The first multituberculate mammal from India

Mesozoic deposits of the former Gondwanaland are depauperate in early mammals, in general, and multituberculate mammals, in particular. Until now, the oldest multituberculate mammals known from the Gondwanan continents come from the Early Cretaceous of Morocco, NW Africa. Here, we report the presence of a new multituberculate mammal, Indobaatar zofiae gen. et sp. nov., from the Lower/Middle Jurassic Kota Formation, Pranhita-Godavari valley in peninsular India. This is the first record of a multituberculate from the Mesozoic rocks of India and possibly predates the oldest known multituberculates from Gondwanan continents. The new specimen, representing an upper premolar (P⁴), compares well with the upper premolar morphology of Eobaatariinae multituberculates known from the Early Cretaceous of Mongolia, China, England, and Spain. Together with the recent findings of cimolodontan multituberculates from the Early Cretaceous of Australia and Late Cretaceous of South America, the new discovery indicates a wide temporal and spatial distribution for multituberculate mammals in the former Gondwanaland.     

Chemistry of fogs at Agra, India: Influence of soil particulates and atmospheric gases

Fog water samples were collected in the months of December and January during 1998–2000 at Agra, India. The samples were analyzed for pH, major anions (F⁻, Cl⁻, SO₄ ²⁻, NO₃ ⁻, HCOO⁻ and CH₃COO⁻), major cations (Ca²⁺, Mg²⁺, Na⁺ and K⁺) and NH₄ ⁺ using ion chromatography, ICP-AES and spectrophotometer methods, respectively. pH of fog water samples ranged between 7.0 and 7.6 with a volume weighted mean of 7.2, indicating its alkaline characteristic. NH₄ ⁺ contributed 40%, SO₄ ²⁻ and NO₃ ⁻ accounted for 28%, while Ca²⁺, Mg²⁺, Na⁺ and K⁺ accounted for 16% of the total ionic concentration. The ratios of Mg²⁺/Ca²⁺ and Na⁺/Ca²⁺ in fog water indicates that 50–75% of fog water samples correspond to the respective ratios in local soil. Significant correlation between Ca²⁺, Mg²⁺, Na⁺ and K⁺ suggests their soil origin. The order of neutralization, NH₄ ⁺ (1.4) > Ca²⁺ (0.28) > Mg²⁺ (0.12), indicates that NH₄ ⁺ is the major neutralizing species. Fog water and atmospheric alkalinity were also computed and were found to be 873 and 903 neqm⁻³, respectively. Both of these values are higher than values reported from temperate sites and thus indicate that at the present level of pollutants, there is no risk of acid fog problem. The study also shows that the alkaline nature of fog water is due to dissolution of ammonia gas and partly due to interaction of fog water with soil derived aerosols.     

Autoregressive models in environmental forecasting time series: a theoretical and application review

Environmental Science and Pollution Research - Though globalization, industrialization, and urbanization have escalated the economic growth of nations, these activities have played foul on the...     

Sensitivity analysis of water quality for Delhi stretch of the River Yamuna,India

Simulation models are used to aid the decision makers about water pollution control and management in river systems. However, uncertainty of model parameters affects the model predictions and hence the pollution control decision. Therefore, it often is necessary to identify the model parameters that significantly affect the model output uncertainty prior to or as a supplement to model application to water pollution control and planning problems. In this study, sensitivity analysis, as a tool for uncertainty analysis was carried out to assess the sensitivity of water quality to (a) model parameters (b) pollution abatement measures such as wastewater treatment, waste discharge and flow augmentation from upstream reservoir. In addition, sensitivity analysis for the “best practical solution” was carried out to help the decision makers in choosing an appropriate option. The Delhi stretch of the river Yamuna was considered as a case study. The QUAL2E model is used for water quality simulation. The results obtained indicate that parameters K ₁ (deoxygenation constant) and K ₃ (settling oxygen demand), which is the rate of biochemical decomposition of organic matter and rate of BOD removal by settling, respectively, are the most sensitive parameters for the considered river stretch. Different combinations of variations in K ₁ and K ₂ also revealed similar results for better understanding of inter-dependability of K ₁ and K ₂. Also, among the pollution abatement methods, the change (perturbation) in wastewater treatment level at primary, secondary, tertiary, and advanced has the greatest effect on the uncertainty of the simulated dissolved oxygen and biochemical oxygen demand concentrations.