Long-term impact of basin-wide wastewater management on faecal pollution levels along the entire Danube River.

The Danube River is, at 2857 km, the second longest river in Europe and the most international river in the world with 19 countries in its catchment. Along the entire river, faecal pollution levels are mainly influenced by point-source emissions from treated and untreated sewage of municipal origin under base-flow conditions. In the past 2 decades, large investments in wastewater collection and treatment infrastructure were made in the European Union (EU) Member States located in the Danube River Basin (DRB). Overall, the share of population equivalents with appropriately biologically treated wastewater (without disinfection) has increased from 69% to more than 85%. The proportion of tertiary treatment has risen from 46 to 73%. In contrast, no comparable improvements of wastewater infrastructure took place in non-EU Member States in the middle and lower DRB, where a substantial amount of untreated wastewater is still directly discharged into the Danube River. Faecal pollution levels along the whole Danube River and the confluence sites of the most important tributaries were monitored during four Danube River expeditions, the Joint Danube Surveys (JDS). During all four surveys, the longitudinal patterns of faecal pollution were highly consistent, with generally lower levels in the upper section and elevated levels and major hotspots in the middle and lower sections of the Danube River. From 2001 to 2019, a significant decrease in faecal pollution levels could be observed in all three sections with average reduction rates between 72 and 86%. Despite this general improvement in microbiological water quality, no such decreases were observed for the highly polluted stretch in Central Serbia. Further improvements in microbiological water quality can be expected for the next decades on the basis of further investments in wastewater infrastructure in the EU Member States, in the middle and lower DRB. In the upper DRB, and due to the high compliance level as regards collection and treatment, improvements can further be achieved by upgrading sewage treatment plants with quaternary treatment steps as well as by preventing combined sewer overflows. The accession of the Western Balkan countries to the EU would also significantly boost investments in wastewater infrastructure and water quality improvements in the middle section of the Danube. Continuing whole-river expeditions such as the Joint Danube Surveys is highly recommended to monitor the developments in water quality in the future.

Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model.

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

Examining the U-shaped relationship between non-agricultural sources water pollution and the urban-rural income gap.

Determining how the economy and society interact with the environment of water quality is essential to determining the financial impact of green development. Based on China's provincial panel data from 2010 to 2021, this research considers non-agricultural sources of water pollution (NASWP) as a negative factor of production, investigates its influence on the urban-rural divide, and explains the mechanism of action. The empirical results show that there is a significant correlation between NASWP and the urban-rural gap, with a "U-shaped" relationship between the two. Water pollution first reduces and then increases the urban-rural income gap, and the results are robust after considering endogeneity. Mechanistic research demonstrates that NASWP cause a loss in food output, which in combination with changes in food prices and food subsidy programs impacts the incomes of rural dwellers, thereby having an influence on the urban-rural income gap. Using the threshold effect model, it is discovered that under the combined influence of agricultural mechanization and food subsidy policy, the relationship between NASWP and urban-rural income divide exhibits an U-shape in areas with high agricultural mechanization and an "inverted U" shape in areas with low agricultural mechanization.

Unlocking policy effects: Water resources management plans and urban water pollution.

Water resource management, as a foundation for supporting sustainable urban development, has garnered increasing attention from scholars. Developing effective water resource management plans is a major challenge faced by countries worldwide. This study uses the 2015 Water Pollution Control and Prevention Action Plan (WPCAP) in China as a natural experiment and employs a Difference-in-Differences (DID) model to estimate the relationship between WPCAP and urban water pollution from 2010 to 2021. The findings are as follows: 1) WPCAP reduces water pollution. 2) WPCAP decreases water pollution in high-policy-pressure cities but increases water pollution in low-policy-pressure cities within a 60 km radius, particularly having a significantly negative impact on water pollution in low-policy-pressure cities with low altitude. 3) optimizing industrial and domestic water use, as well as enhancing sewage treatment capabilities, are crucial pathways through which WPCAP reduces water pollution. Additionally, WPCAP significantly improves water pollution control capabilities in cities with abundant water resources, large cities, and industrialized cities. 4) although WPCAP's ability to control water pollution increases management costs, it also raises residential income and promotes population growth. These findings have important implications for the sustainable development of water resources in emerging countries, including China.

Evaluating water-related health risks in East and Central Asian Islamic Nations using predictive models (2020-2030).

This paper presents a thorough evaluation of health outcomes linked to water-related challenges in Islamic nations across East Asia and Central Asia from 2020 to 2030. It has been examined carefully that the trajectory of deaths and disability-adjusted life years associated with unsafe water sources, lack of sanitation, and absence of handwashing facilities is showing a potential rise in negative health impacts due to water pollution. The direct health influences of water-related problems are thoughtful. The increase in deaths and DALYs due to poor water quality and sanitation leads to a higher occurrence of waterborne diseases such as cholera, diarrhea, and dysentery. These conditions not only cause instant health disasters but also subsidize to long-term health issues which include chronic gastrointestinal disorders and malnutrition that is particularly among susceptible populations like children and the elderly. Employing various predictive models including autoregressive integrated moving average, exponential smoothing, support vector machines, and neural networks. The study evaluates their predictive capabilities by using mean absolute percentage error. Support vector machines is found to be the most accurate in forecasting deaths and disability-adjusted life years which is outperforming autoregressive integrated moving average, exponential smoothing, and neural networks. This research aims to inform stakeholders by providing insights into effective strategies for improving water resource management and public health interventions in the targeted regions.

Greenhouse gas emission potential of tropical coastal sediments in densely urbanized area inferred from metabarcoding microbial community data.

Although benthic microbial community offers crucial insights into ecosystem services, they are underestimated for coastal sediment monitoring. Sepetiba Bay (SB) in Rio de Janeiro, Brazil, holds long-term metal pollution. Currently, SB pollution is majorly driven by domestic effluents discharge. Here, functional prediction analysis inferred from 16S rRNA gene metabarcoding data reveals the energy metabolism profiles of benthic microbial assemblages along the metal pollution gradient. Methanogenesis, denitrification, and N2 fixation emerge as dominant pathways in the eutrophic/polluted internal sector (Spearman; p < 0.05). These metabolisms act in the natural attenuation of sedimentary pollutants. The methane (CH4) emission (mcr genes) potential was found more abundant in the internal sector, while the external sector exhibited higher CH4 consumption (pmo + mmo genes) potential. Methanofastidiosales and Exiguobacterium, possibly involved in CH4 emission and associated with CH4 consumers respectively, are the main taxa detected in SB. Furthermore, SB exhibits higher nitrous oxide (N2O) emission potential since the norB/C gene proportions surpass nosZ up to 4 times. Blastopirellula was identified as the main responsible for N2O emissions. This study reveals fundamental contributions of the prokaryotic community to functions involved in greenhouse gas emissions, unveiling their possible use as sentinels for ecosystem monitoring.

Development of a "big data" groundwater microbial contamination index and spatial comparisons with enteric infection rates in southern Ontario.

Big data have become increasingly important for policymakers and scientists but have yet to be employed for the development of spatially specific groundwater contamination indices or protecting human and environmental health. The current study sought to develop a series of indices via analyses of three variables: Non-E. coli coliform (NEC) concentration, E. coli concentration, and the calculated NEC:E. coli concentration ratio. A large microbial water quality dataset comprising 1,104,094 samples collected from 292,638 Ontarian wells between 2010 and 2021 was used. Getis-Ord Gi* (Gi*), Local Moran's I (LMI), and space-time scanning were employed for index development based on identified cluster recurrence. Gi* and LMI identify hot and cold spots, i.e., spatially proximal subregions with similarly high or low contamination magnitudes. Indices were statistically compared with mapped well density and age-adjusted enteric infection rates (i.e., campylobacteriosis, cryptosporidiosis, giardiasis, verotoxigenic E. coli (VTEC) enteritis) at a subregional (N = 298) resolution for evaluation and final index selection. Findings suggest that index development via Gi* represented the most efficacious approach. Developed Gi* indices exhibited no correlation with well density, implying that indices are not biased by rural population density. Gi* indices exhibited positive correlations with mapped infection rates, and were particularly associated with higher bacterial (Campylobacter, VTEC) infection rates among younger sub-populations (p < 0.05). Conversely, no association was found between developed indices and giardiasis rates, an infection not typically associated with private groundwater contamination. Findings suggest that a notable proportion of bacterial infections are associated with groundwater and that the developed Gi* index represents an appropriate spatiotemporal reflection of long-term groundwater quality. Bacterial infection correlations with the NEC:E. coli ratio index (p < 0.001) were markedly different compared to correlations with the E. coli index, implying that the ratio may supplement E. coli monitoring as a groundwater assessment metric capable of elucidating contamination mechanisms. This study may serve as a methodological blueprint for the development of big data-based groundwater contamination indices across the globe.

Assessing Chlorophyll-a Variations in Caspian Sea during the COVID-19 Pandemic.

The COVID-19 pandemic's disruptions to human activities prompted serious environmental changes. Here, we assessed the variations in coastal water quality along the Caspian Sea, with a focus on the Iranian coastline, during the lockdown. Utilizing Chlorophyll-a data from MODIS-AQUA satellite from 2015 to 2023 and Singular Spectrum Analysis for temporal trends, we found a 22% Chlorophyll-a concentration decrease along the coast, from 3.2 to 2.5 mg/m³. Additionally, using a deep learning algorithm known as Long Short-Term Memory Networks, we found that, in the absence of lockdown, the Chlorophyll-a concentration would have been 20% higher during the 2020-2023 period. Furthermore, our spatial analysis revealed that 98% of areas experienced about 18% Chlorophyll-a decline. The identified improvement in coastal water quality presents significant opportunities for policymakers to enact regulations and make local administrative decisions aimed at curbing coastal water pollution, particularly in areas experiencing considerable anthropogenic stress.

Integration of hydrodynamic and water quality modeling to mitigate the effects of spill pollution into the Nile River, Egypt.

Mitigating spill pollution in the Nile River is crucial to protecting aquatic life, water quality, and public health. Extensive studies focused on the assessment of water quality and hydrodynamics of the Nile River, but there have been relatively few studies that have applied integrated hydrodynamic and water quality modeling approaches to simulate actual accidents in the Nile Fourth Reach. The goal of this study is to develop advanced computational models to simulate accidental spills in the Nile River and track the resulting impacts on water quality. Hydrodynamic and water quality simulations were performed using Delft3D software for 144 km of the Nile River, Egypt, from El-Menia to Assuit. Once the hydrodynamic and water quality models were calibrated, two phosphate spill scenarios were modeled under maximum and minimum flow conditions. The spatial distribution of the spill plume along the studied river section was visualized every 12 h following the spill occurrence for both scenarios. The results of the research were calibrated and validated against measured field data. In addition, various error and performance indicators were calculated to thoroughly assess the rigor and reliability of the results. The results demonstrated that flow velocity was the main factor influencing the spill plume characteristics and behavior. Initially, advection force plays a significant role after a spill occurs. After that, phosphate becomes mixed and diluted through dispersion. The spill plume took less time to reach downstream areas during the period of maximum flow compared to minimum flow. Additionally, the concentration of phosphate decreased as the water flowed downstream. The spatial distribution of the spill over time can assist water treatment facilities in developing mitigation strategies to address the spill impacts. However, complex Nile River dynamics demand extensive computational power. Therefore, the model was simplified for spill events, using the modeling capabilities to analyze hypothetical spills and contaminant spread in the absence of real data.

Evaluating the surface water pollution risk of mineral resource exploitation via an improved approach: a case study in Liaoning Province, Northeastern China.

Pollution from mineral exploitation is an important risk factor affecting surface water environment in mineral regions. It is urgent to construct a simple and accurate model to assess the surface water pollution risk from mineral exploitation in the regional scale. Thus, taking a mining province namely Liaoning in northeastern China as the study area, we proposed a framework to simulate the transport process of pollutants from mineral exploitation points to the surrounding surface water based on the "source-sink" theory. In our framework, we adopted the regional growth method (RGM) to extract the potential polluted water area as the certain "sink" considering the influence of the topography, and then applied Minimum Cumulative Resistance (MCR) model to assess the surface water pollution risk from mineral exploitation. The results revealed that: (1) 9.5% of the water areas were located at the potential impact area of MEPs. (2) The total value of resistance surface in Liaoning is relatively low, and gradually decreased from west to east. (3) MEPs in Liaoning had a high risk and seriously threatened the surface water environment, among 2125 MEPs, 733 MEPs (32.99%) were assessed as extremely high risk level, and about 35% of the MEPs were distributed within 10KM buffer zone of surface water. (4) Water pollution risk of MEPs in Dalian, Tieling, Fuxin and Dandong need to be emphasized. (5) Compared to previous studies, we considered the topographical influence before applying MCR model directly, so the results of water pollution risk were more reliable. This study provides a methodological support and scientific reference for the water environment protection and regional sustainable development.

Assessing groundwater quality and its association with child undernutrition in India.

BACKGROUND AND OBJECTIVES: Groundwater contamination poses a significant health challenge in India, particularly impacting children. Despite its importance, limited research has explored the nexus between groundwater quality and child nutrition outcomes. This study addresses this gap, examining the association between groundwater quality and child undernutrition, offering pertinent insights for policymakers. DATA AND METHODS: The study uses data from the fifth round of the National Family Health Survey (NFHS) and the Central Groundwater Board (CGWB) to analyze the association between groundwater quality and child nutritional status. The groundwater quality data were collected by nationwide monitoring stations programmed by CGWB, and the child undernutrition data were obtained from the NFHS-5, 2019-21. The analysis included descriptive and logistic regression model. The study also considers various demographic and socio-economic factors as potential moderators of the relationship between groundwater quality and child undernutrition. FINDINGS: Significant variation in groundwater quality was observed across India, with numerous regions displaying poor performance. Approximately 26.53 % of geographical areas were deemed unfit for consuming groundwater. Environmental factors such as high temperatures, low precipitation, and arid, alluvial, laterite-type soils are linked to poorer groundwater quality. Unfit-for-consumption groundwater quality increased the odds of undernutrition, revealing a 35 %, 38 %, and 11 % higher likelihood of stunting, underweight, and wasting in children, with higher pH, Magnesium, Sulphate, Nitrate, Total Dissolved Solids, and Arsenic, levels associated with increased odds of stunting, underweight, and wasting. Higher temperatures (>25 °C), high elevations (>1000 m), and proximity to cultivated or industrial areas all contribute to heightened risks of child undernutrition. Children consuming groundwater, lacking access to improved toilets, or living in rural areas are more likely to be undernourished, while females, higher-income households, and those consuming dairy, vegetables, and fruits daily exhibit lower odds of undernutrition. POLICY IMPLICATIONS: Policy implications highlight the urgent need for investment in piped water supply systems. Additionally, focused efforts are required to monitor and improve groundwater quality in regions with poor water quality. Policies should emphasize safe sanitation practices and enhance public awareness about the critical role of safe drinking water in improving child health.

Regulating greenhouse gas dynamics in tidal wetlands: Impacts of salinity gradients and water pollution.

Tidal wetlands play a critical role in emitting greenhouse gases (GHGs) into the atmosphere; our understanding of the intricate interplay between natural processes and human activities shaping their biogeochemistry and GHG emissions remains lacking. In this study, we delve into the spatiotemporal dynamics and key drivers of the GHG emissions from five tidal wetlands in the Scheldt Estuary by focusing on the interactive impacts of salinity and water pollution, two factors exhibiting contrasting gradients in this estuarine system: pollution escalates as salinity declines. Our findings reveal a marked escalation in GHG emissions when moving upstream, primarily attributed to increased concentrations of organic matter and nutrients, coupled with reduced levels of dissolved oxygen and pH. These low water quality conditions not only promote methanogenesis and denitrification to produce CH4 and N2O, respectively, but also shift the carbonate equilibria towards releasing more CO2. As a result, the most upstream freshwater wetland was the largest GHG emitter with a global warming potential around 35 to 70 times higher than the other wetlands. When moving seaward along a gradient of decreasing urbanization and increasing salinity, wetlands become less polluted and are characterized by lower concentrations of NO3-, TN and TOC, which induces stronger negative impact of elevated salinity on the GHG emissions from the saline wetlands. Consequently, these meso-to polyhaline wetlands released considerably smaller amounts of GHGs. These findings emphasize the importance of integrating management strategies, such as wetland restoration and pollution prevention, that address both natural salinity gradients and human-induced water pollution to effectively mitigate GHG emissions from tidal wetlands.

Anthropogenic pollution may enhance natural transformation in water, favouring the spread of antibiotic resistance genes.

Aquatic ecosystems are crucial in the antimicrobial resistance cycle. While intracellular DNA has been extensively studied to understand human activity's impact on antimicrobial resistance gene (ARG) dissemination, extracellular DNA is frequently overlooked. This study examines the effect of anthropogenic water pollution on microbial community diversity, the resistome, and ARG dissemination. We analyzed intracellular and extracellular DNA from wastewater treatment plant effluents and lake surface water by shotgun sequencing. We also conducted experiments to evaluate anthropogenic pollution's effect on transforming extracellular DNA (using Gfp-plasmids carrying ARGs) within a natural microbial community. Chemical analysis showed treated wastewater had higher anthropogenic pollution-related parameters than lake water. The richness of microbial community, antimicrobial resistome, and high-risk ARGs was greater in treated wastewaters than in lake waters both for intracellular and extracellular DNA. Except for the high-risk ARGs, richness was significantly higher in intracellular than in extracellular DNA. Several ARGs were associated with mobile genetic elements and located on plasmids. Furthermore, Gfp-plasmid transformation within a natural microbial community was enhanced by anthropogenic pollution levels. Our findings underscore anthropogenic pollution's pivotal role in shaping microbial communities and their antimicrobial resistome. Additionally, it may facilitate ARG dissemination through extracellular DNA plasmid uptake.

Remotely sensed estimates of long-term biochemical oxygen demand over Hong Kong marine waters using machine learning enhanced by imbalanced label optimisation.

In many coastal cities around the world, continuing water degradation threatens the living environment of humans and aquatic organisms. To assess and control the water pollution situation, this study estimated the Biochemical Oxygen Demand (BOD) concentration of Hong Kong's marine waters using remote sensing and an improved machine learning (ML) method. The scheme was derived from four ML algorithms (RBF, SVR, RF, XGB) and calibrated using a large amount (N > 1000) of in-situ BOD5 data. Based on labeled datasets with different preprocessing, i.e., the original BOD5, the log10(BOD5), and label distribution smoothing (LDS), three types of models were trained and evaluated. The results highlight the superior potential of the LDS-based model to improve BOD5 estimate by dealing with imbalanced training dataset. Additionally, XGB and RF outperformed RBF and SVR when the model was developed using log10(BOD5) or LDS(BOD5). Over two decades, the BOD5 concentration of Hong Kong marine waters in the autumn (Sep. to Nov.) shows a downward trend, with significant decreases in Deep Bay, Western Buffer, Victoria Harbour, Eastern Buffer, Junk Bay, Port Shelter, and the Tolo Harbour and Channel. Principal component analysis revealed that nutrient levels emerged as the predominant factor in Victoria Harbour and the interior of Deep Bay, while chlorophyll-related and physical parameters were dominant in Southern, Mirs Bay, Northwestern, and the outlet of Deep Bay. LDS provides a new perspective to improve ML-based water quality estimation by alleviating the imbalance in the labeled dataset. Overall, the remotely sensed BOD5 can offer insight into the spatial-temporal distribution of organic matter in Hong Kong coastal waters and valuable guidance for the pollution control.

Quantitative source apportionment of faecal indicator bacteria from anthropogenic and zoogenic sources of faecal contamination.

Recreational bathing waters are complex systems with diverse inputs from multiple anthropogenic and zoogenic sources of faecal contamination. Faecal contamination is a substantial threat to water quality and public health. Here we present a comprehensive strategy to estimate the contribution of faecal indicator bacteria (FIB) from different biological sources on two at-risk beaches in Dublin, Ireland. The daily FIB loading rate was determined for three sources of contamination: a sewage-impacted urban stream, dog and wild bird fouling. This comparative analysis determined that the stream contributed the highest daily levels of FIB, followed by dog fouling. Dog fouling may be a significant source of FIB, contributing approximately 20 % of E. coli under certain conditions, whereas wild bird fouling contributed a negligible proportion of FIB (<3 %). This study demonstrates that source-specific quantitative microbial source apportionment (QMSA) strategies are vital to identify primary public health risks and target interventions to mitigate faecal contamination.

A multi-tiered approach to assess fecal pollution in an urban watershed: Bacterial and viral indicators and sediment microbial communities.

Development of effective pollution mitigation strategies require an understanding of the pollution sources and factors influencing fecal pollution loading. Fecal contamination of Turkey Creek in Gulfport, Mississippi, one of the nation's most endangered creeks, was studied through a multi-tiered approach. Over a period of approximately two years, four stations across the watershed were analyzed for nutrients, enumeration of E. coli, male-specific coliphages and bioinformatic analysis of sediment microbial communities. The results demonstrated that two stations, one adjacent to a lift station and one just upstream from the wastewater-treatment plant, were the most impacted. The station adjacent to land containing a few livestock was the least impaired. While genotyping of male-specific coliphage viruses generally revealed a mixed viral signature (human and other animals), fecal contamination at the station near the wastewater treatment plant exhibited predominant impact by municipal sewage. Fecal indicator loadings were positively associated with antecedent rainfall for three of four stations. No associations were noted between fecal indicator loadings and any of the nutrients. Taxonomic signatures of creek sediment were unique to each sample station, but the sediment microbial community did overlap somewhat following major rain events. No presence of Escherichia coli (E. coli) or enterococci were found in the sediment. At some of the stations it was evident that rainfall was not always the primary driver of fecal transport. Repeated monitoring and analysis of a variety of parameters presented in this study determined that point and non-point sources of fecal pollution varied spatially in association with treated and/or untreated sewage.

The collateral effects of COVID-19 on marine pollution.

The COVID-19 pandemic has gained significant attention to the intersection of public health crises and environmental challenges, particularly in the context of marine pollution. This paper examines the various impacts of the pandemic on marine environments, focusing on the pollution attributed to single-use plastics (SUPs) and personal protective equipment (PPE). Drawing on a comprehensive analysis of literature and case studies, the paper highlights the detrimental effects of increased plastic waste on marine ecosystems, biodiversity, and human health. Statistical data and graphical representations reveal the scale of plastic pollution during the pandemic, emphasizing the urgent need for mitigation strategies. The study evaluates innovative monitoring techniques and future recommendations, emphasizing stakeholder collaboration in sustainable waste management. By broadening geographic examples and comparative analyses, it provides a global perspective on the pandemic's impact, highlighting the importance of international cooperation for safeguarding marine ecosystems.