Modeling of energy and emissions from animal manure using machine learning methods: the case of the Western Mediterranean Region,Turkey

Environmental Science and Pollution Research - In Turkey, facilities for the use of biomass resources in energy production are increasing, and new conversion facilities are commissioned every year...     

Magnetic nanoparticles coated with aminated polymer brush as a novel material for effective removal of Pb(II) ions from aqueous environments

Environmental Science and Pollution Research - In the present study, a poly (vinylbenzyl chloride) grafted Fe3O4 nanoparticle (Fe3O4@PVBC) was prepared by surface-initiated reversible addition...     

Ergonomic assessment of airport shuttle driver tasks using an ergonomic analysis toolset

This study aimed to (a) evaluate strength requirements and lower back stresses during lifting and baggage handling tasks with the 3D Static Strength Prediction Program (3DSSPP) and (b) provide additional analyses using rapid entire body assessment (REBA) and the NASA task load index (TLX) to assess the risks associated with the tasks. Four healthy female shuttle drivers of good health aged between 55 and 60 years were observed and interviewed in an effort to determine the tasks required of their occupations. The results indicated that lifting bags and placing them in a shuttle were high risk for injury and possible changes should be further investigated. The study concluded there was a potential for injury associated with baggage storing and retrieval tasks of a shuttle driver.     

Modeling seasonal variations of long-term soil CO<Subscript>2</Subscript> emissions in an orchard plantation in a semiarid area,SE Turkey

Emissions of soil CO₂ under different management systems have a significant effect on the carbon balance in the atmosphere. Soil CO₂ emissions were measured from an apricot orchard at two different locations: under the crown of trees (CO₂-UC) and between tree rows (CO₂-BR). For comparison, one other measurement was performed on bare soil (CO₂-BS) located next to the orchard field. Analytical data were obtained weekly during 8 years from April 2008 to December 2016. Various environmental parameters such as air temperature, soil temperature at different depths, soil moisture, rainfall, and relative humidity were used for modeling and estimating the long-term seasonal variations in soil CO₂ emissions using two different methods: generalized linear model (GLM) and artificial neural network (ANN). Before modeling, data were randomly split into two parts, one for calibration and the second for validation, with a varying number of samples in each part. Performances of the models were compared and evaluated using means absolute of estimations (MAE), square root of mean of prediction (RMSEP), and coefficient of determination (R²) values. CO₂-UC, CO₂-BR, and CO₂-BS values ranged from 11 to 3985, from 9 to 2365, and from 8 to 1722 kg ha^?1 week^?1, respectively. Soil CO₂ emissions were significantly correlated (p?<?0.05) with some environmental variables. The results showed that GLM and ANN models provided similar accuracies in modeling and estimating soil CO₂ emissions, as the number of samples in the validation data set increased. The ANN was more advantageous than GLM models by providing a better fit between actual observations and predictions and lower RMSEP and MAE values. The results suggested that the success of environmental variables for estimations of CO₂ emissions using the two methods was moderate.     

Radioactivity measurements in moss (Hypnum cupressiforme) and lichen (Cladonia rangiformis) samples collected from Marmara region of Turkey

The present study was conducted to compare the ¹³⁷Cs, ⁴⁰K, ²³²Th, and ²³⁸U activity concentrations in epigeic moss (Hypnum cupressiforme) and lichen (Cladonia rangiformis). The activity levels in 37 moss and 38 lichen samples collected from the Marmara region of Turkey were measured using a gamma spectrometer equipped with a high purity germanium (HPGe) detector. The activity concentrations of ¹³⁷Cs, ⁴⁰K, ²³²Th, and ²³⁸U in the moss samples were found to be in the range of 0.36-8.13, 17.1-181.1, 1.51-6.17, and 0.87-6.70 Bq kg⁻¹ respectively, while these values were below detection limit (BDL)-4.32, 16.6-240.0, 1.32-6.47, and BDL-3.57 Bq kg⁻¹ respectively in lichen. The average moss/lichen activity ratios of ¹³⁷Cs, ⁴⁰K, ²³²Th, and ²³⁸U were found to be 1.32 ± 0.57, 2.79 ± 1.67, 2.11 ± 0.82, and 2.19 ± 1.02, respectively. Very low ¹³⁷Cs concentrations were observed in moss and lichen samples compared to soil samples collected from the same locations in a previous study. Seasonal variations of the measured radionuclide activities were also examined in the three sampling stations.     

Spatial variability of salinity and alkalinity of a field having salination risk in semi-arid climate in northern Turkey

Spatial variability of salinity and alkalinity is important for site-specific management since they are the most important factors influencing soil quality and agricultural production. The objectives of this study were to analyze spatial variability in salinity and alkalinity and some soil properties affecting salinity and alkalinity, using classical statistics and geostatistical methods, in an irrigated field with low-quality irrigation water diverted from drainage canals. A field of 5 da was divided into 10 m x 10 m grids (5 lines in the east-west direction and 10 lines in the north-south direction). The soil samples were collected from three depths (0-30, 30-60 and 60-90 cm) at each grid corner. The variation coefficients of OM and sand contents were higher than other soil properties. OM had the maximum variability, with a mean of 1.63% at 0-30 cm depth and 0.71% at 30-60 cm depth. Significant correlations occurred between ESP, EC and each of Ca, Mg, K and CaCO(3) contents of the soils (p<0.01). Experimental semivariograms were fitted to spherical and gaussian models. All geostatistical range values were greater than 36 m. The soil properties had spatial variability at small distances at 60-90 cm depth. EC was variable within short distances at 30-60 cm depth. The nugget effect of ESP increased with soil depth. Kriged contour maps revealed that soils had a salinisation and alkalisation tendency at 60-90 cm depth based on spatial variance structure of the EC and ESP values. Spatial variability in EC and ESP can depend on ground water level, quality of irrigation water, and textural differences.     

Assessment of the Water Quality of Troia for the Multipurpose Usages

The aim of this study was to determine the origin and quality of waters in Troia. For this purpose total of 25 water samples including 2 springs, 14 surfaces and 9 groundwaters, were collected at eight different times. Global positioning system (GPS) was used to determine to coordinates of sampling points. The concentration of 6 minor elements (B, Cu, F, Fe, Pb and Zn), 9 major anions and cations (Na(+), Ca(2+), K(+), Mg(2+), SO(4)(2-), PO(4)(3-), HCO(3)(-), Cl(-) and, CO(3)(2-)) were determined by spectrometric, colorimetric and volumetric methods. Water pH, EC, DO, ORP and TDS were measured in situ using probes. The data showed that the concentrations of most of minor elements were below the EPA and TSE limits except Pb which ranged between 0.001 and 4.832 mg L(-1). Statistically significant relationships (P<0.01 and r>0.70) were observed between Fe and Cu, Cu and K(+), Cu and Ca(2+), B and Na(+), Na(+) and K(+). Assessing the water based on irrigation using Wilcox model showed that some well waters were not suitable for irrigation. Troia water was found to be highly corrosive and the average corrosion coefficients varied from 0.5 to 4.6. According to the Piper and Schoeller diagrams results, the water in Troia was classified as mixed water type.     

Monitoring land use/land cover changes using CORINE land cover data: a case study of Silivri coastal zone in Metropolitan Istanbul

The objective of the present study was to assess changes in land use/land cover patterns in the coastal town of Silivri, a part of greater Istanbul administratively. In the assessment, remotely sensed data, in the form of satellite images, and geographic information systems were used. Types of land use/land cover were designated as the percentage of the total area studied. Results calculated from the satellite data for land cover classification were compared successfully with the database Coordination of Information on the Environment (CORINE). This served as a reference to appraise the reliability of the study presented here. The CORINE Program was established by the European Commission to create a harmonized Geographical Information System on the state of the environment in the European Community. Unplanned urbanization is causing land use changes mainly in developing countries such as Turkey. This situation in Turkey is frequently observed in the city of Istanbul. There are only a few studies of land use–land cover changes which provide an integrated assessment of the biophysical and societal causes and consequences of environmental degradation in Istanbul. The research area comprised greater Silivri Town which is situated by the coast of Marmara Sea, and it is located approximately 60 km west of Istanbul. The city of Istanbul is one of the largest metropolises in Europe with ca. 15 million inhabitants. Additionally, greater Silivri is located near the terminal point of the state highway connecting Istanbul with Europe. Measuring of changes occurring in land use would help control future planning of settlements; hence, it is of importance for the Gretaer Silivri and Silivri Town. Following our evaluations, coastal zone of Silivri was classified into the land use groups of artificial surfaces agricultural areas and forests and seminatural areas with 47.1%, 12.66%, and 22.62%, respectively.     

Dynamics of atmospheric polychlorinated biphenyls (PCBs): concentrations, patterns, partitioning, and dry deposition level estimations in a residential site of Turkey

Air samples were collected at a residential site (Gulbahce, Bursa, Turkey) between August 2004 and April 2005 using a modified high volume air sampler. The mean of gas and particle phase concentrations of polychlorinated biphenyls (PCBs) were 468 ± 297 and 43 ± 37 pg m^???3 (average ± standard deviation, SD), respectively. The dominated PCB homologs were 3-CBs, 4-CBs, and 5-CBs. The measured PCB concentrations were higher than the values of pristine sites. In order to assess the gas/particle partitioning fate, log K _P–log $P_{\rm L}^o$ , log K _P–log K _OA and the Junge–Pankow model were applied to the data. Experimental K _P was well correlated with $\log P_{\rm L}^o$ and log K _OA at p?<?0.05. Then, dry deposition fluxes were estimated using atmospheric concentrations and dry deposition velocities obtained from literature. A correlation matrix was formed for PCB homologs including 27 congeners with concurrently measured 16 polycyclic aromatic hydrocarbons (PAHs) and meteorological parameters. Most of PAH compounds were significantly correlated with 5-CBs and 6-CBs.     

Precipitable water modelling using artificial neural network in Çukurova region

Precipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Çukurova region, south of Turkey. We applied Levenberg–Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Çukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Çukurova region. Precipitable water was the output. Correlation coefficient (R²) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water.