html-bibr">30</a>].</p>
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<p>Classification of the 25 calculated geo-environmental indices of this study.</p>
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<p>Cross-plots of (<b>a</b>) Na<sup>+</sup> vs. Cl<sup>−</sup>, (<b>b</b>) SO<sub>4</sub><sup>2−</sup> vs. Ca<sup>2+</sup>, (<b>c</b>) Na<sup>+</sup> vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>), (<b>d</b>) EC vs. Na<sup>+</sup>/Cl<sup>−</sup>, (<b>e</b>) (HCO<sub>3<sup>−</sup></sub> + SO<sub>4</sub><sup>2−</sup>) vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>), and (<b>f</b>) HCO<sub>3<sup>−</sup></sub> vs. Mg<sup>2+</sup> for the 68 groundwater samples from the Loutraki–Schinos–Gerania Mts. region.</p>
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<p>Ficklin diagram [<a href="#B95-environments-12-00014" class="html-bibr">95</a>] of 68 groundwater samples showing the sum of PTEs vs. pH.</p>
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<p>Boxplots of ionic ratios (<b>a</b>) Ca/Mg, (<b>b</b>) Ca/SO<sub>4</sub>, (<b>c</b>) Ca/Na, (<b>d</b>) Cl/NO<sub>3</sub>, (<b>e</b>) Cl/HCO<sub>3</sub>, and (<b>f</b>) Si/NO<sub>3</sub> for the groundwater samples from the Loutraki–Schinos–Gerania Mts. region.</p>
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<p>The Spearman correlation matrix, along with significance levels (<span class="html-italic">p</span>-values), for the 25 calculated geo-environmental indices in the Loutraki–Schinos–Gerania Mts. region (n = 68 groundwater samples).</p>
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<p>The scree plot of eigenvalues for the components derived from 68 groundwater samples collected from the Loutraki–Schinos–Gerania Mts. region indicates that five components have eigenvalues &gt; 1, signifying their statistical significance within the FA approach.</p>
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<p>Dendrogram of R-mode HCA for 25 variables calculated in 68 groundwater samples from the Loutraki–Schinos–Gerania Mts. region. The red and yellow dashed lines represent the linkage distances used to create different distinct clusters.</p>
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<p>Cross-plot of WQI vs. HPI for the 68 groundwater samples from the Loutraki–Schinos–Gerania Mts. Region.</p>
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<p>Bivariate diagrams of (<b>a</b>) Cl/HCO<sub>3</sub> (molar ratio) vs. Cl (mmol/L) and Cl/HCO<sub>3</sub> (molar ratio) vs. HCO<sub>3</sub> (mmol/L) and (<b>b</b>) Si/NO<sub>3</sub> (molar ratio) vs. NO<sub>3</sub> (mmol/L) and Si/NO<sub>3</sub> (molar ratio) vs. Si (mmol/L) for the 68 groundwater samples from the Loutraki–Schinos–Gerania Mts. region.</p>
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<p>Evaluation of major hydrogeochemical processes affecting water chemistry using Cl/HCO<sub>3</sub> vs. Si/NO<sub>3</sub> diagram.</p>
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<p>Bivariate Cl/HCO<sub>3</sub> vs. Si/NO<sub>3</sub> diagrams of (<b>a</b>) As, and (<b>b</b>) Cr (Q1: first quartile; Q2: second quartile or median; Q3: third quartile).</p>
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15 pages, 1559 KiB  
Article
Impact of Long-Term Changes in Ambient Erythema-Effective UV Radiation on the Personal Exposure of Indoor and Outdoor Workers—Case Study at Selected Sites in Europe
by Gudrun Laschewski
Environments 2025, 12(1), 13; https://doi.org/10.3390/environments12010013 - 4 Jan 2025
Viewed by 489
Abstract
Given the persistently high incidence of skin cancer, there is a need for prevention-focused information on the impact of long-term changes in ambient solar ultraviolet radiation (UVR) on human personal radiation exposure. The exposure categories of the UV Index linked to protection recommendations [...] Read more.
Given the persistently high incidence of skin cancer, there is a need for prevention-focused information on the impact of long-term changes in ambient solar ultraviolet radiation (UVR) on human personal radiation exposure. The exposure categories of the UV Index linked to protection recommendations show long-term shifts in the frequency of occurrence with regional differences in direction and magnitude. The patterns of change for sites in the humid continental climate differ from those for sites in other climate zones such as the humid temperate or Mediterranean climate. The diversity of the individual exposures of indoor and outdoor workers can be described using probability models for personal erythema-effective UVR dose (UVD). For people who work indoors, the largest share of the total individual annual UVD is due to vacation, whereas for people who work outdoors, it is occupational exposure. The change in ambient UVDs at the residential locations is only partially reflected in the individual UVDs. For eight selected European sites between 38° and 60° northern latitude, the median of the individual annual total UVD (excluding travel) during the period 2009–2019 is 0.2 to 2.0% higher for indoor workers and 0.6 to 3.2% higher for outdoor workers compared to the period 1983–2008. Changes in the choice of an exemplary holiday destination offer both indoor and outdoor workers the potential to compensate for the observed long-term trend at their place of residence and work. Full article
(This article belongs to the Special Issue Environmental Pollutant Exposure and Human Health)
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<p>Annual share in the number of days with the various UVI ECs (‘negligible to low (S)’ 0 ≤ UVI &lt; 3, ‘moderate (M)’ 3 ≤ UVI &lt; 6, ‘high (L)’ 6 ≤ UVI &lt; 8, ‘very high (X)’ 8 ≤ UVI &lt; 11, and ‘extreme (E)’ UVI ≥ 11) across all eight locations considered (from 60.4° N to 37.9° N) in the period 1983–2008.</p>
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<p>Share of individual erythema-effective UVD [%] according to the exposure scenarios in <a href="#environments-12-00013-t001" class="html-table">Table 1</a> (W1: working days—indoor work, W2: working days—outdoor work, W3: weekends, H: holidays) at the eight sites considered (from 60.4° N to 37.9° N) in the period 1983–2019.</p>
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<p>Annual individual erythema-effective UVD [SED] at the site Freiburg; periods 1983–2008 (light gray) and 2009–2019 (gray) and exposure scenarios according to <a href="#environments-12-00013-t001" class="html-table">Table 1</a> and <a href="#environments-12-00013-t002" class="html-table">Table 2</a>.</p>
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<p>Monthly individual erythema-effective UVD [SED] the site Freiburg; periods 1983–2008 (light gray) and 2009–2019 (gray); and exposure scenario I (case: outdoor work).</p>
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<p>Monthly individual erythema-effective UVD [SED] at the site Freiburg; periods 1983–2008 (light gray) and 2009–2019 (gray); and exposure scenario I (case: indoor work).</p>
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<p>The share [%] of the change in ambient UVD at the residential locations in the total change in individual UVD from 1983–2008 to 2009–2019 for the scenarios I, II, and III for indoor and outdoor work across the eight sites considered.</p>
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22 pages, 1272 KiB  
Systematic Review
Advancing Environmental Sustainability in Healthcare: Review on Perspectives from Health Institutions
by Bárbara Badanta, Anabel Porcar Sierra, Saray Torner Fernández, Francisco Javier Rodríguez Muñoz, José Miguel Pérez-Jiménez, María Gonzalez-Cano-Caballero, Manuel Ruiz-Adame and Rocío de-Diego-Cordero
Environments 2025, 12(1), 9; https://doi.org/10.3390/environments12010009 - 3 Jan 2025
Viewed by 612
Abstract
Hospitals play a key role in promoting sustainable and healthy living. Few studies have taken this perspective into account. Therefore, we explored the role of hospital institutions in the development and implementation of sustainability strategies linked to the provision of health services. Applying [...] Read more.
Hospitals play a key role in promoting sustainable and healthy living. Few studies have taken this perspective into account. Therefore, we explored the role of hospital institutions in the development and implementation of sustainability strategies linked to the provision of health services. Applying the PRISMA guidelines, we conducted a systematic review of the PubMed, Scopus, CINAHL, PsycINFO and Web of Science databases and the references of the resulting articles in Mendeley Desktop v1.19.8. Articles peer-reviewed between 2016 and 2023 were eligible if they analyzed sustainable healthcare, activities derived from services provided and professional involvement. From the 27 articles that constituted the final sample, two themes were identified: (a) environmental sustainability in healthcare and (b) involvement of healthcare professionals in environmental sustainability. Proposals for sustainable actions to reduce the environmental impact of healthcare related to the use of natural resources, sustainable food, sustainable transport and waste management were reviewed. The role of healthcare workers, their attitudes and perceptions of sustainability and global health improvement were investigated. Reducing health pollution involves addressing excessive or inappropriate consumption of resources and minimizing the environmental footprint of healthcare activities. The different contexts reveal the heterogeneity of the sustainability interventions existing in the healthcare industry, both in terms of subject matter and in terms of the number of publications from each country. Full article
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<p>Flowchart for article selection in this systematic review. From Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. <span class="html-italic">BMJ</span> 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit <a href="http://www.prisma-statement.org" target="_blank">www.prisma-statement.org</a> (accessed on 10 June 2024) [<a href="#B16-environments-12-00009" class="html-bibr">16</a>].</p>
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<p>Dynamics of environmental impact of medical activities and measures to reduce it.</p>
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16 pages, 2438 KiB  
Article
Bioaccumulation Patterns in Different Tissues of Twelve Species of Elasmobranchs from the Tyrrhenian and Ionian Sea (Calabria, Southern Italy)
by Samira Gallo, Francesco Luigi Leonetti, Francesca Romana Reinero, Primo Micarelli, Luigi Passarelli, Gianni Giglio, Concetta Milazzo, Sandra Imbrogno, Donatella Barca, Massimiliano Bottaro and Emilio Sperone
Environments 2025, 12(1), 12; https://doi.org/10.3390/environments12010012 - 3 Jan 2025
Viewed by 442
Abstract
Marine ecosystems are increasingly threatened by pollutants, including trace elements (TEs) such as heavy metals, which bioaccumulate and pose risks to both marine fauna and human health. Sharks and rays are particularly susceptible to metal uptake and retention, making them sentinel species for [...] Read more.
Marine ecosystems are increasingly threatened by pollutants, including trace elements (TEs) such as heavy metals, which bioaccumulate and pose risks to both marine fauna and human health. Sharks and rays are particularly susceptible to metal uptake and retention, making them sentinel species for assessing environmental contamination. This study investigated the bioaccumulation of 16 TEs across 12 elasmobranch species sampled from the Ionian and Tyrrhenian coasts of Calabria, southern Italy, over an 11-year period. Muscle tissue was analyzed for all species, while additional comparisons among skin, muscle, and brain tissues were conducted for Galeus melastomus. Statistical analyses revealed significant variability in TEs concentrations across trophic levels (TRLs), with higher levels observed in species occupying higher trophic positions. Positive correlations were noted for elements such as Al, Ba, and Se, while negative correlations were found for Co, Cu, Mn, and U, indicating species-specific metabolic adaptations. Tissue-specific analyses identified the skin as a primary site for TEs accumulation, likely due to its barrier functions and external exposure. This study highlights the complex interplay of ecological, dietary, and physiological factors influencing TEs bioaccumulation in elasmobranchs and emphasizes the need for further research to understand the implications for marine food webs and conservation strategies. Full article
(This article belongs to the Special Issue Biomonitoring and Risk Assessment of Marine Ecosystems)
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<p>Distribution of the sampling locations. The number shown inside the points represents the number of species coming from that area. The size of the points is proportional to the number of species.</p>
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<p>Concentration peaks in species-specific analysis. The chart shows, in percentage, how often (among the analyzed TEs) the different species recorded higher mean values compared to the others.</p>
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<p>Species-specific comparison. Kruskal–Wallis and Dunn’s post hoc tests performed for five species with more than one individual. The figure includes box plots (with standard deviation) for 16 elements. Elements showing significant differences are highlighted in bold. The figure also shows the species that exhibited significant differences according to Bonferroni correction during Dunn’s post hoc test, as follows: <span class="html-italic">p</span> ≤ 0.05 (*), <span class="html-italic">p</span> ≤ 0.001 (**). On the <span class="html-italic">X</span>-axis, the species are represented with the following abbreviations: “D.l.” for <span class="html-italic">Dalatias licha</span>, “S.a.” for <span class="html-italic">Squalus acanthias</span>, “H.g.” for <span class="html-italic">Hexanchus griseus</span>, “P.g.” for <span class="html-italic">Prionace glauca</span>, and “G.m.” for <span class="html-italic">Galeus melastomus</span>. On the <span class="html-italic">Y</span>-axis, the concentration is reported relative to dry weight and expressed in ppm (mg/Kg).</p>
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<p>Trophic level comparison. Kruskal–Wallis and Dunn’s post hoc tests performed for trophic levels. The figure includes mean and whisker plots (with standard error) for all 16 elements. Elements showing significant differences are highlighted in bold. The figure also shows the TRLs that exhibited significant differences according to Bonferroni correction during Dunn’s post hoc test, as follows: <span class="html-italic">p</span> ≤ 0.05 (*), <span class="html-italic">p</span> ≤ 0.001 (**), <span class="html-italic">p</span> ≤ 0.0001 (***). On the <span class="html-italic">X</span>-axis, the different trophic levels are represented, while on the <span class="html-italic">Y</span>-axis, the concentration is reported relative to dry weight and expressed in ppm (mg/Kg).</p>
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<p>Spearman’s correlation about trophic levels/TEs concentration. The image displays ellipses only for elements where the correlation (positive or negative) showed statistical significance (<span class="html-italic">p</span> &lt; 0.05). The color of the ellipses, as indicated in the legend, ranges from blue (slope coefficient equal to 1) to red (slope coefficient equal to −1). The value displayed at the center of each ellipse represents the coefficient observed.</p>
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<p>Kruskal–Wallis and Dunn’s post hoc tests performed for tissue. The figure includes box plots (with standard deviation) for all 15 elements. Elements showing significant differences are highlighted in bold. The figure also shows the species that exhibited significant differences according to Bonferroni correction during Dunn’s post hoc test, as follows: <span class="html-italic">p</span> ≤ 0.05 (*). On the <span class="html-italic">X</span>-axis, the different tissues analyzed are represented, while on the <span class="html-italic">Y</span>-axis, the concentration is reported relative to dry weight and expressed in ppm (mg/kg).</p>
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15 pages, 8583 KiB  
Article
How Do Vegetation Biomass, Area, and Shape Attributes Influence the Cooling Effect of Urban Green Spaces?
by Zahra Mokhtari, Alessio Russo and Raffaele Lafortezza
Environments 2025, 12(1), 11; https://doi.org/10.3390/environments12010011 - 3 Jan 2025
Viewed by 467
Abstract
Despite the increasing volume of research exploring the impact of various characteristics of urban green spaces (UGS) on land surface temperature (LST), findings remain inconsistent, particularly in arid climatic zones. In this research, we examined UGS change and their temperature and analyzed the [...] Read more.
Despite the increasing volume of research exploring the impact of various characteristics of urban green spaces (UGS) on land surface temperature (LST), findings remain inconsistent, particularly in arid climatic zones. In this research, we examined UGS change and their temperature and analyzed the relationship between pertinent variables of vegetation biomass, area, and shape of green patches and LST in Karaj city, an Iranian semi-arid urban area in 2000 and 2020. Linear regressions were used to model the relationship between green patches’ variables and LST. The results showed that vegetation biomass of green patches was more effective in reducing temperature in comparison with area and shape complexity. Moreover, larger patches with more vegetation biomass and higher shape complexity showed lower temperatures. These results can guide urban landscape optimization by providing a clear understanding of which factors contribute most significantly to temperature mitigation in arid and semi-arid urban areas. For instance, areas identified as green but thermally not significantly cold need to be prioritized for improvements such as planting denser vegetation or introducing more heat resilient species. Full article
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<p>Land use/land cover map of the Karaj Urban Region in 2000 and 2020.</p>
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<p>LST maps in 2000 and 2020.</p>
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<p>NDVI maps, UGS maps, and cold green patches at 99% confidence level.</p>
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<p>Scatter plot between Green Biomass, SHAPE, AREA and LST in 2000 and 2020.</p>
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21 pages, 929 KiB  
Review
Genotoxicity of Microplastics on Living Organisms: Effects on Chromosomes, DNA and Gene Expression
by Kuok Ho Daniel Tang
Environments 2025, 12(1), 10; https://doi.org/10.3390/environments12010010 - 3 Jan 2025
Viewed by 670
Abstract
Microplastic exposure has become unavoidable, leading to their presence in living organisms. One area of particular concern is the genotoxicity of microplastics, which has implications for reproductive health and cancer development. This review aims to highlight the genotoxic effects of microplastics on different [...] Read more.
Microplastic exposure has become unavoidable, leading to their presence in living organisms. One area of particular concern is the genotoxicity of microplastics, which has implications for reproductive health and cancer development. This review aims to highlight the genotoxic effects of microplastics on different organisms, focusing on their impacts on chromosomes, DNA, and gene expression. More than 85 papers, primarily published in the last five years, have been reviewed. This review indicates that microplastics can cause clastogenesis and aneugenesis at the chromosome level. Clastogenesis results in chromosome damage, while aneugenesis leads to failures in chromosome segregation without causing direct damage. Additionally, microplastics can fracture and damage DNA. These effects arise from (1) the direct genotoxicity of microplastics through interactions with chromosomes, DNA, and associated proteins; and (2) their indirect genotoxicity due to the production of reactive oxygen species (ROS) by oxidative stress induced by microplastics. Microplastics can trigger the activation of genes related to oxidative stress and the inflammatory response, leading to increased ROS production. Furthermore, they may alter gene expression in other biological processes. The genotoxicity linked to microplastics can stem from the particles themselves and their associated chemicals, and it appears to be both size- and dose-dependent. Full article
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<p>Nuclear abnormalities.</p>
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<p>MPs and ROS activate the p38 MAPK pathway, which upregulates dual-specificity phosphatases (e.g., DUSP1) that inactivate ERK activity, suppressing ERK-mediated cell proliferation and survival signals. p38 MAPK directly or indirectly suppresses MEK and interferes with PI3K, a critical activator of AKT. This inhibits AKT and suppresses pro-survival and anti-apoptotic signals in the cell, ultimately speeding up cell apoptosis.</p>
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4 pages, 155 KiB  
Editorial
Environmental Impact Assessment—Exploring New Frontiers
by Manuel Duarte Pinheiro
Environments 2025, 12(1), 8; https://doi.org/10.3390/environments12010008 - 31 Dec 2024
Viewed by 523
Abstract
Environmental Impact Assessment (EIA) legislation is a critical component of the decision-making process of projects with potential effects (i [...] Full article
(This article belongs to the Special Issue Environmental Impact Assessment II)
30 pages, 7187 KiB  
Review
Underlying Causes of Long-Term Environmental Pollution by Waste from an Abandoned Metal Mining District: When Legislative and Remediation Measures Are Ineffective
by Gregorio García and Guadalupe Rosique
Environments 2025, 12(1), 7; https://doi.org/10.3390/environments12010007 - 30 Dec 2024
Viewed by 756
Abstract
Since ancient times, mining activities have been recognised as having a strong environmental impact. Due to the extraordinary amount of waste and impacts on the landscape, environmental concerns caused by mining can be found worldwide. The risks associated with mining waste are of [...] Read more.
Since ancient times, mining activities have been recognised as having a strong environmental impact. Due to the extraordinary amount of waste and impacts on the landscape, environmental concerns caused by mining can be found worldwide. The risks associated with mining waste are of great concern, especially when these residues come from metal mining and its associated potentially toxic elements that can be released into the environment. The reality of many of these metal mining areas is that, despite the extensive regulatory frameworks and remediation techniques applied, they continue to have high levels of contamination, posing a source of environmental and public health risk to their surroundings. The issues underlying this situation are details that can only be detected by experience in the management and thorough knowledge of the dynamics of these tailings in the long term. And in many cases, the key is in the details. For this purpose, the case of the former metal mining district of Cartagena-La Unión (SE Spain), one of the most affected areas in the European continent by these metal mining wastes, has been analyzed. In conclusion, it has been shown that the legal status of these waste and mining operations and the lack of control and effectiveness of rehabilitation activities are behind the worrying environmental situation of these areas. The interaction between the legal framework and the environmental and technical knowledge of these tailings and mining areas reveals practical issues beyond the scope of general analysis. This case study, conducted in the main Spanish metal mining area, concerns waste volume, and its findings offer the potential to improve the safety and environmental quality of metal mining regions elsewhere. Full article
(This article belongs to the Special Issue Environmental Pollution Risk Assessment)
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<p>Affection on the Mar Menor of metal mining wastes from the old mining district of Cartagena-La Unión: (<b>a</b>) General view of the dikes and canals of the tourist development known as ‘Veneziola’, in La Manga del Mar Menor, built with rocks and metal mining waste from the nearby Cartagena-La Unión area; (<b>b</b>) pile of smelting slags in the Cartagena-La Unión mining area; this waste has traditionally been used to build the foundations of the roads in the region where this mining area is located.</p>
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<p>A general view of a mining valley of the former Cartagena-La Unión metalliferous mining district, where several mining concessions can be seen with their mining facilities and tailings, some of which are still active but with temporary suspension of activities.</p>
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<p>Example of a false, or ‘fake’, rehabilitation: (<b>a</b>) A general image of the metal tailings dump undergoing an ineffective rehabilitation, in which the restoration appears to have been a success; (<b>b</b>) total potentially toxic elements (Zn, Pb, As, and Cd) in the soil profile of the same restored dump, where there has been a very high enrichment of the cover soils used for restoration, that should be almost free of these elements, due to capillary rise of these elements from the underlying metal mining waste layers; adapted from [<a href="#B78-environments-12-00007" class="html-bibr">78</a>] (unpublished graphic).</p>
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20 pages, 3149 KiB  
Article
Evaluation of Petroleum Hydrocarbon-Contaminated Soil Remediation Technologies and Their Effects on Soybean Growth
by Dengyu Jiang, Tao Li, Xuanhe Liang, Xin Zhao, Shanlong Li, Yutong Li, Kokyo Oh, Haifeng Liu and Tiehua Cao
Environments 2025, 12(1), 6; https://doi.org/10.3390/environments12010006 - 28 Dec 2024
Viewed by 658
Abstract
The application of persulfate (PS) for the remediation of petroleum hydrocarbon contamination is among the most widely employed in situ chemical oxidation (ISCO) techniques, and it has received widespread attention due to its limited impact on soil integrity. This study employed a FeSO [...] Read more.
The application of persulfate (PS) for the remediation of petroleum hydrocarbon contamination is among the most widely employed in situ chemical oxidation (ISCO) techniques, and it has received widespread attention due to its limited impact on soil integrity. This study employed a FeSO4-activated PS oxidation method to investigate the feasibility of remediating soil contaminated with total petroleum hydrocarbons (TPHs). The factors tested included the TPH concentration, different PS:FeSO4 ratios, the reaction time for remediation, soil physical and chemical property changes before and after remediation, and the effect of soil before and after remediation on soybean growth. The TPH degradation rate in soil was highest for high-, medium-, and low-TPHs soils—81.5%, 81.4%, and 72.9%, respectively, with minimal disruption to the soil’s physicochemical properties—when PS:FeSO4 = 1:1. The remediation verification results indicated that the condition of the soybeans was optimal when PS:FeSO4 = 1:1. Under this condition, the net photosynthetic rate, stomatal conductance, intercellular CO2 concentration, and transpiration rate all remained high. Therefore, the best remediation effect was achieved with PS:FeSO4 = 1:1, which also minimized the damage to the soil and the effects on crop growth. Full article
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<p>Changes in soil TPH concentrations with PS oxidation remediation over 14 d: (<b>a</b>) PS:FeSO<sub>4</sub> = 1:0.8; (<b>b</b>) PS:FeSO<sub>4</sub> = 1:1; (<b>c</b>) PS:FeSO<sub>4</sub> = 1:1.2; (<b>d</b>) PS:FeSO<sub>4</sub> = 1:1.4; (<b>e</b>) natural conditions oxidation (contrast). Experimental conditions: initial TPH concentration = 19,070 ± 477.3 mg/kg; pH = 6.84.</p>
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<p>Changes in soil TPH concentrations with PS oxidation remediation over 14 d: (<b>a</b>) PS:FeSO<sub>4</sub> = 1:0.8; (<b>b</b>) PS:FeSO<sub>4</sub> = 1:1; (<b>c</b>) PS:FeSO<sub>4</sub> = 1:1.2; (<b>d</b>) PS:FeSO<sub>4</sub> = 1:1.4; (<b>e</b>) natural conditions oxidation (contrast). Experimental conditions: initial TPH concentration = 14,792 ± 350.5 mg/kg; pH = 6.62.</p>
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<p>Changes in soil TPH concentrations with PS oxidation remediation over 14 d: (<b>a</b>) PS:FeSO<sub>4</sub> = 1:0.8; (<b>b</b>) PS:FeSO<sub>4</sub> = 1:1; (<b>c</b>) PS:FeSO<sub>4</sub> = 1:1.2; (<b>d</b>) PS:FeSO<sub>4</sub> = 1:1.4; (<b>e</b>) natural conditions oxidation (contrast). Experimental conditions: initial TPH concentration = 10,801 ± 200.9 mg/kg, pH = 6.59.</p>
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<p>Changes in soil TPH concentrations with PS oxidation remediation over 14 d: (<b>a</b>) PS:FeSO<sub>4</sub> = 1:0.8; (<b>b</b>) PS:FeSO<sub>4</sub> = 1:1; (<b>c</b>) PS:FeSO<sub>4</sub> = 1:1.2; (<b>d</b>) PS:FeSO<sub>4</sub> = 1:1.4; (<b>e</b>) natural conditions oxidation (contrast). Experimental conditions: Initial TPH concentration = 19,070 ± 477.3 mg/kg; pH = 6.84.</p>
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<p>Changes in soil TPH concentrations with PS oxidation remediation over 14 d: (<b>a</b>) PS:FeSO<sub>4</sub> = 1:0.8; (<b>b</b>) PS:FeSO<sub>4</sub> = 1:1; (<b>c</b>) PS:FeSO<sub>4</sub> = 1:1.2; (<b>d</b>) PS:FeSO<sub>4</sub> = 1:1.4; (<b>e</b>) natural conditions oxidation (contrast). Experimental conditions: Initial TPHs = 14, 792 ± 350.5 mg/kg; pH = 6.62.</p>
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<p>Changes in soil TPH concentrations with PS oxidation remediation over 14 d: (<b>a</b>) PS:FeSO<sub>4</sub> = 1:0.8; (<b>b</b>) PS:FeSO<sub>4</sub> = 1:1; (<b>c</b>) PS:FeSO<sub>4</sub> = 1:1.2; (<b>d</b>) PS:FeSO<sub>4</sub> = 1:1.4; (<b>e</b>) natural conditions oxidation (contrast). Experimental conditions: Initial TPH concentration = 10,801 ± 200.9 mg/kg, pH = 6.59.</p>
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<p>Changes in the TPH degradation rate in soil with PS oxidation remediation for 14 d: PS:FeSO<sub>4</sub> = 1:0.8 (TA1/TB1/TC1), PS:FeSO<sub>4</sub> = 1:1 (TA2/TB2/TC2), PS:FeSO<sub>4</sub> = 1:1.2 (TA3/TB3/TC3), PS:FeSO<sub>4</sub> = 1:1.4 (TA4/TB4/TC4). (<b>a</b>) Experimental conditions: initial TPH concentration = 19,070 ± 477.3 mg/kg, pH = 6.84; (<b>b</b>) Experimental conditions: TPHs = 14,792 ± 350.5 mg/kg, pH = 6.62; (<b>c</b>) Experimental conditions: TPHs = 10,801 ± 200.9 mg/kg, pH = 6.59.</p>
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<p>The effects of chemical oxidation remediation 14 days after planting soybeans: (<b>a</b>) photosynthetic rate; (<b>b</b>) stomatal conductance; (<b>c</b>) intercellular CO<sub>2</sub> concentration; (<b>d</b>) transpiration rate. Measurements were made during the flowering and podding stages under the following experimental conditions: initial TPH concentration = 19,070 ± 477.3 mg/kg, pH = 6.84. Different letters (e.g., a, b, c, d) indicate statistically significant differences between groups. The same letter denotes no significant difference, while different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>The effects of chemical oxidation remediation 14 days after planting soybeans: (<b>a</b>) photosynthetic rate; (<b>b</b>) stomatal conductance; (<b>c</b>) intercellular CO<sub>2</sub> concentration; (<b>d</b>) transpiration rate. Measurements were made during the flowering and podding stages under the following experimental conditions: initial TPH concentration = 14,792 ± 350.5 mg/kg, pH = 6.62. Different letters (e.g., a, b, c, d) indicate statistically significant differences between groups. The same letter denotes no significant difference, while different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>The effects of chemical oxidation remediation 14 days after planting soybeans: (<b>a</b>) photosynthetic rate; (<b>b</b>) stomatal conductance; (<b>c</b>) intercellular CO<sub>2</sub> concentration; (<b>d</b>) transpiration rate. Measurements were made during the flowering and podding stages under the following experimental conditions: initial TPH concentration = 10,801 ± 200.9 mg/kg, pH = 6.70. Different letters (e.g., a, b, c, d) indicate statistically significant differences between groups. The same letter denotes no significant difference, while different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Heatmap of the correlation between different PS:FeSO<sub>4</sub> ratios in the remediation of soil contaminated with various TPH concentrations and the physiological indicators of soybeans. “*” indicates a significant correlation between factors, while “**” indicates a highly significant correlation between factors.</p>
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21 pages, 1513 KiB  
Article
Enhancing Circularity in Urban Waste Management: A Case Study on Biochar from Urban Pruning
by Rocco Pavesi, Luigi Orsi and Luca Zanderighi
Environments 2025, 12(1), 5; https://doi.org/10.3390/environments12010005 - 28 Dec 2024
Viewed by 494
Abstract
This study investigates the potential of biochar production from urban pruning waste as a sustainable solution within a circular economy framework. Urban green waste, often underutilized, typically increases landfill burden and greenhouse gas emissions. Converting pruning waste into biochar reduces landfill reliance while [...] Read more.
This study investigates the potential of biochar production from urban pruning waste as a sustainable solution within a circular economy framework. Urban green waste, often underutilized, typically increases landfill burden and greenhouse gas emissions. Converting pruning waste into biochar reduces landfill reliance while enabling stable carbon sequestration. Utilizing the circular triple-layered business model canvas (CTLBMC), biochar’s impact is evaluated across economic, environmental, and social dimensions. This structured analysis is based on a theoretical framework and uses secondary data to illustrate the model’s applicability. As a result of the conducted studies, it was found that biochar derived from urban green waste not only improves soil phytotoxicity and enables long-term carbon sequestration, but also offers economic benefits, including municipal cost savings in waste management and diversified revenue streams from biochar sales. Socially, biochar production promotes community engagement in sustainable practices and supports urban greening initiatives, enhancing local ecosystems. The findings suggest that biochar production, assessed through the CTLBMC framework, represents a viable circular business model. This approach provides significant environmental, economic, and social benefits over conventional disposal, offering valuable insights for policymakers, waste management professionals, and urban planners advancing circular economy solutions. Full article
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<p>The economic layer of the CTLBMC for urban biochar case study. Source: authors’ elaboration.</p>
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<p>The environmental layer of the CTLBMC for urban biochar case study. Source: authors’ elaboration.</p>
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<p>The social layer of the CTLBMC for urban biochar case study. Source: authors’ elaboration.</p>
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18 pages, 3017 KiB  
Article
Examining Microplastics Along the Calabrian Coastline: Analysis of Key Characteristics and Metal Contamination
by Luana S. Brunetti, Costanza Piersante, Mauro F. La Russa, Emilio Cellini, Eduardo Bolea, Francisco Laborda and Silvestro A. Ruffolo
Environments 2025, 12(1), 4; https://doi.org/10.3390/environments12010004 - 27 Dec 2024
Viewed by 930
Abstract
Plastic pollution is a major concern today. Microplastics (MPs), due to their small size, can enter the food chain and cause serious harm to living organisms. The Mediterranean Sea is the sixth largest accumulation area for plastic waste, including MPs, worldwide. In this [...] Read more.
Plastic pollution is a major concern today. Microplastics (MPs), due to their small size, can enter the food chain and cause serious harm to living organisms. The Mediterranean Sea is the sixth largest accumulation area for plastic waste, including MPs, worldwide. In this study, we analyzed the distribution, shape, color, size, and polymer composition of MPs (having dimensions between 330 µm and 5 mm), collected from the water surface in six areas along the Calabrian coast, Italy. A prevalence of polyethylene was detected, with higher concentrations of MPs found in the Gioia Tauro and Cetraro areas. Additionally, heavy metals were identified within the MPs, suggesting that these particles could act as environmental carriers of such elements into the food chain. Full article
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<p>Sampling areas on the Ionian Coast (Crati, Neto, and Corace) and on the Tyrrhenian Coast (Cetraro, Vibo Marina, and Gioia Tauro).</p>
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<p>Differences among coasts and sampling periods.</p>
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<p>Summary of results of size analysis of MPs.</p>
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<p>Summary of results of shape analysis of MPs.</p>
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<p>Summary of results of color analysis of MPs.</p>
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<p>Summary of results of composition analysis of MPs. The term “others” includes the following: polyvinyl chloride, polyethylene terephthalate, polyurethane, acrylonitrile butadiene styrene, and polyamide.</p>
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<p>Example of image and related spectrum representing the elements detected during the SEM-EDS analysis.</p>
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