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Categories
Chemistry (miscellaneous)
Inorganic Chemistry
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Chemistry
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2019-2025
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Chemistry
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Found
Publications found: 38

Analytic review of the first 15 years of journal functioning
Frolov O.A., Glagolev M.V., Теrentievа I.Е.
This article reviews the activities of the journal Environmental Dynamics and Global Climate Change (EDGCC) over the 15 years since the publication of its first issue. The journal aims to inform readers about scientific and educational developments within the themes of "Environmental Dynamics" and "Global Climate Change." The main objectives of the journal include:
Publishing papers, reviews and discussions addressing topics related to the composition, structure, and functioning of natural and anthropogenically altered systems under the climate change.
Disseminating key findings of research across universities, academic institutions, and industry stakeholders.
Fostering open scientific dialogue to improve the quality of research.
Promoting national and international best practices in applying cutting-edge technologies.
The journal accepts papers in both Russian and English. Submissions may include methodological, theoretical, and experimental works, ranging from regionally focused and federally funded projects to independent research yet to receive formal grant support. Recognizing the scarcity of high-quality Russian-language reviews in certain areas of global ecology and climatology, the journal also welcomes reviews and lectures by leading scientists to fill this gap. Papers undergo a double-blind peer review process, typically involving three reviewers who evaluate manuscripts anonymously without knowledge of the authors or their affiliations.
This article presents scientometric data on the publication activity of EDGCC, along with an analysis of materials deemed most useful to readers. Building on a previous 10-year review, the article evaluates the journal's performance over the past five years. It highlights changes in publication format, particularly the shift to electronic-only articles, and their impact on key metrics. Papers with the highest reader engagement (measured by website views and citations) are identified. The analysis reveals that theoretical studies attract the greatest interest, followed closely by experimental works. Notably, a discussion paper achieved the fastest citation rate, while a "Chronicle" paper recorded the highest number of abstract views in the past five years.
The journal's two-year impact factor has remained stable over the last five years, achieving competitive results compared to 27 peer journals with similar themes, often frequented by EDGCC's regular contributors. The number of authors publishing in EDGCC has remained consistent, averaging 16 authors annually, with approximately 50% being new contributors each year. A trend of increasing article half-life is observed over the past decade, indicating sustained interest in the journal's content. Additionally, the average h-index of EDGCC authors has shown an upward trend over time. In terms of "probability of citation after reading," EDGCC ranks third among the analyzed journals.
This evaluation underscores EDGCC's enduring relevance and growing impact within the scientific community focused on environmental dynamics and climate change.

Catalog of the mire habitats of East European tundra
Lavrinenko I.A., Lavrinenko O.V.
The basis for the existence of Arctic plant and animal species is the presence of suitable habitats (biotopes) – fragments of the earth's surface that are vital for a biological species or communities at a certain period of time. Considering the diversity of habitat types in the Russian Arctic, their inventory, preceded by classification, is firstly necessary. In 2019, with the support of a grant from the Russian Science Foundation (RSF), work began on creating a catalog of Arctic habitats using the East European tundra as the case study. The experience of European countries, which have been implementing a number of national and common European programs for the protection of habitats for decades, was taken into account [ Lavrinenko, 2020].
A multi-level classification of habitats is demonstrated using the case of the mires of the East European tundra. The classification of habitats is based on their location on the geomorphological profile and environmental features, which are identified by the syntaxonomic composition of vegetation. Since the tundra zone is characterized by small-contour and mosaic landscapes, combinations of phytocenoses – territorial units of vegetation (TUV) have to be highlighted even on large-scale maps.
To define habitats based on their syntaxonomic composition, a typological scheme that allows identifying TUVs of varying complexity and rank (from type to class and division) on the map while preserving information on the composition of syntaxa and the spatial structure of the contours has been developed [Lavrinenko, 2020, 2021; Lavrinenko, Lavrinenko, 2020]. The typological scheme and TUV nomenclature are based on the Braun-Blanquet classification. The background vegetation of the Oxycocco-Sphagnetea and Scheuchzerio–Caricetea nigrae bogs and fens in the East European tundra has been studied to the level of associations and subassociations [Lavrinenko et al., 2016, 2022; Lavrinenko, Lavrinenko, 2015, 2021].
The highest unit of the typological scheme is the division, which combines the TUV of the largest landscape elements (watersheds, river valleys with a floodplain regime and low marine terraces). Divisions include classes – topographically expressed TUV, which reflect the ecological uniqueness of genetically homogeneous simple relief forms by the composition of syntaxa and their combinations. Types are the main elementary units of the typological scheme. Their determination is based on two main criteria: the syntaxonomic composition of TUV elements and the type of spatial structure (phytocenoses, ecological-genetic series, ecological series, complexes, complex combinations). It is proposed to use subclasses and groups as secondary units. A scheme for the step-by-step unification of TUV categories from phytocenosis to geobotanical region during map generalization as the scale decreases was developed using coastal marshes as the case study [Lavrinenko, 2020].
According to the habitat classification at the highest level, 4 groups of biotopes were identified: A – watershed habitats, B – habitats of river valleys with a floodplain regime, C – coastal habitats, D – marine habitats, including estuaries. To reflect the zonal position of biotopes, a letter was added to the highest-level index: a – polar deserts, b – tundra, c – forest-tundra belt, d – taiga, etc. Biotopes of the second-level categories (Ab1, …, Cb3) differ in their position on the generalized geomorphological profile – from the highest to the lowest positions. When identifying the third-level categories, along with the position on the profile, the features of substrate, and the fourth and lower levels – physiognomic (color, texture) and spectral (indices, signatures) characteristics are taken into account. Each category of biotopes below the second level is diagnosed by TUV of the corresponding rank and syntaxonomic composition – from class to type, which reflects its complexity and spatial structure well. The classification of habitats of different levels allows using all TUV ranks for naming – from type to class, depending on the scale and degree of vegetation study.
Mire biotopes belong to the Ab3 category – habitats of drainless or semi-drainage accumulative-eluvial landscapes, which in turn is subdivided into 5 categories of the third level: Ab3.1 – marshy marine terraces with grass (sedge and cotton grass) and dwarf shrub-moss (sphagnum and brown and green mosses) communities on acidic peat and peaty waterlogged soils; Ab3.2 – willow (Salix myrsinites)-moss boggy communities with a high proportion of hemicalciphyte species on base-rich substrates; Ab3.3 – peatlands (bogs) in relief depressions, where active peat accumulation occurred in the Holocene; Ab3.4 – arctic mineral mires; Ab3.5 – lowland sedge-brown-moss and sedge-cotton grass-brown-moss fens. Within the categories of the third level, 10 categories of the fourth level are identified and characterized. Each category passport contains: habitat name, compliance with the EUNIS category, TUV name, syntaxonomic composition of vegetation (alliances, associations and subassociations), vegetation definition, diagnostic species (characters, dominants and constants), ecological conditions (location on the geomorphological profile, soil moisture and type, permafrost, etc.), distribution in the Nenets Autonomous Okrug, species in the Red Data Book of the Nenets Autonomous Okrug (2020), threats and limiting factors, photographs.
The proposed habitat monitoring system does not replace, but complements the established and existing system of nature conservation in Russia through the creation and operation of Special Protected Natural Areas. The prepared catalogue of habitats can serve as a basis for studying their dynamics under anthropogenic impacts and climatic change, and for organizing field and remote monitoring.

Methane and carbon dioxide fluxes correlation according to automatic chamber observations at the Mukhrino bog ridge and hollow complex
Dyukarev E.A., Veretennikova E.E., Sabrekov A.F., Kulik A.A., Zarov E.A.
Aim: The paper presents the results of the study of the dynamics of methane and carbon dioxide fluxes for the ridge-hollow oligotrophic bog complex in the middle taiga subzone of Western Siberia. Correlations between carbon dioxide and methane fluxes were revealed and the influence of meteorological parameters on greenhouse gas fluxes was estimated.
Methods: Greenhouse gas fluxes were measured using the KASM8 chamber automatic monitoring system with eight transparent chambers and LI-COR LI-7810 gas analyser to analyze CO₂, CH₄ and H₂O concentrations.
Results: The mean values of CO2 and CH4 fluxes for the study period were obtained; differences in the functioning of the ridge and the hollow are shown: median values of CO2 fluxes indicate a greater uptake on the ridge (-74.4 mgCO2/m2/h) than on the hollow (-52.7 mgCO2/m2/h); methane fluxes on the ridge (0.08 mgCH4/m2/h) are on average 20 times lower than on the hollow (2.76 mgCH4/m2/h). Correlation of greenhouse gas fluxes with environmental factors were revealed: the highest correlations were found with the intensity of incoming solar (r = -0.84 ÷ -0.91) and photosynthetically active radiation (r = -0.85 ÷ -0.92), air temperature (r = -0.51 ÷ -0.63) and relative air humidity (r = +0.56 ÷ +0.62) and wind speed (r = +0.39 ÷ +0.50).
Conclusions: Correlations between specific greenhouse gas fluxes were estimated based on spatial and temporal flux variability data. Correlations between greenhouse gas fluxes are different at night and daytime, which is directly related to external factors and principles of ecosystem functioning.

Consortium “RITM carbon” launches a series of online lectures “The World of wetland ecosystems: from basics to innovations”
Kupriianova I.V., Lapshina E.D.
In the article, an overview of the online lecture series "The World of Wetland Ecosystems: From Basics to Innovations", which will be launched in early 2025 by the Working Group on Wetland Ecosystem Research of the Consortium "RITM CARBON", is given. Wetlands are vital for human survival. They are among the most productive environments in the world; cradles of biodiversity that are indispensable for countless benefits and "ecosystem services" provided to humanity. However, study after study shows that the area and quality of wetlands continue to decline in most regions of the world, and the knowledge for more rational use is still insufficient. In a popular science format, 19 lectures will comprehensively cover swamps, their formation and development, vegetation and wildlife, peat accumulated during the post-glacial period and still continuing to accumulate, their stratigraphy and properties. Much attention will be paid to the issues of the role of wetlands in the biosphere, the economic use of wetlands, their restoration, and research methods. The lecture series is intended for a wide audience, especially young people interested in nature and its life.

The impact of wildfires on the dynamics of vegetation cover in the middle taiga subzone of Western Siberia during the Holocene
Pupysheva M.A., Blyakharchuk T.A.
In the article we present new results on the influence of paleo-fires on the dynamics of vegetation cover and the connections between them using the example of bottom sediments of Lake “S14” in the middle taiga subzone of Western Siberia (Khanty-Mansiysk Autonomous Okrug). The change in vegetation cover is influenced by both climate and fire activity, which acted as a trigger for the evolution of vegetation cover. This is evidenced by the obtained paleoecological data based on the analysis of identified particles of charcoal and pollen in lake sediments. According to the radiocarbon dating, sedimentation of lake "S14" began at 11920 cal. yr. BP. Based on the macro-charcoal analysis and statistical processing of the obtained data in the CharAnalysis program in R, the Holocene history of paleo-fires in the study area was reconstructed. 16 local fire episodes, their time, frequency and intensity were identified (11400, 11100, 10700, 10400, 9800, 9400, 7400, 6100, 5150, 4500, 3800, 2800, 1400, 1100, 400, 250 cal. yr BP).
Using spore-pollen analysis, the dominant landscapes were reconstructed for the entire period of the existence of lake “S14”: 12000-11500 cal. yr BP – larch-spruce forests with an admixture of birch; 11500-9850 cal. yr BP – larch-spruce-birch forests; 9850-4700 cal. yr BP – spruce-pine-birch forests; 4700-3500 cal. yr BP – birch-pine forests; 3500-2250 cal. yr BP – birch-cedar-pine forests; 2250-1000 cal. yr BP – cedar-pine forests with an admixture of birch; 1000 cal. yr BP to present – cedar-birch-pine forests. The resulting reconstruction of the dynamics of vegetation cover is compared with the history of paleo-fires of the study lake and with the climatic periods of the Holocene. This made it possible to identify three periods with maximum pyrogenic activity (11500-10400, 7500-6800 and 400-250 cal. yr BP), as well as to consider the conditions contributing to the intensification of Holocene wildfires. To determine the degree of impact of fires on the change in vegetation cover and the connections between them, a correlation analysis was carried out using the Pearson method in the PAST program. The analysis was made based on a comparison of micro- and macro-particles of charcoals with the pollen content of the predominant plant taxa for lake “S14”.
The most powerful paleo-fires were noted at the end of the Preboreal – beginning of the Boreal periods of the Holocene (11500-10400 cal. yr BP) with 4 local fire episodes and a high rate of accumulation of charcoal particles (1.1 per cm2/year). At the same time, larch-spruce forests with an admixture of birch grew near the lake area. The next maximum of pyrogenic activity was recorded in the mid-Atlantic period of the Holocene (7500-6800 cal. yr BP) with one local fire. The rate of charcoal accumulation decreased slightly compared to the previous period – 0.9 particles per cm2/year. At this time, the territory of the middle taiga subzone was covered with spruce-pine-birch forests. The third peak of local fires occurred at the end of the Subatlantic Holocene period (400-250 cal. yr BP) with a macro-charcoal accumulation rate of 0.6 particles per cm2/year. The vegetation cover included Siberian cedar, birch and pine forests at this time. It was found that the most intense fires occurred during dry climatic periods. The longest fire-free periods (9400-7400, 2800-1400 cal. yr BP) were observed precisely during the period of increasing precipitation.
According to the results of correlation analysis, wildfires had an impact on vegetation dynamics throughout the Holocene. A positive correlation of micro- and macro-charcoal particles with each other was revealed, which confirms the presence of fires at the local and regional levels and connection of local fires with regional fire situation. It has been determined that micro- and macro-charcoals simultaneously have a negative correlation with birch (Betula pendula), Siberian cedar (Pinus sibirica), Scot’s pine (Pinus sylvestris) and fir (Abies sibirica), and a positive correlation with grasses (Poaceae) and spruce (Picea obovata). A positive correlation with grasses and a negative correlation with tree pollen reflects the effect of fires on vegetation cover, probably, the suppression of tree species and the growth of grasses in the first stages of post-pyrogenic succession. The positive correlation with spruce is most likely due to the greater burning of landscapes at the beginning of the Holocene, when larch-spruce forests dominated the landscape and the climate was drier. This confirms the direct influence of fires on the formation of vegetation landscapes in the study region.

Effects of temperature and precipitation anomalies on carbon dioxide and latent heat fluxes in wetland ecosystems
Satosina E.М., Gushchina D.Y., Tarasova M.A., Gibadullin R.R., Zheleznova I.V., Emelianova E.R., Osipov A.M., Olchev A.V.
This study conducted a comprehensive assessment of the response of wetland ecosystems in temperate and polar latitudes, located on different continents, to extreme weather events. These events included temperature anomalies (unusually high/low temperatures) and precipitation anomalies (droughts/intense precipitation). The analysis of the response net ecosystem exchange (NEE) of CO2 and latent heat (LE) fluxes to extreme temperature and precipitation events used ERA5 reanalysis data [Smith, 2011] and observations of CO2 and LE fluxes from the global FLUXNET database [https://fluxnet.org/data/]. Fifteen greenhouse gas flux monitoring stations were selected for the study, representing the longest and most continuous time series of observations. These stations are located on different continents, with eight stations in temperate latitudes and seven in polar regions. It should be noted that this study focused exclusively on the warm season. The beginning and end of the warm season were defined as the sustained crossing of the daily mean air temperature above 0°C for at least seven consecutive days.
For each station, daily anomalies of CO2 and LE fluxes were calculated as the deviation from the long-term mean values for the corresponding day of the year. Extremely high/low values of flux anomalies were identified as exceeding one standard deviation from the overall time series for each calendar month individually.
To identify periods with extreme air temperature values, ERA5 reanalysis data on two-meter air temperature every 3 hours with a spatial resolution of 0.25°×0.25° from 1991 to 2021 were used. To estimate extreme precipitation amounts, data from half-hourly station observations were used. Daily means were calculated from these data in a first step. Thresholds for defining extremely hot/cold periods were calculated as daily mean air temperature exceeding the 95th percentile (for anomalously hot periods) or not exceeding the 5th percentile (for anomalously cold periods) of a normal distribution with mean and standard deviation. The distribution was constructed for a specific month of the year and then averaged over the entire period considered. Two approaches were used to determine the extreme precipitation threshold. In the first approach, extreme precipitation days were defined as days with daily precipitation exceeding the 95th percentile of the probability density function (the Weibull distribution was used for precipitation). The second approach was based on the assessment of the Antecedent Precipitation Index (API), which determines the cumulative effect of precipitation on CO2 fluxes.
For the quantitative assessment of the relationship between temperature and precipitation extremes and flux anomalies, the percentages of days on which both the NEE/LE anomaly exceeded the standard deviation and the temperature/precipitation exceeded the 95th percentile for the upper threshold or the temperature did not reach the 5th percentile for the lower threshold were calculated. The percentage was calculated based on the total number of days when one of the characteristics (air temperature, daily sum of precipitation) exceeded the threshold.
The analysis showed that temperate and polar wetland ecosystems can respond differently to temperature and precipitation anomalies. These differences can be attributed to the geographic location of the ecosystem, regional climatic conditions, plant species composition, and the intensity of temperature and precipitation extremes. During the warm half of the year, periods of extremely high temperatures in temperate latitudes were associated with a positive CO2 flux anomaly, corresponding to an increased emission of CO2 into the atmosphere. In contrast, polar latitudes showed an opposite response - an increase in CO2 uptake by wetland ecosystems under anomalously high temperatures. This opposite response of CO2 fluxes may be related to the different soil moisture regimes in polar wetland ecosystems and the different plant species composition. Extremely high temperatures were accompanied by positive LE anomalies due to the intensification of evaporation processes with rising temperatures, a trend observed in all wetland ecosystems analyzed.
The immediate response of wetland ecosystems to intense precipitation (above the 95th percentile) was manifested as an increase in CO2 flux to the atmosphere at almost all stations analyzed. This observed response could be related to the "Birch effect" [Birch, 1964], which is characterized by an intensification of soil respiration due to a sudden increase in soil moisture and, consequently, an increase in the rate of decomposition and mineralization of organic matter during heavy precipitation and rising groundwater levels. LE flux decreases during intense precipitation, indicating suppression of evaporation due to high humidity and reduced incoming solar radiation. The cumulative effect (API index) of extremely high precipitation is characterized by a predominance of extremely positive CO2 flux anomalies over negative ones in wetland ecosystems at both temperate and polar latitudes. It should also be noted that the percentage of days with increased CO2 uptake during the two weeks following intense precipitation is significantly higher than for the immediate response (10-25% of days in temperate latitudes and 5-20% of days in polar latitudes). The increase in CO2 uptake after heavy precipitation may be related to enhanced photosynthetic rates of the vegetation cover under sunny weather and optimal soil moisture conditions. A prolonged absence of precipitation, represented by extremely low API values, is accompanied by negative CO2 flux anomalies (enhanced uptake) at most of the studied wetland ecosystem stations, indicating a high adaptive potential of the studied wetland ecosystems to short-term (less than 14 days) dry periods. On the other hand, enhanced CO2 uptake could be facilitated by clear weather conditions, which prevail during dry periods and are accompanied by an increase in direct solar radiation and corresponding acceleration of photosynthetic processes.
It is noteworthy that flux anomalies often did not coincide with temperature or precipitation extremes, indicating that the functioning of wetland ecosystems is strongly influenced by multiple abiotic and biotic factors, which vary among different plant communities.

On rare vascular plant species in the mires of the bashkir trans-urals
Muldashev A.A., Ishbulatov M.K., Shirokikh P.S., Martynenko V.B., Baisheva Z.Z., Bikbaev I.G.
The Bashkir Trans-Urals (the eastern part of the Republic of Bashkortostan) includes the low mountains and foothills of the eastern slope of the Southern Urals, as well as the adjacent Sakmaro-Tanalyk and Kizilo-Urtazym plains. The vegetation is forest-steppe and steppe. Despite the small amount of precipitation (350-450 mm/year), there are quite a lot of mires, especially in the northern part of the study area. Mires are located mainly on the slopes of mountains and hills and at their foothills, in endorheic basins, in river floodplains, and often have a large area (up to several hundred hectares). All mires are eutrophic, their vegetation cover includes paludified birch and black alder forests and treeless reed, reed-sedge, moss-sedge plant communities. In 2023, more than 240 mires were identified in the Bashkir Trans-Urals, 60% of the total area of which (more than 8 thousand hectares) were disturbed as a result of drainage and peat extraction. There are very few publications about the distribution and the state of populations of rare plant species in the mires of the study area. The aim of this work is to summarize and to analyze the data on the representation of rare species of vascular plants in the mires of the Bashkir Trans-Urals.
Currently, in the mires of the Bashkir Trans-Urals, 32 rare plant species in need of protection have been identified, including 25 species (Table 1) listed in core list of the Red Data Book of the Republic of Bashkortostan [Martynenko, 2021], as well as 7 species are included in the Appendix II to this book, i.e. “List of flora and fungi that require special attention to their condition in the natural environment and monitoring in the Republic of Bashkortostan” (Carex dioica, Ranunculus lingua, Salix myrtilloides, Salix pyrolifolia, Saussurea parviflora, Baeothryon pumilum, Utricularia intermedia). Two species (Liparis loeselii. and Orchis militaris) are included in the Red Data Book of the Russian Federation [Order ..., 2023].
The largest number of rare mire species belong to the families Orchidaceae (11 species) and Cyperaceae (4 species). About 80% of these rare species are stenotopic and have a fidelity score for the mire ecotope III-V. Therefore, the destruction or degradation of their habitats will lead to the disappearance of their local populations in the Bashkir Trans Urals.
Little is known about the population size of rare species growing in the mires of the Bashkir Trans-Urals. Local populations of these species are often small and usually consist of several dozen, rarely hundreds of individuals (Carex serotina, Dactylorhiza russowii, etc.). For few species, for instance, Orchis militaris, the subpopulation size within the mire can amount to several thousand individuals, but, depending on weather conditions, there are extreme fluctuations in the number of plants in different years. A decrease in the number or disappearance of subpopulations of rare plant species depends on different factors, i.e., fluctuations in the water level in lakeside mires, the habitat degradation along the edges of mires due to grazing and haymaking (Artemisia laciniata), drying out of mires due to a decrease in the groundwater level after droughts (Liparis loeselii, Saxifraga hirculus), drainage, peat extraction, peat fires and recreation.
Currently, populations of rare and protected species of vascular plants have been identified in 58 mires. The most valuable for the protection of rare species of vascular plants are the mire vegetation complexes of the natural monuments “Nurok Mire”, “Karpis Mire”, “Starobalbukovskoye Mire” [Muldashev et al., 2020]. Most of the mires of the Bashkir Trans-Urals, where habitats of rare species have been identified, do not have conservation status. Searching for new locations and monitoring local populations of rare plant species are a necessary for organizing effective protection of the biodiversity of this region, which is characterized by a high degree of agricultural development. Factors causing a reduction or disappearance of local populations of rare species in the mires of the Bashkir Trans-Urals are the consequences of drainage, fluctuations in the water level in lakeside mires, grazing and haymaking along the edges of mires, drought and recreation.

The VII International Field Symposium was held in Yugra “West Siberian peatlands and the carbon cycle: past and present”
Kupriianova I.V., Niyazova A.V., Verevkina E.L., Veretennikova E.E., Shanyeva V.S., Ilyasov D.V., Akhmedova I.D., Zmanovskaya A.S., Lapshina E.D.
The article covers the results of the VII International Field Symposium "West Siberian Peatlands and the Carbon Cycle: Past and Present", which was held in the Khanty-Mansiysk autonomous okrug – Yugra (Khanty-Mansiysk, Beloyarsky) in August 16–26, 2024. The Symposium brought together over 180 scientists specializing in bog science, ecology and carbon balance. The main topics of the plenary and sectional sessions were: the role of bogs in the global carbon cycle, environmental modeling, biodiversity and ecology of bogs, biogeochemical cycles of natural andanthropogenically disturbed bog ecosystems, biogeochemistry of peat and bog waters, paleoecology and history of bog ecosystems development, the impact of modern climate change on forest-bog ecosystems. At the roundtable meetings, the participants discussed the possibilities of using unmanned aerial vehicles (UAVs) in the study and mapping of bog ecosystems, and shared their experience in implementation of forest-climate projects.

Modern spore-pollen spectra of the Altai-Sayan region, their relationship with climate and transfer functions for palaeoclimate reconstructions
Blyakharchuk T.A., Shefer N.V., Lukanina E.A., Van Hardenbroek M., Juggins S., Zhang D.
Quantitative reconstruction of paleoclimate based on spore-pollen data remains an important task in the study of long-term climate dynamics. The construction of transfer pollen-climate functions on a training set of modern spore-pollen spectra is an effective method for such studies, especially necessary in areas that are poorly supported by numerical reconstructions of paleoclimate, which includes Siberia. To solve this problem, a series of 145 modern spore-pollen spectra were collected during summer expedition at different years from various phytocenoses (plant functional types) representing biomes of: mountain forest, lowland forest, forest-steppe, steppe, desert steppe and alpine tundra-steppe on the territory of the Altai-Sayan mountain region and adjacent areas of the plains (Fig. 1). At each sampling point, from 1 to 6 samples were taken in the form of moss pollsters or surface detritus, geographic coordinates were noted, and a geobotanical description of the vegetation was made. After physicochemical sample preparation, spore-pollen analysis was carried out using generally accepted methods. In common 143 pollen types were identified in study set of modern spore-pollen spectra. To create the transfer pollen-climate function, first of all, we studied by using the method of multivariate statistical analysis the relationship between the composition of the obtained spore-pollen spectra and the composition of maternal phytocenoses (based on geobotanical descriptions made during the collecting of samples), as well as with climatic parameters that could influence the composition of spores-pollen spectra. The results of constrained cluster analysis (Fig. 2) showed that each group of spore-pollen spectra characteristic of a particular biome is distinguished by a separate subcluster of the cluster tree, which confirms the possibility of identifying biomes by spore-pollen spectra. In addition, specific phytocoenoses characterizing plant functionl types are also distinguished by independent clusters.
To study the general structure of the calibration set of modern spore-pollen spectra, a PCA analysis of sampling points (grouped by biomes) and pollen taxa was carried out using the “stats” package basic for “r”, as well as the “vegan” packages 2.6-4 [Oksanen et al., 2018] and “ellipse” 0.5.0 [Murdoch et al., 2018]. Based on the distribution of species and their ecology, axis 1 of the PCA biplot (Fig. 3) reflects the moisture gradient, and axis 2 is associated with the temperature gradient. The pollen types were distributed according to these gradients. The fields of the corresponding biomes are highlighted, by different marks united by colored ovals, which are shown in Fig. 3. Thus, in the most humid and warm conditions, in the upper right quarter of the PCA graph there are located pollen types character for biomes of lowland forests and mountain dark coniferous and “chern’” forests with abundance of fir (Abies sibirica), birch (Betual pendula) and linden (Tilia) with tall grass and fern grass cover. In cold and dry conditions (lower left quarter of the PCA plot) pollen types of the alpine tundra-steppe (yellow oval) and desert steppe (pink oval) biomes are located.
To identify the influence of climatic factors on the variability of spore-pollen spectra, RDA analysis was performed on six factors: MAP - mean annual precipitation; TJAN - mean temperature of January; TJUL - mean temperature of July; MAT - mean annual temperature; Altitude and GCI - index continentality of Garchinski. Inflation Factors test showed that Altitude and GCI correlate positive with each other and strongly negative with MAT, hence they are not recommended for transfer function construction. The RDA plot (Fig. 4) revealed a positive correlation between MAP and TJAN, as well as Abies sibirica pollen and spores of ferns (Monolete), which reflects the spreading of dark coniferous tall-herb-fern mountain taiga and “chern’” forests with fir, aspen, and linden on the western macroslope of the Kuznetski Alatau Mountains in an area with maximum precipitation and milder winters with abundant snow cover. A positive correlation was found between the Altitude factor and pollen of Pinus sibirica, Betula nana and Cyperaceae, reflecting the ecological conditions of the upper part of the mountain forest belt and the subalpine belt of sparse cedar forests with thickets of Betula nana, sedges and areas of alpine meadows. The pollen of xerophytic plants, from taxa Artemisia and Chenopodiaceae is strongly correlates with GCI. Pollen of Poaceae is equally correlates with GCI and Altitude factors, reflecting the distribution of grasses in both high-mountain tundra and steppe. Tree pollen of Pinus sylvestris and Betula pendula has maximum positive correlation with MAT, while with TJAN+MAP and TJUL these species correlate less strong.
The cluster analysis, as well as PCA and RDA analyses showed that the composition of the studied spore-pollen spectra adequately reflects not only the peculiarities of the altitudinal belts (biomes) of the vegetation cover and the composition of the parent phytocoenoses (plant functional types), but also the temperature-humidity gradients existing in this area. Consequently, despite the complex structure of the vegetation cover of the mountain region, the presented series of spore-pollen spectra can be used as a training set in the construction of transfer functions for their use in paleoreconstructions based on paleopalynological data.
Transfer function modeling was performed with the R package “rioja” 1.0-6 using the numerical methods WA, WA-PLS, MAT*, MLRC - evaluated by Bootstrap Cross-Validation to identify the strongest model for paleo reconstructions [Hall Wilson, 1991; Payne et al., 2012]. Statistical analysis showed that for the presented set of modern spore-pollen spectra a significant models can be built for the factors TJUL, MAT, TJAN and MAP. Of the 4 types of models (based on WA, WAPLS, MAT* and MLRC methods) which we created for the 5 variables MAT, MAP, TJAN, TJUL and GCI for the presented set of modern spore-pollen spectra, the best model results were obtained by the MAT* method for the factors MAT, MAP, TJAN and GCI (Table 1). However, the transfer function model for TJUL created by the MLRC method (R2=0.7268 and RMSE=1.68°C) was the strongest. By performance characteristics our TJUL model is comparable to previously published models by other authors created for reconstruction the mean July temperature of the Arctic zone of Siberia [Klemm et al., 2013], and for reconstruction the vegetation cover characteristics such as afforestation [Tarasov et al., 2007; Zanon et al., 2018], NDVI [Liu et al., 2013; Chen et al., 2019], and fractional vegetation cover [Li et al., 2024].
Further statistical analysis of the data and comparison of the results with those published for the neighboring region of the central Tienshan Mountains [Li et al., 2024] showed that the leading climatic factor controlling the variability of spore-pollen spectra in the Altai-Sayan mountains of southern Siberia is the temperature of the growing season expressed as TJUL, while in the mountains of the central Tienshan such a factor is the annual precipitation - MAP. This reflects well the natural geographical patterns of vegetation-climate dependence in the more northern, humid and cold Altai and in the more southern hot and continental climate of Tienshan. Taking into account the different leading factors controlling the variability of modern spore-pollen spectra and vegetation in the two regions under consideration, the newly created transfer functions can be recommended for paleoclimatic reconstructions in the Altai-Sayan region.

Features of distribution of a number of rare and protected plant species in the territories of oligotrophic and mesotrophic mires within oil fields of Khanty-Mansi Autonomous Area – Yugra
Shishkonakova E.A.
The technogenic transformation of oligotrophic and mesotrophic mires, which occurs during the functioning of the infrastructure of oil fields, leads to the emergence of new habitats with fundamentally different conditions, previously uncharacteristic for these biogeocenoses. Expansion of areas of eutrophicated soils, pollution with oil and chlorides, subsequent reclamation measures, the formation of embankments from mineral soil – all these disturbance types create the preconditions for the disappearance of rare plants, however, in parallel, the introduction of new species that also have a protected status is noted. Transformed areas of mires can become refugia for endangered and protected plants. Three groups of plants can be distinguished, related to the nature of the technogenically transformed substrates on which they settle. The first group includes plants that settle exclusively on peat, the second includes species found on mineral substrates introduced into mires, the third group includes species that can grow both on mineral substrates and technogenically eutrophicated peat soils. The article provides characteristics of a number of transformed habitats that arose in the mire areas of the – Khanty-Mansi Autonomous Area – Yugra, within which rare and protected species were found. Among the plants whose distribution in the mires of the district is largely due to the expansion of human economic activity, mention should be made of Thelypteris palustris, Lycopodiella inundata, Typha angustifolia, liverwort Heterogemma laxa, etc.

Multy-year dynamics of some physico-chemical parameters in mire water at the site of salt pollution of the raised bog (Vostochno-Surgutskoye oil field, Western Siberia)
Tyurin V.N., Kharbaka V.A., Maslovskaya O.V.
The article discusses the features of changes in some physico-chemical parameters of mire waters in raised bog (pH, electrical conductivity, chloride concentration) over a long period.

Phytomass carbon pools of Koivulambisuo mire system (South Karelia)
Kutenkov S.A., Kuznetsov O.L., Kantserova L.V., Mironov V.L., Ignashov P.A., Talbonen E.L., Vasyuta V.S.
Koivulambisuo mire system (61,80º N, 33,56º E, middle taiga subzone) has a complex structure of vegetation cover, includes south karelian variant of aapa mires, raised bogs, transitional herb-sphagnum fens and forested sites of different trophic levels.The study was carried out within the framework of the National system for monitoring carbon pools and greenhouse gas fluxes in Russia. Phytomass and carbon pools were determined for three types of mire sites: aapa, ridge-hollow sphagnum bog and oligotrophic pine-dwarf shrub-cotton grass-sphagnum.
On each type of mire sites, 3 sample plots 50x50 m in size were set up, with 8–12 sampling points on each. Tree stand estimated by total count and basic measurements of all standing trees on plots, production of needles and branches by model tree method. The above-ground vascular plants phytomass material was collected by the cutting method, mosses and underground phytomass by monoliths method, the production of sphagnum mosses by annual increment method.
Aapa mire extremely poorly afforested, to the least extent among the studied sites, due to strong watering and poor development of strings. The total carbon pool in a forest stand is only 0.01 tC/ha. The carbon pool in the above-ground phytomass of aapa complexes is also minimal – 2.56 tC/ha. It is mainly provided by sphagnum mosses, while herbs and shrubs contain half as much carbon in total. Living underground phytomass deposits 21.56 tC/ha (89% of the total phytomass). Such a high carbon sequestration by living underground plant organs is a feature of aapa sites and is associated with favorable regime of water movement. Some overestimation is also possible due to the difficulty of separating living and dead tightly intertwined roots. The mortmass of a 40 cm surface layer of peat soil contains 38.71 tC/ha, most of it in the sphagnum remains. The annual ground cover production of aapa is minimal: 1.55 tC/ha, which is caused by the development of extensive flarks with sparse vegetation cover. Unlike other sites, the role of vascular plants is higher here (0.68 tC/ha) due to the predominance of herbaceous plants in cover.
The afforestation degree of ridge-hollow bogs varies; the living forest stand of the most afforested sites is contains 0.31 tC/ha, while in the other two, more watered sites, the pool is less than 0.02 tC/ha. The average carbon stock in a tree stand is 0.11 tC/ha. The carbon pool in the ground cover of the ridge-hollow bogs is maximum among the studied mire sites (4.25 tC/ha), the main share in it is sphagnum mosses (3.52 tC/ha), while grasses and shrubs 0.73 tC/ha. Living underground phytomass deposits 8.62 tC/ha (66% of the total phytomass).The mortmass of a 40 cm surface layer of peat soil contains, on average, 61.77 tC/ha, most of it in the sphagnum remains. In cotton grass-sphagnum hollows the stock reaches 80.09 tC/ha due to dense cotton grass tussocks. The above-ground vascular plants annual production is 0.48 tC/ha (the minimum among the studied mire site types) and 1.56 tC/ha by mosses.
Pine-dwarf shrub-cotton grass-sphagnum plots have the most developed forest stands among the studied mire sites. The average tree height here is 1-2 m, individual trees reach a 5-6 m. The carbon pool in the living tree stand is 2.92 tC/ha, in dead wood – 1.66 tC/ha. Accordingly, the forest stand contribution to the total living phytomass carbon stock is maximum here and equal to moss stock. In the ground cover is deposited 4.46 tC/ha, besides sphagnum mosses (2.9 tC/ha), a significant stock in dwarf shrubs (1.11 tC/ha). Living underground phytomass deposits 9.04 tC/ha (56% of the total phytomass). The mortmass of a 40 cm surface layer of peat soil contains 63.43 tC/ha. The total contribution of pine needles and branches to the annual production is 0.04 tC/ha. The above-ground phytomass here also demonstrates the maximum annual production – 2.21 tC/ha per year, mainly provided by sphagnum mosses (1.64 tC/ha).
In general, the main living phytomass carbon pools of mire sites are concentrated in the underground parts of vascular plants. The ground cover main stock is in sphagnum mosses. Significant carbon stock in the tree stands only has pine-cotton grass-dwarf shrub-sphagnum sites, where it equal to the carbon stock of mosses. In all types of sites, carbon pool in the mortmass of the upper 40 cm of the deposit are noticeably higher than the total reserves in living phytomass. The main part of the mortmass consists of the sphagnum mosses remains. Mosses are also characterized by the largest primary annual production of carbon.

State of island spruce forests in the western part of the Bolshezemelskaya tundra after 23 years
Lavrinenko O.V., Lavrinenko I.A., Simonova K.I.
Modern climate warming, which began in the 1970s, has been observed throughout the Arctic including its Russian part [Доклад…, 2023; Druckenmiller et al., 2021]. It is accompanied by a large number of papers by Russian and foreign scientists on the forest boundary advancement to the north, and its upper boundary in the mountains – up the slopes [Шиятов и др., 2007, Harsch et al., 2009; Bolotov et.al., 2012; Grigor'ev et.al., 2013, 2019; Moiseev et.al., 2019; Shiyatov et al., 2020; Timofeev et.al., 2021; Dial et al., 2022; Hansson, 2022, etc.]. Climate change rate is high in the East European sector of the Arctic: over the last 35 years the average annual air temperature increase has reached +0.8°C/10 years [Malkova et.al., 2021], the length of the growing season has increased by an average of 2 weeks and the amount of heat accumulated during this period has increased by an average of 85°C [Lavrinenko et al., 2022].
The northern forest boundary (timberline) in East European Russia is formed by Picea obovata and runs at N 67°30ʹ-67°10ʹ. In the Bolshezemelskaya Tundra, spruce is found rather far north of the forest boundary and even north of N 68°. Spruce islands have been preserved here since the Holocene in refugia – sites with favorable microclimatic and soil conditions. Relict spruce islands are groups of closely spaced, thin-stemmed trees occupying upland landform elements on sandy outcrops of watersheds. Skirt-shaped growth trees are united by a common root system and appear to be clones formed by vegetative propagation [Lavrinenko, Lavrinenko, 2004].
In the framework of the international SPICE project, eight spruce islands were discovered and studied 8 spruce islands at latitude N 67°54'-67°56' (Fig. 1). Complete relevés were carried out within the boundaries of the 5 islands. Species abundance was estimated using the Brown-Blanquet scale [Becking, 1957]. The height of the tallest trunks was measured with a measuring tape and their diameter at the trunk base (in island E2 at a height of 50 cm) – with a caliper. In 2000, a spruce island was described at the northernmost site (N 68°17') near Cape Bolvansky Nos on the coast of the Pechora Bay of the Barents Sea (Fig. 1). The results of the spruce islands structure and cenoflora study have been published [Lavrinenko, Lavrinenko, 2003]. This data provided an opportunity to trace the changes of the islands 23 years later.
All spruce islands in the Ortina Basin were resurveyed between 20 and 30 July 2023. The study included tree morphometric measurements, geobotanical relevés and comparative landscape photography. The surveys on the islet at Cape Bolvansky Nos were carried out in 2000, 2014 and 2020 and included plant community relevés and photography and height measurements of the 6 tallest living spruce tops; photos were taken during a short visit in 2017.
Comparative analysis of the spruce islands composition and structure after almost a quarter of a century have shown:
1) In the Ortina River basin, in relict spruce islands on watersheds (E1, E4-E8), mean tree height has increased by 1.1-1.9 m and mean diameter – by 1.9-3.0 cm, i.e. mean height growth was 4.3-8.3 cm/year and radial growth was 0.41-0.65 mm/year. On a spruce island in the Ortina River valley (E2) with more favorable microclimatic conditions, these values were significantly higher – trees have grown on an average 2.8 m, diameter – 3.7 cm, i.e. height growth was 12.2 cm/year, radial growth – 0.8 mm/year (Table 1, Fig. 2а and б). In 2000 spruce island E3 was located on a sandy mound in the center of a sandy outcrop. By 2023 the mound has been almost completely destroyed by winds, the spruce looked like dying off and most likely it will disappear after some time (Fig. 9).
2) The shape of the tree crowns has changed. In 2000, spruce trees predominantly had "skirts" of well-developed lower branches. The upper part of the trees could have a cylindrical crown or the trunk could be partially devoid of branches with needles only at the top. By 2023, the crown of the most trees has become conical or narrow pyramidal with well-developed lower branches and green branches all over the trunk. On the E2 spruce island in the valley, the cone-shaped crowns of the trees have become lusher.
3) On all islands spruce has been spreading vegetatively by rooting lower branches and subsequently changing their growth from plagiotropic to orthotropic. This process has been especially active on the slopes of southern exposition. As a result, the area of the islands has slightly increased. Despite the abundance of both male strobiles and mixed-aged female cones, no undergrowth or freestanding young spruce trees were found in the surroundings. This indicates the absence of reproduction by seed for 23 years. The results prove the earlier suggestion that the northward advance of forests in watersheds is limited by the lack of quality seeds for sexual reproduction [Andreev, 1954; Norin, 1958; Surso, Barzut, 2010]. The earlier assumption that spruce islands could become a springboard for the spruce introduction into tundra communities under climate warming [Lavrinenko, Lavrinenko, 1999, 2004] is currently not confirmed.
4) Comparative photos taken from the same angles in 2000 and 23 years later are shown for all spruce islands (Fig. 3-8, 10). They display a significant tree state improvement.
5) At Cape Bolvansky Nos in the northernmost spruce islet (N 68°17'), both a surge (in 2014) and a decline in spruce vitality have been recorded over the past 20-year period. There was no increase in island area observed, in 2020 the condition of the spruce was depressed and close to 2000 (Fig. 11).
6) The dwarf shrub green-mossy spruce islands cenoflora was characterised by stability. Changes in the species composition were due to single, predominantly cryptogamous plants (Table 2). Key species, in addition to Picea obovata, are: Betula pubescens subsp. tortuosa, dwarf shrubs Empetrum hermaphroditum, Vaccinium vitis-idaea, Linnaea borealis, Arctous alpina, bryophytes Pleurozium schreberi, Hylocomium splendens and Ptilidium ciliare. Juniperus sibirica and Betula nana were often found in the shrub layer. The most active permanent herbaceous plant was Festuca ovina (Tables 1 and 2).
7) Landscape photos show the "greening" of surrounding tundra communities in watersheds and stream valleys in the Ortina River Basin due to climate warming. On watersheds, Betula pubescens subsp. tortuosa has actively introduced into tundra communities, and juveniles and young trees have gained straight trunks from the base of the tree (Fig. 13). In the river valley and its tributaries, the area and height of bushes of Juniperus sibirica, shrubby willows and especially Alnus fruticosa have increased (Fig. 8а and б, 14).
8) The current position of the island spruce sparse forests` northern boundary in the Ortina River valley recorded on the satellite image is at latitude N 67°53ʹ (Fig. 15) and has not changed over the last 20 years. The reason appears to be the lack of good quality seed for sexual reproduction. Monitoring studies could make it possible to trace the time when the boundaries of spruce sparse forests and spruce islands will close up in case of further possible climate warming. The distance between them is now quite small – 3-6 kilometers

Spatial and temporal structure of mire landscapes: basic concept and approaches to classification
Lapshina E.D., Kupriianova I.V.
The article provides an overview and definition of the key terms and concepts related to the description of the spatio-temporal organization of mire landscapes as well as possible approaches to their classification for assessing carbon stocks and greenhouse gas fluxes.
The Introduction lists the main biospheric functions of peatlands (Ivanov, 1976; Vitt, Short, 2020; Minayeva, Sirin, 2011; Tanneberger et al., 2021), with carbon dioxide sequestration and carbon accumulation/ storage in peat deposits being the primary one (Vitt, Short, 2020; Qiu et al., 2020; Loisel et al., 2021). In this regard, considerable attention is paid to the issues of gas exchange and peatland carbon balance (van Bellen, Larivière, 2020; Dyukarev et al., 2021; Lourenco et al., 2023; Yang et al., 2023; Golovatskaya et al., 2024).
Currently the development of a system for ground-based and remote monitoring of carbon pools and greenhouse gas fluxes of terrestrial ecosystems, including peat mires, (Rhythm of Carbon. 2024. URL: https://ritm-c.ru/) is being implemented in Russia within the framework of the key national innovative project "Russian Climate Monitoring System" (Shirov, 2023; Carbon regulation…, 2023). The development of such a methodology presupposes basic terms and concepts unification for their uniform use in the monitoring system to be created.
Young researchers use exclusively computer-based technologies for information search which results in reduced number of references to classical research works of Russian scientists, while methodological approaches and foreign terminology in peat mires study are increasingly borrowed. Based on extensive experience of Russian mire science, the article makes a comparison of the basic terms and concepts widely used in the literature.
In the section "Methodological Bases for Mire Studies" definitions and comparison of the terms "mire" and "peat mire" or "peatlands" (P’yavchenko, 1963; Bogdanowskaya-Guihéneuf, 1969; Nitsenko, 1967; Boch, Masing, 1979; Boch, Smagin, 1993) are provided, and the criteria for attributing lands to these categories are revealed. Two aspects are distinguished when considering the problem of peat mires classification: what to classify, i.e., the problem of the classification object, and how to classify, i.e., the question of classification activity, including the issues of selecting features and choosing classification units (Masing, 1993).
The section "Levels of Mire Landscapes Organization " discusses in detail territorial units of different dimensions (micro-, meso- and macro- mire landscapes) depending on the scope and objectives of the research. The concepts of "mire microlandscape" and "mire facies" are compared. The concept of "microlandscape" represents an elementary unit of the peatland surface (Ivanov, 1976; Galkina, 1946, 1959; Masing, 1974; Boch, Masing, 1979, et al.). It is comparable to "mire sites" or "wetland sites" or "habitats" as understood by Western authors (Eurola et al., 1984; Wells, Zoltai, 1985; Lindsay, 2016). For assessing the carbon budget and the dynamics of its accumulation by mire biogeocenoses, the concept of mire facies is more preferable, since the facies includes the layer of peat deposited under relatively constant conditions of water-mineral nutrition (L’vov, 1974, 1977, 1979). A facies is easily identified in space and quite stable over time. It is the primary (elementary) unit, both of the peat body and of the modern biogeocenotic cover (Lapshina, 2000, 2004). Examples are used to compare the concept of "biogeocenosis" and "mire facies," with the latter being broader both horizontally and vertically.
For the carbon budget estimation, of the three strands of structure study (composition, spatial construction, totality of connections), the spatial one is of major importance, primarily horizontal (morphological) structure, and functional structure of peat mire facies and biogeocenoses (Masing, 1969; Korchagin, 1976). When describing the horizontal structure, we distinguish three levels of subordination of structural units: biogeocenoses, mosaic elements, and smaller structures (moss hummocks, sedge tussocks, stumps, rotten wood, etc.). The concept of "ecosystem" is more suitable for describing the functional structure because functional connections in the form of flows of matter and energy are more amenable to mathematization and modeling than other parameters of the biogeocenosis, which is very important in connection with the development of modern instrumental methods for studying natural systems.
The second part of the article discusses "Main Principles and Approaches to the Mire Landscapes Classification" The zonal-geographical and landscape-physiognomic levels of classification seem to be the most promising for generalizing information about the typological diversity of pet mires in a large region and the entire country for the purposes of studying the carbon balance. At the zonal-geographical level in Western Siberia, types of polygonal mires, palsa mires, raised sphagnum bogs, flatand slightly convex sedge-moss fens and forest swamps, and concave (sedge and reed) mires are distinguished (Romanova, 1976; Semenova, Lapshina, 2001; Lapshina, 2004). According to the physiognomic features, the entire variety of peat mires falls into four main types (categories) (Warner, Rubec, 1997; Lapshina, 2004): 1 – highly productive grassy (reed-large sedge) floodplain mires (marshes), 2 – wooded peatlands or carrs (swamps), 3 – low-productive sedge-moss peat mires (fens), 4 – raised (pine)-shrub-sphagnum mires (bogs). A classification of peat mires in Western Siberia for the purposes of studying the carbon balance is proposed, in which the entire peat mire variety is summarized in seven main types, which are represented to varying degrees or are absent in a number of bioclimatic zones: 1 – shrub-moss and shrub-lichen frozen palsa-mires; 2 – raised pine-dwarf shrub-Sphagnum bogs; 3 – rain fed (ombrotrophic) Sphagnum hollows; 4 – poor (meso-oligotrophic and mesotrophic) sedge-moss hollows fed by rain, run-off and mixed (incl. poor ground discharge) waters; 5 – sedge-hypnum rich fens fed by groundwater; 6 – forest swamps; 7 – meso-eutrophic grassy (large-sedge, reed) floodplain marshes and ‘zaimishche’. Two types of peat mire ecosystems – raised bogs and poor sedge-moss lawns – are divided into subtypes (Table 2). For general overview at the country level, it is necessary to compile classification schemes of generalized peat mire types in all other meridional sectors of Russia's territory: Eastern European, East Siberian, and Far Eastern, each with its own characteristics.

International conference on environmental observations, modeling and information systems: Enviromis-2024
Golovatskaya E.A., Gordov E.P.
Announcement of the international conference on measurements, modeling and information systems for environmental studies: ENVIROMIS-2024, will be held in Tomsk on July 1-6, 2024.
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6 citations, 0.2%
|
|
Journal of Organometallic Chemistry
6 citations, 0.2%
|
|
Industrial & Engineering Chemistry Research
6 citations, 0.2%
|
|
Chemical Reviews
6 citations, 0.2%
|
|
Gels
6 citations, 0.2%
|
|
Sustainability
6 citations, 0.2%
|
|
AIP Conference Proceedings
6 citations, 0.2%
|
|
Chemical Record
6 citations, 0.2%
|
|
Environmental Science and Pollution Research
6 citations, 0.2%
|
|
Advanced Synthesis and Catalysis
5 citations, 0.17%
|
|
Advanced Science
5 citations, 0.17%
|
|
Bioorganic Chemistry
5 citations, 0.17%
|
|
Biosensors
5 citations, 0.17%
|
|
Mendeleev Communications
5 citations, 0.17%
|
|
Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
5 citations, 0.17%
|
|
Microporous and Mesoporous Materials
5 citations, 0.17%
|
|
Show all (70 more) | |
20
40
60
80
100
120
140
|
Citing publishers
100
200
300
400
500
600
700
800
|
|
Elsevier
711 citations, 24.22%
|
|
MDPI
649 citations, 22.1%
|
|
Wiley
322 citations, 10.97%
|
|
Springer Nature
297 citations, 10.12%
|
|
Royal Society of Chemistry (RSC)
275 citations, 9.37%
|
|
American Chemical Society (ACS)
254 citations, 8.65%
|
|
Taylor & Francis
73 citations, 2.49%
|
|
AIP Publishing
32 citations, 1.09%
|
|
Pleiades Publishing
23 citations, 0.78%
|
|
IOP Publishing
22 citations, 0.75%
|
|
Walter de Gruyter
21 citations, 0.72%
|
|
Bentham Science Publishers Ltd.
21 citations, 0.72%
|
|
Oxford University Press
16 citations, 0.54%
|
|
Frontiers Media S.A.
16 citations, 0.54%
|
|
International Union of Crystallography (IUCr)
13 citations, 0.44%
|
|
Georg Thieme Verlag KG
10 citations, 0.34%
|
|
SAGE
8 citations, 0.27%
|
|
Hindawi Limited
8 citations, 0.27%
|
|
Cold Spring Harbor Laboratory
6 citations, 0.2%
|
|
Trans Tech Publications
4 citations, 0.14%
|
|
King Saud University
4 citations, 0.14%
|
|
Beilstein-Institut
4 citations, 0.14%
|
|
The Russian Academy of Sciences
4 citations, 0.14%
|
|
IGI Global
4 citations, 0.14%
|
|
IntechOpen
4 citations, 0.14%
|
|
Universitas Gadjah Mada
4 citations, 0.14%
|
|
Research Square Platform LLC
4 citations, 0.14%
|
|
Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii
4 citations, 0.14%
|
|
Cambridge University Press
3 citations, 0.1%
|
|
EDP Sciences
3 citations, 0.1%
|
|
American Society for Microbiology
3 citations, 0.1%
|
|
IWA Publishing
3 citations, 0.1%
|
|
American Physical Society (APS)
3 citations, 0.1%
|
|
Asian Journal of Chemistry
3 citations, 0.1%
|
|
European Journal of Chemistry
3 citations, 0.1%
|
|
F1000 Research
3 citations, 0.1%
|
|
SAE International
3 citations, 0.1%
|
|
The Royal Society
2 citations, 0.07%
|
|
Optica Publishing Group
2 citations, 0.07%
|
|
Academic Publication Council - Kuwait University
2 citations, 0.07%
|
|
American Society for Biochemistry and Molecular Biology
2 citations, 0.07%
|
|
The Electrochemical Society
2 citations, 0.07%
|
|
Institute of Electrical and Electronics Engineers (IEEE)
2 citations, 0.07%
|
|
Oriental Scientific Publishing Company
2 citations, 0.07%
|
|
RTU MIREA
2 citations, 0.07%
|
|
National Library of Serbia
2 citations, 0.07%
|
|
Scientific Research Publishing
2 citations, 0.07%
|
|
World Scientific
1 citation, 0.03%
|
|
Emerald
1 citation, 0.03%
|
|
Begell House
1 citation, 0.03%
|
|
Proceedings of the National Academy of Sciences (PNAS)
1 citation, 0.03%
|
|
American Physiological Society
1 citation, 0.03%
|
|
Mary Ann Liebert
1 citation, 0.03%
|
|
Editura Academiei Romane/Publishing House of the Romanian Academy
1 citation, 0.03%
|
|
Scientific Publishers
1 citation, 0.03%
|
|
Editions Technip
1 citation, 0.03%
|
|
Pensoft Publishers
1 citation, 0.03%
|
|
Spandidos Publications
1 citation, 0.03%
|
|
Japan Institute of Metals
1 citation, 0.03%
|
|
Biophysical Society
1 citation, 0.03%
|
|
Nonferrous Metals Society of China
1 citation, 0.03%
|
|
Chinese Academy of Sciences
1 citation, 0.03%
|
|
Brazilian Society of Chemical Engineering
1 citation, 0.03%
|
|
International OCSCO World Press
1 citation, 0.03%
|
|
Open Access House of Science and Technology (OAHOST)
1 citation, 0.03%
|
|
Kazan Federal University
1 citation, 0.03%
|
|
American Vacuum Society
1 citation, 0.03%
|
|
1 citation, 0.03%
|
|
Taiwan Institute of Chemical Engineers
1 citation, 0.03%
|
|
1 citation, 0.03%
|
|
Scientific and Practical Reviewed Journal Pulmonology
1 citation, 0.03%
|
|
Shenyang Pharmaceutical University
1 citation, 0.03%
|
|
eLife Sciences Publications
1 citation, 0.03%
|
|
Medknow
1 citation, 0.03%
|
|
1 citation, 0.03%
|
|
Annual Reviews
1 citation, 0.03%
|
|
Allerton Press
1 citation, 0.03%
|
|
National Academy of Sciences of Ukraine (Co. LTD Ukrinformnauka) (Publications)
1 citation, 0.03%
|
|
SPIE-Intl Soc Optical Eng
1 citation, 0.03%
|
|
Centre for Evaluation in Education and Science (CEON/CEES)
1 citation, 0.03%
|
|
Baishideng Publishing Group
1 citation, 0.03%
|
|
Institute of Research and Community Services Diponegoro University (LPPM UNDIP)
1 citation, 0.03%
|
|
South Florida Publishing LLC
1 citation, 0.03%
|
|
Shanghai Institute of Organic Chemistry
1 citation, 0.03%
|
|
Institute of Physics, Polish Academy of Sciences
1 citation, 0.03%
|
|
OOO Zhurnal "Mendeleevskie Soobshcheniya"
1 citation, 0.03%
|
|
Show all (56 more) | |
100
200
300
400
500
600
700
800
|
Publishing organizations
2
4
6
8
10
|
|
University of Basel
10 publications, 1.62%
|
|
University of Salerno
8 publications, 1.29%
|
|
University of Fribourg
7 publications, 1.13%
|
|
National Interuniversity Consortium of Materials Science and Technology
7 publications, 1.13%
|
|
King Saud University
6 publications, 0.97%
|
|
Budapest University of Technology and Economics
6 publications, 0.97%
|
|
Aristotle University of Thessaloniki
6 publications, 0.97%
|
|
Lomonosov Moscow State University
5 publications, 0.81%
|
|
Saint Petersburg State University
5 publications, 0.81%
|
|
University of Sydney
5 publications, 0.81%
|
|
Osaka University
5 publications, 0.81%
|
|
University of Patras
5 publications, 0.81%
|
|
Masaryk University
5 publications, 0.81%
|
|
N.D. Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences
4 publications, 0.65%
|
|
Kazan Federal University
4 publications, 0.65%
|
|
University of Chinese Academy of Sciences
4 publications, 0.65%
|
|
Jilin University
4 publications, 0.65%
|
|
University of Strasbourg
4 publications, 0.65%
|
|
University of New South Wales
4 publications, 0.65%
|
|
University of Milan
4 publications, 0.65%
|
|
Birla Institute of Technology, Mesra
4 publications, 0.65%
|
|
Florida State University
4 publications, 0.65%
|
|
University of Pavia
4 publications, 0.65%
|
|
University of Trieste
4 publications, 0.65%
|
|
Istituto Officina dei Materiali
4 publications, 0.65%
|
|
San Diego State University
4 publications, 0.65%
|
|
Hunan University
4 publications, 0.65%
|
|
National and Kapodistrian University of Athens
4 publications, 0.65%
|
|
University of Crete
4 publications, 0.65%
|
|
University of Cologne
4 publications, 0.65%
|
|
University of Warsaw
4 publications, 0.65%
|
|
University of Valencia
4 publications, 0.65%
|
|
Texas A&M University
4 publications, 0.65%
|
|
Mansoura University
4 publications, 0.65%
|
|
University of São Paulo
4 publications, 0.65%
|
|
University of Mississippi
4 publications, 0.65%
|
|
Ural Federal University
3 publications, 0.48%
|
|
Imam Mohammad Ibn Saud Islamic University
3 publications, 0.48%
|
|
Jazan University
3 publications, 0.48%
|
|
Vellore Institute of Technology University
3 publications, 0.48%
|
|
Zhejiang University
3 publications, 0.48%
|
|
Shanghai Jiao Tong University
3 publications, 0.48%
|
|
Technical University of Munich
3 publications, 0.48%
|
|
University of Bordeaux
3 publications, 0.48%
|
|
Sidi Mohamed Ben Abdellah University
3 publications, 0.48%
|
|
Nankai University
3 publications, 0.48%
|
|
University of Geneva
3 publications, 0.48%
|
|
University of Warwick
3 publications, 0.48%
|
|
University of Oslo
3 publications, 0.48%
|
|
University of Florence
3 publications, 0.48%
|
|
University of Melbourne
3 publications, 0.48%
|
|
University of Queensland
3 publications, 0.48%
|
|
Rutgers, The State University of New Jersey
3 publications, 0.48%
|
|
Federal University of São Carlos
3 publications, 0.48%
|
|
Institute of Chemistry, Chinese Academy of Sciences
3 publications, 0.48%
|
|
Universidad Andrés Bello
3 publications, 0.48%
|
|
Max Planck Institute for Coal Research
3 publications, 0.48%
|
|
University of Bristol
3 publications, 0.48%
|
|
Keele University
3 publications, 0.48%
|
|
University of Illinois Urbana-Champaign
3 publications, 0.48%
|
|
Changchun Institute of Applied Chemistry, Chinese Academy of Sciences
3 publications, 0.48%
|
|
Ruhr University Bochum
3 publications, 0.48%
|
|
National Autonomous University of Mexico
3 publications, 0.48%
|
|
Beijing National Laboratory for Molecular Sciences
3 publications, 0.48%
|
|
University of Rostock
3 publications, 0.48%
|
|
University of Kaiserslautern-Landau
3 publications, 0.48%
|
|
Okayama University
3 publications, 0.48%
|
|
Chouaib Doukkali University
3 publications, 0.48%
|
|
University of Sfax
3 publications, 0.48%
|
|
University of Belgrade
3 publications, 0.48%
|
|
University of Florida
3 publications, 0.48%
|
|
Al-Azhar University
3 publications, 0.48%
|
|
University of Surrey
3 publications, 0.48%
|
|
University of Zagreb
3 publications, 0.48%
|
|
University of Split
3 publications, 0.48%
|
|
University of Montpellier
3 publications, 0.48%
|
|
A.N.Nesmeyanov Institute of Organoelement Compounds of the Russian Academy of Sciences
2 publications, 0.32%
|
|
Nikolaev Institute of Inorganic Chemistry of the Siberian Branch of the Russian Academy of Sciences
2 publications, 0.32%
|
|
Postovsky Institute of Organic Synthesis of the Ural Branch of the Russian Academy of Sciences
2 publications, 0.32%
|
|
Novosibirsk State University
2 publications, 0.32%
|
|
Samara State Technical University
2 publications, 0.32%
|
|
Institute of Geology Komi SC of the Ural Branch of the Russian Academy of Sciences
2 publications, 0.32%
|
|
Kuban State University
2 publications, 0.32%
|
|
Pitirim Sorokin Syktyvkar State University
2 publications, 0.32%
|
|
Komi Science Center of the Ural Branch of the Russian Academy of Sciences
2 publications, 0.32%
|
|
King Abdulaziz University
2 publications, 0.32%
|
|
King Faisal University
2 publications, 0.32%
|
|
Princess Nourah bint Abdulrahman University
2 publications, 0.32%
|
|
University of Jeddah
2 publications, 0.32%
|
|
United Arab Emirates University
2 publications, 0.32%
|
|
Indian Institute of Technology Palakkad
2 publications, 0.32%
|
|
Chandigarh University
2 publications, 0.32%
|
|
Al-Nahrain University
2 publications, 0.32%
|
|
Indian Association for the Cultivation of Science
2 publications, 0.32%
|
|
Xi'an Jiaotong University
2 publications, 0.32%
|
|
Aix-Marseille University
2 publications, 0.32%
|
|
Petronas University of Technology
2 publications, 0.32%
|
|
Nanjing Tech University
2 publications, 0.32%
|
|
Nanjing University of Posts and Telecommunications
2 publications, 0.32%
|
|
Nanjing University of Chinese Medicine
2 publications, 0.32%
|
|
Show all (70 more) | |
2
4
6
8
10
|
Publishing organizations in 5 years
1
2
3
4
5
6
7
8
|
|
University of Basel
8 publications, 1.32%
|
|
University of Salerno
8 publications, 1.32%
|
|
National Interuniversity Consortium of Materials Science and Technology
7 publications, 1.15%
|
|
King Saud University
6 publications, 0.99%
|
|
University of Fribourg
6 publications, 0.99%
|
|
Budapest University of Technology and Economics
6 publications, 0.99%
|
|
Aristotle University of Thessaloniki
6 publications, 0.99%
|
|
Lomonosov Moscow State University
5 publications, 0.82%
|
|
Saint Petersburg State University
5 publications, 0.82%
|
|
Osaka University
5 publications, 0.82%
|
|
University of Patras
5 publications, 0.82%
|
|
Masaryk University
5 publications, 0.82%
|
|
N.D. Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences
4 publications, 0.66%
|
|
Kazan Federal University
4 publications, 0.66%
|
|
University of Chinese Academy of Sciences
4 publications, 0.66%
|
|
Jilin University
4 publications, 0.66%
|
|
University of New South Wales
4 publications, 0.66%
|
|
University of Milan
4 publications, 0.66%
|
|
Birla Institute of Technology, Mesra
4 publications, 0.66%
|
|
Florida State University
4 publications, 0.66%
|
|
University of Sydney
4 publications, 0.66%
|
|
University of Pavia
4 publications, 0.66%
|
|
University of Trieste
4 publications, 0.66%
|
|
Istituto Officina dei Materiali
4 publications, 0.66%
|
|
San Diego State University
4 publications, 0.66%
|
|
Hunan University
4 publications, 0.66%
|
|
National and Kapodistrian University of Athens
4 publications, 0.66%
|
|
University of Crete
4 publications, 0.66%
|
|
University of Cologne
4 publications, 0.66%
|
|
University of Warsaw
4 publications, 0.66%
|
|
University of Valencia
4 publications, 0.66%
|
|
Texas A&M University
4 publications, 0.66%
|
|
Mansoura University
4 publications, 0.66%
|
|
University of São Paulo
4 publications, 0.66%
|
|
University of Mississippi
4 publications, 0.66%
|
|
Ural Federal University
3 publications, 0.49%
|
|
Imam Mohammad Ibn Saud Islamic University
3 publications, 0.49%
|
|
Jazan University
3 publications, 0.49%
|
|
Vellore Institute of Technology University
3 publications, 0.49%
|
|
Zhejiang University
3 publications, 0.49%
|
|
Technical University of Munich
3 publications, 0.49%
|
|
University of Strasbourg
3 publications, 0.49%
|
|
University of Bordeaux
3 publications, 0.49%
|
|
Sidi Mohamed Ben Abdellah University
3 publications, 0.49%
|
|
Nankai University
3 publications, 0.49%
|
|
University of Geneva
3 publications, 0.49%
|
|
University of Oslo
3 publications, 0.49%
|
|
University of Florence
3 publications, 0.49%
|
|
University of Melbourne
3 publications, 0.49%
|
|
University of Queensland
3 publications, 0.49%
|
|
Rutgers, The State University of New Jersey
3 publications, 0.49%
|
|
Federal University of São Carlos
3 publications, 0.49%
|
|
Institute of Chemistry, Chinese Academy of Sciences
3 publications, 0.49%
|
|
Universidad Andrés Bello
3 publications, 0.49%
|
|
Max Planck Institute for Coal Research
3 publications, 0.49%
|
|
University of Bristol
3 publications, 0.49%
|
|
Keele University
3 publications, 0.49%
|
|
University of Illinois Urbana-Champaign
3 publications, 0.49%
|
|
Changchun Institute of Applied Chemistry, Chinese Academy of Sciences
3 publications, 0.49%
|
|
Beijing National Laboratory for Molecular Sciences
3 publications, 0.49%
|
|
University of Rostock
3 publications, 0.49%
|
|
University of Kaiserslautern-Landau
3 publications, 0.49%
|
|
Okayama University
3 publications, 0.49%
|
|
Chouaib Doukkali University
3 publications, 0.49%
|
|
University of Sfax
3 publications, 0.49%
|
|
University of Belgrade
3 publications, 0.49%
|
|
University of Florida
3 publications, 0.49%
|
|
Al-Azhar University
3 publications, 0.49%
|
|
University of Zagreb
3 publications, 0.49%
|
|
University of Split
3 publications, 0.49%
|
|
A.N.Nesmeyanov Institute of Organoelement Compounds of the Russian Academy of Sciences
2 publications, 0.33%
|
|
Nikolaev Institute of Inorganic Chemistry of the Siberian Branch of the Russian Academy of Sciences
2 publications, 0.33%
|
|
Postovsky Institute of Organic Synthesis of the Ural Branch of the Russian Academy of Sciences
2 publications, 0.33%
|
|
Novosibirsk State University
2 publications, 0.33%
|
|
Samara State Technical University
2 publications, 0.33%
|
|
Institute of Geology Komi SC of the Ural Branch of the Russian Academy of Sciences
2 publications, 0.33%
|
|
Kuban State University
2 publications, 0.33%
|
|
Pitirim Sorokin Syktyvkar State University
2 publications, 0.33%
|
|
Komi Science Center of the Ural Branch of the Russian Academy of Sciences
2 publications, 0.33%
|
|
King Abdulaziz University
2 publications, 0.33%
|
|
King Faisal University
2 publications, 0.33%
|
|
Princess Nourah bint Abdulrahman University
2 publications, 0.33%
|
|
University of Jeddah
2 publications, 0.33%
|
|
Indian Institute of Technology Palakkad
2 publications, 0.33%
|
|
Chandigarh University
2 publications, 0.33%
|
|
Al-Nahrain University
2 publications, 0.33%
|
|
Indian Association for the Cultivation of Science
2 publications, 0.33%
|
|
Shanghai Jiao Tong University
2 publications, 0.33%
|
|
Xi'an Jiaotong University
2 publications, 0.33%
|
|
Aix-Marseille University
2 publications, 0.33%
|
|
Petronas University of Technology
2 publications, 0.33%
|
|
Nanjing Tech University
2 publications, 0.33%
|
|
Nanjing University of Posts and Telecommunications
2 publications, 0.33%
|
|
Nanjing University of Chinese Medicine
2 publications, 0.33%
|
|
Nanjing University
2 publications, 0.33%
|
|
University of Helsinki
2 publications, 0.33%
|
|
Wuhan University
2 publications, 0.33%
|
|
Shandong University of Science and Technology
2 publications, 0.33%
|
|
Technische Universität Dresden
2 publications, 0.33%
|
|
University of Naples Federico II
2 publications, 0.33%
|
|
Show all (70 more) | |
1
2
3
4
5
6
7
8
|
Publishing countries
10
20
30
40
50
60
70
80
90
|
|
China
|
China, 83, 13.41%
China
83 publications, 13.41%
|
USA
|
USA, 64, 10.34%
USA
64 publications, 10.34%
|
Germany
|
Germany, 50, 8.08%
Germany
50 publications, 8.08%
|
Italy
|
Italy, 50, 8.08%
Italy
50 publications, 8.08%
|
United Kingdom
|
United Kingdom, 38, 6.14%
United Kingdom
38 publications, 6.14%
|
India
|
India, 36, 5.82%
India
36 publications, 5.82%
|
Japan
|
Japan, 36, 5.82%
Japan
36 publications, 5.82%
|
Russia
|
Russia, 29, 4.68%
Russia
29 publications, 4.68%
|
Spain
|
Spain, 28, 4.52%
Spain
28 publications, 4.52%
|
France
|
France, 27, 4.36%
France
27 publications, 4.36%
|
Switzerland
|
Switzerland, 24, 3.88%
Switzerland
24 publications, 3.88%
|
Saudi Arabia
|
Saudi Arabia, 18, 2.91%
Saudi Arabia
18 publications, 2.91%
|
Greece
|
Greece, 17, 2.75%
Greece
17 publications, 2.75%
|
Brazil
|
Brazil, 16, 2.58%
Brazil
16 publications, 2.58%
|
Egypt
|
Egypt, 15, 2.42%
Egypt
15 publications, 2.42%
|
Hungary
|
Hungary, 13, 2.1%
Hungary
13 publications, 2.1%
|
Romania
|
Romania, 13, 2.1%
Romania
13 publications, 2.1%
|
Mexico
|
Mexico, 11, 1.78%
Mexico
11 publications, 1.78%
|
Australia
|
Australia, 10, 1.62%
Australia
10 publications, 1.62%
|
Canada
|
Canada, 10, 1.62%
Canada
10 publications, 1.62%
|
South Africa
|
South Africa, 9, 1.45%
South Africa
9 publications, 1.45%
|
Poland
|
Poland, 8, 1.29%
Poland
8 publications, 1.29%
|
Denmark
|
Denmark, 7, 1.13%
Denmark
7 publications, 1.13%
|
Morocco
|
Morocco, 7, 1.13%
Morocco
7 publications, 1.13%
|
Tunisia
|
Tunisia, 7, 1.13%
Tunisia
7 publications, 1.13%
|
Czech Republic
|
Czech Republic, 7, 1.13%
Czech Republic
7 publications, 1.13%
|
Bulgaria
|
Bulgaria, 6, 0.97%
Bulgaria
6 publications, 0.97%
|
Malaysia
|
Malaysia, 6, 0.97%
Malaysia
6 publications, 0.97%
|
Finland
|
Finland, 6, 0.97%
Finland
6 publications, 0.97%
|
Indonesia
|
Indonesia, 5, 0.81%
Indonesia
5 publications, 0.81%
|
Ireland
|
Ireland, 5, 0.81%
Ireland
5 publications, 0.81%
|
Republic of Korea
|
Republic of Korea, 5, 0.81%
Republic of Korea
5 publications, 0.81%
|
Serbia
|
Serbia, 5, 0.81%
Serbia
5 publications, 0.81%
|
Croatia
|
Croatia, 5, 0.81%
Croatia
5 publications, 0.81%
|
Chile
|
Chile, 5, 0.81%
Chile
5 publications, 0.81%
|
Ukraine
|
Ukraine, 4, 0.65%
Ukraine
4 publications, 0.65%
|
Austria
|
Austria, 4, 0.65%
Austria
4 publications, 0.65%
|
Iraq
|
Iraq, 4, 0.65%
Iraq
4 publications, 0.65%
|
Iran
|
Iran, 4, 0.65%
Iran
4 publications, 0.65%
|
Netherlands
|
Netherlands, 4, 0.65%
Netherlands
4 publications, 0.65%
|
Turkey
|
Turkey, 4, 0.65%
Turkey
4 publications, 0.65%
|
Sweden
|
Sweden, 4, 0.65%
Sweden
4 publications, 0.65%
|
Argentina
|
Argentina, 3, 0.48%
Argentina
3 publications, 0.48%
|
Colombia
|
Colombia, 3, 0.48%
Colombia
3 publications, 0.48%
|
Norway
|
Norway, 3, 0.48%
Norway
3 publications, 0.48%
|
Pakistan
|
Pakistan, 3, 0.48%
Pakistan
3 publications, 0.48%
|
Slovenia
|
Slovenia, 3, 0.48%
Slovenia
3 publications, 0.48%
|
Portugal
|
Portugal, 2, 0.32%
Portugal
2 publications, 0.32%
|
Algeria
|
Algeria, 2, 0.32%
Algeria
2 publications, 0.32%
|
Jordan
|
Jordan, 2, 0.32%
Jordan
2 publications, 0.32%
|
Cuba
|
Cuba, 2, 0.32%
Cuba
2 publications, 0.32%
|
Lebanon
|
Lebanon, 2, 0.32%
Lebanon
2 publications, 0.32%
|
Libya
|
Libya, 2, 0.32%
Libya
2 publications, 0.32%
|
Malta
|
Malta, 2, 0.32%
Malta
2 publications, 0.32%
|
Moldova
|
Moldova, 2, 0.32%
Moldova
2 publications, 0.32%
|
New Zealand
|
New Zealand, 2, 0.32%
New Zealand
2 publications, 0.32%
|
UAE
|
UAE, 2, 0.32%
UAE
2 publications, 0.32%
|
Peru
|
Peru, 2, 0.32%
Peru
2 publications, 0.32%
|
Singapore
|
Singapore, 2, 0.32%
Singapore
2 publications, 0.32%
|
Thailand
|
Thailand, 2, 0.32%
Thailand
2 publications, 0.32%
|
Uruguay
|
Uruguay, 2, 0.32%
Uruguay
2 publications, 0.32%
|
Belarus
|
Belarus, 1, 0.16%
Belarus
1 publication, 0.16%
|
Estonia
|
Estonia, 1, 0.16%
Estonia
1 publication, 0.16%
|
Albania
|
Albania, 1, 0.16%
Albania
1 publication, 0.16%
|
Belgium
|
Belgium, 1, 0.16%
Belgium
1 publication, 0.16%
|
Brunei
|
Brunei, 1, 0.16%
Brunei
1 publication, 0.16%
|
Vietnam
|
Vietnam, 1, 0.16%
Vietnam
1 publication, 0.16%
|
Israel
|
Israel, 1, 0.16%
Israel
1 publication, 0.16%
|
Qatar
|
Qatar, 1, 0.16%
Qatar
1 publication, 0.16%
|
Cyprus
|
Cyprus, 1, 0.16%
Cyprus
1 publication, 0.16%
|
Costa Rica
|
Costa Rica, 1, 0.16%
Costa Rica
1 publication, 0.16%
|
Kuwait
|
Kuwait, 1, 0.16%
Kuwait
1 publication, 0.16%
|
Nepal
|
Nepal, 1, 0.16%
Nepal
1 publication, 0.16%
|
Palestine
|
Palestine, 1, 0.16%
Palestine
1 publication, 0.16%
|
Slovakia
|
Slovakia, 1, 0.16%
Slovakia
1 publication, 0.16%
|
Trinidad and Tobago
|
Trinidad and Tobago, 1, 0.16%
Trinidad and Tobago
1 publication, 0.16%
|
Sri Lanka
|
Sri Lanka, 1, 0.16%
Sri Lanka
1 publication, 0.16%
|
Kosovo
|
Kosovo, 1, 0.16%
Kosovo
1 publication, 0.16%
|
Show all (48 more) | |
10
20
30
40
50
60
70
80
90
|
Publishing countries in 5 years
10
20
30
40
50
60
70
80
|
|
China
|
China, 80, 13.18%
China
80 publications, 13.18%
|
USA
|
USA, 63, 10.38%
USA
63 publications, 10.38%
|
Italy
|
Italy, 50, 8.24%
Italy
50 publications, 8.24%
|
Germany
|
Germany, 47, 7.74%
Germany
47 publications, 7.74%
|
United Kingdom
|
United Kingdom, 36, 5.93%
United Kingdom
36 publications, 5.93%
|
Japan
|
Japan, 36, 5.93%
Japan
36 publications, 5.93%
|
India
|
India, 35, 5.77%
India
35 publications, 5.77%
|
Russia
|
Russia, 29, 4.78%
Russia
29 publications, 4.78%
|
Spain
|
Spain, 26, 4.28%
Spain
26 publications, 4.28%
|
France
|
France, 25, 4.12%
France
25 publications, 4.12%
|
Switzerland
|
Switzerland, 20, 3.29%
Switzerland
20 publications, 3.29%
|
Saudi Arabia
|
Saudi Arabia, 18, 2.97%
Saudi Arabia
18 publications, 2.97%
|
Greece
|
Greece, 17, 2.8%
Greece
17 publications, 2.8%
|
Brazil
|
Brazil, 16, 2.64%
Brazil
16 publications, 2.64%
|
Egypt
|
Egypt, 15, 2.47%
Egypt
15 publications, 2.47%
|
Hungary
|
Hungary, 13, 2.14%
Hungary
13 publications, 2.14%
|
Romania
|
Romania, 12, 1.98%
Romania
12 publications, 1.98%
|
Canada
|
Canada, 10, 1.65%
Canada
10 publications, 1.65%
|
Mexico
|
Mexico, 10, 1.65%
Mexico
10 publications, 1.65%
|
Australia
|
Australia, 9, 1.48%
Australia
9 publications, 1.48%
|
Poland
|
Poland, 8, 1.32%
Poland
8 publications, 1.32%
|
Denmark
|
Denmark, 7, 1.15%
Denmark
7 publications, 1.15%
|
Morocco
|
Morocco, 7, 1.15%
Morocco
7 publications, 1.15%
|
Tunisia
|
Tunisia, 7, 1.15%
Tunisia
7 publications, 1.15%
|
Czech Republic
|
Czech Republic, 7, 1.15%
Czech Republic
7 publications, 1.15%
|
South Africa
|
South Africa, 7, 1.15%
South Africa
7 publications, 1.15%
|
Bulgaria
|
Bulgaria, 6, 0.99%
Bulgaria
6 publications, 0.99%
|
Malaysia
|
Malaysia, 6, 0.99%
Malaysia
6 publications, 0.99%
|
Finland
|
Finland, 6, 0.99%
Finland
6 publications, 0.99%
|
Indonesia
|
Indonesia, 5, 0.82%
Indonesia
5 publications, 0.82%
|
Ireland
|
Ireland, 5, 0.82%
Ireland
5 publications, 0.82%
|
Republic of Korea
|
Republic of Korea, 5, 0.82%
Republic of Korea
5 publications, 0.82%
|
Croatia
|
Croatia, 5, 0.82%
Croatia
5 publications, 0.82%
|
Chile
|
Chile, 5, 0.82%
Chile
5 publications, 0.82%
|
Ukraine
|
Ukraine, 4, 0.66%
Ukraine
4 publications, 0.66%
|
Austria
|
Austria, 4, 0.66%
Austria
4 publications, 0.66%
|
Iraq
|
Iraq, 4, 0.66%
Iraq
4 publications, 0.66%
|
Netherlands
|
Netherlands, 4, 0.66%
Netherlands
4 publications, 0.66%
|
Serbia
|
Serbia, 4, 0.66%
Serbia
4 publications, 0.66%
|
Turkey
|
Turkey, 4, 0.66%
Turkey
4 publications, 0.66%
|
Sweden
|
Sweden, 4, 0.66%
Sweden
4 publications, 0.66%
|
Argentina
|
Argentina, 3, 0.49%
Argentina
3 publications, 0.49%
|
Iran
|
Iran, 3, 0.49%
Iran
3 publications, 0.49%
|
Colombia
|
Colombia, 3, 0.49%
Colombia
3 publications, 0.49%
|
Norway
|
Norway, 3, 0.49%
Norway
3 publications, 0.49%
|
Pakistan
|
Pakistan, 3, 0.49%
Pakistan
3 publications, 0.49%
|
Slovenia
|
Slovenia, 3, 0.49%
Slovenia
3 publications, 0.49%
|
Portugal
|
Portugal, 2, 0.33%
Portugal
2 publications, 0.33%
|
Algeria
|
Algeria, 2, 0.33%
Algeria
2 publications, 0.33%
|
Jordan
|
Jordan, 2, 0.33%
Jordan
2 publications, 0.33%
|
Cuba
|
Cuba, 2, 0.33%
Cuba
2 publications, 0.33%
|
Lebanon
|
Lebanon, 2, 0.33%
Lebanon
2 publications, 0.33%
|
Libya
|
Libya, 2, 0.33%
Libya
2 publications, 0.33%
|
Malta
|
Malta, 2, 0.33%
Malta
2 publications, 0.33%
|
Moldova
|
Moldova, 2, 0.33%
Moldova
2 publications, 0.33%
|
New Zealand
|
New Zealand, 2, 0.33%
New Zealand
2 publications, 0.33%
|
Peru
|
Peru, 2, 0.33%
Peru
2 publications, 0.33%
|
Singapore
|
Singapore, 2, 0.33%
Singapore
2 publications, 0.33%
|
Thailand
|
Thailand, 2, 0.33%
Thailand
2 publications, 0.33%
|
Uruguay
|
Uruguay, 2, 0.33%
Uruguay
2 publications, 0.33%
|
Belarus
|
Belarus, 1, 0.16%
Belarus
1 publication, 0.16%
|
Estonia
|
Estonia, 1, 0.16%
Estonia
1 publication, 0.16%
|
Albania
|
Albania, 1, 0.16%
Albania
1 publication, 0.16%
|
Belgium
|
Belgium, 1, 0.16%
Belgium
1 publication, 0.16%
|
Brunei
|
Brunei, 1, 0.16%
Brunei
1 publication, 0.16%
|
Vietnam
|
Vietnam, 1, 0.16%
Vietnam
1 publication, 0.16%
|
Israel
|
Israel, 1, 0.16%
Israel
1 publication, 0.16%
|
Qatar
|
Qatar, 1, 0.16%
Qatar
1 publication, 0.16%
|
Cyprus
|
Cyprus, 1, 0.16%
Cyprus
1 publication, 0.16%
|
Costa Rica
|
Costa Rica, 1, 0.16%
Costa Rica
1 publication, 0.16%
|
Kuwait
|
Kuwait, 1, 0.16%
Kuwait
1 publication, 0.16%
|
Nepal
|
Nepal, 1, 0.16%
Nepal
1 publication, 0.16%
|
UAE
|
UAE, 1, 0.16%
UAE
1 publication, 0.16%
|
Palestine
|
Palestine, 1, 0.16%
Palestine
1 publication, 0.16%
|
Slovakia
|
Slovakia, 1, 0.16%
Slovakia
1 publication, 0.16%
|
Trinidad and Tobago
|
Trinidad and Tobago, 1, 0.16%
Trinidad and Tobago
1 publication, 0.16%
|
Sri Lanka
|
Sri Lanka, 1, 0.16%
Sri Lanka
1 publication, 0.16%
|
Kosovo
|
Kosovo, 1, 0.16%
Kosovo
1 publication, 0.16%
|
Show all (48 more) | |
10
20
30
40
50
60
70
80
|
3 profile journal articles
Alabugin Igor

Florida State University

A.E. Arbuzov Institute of Organic and Physical Chemistry of the Kazan Scientific Center of the Russian Academy of Sciences
236 publications,
10 084 citations
h-index: 59
2 profile journal articles
Konshin Valeriy
🤝
PhD in Chemistry, Associate Professor

Kuban State University
49 publications,
257 citations
h-index: 9
Research interests
Adsorption
Organic synthesis
2 profile journal articles
Varaksin Mikhail
DSc in Chemistry

Postovsky Institute of Organic Synthesis of the Ural Branch of the Russian Academy of Sciences

Ural Federal University
68 publications,
687 citations
h-index: 16
1 profile journal article
Kouznetsov V
103 publications,
1 756 citations
h-index: 17
1 profile journal article
Nikonov Igor
12 publications
h-index: 0
1 profile journal article
Karpenko Kirill
PhD in Chemistry

N.D. Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences
30 publications,
153 citations
h-index: 8
1 profile journal article
Guskov Vladimir
🤝
DSc in Chemistry, Associate Professor

Ufa University of Science and Technology
55 publications,
258 citations
h-index: 10
Research interests
Adsorption
Chirality
Chromatography
Supramolecular chemistry
1 profile journal article
Moseev Timofey
PhD in Chemistry

Ural Federal University
27 publications,
169 citations
h-index: 8
1 profile journal article
Burykina Julia
PhD in Chemistry

N.D. Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences
47 publications,
925 citations
h-index: 15
Research interests
Catalysis
Mass Spectrometry
Photochemistry
1 profile journal article
Konshina Dzhamilya
PhD in Chemistry, Associate Professor

Kuban State University
31 publications,
101 citations
h-index: 6
1 profile journal article
Weil Matthias
71 publications,
183 citations
h-index: 7
1 profile journal article
Mukherjee Sanat
🤝
PhD in Education, Senior lecturer

Birla Institute of Technology, Mesra
95 publications,
1 971 citations
h-index: 27
Research interests
Optoelectronics
Thin solid films
1 profile journal article
MacLeod-Carey Desmond
40 publications,
309 citations
h-index: 10