Ferhat Karaca

Particulate matter (PM(2.5), PM(10-2.5), and PM(10)) and children's hospital admissions for asthma and respiratory diseases: a bidirectional case-crossover study.

Epidemiological studies reported adverse effects of air pollution on the prevalence of respiratory diseases in children. The purpose of this study was to examine the association between air pollution and admissions for asthma and other respiratory diseases among children who were younger than 15 yr of age. The study used data on respiratory hospital admissions and air pollutant concentrations, including thoracic particulate matter (PM10), fine (PM2.5), and coarse (PM10-2.5) particulate matter in Zonguldak, Turkey. A bidirectional case-crossover design was used to calculate odds ratios for the admissions adjusted for daily meteorological parameters. Significant increases were observed for hospital admissions in children for asthma, allergic rhinitis (AR), and upper (UPRD) and lower (LWRD) respiratory diseases. All fraction of PM in children showed significant positive associations with asthma admissions. The highest association noted was 18% rise in asthma admissions correlated with a 10-μg/m3 increase in PM10-2.5 on the same day of admissions. The adjusted odds ratios for exposure to PM2.5 with an increment of 10 μg/m3 were 1.15 and 1.21 for asthma and allergic rhinitis with asthma, respectively. PM10 exerted significant effects on hospital admissions for all outcomes, including asthma, AR, UPRD, and LWRD. Our study suggested a greater effect of fine and coarse PM on asthma hospital admissions compared with PM10 in children.

NN-LEAP: A neural network-based model for controlling leachate flow-rate in a municipal solid waste landfill site

Abstract A method is proposed for modeling leachate flow-rate in a municipal solid waste (MSW) landfill site, based on a popular neural network – the backpropagation algorithm ( n eural n etwork-based lea chate p rediction method; NN-LEAP). After backpropagation training, the neural network model predicts flow-rates based on meteorological data. Depending on output value, relevant control strategies and actions are activated. To illustrate and validate the proposed method, a case study was carried out, based on the data obtained from the Istanbul Odayeri landfill site. As a critical model parameter (neural network outputs), daily flow-rate of leachate from the landfill site was considered. The Levenberg–Marquardt algorithm was selected as the best of 13 backpropagation algorithms. The optimal neural network architecture has been determined, and the advantages, disadvantages and further developments are discussed.

Effect of Meteorological Parameters on Fine and Coarse Particulate Matter Mass Concentration in a Coal-Mining Area in Zonguldak, Turkey

Abstract In this work, the effect of meteorological parameters and local topography on mass concentrations of fine (PM2.5) and coarse (PM2.5–10) particles and their seasonal behavior was investigated. A total of 236 pairs of samplers were collected using an Anderson Dichotomous sampler between December 2004 and October 2005. The average mass concentrations of PM2.5, PM2.5–10, and particulate matter less than 10 μm in aerodynamic diameter (PM10) were found to be 29.38, 23.85, and 53.23 μ/m3, respectively. The concentrations of PM2.5 and PM10 were found to be higher in heating seasons (December to May) than in summer The increase of relative humidity, cloudiness, and lower temperature was found to be highly related to the increase of particulate matter (PM) episodic events. During non-rainy days, the episodic events for PM2.5 and PM10 were increased by 30 and 10.7%, respectively. This is a result of the extensive use of fuel during winter for heating purposes and also because of stagnant air masses formed because of low temperature and low wind speed over the study area.

Ragweed pollen observed in Turkey: detection of sources using back trajectory models.

This paper discusses the pollen season and the source apportionment of ragweed (Ambrosia) grains detected in the atmosphere of Istanbul, Turkey. The dynamic migration of this invasive taxon is a serious environmental issue. Ragweed pollen is highly allergenic and causes sensitization in patients at low concentrations. At present, there is no floristic evidence of this taxon in the region. Aerobiological records presented here, though, indicate a local source. Moreover, we argue that ragweed pollen comes from distant sources through air mass movements. The analysis concerns the ragweed season 2007. Pollens were sampled with a Burkard trap and identified at a magnification of 400 ×. Grains were counted on 12 transverse traverses to estimate bi-hourly changes in concentrations. The peak day was on August 28 with 20 grainsm(-3). Ragweed was observed on 22 days during August and September 2007. On all days, except one, the daily average concentration was below 10 grainsm(-3). Diurnal bi-hourly ragweed concentrations reached a maximum at 11:00 EET. Relatively high concentrations were observed between 21:00 and 01:00 EET. This allowed for the assumption of a local and a remote ragweed pollen source. We used HYSPLIT backward trajectory ensembles to identify possible sources on peak day. A frequency analysis of back trajectories covering the entire ragweed season followed. Firstly, possible local sources were the Istanbul Province and Turkish Thrace; secondly, a likely over-regional source was Bulgaria; and lastly, remote sources of ragweed pollen were the Ukraine, the Russian coastal region of the Black Sea and Moldova. This study provides evidence that pollens detected on our receptor site stem from combined local and remote origins.

NN-AirPol: a neural-networks-based method for air pollution evaluation and control

A method for air pollution evaluation and control, based on one of the most popular neural networks - the backpropagation algorithm, is proposed. After the backpropagation training, the neural network, based on weather forecasting data, determines the future concentration of critical air pollution indicators. Depending on these concentrations, relevant episode warnings and actions are activated. A case study is carried out to illustrate and validate the method proposed, based on Istanbul air pollution data. Sulphur dioxide and inhalable particulate matter are selected as air pollution indicators (neural network outputs). Relevant episode measures are proposed. Among ten backpropagation algorithms, the BFGS algorithm (Quasi-Newton algorithms) is adopted since it showed the lowest training error. A comparison of NN-AirPol method against regression and perceptron models showed significantly better performance.

APPLICATION OF INDUCTIVE LEARNING: AIR POLLUTION FORECAST IN ISTANBUL, TURKEY

Abstract In this study, Istanbul city was taken as the study area. A new and powerful technique, Artificial Intelligence (AI), an Inductive Learning Algorithm (RULES-3), was used in predicting the future (next 24 hours) sulfur dioxide (SOZ) air pollutant on the basis of vazious meteorological parameters. The goal of this study is to forecast the 24-h average SOZ concentration levels in the urban atmosphere using AL As a result of this study, it was seen that AI is a powerful tool in estimating air pollution levels considering the complex and nonlinear structure of the atmospheric parameters, which is the source of the database.

Determination of air quality zones in Turkey

In this study, the particulate matter (with an aerodynamic diameter <10 μm; PM10) profile of Turkey with data from the air quality monitoring stations located throughout the country was used. The number of stations (119) was reduced to 55 after a missing data treatment for statistical analyses. First, a classification method was developed based on ongoing national and international (European Commission directives) legislations to categorize air zones into six groups, from a “Very Clear Air Zone” to a “Polluted Air Zone.” Then, a Geographic Information System (GIS)-based interpolation technique and statistical analyses (correlation analysis and factor analysis) were used to generate PM10 pollution profiles of the annual heating time and nonheating time periods. Finally, the coherent air pollution management zones of Turkey, based on air quality criteria and measured data using a GIS-based model supported by statistical analyses, were suggested. Based on the analysis, four hot spots were identified: (i) the eastern part of the Black Sea region; (ii) the northeastern part of inland Anatolia; (iii) the western part of Northeastern Anatolia; and (vi) the eastern part of Turkey. The possible reasons for the elevated PM10 levels are discussed using topographic, climatologic, land use, and energy utilization parameters. Finally, the suggested air zones were compared with the administrative air zones, which were newly developed by the Turkish Ministry of Environment and Forestry, to evaluate the level of agreement between the two. Implications: The evaluation of air quality profiles of specific regions is important in the development and/or application of an effective air quality management strategy. Factor analysis (FA), together with correlation analysis (CA), provides useful information to classify air pollution management areas over regional networks that have historical time-series air quality data. In this study, which relied on a FA- and CA-based methodology, the coherent air pollution management zones of Turkey after using a GIS-based model were suggested. Policy makers and scientist can use these suggested zones to construct better air quality management strategies.

Rethinking Future of Utilities: Supplying All Services through One Sustainable Utility Infrastructure

Sustainable Utility Infrastructure Fatih Camci,†,* Bogumil Ulanicki,‡ Joby Boxall, Ruzanna Chitchyan, Liz Varga, and Ferhat Karaca† †IVHM Centre, Cranfield University, Bedford, U.K. ‡Department of Engineering, De Montfort University, U.K. Department of Civil and Structural Engineering, University of Sheffield, U.K. Department of Computer Science, University of Leicester, U.K. Complex Systems Research Centre, Cranfield School of Management, U.K.

Traffic Related PM Predictor for Besiktas, Turkey

The main objective of this study was to develop an Artificial Neural Networks (ANN) based model, which could be used as a tool for the prediction of traffic related PM2.5 and PM10 emissions. In this purpose, about 70 pairs of daily PM2.5 and PM2.5-10 samples were collected near to a main artery in Besiktas, Istanbul, Turkey. In addition to the PM data, hourly meteorological data, air quality data (CO, SO2, NO, NO2, NOx) and traffic data (traffic counts, speed, and density) were employed in the model. The results obtained from two different Neural Networks namely Forward NN (FFNN) and Radial Basis Function NN (RBFNN) were compared. While FFNN did not give good results due to limited number of data (60% of 70 data points) in high dimensional space (i.e., 14 dimensional space), more robust results were obtained with RBFNN with 72% prediction performance.

Radiotracer method to study the transport of mercury(II)chloride from water to sediment and air

The fate of dissolved Hg(II) in surface waters is an important component of the Hg cycle. A simple experimental methodology was used to understand and measure the transport of Hg(II) from water to air and sediment. The use of radioactive dissolved Hg tracer for the determination of evasion and deposition is found to be a very useful technique. The evasion of mercury was investigated during a 140-hour period. It was observed that about a quarter of mercury chloride remained in the water phase, the other quarter was emitted via the evasion process and half of it deposited in sediment.