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Field experiments were conducted to evaluate the effect of thiobencarb and oxadiargyl herbicides on rice (Oryza sativa L.) and their possible residual effects on spinach (Spinacea oleracea L.) and lettuce (Lactuca sativa L.) at Dashtnaz and Gharakhil Agricultural Research Stations, Iran. Treatments included thiobencarb at 3.16 and 6.33 kg a.i. ha-1, oxadiargyl at 0.15 and 0.30 kg a.i. ha-1 and a non-treated control. After harvesting rice, trial plots were kept undisturbed until late September when spinach was seeded in half of each plot. In November lettuce was transplanted in another half of the plots. Soil residual oxadiargyl at 0.30 kg a.i. ha-1 stunted rice up to 31%, but this injury was transient and did not reduce yield. The adverse effect of oxadiargyl on rice was lower at Gharakhil possibly due to the greater binding by soil organic matter (OM). At Dashtnaz, spinach fresh yield was significantly affected by soil residues of oxadiargyl. Whereas lettuce fresh yield was significantly reduced in both thiobencarb and oxadiargyl treated plots. At Gharakhil, fresh yield of lettuce was not affected significantly. The experimental results revealed that soil characteristics, in particular OM content, are the main factors controlling the effect of thiobencarb and oxadiargyl residues. Furthermore, it could be concluded that oxadiargyl affected rice and spinach fresh yield greater than thiobencarb. Since no statistically significant differences were found in rice, spinach, and lettuce yield between the two applied doses of thiobencarb, from economical and environmental point of view, the lower thiobencarb dose is recommended to be used in paddy fields of northern Iran.

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In this study, micromorphological properties of some samples collected from pedons polluted with petroleum refinery wastes and some adjacent unpolluted pedons were studied. After description of the studied pedons, disturbed and undisturbed samples were collected for physicochemical and micromorphological analyses. The results showed that the physicochemical properties (i.e. structure, bulk density, pH, EC and organic matter) of the soils polluted with petroleum wastes were strongly changed. Prolonged exposure of soils to the petroleum wastes resulted in the formation of specific and distinctive micromorphological features. Strongly developed granular microstructure and infillings of solid petroleum wastes alone or mixed with soil aggregates were some of the most important pedofeatures which were observed in deeper horizons. The existence of excrement belonging to different soil micro and macro fauna, coatings, hypocoatings, quasicoatings, and zones depleted from petroleum dissolvable materials at different depths were the other features throughout the pedons. The type of developed pedofeatures was correlated with the state of petroleum wastes and their fluidity in penetration, deposition, or dissolving and removal of soil compounds. This study demonstrated that micromorphology can be used as a powerful technique in characterization of petroleum polluted soils.

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Mines and their related-industries are able to affect their surrounding environment, not only by their activities, but also after being abandoned. Among their different harmful effects, under water and surface water contaminations, and soil contamination can be mentioned. In order to manage these environmental effects, it is necessary to use reasonable methods for modelling heavy metal concentration in soil. This study aims to present a framework for modelling heavy metal soil contamination based on spectroscopy and statistical models. For this purpose, the spectral curves of the 53 soil samples, derived from an abandoned mine and its surrounding areas in New South Wales, Australia, were collected using a spectroradiometer in visible to short wavelength infrared (SWIR) wavelengths. Calculating the second derivative of the collected spectral data, random forest feature selection method (RFFS) was used to determine the most important spectral data for modelling heavy metal concentrations including lead, silver, cadmium and mercury. Then, the modelling techniques including multiple linear regression, random forest regression, and support vector regression (SVR) were applied on the selected spectral data. The results indicated that SWIR wavelengths are the most important spectral data for modelling heavy metal concentrations. Moreover, the non-linear machine learning methods, especially random forest with RMSE of 0.8 ppm and R² of 0.51 for lead and RMSE of 9.4 ppm and R² of 0.46 for cadmium performed better than multiple linear regression.