Search Results (7)

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. Full article

This study presents detailed suspended sediment budget for the four Siberian river deltas, representing contrasting conditions between Northern and Southern environments. Two of the studied rivers empty their water and sediments into the marine located in the permafrost zone in the Arctic region (Lena and Kolyma), and the other two (Selenga and Upper Angara) flow into Lake Baikal located in the steppe and forest-steppe zone of Southern Siberia. For the first time, these poorly monitored areas are analyzed in terms of the long-term and seasonal changes of spatial patterns of suspended sediment concentrations (SSC) over distributaries systems. Remote sensing reflectance is derived from continuous time series of Landsat images and calibrated with the onsite field measurements of SSC. Seasonal variability of suspended sediment changes over deltas was captured for the period from 1989 to 2020. We identify significant variability in the sedimentation processes between different deltas, which is explained by particularities of deltas networks and geomorphology and the existence of specific drivers—continuous permafrost impact in the North and abundant aquatic vegetation and wetland-dominated areas in the South. The study emphasizes that differences exist between Northern and Southern deltas regarding suspended sediments transport conditions. Mostly retention of suspended sediment is observed for Southern deltas due to sediment storage at submerged banks and marshlands located in the backwater zone of the delta during high discharges. In the Northern (arctic) deltas due to permafrost impacts (melting of the permafrost), the absence of sub-aquatic banks and river to ocean interactions of suspended sediment transport is mostly increased downwards, predominantly under higher discharges and along main distributary channels. These results shine light on the geochemical functions of the deltas and patterns of sequestering various metals bound to river sediments. Full article

Lake Baikal is the largest freshwater body on Earth, once famous for its pristine conditions. However, the lake and its drainage basin with their unique ecosystems have in recent decades been subject to both climate warming above the world average and severe anthropogenic pressures from mining and agriculture. Although previous studies have targeted various hydroclimatic, geochemical, and biological conditions of the Lake Baikal basin, the heterogeneous nature and large size of the basin leave considerable knowledge gaps regarding ongoing metal contamination of the basin’s suspended sediments and waters. To address these knowledge gaps, the main objectives of this study are to (i) determine regional background values for water and suspended sediment quality with respect to multiple metals (representing undisturbed conditions) and (ii) further evaluate spatio-temporal concentration patterns of these metals, including regions with heavy anthropogenic impacts. We synthesize data from extensive field measurements within the Selenga River basin performed between 2011 and 2016, covering over 100 sampling locations. Results show that although the background metal concentrations (of both dissolved and suspended metal forms) in the alkaline Selenga River waters were close to the world averages, metal concentrations of up to two orders of magnitude above the background values were seen for Zn, As, Cd, Cu, Mo, and Pb in regions subject to anthropogenic impacts (cities and the mining industry). Specifically, dissolved As levels within the Selenga River basin were 2–5 times higher than the world average and well above the global guideline value in several regions. Notable hotspots for anthropogenic impacts of Cd were particularly found in Zakamensk and Ulaanbaatar. Our results highlight clear anthropogenic impacts and large-scale spreading of several pollutants of concern, with risks even to downstream parts including the Selenga delta and Lake Baikal. We expect that these results will aid in increasing the understanding of large-scale metal transport processes, as well as for designing relevant measures to mitigate further spreading of metals to Lake Baikal. Full article

In this study, the downstream effects of pollutants spreading due to hydromorphological gradients and associated changes in sediment transport conditions along the braided-meandering and deltaic distributary reach of a large river downstream section are discussed. We demonstrate the significance of hydrodynamic control for sediment-associated metal partitioning along the river. Typically, the downward decline of the sediment and metals spreading towards Lake Baikal is observed due to buffer effects in the delta. During peak flow, the longitudinal gradients in heavy metal concentration along the distributary delta reach are neglected due to higher concentrations delivered from the upper parts of the river. In particular, significant variations of heavy metal concentrations associated with the river depth are related to sediment concentration and flow velocity profiles. Various particulate metal behavior in silt-sand delta channels and the sand–gravel Selenga main stem emphasize the importance of near-bottom exchange for particles spreading with the river flow. Using empirically derived Rouse numbers, we found quantitative relationships between the ratio of particulate metals sorting throughout depth in a single river channel and the hydrodynamic conditions of sediment transport. Full article

Classifying and mapping natural systems such as wetlands using remote sensing frequently relies on data derived from regions of interest (ROIs), often acquired during field campaigns. ROIs tend to be heterogeneous in complex systems with a variety of land cover classes. However, traditional supervised image classification is predicated on pure single-class observations to train a classifier. This ultimately encourages end-users to create single-class ROIs, nudging ROIs away from field-based points or gerrymandering the ROI, which may produce ROIs unrepresentative of the landscape and potentially insert error into the classification. In this study, we explored WorldView-2 images and 228 field-based data points to define ROIs of varying heterogeneity levels in terms of class membership to classify and map 22 discrete classes in a large and complex wetland system. The goal was to include rather than avoid ROI heterogeneity and assess its impact on classification accuracy. Parametric and nonparametric classifiers were tested with ROI heterogeneity that varied from 7% to 100%. Heterogeneity was governed by ROI area, which we increased from the field-sampling frame of ~100 m² nearly 19-fold to ~2124 m². In general, overall accuracy (OA) tended downwards with increasing heterogeneity but stayed relatively high until extreme heterogeneity levels were reached. Moreover, the differences in OA were not statistically significant across several small-to-large heterogeneity levels. Per-class user’s and producer’s accuracies behaved similarly. Our findings suggest that ROI heterogeneity did not harm classification accuracy unless heterogeneity became extreme, and thus there are substantial practical advantages to accommodating heterogeneous ROIs in image classification. Rather than attempting to avoid ROI heterogeneity by gerrymandering, classification in wetland environments, as well as analyses of other complex environments, should embrace ROI heterogeneity. Full article

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (n = 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the classification OA. However, including vegetation/soil/water metrics (based on WorldView-2 band combinations), hydrogeomorphic data layers, and elevation data layers to increase the descriptive content of the input parameters surprisingly did not markedly improve the OA. We conclude that, in most cases, RF should be the classifier of choice. The potential exception to this recommendation is under the circumstance where the end user requires narrative rules to best manage his or her resource. Though not useful in this study, continuously increasing satellite imagery resolution and band availability suggests the inclusion of ancillary contextual data layers such as soil metrics or elevation data, the granularity of which may define its utility in subsequent wetland classifications. Full article

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system. Full article