Implications for biological invasion of non-native plants for sale in the world's largest online market

Internet trade is increasingly recognized as a dispersal pathway of non-native plant species that is difficult to monitor. We sought to identify non-native flora present in the Chinese online market, the largest e-commerce market globally, and to decipher the effect of existing trade regulations, among other variables, on e-trading patterns and to inform policy. We used a comprehensive list of 811 non-native plant species in China present in 1 of the 3 phases of the invasion continuum (i.e., introduced, naturalized, and invasive). The price, propagule types, and quantities of the species offered for sale were retrieved from 9 online stores, including 2 of the largest platforms. Over 30% of the non-native species were offered for sale in the online marketplaces; invasive non-native species dominated the list (45.53%). No significant price difference was observed across the non-native species of the 3 invasion categories. Among the 5 propagule types, a significantly higher number of non-native species were offered for sale as seeds. The regression models and path analyses consistently revealed a direct positive effect of the number of uses and species’ minimum residence time and an indirect effect of biogeography on the pattern of trade in non-native plant species when minimal phylogenetic signal was detected. A review of the existing phytosanitary regulations in China revealed their inadequacy in managing e-trading of non-native plant species. To address the problem, we propose integration of a standardized risk assessment framework that considers perceptions of stakeholders and is adaptable based on continuous surveillance of the trade network. If implemented successfully, the measures could provide a template for other countries to strengthen trading regulations for non-native plant species and take proactive management measures.     

Mechanistic insights to lactic and formic acid toxicity on benthic oligochaete worm Tubifex tubifex

Environmental Science and Pollution Research - Lactic and formic acid are two commonly found monocarboxylic organic acids. Lactic acid is discharged into the water bodies as acidic industrial...     

Recycling of distillation by-products of Ocimum sp. as potential source of antioxidants as well as feedstock for biochar

Journal of Material Cycles and Waste Management - Distillation by-products of two Ocimum sp. were recycled through extraction of phenolic compounds and development of functional material like...     

Performance assessment of the indigenous ceramic UF membrane in bioreactor process for highly polluted tannery wastewater treatment

Environmental Science and Pollution Research - The present study evaluates the performance of an indigenously developed ceramic ultrafiltration (UF) membrane in a lab-scale membrane bioreactor...     

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).