Leachate derived humic-like substances drive the variation in microbial communities in landfill-affected groundwater.

Landfills are commonly used for waste disposal in many countries, and pose a significant threat of groundwater contamination. Dissolved organic matter (DOM) plays a crucial role as a carbon and energy source, supporting the growth and activity of microorganisms. However, the changes in the DOM signature and microbial community composition in landfill-affected groundwater and their bidirectional relationships remain inadequately explored. Herein, we showed that DOM originating from more recent landfills mainly comprises microbially produced substances resembling tryptophan and tyrosine. Conversely, DOM originating from older landfills predominantly comprises fulvic-like and humic-like compounds. Leachate leakage increases microbial diversity and richness and facilitates the transfer of foreign bacteria from landfills to groundwater, thereby increasing the vulnerability of the microbial ecosystem in groundwater. Deterministic processes dominated the assembly of the groundwater microbial community, while stochastic processes accounted for an increased proportion of the microbial community in the old landfills. The dominant phyla observed in groundwater were Proteobacteria, Bacteroidota, and Actinobacteriota, and humic-like substances play a crucial role in driving the variation in microbial communities in landfill-affected groundwater. Predictions using PICRUSt2 suggested significant associations between various metabolic pathways and microbial communities, with the Kyoto Encyclopedia of Genes and Genomes pathway "Metabolism" being the most predominant. The findings contribute to advancing our understanding of the transformation of DOM and its interplay with microbial communities and can serve as a scientific reference for decision-making regarding groundwater pollution monitoring and remediation.

Multifaceted anomaly detection framework for leachate monitoring in landfills.

The imperative to preserve environmental resources has transcended traditional conservation efforts, becoming a crucial element for sustaining life. Our deep interconnectedness with the natural environment, which directly impacts our well-being, emphasizes this urgency. Contaminants such as leachate from landfills are increasingly threatening groundwater, a vital resource that provides drinking water for nearly half of the global population. This critical environmental threat requires advanced detection and monitoring solutions to effectively safeguard our groundwater resources. To address this pressing need, we introduce the Multifaceted Anomaly Detection Framework (MADF), which integrates Electrical Resistivity Tomography (ERT) with advanced machine learning models-Isolation Forest (IF), One-Class Support Vector Machines (OC-SVM), and Local Outlier Factor (LOF). MADF processes and analyzes ERT data, employing these hybrid machine learning models to identify and quantify anomaly signals accurately via the majority vote strategy. Applied to the Chaling landfill site in Zhuzhou, China, MADF demonstrated significant improvements in detection capability. The framework enhanced the precision of anomaly detection, evidenced by higher Youden Index values (≈ 6.216%), with a 30% increase in sensitivity and a 25% reduction in false positives compared to traditional ERT inversion methods. Indeed, these enhancements are crucial for effective environmental monitoring, where the cost of missing a leak could be catastrophic, and for reducing unnecessary interventions that can be resource-intensive. These results underscore MADF's potential as a robust tool for proactive environmental management, offering a scalable and adaptable solution for comprehensive landfill monitoring and pollution prevention across varied environmental settings.

Using a combination of δ13CDIC-DOC-difference in dissolved inorganic and organic carbon, δ2H, and δ18O to localize leachate leaks at landfill sites in China.

The investigation of leachate leakage at numerous landfill sites is urgently needed. This study presents an exploration of environmental tracing methods using δ2H and δ13C-difference in dissolved carbon (δ13CDIC-DOC) to localize leachate leak points at landfill sites. δ2H, δ13CDIC, δ13CDOC, δ18O, and an array of physicochemical indices (e.g., total dissolved solids, temperature, and oxidation reduction potential) were monitored in both leachate and groundwater from different zones of a landfill site in China during the year of 2021-2023. Moreover, data for these parameters (i.e., the isotopic composition and physicochemical indices) from twelve published landfill cases were also collected, and these groundwater/leachate data points were located within 1 km away from the landfill boundary. Then statistical analyses, such as Pearson correlation analysis and redundancy analysis (RDA), were performed using both the detected and collected parameters at landfill sites. Consequently, the intensity of interaction between leachate and background groundwater was found to significantly control the isotopic fractionation features of hydrogen and carbon, and both the content of major contamination indicators (total dissolved solids, chemical oxygen demand, and ammoniacal nitrogen) and the oxidation reduction potential were the key impact factors. Accordingly, the water type used to indicate leachate leakage points was determined to be leachate that significantly interacted with the background groundwater or precipitation (LBGP). δ2H showed a perfect linear correlation (0.81 ≤ r2 < 1.0) with δ13CDIC-DOC in leachate under highly anaerobic landfill conditions, and the δ2H & δ13CDIC-DOC combinations in the LBGP were significantly different from those in the other water types. For groundwater with total dissolved solids lower than 1400 mg/L at landfill sites, a strong positive linear correlation (r = 0.83) was revealed between δ13CDIC and δ13CDOC. Based on these insights, δ2H versus δ13CDIC-DOC plots and RDA using δ2H and δ13CDIC-DOC as response variables were proposed to localize leak points at both lined landfills and leachate facilities. These findings further understanding of the isotopic fractionation features of hydrogen, carbon, and oxygen and provide novel environmental tracer methods for investigating leachate leak points at MSW landfill sites.

Migration and natural attenuation of leachate pollutants in bedrock fissure aquifer at a valley landfill site.

Groundwater pollution from valley type landfills is concerning, and natural attenuation by contaminants is increasingly relied upon. However, the reliability of natural attenuation in such complex sites has been called into question due to incomplete understanding of their attenuation mechanisms. Therefore, we conducted field investigations, monitoring analyses, mathematical statistics, and machine learning techniques to elucidate the natural attenuation mechanisms of pollutants within bedrock fissures at a prototypical valley type landfill located in the east Yanshan Mountains, China. Our results indicate that 50% of the monitored indicators showed extreme pollution in bedrock fissure aquifers, due to seepage from the valley type landfill site. Ammonia nitrogen, arsenic, cadmium, lead, iron, manganese, and mercury were among the contaminants that could pose serious risks to human health. Pollutant concentrations in bedrock fissure aquifers were lower during the rainy season compared to the dry season as the aquifer was rapidly recharged by strong rainfall runoff. The initial concentration of bedrock fissure water generally increased during the flow through the landfill. However, significant natural attenuation of total dissolved solids, oxygen consumption, ammonia, cadmium, and lead occurred after passing through the landfill (p<0.05), with attenuation coefficients of 0.0041 m-1, 2.56×E-5m-2, 4.18×E-5m-2、0.0015 m-0.99, and 6.83×E-33m-12.49, respectively. The driving mechanisms for natural attenuation include physical migration, leaching, microbiological degradation, and adsorption, primarily occurring within 600-650 m downstream of the landfill boundary. This study makes fundamental contribution to the understanding of the migration and natural attenuation process of leachate pollutants in bedrock fissure aquifer, which will provide a scientific basis for implementation of natural attenuation strategies in complex site remediation. Future research should examine more precise evidence of natural attenuation feasibility in complex sites in conjunction with monitoring networks.

Characteristic pollutants and microbial community in underlying soils for evaluating landfill leakage.

Leachate leakage poses a serious environmental risk to the safety of surrounding soils and groundwater. A much faster approach to reflect landfill leakage is the premise to mitigate the ecological risk of landfills. In this study, two landfills (BJ and WZ) were selected to investigate the leaching characteristics of various pollutants along the vadose soil depths. The physiochemical properties of underlying soils including NO3--N, NO2--N, NH4+-N, OM, TN, EC and Cl- exhibited a typical leaching dynamic along the depths. Among them, TN, NH4+-N, OM, NO3--N, and EC might be used as characteristic pollutants to evaluate the leachate leakage issues in landfilled sites. The genera Thiopseudomonas, Acinetobacter, Pseudomonas, and Hydrogenispora dominated in underlying soils. Compared to BJ samples, a more diverse and active microbiome capable of carbon and nitrogen cycles was observed in WZ samples, which was mainly ascribed to nutrients and elements contained in different types of soils. Among the environmental factors, nitrogenous compounds, SO42-, pH and EC had significant effects on the microbial community structures in the underlying soils. The relative abundances of Hydrogenispora and Caldicoprobacter might be used as characteristic microorganisms to evaluate the leachate leakage issues in landfilled sites. These results provided a deep insight into effects of leachate leakage in underlying soils, especially the pollutants vertical distribution and the corresponding microbial community structures.

Leachate leakage enhances the microbial diversity and richness but decreases Proteobacteria and weakens stable microbial ecosystem in landfill groundwater.

Sanitary landfill is the most prevalent and economic method for municipal solid waste disposal, and the resultant groundwater pollution has become an environmental problem due to leachate leakage. The pollution characteristics in groundwater near landfill sites have been extensively investigated, although the succession characteristics and driving mechanisms of microbial communities in leachate-contaminated groundwater and the sensitive microbial indicators for leachate leakage identification remain poorly studied. Herein, results showed that leachate leakage enhanced the microbial diversity and richness and transferred endemic bacteria from landfills into groundwater, producing an average decrease of 17.73% in the relative abundance of Proteobacteria. The key environmental factor driving the evolution of microbial communities in groundwater due to leachate pollution was organic matter, which can explain 16.13% of the changes in microbial community composition. The |ßNTI| values of the bacterial communities in all six landfills were <2, and the assembly process of microbial communities was primarily dominated using stochastic processes. Leachate pollution changed the assembly mechanism, transforming the community assembly process from an undominated process to a dispersal limitation process. Leachate pollution reduced the efficiency and stability of microbial communities in groundwater, increasing the vulnerability of the stable microbial ecosystems in groundwater. Notably, microbial indicators are more sensitive to leachate leakage and could accurately identify landfills where leachate leakage occurred and other extraneous pollutants. The phylum Proteobacteria and mcrA could act as appropriate indicators for the identification of leachate leakage. These results provide a novel insight into the monitoring, identification of groundwater pollution and the scientific guidance for appropriate remediation strategies for leachate-contaminated groundwater.

A supervised machine learning approach to discriminate the effect of carcass leachate on shallow groundwater quality around on-farm livestock mortality burial sites.

The evaluation of leachate leakage at livestock mortality burial sites is challenging, particularly when groundwater is previously contaminated by agro-livestock farming. Supervised machine learning was applied to discriminate the impacts of carcass leachate from pervasive groundwater contamination in the following order: data labeling, feature selection, synthetic data generation, and classification. Physicochemical data of 359 water samples were collected from burial pits (LC), monitoring wells near pits (MW), pre-existing shallow household wells (HW), and background wells with pervasive contamination (BG). A linear classification model was built using two representative groups (LC and BG) affected by different pollution sources as labeled data. A classifier was then applied to assess the impact of leachate leakage in MW and HW. As a result, leachate impacts were observed in 40% of MW samples, which indicates improper construction and management of some burial pits. Leachate impacts were also detected in six HW samples, up to 120 m downgradient, within one year. The quantitative decision-making tool to diagnose groundwater contamination with leachate leakage can contribute to ensuring timely responses to leakage. The proposed machine learning approach can also be used to improve the environmental impact assessment of water pollution by improper disposal of organic waste.

Molecular structure and evolution characteristics of dissolved organic matter in groundwater near landfill: Implications of the identification of leachate leakage.

Landfills can cause groundwater contamination, the pollution characteristics in groundwater near landfill sites have been extensively investigated, while the rapid identification of leachate leakage remained unclear. Comprehensively characterizing dissolved organic matter (DOM) is crucial for tracing the source, species, and migration of contaminants within groundwater and protecting groundwater sources. Here, we showed that DOM composition from newer landfills was mainly composed of newly-produced tryptophan and tyrosine, and protein-like and humic-like substances were more abundant in landfills that were relatively older. DOM in landfill groundwater was initially dominated by outputs from microbial activities, followed by terrigenous input. Leaked leachate contained an additional dye-derived fluorescent matter at the excitation/emission wavelength of 240-260/440-460 nm that was absent in uncontaminated groundwater. Leachate leakage increased the concentrations of humic-like substance, DOM molecular weight, and microbial activity in the downstream groundwater, resulting in the microorganisms rapidly multiply and secrete large amounts of microbial metabolism by-products, making them suitable indicators of groundwater pollution. Three criteria were proposed to establish an interpretable fluorescence method to identify leachate pollution. The obtained results provide a novel insight into not only the monitoring, early warning, and identification but also the transport, fate and removal or transformation of groundwater leachate in landfills.

Long-term groundwater protection efficiency of different types of sanitary landfills: Model description.

A new risk assessment method intended for comparing long-term environmental performance of different types of sanitary landfills was customized. Processes occurring within the hydrogeological environment were excluded from modeling, because they can be addressed separately. Only parameters directly related to leachate composition at the bottom of the landfill and leachate losses into the subsoil after landfill closure which can be reliably determined by evaluating already available information from the scientific literature were considered as necessary inputs for quantitative modeling. Once the simulated outcomes for a primary output ''fugitive emissions of a reference pollutant into the subsoil'' are acquired, more complex outputs can be derived, too. Commercially available risk assessment software which operates within an Excel environment was used to fulfill the task.•Uncertainty of data as well as heterogeneity and complexity of landfill systems was considered by attributing the selected input parameters with adequate probability density functions•Probability density functions attributed to the inputs differ considerably between the antagonistic landfill types•Risk assessments related outputs were defined as probabilities that an aquifer would be polluted due to landfill derived emissions into the subsoil.