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 59.9 to 758 ng/L, with an average of 290 ng/L. The most abundant was Sc, followed by La, accounting for 54.2% and 21.4% of the total REE concentration, respectively. Geospatial analysis revealed the REE enrichment at several hotspots near the TPP. The highest REE concentrations were observed near the TPP and ash landfill, decreasing with the distance from the plant and the landfill. REE fractionation ratios and anomalies suggested the Light REE dominance, comprising over 78% of the total REEs. Correlation and principal component analyses indicated similar behavior and sources for most REEs. Health risk assessment found hazard indices (HI) of 1.36 × 10-3 and 1.98 × 10-3 for adults and children, respectively, which are far below the permissible limit (HI = 1). Likewise, incremental lifetime cancer risks (ILCR) were all below 1 × 10-6. Nevertheless, ongoing ash disposal and potential accumulation in the environment could elevate the REE exposure over time.

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. Sequential analysis shows that the trace elements portion is the lowest for water-soluble and exchangeable fractions. The highest enrichment levels in FAs are noticed for As and Hg. Based on toxic trace elements content, FAs from TPPs represent a very high ecological risk, whereas fly ash from FBB poses a moderate ecological risk but has the highest benzo[a]pyrene equivalent concentration, indicating its increased carcinogenic potential. Lead isotope ratios for Serbian coals and FAs can contribute to a lead pollution global database.

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.g., dynamic systems, plant engineering, and logistics, it is less common for edge computing. This work discusses different use cases concerning edge computing in IIoT that can profit from the use of OT simulation methods. In addition to enabling machine learning, the focus of this work is on the virtual commissioning of data stream processing systems. To evaluate the proposed approach, an exemplary application of the middleware layer, i.e., a multi-agent reinforcement learning system for intelligent edge resource allocation, is combined with a physical simulation model of an industrial plant. It confirms the feasibility of the proposed use of simulation for virtual commissioning of an industrial edge computing system using Hardware-in-the-Loop. In summary, edge computing in IIoT is highlighted as a new application area for existing simulation methods from the OT perspective. The benefits in IIoT are exemplified by various use cases for the logic or middleware layer using physical simulation of the target environment. The relevance for real-life IIoT systems is confirmed by an experimental evaluation, and limitations are pointed out.

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, Pb2+, Cd2+, and Zn2+ 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. The amino-modified starch was the most effective in removing crystal violet (CV) (65.31-80.46 %) and Pb2+ (67.44-80.33 %) within the optimal adsorption conditions (pH 5, 10 mg dm-3, 25 °C, 180 min). The adsorption of CV could be described by both Langmuir and Freundlich adsorption isotherms, while the adsorption of Pb2+ ions was better described by the Langmuir isotherm. The pseudo-second order model can be used to describe the adsorption kinetics of CV and Pb2+ on all tested samples. The thermodynamic study indicated that the adsorption of CV was exothermic, while the Pb2+ adsorption was endothermic. The simultaneous removal of CV and Pb2+ from the binary mixture has shown their competitive behavior. Thus, the amino-modified starch is a promising eco-friendly adsorbent for the removal of dyes and heavy metals from polluted water.

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). Due to the instability, toxicity, and high cost of the most commonly used GTMAC, it should be replaced with more eco-friendly reagents, such as BHC, which is derived from betaine found in most green plants (e.g., spinach - Spinacia oleracea, beets - Beta vulgaris). The influence of processing conditions such as temperature, concentration of cationic reagents, presence and concentration of natural plasticizers/catalyst on physico-chemical and structural properties of cationic starches have also been studied. The cationic degree varied from 0.045-0.204 for the starch-BHC samples and within the range of 0.066-0.245 for the starch-GTMAC samples. The modification of starch with cationic reagents resulted in an increased solubility and swelling capacity, followed by decreased viscosity of the modified starches.

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. The surface of raw and modified cotton yarn were characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, and streaming potential method for determination of an isoelectric point. Sorption studies were performed on the basis of pH, kinetics, isotherms, and desorption results. It has been shown that waste cotton yarn modification, typically, does not improve the sorption capacity of the material and that the unmodified material could be used for the removal of examined heavy metal ions. Selectivity was in order Pb > Cd > Cr > As. Desorption studies have indicated to the possible reusability of the sorbent only in the case of Pb removal. A potential application of spent waste sorbent for the soil quality improvement has been considered.

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 m2 g-1 surface area and 7.7 mmol g-1 of terminal amino groups. Structural and surface characteristics were obtained from Brunauer-Emmett-Teller method, Fourier Transform-Infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy and porosity determination. In a batch test, the influence of pH, A-LMS_5 dose, temperature, contact time on adsorption efficiency of Ni2+, Cd2+, As(V) and Cr(VI) ions were studied. The adsorption is spontaneous and feasible with maximum adsorption capacity of 74.84, 54.20, 53.12 and 49.42 mg g-1 for Cd2+, Cr(VI), As(V) and Ni2+ ions, respectively, obtained by using Langmuir model. Modeling of kinetic data indicated fast adsorbate removal rate with pore diffusional transport as rate limiting step (pseudo-second order model and Weber-Morris equations), thus further confirming high performances of produced bio-adsorbent for heavy metal ions removal.

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. Available ozone precursors' emissions, population and climate data for the period 2003-2013 were used as inputs. Trend analysis had been performed using the linear regression of SOMO35 over time, and it has demonstrated that majority of the studied countries have a decreasing trend of SOMO35 values. The created models have made majority of predictions (≈60%) with satisfactory accuracy (relative error <20%) on testing, while the best performing model had R2 = 0.87 and overall relative error of 33.6%. The domain of applicability of the created models was analyzed using slope/mean ratio derivate from the trend analysis, which was successful in distinguishing countries with high from countries with low prediction errors. The overall relative error was reduced to <14%, after the pool of countries was reduced based on the abovementioned criterion.

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. Those different modeling approaches were considered in order to determine the most suitable combination of models. The variable selection and sensitivity analysis in the case of simultaneous models were performed using a modified procedure based on Monte Carlo Simulations (MCS). In general, the multi-target models tend to be more accurate than single target ones, while single output models outperform the simultaneous ones. Hence, for particular monitoring network and set of water quality parameters the optimal combination of models must be defined based on model's accuracy and computational effort needed. The MCS selection procedure has proved to be efficient only in the case of simultaneous multi-target model. MCS based analysis of input-output interactions has shown all significant interactions in the case of simultaneous single-target are grouped as a complex cluster of interactions, where majority of inputs influence on several outputs. In the case multi-target model those interactions were portioned in five separate clusters, there majority of them mimic the input-output interactions that are present in single output models. The modeling strategy for study area was proposed on the basis of the performance of created models (mean average percentage error < 10%): simultaneous multi-target model for pH, alkalinity, conductivity, hardness, dissolved oxygen, HCO3-, SO42- and Ca, single-output multi-target models for temperature and Cl-, simultaneous single-target models for Mg and CO2, single output single target models for NO3-.

Assessment of spatial and temporal variations in trace element concentrations using honeybees (Apis mellifera) 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. Concentrations of As, Cs, Hg, Mo, Sb, Se, U and V were measured. The aim of this study was to assess spatial and temporal variations of metal concentrations in honeybees. Samples of honeybees were taken at five different regions in Serbia (Belgrade - BG, Pancevo - PA, Pavlis - PV, Mesic - MS, and Kostolac - TPP) during 2014. Spatial variations were observed for Sb, which had higher concentrations in BG compared to all other regions, and for U, with higher concentrations in the TPP region. High concentrations of Sb in BG were attributed to intense traffic, while higher U concentrations in the TPP region are due to the vicinity of coal fired power plants. In order to assess temporal variations at two locations (PA and PV) samples were taken during July and September of 2014 and June, July, August and September of 2015. During 2014 observing months of sampling higher concentrations in July were detected for Sb and U in BG, which is attributed to lifecycle of plants and honeybees. During the same year higher concentrations in September were observed for As, Sb in PA and Hg in PV. This is due to high precipitation during the peak of bee activity in spring/summer of 2014. No differences between months of sampling were detected during 2015. Between 2014 and 2015 statistically significant differences were observed for Hg, Mo and V; all elements had higher concentrations in 2014. This is in accordance with the trend of reduction of metal concentrations in the bodies of honeybees throughout the years in this region.

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. At PA and PV locations, samples were taken during multiple years. This is the first use of Kohonen self-organizing map (SOM) in combination with honeybees as bioindicators to determine spatio-temporal variations and origin of Pb pollution. It was observed that during the years Pb concentrations were in decline. Anthropogenic sources are most dominant in BG and TPP, in PA there are mixed sources of natural and anthropogenic origin and in PV Pb is of natural origin. It can be concluded that honeybees in combination with SOM can be used to differentiate between slight changes in spatio-temporal variations of Pb, as well as for source appointment.

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. The prediction of DO was performed using a feed-forward back-propagation neural network (BPNN), while the selection of most important inputs was done off-model using multi-filter approach that combines a chi-square ranking in the first step with a correlation-based elimination in the second step. The contour plots of absolute and relative error response surfaces were utilized to determine the optimal values of design factors. From the contour plots, two BPNN models that cover entire Danube flow through Serbia are proposed: an upstream model (BPNN-UP) that covers 8 monitoring sites prior to Belgrade and uses 12 inputs measured in the 7-year period and a downstream model (BPNN-DOWN) which covers 9 monitoring sites and uses 11 input parameters measured in the 6-year period. The main difference between the two models is that BPNN-UP utilizes inputs such as BOD, P, and PO43-, which is in accordance with the fact that this model covers northern part of Serbia (Vojvodina Autonomous Province) which is well-known for agricultural production and extensive use of fertilizers. Both models have shown very good agreement between measured and predicted DO (with R2 ≥ 0.86) and demonstrated that they can effectively forecast DO content in the Danube River.

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. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R2=0.82), but it was not robust in extrapolation (R2=0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD.

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. The data for 16 countries from the European Union and Norway for the period 2006-2015 was used for the development of the model. The model with the best performance (coefficient of determination R2 = 0.995 and the mean absolute percentage error MAPE = 7.757%) was applied to the data for the Balkan countries from 2006 to 2015. The obtained results indicate that there is a significant potential for utilization of municipal solid waste for energy production, which should lead to substantial savings of fossil fuels, primarily lignite which is the most common fossil fuel in the Balkans.

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. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.

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. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

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. The final ANN model was trained using only five input variables, and it has demonstrated similar accuracy in predicting NMVOC emissions for the selected EU countries that were used for the development of the model and then for China for which the input dataset was previously unknown to the ANN model. The obtained mean absolute percentage error (MAPE) values were 8 % for EU countries and 5 % for China. Also, the temporal trend of NMVOC emissions predicted in this study is generally consistent with the trend obtained using inventory emission models. The proposed ANN approach can represent a viable alternative for the prediction of NMVOC emissions at the national level, in particular for developing countries which are usually lacking emission data.

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 combined approach of self-organizing networks (SON) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) aided by Geometrical Analysis for Interactive Aid (GAIA) was used in the interpretation of multiple element loads on/in the tree leaves. The morphological characteristics of the leaf surfaces and the elemental composition of particulate matter (PM) deposited on tree leaves were studied by using scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) detector. The results showed that the amounts of retained and accumulated element concentrations depend on several parameters, such as chemical properties of the element and morphological properties of the leaves. Among the studied species, Tilia spp. was found to be the most effective in the accumulation of elements in leaf tissue (70% of the total element concentration), while A. hippocastanum had the lowest accumulation (54%). After water and chloroform washing, the highest percentages of removal were observed for Al, V, Cr, Cu, Zn, As, Cd and Sb (>40%). The PROMETHEE/SON ranking/classifying results were in accordance with the results obtained from the GAIA clustering techniques. The combination of the techniques enabled extraction of additional information from datasets. Therefore, the use of both the ranking and clustering methods could be a useful tool to be applied in biomonitoring studies of trace elements.