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.     

Non-target effect of organic insecticides: effect of two plant extracts on soil microbial biomass and enzymatic activities in soil

Efficacious botanical derivatives can provide an alternative to synthetic pesticides for organic farming systems. However, there is lack of information regarding the side effects of organic pesticides on key soil ecological processes. In this study, we investigated the effects of aqueous extracts from Urginea maritima and Euphorbia myrsinites exhibiting translaminar and systemic activity against pests on microbial biomass and enzymatic activities in soil. Two grams of plant material was extracted with 100 ml of water and then diluted 1:100, 2:100, and 4:100 with distilled water. Diluted plant extracts were applied around hypocotyl of tomato by soil drench. The effect of both plant extracts on microbial biomass C, amount of total N and organic C, and enzymatic activity in soil was significant. After the last application, the highest microbial biomass C was determined in the lowest U. maritima concentration (U 1:100). Soils treated with the highest concentration of U. maritima (U 4:100) had always lower SMBC content than control soil. All concentrations of E. myrsinites decreased microbial biomass C by 18% to 27% compared to the control. Total nitrogen and organic carbon decreased in soils without (control) and with treated U. maritima extract from first application to last application. Phosphatase, urease, and β-glucosidase activities were monitored in plant extract-treated soils. Except U. maritima 1:100 treatments of second and fourth applications, the other treatments of plant extracts negatively affected enzymatic activity in soil. U. maritima and E. myrsinites plant extracts exhibited different effects on soil microbial biomass and activity, probably because of their different chemical contents.     

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the “Enter” method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685–3.65% and 0.9988–0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.     

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.     

Determination of 7-ethoxyresorufin-o-deethylase (EROD) induction in leaping mullet (Liza saliens) from the highly contaminated Aliaga Bay, Turkey

Pollution of the aquatic environment is a global concern owing to the devastating effects of contaminants whose levels are growing at an alarming rate, and it has become a major threat for marine organisms, as well as to humans as consumers. This study has been carried out on leaping mullet obtained from Aliaga Bay, which is located on the west coast of the Aegean Sea near Izmir and hosts the world’s fifth largest shipyard, plus a broad range of industrial activities, including an oil refinery and a paper factory. The waste from these industries, combined with municipal sewer discharges, is the main cause of pollution in this region. There is no national documentation or research on the determination of pollution resulting from the industrial activities in this area. In the present study, the degree of induction of CYP4501A-associated 7-ethoxyresorufin O-deethylase (EROD) activity and immunochemical detection of CYP1A1 in the liver of leaping mullet (Lisa saliens) were used as biomarkers for the assessment of polycyclic aromatic hydrocarbon (PAH)-type organic pollutants in Aliaga Bay. Mullet caught from different locations of the bay had approximately 52 times more EROD activity than the feral fish sampled from a clean reference site near Foca, Izmir. The results of this study indicate that Aliaga Bay is highly contaminated with PAH-type organic pollutants.     

Heavy metals in water, sediment and tissues of Leuciscus cephalus from a stream in southwestern Turkey

Concentrations of heavy metals (Cd, Cr, Cu, Pb and Zn) were measured in water, bottom sediment and tissues (muscle and gills) of Leuciscus cephalus from the Dipsiz stream in the Yatagan basin (southwestern Turkey), the site of a thermal power plant. Results for levels in water were compared with national and international water quality guidelines, as well as literature values were reported for streams and rivers. Comparisons were made of metal concentrations in water and sediment with those in the muscle and gills of L. cephalus caught from the Dipsiz stream. We found that there was metal accumulation in the gills compared to the muscle. Concentrations of Cd, Pb, Zn and Cr in the gills were higher than that in the muscle; however, Cu levels were higher in muscle than that in gills. Concentrations of heavy metals in L. cephalus muscle were below the legal limits for human consumption, although Cr, Pb and Zn levels in the gills were above the limits in the fish taken from the Dipsiz stream. On the other hand, no correlation was found between metal concentrations in water and sediment or between metal concentrations in water and muscle and gills of L. cephalus. A positive correlation was found between concentrations of Cu and Zn in the sediment and in fish tissue, whereas there was no relationship between other metal concentrations in the sediment and water, and muscle and gills of L. cephalus. As with water, Pb and Cd concentrations in particular were higher in sediment than that in background levels. The results show that the pollutants from the thermal power plant may be a source of these elements.     

Determination of arsenic levels in the water resources of Aksaray Province, Turkey

Arsenic levels were determined in 62 stations utilized as drinking and potable water resources by local community for Turkey's Aksaray Province (4589 km(2); 980 m above sea level). The samplings were implemented every two months for 1 year. The arsenic values were found to be ranging between 10 and 50 μg/L in 22 points and were found to be >50 μg/L in 5 stations, according to the mean value of the 6 samples. WHO and the Turkish Standards have permitted an arsenic concentration of 10 μg/L in drinking waters. The multivariate statistical technique, cluster analysis (CA), followed by principal component analysis (PCA) were applied to the data on 17 water quality parameters in 47 stations that are used for drinking and other domestic resources. Two significant sampling locations were detected based on the similarity of their water quality. The chemical correlations were observed in the two sub-sampling locations by Principal Component Analysis.     

The effects of exposure of 60Co on the oxidant/antioxidant status among radiation victims

This retrospective study has been performed with radiation victims who were accidentally exposed to a 60Co source and its release into the environment. The aim of the study was to assess the effects of elevated radiation exposures on plasma level, on erythrocyte thio barbituric acid reactive substance (TBARS) level and on erythrocyte glutathione (GSH) levels. Patients were treated in different hospitals with different symptoms such as nausea, vomiting, dizziness, along with severe anemia in some patients. Blood samples were collected 3-5 days following the radiation accident. Increases in plasma (6.25 +/- 0.90 nmol ml(-1)) and erythrocyte TBARS levels (330.5 +/- 30.5 micromol gHb(-1)) were found in comparison to a healthy group (3.72 +/- 0.68 nmol ml(-1) and 150.7 +/- 20.5 micromol gHb(-1), respectively) at a significant level (p<0.001). Erythrocyte GSH levels (5.2 +/- 0.30 micromol gHb(-1)) were found to be decreased among the victims (healthy group: 10.2 +/- 0.7 micromol gHb(-1)) at the same significance level (p<0.001). These observations confirm a significant change induced by radiation in the oxidant/antioxidant status among the victims. It is suggested here that antioxidant supplementation therapy might be effective in preventing the harmful effects of 60Co radiation among radiation victims.     

A cluster-based decision support system for estimating earthquake damage and casualties

This paper describes a Decision Support System for Disaster Management (DSS-DM) to aid operational and strategic planning and policy-making for disaster mitigation and preparedness in a less-developed infrastructural context. Such contexts require a more flexible and robust system for fast prediction of damage and losses. The proposed system is specifically designed for earthquake scenarios, estimating the extent of human losses and injuries, as well as the need for temporary shelters. The DSS-DM uses a scenario approach to calculate the aforementioned parameters at the district and sub-district level at different earthquake intensities. The following system modules have been created: clusters (buildings) with respect to use; buildings with respect to construction typology; and estimations of damage to clusters, human losses and injuries, and the need for shelters. The paper not only examines the components of the DSS-DM, but also looks at its application in Besiktas municipality in the city of Istanbul, Turkey.