Field Monitoring Research Articles

Cognitive Computing Advancements: Improving Precision Crop Protection through UAV Imagery for Targeted Weed Monitoring

Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was to create an automated multiclass discrimination system using cognitive computing, regardless of the weed growth stage. Initially, the model was trained and tested on a dataset of 31,002 UAV images, including ten weed species manually identified by experts at the early phenological stages of maize (BBCH14) and tomato (BBCH501). The images were captured at 11 m above ground level. This resulted in a classification accuracy exceeding 99.1% using the vision transformer Swin-T model. Subsequently, generative modeling was employed for data augmentation, resulting in new classification models based on the Swin-T architecture. These models were evaluated on an unbalanced dataset of 36,556 UAV images captured at later phenological stages (maize BBCH17 and tomato BBCH509), achieving a weighted average F1-score ranging from 94.8% to 95.3%. This performance highlights the system’s adaptability to morphological variations and its robustness in diverse crop scenarios, suggesting that the system can be effectively implemented in real agricultural scenarios, significantly reducing the time and resources required for weed identification. The proposed data augmentation technique also proved to be effective in implementing the detection transformer architecture, significantly improving the generalization capability and enabling accurate detection of weeds at different growth stages. The research represents a significant advancement in weed monitoring across phenological stages, with potential applications in precision agriculture and sustainable crop management. Furthermore, the methodology showcases the versatility of the latest generation models for application in other knowledge domains, facilitating time-efficient model development. Future research could investigate the applicability of the model in different geographical regions and with different types of crops, as well as real-time implementation for continuous field monitoring.

50 Hz Temporal Magnetic Field Monitoring from High-Voltage Power Lines: Sensor Design and Experimental Validation.

A low-cost, tri-axial 50 Hz magnetic field monitoring sensor was designed, calibrated and verified. The sensor was designed using off-the-shelf components and commercially available coils. It can measure 50 Hz magnetic fields originating from high-voltage power lines from 0.08 µT to 364 µT, divided into two measurement ranges. The sensor was calibrated both on-board and in-lab. The on-board calibration takes the circuit attenuation, noise and parasitic components into account. In the in-lab calibration, the output of the developed sensor is compared to the benchmark, a narrowband EHP-50. The sensor was then verified in situ under high-voltage power lines at two independent measurement locations. The measured field values during this validation were between 0.10 µT and 13.43 µT, which is in agreement with other reported measurement values under high-voltage power lines in literature. The results were compared to the benchmark, for which average deviations of 6.2% and 1.4% were found, at the two independent measurement locations. Furthermore, fields up to 113.3 µT were measured in a power distribution sub-station to ensure that both measurement ranges were verified. Our network, four active sensors in the field, had high uptimes of 96%, 82%, 81% and, 95% during a minimum 3-month interval. In total, over 6 million samples were gathered with field values that ranged from 0.08 µT to 45.48 µT. This suggests that the proposed solution can be used for this monitoring, although more extensive long-term testing with more sensors is required to confirm the uptime under multiple circumstances.

Field Monitoring and the Impact of Green Space on the Local Climate of Summer and Winter for the Duration (2023-2024)

Field Monitoring and the Impact of Green Space on the Local Climate of Summer and Winter for the Duration (2023-2024)

Wind loading on gable and multi-span roof buildings: Comparison between field monitoring, wind tunnel experiments, and design code provisions

The wind loading provisions in the National Building Code of Canada (NBCC) have not been updated for decades despite developments in wind tunnel methods since the original source data. This study aims to review the Nbcc 2020 provisions compared to wind tunnel experiments, field measurements, and the ASCE 7–22 provisions for single and multi-span gable roof buildings. Field measurements and wind tunnel tests are used to collect data for an isolated building and later for a multi-span gable. The pressure coefficients from the field measurements were mostly higher than the wind tunnel, which is consistent with previous studies. The results provided evidence that NBCC provisions for single-gabled roofs (with slope 7∘<α≤27∘) and multi-span gabled roofs (with slope 10∘<α≤30∘) are currently underestimated. New provisions are suggested for the corner (c), edge (s), and field (r) zones of the single-span gabled roof and for gable-end-edge (s’) zone of the multi-span. It is suggested to merge Zone s into Zone c to form a new perimeter Zone p for the single gabled roof and to merge Zone s’ into Zone c for the multi-span gabled roof. The proposed changes correct the current provision underestimation and add simplicity for easier construction.

A full computational framework for the temperature analysis of a flax-reinforced composite footbridge with field monitoring validation

This research presents a computational framework for predicting temperatures in flax-reinforced composite sandwich footbridges. The case study is a 15m-span footbridge situated in Almere, the Netherlands, instrumented with 82 fiber Bragg grating (FBG) sensors and 8 thermocouples within an IoT-based structural health monitoring (SHM) system, enabling real-time evaluation. The main focus of this article is to enhance the understanding of structural temperature distributions in FRP sandwich cross-sections for bridge engineering applications. Furthermore, providing a comprehensive and accurate framework for temperature normalization of FBG strain measurements. The methodology involves environmental data, point-by-point solar radiation analysis with sun sheltering, and thermal properties characterization, culminating in a numerical analysis using Abaqus. A k-d tree binary search is performed previous to the numerical analysis to solve compatibility between Abaqus and Rhino's meshing algorithms. The non-uniform heat flux is used as input for the numerical analysis and handled through UEXTERNALDB and DFLUX subroutines. The use of a sub-modeling technique, allows the isolation, in correspondence of the sensor locations, of one critical section of the bridge for detailed analysis. Verification against in-situ measurements demonstrates the framework's efficacy. It was concluded that the framework restitutes errors in the range of ±1 ÷ 3 °C across varying weather conditions, during different seasons, and consistently for multiple sensor locations. Finally, the results are used to compensate strain measurements of nearby selected FBG sensors from the temperature counterparts.

Real-Time Prediction of Oil-Type Gas Emissions in Coal-Oil-Gas Coexistence Mines.

Oil-type gas disasters are a frequently occurring concern in coal-oil-gas coexistence mines. In order to actively predict the volume of oil-type gas emissions from floor rocks, this study presents an investigative methodology to forecast the geological conditions of floor rocks in front of the roadway face by utilizing the Direct Current (DC) method. The assessment of electrical resistance in rock formations, which is widely employed for identifying geological characteristics, serves as the basis for proposing a geological anomaly index derived from rock resistivity. This index effectively characterizes the stability of rock strata, providing an indirect evaluation of fracture development. As a real-time geological detection index for floor rocks that are 100 m ahead of the roadway face, it enhances predictive capabilities. Moreover, when combined with parameters such as floor rock thickness and permeability, the paper presents simulations of oil-type gas emissions under varying geological conditions. Subsequently, an adaptive optimization of the Back Propagation (BP) neural network is achieved through the Genetic Algorithm Back Propagation Neural Network (GA-BP) model to evaluate the quantity of oil-type gas emissions in roadways. This advanced real-time prediction method is applied in Huangling coal mining to predict the oil-type gas emissions from the floor rocks in the excavation roadway area. The results show consistency with field monitoring outcomes, confirming the accuracy of the predictive model. In conclusion, this advanced real-time prediction technique enables continuous monitoring and real-time forecasting of oil-type gas emissions in front of roadways. This capability facilitates the implementation of specific measures for pre-extraction in gas disaster prevention and control, thereby ensuring the safety of coal mine production. Moreover, the versatility of this advanced real-time prediction method extends to early warnings of rock mass instability-related disasters. Through a comprehensive understanding of subsurface conditions, continuous monitoring of changes, and the application of predictive models, timely actions can be taken to reduce risks and uphold safety standards.

How do plateau pikas use burrows during population reestablishment?

The construction of burrow systems is one of the most important challenges for burrowing animals, especially during population reestablishment. Efficient use of the existing resources could help improve the survival chances. To explore the strategies burrowing animals take in using the burrow resources, we conducted monthly field monitoring of the four features (Active burrows (AcB), abandoned burrows (AbB), latrine pits (LP), and shallow duck holes (SDH)) of plateau pika (Ochotona curzoniae) burrow systems after population extermination. The results indicated that the existing burrows were largely utilized in rebuilding their new burrow systems. The use rates of the four features were in the order of AcBs > AbBs > LPs > SDHs. More energy-saving measures were taken in using the existing burrows during population reestablishment than in the undisturbed natural ecosystems, including higher maintenance rates, higher transformation rates to AcBs, lower disappearing rates and new adding rates, especially in short time intervals; and burrows at the early breeding season are used more stable than at other times. Our study has implications for the population reestablishment of endangered burrowing animals.

Environmental controls on bioluminescent dinoflagellate density, in Laguna Grande, Fajardo, Puerto Rico

Introduction: Bio bays in Puerto Rico play an important socio-economic role and declines in dominant bioluminescent dinoflagellate Pyrodinium bahamense are concerning. Studies show erratic blooms with is weak correlation to in situ environmental factors. Our study examines shorter field and longer proxy records on dinoflagellate density at Laguna Grande de Fajardo (LGF). Objectives: To quantify temporal changes in dinoflagellate density in a long-term monitoring study, understand how the marine environment modulates those changes, and determine the wider impacts of a fluctuating climate and extreme events on proxies for dinoflagellate density. Methods: Bimonthly samples were collected from 2016 to 2021 at three sampling sites in LGF. Dinoflagellates density was estimated by Sedgewick Rafter counting cells. Environmental conditions were obtained from Rio Fajardo 5007100 station and NOAA buoy 41056. Marine climate and biotic proxies were obtained from remote sensing measurements. Kruskal Wallis, Spearman correlations and cross-correlations in the shorter field and longer proxy records were used to evaluate environmental controls on LGF dinoflagellate blooms. Results: Six years of field monitoring densities found a low period in 2016-2017, frequent and intense blooms in 2018-2021 punctuated by hurricanes. Generally low values were recorded in late winter in contrast with higher values in late summer (Aug-Nov). Light winds and mixed layer response to seasonal warming in the form of high tides and low salinity, were found to sustain dinoflagellate reproduction. Conclusions: Bioluminescent dinoflagellates are vital to coastal tourism and require resource management. LGF results show that: 1) dinoflagellate counts fluctuate widely, 2) fluorescing dinoflagellates are sensitive to environmental conditions because of limited seasonality and narrow physiological range, 3) hurricanes play a role by ‘raking and refreshing’ the coastal lagoon for subsequent biotic reproduction, and 4) intra-seasonal fluctuations of density and proxies relate to air-sea thermodynamic conditions, the salinity budget and sea level.

Investigation on instability mechanism and control of abandoned roadways in coal pillars recovery face: A case study

The abandoned roadways (ARs) in front of the longwall face catastrophic instability will seriously hamper mining progress, which is a complicated process related to the stress environment, the roadway section, and the mechanical properties of the surrounding rock. The cusp catastrophe theory is employed to establish a state identification model for the irregular coal pillar-roof system (CPRS) formed by the ARs and re-mining entries. To begin, the state discrimination equation (Δp) for the gradual CPRS is derived, and the critical value at which the system transitions into an unstable state under quasi-static conditions is determined. The results indicated that when 16.49 m ≤ L ≤ 22.63 m (L denotes the equivalent span of the intersection roof) and 0 < Re ≤ 2.61 m (Re denotes the width of the elastic zone within the triangular coal pillar), the triangular CPRS is inherently unstable. Similarly, for trapezoidal CPRS configurations where the length Lm (the span of the right-angled trapezoid roof in the propulsion direction) varies from 4.0 to 12.60 m, the system is unstable as well. Subsequently, the model was further enhanced by accounting for the impact of the Pc (advance stress increment load), where a critical criterion for the catastrophic instability of the CPRS was proposed, which represented the external energy required to transition the CPRS from an unstable state to catastrophic instability in different mining stages. After that, the stability degree of the irregular coal pillar was categorized, and a coupling zoning control technology was applied to CPR operations. Field monitoring results demonstrated the effectiveness of the zoning control technology, providing valuable guidance for similar mining practices.

Thermokarst landslides susceptibility evaluation across the permafrost region of the central Qinghai-Tibet Plateau: Integrating a machine learning model with InSAR technology

Thermokarst landslides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-layer detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tibet Plateau (QTP) permafrost area. TLs induce many environmental problems and threaten the safety of infrastructure. A landslide susceptibility map is crucial to prevent the negative impacts of these landslides. However, traditional thermokarst landslides susceptibility (TLS) evaluations do not consider real-time surface deformation information. Therefore, we propose a novel method that integrates a conventional machine learning model with ground surface deformation. Nine influencing factors, including slope, normalized difference vegetation index, elevation, precipitation, thawing degree days, soil content, active layer thickness, water content, and vegetation type were selected based on the q-index detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accuracy of the InSAR results was validated through comparison with data from two field monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribution matrix, and confirmed the fusion model’s higher rationality and accuracy through comparison with the IM. Furthermore, we examined the impact of the quadtree segmentation method on atmospheric correction and validated the TLS results obtained using the ITM with high-resolution optical remote sensing imagery from GF6. Finally, the influencing factors and distribution characteristics of TLS was analyzed, and the results indicated that climatic conditions are the primary factors affecting the distribution of TLS.

Compensation mechanics application of NPR anchor cable to large deformation tunnel in soft rock

NPR anchor cable is a new type of support material with negative Poisson's ratio effect, which is widely used in mine support because of its superb compensating mechanical effect. In order to study more deeply the support effect of NPR anchor cable in soft rock large deformation tunnel, indoor test, numerical simulation and field monitoring were used to study the strong weathering carbonaceous slate tunnel in Min County. The study shows that NPR anchor cable has extraordinary compensating mechanical behavior for soft rock large deformation tunnel, which can control the deformation of tunnel surrounding rock below 300 mm and keep the constant resistance value around 350 kN, which has obvious effect on the control of broken rock. To provide a basis for other research on support for large deformation tunnels in soft rock.

From Farm to Fork: Enhancing Agricultural and Food Safety Monitoring with Laser-Engineered Graphene Sensors

In the critical fields of precision agriculture and food safety, the demand for accurate and sensitive monitoring tools for fertilizers, pesticides, and food-borne pathogens cannot be overstated. Our collaborative research with commercial entities seeks to overcome the challenges faced by current electrochemical ion-selective, enzymatic, and immunosensors, which often fall short in selectivity, stability, and sensitivity, particularly under field conditions. We showcase the tunable properties of Laser-Induced Graphene (LIG) – such as surface wettability, roughness/area, and electrical conductivity – and their instrumental role in enhancing the commercial viability of these sensor technologies.We demonstrate the conversion of hydrophilic Laser-Induced Graphene (LIG) to a near-superhydrophobic state using an innovative dual-laser process in ambient conditions. This method intricately sculpts the surface into micro and nanoscale structures reminiscent of a lotus leaf, resulting in nearly superhydrophobic characteristics. By modifying the laser's power, speed, and fluence, we can also fine-tune the electroactive surface area and electrical conductivity, significantly impacting sensor functionality. For solid contact ion-selective electrodes (SC-ISEs), enhancing the LIG surface with high hydrophobicity prevents water layer accumulation at the ion-selective membrane and electrode interface. This design expels water from within the membrane, fosters stronger membrane-electrode adhesion, and consequently, diminishes signal interference. Such improvements are instrumental in boosting the sensor's accuracy and extending its operational lifespan. In contrast, a hydrophilic LIG with a large surface area enhances catalytic efficiency and allows more biorecognition agents to adhere to its surface, thereby amplifying the sensitivity of enzymatic pesticide sensors. Lastly, a flat, hydrophilic LIG surface ensures an even distribution of antibodies, which enhances the performance of sensors detecting foodborne pathogens.These modifications to LIG and the resulting sensors have substantial commercial potential. We have developed ions sensors for nitrate, ammonium, potassium, calcium, and magnesium have demonstrated near-Nernstian sensitivities, extensive sensing ranges across four orders of magnitude, and the ability to detect concentrations as low as 1 ppm in soil and water samples. These sensors are currently being integrated into commercial soil moisture and temperature probes for monitoring in farm fields. The pesticide sensing capability of the sensor can reach an extremely low picomolar detect limit for organophosphates, the nanomolar regime for neonicotinoids, and micromolar regime for glyphosate in surface waters. These sensors are currently being interfaced with LIG-based mist/fog harvesting devices to monitor pesticide and spray drift in farm fields. Finally, the LIG-based Salmonella sensors are capable of monitoring bacteria such as Salmonella to about 10 CFU/mL in actual food matrices such as chicken broth without the need for sample preconcentration or costly lab analyses.This research forges innovative avenues in agriculture and food safety monitoring, laying the groundwork for mass production of efficient and economical sensors. We believe this technology will revolutionize field monitoring, significantly advancing sustainable agriculture and improving public health safety. Figure 1

Laser-Induced Graphene for Electrochemical Analysis of Food and Agricultural Contaminants

Carbon-nanomaterial sensors offer promising routes toward low-cost, high-throughput electrochemical sensors. Carbon nanomaterials, such as graphene and carbon nanotube (CNT), have been studied with various fabrication methods to produce sensors, including high-quality thin film graphene and CNT by chemical vapor deposition. However, the complexity and cost associated with their fabrication have hindered their implementation into single-use test strip sensors. Even high-yield mechanical and/or chemical exfoliation of graphene from graphite followed by solution phase printing of electrodes can be costly due to the complexity of ink formulation and the need for post-print annealing requirements. However, laser-induced graphene (LIG) avoids the need to exfoliate graphene from graphite or to create a solution-phase graphene ink by converting sp3 carbon found in carbon-rich substrates into conductive sp2-hybridized carbon found in graphene through a laser scribing technique. The resulting LIG also exhibits a high surface area and is porous with a high density of edge plane sites, which are beneficial for biofunctionalization and heterogeneous charge transport during electrochemical sensing. Furthermore, we have demonstrated that the surface area and wettability of the LIG can be tuned to greatly improve the sensitivity of electrochemical enzymatic biosensors (detection limits down to the picomolar range) and reduce the water layer buildup between the ion-selective membrane and the electrode, which improves the accuracy of the sensor readings. The fabricated LIG electrodes have been used in a wide variety of electrochemical sensing applications, including in food safety (foodborne pathogens and other contaminants), environmental monitoring (agrochemicals), and health monitoring sensors (salivary potassium and lactate, and urinary potassium and ammonium). Different electroanalytical methods are used, including amperometric, potentiometric, coulometric, and impedimetric, to quantify the analytes. We demonstrate how the ISE (ion-selective electrodes) can be functionalized with PVC-based ion-selective membranes for potassium, nitrate, nitrite, ammonium, sodium, and pH in various biological solutions, including soil and water for nutrient/fertilizer monitoring in farm fields. Additionally, we demonstrate how the hydrophilic LIG graphene can be used to create other highly sensitive electrochemical biosensors including enzymatic pesticide biosensors and Salmonella spp. immunosensors. All these electroanalytical sensors were validated in real-world samples and demonstrated to have limits of detection and linear sensing ranges that are relevant to their sensing applications. Results demonstrate the utility of LIG-based sensors for rapid and sensitive electrochemical measurement of contaminants as a readily modified platform for scalable production of electroanalytical devices applied toward point-of-service environmental and food monitoring.

Field monitoring and numerical simulation for force characteristics of pipe jacking in deep buried moderately weathered slate

Understanding the force characteristics of pipe jacking in rock formations is crucial for ensuring the stability of the structure and construction safety during construction. Yet, very little is explored about its characteristics in rock formations of pipe jacking. This paper presents a case study of constructing a deeply buried moderately weathered slate sewage pipeline using pipe jacking method in Changsha, China. To this end, we propose a novel method to combine field monitoring and numerical simulation representation in an efficacious way. Field monitoring was conducted to investigate the spatial distribution characteristics of jacking force, axial stress, and hoop stress in moderately weathered slate. Numerical simulation methods were employed to discuss the influences of several factors on pipe stress: the pipe-rock contact area, contact relationships at pipe joint interfaces, the height of the jacking force position, and the depth of pipe burial. The results show that hand shield is influenced by the excavation technology, and the distribution of jacking force exhibits a stepped or oscillating upward pattern in moderately weathered slate. The maximum axial stress occurs at the mid-span position of the arched roof of pipe at 30 MPa. Hoop stresses are dominated by compressive stresses with the maximum at-8 MPa. As the pipe burial depth increases, so does the axial stress on the pipe. Lower positioning of the jacking force heightens this stress effect. A larger pipe-rock contact area correlates with reduced axial stress levels. The weakening of the contact interface between pipe joints minimally affects axial stress, as stress primarily transmits through non-weakened areas. To ensure the reliability of the data, automatic monitoring and measuring instruments calibrated for on-site monitoring are used. The results of this study can provide beneficial guidance for the design and construction of pipe jacking.

A call to action for inventorying and monitoring of cliff ecosystems to support conservation

Cliffs harbor unique and specialized biodiversity that warrants attention and conservation. At the same time, cliffs are under increased threat from anthropogenic disturbance and climate change. Since cliffs are highly heterogeneous, spatially isolated, and often inaccessible compared to nearby habitats, land managers require up-to-date and site-specific information to protect them. Cliffs are often overlooked due to the technical and logistical challenges posed by surveying these environments, but field inventorying and monitoring can fill this gap. We present three case studies of cliff monitoring in action: mapping populations of an endemic rare plant in the Southern Appalachian Mountains (US), photo-sampling of cliff specialist plants in Spain, and surveying peregrine falcons in Western North Carolina (US). These case studies highlight the application of various monitoring techniques, the possibilities for collaboration among stakeholders, and some ways that data from monitoring can inform cliff conservation and stewardship. To facilitate the development of easy-to-implement monitoring, we outline three approaches and associated best practices for monitoring cliff plants. Methods range from simple photo point monitoring to more in-depth species inventories and could be implemented by community scientists alongside a broader audience interested in providing up-to-date data on cliff environments. We call for action, urging the expansion and advancement of cliff biodiversity monitoring.

Meta-analysis of Heavy Metal Pollution in Soil-crop Systems in China's Urban-Rural Fringe

Owing to the influence of human activities, the issue of heavy metal pollution in farmland soil at the urban-rural fringe has become increasingly prominent. An accurate understanding of the characteristics of soil-crop heavy metal pollution in these areas is of great significance for ensuring food safety and promoting social sustainable development. Most of the existing studies rely on small-scale field monitoring, but research at the national level has not effectively captured the unique pollution dynamics of this urban-rural interface. Based on the published literature, the present study investigated the status of heavy metal pollution in the soil-crop system within China's urban-rural fringe through Meta-analysis. On this basis, the study evaluated the risks to human health associated with heavy metals in major crops in a given region. The results showed that heavy metals were accumulated in farmland soil in the urban-rural fringe of China, especially in Cd （Igeo = 0.89） pollution, and its distinct spatial heterogeneity patterns emerged. When considering different types of cultivated land, paddy fields and irrigated land exhibited a higher tendency of heavy metal accumulation. From the varying urbanization levels, the accumulation of heavy metals in soils of small and medium-sized cities was more obvious. For crops in the urban-rural fringe, Cd and Pb had the highest exceedance rate in rice （28.25% and 39.64%） and wheat （27.72% and 42.72%）. The health risk assessment results of heavy metals in crops showed that Cd was the main element that posed human health risks, which had some degree of non-carcinogenic risk and unacceptable carcinogenic risk to both children and adults.

The Implementation of “Smart” Technologies in the Agricultural Sector: A Review

The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring.

Validation of the Lead Care II System in Cape vultures (Gyps coprotheres) in comparison to ICP-MS using pure standards

Lead toxicosis remains a concern in raptors, especially following feeding on carcasses sourced from hunting. Rapid diagnosis of lead exposure and easy field monitoring is desirable. The LeadCareII analytical system, validated for rapid diagnoses of lead toxicity in humans, has been described as a useful evaluation system in various species. For this study we attempt to validate the LeadCareII system in the Cape Vulture (CV) (Gyps coprotheres). Blood samples from CV housed under captive conditions and low background lead exposure, were pooled and spiked with known concentrations of a lead standard (0–60 µg/dL). Samples were analyzed by the LeadCareII system and by ICP-MS. The final results showed that despite good linearity the LeadCareII system underestimated lead concentrations by up to 50 %. While the results can be corrected by the derived equation, this is not supported due to the large underestimations evident. The reason for the underestimation is presently unknown.

The Content of Soil Glomalin Concerning Selected Indicators of Soil Fertility

The glomalin content is generally considered an indicator of the soil organic matter (SOM) quality. The content of easily extractable glomalin (EEG) and the total glomalin (TG) content was investigated across 71 different sites in the Czech Republic with arable soil and crop production (12 chernozems, 30 luvisols, 17 cambisols, and 12 fluvisols). The majority of the crops in the crop rotation were cereals (45.5%—mainly winter wheat, winter barley, and spring barley). The proportion of winter canola within the crop rotation was 15.9%. The contribution of other crops was substantially smaller (alfalfa, clover, potatoes, beet, silage maize, grain maize). The representation of crops in the crop rotation is standard for conventional farming in the Czech Republic. Based on the results of long-term field monitoring at 71 sites in different soil–climate conditions, we can state the following. The TG content was significantly correlated with the soil organic matter carbon content (CSOM), as well as another important indicator of SOM quality (humic and fulvic acid carbon content ratio—CHA/CFA). A significant and positive correlation was also determined for the TG and clay content (size &lt; 0.002 mm), as well as particles smaller than 0.01 mm. The easily extractable glomalin content (EEG) did not differ based on the reference soil group (RSG). On the other hand, the total glomalin content (TG) was significantly higher in the chernozem RSG in comparison with other RSGs (luvisols, cambisols, fluvisols). There was no relationship between the pHCaCl2 and glomalin (EEG; TG). The same can be said about the relationship between glomalin (EEG; TG) and the bulk density and porosity. No link was established between the glomalin content (EEG; TG) and phosphorus plant-available content. There was no relationship between the amount of applied organic matter (carbon inputs) and the soil glomalin content (EEG; TG). This relationship was not influenced by the type of applied organic fertilizer. No significant relationship was found for either straw, manure, or compost. The data on the glomalin content are significantly influenced by the site (soil type and soil texture).

Research on the technology of uniformly injecting nitrogen into the porous long pipes in the gob of the gob-side entry retaining mining mode with roof cutting and pressure relief

The implementation of the Gob-Side Entry Retaining Mining Mode with Roof Cutting and Pressure Relief (GERRCPR) results in the gob connecting to the retaining roadway, creating an open space that causes significant air leakage and increases the risk of spontaneous combustion. A study was conducted during the implementation of the GERRCPR in the Xiaonan Coal Mine N1-1502 working face to investigate spontaneous combustion characteristics, along with fire prevention and extinguishing measures. To analyze gob airflow, Computational Fluid Dynamics (CFD) was employed to collect data on airflow conditions, O2 concentration, and temperature. Based on this, this study focuses on exploring the effects of nitrogen injection treatment under various rates and positions to optimize parameters for buried pipe nitrogen injection. Results indicated that within the GERRCPR, air leakage in the gob increased, leading to an increase in O2 concentration, expansion of the oxidation zone, and an elevated risk of spontaneous combustion. Air leakage primarily occurred from the retaining roadway and the working face near the intake-air roadway, peaking at a retaining roadway length of 500 m, with a flow rate of 226 m3/min. Following nitrogen injection treatment, the oxidation zone was significantly reduced, with optimal treatment achieved at a nitrogen injection depth of 70 m and a rate of 600 m3/h. Field monitoring data showed that the inertization measure of using porous long pipes, a nitrogen injection spacing of 30 m, and a nitrogen injection rate of 600 m3/h significantly decreased the O2 concentration within the gob. This reduction meets safety production requirements and outperforms the effectiveness of traditional buried-pipe nitrogen injection methods, thereby validating the simulation accuracy. Understanding the laws governing spontaneous coal combustion in the GERRCPR and enacting preventive measures for nitrogen injection can improve safety standards in mining operations. This proactive approach can effectively prevent spontaneous coal combustion accidents, resulting in substantial social benefits.