Concentration Measurements Research Articles

Differential absorption laser spectroscopy at 8 kHz using precompensated current modulation

We present a differential laser absorption spectroscopy (DLAS) system operating at 1550 nm for rapid and sensitive gas concentration measurements. A dual-wavelength toggling mechanism is presented, which significantly reduces data processing, hence supporting a high update rate and data robustness against fast-changing environmental conditions. We showcase the ability to toggle between two wavelengths separated by 90 pm in 14 μs and with minimal chirp (∼1 pm), facilitating sensitive DLAS measurements at 8 kHz update rate. This performance is achieved by driving a 1550 nm diode laser with a modified square-wave current pulse, overcoming the thermal time constant limited wavelength-modulation response of the diode laser. A sensitive feedback mechanism ensures excellent long-term wavelength stability better than 1.4 pm peak-to-peak at 8 kHz toggling over 20 h. As a performance test, we measured the volumetric ratio (VMR) of hydrogen cyanide (HCN) gas in a fiber-coupled gas cell with less than 0.2% peak-to-peak variation over 20 h at 40 Hz. A best sensitivity in VMR of 8×10−6 was achieved at 25 ms integration time. The simplicity and high update rate of our system make it well-suited for gas monitoring in dynamic atmospheric and industrial environments. Further, it offers potential utility in applications requiring precise wavelength control, such as injection seeding of pulsed lasers. A simple analytical model is derived, which, in detail, supports the experimental results, hence offering a tool for future design optimization.

A Fabry–Pérot Sensing Setup Based on a Coreless Fiber for the Measurement of Ethanol Concentration in Water-based Solutions

A Fabry–Pérot Sensing Setup Based on a Coreless Fiber for the Measurement of Ethanol Concentration in Water-based Solutions

Airborne observations reveal the fate of the methane from the Nord Stream pipelines

Abstract The Nord Stream pipeline leaks on 26 September 2022 released 465 ± 20 kt of methane into the atmosphere, which is the largest recorded transient anthropogenic methane emission event. While most of the gas escaped directly to the atmosphere, a fraction dissolved in the water. So far, studies on the fate of this dissolved methane rely on pipeline volumetric estimates or spatially sparse concentration measurements and ocean models. Here, we use atmospheric measurements with broad spatial coverage obtained from an airborne platform to estimate outgassing of 19-48 t h−1 on 5 October 2022. Our results broadly agree with ocean models but reveal uncertainties such as inaccuracies in their spatial emission distribution. Thus, we provide a data-driven constraint on the fate of the methane from the Nord Stream pipelines in the Baltic Sea. These results demonstrate the benefit of a fast-response airborne mission to track a dynamic methane emission event.

On-Site Detection of Ca and Mg in Surface Water Using Portable Laser-Induced Breakdown Spectroscopy

Ca and Mg are key constituents in surface water that are essential nutrients and vital indicators of water hardness. Rapid on-site measurement of Ca and Mg concentrations in surface water is important. However, traditional laboratory detection methods are complex and time-consuming, and on-site detection is difficult. In this study, a portable surface water detection method was developed using laser-induced breakdown spectroscopy with a miniaturized spectrometer LIBS and a liquid jet device for sample introduction. The device enables the rapid online in situ measurement of elemental concentrations in the water. The limits of detection for the rapid on-site detection of Ca and Mg in surface water were 11.58 and 2.57 mg/L, respectively. We applied this method to assess the concentrations of Ca and Mg in authentic water samples collected from rivers and ponds. The recovery rates for Ca and Mg were 90.83–101.74% and 93.43–108.74%, respectively. This method is suitable for rapid, on-site, and highly sensitive monitoring of Ca and Mg concentrations in the environment.

Correction: A novel method for simultaneous measurement of 222Rn and 220Rn progeny concentrations measured by an alpha spectrometer

Correction: A novel method for simultaneous measurement of 222Rn and 220Rn progeny concentrations measured by an alpha spectrometer

Determination of Location Quotient of Organic Agriculture in Türkiye

The main objective of the study is to determine in which provinces organic agriculture is clustered in Türkiye. Location quotient is one of the most widely used indices in regional concentration measurements and expresses the degree of specialization of a certain production activity within a country/region/sector or the relative concentration of a certain production activity in a certain region. In this direction, within the scope of the study, it is aimed to determine the location quotient of organic agriculture, which provides competitive advantage throughout Türkiye, and it is envisaged to determine the location quotient in this direction. In determining the location quotient, the amount of production, which is one of the best indicators of economic performance, was used. As a matter of fact, the amount of production is the most important indicator showing which production activity is more dominant and the location quotient to be calculated takes a value between 0 and infinity (∞). If the relevant production activity has a score greater than 1, it indicates that the activity is the main production activity that is concentrated/specialised in the region. However, if the score is less than 1, it indicates that the activity is a local one that is not sufficiently concentrated/specialised in the region. Following the analysis, Eastern Anatolia and Central Anatolia are the regions with the highest concentration of organic agriculture production. The top 10 provinces with the most concentrated organic agriculture production are Van (7), Ağrı (6), Ankara (6), Bayburt (6), Erzurum (6), Çanakkale (5), Kars (5), Muş (5), Niğde and Sivas. The most widely produced crops are alfalfa (18), wheat (16), tomato (15), apple (14), maize (12), barley (12), hazelnut (9), sainfoin (9), olive (9), oat (9) and vetch (9). Localized and intensified organic farming systems have proven effective in generating high income per unit of land, optimizing agricultural land utilization, and ensuring sustainability. They also provide specialized labour forces, sector-specific inputs, and the use of advanced technologies. Therefore, it is believed that the formations will enhance the economic, social, and environmental sustainability of the region by increasing productivity and promoting the growth of organic agriculture. This will lead to significant benefits for the region's development.

Multi-AUV sediment plume estimation using Bayesian optimization

Sediment plumes created by dredging or mining activities have an impact on the ecosystem in a much larger area than the mining or dredging area itself. It is therefore important and sometimes mandatory to monitor the developing plume to quantify the impact on the ecosystem including its spatial-temporal evolution. To this end, a Bayesian Optimization (BO)-based approach is proposed for plume monitoring using autonomous underwater vehicles (AUVs), which are used as a sensor network. Their paths are updated based on the BO, and additionally, a split-path method and the traveling salesman problem are utilized to account for the distances the AUVs have to travel and to increase the efficiency. To address the time variance of the plume, a sliding-window approach is used in the BO and the dynamics of the plume are modeled by a drift and decay rate of the suspended particulate matter (SPM) concentration measurements. Simulation results with SPM data from a simulation of a dredge experiment in the Pacific Ocean show that the method is able to monitor the plume over space and time with good overall estimation error.

Direct measurement of brake wear particles from a light-duty vehicle under real-world driving conditions

Abstract As tailpipe emissions have decreased, there is a growing focus on the relative contribution of non-exhaust sources of vehicle emissions. Addressing these emissions is key to better evaluating and reducing vehicles’ impact on air quality and public health. Tailoring solutions for different non-exhaust sources, including brake emissions, is essential for achieving sustainable mobility. Studying emissions from vehicles in real-world scenarios provides a better understanding of their environmental impact compared to laboratory testing alone. This study presents findings on the direct measurement of brake particles and the characterization of this source of particulate matter in real-world conditions using a mobile laboratory. In situ measurements of particle concentration and size distribution showed good agreement with previous laboratory studies, indicating the suitability of the approach to investigate break particle emissions during real-world operation. The study demonstrates that particle size distributions can vary based on the temperature of the brake disk, which is influenced by the initial braking speed, with significant variations observed between speeds of 60, 80, 100, and 120 km/h. Particles with sizes between 6 and 523 nm were released into the air from the brake system, although it is likely that larger particles were also emitted but not captured due to the upper detection limit of the Engine Exhaust Particle Sizer. During harsh braking events, such as decelerations of 4.2 m/s2 from 120 km/h, a concentration of up 106 (#/cm3) was measured for particles under 8 nm. Moreover, scanning electron microscope analysis revealed that nanoparticles are present in the form of agglomerates, whose shape can change depending on the formation process. Elements present in the particles comprised mainly iron, copper, and aluminium, indicating wear of the brake pad materials and disk components.

A Prediction Model for Methane Concentration in the Buertai Coal Mine Based on Improved Black Kite Algorithm–Informer–Bidirectional Long Short-Term Memory

Accurate prediction of methane concentration in mine roadways is crucial for ensuring miner safety and enhancing the economic benefits of mining enterprises in the field of coal mine safety. Taking the Buertai Coal Mine as an example, this study employs laser methane concentration monitoring sensors to conduct precise real-time measurements of methane concentration in coal mine roadways. A prediction model for methane concentration in coal mine roadways, based on an Improved Black Kite Algorithm (IBKA) coupled with Informer-BiLSTM, is proposed. Initially, the traditional Black Kite Algorithm (BKA) is enhanced by introducing Tent chaotic mapping, integrating dynamic convex lens imaging, and adopting a Fraunhofer diffraction search strategy. Experimental results demonstrate that the proposed improvements effectively enhance the algorithm’s performance, resulting in the IBKA exhibiting higher search accuracy, faster convergence speed, and robust practicality. Subsequently, seven hyperparameters in the Informer-BiLSTM prediction model are optimized to further refine the model’s predictive accuracy. Finally, the prediction results of the IBKA-Informer-BiLSTM model are compared with those of six reference models. The research findings indicate that the coupled model achieves Mean Absolute Errors (MAE) of 0.00067624 and 0.0005971 for the training and test sets, respectively, Root Mean Square Errors (RMSE) of 0.00088187 and 0.0008005, and Coefficient of Determination (R2) values of 0.9769 and 0.9589. These results are significantly superior to those of the other compared models. Furthermore, when applied to additional methane concentration datasets from the Buertai Coal Mine roadways, the model demonstrates R2 values exceeding 0.95 for both the training and test sets, validating its excellent generalization ability, predictive performance, and potential for practical applications.

Integrated learning‐based estimation and nonlinear predictive control of an ammonia synthesis reactor

AbstractThis paper presents an advanced machine learning‐based framework designed for predictive modeling, state estimation, and feedback control of ammonia synthesis reactor dynamics. A high‐fidelity two‐dimensional multiphysics model is employed to generate a comprehensive dataset that captures variable dynamics under various operational conditions. Surrogate long short‐term memory neural networks are trained to enable real‐time predictions and model‐based control. Additionally, a feedforward neural network is developed to estimate the outlet ammonia concentration and hotspot temperature using spatially distributed temperature readings, thereby addressing the challenges associated with real‐time concentration and maximum temperature measurements. The machine learning‐based predictive modeling and state estimation methods are integrated into a model predictive control architecture to regulate ammonia synthesis. Simulation results demonstrate that the machine learning surrogates accurately represent the nonlinear process dynamics with minimal discrepancy while reducing optimization costs compared to the high‐fidelity model, ensuring adaptability and effective guidance of the reactor to desired set points.

Exercise intensity and training alter the innate immune cell type and chromosomal origins of circulating cell-free DNA in humans

Exercising regularly promotes health, but these benefits are complicated by acute inflammation induced by exercise. A potential source of inflammation is cell-free DNA (cfDNA), yet the cellular origins, molecular causes, and immune system interactions of exercise-induced cfDNA are unclear. To study these, 10 healthy individuals were randomized to a 12-wk exercise program of either high-intensity tactical training (HITT) or traditional moderate-intensity training (TRAD). Blood plasma was collected pre- and postexercise at weeks 0 and 12 and after 4 wk of detraining upon program completion. Whole-genome enzymatic methylation sequencing (EM-seq) with cell-type proportion deconvolution was applied to cfDNA obtained from the 50 plasma samples and paired to concentration measurements for 90 circulating cytokines. Acute exercise increased the release of cfDNA from neutrophils, dendritic cells (DCs), and macrophages proportional to exercise intensity. Exercise training reduced cfDNA released in HITT participants but not TRAD and from DCs and macrophages but not neutrophils. For most participants, training lowered mitochondrial cfDNA at rest, even after detraining. Using a sequencing analysis approach we developed, we concluded that rapid ETosis, a process of cell death where cells release DNA extracellular traps, was the likely source of cfDNA, demonstrated by enrichment of nuclear DNA. Further, several cytokines were induced by acute exercise, such as IL-6, IL-10, and IL-16, and training attenuated the induction of only IL-6 and IL-17F. Cytokine levels were not associated with cfDNA induction, suggesting that these cytokines are not the main cause of exercise-induced cfDNA. Overall, exercise intensity and training modulated cfDNA release and cytokine responses, contributing to the anti-inflammatory effects of regular exercise.

Raman-based in situ concentration measurement of boric solution with high accuracy and stability

In this paper, a quantitative analysis of boric acid solutions’ concentration with high accuracy is demonstrated by Raman spectroscopy. The in-situ concentration measurement model is developed by an elastic net against Raman spectral data. The R 2 of the elastic net model in the test set prediction can reach 0.9960. Compared with the partial least squares method used in traditional quantitative model, the method proposed in this paper shows excellent performance with low measurement error and high stability. One hundred and twenty groups of Raman spectra of boric solution with different concentrations and flow rates are used for training and validation of the model. The mean relative prediction error of the elastic net model was reduced from 7.1 to 1.9% in comparison with the models based on linear regression and the partial least squares. The verification test of various concentrations of boric acid solution shows that the detection error is <2% when the concentration is below 2000 mg/L. The study provides a reference for improving the in-situ quantitative detection accuracy by using Raman spectroscopy.

Microstructural Analysis and Radiological Characterization of Alkali-Activated Materials Based on Aluminosilicate Waste and Metakaolin

The formation of an aluminosilicate gel structure made of alkali-activated materials (AAMs) was conducted through an alkali-activation reaction of the solid precursors (fly ash, metakaolin, and wood ash). Fly and wood ash are by-products of the burning process of coal and wood, respectively. Alkali-activated materials of aluminosilicate origin, made from the different ashes, fly and wood, are very attractive research targets and can be applied in various technological fields due to their thermal stability, resistance to thermal shock, high porosity, high sustainability, and finally, low energy loss during production. In this paper, we evaluate physico-chemical properties, microstructure, and radiological environmental impacts when wastes that contain elevated levels of naturally occurring radionuclides (NORs) such as fly ash and wood ash are made into “green cements” such as AAMs. The determination of radionuclide content was performed by means of gamma-ray spectrometry. Results showed that the AAMs have a lower value in the activity concentration of radionuclides than raw materials. The external absorbed gamma dose rate was 74.7–107.3 nGy/h, and the external radiation hazard index values were in range of 0.445–0.628 Bq/kg. The results of the activity concentration measurements for alkali-activated materials indicate the potential of their safe application in building construction. In terms of the structural characterizations, the obtained alkali-activated materials were examined using XRD, DRIFT, FESEM, and TEM analyses.

Chronic oral dosing of cannabidiol and cannabidiolic acid full-spectrum hemp oil extracts has no adverse effects in horses: a pharmacokinetic and safety study.

To compare the pharmacokinetics of cannabidiol (CBD) and cannabidiolic acid (CBDA) in horses and to evaluate the safety of their chronic administration. CBD- and CBDA-rich oil (1 mg/kg) were administered orally twice daily to 7 adult horses over 6 weeks in a randomized, crossover design with a 2-week washout period. A 12-hour pharmacokinetic analysis was conducted on day 1 of each 6-week trial, followed by the measurement of peak and trough concentrations at weeks 1, 2, 4, and 6. The cannabinoids safety was assessed via daily physical examination, periodic bloodwork, and liver biopsy at the beginning and end of the study. 12-hour pharmacokinetics revealed a higher maximum serum concentration (103 vs 12 ng/mL) and greater area under the curve (259 vs 62 ng·h/mL) for CBDA when compared to CBD. Cannabidiolic acid nadir and peak serum levels over time ranged from 46 to 122 ng/mL, which was higher than CBD (12 to 38 ng/mL). Complete blood count and serum chemistry revealed no clinically relevant changes with either CBD or CBDA. No significant abnormalities were detected on liver ultrasonographic and histopathologic evaluation on day 0 and after both phases of the study. A dose of either 1 mg/kg of CBD or CBDA administered long term appears safe; however, CBDA serum concentrations suggest superior absorption/retention. Chronic cannabinoid supplementation in horses is safe. Considering the higher absorption of CBDA, its use is recommended to evaluate the therapeutic efficacy of this common hemp derived cannabinoid.

Dose Escalation of Adalimumab in Patients with Hidradenitis Suppurativa: A Retrospective Case Series.

Adalimumab is a central treatment for moderate-to-severe hidradenitis suppurativa (HS). However, half of patients treated with adalimumab for HS do not achieve a clinically significant response. Data on non-immunological factors correlating clinical response and adalimumab blood concentration are scarce. To determine whether increasing adalimumab dose to 80 mg weekly can improve the clinical outcome of patients unresponsive to adalimumab 40 mg weekly. To identify parameters influencing disease activity and those modifying serum adalimumab concentration. This is a retrospective case series from a tertiary dermatology clinic, comprising 40 patients with moderate-to-severe HS with suboptimal response to the FDA-approved dose of adalimumab. All patients had a measurement of blood adalimumab concentration. Depending on their dosage, some patients had their adalimumab dose increased to 80 mg weekly while others stayed at 40 mg dose weekly. Chi-squared and ANOVA tests were used for data analysis. 43.8% of patients who increased to a weekly dose of 80 mg clinically improved at their follow-up, compared with 33.3% of those who stayed at a weekly dose of 40 mg. The dose increase led to a statistically significant increase in serum concentration of adalimumab (P < .001). Higher serum adalimumab concentration is associated with lower disease activity (P = .043). Normal-weight patients had significantly higher concentrations than overweight patients (P = .011). An increase in the weekly dose of adalimumab led to a nonstatistically significant improvement in clinical response but led to a statistically significant increase in serum concentration.

Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities.

In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO2), fine particulate matter (PM2.5), and coarse particulate matter (PM10). A hybrid forecasting strategy was employed, integrating Autoregressive Integrated Moving Average (ARIMA) models with Artificial Neural Networks (ANN), incorporating external covariates such as wind speed and direction to enhance prediction accuracy. Vital monitoring stations, including Quintero, Ventanas, Coyhaique I, and Coyhaique II, played a pivotal role in data collection and model development. Emphasis on industrial and residential zones highlighted the significance of discerning pollutant origins and the influence of wind direction on concentration measurements. Geographical and climatic factors, notably in Coyhaique, revealed a seasonal stagnation effect due to topography and low winter temperatures, contributing to heightened pollution levels. Model performance underwent meticulous evaluation, utilizing metrics such as the Akaike Information Criterion (AIC), Ljung-Box statistical tests, and diverse statistical indicators. The hybrid ARIMA-ANN models demonstrated strong predictive capabilities, boasting an R2 exceeding 0.90. The outcomes underscored the imperative for tailored strategies in air quality management, recognizing the intricate interplay of environmental factors. Additionally, the adaptability and precision of neural network models were highlighted, showcasing the potential of advanced technologies in refining air quality forecasts. The findings reveal that geographical and climatic factors, especially in Coyhaique, contribute to elevated pollution levels due to seasonal stagnation and low winter temperatures. These results underscore the need for tailored air quality management strategies and highlight the potential of advanced modeling techniques to improve future air quality forecasts and deepen the understanding of environmental challenges in Chile.

Practical, low-cost integrity testing of diesel particle filters using remote sensing measurement at the campus entrance gate.

This work investigates the detection of defunct or absent diesel particle filters by drive-through remote sensing measurement at the Czech University of Life Sciences main vehicular entrance gate. An exhaust sample was collected by a line attached to the road surface in the center of the travel lane. A non-volatile particle number (nvPN) counter and electric mobility particle size classifier were used to measure particle number concentrations, and an FTIR analyzer was used to measure CO2, CO, and NO concentrations. Of 59.7% of 526 entering vehicles, peak CO2 concentrations above 100ppm were observed, suggesting that the entrance gate is ideal for sampling-style remote sensing measurements due to high signal strength and controlled spacing between vehicles. On 48 vehicles, the measurements were compared to a 10-s periodic technical inspection (PTI) style measurement of tailpipe nvPN concentrations at idle. The results indicate nearly absolute agreement in distinguishing diesel vehicles with and without a functional DPF, with one outlier with a relatively weak CO2 signal and weak correlation between roadside nvPN and CO2 concentrations. Using a linear regression approach to calculate the remote sensing emissions factors (EF) and restricting the data to those with peak CO2 at least 100ppm yielded an absolute agreement not only between tailpipe nvPN concentrations at idle and remote sensing nvPN EF but also between tailpipe nvPN and roadside concentration of both nvPN and total particle number in 25-560nm size bins. The reported setup presents, in the authors' opinion, a relatively simple, robust, non-obtrusive, and practical means of checking DPF functionality in locations interested in excluding high emitters, such as university campuses, airports, large parking garages, national park entrances, and similar locations where entering vehicles are fully warmed up and not cooled down by extended waiting in line. Relatively strong CO2 signal yielding high accuracy of detection of non-DPF diesels with laboratory instrument presents, in the opinion of the authors, a very high chance of successful exploitation of diffusion chargers, nvPN counters intended for PTI use in several EU countries, and other low-cost particle number sensors, with detection limit in thousands of #/cm3, and possibly low-cost black carbon monitors, along with a low-cost NDIR CO2 sensor and a camera or other means of identifying individual tested vehicles, for detecting defunct diesel particle filters. The CO2 signal alone can identify vehicles using internal combustion engines. Extending this measurement to CO and NO concentrations may also detect non-functional three-way and NOx-reducing catalysts, as demonstrated on three L-category vehicles, one quad, and two motorcycles. The setup also could be used as a drive-through, loaded-mode PTI test.

Fast response solid electrolyte oxygen sensors with porous thin film electrodes.

A novel solid electrolyte sensor with considerably improved response times is presented. The new so-called eFIPEX [etched flux (Φ) probe experiment] is based on the FIPEX [flux (Φ) probe experiment] sensor applied for the measurement of molecular and atomic oxygen concentrations. A main application is the measurement of atmospheric atomic oxygen aboard sounding rockets up to altitudes of 250 km. eFIPEX employs a new manufacturing technique for its electrodes combining two manufacturing steps-the deposition of platinum films with a polyol process and electrochemical etching to carve out the electrode geometry. Selectivity toward atomic oxygen is achieved through gold plating. All work steps can be completed in ambient air. Electrodes with thicknesses of 200nm to 1.5μm are manufactured and characterized with optical and electron microscopy as well as with energy dispersive x-ray spectroscopy. It is shown that the significantly faster response times are related to pores in the platinum film reaching down to the substrate. The new eFIPEX were flown in comparison with conventional FIPEX sensors on the PMWE-2 sounding rocket flight showing significantly improved performance. Due to the easier fabrication and the superior transient behavior, this new sensor system will be preferentially used in future missions.

End-to-End Methane Concentration Measurement Model with Deep Residual Shrinkage Network for Direct Absorption Spectroscopy

End-to-End Methane Concentration Measurement Model with Deep Residual Shrinkage Network for Direct Absorption Spectroscopy

Automated-Cleaning Quartz Crystal Microbalance Sensor for PM2.5 Mass Concentration Measurement with Enhanced Reliability

Automated-Cleaning Quartz Crystal Microbalance Sensor for PM2.5 Mass Concentration Measurement with Enhanced Reliability