Volume 18 - 2016

During the last century, the European Alps have faced intense socio-economic changes, which have led to respective land-use changes with immediate impact on ecosystem services (ESs). The aim of the study is to present the land-use changes and their effects on the economical contribution of ESs in the alpine environment of Ledro Valley, in Northern Italy. Data were collected through historical cartography and photographic material from 1859, 1973 and 2011. The analysis of landscape evolution highlighted the forest expansion, and partially the urban expansion, at the expense of grasslands and croplands due to a transition from a rural to a touristic and handcrafting economy. The land-use changes led to an overall reduction of the economical contribution of ESs, while further analysis on individual services identified advantages and disadvantages caused by the re-naturalization process of forest expansion. The results suggested that the profit maximization of ESs under the pressure of such land-use changes can be achieved by a) a respective profit maximization from services related to recreation activities (tourism) which have an immediate economical impact on local economies and b) the maintenance and sustainable management of the typical rural landscape and grasslands in order to preserve some of their economical benefits.

Estradiol is known as the indicator of the presence of hormones as Endocrine Disruptor Compounds (EDCs) in water and wastewater. The entrance of these compounds into water resources through daily liquid wastes of societies as well as pharmaceutical industries, ranching, and pathology laboratories leads to an increase in their concentrations in these resources. Consequently, due to the metabolic properties of these substances they can cause adverse effects on consumers and aquatics. The main purpose of this research is to determine the occurrence and the fate of estradiol as indicator of endocrine-disrupting compounds in drinking water, surface water, and wastewater in Ahvaz, Iran. 17β-estradiol was detected in 37.5% of samples obtained from drinking water and 68.75% of samples from the Karun River. In the urban drinking water and Karun River, the mean concentrations of 17β-estradiol were 2.96 and 13.66
ng l-1, respectively.

In the domestic and industrial wastewater, the concentrations of 17β-estradiol were higher than that detected in the drinking and surface waters. The mean concentrations of this hormone in these wastewaters were 57.46 and 70.6 ng l-1, respectively. The highest amount of 17β-estradiol was measured in the hospital wastewater. The fate of 17β-estradiol in the slaughterhouse wastewater treatment plant, in which a septic tank, an anaerobic pond, and an aerobic tank were used for wastewater treatment, was higher than 75%. The highest level of removal in the aerobic stage was also obtained by breaking estradiol down to other metabolites.

Two different methodologies for hydraulic head simulation were compared in this study. The first methodology is a classic numerical groundwater flow simulation model, Princeton Transport Code (PTC), while the second one is a black-box approach that uses Artificial Neural Networks (ANNs). Both methodologies were implemented in the Bavaria region in Germany at thirty observation wells. When using PTC, meteorological and geological data are used in order to compute the simulated hydraulic head following the calibration of the appropriate model parameters. The ANNs use meteorological and hydrological data as input parameters. Different input parameters and ANN architectures were tested and the ANN with the best performance was compared with the PTC model simulation results. One ANN was trained for every observation well and the hydraulic head change was simulated on a daily time step. The performance of the two models was then compared based on the real field data from the study area. The cases in which one model outperforms the other were summarized, while the use of one instead of the other depends on the application and further use of the model. 

Natural organic matter (NOM) has been identified as the prominent precursor for disinfection by-products (DBPs) formation during chlorination. Various studies have shown that the characteristics of NOM influence the Trihalomethanes (THMs) formation mechanism. The present study represents NOM categorization in terms of total organic carbon (TOC), dissolved organic carbon (DOC), UV absorbance at 254 nm wavelengths (UV254) and specific ultraviolet absorbance (SUVA) to investigate the effects of NOM on THMs formation mechanism. The high rate of dependency was found for each representative of NOM with respect to water quality characteristics and operational condition of disinfection process. Values of SUVA and UV254 is drastically reduced with respect from higher to a moderate chlorine dose which represent the chlorine contact is more predominant with hydrophobic fractions of NOM. The value of SUVA is decreasing with respect to temperature and reaction time, which reveled higher rate of utilization for hydrophobic fractions of NOM. Predictive modeling approach was carried out using multiple regression analysis with the combination of two surrogates at each stage of modeling with help of operational condition of disinfection process and water quality characteristics. The R2 value of the model was found in the range of 0.927 to 0.937 from the developed model and a model could be recommended for prediction of THMs in drinking water.