Winter arsenic pollution in 10 forest ecosystems in the mountainous border regions of the Czech Republic

Environmental Science and Pollution Research - Arsenic (As) concentrations and deposition fluxes were measured in snow and rime at 10 mountain-top sites near the borders between the Czech Republic...     

Dynamic effects of fiscal and monetary policy instruments on environmental pollution in ASEAN

Environmental Science and Pollution Research - This study aims to re-examine the impacts of monetary and fiscal policy on environmental quality in ASEAN countries from 1990 to 2019. We utilized the...     

Is ionizing radiation effective in removing pharmaceuticals from wastewater?

Environmental Science and Pollution Research - Wastewater and effluent discharges are the main causes of receiving water body pollution and important challenges in water quality management. Among...     

Simultaneous removal of metronidazole and Pb(II) from aqueous solution onto bifunctional activated carbons

Environmental Science and Pollution Research - In this work, it was analyzed the behavior of three commercial activated carbons with different textural and chemical properties to adsorb...     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.     

Carbon stocks and losses to deforestation in protected areas in Brazilian Amazonia

Regional Environmental Change - By 2014 approximately 2.2 million km2 (~43%) of Brazil’s Legal Amazonia region had been incorporated into an extensive network of 718 protected areas, which...     

An integrated approach for assessing the migration behavior of chlorpyrifos and carbaryl in the unsaturated soil zone

Chlorpyrifos (O, O-diethyl O-3,5,6-trichloropyridin-2-yl phosphorothioate) and carbaryl (1-naphthyl methylcarbamate) are often applied concurrently as insecticides in food production. The aim of this study was to research their migration behavior in a real environment. We researched the leaching of both pesticides by setting up field lysimeters on a farm with the typical soil used in fruit production today. In order to analyze the variables involved in this process, we performed complementary adsorption studies, we performed complementary adsorption studies using batches and undisturbed soil laboratory columns for both compounds. The results for pesticide transport through the lysimeters showed that less than 1% of chlorpyrifos was recovered in the leachates, while almost 17% was recovered for carbaryl. Having completed the experiment in undisturbed laboratory columns, soil analysis showed that chlorpyrifos mainly remained in the first 5 cm, while carbaryl moved down to the lower sections. These results can be explained in view of the sorption coefficient values (K_D) obtained in horizons A and B for chlorpyrifos (393 and 184 L kg^?1) and carbaryl (3.1 and 4.2 L kg^?1), respectively. By integrating the results obtained in the different approaches, we were able to characterize the percolation modes of these pesticides in the soil matrix, thus contributing to the sustainable use of resources.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.