Seasonal variations of heavy metals in some organs of carp (Cyprinus carpio L., 1758) from Beyşehir Lake (Turkey)

In this study which was carried out between March 2003 and February 2005 Fe, Cu, Zn, Mn, Cr, Pb and Cd contents were determined in muscle, liver and gill of carp (Cyprinus carpio L., 1758) caught from Beyşehir Lake. Among the heavy metals analyzed Cr, Pb and Cd were below the detection limit (<0.03). Heavy metal concentrations varied significantly depending on the type of the tissue and season. The highest metal concentrations were found in the liver, followed by gill and muscle. Heavy metal levels in tissues of carp were increased in summer and winter, while were decreased in autumn and spring. The present study shows that precautions need to be taken in Beyşehir Lake in order to prevent heavy metal pollution that can occur in the future.     

The fatty acid compositions of several plant seed oils belong to Leguminosae and Umbelliferae families

In samples with 1,009, 7,723, 7,618, 7,618, 1,004 and 1,009 number, oleic acid were found as 62.0, 77.0, 74.84, 71.55, 54.52 and 62.30 %, respectively. In other samples, oleic acid content was determined between 17.43 % (1,589) and 34.86 % (1,298). Linoleic acid content of seed oils ranged from 6.52 % (7,727) to 57.29 % (1,501). In addition, linolenic acid content was found between 0.22 % (7,618) and 46.91 % (1,589). Palmitic acid content of samples changed between 2.03 % (7,727) and 19.81 % (1,298). Capric acid was found at high level in 1,009 (8.53 %), 7,727 (37.31 %) and 1,004 (8.28 %) samples. Caproic acid was found in only 7,727 (3.38 %).     

Evaluation of land-use regression models used to predict air quality concentrations in an urban area

Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM_2.5, NO_x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.     

Scenario Development as a Basis for Formulating a Research Program on Future Agriculture: A Methodological Approach

To increase the awareness of society to the challenges of global food security, we developed five contrasting global and European scenarios for 2050 and used these to identify important issues for future agricultural research. Using a scenario development method known as morphological analysis, scenarios were constructed that took economic, political, technical, and environmental factors into account. With the scenarios as a starting point future challenges were discussed and research issues and questions were identified in an interactive process with stakeholders and researchers. Based on the outcome of this process, six socioeconomic and biophysical overarching challenges for future agricultural were formulated and related research issues identified. The outcome was compared with research priorities generated in five other research programs. In comparison, our research questions focus more on societal values and the role of consumers in influencing agricultural production, as well as on policy formulation and resolving conflicting goals, areas that are presently under-represented in agricultural research. The partly new and more interdisciplinary research priorities identified in Future Agriculture compared to other programs analyzed are likely a result of the methodological approach used, combining scenarios and interaction between stakeholders and researchers.     

Assessment of climate change simulations over climate zones of Turkey

Projected climate change over Turkey has been analyzed by using the reference (1961–1990) and future (2071–2100) climate simulations produced by ICTP-RegCM3. Since examining Turkey as a single region could be misleading due to the existence of complex topography and different climatic regions, Turkey has been separated into seven climatic regions to evaluate the surface temperature and precipitation changes. Comparison of the reference simulation with observations was made spatially by using a monthly gridded data set and area-averaged surface data compiled from 114 meteorological stations for each climatic region of Turkey. In the future simulation, warming over Turkey’s climatic regions is in the range of 2–5 °C. Summer warming over western regions of Turkey is 3 °C higher than the winter warming. During winter, in the future simulation, precipitation decreases very significantly over southeastern Turkey (24 %), which covers most of the upstream of Euphrates and Tigris river basin. This projected decrease could be a major source of concern for Turkey and the neighboring countries. Our results indicate that a significant increase (48 %) in the autumn season precipitation is simulated over southeastern Turkey, which may help to offset the winter deficit and therefore reduce the net change during the annual cycle.     

Cytogenetic biomonitoring of primary school children exposed to air pollutants: micronuclei analysis of buccal epithelial cells

There is an increasing attempt in the world to determine the exposures of children to environmental chemicals. To analyze the genotoxic effect of air pollution, micronucleus (MN) assay was carried out in buccal epithelial cells (BECs) of children living in an urban city of Turkey. Children from two schools at urban-traffic and suburban sites were investigated in summer and winter seasons for the determination of BEC-MN frequency (per mille) and frequency of BEC with MN (per mille). The same children were also recruited for lung function measurements within a MATRA project (“Together Towards Clean Air in Eskisehir and Iskenderun”) Measured NO₂ and SO₂ concentrations did not exceed the European Union (EU) limit levels either in urban-traffic or suburban regions. Higher O₃ concentrations were measured in the suburban site especially in the summer period. Particulate matter (PM_2.5 and PM₁₀) levels which did not differ statistically between two regions were above the EU limits in general. Although BEC-MN frequencies of children living in the suburban sites were higher in general, the difference between two regions was not significant either in the summer or winter periods. BEC-MN frequencies of the urban-traffic children were found to be significantly higher in summer period (mean ± SD, 2.68?±?1.99) when compared to winter period (1.64?±?1.59; p?=?0.004). On the other hand, no seasonality was observed for the suburban children. Similar results have been obtained in the BEC frequency with MN in our study. In summer, BEC-MN frequencies were significantly increased with the decrease in pulmonary function levels based on forced expiratory flow between 25 and 75 % of vital capacity (FEF_25–75 %) levels (p?<?0.05). As a conclusion, children living in urban-traffic and suburban areas in the city of Eski?ehir exhibited similar genotoxicity. Seasonal variation in genotoxicity may be interpreted as relatively high ozone levels and increasing time spent at outdoors in the summer.     

Development of Models for Predicting the Distribution of Indoor Nitrogen Dioxide Concentrations

Extensive data on residential indoor and outdoor NO₂ levels have been collected in a limited number of U.S. locations. To date, researchers have analyzed these data sets individually, but have not analyzed them in the aggregate. Results have not, therefore, been suitable for application in a nationwide exposure assessment. This paper presents an analysis of indoor and outdoor NO₂ field measurements from five U.S. metropolitan areas for homes with gas-fueled ranges and discusses potential applications of the results. Using linear regression analysis, the relationship between indoor NO₂ and various predictor variables was explored. Results indicated that ambient NO2 levels alone explain an estimated 37 percent of the variability in indoor NO₂ levels, that the relationship between indoor and outdoor NO₂ concentrations differs significantly from summer to winter months, and that homes with range pilot lights have indoor levels approximately 7 ppb greater than homes without pilot lights. A logistic regression model which predicts the distribution of indoor NO₂ levels based on ambient NO₂ concentrations was developed. Estimation and testing of the logistic model indicated good model performance. The model is particularly useful for addressing policy-oriented questions that involve the concept of "acceptable" threshold levels for human exposure to NO₂.     

Parameter Evaluation and Model Validation of Ozone Exposure Assessment Using Harvard Southern California Chronic Ozone Exposure Study Data

Abstract

To examine factors influencing long‐term ozone (O₃) exposures by children living in urban communities, the authors analyzed longitudinal data on personal, indoor, and outdoor O₃ concentrations, as well as related housing and other questionnaire information collected in the one‐year‐long Harvard Southern California Chronic Ozone Exposure Study. Of 224 children contained in the original data set, 160 children were found to have longitudinal measurements of O₃ concentrations in at least six months of 12 months of the study period. Data for these children were randomly split into two equal sets: one for model development and the other for model validation. Mixed models with various variance‐covariance structures were developed to evaluate statistically important predictors for chronic personal ozone exposures. Model predictions were then validated against the field measurements using an empirical best‐linear unbiased prediction technique.The results of model fitting showed that the most important predictors for personal ozone exposure include indoor O₃ concentration, central ambient O₃ concentration, outdoor O₃ concentration, season, gender, outdoor time, house fan usage, and the presence of a gas range in the house. Hierarchical models of personal O₃ concentrations indicate the following levels of explanatory power for each of the predictive models: indoor and outdoor O₃ concentrations plus questionnaire variables, central and indoor O₃ concentrations plus questionnaire variables, indoor O₃ concentrations plus questionnaire variables, central O₃ concentrations plus questionnaire variables, and questionnaire data alone on time activity and housing characteristics. These results provide important information on key predictors of chronic human exposures to ambient O₃ for children and offer insights into how to reliably and cost‐effectively predict personal O₃ exposures in the future. Furthermore, the techniques and findings derived from this study also have strong implications for selecting the most reliable and cost‐effective exposure study design and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.