Davor Antanasijević

PM10 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.

The forecasting of municipal waste generation using artificial neural networks and sustainability indicators

The feasibility of modeling municipal waste generation (MWG) for countries at different levels of development using artificial neural networks (ANN) and selected generic indicators of sustainability was investigated. The main goals of this research were to develop ANN-based models for predicting MWG, to overcome the problem of incomplete MWG data, which is notable in developing countries, and to provide a new method for the planning of municipal solid waste management systems as well as for the simulation of various other scenarios. Data from 26 European countries was used in this study as training, test and validation datasets for the developing of ANN models. Since this kind of modeling is particularly important for developing countries where MWG data is missing or incomplete, emphasis was placed on modeling of MWG for Bulgaria and Serbia. Based on a comparison of actual MWG data with predictions given by the model, we show that ANNs can be applied successfully to modeling and forecasting MWG on a national scale. Moreover, the scope for possible application of the model is broad, since it uses generic indicators of sustainability such as gross domestic product, domestic material consumption and resource productivity, and performs well for countries with highly diversified levels of economic development, industrial structure, productivity and output.

Review: the approaches for estimation of limit of detection for ICP-MS trace analysis of arsenic.

The analytical properties of an analytical method must be evaluated through validation protocols. Beside specificity and/or selectivity, linearity of calibration, repeatability and accuracy, the most important parameters are: LOD (limit of detection) and LOQ (limit of quantification). Through these limits, it is possible to define the smallest concentration of analyte that can be reliably detected and quantified. To establish these limits, an analyst should apply several estimation methods and test a large number of sample replicates. It is difficult to make a compromise between complex statistical programs and the simple analytical demand to have reliable analytical parameters. The differences and equivalency of estimation methods and approaches for analytical limits could be overcome by an experimental comparison. In this paper, the focus is the LOD of inductively coupled plasma-mass spectrometry (ICP-MS) measurements employed for the determination of arsenic. The current approaches for the calculation of the LOD are summarized and critically discussed.

Forecasting human exposure to PM10 at the national level using an artificial neural network approach

A neural network model for predicting country‐level concentrations of the fraction of particulates in the air with sizes less than 10 µm (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country‐level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country‐level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level. Copyright

Concentrations of selected trace elements in mineral and spring bottled waters on the Serbian market

Eight selected trace elements, which are generally included in regulations, were analyzed in 23 types of bottled waters. Ten mineral and seven spring bottled waters were from the Serbian market and six mineral bottled waters were obtained in different EU countries. For the purpose of comparison, selected tap waters were also analyzed. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the analysis of trace elements (arsenic, cadmium, copper, manganese, nickel, lead and antimony). Results were compared with the Serbian regulations for bottled water, EU regulations and guideline values set by the World Health Organization for drinking water. With few exceptions, the trace element levels of most bottled waters were below the guideline values. However, a higher content of antimony was observed in waters from polyethylene terephthalate (PET) containers, indicating a potential leaching of this element from the plastic packaging.

Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model

Nitrogen oxides (NOx) emissions into the atmosphere have multiple negative effects on the environment and effects directly and indirectly on human health. This paper describes the development of a model for NOx emission prediction at the national level based on artificial neural networks (ANNs) and on widely available sustainability, industrial, and economical parameters as input variables. In this study, 11 sustainability, industrial, and economical parameters were chosen as potential input variables. The ANN models were trained, validated, and tested with available data for 17 European countries, USA, China, Japan, Russia, and India for the years 2001 to 2008. The ANN modeling was performed using general regression neural network (GRNN), and correlation and variance inflation factor (VIF) analysis were applied to reduce the number of input variables. The best results were obtained using the selection of inputs based on the correlation between input variables, which provided a more accurate prediction than the GRNN model created with all initial selected input variables. Sensitivity analysis showed that the input variables with the largest influences on the GRNN model results were (in descending order) electricity production from oil sources, agricultural land, fossil fuel energy consumption, number of vehicles, gross domestic product, energy use, and electricity production from coal sources.

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.

Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines

ABSTRACT Accurate prediction of transition temperature is very helpful for the design of new liquid crystals (LCs) because even small changes in structure can dramatically alter the transition temperature, and therefore the synthesis of LCs should not be governed only by chemical intuition. A quantitative structure–property relationship (QSPR) study was performed on 243 five-ring bent-core LCs in order to predict their clearing temperatures using molecular descriptors. Decision tree and multivariate adaptive regression splines (MARS), techniques well suited for high-dimensional data analysis, were applied to select important descriptors (dimension reduction) and to generate nonlinear models. These techniques were applied both on two-dimensional (2D) descriptors only and on the pool of 2D and 3D descriptors (2&3D). The obtained QSPR models were tested using 15% of available data, and their performance and ability to generalise were analysed using multiple statistical metrics. The best results for the external test set were obtained using the MARS model created with 2&3D descriptors, with a high correlation coefficient of r = 0.95 and a root mean squared error of 7.41 K. All metrics suggest that the proposed QSPR model, generated by MARS, is a robust and satisfactorily accurate approach for the prediction of clearing temperatures of bent-core LCs. GRAPHICAL ABSTRACT

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.

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). A. hippocastanum seems to be a more appropriate biomonitor since it showed more consistent results in the analysis of trace elements and Pb isotopes. The SOM proved to be a suitable and sensitive tool for assessing differences in trace element concentrations and for the Pb isotopic composition in leaves of different species. In addition, the SON provided more clear data on seasonal and temporal accumulation of trace elements in the leaves and could be recommended complementary to the SOM analysis of trace elements in biomonitoring studies.