TY - JOUR AU - Balla, Alexia AU - Teofilovic, Vesna AU - Kiss, Tímea TI - Microplastic Contamination of Fine-Grained Sediments and Its Environmental Driving Factors along a Lowland River: Three-Year Monitoring of the Tisza River and Central Europe JF - HYDROLOGY J2 - HYDROLOGY-BASEL VL - 11 PY - 2024 IS - 1 SP - 11 SN - 2306-5338 DO - 10.3390/hydrology11010011 UR - https://m2.mtmt.hu/api/publication/34522726 ID - 34522726 AB - The hydro-geomorphological background in microplastic (MP) deposition and mobilization is often neglected, though the sampling environment is the key point in a monitoring scheme. The aim of the study was to analyze the environmental driving factors of MP transport over three years (2020–2022) along a 750 km-long section of the Tisza River, Central Europe. The mean MP content of the fresh clayey sediments was 1291 ± 618 items/kg in 2020, and then it decreased (2021: 730 ± 568 items/kg; 2022: 766 ± 437 items/kg). The upstream and downstream sections were the most polluted due to improper local sewage treatment. In 2020, 63% of the sites were hotspot (≥2000 items/kg), but their number decreased to one-third in 2021 and 2022. MP pollution is influenced by highly variable environmental factors. (1) The geomorphological setting of a site is important, as most of the hotspots are on side bars. (2) The tributaries convey MP pollution to the Tisza River. (3) The bankfull or higher flood waves effectively rearrange the MP pollution. (4) The dams and their operation influence the downstream trend of MP pollution in the reservoir. (5) Downstream of a dam, the clear-water erosion increases the proportion of the pristine sediments; thus, the MP concentration decreases. LA - English DB - MTMT ER - TY - JOUR AU - Vizi, Zsolt AU - Batki, Bálint AU - Rátki, Luca AU - Szalánczi, S AU - Fehérváry, István AU - Kozák, Péter AU - Kiss, Tímea TI - Water level prediction using long short-term memory neural network model for a lowland river: a case study on the Tisza River, Central Europe JF - ENVIRONMENTAL SCIENCES EUROPE J2 - ENVIRON SCI EUR VL - 35 PY - 2023 IS - 1 PG - 18 SN - 2190-4707 DO - 10.1186/s12302-023-00796-3 UR - https://m2.mtmt.hu/api/publication/34531362 ID - 34531362 LA - English DB - MTMT ER - TY - JOUR AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kovács, Ferenc AU - Kiss, Tímea TI - Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy JF - SENSORS J2 - SENSORS-BASEL VL - 23 PY - 2023 IS - 23 PG - 26 SN - 1424-8220 DO - 10.3390/s23239505 UR - https://m2.mtmt.hu/api/publication/34407342 ID - 34407342 AB - Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy. LA - English DB - MTMT ER - TY - JOUR AU - Kiss, Tímea AU - Fehérváry, István TI - Increased Riparian Vegetation Density and Its Effect on Flow Conditions JF - SUSTAINABILITY J2 - SUSTAINABILITY-BASEL VL - 15 PY - 2023 IS - 16 PG - 21 SN - 2071-1050 DO - 10.3390/su151612615 UR - https://m2.mtmt.hu/api/publication/34107572 ID - 34107572 AB - The physical and biological structure of riparian vegetation fundamentally influences floodplain roughness, and thus the flood velocity and flood levels of a river. The study aims to provide detailed spatial data on the vegetation density of a floodplain, and to model the effect of the actual vegetation and various scenarios on flow conditions. LiDAR data were applied to evaluate the density and roughness of the submerged understory vegetation over the densely vegetated floodplain of Lower Tisza, Hungary. Then, HEC–RAS 2D modelling was applied to analyse the effect of the actual vegetation on flow conditions. Further scenarios were also created to predict the effect of (i) invasive plant control, (ii) no maintenance, and (iii) riparian vegetation restoration (meadows). According to the results, since the 19th Century, the increased vegetation density is responsible for a 17-cm flood level increase, and if the vegetation grows even denser, a further 7 cm could be expected. As the vegetation density increases, the overbank flow velocity decreases, and the crevasses and flood conveyance zones gradually lose their function. Simultaneously, the flow velocity increases in the channel (from 1 m/s to 1.4 m/s), resulting in an incision. Applying LiDAR-based 2D flow modelling makes it possible to plan sustainable riparian vegetation maintenance (e.g., forestry, invasive species clearance) from both ecology and flood control perspectives. LA - English DB - MTMT ER - TY - JOUR AU - Saadu, Ibrahim AU - Farsang, Andrea AU - Kiss, Tímea TI - Quantification of macroplastic litter in fallow greenhouse farmlands: case study in southeastern hungary JF - ENVIRONMENTAL SCIENCES EUROPE J2 - ENVIRON SCI EUR VL - 35 PY - 2023 IS - 1 PG - 13 SN - 2190-4707 DO - 10.1186/s12302-023-00777-6 UR - https://m2.mtmt.hu/api/publication/34094864 ID - 34094864 N1 - Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem Str. 2-6, Szeged, 6722, Hungary Geography Department, Usmanu Danfodiyo University, P.M.B, Sokoto, 2346, Nigeria Cited By :1 Export Date: 22 January 2024 Correspondence Address: Saadu, I.; Department of Geoinformatics, Egyetem Str. 2-6, Hungary; email: sibrahim@geo.u-szeged.hu Funding text 1: We thank the owners of the greenhouse farmlands for granting us access to their respective farmlands. We also acknowledge the efforts of Dr. Károly Barta and László Makó, who helped with the fieldwork during this research. We thank Dr. Krisztián Fintor of the Department of Mineralogy, Geochemistry, and Petrology, University of Szeged, for his assistance with the Raman spectroscopy analysis. LA - English DB - MTMT ER - TY - JOUR AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Balla, Alexia AU - Kiss, Tímea TI - High spatiotemporal resolution analysis on suspended sediment and microplastic transport of a lowland river JF - SCIENCE OF THE TOTAL ENVIRONMENT J2 - SCI TOTAL ENVIRON VL - 902 PY - 2023 PG - 15 SN - 0048-9697 DO - 10.1016/j.scitotenv.2023.166188 UR - https://m2.mtmt.hu/api/publication/34093571 ID - 34093571 LA - English DB - MTMT ER - TY - JOUR AU - Bartyik, Tamás AU - Urdea, P AU - Kiss, Tímea AU - Hegyi, A AU - Sipos, György TI - The Role of Past Climatic Variability in Fluvial Terrace Formation, a Case Study from River Mureş (Maros), Romania JF - QUATERNARY J2 - Quaternary VL - 6 PY - 2023 IS - 2 PG - 16 SN - 2571-550X DO - 10.3390/quat6020035 UR - https://m2.mtmt.hu/api/publication/34007585 ID - 34007585 AB - Fluvial terrace formation is a complex process governed by the interplay of climatic and tectonic forcings. From a climatic perspective, an incision is usually related to climatic transitions, while valley aggradation is attributed to glacial periods. We have reconstructed the formation of Late Pleistocene fluvial terraces along the middle, mountainous section of a temperate zone river (Mureş/Maros) in order to identify the roles of different climatic periods and potential vertical displacement in terrace development. Investigations were based on two profiles representing two different terrace levels. The profiles were subjected to sedimentological and detailed geochronological analyses using optically stimulated luminescence (OSL). The results indicated that the investigated terraces represent different incision events coinciding with climatic transition periods. However, a joint MIS 3 valley aggradation period can be identified at both of them. Thus, the relatively mild but highly variable climate of the MIS 3 facilitated sediment mobilization from upland catchments. On the other hand, there is no evidence of aggradation under the cold and stable climate of MIS 2. However, the tectonic setting favours incision at the site. Based on our results, we concluded that the timing of the main events was controlled primarily by climatic forcing. The terrace formation model recognised might also be applied at other rivers in the region. LA - English DB - MTMT ER - TY - JOUR AU - Sheishah, Diaa Elsayed Hamed Abdallah Hamed AU - Kiss, Tímea AU - Borza, T. AU - Fiala, K. AU - Kozák, P. AU - Abdelsamei, Enas AU - Tóth, Csaba AU - Grenerczy, G. AU - Páll, D.G. AU - Sipos, György TI - Mapping subsurface defects and surface deformation along the artificial levee of the Lower Tisza River, Hungary JF - NATURAL HAZARDS J2 - NAT HAZARDS VL - 117 PY - 2023 IS - 2 SP - 1647 EP - 1671 PG - 25 SN - 0921-030X DO - 10.1007/s11069-023-05922-1 UR - https://m2.mtmt.hu/api/publication/33879836 ID - 33879836 N1 - Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem U. 2-6., Szeged, 6722, Hungary National Research Institute of Astronomy and Geophysics, El Marsad St., Helwan, Cairo, 11421, Egypt Lower Tisza District Water Directorate, Stefánia 4., Szeged, 6720, Hungary Department of Highway and Railway Engineering, Budapest University of Technology and Economics, Műegyetem Rakpart 3., Budapest, 1111, Hungary Geo-Sentinel Ltd., Kacsoh Pongrac U. 13., God, 2132, Hungary Export Date: 26 May 2023 Correspondence Address: Sipos, G.; Department of Geoinformatics, Egyetem U. 2-6., Hungary; email: gysipos@geo.u-szeged.hu LA - English DB - MTMT ER - TY - JOUR AU - Liro, Maciej AU - Zielonka, Anna AU - van Emmerik, Tim H.M. AU - Grodzińska-Jurczak, Małgorzata AU - Liro, Justyna AU - Kiss, Tímea AU - Mihai, Florin-Constantin TI - Mountains of plastic: Mismanaged plastic waste along the Carpathian watercourses JF - SCIENCE OF THE TOTAL ENVIRONMENT J2 - SCI TOTAL ENVIRON VL - 888 PY - 2023 PG - 12 SN - 0048-9697 DO - 10.1016/j.scitotenv.2023.164058 UR - https://m2.mtmt.hu/api/publication/33862864 ID - 33862864 LA - English DB - MTMT ER - TY - JOUR AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kiss, Tímea AU - Kovács, Ferenc TI - Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery JF - ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH J2 - ENVIRON SCI POLLUT R VL - 30 PY - 2023 IS - 25 SP - 67742 EP - 67757 PG - 16 SN - 0944-1344 DO - 10.1007/s11356-023-27068-0 UR - https://m2.mtmt.hu/api/publication/33785558 ID - 33785558 AB - Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary. LA - English DB - MTMT ER -