ABSTRACT
The majority of anthraquinone dye released to the environment come from antrapogenic sources. Several techniques are available for dyes' removal. In this study removal of reactive blue 29 (RB29) by an advanced oxidation process sequenced with single wall carbon nanotubes was investigated. Advanced oxidation process was optimized over a period of 60 minutes by changing the ratio of acetic acid to hydrogen peroxide, the compounds which form peroxy acid. Reduction of 20.2% -56.4% of reactive blue 29 was observed when the ratio of hydrogen peroxide/acetic acid/dye changed from 344/344/1 to 344/344/0.08 at different times (60, 120 and 180 min). The optimum ratio of acetic acid/hydrogen peroxide/dye was found to be 344/344/0.16 over 60 min. The resultant then was introduced for further removal by single wall carbon nanotubes(SWCNTs) as adsorbent. The adsorption of reactive blue 29 onto SWCNTs was also investigated. Langmuir, Freundlich and BET isotherms were determined and the results revealed that the adsorption of RB29 onto SWCNTs was well explained by BET model and changed to Freundlich isotherm when SWCNTs was used after the application of peroxy acid. Kinetic study showed that the equilibrium time for adsorption of RB 29 on to SWCNT is 4 h. Experiments were carried out to investigate adsorption kinetics, adsorbent capacity and the effect of solution pH on the removal of reactive blue29. The pseudo-second order kinetic equation could best describe the sorption kinetics. The most efficient pH for color removal (amongst pH=3, 5 and 8) was pH= 5. Further studies are needed to identify the peroxy acid degradation intermediates and to investigate their effects on SWCNTs.
ABSTRACT
INTRODUCTION: The impact of a driver's cognitive capability on traffic safety has not been adequately studied. This study examined the relationship between cognitive failures, driving errors and accident data. METHOD: Professional drivers from Iran (160 males, ages 18-65) participated in this study. The cognitive failures questionnaire (CFQ) and the driver error questionnaire were administered. The participants were also asked other questions about personal driving information. A principal component analysis with varimax rotation was performed to determine the factor structure of the CFQ. Poisson regression models were developed to predict driving errors and accidents from total CFQ scores and the extracted factors. RESULTS: Total CFQ scores were associated with driving error rates, but not with accidents. However, the 2 extracted factors suggested an increased effect on accidents and were strongly associated with driving errors. DISCUSSION: Although the CFQ was not able to predict driving accidents, it could be used to identify drivers susceptible to driving errors. Further development of a driving-oriented cognitive failure scale is recommended to help identify error prone drivers. Such a scale may be beneficial to licensing authorities or for developing driver selection and training procedures for organizations.
