Design of an Integrated System for Modeling of Functional Air Quality Index Integrated with Health-GIS Using Bayesian Neural Network

Air pollution is a major problem, conscious both for health and surroundings. This is a novel approach for the design & development of a system for the monitoring of different air pollutants especially at remote places where it is difficult to install any conventional air quality monitoring stations as well as for the cities. In this research work, a framework of Functional air quality index which is an indicator of susceptibility to respiratory illness has been built using the Bayesian neural network to provide the random real-time data about a location through wireless communication. The monitoring system is integrated with different types of sensors to measure the level of different air pollutants or air quality parameters such as Suspended particulate matters, (PM_2.5), Nitrogen dioxide, Sulphur dioxide, Ozone which are directly associated with airways inflammatory diseases such as Asthma, Bronchitis, COPD. Each location in Map (GPS) can be updated automatically with fAQI to the user through mobile computing and satellite commutation. The user gets information about the neighborhood location with health-related information such as- whether a particular location is sensitive to respiratory diseases such as Bronchitis, asthma, COPD etc. due to suspended allergen/pollutants in the ambient air. This novel approach is designed with its’ own prototype and an application of Inter of Things in Health GIS for the benefit of humanity.     

Effects of quasar feedback in galaxy groups

We study the effect of quasar feedback on distributions of baryons in galaxy groups using high-resolution numerical simulations. We use the entropy-conserving gadget code that includes gas cooling and star formation, modified to include a physically based model of quasar feedback. For a sample of 10 galaxy group-sized dark matter haloes with masses in the range of  1–5 × 10¹³ M_⊙  h ⁻¹  , star formation is suppressed by more than 50 per cent in the inner regions due to the additional pressure support by quasar feedback, while gas is driven from the inner region towards the outer region of the haloes. As a result, the average gas density is 50 per cent lower in the inner region and 10 per cent higher in the outer region in the simulation, compared to a similar simulation with no quasar feedback. Gas pressure is also higher in the outer region, while temperature and entropy are enhanced in the inner region. The total group gas fraction in the two simulations generally differs by less than 10 per cent. We also find a small change of the total thermal Sunyaev–Zeldovich distortion, leading to 10 per cent changes in the microwave angular power spectrum at angular scales below 2 arcmin.     

Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark

Today the health of ocean is in danger as it was never before mainly due to man-made pollutions. Operational activities show regular occurrence of accidental and deliberate oil spill in European waters. Since the areas covered by oil spills are usually large, satellite remote sensing particularly Synthetic Aperture Radar represents an effective option for operational oil spill detection. This paper describes the development of a fully automated approach for oil spill detection from SAR. Total of 41 feature parameters extracted from each segmented dark spot for oil spill and ‘look-alike’ classification and ranked according to their importance. The classification algorithm is based on a two-stage processing that combines classification tree analysis and fuzzy logic. An initial evaluation of this methodology on a large dataset has been carried out and degree of agreement between results from proposed algorithm and human analyst was estimated between 85% and 93% respectively for ENVISAT and RADARSAT.     

Satellite-based technique for nowcasting of thunderstorms over Indian region

India experiences severe thunderstorms during the months, March–June. But these systems are not predicted well, mainly due to the absence of mesoscale observational network over Indian region and the expert system. As these are short lived systems, the nowcast is attempted worldwide based on satellite and radar observations. Due to inadequate radar network, satellite plays the dominant role for nowcast of these thunderstorms. In this study, a nowcast based algorithm ForTracc developed by Vila et al. (Weather Forecast 23:233–245, 2008) has been examined over the Indian region using Infrared Channel \((10.8~\upmu \hbox {m})\) of INSAT-3D for prediction of Mesoscale Convective Systems (MCS). In this technique, the current location and intensity in terms of Cloud Top Brightness Temperature (CTBT) of the MCS are extrapolated. The purpose of this study is to validate this satellite-based nowcasting technique for Convective Cloud Clusters that helps in optimum utilization of satellite data and improve the nowcasting. The model could predict reasonably the minimum CTBT of the convective cell with average absolute error (AAE) of \({<}7\hbox { K}\) for different lead periods (30–180 min). However, it was underestimated for all the lead periods of forecasts. The AAE in the forecasts of size of the cluster varies from about \(3\times 10^{4}\hbox { km}^{2}\) for 30-min forecast to \(7\times 10^{4}\hbox { km}^{2}\) for 120-min forecast. The mean absolute error in prediction of size is above 31–38% of actual size for different lead periods of forecasts from 30 to 180 min. There is over estimation in prediction of size for 30 and 60 min forecasts (17% and 2.6% of actual size of the cluster, respectively) and underestimation in 90 to 180-min forecasts (–2.4% to –28%). The direct position error (DPE) based on the location of minimum CTBT ranges from 70 to 144 km for 30–180-min forecast respectively.     

Hydrocarbon biodegradation and soil microbial community response to repeated oil exposure

Bioremediation strategies continue to be developed to mitigate the environmental impact of petroleum hydrocarbon contamination. This study investigated the ability of soil microbiota, adapted by prior exposure, to biodegrade petroleum. Soils from Barrow Is. (W. Australia), a class A nature reserve and home to Australia’s largest onshore oil field, were exposed to Barrow production oil (50 ml/kg soil) and incubated (25 °C) for successive phases of 61 and 100 days. Controls in which oil was not added at Phase I or II were concurrently studied and all treatments were amended with the same levels of additional nutrient and water to promote microbial activity. Prior exposure resulted in accelerated biodegradation of most, but not all, hydrocarbon constituents in the production oil. Molecular biodegradation parameters measured using gas chromatography–mass spectrometry (GC–MS) showed that several aromatic constituents were degraded more slowly with increased oil history. The unique structural response of the soil microbial community was reflected by the response of different phospholipid fatty acid (PLFA) sub-classes (e.g. branched saturated fatty acids of odd or even carbon number) measured using a ratio termed Barrow PLFA ratio (B-PLFAr). The corresponding values of a previously proposed hydrocarbon degrading alteration index showed a negative correlation with hydrocarbon exposure, highlighting the site specificity of PLFA-based ratios and microbial community dynamics. B-PLFAr values increased with each Phase I and II addition of production oil. The different hydrocarbon biodegradation rates and responses of PLFA subclasses to the Barrow production oil probably relate to the relative bioavailability of production oil hydrocarbons. These different effects suggest preferred structural and functional microbial responses to anticipated contaminants may potentially be engineered by controlled pre-exposure to the same or closely related substrates. The bioremediation of soils freshly contaminated with petroleum could benefit from the addition of exhaustively bioremediated soils rich in biota primed for the impacting hydrocarbons.     

Palaeoecological implications of corallinacean red algae and halimedacean green algae from the prang formation of south shillong plateau, meghalaya

The southwestern part of south Shillong plateau (Meghalaya, N-E India), designated as Sylhet Limestone Group is sub-divided into three lithounits i.e., Lakadong, Umlatdoh and Prang formations in ascending order. The Prang Formation is the youngest lithostratigraphic unit of the Sylhet Limestone Group and has been dated as Middle to early Upper Eocene based on the benthic foraminifera studies. Thin section analysis of carbonate rocks from Prang Formation, exposed in the Bholaganj limestone quarry yielded a rich assemblage of calcareous algae. The coralline algal assemblage comprises both non-geniculate and geniculate forms. The green algae are represented by species of Halimeda belonging to the family Halimedaceae. Palaeoecological interpretation based on diversity, growth-form analysis and taphonomic aspects of the algal assemblage indicate that in all probabilities the deposition of Prang Formation occurred in shallow, warm, shelf environment of normal salinity within the transgressive phase.