An investigation of heavy metal and migration through groundwater from the landfill area of Eskisehir in Turkey

This paper was conducted in order to determine the groundwater and soil pollution within and around the landfill of Eskisehir, Turkey. In this paper, mud, leachate and groundwater samples were collected seasonally a year from near Eskisehir landfill-site to investigate the possible impact of leachate which affects soil and groundwater quality. Concentrations of various heavy metals (Fe, Cu, Zn, Mn, Co, Pb, Cr, Ni and Mo) were determined in mud, leachate and groundwater samples. In addition, the heavy metal transportation infiltrated from landfill through a porous medium into the groundwater was modelled in order to determine the potential groundwater pollution caused by the leachate of the landfill. The modelling of the contaminant transportation was carried out by using a multiflow computer programme which simulates the distribution of heavy metal concentrations. As a result of this study, the distribution of the contaminant concentration was modelled and determined with respect to time and distance. Hence, the contaminant concentrations were determined at any time interval according to distance. The heavy metal contamination in groundwater does not affect the wells found at far points from the source in a short time, e.g. 10, 20 and 30 days according to the obtained experimental results. When the time intervals extended more than 1 year, heavy metal concentrations decrease with distance but the concentration of the contamination increases when it gets closer to the pollution source. In this study, the potential contamination of groundwater was effectively estimated.     

Long-term spatiotemporal patterns of CH4 and N2O emissions from livestock and poultry production in Turkey

This study quantified spatiotemporal patterns of CH₄ and N₂O emissions from livestock and poultry production in Turkey between 1961 and 2007. CH_4(enteric) (from enteric fermentation), CH_4(manure) (from manure management), and N₂O_(AWM) (from animal waste management) emissions in Turkey were estimated at 1,164, 216, and 55 Gg in 1961 and decreased to 844, 187, and 39 Gg in 2007, contributing a share of roughly 2% to the global livestock-related CH₄ emissions and %1.5 to the global N₂O_(AWM) emissions, respectively. Total CO₂-eq emissions were estimated at 50.7 Tg in 1961 and declined from a maximum value of 60.7 Tg in 1982 to a minimum value of 34.5 Tg in 2003, with a mean emission rate of 48 Tg year^???1 due to a significant reduction in the number of ruminant livestock. The highest mean share of emissions belonged to West Black Sea (14% and 16%) for CH_4(enteric) and CH_4(manure) and to North East Anatolia (12% and %13) for N₂O_(AWM) and total CO₂-eq emissions, respectively. The highest emission density was 1.7 Mg km^???2 year^???1 for CH_4(enteric), 0.3 Mg km^???2 year^???1 for CH_4(manure), and 0.07 Mg km^???2 year^???1 for the total CO₂-eq emissions in the West and North East Anatolia regions and 0.09 Mg km^???2 year^???1 for N₂O_(AWM) in the East Marmara region. Temporal and spatial variations in CH_4(enteric), CH_4(manure), and N₂O_(AWM) emissions in Turkey were estimated using regression models and ordinary kriging at a 500-m resolution, respectively.     

New kinetic modelling parameters for composting process

This study aims to assess the composting process of sawdust, wheat-straw and chicken manure and to define the best blend proportion as a function of organic matter loss. Chicken manure, sawdust, and wheat-straw were mixed at different ratios and composted in reactors. The obtained outcomes revealed that the optimum mixture ratio was found in a mixture of 60 % chicken manure, 30 % sawdust, and 10 % wheat-straw. Three kinetic parameters were used in the models including daily process average temperature, area characterized by temperatures under the process temperature curve, and area characterized by temperatures between the ambient and process temperature (ALAT) as an alternative of process temperature. In addition to these statistical values, modelling efficiency was defined. Statistical analyses revealed that all the evaluated models were found suitable for this study; however, when ALAT was used as a function of temperature, the predictability level of all the models improved.