Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.     

Pier scour within long contraction in cohesive sediment bed

In the present study, an attempt has been made to study the bridge pier scour embedded long contraction for the bed sediment prepared by a mixture of clay and fine sand with varying proportions having a specific range of antecedent moisture content. Results particularly focused on the clay–sand mixed cohesive bed at varying clay fractions of the sediment bed, approach flow velocity, contraction ratio and different pier shapes, on maximum equilibrium scour depth for pier scour within long contraction. Further, regression based equations for the estimation of non-dimensional maximum scour depth for piers within long contraction in clay–sand mixed cohesive bed embedded were proposed.     

Are South Indian farmers adaptable to global change? A case in an Andhra Pradesh catchment basin

Global changes are already having an impact on South Indian farmers. Climate change is affecting the agricultural sector since it is dependent on climatic conditions and water resource availability. The impacts tend to be greater in semi-arid hard rock areas with few water resources. Furthermore, South India area is experiencing a profound agrarian crisis, which is linked, among others, to debt and credit problems. The study reported in this paper aims to develop a methodology to compare and rank farmers according to their ability to adapt to global change. The definition of adaptive capacity is based on a livelihood assets approach. Indicators are evaluated through individual surveys among farmers, then, weighted using the analytic hierarchy process and aggregated via compromise programming. The result is a standardized score measuring the distance of each farmer from an ideal adaptive capacity. Farmers are ranked according to this distance, which allows a comparison of their relative ability to adapt. At the basin scale, it shows that the geographic position of farmers is a significant factor in adaptation performance. The proximity of an administrative center contributes to an increase of their adaptive capacity. Small farming areas limit the adaptive capacities of marginal and small farmers while the largest farmers are constrained by economic factors such as large loans. These study findings offer interesting indications on the variability of farmers’ weaknesses and are bringing a better understanding of the causes of poor performance.     

Impact of COVID-19 lockdown on the elemental profile of PM10 present in the ambient aerosol of an educational institute in Kolkata,India

Reduction in air pollution level was prime observation during COVID-19 lockdown globally. Here, the study was conducted to assess the impact of lockdown on the elemental profile of PM₁₀ in ambient aerosol to quantify the elemental variation. To quantify the variation, phase-wise sampling of air pollutants was carried out using the gravimetric method for PM₁₀, while NO₂ and SO₂ were estimated through the chemiluminescence and fluorescent spectrometric method respectively. The elemental constituents of PM₁₀ were carried out using an Inductively Coupled Plasma Optical Emission Spectrometer and their source apportionment was carried out using the Positive Matrix Factorization model. The results showed that PM₁₀, NO₂ and SO₂ reduced by 86.97%, 83.38%, and 88.60% respectively during the lockdown sampling phase. The highest mean elemental concentration reduction was found in Mn (97.47%) during the lockdown. The inter-correlation among the pollutants exhibited a significant association indicating that they originate from the same source. The metals like Mn and Cu were found at a higher concentration during the lockdown phase corresponding to vehicular emissions. The comparative analysis of the elemental profile of PM₁₀ concluded that the lockdown effectuated in reduction of the majority of elements present in an aerosol enveloping metropolitan like Kolkata.     

Sediment quality,elemental bioaccumulation and antimicrobial properties of mangroves of Indian Sundarban

Environmental Geochemistry and Health - Mangroves have wide applications in traditional medicines due to their several therapeutic properties. Potentially toxic elements (PTEs), in mangrove...     

Assessing the potential ecological risk of Co,Cr, Cu,Fe and Zn in the sediments of Hooghly–Matla estuarine system,India

Environmental Geochemistry and Health - Hooghly–Matla estuarine system along with the Sundarbans mangroves forms one of the most diverse and vulnerable ecosystems in the world. We have...     

Chemical oxidation of C. I. Reactive Red 2 using Fenton-like reactions

A detailed investigation on the kinetics of the oxidative degradation of a reactive dye, C. I. Reactive Red 2 by hydroxyl radicals generated by H202 and Fe2+ has been carried out in aqueous acidic media. Effects of different parameters like initial concentration of dye, H2O2, Fe2+, pH of the solution, reaction temperature and added electrolytes on the oxidation process have been studied. The results indicate that 1.63 x 10(-4) mol dm(-3) dye can be most effectively degraded at a dye: Fe2+: H2O2 molar ratio of 1:0.22: 8.13 at pH approximately 2.7 and at 299 K. The addition of excess 2-propanol or t-butyl alcohol, well known scavengers of hydroxyl radicals, almost stopped the degradation of the dye indicating the absence of any possible reductive pathways in the degradation. The results may be useful for designing the treatment systems of wastewater containing various reactive dyes.     

Characterization of ammonia remover Bacillus albus (ASSF01) in terms of biofilm-forming ability with application in aquaculture effluent treatment

Environmental Science and Pollution Research - Presence of higher concentration of ammonia (> 0.5 mg/L) as well as nitrite (> 0.2 mg/L) in aquaculture environment create difficulties...