Atmospheric ambient trace element concentrations of PM10 at urban and sub-urban sites: source apportionment and health risk estimation.

In this study, PM10 concentrations and elemental (Al, Fe, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Sn, Sb, Ba, Pb, and Bi) contents of particles were determined in Düzce, Turkey. The particulate matter samplings were carried out in the winter and summer seasons simultaneously in both urban and sub-urban sampling sites. The average PM10 concentration measured in the winter season was 86.4 and 27.3 µg/m3, respectively, in the urban and sub-urban sampling sites, while it was measured as 53.2 and 34.7 µg/m3 in the summer season. According to the results, it was observed that the PM10 levels and the element concentrations reached higher levels, especially at the urban sampling site, in the winter season. The positive matrix factorization model (PMF) was applied to the data set for source apportionment. Analysis with the PMF model revealed six factors for both the urban (coal combustion, traffic, oil combustion, industry, biomass combustion, and soil) and sub-urban (industry, oil combustion, traffic, road dust, soil resuspension, domestic heating) sampling sites. Loadings of grouped elements on these factors showed that the major sources of the elements in the atmosphere of Düzce were traffic, fossil fuel combustion, and metal industry-related emissions.

Improving artificial neural network model predictions of daily average PM10 concentrations by applying principle component analysis and implementing seasonal models.

UNLABELLED: This study introduces a seasonal modeling approach in the prediction of daily average PM10 (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 PM10 concentrations in the period of 2007-2014. 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 R2 values ranged in 0.681-0.727, which outperformed nonseasonal models. The best testing R2 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 PM10 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 PM10 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.

Assessment of heavy metals in agricultural soils and their source apportionment: a Turkish district survey.

This study aimed at investigating the impact of industrialization on the quality of agricultural soils in the district of Bandirma, 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.

Health risk assessment of BTEX emissions in the landfill environment.

This study focuses on a health risk assessment related to benzene, toluene, ethylbenzene, and m,p,o-xylene (xylenes) (BTEX) exposure via inhalation for workers at a landfill (LF) site. First, the landfill gas (LFG) samples were collected and analyzed accordance with US EPA method TO-17. The mean concentrations of benzene, toluene, ethylbenzene, and xylenes were determined as 140.3, 1271.7, 239.9, and 341.3 microg/m(3), respectively. Then, a risk assessment methodology was employed to evaluate the potential adverse health effects of the individual BTEX compounds according to their carcinogenicities. The corresponding mean cancer risk for benzene was estimated to be 6.75E-05 that is lower than the designated acceptable risk level of 1.0E-04. With respect to mean non-carcinogenic risks for toluene, ethylbenzene, and xylenes, both individually and cumulatively, they were lower than the specified level of 1.0. These findings reveal that landfill BTEX emissions do not pose a health threat to workers at the landfill site. In addition, as far as the risks are concerned for the population in the neighborhood area of the landfill, air dilution of BTEX emitted from LF site is widely sufficient to guarantee their protection.

Removal of PCDD/Fs from flue gas by a fixed-bed activated carbon filter in a hazardous waste incinerator.

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.