Investigation of Soil Concentrations of Persistent Organic Pollutants, Trace Elements, and Anions Due to Iron–Steel Plant Emissions in an Industrial Region in Turkey

The spatial distribution of several air pollutants emitted from iron–steel industries in Hatay-Iskenderun industrial region in Turkey was investigated by measuring their concentrations in soil. Collected soil samples (n?=?20) were analyzed for persistent organic pollutants (POPs; i.e., polycyclic aromatic hydrocarbons, polychlorinated biphenyls, polybrominated diphenyl ethers, and polychlorinated naphthalenes), anions, and trace elements. Generally, all measured POP concentrations and marker trace elements for iron–steel industry (Zn, Pb, and Cu) were high relative to those reported previously for rural and urban areas around the world, and they were comparable to those measured recently in a similar industrial region (Aliaga, Izmir, Turkey). For all investigated POPs, elements, and anions, the highest concentrations were measured at points located north of the iron–steel industries where their emissions are transported by the prevailing southerly winds. The spatial distribution and levels of POPs, elemental, and anionic concentrations indicated that the iron–steel plants in Hatay-Iskenderun region are important sources for these pollutants. Statistically significant relationships between several organic and inorganic pollutants further confirmed that they are emitted by common sources (iron–steel plants).     

Modeling surface fuels moisture content in Pinus brutia stands

Fuel moisture content is an important variable for forest fires because it affects fuel ignition and fire behavior. In order to accurately predict fuel ignition potential, fuel moisture content must be assessed by evaluating fire spread, fireline intensity and fuel consumption.Our objective here is to model moisture content of surface fuels in normally stocked Calabrian pine(Pinus brutia Ten.) stands in relation to weather conditions, namely temperature, relative humidity, and wind speed in the Mugla province of Turkey. All surface fuels were categorized according to diameter classes and fuel types. Six fuel categories were defined: these were 0–0.3, 0.3–0.6, and0.6–1 cm diameter classes, and cone, surface litter, and duff. Plastic containers 15 9 20 cm in size with 1 9 1 mm mesh size were used. Samples were taken from 09:00 to19:00 h and weighed every 2 h with 0.01 g precision for10 days in August. At the end of the study, samples were taken to the laboratory, oven-dried at 105 °C for 24 h and weighed to obtain fuel-moisture contents. Weather measurements were taken from a fully automated weather station set up at the study site prior to the study. Correlation and regression analyses were carried out and models were developed to predict fuel moisture contents for desorption and adsorption phase for each fuel type categories. Practical fuel moisture prediction models were developed for dry period. Models were developed that performed well with reasonable accuracy, explaining up to 92 and 95.6%of the variability in fuel-moisture contents for desorption and adsorption phases, respectively. Validation of the models were conducted using an independent data set and known fuel moisture prediction models. The predictive power of the models was satisfactory with mean absolute error values being 1.48 and 1.02 for desorption and adsorption as compared to the 2.05 and 1.60 values for the Van Wagner's hourly litter moisture content prediction model. Results obtained in this study will be invaluable for fire management planning and modeling.