Explore the vast range of titles available.

Groundwater sampling in challenging environments often leads to compromises in following best practices to obtain representative samples from aquifers. This includes collecting samples from existing production or domestic wells instead of using properly constructed monitoring wells or using a bailer instead of a submersible pump for sampling. To address unusual patterns and trends in groundwater chemistry data collected in Niamey, Niger from 2012–2021, a state-of-the-art monthly sampling routine was established for eight wells tapping the basement aquifer. This was based on the hypothesis that the observed changes in groundwater composition were mainly due to differences in sampling technique, and the aim of the study was to gain insights into possible seasonal variations in water composition, to examine if the previously observed trends could be validated and to provide baseline data for future studies. The results indicate that in most cases the long well response zones in the stratified aquifer system led to the collection of water from different strata/aquifers or of strongly mixed samples. Therefore, any sample from those wells is only of limited value for the interpretation of hydrogeological processes. To obtain sound data for the development of groundwater management strategies, the monitoring has to be shifted from existing production wells to properly constructed monitoring wells. In the complex hydrogeological setting of Niamey, with hydraulically interacting aquifers and occurrences of density layering, it is fundamental to ensure that a monitoring well taps one specific depth of one target aquifer and that well-volume purging is applied properly.

Groundwater sampling is critical for hydrogeological investigations and accurate hydrogeochemical analyses, influenced by two key processes within the well: depletion of casing storage and completion of wellbore transport. This study characterizes their combined effects on purging efficiency in groundwater sampling across various chemical and hydrogeological settings. A coupled multiphysics flow and transport model is developed, accounting for transient laminar flow and solute transport within the wellbore, and Darcy flow in the aquifer. Results indicate that sufficient well purge is constrained by the process with the longer characteristic time scale. In high‐yield aquifers, purging until water level stabilizes or purging 3 to 5 well screen volumes is inadequate for representative samples, as wellbore transport is the limiting process. Thus, multiple successive readings of water quality indicators or contaminant concentrations over an extended period are required. In low‐yield aquifers, the casing storage effect is significant, and drawdown stabilization suggests a sufficient well purge. However, purging time and wastewater volume tend to be excessive. The “High‐Stress Low‐Flow” (HSLF) strategy can reduce purging time and establish a predictable sampling window. A modified analytical solution, assuming complete mixing in the wellbore, provides an accurate approximation of the multiphysics model for both homogeneous and stratified formations. In heterogeneous aquifers, high variance of mass inflow at the screen leads to a lower degree of mixing within the screen. However, stabilized sample concentrations at the intake consistently reflect a weighted average of the total mass inflow, regardless of intake placement or formation stratification.

Seagrass beds are a target for conservation worldwide and are frequently the focus of bioassessment and biomonitoring surveys. However, these surveys often employ destructive methods and involve considerable effort and cost. Recently, environmental DNA (eDNA) techniques have been attracting attention as a low-impact alternative for evaluating species diversity. However, the relationship between the detection ability of eDNA metabarcoding and the ecology of the organisms from which eDNA is derived has not been explicitly investigated. In this study, we examined this relationship for fishes in 2 eelgrass Zostera marina beds in temperate Japan, with a focus on 2 ecological characteristics (swimming position and appearance frequency in seagrass beds). We used eDNA metabarcoding to identify fish DNA collected from seawater samples and compared species inventories between 2 sampling positions (within vs. above eelgrass meadows) and 2 survey methods (eDNA vs. sledge-net sampling). Our results show that eDNA metabarcoding is much more effective than sledge-net sampling in detecting fish species, and that the detection ability of eDNA metabarcoding differs with water-sampling position. In particular, the relationship between fish ecology and survey detection ability appears to differ between the 2 eelgrass beds. Our results indicate that prior consideration of the spatial structure and fish communities of eelgrass beds is necessary for a reliable estimation of fish diversity in vegetated marine habitats.

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

This study contributes to identifying and spatializing the different types of nitrate sources by combining hydrogeochemical and isotopic data with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) multicriteria statistical methods. The methodology is applied to the strategic Mons Basin chalk aquifer (Belgium). The results are based on a whole dataset containing 72 water samples with analyses of the hydrogeochemical parameters (temperature, pH, electrical conductivity (EC), redox potential, dissolved O2), alkalinity, total organic carbon (TOC), silica (SiO2), major and minor ions (NO3–, NH4+, Ca2+, dissolved Fe and Mn, K+, Mg2+, Na+, Sr2+, Cl–, F–, SO4–, B) and multiple stable isotope ratios (δ11B, δ15N–NO3–, δ18O–NO3–). Compared to classical PCA, the recently developed t-SNE method, which considers nonlinear relationships between variables and preserves local-scale similarities in a low-dimensional space, showed much better performance in discriminating different groups of samples and related zones in the aquifer. t-SNE results combined with isotope ratios highlighted four zones in the aquifer (grouped as A–D) and the presence of denitrification fronts. Group A presents a manure signature (δ15N–NO3– – mean (μ) +12.78‰, standard deviation (σ) 6.48‰; δ11B – μ 29.96‰, σ 6.91‰). Group B exhibits both manure and inorganic fertilizer signatures (δ15N–NO3– – μ 6.27‰, σ 2.55‰; δ11B – μ 15.86‰, σ 9.69‰). Group C shows a contamination by sewage (δ15N–NO3– – μ 12.67‰, σ 5.60‰; δ11B – μ 9.97‰, σ 7.08‰). Group D presents a mixed signature (δ15N–NO3– – μ 9.25‰, σ 2.94‰; δ11B – μ 20.00‰, σ 6.70‰).

Marine water resources (including seawater and pore-water) provide important information for understanding the marine environment, studying marine organisms, and developing marine resources. Obtaining high-quality marine water samples is significant to marine scientific research and monitoring of marine resources. Since the 20th century, marine water resources sampling technology has become the key research direction of marine equipment. In order to have a comprehensive understanding of marine water resource sampling technology, promote the development of marine water resource sampling technology, and obtain high-quality marine water samples, this paper summarizes the current development status of the sampling technology of marine water resources from the aspects of research and application. This paper first provides an overview of seawater and pore water sampling techniques. The two sampling technologies are categorized and discussed according to different sampling means, and the advantages of different sampling means are compared. We also found similarities between seawater and pore water sampling means. Then, a comprehensive analysis of existing technologies and equipment reveals the development trend of marine water resources sampling technology, for example, the need for high temporal and spatial accuracy in sampling, etc. Finally, it explores the challenges facing deep-sea water sampling technology regarding future research, development and equipment industrialization. These reviews not only help researchers better understand the current development of marine water sampling technologies but also provide an important reference for the future development of marine water sampling technology, which provides guidance and support for in-depth marine scientific research and effective use of marine resources.

The chemical composition of cloud water can be used to infer the sources of particles upon which cloud droplets and ice crystals have formed. In order to obtain cloud water for analysis of chemical composition for elevated clouds in the pristine high Arctic, balloon-borne active cloud water sampling systems are the optimal approach. However, such systems have not been feasible to deploy previously due to their weight and the challenging environmental conditions. We have taken advantage of recent developments in battery technology to develop a miniaturised cloud water sampler for balloon-borne collection of cloud water. Our sampler is a bulk sampler with a cloud drop cutoff diameter of approximately 8 µm and an estimated collection efficiency of 70%. The sampler was heated to prevent excessive ice accumulation and was able to operate for several hours under the extreme conditions encountered in the high Arctic. We have tested and deployed the new sampler on a tethered balloon during the Microbiology-Ocean-Cloud-Coupling in the High Arctic (MOCCHA) campaign in August and September 2018 close to the North pole. The sampler was able to successfully retrieve cloud water samples that were analysed to determine their chemical composition as well as their ice-nucleating activity. Given the pristine conditions found in the high Arctic we have placed significant emphasis on the development of a suitable cleaning procedure to minimise background contamination by the sampler itself.

Wastewater surveillance of pathogens may be a useful tool to help determine whether clinical surveillance of disease is effective or inadequate due to under-reporting and under-detection. In addition, tracking of pathogen concentrations over time could potentially provide a measure of the effectiveness of public health control measures and the impact of the gradual relaxation of these controls. Analysis of wastewater using quantitative molecular methods offers a real-time measure of infections in the community, and thus is expected to provide a more sensitive and rapid indication of changes in infection rates before such effects become detectable by clinical health surveillance. Models may help to back-calculate wastewater prevalence to population prevalence or to correct pathogen counts for wastewater catchment-specific and temporal effects. They may also help to design the wastewater sampling strategy. This article provides a brief summary of the history of pathogen wastewater surveillance to help set the context for the SARS-CoV-2 wastewater-based epidemiology (WBE) programmes currently being undertaken globally.

Background Workers in artisanal and small-scale gold mining (ASGM) are exposed to metals and metalloids (referred to here as “metals”) while mixing milled ore and elemental mercury to produce a gold-mercury amalgam. Although concentrations of metals in surface waters near ASGM activities have been described, little is known about concentrations of metals in the water-ore-mercury slurry with which workers have extensive dermal contact. We sought to characterize those concentrations. Methods Water samples ( n  = 76) were collected from amalgamation basins and milled ore washing ponds at 13 ASGM sites in Western Kenya. Samples were filtered and metals in the filtrate and metals retained on filters were analyzed for trace elements by inductively coupled plasma mass spectrometry (ICP-MS). Based upon the volume filtered and ICP-MS results, total metal concentrations in the original samples (pre-filtration), were calculated. Results Concentrations of metals in the amalgamation basins were high. The median concentrations of arsenic (240.23 µg/L), chromium (312.97 µg/L) and total mercury (3.52 µg/L) all exceeded Kenya’s drinking water standard by several fold. Only 3.38% of arsenic, 0.28% of chromium, 40.51% of manganese, 0.22% of mercury and 0.01% of lead mass were in filtrate, with the remainder of the metal mass retained on filters. Conclusions Concentrations of arsenic, chromium, manganese and lead to which ASGM workers are exposed in the amalgamation process were approximately 5-100-fold higher concentrations than reported in prior studies of metals in surface waters near ASGM sites. These findings should be useful in assessments of exposure and health risk of the many thousands ASGM workers who amalgamate milled ore. The high concentrations of As, Mn and Hg put at risk the health of children who live near or work at ASGM sites. Policy measures and changes in occupational practices are urgently needed to reduce Hg use in ASGM and to protect individuals from metals present in milled ore.

Residual pit lakes from mining are often dangerous to sample for water quality. Thus, pit lakes may be rarely (or never) sampled. This study developed new technology in which water-sampling devices, mounted on an unmanned aerial vehicle (UAV), were used to sample three pit lakes in Nevada, USA, during 1 week in 2017. Water-quality datasets from two of the three pit lakes on public lands, Dexter and Clipper, are presented here. The current conditions of the Dexter pit lake were assessed by examining cation and anion concentration changes that have occurred over a 17-year period since the pit lake was last sampled in 2000. Data gathered during this sampling campaign assessed 2017 conditions of the Dexter and Clipper pit lakes by comparing constituent concentrations to the Nevada Division of Environmental Protection (NDEP) pit lake water-quality requirements, indicating that selenium concentrations exceeded regulatory standards. We compared our sampling data for Dexter lake to prior water-quality data from the Dexter pit lake collected in 1999 and 2000. This comparison for the Dexter pit lake indicates that evapoconcentration may have caused increasing cation and anion concentrations. This UAV sampling approach can potentially incorporate the use of additional multiparameter probes: pH, oxygen concentration, turbidity, or chlorophyll. Some limitations of this UAV water-sampling methodology are battery duration, weather conditions, and payload capacity.