Novy Urengoy Research Articles

Additions to the flora of northern Asia: alien vascular plant records in the Yamal-Nenets Autonomous District (Russia)

The north of Western Siberia has seen intensive economic development. Exploration and mining for mineral resources, active road and pipeline construction, urban development, and agriculture favor invasion and dispersal of alien plant species across the Subarctic region. The paper reports new records for eight alien species and hybrids previously unknown from northwest Siberia and the flora of northern Asia: Alopecurus geniculatus L., Anthyllis vulneraria L. subsp. vulneraria, Aquilegia atrata W.D.J. Koch, A. vulgaris L., Epilobium franciscanum Barbey, Galium album Mill. subsp. album, Petunia atkinsiana (Sweet) D. Don ex W.H. Baxter, Primula elatior (L.) Hill. These species were spotted in 2012–2014 in the towns of Salekhard, Nadym, Novy Urengoy, Tarko-Sale, Gubkinsky, and Noyabrsk in the Yamal-Nenets Autonomous District, Western Siberia (Russia). Species, synonyms, overall distribution, habitat preferences, and species taxonomy with remarks on identification and differentiation from the most similar taxa occurring in the study area, as well as the list of localities are presented.

Divisions and Unity of the Novy Urengoy Muslim Community

Novy Urengoy, in the oil-and-gas rich Yamalo-Nenets autonomous Okrug, became a central place for migrants, giving birth to an active Muslim community that found itself several times under the Russian media spotlight for its division and radicalization. The article looks first at institutional struggle to control the city’s Muslim community, before delving deeper into its ideological divisions and its unity in adapting practices to Far North. It concludes by discussing the arrival of new, power-related interactions between the North Caucasus and the largest Muslim community in Russia’s gas capital. This article is based on fieldwork conducted in March 2018.

Урянхай и урянхайцы. Проблема поисков локуса и этносов

Статья посвящена проблеме наименований местности Урянхай и этнонима урянхайцы, занимавших значительное место в древней и новой истории Центральной Азии. Как известно, Тува в русской научной литературе именовалась Урянхайский край, а тувинцы — урянхайцами. Эти названия, известные еще по средневековым источникам по истории тюркских и монгольских народов и широко распространившиеся в географическом отношении далеко в Забайкалье до территории Дальнего Востока, до сих пор не имели даже сколько-нибудь правдоподобного объяснения. Не была ясна и языковая принадлежность наименования местности (историко-географической провинции) Урянхай, и можно было только догадываться, что живущий там этнос и небольшие этнические группы получили свое название по месту проживания. Использование в якутском фольклоре названия (по существу фольклорного самоназвания) ураангхай-сахалар связывает поставленные вопросы с проблемой этногенеза и этнической истории якутов и с этнической историей народов окрестностей озера Байкал.Автор собрал топонимические параллели для географического имени Урянхай. Среди них: Уренгой — название реки в истоках Оби или Иртыша, отмеченное в дневнике путешествия Н. Г. Спафария 1675 г., Новый Уренгой — название города в низовьях Оби. Объяснение происхождения этих топонимов найдено на материале самодийских языков: на ненецком языке варё-нг-хой — «гора с проталинами, гора, на которой раньше всего тает снег». Такой осложненный географический апеллятив закрепился в качестве конкретного топонима лишь в некоторых местах, и, учитывая горный рельеф территории Тувы, закрепился как название территории в целом. Название «урянхайцы», которое не могло быть самоназванием ни одного из народов Тувы, распространилось, по-видимому, в связи с миграциями тюркоязычных народов в Прибайкалье и Забайкалье, а также на Дальний Восток. Вероятно, предки якутов принесли название Ураангкай как часть самоназвания из районов Прибайкалья, соседствующих с территорией Тувы, но, возможно, что наименование ураангхай-саха является благоприобретенной особенностью языка якутского фольклора, не связанной с дискуссионными проблемами этнической истории якутов.

The Effect of Splitting of Raw Data into Training and Test Subsets on the Accuracy of Predicting Spatial Distribution by a Multilayer Perceptron

The paper discusses the influence of various methods for splitting raw data into test and training subsets on the accuracy of the prediction of the spatial distribution of the variable for the model based on a multilayer perceptron. A comparison of models was performed taking into account both spatial heterogeneity and the spread of values of the modeled variable with a completely random splitting option. The study was based on the results obtained during soil screening of urbanized territories in the Russian subarctic zone, Novy Urengoy city and Noyabrsk city. The spatial distribution of the chemical element chromium (Cr) in the surface layer of the soil was modeled. For both surveyed areas, the models in which a controlled splitting method was used for training demonstrated more accurate results than models trained using completely random splitting.

Sources and accumulation of 7Be, 210Pb and 137Cs isotopes in the annual needles of larch and cedar in Novy Urengoy region (Arctic part of Western Siberia)

Annual needles of larch and cedar were sampled in the tundra zone of the Arctic part of Western Siberia in order to determine 7Be, 210Pb and 137Cs specific activities. Higher concentrations of 137Cs in the needles of cedar relative to the needles of larch can be associated with a clear difference in the properties of accumulation and translocation of caesium among these species. Higher concentrations of 7Be and 210Pb in the needles of larch relative to cedar needles and close isotopic ratios (7Be/210Pb) are probably due to more efficient aerosol deposition on vegetative canopies of larch.

Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals

A hybrid approach was proposed to simulate the spatial distribution of a number of heavy metals in the surface layer of the soil. The idea of the method is to simulate a nonlinear large-scale trend using an artificial neural network (ANN) and the subsequent modelling of the residuals by geostatistical methods. A comparison was made with the basic modelling methods based on ANN: generalised regression neural network (GRNN) and multilayer perceptron (MLP). The raw data for the surface layer modelling of Cuprum (Cu), Manganese (Mn) and Niccolum (Ni) were obtained as a result of the soil screening in the subarctic city Novy Urengoy, Russia. The ANN structures were selected by the computer simulation based on the root mean square error (RMSE) minimization. The predictive accuracy of each selected approach was verified by the correlation coefficient, the coefficient of determination, RMSE, Willmott's index of agreement (d), a ratio of performance to interquartile distance (RPIQ) between the prediction and raw data from the test data set. It was confirmed that the use of the hybrid approach provides an increase in prediction accuracy in comparison with the basic ANN models. The proposed hybrid approach for each element showed the best predictive accuracy in comparison with other models based on ANN.

A high density urban temperature network deployed in several cities of Eurasian Arctic

Dense meteorological networks are needed to advance understanding of urban climatology in the northern polar region where global warming is rapid and amplified. High quality and density urban temperature datasets are required to monitor thawing processes in urban soils, properly assess and project climatic trends in human comfort, air quality and weather extremes. This study presents an Urban Heat Island Arctic Research Campaign (UHIARC) observational network, which has been deployed in several mid-sized cities (Salekhard, Vorkuta, Nadym, Novy Urengoy) of the Eurasian Arctic region in the winter of 2016–2017. The network comprises an array of air temperature loggers and one automatic weather station in each of these four cities. The UHIARC observations revealed strong warm temperature anomalies in all four cities. Such persistent temperature anomalies are frequently referred to as urban heat islands (UHIs). The mean wintertime magnitude of these temperature anomalies (the UHI intensity) was found to be between 0.8 K and 1.4 K. Extreme UHI intensities up to 7 K were observed during cold anticyclonic weather conditions. Such a strong mediation of cold temperature spells by UHI might induce considerable socio-economic and environmental impacts in the cities. The UHIARC dataset is available for further analysis from http://urbanreanalysis.ru/uhiarc.html.

High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging

The work deals with the application of artificial neural networks combined with residual kriging (ANNRK) to the spatial prediction of the anomaly distributed chemical element Chromium (Cr). In the work, we examined and compared two neural networks: generalized regression neural network (GRNN) and multi-layer perceptron (MLP) as well as two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multi-layer perceptron residual kriging (MLPRK). The case study is based on the real measurements of surface contamination by Cr in subarctic city Novy Urengoy, Russia. The networks structures have been chosen during a computer simulation based on a minimization of the root mean square error (RMSE). Different prediction approaches are compared by a Spearman's rank correlation coefficient, the mean absolute error (MAE), and RMSE. MLPRK and GRNNRK show the best predictive accuracy comparing to kriging and even to MLP and GRNN, that is hybrid models are more accurate than solo models. The most significant improvement in RMSE (15.5% compared to kriging) is observed in the MLPRK model. The proposed hybrid approach improves the high variation topsoil spatial pollution forecasting, which might be utilized in the environmental modeling.

NORTHERN LATITUDINAL RAILWAY

[For the English full text of the article please see the attached PDF-File (English version follows Russian version)].Russian Railways and Pubic Joint Stock Company Gazprom have signed on 30 March 2017 an agreement on the joint implementation of an investment project to construct the Northern Latitudinal Railway (NLR) Obskaya- Salekhard- Nadym- Pangody- Novy Urengoy- Korotchaevo and its railway approaches. Based on releases of press service of JSC Russian Railways