Laboratory Test Methods Research Articles

Laboratory test and design methods of horizontal-ellipse corrugated-steel-plate–utility-tunnel with varying top–side plate ratios

Corrugated steel-plate culverts, particularly in horizontal ellipse form, are commonly used in large-span projects. Despite the guidelines on plate radius ratios, the impact of these ratios on mechanical properties remains unexplored. This gap highlights the need for research to guide utility tunnel design because existing studies mainly focus on round culverts compressed into elliptical shapes. Therefore, this study conducted backfill, simulated vehicle live load, and ultimate-load tests on two horizontal-ellipse corrugated steel utility tunnel structures with different top-side plate ratios to examine their response characteristics under various load conditions. Moreover, they were compared with those of existing design methods to offer new insights for the design analysis of soil–steel structures. The results demonstrated that the ratio significantly influenced bending moment distribution, and the critical section was concentrated beneath the loading pad for live loads. The ultimate capacity varied with the ratio, with the higher ratio specimen reaching approximately 92.5 % of the capacity of its counterpart. Both specimens failed via tri-plastic hinge mechanisms, with reduced capacity as corrugations flattened. The Canadian Highway Bridge Design Code, which considers thrust force and bending moment, accurately predicted bearing capacity than the other methods in this study. These findings are vital for optimising design and ensuring safety in horizontal-ellipse corrugated steel utility tunnels.

Ukrainian adaptation of the nomenclature of the International Consensus on Antinuclear Antibody Patterns

Background. Diagnosis of autoimmune diseases remains a complex multi-stage process that includes various methods of laboratory testing. The gold standard for serological diagnosis of autoimmune connective tissue disorders is the indirect immunofluorescence assay (IFA) on HEp-2 cells. The result of the test is a description of the morphological pattern observed in the assay, which correlates with the corresponding autoantibodies and allows to direct further diagnostics. The most recent and most comprehensive attempt to describe HEp-2 IFA patterns was made by the International Consensus on Antinuclear Antibody (ANA) Patterns (ICAP). Purpose. Ukrainian adaptation of the ICAP nomenclature was developed to provide for seamless harmonization and integration with international protocols. Materials and methods. Ukrainian adaptation was based on the materials the International Consensus on Antinuclear Antibody (ANA) Patterns (ICAP). Results. Ukrainian nomenclature of HEp-2 IFA patterns is provided in accordance with ICAP nomenclature, to be used a tool for comprehensive description of HEp-2 IFA results and guidance for clinicians Conclusions. With the introduction and universal use of the Ukrainian adaptation of ICAP nomenclature, the laboratory diagnosis of autoimmune connective tissue diseases is approaching international standards, which creates conditions for the integration of international protocols for the diagnosis and treatment of these pathologies.

Angiotensin II Type-1 Receptor Antibody in Solid Organ Transplantation - Is It Time to Test?

Angiotensin II type-1 receptor antibody (AT1R-Ab) has been mooted as a potential effector of both acute and chronic antibody mediated rejection (AMR). A growing body of literature on the topic is now coming under scrutiny in the context of the evolving Banff AMR diagnostic classification system and refinement of recommendations for histocompatibility testing by the Sensitization in Transplantation Assessment of Risk (STAR) workgroup. This mini-review discusses the latest understanding of pathophysiological mechanisms, clinical evidence for the pathogenicity of AT1R-Ab, and methods of laboratory testing.

Operator exposure and cabin protection in plant protection product application

AbstractTightly sealed driver's cabs can effectively protect the user from exposure when applying crop protection products. Therefore, under certain conditions, users in such cabins can avoid wearing personal protective equipment for skin and eyes. The protective effect depends on the technical design and handling of the different cabin types and can be divided into categories with different levels of protection. Since the level of protection of the different types of cabins and the associated possibility of not wearing personal protective equipment has not been sufficiently scientifically verified in the past, the present study was carried out to close this gap. Field and laboratory test methods were developed and used to assess exposure levels. External contamination of the cabin was measured using various dosimeters. Exposure inside the cabins was measured on coveralls and gloves to measure dermal exposure and with aerosol sampling pumps to determine inhalation exposure. Tests were conducted on tractors with different cab categories using an air-assisted orchard sprayer as a worst-case scenario for exposure to spray drift. Additionally, an alternative laboratory test method was developed and conducted to evaluate filter efficiency under controlled conditions and compare it with the field tests. The results showed that exposure inside closed cabins was generally low and was not notably correlated with the cabin category. All types of cabins tested were found to provide significant protection from dermal and inhalation exposure.

Evaluation of the utilization of Ginkgo biloba leaf (GBL) extract as an eco-friendly wood preservative

Ginkgo biloba leaf (GBL) extract was evaluated as a wood preservative, considering its sustainable availability in forests and cities worldwide. Previous research has demonstrated the antibacterial and antifungal properties of GBL extracts. Based on this information, it was hypothesized that GBL extract could effectively combat wood decay. The wood preservation properties of GBL extracts were evaluated using the “Laboratory test method of natural decay resistance of wood” KS F 2213 (2018). Its performance was compared to that of ACQ-2, a commercial wood preservative. The GBL extract was tested against two common wood decay fungi, namely Fomitopsis palustris and Trametes versicolor. The results showed mass loss rates of 13.6% and 9.9% after culturing these fungi, respectively. Although the GBL extract did not surpass the performance of ACQ-2, it achieved a “resistant” grade according to KS F 2213 (2018). This indicates that GBL extract exhibits a significant wood preservation effect. Furthermore, the GBL extract retains the natural color of wood, which is an advantageous characteristic.

Prediction of Resilient Modulus Value of Cohesive and Non-Cohesive Soils Using Artificial Neural Network.

This paper investigates the application of Artificial Neural Networks (ANNs) for predicting the resilient modulus (Mr) of subgrade and subbase soils, which is a critical parameter in pavement design. Utilizing a dataset of 1683 Mr observations, the ANN model incorporates eight input variables, including soil gradation, plasticity, and stress conditions. The model was optimized using a quasi-Newton method, achieving high predictive accuracy, with a coefficient of determination (R2) of 0.9613 and low error rates for both selection and testing datasets. To further enhance model interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted, revealing the significant influence of specific input parameters, such as saturation ratio, plasticity index and soil gradation, on Mr predictions. This study underscores the potential of ANNs as a practical tool for estimating resilient modulus, offering a reliable alternative to conventional laboratory testing methods. The findings suggest that integrating ANNs into pavement design processes can lead to more accurate predictions of pavement performance, ultimately supporting the development of more efficient and durable road structures.

Cobalt and Tungsten Extraction from Diamond Core Drilling Crowns by Aqua Regia Leaching.

In this work, a hydrometallurgical process for the recycling of diamond core drilling crowns by means of aqua regia leaching and subsequent alkali leaching was investigated. This investigation continues a previous study in which nitric acid was used for the acid leaching phase. In the current study, higher tungsten recovery was achieved, reaching 98.2%, which is an improvement of about 1.5%. Another advancement of this study was the high Co recovery (97.21%) and the high purity of the tungsten trioxide obtained, comparable to the previously proposed technological process. Furthermore, a novel laboratory method for testing recycled diamond drilling crowns based on infrared thermography was introduced. Although this innovative approach is not the most accurate, it is fast and cost-effective and provides valuable results before the actual field test is conducted as a final evaluation. In addition, the infrared thermography method offers the advantage of non-destructive testing, ensuring that the diamond drilling crowns can be assessed without compromising their structural integrity. Other instrumental methods used to characterize the products and intermediates were X-ray diffraction (XRD), scanning electron microscope with energy dispersive X-ray spectroscopy (SEM-EDS), and laser desorption ionization mass spectrometry (LDI-MS). The analytical method for the concentrations in all working solutions was ICP-AES.

A-324 Performance and Turnaround Time of Plasma Lactate Assay on POCT, Blood Gas Instrument, and Chemistry Analyzer

Abstract Background Time is a critical factor of plasma lactate testing in the management of patients with sepsis. To best serve these patients, we must optimize testing methods to provide a turnaround time (TAT) that guides efficient patient care. Point of care tests (POCTs) provide rapid results for emergency department and medical intensive care unit but are not feasible for all hospital settings. Laboratory testing methods on an automation line provide greater efficiency but result slowly due to need of specimen preparation and longer analytical time. The goal of this study is to provide detailed TATs of plasma lactate testing among the methods of POCT, chemistry analyzer in an automation line, and blood gas analyzer. Methods Plasma lactate testing was performed by three methods, GEM Premier ChemSTAT (POCT in ER only), Beckman AU5800 (chemistry analyzer), and Radiometer ABL800 (blood gas instrument). The TAT of lactate for each method was measured and include the time from specimen collection to laboratory receipt (except for POCT method), and time from specimen receipt to resulting (including specimen preparation for Beckman AU5800). Results The average TATs of lactate testing for each method in all hospital sites are detailed with respect to each event and subcategorized (Table 1). The average TAT of 12.0 min on Radiometer ABL800 is higher than the TAT of 2.5 on GEM Premier ChemSTAT method but is greatly reduced compared to the TAT of 35.4 min on Beckman AU5800. Conclusions Radiometer ABL800 method could be the optimal laboratory method for plasma lactate testing fulfilling the TAT (average 12 min), with less test cancellation due to specimen stability issue, and combines testing with blood gases. Using three Radiometer ABL800 instruments, we manage the performance of around 70 plasma lactate tests daily.

Improvement of biodegradability prediction QSAR system through optimal combination of algorithms

Abstract This paper introduces a novel quantitative structure-activity relationship (QSAR) system for predicting the biodegradability of chemicals. The system focuses on evaluating the ratio of biochemical oxygen demand (BOD) to theoretical oxygen demand (ThOD) (%BOD/ThOD) and the presence of degradation products to categorize chemicals as “readily biodegradable” (RB) or “not readily biodegradable” (NB). The conventional laboratory testing method for biodegradability assessment is time-consuming and expensive, thus highlighting the need for a quantitative structure-activity relationship (QSAR) system that can predict biodegradability without the need for extensive testing. The authors acquired a dataset of 3948 chemicals from the National Institute of Technology and Evaluation (NITE) database, which included information on molecular structure, %BOD/ThOD values, and the presence of degradation products. The QSAR system consists of two prediction models: a %BOD/ThOD prediction model and a discrimination prediction model. Multiple algorithms were employed in each model to enhance prediction accuracy. The %BOD/ThOD prediction model utilizes eight algorithms, including linear, nonlinear, and tree-structured models. The discrimination prediction model employs ten algorithms, and the outcomes are combined using majority voting. Explanatory variables for both models include molecular descriptors and spatial structure derived from the chemical’s molecular representation. The performance of the QSAR system was evaluated based on training and validation datasets. Results showed that the combination of three %BOD/ThOD prediction algorithms improved the average correction rate compared to using all algorithms. Similarly, selecting the discrimination prediction algorithm further enhanced the average correction rate.

Assessment, identifying, and presenting a plan for the stabilization of loessic soils exposed to scouring in the path of gas pipelines, case study: Maraveh-Tappeh city

Dealing with collapsible soils consistently presents a crucial challenge for geological and geotechnical engineers. Loess soil is among the most widely recognized types of collapsible soils, covering approximately 10 % of the Earth's land surface. Loessic soil is a sedimentary deposit primarily composed of silt-size grains, loosely bound together by calcium carbonate. In Iran, approximately 17 % of Golestan province is covered by silty, clayey, and sandy loesses, primarily composed of loessic soil. Additionally, several energy transmission lines in this province traverse these loess-covered areas. Based on the reports from Golestan Gas Company experts, the scouring of gas pipeline channels in various regions, such as Dashli-Alum in Maraveh-Tappeh city, causes significant risks in the traffic roads and is one of the most critical issues facing this company. This research assessed the dispersion and collapse potentials of loess soil using a range of field exploration and laboratory testing methods. These methods included atomic absorption spectroscopy, the double hydrometer, scanning electron microscope photography, wavelength-dispersive X-ray fluorescence spectrometry, and consolidation tests. The results indicate that soil collapsibility was acquired as one of the components of the scouring phenomenon occurrences. To achieve an optimal solution, the effectiveness of the chemical stabilization method involving cement, bentonite, micro-silica, and synthesized nano‑titanium additives was evaluated through an oedometer, Atterberg limits, uniaxial compression, and direct shear tests. Additives dry mixing of cement and nano‑titanium were obtained as the optimal stabilization solutions against scouring compared to other additives. However, considering the environmental impacts of cement production and use, nano‑titanium presents a more environmentally sustainable option due to CO2 absorption and reduced damage potential to vegetation.

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive toxicity, but none of them make full use of the topological information of compounds. In addition, most existing atom-based graph neural network methods focus on attributing model predictions to individual nodes or edges rather than chemically meaningful fragments or substructures. In current studies, we develop a novel fragment-based graph transformer network (FGTN) approach to generate the QSAR model of human reproductive toxicity by considering internal topological structure information of compounds. In the FGTN model, the compound is represented by a graph architecture using fragments to be nodes and bonds linking two fragments to be edges. A super molecule-level node is further proposed to connect all fragment nodes by undirected edges, obtaining global molecular features from fragment embeddings. The FGTN model achieved an accuracy (ACC) of 0.861 and an area under the receiver operating characteristic curve (AUC) value of 0.914 on nonredundant blind tests, outperforming traditional fingerprint-based machine learning models and atom-based GCN model. The FGTN model can attribute toxic predictions to fragments, generating specific structural alerts for the positive compound. Moreover, FGTN may also have the capability to distinguish various chemical isomers. We believe that FGTN can be used as a reliable and effective tool for human reproductive toxicity prediction in contribution to the advancement of chemical safety assessment.

Hygienic assessment of the protective properties of personal hand protection equipment during vibration testing

Introduction. In industrial environments, at the workplaces of hand tool operators, it is not always possible to completely eliminate increased levels of vibration exposure. The power and speed characteristics of modern hand-held machines are constantly increasing. The impact of local vibration on a person has a negative effect on his body. When working with hand tools, personal protective equipment is used. The purpose of personal hand protection against vibration is to reduce the vibration impact on a person. Materials and methods. Bench tests were carried out on hand protection products consisting of damping materials of various shapes and locations in the product. The vibration sensor was installed and fastened in places where the operator’s hands came into contact with vibration and on the forearm. The effectiveness of protection was determined by the difference in measurement levels. Results. During the tests, vibration levels were recorded on the handle of the vibration stand, the palmar surface, the fingers, and the operator’s forearm. In the low-frequency region of the spectrum, vibration attenuation is minimal, and as the frequency range increases, the attenuation increases. Across all areas of the spectrum, performance levels for mittens are higher than for gloves. In places where the fingers come into contact with vibration, the efficiency is less than when in contact with the hand, and in the low-frequency region of the spectrum a negative efficiency of up to 0.8 dB is noted. Vibration measurements on the operator’s forearm showed vibration levels in the forearm to be lower than in the hand. Limitations. The study was conducted over a period of one year, in laboratory conditions, and was limited to two thousand measurements. Conclusion. Hygienic assessment of the effectiveness of the protective properties of personal protective equipment for hands was carried out in accordance with developed laboratory test methods when installing and securing a vibration sensor on the handle, hand, and forearm of the operator. Determining the effectiveness of the entire product model should be considered when designing and manufacturing new hand protection against vibration.

Experimental behavior and fracture prediction of a novel high-strength and high-toughness steel subjected to tension and shear loading tests

In this study, laboratory testing and numerical simulation methods are used to investigate the mechanical behavior and perform fracture prediction of a novel high-strength and high-toughness steel with a negative Poisson's ratio (NPR) effect under combined tensile-shear loading conditions. A test device capable of meeting different tensile-shear combination test angles is designed and manufactured, wherein the mechanical experiments on the NPR (Negative Poisson's Ratio) steel specimens are carried out at various testing angles. Q235 steel and MG400 steel are used as experimental control groups. The results show that the mechanical deformation of NPR steel is significantly better than that of Q235 steel and MG400 steel. Its tensile-shear test curve has no yield plateau and it has quasi-ideal elastic-plastic mechanical properties. The loading direction gradually changes from tension-dominated to shear-dominated as the tension-shear angle increases, and the strength and deformation of the specimens show a decreasing trend. Based on the laboratory test results, a finite element numerical model of NPR steel is established. A series of numerical simulations are carried out under the conditions of different tension and shear angles and the average stress triaxiality and fracture strain data are obtained. The fracture data of NPR steel are fitted using the Johnson-Cook fracture criterion, and the Johnson-Cook fracture parameters under the tensile-shear test conditions of NPR steel are thus obtained. The numerical simulation verifies that the fracture model can accurately predict the tensile-shear fracture behavior of NPR steel.

Multiprobe Amplification (MPA) with Melting Curve Analysis: A Highly Stable and Cost-Effective Platform for the Simultaneous Detection of Eight Potential Bacterial Bioterrorism Agents in Complex Samples.

We aimed to develop an efficient detection platform that can identify a larger number of suspicious samples in a single test, saving time, manpower, and material costs, and providing vital support to the public health system in coping with the current challenging and dynamic bioterrorism threat landscape, particularly in regions of turmoil and conflict. We have successfully developed a high-throughput, multitarget fluorescent array detection platform by effectively combining integrating multiprobe amplification (MPA) with melting curve analysis. Specifically, we have established reliable laboratory testing methods for eight highly pathogenic bacteria, including Bacillus anthracis, Yersinia pestis, Brucella spp., Burkholderia pseudomallei, Francisella tularensis, Vibrio cholerae, Salmonella typhi, and Staphylococcus aureus. Our method achieves sensitive and specific simultaneous detection of eight target bacteria in one well by optimizing the reaction conditions of MPA. In the assessment of 192 simulated environmental samples, both positive and negative coincidence rates were 100.00%. Among 48 simulated clinical samples, the positive coincidence rate reached 97.73%, while maintaining a perfect negative coincidence rate of 100.00%. Moreover, the detection platform holds immense potential for attaining a more comprehensive bioterrorism screening, and its high cost-effectiveness enables the provision of diverse and adaptable diagnostic methods for public health quarantine in underdeveloped countries and regions.

Establishment and application of PDCoV antigen-specific DAS-ELISA detection method

BackgroundPorcine deltacoronavirus (PDCoV) is a swine enteropathogenic coronavirus that affects young pigs, causing vomiting, acute diarrhea, dehydration, and even death. There is growing evidence that PDCoV can undergo cross-species as well as zoonotic transmissions. Due to the frequent outbreaks of this deadly virus, early detection is essential for effective prevention and control. Therefore, developing a more convenient and reliable method for PDCoV detection is the need of the hour.ResultsThis study utilized a high-affinity monoclonal antibody as the capture antibody and a horseradish peroxidase labeled polyclonal antibody as the detection antibody to develop an enzyme-linked immunosorbent assay (DAS-ELSA) for PDCoV detection.Both antibodies target the PDCoV nucleocapsid (N) protein. The findings of this study revealed that DAS-ELISA was highly specific to PDCoV and did not cross-react with other viruses to cause swine diarrhea. The limit of detection of the virus titer using this method was 103 TCID50/mL of PDCoV particles. The results of a parallel analysis of 239 known pig samples revealed a coincidence rate of 97.07% (κ = 0.922) using DAS-ELISA and reverse transcriptase PCR (RT-PCR). The DAS-ELISA was used to measure the one-step growth curve of PDCoV in LLC-PK cells and the tissue distribution of PDCoV in infected piglets. The study found that the DAS-ELISA was comparable in accuracy to the TCID50 method while measuring the one-step growth curve. Furthermore, the tissue distribution measured by DAS-ELISA was also consistent with the qRT-PCR method.ConclusionThe developed DAS-ELISA method can be conveniently used for the early clinical detection of PDCoV infection in pigs, and it may also serve as an alternative method for laboratory testing of PDCoV.

Establishment of collection methods and laboratory testing methods for active pharmaceutical ingredient in the workplace air

Objective: To establish collection methods and laboratory testing methods for qualitative and quantitative analysis of 9 typical active pharmaceutical ingredient in the workplace air. Methods: In December 2021, a mixed solution of nine analytes was prepared and then dispersed in aerosol state to simulate sampling. Glass fiber filter membrane was selected as air collector and collected active pharmaceutical ingredient in the air at a rate of 2.0 L/min for 15 minutes. Then, the obtained filter membrane samples were eluted with 25%ACN/75%MeOH. Finally, the eluent was qualitatively and quantitatively analyzed with liquid chromatography-triple quadrupole mass spectrometer. Results: This method could effectively collect active pharmaceutical ingredient in the air, with an average sampling efficiency of more than 98.5%. The linear correlation coefficient r was greater than 0.9990. The lower limit of quantification for each analyte ranged from 0.6~500.0 ng/ml, and the average recovery rate ranged from 97.6%~102.5%. Conclusion: This method could simultaneously collect 9 active pharmaceutical ingredient in the workplace air, and could provide accurate qualitative and quantitative analysis in subsequent laboratory tests.

General Applicability of Existing College of American Pathologists Accreditation Requirements to Clinical Implementation of Machine Learning-Based Methods in Molecular Oncology Testing.

The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based models share some features of conventional laboratory testing methods. Accreditation requirements that specifically address clinical laboratories' use of ML remain in the early stages of development. To identify relevant CAP accreditation requirements that may be applied to the clinical adoption of ML-based molecular oncology assays, and to provide examples of current and emerging ML applications in molecular oncology testing. CAP accreditation checklists related to molecular pathology and general laboratory practices (Molecular Pathology, All Common and Laboratory General) were reviewed. Examples of checklist requirements that are generally applicable to validation, revalidation, quality management, infrastructure, and analytical procedures of ML-based molecular oncology assays were summarized. Instances of ML use in molecular oncology testing were assessed from literature review. Components of the general CAP accreditation framework that exist for traditional molecular oncology assay validation and maintenance are also relevant for implementing ML-based tests in a clinical laboratory. Current and emerging applications of ML in molecular oncology testing include DNA methylation profiling for central nervous system tumor classification, variant calling, microsatellite instability testing, mutational signature analysis, and variant prediction from histopathology images. Currently, much of the ML activity in molecular oncology is within early clinical implementation. Despite specific considerations that apply to the adoption of ML-based methods, existing CAP requirements can serve as general guidelines for the clinical implementation of ML-based assays in molecular oncology testing.

Concrete Compressive Strength Prediction by Ensemble Machine Learning Approach

The prediction of concrete compressive strength is a crucial aspect of ensuring the structural integrity and durability of construction projects. In recent years, machine learning approaches have improved upon the limitations of empirical formulas and laboratory testing methods for predicting concrete compressive strength. This study utilizes ensemble machine-learning techniques, such as Bagging, XGBoost, and Stacking models, to enhance the accuracy of concrete compressive strength prediction models. A five-fold cross-validation technique was applied to mitigate the problems of underfitting or overfitting in the regression model. Furthermore, various statistical indices were employed to compare the forecasting performance of these ensemble techniques. The prediction performance of this research revealed that XGBoost achieved the highest R-squared value of 93%, followed by Stacking and Bagging regression models at 92%. Consequently, this research underscores the potential of ensemble techniques as valuable tools in the domain of civil engineering, paving the way for more reliable and efficient construction practices.

SEDIMENTATION AND WAVE ATTENUATION BY<i> Avicennia marina </i>MANGROVES: IMPLICATIONS FOR SUSTAINABLE COASTAL PROTECTION

Mangrove ecosystems protect coastlines from abrasion and erosion, attenuate strong sea breezes, and bind sediments periodically. A mangrove ecosystem can be set up to be a coastal building capable of breaking and reducing natural waves, but only through certain calculation methods, so that it serves as a solution by being an environmentally friendly breakwater alternative for sustainable coastal development. The objective of this study was to determine the effects of sedimentation by Avicennia marina mangroves on wave attenuation. Spot-check, transect-squared, and laboratory test methods were employed in this research. The wave data measurement performed using with SBE 26 and RBRDuo T.D. The measurement was performed at five stations at distances of 3 m, 5 m, 10 m, 20 m, and 50 m. The wave attenuation at the 5 stations was 49.5%, 53.8%, 82.9%, 91.4%, and 92.3% with grain diameters of 19.36%, 19.75%, 19.87%, 21%, and 27%. The higher the wave attenuation percentage, the lower the grain diameter percentage, and vice versa. When the waves came, the energy was so large that all the large particles of the sediment in the sea were carried back to the mangrove ecosystem, and when the waves left, the energy released was so small that only the small particles were carried back to the sea. The conclusion is that the diameter percentage of mangrove sediment is related to the percentage of wave attenuation. The higher the wave attenuation percentage is, the smaller the grain diameter percentage will be.

Pengaruh Konsentrasi Koagulan Terhadap Proses Pengolahan Air Di PDAM Surya Sembada Kota Surabaya

Coagulation is one of the processes of water treatment by the addition of chemicals, the purpose of which is to bind particles like particles or dissolve so that they clump. Coagulation concentration definitely affects the coagulation process carried out. In addition, the addition of coagulation also affects the water produced for distribution. Testing of the impact of coagulant concentrations on raw water treatment processes has been carried out on the PDAM Surya Sembada Surabaya. The use of laboratory testing methods is done to see the water quality when using different coagulants. The results of the analysis obtained that the difference in the concentration of coagulants has an influence on the process of water treatment, this is due to several factors that support the existence of variation in the use of the coagulation concentration.