Automated Sampling System Research Articles

Breath Analysis: Identification of Potential Volatile Biomarkers for Non-Invasive Diagnosis of Chronic Kidney Disease (CKD).

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal-Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild-moderate CKD (G) and ESKD (DIAL), respectively.

A high‐frequency greenhouse gas flux analysis tool: Insights from automated non‐steady‐state transparent soil chambers

AbstractNon‐steady‐state chambers are widely employed for quantifying soil emissions of CO2, CH4, and N2O. Automated non‐steady‐state (a‐NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high‐frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post‐measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high‐resolution data obtained from an a‐NSS sampling system. Applied to a 38‐day dataset, the algorithm captured concurrent field measurements of CO2, CH4, and N2O fluxes. The automated sampling system enables the acquisition of high‐frequency data, allowing the detection of episodic gas flux events. By using shape‐constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO2 and N2O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO2, CH4, and N2O fluxes, providing both instantaneous and diel values directly from raw, high‐temporal‐resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a‐NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics.

Spatial distribution of small microplastics in the Norwegian Coastal Current

High concentrations of microplastic (MP) particles have been reported in the Arctic Ocean. However, studies on the high-resolution lateral and vertical transport of MPs from the European waters to the Arctic are still scarce. Here, we provide information about the concentrations and compositions of MPs in surface, subsurface, and deeper waters (< 1 m, ∼ 4 m, and 17–1679 m) collected at 18 stations on six transects along the Norwegian Coastal Current (NCC) using an improved Neuston Catamaran, the COntinuos MicroPlastic Automatic Sampling System (COMPASS), and in situ pumps, respectively. FTIR microscopy and spectroscopy were applied to measure MP concentration, polymer composition, and size distribution. Results indicate that the concentrations of small microplastics (SMPs, <300 μm) varied considerably (0–1240 MP m−3) within the water column, with significantly higher concentrations in the surface (189 MP m−3) and subsurface (38 MP m−3) waters compared to deeper waters (16 MP m−3). Furthermore, the average concentration of SMPs in surface water samples was four orders of magnitude higher than the abundance of large microplastics (LMPs, >300 μm), and overall, SMPs <50 μm account for >80 % of all detected MPs. However, no statistically significant geographical patterns were observed in SMP concentrations in surface/subsurface seawaters between the six sampling transects, suggesting a relatively homogeneous horizontal distribution of SMPs in the upper ocean within the NCC/Norwegian Atlantic Current (NwAC) interface. The Lagrangian particle dispersal simulation model further enabled us to assess the large-scale transport of MPs from the Northern European waters to the Arctic.

Drone-Based Automatic Water Sampling System

Drone-Based Automatic Water Sampling System

Development of Responsive Promoters and their Utilization for Stable CHO Sensor Cell Lines.

Chinese hamster ovary (CHO) cells are the most important mammalian expression systems to produce recombinant proteins. To ensure a proper expression of the desired molecule, it is important to monitor and adjust bioprocess parameters like oxygen concentration as well as osmolality. However, the observation of crucial cultivation parameters can be an elaborate procedure requiring lots of hands-on work. In addition, for emerging modeling approaches for bioprocesses, a model cell line responding with a measurable signal to an external influence would be highly valuable. This protocol describes in detail the procedure to generate responsive promoters reacting to limiting conditions as well as the generation of stable sensor cell lines communicating with the operator. Thereby, hypoxia and osmolality sensing response elements established in CHO cells will be utilized to trigger the expression of a minimal CMV promoter. To assess the activity of the responsive promoter in close to real time, unstable variants of GFP and BFP will be expressed, which can be analyzed via flow cytometry. Finally, an automated sampling system coupled to a fluorescence microscope enables a continuous observation of CHO cells and reports emerging limiting conditions by detecting increasing amounts of a specific fluorescent protein.

Continuous Online Titer Monitoring in CHO Cell Culture Supernatant Using a Herringbone Nanofluidic Filter Array.

Online monitoring of monoclonal antibody product titers throughout biologics process development and production enables rapid bioprocess decision-making and process optimization. Conventional analytical methods, including high-performance liquid chromatography and turbidimetry, typically require interfacing with an automated sampling system capable of online sampling and fractionation, which suffers from increased cost, a higher risk of failure, and a higher mechanical complexity of the system. In this study, a novel nanofluidic system for continuous direct (no sample preparation) IgG titer measurements was investigated. Tumor necrosis factor α (TNF-α), conjugated with fluorophores, was utilized as a selective binder for adalimumab in the unprocessed cell culture supernatant. The nanofluidic device can separate the bound complex from unbound TNF-α and selectively concentrate the bound complex for high-sensitivity detection. Based on the fluorescence intensity from the concentrated bound complex, a fluorescence intensity versus titer curve can be generated, which was used to determine the titer of samples from filtered, unpurified Chinese hamster ovary cell cultures continuously. The system performed direct monitoring of IgG titers with nanomolar resolution and showed a good correlation with the biolayer interferometry assays. Furthermore, by variation of the concentration of the indicator (TNF-α), the dynamic range of the system can be tuned and further expanded.

Online monitoring of higher alcohols and esters throughout beer fermentation by commercial Saccharomyces cerevisiae and Saccharomyces pastorianus yeast.

Higher alcohols and esters are among the predominant classes of volatile organic compounds (VOCs) that influence the quality of beer. The concentrations of these compounds are determined through a specific yeast strain selection and fermentation conditions. The effect of yeast strains on the formation of higher alcohols and esters throughout fermentations (at 20°C) was investigated. Flavour-relevant esters (ethyl acetate, isoamyl acetate, ethyl hexanoate and ethyl octanoate) and higher alcohols (isoamyl alcohol, isobutyl alcohol and phenylethyl alcohol) were monitored throughout the fermentation using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) coupled with an automated sampling system for continuous measurements. Compound identification was confirmed by analysis of samples using gas chromatography-mass spectrometry (GC-MS). Results demonstrated the specific time points where variation in higher alcohol and ester generation between yeast strains occurred. In particular, the concentrations of isoamyl acetate, ethyl octanoate and isoamyl alcohol between yeast strains were significantly different over the first 2 days of fermentation; whereas, after Day 3, no significant differences were observed. The two Saccharomyces pastorianus strains produced comparable concentrations of the key higher alcohols and esters. However, the key higher alcohol and ester concentrations varied greatly between the two S.cerevisiae strains. The use of PTR-ToF-MS to rapidly measure multiple yeast strains provides new insights on fermentation for brewers to modify the sensory profile and optimise quality.

Optimization of bioanalysis of dried blood samples

IntroductionPharmacokinetic/pharmacodynamic modelling has emerged as a valuable technique for understanding drug exposure and response relationships in drug development. Pharmacokinetic data are often obtained by taking multiple blood samples, which may disturb physiological parameters and complicate study designs. Wearable automatic blood sampling systems can improve this limitation by collecting dried blood samples at programmable time points without disrupting cardiovascular parameters. It is the objective of this study to evaluate the bioanalysis of DBS in comparison to conventional blood sampling techniques and to optimize the recovery of various compounds spiked into canine blood dried on filter paper tape. MethodsIncubated blood samples from Beagle dogs were spiked with 16 different compounds and half of the whole blood sample was centrifuged to obtain plasma. After the dried blood sample drops were dried, liquid chromatography-mass spectrometry methods were used to analyze the samples. The study explored different anticoagulants, sample preparation methods and technical approaches to best determine the compound concentrations in dried blood samples. ResultsWith the two anticoagulants tested and using the optimized sample preparation methods and technical approaches we employed, the bioanalysis of dried blood samples can provide equivalent results to conventional blood sampling techniques. DiscussionAutomated blood sampling systems have the potential to provide increased numbers of blood samples, providing substantially more Pharmacokinetic data within safety pharmacology studies without disrupting physiological parameters. They can provide a viable alternative to traditional methods of obtaining blood for various other types of studies or analyses.

Identification of a characteristic VOCs pattern in the exhaled breath of post-COVID subjects: are metabolic alterations induced by the infection still detectable?

SARS-CoV-2 is expected to cause metabolic alterations due to viral replication and the host immune response resulting in increase of cytokine secretion and cytolytic activity. The present prospective observational study is addressed at exploring the potentialities of breath analysis in discrimination between patients with a documented previous history of symptomatic SARS-CoV-2 infection and, at the moment of the enrollment, exhibiting a negative nasopharyngeal swab and acquired immunity (post-COVID) and healthy subjects with no evidence of previous SARS-CoV-2 infection (no-COVID). The main purpose is to understand if traces of metabolic alterations induced during the acute phase of the infection are still detectable after negativization, in the form of a characteristic volatile organic compound (VOC) pattern. An overall number of 60 volunteers aged between 25 and 70 years were enrolled in the study (post-COVID: n.30; no-COVID: n. 30), according to well-determined criteria. Breath and ambient air samples were collected by means of an automated sampling system (Mistral) and analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC/MS). Statistical tests (Wilcoxon/Kruskal–Wallis test) and multivariate data analysis (principal component analysis (PCA), linear discriminant analysis) were performed on data sets. Among all compounds detected (76 VOCs in 90% of breath samples), 5 VOCs (1-propanol, isopropanol, 2-(2-butoxyethoxy)ethanol, propanal and 4-(1,1-dimethylpropyl)phenol) showed abundances in breath samples collected from post-COVID subjects significantly different with respect to those collected from no-COVID group (Wilcoxon/Kruskal–Wallis test, p-values <0.05). Although not completely satisfactory separation between the groups was obtained, variables showing significant differences between the two groups and higher loadings for PCA are recognized biomarkers of COVID-19, according to previous studies in literature. Therefore, based on the outcomes obtained, traces of metabolic alterations induced by SARS-CoV-2 infection are still detectable after negativization. This evidence raises questions about the eligibility of post-COVID subjects in observational studies addressed at the detection of COVID-19. (Ethical Committee Registration number: 120/AG/11).

Concept of a Crossed Czerny–Turner Spectrometer With an Integrated Automatic Sampling System for Biodetection Using Ultrastable Gold Nanoparticles

This work presents the design and implementation of a portable optofluidic system for biodetection based on ultrastable gold nanoparticles functionalized with a dopamine binding aptamer. The automatic sampling and mixing fluidic system integrated into a spectrometer is configured to measure the gold nanoparticles’ plasmon band position. The novel molecule detection method based on ultrastable gold nanoparticles has been previously tested using dopamine as the target molecule. The optofluidic system designed for automatic bathochromic shift detection has an optical resolution of 1nm using a 50 μm input slit. The proposed system, compared to other existing molecules detection techniques, has several advantages including portability and automatic detection for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> applications.

Automated Sampling System for Monitoring 85Kr in Air

Radioactive krypton-85 (85Kr) gas, a chemically inert and non-proliferation indicator, is derived from fission products. Its detection relies on the Budesamt für Strahlenschutz–Institute of Atmospheric Radioactivity (BfS-IAR) method, which necessitated impurity removal using soda lime, silica gel, and liquid nitrogen for cryogenic adsorption. This manual process requires frequent replacements, posing challenges for its automation. To address this, we developed a prototype krypton sampling system as an interim research product for the fully automated remote monitoring of covert nuclear activity. The system incorporates a hollow fiber membrane for impurity removal, a computer-controlled multi-position valve for sampling, and an electric cooler for adsorption. The impurity removal modules demonstrated high efficiencies, removing H2O and CO2 at 99.8% and 97.8% rates, respectively. Further, the custom-made sampling system can process 16 samples in a single run without analyst intervention. We conducted experiments to verify the automatic krypton sampling capability. The activity concentration of 85Kr in ambient air was measured using the BfS-IAR processing and detection system. The system exhibited a recovery rate of ~7.8% for krypton in 1000 L air, demonstrating good continuous remote monitoring capability. This study promotes the development of an automated analysis system for the detection of 85Kr in ambient air.

A strong temperature dependence of soil nitric oxide emission from a temperate forest in Northeast China

Nitric oxide (NO) is a highly reactive trace gas affecting atmospheric chemistry and air quality. Although forest soils have been recognized as an important source of atmospheric NO, there are large uncertainties in global forest soil NO emission inventories (ranged from 0.03 to 8.00 kg N ha−1, averaged 1.34 ± 0.28 kg N ha−1), partly due to the paucity of high-frequency monitoring from unmanaged forests. In this study, we used an automated sampling system to measure NO fluxes with a high temporal resolution over two years (daily measurements from January 2019 to December 2020) in a mixed forest in Northeast China. We found that the mean annual NO emission was 0.42 ± 0.04 kg N ha−1, being 31% of the global forest average. The contribution of NO emission during the growing season was 92% of the yearly NO flux. Soil temperature was the most important edaphic factor in regulating NO emission, explaining 90–92% of the seasonal variation. The apparent temperature sensitivity (Q10) of NO flux was 3.67. In the growing season, NO emission was also influenced by soil moisture, with optimum soil moisture of 37% WFPS. By providing a detailed measurement of diurnal, seasonal, and annual dynamics of NO emissions and their environmental controls from forest soils, our data are useful to develop more accurate biogeochemical models that will improve upscaled global NO budgets.

AN ANALYSIS OF THE INFLUENCE OF THE NUMBER OF OBSERVATIONS IN A RANDOM FOREST TIME SERIES CLASSIFICATION TO MAP THE FOREST AND DEFORESTATION IN THE BRAZILIAN AMAZON

Abstract. Remote sensing has been an essential tool in combating deforestation. However, the ever-rising deforestation rates require new remote sensing techniques. This paper presents a study to determine the effects on the accuracy of the data analysis of varying the number of satellite observations, using a Random Forest classification algorithm. We carried out experiments on the Landsat-8 data cube with 22 images and developed an automatic sampling system based on PRODES to generate the labeled time series. We split the time series dataset to build data subsets with different number of observations. The results showed that a fewer number of observations negatively effects the accuracy of the RF algorithm when analyzing deforested areas, but not forest areas. The RF classifiers were compared using a random test data set, where all classifiers presented an Overall Accuracy (OA), Balanced Accuracy (BA), and f1-score (F1) above 97%. In the first evaluation, the variation in the number of observations appears to cause little influence on the classification accuracy. The analysis used the reference map to contrast the RF classifier’s results. The results showed that the best results in OA occurred with fewer observations. The best performance of 96% happened with four observations. We evaluated the performance of the classes, deforestation, and forest individually. The results showed that a fewer number of observations had negative effects on the accuracy of the RF algorithm when analyzing deforested areas, but not forest areas. Finally, we evaluated the visual quality of the land cover maps produced.

The state of organized sampling work for minerals in China since WCSB9

The state of organised sampling work for ore products in China since WCSB9 is introduced in this paper, including national standards, industry standards and group standards in China. In particular, the group standards organized by CSTM Material Sampling and Blending Technical Committee are introduced, meanwhile, the development of automatic sampling and sample preparation system, slurry on-line samplers and multiplexer dividers, on-line sampling and LIBS analysis technology integration system in China in recent years are introduced.

Real-time monitoring of antibody quality attributes for cell culture production processes in bioreactors via integration of an automated sampling technology with multi-dimensional liquid chromatography mass spectrometry

Online monitoring of quality attributes (QAs) directly within the bioreactor can provide the basis for advanced modes of protein production including process intensification, smart manufacturing, and real-time release testing. The development of technologies to enable monitoring of QAs has been highly challenging due to the relative immaturity of commercial technologies for online analysis, generally low abundance of the attributes requiring highly specialized analytics not always amenable to automation, and the significant burden on development organizations to demonstrate the comparability and suitability of the online technologies resulting in low investment interest. In this study, we present for the first time a fully automated and highly flexible method for direct monitoring of QAs from the bioreactor. The method combines an automated sampling system and multi-dimensional (mD) LC-MS/MS technology to provide a means of quantifying post-translational modifications (PTMs) during the cell culture process and making real-time process decisions based on the resulting peptide mapping data. In doing so, a wide variety of PTMs can be identified and quantified including, but not limited to, oxidation, succinimidation, deamidation, isomerization, and glycosylation. The potential of this analytical workflow for the monitoring and trending of multiple attributes during cell culture production processes was first demonstrated with a standard IgG1 antibody over the production process. Then, the online workflow was applied to a complex format Fc-fusion protein to monitor sialylation. The ability to monitor sialylation offers a unique opportunity to develop process control schemes to ensure the final product meets quality specifications, showing the potential of this workflow in the context of online process analytical technology (PAT).

Rapid screening of 23 synthetic cannabinoids in blood by direct analysis in real time - Tandem mass spectrometry

New psychoactive substances of synthetic cannabinoids are cannabinoid receptor agonists which interact with the endocannabinoid system. They have become alternatives to traditional drugs by criminals for profit. Rapid detection of synthetic cannabinoids became the primary task in forensic science. Direct analysis in real time-tandem mass spectrometry (DART-MS/MS) was used to establish a rapid screening method on 23 synthetic cannabinoids in human whole blood. First, 100 ng/mL synthetic cannabinoid standards were used for mass spectrometry optimization to determine the mass spectrum conditions. Then the temperature of DART ion source was optimized to 400 °C. Acetonitrile - Methanol (V/V = 4/1) was used for the pretreatment of biological samples. Finally，the samples were centrifuged and the supernatant was extracted for detection by the DART 12Dip-it automatic sampling system. The results show that the linear relationships range between 0.01 and 5 μg/mL, the coefficients of determination (R2) range from 0.99 to 1, the limits of detection (LODs) are all between 0.01 and 1 ng/mL, the relative standard deviations (RSDs) of repeatability and intra-day and inter-day precision are all below 14%. The results indicate that the method would be suitable for the rapid screening of analytes which may contain synthetic cannabinoids. Meanwhile, the method has the characteristics of simple, efficient, fast and environmentally friendly, which provides effective data support for rapid screening of synthetic cannabinoids in forensic science. However, there are also some challenges such as screening in crime scene and manual error. In the future, with the development of science and technology, the improvement of portable equipment or others can effectively overcome the challenges.

Interannual and seasonal variabilities in soil NO fluxes from a rainfed maize field in the Northeast China

Nitric oxide (NO) plays a critical role in atmospheric chemistry and also is a precursor of nitrate, which affects particle matter formation and nitrogen deposition. Agricultural soil has been recognized as a main source of atmospheric NO. However, quantifying the NO fluxes emitted from croplands remains a challenge and in situ long-term measurements of NO are still limited. In this study, we used an automated sampling system to measure NO fluxes with a high temporal resolution over two years (April 2017 to March 2019) from a rainfed maize field in the Northeast China. The cumulative annual NO emissions were 8.9 and 2.3 kg N ha−1 in year 1 (April 2017 to March 2018) and year 2 (April 2018 to March 2019), respectively. These interannual differences were largely related to different weather conditions encountered. In year 1, a month-long drought before and after the seeding and fertilizing reduced plant N uptake and dramatically increased soil N concentration. The following moderate rainfalls promoted large amount of NO emissions, which remained high until late September. The NO fluxes in both years showed clearer seasonal patterns, being highest after fertilizer application in summer, and lowest in winter. The seasonal patterns of NO fluxes were mainly controlled by soil available N concentrations and soil temperatures. The contribution of NO fluxes during the spring freeze-thaw in both years was no more than 0.2% of the annual NO budget, indicating that the freeze-thaw effect on agricultural NO emissions was minimal. In addition, with high-resolution monitoring, we found that soil not only act as a NO source but also a sink. Long-term and high-resolution measurements help us better understand the diurnal, seasonal, and annual dynamics of NO emissions, build more accurate models and better estimate global NO budget and develop more effective policy responses to global climate change.

Development of an Automated Multidirectional Pest Sampling Detection System Using Motorized Sticky Traps

Insect detection and counting constitute a considerable challenge in the field of agriculture. However, among various biotic issues of agricultural production, pest infestation is a major challenge, with the humid environment surrounding the crops encouraging the survival and proliferation of pests. In addition, electronic traps need protection, especially from rain. This study describes the design and development of a prototype for an automatic pest sampling and detection system for agricultural crops. To the best of our knowledge, the proposed system is the first motorized automatic trap developed to handle monitoring operations in two directions of precise movement (i.e., clockwise and counterclockwise) and cover four directions of insect sampling that provides additional details of insect infestation direction. A square-shaped sticky box was designed, and an optical sensor was attached to a scalable arm. The movements of the sticky box and the camera arm were generated by motors. Preprocessing was conducted by using morphological operations, whereas insect detection and counting were implemented by an algorithm of connected components labeling that applied by using MATLAB image processing toolbox. Different kernel functions, such as disk, diamond, square, and sphere, were used as matching functions for the insect detection and counting algorithm. The average accuracy of the highest sphere kernel was 85.2%. Test results of the hardware show the reliability, flexibility, and system protection of the automatic system to provide accurate movements in two degrees of freedom.

Differential Influence of No-Tillage and Precipitation Pulses on Soil Heterotrophic and Autotrophic Respiration of Summer Maize in the North China Plain

It is important to strengthen the studies on the response of soil respiration components to tillage practices and natural precipitation in cropland. Therefore, soil heterotrophic respiration (RH) and autotrophic (RA) respiration were monitored by a root exclusion method in the North China Plain (NCP). The tillage practices included no-tillage (NT) and conventional tillage (CT), and the study periods were the summer maize growth stages in 2018 and 2019. RH, RA, soil water content and temperature were measured continuously for 113 days by an automatic sampling and analysis system. The soil RH values on bright days and rain-affected days were higher under NT in 2018 (14.22 and 15.06 g CO2 m−2 d−1, respectively) than in 2019 (8.25 and 13.30 g CO2 m−2 d−1, respectively). However, the RA values on bright days and rain-affected days were lower under NT in 2018 (4.74 and 4.97 g CO2 m−2 d−1, respectively) than in 2019 (5.67 and 6.93 g CO2 m−2 d−1, respectively). Moreover, NT decreased RH but increased RA compared to CT in 2019. Compared to bright days, the largest increase in both RH and RA after rain pulses was under CT in 2019 (6.75 and 1.80 g CO2 m−2 d−1, respectively). Soil water content and soil temperature were higher in 2018 than in 2019. Moreover, NT increased soil water content and decreased soil temperature on bright days compared to CT in 2019. Furthermore, soil temperature accounted for more variations in RH on bright days and rain-affected days, but soil water content had a greater influence on RA on bright days. However, after precipitation, higher soil water content decreased RA under NT in 2018, while soil water content was positively related to RA under CT in 2019. This study determined the differential response of RH and RA to tillage practices and natural precipitation pulses, and we confirmed that excessively dry soil increases soil carbon loss after rain events in the NCP.

Особенности применения метода атомно-эмиссионной спектрометрии с индуктивно-связанной плазмой для элементного анализа взвешенных частиц атмосферного воздуха в черте города

The paper presents the parameters of elemental analysis by inductively coupled plasma atomic emission spectrometry (ICP-AES) of suspended atmospheric air particles in the city, collected on PTFE filters by the automatic dust sampling system (aspirator) Derenda PNS8TDM-6.1. The optimal wavelengths were selected for the quantitative determination of the contents of Co, Cu, Fe, Mn, Ni, Pb, and Zn, the detection limits were calculated and the parameters were calculated for automatically taking into account the spectral effects.