Location Of Sampling Sites Research Articles (Page 1)

This volume, Sedimentary Successions of the Arctic Region and their Hydrocarbon Prospectivity , developed around maps of the sedimentary successions of the Arctic Region, and contains a brief, but comprehensive compilation of geological and geophysical data characterizing all significant sedimentary successions in the Arctic, which cover 57% of the polar area north of 64° N. Its two main goals are to provide, based on present-day knowledge and data, a characterization of all Arctic sedimentary successions (or sedimentary accumulations) and to supply a snapshot of hydrocarbon-related exploration in the Arctic at the end of the first quarter of this century. To achieve these goals, we represent sedimentary successions as consisting of one or several ‘tectono-sedimentary elements’ (TSEs) based on the main tectonic regimes that formed accommodation space for accumulation of sediments. A TSE characterization template has been developed as an efficient method of organizing and presenting the most important information about the stratigraphy, structure and petroleum geology of a TSE, including the most significant exploration facts. This organizational architecture is the backbone of the volume and is a key feature that distinguishes it from other studies of Arctic sedimentary basins. The online volume includes six large-size foldout maps portraying the mapped TSEs in the Circum-Arctic context, including tectonic grain of the consolidated basement, anomalous gravity and magnetic fields, location of the Arctic sampling sites and seismic profiles.

The brook lamprey (Lampetra planeri) is a key species in Danish running waters and is listed as an Annex II species in the European Habitat Directive. Still, its ecological role is very poorly studied, which restricts the use of efficient conservation measures. Larvae (ammocoetes), identified as the genus Lampetra, were sampled and assessed in a small Danish brook, Ovnstrup bæk. We used a 300 × 400 mm bottom sampler, which has been found useful to collect ammocoetes in previous studies. This proved efficient in quantifying ammocoetes in a brook where previously conducted surveys, based on electrofishing, did not report catches of lamprey. The method generated 390 samples and 179 ammocoetes were caught enabling mapping of the spatial distribution, along with substrate types and stream parameters, throughout the brook. Larvae were not found in all areas of the brook. Our results suggest that larvae prefer to burrow at the margins of the brook in sediments characterized as mud (< 0.25 mm) and lacking stone (60–300 mm) and gravel (30–60 mm). The average density was 4.2 No./m2 with no ammocoetes observed in the upper part where the brook is smallest, and up to 8.5 No./m2 in the middle part. No differences in average length between sample-site locations were found, suggesting that ammocoetes size classes are heterogeneously distributed in the brook. Areas of the brook where ammocoetes were not found or where densities were low could be targeted for habitat restoration. A bottom sampler can effectively be used to assess the distribution and habitat use of larval brook lampreys in a small stream.

Societal Impact StatementSeedbanks are vital for biodiversity conservation, but their potential remains underutilised due to a limited understanding of the intraspecific genetic diversity they hold. By leveraging digitised data associated with seedbank collections, such as sampling locations, number of maternal plants and seed traits, we can attempt the estimation of genetic variation and identify gaps in collections, enabling better prioritisation of species for conservation efforts. These advancements can inform policy targets like those of the Kunming‐Montreal Global Biodiversity Framework, promoting more effective conservation strategies. Digitisation and emerging machine‐learning technologies offer scalable, cost‐efficient solutions to enhance conservation knowledge, ensuring biodiversity resilience for future generations.SummarySeedbank collections hold significant untapped potential for advancing conservation science and practice, but the intraspecific genetic diversity (i.e. diversity within a species) stored in worldwide seedbank collections remains largely unknown, hindering the effective use of seeds for both informing and implementing in situ interventions. As producing genetic data is time‐consuming and expensive, other data associated with seedbank collections can greatly enhance our understanding of the genetic variation stored in seed collections when genetic data are unavailable. Information such as the location of sampling sites, estimated population size and the number of mother plants from which seeds were collected can facilitate the estimation of the genetic diversity captured in the collections. This information can also be used to estimate the sampling effort required to fill gaps in seedbank collections to better represent genetic diversity, through comparison with existing baselines from species where genetic diversity is characterised, and through simulations. Digitisation of the data associated with seedbank collections makes the approaches above practicable at scale. In addition, digital images of the seeds themselves may identify intraspecific phenotypic variation and can, therefore, be used to prioritise populations for future genetic studies.In this article, we explore the potential of digitised information made available by seedbanks for improving our understanding of the intraspecific genetic diversity preserved in collections. We describe possible improvements that might enhance the predictive power of digital information for genetic studies, and discuss the challenges and opportunities associated with these.

Machine learning (ML) models have proven to be an efficient technique for better understanding and quantification of surface water quality, especially in agricultural watersheds where considerable anthropogenic activities occur. However, there is a lack of systematic investigations that can examine the application of different ML regression models in agricultural settings to predict the surface water quality using a group of input variables, including hydrological (e.g., surface flow), meteorological (e.g., precipitation), and field (e.g., crop cover) conditions. In this study, multiple ML regression models, including support vector machine (SVM) and regression trees (RT), were employed on a 2-year dataset collected from a sand plain agricultural sub-watershed in southwestern Ontario, Canada (i.e., Lower Whitemans Creek) to predict the nitrate and chloride concentrations in surface water at nine sampling sites within the sub-watershed. The prediction capabilities of these ML models were determined using a group of evaluation metrics including the coefficient of determination (R2) and root-mean squared error (RMSE). In general, the Gaussian Process Regression (GPR) model was the optimal algorithm to predict the nitrate and chloride concentrations in surface water (R2 was 0.99 and 0.98 respectively for training and testing). According to the results of a feature importance analysis, it was found that the field conditions (specifically the location of sampling site (main channel or tributary site) and crop cover) were the most crucial model input variables for accurate predictions of the output variables. This study underscores that ML regression models can be implemented to effectively quantify the water quality properties of surface water in agricultural watersheds using easily measurable parameters. These models can assist decision makers in advancing successful actions and steps towards protecting the available surface water resources.

We hosted a nine-week NIH-funded summer undergraduate research experience in Environmental Health Sciences, the New College Environmental Health Science Scholars program, in which undergraduate students who were rising sophomores, juniors, and seniors receive both professional development and mentored research opportunities. In addition to this standard model of a summer research program, we added an additional professional development and skill-building activity, a course-based undergraduate research experience (CURE) performed by the whole group. Students designed and carried out an experiment in the CURE research project looking at the relationship between soil elemental content and sampling site location. They worked collaboratively over several days on experimental design, the experiment itself, data analysis, and finally, poster design for dissemination. The CURE is intentionally different from any mentored research experiences so that students obtain additional and varied research skills from the summer program. Qualitative and quantitative feedback was positive. Additionally, students worked with different students than those in their research lab, broadening their network. In conclusion, a CURE embedded in a summer research program is an effective pedagogical tool for science and additional skill building and enriches an existing summer research program.

Soil-transmitted helminths (STH) are one of the most prevalent enteric infections world-wide. To control STH-related morbidity, the World Health Organization recommends targeted deworming and improvements in water, sanitation and hygiene. Current surveillance strategies for STH focus on identifying and quantifying eggs in stool samples via microscopy, which exhibits poor specificity and sensitivity, especially in settings with low-intensity infections. Wastewater-based epidemiology is a surveillance tool used to monitor pathogen circulation and could replace stool-based approaches for STH detection. However, sampling strategies for settings lacking networked sanitation outside large urban settlements are not well developed. Here, we report evaluation of sampling strategies for soil and wastewater STH surveillance in rural and peri-urban settings without networked sanitation. We used multi-parallel qPCR assays to detect STH DNA in soil collected from high foot-traffic locations and three types of wastewater samples (passive Moore swabs, grab samples, and sediment from drainage ditches) in Comé, Benin and Timiri and Jawadhu Hills in Tamil Nadu, India. We detected STH in soil (India = 32/95, Benin = 39/121) and wastewater (India = 24/60, Benin = 8/64) with a detection frequency across all sample types of 36% in India and 25% in Benin. We evaluated which sample locations and types allowed for more sensitive detection of STH DNA and determined that STH prevalence varied by sample site but did not vary significantly within a given sample site location (e.g., samples collected from multiple locations within one market). Further, we determined that wastewater sediment samples outperformed grab and Moore swab sample types for STH detection. Finally, we expanded our methods to include detection of other enteric pathogens using multiplexed qPCR for wastewater samples. Our results establish sampling strategies for environmental and wastewater surveillance of a wide range of enteric pathogens in settings without networked sanitation.

To develop methods for early detection of natural killer/T-cell lymphoma, nasal type (nasal NKTL) and to evaluate the use of apparent diffusion coefficient (ADC) measurements from diffusion-weighted imaging (DWI) for selecting biopsy sites to enhance the positive biopsy rate. The clinical data of 32 cases of nasal NKTL were retrospectively collected and analyzed. Cases were graded from 1 to 5 based on endoscopic and imaging findings. Biopsy methods included the superficial forceps method and deep incisional method. Data recorded included biopsy method, time to confirmed diagnosis, number of biopsies required, and ADC values of biopsy sites. Both endoscopic and MRI lesion severity did not affect biopsy outcomes (p = 0.8248, 0.7571). The deep incisional biopsy method had a significantly higher probability of yielding positive pathological results compared to the superficial clamping method [OR (95% CI) = 23.2143 (6.0448, 89.1513) (χ2 = 26.1089, p < 0.0001)]. The ADC value at the site with a positive biopsy result is significantly lower than at the site with a negative result (p = 0.0003). The diagnostic threshold for ADC value to predict a positive biopsy result was 0.603 × 10-3 mm2/s, with a Youden index of 0.43. Nasal NKTL has distinct characteristics distinguishing it from sinusitis and other tumors. Deep incision biopsy under general anesthesia is superior to the superficial method. The location of the positive sampling site can be guided by measuring the ADC value, and the optimal diagnostic threshold of this proposed method is 0.603 × 10-3 mm2/s. 4 Laryngoscope, 2024.

Gas stations distributed around densely populated areas are responsible for toxic pollutant emissions such as volatile organic compounds (VOCs). This study aims to measure VOCs emission from three different kinds of gas stations to determine the extent of pollution from the gas stations and the most frequent type of VOC compound emitted. The concentrations of ambient VOCs at three refueling stations with a different type of fuels in Mashhad were monitored. The result of this study showed that CNG fuel stations are less polluting than petrol stations. In all the studied sites, the highest concentrations were related to xylene isomers, irrespective of the fuel type. Total VOCs at the supply of both compressed natural gas (CNG) and gasoline stations was 482.36 ± 563.45 µg m−3. At a CNG station and a gasoline station, total VOC concentrations were 1363.4 ± 1975 µg m−3 and 410.29 ± 483.37 µg m−3, respectively. The differences in concentrations of toluene and m,p-xylene between the fuel stations can be related to the quality and type of fuel, vapor recovery technology, fuel reserves, dripless nozzles, traffic density in these stations, meteorological conditions and the location of sampling sites. The combination of a sine function and a quadratic function could model the fluctuation behavior of air pollutants like m,p-xylene. In all the sites, the highest concentrations were related to xylene isomers, irrespective of the type of fuel. The changing rate of m,p-xylene pollutant in each station was also modeled in this study.

Solar power is an important renewable energy resource that plays a pivotal role in replacing fossil fuel generators and lowering carbon emissions. Since sunlight, which is highly dependent on meteorological factors, is highly volatile, the difficulty in collecting real-time data from renewable energy power plants poses a major threat to maintaining the stability of the entire power system in the target area. A high-performance wireless metering modem is required to monitor the renewable energy generation power of the entire target area in real-time. However, installing such devices on all sites is expensive, so we propose a system that uses deep learning to estimate the generation power of a target site based on the power generations of some sample sites. We use clustering and distance-based sampling to extract a sample site corresponding to each target site and use the recurrent neural network (RNN)-based attention techniques to estimate the generation of target sites from the sample sites. Our experiments show that the proposed RNN-based attention models significantly improve estimation accuracy compared to the baseline model or other deep learning models, irrespective of the number or location of sample sites.

Soil nutrients are the key factors that influence crop productivity and plant growth. The present study gives information about the soil nutrient status of Jabalpur Block in Jabalpur District, Madhya Pradesh. 30 soil samples were collected from the field at a depth of 0-15 cm. Soil samples were collected using grid sampling techniques and the location of soil sampling sites was determined using GPS. Soil samples were further analysed to determine the soil nutrient index (SNI) using the soil chemical properties such as pH, OC, EC, and soil macronutrients i.e. N, P, K and Sulphur (S), etc. The nutrients are further categorized as low, medium, and high as per the soil nutrient index (NI). The spatial variation of soil nutrient parameters was represented by using the interpolation method in the ArcGIS environment. Based on the fertility measurement, the pH of the Jabalpur block ranges from 7.1 to 7.9, which is indicative of Neutral to alkaline nature, and organic carbon content ranges from 0.7 % to 1.78 %, indicating that the amount of SOC is medium to high along with available N ranging from (90kg/ha to 320kg/ha), available P with (20.16kg/ha to 47.04kg/ha) and available K of (29.12 kg/ha to 118kg/ha). The fertility status of Jabalpur block in this study is primarily constrained by soil pH, as well as N, P, K and S. Therefore, to enrich the soil fertility status of Jabalpur block, proper nutrient management practices, application of different organic manures, suitable cropping pattern and sustainable use of agricultural land are very essential.

Characterization of fungal communities in aerosols: Coastal waters vs. open oceans

The rhizosphere is the narrow region of soil surrounding the roots of plants that is influenced by root exudates, root secretions, and associated microbial communities. This region is crucial to plant growth and development and plays a critical role in nutrient uptake, disease resistance, and soil transformation. Understanding the function of exogenous compounds in the rhizosphere starts with determining the spatiotemporal distribution of these molecular components. Using liquid microjunction surface-sampling probe mass spectrometry (LMJ-SSP-MS) and microfluidic devices with attached microporous membranes enables in situ, nondisruptive, and nondestructive spatiotemporal measurement of exogenous compounds from plant roots. However, long imaging times (>2 h) can negatively affect plant heath and limit temporal studies. Here, we present a novel strategy to optimize the number and location of sampling sites on these microporous membrane-covered microfluidic devices. This novel, "structure-driven" sampling workflow takes into consideration the channel structure of the microfluidic device to maximize sampling from the channels and minimize acquisition time (∼4× less time in some cases while providing similar chemical image accuracy), thus reducing stress on plants during in situ LMJ-SSP-MS analysis.

Agricultural soils are considered as “hot-spots” of plastic particles; however, due to a lack of standardized method of microplastic determination in soils, as well as no legal regulations requiring the monitoring of the soil environment in the context of microplastic contamination, the data on MP abundance and occurrence in European soils are very limited. In this first study of MPs pollution in agricultural soils in Poland, we developed a method of microplastic extraction from soil samples with different properties (particle size distribution, clay and organic matter content) and used optical microscopy for MP determination and quantification. In this study, we analyzed 44 soil samples from five sampling site locations with differing soil type, agricultural activity, including farmland soils on floodplains and past records of sewage sludge and compost applications. We found evidence that 93% of cultivated soils in the SW part of Poland contained MPs. The content of MP varied between soil types and present/former use of the land. Loamy and clay soils contained more MPs, 1540 ± 912 particles per kg soil and 933 ± 682 particles per kg, respectively, compared with sandy soils at 383 ± 188 particles per kg of soil. The highest MP concentrations were determined in soils amended with sewage sludge, wastewaters and green-waste composts (up to 4050 ± 2831 particles per kg of soil). The wide distribution of MPs with a dominance of plastic fibers (up to 60% of determined MP types) can be associated with agricultural sources such as soil mulching, the use of organic fertilizers, seed coating or unintentional waste dumping and air deposition.

The urban area is a source of invasive plants that enter through human activities such as agriculture and land-use conversion. Studying the invasive plant in urban areas is essential to understanding the city’s ecosystem health condition. Therefore, this study aims to inventory invasive plants, map their distribution, and explain the relationship between land use with the community diversity and species richness of invasive plants in Bandung. The vegetation analysis was performed using line-transect in 22 study sites distributed using a systematic random sampling method in Bandung to observe the plant species composition. The study plots were placed based on the land-use type. The species name, individual number, frequency, and sampling site locations were noted and analyzed to calculate the important value index (IVI) and the invasive species distribution pattern using the principal component analysis (PCA). The dominant invasive species was spatially mapped. Six types of land use were used in this study, i.e., settlements, street green lanes, gardens, paddy fields, urban parks, and urban forests. There were 187 species found in Bandung, which can be categorized into alien invasive species (39%), invasive native plants (25%), non-invasive alien species (18%), non-invasive native species (15%), and unidentified plants (3%). The most common invasive plants found were Eleusine indica (IVI=10.50%), Trimezia martinicensis (IVI=7.22%), and Cyperus rotundus (IVI=6.74%). Based on the plant community similarity index, the study area with the highest similarities were paddy fields with gardens (50.5%), settlements with road lanes (44.4%), urban parks with road lanes (26.2%), and urban forests with road lane (17.5%). PCA showed Swietenia macrophylla as the most common invasive plant found in urban forests, urban parks, and road lanes, with air humidity as the most influencing environmental factor. Trimezia martinicensis is the most common species in the settlement area affected by high air humidity. Bidens pilosa is an invasive plant commonly found on paddy fields, gardens, settlements, road lanes, and urban park edges. This species can easily and rapidly reproduce with a high survival rate. The many invasive plants found in Bandung must be managed to maintain the urban ecosystem’s health.

Khanspur, a small town located near Ayubia National Park, is a famous mountainous resort lying in Galyat area of Khyber Pakhtunkhwa. It is included in one of the richest biodiversity hotspots in the country. Despite of extensive previous studies, many new species are awaiting to be documented, including macrofungi. In this study, a new macrofungus Pseudoomphalina khanspurensis is analyzed by both light and scanning electron microscopy as well as DNA sequences based on nrITS and nrLSU regions. The sister species P. cokeri differs by its red to purple, dark to reddish brown, broadly convex to applanate pileus, purple blue to brownish stipe, and numerous cylindrical to lageniform cheilocystidia. HIGHLIGHTS: This is the first report of genus Pseudoomphalina from Pakistan and first study based on Scanning electron microscopy. These species have been described with detailed micro-morphological and molecular makers (nrITS and nrLSU). General distribution, ecology, diagnostic features and comparisons with alies have been provided. Graphical representation of DNA extraction and geographical locations of sampling sites are also illustrated. Softwares such as CIPRES Science Gateway Portal, MUSCLE, BioEdit, FigTree, Adobe illustrator and Adobe Photoshop are used in the current study. No member of this genus is already described from Pakistan.

Two species of the genus Laccaria, are described as new reports for Pakistan. Laccaria murina has been collected from a Himalayan moist temperate forest in Khanspur, KP, while L. pumila was found in the moist temperate forests of Kumrat Valley in Dir Upper, KP, and at higher altitudes of the Deosai plains of Gilgit-Baltistan, Pakistan. Phylogenetic analysis based on the nrITS region clustered the Pakistani species with L. murina sequences with other Asian collections and L. pumila from USA and Netherlands. SEM of basidiospores along with detailed micro-morphological data are provided. General distribution, habitat, ecology, and diagnostic features are also discussed. RESEARCH HIGHLIGHTS: It has new reports from Pakistan, studies based on light, scanning electron microscopy, and nrITS molecular markers. These species have been described with detailed micro-morphological and molecular phylogenetic analyses. General distribution, ecology, diagnostic features, and comparisons with closely related specimens have been provided. Graphical representation of DNA extraction and geographical locations of sampling sites are also illustrated (Figures1 and 2). Very few members of this genus are already described from Pakistan.

The Ba, Pb and Zn ore deposit Pleše near Ljubljana is one of the formerly productive mines. The stream sediments were sampled and analysed by XRF to establish the effect of grain size, mineralization, and downstream location of sampling sites on geochemical composition based on various statistical analyses. Statistical analyses of the geochemical data confirm the impact of mineralization. The parametric t-test, non-parametric Mann-Whitney test and cluster analysis showed only minor differences in the geochemical composition of the samples with different grain sizes (&lt; 0.063 mm and 0.063-2 mm). The parametric and non-parametric correlation coefficients as well as cluster analysis indicate that the contents of Si, Al, K, Rb, and Fe are associated with weathered rock forming minerals such as micas, and clay minerals, whereas Nb and Zr are associated with minerals resistant to weathering. Ca and Mg are associated with carbonates. S, Ba, Sr, Pb, Zn, and Mn indicate local mineralization with sulphates and sulphides. The results of the t-test and analysis of variance, Mann-Whitney tests and Kruskal-Wallis ANOVA of the groups established by the cluster analysis confirm that the contents of Ba, Pb and Sr have a statistically significant influence on the classification of the cluster group - i.e., the influence of sediment mineralization. There are no differences in elemental contents in the sediment samples downstream. The statistical approach to evaluate the geochemical data has proven useful and provides a good basis for further interpretation.

The aim of the study was to assess the quality of agricultural soils, which accumulate additional amounts of heavy metals from fertilization and modify their bioavailability, with the use of interdependencies between their biological and chemical properties conditioned by the distance from communication routes. Our results indicated that heavy metals had an impact on enzyme activity in soils and their accumulation was significantly related to the distance from the edge of the road, location of sampling sites, date of soil sampling, and years of research. It was found that the greatest amounts of zinc, cadmium, lead, and copper were accumulated at a distance of 5–20 m from the edge of the road. The highest enrichment factor and geoaccumulation index values were recorded for Pb, followed by Zn, Cu, and Cd. Principal component analysis and regression models showed that the activities of phosphatase and dehydrogenases seem to be the best bioindicators of contamination of roadside soils used for agricultural purposes. Since their activity is related to soil’s organic carbon content, inputs of organic fertilizers and crop residues should be ensured in the agroecosystems along roadsides.

Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference dataset produced by this national programme, from May 2020 to February 2022. Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 162 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs. We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.

The mussel M. galloprovincialis is one of the most popular species in world's mariculture and environment pollution bioindicators. Although this mollusk was in a focus of numerous publications, the detoxifying and accumulating role of some of its body parts was insufficiently studied. The goals of the present work were as follows: (a) to study the distribution of potentially toxic elements (Cr, Co, Ni, Cu, Zn, As, Cd, Hg, Pb) in soft tissues, byssus, and shell liquor of this bivalve as a function of sampling location in the Black Sea near the southwestern coast of Crimea and (b) to assess human health risks from consuming soft tissues of mussels cultivated on a mollusk farm. Multivariate analysis showed significant differences in the overall distribution of the elements among the body parts and sampling sites under consideration. The trace element contents in soft tissues of M. galloprovincialis decreased in the following order: Zn > Cu > As > Ni > Pb > Cd > Cr > Co > Hg. The noncarcinogenic hazard index from the cultivated mussel consumption was found to be well below one and the carcinogenic risk index was found within the tolerable limits, which indicate the safety of consuming these mussels for humans. Byssus of M. galloprovincialis was characterized as a perfect indicator of marine environment pollution with Ni, Cu, Pb, Сo, and Cr. For the first time, the concentrations of trace elements were determined in the shell liquor and the function of byssus and shell liquor as the systems of trace element excretion from soft tissues was demonstrated.