Urban deciduous tree leaves as biomonitors of trace element (As, V and Cd) atmospheric pollution in Belgrade, Serbia

Leaves of common deciduous trees: horse chestnut (Aesculus hippocastanum) and linden (Tilia spp.) from three parks within the urban area of Belgrade were studied as biomonitors of trace element (As, V, and Cd) atmospheric pollution. The May–September trace element accumulation in the leaves, and their temporal trends, were assayed in a multi-year period (2002–2006). Significant accumulation in the leaves was evident for As and V, but not so regularly for Cd. Slightly decreasing temporal trends of V and As ac-cumulated in the leaf tissues were observed over the years. During the time span, the concentrations of Cd remained approximately on the same level, except in May 2002 and September 2005, when a rapid increase was observed. The May–September accumulations of As and V were higher in horse chestnut than in linden, although both may be used as biomonitors for these elements, and optionally for Cd in conditions of its high atmospheric loadings

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. (C) 2012 Elsevier B.V. All rights reserved

Analysis of selected elements in water in the drinking water preparation plants in Belgrade, Serbia

Belgrade's water supply relies mainly on the River Sava and groundwater supply wells, which are located in the vicinity of the river and Ada Ciganlija. In this paper, the content of aluminum, boron, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, cadmium, barium and lead was analyzed in raw water as well as drinking water distributed by the Water Supply and Sewage of Belgrade. A total of 14 samples were examined from all water treatment plants that are part of the distribution system. The measurements were conducted using the inductively coupled plasma-mass spectrometry (ICP-MS) technique. The aim of this research was to examine the effectiveness of drinking water preparation process in the plants belonging to the Water Supply and Sewage of Belgrade. The content of certain elements varies considerably in raw water (river and groundwater): the concentration of boron in river water is two to three times lower than the concentration in groundwater; the concentration of arsenic in river water is ten to twenty five times lower than the concentration in groundwater; the concentration of aluminum in all groundwater samples was below the detection limit of the instrument (0.50 μg/dm3), whilst in the river water the content of aluminum was about 50 μg/dm3 and the concentration of manganese in the river water was up to 10 times lower than the concentrations in groundwater. In all drinking water samples the concentration of the elements were bellow the maximum allowed levels according to the Serbian regulations. Correlation coefficients determined for boron, manganese, cobalt, nickel, copper, zinc, arsenic, barium and lead, which were analyzed in raw waters, show that four groups of elements can be distinguished. Boron, manganese, arsenic and barium are related to each other and probably have a common natural origin; copper and lead probably have a common anthropogenic origin; correlation of nickel and cobalt was observed, while zinc was not in correlation with any other element

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

An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level

This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual quantities of hazardous chemical and healthcare waste at the national level. Hazardous waste is being generated from many different sources and therefore it is not possible to conduct accurate predictions of the total amount of hazardous waste using traditional methodologies. Since they represent about 40% of the total hazardous waste in the European Union, chemical and healthcare waste were specifically selected for this research. Broadly available social, economic, industrial and sustainability indicators were used as input variables and the optimal sets were selected using correlation analysis and sensitivity analysis. The obtained values of coefficients of determination for the final models were 0.999 for the prediction of chemical hazardous waste and 0.975 for the prediction of healthcare and biological hazardous waste. The predicting capabilities of the models for both types of waste are high, since there were no predictions with errors greater than 25%. Also, results of this research demonstrate that the human development index can replace gross domestic product and in this context even represent a better indicator of socio-economic conditions at the national level

Unsupervised classification and multi-criteria decision analysis as chemometric tools for the assessment of sediment quality: A case study of the Danube and Sava River

The aim of this study was to evaluate the quality of freshwater sediments by means of three chemometric techniques for multi-criteria analysis and decision: self-organizing network (SON), self-organizing map (SOM) and PROMETHEE&GAIA (Preference Ranking Organization Method for Enrichment Evaluation with Geometrical Analysis for Interactive Aid). Selected chemometric techniques were applied to the results of Pb, Cd, Zn, Cu, Ni, Cr, Hg and As content in thirty Danube and fourteen Sava river sediment samples from Serbia. The potential toxicity of sediments was estimated using Probable Effect Concentrations quotients (mean PEC-Q). According to the SON analysis the Danube sediment samples were divided into three classes, Class I (mean PEC-Q range 0.27-0.51), Class II (mean PEC-Qrange 0:50-0.70), and Class III (mean PEC-Qrange 0.77-0.97), while the Sava samples were divided into two classes, Class II (two samples, mean PEC-Qvalues 0.65 and 0.69) and Class III (mean PEC-Q range 0.69-1.00). Using the SOM cluster analysis, both Danube and Sava sediment samples were classified into five subclusters, on the basis of the metal concentration level and further ranked into three levels (for remediation, moderately polluted and not polluted) by the use of multi-criteria ranking PROMETHEE method. Graphical presentation of the results obtained by PROMETHEE method using GAIA descriptive tool has provided an insight into the distribution of examined elements in sediments and has shown a significant correlation between some elements. On the basis of the results obtained, it has been concluded that the proposed chemometric approach could provide useful information in the sediment quality assessment