This first nationwide survey was conducted to evaluate the overall performance of the circulating fluidized bed (CFB) incineration of municipal solid waste (MSW) in 2014-2015 in China. Total 23 CFB incineration power plants were evaluated. The data for monthly average flue gas emission of particles, CO, NOx, SO2 and HCl were collected over 12 consecutive months. The data were analyzed to assess the overall performance of CFB incineration by applying the Mahalanobis distance as a multivariate outlier detection method. Although the flue gas emission parameters had met the Chinese national emission standards, there were 11 total outliers (abnormal behavior) detected in 6 out of 23 CFB incineration power plants from the perspective of the MSWincineration performance. The results demonstrate that it is more important for a better performance of CFBs to reduce the frequencies of the MSW load changes, rather than the magnitudes of the MSW load changes, particularly reducing the frequencies in the range of 10% and more of the load changes, under the same and stable conditions. Furthermore, the overloading occurs more often than the underloading during the operation of the CFB incineration power plants in China. The frequent overloading is 0% to 30% of the designed capacity. To achieve the stable performance of CFBs in practice, an appropriately designed MSW storage capacity is suggested to build in a plant to buffer and reduce the frequency of the load changes.
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.