Effects of land conversion from native shrub to pistachio orchard on soil erodibility in an arid region

Land-use change through degrading natural vegetation for agricultural production adversely affects many of soil properties particularly organic carbon content of soils. The native shrub land and grassland of Gaziantep-Adiyaman plateau that is an important pistachio growing eco-region have been cleared to convert into pistachio orchard for the last 50 to 60 years. In this study, the effects of conversion of natural vegetation into agricultural uses on soil erodibility have been investigated. Soil samples were collected from surface of agricultural fields and adjacent natural vegetation areas, and samples were analyzed for some soil erodibility indices such as dispersion ratio (DR), erosion ratio (ER), structural stability index (SSI), Henin’s instability index (I _s ), and aggregate size distribution after wet sieving (AggSD). According to the statistical evaluation, these two areas were found as different from each other in terms of erosion indices except for I _s index (P < 0.001 for DR and ER or P < 0.01 for SSI). In addition, native shrub land and converted land to agriculture were found different in terms of AggSD in all aggregate size groups. As a contrary to expectations, correlation tests showed that there were no any interaction between soil organic carbon and measured erodibility indices in two areas. In addition, significant relationships were determined between measured variables and soil textural fractions as statistical. These obtaining findings were attributed to changing of textural component distribution and initial aggregate size distribution results from land-use change in the study area. Study results were explained about hierarchical aggregate formation mechanism.     

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.     

Characterization of an urban landfill soil by using physicochemical analysis and solid phase microextraction (SPME)—GC/MS

We have aimed at characterizing top soil samples taken in-situ from five different locations of the unregulated dumping site in Eskişehir/Turkey for a period of six months. The study is the first attempt in the city and in Turkey, regarding particularly the SPME (Solid Phase Microextraction Technique) analysis method utilized. A comprehensive research has been conducted to produce critical soil data to be used for indicating current risks as well as the urgency of rehabilitating the site and establishing a sanitary landfill in the site. Conventional physicochemical analytical methods and SPME technique were used to analyze the samples. Physicochemical analyses were performed for determining the pH, total dried matter, volatile matter, total nitrogen, phosphorus, macro elements and heavy metals. Meteorological data were also recorded for the same period. SPSS.10.0 statistical program was used to determine the correlation between meteorological data and physicochemical analysis results. Mean values were used in the correlation analyses. These data indicated that the air temperature and precipitation have significant effects on soil characteristics. SPME, coupled with GC/MS, was used to identify eighty six volatile and semi-volatile organic compounds contained in soil samples. The samples were extracted by headspace SPME with heating (δHS-SPME). SPME analyses were conducted using a commercially available polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber having a film thickness of 65 μm (Supelco) as a capture medium. The experimentally optimized headspace sampling conditions were arranged (15 min. at 50^˚C) before a 30 min. sampling period.     

Delineation of soil and groundwater contamination using geophysical methods at a waste disposal site in Çanakkale, Turkey

Direct current (DC) resistivity, self potential (SP) and very low frequency electromagnetic (VLF-EM) measurements are carried out to detect the spread of groundwater contamination and to locate possible pathways of leachate plumes, that resulted from an open waste disposal site of Canakkale municipality. There is no proper management of the waste disposal site in which industrial and domestic wastes were improperly dumped. Furthermore, because of the dumpsite is being located at the catchment area borders of a small creek and is being topographically at a high elevation relative to the urban area, the groundwater is expected to be hazardously contaminated. Interpretations of DC resistivity geoelectrical data showed a low resistivity zone (<5 ohm-m), which appears to be a zone, that is fully saturated with leachate from an open dumpsite. The VLF-EM and SP method, support the results of geoelectrical method relating a contaminated zone in the survey area. There is a good correlation between the geophysical investigations and the results of previously collected geochemical and hydrochemical measurements.