Hakan Pekey

Fine particulate matter in the indoor air of barbeque restaurants: Elemental compositions, sources and health risks

Cooking is a significant source of indoor particulate matter that can cause adverse health effects. In this study, a 5-stage cascade impactor was used to collect particulate matter from 14 restaurants that cooked with charcoal in Kocaeli, the second largest city in Turkey. A total of 24 elements were quantified using ICP-MS. All of the element contents except for Mn were higher for fine particles (PM2.5) than coarse particles (PM>2.5), and the major trace elements identified in the PM2.5 included V, Se, Zn, Cr, As, Cu, Ni, and Pb. Principle component analysis (PCA) and enrichment factor (EF) calculations were used to determine the sources of PM2.5. Four factors that explained over 77% of the total variance were identified by the PCA. These factors included charcoal combustion, indoor activities, crustal components, and road dust. The Se, As, Cd, and V contents in the PM2.5 were highly enriched (EF>100). The health risks posed by the individual metals were calculated to assess the potential health risks associated with inhaling the fine particles released during charcoal cooking. The total hazard quotient (total HQ) for a PM2.5 of 4.09 was four times greater than the acceptable limit (i.e., 1.0). In addition, the excess lifetime cancer risk (total ELCR) for PM2.5 was 1.57×10(-4), which is higher than the acceptable limit of 1.0×10(-6). Among all of the carcinogenic elements present in the PM2.5, the cancer risks resulting from Cr(VI) and As exposure were the highest (i.e., 1.16×10(-4) and 3.89×10(-5), respectively). Overall, these results indicate that the lifetime cancer risk associated with As and Cr(VI) exposure is significant at selected restaurants, which is of concern for restaurant workers.

Indoor/outdoor concentrations and elemental composition of PM10/PM2.5 in urban/industrial areas of Kocaeli City, Turkey.

This study presents indoor/outdoor PM2.5 and PM10 concentrations measured during winter and summer in 15 homes in Kocaeli, which is one of the most industrialized areas in Turkey. Indoor and outdoor PM2.5 and PM10 mass concentrations and elemental composition were determined using an X-ray fluorescence spectrometer. Quantitative information was obtained on mass concentrations and other characteristics such as seasonal variation, indoor/outdoor (I/O) ratio, PM2.5/PM10 ratio, correlations and sources. Average indoor and outdoor PM2.5 concentrations were 29.8 and 23.5 microg/m(3) for the summer period, and 24.4 and 21.8 microg/m(3) for the winter period, respectively. Average indoor and outdoor PM10 concentrations were 45.5 and 59.9 microg/m(3) for the summer period, and 56.9 and 102.3 microg/m(3) for the winter period, respectively. A varimax rotated factor analysis (FA) was performed separately on indoor and outdoor datasets in an effort to identify possible heavy metal sources of PM2.5 and PM10 particle fractions. FA of outdoor data produced source categories comprising polluted soil, industry, motor vehicles, and fossil fuel combustion for both PM fractions, while source categories determined for indoor data for both PM2.5 and PM10 comprised industry, polluted soil, motor vehicles, and smoking, with an additional source category of cooking activities detected for the PM2.5 fraction. Practical Implications In buildings close to industrial areas or traffic arteries, outdoor sources may have an important effect on indoor air pollution. Therefore, indoor and outdoor investigations should be conducted simultaneously to assess the relationship between indoor and outdoor pollution. This study presents the simultaneous measurement of PM fractions (PM2.5 and PM10) and their elemental compositions to determine the sources of respirable PM and the heavy metals bound to these particles in indoor air. Factor analysis of indoor data indicated that the contribution of outdoor pollutant sources to indoor pollution was about 70%, making these sources the most significant for indoor heavy metal pollution, wheras other sources of indoor pollution included smoking and cooking activities.

Metals in the surface sediments of Istanbul Strait (Turkey)

Surface sediments from 17 stations in the Istanbul Strait and Marmara Sea were collected and analysed for major and trace elements by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF). Metal concentrations in surface sediments varied from 1.3 to 7.2 % for Al, 4.8 to 18 mg kg− 1 for As, 119 to 599 mg kg− 1 for Ba, below detection limit (bdl) to 6.6 mg kg− 1 for Cd, 18 to 222 mg kg− 1 for Cr, 7.6 to 180 mg kg− 1 for Cu, 1.0 to 5.5 % for Fe (10 000 to 55 000 mg kg− 1), 171 to 718 mg kg− 1for Mn, 3.3 to 64 mg kg− 1 for Ni, 4.5 to 461 mg kg− 1 for Pb, 1.3 to 68 mg kg− 1 for Sn, 19 to 170 mg kg− 1 for V and 16 to 859 mg kg− 1 for Zn. Three tools have been applied in order to evaluate metal pollution in the sediments; Sediment quality guidelines (SQGs), enrichment factors (EFs) and geoaccumulation index (Igeo). SQGs values indicate that Pb and Ni are the most likely contaminants to cause adverse biological effects. On the other hand, both metal enrichment factors and geoaccumulation index show that As, Zn, Pb and Cd contaminations exist in the entire study area and contamination of other metals is also present in some sites depending on the sources. Factor analysis (FA) receptor modelling technique was applied to investigate the sources affecting surface sediment samples at the Istanbul Strait.

Sources of heavy metals in the Western Bay of Izmit surface sediments

The study aimed to examine source apportionment of heavy metals of the surface sediments in the <63 µm size fraction. The sediment samples collected from 34 sites at the Western Bay of Izmit were subjected to a total digestion technique and analysed for major (total organic carbon, Al, Fe, Mg, and S) and trace (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sn, V, and Zn) elements by inductively coupled plasma-atomic emission spectrometry. The results were compared with the marine sediment quality standards, as well as literature values reported to assess the pollution status of the sediments. A factor analysis/multiple regression (FA/MR) multivariate receptor modelling technique was used for quantitative source apportionment to estimate the contributions from each source of contamination. Source fingerprints were obtained from the literature. A varimax rotated factor analysis was applied to the whole data set, and four probable source types were identified as the iron and steel industry, paint industry, crustal and sewage for heavy metals, explaining about 84% of the total variance. Source apportionment results derived from the FA and FA/MR methods agree well with each other.

Determination of the personal, indoor and outdoor exposure levels of inorganic gaseous pollutants in different microenvironments in an industrial city

We measured SO2, NO2 and O3 concentrations during the summer and winter in Kocaeli, Turkey. The sampling was carried out indoors and outdoors at homes, schools and offices. Personal samplers were also used to determine personal exposures to these pollutants. High NO2 and SO2 concentrations were observed in outdoor samples collected close to locations characterized by heavy urban traffic. Concentrations of O3, on the other hand, were higher in rural areas around the city due to ozone distillation. For both sampling periods, the concentrations of outdoor SO2 and O3 were higher than for indoor and personal samples; however, the NO2 concentrations were higher in indoor and personal samples, indicating that outdoor sources significantly contribute to indoor SO2 and O3 levels and that indoor NO2 concentrations are primarily modulated by sources within buildings. Seasonal variations in pollutant concentrations showed statistically significant differences. Indoor and outdoor concentrations of NO2 and SO2 measured in the winter were higher than the levels measured in the summer; O3 concentrations, on the other hand, exhibited the opposite trend. Active-to-passive concentration ratios for NO2, SO2 and O3 were 0.99, 1.08 and 1.16, respectively; the corresponding outdoor ratios were 0.95, 0.99 and 1.00.

Odor dispersion modeling with CALPUFF: Case study of a waste and residue treatment incineration and utilization plant in Kocaeli, Turkey

ABSTRACT This study addresses the odor problem at a waste and residue treatment incineration and utilization plant located within the borders the Alikahya district of the Kocaeli province in Turkey. In the first stage of the study, odor measurements were made at designated sampling points, while in the second stage, odor concentrations were determined at the receptor points through dispersion modelling using a USEPA (United States Environmental Protection Agency) certified long-range (>50 km) CALPUFF lagrangian puff model. In the final stage, an analysis of the predicted and observed values was carried out using such statistical methods as geometric mean bias (MG), geometric variance (VG) and fractions of predictions within a factor of two observations (FAC2). During the modelling study, the highest one-hour concentration level was found to be 1868.10 OU (Odor Units), and the 24-hour concentration level was found to be 1316 OU, representing a decrease of approximately 30 percent. According to the measurement made, the maximum concentration value was 2455 OU. Odor measurements were carried out at 13 points within the area in order to assess the prediction results. When the results were assessed using the MG, VG and FAC2 statistical methods, it was observed that an acceptable model performance was not achieved for the whole sampling point. When the reason for this was investigated, it was concluded that the observed values were lower than the predicted values due to the fact on the measurement days, the odor was dispersed by wind. It was further concluded that the observed values were higher than the predicted values as a result of odors emitted by other plants in the area. When the measurements in residential areas were examined to identify the effect of the odors, it was determined that although the primary density of settlement is to the southwest of the plant, it was not this area that was affected most, but rather the area to the northeast, where there is a lower settlement density.