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.

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.

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. The Multilayer Perceptron (MLP) architecture was optimized using a trial-and-error procedure, including the number of hidden neurons, activation function, and a back-propagation algorithm. Principal component analysis (PCA) was applied to reduce mutual correlation between the inputs. The obtained results demonstrate that the MLP model created using the PCA transformed inputs (PCA-MLP) provides a more accurate prediction than the MLP model based on the original inputs. In the validation stage, the MLP and PCA-MLP models were tested for ammonia emission predictions for up to 2 years and compared with a principal component regression model. Among the three models, the PCA-MLP demonstrated the best performance, providing predictions for the USA and the majority of EU countries with a relative error of less than 20%.

PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization.

This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables.

Sorption of zinc by novel pH-sensitive hydrogels based on chitosan, itaconic acid and methacrylic acid.

Novel pH-sensitive hydrogels based on chitosan, itaconic acid and methacrylic acid were applied as adsorbents for the removal of Zn(2+) ions from aqueous solution. In batch tests, the influence of solution pH, contact time, initial metal ion concentration and temperature was examined. The sorption was found pH dependent, pH 5.5 being the optimum value. The adsorption process was well described by the pseudo-second order kinetic. The hydrogels were characterized by spectral (Fourier transform infrared-FTIR) and structural (SEM/EDX and atomic force microscopy-AFM) analyses. The surface topography changes were observed by atomic force microscopy, while the changes in surface composition were detected using phase imaging AFM. The negative values of free energy and enthalpy indicated that the adsorption is spontaneous and exothermic one. The best fitting isotherms were Langmuir and Redlich-Peterson and it was found that both linear and nonlinear methods were appropriate for obtaining the isotherm parameters. However, the increase of temperature leads to higher adsorption capacity, since swelling degree increased with temperature.

Danube and Sava river sediment monitoring in Belgrade and its surroundings.

Belgrade is the largest city in Serbia located at the confluence of river Sava to the Danube river. The quality of water and sediments of rivers which run through Belgrade is of a significant importance, since water from these rivers is a source of Belgrade drinking water supply system and probable anthropogenic contamination is related to industrialization and inputs of sewage water. In order to follow the sediment quality of river Sava (km 62-1) and river Danube (km 1193-1124) in Belgrade and its surroundings, the content of As, Cd, Cr, Cu, Zn, Ni, Pb and Hg were measured in the period 2001--2005. The content of 16 polycyclic aromatic hydrocarbons (PAHs) was measured in 2005. The results have shown that, due to the metal content, examined Danube sediment quality varies from class 1 to class 3, predominantly nickel being the class determining parameter. Elevated copper, zinc and mercury concentrations were measured at some profiles, as well. Typically due to the nickel content, Sava sediment quality belongs to class 3 in the period 2001--2004. Elevated concentrations of cadmium, zinc and mercury were observed in 2001, as well. Moreover, in 2005, sediments from three profiles were extremely polluted with nickel, leading the Sava sediment to class 4, when highest urgency measures are needed. Total PAH concentration in the sediments from Danube (213.1-575.4 microg kg(- 1)) was lower than total PAH concentration from Sava sediments (416.2-595.3 microg kg(- 1)). Nevertheless, according to the Dutch regulatory system, it has been concluded that river sediments in Belgrade and its surroundings were not polluted with PAHs in 2005.