Predicting field capacity,wilting point,and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches

In this study, we examined the ability of reflectance spectroscopy to predict some of the most important soil parameters for irrigation such as field capacity (FC), wilting point (WP), clay, sand, and silt content. FC and WP were determined for 305 soil samples. In addition to these soil analyses, clay, silt, and sand contents of 145 soil samples were detected. Raw spectral reflectance (raw) of these soil samples, between 350 and 2,500-nm wavelengths, was measured. In addition, first order derivatives of the reflectance (first) were calculated. Two different statistical approaches were used in detecting soil properties from hyperspectral data. Models were evaluated using the correlation of coefficient (r), coefficient of determination (R ²), root mean square error (RMSE), and residual prediction deviation (RPD). In the first method, two appropriate wavelengths were selected for raw reflectance and first derivative separately for each soil property. Selection of wavelengths was carried out based on the highest positive and negative correlations between soil property and raw reflectance or first order derivatives. By means of detected wavelengths, new combinations for each soil property were calculated using rationing, differencing, normalized differencing, and multiple regression techniques. Of these techniques, multiple regression provided the best correlation (P?<?0.01) for selected wavelengths and all soil properties. To estimate FC, WP, clay, sand, and silt, multiple regression equations based on first(2,310)-first(2,360), first(2,310)-first(2,360), first(2,240)-first(1,320), first(2,240)-first(1,330), and raw(2,260)-raw(360) were used. Partial least square regression (PLSR) was performed as the second method. Raw reflectance was a better predictor of WP and FC, whereas first order derivative was a better predictor of clay, sand, and silt content. According to RPD values, statistically excellent predictions were obtained for FC (2.18), and estimations for WP (2.0), clay (1.8), and silt (1.63) were acceptable. However, sand values were poorly predicted (RDP?=?0.63). In conclusion, both of the methods examined here offer quick and inexpensive means of predicting soil properties using spectral reflectance data.     

Detecting micronuclei frequency in some aquatic organisms for monitoring pollution of Izmir Bay (Western Turkey)

Micronuclei tests is a system of mutagenicity testing used for determining the pollution and chemicals causing changes in DNA fragments such as micronuclei in the cytoplasm of interphase cells. Damage caused on the DNA by genotoxic pollutants is the first consequence occurring in the aquatic organisms. Thus, it was attempted to determine whether pollution affected the erythrocytes and gills of fish Gobius niger and haemolymph and gills of mussels Mytilus galloprovincialis living in Izmir Bay at the level of DNA by the means of micronuclei (MN) test. Organisms used in the MN test were collected from seven locations (Alsancak, Alaybey Shipyard, Karsiyaka, Bostanli, Göztepe, Konak and Pasaport) which are known as the most polluted part of inner Bay of Izmir (Western Coast of Turkey). According to the results of the present study, frequency of MN was found at high level in Alaybey Shipyard and Pasaport where wastes from existing dockyard contributed to high level of pollution. In conclusion, this study indicates that the micronuclei test gives sensitive results in monitoring the pollution, especially the pollution of harbor, and thus it might be used as standard method in regular monitoring of pollution of coastal ecosystem.     

Detoxification and reproductive system-related gene expression following exposure to Cu(OH)2 nanopesticide in water flea (Daphnia magna Straus 1820)

Environmental Science and Pollution Research - The extensive use of copper-based nanopesticides in agriculture has led to their release into the aquatic environment and causes a potential risk to...     

Elemental composition of Marrubium astracanicum Jacq. growing in tungsten-contaminated sites

Environmental Science and Pollution Research - This study evaluates the elemental (W, Mo, Zn, Fe, Cu, Cd, Mn, Pb, Cr, Co, B, and Bi) composition of Marrubium astracanicum Jacq. (Lamiaceae), around...     

Prediction of Ground Level SO2 Concentration using Artificial Neural Networks

Water, Air, & Soil Pollution: Focus - Future (24 h later) daily ground level SO2 concentration in Istanbul was modeled and predicted using a new and powerful technique, Artificial Neural...     

Cytogenetic and epigenetic alterations by cobalt and nickel on Zea mays L

Changes in DNA methylation in Zea mays plantlets in response to cobalt and nickel (5, 10, 20, and 40 mmol L^?1) evaluated by the coupled restriction enzyme digestion-random amplification technique revealed dose-related increases in hypermethylation. The effects on mitotic index and phytohormone levels were also determined. Co and Ni caused a decrease in mitotic index and an attenuation in the growth-promoting hormones gibberellic acid, zeatin, and indole acetic acid, while abscisic acid levels rose. These findings have shown that the increase in abscisic acid levels and DNA methylation depend on the concentrations of Co and Ni.     

Characterization and assessment of “Kullar Domestic Wastewater Treatment Plant” wastewaters

We investigated conventional characterization of wastewaters of the "Kullar Domestic Wastewater Treatment Plant." In the study of conventional characterization; 23 composite samples, which were taken during 10 months, were used and analysed as COD, BOD5, TKN, NH3-N, SS, VSS, TP, RP, TS, Alkalinity, oil and grease, detergent, chloride parameters. For determine of the plant efficiency, 8 output grab samples were taken. The COD and BOD5 values of influent, respectively, ranging from 37 to 1,056 mg/L and 8 to 140 mg/L in total wastewater. The meanly BOD5/COD ratio was calculated as 0.34 in total wastewaters.     

Spatial variability of depth and salinity of groundwater under irrigated ustifluvents in the Middle Black Sea Region of Turkey

Information on the potential risk for soil salinity buildup can be very helpful for soil salinity management in irrigated areas. We evaluated the spatial and temporal variability of groundwater salinity (GWS) and groundwater depth (GWD), which are two of the most important indicators of soil salinity, by indicator kriging technique in a large irrigated area in northern Turkey. GWS and GWD were measured on a monthly basis from irrigation season (August 2003) to rainy season (April 2004) at 60 observation wells in the 8,187-ha irrigated area. Five indicator thresholds were used for GWS and GWD. The semivariogram for each of the thresholds for both variables was analyzed then used together with experimental data to interpolate and map the corresponding conditional cumulative distribution functions (CCDF). Risk for soil salinity buildup was greater in the irrigation season compared to that in the rainy season. The greatest risk for soil salinity buildup occurred in the eastern part of the study area, suffering from poor drainage problem due to malfunctioning drainage infrastructure, as indicated by the CCDF of GWS and GWD obtained in both seasons. It was concluded that a combination of mechanical and cultural measures should be taken in high-risk locations to avoid further salinity problems.     

Estimation of Biogas Production Rate in a Thermophilic UASB Reactor Using Artificial Neural Networks

Biogas production rate was modeled and estimated in a thermophilic upflow anaerobic sludge blanket digester. Data set covers a time period of both steady-state conditions and an abnormal operation condition, i.e., organic loading shocks. Multilayer neural networks topology was used as the modeling tool. Half of the experimental data were used for the training of the model and the remaining half were used for the testing stage. Model results were evaluated from the point of view of both steady conditions and abnormal conditions. It was seen from the time series trends of the estimated data that biogas production rates at steady state operation conditions were closely estimated by the model while the results for organic loading shocks were sufficiently followed. Artificial neural network models gave encouraging estimation results for the online control of thermophilic reactors.     

An investigation of engine and fuel system performance in a diesel engine operating on waste cooking oil

Waste cooking oil (WCO) was experimentally examined to determine whether it can be used as an alternative fuel in a 3-cylinder, 4-stroke, direct injection, 48 kW power tractor engine. The test engine was operated under full load conditions using diesel fuel and waste vegetable oil from the 2400 to 1100 rpm and performance values were recorded. Tests were performed in two stages to evaluate the effect of the waste oils on the engine life cycle. When the test engine was operated with diesel fuel and waste cooking oil; engine torque decreased between at ratio of 0.09 % and 3% according to the engine speed. While no significant difference occurs in the diesel fuel tests at the end of 100 hours of operation, an important reduction was observed in the engine torque of the WCO engine between 4.21% and 14.48% according to the engine speed, and an increase in average smoke opacity ratio was also observed. In accordance with the results obtained from the studies, it was determined that the engine performance values of waste cooking oil show similar properties with diesel fuel, but in long-term usage, performance losses increased. In the SEM analysis performed on the fuel system, there were dark deposits at the nozzle tip and stem. According to an EDX analysis at the nozzle tips, the detected elements point to engine oil ash in the combustion chamber and show coking products (C and O). The other elements (Na, S, Ca, P, Cl, and K) point to used WCO.