Environmental Conditions Of Zakamensk Town (Dzhida River Basin Hotspot)

Ecological problems of Zakamensk town are associated with sand deposits that were formed as a result of mining activities of former Dzhidinsky tungstenmolybdenum plant. Sands are accumulated in large quantities and they contain dangerous concentrations of heavy metals. Desertification in an urbanized area is manifested locally, but it differs from agricultural desertification by a profound and comprehensive destructive change in the components of the environment. Maps of soils, vegetation, types of lands, as well as ecological zoning maps of Zakamensk were created. The basis for the creation of electronic maps using GIS were stock, archive and own materials, topographic maps and remote sensing data. Urbanized desertification in Zakamensk is caused by chemical contamination of sandy eluvium, the spreading of pollutants by water flows and wind currents. Erosion occurs both in the form of flat flushing and linear erosion. The most intensive is gully erosion. Quantitative parameters of temporal variability of the erosive rainfall potential for the Zakamensk town are received. The quantitative characteristics of loads of pollutants on the territory of the town are determined on the basis of the erosion-deflation models. The calculations showed that 204 tons/ha of contaminated sand annually falls into the settlement area with water-erosion flows (Pb – 3.7 tons, W – 4.3 tons). Moreover, active wind activity led to the deposition of more metals (Pb – 5.6 tons, W – 6.5 tons) in the town

Algorithm of Assessment of the MODIS NDVI Long-Term Variations

Разработан алгоритм оценки долговременных вариаций характеристик растительного покрова, использующий значения вегетационного индекса NDVI спектрорадиометра MODIS спутников Terra и Aqua. Алгоритм содержит процедуры предварительной обработки данных – восстановление отсутствующих значений, сглаживание фильтром Савицкого-Голея. Для анализа территорий со снежным покровом определяется минимальная длина вегетационного периода для всей длины временного ряда. Полученные после удаления сезонной компоненты значения NDVI используются для построения линейной регрессии и определения тренда. В результате применения алгоритма создана карта пространственного распределения линейных трендов NDVI с 2000 по 2016 гг. для территории Западного Забайкалья. Представлены примеры визуальной верификации изменения растительного покрова с использованием спутниковых изображений сверхвысокого пространственного разрешенияAn algorithm was developed for assessment of long-term variations of vegetation characteristics. The algorithm use NDVI data from spectroradiometer MODIS of Terra and Aqua satellites. The algorithm includes pre-processing procedures – the restoration of missing values, smoothing using Savitsky-Golay filter. To analyze the areas with snow cover the minimum length of the growing season is determined for the full length of the time series. Obtained after removing the seasonal component NDVI values are used to construct a linear regression and determine the trend. As a result of applying the algorithm the map of the spatial distribution of NDVI linear trends was created from 2000 to 2016 for the West Transbaikalia. Examples of visual verification of vegetation cover changes using satellite images of ultra-high spatial resolution are presente

Algorithm of Assessment of the MODIS NDVI Long-Term Variations

Разработан алгоритм оценки долговременных вариаций характеристик растительного покрова, использующий значения вегетационного индекса NDVI спектрорадиометра MODIS спутников Terra и Aqua. Алгоритм содержит процедуры предварительной обработки данных – восстановление отсутствующих значений, сглаживание фильтром Савицкого-Голея. Для анализа территорий со снежным покровом определяется минимальная длина вегетационного периода для всей длины временного ряда. Полученные после удаления сезонной компоненты значения NDVI используются для построения линейной регрессии и определения тренда. В результате применения алгоритма создана карта пространственного распределения линейных трендов NDVI с 2000 по 2016 гг. для территории Западного Забайкалья. Представлены примеры визуальной верификации изменения растительного покрова с использованием спутниковых изображений сверхвысокого пространственного разрешенияAn algorithm was developed for assessment of long-term variations of vegetation characteristics. The algorithm use NDVI data from spectroradiometer MODIS of Terra and Aqua satellites. The algorithm includes pre-processing procedures – the restoration of missing values, smoothing using Savitsky-Golay filter. To analyze the areas with snow cover the minimum length of the growing season is determined for the full length of the time series. Obtained after removing the seasonal component NDVI values are used to construct a linear regression and determine the trend. As a result of applying the algorithm the map of the spatial distribution of NDVI linear trends was created from 2000 to 2016 for the West Transbaikalia. Examples of visual verification of vegetation cover changes using satellite images of ultra-high spatial resolution are presente

Estimation of Anthropogenic Forest Disturbancy Using Modis Ndvi Data (on the Example of Zaigraevskoe Forestry, Republic of Buryatia)

В работе представлены результаты оценки изменения лесного покрова после природно- антропогенного воздействия (пожары, рубки) на примере модельного участка, а также сравнительный анализ результатов, полученных по статистическим архивным данным и данным дистанционного зондирования. Выявлено, что по снимкам MODIS детектируются только крупные лесные пожары и сплошные рубки леса большой протяженности. Также обнаружено, что не детектируются разнесенные в пространстве небольшие рубки в пределах пикселяThe paper presents the results of assessment the forest cover changes as a result of natural and anthropogenic impact (fires, forest felling) by the example of a model area. A comparative analysis of the results obtained from statistical archival data and remote sensing data was conducted. It has been revealed that according to MODIS images only large forest fires and continuous long-length felling of forests are detected. It has also been discovered that small felling within a pixel is not detecte

Ecological State of Lake Gusinoe—A Cooling Pond of the Gusinoozersk GRES

The study of the transformation of substances in the basin of the Selenga River—the main tributary of Lake Baikal—under anthropogenic pressure and in the context of global climate change, is especially important for the lake, a globally important source of drinking water. The ecosystem of Lake Gusinoe is one of the key objects in the Selenga River basin that is exposed to significant anthropogenic pressure. This study presents the results of an analysis of water level changes and physicochemical parameters of the water mass of Lake Gusinoe; literature data from 1951 to 2017 and own data from 2017 to 2021. The water level in the lake had depended on natural factors before the Gusinoozersk GRES was launched; however, since the plant has begun using the lake as a cooling pond, its level has actually been regulated by the economic entity. Over the years, there has been a significant increase in mineralization, sulfate, sodium, fluoride and organic matter fractions resistant to oxidation. Seasonal increases in iron and manganese concentrations in water were detected. Increased concentrations of nutrients and organic matter fractions resistant to oxidation were registered at the wastewater discharge sites. Heavy metals in the bottom sediments of Lake Gusinoe accumulate mainly in the silt of the deep zone of the lake. Plants growing in the zones of influence of the Gusinoozersk GRES and Gusinoozersk wastewater discharge accumulate the largest amount of metals

Phthalates in Surface Waters of the Selenga River (Main Tributary of Lake Baikal) and Its Delta: Spatial-Temporal Distribution and Environmental Risk Assessment

The Selenga River provides about half of the water and chemical runoff into Lake Baikal and plays an important role in the sustainability of the ecosystem of this large natural freshwater lake. Phthalate esters (PAEs) are organic compounds that can disrupt reproductive and endocrine systems. This study focused on investigating the distribution of six priority phthalates in the Selenga River and its delta utilizing SPE-GC/MS. The study found that the highest levels of Σ6PAE were observed during the high-water years, 2021 and 2023, and were evenly distributed along the river from the sampling sites upstream of Ulan-Ude to the delta channels. In contrast, the mean annual Σ6PAE content was relatively low in the low water period of 2022. Dibutyl phthalate (DBP) and di-(2-ethylhexyl) phthalate (DEHP) are the two dominant phthalates found in the surface waters of the Selenga River and delta channels. In 2021, the average total concentration of six phthalates (Σ6PAE) ranged from 8.84 to 25.19 µg/L, while in 2022 it ranged from 0.45 to 4.01 µg/L, and in 2023 it ranged from 5.40 to 21.08 µg/L. The maximum level for the sum of phthalates was 61.64 µg/L in 2021, 13.57 µg/L in 2022, and 30.19 µg/L in 2023. The wastewater treatment facilities in Ulan-Ude were identified as a stable local source of phthalates. In some cases, PAE concentrations exceeded maximum allowable concentrations, particularly for DEHP. This could have adverse effects on aquatic organisms