Abstract: Plastic pollution represents a systemic governance failure with disproportionate social, environmental, and health impacts on grassroots communities, particularly in low- and middle-income contexts. Conventional top-down regulatory and technological responses have proven insufficient to address the complexity of plastic pollution, often excluding those most affected from decision-making and solution design. This paper examines how democratizing plastic governance through effective engagement of grassroots communities can generate equitable, effective, and scalable responses to plastic pollution. Drawing on empirical evidence from the #RestorationX10000 initiative led by Community Action Against Plastic Waste (CAPws), the paper documents implementation processes and outcomes achieved between 2021 and 2025 across 71 impacted communities in 21 countries spanning Africa, Asia-Pacific, and Latin America. The initiative was designed to empower 10,000 youths and women as community leaders, practitioners, and advocates by equipping them with leadership, technical, and policy engagement skills to drive systemic change in plastic governance and circular economy practice. Using a transdisciplinary, community-based action research approach, the initiative integrates capacity building, citizen science, circular economy interventions (collection, sorting, repair, reuse, repurposing, and recycling), and policy advocacy. Quantitative and qualitative evidence demonstrates that grassroots-led interventions can simultaneously reduce plastic leakage, create decent green livelihoods, and strengthen environmental governance. Overall, this paper contributes to emerging scholarship on inclusive environmental governance by providing evidence that democratized plastic governance, rooted in grassroots participation and circular economy principles, can deliver durable environmental restoration, socio-economic resilience, and policy-relevant outcomes. The results have direct implications for national plastic action plans, extended producer responsibility frameworks, and ongoing global negotiations toward a legally binding instrument on plastic pollution.

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Combined sewer overflow (CSO) pollution has consequently become a critical challenge, yet its formation depends on tightly coupled dry-and-wet weather processes. This study aims to integrate high-resolution field monitoring with statistical analysis to characterize the full “accumulation-transport-discharge” cycle of CSO pollution. Results indicated that during dry periods, domestic sewage exhibited strong enrichment, with concentrations of TIN, COD, and TP being 2.1-, 2.3-, and 1.9-fold higher, respectively, than the Chinese secondary discharge standards (GB 18918-2002). Surface sediment showed pronounced spatial heterogeneity, with greater loads in residential than transportation areas and substantial fine-particle accumulation on roofs (particle size <150 μm, accounting for 73% by mass). Sewer sediments, dominated by coarse inorganic particles (over 77% by mass), represented the main pollutant reservoir. Rainfall produced distinct hydrodynamic and water-quality responses. Light rain following long antecedent dry periods generated a high-concentration but low-load regime with a strong first flush, whereas moderate rain yielded lower concentrations but higher loads. Overflow occurred when rainfall exceeded ~14 mm, with pollutant peaks lagging rainfall by 20–45 min in the studied area. TIN and TP peaked sharply at rainfall event onset, and first-flush intensities followed TIN > TP > COD > SS. Source apportionment identified sewer sediments as the dominant CSO source, followed by surface runoff and domestic sewage. These findings clarify the mechanisms linking dry-weather accumulation to wet-weather transport and support targeted CSO pollution control and urban water-quality management.

Abstract: Despite the Minamata Convention’s targeted reductions in mercury consumption, global trade data exhibits a ‘Compliance Paradox’ where reported flows vanish while artisanal gold mining output remains stable. This research proposes a ‘Mineral Intelligence’ pipeline utilizing unsupervised machine learning to detect illicit mercury trafficking disguised as Electronic Waste (HS 8549). By applying Gaussian Mixture Models (GMM) and Isolation Forest algorithms to UN Comtrade data (2020–2024), we identify a systemic ‘Balloon Effect’: as elemental mercury bans took effect in 2022, illicit volumes were structurally displaced into ‘fake waste’ classifications. Forensic analysis reveals a statistically significant ‘Smuggler’s Signature’ within these flows, characterized by a price anomaly of $24–$80/kg (mirroring liquid mercury markets) and a Net to-Gross weight ratio exceeding 90%, physically corresponding to standard 34.5 kg steel mercury flasks. Furthermore, Node2Vec and spectral embedding analysis exposes a ‘Decoupling Chasm’ (Manifold Distance: 2.06) that topologically separates financial gold hubs from mercury-intensive mining zones. Finally, Recursive LSTM forecasts predict a ‘burnout’ of the current HS 8549 smuggling vector (-618M kg/yr), warning of an imminent regime shift toward chemically masked commodities.

Abstract: The catchments that contain ecologically critical wetlands supplying the Mohale and Polihali dams under the Lesotho Highlands Water Project (LHWP) are increasingly threatened by expanding agriculture, mining activities, and uncontrolled livestock grazing. Hence, the present study was conducted to assess heavy metal contamination and wetland health across the three higher-altitude sub-catchments in Lesotho: Senqunyane, Khubelu, and Sani. A total of 24 water samples were collected from six wetlands in March 2025 to determine concentrations of copper, iron, manganese, lead, and zinc in accordance with APHA standards. Pollution Load Index (PLI) and Heavy Metal Pollution Index (HPI) were calculated to evaluate water quality. All sites exceeded the HPI safety threshold of 100, with Sani Top showing the highest PLI (5.54), indicating severe contamination primarily driven by manganese and lead. Lead emerged as the dominant pollutant due to its low permissible limits, exacerbating HPI scores across wetlands. Heavy metal concentrations generally declined with increasing altitude, with lead and copper displaying the steepest decreases, while manganese peaked at mid-altitudes (2750 m), potentially linked to local geochemical processes and organic matter decomposition. Principal Component Analysis (PCA) explained 40.6% of total data variance, revealing tight clustering at higher altitudes (3000 m), reflecting uniform, geogenically controlled water quality, whereas lower elevations displayed more variable and anthropogenically influenced patterns. Despite high-altitude sites appearing chemically stable, they carry higher dissolved ions, suggesting treatment needs for water hardness. In contrast, low-to-mid elevation wetlands showed more variable and hazardous metal loads, necessitating targeted management strategies including buffer zones, liming, and pollution source tracing. Findings highlight land use and hydrology in wetland water quality. Continuous monitoring of Lesotho’s alpine wetlands is vital to address heavy metal pollution, guide evidence-based policy, and support prioritized monitoring, mitigation, and restoration for sustainable downstream water management.

Abstract: Located in northeastern Algeria, the Tébessa region is characterized by a semi-arid climate. Due to the scarcity of surface water resources, groundwater has become the primary source for domestic, agricultural, and industrial use. However, the shallow aquifer system in the area is highly vulnerable to contamination and is increasingly impacted by various pollution phenomena. To investigate the extent and spatial distribution of groundwater pollution, hydrochemical data from about seventy sampling campaigns conducted in 2005, 2006, and 2018 were analyzed. Special attention was given to nitrate (NO₃⁻) concentrations, a key indicator of agricultural and domestic pollution, alongside other chemical tracers such as chloride (Cl⁻), sulfate (SO₄²⁻), and sodium (Na⁺). Multivariate statistical analyses, including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were employed to identify the dominant factors controlling groundwater chemistry and to classify water types and pollution levels across the study area. The results reveal significant spatial variability in groundwater quality, with elevated nitrate levels and other contamination indicators (Na⁺, Cl⁻, SO₄²⁻) particularly concentrated in recharge zones affected by unregulated agricultural practices and wastewater discharge. This situation, suggesting significant anthropogenic influence and highlighting the urgent need for groundwater protection strategies.

Abstract: European Air Quality Directive defines benzo(a)pyrene as the chemical index for Polycyclic Aromatic Hydrocarbons (PAHs) carcinogenicity and sets a limit for its concentration in PM10 to address the exposure risk associated with the class. It also mandates monitoring six additional PAHs at a limited number of selected sites to assess the benzo(a)pyrene's contribution to the class in ambient air. For this aim, as part of the "Reti Speciali" project, benzo(a)pyrene and seven other PAHs were measured at 10 urban sites across Italy in 2016-2019 and the spatial and temporal pattern of these compounds were analyzed to evaluate benzo(a)pyrene's effectiveness in representing the carcinogenicity of the entire PAHs class. Results showed that in Italy, benzo(a)pyrene accounted for 61% ± 4.4% of total carcinogenicity when benzo(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, dibenzo(a-h)anthracene, and indenopyrene were considered, and about 5% less when chrysene was also added. This value varies by site (from 51%± 11% in Taranto to 66% ± 7.5% in Cosenza) and decreases in summer due to benzo(a)pyrene's strong photochemical degradation. In Europe, this percentage is generally similar or lower. For instance, in the United Kingdom, across 24 urban sites, it averages 56%± 2.9%. These findings suggest that benzo(a)pyrene does not represent the overall carcinogenicity of PAHs nor a constant percentage, highlighting the need to further investigate the use of benzo(a)pyrene as the sole marker of PAHs toxicity.

Abstract: Air pollution remains a major environmental challenge, with severe impacts on human health and ecosystems. Recent advances in satellite technology have transformed air quality monitoring by enabling global, continuous observations of atmospheric pollutants. However, satellite data often lack the precision of ground-based stations. This study aims to develop a machine learning model to predict daily surface concentrations of key air pollutants (NO2, O3, PM10, PM2.5) at high spatial resolution (300 m) in the Apulia region. Using Regional Environmental Protection Agency (ARPA) station data from 2019 to 2022 and meteorological, geographic, land-use, and temporal variables, we trained an XGBoost model on a 300 m grid. Model performance, assessed by repeated cross-validation, showed an average R^2 of 0.71, with values of 0.77 for NO2, 0.78 for O3, 0.67 for PM2.5, and 0.64 for PM10. eXplainable AI (XAI) methods confirmed strong alignment with established scientific knowledge, enhancing model reliability and offering insights into pollutant distribution drivers.

Abstract: Atmospheric deposition of emerging contaminants, including toxic trace elements, remains a critical environmental and public health concern. Moss biomonitoring offers a sensitive and cost-effective tool for assessing airborne pollutants, yet traditional analyses rely on descriptive statistics and lack predictive and mechanistic insight. Here, we introduce Mosses ML, a machine-learning–enhanced framework that integrates moss biomonitoring with bulk and dry deposition measurements to improve detection, interpretation and risk assessment of atmospheric contaminants. Using Hylocomium splendens transplants exposed for 90 days across industrial, urban and rural sites in Upper Silesia (Poland), we combined trace-element accumulation (Cd, Pb, Zn, Ni, Cr, Fe), relative accumulation factors (RAF), PCA-derived gradients, and site-level metadata with Random Forest and Gradient Boosting models. ML algorithms achieved high predictive performance (R² up to 0.91), accurately estimating moss metal concentrations from deposition metrics and environmental variables. SHAP feature-importance analysis identified dry deposition load and co-occurring metal signals as the dominant predictors of contamination, confirming the primary role of particulate emissions in shaping moss chemistry. Compared with classical threshold-based classification, the ML approach improved high-risk site identification by 24–38%. Mosses ML combines biologically meaningful indicators with modern computational tools, strengthening the role of mosses as early-warning systems for atmospheric pollution. The framework is broadly applicable to bryophyte biomonitoring and supports regulatory decision-making for emerging contaminants.

Abstract: New methods are needed to monitor environmental treaties, like the Montreal Protocol, by reviewing large, complex customs datasets. This paper introduces a framework using unsupervised machine learning to systematically detect suspicious trade patterns and highlight activities for review. Our methodology, applied to 100,000 trade records, combines several ML techniques. Unsupervised Clustering (K-Means) discovers natural trade archetypes based on shipment value and weight. Anomaly Detection (Isolation Forest and IQR) identifies rare "mega-trades" and shipments with commercially unusual price-per-kilogram values. This is supplemented by Heuristic Flagging to find tactics like vague shipment descriptions. These layers are combined into a priority score, which successfully identified 1,351 price outliers and 1,288 high-priority shipments for customs review. A key finding is that high-priority commodities show a different and more valuable value-to-weight ratio than general goods. This was validated using Explainable AI (SHAP), which confirmed vague descriptions and high value as the most significant risk predictors. The model's sensitivity was validated by its detection of a massive spike in "mega-trades" in early 2021, correlating directly with the real-world regulatory impact of the US AIM Act. This work presents a repeatable unsupervised learning pipeline to turn raw trade data into prioritized, usable intelligence for regulatory groups.

Abstract: Sabalan Dam Lake, located at ~4,800 m above sea level in northwestern Iran, serves as a strategic freshwater source for Ardabil Province. Despite its high-altitude and volcanic setting, recent evidence suggests emerging water quality degradation due to a combination of geogenic and anthropogenic stressors. This study presents the first integrated assessment of pollution status, sources, and spatial distribution in the lake and its inflowing rivers during the spring season. Water samples from nine representative stations were analyzed for physicochemical, microbiological, and heavy metal parameters (Pb, Cd, Zn, Cu), and results were evaluated against WHO, EPA, and Iranian national standards. GIS-based spatial interpolation was employed to identify pollution hotspots and link them to potential sources. Findings revealed that while most physicochemical parameters remained within permissible limits, lead (28.85–62.72 µg/L) and cadmium (11–23.94 µg/L) concentrations exceeded WHO and EPA thresholds by 2–8 times across all stations attributed to natural leaching from Sabalan’s andesitic-basaltic formations and anthropogenic inputs from upstream rural settlements. Microbiological contamination was widespread, with total coliforms (260–1,100 MPN/100mL) and fecal coliforms (0–1,150 MPN/100mL) indicating significant fecal intrusion, particularly near villages and livestock areas. The lake has shifted from an oligotrophic to a mesotrophic state (mean TP ≈ 20 µg/L), signaling early eutrophication. Spatial analysis identified three critical hotspots (Stations 2, 6, and 8) where geogenic metal release and rural pollution converge. These results challenge the assumption that high-altitude lakes are inherently pristine and underscore the dual vulnerability of volcanic reservoirs to natural and human-induced pressures. We recommend a dual-track management strategy: (1) geochemical mitigation (e.g., lime softening to reduce metal bioavailability) and (2) source control (e.g., decentralized wastewater systems, livestock exclusion zones). This study provides a replicable framework for assessing data-scarce, high-elevation reservoirs in arid and semi-arid regions globally.

Abstract: Plastic pollution is a global challenge. Despite plastics being complex chemical mixtures, hazard research has focused on particulate forms and the risks of plastics additives, especially for environmental organisms, remain poorly understood. This is a significant knowledge gap considering ubiquitous organismal exposure to plastics and the associated 16 000+ additives. The aim of this study was to provide ecotoxicological characterization of aqueous eluates of foamed plastic consumer products and propose a test battery for toxicity screening. For that, hazard of eluates of six randomly selected foamed plastic products was evaluated using aquatic decomposers, autotrophs and heterotrophs (Vibrio fischeri, Raphidocelis subcapitata, Lemna minor, Thamnocephalus platyurus, Heterocypris incongruens, Daphnia magna). Alarmingly, all plastic eluates affected the organisms, though toxicity varied among materials and species. Results showed that short-term contact may underestimate plastic eluate toxicity. To increase environmental relevance of hazard assessment of foamed plastic eluates, harmonizing leachate preparation, using natural water and avoiding (excessive) filtration of eluates should be considered. OECD/ISO assays with R. subcapitata, H. incongruens and D. magna (96 h) can be recommended as a minimal sensitive battery for effective screening of plastic eluate toxicity.

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Wetlands are delicate ecosystems that host diverse species and face ongoing environmental stress. The “Carlos Anwandter” Ramsar Site in Valdivia, Chile, is the world’s main breeding ground for the black-necked swan, which strongly relies on the aquatic plant Egeria densa. This plant has been impacted by deposition of particulate iron (Fe) and zinc (Zn). However, current methodological approaches typically remove particulate matter during sample pre-treatment through washing, following agricultural plant-tissue protocols. This study aimed to evaluate how sample treatments and plant sectioning affect Fe and Zn concentrations in E. densa. Samples were collected from both the Ramsar site (Cruces River) and a control site (Calle-Calle River). Results showed that washing samples (both in the field and lab) significantly reduced reported metal concentrations, underscoring the importance of standardized sampling and pre-treatment protocols. Fe concentrations were notably higher at the Ramsar site (11,155 mg kg^-1) compared to the control (3,783 mg kg^-1). The same is true for Zn (108 mg kg^-1 and 60 mg kg^-1, respectively). Over time, Fe concentrations remained stable, while Zn concentrations declined, suggesting a consistent Fe input and a decreasing Zn trend in the wetland. These findings are crucial for interpreting metal pollution and understanding spatial-temporal variability in aquatic plant contamination.

Abstract: Air quality prediction is critical for public health management and environmental policymaking, as poor air quality contributes to respiratory diseases, cardiovascular conditions, and premature mortality. Previous research has demonstrated that machine learning models can effectively forecast air quality indices by capturing complex relationships between meteorological variables and pollutant concentrations, with ensemble methods consistently outperforming traditional linear approaches. This study aims to develop and evaluate predictive models for daily Air Quality Index (AQI) in Cook County, Illinois, to support proactive environmental health interventions. Daily air quality data spanning from January 2015 to October 2025 were obtained from the EPA Air Quality System, encompassing 20 environmental parameters including PM2.5, ozone, nitrogen dioxide, and meteorological conditions. The dataset was enhanced through feature engineering, creating 50+ features including temporal patterns, lag variables, rolling averages, and interaction terms. Eleven machine learning models were trained and evaluated, ranging from traditional regression algorithms to advanced ensemble methods (XGBoost) and deep learning architectures (MLP, LSTM). XGBoost with hyperparameter tuning emerged as the best-performing model, achieving 88.4% variance explanation (R²=0.8842) with a mean absolute error of 7.28 AQI points. Feature importance analysis revealed that ozone, PM2.5, and nitrogen dioxide were the strongest predictors, with temporal lag features significantly improving model accuracy. These findings enable environmental agencies to implement early warning systems for poor air quality days, optimize sensor deployment strategies across Cook County's 155 monitoring sites, and develop targeted interventions during high-risk periods such as summer months when ozone levels peak.

Abstract: Mesoporous TiO2 microspheres with a large surface area were synthesized via a hydrothermal reaction using titanium glycolate. The samples were subsequently subjected to different vacuum oven treatment times (2, 4, 6, and 8 hours), resulting in Ti³⁺ self-doping. Comprehensive characterization was performed using transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The synthesized TiO2 microspheres exhibited significantly enhanced photocurrent and efficient photocatalytic activity under visible light irradiation, demonstrating their potential for applications in solar-driven water splitting. The results highlight the influence of Ti³⁺ self-doping on improving the photoactivity and photosensitivity of the material.

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The competition for desalination is currently underway. A mere decade ago, nations within the Maghreb region and, rather unexpectedly, European countries, were fortunate enough to evade humanity's primary adversary: drought. However, the unpredictable nature of climate change has since altered this reality. Consequently, an increasing number of countries are contemplating the serious prospect of utilizing desalination to fulfill their potable water requirements from the seas and oceans bordering their coastlines. Regrettably, research and experience have indicated that highly saline water presents a significant threat to marine ecosystems. This scholarly investigation aims to contribute to the discovery of a solution that will enable the continuation of seawater desalination without inflicting harm on the marine flora and fauna, and this work can be considered as a prototype that need to be studied closely, because the results are here and undeniable, plus this is all what we going to need more and more in near future, namely water and energy.

Abstract: Rural and agricultural runoff continues to pose a threat to water quality and human health despite a plethora of research identifying likely causes. Large livestock operations and leaking septic systems have proven to be significant sources of both nutrients and bacteria in the form of algal blooms and antibiotic-resistant Escherichia coli. Many times, these impacts are witnessed on a watershed scale. Implementing remedies are complicated by livestock operations defined as point source facilities under the USA Clean Water Act (CWA) but regulated as non-point source agricultural runoff. Additionally, the State of Michigan is the only state in the USA without a comprehensive rural septic system law. Pollutant assessment of watersheds involves a wide array of sampling parameters that focus primarily on impacts after-the-fact. Non-point source pollution, particularly in rural areas, lacks regulatory teeth; this watershed management approach is not sustainable as evidenced by continual degradation of our rural watersheds. This study lays out a streamlined methodology incorporating focused parameters that can infer pollutant pathways and processes. We illustrate the methodology using data collected in the Pine River watershed (central Michigan) where multiple pollutant inputs were defined as exceeding water quality standards in channels and reservoirs. The results of this work beg for better understanding of what should be defined as sustainable and unsustainable land use/watershed management. Using simplified field and laboratory techniques, it is possible for local communities, educational institutions, and regulatory agencies to identify likely pollutant sources violating water quality standards regardless of point or nonpoint designation.

Abstract: Plastics are accumulating in the environment, and due to their extremely low biodegradability, this issue is expected to persist for centuries. Historically, oceans were used as dumping grounds for waste, leading to the accumulation of long-lasting materials that now cause severe pollution problems. Macro- and microplastic waste pose serious environmental, social, and economic threats, such as injuring or killing marine organisms and entering the food chain, resulting in potential health risks for humans. Microplastics have become one of the most critical global concerns, as they disrupt the balance of terrestrial and marine ecosystems. The growing presence of microplastics in the environment threatens biodiversity and endangers vulnerable marine species. Moreover, their ingestion by marine organisms can impact the entire food chain, affecting both wildlife and human health. Addressing this challenge requires the development of efficient and sustainable solutions for the control and mitigation of microplastics. This study focuses on the advancement of filtration processes and membrane technologies specifically designed to capture and remove microplastics based on their size, quantity, and origin. By evaluating the performance and suitability of various filtration methods, this research seeks to promote effective recovery, control, and final elimination of microplastics while increasing awareness of sustainable environmental management practices.

Abstract: The Eastern Mediterranean is a rapidly warming climate-change hotspot where heat and air pollution increasingly interact to affect human health. This study quantifies the mortality burden attributed to the synergistic effects of thermal stress and air pollution in Thessaloniki, Greece. Daily mortality data (2001–2019) were analyzed together with pollutant concentrations (PM10, NO₂, O₃) and the modified Physiologically Equivalent Temperature (mPET) using a hierarchical Generalized Additive Model with Distributed Lag Non-Linear terms to capture combined, lagged, and age-specific responses. A re-fined, count-independent definition of the Attributable Fraction (AF) was introduced to improve stability in small strata. Results show that heat and pollution act synergistically, explaining on average 20–30% of daily mortality during severe co-occurrence events. Seniors were most affected during hot, polluted summers (AF ≈ 27%), while adults showed higher burdens during cold, polluted winters (AF ≈ 30%). Intra-urban analyses revealed stronger compound effects in the western, more industrial districts, reflecting combined environmental and socioeconomic vulnerability. The findings demonstrate that temperature extremes amplify pollution-related mortality and underline the need to integrate air-quality and bioclimatic indicators into early-warning and adaptation systems in Eastern Mediterranean cities.

Abstract: Microplastics represent an emerging threat to marine ecosystems, human health, and coastal aesthetics, with increasing concern about their accumulation on beaches due to ocean currents, wave action, and accidental spills. Despite their environmental impact, current methods for detecting and quantifying microplastics remain largely manual, time-consuming, and spatially limited. In this study, we propose a deep learning-based approach for the semantic segmentation of microplastics on sandy beaches, enabling pixel-level localization of small particles under real-world conditions. Twelve segmentation models were evaluated, including U-Net and its variants (Attention U-Net, ResUNet), as well as state-of-the-art architectures such as LinkNet, PAN, PSPNet, and YOLOv11 with segmentation heads. Models were trained and tested on augmented data patches, and their performance was assessed using Intersection over Union (IoU) and Dice coefficient metrics. LinkNet achieved the best performance with a Dice coefficient of 80% and an IoU of 72.6% on the test set, showing superior capability in segmenting microplastics even in the presence of visual clutter such as debris or sand variation. Qualitative results support the quantitative findings, highlighting the robustness of the model in complex scenes.

Abstract: Wastewater treatment systems retain a significant proportion of microplastics (MPs) derived from domestic and industrial discharges; however, these emerging pollutants are not completely removed and tend to accumulate in the biological sludge generated during the treatment process. In this study, three biological-type wastewater treatment plants (WWTPs) located in Acapulco, Mexico, were analyzed. The concentrations of MPs in the biological sludge ranged from 830 to 9,300 items per liter. Using differential scanning calorimetry (DSC), the predominant polymers identified were high-density polyethylene (HDPE), polyethylene terephthalate (PET), and polypropylene (PP). It was estimated that the monthly concentrations of MPs in the sludge could reach up to 5.36 × 109 items/liter, while the annual concentrations could rise to 3.55 × 1010 items/liter. These findings highlight the urgent need to review and update the regulatory framework re-lated to the use of residual sludge for agricultural purposes since high loads of MPs and their transfer pose a potential risk to soil quality, ecosystem health, and long-term en-vironmental sustainability.