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Environments, Volume 12, Issue 2 (February 2025) – 36 articles

Cover Story (view full-size image): The energy sector plays a key role in advancing sustainability. Despite this, inconsistencies in the reporting of emissions and renewable energy integration persist. In this study, we analyze 293 EMAS Environmental Statements from Italian thermal power plants, hydropower plants, and waste incinerators to assess greenhouse gas (GHG) emissions (Scopes 1, 2, and 3) and renewable energy use. The findings revealed gaps in Scope 2 and 3 reporting, limited prioritization of renewables in fossil fuel-reliant sectors, and improvement objectives often tied to operational efficiency rather than sustainability. Standardized reporting and clearer renewable energy targets are needed to enhance transparency and decarbonization efforts. View this paper
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28 pages, 3557 KiB  
Article
Spatial and Temporal Climate Change Vulnerability Assessment in the West Bank, Palestine
by Sandy Alawna and Xavier Garcia
Environments 2025, 12(2), 69; https://doi.org/10.3390/environments12020069 - 18 Feb 2025
Viewed by 316
Abstract
Climate change is widely recognized as an inevitable phenomenon, with the Mediterranean region expected to experience some of the most severe impacts. Countries in this region, including Palestine, are already observing significant effects on key sectors such as agriculture, water resources, industry, and [...] Read more.
Climate change is widely recognized as an inevitable phenomenon, with the Mediterranean region expected to experience some of the most severe impacts. Countries in this region, including Palestine, are already observing significant effects on key sectors such as agriculture, water resources, industry, and health. Consequently, there is a need for multidimensional analyses of vulnerability. This study applied a Climate Change Vulnerability (CCV) index to assess spatial and temporal changes in vulnerability across different governorates in the West Bank, Palestine. Climate change vulnerability maps for the West Bank were developed using Geographic Information System (GIS) tools and Analytical Hierarchy Process (AHP) matrices, incorporating various indicators across categories such as Health, Socio-demographic, Agriculture, Service, Housing, and Economic components. The findings indicate that socio-demographic factors contribute significantly to the West Bank’s overall vulnerability to climate change. Although the overall vulnerability has decreased over time, the developed maps reveal that 76% of the West Bank’s population resides in areas classified as highly vulnerable to climate change impacts. In contrast, 10% of the population lives in areas classified as low to very low in terms of vulnerability, including the governorates of Tubas, Salfit, Qalqiliya, and Jericho and Al-Aghwar. These results are invaluable for policymakers, offering guidance on selecting appropriate mitigation and adaptation measures, particularly in highly vulnerable areas, to reduce the impacts of climate change across the region. Full article
(This article belongs to the Special Issue Environmental Risk and Climate Change III)
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<p>Map locating the study area.</p>
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<p>Spatial climate change vulnerability analysis methodological approach.</p>
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<p>Uncertainty analysis of the temporal evolution analysis of the climate change vulnerability indices ((<b>a</b>) Health, (<b>b</b>) Agriculture, (<b>c</b>) Socio-demographic, (<b>d</b>) Housing, (<b>e</b>) Services, (<b>f</b>) Economic, (<b>g</b>) Overall).</p>
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<p>Spider diagram for the climate change vulnerability index for the different West Bank governorates.</p>
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<p>Temporal change in the average climate change vulnerability index for the different climate change vulnerability components ((<b>a</b>) Health, (<b>b</b>) Agriculture, (<b>c</b>) Socio-demographic, (<b>d</b>) Housing, (<b>e</b>) Services, (<b>f</b>) Economic, (<b>g</b>) Overall).</p>
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<p>Temporal and spatial evolution of climate change vulnerability maps (<b>left</b>: past, <b>right</b>: present).</p>
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<p>Temporal evolution of climate change vulnerability mapping for Health (<b>A</b>), Economic (<b>B</b>), Agriculture (<b>C</b>), Housing (<b>D</b>), Services (<b>E</b>), and Socio-demographic (<b>F</b>) components.</p>
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18 pages, 1549 KiB  
Article
Advancing Waste Electrical and Electronic Equipment (WEEE) Recycling: A Random Forest Approach to Classifying WEEE Plastics for Sustainable Waste Management
by Cecilia Chaine, Miguel Mitsou Errandonea and Belen Morales Vega
Environments 2025, 12(2), 68; https://doi.org/10.3390/environments12020068 - 17 Feb 2025
Viewed by 224
Abstract
The rapid growth of waste electrical and electronic equipment (WEEE) highlights its significance as a critical waste stream, with plastics comprising 30% of its volume. These plastics often contain hazardous brominated flame retardants (BFRs), which are regulated to prevent negative environmental and public [...] Read more.
The rapid growth of waste electrical and electronic equipment (WEEE) highlights its significance as a critical waste stream, with plastics comprising 30% of its volume. These plastics often contain hazardous brominated flame retardants (BFRs), which are regulated to prevent negative environmental and public health impacts, but are predominantly managed through incineration, challenging circular economy goals. Addressing this issue requires innovation in sorting technologies and predictive methodologies to reduce reliance on incineration and enhance recycling efficiency. Despite progress, existing recycling practices are hindered by overly conservative contamination assumptions and a lack of detailed data on WEEE characteristics, leading to resource inefficiencies and missed opportunities for material recovery. This research aimed to bridge these gaps by developing a Random Forest-based predictive model to classify WEEE plastics as recyclable or non-recyclable, thereby supporting sustainable waste management. Using a dataset of over 15,000 samples analysed for polymer type, bromine concentration as an indicator of recyclability, and five additional variables, the model demonstrated 80–88% accuracy in validation tests. Polymer type appeared as the most significant predictor, followed by manufacturer and year of manufacture. Regional testing highlighted the adaptability of the model but also underscored the need for extended datasets and improved data management to simplify variable retrieval, as the model relies on hard-to-access data. The findings of this study have broad implications, including enhanced sorting efficiency, regulatory compliance, and alignment with circular economy principles. By refining classification accuracy and expanding its application, the model offers a scalable solution to advancing WEEE recycling and optimizing resource recovery, thereby promoting sustainability and reducing the environmental impact. Full article
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<p>Evolution of waste FPDs and FPDs placed on the market (POM) in the UK. Source: <a href="https://www.gov.uk/guidance/regulations-waste-electrical-and-electronic-equipment" target="_blank">https://www.gov.uk/guidance/regulations-waste-electrical-and-electronic-equipment</a> (10 May 2024).</p>
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<p>Cognitive Project Management for AI Methodology (source: <a href="https://www.ibm.com" target="_blank">https://www.ibm.com</a>).</p>
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<p>Confusion matrix.</p>
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<p>Confusion matrices (results).</p>
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14 pages, 1607 KiB  
Article
Global NDVI-LST Correlation: Temporal and Spatial Patterns from 2000 to 2024
by Ehsan Rahimi, Pinliang Dong and Chuleui Jung
Environments 2025, 12(2), 67; https://doi.org/10.3390/environments12020067 - 17 Feb 2025
Viewed by 195
Abstract
While numerous studies have investigated the NDVI-LST relationship at local or regional scales, existing global analyses are outdated and fail to incorporate recent environmental changes driven by climate change and human activity. This study aims to address this gap by conducting an extensive [...] Read more.
While numerous studies have investigated the NDVI-LST relationship at local or regional scales, existing global analyses are outdated and fail to incorporate recent environmental changes driven by climate change and human activity. This study aims to address this gap by conducting an extensive global analysis of NDVI-LST correlations from 2000 to 2024, utilizing multi-source satellite data to assess latitudinal and ecosystem-specific variability. The MODIS dataset, which provides global daily LST data at a 1 km resolution from 2000 to 2024, was used alongside MODIS-derived NDVI data, which offers global vegetation indices at a 1 km resolution and 16-day temporal intervals. A correlation analysis was performed by extracting NDVI and LST values for each raster cell. The analysis revealed significant negative correlations in regions such as the western United States, Brazil, southern Africa, and northern Australia, where increased temperatures suppress vegetation activity. A total of 38,281,647 pixels, or 20% of the global map, exhibited statistically significant correlations, with 80.4% showing negative correlations, indicating a reduction in vegetation activity as temperatures rise. The latitudinal distribution of significant correlations revealed two prominent peaks: one in the tropical and subtropical regions of the Southern Hemisphere and another in the temperate zones of the Northern Hemisphere. This study uncovers notable spatial and latitudinal patterns in the LST-NDVI relationship, with most regions exhibiting negative correlations, underscoring the cooling effects of vegetation. These findings emphasize the crucial role of vegetation in regulating surface temperatures, providing valuable insights into ecosystem health, and informing conservation strategies in response to climate change. Full article
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<p>Correlation map of LST-NDVI in six classes (<b>a</b>), and significant and non-significant pixels (<b>b</b>).</p>
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<p>Latitudinal distribution of significant correlations (<b>a</b>), and proportions of positive and negative significant correlations (<b>b</b>).</p>
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13 pages, 1637 KiB  
Article
Evaluation of Fish and Seafood Consumption in the Adult Population of an Italian Coastal Region and Health Risk Perception from Exposure to Methylmercury
by Anna Maria Spagnolo, Cristiana Maurella, Marina Sartini and Elena Bozzetta
Environments 2025, 12(2), 66; https://doi.org/10.3390/environments12020066 - 17 Feb 2025
Viewed by 169
Abstract
Fish is a nutrient-rich food important for the well-being of all age groups. However, through fish ingestion, organisms are also exposed to various contaminants such as mercury, which can be biomagnified to reach the highest levels of concentration in predatory fishes. The aim [...] Read more.
Fish is a nutrient-rich food important for the well-being of all age groups. However, through fish ingestion, organisms are also exposed to various contaminants such as mercury, which can be biomagnified to reach the highest levels of concentration in predatory fishes. The aim of this study was to evaluate the consumption of fish and seafood products in the population of an Italian coastal region and to investigate the perception of risk by consumers. A closed-ended questionnaire was administered for this purpose. Regarding the habit of eating fish and seafood products, 92% of the interviewees reported consuming these regularly. Fresh, frozen, and canned products were eaten one to three times a week by 55.0%, 52.1%, and 65.6% of participants, respectively. Swordfish and tuna, species at high risk of methylmercury contamination, were consumed one or more times a month by 79.5% of respondents. Only 37.4% showed awareness of the possible health risk, with 81% citing chemical causes (e.g., methylmercury). Combined actions are needed to contain the risk of exposure to contaminants, such as mercury, through the ingestion of fish and seafood products, including greater consumer information on species to be limited, fisheries monitoring, and controls on contaminant emissions. Full article
(This article belongs to the Special Issue Environmental Risk Assessment of Aquatic Environments)
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<p>Ligurian region of Italy [Google Earth image].</p>
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<p>Quantitative images of foods, Photographic Atlas of Food Portions, Scotti Bassani Institute [<a href="#B16-environments-12-00066" class="html-bibr">16</a>].</p>
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<p>Percent frequency (%) of fish/seafood (fresh, frozen, and canned with long shelf life) eating (N. 511). Possibility of giving only one answer for each product category.</p>
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<p>Percentage of consumption of the various categories of fish/seafood products with a frequency of one or more times per month. Multiple response options. The bar colors refer to different concentration limit values for mercury according to Commission Regulation (EU) 2022/617. Red bars: 1 mg/kg; dark gray bars: 0.5 mg/kg; light gray bars: 0.3 mg/kg of wet weight.</p>
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<p>Percentages of responses regarding the size of the average portion of fish/seafood products consumed per meal (No. 504).</p>
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<p>Perception about the possibility that fish/seafood products may pose a health risk (N. 511). Only one response was chosen.</p>
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18 pages, 11905 KiB  
Article
The Structural Evolution of Bimetallic Fe/Ag Mediated by Montmorillonite and Its Effect on Triclosan in the Environment
by Liting Ju, Qunyi Liu, Hongye Feng, Pingxiao Wu, Yiwen Ju, Li Zhang and Junbo Wang
Environments 2025, 12(2), 65; https://doi.org/10.3390/environments12020065 - 14 Feb 2025
Viewed by 402
Abstract
Montmorillonite (Mont) is a natural two-dimensional material with a 2:1 layered silicate crystal structure. It possesses abundant surface groups, cation exchange capacity, and adsorption performance. In addition, it has other advantages such as abundant reserves, environmental friendliness, strong mechanical stability, and a large [...] Read more.
Montmorillonite (Mont) is a natural two-dimensional material with a 2:1 layered silicate crystal structure. It possesses abundant surface groups, cation exchange capacity, and adsorption performance. In addition, it has other advantages such as abundant reserves, environmental friendliness, strong mechanical stability, and a large specific surface area. As such, it shows excellent potential for application in environmental remediation. In the following paper, we focus on the removal of TCS (triclosan) from an aqueous environment by utilizing montmorillonite-supported bimetallic Fe/Ag particles. We use scanning electron microscopy, X-ray diffraction patterns, Fourier-transform infrared spectra, and specific surface area to analyze the structure, morphology, and composition of these nanocomposites. The effects of the pH, different materials, contact time, and different initial concentrations on the degradation efficiency of TCS were studied systematically. Based on the results of our study, montmorillonite-supported bimetallic Fe/Ag nanoparticles (Fe/Ag-Mont) should be categorized as a type of mesoporous material of high uniformity because the pore size of all its catalysts ranges from 10 to 20 nm, and they are well-distributed. The Si-O stretching vibrations of montmorillonite can be changed by adding Fe/Ag. We found that Fe or Ag combined with -O to form a new bond and interacted with Si-O, and the incorporation of Fe/Ag-Mont nanoparticles removed TCS with better reduction rates. By enhancing reduction capacity, the pH was below 4 due to H• species generation by Fe/Ag. H• was the main factor enhancing the redox reaction in reducing TCS. The pH controlled the competition between Fe corrosion and silver formation, which enabled the system to self-regulate. In addition, this study provided a suitable method of efficiently synthesizing clay-supported bimetallic nano-system materials for reduction. Full article
(This article belongs to the Special Issue Advanced Nanomaterials for Wastewater Treatment)
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<p>SEM images of the prepared materials: (<b>a</b>) Fe/Ag-Mont; (<b>b</b>) Mont/Fe; (<b>c</b>) Fe/Ag; (<b>d</b>) Mont; (<b>e</b>) Mont/Ag; (<b>f</b>) Fe.</p>
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<p>EDS images of the prepared materials: (<b>a</b>) Fe/Ag-Mont; (<b>b</b>) Mont/Fe; (<b>c</b>) Fe/Ag; (<b>d</b>) Mont; (<b>e</b>) Mont/Ag; (<b>f</b>) Fe.</p>
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<p>(<b>a</b>) N<sub>2</sub> adsorption–desorption isotherms and (<b>b</b>) pore size distribution of the obtained samples.</p>
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<p>Powder X-ray diffraction patterns of Fe/Ag nanoparticles supported Fe/Ag nanoparticles, zero-valent Fe nanoparticles, supported zero-valent Fe nanoparticles, supported zero-valent Ag nanoparticles, and montmorillonite particles. The 2θ is from 2° to 70°. The number marked with nm unit is the value of the material interlayer domain. The amplified XRD data in the red box are shown in the right figure.</p>
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<p>FTIR spectra of Mont materials, Fe/Ag nanoparticles, zero-valent Fe nanoparticles, supported iron nanoparticles, supported zero-valent Ag nanoparticles, and supported Fe/Ag nanoparticles.</p>
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<p>XPS spectra of the high-resolution Fe<sub>2p</sub> spectrum: (<b>a</b>) Fe/Ag-Mont and Fe; (<b>b</b>) used Fe/Ag-Mont and Fe/Ag-Mont.</p>
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<p>(<b>a</b>) The effect of Ag loading with different compositions on the reduction of triclosan (experimental conditions: 10 mg/L TCS, reaction dose 2 g/L, pH 8, and temperature 28 °C). (<b>b</b>) The dates was calculated based on the Langmuir–Hinshelwood model.</p>
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<p>(<b>a</b>) The effect of contact time on the reduction of triclosan (experimental conditions: 10 mg/L TCS, reaction dose 2 g/L, pH 8, and temperature 28 °C). (<b>b</b>) The dates was calculated based on the Langmuir–Hinshelwood model.</p>
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<p>(<b>a</b>) The effect of different initial concentrations on the reduction of triclosan (experimental conditions: reaction dose 2 g/L, pH 8, and temperature 28 °C). (<b>b</b>) The removal rate of different triclosan initial concentrations. (<b>c</b>) The dates was calculated based on the Langmuir–Hinshelwood model.</p>
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<p>The effect of different initial pH on the reduction of triclosan (experimental conditions: 10 mg/L TCS, reaction dose 2 g/L, and temperature 28 °C).</p>
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19 pages, 1247 KiB  
Article
A Resilience-Augmented Approach to Compound Threats and Risk Governance: A Systems Perspective on Navigating Complex Crises
by Katarzyna Klasa, Benjamin D. Trump, Sam Dulin, Madison Smith, Holly Jarman and Igor Linkov
Environments 2025, 12(2), 64; https://doi.org/10.3390/environments12020064 - 12 Feb 2025
Viewed by 739
Abstract
Compound threats—two or more relatively rare and high-consequence events that co-occur in time and space, amplifying their effects—present difficult-to-predict events that can impose potentially grave consequences. While there has been increasing attention placed on modeling the probabilities and outcomes of compounding threats, there [...] Read more.
Compound threats—two or more relatively rare and high-consequence events that co-occur in time and space, amplifying their effects—present difficult-to-predict events that can impose potentially grave consequences. While there has been increasing attention placed on modeling the probabilities and outcomes of compounding threats, there are no proposed governance models for compound threats, limiting the ability of policymakers and decisionmakers to manage such crises in the future. We visualize resilience for compound threats to understand how critical functioning and system utility to contain hazards, to absorb losses, and to recover from stressors shifts over time. Using North Carolina as a case study, we conduct a compound threats assessment for disaster risk to showcase its effectiveness in more accurately predicting disaster risk areas, as well highlight the limitations of existing risk models used by policymakers. We propose a resilience-augmented conceptual framework to rethink risk governance for compound threats that allows for speed (specifically flexibility and adaptability) in situations of high uncertainty while working within the rigid, slow-moving boundaries of government and bureaucracy. Finally, we discuss strategies for key actors to apply a resilience-augmented governance approach to compound threats into operational decision-making during crisis situations. Full article
(This article belongs to the Special Issue Environments: 10 Years of Science Together)
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<p>Resilience phases for compound threats. In the figure, red signifies a decrease in critical functioning (damage, systemic shock, disruption, etc.); green signifies adaptation from the damage or shock that allows the system to improve critical functioning above its prior baseline; and gray signifies a system that was unable to recover or adapt, experiencing catastrophic, irrecoverable failure that made the critical functioning of the system no longer operational.</p>
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<p>Compound threats in North Carolina (2024). In this figure, panel a is on the left and panel b is on the right: (<b>a</b>) This image shows the county-level disaster risks in North Carolina, particularly for flooding, using traditional risk analyses which do not consider compound threats; (<b>b</b>) this image shows the county-level disaster risks in North Carolina, particularly for flooding, using a novel method that includes compound risks. Specifically, 1 (yellow to light green) means there is a large compounding effect; 0 (dark green to blue) means there is a small or negligible compounding effect; and −1 (dark blue to purple) means there is a mitigative compounding effect.</p>
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<p>Operationalizing compound threats: governance vs. leadership. TAPIC is a governance framework that stands for Transparency, Accountability, Participation, Integrity, and Capacity [<a href="#B48-environments-12-00064" class="html-bibr">48</a>].</p>
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21 pages, 4875 KiB  
Article
Late 20th Century Hypereutrophication of Northern Alberta’s Utikuma Lake
by Carling R. Walsh, Fabian Grey, R. Timothy Patterson, Maxim Ralchenko, Calder W. Patterson, Eduard G. Reinhardt, Dennis Grey, Henry Grey and Dwayne Thunder
Environments 2025, 12(2), 63; https://doi.org/10.3390/environments12020063 - 11 Feb 2025
Viewed by 393
Abstract
Eutrophication in Canadian lakes degrades water quality, disrupts ecosystems, and poses health risks due to potential development of harmful algal blooms. It also economically impacts the general public, industries like recreational and commercial fishing, and tourism. Analysis of a 140-year core record from [...] Read more.
Eutrophication in Canadian lakes degrades water quality, disrupts ecosystems, and poses health risks due to potential development of harmful algal blooms. It also economically impacts the general public, industries like recreational and commercial fishing, and tourism. Analysis of a 140-year core record from Utikuma Lake, northern Alberta, revealed the processes behind the lake’s current hypereutrophic conditions. End-member modeling analysis (EMMA) of the sediment grain size data identified catchment runoff linked to specific sedimentological processes. ITRAX X-ray fluorescence (XRF) elements/ratios were analyzed to assess changes in precipitation, weathering, and catchment runoff and to document changes in lake productivity over time. Five end members (EMs) were identified and linked to five distinct erosional and sedimentary processes, including moderate and severe precipitation events, warm and cool spring freshet, and anthropogenic catchment disturbances. Cluster analysis of EMMA and XRF data identified five distinct depositional periods from the late 19th century to the present, distinguished by characteristic rates of productivity, rainfall, weathering, and runoff linked to natural and anthropogenic drivers. The most significant transition in the record occurred in 1996, marked by an abrupt increase in both biological productivity and catchment runoff, leading to the hypereutrophic conditions that persist to the present. This limnological shift was primarily triggered by a sudden discharge from a decommissioned sewage treatment lagoon into the lake. Spectral and wavelet analysis confirmed the influence of the Arctic Oscillation, El Niño Southern Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation on runoff processes in Utikuma Lake’s catchment. Full article
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<p>Map showing Utikuma Lake, its location within Canada (star, upper right inset), as well as the location of the community of Atikameg (Whitefish Lake First Nation #459) on the western shore. The coring site is indicated by a red star, at which four closely spaced gravity cores were collected. Location and core details are presented in <a href="#environments-12-00063-t001" class="html-table">Table 1</a>. Bathymetric contours are given in meters.</p>
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<p>Photographs showing (<b>A</b>) collection of a UWITEC gravity core from Utikuma Lake as the bottom of the core is capped upon recovery, (<b>B</b>) a UWITEC core barrel filled with a core mounted on a custom-built portable extruder that can be deployed in the field and which can produce 1 mm resolution subsamples, and (<b>C</b>) a 1 mm resolution subsample being transferred from the extruder into a sample bag for subsequent analysis.</p>
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<p><sup>210</sup>Pb age model (black) and standard deviation (grey) for Utikuma Lake, with an average sedimentation rate of 1.5 mm/yr.</p>
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<p>Utikuma Lake Core BU1 grain size frequency distributions for the 200 subsamples (gray plots), as well as the distribution of five robust end members (EMs) derived from the model that best explains the sediment grain size distributions.</p>
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<p>Down core EMMA and normalized ITRAX elemental profiles for Utikuma Lake in core BU1, plotted against depth and estimated age. The CONISS cluster dendrogram (right) indicates five zones, the boundaries of which are marked by horizontal red lines.</p>
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<p>Spectral and wavelet time series analysis results of the normalized ITRAX elemental profiles for Utikuma Lake Core BU1. Red noise (AR1) and confidence levels are indicated on the spectrogram (<b>left</b>). Periodicities are indicated when the spectral power is statistically significant at 90%. Likewise, time-frequency areas of high spectral power, shown in red, which are statistically significant at 90% are indicated with black contouring on the CWT scalogram (<b>right</b>). CONISS boundaries (white dashed lines) overlay the wavelet scalograms.</p>
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<p>Spectral and wavelet analysis results for the five identified end members (EMs) identified in Utikuma Lake Core BU1. Oscillations above the 90% confidence level are labeled in the spectral time series results (<b>left</b>) and encircled in a solid black line in the wavelet time series analyses (<b>right</b>). CONISS boundaries (white dashed lines) overlay the wavelet scalograms.</p>
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<p>Cross wavelet transforms (XWTs) of EM02 and EM03 with annual snowfall total in Campsie, AB, and each of the PDO, ENSO, AO, and NAO. Oscillations above the 90% confidence level are encircled in a solid black line.</p>
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<p>Cross wavelet transforms (XWTs) of EM01 and EM04 with annual rainfall total in Campsie, AB, and each of the PDO and ENSO. Oscillations above the 90% confidence level are encircled in a solid black line.</p>
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<p>Continuous wavelet transform (CWT) of the Pacific Decadal Oscillation. Oscillations above the 90% confidence level are encircled in a solid black line.</p>
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36 pages, 6411 KiB  
Article
Ecological Flow Deficit in a Context of Institutional Rigidities and Climate Change: The Case of Mapocho Alto, Central Chile
by Gino Sturla and Eugenio Figueroa
Environments 2025, 12(2), 62; https://doi.org/10.3390/environments12020062 - 10 Feb 2025
Viewed by 587
Abstract
Climate change impacts on runoff, coupled with population and production growth, pose significant risks to aquatic ecosystems. These risks are heightened in countries with rigid institutional frameworks that prevent water extraction from adapting to ecological requirements. Central Chile presents a particularly compelling case [...] Read more.
Climate change impacts on runoff, coupled with population and production growth, pose significant risks to aquatic ecosystems. These risks are heightened in countries with rigid institutional frameworks that prevent water extraction from adapting to ecological requirements. Central Chile presents a particularly compelling case due to the coexistence of private water rights, challenges in establishing ecological flows, projected reductions in runoff, and the high country’s population share. This study aims to determine current and future ecological flow deficits using two indicators: the accumulated water volume deficit and the frequency of runoff falling below ecological flow thresholds. Given the absence of defined ecological flows in some basins and uncertainties about future water system operations, an original methodology tailored to the Chilean context is proposed. This analysis focuses on the Mapocho Alto system (five basins), which is highly affected by water extraction and outdated ecological flow definitions. Results indicate that annual deficit volumes during the historical period are low across basins and are concentrated between September and November. Under climate change scenarios (three basins), the deficits remain relatively stable in two basins but shift in January–July. However, in the Arrayán en la Montosa basin, climate change significantly increases the deficit volumes and frequencies due to imbalances between the natural water supply and water demand. The conclusions underscore the necessity of addressing institutional constraints, such as static ecological flow definitions, to prevent severe ecosystem issues in basins where runoff is projected to decline while demand remains constant or increases, a concern applicable to other countries with similar institutional frameworks. Full article
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<p>Study basins. Source: Own elaboration. The axes correspond to UTM East (<span class="html-italic">x</span>-axis) and UTM North (<span class="html-italic">y</span>-axis) coordinates, datum WGS84, and zone 18 s.</p>
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<p>Ecological flows, as defined by the DGA (measured in cubic meters per second). Source: Own elaboration.</p>
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<p>Scheme of the methodology. Source: Own elaboration.</p>
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<p>Ecological flows for the sub-basins of YL, SF, and MO for the 1985–2020 period. In the statistics of the Molina River, there are two outlier values with very low monthly flows (0.01 m<sup>3</sup>/s), which were probably poorly recorded by the measurement stations. These values have been corrected based on linear correlations with the monthly flows of October, which present a high correlation (R<sup>2</sup> = 0.8) with the monthly flows of September. This implies that the ecological flow for the month of September is higher than it would otherwise have been, considering the outlier data. In fact, in this case, the ecological flow takes the value of 20% of the annual flow (0.81 m<sup>3</sup>/s). Source: Own elaboration.</p>
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<p>High exceedance probability runoff (3 lowest flows) and monthly ecological flow for Mapocho en Los Almendros. Source: Own elaboration.</p>
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<p>High exceedance probability runoff (3 lowest flows) and monthly ecological flow for Arrayán en La Montosa. Source: Own elaboration.</p>
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<p>High exceedance probability runoff (3 lowest flows) and monthly ecological flow for Yerba Loca. Source: Own elaboration.</p>
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<p>High exceedance probability runoff (3 lowest flows) and monthly ecological flow for San Francisco. Source: Own elaboration.</p>
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<p>High exceedance probability runoff (3 lowest flows) and monthly ecological flow for Molina. Source: Own elaboration.</p>
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<p>Total deficit volume per basin/sub-basin for the historical period of 1985–2020. Source: Own elaboration.</p>
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<p>Minimum monthly remaining runoff, shown per model, for Mapocho en Los Almendros. Source: Own elaboration.</p>
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<p>Minimum monthly remaining runoff, shown per model, for Arrayán en La Montosa. Source: Own elaboration.</p>
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<p>Minimum monthly remaining runoff, shown per model, for Yerba Loca. Source: Own elaboration.</p>
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<p>Expected total deficit volume per basin (future period, 2030–2050). Source: Own elaboration.</p>
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<p>Historical monthly runoff (1985–2020) in Mapocho en Los Almendros (average and coefficient of variation) for the hydrological year (April to March). Source: Own elaboration.</p>
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<p>Historical monthly runoff (1985–2020) in Arrayán en La Montosa (average and coefficient of variation) for the hydrological year (April to March). Source: Own elaboration.</p>
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<p>Historical monthly runoff (1985–2020) in Yerba Loca (average and coefficient of variation) for the hydrological year (April to March). Source: Own elaboration.</p>
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<p>Historical monthly runoff (1985–2020) in San Francisco (average and coefficient of variation) for the hydrological year (April to March). Source: Own elaboration.</p>
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<p>Historical monthly runoff (1985–2020) in Molina (average and coefficient of variation) for the hydrological year (April to March). Source: Own elaboration.</p>
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<p>Monthly average, grouped by model, of the projected runoff for the 2030–2050 period and the monthly average of the measured runoff for the historical period of 1985–2020 for Mapocho en Los Almendros. Source: Own elaboration.</p>
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<p>Monthly coefficient of variation, by model, of the projected runoff for the 2030–2050 period and the coefficient of the monthly variation of the measured runoff for the historical period of 1985–2020 for Mapocho en Los Almendros. Source: Own elaboration.</p>
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<p>Monthly average, by model, of the projected runoff for the 2030–2050 period and the monthly average of the measured runoff for the historical period of 1985–2020 for Arrayán en La Montosa. Source: Own elaboration.</p>
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<p>Monthly coefficient of variation, by model, of the projected runoff for the 2030–2050 period and the coefficient of monthly variation of the measured runoff for the historical period of 1985–2020 for Arrayán en La Montosa. Source: Own elaboration.</p>
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<p>Monthly average, by model, of the projected runoff for the 2030–2050 period and monthly average of the measured runoff for the historical period of 1985–2020 for Yerba Loca. Source: Own elaboration.</p>
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<p>Monthly coefficient of variation, by model, of the projected runoff for the 2030–2050 period and the coefficient of monthly variation of the measured runoff for the historical period of 1985–2020 for Yerba Loca. Source: Own elaboration.</p>
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18 pages, 1248 KiB  
Review
Best Practices in Scenario Planning and Mapping for Salmon Recovery in the Columbia River Basin
by Gregory M. Hill and Steven A. Kolmes
Environments 2025, 12(2), 61; https://doi.org/10.3390/environments12020061 - 10 Feb 2025
Viewed by 565
Abstract
Salmon recovery planning in the Columbia River Basin depends upon what we argue are best practices of scenario planning in social–ecological systems. We examine how resilience science informs the concepts of stability landscapes and scenario mapping, and how this fits into the current [...] Read more.
Salmon recovery planning in the Columbia River Basin depends upon what we argue are best practices of scenario planning in social–ecological systems. We examine how resilience science informs the concepts of stability landscapes and scenario mapping, and how this fits into the current state of salmonid recovery planning. We analyze proposed “scenarios” and “perspectives” that reflect the current state of the U.S. federal planning process for salmonid recovery. We argue that only proposed “scenarios” that adhere to best practices, employ the resilience perspective, and adopt holistic social–ecological thought can be mapped onto a stability landscape. We demonstrate how such scenarios have the potential to increase insight into the viability of proposed recovery actions and avoid self-contradictory efforts stemming from a failure to see the basin-wide social–ecological system as a whole. We discuss and illustrate the potential of employing backcasting and post-normal science in terms of indigenous perspectives on salmon recovery. Full article
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<p>The common stability landscape for visualizing scenarios for salmonid recovery in the Columbia River Basin. On this landscape, an example of the current state of the system (red ball) is shown as being in the Historic Regime on the left, past the dendritic complexity threshold; it represents the full dendritic complexity of the river system and is an ideal goal state for many stakeholders. To the right, past a Quasi-Extinction Threshold, is the Remnant Regime, which represents a diminished population in size and metapopulation structure. Between and below the Historic Regime and the Refugia Regime is the Techno Regime, dominated by anthropogenic forces of dams and hatcheries [<a href="#B7-environments-12-00061" class="html-bibr">7</a>].</p>
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<p>An example scenario mapped onto a stability landscape. It shows a path from the current state in the Refugia Regime (the red ball) to an intermediate goal state in the Refugia Regime (the first green ball) and then to the goal state in the Historic Regime (second green ball) [<a href="#B7-environments-12-00061" class="html-bibr">7</a>].</p>
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<p>Backcasting and forecasting planning modalities. Backcasting escapes the limits of incremental change, distinguishing between forecasted futures and desirable futures. The blue lines on the left and right of the Figure indicate the limits of the possible futures. This extends the space of possible futures. Originally published in [<a href="#B8-environments-12-00061" class="html-bibr">8</a>], courtesy of Oregon State University Press.</p>
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21 pages, 2663 KiB  
Article
The Phytoremediation of Arsenic-Contaminated Waste by Poa labillardieri, Juncus pauciflorus, and Rytidosperma caespitosum
by Feizia Huslina, Leadin S. Khudur, Julie A. Besedin, Kamrun Nahar, Kalpit Shah, Aravind Surapaneni, Pacian Netherway and Andrew S. Ball
Environments 2025, 12(2), 60; https://doi.org/10.3390/environments12020060 - 10 Feb 2025
Viewed by 413
Abstract
Phytoremediation represents a potentially effective and environmentally friendly technology to remediate arsenic (As) in mine waste soils. However, soil amendments are often required to improve phytoremediation due to depleted nutrients in mine waste. This study aims to assess the effect of biosolids biochar, [...] Read more.
Phytoremediation represents a potentially effective and environmentally friendly technology to remediate arsenic (As) in mine waste soils. However, soil amendments are often required to improve phytoremediation due to depleted nutrients in mine waste. This study aims to assess the effect of biosolids biochar, applied at different rates (0%, 5%, and 10%) on As phytoremediation using three plant species: Poa labillardieri, Rytidosperma caespitosum, and Juncus pauciflorus. This study was conducted in a replicated greenhouse pot study using soil from an abandoned mine site. Dry plant biomass, As concentration in plants and soil, and soil microbial abundance were investigated. Juncus pauciflorus produced eight times more root and shoot biomass than R. caespitosum in soils amended with 10% biochar. The highest As uptake was also observed in J. pauciflorus grown in soils amended with 10% biochar (7.10 mg/plant), while R. caespitosum had the lowest As uptake in soils without biochar (0.16 mg/plant). In soils amended with 10% biochar, the total bacterial community decreased to approximately 8.50 log10 copies/g, compared to the initial soil (9.05 log10 copies/g), while the number of gene copies of the nifH gene increased, suggesting the importance of nitrogen-fixing bacteria to promote plant growth. Taguchi analysis confirmed that plant species was the key factor for As phytoremediation, followed by biochar application dose. This study showed that J. pauciflorus and the addition of 10% biochar was the best treatment for remediating As-contaminated mine waste, offering the potential for use commercially. Moreover, the utilisation of biochar derived from biosolids as a soil amendment for enhancing phytoremediation represents good circular economy practice to manage excessive biosolids production. Full article
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<p>Map of the soil sampling site.</p>
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<p>Shoots and roots dry biomass following growth for 100 days in As-contaminated soils with biochar addition at different doses (0%, 5%, and 10%). Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 5). Error bars represent the standard deviation.</p>
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<p>(<b>a</b>) Arsenic accumulation in shoots and roots following biochar addition with different doses (0%, 5%, and 10%). Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 5). Error bars represent the standard deviation. (<b>b</b>) Total arsenic uptake per plant (shoots and roots) at different biochar doses (0%, 5%, and 10%).</p>
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<p>(<b>a</b>) Arsenic accumulation in shoots and roots following biochar addition with different doses (0%, 5%, and 10%). Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 5). Error bars represent the standard deviation. (<b>b</b>) Total arsenic uptake per plant (shoots and roots) at different biochar doses (0%, 5%, and 10%).</p>
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<p>Arsenic concentration in soils before and after biochar addition (0%, 5%, and 10%). Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 5). Error bars represent the standard deviation.</p>
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<p>Plot of the mean effect of the experimental factors. PL: <span class="html-italic">Poa labillardieri</span>; JP: <span class="html-italic">Juncus pauciflorus</span>; RC: <span class="html-italic">Rytidosperma caespitosum</span>. Biochar dose (0%, 5%, and 10%).</p>
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<p>(<b>a</b>) Variation in the number of gene copies (16S rRNA) (log<sub>10</sub> copies/g) measured using qPCR for the initial soils, treated soils, and rhizosphere after biochar addition (0%, 5%, and 10%). PL: <span class="html-italic">P. labillardieri</span>; JP: <span class="html-italic">J. pauciflorus</span>; RC: <span class="html-italic">R. caespitosum</span>. Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation. (<b>b</b>) Variation in the number of gene copies (<span class="html-italic">nifH</span>) (log<sub>10</sub> copies/g) measured using qPCR for the initial soils, treated soils, and rhizosphere after biochar addition (0%, 5%, and 10%). PL: <span class="html-italic">P. labillardieri</span>; JP: <span class="html-italic">J. pauciflorus</span>; RC: <span class="html-italic">R. caespitosum</span>. Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation.</p>
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<p>(<b>a</b>) Variation in the number of gene copies (16S rRNA) (log<sub>10</sub> copies/g) measured using qPCR for the initial soils, treated soils, and rhizosphere after biochar addition (0%, 5%, and 10%). PL: <span class="html-italic">P. labillardieri</span>; JP: <span class="html-italic">J. pauciflorus</span>; RC: <span class="html-italic">R. caespitosum</span>. Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation. (<b>b</b>) Variation in the number of gene copies (<span class="html-italic">nifH</span>) (log<sub>10</sub> copies/g) measured using qPCR for the initial soils, treated soils, and rhizosphere after biochar addition (0%, 5%, and 10%). PL: <span class="html-italic">P. labillardieri</span>; JP: <span class="html-italic">J. pauciflorus</span>; RC: <span class="html-italic">R. caespitosum</span>. Different letters above the bars indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Tukey’s test). Data are presented as the mean (<span class="html-italic">n</span> = 3). Error bars represent the standard deviation.</p>
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18 pages, 1133 KiB  
Article
Unveiling the Non-Market Value of a Fragile Coastal Wetland: A CVM Approach in the Amvrakikos Gulf, Greece
by Dimitra Pappa and Dimitris Kaliampakos
Environments 2025, 12(2), 59; https://doi.org/10.3390/environments12020059 - 10 Feb 2025
Viewed by 289
Abstract
Wetlands are highly productive ecosystems with multidimensional value and significant social and economic impacts. Estimating the economic value of their non-marketed goods and services—benefits not traded in conventional markets—can provide essential insights to guide protection, restoration, and sustainable management strategies for these sensitive [...] Read more.
Wetlands are highly productive ecosystems with multidimensional value and significant social and economic impacts. Estimating the economic value of their non-marketed goods and services—benefits not traded in conventional markets—can provide essential insights to guide protection, restoration, and sustainable management strategies for these sensitive ecosystems. The present study employs environmental economics tools, specifically the Contingent Valuation Method (CVM), to assess the value of the Amvrakikos Gulf in northwestern Greece. This semi-enclosed wetland system is particularly fragile due to its low water renewal rate, while being a primary source of income and an integral component of local cultural identity. Despite its high ecological importance, the Amvrakikos Gulf has experienced substantial environmental degradation stemming from its geomorphological characteristics and external anthropogenic pressures. This investigation was designed to explore residents’ perceptions of the wetland’s value and its correlation with the need for restoration. In total, 383 coastal area residents participated in this study. Data analysis was conducted using appropriate econometric methods based on both parametric and non-parametric models. Approximately 46.2% of respondents expressed willingness to pay, and the environmental restoration of the Amvrakikos Gulf was valued at EUR 715,968.36. Additionally, this study examined potential associations between willingness to pay and various socio-cultural and demographic factors recorded during the interviews. In conclusion, the need for the restoration and preservation of the Amvrakikos Gulf’s natural wealth was made evident, affirming the contribution of the CVM in valuing wetlands and enriching the existing literature, while explicitly recognizing the subjectivity inherent in WTP assessments. Full article
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<p>The Amvrakikos Gulf: Study area overview.</p>
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<p>Kaplan–Meier survival curve.</p>
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<p>Maximum likelihood estimation (MLE) based on the histogram data.</p>
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17 pages, 2830 KiB  
Article
Understanding the Origin of Wet Deposition Black Carbon in North America During the Fall Season
by Piyaporn Sricharoenvech, Ross Edwards, Müge Yaşar, David A. Gay and James Schauer
Environments 2025, 12(2), 58; https://doi.org/10.3390/environments12020058 - 10 Feb 2025
Viewed by 387
Abstract
Black carbon (BC) aerosols emitted from biomass, fossil fuel, and waste combustion contribute to the radiation budget imbalance and are transported over extensive distances in the Earth’s atmosphere. These aerosols undergo physical and chemical modifications with co-existing aerosols (e.g., nitrate, sulfate, ammonium) through [...] Read more.
Black carbon (BC) aerosols emitted from biomass, fossil fuel, and waste combustion contribute to the radiation budget imbalance and are transported over extensive distances in the Earth’s atmosphere. These aerosols undergo physical and chemical modifications with co-existing aerosols (e.g., nitrate, sulfate, ammonium) through aging processes during long-range transport and are primarily removed from the troposphere by wet deposition. Using precipitation samples collected in North America between 26 October and 1 December 2020 by the National Atmospheric Deposition Program (NADP), we investigated the relationships between BC and both water-soluble ions and water-soluble organic carbon (WSOC) using Spearman’s rank coefficients. We then attempted to identify the sources of BC in the wet deposition using factor analysis (FA) and satellite data of fire smoke. BC showed a very strong correlation with nitrate (ρ = 0.83). Strong correlations were also found with WSOC, ammonium, calcium, and sulfate ions (ρ = 0.78, 0.74, 0.74, and 0.67, respectively). FA showed that BC was in the same factor as nitrate, ammonium, sulfate, and WSOC, indicating that BC could originate from secondary aerosol formation and biomass burning. Supported by satellite data of fire and smoke, BC and other correlated pollutants were believed to be associated with wildfire outbreaks in several states in the United States (US) during November 2020. Full article
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<p>The locations of 209 North American sampling sites were included in the study from 26 October to 1 December 2020.</p>
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<p>Concentrations of 6 main air pollution parameters in wet deposition in thecontiguous US and Alaska in November 2020: (<b>a</b>) rBC; (<b>b</b>) nitrate; (<b>c</b>) ammonium; (<b>d</b>) WSOC; (<b>e</b>) calcium; (<b>f</b>) sulfate. Gray circles indicate locations without WSOC data.</p>
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<p>Comparisons between weekly precipitation and concentrations of (<b>a</b>) rBC, (<b>b</b>) WSOC, (<b>c</b>) sulfate, (<b>d</b>) nitrate, (<b>e</b>) ammonium, (<b>f</b>) chloride, (<b>g</b>) orthophosphate, (<b>h</b>) calcium ion, (<b>i</b>) magnesium ion, (<b>j</b>) sodium ion, and (<b>k</b>) potassium ion.</p>
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<p>Spearman’s rank correlations between BC and air pollution parameters in wet deposition in North America in November 2020 using moderate-rainfall samples (n = 95).</p>
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<p>Comparisons between the concentrations of rBC and (<b>a</b>) NO<sub>3</sub><sup>−</sup>, (<b>b</b>) NH<sub>4</sub><sup>+</sup>, (<b>c</b>) WSOC, (<b>d</b>) SO<sub>4</sub><sup>2−</sup>, and (<b>e</b>) Ca<sup>2+</sup>.</p>
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<p>HMS smoke map showing collective smoke data from 25 October to 1 December 2020. Blue dots indicate NADP NTN sites included in the study.</p>
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32 pages, 48948 KiB  
Article
Repeatability of SOPRANOISE Quick Method for Measuring Sound Reflection and Sound Insulation of Noise Barriers
by Paolo Guidorzi and Massimo Garai
Environments 2025, 12(2), 57; https://doi.org/10.3390/environments12020057 - 8 Feb 2025
Viewed by 416
Abstract
The measurement method employed for the estimation of the intrinsic acoustic characteristics of noise barriers installed along European highways is outlined in the EN 1793-5 and 1793-6 standards, commonly named the “Adrienne method”. This method has been shown to have repeatability and reproducibility [...] Read more.
The measurement method employed for the estimation of the intrinsic acoustic characteristics of noise barriers installed along European highways is outlined in the EN 1793-5 and 1793-6 standards, commonly named the “Adrienne method”. This method has been shown to have repeatability and reproducibility comparable to or better than laboratory methods. However, its correct application requires skilled operators managing with great care the equipment on site, thus limiting the number of measurements made in a working day on different positions of the noise barrier under test. To overcome this limitation and perform fast measurements in the field, the Quick Method, a simplified version of the Adrienne measurement method, was developed in the context of the European SOPRANOISE project. The Quick Method needs only lightweight and easy-to-use equipment, called the Quick System; this allows extensive measurement campaigns to be carried out at many points of the noise barrier under test. However, the repeatability of the Quick Method has not yet been assessed. This article reports and analyses a series of repeatability tests of the Quick Method conducted in the laboratory and on site; moreover, comparisons with the Adrienne method are systematically presented. These results can be considered the first validation of the Quick Method and its measuring equipment. Full article
(This article belongs to the Special Issue New Solutions Mitigating Environmental Noise Pollution III)
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<p>The Quick System. (<b>a</b>) Main system box; (<b>b</b>) internal circuits; (<b>c</b>) an example of a Quick System measurement screen.</p>
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<p>The Adrienne system. (<b>a</b>) Sound Reflection Index measurement, barrier measurement; (<b>b</b>) free-field measurement; (<b>c</b>) Sound Insulation Index measurement: the microphone grid is placed on the receiver side of the noise barrier (opposed to the roadside).</p>
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<p>The Quick (SOPRA) method. (<b>a</b>) Sound Reflection Index measurement, barrier measurement; (<b>b</b>) free-field measurement; (<b>c</b>) Sound Insulation Index measurement: the microphone antenna is placed on the rear side of the noise barrier (opposite to the roadside); the source placed on the roadside is visible through the transparent panel.</p>
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<p>Quick System measurement configurations. (<b>a</b>) Sound Reflection Index measurement, barrier measurement; (<b>b</b>) free-field measurement for Sound Reflection Index; (<b>c</b>) Sound Insulation Index measurement, barrier measurement; (<b>d</b>) free-field measurement for Sound Insulation Index; 1: barrier reference surface; 2: height of sound source = 2 m; 3: sound source front surface; 4: distance between sound source and barrier reference surface = 1.5 m for RI measurement and = 1 m for SI measurement; 5: distance between sound source and microphone grid = 1.25 m; 6: microphone grid-reference surface distance = 0.25 m (on front side of barrier in case of RI measurement or on the read side of the barrier for SI measurement); 7: microphone grid; 8: barrier height; 9: barrier thickness.</p>
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<p>Laboratory repeatability measurements with Quick System. (<b>a</b>) Sound Reflection Index measurement on perforated metal barrier; (<b>b</b>) Sound Reflection Index measurement on the OSB3 non-flat barrier; (<b>c</b>) free-field measurement; (<b>d</b>) Sound Insulation Index measurement.</p>
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<p>Laboratory repeatability measurements with Adrienne system. (<b>a</b>) Sound Reflection Index measurement on the OSB3 non-flat barrier; (<b>b</b>) Sound Reflection Index measurement on perforated metal barrier; (<b>c</b>) free-field measurement.</p>
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<p>Repeatability measurements with Quick System and in-place instrumentation on the OSB3 barrier, microphone 3; 10 measurements. (<b>a</b>) Sound Reflection Index; (<b>b</b>) standard deviation on measurements in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub>, calculated in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>Repeatability measurements with Quick System and in-place instrumentation on the OSB3 barrier, average from microphones 2, 3, and 4; 10 measurements. (<b>a</b>) Sound Reflection Index; (<b>b</b>) standard deviation on values in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub>, calculated in the 200 Hz–5 kHz range, of values in (<b>a</b>).</p>
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<p>Repeatability measurements with Adrienne system and in-place instrumentation measuring the OSB3 barrier, average from microphones 8, 5, and 2; 10 measurements. (<b>a</b>) Sound Reflection Index; (<b>b</b>) standard deviation of values in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub>, calculated in the 200 Hz–5 kHz range, of values in (<b>a</b>).</p>
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<p>Sound Reflection Index measurements on the OSB3 barrier with Adrienne and Quick System. (<b>a</b>) RI from microphones 2, 5, and 8 from the Adrienne system; (<b>b</b>) RI from microphones 4, 3, and 2 from the Quick System.</p>
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<p>Repeatability measurements with Quick System and instrumentation repositioned in front of the sound-absorbing perforated metal barrier, microphone 3; 10 measurements. (<b>a</b>) Sound Reflection Index; (<b>b</b>) standard deviation on values in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>Repeatability measurements with Quick System and instrumentation repositioned in front of the sound-absorbing perforated metal barrier, average of microphones 2, 3, and 4; 10 measurements. (<b>a</b>) Sound Reflection Index; (<b>b</b>) standard deviation of values in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>Repeatability measurements with Adrienne system and instrumentation repositioned in front of the sound-absorbing perforated metal barrier, average of microphones 8, 5, and 2; 10 measurements. (<b>a</b>) Sound Reflection Index; (<b>b</b>) standard deviation of values in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>Sound Reflection Index measurements on the flat metal barrier with the Adrienne and Quick System. (<b>a</b>) RI from microphones 2, 5, and 8 from the Adrienne system; (<b>b</b>) RI from microphones 4, 3, and 2 from the Quick System processed without time subtraction; (<b>c</b>) RI from microphones 4, 3, and 2 from the Quick System processed with time subtraction from the Adrienne software, version 1.8.</p>
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<p>(<b>a</b>) Average of measurements from three microphones presented in <a href="#environments-12-00057-f014" class="html-fig">Figure 14</a>; (<b>b</b>) DL<sub>RI</sub>, in the 200 Hz–5 kHz range, from data in (<b>a</b>); (<b>c</b>) normalized traffic noise spectrum, in dB(A).</p>
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<p>Sound Reflection Index measured on the flat metal barrier. (<b>a</b>) RI measured with Adrienne system, values from individual grid microphones; (<b>b</b>) RI measured with Quick System and ‘virtual grid’, values from individual virtual microphones.</p>
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<p>SI repeatability measurements with Quick System and in-place instrumentation on the OSB3 barrier, microphone 3; 10 measurements. (<b>a</b>) Sound Insulation Index; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>Repeatability measurements of SI with Quick System and in-place instrumentation on the OSB3 barrier, average of microphones 2, 3, and 4; 10 measurements. (<b>a</b>) Sound Insulation Index; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>SI repeatability measurements with Adrienne system and in-place instrumentation on OSB3 barrier, average of microphones 8, 5, and 2; 10 measurements. (<b>a</b>) Sound Insulation Index; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>SI repeatability measurements with Quick System and instrumentation repositioned at each measurement on OSB3 barrier, microphone 3 only; 10 measurements. (<b>a</b>) Sound Insulation Index; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>SI repeatability measurements with Quick System and instrumentation repositioned at each measurement on the OSB3 barrier, average of microphones 2, 3, and 4; 10 measurements. (<b>a</b>) Sound Insulation Index; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>SI repeatability measurements with Adrienne system and instrumentation repositioned on OSB3 barrier, average of microphones 8, 5, and 2; 10 measurements. (<b>a</b>) Sound Insulation Index; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub>, in the 200 Hz–5 kHz range, of data in (<b>a</b>).</p>
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<p>In-situ measurements on a flat metal barrier. (<b>a</b>) RI measurement on the roadside surface of the noise barrier; (<b>b</b>) free-field measurement; (<b>c</b>) SI measurement, loudspeaker on the roadside; (<b>d</b>,<b>e</b>) SI measurement with microphone antenna on the noise barrier side opposite to the roadside.</p>
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<p>RI repeatability measurements with Quick System in situ and instrumentation repositioned on a flat metal barrier, microphone 3; 10 measurements. (<b>a</b>) Sound Reflection Index values; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub> in the 200 Hz–5 kHz range of data in (<b>a</b>).</p>
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<p>RI repeatability measurements with Quick System in situ and instrumentation repositioned on a flat metal barrier, average of microphones 2, 3, and 4; 10 measurements. (<b>a</b>) Sound Reflection Index values; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub> in the 200 Hz–5 kHz range of data in (<b>a</b>).</p>
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<p>Repeatability measurements of RI with the Adrienne system made in situ near Verona on a noise barrier made in wooden panels and concrete, repositioning the instrumentation, average of microphones 8, 5, and 2; 10 measurements. (<b>a</b>) Sound Reflection Index values; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>RI</sub> in the 200 Hz–5 kHz range of data in (<b>a</b>).</p>
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<p>Comparison between in-situ measurements with Adrienne system and with Quick System, conducted near Verona on a noise barrier made in wooden panels and concrete. Average of microphones 2, 3, and 4 of the QS and 8, 5, and 2 of the Adrienne system.</p>
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<p>SI repeatability measurements with Quick System in situ and instrumentation repositioned on a flat metal noise barrier, microphone 3 only; 10 measurements. (<b>a</b>) Sound Insulation Index values; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub> in the 200 Hz–5 kHz range of data in (<b>a</b>).</p>
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<p>SI repeatability measurements with Quick System in situ and instrumentation repositioned on a flat metal noise barrier, average of microphones 2, 3, and 4; 10 measurements. (<b>a</b>) Sound Insulation Index values; (<b>b</b>) standard deviation of measurements in (<b>a</b>); (<b>c</b>) DL<sub>SI</sub> in the 200 Hz–5 kHz range of data in (<b>a</b>).</p>
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<p>Signal-to-noise ratio of SI measurements shown in <a href="#environments-12-00057-f029" class="html-fig">Figure 29</a>.</p>
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<p>In-situ Sound Insulation Index measurement carried out with Adrienne and Quick Systems on the same noise barrier (location Verona Nord).</p>
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<p>Example of time–domain subtraction between reflected impulse response and free-field impulse response, microphone 5. (<b>a</b>) Reflected impulse response (gray) superimposed on direct impulse response (green); (<b>b</b>) detail of measured reflected impulse response only; (<b>c</b>) reflected impulse response after subtraction; (<b>d</b>) reflected impulse response and residual of direct one after imperfect subtraction. The blue line is the direct impulse response window; the red line is the reflected impulse response window.</p>
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12 pages, 214 KiB  
Article
Predicting Acute Oral Toxicity in Bobwhite Quail: Development of QSAR Models for LD50
by Nadia Iovine, Alessandra Roncaglioni and Emilio Benfenati
Environments 2025, 12(2), 56; https://doi.org/10.3390/environments12020056 - 8 Feb 2025
Viewed by 435
Abstract
The development of a predictive model for estimating oral acute toxicity (LD50) in wildlife species is essential for environmental risk assessments. In this study, a quantitative structure–activity relationship (QSAR) model was developed to predict the acute oral toxicity of pesticides toward [...] Read more.
The development of a predictive model for estimating oral acute toxicity (LD50) in wildlife species is essential for environmental risk assessments. In this study, a quantitative structure–activity relationship (QSAR) model was developed to predict the acute oral toxicity of pesticides toward Bobwhite quail, categorizing them into three toxicity classes: low, moderate, and high. This model was built using the SARpy softwareA dataset of pesticides collected from OpenFoodTox and the ECOTOX database was used to identify training and test datasets, while data collected from the PPDB were used as an external validation. The model’s performance was evaluated using these three sets. The accuracy achieved on the training set was 0.75, indicating good performance during model development. However, the model’s accuracy dropped to 0.55 for the test set, suggesting some overfitting. The external validation accuracy was 0.69, reflecting the model’s ability to generalize to new, unseen data. While these results demonstrate the potential of the QSAR models for predicting toxicity in Bobwhite quail, they also highlight the need for further refinement to improve predictive accuracy, particularly for unseen compounds. This work contributes to the development of computational tools for wildlife risk assessment and toxicological predictions. Full article
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22 pages, 1127 KiB  
Article
Evaluating GHG Emissions and Renewable Energy Use in the Italian Energy Sector: Monitoring, Reporting, and Objectives
by Stefano Castelluccio, Silvia Fiore and Claudio Comoglio
Environments 2025, 12(2), 55; https://doi.org/10.3390/environments12020055 - 6 Feb 2025
Viewed by 447
Abstract
This study investigates the greenhouse gas (GHG) and renewable energy use reporting practices among thermal power plants (TPPs), waste incinerators (WIs), and hydropower plants (HPPs) in Italy, as reflected in their EMAS environmental statements. The analysis focuses on GHG emissions (Scope 1, 2, [...] Read more.
This study investigates the greenhouse gas (GHG) and renewable energy use reporting practices among thermal power plants (TPPs), waste incinerators (WIs), and hydropower plants (HPPs) in Italy, as reflected in their EMAS environmental statements. The analysis focuses on GHG emissions (Scope 1, 2, and 3) and renewable energy utilization reporting, and on the objectives set by the companies for reducing emissions and fossil fuels use. TPPs and WIs reported positive Scope 1 emissions extensively but reporting on Scope 2 and Scope 3 resulted inconsistent for all facilities. Negative emissions reporting was generally lacking, except for HPPs. Renewable energy use reporting was also limited, especially in TPPs and WIs, despite some facilities producing energy from renewable sources. The study also evaluated the objectives set by the companies on GHG reduction and renewable energy use increase, finding that GHG reduction was prioritized over renewable energy use. However, both were often a secondary goal integrated into planned operational improvements. The findings highlight that, to ensure transparency of sustainability data and the possibility of performances benchmarking in the energy production sector, there is the need for defining stronger reporting guidelines on GHG emissions, especially regarding Scope 3 emissions, and to prioritize increasing the share of renewable energy among strategic objectives. Future research should investigate factors affecting reporting behavior and the barriers to renewable energy adoption in fossil fuel-reliant sectors. Full article
(This article belongs to the Special Issue Greenhouse Gas Emission Reduction and Green Energy Utilization)
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<p>Scope 1, 2, and 3 greenhouse gas (GHG) emissions sources, including both positive and negative (avoided) emissions, reported by thermal power plants. Bar colors indicate the reporting approach: “mentioned” (blue-gray) and “quantified” as measured (teal), calculated (yellow), or unspecified (red). The total counts for each source are shown to the right of each bar.</p>
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<p>Scope 1, 2, and 3 greenhouse gas (GHG) emissions sources, including both positive and negative emissions, reported by hydropower companies. Bar colors indicate the reporting approach: “mentioned” (blue-gray) and “quantified” as measured (teal), calculated (yellow), or unspecified (red). The total counts for each source are shown to the right of each bar.</p>
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<p>Scope 1, 2, and 3 greenhouse gas (GHG) emissions sources, including both positive and negative emissions, reported by waste incinerators. Bar colors indicate the reporting approach: “mentioned” (blue-gray) and “quantified” as measured (teal), calculated (yellow), or unspecified (red). The total counts for each source are shown to the right of each bar.</p>
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<p>Percentage distribution of total objectives and budget allocations across thermal power plants (TPPs), waste incinerators (WIs), and hydropower plant (HPP) companies with ‘GHG reduction’ and ‘renewable energy use increase’ as a goal. ‘1st’ indicates the goal is primary within the objective, while ‘2nd’ that the goal is secondary. Bars represent the percentage of total objectives set or budget allocated, with exact values labeled next to each bar.</p>
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25 pages, 4478 KiB  
Article
Advancing Human Health Risk Assessment Through a Stochastic Methodology for Mobile Source Air Toxics
by Mohammad Munshed, Jesse Van Griensven Thé and Roydon Fraser
Environments 2025, 12(2), 54; https://doi.org/10.3390/environments12020054 - 6 Feb 2025
Viewed by 584
Abstract
Mobile source air toxics (MSATs) are major contributors to urban air pollution, especially near high-traffic roadways, where populations face elevated pollutant exposures. Traditional human health risk assessments, based on deterministic methods, often overlook variability in exposure and the vulnerabilities of sensitive subpopulations. This [...] Read more.
Mobile source air toxics (MSATs) are major contributors to urban air pollution, especially near high-traffic roadways, where populations face elevated pollutant exposures. Traditional human health risk assessments, based on deterministic methods, often overlook variability in exposure and the vulnerabilities of sensitive subpopulations. This study introduces and applies a new stochastic modeling approach, utilizing Monte Carlo simulations to evaluate cumulative cancer risks from MSATs exposure through inhalation and ingestion pathways. This method captures variability in exposure scenarios, providing detailed health risk assessments, particularly for vulnerable groups such as children and the elderly. This approach was demonstrated in a case study conducted in Saint Paul, Minnesota, using 2019 traffic data. Deterministic models estimated cumulative cancer risks for adults at 6.24E-02 (unitless lifetime cancer risk), while stochastic modeling revealed a broader range, with the 95th percentile reaching 4.98E-02. The 95th percentile, used in regulatory evaluations, identifies high-risk scenarios overlooked by deterministic methods. This research advances the understanding of MSATs exposure risks by integrating spatiotemporal dynamics, identifying high-risk zones and vulnerable subpopulations, and supporting resource allocation for targeted pollution control measures. Future applications of this methodology include expanding stochastic modeling to evaluate ecological risks from mobile emissions. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas III)
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<p>Urban resident exposure scenarios and associated exposure pathways.</p>
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<p>Distribution of body weights, showing close alignment between the input mean (72 kg) and the calculated mean (~72.02 kg), along with their respective standard deviations (±1 standard deviation, SD).</p>
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<p>Q-Q plot for body weights, showing the alignment of the simulation data with the theoretical quantiles. The red trend line represents the ideal fit for a theoretical normal distribution.</p>
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<p>Distribution of exposure duration (years) for the child scenario, showing alignment between the input minimum (1 year) and maximum (6 years) and their respective calculated values.</p>
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<p>Q-Q plot for exposure duration (years) for the child scenario, showing the alignment of the simulation data with theoretical quantiles. The red trend line represents the ideal fit for a uniform distribution.</p>
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<p>Visual breakdown of the stochastic risk characterization process.</p>
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<p>Map of the study area showing high-traffic roadways in Saint Paul, Minnesota. Red circles indicate traffic count points, with the blue marker identifying sequence number 11508 and the red box highlighting its location. Map data sourced from Esri Community Maps contributors, County of Ramsey, Metropolitan Council, MetroGIS, © OpenStreetMap, Microsoft, Esri, TomTom, Garmin, SafeGraph, GeoTechnologies, Inc., METI/NASA, USGS, EPA, NPS, US Census Bureau, USDA, and USFWS.</p>
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<p>AADT at the study site with 3-year moving average. The 3-year moving average excludes the data points at the beginning (1998) and end (2022) due to insufficient data for a complete 3-year window. Linear interpolation was applied to estimate the missing data points in the early years (e.g., 1999 and 2001) to enable the calculation of a valid 3-year moving average.</p>
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<p>Relative frequency density plot (<b>a</b>) and cumulative probability plot (<b>b</b>) for adult cumulative cancer risk estimates, showing a positively skewed distribution, distributional spread, and risk thresholds.</p>
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<p>Relative frequency density plot (<b>a</b>) and cumulative probability plot (<b>b</b>) for child cumulative cancer risk estimates, showing a positively skewed distribution, distributional spread, and risk thresholds.</p>
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19 pages, 2939 KiB  
Article
Improving Groundwater Quality Through Biosphere Reserve Management: Insights from the Anaga Reserve, Tenerife
by Joselin S. Rodríguez-Alcántara, Noelia Cruz-Pérez, Jesica Rodríguez-Martín, Alejandro García-Gil, Jelena Koritnik and Juan C. Santamarta
Environments 2025, 12(2), 53; https://doi.org/10.3390/environments12020053 - 5 Feb 2025
Viewed by 590
Abstract
The Canary Islands, an outermost Spanish territory in the Atlantic Ocean, are renowned for their subtropical climate and significant tourism. However, substantial areas are designated for environmental protection, notably the Anaga Rural Park in Tenerife, a UNESCO Biosphere Reserve, which is the focus [...] Read more.
The Canary Islands, an outermost Spanish territory in the Atlantic Ocean, are renowned for their subtropical climate and significant tourism. However, substantial areas are designated for environmental protection, notably the Anaga Rural Park in Tenerife, a UNESCO Biosphere Reserve, which is the focus of this study. This research investigates the influence of Biosphere Reserve designation on groundwater quality, a crucial resource for Tenerife’s population. We analysed the physicochemical properties of groundwater within the Anaga region over a decade (2007–2016). Our findings demonstrate that groundwater quality consistently meets regulatory standards, exhibiting no evidence of pollution. This high quality is attributed to several factors, including the low population density, limited tourism impact within the reserve, and crucially, the effective soil protection measures implemented within the Biosphere Reserve. The compact geology of the region further limits infiltration and potential pollution. The sustained high quality of groundwater, even in the absence of detectable pollution, highlights the importance of ongoing monitoring to maintain this valuable resource and support local biodiversity. This case study provides a valuable model for sustainable groundwater management and soil protection strategies in other areas of Tenerife and beyond. Full article
(This article belongs to the Special Issue Research Progress in Groundwater Contamination and Treatment)
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<p>Location of the study area.</p>
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<p>Sampling locations for water quality analysis, Anaga Biosphere Reserve (Tenerife, Canary Islands).</p>
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<p>The pH values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>The laboratory turbidity (<b>a</b>) and turbidity in situ and (<b>b</b>) values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>The color values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>The residual combined chlorine (<b>a</b>), chlorine in-situ (<b>b</b>), free residual chlorine (<b>c</b>) and chloride (<b>d</b>) values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>The residual combined chlorine (<b>a</b>), chlorine in-situ (<b>b</b>), free residual chlorine (<b>c</b>) and chloride (<b>d</b>) values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>Ammonium values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>Sulphate values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>Nitrate values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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<p>The Fe (<b>a</b>) and Na (<b>b</b>) values of the samples for every location and Anaga as a whole during the study period (2007–2016).</p>
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12 pages, 2443 KiB  
Article
The Fate of the Cyanotoxin Dihydroanatoxin-a in Drinking Water Treatment Processes
by Armin Dolatimehr, Jutta Fastner and Aki Sebastian Ruhl
Environments 2025, 12(2), 52; https://doi.org/10.3390/environments12020052 - 5 Feb 2025
Viewed by 403
Abstract
Only recently has the cyanotoxin dihydroanatoxin-a (dhATX-a) been detected more frequently in different surface waters, some of which are used for supplying drinking water. As data about the fate of dhATX-a in drinking water treatment processes are still scarce, the present study investigated [...] Read more.
Only recently has the cyanotoxin dihydroanatoxin-a (dhATX-a) been detected more frequently in different surface waters, some of which are used for supplying drinking water. As data about the fate of dhATX-a in drinking water treatment processes are still scarce, the present study investigated the behavior of dhATX-a in different water treatment steps: slow sand filtration, flocculation, adsorption onto activated carbon, ozonation and chlorination. The almost complete removal (>95%) of dhATX-a was observed in sand columns simulating slow sand filtration without showing a long adaptation phase. The results further indicate that dhATX-a can be removed using powdered activated carbon at dosages of 50 mg/L with removal rates between 75 and 93% and also by using ozonation with dosages above 1 mg/L at a concentration of ca. 4.5 mg/L background organic carbon. In contrast, no elimination of dhATX-a was observed in flocculation and chlorination experiments. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water)
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<p>Scheme of the experimental setup with two parallel columns fed with tap water (spiked with dhATX-a) as influent in up-flow mode.</p>
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<p>Residual dhATX-a concentrations after flocculation tests with different ferric chloride dosages.</p>
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<p>Removals of dhATX-a with different dosages of PAC AS in 30 min contact time and in equilibrium (48 h contact time).</p>
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<p>Adsorptive removals of dhATX-a obtained with the three different dosages of PAC AS, HCR and HCC within 30 min contact time (<b>left</b>), the respective abatements of UV<sub>254</sub> (<b>middle</b>) and the relation between both parameters (<b>right</b>).</p>
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<p>LC-OCD (<b>left</b>) and LC-UVD (<b>right</b>) chromatograms of the tap water with dhATX-a before and after 30 min contact time with 50 mg/L of the three different PACs.</p>
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<p>Percentual dhATX-a removals with initial dhATX-a concentrations of 10.4 µg/L (experiment A) and 7.1 µg/L (experiment B) depending on the ozone consumption (<b>left</b>), (for experiment B) UVA<sub>254</sub> abatements (<b>middle</b>) and the relation between UVA<sub>254</sub> abatements and dhATX-a removals (<b>right</b>).</p>
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<p>LC-OCD (<b>left</b>) and LC-UVD (<b>right</b>) chromatograms of the tap water with dhATX-a before and after ozone treatment with ozone consumptions of 2.0 and 3.1 mg/L, respectively.</p>
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<p>The elimination of dhATX-a in the two parallel sand columns within 11 days of operation with dhATX-a-containing tap water.</p>
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<p>Residual dhATX-a percentages at dosages of up to 1.0 mg/L and contact times of up to 30 min.</p>
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11 pages, 2497 KiB  
Article
A Bioassay Analysis of Uranium and Lead in Urine Samples from a High Natural Background Radiation Area in Indonesia
by Very Susanto, Radhia Pradana, Eka Djatnika Nugraha, Prihadi Sumintadireja, Oumar Bobbo Modibo, Ilsa Rosianna, Nastiti Rahajeng, Haeranah Ahmad, Rusbani Kurniawan, Leons Rixson, Atika Yuningsih, Yusraini Dian Inayati Siregar, Asep Saepuloh, Wahyudi Wahyudi, Hirofumi Tazoe, Naofumi Akata and Shinji Tokonami
Environments 2025, 12(2), 51; https://doi.org/10.3390/environments12020051 - 4 Feb 2025
Viewed by 738
Abstract
Heavy metal pollution is a major environmental concern due to the high toxicity of heavy metals in humans. High natural background radiation areas (HNBRAs) contain high concentrations of the radioactive element 238U, which decays into 206Pb, in their soil, crops, and [...] Read more.
Heavy metal pollution is a major environmental concern due to the high toxicity of heavy metals in humans. High natural background radiation areas (HNBRAs) contain high concentrations of the radioactive element 238U, which decays into 206Pb, in their soil, crops, and water. Concentrations of the heavy metals lead (Pb) and uranium (U) are, thus, correlated with HNBRAs. Mamuju in Indonesia is a recently studied HNBRA where high concentrations of Pb and U in the soil have been reported. The present study analyzes Mamuju residents’ exposure to Pb and U. Two zones in the study area were selected for comprehensive assessment. North Botteng was chosen to represent the HNBRA, and Topoyo was selected as the control zone, with 22 urine samples collected from each zone. The samples were analyzed using a quadrupole inductively coupled plasma mass spectrometer (ICP-MS). The average concentrations of Pb measured in the urine samples were 1.31 mg L−1 and 0.77 mg L−1 in North Botteng and Topoyo, respectively. These values are higher than the urine Pb reference value of 5 µg L−1. The urine Pb concentrations in both studied zones were alarmingly high, which may have serious health effects on the population and should warrant action to reduce Pb exposure in this area. The committed effective dose from the ingestion of 238U in North Botteng was higher than in Topoyo, measuring 36.0 mSv and 8.9 mSv, respectively. The area most affected by the ingestion of 238U was the red bone marrow, followed by the bone surface. Full article
(This article belongs to the Special Issue Environmental Pollutant Exposure and Human Health)
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<p>The location of the study area, (<b>a</b>) the location of Topoyo (control area), and (<b>b</b>) the location of Northern Botteng (HNBRA). This map was created using a mapping program (Mapinfo v10.5, Precisely, USA) and a base map provided by the National Mapping Agency of Indonesia (<a href="https://tanahair.indonesia.go.id/portal-web" target="_blank">https://tanahair.indonesia.go.id/portal-web</a> accessed on 11 September 2024).</p>
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<p>Summary of reported health issues in North Botteng, Mamuju.</p>
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<p>Summary of reported health issues in Topoyo.</p>
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<p>Pb and <sup>238</sup>U concentrations in urine samples.</p>
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<p>Committed equivalent dose received by each organ through ingestion of <sup>238</sup>U.</p>
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<p>Committed annual effective dose received by each organ from ingestion of <sup>238</sup>U.</p>
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15 pages, 4146 KiB  
Article
Spatial Distribution and Influencing Factors of Chlorophyll a in Lianzhou Bay, Guangxi Province, China
by Xiao Tan, Bingliang Qu, Yinling Zhu and Hui Zhao
Environments 2025, 12(2), 50; https://doi.org/10.3390/environments12020050 - 4 Feb 2025
Viewed by 816
Abstract
Phytoplankton is essential in coastal marine ecosystems, aiding ecosystem stability and development of marine economy. Coastal ecosystems, as a transitional zone, feature complex, variable environmental factors that significantly affect phytoplankton growth. To assess the factors influencing the growth of phytoplankton in the bay [...] Read more.
Phytoplankton is essential in coastal marine ecosystems, aiding ecosystem stability and development of marine economy. Coastal ecosystems, as a transitional zone, feature complex, variable environmental factors that significantly affect phytoplankton growth. To assess the factors influencing the growth of phytoplankton in the bay area, this study measured chlorophyll a (Chla), nutrients, and four antibiotics (sulfamethoxazole, sulfadiazine, ciprofloxacin, and enrofloxacin) in seawater, as well as total nitrogen and total phosphorus contents in sediments at 25 stations in Lianzhou Bay. Principal component analysis and the risk quotient (RQ) were utilized for analysis and assessment. The results indicate that the factors influencing Chla concentrations are inconsistent between the nearshore and offshore areas of Lianzhou Bay. Specifically, abundant nutrients, high ammonia levels, and low enrofloxacin concentrations are the primary factors contributing to high Chla concentrations in the nearshore area. In contrast, hydrodynamic conditions, feeding by cultured shellfish, and adequate lighting collectively shape the distribution characteristics of Chla in the offshore area. Additionally, the ecological risk posed by antibiotics in this bay is relatively low. The findings of this study provide scientific evidence for local management of marine pollution sources and the optimization of aquaculture models, which is of great significance for sustainable utilization of marine ecological resources. Full article
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<p>Distribution of sampling stations in Lianzhou Bay, Guangxi.</p>
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<p>Distribution of Chla in Lianzhou Bay.</p>
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<p>Distribution of nitrogen and phosphorus in Lianzhou Bay. (<b>a</b>) <math display="inline"><semantics> <msubsup> <mrow> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">H</mi> </mrow> <mn>4</mn> <mo>+</mo> </msubsup> </semantics></math>-N, (<b>b</b>) <math display="inline"><semantics> <msubsup> <mrow> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </semantics></math>-N, (<b>c</b>) DIN, (<b>d</b>) TN, (<b>e</b>) <math display="inline"><semantics> <msubsup> <mrow> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>4</mn> <mrow> <mn>3</mn> <mo>−</mo> </mrow> </msubsup> </semantics></math>-P, and (<b>f</b>) TP. The numbers 1 to 25 correspond one-to-one with the sampling station numbers.</p>
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<p>Distribution of DIN/DIP ratio (<b>a</b>) and turbidity (<b>b</b>) in Lianzhou Bay. The numbers 1 to 25 correspond one-to-one with the sampling station numbers.</p>
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<p>Distribution of four kinds of antibiotics in Lianzhou Bay. The size of the circle represents the total concentration of the four antibiotics.</p>
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<p>Factor loading result of principal component analysis based on physicochemical index of seawater.</p>
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17 pages, 755 KiB  
Review
Living Under the Volcano: Effects on the Nervous System and Human Health
by Alicia Navarro-Sempere, Raúl Cobo, Ricardo Camarinho, Patricia Garcia, Armindo Rodrigues, Magdalena García and Yolanda Segovia
Environments 2025, 12(2), 49; https://doi.org/10.3390/environments12020049 - 4 Feb 2025
Viewed by 629
Abstract
Volcanoes, during their explosive and post-explosive phases, as well as through continuous degassing processes, release a range of pollutants hazardous to human health, including toxic gases, fine particulate matter, and heavy metals. These emissions impact over 14% of the global population living in [...] Read more.
Volcanoes, during their explosive and post-explosive phases, as well as through continuous degassing processes, release a range of pollutants hazardous to human health, including toxic gases, fine particulate matter, and heavy metals. These emissions impact over 14% of the global population living in proximity to volcanoes, with effects that can persist for days, decades, or even centuries. Living conditions in these regions often involve chronic exposure to contaminants in the air, water, and soil, significantly increasing the risk of developing neurological disorders. Prolonged exposure to elements such as lead (Pb), mercury (Hg), and cadmium (Cd), among others, results in the accumulation of metals in the brain, which increases oxidative stress and causes neuronal damage and severe neurotoxicity in animals. An examination of metal accumulation in brain cells, particularly astroglia, provides valuable insights into the developmental neurotoxicity of these metals. Moreover, microglia may activate itself to protect from cytotoxicity. In this review, we consider the implications of living near an active volcano for neurotoxicity and the common neurodegenerative diseases. Additionally, we encourage governments to implement public health strategies and mitigation measures to protect vulnerable communities residing near active volcanoes. Full article
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<p>Effects of volcanic-origin contaminants on health.</p>
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13 pages, 1362 KiB  
Article
The Distribution and Seasonality of Per- and Polyfluoroalkyl Substances (PFAS) in the Vertical Water Column of a Stratified Eutrophic Freshwater Lake
by Patrick R. Gorski
Environments 2025, 12(2), 48; https://doi.org/10.3390/environments12020048 - 4 Feb 2025
Viewed by 458
Abstract
The vertical distribution and potential variability of Per- and Polyfluoroalkyl substances (PFAS) in the water column of lacustrine systems is important to know for sampling and monitoring purposes, but could also relate to details of their fate, transport, and distribution. In this study, [...] Read more.
The vertical distribution and potential variability of Per- and Polyfluoroalkyl substances (PFAS) in the water column of lacustrine systems is important to know for sampling and monitoring purposes, but could also relate to details of their fate, transport, and distribution. In this study, the water column of a eutrophic freshwater lake (Lake Monona, Madison, WI, USA) was sampled vertically for PFAS during summer stratification at several depths (surface microlayer to 1 m from the bottom) and then monitored at four dates and three depths the following year to assess seasonality. PFAS concentration did not exhibit vertical stratification or large variability in the water column. However, seasonal variation in PFAS concentration was detected, as well as an increase in PFAS concentration related to drought conditions. This study suggests that a surface water grab sample may be a sufficient representative of the water column for the basic monitoring of PFAS. But a single sample during the year may not provide a complete understanding of the lake, and multiple samples should be taken to capture and understand important seasonal events. Full article
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<p>Water column profiles of parameters in Lake Monona on August 4, 2022. (<b>a</b>) Chemical and biological parameters (Temp, DO, pH, Chl <span class="html-italic">a</span>, TSS, Org TSS, and alkalinity) plotted against water column depth. Parameters show the lake was highly stratified, with Chl <span class="html-italic">a</span>, TSS, and Org TSS at much higher levels in the epilimnion than the hypolimnion. (<b>b</b>) PFOS and PFOA concentrations plotted against water column depth. Symbols represent individual measurements, except 6 m, which is an average. PFOS and PFOA concentrations were consistent with depth during stratification unlike most other parameters.</p>
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<p>(<b>a</b>) Concentrations of 13 PFAS at each sample depth, in the surface microlayer (SML) and in a surface water grab sample (grab). Concentrations at each depth are for individual samples except at 6 m, which is the average of two samples. Only detectable PFAS are shown. The concentration of most compounds was similar across all depths. (<b>b</b>) The same data as in (<b>a</b>) but expressed in relative percentage of PFAS at each depth.</p>
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<p>Lake Monona water column profiles of temperature on four sampling dates in 2023: May 11, July 11, September 20, and December 21.</p>
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<p>(<b>a</b>) Concentrations of 13 PFAS on four dates and at three depths (grab, mid-epilimnion, and mid-hypolimnion) in Lake Monona. Only detectable PFAS are shown. Total PFAS concentration changes during the year, with a peak during September. (<b>b</b>) Relative percentages of individual PFAS were similar across depths on each date and consistent throughout the year despite monthly differences in concentration.</p>
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<p>Lake Monona (MON) grab sample PFAS concentrations compared with Starkweather Creek (STKW) grab samples on the same dates in 2023. NS = no sample.</p>
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<p>The 2023 rainfall in Madison, WI [<a href="#B24-environments-12-00048" class="html-bibr">24</a>] highlighting the 4 sampling dates with red arrows on the <span class="html-italic">x</span>-axis.</p>
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22 pages, 2862 KiB  
Article
Short-Term Ground Vegetation Responses to Fertilization in Latvian Forests: Effects on Species Richness and Diversity
by Guna Petaja, Didzis Elferts, Arta Bārdule, Zaiga Anna Zvaigzne, Dana Purviņa and Ilona Skranda
Environments 2025, 12(2), 47; https://doi.org/10.3390/environments12020047 - 4 Feb 2025
Viewed by 457
Abstract
This study investigated the impact of forest fertilization on ground vegetation in deciduous and conifer stands across different forest site types (forests with drained mineral soils, forests with drained organic soils, and dry upland forests), stand age groups (young, middle-aged, and pre-mature), and [...] Read more.
This study investigated the impact of forest fertilization on ground vegetation in deciduous and conifer stands across different forest site types (forests with drained mineral soils, forests with drained organic soils, and dry upland forests), stand age groups (young, middle-aged, and pre-mature), and fertilizer types (ammonium nitrate (NH4NO3) and wood ash alone, and both together). Ground vegetation was surveyed one to three years after fertilizer application, with the projected ground cover of individual species in the moss and herb layers determined. Thus, results reflect short-term impact of fertilization. Species richness and diversity (Shannon diversity index, H′) were compared between fertilized and control (unfertilized) plots. The results show that species diversity in the moss layer of silver birch stands was significantly affected by fertilization, while species richness was significantly influenced by the interaction between fertilization and forest site type. Differences between control and fertilized plots in birch stands suggest a potentially negative response of the moss layer to fertilization. In contrast, no significant effect of fertilization was observed in Norway spruce stands, where site type and stand age emerged as significant factors. In Scots pine stands, where NH4NO3 was applied alone, fertilization had a significant impact on both species richness and diversity in the herb layer. In the moss layer, a marginally significant effect was found for the interaction between fertilization and stand age. NH4NO3 alone appeared to enhance herb layer richness, although its effect on species diversity was more variable. Our study highlights the context-dependent nature of fertilization effects on species richness and diversity in Latvian hemiboreal forest ecosystems. Full article
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<p>The number of species (mean values and 95% confidence intervals) in the moss and herb layers of silver birch (<b>A</b>), Norway spruce (<b>B</b>–<b>D</b>), and Scots pine (<b>E</b>) stands, based on site type, age group, and treatment combinations. WA—wood ash; NH<sub>4</sub>NO<sub>3</sub>—ammonium nitrate. Letters above the error bars show the results of the Tukey test comparison of the groups—if two groups have the same letter, the difference is not statistically significant (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>The Shannon diversity index (H′, mean values, and 95% confidence intervals) in the moss and herb layers of silver birch (<b>A</b>), Norway spruce (<b>B</b>–<b>D</b>), and Scots pine (<b>E</b>) stands, based on site type, age group, and treatment combinations. WA—wood ash; NH<sub>4</sub>NO<sub>3</sub>—ammonium nitrate. Letters above the error bars show the results of the Tukey test comparison of the groups—if two groups have the same letter, the difference is not statistically significant (<span class="html-italic">p</span> &gt; 0.05).</p>
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23 pages, 2291 KiB  
Review
Impact of Air Pollution and Smog on Human Health in Pakistan: A Systematic Review
by Shazia Iram, Iqra Qaisar, Rabia Shabbir, Muhammad Saleem Pomee, Matthias Schmidt and Elke Hertig
Environments 2025, 12(2), 46; https://doi.org/10.3390/environments12020046 - 3 Feb 2025
Viewed by 797
Abstract
Air pollution is a serious public health issue in Pakistan’s metropolitan cities, including Lahore, Karachi, Faisalabad, Islamabad, and Rawalpindi. Pakistan’s urban areas are vulnerable due to air pollution drivers such as industrial activities, vehicular emissions, burning processes, emissions from brick kilns, urbanization, and [...] Read more.
Air pollution is a serious public health issue in Pakistan’s metropolitan cities, including Lahore, Karachi, Faisalabad, Islamabad, and Rawalpindi. Pakistan’s urban areas are vulnerable due to air pollution drivers such as industrial activities, vehicular emissions, burning processes, emissions from brick kilns, urbanization, and other human activities that have resulted in significant human health issues. The purpose of this study was to examine the impact of air pollutants and smog, as well as their causes and effects on human health. The PRISMA technique was used to assess the impact of environmental contaminants on human health. This study looked at air pollution sources and pollutants such as PM2.5, PM10, CO2, CO, SOX, and NOx from waste combustion and agriculture. The population included people of all ages and sexes from both urban and rural areas of Pakistan. Data were retrieved and analyzed using SRDR+ software and Microsoft Excel spreadsheets. The data suggested that Karachi and Lahore had the highest levels of air pollution and disease prevalence, which were attributed to heavy industrial activity and traffic emissions. Smog was a serious concern in Lahore during winter, contributing to the spread of several diseases. Other cities, including Islamabad, Rawalpindi, Jhang, Sialkot, Faisalabad, and Kallar Kahar, were impacted by agricultural operations, industrial pollutants, brick kilns, and urbanization. Due to these drivers of air pollution, some diseases such as respiratory and cardiovascular diseases had notably higher incidences in these cities. Other diseases were connected with air pollution exposure, asthma, eye and throat problems, allergies, lung cancer, morbidities, and mortalities. To reduce air pollution’s health effects, policies should focus on reducing emissions, supporting cleaner technologies, and increasing air quality monitoring. Full article
(This article belongs to the Special Issue Environments: 10 Years of Science Together)
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<p>Schematic representation of sources (drivers) of air pollution and effects on human health in Pakistan [SOURCE: authors].</p>
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<p>Flowchart of inclusion and exclusion of articles.</p>
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<p>Geographically distribution of selected studies in Pakistan.</p>
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<p>Disease types due to air pollution in selected studies.</p>
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<p>Annual distribution of the selected studies in Pakistan.</p>
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<p>Sources describing air pollution drivers.</p>
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20 pages, 4815 KiB  
Article
Fungicides in English Rivers: Widening the Understanding of the Presence, Co-Occurrence and Implications for Risk Assessment
by Nick Porter and Rob Collins
Environments 2025, 12(2), 45; https://doi.org/10.3390/environments12020045 - 3 Feb 2025
Viewed by 634
Abstract
Fungicides are commonly found in freshwater; however, the understanding of their wider presence, co-occurrence, and potential risk remains limited. This study examined English national datasets to highlight knowledge gaps and identify improvements to monitoring and risk assessment. The analysis found that at least [...] Read more.
Fungicides are commonly found in freshwater; however, the understanding of their wider presence, co-occurrence, and potential risk remains limited. This study examined English national datasets to highlight knowledge gaps and identify improvements to monitoring and risk assessment. The analysis found that at least one fungicide was present in 91% of samples collected from English rivers over a 5-year period, with four fungicides detected at rates exceeding 50%. Co-occurrence occurs widely, with up to nine different fungicides detected within the same sample and four detected the most frequently, raising concerns for synergistic interactions. The semi-quantitative nature of much of the available data precludes a clear determination of the potential risk of detrimental effects on aquatic biota. Fully quantitative analysis is required, and ecotoxicity-based water quality standards need to be agreed upon. The monthly sampling regime reflected in the national datasets will infrequently capture high flow events and so is unlikely to fully represent fungicides transported to rivers via rainfall-driven processes. Several information gaps exist, including the risk posed by fungicides in sewage sludge applied to land and the extent to which fungicides in the aquatic and terrestrial environments contribute to antifungal resistance. Improvements in spatial and temporal information on fungicide use are needed. Full article
(This article belongs to the Special Issue Advanced Research on Micropollutants in Water, 2nd Edition)
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<p>(<b>A</b>) Histogram showing number of unique fungicides found in a given sample between 2019 and 2023. (<b>B</b>) Histogram showing number of unique fungicides detected each year at a sampling site.</p>
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<p>Location of fungicide detection, including co-occurrence, under LC-MS during 2022. Data are derived from the Environment Agency’s LC-MS database.</p>
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<p>Boxplots of detected epoxiconazole concentrations by LC-MS for 2019 (<span class="html-italic">n</span> = 227), 2020 (<span class="html-italic">n</span> = 85), 2021 (<span class="html-italic">n</span> = 212), 2022 (<span class="html-italic">n</span> = 163), and 2023 (<span class="html-italic">n</span> = 43). The boxes represent the upper and lower quartiles, the horizontal bars represent the median, the whiskers represent the minimum and maximum (excluding outliers), and the dots represent outliers.</p>
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<p>Bar plots showing the monthly average LC-MS concentrations of selected fungicides for 2019 (<span class="html-italic">n</span> = 349 for azoxystrobin, 227 for epoxiconazole, 116 for fludioxonil, and 157 for fluoxastrobin), 2021 (<span class="html-italic">n</span> = 297 for azoxystrobin, 212 for epoxiconazole, 65 for fludioxonil, and 153 for fluoxastrobin), and 2022 (<span class="html-italic">n</span> = 470 for azoxystrobin, 163 for epoxiconazole, 205 for fludioxonil, and 109 for fluoxastrobin). Absence of bars indicate no detections. Data used were &gt;LoD only.</p>
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<p>Boxplots of concentrations of azoxystrobin and triclosan from the LC-MS and Water Quality Archive (WQA) datasets. The boxes represent the upper and lower quartiles, the horizontal bars represent the median, the whiskers represent the minimum and maximum (excluding outliers), and the dots represent outliers. Asterisks indicate significant difference between LC-MS and WQA concentrations by the Wilcoxon rank sum test, at <span class="html-italic">p</span> &gt; 0.05 (ns), <span class="html-italic">p</span> &lt; 0.05 (*), <span class="html-italic">p</span> &lt; 0.01 (**), <span class="html-italic">p</span> &lt; 0.001 (***) and <span class="html-italic">p</span> &lt; 0.0001 (****). Red dashed lines represent the PNEC value for azoxystrobin, and blue dashed lines represent the AA and MAC EQS for triclosan. Triclosan: WQA <span class="html-italic">n</span> = 76 (2019), 3 (2020), 34 (2021), 83 (2022), and 4 (2023); LC-MS <span class="html-italic">n</span> = 252 (2019), 62 (2020), 183 (2021), 313 (2022), and 47 (2023). Azoxystrobin: WQA <span class="html-italic">n</span> = 14 (2021), 8 (2022), and 50 (2023); LC-MS <span class="html-italic">n</span> = 297 (2021), 470 (2022), and 95 (2023). Data used were &gt;LoD only.</p>
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<p>Boxplots of 2021 riverine concentrations from the LC-MS, GC-MS, and WQA datasets and 2021 treated effluent concentrations from the UKWIR-CIP for azoxystrobin, epoxiconazole, fludioxonil, propoconazole, tebuconazole, and triclosan. The boxes represent the upper and lower quartiles, the horizontal bars represent the median, the whiskers represent the minimum and maximum (excluding outliers), and the dots represent outliers. Asterisks indicate significant difference by the Wilcoxon rank sum test at, <span class="html-italic">p</span> &gt; 0.05 (NS.), <span class="html-italic">p</span> &lt; 0.05 (*), <span class="html-italic">p</span> &lt; 0.01 (**) and <span class="html-italic">p</span> &lt; 0.001 (***). Azoxystrobin: <span class="html-italic">n</span> = 5 (GC-MS), 297 (LC-MS), 82 (UKWIR), and 14 (WQA); Epoxiconazole: <span class="html-italic">n</span> = 6 (GC-MS), 212 (LC-MS), and 5 (UKWIR); Fludioxonil: <span class="html-italic">n</span> = 8 (GC-MS), 65 (LC-MS), and 67 (UKWIR); Propiconazole: <span class="html-italic">n</span> = 13 (GC-MS), 195 (LC-MS), and 72 (UKWIR); Tebuconazole: <span class="html-italic">n</span> = 36 (GC-MS), 248 (LC-MS), and 12 (UKWIR); Triclosan: <span class="html-italic">n</span> = 183 (LC-MS),1127 (UKWIR), and 34 (WQA). Data used were &gt;LoD only.</p>
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<p>Boxplots portraying concentrations (mg/kg) of triclosan in sewage sludge in 2020 (<span class="html-italic">n</span> = 58) and 2021 (<span class="html-italic">n</span> = 144). The boxes represent the upper and lower quartiles, the horizontal bars represent the median, the whiskers represent the minimum and maximum (excluding outliers), and the dots represent outliers. Data used were &gt;LoD only.</p>
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19 pages, 7288 KiB  
Article
Atmospheric Radon in the Central Mediterranean: Seasonal and Diurnal Variations Measured in Gozo, Malta
by Beatriz Defez, Raymond Ellul, Martin Saliba, Rebecca Muscat, Marvic Grima, Alfred Micallef, Charles Galdies, María Moncho-Santonja, Silvia Aparisi-Navarro and Guillermo Peris-Fajarnés
Environments 2025, 12(2), 44; https://doi.org/10.3390/environments12020044 - 2 Feb 2025
Viewed by 602
Abstract
This paper presents the findings of a 12-year study on radon conducted from January 2011 to December 2022 at the Giordan Lighthouse station on the island of Gozo, Malta. Located in the Central Mediterranean, Gozo’s strategic position enables effective monitoring of air mass [...] Read more.
This paper presents the findings of a 12-year study on radon conducted from January 2011 to December 2022 at the Giordan Lighthouse station on the island of Gozo, Malta. Located in the Central Mediterranean, Gozo’s strategic position enables effective monitoring of air mass movements between Africa and Europe (from south to north) and between Europe and Central Asia (from west to east). Our research involves an analysis of seasonal and diurnal variations in radon levels, alongside analysis of relevant meteorological variables, clustering of air mass back trajectories, and assessment of local and remote radon production. The findings provide critical insights into the dynamics of atmospheric radon, which are significant not only for the Maltese islands, but also for enhancing our understanding of transcontinental radon transport in the Central Mediterranean, a region that has remained largely unexplored. Full article
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<p>The geographical location of the monitoring site at the Giordan Lighthouse (GLH) on the island of Gozo, Malta: (<b>a</b>) the global location of Malta and (<b>b</b>) the local location of the GLH.</p>
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<p>Seasonal composite of radon. Mean, 10th, and 90th percentile values: (<b>a</b>) monthly timescale and (<b>b</b>) daily timescale. Standard deviation is 1.46 Bqm<sup>−3</sup> and 1.23 Bqm<sup>−3</sup> for monthly and daily scales, respectively. Whiskers are excluded in subplot “b” for visualization purposes.</p>
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<p>Seasonal composite of meteorological variables. Mean values and mean, 10th, and 90th percentile values at monthly and daily timescales, respectively: (<b>a1</b>,<b>a2</b>) WD, (<b>b1</b>,<b>b2</b>) WS, (<b>c1</b>,<b>c2</b>) AT, and (<b>d1</b>,<b>d2</b>) RH.</p>
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<p>Histograms per season (1 = Spring, 2 = Summer, 3 = Fall, 4 = Winter): (<b>a1</b>–<b>a4</b>) radon, (<b>b1</b>–<b>b4</b>) WD, (<b>c1</b>–<b>c4</b>) WS, (<b>d1</b>–<b>d4</b>) AT, and (<b>e1</b>–<b>e4</b>) RH.</p>
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<p>Radon diurnal composite per season: (<b>a</b>) all seasons, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) fall, and (<b>e</b>) winter. Subplots (<b>b</b>–<b>e</b>) provide insight into seasons individually for better scaled representation of diurnal evolution of radon. Standard deviations are 1.37 Bqm<sup>−3</sup>, 1.73 Bqm<sup>−3</sup>, 1.70 Bqm<sup>3</sup>, and 1.21 Bqm<sup>−3</sup>, and daytime standard deviations differ by less than 11%, 10%, 13%, and 18% compared to their seasonal values for spring, summer, fall, and winter, respectively.</p>
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<p>Diurnal composite of meteorological variables per season: (<b>a</b>) WD, (<b>b</b>) WS, (<b>c</b>) AT, and (<b>d</b>) RH.</p>
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<p>Air mass source clustering based on Hysplit 14-day back trajectories (markers every 24 h): (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) fall, and (<b>d</b>) winter. Colors are automatically assigned and solely represent different cluster trajectories.</p>
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<p>Air mass source clustering based on Hysplit 7-day back trajectories (markers every 24 h): (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) fall, and (<b>d</b>) winter. Colors are automatically assigned and solely represent different cluster trajectories.</p>
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<p>Seasonal radon flux maps calculated from monthly results published by “traceRadon” during period of the study (2011 to 2022): (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) fall and (<b>d</b>) winter.</p>
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27 pages, 2018 KiB  
Review
Advances in Graphene-Based Materials for Metal Ion Sensing and Wastewater Treatment: A Review
by Akram Khalajiolyaie and Cuiying Jian
Environments 2025, 12(2), 43; https://doi.org/10.3390/environments12020043 - 2 Feb 2025
Viewed by 879
Abstract
Graphene-based materials, including graphene oxide (GO) and functionalized derivatives, have demonstrated exceptional potential in addressing environmental challenges related to heavy metal detection and wastewater treatment. This review presents the latest advancements in graphene-based electrochemical and fluorescence sensors, emphasizing their superior sensitivity and selectivity [...] Read more.
Graphene-based materials, including graphene oxide (GO) and functionalized derivatives, have demonstrated exceptional potential in addressing environmental challenges related to heavy metal detection and wastewater treatment. This review presents the latest advancements in graphene-based electrochemical and fluorescence sensors, emphasizing their superior sensitivity and selectivity in detecting metal ions, such as Pb2⁺, Cd2⁺, and Hg2⁺, even in complex matrices. The key focus of this review is on the use of molecular dynamics (MD) simulations to understand and predict ion transport through graphene membranes, offering insights into their mechanisms and efficiency in removing contaminants. Particularly, this article reviews the effects of external conditions, pore radius, functionalization, and multilayers on water purification to provide comprehensive insights into filtration membrane design. Functionalized graphene membranes exhibit enhanced ion rejection through tailored electrostatic interactions and size exclusion effects, achieving up to 100% rejection rates for selected heavy metals. Multilayered and hybrid graphene composites further improve filtration performance and structural stability, enabling sustainable, large-scale water purification. However, challenges related to fabrication scalability, environmental impact, and cost remain. This review also highlights the importance of computational approaches and innovative material designs in overcoming these barriers, paving the way for future breakthroughs in graphene-based filtration technologies. Full article
(This article belongs to the Special Issue Monitoring of Contaminated Water and Soil)
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<p>An MD flowchart. MD begins with the definition of initial input data, including interaction potential, the initial coordinates of atoms, and their velocities. Once the system is initialized, potential energy is used to calculate the forces acting on each atom. Next, the positions and velocities of the atoms are updated according to the equations of motion derived from Newton’s second law. Throughout the simulation, data such as atomic positions, velocities, and energy values are periodically recorded. The simulation continues until the specified number of time steps is reached.</p>
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<p>(<b>a</b>) Graphene membrane with dimensions of 30 × 30 Å<sup>2</sup> and different pore diameters 9, 11, 13, and 15 Å. Reprinted from [<a href="#B112-environments-12-00043" class="html-bibr">112</a>], copyright 2022, with permission from Springer Nature. (<b>b</b>) Different pore shapes of graphene membrane. Reprinted from [<a href="#B107-environments-12-00043" class="html-bibr">107</a>], copyright 2024, with permission from Elsevier.</p>
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<p>An overhead perspective of three functionalized graphene materials with B, NH, and OH. The membrane's carbon atoms are shown in gray, boron in pink, nitrogen in blue, oxygen in red, and hydrogen in white. Reprinted from [<a href="#B53-environments-12-00043" class="html-bibr">53</a>], copyright 2017, with permission from Elsevier.</p>
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<p>(<b>a</b>) The top view of the GO nanosheets utilized in MD simulations, featuring varying oxidation levels and dimensions. Reprinted from [<a href="#B117-environments-12-00043" class="html-bibr">117</a>], copyright 2022, with permission from Elsevier. (<b>b</b>) A depiction of multilayer graphene membranes utilized for separating metal ions from solutions. The geometrical parameters, <span class="html-italic">w</span> and <span class="html-italic">d</span>, represent the width of the channel and the interlayer separation, respectively. The dark blue arrows indicate the solution flow direction. Reprinted from [<a href="#B118-environments-12-00043" class="html-bibr">118</a>], copyright 2021, with permission from Elsevier.</p>
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10 pages, 320 KiB  
Article
Carbon Footprint of By-Product Concentrate Feed: A Case Study
by Emilio Sabia, Ada Braghieri, Luca Vignozzi, Rosanna Paolino, Carlo Cosentino, Adriana Di Trana and Corrado Pacelli
Environments 2025, 12(2), 42; https://doi.org/10.3390/environments12020042 - 2 Feb 2025
Viewed by 461
Abstract
Using by-products in livestock feed can be an additional strategy for safeguarding land use in agriculture and reducing the environmental impact of animal production. Studies conducted on farms to assess the environmental impact of milk and meat production using life-cycle assessment (LCA) tools [...] Read more.
Using by-products in livestock feed can be an additional strategy for safeguarding land use in agriculture and reducing the environmental impact of animal production. Studies conducted on farms to assess the environmental impact of milk and meat production using life-cycle assessment (LCA) tools reveal that feeding accounts for approximately one-third. This study aimed to calculate the carbon footprint (CF) of three different concentrated feeds for livestock, both with and without the inclusion of by-products in the formulation. Three different formulations of concentrated feeds for dairy cows were developed homogeneously regarding energy content and crude protein. The LCA approach assessed CF in kg CO2 eq.; the functional unit was 1 kg of concentrate feed. A sensitive analysis of soybean meal’s association with deforestation was formulated. The concentrated feed with by-products demonstrated a lower impact on CF of 23.7% and 37.0% compared to concentrated feed with a mix of raw material and by-products, and solely with raw material, respectively. Using agricultural by-products to produce concentrated feed for livestock sectors can be an environmentally sound alternative in terms of carbon footprint. Full article
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<p>Carbon footprint of different concentrate feed. By-product-concentrate feed (BCF); mixed concentrate feed (MCF); conventional raw feed (CRF); mixed concentrate feed with soybean meal from deforestation (MCFSD); conventional concentrate feed with soybean meal from deforestation (CRFSD); FU = functional unit—1 kg of concentrate feed.</p>
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19 pages, 2516 KiB  
Article
Application of Electronic Tongue for Detection and Classification of Lead Concentrations in Coal Mining Wastewater
by Jeniffer Katerine Carrillo Gómez, Laura Daniela Patiño Barrera and Cristhian Manuel Durán Acevedo
Environments 2025, 12(2), 41; https://doi.org/10.3390/environments12020041 - 1 Feb 2025
Viewed by 497
Abstract
This study evaluates the potential of an electronic tongue (E-tongue) as an innovative and alternative method for detecting and classifying lead concentrations in wastewater generated by coal mining activities in North Santander, Colombia. The E-tongue aims to complement traditional environmental monitoring techniques with [...] Read more.
This study evaluates the potential of an electronic tongue (E-tongue) as an innovative and alternative method for detecting and classifying lead concentrations in wastewater generated by coal mining activities in North Santander, Colombia. The E-tongue aims to complement traditional environmental monitoring techniques with a more efficient and accurate solution. A total of 110 wastewater samples were collected from two locations at a coal mine in the municipality of Toledo: one inside the mine (Point 2) and another outside the mine (Point 1). This research involved the physicochemical analysis of parameters such as pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), hardness, and alkalinity, conducted at the University of Pamplona’s laboratories. The integration of PCA with machine learning algorithms highlighted the E-tongue’s capability for the real-time, on-site detection and discrimination of lead concentrations in coal mining wastewater. Achieving a precision and accuracy above 90%, the SVM classifier outperformed alternative models such as the k-NN, Random Forest, Naïve Bayes, and Quadratic Discriminant Analysis. This demonstrates the system’s robustness and reliability in environmental monitoring, enabling the accurate classification of lead concentrations within the critical range of 0.05 to 1 ppm, essential for assessing contamination levels and ensuring water safety. These findings highlight the E-tongue system’s capability as a rapid, cost-effective tool for monitoring lead contamination in mining wastewater, presenting a viable alternative to conventional methods such as atomic absorption spectroscopy. Full article
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<p>Methodology established for lead quantification using electronic tongue.</p>
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<p>The response of the C110 electrode of the E-tongue to lead concentrations of 0.05 ppm and 1 ppm in wastewater.</p>
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<p>PCA plot of lead concentration (0.5 ppm to 100 ppm) categories in wastewater using E-tongue.</p>
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<p>Confusion matrix obtained from PCA-SVM classification model of lead concentrations in wastewater using E-tongue (C110 electrode).</p>
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<p>PCA analysis: Lead concentration (0.05 ppm to 1.0 ppm) categories in wastewater samples from Point 1 and Point 2, analyzed using E-tongue system.</p>
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<p>Confusion matrix obtained from PCA-RF classification model of lead concentrations in wastewater from Points 1 and 2 using E-tongue (C110 electrode).</p>
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55 pages, 1183 KiB  
Review
Chemicals in European Residences—Part II: A Review of Emissions, Concentrations, and Health Effects of Semi-Volatile Organic Compounds (SVOCs)
by Charlotte Landeg-Cox, Alice Middleton, Christos Halios, Tim Marczylo and Sani Dimitroulopoulou
Environments 2025, 12(2), 40; https://doi.org/10.3390/environments12020040 - 30 Jan 2025
Viewed by 569
Abstract
This comprehensive review reports on concentrations, sources, emissions, and potential health effects from Semi-Volatile Organic Compounds (SVOCs) identified in the internal home environment in European residences. A total of 84 studies were identified, and concentrations were collated for inhalation exposure from dust, air [...] Read more.
This comprehensive review reports on concentrations, sources, emissions, and potential health effects from Semi-Volatile Organic Compounds (SVOCs) identified in the internal home environment in European residences. A total of 84 studies were identified, and concentrations were collated for inhalation exposure from dust, air and aerosol. A total of 298 individual SVOCs were identified and 67 compounds belonging to eight chemical classes: phthalates, flame retardants, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), per- and polyfluorinated alkyl substances (PFAS), biocides, bisphenols and musks were prioritised. Phthalates are the most abundant SVOCs with DEHP being the most abundant in both the dust and aerosol phases (WAGMs 426.4 μg g−1 and 52.2 ng m−3, respectively) followed by DBP for dust (WAGMs are 95.9 μg g−1). In the air, the most abundant SVOCs are DiBP (284.1 ng m−3), DBP (179.5 ng m−3), DEHP (106.2 ng m−3) and DMP (27.79 ng m−3). Chemicals from all SVOC categories are emitted from building and construction materials, furnishings and consumer products, especially phthalates. Both legacy chemicals and their alternatives were detected. Complexities of reporting on SVOCs included differing sampling methodologies, multiple standards in their definition, lack of industry data, and toxicological data focused primarily on ingestion not inhalation exposures. Further research is recommended to develop the evidence base for potential health effects including via inhalation, reporting of emission rates and undertaking future monitoring studies. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas III)
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Figure 1
<p>PRISMA diagram.</p>
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<p>Categorization of health endpoints for the 67 identified health relevant SVOCs in European residences.</p>
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<p>Concentrations of selected SVOCs in residences, with each point representing a weighted average geometric mean concentration. Note that vertical axis is presented on a logarithmic scale. Vertical coloured stripes correspond to number of health endpoints associated with each chemical (i.e., red (six), orange (five), yellow (four), green (three), light blue (two), dark blue (one)).</p>
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<p>Categorization of sources for the 67 identified health relevant SVOCs in the European residences.</p>
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