A wide range of waste biomass/waste wood feedstocks abundantly available at mine sites provide the opportunity to produce biochars for cost-effective improvement of mine tailings and contaminated land at metal mines. In the present study, soft- and hardwood biochars derived from pine and jarrah woods at high temperature (700 °C) were characterized for their physiochemical properties including chemical components, electrical conductivity, pH, zeta potential, cation-exchange capacity (CEC), alkalinity, BET surface area and surface morphology. Evaluating and comparing these characteristics with available data from the literature have affirmed the strong dictation of precursor type on the physiochemical properties of the biochars. The pine and jarrah wood feedstocks are mainly different in their proportions of cellulose, hemicellulose and lignin, resulting in biochars with heterogeneous physiochemical properties. The hardwood jarrah biochar exhibits much higher microporosity, alkalinity and electrostatic capacity than the softwood pine. Correlation analysis and principal component analysis also show a good correlation between CEC–BET–alkalinity, and alkalinity–ash content. These comprehensive characterization and analysis results on biochars’ properties from feedstocks of hardwood (from forest land clearance at mine construction) and waste pine wood (from mining operations) will provide a good guide for tailoring biochar functionalities for remediating metal mine tailings. The relatively inert high-temperature biochars can be stored for a long term at mine closure after decades of operations. 相似文献
This study was conducted to assess the merits and limitations of various high-pressure membranes, tight nanofiltration (NF) membranes in particular, for the removal of trace organic compounds (TrOCs). The performance of a low-pressure reverse osmosis (LPRO) membrane (ESPA1), a tight NF membrane (NF90) and two loose NF membranes (HL and NF270) was compared for the rejection of 23 different pharmaceuticals (PhACs). Efforts were also devoted to understand the effect of adsorption on the rejection performance of each membrane. Difference in hydrogen bond formation potential (HFP) was taken into consideration. Results showed that NF90 performed similarly to ESPA1 with mean rejection higher than 95%. NF270 outperformed HL in terms of both water permeability and PhAC rejection higher than 90%. Electrostatic effects were more significant in PhAC rejection by loose NF membranes than tight NF and LPRO membranes. The adverse effect of adsorption on rejection by HL and ESPA1 was more substantial than NF270 and NF90, which could not be simply explained by the difference in membrane surface hydrophobicity, selective layer thickness or pore size. The HL membrane had a lower rejection of PhACs of higher hydrophobicity (log D>0) and higher HFP (>0.02). Nevertheless, the effects of PhAC hydrophobicity and HFP on rejection by ESPA1 could not be discerned. Poor rejection of certain PhACs could generally be explained by aspects of steric hindrance, electrostatic interactions and adsorption. High-pressure membranes like NF90 and NF270 have a high promise in TrOC removal from contaminated water.
Observed effects of metal mixtures on animals and plants often differ from the estimates, which are commonly calculated by adding up the biological responses of individual metals. This difference from additivity is commonly referred to as being a consequence of specific interactions between metals. The science of how to quantify metal interactions and whether to include them in risk assessment models is in its infancy. This review summarizes the existing predictive tools for evaluating the combined toxicity of metals present in mixtures and indicates the advantages and disadvantages of each method. We intend to provide eco-toxicologists with background information on how to make good use of the tools and how to advance the methods for assessing toxicity of metal mixtures. It is concluded that statistically significant deviations from additivity are not necessarily biologically relevant. Incorporation of interactions between metals in a model does not on forehand mean that the model is more accurate than a model developed based on additivity only. It is recommended to first use a relatively simple method for effect prediction of uninvestigated metal mixtures. To improve the reliability of toxicity modeling for metal mixtures, further efforts should focus on balancing the relationship between the significance of statistics and the biological meaning, and unraveling the toxicity mechanisms of metals and their mixtures.