CO<Subscript>2</Subscript>-assisted removal of nutrients from municipal wastewater by microalgae <Emphasis Type="Italic">Chlorella vulgaris</Emphasis> and <Emphasis Type="Italic">Scenedesmus obliquus</Emphasis>

Axenic culture of microalgae Chlorella vulgaris ATCC^® 13482 and Scenedesmus obliquus FACHB 417 was used for phycoremediation of primary municipal wastewater. The main aim of this study was to measure the effects of normal air and CO₂-augmented air on the removal efficacy of nutrients (ammonia N and phosphate P) from municipal wastewater by the two microalgae. Batch experiments were carried out in cylindrical glass bottles of 1 L working volume at 25 °C and cool fluorescent light of 6500 lux maintaining 14/10 h of light/dark cycle with normal air supplied at 0.2 L min^?1 per liter of the liquid for both algal strains for the experimental period. In the next set of experiments, the treatment process was enhanced by using 1, 2 and 5% CO₂/air (vol./vol.) supply into microalgal cultures. The enrichment of inlet air with CO₂ was found to be beneficial. The maximum removal of 76.3 and 76% COD, 94.2 and 92.6% ammonia, and 94.8 and 93.1% phosphate after a period of 10 days was reported for C. vulgaris and S. obliquus, respectively, with 5% CO₂/air supply. Comparing the two microalgae, maximum removal rates of ammonia and phosphate by C. vulgaris were 4.12 and 1.75 mg L^?1 day^?1, respectively, at 5% CO₂/air supply. From kinetic study data, it was found that the specific rates of phosphate utilization (q _phsophate) by C. vulgaris and S. obliquus at 5% CO₂/air supply were 1.98 and 2.11 day^?1, respectively. Scale-up estimation of a reactor removing phosphate (the criteria pollutant) from 50 MLD wastewater influent was also done.     

Hydromagnetic flow past a continuously moving semi-infinite plate for large suction

The similarity solution for hydromagnetic flow of an incompressible viscous electrically conducting fluid past a continuously moving semi-infinite porous plate in the presence of a magnetic field has been obtained for the case of small magnetic Reynolds number. The perturbation method has been used to solve the similarity equations at large suction. The resulting equations have been solved by analytical method. The effect of the magnetic parameter is to increase the skin-friction coefficient while it has no significant effect on the Nusselt number.     

Impact of Three-Parameter Weibull Models in Probabilistic Assessment of Earthquake Hazards

This paper investigates the suitability of a three-parameter (scale, shape, and location) Weibull distribution in probabilistic assessment of earthquake hazards. The performance is also compared with two other popular models from same Weibull family, namely the two-parameter Weibull model and the inverse Weibull model. A complete and homogeneous earthquake catalog (Yadav et al. in Pure Appl Geophys 167:1331–1342, 2010) of 20 events (M ≥ 7.0), spanning the period 1846 to 1995 from north–east India and its surrounding region (20°–32°N and 87°–100°E), is used to perform this study. The model parameters are initially estimated from graphical plots and later confirmed from statistical estimations such as maximum likelihood estimation (MLE) and method of moments (MoM). The asymptotic variance–covariance matrix for the MLE estimated parameters is further calculated on the basis of the Fisher information matrix (FIM). The model suitability is appraised using different statistical goodness-of-fit tests. For the study area, the estimated conditional probability for an earthquake within a decade comes out to be very high (≥0.90) for an elapsed time of 18 years (i.e., 2013). The study also reveals that the use of location parameter provides more flexibility to the three-parameter Weibull model in comparison to the two-parameter Weibull model. Therefore, it is suggested that three-parameter Weibull model has high importance in empirical modeling of earthquake recurrence and seismic hazard assessment.     

A new approach for finding an appropriate combination of texture parameters for classification

This article is an attempt to suggest a new approach for eliminating the lengthy process of selecting various parameters for extracting texture features and to quantify the relative importance of the parameters affecting textural classification. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyse the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. Results of the classification of an Indian urban environment using spatial property (texture) have also been reported. It was observed that the classification incorporating texture features using grey level co-occurrence matrix and wavelet-based approach improves the overall accuracy in a statistically significant manner in comparison to pure spectral classification.     

Integration of high-resolution imagery and LiDAR data for object-based classification of urban area

This paper investigates the synergistic use of high-resolution multispectral imagery and Light Detection and Ranging (LiDAR) data for object-based classification of urban area. The main contribution of this paper is the development of a semi-automated object-based and rule-based classification method. In the implemented approach, the diverse knowledge about land use/land cover classes are transformed into a set of specialized rules. Further, this paper explores supervised Gaussian Mixture Models for classification, which have been primarily used for unsupervised classification. The work is carried out on test data from two different sites. Contribution of the LiDAR data resulted in a significant improvement of overall Kappa. Accuracy assessment carried out for aforementioned classification methods shows higher overall kappa for both the study sites.     

Comparative study on projected clustering methods for hyperspectral imagery classification

In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction.     

Human health risk as a basis for prioritizing air quality monitoring stations in a megacity: a case study

Arabian Journal of Geosciences - The Ahnet and Tin Zaouatine terranes are located in the southern sector of the Algerian Desert and are part of the Tuareg Shield, which was built up during...     

Potential of multispectral reflectance for assessment of snow geophysical parameters in Solang valley in the lower Indian Himalayas

ABSTRACT

Snow geophysical parameters such as wetness, density and permittivity are a significant input in hydrological models and water resource management. In this paper, we utilize the triangle method based on a feature space developed with the near-infrared (NIR) reflectance and the Normalized Differenced Snow Index (NDSI) for the estimation of surface snow wetness, permittivity and density. The triangular feature space based on NIR reflectance and NDSI is parameterized to yield a linear relationship between the snow wetness and the NIR reflectance. Snow density and permittivity are derived based on the least squares solution of empirical relations based on the observations of surface snow wetness. The proposed methodology was evaluated using Sentinel-2 data, and the modeled snow geophysical parameters were validated with respect to field measurements. Based on the results, it was inferred that the NIR reflectance varies linearly with the liquid water content in the snow. A good agreement was determined between the modeled and measured parameters for wet snow conditions as observed by the coefficient of determination of 0.968, 0.521 and 0.969 for the snow wetness, density and permittivity (real part), respectively. The proposed approach can be significantly utilized with unmanned aerial sensors for monitoring of physical properties of fresh or wet snow and is thus expected to contribute considerably in hydrological applications and avalanche studies.     

Did unusual warming over the mid and higher latitudes play some role in causing the unprecedented failure of the southwest monsoon during July 2002?

Summary Southwest monsoon rainfall over India during July 2002 was the lowest since instrumental observations of monsoon rainfall began. The present study is an attempt to examine some of the probable causes for this unprecedented rainfall deficit. It is found that mid and higher latitudes in the northern hemisphere were abnormally warm during the spring and summer months. Associated with this unusual warming were two blocking highs, one each to the east and west of the Indian subcontinent. These were separated by an anomalous low (low temperature) just to the north of the subcontinent. This anomalous stationary wave-like configuration was consistently present from March to August and the blocking highs were found to be closer to each other during July. This configuration was apparently responsible for the advection of anomalously dry air over the Indian region during July, which may be one of the causes of the suppressed monsoon flow and, ultimately, rainfall activity.