| 1. |
- Abd El‑Hameed, Mona M., et al.
(författare)
-
Phycoremediation of contaminated water by cadmium (Cd) using two cyanobacterial strains (Trichormus variabilis and Nostoc muscorum)
- 2021
-
Ingår i: Environmental Sciences Europe. - Germany : Springer Nature. - 2190-4707 .- 2190-4715. ; 33:1
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundWater pollution with heavy metals is a severe dilemma that concerns the whole world related to its risk to natural ecosystems and human health. The main objective was to evaluate the removal efficiency of Cd of various concentrations from contaminated aqueous solution by use of two cyanobacterial strains (Nostoc muscorum and Trichormus variabilis). For this purpose, a specially designed laboratory pilot-scale experiment was conducted using these two cyanobacterial strains on four different initial concentrations of Cd (0, 0.5, 1.0 and 2.0 mg L−1) for 21 days.ResultsN. muscorum was more efficient than T. variabilis for removing Cd (II), with the optimum value of residual Cd of 0.033 mg L−1 achieved by N. muscorum after 21 days with initial concentration of 0.5 mg L−1, translating to removal efficiency of 93.4%, while the residual Cd (II) achieved by T. variabilis under the same conditions was 0.054 mg L−1 (89.13% removal efficiency). Algal growth parameters and photosynthetic pigments were estimated for both cyanobacterial strains throughout the incubation period.ConclusionsHigh Cd concentration had a more toxic impact on algal growth. The outcomes of this study will help to produce treated water that could be reused in agrarian activities.
|
|
| 2. |
- Mokhtar, Ali, et al.
(författare)
-
Prediction of irrigation water quality indices based on machine learning and regression models
- 2022
-
Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12
-
Tidskriftsartikel (refereegranskat)abstract
- Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies. The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. To achieve this goal, three artificial intelligence (AI) models (Support vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression models (Stepwise Regression, SW; Principal Components Regression, PCR; Partial least squares regression, PLS; Ordinary least squares regression, OLS) were applied and validated for predicting six irrigation water quality criteria (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; residual sodium carbonate, RSC; potential of salinity, PS; permeability index, PI; Kelly’s ratio, KR). Electrical conductivity (EC), sodium (Na+), calcium (Ca2+) and bicarbonate (HCO3−) were used as input exploratory variables for the models. The results indicated the water source is not suitable for irrigation without treatment. A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. Based on the performance statistics of the root mean square error (RMSE) and the scatter index (SI), SW emerged as the best (0.21% and 0.03%) followed by PCR and PLS with RMSE 0.22% and 0.21% for SAR, respectively. Based on the classification of the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC. These results highlight potential of using multiple regressions and the developed machine learning methods in predicting the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality.
|
|
