@article{MTMT:34494893, title = {New approach into human health risk assessment associated with heavy metals in surface water and groundwater using Monte Carlo Method}, url = {https://m2.mtmt.hu/api/publication/34494893}, author = {Hemida, Mohamed Hamdy Eid and Eissa, Mustafa and Mohamed, Essam A. and Ramadan, Hatem Saad and Madarász, Tamás and Kovács, Attila and Szűcs, Péter}, doi = {10.1038/s41598-023-50000-y}, journal-iso = {SCI REP}, journal = {SCIENTIFIC REPORTS}, volume = {14}, unique-id = {34494893}, issn = {2045-2322}, abstract = {This study assessed the environmental and health risks associated with heavy metals in the water resources of Egypt's northwestern desert. The current approaches included the Spearman correlation matrix, principal component analysis, and cluster analysis to identify pollution sources and quality-controlling factors. Various indices (HPI, MI, HQ, HI, and CR) were applied to evaluate environmental and human health risks. Additionally, the Monte Carlo method was employed for probabilistic carcinogenic and non-carcinogenic risk assessment via oral and dermal exposure routes in adults and children. Notably, all water resources exhibited high pollution risks with HPI and MI values exceeding permissible limits (HPI > 100 and MI > 6), respectively. Furthermore, HI oral values indicated significant non-carcinogenic risks to both adults and children, while dermal contact posed a high risk to 19.4% of samples for adults and 77.6% of samples for children (HI > 1). Most water samples exhibited CR values exceeding 1 × 10 –4 for Cd, Cr, and Pb, suggesting vulnerability to carcinogenic effects in both age groups. Monte Carlo simulations reinforced these findings, indicating a significant carcinogenic impact on children and adults. Consequently, comprehensive water treatment measures are urgently needed to mitigate carcinogenic and non-carcinogenic health risks in Siwa Oasis.}, year = {2024}, eissn = {2045-2322} } @article{MTMT:34061120, title = {Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model}, url = {https://m2.mtmt.hu/api/publication/34061120}, author = {Elsayed, Salah and Gupta, Meenu and Chaudhary, Gopal and Taneja, Soham and Gaur, Harshit and Gad, Mohamed and Hemida, Mohamed Hamdy Eid and Kovács, Attila and Szűcs, Péter and Gaagai, Aissam and Schmidhalter, Urs}, doi = {10.51526/kbes.2023.4.1.55-77}, journal-iso = {Knowledge-based Engineering and Sciences}, journal = {Knowledge-based Engineering and Sciences}, volume = {4}, unique-id = {34061120}, issn = {2788-7820}, abstract = {The importance of soil temperature (ST) quantification can contribute to diverse ecological modelling processes as well as for agricultural activities. Over the literature, it was evident that soil supports more than 95% of living habitats and food production on earth, and this demand will increase to 500 years’ times in expected consumption in 2060. This paper aims to analyses the contrastive approach to predict the ST of a certain region with the help of different machine learning models, including Random Forest (RF), Support Vector, Neural Network (NN), Linear Regression (LR) and Long Short-Term Memory Network (LSTM). The study was utilized the hourly humidity, dew point, rainfall, solar radiation, and barometer readings for the formulation of the models. Various performance criteria were employed to evaluate the prediction skills of the models and the results depicted that the promising ability belong to LSTM despite the acceptable prediction accuracy achieved by other models. The modelling outcomes revealed that LSTM model attained the lowest root mean square error (RMSE = 3.3255) decreased the average prediction error by 6% with regards to NN (RMSE = 3.4796), SVM (RMSE = 3.5766), and RF (RMSE = 3.8128), and improved the prediction accuracy of LR by 15%. The model is in compliance with the latest machine learning industry standards and allows low-cost experimental performances on low powered edge computing devices.}, year = {2023}, eissn = {2788-7839}, pages = {55-77} } @article{MTMT:33699850, title = {A Combined Stochastic–Analytical Method for the Assessment of Climate Change Impact on Spring Discharge}, url = {https://m2.mtmt.hu/api/publication/33699850}, author = {Kovács, Attila and Stevanović, Zoran}, doi = {10.3390/w15040629}, journal-iso = {WATER-SUI}, journal = {WATER}, volume = {15}, unique-id = {33699850}, abstract = {This study describes a novel methodology for the prediction of spring hydrographs based on regional climate model (RCM) projections, with the goal of evaluating climate-change impact on karstic-spring discharge. A combined stochastic–analytical modeling methodology to predict spring discharge was developed and demonstrated on the Bukovica spring catchment at the Durmitor National Park, Montenegro. As a first step, climate model projections of the EURO-CORDEX ensemble were selected; and then bias correction was applied based on historical climate data. The regression function between rainfall and peak discharge was established by using historical data. Baseflow recession was described by using a double-component exponential model, where hydrograph decomposition and parameter fitting were performed on the Master Recession Curve. Rainfall time series from two selected RCM scenarios were applied to predict future spring-discharge time series. Bias correction of simulated hydrographs was performed, and bias-corrected combined stochastic–analytical models were applied to predict spring hydrographs based on RCM-simulated rainfall data. Both simulated climate scenarios predict increasing peak discharges and decreasing baseflow discharges throughout the 21st century. The model results suggest that climate change is likely to exaggerate the extremities both in terms of climate parameters and spring discharge by the end of the century both for moderate (RCP 45) and pessimistic (RCP 85) CO2 emission scenarios. To investigate the temporal distribution of extremities throughout the simulated time periods, the annual numbers of flood and drought days were calculated. Annual predicted flood days show an increasing trend during the first simulation period (2021–2050) and a slightly decreasing trend during the second simulation period (2071–2100), according to the RCP45 climate scenario. The same parameter shows a stagnant trend for the RCP 85 climate scenario. Annual predicted drought days show a decreasing trend both for the RCP 45 and RCP 85 climate scenarios. However, the annual number of drought days shows a large variation over time. There is a periodicity of extremely dry years with a frequency between 5 and 7 years. The number of drought days seems to increase over time during these extreme years. The study confirmed that the applied methodology can successfully be applied for spring-discharge prediction and that it offers a new prospect for its wider application in studying karst aquifers and their behavior under different climate-change scenarios.}, year = {2023}, eissn = {2073-4441}, orcid-numbers = {Stevanović, Zoran/0000-0001-6422-0254} } @article{MTMT:33634210, title = {Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study}, url = {https://m2.mtmt.hu/api/publication/33634210}, author = {Ibrahim, Hekmat and Yaseen, Zaher Mundher and Scholz, Miklas and Ali, Mumtaz and Gad, Mohamed and Elsayed, Salah and Khadr, Mosaad and Hussein, Hend and Ibrahim, Hazem H. and Hemida, Mohamed Hamdy Eid and Kovács, Attila and Szűcs, Péter and Khalifa, Moataz M.}, doi = {10.3390/w15040694}, journal-iso = {WATER-SUI}, journal = {WATER}, volume = {15}, unique-id = {33634210}, abstract = {Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.}, year = {2023}, eissn = {2073-4441}, pages = {694-719}, orcid-numbers = {Yaseen, Zaher Mundher/0000-0003-3647-7137; Scholz, Miklas/0000-0001-8919-3838; Ali, Mumtaz/0000-0002-6975-5159; Gad, Mohamed/0000-0002-8982-7201; Elsayed, Salah/0000-0002-5808-3561; Khadr, Mosaad/0000-0002-8322-061X; Hussein, Hend/0000-0002-2770-8899; Ibrahim, Hazem H./0000-0003-4274-8888; Khalifa, Moataz M./0000-0001-8134-1031} } @article{MTMT:33571440, title = {Synthesis of K+ and Na+ Synthetic Sodalite Phases by Low-Temperature Alkali Fusion of Kaolinite for Effective Remediation of Phosphate Ions: The Impact of the Alkali Ions and Realistic Studies. The Impact of the Alkali Ions and Realistic Studies}, url = {https://m2.mtmt.hu/api/publication/33571440}, author = {Bellucci, Stefano and Hemida, Mohamed Hamdy Eid and Kruppiné Fekete, Ilona and Szűcs, Péter and Kovács, Attila and Othman, Sarah I. and Ajarem, Jamaan S. and Allam, Ahmed A. and Abukhadra, Mostafa R.}, doi = {10.3390/inorganics11010014}, journal-iso = {INORGANICS}, journal = {INORGANICS}, volume = {11}, unique-id = {33571440}, abstract = {Two sodalite phases (potassium sodalite (K.SD) and sodium sodalite (Na.SD)) were prepared using alkali fusion of kaolinite followed by a hydrothermal treatment step for 4 h at 90 °C. The synthetic phases were characterized as potential adsorbents for PO43− from the aqueous solutions and real water from the Rákos stream (0.52 mg/L) taking into consideration the impact of the structural alkali ions (K+ and Na+). The synthetic Na.SD phase exhibited enhanced surface area (232.4 m2/g) and ion-exchange capacity (126.4 meq/100 g) as compared to the K.SD phase. Moreover, the Na.SD phase exhibited higher PO43− sequestration capacity (Qmax = 261.6 mg g−1 and Qsat = 175.3 mg g−1) than K.SD phase (Qmax = 201.9 mg g−1 and Qsat = 127.4 mg g−1). The PO43− sequestration processes of both Na.SD and K.SD are spontaneous, homogenous, and exothermic reactions that follow the Langmuir isotherm and pseudo-first-order kinetics. Estimation of the occupied active site density validates the enrichment of the Na.SD phase with high quantities of active sites (Nm = 86.1 mg g−1) as compared to K.SD particles (Nm = 44.4 mg g−1). Moreover, the sequestration and Gaussian energies validate the cooperation of physisorption and weak chemisorption processes including zeolitic ion exchange reactions. Both Na.SD and K.SD exhibit significant selectivity for PO43− in the coexisting of other common anions (Cl−, SO42−, HCO3−, and NO3−) and strong stability properties. Their realistic application results in the complete adsorption of PO43- from Rákos stream water after 20 min (Na. SD) and 60 min (K.SD).}, year = {2023}, eissn = {2304-6740}, orcid-numbers = {Bellucci, Stefano/0000-0003-0326-6368; Abukhadra, Mostafa R./0000-0001-5404-7996} } @article{MTMT:33571435, title = {Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria}, url = {https://m2.mtmt.hu/api/publication/33571435}, author = {Hemida, Mohamed Hamdy Eid and Elbagory, Mohssen and Tamma, Ahmed A. and Gad, Mohamed and Elsayed, Salah and Hussein, Hend and Moghanm, Farahat S. and Omara, Alaa El-Dein and Kovács, Attila and Szűcs, Péter}, doi = {10.3390/w15010182}, journal-iso = {WATER-SUI}, journal = {WATER}, volume = {15}, unique-id = {33571435}, abstract = {Irrigation has made a significant contribution to supporting the population’s expanding food demands, as well as promoting economic growth in irrigated regions. The current investigation was carried out in order to estimate the quality of the groundwater for agricultural viability in the Algerian Desert using various water quality indices and geographic information systems (GIS). In addition, support vector machine regression (SVMR) was applied to forecast eight irrigation water quality indices (IWQIs), such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), Kelly index (KI), permeability index (PI), potential salinity (PS), permeability index (PI), and residual sodium carbonate (RSC). Several physicochemical variables, such as temperature (T°), hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), K+, Na2+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, were measured from 45 deep groundwater wells. The hydrochemical facies of the groundwater resources were Ca–Mg–Cl/SO4 and Na–Cl−, which revealed evaporation, reverse ion exchange, and rock–water interaction processes. The IWQI, Na%, SAR, SSP, KI, PS, PI, and RSC showed mean values of 50.78, 43.07, 4.85, 41.78, 0.74, 29.60, 45.65, and −20.44, respectively. For instance, the IWQI for the obtained results indicated that the groundwater samples were categorized into high restriction to moderate restriction for irrigation purposes, which can only be used for plants that are highly salt tolerant. The SVMR model produced robust estimates for eight IWQIs in calibration (Cal.), with R2 values varying between 0.90 and 0.97. Furthermore, in validation (Val.), R2 values between 0.88 and 0.95 were achieved using the SVMR model, which produced reliable estimates for eight IWQIs. These findings support the feasibility of using IWQIs and SVMR models for the evaluation and management of the groundwater of complex terminal aquifers for irrigation. Finally, the combination of IWQIs, SVMR, and GIS was effective and an applicable technique for interpreting and forecasting the irrigation water quality used in both arid and semi-arid regions.}, year = {2023}, eissn = {2073-4441}, orcid-numbers = {Elbagory, Mohssen/0000-0002-0400-600X; Gad, Mohamed/0000-0002-8982-7201; Elsayed, Salah/0000-0002-5808-3561; Hussein, Hend/0000-0002-2770-8899; Moghanm, Farahat S./0000-0003-3318-5358; Omara, Alaa El-Dein/0000-0001-5622-7501} } @inproceedings{MTMT:33535343, title = {HYDROGEOCHEMICAL EVALUATION OF GROUNDWATER AND ITS SUITABILITY FOR DRINKING AND IRRIGATION USING WATER QUALITY INDEX, STATISTICAL AND GEOCHEMICAL MODELLING, NORTH EASTERN DESERT OF ALGERIA}, url = {https://m2.mtmt.hu/api/publication/33535343}, author = {Hemida, Mohamed Hamdy Eid and Ahmed, A. Tamma and Szűcs, Péter and Kovács, Attila}, booktitle = {XXII Conference of PhD Students and Young Scientists}, unique-id = {33535343}, year = {2022}, pages = {45-49} } @article{MTMT:33364606, title = {Problems threatening sustainability in Siwa Oasis and recommendations for understanding the sources of water quality deterioration}, url = {https://m2.mtmt.hu/api/publication/33364606}, author = {Hemida, Mohamed Hamdy Eid and Szűcs, Péter and Kovács, Attila}, doi = {10.33030/geosciences.2022.15.138}, journal-iso = {GEOSCIENCES AND ENGINEERING}, journal = {GEOSCIENCES AND ENGINEERING: A PUBLICATION OF THE UNIVERSITY OF MISKOLC}, volume = {10}, unique-id = {33364606}, issn = {2063-6997}, year = {2022}, pages = {138-153} } @inproceedings{MTMT:32262894, title = {Pricking Probe (PriP) method and its applicability}, url = {https://m2.mtmt.hu/api/publication/32262894}, author = {Szalai, Sándor and Kuslics, Lukács and Kis, Árpád and Lemperger, István and Baracza, Mátyás Krisztián and Kovács, Attila}, booktitle = {Proceedings of 6th International Conference on Geotechnical and Geophysical Site Characterisation}, unique-id = {32262894}, year = {2021} } @article{MTMT:32082237, title = {Long-term time series of environmental tracers reveal recharge and discharge conditions in shallow karst aquifers in Hungary and Slovakia}, url = {https://m2.mtmt.hu/api/publication/32082237}, author = {Palcsu, László and Gessert, A and Túri, Marianna and Kovács, Attila and Futó, István and Orsovszki, J and Puskás-Preszner, Anita and Temovski, Marjan and Koltai, Gabriella}, doi = {10.1016/j.ejrh.2021.100858}, journal-iso = {J HYDROL-REG STUD}, journal = {JOURNAL OF HYDROLOGY: REGIONAL STUDIES}, volume = {36}, unique-id = {32082237}, year = {2021}, eissn = {2214-5818}, orcid-numbers = {Gessert, A/0000-0001-7239-4174} }