Search Results (189)

In-situ chemical oxidation (ISCO) is a common method to remediate chlorinated ethene contaminants in groundwater. Monitoring the effectiveness of ISCO can be hindered because of insufficient observations to assess oxidant delivery. Advantageously, potassium permanganate, one type of oxidant, provides the opportunity to use its strong electrical signal as a surrogate to track oxidant delivery using time-series borehole geophysical methods, like electromagnetic (EM) induction logging. Here we report a passive ISCO (P-ISCO) experiment, using potassium permanganate cylinders emplaced in boreholes, at a chlorinated ethene contamination site, Naval Air Station Pensacola, Florida. The contaminants are found primarily at the base of a shallow sandy aquifer in contact with an underlying silty-clay confining bed. We used results of the time-series borehole logging collected between 2017 and 2022 in 4 monitoring wells to track oxidant delivery. The EM-induction logs from the monitoring wells showed an increase in EM response primarily along the contact, likely from pooling of the oxidant, during P-ISCO treatment in 2021. Interestingly, concurrent natural gamma-ray (NGR) logging showed a decrease in NGR response at 3 of the 4 wells possibly from the formation of manganese precipitates coating sediments. The coupling of time-series logging and well-chemistry data allowed for an improved assessment of passive ISCO treatment effectiveness. Full article

Prediction of the spatial configuration of the umbilical cable during deep-sea mining in-situ tests is of great significance because dynamic change may cause the umbilical cable to touch the ground or overturn the mining vehicle. In the present paper, a real-time prediction method for the dynamic spatial configuration of the umbilical cable during the deep-sea mining process is proposed. At first, the environmental information, position and motion of the vessel–umbilical cable–mining system were collected by sensors arranged at different locations. Then, the data were converted and transformed to the local vessel coordinate system. After that, the commercial software OrcaFlex was employed to conduct real-time simulation, in which the spatial configuration of the umbilical cable was predicted by the lumped mass method. Furthermore, the proposed real-time simulation method was employed in a sea trial test of deep-sea mining in an area with a water depth of 1100 m. Comparing the prediction results with the trajectory of the USBL beacon obtained from the monitoring data, the maximum distance of some specific points was close to 5 m, and most of them were less than 3 m. Meanwhile, it could also give the dynamic responses of the deep-sea mining system. For example, the maximum top tension of the umbilical cable was less than 15 kN, which could be used to evaluate the health condition of the system. During the sea trial test, the proposed method played an important role in ensuring the safety of the umbilical cable during wide-range movement of the mining vehicle. With characteristics of good real-time performance, accurate prediction, high reliability and stability, the proposed method could enhance the confidence of engineers for on-site operation as a powerful digital tool for visualization of the subsea working state. Full article

Different sulfates (Ca-, Mg, and Fe- sulfates) have been extensively detected on the Martian surface. As one of the Martian sulfates, the presence of ferrous sulfates will provide valuable clues about the redox environment, hydrological processes, and climatic history of ancient Mars. In this study, three hydrated ferrous sulfates were prepared in the laboratory by heating dehydration reactions. These samples were analyzed using X-ray Diffraction (XRD) to confirm their phase and homogeneity. Subsequently, Raman, mid-infrared (MIR) spectra, visible near-infrared (VNIR) spectra, and laser-induced breakdown spectroscopy (LIBS) were measured and analyzed. The results demonstrate that the spectra of three hydrated ferrous sulfates exhibit distinctive features (e.g., the v₁ and v₃ features of ${\mathrm{S}\mathrm{O}}_4^{2-}$ tetrahedra in their Raman and MIR spectra) that can offer new insights for identifying different ferrous sulfates on Mars and aid in the interpretation of in-situ data collected by instruments such as the Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC), SuperCam, and ChemCam, etc. Full article

The latest satellite in the Landsat series, Landsat-9, was successfully launched on 27 September 2021, equipped with the Operational Land Imager-2 (OLI-2) sensor, continuing the legacy of OLI/Landsat-8. To evaluate the uncertainties in water surface reflectance derived from OLI-2, this study conducts a comprehensive performance assessment of six atmospheric correction (AC) methods—DSF, C2RCC, iCOR, L2gen (NIR-SWIR1), L2gen (NIR-SWIR2), and Polymer—using in-situ measurements from 14 global sites, including 13 AERONET-OC stations and 1 MOBY station, collected between 2021 and 2023. Error analysis shows that L2gen (NIR-SWIR1) (RMSE ≤ 0.0017 sr⁻¹, SA = 6.33°) and L2gen (NIR-SWIR2) (RMSE ≤ 0.0019 sr⁻¹, SA = 6.38°) provide the best results across four visible bands, demonstrating stable performance across different optical water types (OWTs) ranging from clear to turbid water. Following these are C2RCC (RMSE ≤ 0.0030 sr⁻¹, SA = 5.74°) and Polymer (RMSE ≤ 0.0027 sr⁻¹, SA = 7.76°), with DSF (RMSE ≤ 0.0058 sr⁻¹, SA = 11.33°) and iCOR (RMSE ≤ 0.0051 sr⁻¹, SA = 12.96°) showing the poorest results. By comparing the uncertainty and consistency of Landsat-9 (OLI-2) with Sentinel-2A/B (MSI) and S-NPP/NOAA20 (VIIRS), results show that OLI-2 has similar uncertainties to MSI and VIIRS in the blue, blue-green, and green bands, with RMSE differences within 0.0002 sr⁻¹. In the red band, the OLI-2 uncertainties are lower than those of MSI but higher than those of VIIRS, with an RMSE difference of about 0.0004 sr⁻¹. Overall, OLI-2 data processed using L2gen provide reliable surface reflectance and show high consistency with MSI and VIIRS, making it suitable for integrating multi-satellite observations to enhance global coastal water color monitoring. Full article

Transformative learning processes that can trigger deep and long-lasting behaviour changes are highly sought after for targeted improvements ranging from human diet and health to pro-environmental behaviours. A step forward was the reporting of a method that reliably produces transformative learning outcomes (TLOs) as an (incidental) effect of group shared values crystallisation, but the theme of the TLOs could not be targeted, e.g., for pro-environmental behaviours. A recent exploratory study bolted on environmentally themed pre-Nudging and unexpectedly produced a heavy bias towards pro-environmental behaviour changes. Here, we investigated more systematically the influences of different Nudging types upon TLO themes produced using two further case study designs created for comparability with earlier studies and using the same process (WeValue InSitu) and post-event data collection of TLOs categorised as environmental/not and behavioural/not. Our findings show that most Nudging had no effect, including raising the profile of environmental photos and the materials used in the crystallisation process, having participants reflect on their environmental identities, or emphasising environmental topics before going home. However, Nudging which involved answering written questions on specific personal pro-environmental actions such as recycling, applied early on, was linked to desired results. This has pragmatic significance for sustainability practitioners and raises questions for further research on the mechanisms of both active learning and Nudging. Full article

This study uses a triple-sensor collocation approach to evaluate the performance of small-holder irrigation schemes in the Zamra catchment of Northern Ethiopia. Crop water productivity (CWP), as an integrator of biomass production and water use, was used to compare the overall efficiencies of three types of irrigation systems: traditional and modern diversions, and dam-based irrigation water supply. Farmer-reported data often rely on observations, which can introduce human estimation and measurement errors. As a result, the evaluation of irrigation scheme performance has frequently been insufficient to fully explain crop water productivity. To overcome the challenges of using one single estimation method, we used a triple-sensor collocation approach to evaluate the efficiency of three small-scale irrigation schemes, using water productivity as an indicator. It employed three independent methods: remotely sensed data, a model-based approach, and farmer in-situ estimates to assess crop yields and water consumption. To implement the triple collocation appraisal, we first applied three independent evaluation methods, i.e., remotely sensed, model-based, and farmer in-situ estimates of crop yields and water consumption, to assess the crop water productivities of the systems. Triple-sensor collocation allows for the appraisal and comparison of estimation errors of measurement sensor systems, and enables the ranking of the estimators by their quality to represent the de-facto unknown true value, in our case: crop yields, water use, and its ratio CWP, in small-holder irrigated agriculture. The study entailed four main components: (1) collecting in-situ information and data from small-holder farmers on crop yields and water use; (2) derivation of remote sensing-based CWP from the FAO WaPOR open database and time series; (3) evaluation of biomass, crop yields and water use (evapotranspiration) using the AquaCrop model, integrating climate, soil data, and irrigation management practices; (4) performing and analysis of a categorical triple collocation analysis of the independent estimator data and performance ranking of the three sensing and small-holder irrigation systems. Maize and vegetables were used as main crops during three consecutive irrigation seasons (2017/18, 2018/19, 2019/20). Civil war prevented further field surveying, in-situ research, and data collection. The results indicate that remote sensing products are performed best in the modern and dam irrigation schemes for maize. For vegetables, AquaCrop performed best in the dam irrigation scheme. Full article

A continuous flow with reagent injection method on a novel inlaid microfluidic platform for nitrite determination has been successfully developed. The significance of the high-frequency monitoring of nutrient fluctuations in marine environments is crucial for understanding our impacts on the ecosystem. Many in-situ systems face limitations in high-frequency data collection and have restricted deployment times due to high reagent consumption. The proposed microfluidic device employs automatic colorimetric absorbance spectrophotometry, using the Griess assay for nitrite determination, with minimal reagent usage. The sensor incorporates 10 solenoid valves, four syringes, two LEDs, four photodiodes, and an inlaid microfluidic technique to facilitate optical measurements of fluid volumes. In this flow system, Taylor–Aris dispersion was simulated for different injection volumes at a constant flow rate, and the results have been experimentally confirmed using red food dye injection into a carrier stream. A series of tests were conducted to determine a suitable injection frequency for the reagent. Following the initial system characterization, seven different standard concentrations ranging from 0.125 to 10 µM nitrite were run through the microfluidic device to acquire a calibration curve. Three different calibrations were performed to optimize plug length, with reagent injection volumes of 4, 20, and 50 µL. A straightforward signal processing method was implemented to mitigate the Schlieren effect caused by differences in refractive indexes between the reagent and standards. The results demonstrate that a sampling frequency of at least 10 samples per hour is achievable using this system. The obtained attenuation coefficients exhibited good agreement with the literature, while the reagent consumption was significantly reduced. The limit of detection for a 20 µL injection volume was determined to be 94 nM from the sample intake, and the limit of quantification was 312 nM. Going forward, the demonstrated system will be packaged in a submersible enclosure to facilitate in-situ colorimetric measurements in marine environments. Full article

The Vaal Dam catchment, which is the source of potable water for Gauteng province, is characterized by diverse human activities, and the dam encounters significant nutrient input from multiple sources within its catchment. As a result, there has been a rise in Harmful Algal Blooms (HABs) within the reservoir of the dam. In this study, we employed time series analysis on nutrient data to explore the relationship between HABs, using chlorophyll-a (Chl−a) as a proxy, and nutrient levels. Additionally, Chl−a data extracted from Landsat-8 satellite images was utilized to visualize the spatial distribution of HABs in the reservoir. Our findings revealed that HAB productivity in the Vaal Dam is influenced by the levels of total phosphorus (TP) and organic nitrogen (KJEL_N), which exhibited a positive correlation with chlorophyll-a (Chl−a) concentration. Long-term analysis of Chl−a in-situ data (1986–2022) collected at a specific point within the reservoir showed an average concentration of 11.25 μg/L. However, on certain stochastic dates, Chl−a concentration spiked to very high values, reaching a maximum of 452.8 μg/L, coinciding with elevated records of TP and KJEL_N concentrations on those dates, indicating their effect on productivity levels. The decadal time series and trend analysis demonstrated an increasing trend in Chl−a productivity over the studied period, rising from 4.75 μg/L in the first decade (1990–2000) to 10.51 μg/L in the second decade (2000–2010), and reaching 16.7 μg/L in the last decade (2010–2020). The rising averages of the decadal values were associated with increasing decadal averages of its driving factors, TP from 0.1043 to 0.1096 to 0.1119 mg/L for the three decades, respectively, and KJEL_N from 0.80 mg/L in the first decade to 1.14 mg/L in the last decade. Satellite data analysis during the last decade revealed that the spatial dynamics of HABs are influenced by the dam’s geometry and the levels of discharge from its two feeding rivers, with higher concentrations observed in meandering areas of the reservoir and within zones of restricted water circulation. Full article

Groundwater is an important part of the water resources, crucial for human production and life. With the rapid development of industry and agriculture, organic pollution of groundwater has attracted great attention. Enhanced in-situ bioremediation of groundwater technology has gradually gained attention because of its high efficiency and low environmental impact. Bioremediation agents are crucial for bioremediation technology. In this review, bioremediation agents were classified into three categories: biological nutrition agents, slow-release agents, and microbial agents. Biological nutrition agents are a specific mixture of mineral salt and carbon source; slow-release agents may contain mineral salt, carbon source, pH buffers, and oxygen-releasing material and microbial agents with specific microbial culture. By adding bioremediation agents to the polluted sites, they can improve population density and degradation efficiency for microbial degradation of pollutants. To assist future development and application of bioremediation agents, the development of different agents in laboratory and commercial to date was retrieved online via publisher sites and cooperation case studies. The data collected were analyzed and reviewed, as well as application and remediation effects of enhanced in-situ bioremediation agents were summarized. Current studies mainly focus on laboratory development and experiments, while field tests and remediation effects between different agents are of less concern. Further study may focus on developing new materials, especially coating or loading materials, and systematic evaluation of different agents, considering both laboratory research and on-site experiments, in order to improve the efficiency of in situ organically contaminated groundwater bioremediation. Full article

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final classification was performed using another ML model referred to as the Meta-model. The Pa-PCA-Ca model was evaluated using in-situ data collected from extensive field surveys in the northwest part of Iran. The results demonstrated the superior performance of the proposed structure, achieving an Overall Accuracy (OA) of 96.25% and a Kappa coefficient of 0.955. The incorporation of PCA led to an OA improvement of over 6%. Furthermore, the proposed model significantly outperformed conventional classification approaches, which simply stack RS data sources and feed them to a single ML model, resulting in a 10% increase in OA. Full article

In 2017, Bureau of Economic Geology (BEG) researchers at the University of Texas at Austin (UT Austin) conducted an airborne lidar survey campaign, collecting topographic and bathymetric data over Lower Laguna Madre, which is a shallow hypersaline lagoon in south Texas. Researchers acquired 60 hours of lidar data, covering an area of 1600 km² with varying environmental conditions influencing water quality and surface heights. In the southernmost parts of the lagoon, in-situ measurements were collected from a boat to quantify turbidity, water transparency, and depths. Data analysis included processing of Sentinel-2 L1C satellite imagery pixel reflectance to classify locations with intermittent turbidity. Lidar measurements were compared to sonar recordings, and results revealed height differences of 5–25 cm where the lagoon was shallower than 3.35 m. Further, researchers analyzed satellite bathymetry at relatively transparent lagoon locations, and the results produced height agreement within 13 cm. The study concluded that bathymetric efforts with airborne lidar and optical satellite imaging have practical limitations and comparable results in large and dynamic shallow coastal estuaries, where in-situ measurements and tide adjustments are essential for height comparisons. Full article

High spatial and temporal resolution hydrographic data collected by Southern Elephant Seals (Mirounga leonina, SESs) and satellite remote sensing data allow a detailed oceanographic description of the Argentine Continental Shelf (ACS). In-situ data were obtained from the CTD (Conductivity, Temperature, and Depth), accelerometer, and hydrophone sensors attached to five SESs that crossed the ACS between the 17th and 31st of October 2019. The analysis of the temperature (T) and salinity (S) along the trajectories allowed us to identify two different regions: north and south of 42°S. Satellite Sea Surface Temperature (SST) data suggests that north of 42°S, warm waters are coming from the San Matias Gulf (SMG). The high spatio-temporal resolution of the in-situ data shows regions with intense gradients along the T and S sections that were associated with a seasonal front that develops north of Península Valdés in winter due to the entrance of cold and fresh water to the SMG. The speed of the SESs is correlated with tidal currents in the coastal portion of the northern region, which is in good agreement with the macrotidal regime observed. A large number of Prey Catch Attempts (PCA), a measure obtained from the accelerometer sensor, indicates that SESs also feed in this region, contradicting suggestions from previous works. The analysis of wind intensity estimated from acoustic sensors allowed us to rule out the local wind as the cause of fast thermocline breakups observed along the SESs trajectories. Finally, we show that the maximum depth reached by the elephant seals can be used to detect errors in the bathymetry charts. Full article

Accurate crop yield Mapping is paramount in agricultural monitoring and food security. In this study, we present a comprehensive investigation into estimating winter wheat yield in the Qazvin plane of Iran, leveraging the synergy between machine learning algorithms and the fusion of remote sensing data from radar and optical satellite sensors. The research is based on the availability of high-quality in situ yield data gathered by the Ministry of Agriculture in collaboration with the Food and Agriculture Organization (FAO), collected during the 2019–2020 crop year. The study area encompasses the Qazvin plane, an agriculturally significant region renowned for winter wheat production in Iran. In-situ data from various agricultural fields and seed types as reference measurements enabled us to conduct rigorous validation of the performance of machine learning algorithms and the effectiveness of the fused remote sensing data. The primary objective of this study is to assess and compare the performance of seven prominent machine learning algorithms for accurate estimation of the annual winter wheat yields. Furthermore, we investigate the individual and synergistic capabilities of radar and optical satellite sensors in estimating winter wheat yield. Through rigorous analysis of the pixel-level confusion matrices, we identify the most effective model for yield estimation, evaluating the complementarity and information redundancy between the two types of remote sensing data. In this study, we conducted an extensive comparison of various machine learning algorithms for winter wheat crop yield estimation in the Qazvin plane of Iran. Among the four best-performing algorithms examined, namely polynomial regression (RMSE = 0.5657 $\mathrm{t}/{\mathrm{h}\mathrm{a}}^{-1}$), random forest (RMSE = 0.1632 $\mathrm{t}/{\mathrm{h}\mathrm{a}}^{-1}$), XGBoost (RMSE = 0.3153 $\mathrm{t}/{\mathrm{h}\mathrm{a}}^{-1}$), and the proposed Multi-Layer Perceptron (MLP) (RMSE = 0.1324 $\mathrm{t}/{\mathrm{h}\mathrm{a}}^{-1}$), the MLP demonstrated superior performance. The MLP’s yield estimation exceeded the total yearly agricultural statistics of Qazvin by 0.19 percent. However, this discrepancy can be attributed to various factors, including errors in wheat and barley field mapping, miscalculation in cumulative statistics, and the inherent limitations of yield estimation algorithms in capturing the dynamic nature of agricultural systems. The findings of this research provide valuable insights into the potential of machine learning algorithms and remote sensing data fusion for accurate crop yield estimation, paving the way for enhanced agricultural monitoring and decision-making processes in the region. Full article

Background: Multiple primary melanoma (MPM) is a diagnostic challenge even with ancillary imaging technologies available to dermatologists. In selected patients’ phenotypes, the use of imaging approaches can help better understand lesion characteristics, and aid in early diagnosis and management. Methods: Under a 5-year prospective single-center follow-up, 58 s primary melanomas (SPMs) were diagnosed in two first-degree relatives, with fair skin color, red hair, green eyes, and personal history of one previous melanoma each. Patients’ behavior and descriptive demographic data were collected from medical records. The information on the first two primary melanomas (PMs) were retrieved from pathology reports. The characteristics of 60 melanomas were collected from medical records, video dermoscopy software, and pathology reports. Reflectance confocal microscopy (RCM) was performed prior to excision of 22 randomly selected melanomas. Results: From February 2018 to May 2023, two patients underwent a pooled total of 214 excisional biopsies of suspect lesions, resulting in a combined benign versus malignant treatment ratio (NNT) of 2.0:1.0. The number of moles excised for each melanoma diagnosed (NNE) was 1.7:1.0 and 6.9:1.0 for the female and male patient respectively. The in-situ melanoma/invasive melanoma ratio (IIR) demonstrated a higher proportion of in-situ melanomas for both patients. From June 2018 to May 2023, a total of 58 SPMs were detected by the combination of total body skin exam (TBSE), total body skin photography (TBSP), digital dermoscopy (DD), and sequential digital dermoscopy imaging (SDDI) via comparative approach. The younger patient had her PM one month prior to the second and third cutaneous melanomas (CMs), characterizing a case of synchronous primary CM. The male older relative had a total of 7 nonsynchronous melanomas. Conclusions: This CM cohort is composed of 83.3% in-situ melanoma and 16.7% invasive melanoma. Both patients had a higher percentage of SPM with clinical nevus-like morphology (84.5%), global dermoscopic pattern of asymmetric multiple component (60.3%) and located on the lower limbs (46.6%). When RCM was performed prior to excision, 81% of SPM had features suggestive of malignancy. As well, invasive melanomas were more frequent in the lower limbs (40%). In the multivariate model, for the two high-risk patients studied, the chance of a not associated with nevus (“de novo”) invasive SPM diagnosis is 25 times greater than the chance of a diagnosis of a nevus-associated invasive SPM. Full article

Monitoring turbidity is essential for sustainable coastal management because an increase in turbidity leading to diminishing water clarity has a detrimental ecological impact. Turbidity in coastal waters is strongly dependent on the concentration and physical properties of particles in the water column. In the Belgian part of the North Sea, turbidity and suspended particulate matter (SPM) concentrations have been monitored for decades by satellite remote sensing, but this technique only focuses on the surface layer of the water column. Within the water column, turbidity and SPM concentrations are measured in stations or transects with a suite of optical and acoustic sensors. However, the dynamic nature of SPM variability in coastal areas and the recent construction of offshore windmill parks and dredging and dumping activities justifies the need to monitor natural and human-induced SPM variability in 3D instead. A possible solution lies in modern multibeam echosounders (MBES), which, in addition to seafloor bathymetry data, are also able to deliver acoustic backscatter data from the water column. This study investigates the potential of MBES as a 3D turbidity and SPM monitoring tool. For this purpose, a novel empirical approach is developed, in which 3D MBES water column and in-situ optical sensor datasets were collected during ship transects to yield an empirical relation using linear regression modeling. This relationship was then used to predict SPM volume concentrations from the 3D acoustic measurements, which were further converted to SPM mass concentrations using calculated densities. Our results show that these converted mean mass concentrations at the Kwinte and Westdiep swale areas are within the limits of the reported yearly averages. Moreover, they are in the same order of magnitude as the measured mass concentrations from Niskin water samples during each campaign. While there is still need for further improvement of acquisition and processing workflows, this study presents a promising approach for converting MBES water column data to turbidity and SPM measurements. This opens possibilities for improving future monitoring tools, both in scientific and industrial sectors. Full article