Omar Alagha

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.

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.

Investigation of soil multi-element composition in Antalya, Turkey

The chemical composition of 73 surface soil samples collected at the city of Antalya were analyzed for major, minor and trace elements to assess distribution of soil pollution, due to deposition of pollution-derived particles from the atmosphere. Comparison with data from rural area and distribution maps demonstrated that the composition of soil by metals is not significantly modified by anthropogenic activities in most of the city and its surroundings. In these areas, observed concentrations can be largely accounted for by occurrence of elements in aluminasilicate matrix of soil. However, soil composition is altered substantially close to major industries and at the settlement districts. In such limited areas, concentrations of anthropogenic elements such as Cd, Pb, Zn, Cu and Cr are factors of 20-50 higher than their concentrations in unperturbed soil. Factor analysis revealed three groups of elements that differ in their distributions. One of these components is unperturbed soil component, which is distributed uniformly in the study area; the second one is polluted soil, which is mostly confined to settlement areas and around industries; and the third is a mixed marine and motor vehicle impacted soil component, which occurred at the coastal parts of the city.

Study of Trace and Heavy Metals in Rural and Urban Aerosols of Uludağ and Bursa (Turkey)

The concentrations of heavy, trace elements and major ions measuredin the Uludağ and Bursa aerosols were investigated to assess size distributions, spatial and temporal variability, sources and source regions affecting the composition of aerosols in Uludağ and Bursa. A total of 81 samples were collected in two sites, one in Bursa city and another in the Uludağ Mountain during two sampling campaigns. Daily samples were collected using a high volume sampler on Whatman 41 cellulose filters in Uludağ, while three days interval samples were collected in Bursa using an automatic dichotomous sampler on PTFE Teflon filters. Samples were analysed for 15 trace and heavy metals (Al, Fe, Ba, Na, Mg, K, Mn, Ca, Cu), (V, Pb, Cd, Cr, Ni, Zn), and 4 major ions (SO42-, NO3-, Cl-), (NH4+) using ICP-AES, GFAAS, HPLC and UV/VIS Spectrophotometer,respectively. In general, concentrations of the metals measured inUludağ aerosols were lower than those in Bursa. The concentrations of crustal elements were higher in summer than winter, while anthropogenic elements had higher concentrations in winter than summer. Most of the mass of crustal elements was concentrated in the coarse mode while the mass of the heavy metals was concentrated in the fine mode. Factor analysis revealed four factors with sources including crustal, industrial and combustion. Back trajectory calculations were used to determine long range contributions. These calculations showed that contributions were mostly from European countries, former Soviet Union countries, Black Sea and North Africa.

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.

Trace metals solubility in rainwater: evaluation of rainwater quality at a watershed area, Istanbul

In this study, 79 bulk precipitation samples were collected at two sampling sites near Büyükçekmece Lake, one of the important drinking water sources of Istanbul, for the period of October 2001 to July 2002. The study comprised the determination of trace and toxic metals concentrations in rain water. The concentrations of the metals in this study were found to be higher than those reported by other researchers around the world. The solubility of toxic metals was found in the order of Cd > Cu > V > Zn > Ni > Pb > Cr. Solubility of metals under acidic conditions (pH < 5.5) was approximately five times higher than those under neutral conditions with Cd as the most soluble metal (50% soluble). Statistical evaluations including seasonal variations, crustal enrichment factors, and correlation matrix were discussed to identify the possible sources of these pollutants. The study revealed that anthropogenic elements were highly enriched especially for Cd > Cu > Pb which were found to be highly enriched. Significant portion of Cu and Pb could be increased by the effect of local sources like cement industry in the area; however, the rest of the investigated trace metals could be brought to the sampling site by long-range transport to the Büyükçekmece Lake watershed area.

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.

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.

Atmospheric lead concentrations near roadways in a suburban part of Istanbul

In this study, daily aerosol samples of fine (PM2.5) and coarse (PM2.5−10) particles were collected at Buyukcekmece (41°2′35″N, 28°35′25″E), a sub-urban part of Istanbul, Turkey. Sampling area is under the impact of two main transport arteries, namely the Trans European Motorway (TEM) and E5 motorways. Atmospheric lead levels measured in this study were compared with those reported previously from various locations in Turkey and other parts of the world. These values lie midway between those typical of remote and urban sites. The data were statistically evaluated to determine seasonal changes. Then, wind sector analysis was carried out. The analysis showed that the major proportion of the episodes, about 54%, was generated from the direction of TEM motorway, located to the north, while the second major lead source, with a contribution of 20%, was found to be the direction of E-5 motorway, located to the south.

Relation of earth probe TOMS/AI data and ground level measured atmospheric aerosols over Marmara region

The main aim of this study is to statistically investigate the correlation between TOMS/Aerosol Index (AI) remote sensing particulate data and Ground Level (GL) aerosol concentrations. GL fine and coarse particles are collected from watershed area of Buyukcekmece at Marmara region in Istanbul, Turkey (41°0.04′ N; 28°0.59′ E). Randomly collected, 24 hr GL samples are statistically compared with the two years (2002-2003) TOMS/AI data. It is found that there is a significant relationship (R² = 0.47, p < 0.001) between TOMS/AI data and wintertime GL aerosols. The relation of TOMS/AI data and GL aerosol data are modelled using curvilinear models. Very good agreements between the data sets are obtained. The obtained models are first level exponential model and third level polynominal model for TOMS/AI versus fine and TOMS/AI versus coarse data. R-square values of the models are calculated as 0.92 and 0.67, respectively.