Effects of turbulent Schmidt number on CFD simulation of $$45^\circ $$ 45 ∘ inclined negatively buoyant jets

Environmental Fluid Mechanics - Employing inclined negatively buoyant jets is one of the most advantageous means to discharge brine or waste in coastal environments. However, numerical prediction...     

Analysis and prediction of $$\mathrm{PM}_{10}$$ concentration levels in Tunisia using statistical learning approaches

Over the past years, the health impact of airborne particulate matter \(\mathrm{PM}_{10}\) has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient \(\mathrm{PM}_{10}\) concentrations. In this paper, we are interested in the statistical classification of the daily mean \(\mathrm{PM}_{10}\) concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of \(\mathrm{PM}_{10}\) concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the \(\mathrm{PM}_{10}\) concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of \(\mathrm{PM}_{10}\) concentration may be crucial to get an accurate prediction.     

Investigating the link between $$\hbox {PM}_{2.5}$$ and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (\(\hbox {PM}_{2.5}\)) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level \(\hbox {PM}_{2.5}\). Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of \(\hbox {PM}_{2.5}\) data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both ‘typical’ and ‘high’ \(\hbox {PM}_{2.5}\) events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location.     

Markov regression model for analyzing big data to predict trajectories of repeated categorical outcomes: an application to $$\hbox {PM}_{2.5}$$ PM 2.5 air pollution data

Environmental and Ecological Statistics - Fine particulate matter ( $$\text{ PM}_{2.5}$$ ), tiny particles in the air, is air contamination that negatively impacts the environment and human health...     

锐钛矿{001}面TiO2纳米片对NOx的光催化氧化性能

以钛酸丁酯为前驱物,HF为形貌控制剂,采用水热法合成{001}面TiO2光催化材料.通过X射线衍射、紫外\可见光吸收光谱、扫描电镜及透射电镜等手段对样品形貌及结构进行表征,并在紫外光下考察其对NOx的光催化氧化活性.结果表明,{001}面TiO2具有良好的锐钛矿晶型,为{001}面暴露率约50%的纳米级片状结构,在紫外波段具有强烈的光吸收.经过碱液洗涤去除表面F离子后的{001}面TiO2与{101}面TiO2及P25相比,其对NOx去除效果显著.在催化反应达到平衡后,其NO转化率为{101}面TiO2的2倍及P25的3倍.并且NO去除量增加的同时NO2的相对生成量却减少,导致光催化反应进行得更彻底.     

A statistical model to forecast the profile of the index structure constant $${C_N^2}$$

A statistical study was performed over 145 profiles of meteorological balloons, equipped with microthermal sensors, from the ground to the midstratosphere. This study put into evidence the lognormal distribution of the fluctuations of the structure constant of temperature, the fluctuations of the buoyancy force and the vertical shear of the horizontal wind speed. We show that these quantities, computed over a large scale (100 m), are correlated up to the midstratosphere. A model is adjusted to estimate the optical turbulence strength from the macroscopic meteorological parameters. The model performances, to estimate and forecast the strength and the altitude of the optical turbulent layers, are quantified and compared with other already defined models. This model was shown to have the best performances and put into evidence a new relation to describe the turbulence on a large scale.     

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.     

Investigating the complex relationship between in situ Southern Ocean pCO_{2} and its ocean physics and biogeochemical drivers using a nonparametric regression approach

The objective in this paper is to investigate the use of a non-parametric approach to model the relationship between oceanic carbon dioxide \((pCO_2)\) and a range of ocean physics and biogeochemical in situ variables in the Southern Ocean, which influence its in situ variability. The need for this stems from the need to obtain reliable estimates of carbon dioxide concentrations in the Southern Ocean which plays an important role in the global carbon flux cycle. The main challenge involved in this objective is the spatial limitation and seasonal bias of the in situ data. Moreover, studies have also reported that the relationship between \(pCO_2\) and its drivers is complex. As such, in this paper, we use the non-parametric kernel regression approach since it is able to accurately capture the complex relationships between the response and predictor variables. In this analysis we use the in situ data obtained from the SANAE49 return leg journey between Antarctic to Cape Town. To the best of our knowledge, this is the first time this data set has been subjected to such analysis. The model variants were developed on a training data subset, and the ‘goodness’ of the models were assessed on an “unseen” test data subset. Results indicate that the nonparametric approach consistently captures the relationship more accurately in terms of mean square error, root mean square error and mean absolute error, over a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict \(pCO_2\) for a larger spatial region in the Southern Ocean based on satellite biogeochemical measurements of predictor variables, given that satellites do not measure \(pCO_2\) .     

Bayesian hierarchical spatio-temporal modelling of trends and future projections in the ocean wave climate with a \text{ CO }_2 regression component

Bad weather and rough seas continue to be a major cause for ship losses and is thus a significant contributor to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account, with due treatment of the uncertainties involved, in ship design and operation in order to enhance safety. Hence, there is a need for appropriate stochastic models describing the variability of sea states. These should also incorporate realistic projections of future return levels of extreme sea states, taking into account long-term trends related to climate change and inherent uncertainties. The stochastic ocean wave model presented in this paper exploits the flexible framework of Bayesian hierarchical space-time models. It allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet at the same time remains intuitive and easily interpreted. Furthermore, by taking a Bayesian approach, the uncertainties of the model parameters are also taken into account. A regression component with $\text{ CO }_2$ as an explanatory variable has been introduced in order to extract long-term trends in the data. The model has been fitted by monthly maximum significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined in the paper, and the results will be discussed. Furthermore, a discussion of possible extensions to the model will be given.     

In vitro antioxidant and antibacterial activities of certain 3-{4-(5-mercapto-1,3,4-oxadiazole-2-yl)phenylimino}-indolin-2-one derivatives

A new series of 5- or 7-substituted 3-{4-(5-mercapto-1,3,4-oxadiazol-2-yl)phenylimino}-indolin-2-one derivatives were screened for in vitro antioxidant and antimicrobial activities. The results of this study revealed that all synthesized compounds were significantly scavenged DPPH free radicals in a concentration-dependant manner. The IC₅₀ values of all test compounds were found to be between 18.34 and 50.54?µM. All compounds significantly inhibited growth of bacteria and most of the minimum inhibitory concentration values were between 20 and 100?mg?mL^?1 indicating potent to moderate antibacterial activity. In conclusion, 3-{4-(5-mercapto-1,3,4-oxadiazole-2-yl)phenylimino)-5 or 7-substituted indolin-2-ones derivatives exhibited moderate antioxidant and antimicrobial activities.     

Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial $$\hbox {CO}_2$$ CO 2

Environmental and Ecological Statistics - Advances in sensor technology enable environmental monitoring programmes to record and store measurements at a high temporal resolution, enhancing the...