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.     

Determinants of CO2 emissions: empirical evidence from Egypt

Environmental and Ecological Statistics - This paper aims to explore the main determinants of environmental quality in Egypt by utilizing the data covering the years from 1971 to 2014. These...     

Dimethoate-induced oxidative stress and DNA damage in Oncorhynchus mykiss

The present study was conducted in order to investigate pro-oxidant activity of dimethoate in liver and brain tissues following sublethal pesticide exposure for 5, 15 and 30 d by using SOD, GPx, CAT enzyme activities and lipid peroxidation as biomarkers as well as DNA damaging potential via detecting% Tail DNA, Tail moment and Olive tail moment as endpoints in erythrocytes of Oncorhynchus mykiss in an in vitro experiment. Antioxidant enzyme activities were found to elicit two staged response which was an initial induction followed by a sharp inhibition in liver tissue while a sustained increase in GPx activity and slight stimulation in SOD activity were detected in brain tissue. Lipid peroxidation showed an ascending pattern throughout the exposure period in both tissues and a decreasing trend was determined in tissue protein levels which was proved to be positively correlated with duration. Similar findings were obtained from outcomes preferred to quantify DNA damage and TM was decided to reflect the extent of damage more sensitively because of determined positive correlation with concentrations applied. Considering these results, it can be concluded that oxidative stress condition evoked by dimethoate could not be responded effectively and genotoxic nature of pesticide was proven by determined clastogenic effect possibly via being an alkylation agent or stimulating the production of reactive species.     

Assessment of air pollution genotoxicity by RAPD in Evernia prunastri L. Ach. from around iron-steel factory in Karab¨uk, Turkey

Lichens are widely used in biomonitoring studies of air pollution, either as bioindicators of air quality or as bioaccumulators of atmospheric deposition. Over the past decade, several molecular techniques have been developed to provide information on diversity, genotoxicology, genetic relationships, etc. The heavy metal contents of Evernia prunastri samples were determined by atomic absorption spectrometry. The Random Amplified Polymorphic DNA Polymerase Chain Reaction (RAPD-PCR) method was used to describe the pattern of DNA band variation in the samples influenced by the environmental pollution. The study was designed to describe the level of pollution in an area contaminated with smoke and waste from an iron-steel factory, and to reveal the level of potential genotoxic agents around this source of pollution. The study also examined the suitability of the lichen samples for the detection of genotoxicity.