Environmental and Earth Sciences

Abstract: Soil moisture is a key forecast variable of land surface models. Direct assimilation of microwave brightness temperature data to optimize soil moisture initial fields is an effective approach to improve simulation accuracy of soil moisture. However, most existing direct assimilation methods adopt physical radiative transfer models as observation operators, and their complex parametric errors greatly restrict the improvement of assimilation performance. This study introduces a high-precision MLP-based surrogate radiative transfer model as the observation operator. Combined with the Simplified Extended Kalman Filter (SEKF), it develops a direct radiance data assimilation system for the Common Land Model (CoLM). Assimilation experiments are conducted using brightness temperature data from the Microwave Radiation Imager (MWRI) onboard the FY-3D satellite. Their performance over China's land areas is systematically assessed through comparison with the assimilation scheme based on the Community Microwave Emission Model (CMEM). The results show that the MLP-based assimilation scheme can effectively improve soil moisture simulation accuracy, yet the improvement varies across vegetation types: grassland areas achieve the largest error reduction (10.2%), while semidesert areas present the most prominent increase in correlation coefficient (53.9%). Compared with the CMEM scheme, the MLP scheme exhibits better error stability and produces generally improved assimilation effects—specifically, in semidesert areas, the error decreases by 9.4% and the correlation coefficient increases by 62.8%. This study demonstrates that deep learning-based observation operators have strong application potential for land surface data assimilation under complex physical mechanisms.

Abstract: Whether economic growth decouples from municipal solid waste (MSW) generation in upper-middle-income economies remains contested. We test the Waste Kuznets Curve and a disposal-to-recovery substitution effect using a 13-year panel of 1,101 Colombian municipalities, combining step-wise fixed-effects models with a non-parametric generalised additive model (GAM), a spatial autoregressive (SAR) check, and a selection-aware recovery model. We find no evidence of income-driven decoupling in landfilling. Once urban density and demographic structure are controlled, the income terms lose significance, the non-parametric estimate is predominantly monotonic, and density emerges as the main structural driver. Material recovery grows faster than disposal with income (relative substitution), but this signal is concentrated where recovery is measured—only 27% of municipalities report it, and coverage falls from 86% in metropolitan tiers to 19% in the rural periphery—so that once selection is corrected the recovery elasticity falls from about 5.9 to a non-significant 1.3. Rather than spontaneous decoupling, Colombia exhibits persistent coupling alongside an institutionally engineered, spatially unequal recovery capacity. Achieving SDG 12 therefore requires stratified policies that mandate consumption reduction in mature urban economies while subsidising shared circular infrastructure for historically neglected rural jurisdictions.

Abstract: Accurate urban floodwater depth estimation is vital for disaster management but traditionally relies on data-intensive hydrodynamic models or supervised deep learning restricted by labeled data requirements. To address these bottlenecks, this study proposes a fully unsupervised framework for rapid flood depth estimation using post-event remote sensing imagery and Digital Terrain Models (DTMs). The methodology operates in two steps: first, a binary flood extent map is automatically delineated using a label-free unsupervised approach. Second, leveraging the hydrostatic equilibrium principle, floodwater depth is computed by integrating the extracted flood footprint with the underlying DTM. This framework was evaluated using the Inundation2Depth dataset, encompassing twelve urban and peri-urban sites in the Southeastern United States impacted by Hurricanes Matthew and Florence. Experimental results across all remote sensing sites demonstrated the framework’s viability, with segmentation F1-score and flood depth normalized-RMSE ranging from 64% to 95%, and 0.14 to 0.26, respectively. By eliminating the need for manual annotations and task-specific training, the proposed framework offers a scalable, transferable, and rapidly deployable solution for flood mapping and depth estimation in data-scarce environments, enabling efficient adaptation to new regions and disaster events without retraining or prior ground truth labels.

Abstract: Air pollution is a public health threat that requires urgent action. This study conducted a bibliometric analysis of air pollution and human health in Indian cities to examine trends and the geography of the scientific literature, the evolution of research, and co-occurrence patterns of pollution sources, types, and health impacts. Furthermore, a narrative review of air pollution mitigation strategies was conducted using scholarly articles, policy documents, and reports. Relevant publications from the Web of Science (WoS) and Scopus databases were downloaded. The search identified 3307 articles published between 1987 and 2024, of which 172 met the inclusion criteria. The bibliometric analysis was conducted using VOSviewer and R software. The results indicate a steady rise in studies on air pollution and health issues in India. Initially, research concentrated on various sources and types of pollution, subsequently transitioning to the evaluation of exposure risks, risk assessment, and health implications, ultimately narrowing its focus to risk assessment concerning human health. Over the course of forty years, there has been a growing emphasis on the influence of indoor air quality, including ‘PM2.5’, ‘PM10’, dust, chemical pollutants, heavy metals, and exhaust dust, on human health. Research on pollution-related health effects has moved from examining general impacts to focusing on long-term, chronic consequences of pollutant exposure. Notably, most studies are centred in large metropolitan areas, whereas medium and small towns are underrepresented. Urban areas face severe air-quality challenges, requiring strategies such as monitoring pollution, promoting renewable energy, reusing materials, installing green walls or buffers in pollution zones, improving transport infrastructure, and reducing dust with grass covers. This study underscores the importance of implementing effective air pollution control measures across various geographic regions and integrating air pollution mitigation strategies into comprehensive urban development and planning frameworks.

Abstract: Household food waste remains a huge challenge for solid waste management in municipalities worldwide, especially in the Global South. Existing studies that measured food waste (FW) in cities are scarce, have limited geographic scope, and have limited timeframes. In that direction, the current investigation provides data on the FW composition of nine regions of the municipality of Rio de Janeiro (Brazil), based on a three-year sampling across 155 neighborhoods. Waste samples were collected from 2021 to 2023. In total, about 24,038 kg (fresh weight) were analyzed. Results showed that FW accounts for an average of 47.7±1.9% of household waste in the study period. The FW composition in the city of Rio de Janeiro ranged from 60.3 – 76.5% for fruits, vegetables, and salads, 15.0 – 25.1% for fine aggregate (small-sized food residues < 2.54 cm, like rice, beans, grains, and fragmented food particles), and 3.2 – 5.8% for proteins (discarded animal-based protein foods like chicken and meat). The chi-square good-ness-of-fit test was applied to evaluate whether the FW composition in each of the nine regions differed from the mean FW composition of the Rio de Janeiro municipality. The findings revealed statistically significant differences (p-value < 0.05) in the average FW fractions in specific regions and years compared with the city’s average composition. Thus, one of the key takeaways of this investigation was that the percentages of discharged food waste fractions vary over time and across locations, even within the same municipality. The present research took a first step toward understanding the food waste problem in Rio de Janeiro (Brazil) and underscores the importance of monitoring food waste data to guide the development of locally specific strategies for sustainable urban food systems, including waste prevention, recycling, and food recovery.

Abstract: This study presents a 3D geological model and structural interpretation of the Masa Valverde volcanogenic massive sulphide (VMS) deposit in the Iberian Pyrite Belt. The deposit is hosted by felsic porphyritic volcanic rocks, volcanic tuffs and black shales. A 3D geological model of the orebodies and host rocks, constructed from 145 drillcore logs, allowed us to build 16 cross-sections spaced 100 m apart, and constrain the mineralisation geometry and its structural evolution. Mineralization formed during Early Carboniferous transtensional tectonics within an extensional basin, where an extensional duplex structure controlled the development of the primary massive sulfide body and its associated stockwork. Subsequent counterclockwise rotation of the principal stress axes reactivated extensional faults as reverse faults during tectonic inversion. This deformation strongly modified the VMS system through buttressing, generating extensive open spaces and promoting brecciation and recrystallization of both the stockwork and massive sulfides. These processes produced a new paragenesis dominated by chalcopyrite and sphalerite, with minor galena among other minerals, which cemented the breccias, partially replaced earlier mineral assemblages, and filled open fractures. The resulting Cu-Zn enrichment, spatially associated with buttressed zones, provides new insights into ore remobilization with direct implications for the development of the ongoing underground mine.

Abstract: This study uses causal loop diagram (CLD) to explore the system dynamics of urban waste management analysing the interdependencies among waste generation, operational efficiencies, governance, public behaviour, and environmental outcomes. The model identifies key reinforcing and balancing feedback loops that drive system performance, including data–policy–performance, awareness–segregation, and technology–innovation dynamics. It also highlights critical constraints such as collection inefficiencies under system overload and the adverse effects of inadequate monitoring and data availability. Findings suggest that strengthening monitoring systems, enhancing public awareness, and investing in technological innovation, concomitantly, can create reinforcing improvements across the waste management system. On the other hand, weak governance and data gaps undermine system responsiveness and environmental outcomes. The study contributes to policy design by providing a holistic framework for understanding leverage points that can improve sustainability and resilience in waste management systems.

Abstract: The sustainability of territorial agri-food systems depends on whether high-value produce can reach a paying market. The yam supply chain of Montes de María, a post-conflict subregion of the Colombian Caribbean, sorts its product into a non-substitutable export grade and a local grade. This study assesses the structural resilience of the chain and its food-security implications by integrating network analysis with a household survey of the territory. The chain is modeled as a weighted directed graph in three layers: value concentration, structural criticality via Menger edge connectivity, and a differential-equation model of export-grade erosion under a demand shock with saturating absorption and storage; a parallel food-insecurity and dietary-diversity survey sets the baseline. Results, reported as conditional scenarios, show the export value hangs on a single non-redundant channel: one cut disconnects the export outlet. Under a shock, permanent loss nears 18% of the diverted product versus 50% without storage, while the export-income collapse, about 40% or 57% when severe, is not buffered. With 41% of surveyed households food-insecure, this fragility erodes rural livelihoods through economic access. Diversifying the export outlet and strengthening local storage emerge as levers for a more resilient and sustainable chain, supporting food security and territorial peace.

Abstract: The Huizhou Cultural-Ecological Reserve (HCER), China’s first nationally designated Cultural-Ecological Protection Zone, offers a distinctive setting where wetland conservation interacts with a millennia-old cultural landscape. We assemble a 26-year, 1-km grid panel (2000–2025; 14 011 grids; 364 286 grid-year observations) over the nine HCER counties, infer four-dimensional cultural ecosystem services (CES) – Aesthetic, Recreation, Heritage, Education – with Random Forest and XGBoost from a 14-variable predictor stack (mean XGBoost R² = 0.725), and apply a two-way fixed-effects panel regression with a distance-decay exposure kernel (5 km) to eight wetland protection units, using county-clustered standard errors. Reserve-wide CES-Total declines by 47% between 2000 and 2025. Once grid and year effects are absorbed, boundary cells show 4.1-percentage-points higher CES-Total than distant cells (β = +0.041, p = 0.012); Aesthetic (+8.1 pp, p = 0.029) and Recreation (+7.2 pp, p = 0.007) respond most strongly, Education positively but modestly (+0.6 pp, p = 0.001), Heritage not detectably. A halo peaks at 5–10 km rather than on the water surface. We formalise this as a Conservation Zone Externalities (CZE) framework and derive three planning levers for the HCER.

Abstract: Earth System Models (ESMs) and General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) still exhibit substantial biases over West Africa, particularly for daily precipitation extremes. This study evaluates raw CMIP6 simulations and simulations bias-corrected with the Quantile Delta Mapping (QDM) and CDF-transform (CDF-t) methods, for mean precipitation and two extreme indices: consecutive dry days (CDD) and total precipitation from very wet days (R95pTOT). Three observational datasets (CHIRPS, TAMSAT, MSWEP) are used over 1983-2012 for the West African Monsoon season (JJAS), with projections for 2071-2100 under SSP5-8.5. Raw simulations reproduce the main precipitation gradients but show considerable biases and inter-model spread, especially over the Guinean coast and Sahel. Both methods considerably improve performance, increasing spatial correlations and bringing normalized standard deviations closer to unity. CDF-t performs slightly better for mean precipitation and CDD, with comparable performance for R95pTOT; no single method is optimal for all indices. Projections indicate wetter conditions over the central and eastern Sahel, longer dry spells over the western Sahel, and a widespread intensification of extreme rainfall. Based on the Wilcoxon signed-rank test, this intensification is significant mainly at the ensemble level, while CDD changes show the weakest significance.

Abstract: Indoor air quality (IAQ) is an important determinant of public health, especially for individuals with multiple chemical sensitivity (MCS). Fragrances emit volatile organic compounds (VOCs) that can contribute to poor IAQ. Although scent-free policies are being widely implemented as an accommodation for those impacted by fragrances, their ability to improve IAQ has not been examined. This study assessed if scent-free policies can improve IAQ across Canadian offices. IAQ testing was conducted between December 2023 and June 2024 in 34 offices (17 with scent-free policies, 17 without). Sampling included total VOCs (TVOCs), top 35 VOCs, formaldehyde, CO, CO₂, PM₂.₅, temperature, and relative humidity. Analyses controlled for room size, occupancy, and ventilation rate using Mann-Whitney U tests and generalized linear models. Regression analysis revealed significantly higher VOC concentrations in spaces without scent-free policies, including acetaldehyde (OR = 2.2, p <.05), acetone (OR = 7.7, p <.001), toluene (OR = 3.4, p <0.05), m-/p-xylene (OR = 6.9, p <.001), o-Xylene (OR = 15.5, p <.001), and TVOCs (ORs = 3.9, p<.001). This study provides findings that support scent-free policies as a low-cost source control strategy to help lower VOC concentrations and improve IAQ accessibility.

Abstract: Accurate quantification of wildfire risk is essential for balancing wildfire mitigation and prescribed fire management in grassland ecosystems, yet existing fire danger indices do not explicitly distinguish seasonal fuel dynamics from daily weather variability. This study presents a hierarchical framework for quantifying wildland fire risk by explicitly separating seasonal wildfire potential from daily weather-driven fire activity. The framework introduces the Daily Burned Area Ratio (DBAR) as a quantitative measure of realized wildfire risk and decomposes it into the Seasonal Burned Area Ratio (SBAR) and the Daily Burn Activity Index (DBAI). Wildfire records from the U.S. Forest Service Fire Program Analysis Fire-Occurrence Database, Oklahoma Mesonet weather observations, and remotely sensed vegetation data collected between 1995 and 2020 were used to develop and evaluate the framework in the Flint Hills of Kansas and Oklahoma. SBAR was modeled using grass curing and air temperature to characterize the seasonal baseline of wildfire activity, whereas DBAI was modeled using dead fuel moisture content (DFMC) and wind speed to quantify day-to-day departures from that seasonal baseline. The SBAR model accurately reproduced the characteristic bimodal wildfire regime of the Great Plains, whereas the DBAI model identified DFMC as the dominant control on daily wildfire activity, with wind speed providing an important secondary influence. Compared with the Burning Index (BI) and the Grassland Fire Danger Index (GFDI), the hierarchical framework achieved superior performance in discriminating fire days from non-fire days. Global sensitivity analysis further demonstrated that the framework provides a more balanced representation of the influences of grass curing, relative humidity, air temperature, and wind speed than the conventional indices. By explicitly separating seasonal fuel dynamics from short-term weather variability, the proposed framework provides an ecologically interpretable, locally calibratable, and operationally practical approach to wildfire risk assessment. Because the seasonal and daily components can be calibrated independently, the framework is readily transferable to other grassland ecosystems and provides a flexible foundation for adaptive wildfire and prescribed fire management under changing climatic conditions.

Abstract: Stachys fontqueri is a strict endemic species of the Moroccan Rif that depends on specific ecological conditions. To understand the effect of climate change on the potential evolu-tion of the species' geographic range under current and future climatic scenarios, ecologi-cal niche modelling was performed using MaxEnt algorithm based on five bioclimatic variables. To model the effect of climate change, four climatic scenarios were used, namely CSM2- SSP1-2.6, CSM2-SSP5-8.5, MIROC6-SSP1-2.6, and MIROC6-SSP5-8.5, for the period 2061–2080. The results demonstrated high model performance, with an AUC ranging from 0.921 to 0.930 and a TSS from 0.81 to 0.83. Three bioclimatic variables contributed significantly to determining the suitable potential distribution area of the species, namely Precipitation Seasonality (Bio15), Temperature Annual Range (Bio7), and Annual Mean Temperature (Bio1). The suitable area covered 3,244 km² under the current climate and is projected to decrease by 23.25% to 29.59% under future climate scenarios. This contraction of suitable habitat due to climate change could be exacerbated by human activities, there-by requiring urgent in situ and ex situ conservation measures to ensure the species' resilience.

Abstract: Green buildings (GB) have proven to be an important way to respond to environmental degradation, depletion of resources, climate change caused by rapid urbanization. Due to the increasing rate of urban growth in Nigeria, and the demands for increased infrastructure, there is a growing need to include GB principles in the development of sustainable urban planning. In this study, Nigerian GB Policy Frameworks (GBPFs) are reviewed and analysed concerning their degree of integration within sustainable urban planning. A Narrative Literature Review with systematic elements was using secondary data, including policy documents and international GB standards from 2000 to 2025. The review focused on four primary Nigerian policy documents (the National Building Code (NBC), National Housing Policy (NHP), Energy Efficiency Building Code (EEBC) and the Green Building Council of Nigeria (GBCN)) and provided a comparative analysis between the Nigerian GBPFs and the international frameworks - LEED, BREEAM, EDGE, Green Star and Green Mark. The findings indicate there has been progress in GB construction practices in Nigeria; however, the existing GB policies (GBPs) in Nigeria are largely fragmented, poorly enforced, and poorly integrated into urban planning processes. Institutional overlaps, lack of adequate financial resources, lack of technical capacity, lack of stakeholder engagement and the absence of a nationally sanctioned GB rating system present ongoing barriers to implementation. The comparative analysis indicates that countries which have implemented stronger regulatory frameworks, greater financial incentives, and introduced compulsory certification systems achieve greater success in the integration of GB principles into urban development. The study advocates a legally binding national GB framework that would provide for improved institutional coordination, the provision of financial incentives, the establishment of climate-responsive standards, and robust monitoring mechanisms to support sustainable urban development in Nigeria.

Abstract: Rock physics model provides an essential theoretical tool to quantify the impacts of reservoir physical parameters (porosity, water saturation, etc.) on seismic elastic properties including P- wave velocity and S-wave velocity. As a core module for deriving physical properties through joint well-seismic inversion, rock physics modeling accuracy directly determines the reliability of reservoir property prediction. Simultaneous characterization of pores and oriented fractures is required to refine modeling precision, yet the resulting excessive model parameters severely limit applications to field seismic data. To address the critical issue of applying pore and fracture anisotropic rock physics models to azimuthal seismic interpretation, this work decouples pores and oriented fractures during modeling. We postulate that pore variations control the model’s isotropic elastic properties, whereas oriented fracture characteristics dominate anisotropic parameters. Well-log data are utilized to invert key modeling parameters (matrix mineral elastic moduli, fracture porosity, etc.) for model correction. The corrected rock physics model is further coupled with the Rüger reflectivity equation to analyze how porosity and fracture azimuth alter seismic reflection coefficients and synthetic seismic records. The presented anisotropic rock physics correction method via parameter inversion greatly elevates modeling accuracy. Rock-physics-based analysis of azimuthal seismic reflection shows promising prospects for widespread use in fractured reservoir characterization.

Abstract: The evaluation of Gridded Precipitation Products (GPPs) must account for the zero-inflated nature of precipitation data and the differences in spatial support between rain gauges and satellite grids. This study assesses the timing, precipitation event detection, and volume of ERA5, IMERG, CHIRPS, and TAMSAT against the Trans-African Hydro-Meteorological Observatory (TAHMO) network. We employ variance-stabilising transformations, detect rainfall events, and cluster diurnal precipitation cycles into different regimes. Our clustering results reveal spatial variability in performance, with GPP and TAHMO derived diurnal regimes differing at 57.3% of the stations. Analysis of the diurnal precipitation reveals that ERA5 satisfies its daily water budget through persistent drizzle. At the daily scale, IMERG exhibits superior event detection, timing, and volume accuracy. On the other hand, CHIRPS and TAMSAT show a wet bias. We conclude that GPP selection should consider the use-case, and future meteorological and AI-driven applications should incorporate verification metrics that account for timing, event detection and volume accuracy.

Abstract: Rapid flood mapping using optical sensors such as Sentinel-2 is frequently challenged by spectral confusion, where turbid water, urban shadows, and wet soils exhibit similar reflectance signatures that undermine single-index detectors. We present the FLOod Oriented Detection Hybrid Index (FLOOD-HI), a statistically calibrated multi-index fusion that combines six complementary spectral indices (NDWI, IMP, AWEI, TCW, NDVI, and SAVI) through a multivariate linear model. Trained against high-fidelity Directly Affected Area (DAA) ground-truth maps from the catastrophic May 2024 Rio Grande do Sul floods, the model is implemented end-to-end in Google Earth Engine (GEE). Inundation extent is mapped using a straightforward sign-based decision rule (FLOOD-HI > 0), with spectral masks incorporated as pragmatic refinements. Moving beyond traditional small-sample paradigms, FLOOD-HI is validated wall-to-wall over two municipality-scale Regions Of Interest (ROI), with metrics computed across all pixels rather than on hand-picked water and non-water samples. In the external testing domain (ROI-2), FLOOD-HI achieves an F₁ ≈ 0.80, IoU ≈ 0.66, Precision ≈ 0.73 and Recall ≈ 0.88, substantially outperforming the best-performing single index (TCW, F₁ ≈ 0.63 and IoU ≈ 0.46). This represents an approximate 26,50% relative improvement in F₁ (absolute gain ≈ 0.17) and a 44% improvement in IoU (absolute gain ≈ 0.20). The major contribution is methodological, offering a reproducible multivariate index formulation, a conservative municipality-scale framework, and an open-access GEE implementation that includes a localized calibration workflow.

Abstract: Accurate monitoring of forest carbon stocks represents a critical prerequisite for achieving carbon neutrality. However, conventional remote sensing‑based estimation methods frequently overlook forest heterogeneity, causing systematic overestimation or underestimation. To address this gap, we propose a novel forest carbon stock esti-mation framework that integrates two complementary strategies: (1) forest type‑specific modeling to account for forest heterogeneity, and (2) hyperparameter op-timization to enhance Random Forest model performance. Using ground‑measured carbon stocks and a CCDC‑derived forest vegetation classification map for Hangzhou City, China, we built forest‑type‑specific Random Forest models based on ICESat‑2 canopy height metrics and optimized each model via hyperparameter tuning. The re-sults show that the 70th-90th percentiles and the mean canopy height are relatively highly correlated with carbon stock. Forest‑type‑specific modeling improves estima-tion accuracy, yielding R² gains of 0.10–0.17 and reduced RMSE by 2.28–7.43 Mg C/ha over the non‑stratified model. Integrating forest classification and hyperparameter op-timization strategies improved model R² by 0.16–0.23 and lowered RMSE by 3.05–8.20 Mg C/ha. Overall, this study demonstrates that accounting for forest heterogeneity and applying hyperparameter optimization can significantly enhance the accuracy of for-est carbon stock estimation.

Abstract: The topology and spatial extent of regional surface-water bodies fluctuate seasonally, with the most pronounced changes during high-intensity rainfall and flooding. To capture these dynamics, we introduce a novel geomorphological metric, the catchment-to-destination area ratio (C/D ratio). The catchment area represents the upstream contributing surface area, and the destination area represents the spatial extent of the receiving sink, such as a depression or existing water body. Theoretically, the C/D ratio scales proportionally with the rate of water-level (stage) rise in destination basins during wet seasons. As water levels increase, the lateral expansion of receiving basins drive fundamental topological shifts in watershed-network connectivity. Using the C/D ratio and upstream catchment area, we develop a computational simulation algorithm for modeling the seasonal and climate-driven evolution of surface-water configurations from digital elevation models (DEMs). The algorithm distinguishes natural topographic depressions from spurious digital artifacts, supports automated channel routing across low-relief terrain, preserves two-dimensional channel widths, and estimates depression storage capacity for flood-buffer assessment. Evaluation across multiple DEM datasets demonstrates the algorithm’s ability to simulate seasonal variation of the watershed network and identify flood-prone terrain configurations. This framework allows predictions for regional flood-hazard mapping, water-resource planning, and environmental and ecosystem management. Available at https://cs.stanford.edu/people/zjl/flow.

Abstract: Rapid urbanization and inadequate sanitation infrastructure threaten water security in many sub-Saharan African cities. This study presents the first integrated assessment of groundwater, surface water, and wastewater quality in Bangui, Central African Repub-lic, using physicochemical, trace metal, and microbiological indicators. A total of 28 sampling sites were analyzed using standardized methods, including ion chromatog-raphy, ICP-OES, ICP-MS, and membrane filtration. Results revealed a clear contamina-tion gradient. Wastewater showed the highest electrical conductivity, turbidity, chlo-ride concentrations, and microbial loads, reaching 2.41 × 10⁶ CFU/100 mL for total coli-forms and 1.93 × 10⁶ CFU/100 mL for fecal coliforms. Groundwater exhibited high ni-trite levels and low dissolved oxygen, indicating vulnerability to sewage infiltration. Surface waters were characterized by high turbidity and widespread fecal contamina-tion despite relatively good oxygenation. In contrast, trace metal concentrations gener-ally remained below World Health Organization guideline values. Geochemical anal-yses identified distinct elemental signatures for each water type. Microbial contamina-tion emerged as the dominant factor affecting water quality. High fecal coliform/fecal streptococci ratios (13.08-22.16) indicated predominantly human-derived pollution linked to untreated wastewater and inadequate sanitation systems. The association between elevated nitrite concentrations and fecal indicators suggests active contami-nation pathways connecting wastewater, surface water, and shallow aquifers. These findings highlight the urgent need for improved wastewater management, groundwa-ter protection, and long-term monitoring to ensure sustainable urban water security in Bangui.