Journal of Environmental Engineering

Deterioration of indoor air quality attributable to airborne bacterial consortia is a widespread environmental problem. The main objective of this study is to evaluate the feasibility of applying the syngetic effect of nano-$\mathrm{Ag}/{\mathrm{TiO}}_2$ as a photocatalyst and UV light to enhance the disinfecting capability of full-scale bacterial restraining equipment on-site in the National Museum of Natural Science and a medical-nursing institute. The influence of initial counts of total airborne bacteria and volume of space on the efficiency of bacterial restraining have been studied. In the case of museum application, a higher initial total bacterial count leads to better bacterial restraining rates; Site A (initial total bacterial counts = $506\mathrm{CFU}/{\mathrm{m}}^3$) has the best bacterial restraining rate (92%) as compared with Site B (69%, initial total bacterial counts = $158\mathrm{CFU}/{\mathrm{m}}^3$) and Site C (80%, initial total bacterial counts = $338\mathrm{CFU}/{\mathrm{m}}^3$) after 24 h of operation. Higher initial counts of total airborne bacteria lead to an increasing bacterial restraining rate. Approximately 92% (Site A) and 74% of restrained bacterial rate were observed in a museum and nursing institutions, respectively, under the similar initial total airborne bacterial counts (506, $598\mathrm{CFU}/{\mathrm{m}}^3$). The results illustrate that changes in the volume of space do not have significant inhibitory effects on the efficiency. The proposed equipment can disinfect air to restrain bacteria effectively, as demonstrated on-site in museums and nursing institutions; the results will be valuable references for designing a full-scale commercialized device for large-scale applications in the future.

Nitrogen and nickel codoped nanotitania ( $\mathrm{N}\mathrm{\text{-}}\mathrm{Ni}/{\mathrm{TiO}}_2$ ) photocatalysts for producing methanol by photocatalytic reduction of ${\mathrm{CO}}_2$ were prepared by an improved sol-gol method. The catalysts were characterized by XRD, HRTEM, DES, FTIR, TG-DSC, and UV-Vis, respectively. The experimental results indicated that nano- $\mathrm{N}\mathrm{\text{-}}\mathrm{Ni}/{\mathrm{TiO}}_2$ had better properties than the pure nanotitania ( ${\mathrm{TiO}}_2$ ), N doped titania ( $\mathrm{N}/{\mathrm{TiO}}_2$ ), and ${\mathrm{Ni}}^{2+}$ doped titania ( $\mathrm{Ni}/{\mathrm{TiO}}_2$ ), considering their performance of photoresponse and photocatalytic reduction of ${\mathrm{CO}}_2$ . The methanol yield could reach 482.0μmol/g-cat under optimal conditions. Additionally, the synergetic effect of N and ${\mathrm{Ni}}^{2+}$ on nano- ${\mathrm{TiO}}_2$ in photocatalytic reduction of ${\mathrm{CO}}_2$ was explained, and the mechanism of photocatalytic reduction ${\mathrm{CO}}_2$ on $\mathrm{N}\mathrm{\text{-}}\mathrm{Ni}/{\mathrm{TiO}}_2$ catalyst was proposed.

Every model is, by definition, a simplification of the system under investigation. Although it would be desirable to reduce the gap between the simulated and the observed behaviors of the system to zero, this reduction is generally impossible owing to the unavoidable uncertainties inherent in any modeling procedure. Uncertainty analyses can provide useful insights into the best model approach to be used for obtaining results with a high level of significance and reliability. The evaluation of parameter uncertainties is necessary for calibration and for estimating the impact of these uncertainties on model performance. In this context, the uncertainty of a river water-quality model developed in previous studies is presented. The main goal is to gain insights into the modeling approaches concerning small rivers. Previous works generally focused on modeling the large river while neglecting the small one. Following a model calibration, the model uncertainty has been assessed by means of the generalized likelihood uncertainty estimation (GLUE). The results showed that the biological process related to the biological oxygen demand (BOD) removal influenced mainly by the parameters characterizing deoxygenation and nitrogen removal. On the other hand, the biological processes related to nitrogen removal were influenced not only by the parameters related to the nitrogen removal but also to oxygen concentration. The application of the GLUE methodology shows that the river quality model considered is suitable for simulating the important processes involved. Uncertainty bounds showed different amplitudes with respect to the pollutant species considered. In particular, the oxygen uncertainty bounds were narrower with respect to the other model components suggesting much attention must be paid to both model algorithms and quality data to be gathered. The study confirmed the suitability of the GLUE methodology as a powerful tool as a simplified screening methodology to assess uncertainty.

The formation of bromate by ozone–vacuum ultraviolet (VUV) ($185+254\mathrm{nm}$) process in comparison with ozone and ozone-ultraviolet (UV) (254 nm) processes of coagulated and softened water was studied. The effects of pH (7, 9, and 11), ozone dosage (1, 2, and 4 mg ${\mathrm{O}}_3/\mathrm{mg}$ C), and VUV power (30, 60, and 120 W) were investigated. Bromate concentrations formed by the ozone-VUV process were up to four and six times less than those by the ozone and ozone-UV processes, respectively. Among the variables studied, ozone dosage had the most effect on bromate formation by the ozone-VUV process. Approximately 64 and 213% increases of bromate concentration were observed when the ozone dosage was increased from 1 to 2 and 4 mg ${\mathrm{O}}_3/\mathrm{mg}$ C with VUV power of 120 W at pH 7. The bromate formation also increased as VUV power and pH increased. Hydroxyl radical exposure had a positive relationship with ozone dosage and bromate formation. Results further indicated that it might be difficult to achieve the drinking water standard for bromate and high organic matter removal concurrently.

A numerical model for Type I circular sedimentation tanks (center feed) has been developed to provide useful information for circular tank operation. The unsteady flow process in circular sedimentation tanks is divided by numerous time intervals in which flow and sediment transport are considered to be steady. The other feature for this model is that particle size distribution of raw water is nonuniform. A numerical experiment based on the proposed model is carried out, and the results were compared with results from other models. The comparison shows that this model can provide more information, such as variations of water elevation, overall removal efficiency, bottom sludge thickness, and particle size distribution at outlet. In addition, this model had the advantage of optimizing the tank dimensions based on the sludge raking frequency and preferred removal efficiency. After simplification of the model, this model is practical in determining the overall removal efficiency and dimension of the tank based on different inflow situations for tank design.

This paper describes the development and validation of an equilibrium model of an industrial hot-lime-softening boiler-water-treatment unit for a large-scale nickel processing facility in which approximately 6.6 ML per hour of water is processed. In the industrial process, multiple water sources of varying quality are combined before the softening treatment, which makes control and optimization of the softening unit complicated and has brought about the necessity of a robust numerical model of water treatment. In this paper, the numerical thermodynamic and adsorption relations describing the softening treatment process are presented. Lime, magnesia, and soda ash additions are modeled. Emphasis has been placed on calcium, magnesium, and silica treatments as these are of most relevance to the industry. Jar tests described in this paper are used to determine adsorption relations, estimate statistical uncertainties, validate the model performance, and optimize the model parameters. Parameter estimations for equilibrium constants are undertaken and provide insights into the range of model validity and interactions between additions and softened water quality. Further jar testing is utilized to evaluate the effectiveness of using the model to numerically derive optimal chemical additions.