Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks.

The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the landfill needs more precise management. To solve this problem, based on the "pressure-state-response" framework, a method for the identification and evaluation of groundwater pollution around the landfill was constructed. The LPI method was used to assess the contamination potential of the leachate. The comprehensive quality of groundwater was evaluated by the entropy-AHP water quality assessment method, sodium adsorption ratio and sodium percentage. The probabilistic health risks of groundwater were assessed based on a Monte Carlo algorithm. The sources of pollutants were identified by comprehensively using the PCA-APCS-MLR model and the PMF model. Finally, the self-organizing map algorithm and the Kmeans algorithm were integrated to enhance the precision of groundwater management and control measures. The results showed that the leachate of the landfill was in the mature stage, and the concentration of inorganic substances was relatively high. Leachate had the potential to contaminate surrounding groundwater. The groundwater quality of 68.14% of the study area was in the poor or lower level. The groundwater near the landfill was unsuitable not only for drinking but also for irrigation purposes. Cl- was the main non-carcinogenic risk factor. Reducing pollutant concentration and controlling exposure time are effective strategies for mitigating health risks caused by high-concentration pollutants (Cl-, NO3-) and low-concentration pollutants (F-), respectively. The groundwater around the landfill was jointly affected by six pollution sources. The PMF model has better analytical ability in mixed pollution areas. The groundwater in the study area was divided into five clusters, of which cluster Ⅰ was significantly affected by leachate, and cluster Ⅴ had the lowest pollution and health risk.

Causal diagnostic model and governance strategies to reduce pollution from ship accidents in Chinese waters.

Ship transportation is a primary mode for global trade and cargo transport, yet even minor discrepancies can lead to ship accidents, causing severe secondary environmental pollution. Maritime accidents involve complex and numerous factors. Formal Concept Analysis (FCA) can identify the key contributing factors and their impact levels by eliminating homogenization factors in maritime accidents. This study constructs an innovative FCA model of ship accidents in Chinese waters, utilizing 172 ship accident reports released by the China Maritime Safety Administration. The analysis reveals seven reduced sets and 23 diagnostic rules of ship accidents. Results show that failed ship registration/security inspection, deficient nautical data and instruments, and management issues are the most critical factors. Three accident chains are identified and corresponding mitigation strategies are proposed to reduce potential pollution from ship accidents. These strategies offer significant reference value for preventing ship accidents and reducing their environmental impact in China and globally.

Quantitative fecal pollution assessment with bacterial, viral, and molecular methods in small stream tributaries.

Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under 'wet' and 'dry' conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.

Native and non-native freshwater bivalves in the bioremediation of bacterial pollution caused by the disposal of sewage.

Treated sewage contains a large diversity of pathogens that can be transmitted to the environment and, directly or indirectly, infect humans through water use (i.e., consumption, bathing, or irrigation). In urban environments, wastewater normally flows into wastewater treatment plants (WWTPs), where it is subjected to different processes in order to eliminate the greatest amount of waste. However, there are inequalities among European countries concerning wastewater management. In this context, we evaluate the potential of freshwater mussels to improve water quality (i.e., reduce bacterial abundance) in rivers receiving primary, secondary, or tertiary sewage-treated effluents. Additionally, because freshwater mussels are declining at a global scale and empty niches are progressively occupied by non-native counterparts, we evaluate if depauperate communities and the Asian clams, Corbicula genus, can provide equivalent ecosystem services (i.e., water quality improvement by biofiltration) formerly provided by diverse native communities. For this, an analysis of the bacterial biodiversity of the samples filtered by the different bivalve communities was carried out. The experimental approach was performed by metabarcoding the 16S rRNA gene using Illumina technologies. According to the results obtained, secondary treatment processes were effective in reducing the bacterial diversity. Furthermore, the waters filtered by the bivalves presented a lower bacterial abundance for certain genera. Biofiltration differs, however, among species, with Corbicula reducing a large number of taxa much more efficiently than native freshwater mussels in both diverse and depauperated communities. These results are likely related to Corbicula being a generalist species in front of native mussels, which may be more selective. Considering it is not possible to eradicate Corbicula from European rivers, its filtering capacity should be considered when managing freshwater ecosystems.

A critical review of pollution active biomonitoring using sentinel fish: Challenges and opportunities.

Water pollution is a significant threat to aquatic ecosystems. Various methods of monitoring, such as in situ approaches, are currently available to assess its impact. In this paper we examine the use of fish in active biomonitoring to study contamination and toxicity of surface waters. We analysed 148 previous studies conducted between 2005 and 2022, including both marine and freshwater environments, focusing on the characteristics of the organisms used as well as the principal goals of these studies. The main conclusions we drew are that a wide range of protocols and organisms have been used but there is no standardised method for assessing the quality of aquatic ecosystems on a more global scale. Additionally, the most commonly used developmental stages have been juveniles and adults. At these stages, the most frequently used species were the fathead minnow (Pimephales promelas) and two salmonids: rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta). Few studies used earlier stages of development (embryos or larvae), mostly due to the difficulty of obtaining fish embryos and caging them in the field. Finally, we identified research gaps in active biomonitoring for water quality assessment which could indicate useful directions for future research and development.

The collateral effects of COVID-19 on coastal and marine environments.

The COVID-19 pandemic has generated a global health and economic crisis, significantly impacting coastal and marine environments. Lockdowns and restrictions, while necessary for public health, led to both positive and negative environmental consequences. Reduced human activity resulted in decreased pollution and habitat disruption, allowing for ecosystem recovery and improved water quality. However, the surge in single-use plastics and personal protective equipment (PPE) during the pandemic exacerbated plastic pollution. Additionally, the economic downturn severely affected coastal communities reliant on tourism and fisheries, highlighting the need for sustainable recovery strategies. This Special Issue explores these collateral effects, emphasizing the importance of adaptive management and resilient governance in safeguarding coastal and marine ecosystems against future crises.

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

Identify the seasonal differences in water quality and pollution sources between river-connected and gate-controlled lakes in the Yangtze River basin.

The river-connected Dongting Lake (DT) and Poyang Lake (PY), and the gate-controlled Taihu Lake (TH) and Chaohu Lake (CH) are the four important lakes in the Yangtze River Basin. The comprehensive Water Quality Index (WQI), the Eutrophication Integrated Index (TLI(Σ)), and the Positive Matrix Factorization (PMF) model were employed to evaluate water quality and the contribution of pollution sources for these lakes. The results show that WQI for all lakes indicated generally good water quality, with DT scoring 73.52-86.18, the highest among them. During the wet season, the eutrophication degree of river-connected lake was medium, and that of gate-controlled lakes was high. The surface runoff and agricultural non-point sources are the main pollution sources for both types of lakes, but their impact is more pronounced in gate-controlled lakes during the wet season. The study provides evidence support for scientific understanding of water quality problems and management strategies in these areas.

The Brazilian National Plan to Combat Marine Litter: A critical assessment.

The Brazilian National Plan to Combat Marine Litter was launched in March 2019 and in its context, 577 campaigns were carried out to clean up beaches, rivers, estuaries and mangroves, until January 2023, at an estimated cost of US$ 16,733,000.00. The volume collected in four years represented only 0.0041 % of the total solid waste discharged annually in the maritime waters under the country's jurisdiction. The direct benefit to society was only US$ 173,751.41 at market value for the litter collected. Litter lesser than 2.5 cm was left out, not been characterized. The results achieved deserve reflection and critical evaluation, in order to allow improvements, serving as reference. The participation of around 43,000 people in cleanup campaigns is an indication of environmental citizenship and an asset that cannot be overlooked by managers, demanding focus, tangible results and benefits in terms of environmental quality, well-being and savings on public cleaning services costs.

Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method.

The escalating growth of the global population has led to degraded water quality, particularly in seawater environments. Water quality monitoring is crucial to understanding the dynamic changes and implementing effective management strategies. In this study, water samples from the southwestern regions of Iran were spatially analyzed in a GIS environment using geostatistical methods. Subsequently, a water quality map was generated employing large and small fuzzy membership functions. Additionally, advanced prediction models using neural networks were employed to forecast future water pollution trends. Fuzzy method results indicated higher pollution levels in the northern regions of the study area compared to the southern parts. Furthermore, the water quality prediction models demonstrated that the LSTM model exhibited superior predictive performance (R2 = 0.93, RMSE = 0.007). The findings also underscore the impact of urbanization, power plant construction (2010 to 2020), and inadequate urban wastewater management on water pollution in the studied region.

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.

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.

Microplastic ingestion by sea turtles around Tokyo Bay: Level of water pollution influences ingestion amounts.

We investigated the contents inside the esophagus and stomach of turtles inside and outside of the Tokyo Bay area, which face high and low risks of microplastic (MP) exposure, respectively. 65 synthetic particles were recovered from 8 out of 22 turtles, using ATR-FTIR followed by density separation with calcium chloride solution. Statistical analysis indicated that turtles in high-risk areas ingested significantly more MPs than those in low-risk areas. As the inflow of MPs from major rivers influences pollution levels in the ocean, the results of this study highlight the importance of major rivers for MP ingestion by turtles. Additionally, we discussed the current methodology's shortcomings and addressed scope for subsequent research, along with suggestions on future conservation.

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.

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.

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.

Tide of change: Urgency of a national marine litter policy in India.

The rise of plastics in the 20th century revolutionized modern life but inadvertently exacerbated the marine litter crisis. The proliferation of wastes such as single-use plastics has escalated pollution along the coastline, demanding coordinated, decisive, and unified action. While the absence of specific national marine litter policies is concerning, there is growing recognition of urgency to address this issue. A group of experts and stakeholders was involved through a multi-staged workshop to assess the generated information based on scientific evidence and formulate a framework for the National Marine Litter Policy (NMLP). This paper proposes policy options (4 targets & 20 strategies) to address marine litter pollution, especially plastics, and aims to elucidate the urgency and significance of implementing an NMLP as a comprehensive strategy to combat plastics pollution. Implementing dedicated policies and action plans tailored to the unique challenges faced by each country is a vital step towards sustainable oceans.

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.

Deciphering assembly processes, network complexity and stability of potential pathogenic communities in two anthropogenic coastal regions of a highly urbanized estuary.

The existence of potential pathogens may lead to severe water pollution, disease transmission, and the risk of infectious diseases, posing threats to the stability of aquatic ecosystems and human health. In-depth research on the dynamic of potential pathogenic communities is of significant importance, it can provide crucial support for assessing the health status of aquatic ecosystems, maintaining ecological balance, promoting sustainable economic development, and safeguarding human health. Nevertheless, the current understanding of the distribution and geographic patterns of potential pathogens in coastal ecosystems remains rather limited. Here, we investigated the diversity, assembly, and co-occurrence network of potential pathogenic communities in two anthropogenic coastal regions, i.e., the eight mouths (EPR) and nearshore region (NSE), of the Pearl River Estuary (PRE) and a total of 11 potential pathogenic types were detected. The composition and diversity of potential pathogenic communities exhibited noteworthy distinctions between the EPR and NSE, with 6 shared potential pathogenic families. Additionally, in the NSE, a significant pattern of geographic decay was observed, whereas in the EPR, the pattern of geographic decay was not significant. Based on the Stegen null model, it was noted that undominant processes (53.36%/69.24%) and heterogeneous selection (27.35%/25.19%) dominated the assembly of potential pathogenic communities in EPR and NSE. Co-occurrence network analysis showed higher number of nodes, a lower average path length and graph diameter, as well as higher level of negative co-occurrences and modularity in EPR than those in NSE, indicating more complex and stable correlations between potential pathogens in EPR. These findings lay the groundwork for the effective management of potential pathogens, offering essential information for ecosystem conservation and public health considerations in the anthropogenic coastal regions.