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The adsorption of polychlorinated dibenzodioxins and dibenzofurans (PCDD/Fs) by activated carbon (AC) was examined in a fixed-bed AC unit in a hazardous waste incinerator (IZAYDAS) in Turkey. Results showed that the removal efficiencies of PCDD/Fs decrease as the chlorination level increases, which was explained by the difference in gas/particle partitioning of the compounds. Since dioxins are tightly adsorbed by activated carbon, other flue gas constituents showed no clear effect on the dioxin removal. Adsorption kinetics indicated that the adsorption of volatile congeners and homologues fits well with Henry's law, possibly due to the higher gaseous fractions, while the correlation was lower for lowly volatile ones. PCDD/F congeners and homologues had a concentration value up to which no adsorption occurred, which could be attributed to the insufficient contact times at the low concentrations.

This study introduces a seasonal modeling approach in the prediction of daily average PM 10 (particulate matter with an aerodynamic diameter <10 μm) levels 1 day ahead based on multilayer perceptron artificial neural network (MLP-ANN) forecasters. The data set covered all daily based meteorological parameters and PM 10 concentrations in the period of [2007][2008][2009][2010][2011][2012][2013][2014]. Seasonal ANN models for winter and summer periods were separately developed and trained by using a lagged time series data set. The most significant lagged terms of the variables within a 1-week period were determined by principal component analysis (PCA) and assigned as input vectors of ANN models. Cascading training with error back-propagation method was applied in model building. The use of seasonal ANN models with PCA-based inputs showed an increased prediction performance compared with nonseasonal models. For seasonal ANN models, the overall model agreement in training between modeled and observed values varied in the range of 0.78-0.83 and R 2 values ranged in 0.681-0.727, which outperformed nonseasonal models. The best testing R 2 values of seasonal models for winter and summer periods ranged in 0.709-0.727 with lower testing error, and the models did not show a tendency towards overpredicting or underpredicting the PM 10 levels. The approach demonstrated in the study appeared to be promising for predicting short-term levels of pollutants through the data sets with high irregularities and could have significant applicability in the case of large number of considered inputs.Implications: This study provides an alternative approach to predict PM 10 levels 1 day ahead by building seasonal ANN models. Applying PCA on a lagged data set resulted in selection of the most significant lags of variables reducing model complexity. Cascading training with error back-propagation method appropriately determined hidden layer neurons. Separately building ANN models for winter and summer periods over years, even though it required much more effort compared with building regular nonseasonal models, yielded better model agreements and smaller testing errors. This approach can be applied on the data sets with irregularities and a large number of considered inputs.

This study aimed at investigating the impact of industrialization on the quality of agricultural soils in the district of Bandırma, Turkey, in terms of soil heavy metal contamination. Many soil and phosphogypsum samples were analyzed, and enrichment factors (EFs) were calculated. The average concentration gradient of metals in the soil (mg/kg) was As < Se < Sb < Pb < Co < Cd < V < Cu < Ni < Zn < Cr < P < Mn < Na < K < Mg < Fe < Ca < Al. According to the Pearson cross-correlation results for the element pairs of Fe-Mg (0.635), Fe-Cu (0.863), Fe-Cd (0.545), Cu-Cd (0.630), Mn-Cr (0.698), Mn-Al (0.523), Cr-Mg (0.543), Al-P (0.508), Na-K (0.616), and C-Zn (0.703), the metal enrichments in the soil were found to be moderately high and significant. In the majority of soil samples, Ni, Cu, Co, Zn, Se, Pb, and Cr were moderately enriched whereas Sb and Cd were extremely highly enriched. A factor analysis (FA) was applied to the cross-correlations of the elements to identify their sources. Six significant factors were extracted with the help of FA, explaining 77.22 % of the total variance, and the elements loaded on these factors were interpreted. The evaluations of the factors showed that the study area has been exposed to heavy metal pollution from anthropogenic sources considering the high levels of Cr, Cd, Cu, P, V, Zn, Ni, Sb, and Pb in the soil and the higher EFs falling in the range of 2.54-372.87. Moreover, the soil concentrations of Mn, Mg, Co, Al, K, and Ca were also high, but they were of lithogenic in origin according to the FA.