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The objectives of this study were to determine the selected physicochemical properties of two biochars, one commercially produced from rice husks and the other from oil palm empty fruit bunches, and to evaluate their adsorption capacities for Zn, Cu, and Pb using a batch equilibrium method. The results showed that there was no significant difference between the carbon content of biochars formed from empty fruit bunches (EFBB) and rice husks (RHB). However, the EFBB did present higher quantities of O, H, S, N, and K, compared to the RHB. Although the EFBB had a much lower surface area than the RHB, the former adsorbed much more Zn, Cu, and Pb than the RHB. The higher adsorption capacity of the EFBB over the RHB was a result of the EFBB having higher amounts of oxygen-containing functional groups, a higher molar ratio of O/C, and a higher polarity index [(O ? N)/C]. This suggests that the biochar's chemical properties were more important than its surface area in the adsorption of Zn, Cu, and Pb.

Soil moisture regime (SMR) and soil temperature regime (STR) classes as soil classification criterions are required by US Soil Taxonomy because they affect genesis, use, and management of soils. The lack of sufficient soil moisture and temperature data requires the characterization of the pedoclimate on the basis of climatic data processed by simulation models. This research was conducted to consider the new approach for SMR and STR mapping. The objectives of this study were to compare the four interpolation schemes including ordinary kriging (OK), cokriging (Co-K), inverse distance weighting, and conditional simulation for interpolating the monthly mean total precipitation (MMTP) and monthly mean air temperature (MMAT) and to apply the Java Newhall simulation model for the MMTP and MMAT predictive values at each node of 1 km 2 grids across the Mazandaran province, northern Iran, for delineating the SMR and STR classes. The semivariogram analyses showed moderate to strong spatial dependence of data sets. The accuracy of interpolators varied within months for both MMTP and MMAT data sets. In most cases, OK and Co-K methods had the highest accuracy with lower mean error, root mean square error, and higher concordance correlation coefficient. The predictive maps show high diversity of SMR classes including Aridic, Ustic, Udic, and Xeric. The STR classes comprise Mesic, Thermic, and Cryic regimes. Results herein indicated that geostatistical approaches can potentially provide the opportunity for mapping of SMR and STR classes in data scarce regions.

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