Embedded large eddy simulation approach for pollutant dispersion around a model building in atmospheric boundary layer

In the present article, the potential of embedded large eddy simulation (ELES) approach to reliably predict pollutant dispersion around a model building in atmospheric boundary layer is assessed. The performance of ELES in comparison with large eddy simulation (LES) is evaluated in several ways. These include a number of qualitative and quantitative comparisons of time-averaged and instantaneous results with wind tunnel measurements supplemented by statistical data analyses using scatter plots and standard evaluation metrics. Results obtained by both LES and ELES approaches show very good agreement with the experiment. However, addition of turbulence to mean flow at Reynolds averaged Navier–Stokes (RANS)–LES interface in ELES approach not only increases the turbulence intensity, it also results in larger values of turbulent kinetic energy (TKE) as well as a shorter reattachment length in the wake region. Accordingly, higher levels of TKE predicted by ELES increase the local intensity of concentration leading to shorter plume shapes as compared with LES. In general, ELES shows better agreement with experiment on the surfaces of model building and also in the downstream wake region. In terms of computational costs, the CPU time required to obtain statistical values in ELES is about 49 % lower than that of LES and the number of iterations per time step is also reduced by 55 % as compared with LES.     

Degrading a mixture of three textile dyes using photo-assisted electrochemical process with BDD anode and O2–diffusion cathode

In this paper, degradation of a mixture of three azo dyes was studied by the photo-assisted electrochemical process using an O₂-diffusion cathode containing carbon nanotubes and boron-doped diamond (BDD) anode. The concentration of three textile dyes (C.I. Acid Orange 8 (AO8), C.I. Acid Orange 10 (AO10), and C.I. Acid Orange 12 (AO12)) was determined simultaneously despite the severe overlap of their spectra. For this purpose, partial least square (PLS), as a multivariate calibration method, was utilized based on recording UV–Vis spectra during the decolorization process. Moreover, the central composite design was used for the modeling of photo-assisted electrochemical decolorization of the aqueous solutions containing three dyes. The investigated parameters were the initial concentration of three dyes, applied current and reaction time. Analysis of variance (ANOVA) revealed that the obtained regression models match the experimental results well with R (Khataee et al. 2010, Clean-Soil Air Water 38 (1):96–103, 2010) of 0.972, 0.971, and 0.957 for AO8, AO10, and AO12, respectively. Three-dimensional surface and contour plots were applied to describe the relation between experimental conditions and the observed response. The results of TOC analysis confirmed good ability of proposed photo-assisted electrochemical process for degradation and mineralization of textile industry wastewater.     

Using geospatial business intelligence paradigm to design a multidimensional conceptual model for efficient coastal erosion risk assessment

One of the main challenges in Coastal Erosion Risk Assessment (CERA) is integrating and analysis of conflicting data in various time periods and spatial scales through dissimilar environmental, social, and economic criteria. Currently, Geographical Information Systems (GIS) are widely used in risk assessment despite their drawbacks and limitations as transactional systems for multi-scales, multi-epochs, and multi-themes analysis. Hence, an analytical conceptual framework is proposed in this paper based on geospatial business intelligence paradigm to develop a Spatial Multidimensional Conceptual Model (SMCM) to assess coastal erosion risk. The model is designed based on Spatial On-Line Analytical Processing (SOLAP) platform, on the top of both analytical and transactional paradigms, to allow fast synthesis of cross-tabulated data and easy comparisons over space, scales, epochs, and themes. This objective is achieved through a comprehensive integration of multiple environmental, social, and economic criteria as well as their interactions at various scales. It also takes into account multiple elements at risk such as people, infrastructure, and built environment as different dimensions of analysis. Using this solution allows decision makers to benefit from on-demand, interactive, and comprehensive information in a way that is not possible using GIS alone. The developed model can easily be adapted for any other coastal region through the proposed framework to perform risk assessment. The advantages and drawbacks of the proposed framework are also discussed and new research perspectives are presented.     

Risk factors and the psychological impacts of the COVID-19 outbreak: perspectives and experiences of Iranian healthcare workers on the frontline

Frontline healthcare workers (HCWs) looking after COVID-19 patients are at high risk of developing mental health problems. Both preventive and interventional initiatives are essential, therefore, to maintain and improve the mental health of HCWs and ultimately to enhance the quality of care that they provide. This qualitative content analysis study, conducted in Iran between September 2020 and February 2021, involved the gathering of data on the issue through face-to-face interviews with 22 HCWs working directly with COVID-19 patients. Data analysis led to the generation of 163 codes, 45 categories (16 risk factors and 29 consequences), and 9 themes. Overall, the results suggest that caring for a patient with COVID-19 is associated with positive and negative psychological impacts. Measures such as increasing sensitivity to crisis situations, self-adjustment skills training, and identifying signs of job burnout owing to moral conflicts can reduce or eliminate the risk of negative psychological consequences among HCWs.     

Large eddy simulation of dispersion around an isolated cubic building: evaluation of localized dynamic k_\mathrm{SGS}-equation sub-grid scale model

In the present study, the prediction accuracy of a dynamic one-equation sub-grid scale model for the large eddy simulation of dispersion around an isolated cubic building is investigated. For this purpose, the localized dynamic $k_\mathrm{SGS} $ -equation model (LDKM) is employed and the results are compared with the available experimental data and two other classic sub-grid scale models, namely, standard Smagorinsky–Lilly model (SSLM) and dynamic Smagorinsky–Lilly model (DSLM). It is shown that the three SGS models give results in good agreement with experiment. However, near the ground level of the leeward wall, dimensionless time-averaged concentration, $\langle K\rangle $ , profile is not quite similar to the experimental data. It is also demonstrated that the LDKM predicts the values of $\langle K\rangle $ on the roof, leeward and side walls more acceptably than the SSLM and DSLM. Whereas, the streamwise elongation of time-averaged structures of the plume shape is more over-estimated with the LDKM than with the other two SGS models. In terms of numerical difficulty, the LDKM is found to be stable and computationally reasonable. In addition, it does not suffer from a flow dependent constant such as the Smagorinsky coefficient employed in the SSLM model.     

Experimental studies on solar chimneys for natural ventilation in domestic applications: a comprehensive review

Environmental Science and Pollution Research - Solar chimneys are among relatively modern mechanisms in the field of renewable energy which can be employed for power generation or indoor...