Factors Influencing Unsafe Behaviors and Accidents on Construction Sites: A Review

Objective. Construction is a hazardous occupation due to the unique nature of activities involved and the repetitiveness of several field behaviors. The aim of this methodological and theoretical review is to explore the empirical factors influencing unsafe behaviors and accidents on construction sites. Methods. In this work, results and findings from 56 related previous studies were investigated. These studies were categorized based on their design, type, methods of data collection, analytical methods, variables, and key findings. A qualitative content analysis procedure was used to extract variables, themes, and factors. In addition, all studies were reviewed to determine the quality rating and to evaluate the strength of provided evidence. Results. The content analysis identified 8 main categories: (a) society, (b) organization, (c) project management, (d) supervision, (e) contractor, (f) site condition, (g) work group, and (h) individual characteristics. The review highlighted the importance of more distal factors, e.g., society and organization, and project management, that may contribute to reducing the likelihood of unsafe behaviors and accidents through the promotion of site condition and individual features (as proximal factors). Conclusion. Further research is necessary to provide a better understanding of the links between unsafe behavior theories and empirical findings, challenge theoretical assumptions, develop new applied theories, and make stronger recommendations.     

Simultaneous removal of Congo red and cadmium(II) from aqueous solutions using graphene oxide–silica composite as a multifunctional adsorbent

Graphene oxide is a very high capacity adsorbent due to its functional groups and π?π interactions with other compounds. Adsorption capacity of graphene oxide, however, can be further enhanced by having synergistic effects through the use of mixed-matrix composite. In this study, silica-decorated graphene oxide (SGO) was used as a high-efficiency adsorbent to remove Congo red (CR) and Cadmium (II) from aqueous solutions. The effects of solution initial concentration (20 to 120 mg/l), solution pH (pH 2 to 7), adsorption duration (0 to 140 min) and temperature (298 to 323 K) were measured in order to optimize the adsorption conditions using the SGO adsorbent. Morphological analysis indicated that the silica nanoparticles could be dispersed uniformly on the graphene oxide surfaces. The maximum capacities of adsorbent for effective removal of Cd (II) and CR were 43.45 and 333.33 mg/g based on Freundlich and Langmuir isotherms, respectively. Langmuir and Freundlich isotherms displayed the highest values of Q_max for CR and Cd (II) adsorption in this study, which indicated monolayer adsorption of CR and multilayer adsorption of Cd (II) onto the SGO, respectively. Thermodynamic study showed that the enthalpy (ΔH) and Gibbs free energy(ΔG) values of the adsorption process for both pollutants were negative, suggesting that the process was spontaneous and exothermic in nature. This study showed active sites of SGO (π-π, hydroxyl, carboxyl, ketone, silane-based functional groups) contributed to an enormous enhancement in simultaneous removal of CR and Cd (II) from an aqueous solution, Therefore, SGO can be considered as a promising adsorbent for future water pollution control and removal of hazardous materials from aqueous solutions.     

BANK STABILITY ANALYSIS FOR PREDICTING REACH SCALE LAND LOSS AND SEDIMENT YIELD1

ABSTRACT: When extensive lengths of river become stabilized (e.g., by channel incision), riverbank erosion can result in considerable riparian land loss and the delivery of large volumes of sediment downstream. The ability to predict the stability and failure geometry of eroding riverbanks is therefore an important prerequisite in estimating the rate of bank erosion and sediment yield associated with bank erosion. In this paper, a new stability analysis for layered river banks is introduced. The new analysis differs from many previous analyses in that it takes into consideration the effects of positive pore water pressure in the saturated portion, and negative pore water pressure in the unsaturated portion, of the bank as well as the influence of hydrostatic confining pressure due to the water level in the river. In addition, the failure plane is not constrained to pass through the toe of the bank and the bank profile geometry is not restricted to an idealized special case. The predictive ability of the new bank stability analysis is assessed using data from two field sites. Subsequently, a methodology for applying the bank stability analysis at the scale of the river reach (0.1 to 10 km) is discussed. This method involves the use of empirical models of bed level adjustment to estimate the magnitude of incision at specific locations along the reach, with these estimates used to drive the stability analysis. Application of the new method is demonstrated with an example.     

Study of MoO3-γAl2O3 catalysts behavior in selective catalytic reduction of SO2 toxic gas to sulfur with CH4

Environmental Science and Pollution Research - In the present study, a detailed investigation was carried out on MoO3 alumina-supported catalysts behavior in selective catalytic reduction of SO2 to...     

Equilibrium, kinetic, and thermodynamic biosorption of Pb(II), Cr(III), and Cd(II) ions by dead anaerobic biomass from synthetic wastewater

Purpose

Heavy metals are toxic pollutants released into the environment as a result of different industrial activities. Biosorption of heavy metals from aqueous solutions is a new technology for the treatment of industrial wastewater. The aim of the present research is to highlight the basic biosorption theory to heavy metal removal.

Materials and methods

Heterogeneous cultures mostly dried anaerobic bacteria, yeast (fungi), and protozoa were used as low-cost material to remove metallic cations Pb(II), Cr(III), and Cd(II) from synthetic wastewater. Competitive biosorption of these metals was studied.

Results

The main biosorption mechanisms were complexation and physical adsorption onto natural active functional groups. It is observed that biosorption of these metals was a surface process. The main functional groups involved in these processes were hydroxyl (–OH) and carboxylic groups (C=O) with 37, 52, and 31 and 21, 14, and 34 % removal of Pb(II), Cr(III), and Cd(II), respectively. Langmuir was the best model for a single system. While extended Langmuir was the best model for binary and ternary metal systems. The maximum uptake capacities were 54.92, 34.78, and 29.99 mg/g and pore diffusion coefficients were 7.23, 3.15, and 2.76?×?10^?11 m²/s for Pb(II), Cr(III), and Cd(II), respectively. Optimum pH was found to be 4. Pseudo-second-order was the best model to predict the kinetic process. Biosorption process was exothermic and physical in nature.

Conclusions

Pb(II) offers the strongest component that is able to displace Cr(III) and Cd(II) from their sites, while Cd(II) ions are the weakest adsorbed component.     

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R² were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R² confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.     

Nitrate adsorption onto surface-modified red mud in batch and fixed-bed column systems: equilibrium,kinetic, and thermodynamic studies

Environmental Science and Pollution Research - This research aimed to develop a novel composite as a low-cost adsorbent for the removal of nitrate ion from aqueous solutions. The characterization...