Spatial Variability of Rare Earth Elements in Groundwater in the Vicinity of a Coal-Fired Power Plant and Associated Health Risk.

This study investigated the occurrence and distribution of rare earth elements (REEs), including 14 lanthanoids, scandium (Sc), and yttrium (Y), in groundwater around a large coal-fired thermal power plant (TPP). The ICP-MS technique was used to analyze 16 REEs in groundwater samples collected from monitoring wells. REE concentrations ranged from 59.

Risk Evaluation of Pollutants Emission from Coal and Coal Waste Combustion Plants and Environmental Impact of Fly Ash Landfilling.

Emission factors (EFs) of gaseous pollutants, particulate matter, certain harmful trace elements, and polycyclic aromatic hydrocarbons (PAHs) from three thermal power plants (TPPs) and semi-industrial fluidized bed boiler (FBB) were compared. EFs of particulate matter, trace elements (except Cd and Pb), benzo[a]pyrene, and benzo[b]fluoranthene exceed the upper limits specified in the EMEP inventory guidebook for all combustion facilities. The comparison of trace elements and PAHs content in fly ashes (FAs) from lignite and coal waste combustion in TPPs and FBB, respectively, as well as the potential environmental impact of FAs disposal, was performed by employing a set of ecological indicators such as crustal enrichment factor, risk assessment code, risk indices for trace elements, and benzo[a]pyrene equivalent concentration for PAHs.

Physico-chemical, structural, and adsorption properties of amino-modified starch derivatives for the removal of (in)organic pollutants from aqueous solutions.

In this study, an environmentally sustainable process of crystal violet, congo red, methylene blue, brilliant green, Pb, Cd, and Zn ions adsorption from aqueous solutions onto amino-modified starch derivatives was investigated. The degree of substitution, elemental analysis, swelling capacity, solubility, and FTIR, XRD, and SEM techniques were used to characterize the adsorbents. The influence of pH, contact time, temperature, and initial concentration has been studied to optimize the adsorption conditions.

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things.

With the convergence of information technology (IT) and operational technology (OT) in Industry 4.0, edge computing is increasingly relevant in the context of the Industrial Internet of Things (IIoT). While the use of simulation is already the state of the art in almost every engineering discipline, e.

A green approach to starch modification by solvent-free method with betaine hydrochloride.

In this study, a novel simple and eco-efficient, semi-dry method with a spray system for starch modification has been developed. Compared to conventional semi-dry methods, this method does not use solvents so that no slurry or semi-liquid mixture is obtained, the material is in a moisted/semi-moisted state. The modification of starch was performed using betaine hydrochloride (BHC) as the cationic reagent, and the characteristics of such starch derivates were compared with cationic starches obtained using glycidyltrimethylammonium chloride (GTMAC).

Application of waste cotton yarn as adsorbent of heavy metal ions from single and mixed solutions.

In this study, waste cotton yarn was used for the removal of Pb (II), Cd (II), Cr (III), and As (V) from aqueous solution. Adsorption of heavy metal ions was tested from single ion solutions, while competitive studies were performed using two- and four-ion mixtures. In order to change the structure of the material, cotton yarn was modified by sodium hydroxide solution.

Novel amino-functionalized lignin microspheres: High performance biosorbent with enhanced capacity for heavy metal ion removal.

Novel highly effective amino-functionalized lignin-based biosorbent in the microsphere geometry (A-LMS) for removal of heavy metal ions, was synthesized via inverse suspension copolymerization of kraft lignin with poly(ethylene imine) grafting-agent and epoxy chloropropane cross-linker. Optimization of A-LMS synthesis, performed with respect to the quantity of sodium alginate emulsifier (1, 5 and 10 wt%), provides highly porous microspheres A-LMS_5, using 5 wt% emulsifier, with 800 ± 80 μm diameter, 7.68 m g surface area and 7.

Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia).

Rationalization of water quality monitoring stations nowadays is applied in many countries. In some cases, missing data from abandoned/inactive stations, spatial and temporal, could be very important, hence the use of artificial neural networks (ANNs) for virtual water quality monitoring at inactive monitoring sites was investigated. The aim was to develop single-output and simultaneous ANNs for the spatial interpolation of 18 water quality parameters at single- and multi-inactive monitoring sites on Danube River course through Serbia.

Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks.

Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members.

An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries.

Although the use of municipal solid waste to generate energy can decrease dependency on fossil fuels and consequently reduces greenhouse gases emissions and areas that waste occupies, in many countries municipal solid waste is not recognized as a valuable resource and possible alternative fuel. The aim of this study is to develop a model for the prediction of primary energy production from municipal solid waste in the European countries and then to apply it to the Balkan countries in order to assess their potentials in that field. For this purpose, general regression neural network architecture was applied, and correlation and sensitivity analyses were used for optimisation of the model.

Assessment of spatial and temporal variations in trace element concentrations using honeybees () as bioindicators.

With the increase in anthropogenic activities metal pollution is also increased and needs to be closely monitored. In this study honeybees were used as bioindicators to monitor metal pollution. Metal pollution in honeybees represents pollution present in air, water and soil.

Honeybees as sentinels of lead pollution: Spatio-temporal variations and source appointment using stable isotopes and Kohonen self-organizing maps.

In this study, honeybees were used to determine spatio-temporal variations and origin sources of Pb. Lead concentrations and isotopic composition were used in combination with selected statistical methods. The sampling was carried out at five different locations in Serbia: urban region (BG), petrochemical industry (PA), suburban region (PV), rural region (MS) and thermal power plant region (TPP) during 2014.

Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction.

This paper presents an application of experimental design for the optimization of artificial neural network (ANN) for the prediction of dissolved oxygen (DO) content in the Danube River. The aim of this research was to obtain a more reliable ANN model that uses fewer monitoring records, by simultaneous optimization of the following model parameters: number of monitoring sites, number of historical monitoring data (expressed in years), and number of input water quality parameters used. Box-Behnken three-factor at three levels experimental design was applied for simultaneous spatial, temporal, and input variables optimization of the ANN model.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis.

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training.

Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis.

This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model.

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters.

Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs.

This paper describes the development of an artificial neural network (ANN) model based on economical and sustainability indicators for the prediction of annual non-methane volatile organic compounds (NMVOCs) emissions in China for the period 2005-2011 and its comparison with inventory emission factor models. The NMVOCs emissions in China were estimated using ANN model which was created using available data for nine European countries, which NMVOC emission per capita approximately correspond to the Chinese emissions, for the period 2004-2012. The forward input selection strategy was used to compare the significance of particular inputs for the prediction of NMVOC emissions in the nine selected EU countries and China.

Chemometrics in biomonitoring: Distribution and correlation of trace elements in tree leaves.

The concentrations of 15 elements were measured in the leaf samples of Aesculus hippocastanum, Tilia spp., Betula pendula and Acer platanoides collected in May and September of 2014 from four different locations in Belgrade, Serbia. The objective was to assess the chemical characterization of leaf surface and in-wax fractions, as well as the leaf tissue element content, by analyzing untreated, washed with water and washed with chloroform leaf samples, respectively.

The novel approach to the biomonitor survey using one- and two-dimensional Kohonen networks.

To compare the applicability of the leaves of horse chestnut (Aesculus hippocastanum) and linden (Tilia spp.) as biomonitors of trace element concentrations, a coupled approach of one- and two-dimensional Kohonen networks was applied for the first time. The self-organizing networks (SONs) and the self-organizing maps (SOMs) were applied on the database obtained for the element accumulation (Cr, Fe, Ni, Cu, Zn, Pb, V, As, Cd) and the SOM for the Pb isotopes in the leaves for a multiyear period (2002-2006).

Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach.

Ammonia emissions at the national level are frequently estimated by applying the emission inventory approach, which includes the use of emission factors, which are difficult and expensive to determine. Emission factors are therefore the subject of estimation, and as such they contribute to inherent uncertainties in the estimation of ammonia emissions. This paper presents an alternative approach for the prediction of ammonia emissions at the national level based on artificial neural networks and broadly available sustainability and economical/agricultural indicators as model inputs.

Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations.

Biological oxygen demand (BOD) is the most significant water quality parameter and indicates water pollution with respect to the present biodegradable organic matter content. European countries are therefore obliged to report annual BOD values to Eurostat; however, BOD data at the national level is only available for 28 of 35 listed European countries for the period prior to 2008, among which 46% of data is missing. This paper describes the development of an artificial neural network model for the forecasting of annual BOD values at the national level, using widely available sustainability and economical/industrial parameters as inputs.

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study.

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity.