Effect of diurnal changes in VOC source strengths on performances of receptor models

Introduction

The effect of diurnal changes in strengths of volatile organic compound (VOC) sources on the performances of positive matrix factorization (PMF) and principal component analysis (PCA) was investigated using ambient measurement results that were taken during daytime and nighttime hours between March 24 and May 14, 2011, within Davutpasa Campus of Yildiz Technical University (Istanbul, Turkey).

Methods

Forty-five VOC species, ranging from C₅ to C₁₁ in volatility, were measured in the samples, 40 of which are included in the analyses. Ambient samples were grouped as daytime, nighttime, and all day datasets, and both PMF and PCA were applied to each dataset. A total of six source groups were extracted from each dataset: solvent use, general industrial paint use, gasoline and diesel vehicle exhausts, and biogenic as well as evaporative emissions. Estimated source contributions showed great diurnal variations.

Results

The results suggested that extraction of possible sources by PCA depends greatly on the number of samples and the strength of the sources, while PMF produced stable results regardless of number of samples and source strengths.

Conclusion

Although PMF was unable to resolve gasoline vehicle and evaporative emissions, it was found to be successful in explaining diurnal fluctuations in source strengths, while the performance of PCA depends on the strength of emission source.     

A detailed investigation of ambient aerosol composition and size distribution in an urban atmosphere

This research was executed between March 2009 and March 2010 to monitor particulate matter size distribution and its composition in Istanbul. Particulate matter composition was determined using ion chromatography and inductively coupled plasma optical emission spectrometry. The sampling point is adjacent to a crowded road and the Bosporus Strait. Two prevailing particulate modes are found throughout PM₁₀ by sampling with a nine-stage low-volume cascade impactor. First mode in the fine mode is found to be between 0.43 and 0.65 μm, whereas the other peak was observed between 3.3 and 4.7 μm, referring to the coarse mode. The mean PM₁₀ concentration was determined as 41.2 μg/m³, with a standard deviation of 16.92 μg/m³. PM_0.43 had the highest mean concentration value of 10.67 μg/m³, making up nearly one fourth of the total PM₁₀ mass. For determining the effect of traffic on particulate matter (PM) composition and distribution, four different sampling cycles were applied: entire day, nighttime, rush hour, and rush hour at weekdays. SO ₄ ^?2 and organic carbon/elemental carbon proportions are found to be lower in night samples, representing a decrease in traffic. The long-range transports of dust storms were observed during the sampling periods. Their effects were determined analytically and their route models were run by the HYSPLIT model and validated through satellite photographs taken by the NASA Earth Observatory.     

Prediction of environmental impacts of quarry blasting operation using fuzzy logic

Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. In this study, an attempt has been made to predict the peak particle velocity (PPV) with the help of fuzzy logic approach using parameters of distance from blast face to vibration monitoring point and charge weight per delay. The PPV and charge weight per delay were recorded for 33 blast events at various distances and used for the validation of the proposed fuzzy model. The results of the fuzzy model were also compared with the values obtained from classical regression analysis. The root mean square error estimated for fuzzy-based model was 5.31, whereas it was 11.32 for classical regression-based model. Finally, the relationship between the measured and predicted values of PPV showed that the correlation coefficient for fuzzy model (0.96) is higher than that for regression model (0.82).     

Attenuation of sodium arsenite-induced cardiotoxicity and neurotoxicity with the antioxidant,anti-inflammatory,and antiapoptotic effects of hesperidin

Environmental Science and Pollution Research - In the scope of the study, the protective effect of hesperidin (HES), a flavanone glycoside, was investigated against sodium arsenite (NaAsO2, SA)...