Seasonal variability of ambient NH3, NO, NO2 and SO2 over Delhi

We present the diurnal and seasonal variability of ambient NH3, NO, NO2 and SO2 over Delhi, India. Ambient NH3, NO and NO2 were measured continuously during winter, summer and autumn seasons using NH3- and NOx-analyzer, which operates by chemiluminescence method with a higher estimation e ciency (> 90%) than the chemical trap method (reproducibility 4.7%). Prominent diurnal, day-to-day and seasonal variations of ambient mixing ratio of NH3, NO, NO2 and SO2 were observed during the study period. Seasonal variation with higher mixing ratio in winter was observed for all measured trace gases except NO. Day-night variation of all measured trace gases observed was higher in winter in comparison with summer. Late morning increase in NO2 mixing ratio might be attributed to conversion of NO to NO2 with the interaction of O3.     

Correction to: Review on evolution of municipal solid waste management in India: practices,challenges and policy implications

Journal of Material Cycles and Waste Management -     

Sources,controls, and probabilistic health risk assessment of fluoride contamination in groundwater from a semi-arid region in Gujarat,Western India: An isotope–hydrogeochemical perspective

Environmental Geochemistry and Health - Fluoride contamination in groundwaters of a rural region in semi-arid Western India has been studied using combination of geochemical-and-isotopic...     

Correction to: Method development,validation, monitoring,seasonal effect and risk assessment of multiclass multi pesticide residues in surface and ground water of new alluvial zone in eastern India

Environmental Science and Pollution Research -     

Pollution resistance assessment of plants around chromite mine based on anticipated performance index,dust capturing capacity and metal accumulation index

Environmental Science and Pollution Research - Plant species sustaining under a polluted environment for a long time are considered as potentially resistant species. Those plant species can be...     

Modeling and multi-objective optimization of commercial ethylene oxide reactor to strike a delicate balance between profit and negative environmental impact

Environmental Science and Pollution Research - The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to...     

Enhancement of nutritional value of fried fish using an artificial intelligence approach

Frying affects the nutritional quality of fish detrimentally. In this study, using Catla catla and mustard oil, experiments were carried out in varying temperatures (140–240 °C), times (5–20 min), and oil amounts (25–100 ml/kg of fish) which established drastic reduction of 44.97% and 99.40% for polyunsaturated fatty acid (PUFA)/saturated fatty acids (SFA) and index of atherogenicity (IA) profile, respectively. Artificial neural network (ANN) was implemented successfully to provide an association between the independent inputs with dependent outputs (values of R² were 0.99 and 0.98; RMSE were 0.038 and 0.046; and performance were 0.038 and 0.067 for PUFA/SFA and IA, respectively) by exhaustive search of various algorithms and activation functions available in literature. ANN model–based meta-heuristic, stochastic optimization formalisms, genetic algorithm (GA) and particle swarm optimization (PSO), were applied to optimize the combination of cooking parameters for improving the nutritional quality of food which improved the nutritional value by maximizing the PUFA/SFA profile up to 63.05% and minimizing the IA profile to 99.64%. Multi-objective genetic algorithm (MOGA) was also employed to tune the inputs by maintaining a balance between the contrasting outputs and enhance the overall food value simultaneously with multi-objective (beneficial for health, economic, and environment-friendly) proposal. MOGA was able to improve the PUFA/SFA profile up to 44.76% and reduce the IA profile to 92.94% concurrently with the reduction of wastage of culinary media and energy consumption, following the optimized cooking condition (118.92 °C, 6.06 min, 40 ml oil/kg of fish).

     

Assessing the impact of ecological insecurity on ecosystem service value in stone quarrying and crushing dominated areas

The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link it with ecosystem service value (ESV). Satellite image-based landscape metrics have been used for developing machine learning-oriented EIM, and the global coefficient of Costanza et al. (Glob Environ Change 26:152–158, 2014) has been used for computing ESV. Field parameter-based ecological insecurity index (EII) has been developed for validating the EIMs along with the statistical methods. Applied Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) revealed that 21.88 to 60.79% area has predicted as highly ecologically insecure in all the selected four stone quarrying and crushing dominated clusters and this is has inflated from 2000 to 2020. All the applied models are acceptable in terms of their performances, but the RF model is found to be the best representative in relation to EII. It causes considerable loss of ESV which ranges from 160,845.18 US$ to 757,445.17 US$ in all the clusters from 2000 to 2020. The findings of the study are useful for ecological management in this area. It further recommends applying such an approach in such similar fields to establish the general finding and provides knowledge to the state of arts.