Quality Assurance And Quality Control Research Articles

Characterization of concrete reference material for analysis of artificial radionuclides in nuclear power plant decommissioning waste

The decommissioning of nuclear power plants has led to a surge in the production of radioactive waste, necessitating the development of accurate and reliable measurement methods for safe waste disposal. This study presents the development of a new concrete reference material (RM) containing beta, and gamma nuclides for quality assurance and quality control (QA/QC) in concrete measurements. The newly developed RM manufacturing method was used to ensure sample homogeneity; rigorous processes were followed according to ISO guidelines. The RM, designed to mimic real waste matrices, targeted six radionuclides: 60Co, 134Cs, 137Cs, 152Eu, 154Eu, and 90Sr. Homogeneity and stability tests, conducted according to ISO guidelines, confirmed the suitability of the concrete RM for QA/QC purposes. Reference values for the six radionuclides in RM ranged from 66.0 to 172.6 Bq kg−1. The expanded uncertainties of the reference values, at a 95 % confidence level (k = 2), ranged from approximately 7.1 %–17.2 %. The developed RM holds potential as proficiency test material and for validating and verifying analytical methods in radioactive waste analysis. Additionally, the manufacturing method introduced here opens doors for diverse applications, enabling the production of accurate and reliable reference materials for various waste matrices.

Service Life Design of Reinforced Concrete Maritime Infrastructure: Holistic Approach with Experiences from Chilean Cases

The consideration of the useful life of reinforced concrete in the different stages of a structure's life cycle, as a distinguishing factor in the design process for performance under aggressive environmental conditions, has presented new challenges in construction. This includes the development of new regulations incorporating innovative engineering materials, performance tests and modeling processes to estimate concrete deterioration over time, and construction processes ensuring adequate Quality Control and Quality Assurance (QAQC). As a result, infrastructure projects have been able to exceed their intended lifespan, maintaining serviceability levels for more than 100 years. One such example is the Chacao Bridge project currently under construction in southern Chile. This paper provides an overview of project stages from a holistic approach, focusing on aspects inherent to planning including design and construction processes, control stages for monitoring deterioration levels through regular diagnostics and appropriate monitoring to support ongoing structural health management. Additionally, it presents real case design examples carried out with specific models created for each case.

The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics.

Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5'-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators.

Practical applications of quality assurance and quality control in mineral exploration, resource estimation and mining programmes: a review of recommended international practices

Independent quality assurance and quality control (QA/QC) programmes are required by reporting codes for publicly listed companies and are necessary to optimize data quality at all stages of the sampling, preparation and analytical processes involved in mineral exploration, resource estimation and mining grade control. QA/QC programmes should be adjusted over time to meet changing requirements in data quality at different stages of mineral resource development and exploitation. Certified reference materials are used to monitor accuracy and bias at the project laboratory relative to consensus values for the material from round-robin certification analyses. They are also used to monitor drift over time within an individual laboratory and to identify significant failures in QC at the analytical batch level caused by abrupt changes in concentration related to re-calibration of instruments or procedural changes at the laboratory. Duplicate analyses of sample material are generated at key stages of sampling and preparation to estimate the precision of data generated at each stage. Invariably, the largest source of uncertainty occurs during the initial sampling. Coarse blanks are used to monitor cross-contamination between samples or from sample preparation equipment. Furthermore, each of these QC sample types can be used to discover possible sample mix-ups. Supplementary material: An Excel spreadsheet for the calculation of the average coefficient of variation (CV AVE ) (Appendix C) is available at https://doi.org/10.6084/m9.figshare.c.7070137 Thematic collection: This article is part of the Reviews in Exploration Geochemistry collection available at: https://www.lyellcollection.org/topic/collections/reviews-in-exploration-geochemistry

Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys

Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.

Nano- and microplastic PBK modeling in the context of human exposure and risk assessment

Insufficient data on nano- and microplastics (NMP) hinder robust evaluation of their potential health risks. Methodological disparities and the absence of established toxicity thresholds impede the comparability and practical application of research findings. The diverse attributes of NMP, such as variations in sizes, shapes, and compositions, complicate human health risk assessment. Although probability density functions (PDFs) show promise in capturing this diversity, their integration into risk assessment frameworks is limited. Physiologically based kinetic (PBK) models offer a potential solution to bridge the gap between external exposure and internal dosimetry for risk evaluation. However, the heterogeneity of NMP poses challenges for accurate biodistribution modeling.A literature review, encompassing both experimental and modeling studies, was conducted to examine biodistribution studies of monodisperse micro- and nanoparticles. The literature search in PubMed and Scopus databases yielded 39 studies that met the inclusion criteria. Evaluation criteria were adapted from previous Quality Assurance and Quality Control (QA-QC) studies, best practice guidelines from WHO (2010), OECD guidance (2021), and additional criteria specific to NMP risk assessment. Subsequently, a conceptual framework for a comprehensive NMP-PBK model was developed, addressing the multidimensionality of NMP particles.Parameters for an NMP-PBK model are presented. QA-QC evaluations revealed that most experimental studies scored relatively well (>0) in particle characterizations and environmental settings but fell short in criteria application for biodistribution modeling. The evaluation of modeling studies revealed that information regarding the model type and allometric scaling requires improvement. Three potential applications of PDFs in PBK modeling of NMP are identified: capturing the multidimensionality of the NMP continuum, quantifying the probabilistic definition of external exposure, and calculating the bio-accessibility fraction of NMP in the human body. A framework for an NMP-PBK model is proposed, integrating PDFs to enhance the assessment of NMP’s impact on human health.

Quality Control Assessment of Human Adipose-Derived Hydrogels.

Decellularized human-adipose tissue (hDAT) can serve as an alternative to two-dimensional monolayer culture and current ECM hydrogels due to its unlimited availability and cytocompatibility. A major hurdle in the clinical translation and integration of hDAT and other hydrogels into current in vitro culture processes is adherence to current good manufacturing practices (cGMP). Transferring of innovative technologies, including hydrogels, requires the establishing standardized protocols for quality assurance and quality control (QA/QC) of the material.Integration of basic characterization techniques, including physiochemical characterization, structural/morphological characterization, thermal and mechanical characterization, and biological characterization, in addition to the reduction of batch-to-batch variability and establishment of proper sterilization, storage, and fabrication processes verifies the integrity of the hydrogel. Obatala Sciences has established a characterization protocol that involves a series of assays including the evaluation of gelation properties, protein content, glycosaminoglycan content, soluble collagen content, and DNA content of hDAT.

A random forest approach to quality-checking automatic snow-depth sensor measurements

Abstract. State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models.

Efforts to enhance reproducibility in a human performance research project.

Background: Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. Methods: We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Results: Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. Conclusions: In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.

Long-Term Assessment of PurpleAir Low-Cost Sensor for PM2.5 in California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to-use, low-cost, sensor-based monitors which have given a new dimension to air pollutant monitoring and have democratized the air quality monitoring process by making monitors and results directly available at the community level. This study used PurpleAir © sensors for PM2.5 assessment in California, USA. The evaluation of PM2.5 from sensors included Quality Assurance and quality control (QA/QC) procedures, assessment concerning reference-monitored PM2.5 concentrations, and the formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitors also captured the peak PM2.5 concentrations due to incidents such as forest fires. In comparison with reference-monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir’s higher PM2.5 concentrations was the inclusion of moisture or water vapor as an aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. Long-term assessment (2016–2023) revealed that R2 values were between 0.54 and 0.86 for selected collocated PurpleAir sensors and regulatory monitors for hourly PM2.5 concentrations. Past research studies that were conducted for mostly shorter periods resulted in higher R2 values between 0.80 and 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor-based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring and developing mitigation strategies.

AmeriFlux BASE data pipeline to support network growth and data sharing

AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

Purification and characterization of pure curcumin, desmethoxycurcumin, and bisdemethoxycurcumin from North-East India Lakadong turmeric (Curcuma longa)

Lakadong turmeric has been outlined for its high content of curcuminoids across the globe. Three significant molecular markers are widely present in turmeric viz, curcumin, desmethoxycurcumin, and bisdemethoxycurcumin, and they are present very high amount in Lakadong turmeric. Curcuminoids have been reported for structural and spectrum similarity of 3 to 4 nm (432, 434, and 436 nm, respectively). Current purification methods are based on recrystallisation where it is difficult to get highly pure material and preparative methods associated with tedious separation with high cost. Lakadong turmeric has not been explored commercially since long time. No reports are available in the literature with highly pure reference materials with in-depth characterization data and purity assessment. Curcumin, desmethoxycurcumin, and bisdemethoxycurcumin were characterized using different analytical techniques viz, UV–Visible Spectroscopy, Fourier Transform Infrared Spectroscopy (FT-IR), Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA), Proton Nuclear Magnetic Resonance (1HNMR), Carbon-13 Nuclear Magnetic Resonance (13CNMR), High-Resolution Mass Spectrometry (HR-MS) and Inductive Coupled Plasma Mass Spectrometry (ICP-MS). Purified 3 markers has shown High-Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) purity more than 99.5%. DSC the melting peaks of curcumin, desmethoxycurcumin and bisdemethoxycurcumin were observed at 168 °C, 165 °C, and 210 °C, respectively. These plant-based markers have high commercial potential as reference material for routine Quality Assurance and Quality Control (QAQC) in herbal industries.

Quality assurance for building components through point cloud segmentation leveraging synthetic data

Quality Assurance and Quality Control (QA/QC) play a crucial role in the building project life cycle, especially during construction, as discrepancies between as-built structures and as-designed models can lead to cost overruns and schedule delays. Ensuring building quality is of utmost importance, but traditional manual inspections suffer from errors, consume time, and incur significant expenses. This paper describes a deviation detection method for building components using synthetic point clouds and semantic segmentation models. The method entails training a three-dimensional semantic segmentation model using synthetic point clouds generated from BIM to label each point with an object class, resulting in a mean IoU of 41.1% in semantic segmentation. Subsequently, real point clouds collected onsite are segmented using the same model and then compared with the synthetic point cloud to assess the disparities between the building components of the as-designed and as-built structures. This approach can improve the efficiency of QA/QC by reducing the manual workload of field inspectors.

Study of Regression Algorithms and Influent Factors between Intelligent Compaction Measurement Values and In-Situ Measurement Values

Intelligent compaction (IC) technology have been used for quality control and quality assurance (QC/QA) in subgrade construction. The effective regression correlations between Intelligent Compaction Measurement Values (ICMV) and In-situ Measurement Values (ISMV, including compaction degree and subgrade modulus ELWD) are the essential prerequisite of using IC technology for QC/QA. This paper presents the results from an experimental research study that was conducted from a practical subgrade project of China to explore the regression relationships between ICMV and ISMV. Three types of ICMV, including CMV, CCV and Evib, were collected along with the corresponding positions of the rollers. Two types of ISMV, containing compaction degree and ELWD, were measured by ring sampler method and light weight deflectometer (LWD) at specified test points, respectively. Based on these data, the influences of roller parameters and subgrade properties on the regression relationships of ICMV and ISMV were investigated. In addition, linear regression and 5 nonlinear regression algorithms were compared. The results suggest that ICMV reflect the stiffness of subgrade more than reflecting the density. In the regression of ICMV and ISMV, subgrade properties are more important than roller parameters while both of them should not be neglected. The influences of underlying stiffness and roller amplitude are linear while that of roller speed and moisture content are nonlinear. Nonlinear algorithms, especially the random forest, have the better performance compared to linear algorithm. Moreover, the combination of random forest and linear algorithm can further improve the accuracy of ISMV prediction.

Patterns of microparticles in blank samples: A study to inform best practices for microplastic analysis

Quality assurance and quality control (QA/QC) techniques are critical to analytical chemistry, and thus the analysis of microplastics. Procedural blanks are a key component of QA/QC for quantifying and characterizing background contamination. Although procedural blanks are becoming increasingly common in microplastics research, how researchers acquire a blank and report and/or use blank contamination data varies. Here, we use the results of laboratory procedural blanks from a method evaluation study to inform QA/QC procedures for microplastics quantification and characterization. Suspected microplastic contamination in the procedural blanks, collected by 12 participating laboratories, had between 7 and 511 particles, with a mean of 80 particles per sample (±SD 134). The most common color and morphology reported were black fibers, and the most common size fraction reported was 20–212 μm. The lack of even smaller particles is likely due to limits of detection versus lack of contamination, as very few labs reported particles <20 μm. Participating labs used a range of QA/QC techniques, including air filtration, filtered water, and working in contained/‘enclosed’ environments. Our analyses showed that these procedures did not significantly affect blank contamination. To inform blank subtraction, several subtraction methods were tested. No clear pattern based on total recovery was observed. Despite our results, we recommend commonly accepted procedures such as thorough training and cleaning procedures, air filtration, filtered water (e.g., MilliQ, deionized or reverse osmosis), non-synthetic clothing policies and ‘enclosed’ air flow systems (e.g., clean cabinet). We also recommend blank subtracting by a combination of particle characteristics (color, morphology and size fraction), as it likely provides final microplastic particle characteristics that are most representative of the sample. Further work should be done to assess other QA/QC parameters, such as the use of other types of blanks (e.g., field blanks, matrix blanks) and limits of detection and quantification.

External Quality Assurance Schemes (EQUASs) and Inter-laboratory Comparison Investigations (ICIs) for human biomonitoring of polycyclic aromatic hydrocarbon (PAH) biomarkers in urine as part of the quality assurance programme under HBM4EU

Polycyclic aromatic hydrocarbons (PAHs) were included as priority substances for human biomonitoring (HBM) in the European Human Biomonitoring Initiative (HBM4EU), which intended to harmonise and advance HBM across Europe. For this project, a specific Quality Assurance and Quality Control (QA/QC) programme applying Inter-laboratory Comparison Investigations (ICIs) and External Quality Assurance Schemes (EQUASs) was developed to ensure the comparability and accuracy of participating analytical laboratories. This paper presents the results of four ICI/EQUAS rounds for the determination of 13 PAH metabolites in urine, i.e. 1-naphthol, 2-naphthol, 1,2-dihydroxynaphthalene, 2-, 3- and 9-hydroxyfluorene, 1-, 2-, 3-, 4- and 9-hydroxyphenanthrene, 1-hydroxypyrene and 3-hydroxybenzo(a)pyrene. However, 4 PAH metabolites could not be evaluated as the analytical capacity of participating laboratories was too low. Across all rounds and biomarkers, 86% of the participants achieved satisfactory results, although low limits of quantification were required to quantify the urinary metabolites at exposure levels of the general population. Using high-performance liquid or gas chromatography coupled with mass spectrometry (HPLC-MS; GC-MS) and isotope dilution for calibration as well as performing an enzymatic deconjugation step proved to be favourable for the accurate determination of PAHs in urine. Finally, the HBM4EU QA/QC programme identified an international network of laboratories providing comparable results in the analysis of urinary PAH biomarkers, although covering all parameters initially selected was still too challenging.

Innovative reference materials for method validation in microplastic analysis including interlaboratory comparison exercises

Reference materials (RMs) are vital tools in the validation of methods used to detect environmental pollutants. Microplastics, a relatively new environmental pollutant, require a variety of complex approaches to address their presence in environmental samples. Both interlaboratory comparison (ILC) studies and RMs are essential to support the validation of methods used in microplastic analysis. Presented here are results of quality assurance and quality control (QA/QC) performed on two types of candidate microplastic RMs: dissolvable gelatin capsules and soda tablets. These RMs have been used to support numerous international ILC studies in recent years (2019–2022). Dissolvable capsules containing polyethylene terephthalate (PET), polyvinyl chloride (PVC), polyethylene (PE), and polystyrene (PS), in different size fractions from 50 to 1000 µm, were produced for one ILC study, obtaining relative standard deviation (RSD) from 0 to 24%. The larger size fraction allowed for manual addition of particles to the capsules, yielding 0% error and 100% recovery during QA/QC. Dissolvable capsules were replaced by soda tablets in subsequent ILC studies and recovery test exercises because they were found to be a more reliable carrier for microplastic RMs. Batches of soda tablets were produced containing different single and multiple polymer mixtures, i.e., PE, PET, PS, PVC, polypropylene (PP), and polycarbonate (PC), with RSD ranging from 8 to 21%. Lastly, soda tablets consisting of a mixture of PE, PVC, and PS (125–355 µm) were produced and used for recovery testing during pretreatment of environmental samples. These had an RSD of 9%. Results showed that soda tablets and capsules containing microplastics >50 µm could be produced with sufficient precision for internal recovery tests and external ILC studies. Further work is required to optimize this method for smaller microplastics (< 50 µm) because variation was found to be too large during QA/QC. Nevertheless, this approach represents a valuable solution addressing many of the challenges associated with validating microplastic analytical methods.

Oxygen Diffusion Coefficients of Asphalt Mixtures and Their Impacts on Mixture Aging

The oxidative aging of asphalt binder affects the long-term performance of flexible pavement. Apart from the intrinsic property of asphalt binder, binder aging in an asphalt mixture is also affected by the mixture’s oxygen transport efficiency, which can be characterized by the oxygen diffusion coefficient. In this study, the diffusion coefficients ( Ds) of compacted asphalt mixtures of different designs were determined. The same mixture samples were also aged in one dimension for a relatively long period of time. Asphalt binders from different slices of the aged specimens were extracted and recovered, and their aging states were determined. Influences of Ds and air void content on binder aging gradient and binder aging extent in the mixture samples were systematically examined and compared. The results indicate that, as compared with air voids, Ds is more closely related to the aging susceptibility of asphalt binders in asphalt mixtures. It may serve as a useful and convenient parameter in mixture design optimization, modeling and prediction of in situ pavement aging, and quality control and quality assurance (QC/QA) in pavement construction.

ВИКОРИСТАННЯ КОНТРОЛЬНИХ ПРОБ ДЛЯ ЗАБЕЗПЕЧЕННЯ І КОНТРОЛЮ ЯКОСТІ (QA/QC)

Іn geological exploration, the quality of the data underlying of resource and reserve estimation is critically important. According to various international regulations and current world best practices, Quality Assurance and Quality Control (QA/QC) programs are a necessary part of geological exploration. Quality Assurance (QA) is used to avoid the problems with quality, Quality Control (QC) is aimed at detecting them in case of their occurrence, and together they form the overall Quality System – QA/QC. The article considers the main stages of the history of QA/QC development, which begins in the Middle Ages and continues in our time. Control samples used to control analytical tests have different types and purposes / functions: standards, preparation blanks, coarse blanks, analytical blanks, field duplicates, coarse duplicates, pulp duplicates and umpire laboratory control. Their amount and ratio is the main topic of the research. According to the published data of nine different authors, the recommended control sample amount of each type is different. On average, the amount of control samples of all types is about 20% of the total number of routine samples. In order to highlight the current state of the issue, the authors of the article have analyzed 111 QA/QC programs for 87 ore projects according to the public reports disclosed by the issuers of the Toronto Stock Exchange. So, in practice, the control sample amount does not necessarily reach the recommended 20 % and is slightly more than 16 %. The main conclusions regarding the amount and ratio of control samples can be presented as follows: (1) the general increasing trend in the amount and variety of control samples is observed; (2) among the control sample types, the so-called standards are most often used, and the least used are coarse blanks; (3) the control sample amount and variety also depends on the mineral type; (4) at more advanced exploration stages of the project, the control sample relative number usually increases, but in some cases it may decrease if the results of the previous stages are satisfactory, and the methodology and laboratory are not changed.

Study of Cu Recovery Comparison of Fresh, Stockpiled, and Mixed Ores Processing in Batu Hijau Mine – Sumbawa Island, Indonesia

Batu Hijau mine is one of the mining areas in Indonesia that have been operated for 20 years. The main concentrate product in Batu Hijau contains the primary minerals copper (Cu) and gold (Au). However, the mine is not only processing the fresh mined ore but also store the low-grade ore in the stockpile. Therefore, this study compares the Cu recovery in different ore stockpile processing on and changes in the concentration of dissolved Cu in the tailings that potentially contaminated the environment. Chemical analysis of solid and dissolved Cu uses standard methods by strictly carrying out quality assurance and quality control (QA/QC) procedures to ensure the resulting data has high validity. The results showed that there is a slight increase in Cu’s solubility in liquid fraction of tailings and the recovery decrease when ores were processed from stockpiles. However, the recovery reduction is anticipated by flotation with controlled potential sulphidisation (CPS) using sodium hydrosulphide (NaHS). Besides, the CPS also ensure that only small amount of dissolved Cu that dumped in the tailings. Hence, the dissolved Cu concentration can be monitories to reduce the possible impact on the environment.