Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 362, P. 110346 - 110346
Published: Dec. 6, 2024
Language: Английский
Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 362, P. 110346 - 110346
Published: Dec. 6, 2024
Language: Английский
Soil & Environmental Health, Journal Year: 2024, Volume and Issue: 2(2), P. 100080 - 100080
Published: April 2, 2024
Phytoremediation of contaminated soil is an environmentally friendly approach to minimize the contamination nutrients and heavy metals in ecosystem. Hence, selecting appropriate plants with phytoextraction potential paramount remediating soils. This study aimed investigate nutrient metal contents four natural aquatic i.e. Cyperus rotundus (L), Eleocharis dulcis (Burm.f.), Typha angustifolia (L.), Schoenoplectus grossus (L. F.) grown meadow a small reservoir at Mankadawala Anuradhapura, Sri Lanka, assess their ability. Nutrient these were assessed using plant samples collected 32 sampling points meadow. Biological concentration, accumulation, translocation factors determined element mobility Total K, Na, Mg, Ca, Zn, Cu, Fe, Mn, As, Pb, Cd measured Inductivity Couple Plasma Optical Emission Spectrophotometer. ANCOVA, mean separation by Bonferroni 95% confidence level, was used as statistical test best type terms absorption. Shoots exhibited significantly greater values (p<0.05) for P, Cd, Fe than roots. translocation, concentration not different among species. However, >1 all species, indicating ability be hyperaccumulators. angustifolia, its' high accumulation highest aesthetic appeal, selected overall wetland species phytoremediation purposes.
Language: Английский
Citations
2Scientifica, Journal Year: 2024, Volume and Issue: 2024(1)
Published: Jan. 1, 2024
Rapid industrialization, mining, and other anthropogenic activities have poisoned our environment with heavy metals, negatively impacting all forms of life. Heavy metal pollution causes physiological neurological disorders, as metals are endocrine disrupters, carcinogenic, teratogenic. Therefore, it becomes mandatory to address the challenge contamination on a global scale. Physical chemical approaches been employed for pollutant removal detoxification, but these methods cannot be adopted universally due high cost, labor intensiveness, possible negative impact natural microflora. Phytoremediation is one preferred safest environmental management its efficiency low cost investment. The plant can uptake pollutants from water soil through an intense root network via rhizofiltration process phytostabilization, phytovolatilization, accumulation. At cellular level, phytoremediation relies mechanisms cells, e.g., absorption, transpiration, intracellular storage, accumulation counter detrimental effects pollutants. It widely accepted because novelty, efficiency; however, comparatively slower. In addition, plants store long time again become at end life cycle. current review summarizes potential cure pollutants, released well sources. will provide insight into advancement evolution advanced techniques like nanoremediation that improve rate phytoremediation, along making sustainable, cost‐effective, economically viable.
Language: Английский
Citations
1Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 17, 2024
Language: Английский
Citations
0Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3488 - 3488
Published: Dec. 3, 2024
Streamflow forecasting is of great importance in water resource management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time-series data on flows, weather patterns, other relevant factors, machine models learn patterns relationships present predictions about future flows. In this study, an autoregressive integrated moving average (ARIMA) model was constructed predict the monthly flows Athabasca River at three monitoring stations: Hinton, Athabasca, Fort MacMurray Alberta, Canada. The stations upstream, midstream, downstream were selected represent different climatological regimes River. Time-series used for training identify correlations using averages, exponential smoothing, Holt–Winters’ method. model’s compared against observed data. results show that determination coefficients 0.99 all stations, indicating strong correlations. root mean square errors (RMSEs) 26.19 61.1 15.703 MacMurray, respectively, absolute percentage (MAPEs) 0.34%, 0.44%, 0.14%, respectively. Therefore, ARIMA captured seasonality trends stream demonstrated a robust performance hydrological This provides insights
Language: Английский
Citations
0Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 362, P. 110346 - 110346
Published: Dec. 6, 2024
Language: Английский
Citations
0