Microplastic (MP) pollution poses serious environmental and public health concerns, requiring efficient detection methods. Conventional techniques have the limitations of labor-intensive workflows and complex instrumentation, hindering rapid on-site field analysis. Here, we present the Machine learning (ML)-Integrated Droplet-based REal-time Analysis of MP (MiDREAM) system. Utilizing a compact peristaltic

The authors regret < In March, the mean δ18O isotopic signal of groundwater was -13.16 ‰, while in May it was -13.10 ‰. The drinking water, on the other hand, had more depleted signals of -11.53 ‰ in March and -12.26 ‰ in May. >.

Monitoring groundwater tables (GWTs) remains challenging due to limited spatial and temporal observations. This study introduces an innovative approach combining an artificial neural network, specifically a multilayer perceptron (MLP), with continuous passive Multichannel Analysis of Surface Waves (passive-MASW) to construct GWT depth maps. The geologically well-constrained study site includes two

Acoustic Doppler current profilers (ADCPs) are a global standard in observing flow fields in rivers, estuaries and the coastal ocean. To date, it remains a labor intensive challenge to isolate mean flow fields governed by river discharge, tides and atmospheric forcing on the one hand, from small-scale turbulence, positioning imprecision, Doppler noise and erroneous backscatter, on the other hand. Here

The reliable prediction of sediment transport capacity (Tc) is essential for soil erosion models. Although rock fragments are a common surface cover type, quantitative studies on their relationship with Tc are limited. Tc typically follows a power function with slope gradient (S) and flow discharge (q) under bare flumes, but varying exponents complicate practical application. This study aims to investigate

Quantitative analytical chemistry is an important tool for the aquatic sciences, turning environmental samples into meaningful data that support waterbody management and novel research. However, proper use of analytical data must include full and transparent reporting of quality assurance and control (QA/QC) information, something we have noted as consistently lacking in recent literature. Herein,

The authors regret incorrect gene copy numbers of opportunistic pathogens in chapter 3.2 and an incorrect sample volume in chapter 3.3. The conclusions and figures in the article are correct. Also, authors noticed errors in the significance of Pseudomonas gene copy number results. The corrected text is below. The authors would like to apologise for any inconvenience caused.

Recent studies show variations in precipitation-gridded data set accuracy with changing geographical parameters. Ensemble precipitation products, combining diverse data sets, offer global-scale effectiveness, but applying them to regional studies, particularly in small to medium-sized sub-basins, presents challenges in addressing precipitation dependence on specific geographical conditions. Here, we

Pollutant transport in discrete fracture networks (DFNs) exhibits complex dynamics that challenge reliable model predictions, even with detailed fracture data. To address this issue, this study derives an upscaled integral-differential equation to predict transient anomalous diffusion in two-dimensional (2D) DFNs. The model includes both transmissive and dead-end fractures (DEFs), where stagnant water

Snow drought, characterized by an anomalous reduction in snowpack, exerts profound hydrological and socioeconomic impacts in cold regions. Despite its significance, the influence of diverse snow drought types, including warm, dry, and warm-and-dry variants, on streamflow remains inadequately understood. Here we present the first hemispheric-scale, observation-based assessment of snow drought patterns

Here, we explore how differences in morphologic heterogeneity due to logjams and secondary channels drive transient storage across discharge in two stream reaches within the Front Range of Colorado, USA. During three tracer tests conducted from baseflow to near-peak snowmelt, we collected instream fluid conductivity measurements and conducted electrical resistivity surveys to characterize tracer movement

Urban water utilities are significant energy users and also key actors in decarbonisation. However, the integrated perspective of urban water supply and wastewater system emissions, the relevant driving forces, and the boundaries of inclusions or exclusions, are rarely discussed. This is due to widely disaggregated data, and complex issues regarding the boundary of the system being investigated. This

Flooding is one of the most impactful weather-related natural hazards. Numerical models that solve the two dimensional (2D) shallow water equations (SWE) represent the first-principles approach to simulate all types of spatial flooding, such as pluvial, fluvial, and coastal flooding, and their compound dynamics. High spatial resolution (e.g., O$\mathcal{O}$ (100−101${10}^{0}-{10}^{1}$) m) is needed

Physics-informed neural networks (PINNs) are increasingly being used in various scientific disciplines. However, dealing with non-stationary physical processes remains a significant challenge in such models, whereas fluid motions are typically non-stationary. In this study, a PINN-based method was designed and optimized to solve non-stationary fluid dynamics with shallow water equations in a polar

Streamflow prediction is crucial for flood monitoring and early warning, which often hampered by bias and uncertainties arising from nonlinear processes, model parameterization, and errors in meteorological forecast. We examined the utility of multiple hydrological models (VIC, H08, CWatM, Noah-MP, and CLM) and machine learning (ML) methods to improve streamflow simulations and prediction. The hydrological

The tidal behavior of a well in semiconfined aquifers can be described by a diffusion equation including a leakage term. This approach is valid for thin aquifers, as long as the aquitard has low permeability relative to the aquifer. However, in cases where the aquifer is thick and the permeability of the aquitard is not low, using the existing solutions based on these approximations leads to unsatisfactory

In petroleum and natural gas exploration, lithology identification—analyzing rock types beneath the Earth’s surface—is crucial for assessing hydrocarbon reservoirs and optimizing drilling strategies. Traditionally, this process relies on logging data such as gamma rays and resistivity, which often require manual interpretation, making it labor-intensive and prone to errors. To address these challenges

Sediment control is a major concern in sewer management. Early studies focused on the parameters affecting the efficiency of existing dredging facilities, and novel long-term sediment reduction measures have not been developed. Superior sediment reduction performance has been demonstrated for plates folded at 25° placed in a pipe. In this study, flushing experiments are carried out to validate the

Subsurface storage of C⁢O2${\mathrm{C}\mathrm{O}}_{2}$ is an important means to mitigate climate change, and the North Sea hosts considerable potential storage resources. To investigate the fate of C⁢O2${\mathrm{C}\mathrm{O}}_{2}$ over decades in vast reservoirs, numerical simulation based on realistic models is essential. Faults and other complex geological structures introduce modeling challenges

Coastal aquifers experience water table fluctuations that push and pull water and air through organic-rich soils. This exchange affects the supply of oxygen, dissolved organic carbon (DOC), and nitrogen (N) to shallow aquifers and influences groundwater quality. To investigate the fate of N species, we used a meter-long column containing a sequence of natural organic topsoil and aquifer sediments.