RESUMEN
Shrinkage of lakes is considered a serious ecological challenge. The impact of this phenomenon on the environment can be devastating. Monitoring the spatiotemporal variations of lake's water surface and its salinization is essential for planning and adopting mitigation measures. In this study, a new multiscale-kernel-based method was used to assess the spatiotemporal variations of the shrinkage of the Urmia Lake and develop soil salinity vulnerability map for its basin. For this aim, remote sensing, empirical wavelet transform (EWT), differential symbolic entropy (DSE), and Gaussian regression process (GPR) techniques were used. Vulnerable areas were identified using geo-environmental parameters extracted from the in situ observations and satellite datasets. In the next step, considering three time periods including the lake normal period, lake drying period, and lake restoration period, the variations in the quality of groundwater were investigated. Results showed that the east and south sections of the lake were more prone to severe salinization. Saline lands caused negative impacts on air quality and agricultural activities in these areas. It was found that both climate change and human activities had contributed to the shrinking of the lake. Results showed that the quality of groundwater in the area around the lake has been affected by the excessive salinity of the lake water and the encroachment process of saline water. The water quality index has increased during the drying period of the lake and caused negative effects on the quality of water used for drinking and agricultural activities. In the lake restoration period, a slight increase in water quality was observed.
Asunto(s)
Agua Subterránea , Lagos , Humanos , Suelo , Monitoreo del Ambiente/métodos , SalinidadRESUMEN
Wastewater treatment plants (WWTPs) are highly complicated and dynamic systems and so their appropriate operation, control, and accurate simulation are essential. The simulation of WWTPs according to the process complexity has become an important issue in growing environmental awareness. In recent decades, artificial intelligence approaches have been used as effective tools in order to investigate environmental engineering issues. In this study, the effluent quality of Tabriz WWTP was assessed using two intelligence models, namely support Vector Machine (SVM) and artificial neural network (ANN). In this regard, several models were developed based on influent variables and tested via SVM and ANN methods. Three time scales, daily, weekly, and monthly, were investigated in the modeling process. On the other hand, since applied methods were sensitive to input variables, the Monte Carlo uncertainty analysis method was used to investigate the best-applied model dependability. It was found that both models had an acceptable degree of uncertainty in modeling the effluent quality of Tabriz WWTP. Next, ensemble approaches were applied to improve the prediction performance of Tabriz WWTP. The obtained results comparison showed that the ensemble methods represented better efficiency than single approaches in predicting the performance of Tabriz WWTP.
Asunto(s)
Eliminación de Residuos Líquidos , Purificación del Agua , Inteligencia Artificial , Redes Neurales de la Computación , Incertidumbre , Aguas ResidualesRESUMEN
In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO3) and sulfate (SO4) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from - 0.35 to 0.43 for NO3 and from - 0.28 to 0.33 for SO4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO3 and SO4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO4). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.